The Best Times to Try Abiraterone

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Until recently, there have been few treatment options for advanced prostate cancer that is resistant to androgen-directed therapies. Newer treatments that target residual androgen production offer some hope of prolonging the interval before chemotherapy, with fewer adverse effects (AEs) and better efficacy. One of those is abiraterone, which blocks extragonadal, testicular, and tumor androgen biosynthesis.

An ongoing multinational phase 3 study is evaluating the clinical benefits of abiraterone plus prednisone vs prednisone alone in patients with progressive metastatic castration-resistant prostate cancer (mCRPC). Follow-up for the study has now exceeded 27 months, giving a good opportunity to evaluate safety and efficacy. Thus, after having reviewed outcomes so far, the independent data-monitoring committee recommended that the study be unblinded and patients be allowed to cross over from prednisone to abiraterone. The researchers reported the results of the third interim analysis, with updated analysis.

Patients were stratified by Eastern Cooperative Oncology Group performance status (ECOG-PS) and randomly assigned to receive abiraterone 1,000 mg plus prednisone 5 mg twice daily or placebo plus prednisone.

Patients who received abiraterone had, compared with those on prednisone, statistically significant improvement in radiographic progression-free survival (PFS), with a median time to disease progression or death of 16.5 months, vs 8.2 months (95% CI, 0.45-0.61).

Overall survival also lengthened, from a median of 35.3 months vs 30.1 months (95% CI, 0.66-0.95).

All secondary endpoints also favored abiraterone over prednisone. For instance, abiraterone treatment delayed the time to the need for opiates for cancer-related pain and the time to initiation of chemotherapy. Abiraterone also delayed the time to deterioration in ECOG-PS and prostate-specific antigen (PSA) progression. Abiraterone more than doubled the PSA response rate: 68% vs 29% with prednisone.

Patients reported more pain relief. Those receiving abiraterone had statistically significant improvement in pain interference (P = .005), although the improvement in mean pain intensity was not significant.

Adverse effects leading to dose modifications or interruption of treatment were reported in 21% of patients on abiraterone, compared with 12% of the prednisone group. Six patients (1%) in each group died of drug-related treatment-emergent AEs. The AEs of “special interest,” the researchers say, included events related to mineralocorticoid excess, such as hypertension, hypokalemia, and fluid retention—all unsurprising, given the known mechanism of action of abiraterone. Grade 3 or 4 AEs with increased alanine aminotransferase and aspartate aminotransferase were more common in the abiraterone group.

The most common subsequent therapy for patients who terminated the study was docetaxel. However, another recent study, from Johns Hopkins researchers in Baltimore, Maryland, indicates the transition warrants caution: The findings suggest a potential cross-resistance between docetaxel and abiraterone.

Their study compared outcomes in 24 men who received abiraterone before docetaxel with 95 who were abiraterone-naïve. Men who were on abiraterone were less likely to achieve a PSA response, and their cancer was more likely to progress.

The researchers concede that their study groups were small; they also say it is possible that differences in disease severity may have influenced the time to progression. However, they say the fact that PSA-PFS was significantly different between the 2 groups (P = .002) supports their initial hypothesis—that is, that abiraterone pretreatment reduces responsiveness to docetaxel.

In spite of its limitations, the researchers say their study represents the only comparative analysis of PSA-PFS and PFS after docetaxel treatment for patients who have or have not received prior abiraterone. Their report, they add, offers the “strongest available evidence to date” of a clinically meaningful cross-resistance between abiraterone and docetaxel. They conclude that their findings provide “valuable information” about which patients are likely to derive the most benefit from docetaxel.

Sources
Rathkopf DE, Smith MR, de Bono JS, et al. Eur Urol. 2014;66(5):815-825.
doi: 10.1016/j.eururo.2014.02.056.

Schewizer MT, Zhou XC, Wang H, et al. Eur Urol. 2014;66(4):646-652.
doi: 10.1016/j.eururo.2014.01.018.

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Until recently, there have been few treatment options for advanced prostate cancer that is resistant to androgen-directed therapies. Newer treatments that target residual androgen production offer some hope of prolonging the interval before chemotherapy, with fewer adverse effects (AEs) and better efficacy. One of those is abiraterone, which blocks extragonadal, testicular, and tumor androgen biosynthesis.

An ongoing multinational phase 3 study is evaluating the clinical benefits of abiraterone plus prednisone vs prednisone alone in patients with progressive metastatic castration-resistant prostate cancer (mCRPC). Follow-up for the study has now exceeded 27 months, giving a good opportunity to evaluate safety and efficacy. Thus, after having reviewed outcomes so far, the independent data-monitoring committee recommended that the study be unblinded and patients be allowed to cross over from prednisone to abiraterone. The researchers reported the results of the third interim analysis, with updated analysis.

Patients were stratified by Eastern Cooperative Oncology Group performance status (ECOG-PS) and randomly assigned to receive abiraterone 1,000 mg plus prednisone 5 mg twice daily or placebo plus prednisone.

Patients who received abiraterone had, compared with those on prednisone, statistically significant improvement in radiographic progression-free survival (PFS), with a median time to disease progression or death of 16.5 months, vs 8.2 months (95% CI, 0.45-0.61).

Overall survival also lengthened, from a median of 35.3 months vs 30.1 months (95% CI, 0.66-0.95).

All secondary endpoints also favored abiraterone over prednisone. For instance, abiraterone treatment delayed the time to the need for opiates for cancer-related pain and the time to initiation of chemotherapy. Abiraterone also delayed the time to deterioration in ECOG-PS and prostate-specific antigen (PSA) progression. Abiraterone more than doubled the PSA response rate: 68% vs 29% with prednisone.

Patients reported more pain relief. Those receiving abiraterone had statistically significant improvement in pain interference (P = .005), although the improvement in mean pain intensity was not significant.

Adverse effects leading to dose modifications or interruption of treatment were reported in 21% of patients on abiraterone, compared with 12% of the prednisone group. Six patients (1%) in each group died of drug-related treatment-emergent AEs. The AEs of “special interest,” the researchers say, included events related to mineralocorticoid excess, such as hypertension, hypokalemia, and fluid retention—all unsurprising, given the known mechanism of action of abiraterone. Grade 3 or 4 AEs with increased alanine aminotransferase and aspartate aminotransferase were more common in the abiraterone group.

The most common subsequent therapy for patients who terminated the study was docetaxel. However, another recent study, from Johns Hopkins researchers in Baltimore, Maryland, indicates the transition warrants caution: The findings suggest a potential cross-resistance between docetaxel and abiraterone.

Their study compared outcomes in 24 men who received abiraterone before docetaxel with 95 who were abiraterone-naïve. Men who were on abiraterone were less likely to achieve a PSA response, and their cancer was more likely to progress.

The researchers concede that their study groups were small; they also say it is possible that differences in disease severity may have influenced the time to progression. However, they say the fact that PSA-PFS was significantly different between the 2 groups (P = .002) supports their initial hypothesis—that is, that abiraterone pretreatment reduces responsiveness to docetaxel.

In spite of its limitations, the researchers say their study represents the only comparative analysis of PSA-PFS and PFS after docetaxel treatment for patients who have or have not received prior abiraterone. Their report, they add, offers the “strongest available evidence to date” of a clinically meaningful cross-resistance between abiraterone and docetaxel. They conclude that their findings provide “valuable information” about which patients are likely to derive the most benefit from docetaxel.

Sources
Rathkopf DE, Smith MR, de Bono JS, et al. Eur Urol. 2014;66(5):815-825.
doi: 10.1016/j.eururo.2014.02.056.

Schewizer MT, Zhou XC, Wang H, et al. Eur Urol. 2014;66(4):646-652.
doi: 10.1016/j.eururo.2014.01.018.

Until recently, there have been few treatment options for advanced prostate cancer that is resistant to androgen-directed therapies. Newer treatments that target residual androgen production offer some hope of prolonging the interval before chemotherapy, with fewer adverse effects (AEs) and better efficacy. One of those is abiraterone, which blocks extragonadal, testicular, and tumor androgen biosynthesis.

An ongoing multinational phase 3 study is evaluating the clinical benefits of abiraterone plus prednisone vs prednisone alone in patients with progressive metastatic castration-resistant prostate cancer (mCRPC). Follow-up for the study has now exceeded 27 months, giving a good opportunity to evaluate safety and efficacy. Thus, after having reviewed outcomes so far, the independent data-monitoring committee recommended that the study be unblinded and patients be allowed to cross over from prednisone to abiraterone. The researchers reported the results of the third interim analysis, with updated analysis.

Patients were stratified by Eastern Cooperative Oncology Group performance status (ECOG-PS) and randomly assigned to receive abiraterone 1,000 mg plus prednisone 5 mg twice daily or placebo plus prednisone.

Patients who received abiraterone had, compared with those on prednisone, statistically significant improvement in radiographic progression-free survival (PFS), with a median time to disease progression or death of 16.5 months, vs 8.2 months (95% CI, 0.45-0.61).

Overall survival also lengthened, from a median of 35.3 months vs 30.1 months (95% CI, 0.66-0.95).

All secondary endpoints also favored abiraterone over prednisone. For instance, abiraterone treatment delayed the time to the need for opiates for cancer-related pain and the time to initiation of chemotherapy. Abiraterone also delayed the time to deterioration in ECOG-PS and prostate-specific antigen (PSA) progression. Abiraterone more than doubled the PSA response rate: 68% vs 29% with prednisone.

Patients reported more pain relief. Those receiving abiraterone had statistically significant improvement in pain interference (P = .005), although the improvement in mean pain intensity was not significant.

Adverse effects leading to dose modifications or interruption of treatment were reported in 21% of patients on abiraterone, compared with 12% of the prednisone group. Six patients (1%) in each group died of drug-related treatment-emergent AEs. The AEs of “special interest,” the researchers say, included events related to mineralocorticoid excess, such as hypertension, hypokalemia, and fluid retention—all unsurprising, given the known mechanism of action of abiraterone. Grade 3 or 4 AEs with increased alanine aminotransferase and aspartate aminotransferase were more common in the abiraterone group.

The most common subsequent therapy for patients who terminated the study was docetaxel. However, another recent study, from Johns Hopkins researchers in Baltimore, Maryland, indicates the transition warrants caution: The findings suggest a potential cross-resistance between docetaxel and abiraterone.

Their study compared outcomes in 24 men who received abiraterone before docetaxel with 95 who were abiraterone-naïve. Men who were on abiraterone were less likely to achieve a PSA response, and their cancer was more likely to progress.

The researchers concede that their study groups were small; they also say it is possible that differences in disease severity may have influenced the time to progression. However, they say the fact that PSA-PFS was significantly different between the 2 groups (P = .002) supports their initial hypothesis—that is, that abiraterone pretreatment reduces responsiveness to docetaxel.

In spite of its limitations, the researchers say their study represents the only comparative analysis of PSA-PFS and PFS after docetaxel treatment for patients who have or have not received prior abiraterone. Their report, they add, offers the “strongest available evidence to date” of a clinically meaningful cross-resistance between abiraterone and docetaxel. They conclude that their findings provide “valuable information” about which patients are likely to derive the most benefit from docetaxel.

Sources
Rathkopf DE, Smith MR, de Bono JS, et al. Eur Urol. 2014;66(5):815-825.
doi: 10.1016/j.eururo.2014.02.056.

Schewizer MT, Zhou XC, Wang H, et al. Eur Urol. 2014;66(4):646-652.
doi: 10.1016/j.eururo.2014.01.018.

References

References

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abiraterone, advanced prostate cancer treatment, prostate cancer resistant to androgen-directed therapies, residual androgen production, abiraterone plus prednisone, progressive metastatic castration-resistant prostate cancer, mCRPC, Eastern Cooperative Oncology Group performance status, ECOG-PS, prostate-specific antigen, PSA, mineralocorticoid excess, increased alanine aminotransferase, asparate aminotransferase, docetaxel
Legacy Keywords
abiraterone, advanced prostate cancer treatment, prostate cancer resistant to androgen-directed therapies, residual androgen production, abiraterone plus prednisone, progressive metastatic castration-resistant prostate cancer, mCRPC, Eastern Cooperative Oncology Group performance status, ECOG-PS, prostate-specific antigen, PSA, mineralocorticoid excess, increased alanine aminotransferase, asparate aminotransferase, docetaxel
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Disordered methylation compromises CLL treatment

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Disordered methylation compromises CLL treatment

DNA methylation

Credit: Christoph Bock

New research suggests disordered methylation is one of the defining characteristics of cancer and helps tumors adapt to changing circumstances.

The study, published in Cancer Cell, showed that disordered methylation has a direct bearing on the effectiveness of cancer therapy.

In patients with chronic lymphocytic leukemia (CLL), researchers found that treatment produced shorter remissions if the tumor tissue showed signs of highly disordered methylation.

The findings indicate that such disorganization can actually benefit tumors and render them less vulnerable to anticancer drugs.

“The behavior of a cancer cell is dictated not only by genetics . . . but also by epigenetics,” said study author Catherine Wu, MD, of the Dana-Farber Cancer Institute in Boston.

“We know that tumors are composed of many subgroups of cells, each with its own array of gene mutations. In this study, we wanted to see if that type of genetic diversity coincides with epigenetic diversity. In other words, does the range of methylation patterns mirror the genetic variety we find in tumors?”

To find out, the researchers used bisulfite sequencing, which allows scientists to track the presence or absence of methyl groups at specific rungs on the DNA ladder.

They also devised a simple measure called PDR—percent discordant reads—for quantifying the extent of irregular methylation within a tissue sample. The higher the PDR, the more variability in how the methyl groups are arranged.

They measured the PDR and the amount of genetic diversity in 104 CLL samples and 27 samples of normal B cells.

“We thought the epigenetic structure would map right onto the genetic structure,” said study author Alexander Meissner, PhD, of the Broad Institute of MIT and Harvard in Cambridge, Massachusetts.

“That is, the degree of genetic diversity in each sample would match the variation in methylation marks in an organized fashion.”

To the researchers’ surprise, the methylation patterns showed a tremendous degree of random disarray.

“We know that individual tumors are checkered with genetically distinct groups of cells,” Dr Meissner explained. “Bisulfite sequencing enabled us to see that the placement of methyl groups across tumor cell DNA also varies substantially among cells in the same tumor. In fact, disorderly methylation pervades the entire tumor.”

The results revealed that the diversity within individual tumors apparently proceeds along two independent, yet interrelated tracks: one resulting in a genetic hodgepodge of cell groups, the other resulting in haphazard methylation.

The methylation irregularities, technically known as “local methylation disorder,” were highly evident in CLL and other types of cancer.

Because methyl groups control the expression of genes, disorderly methylation might be expected to cause wildly inconsistent gene activity even within a single tumor. This, in fact, is what the researchers found.

The disruption of methylation machinery might seem hazardous to tumor survival, but the researchers theorize that tumors can turn the disorderliness to their own advantage.

“Just as in the case of genetic heterogeneity within tumors, increased random variation of the epigenetic profile may augment the diversity of malignant cells,” said study author Dan Landau, MD, PhD, of Dana-Farber and the Broad Institute.

“The ability of cancers to maintain high levels of diversity is an effective hedging strategy, enabling them to better adapt to therapy, as well as enhancing the ‘trial and error’ process in search of better evolutionary trajectories.”

“Cancer survives through some wildly inventive ways,” Dr Wu added. “Methylation disorder is one of the ways it creates the conditions that enable it to adapt.”

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DNA methylation

Credit: Christoph Bock

New research suggests disordered methylation is one of the defining characteristics of cancer and helps tumors adapt to changing circumstances.

The study, published in Cancer Cell, showed that disordered methylation has a direct bearing on the effectiveness of cancer therapy.

In patients with chronic lymphocytic leukemia (CLL), researchers found that treatment produced shorter remissions if the tumor tissue showed signs of highly disordered methylation.

The findings indicate that such disorganization can actually benefit tumors and render them less vulnerable to anticancer drugs.

“The behavior of a cancer cell is dictated not only by genetics . . . but also by epigenetics,” said study author Catherine Wu, MD, of the Dana-Farber Cancer Institute in Boston.

“We know that tumors are composed of many subgroups of cells, each with its own array of gene mutations. In this study, we wanted to see if that type of genetic diversity coincides with epigenetic diversity. In other words, does the range of methylation patterns mirror the genetic variety we find in tumors?”

To find out, the researchers used bisulfite sequencing, which allows scientists to track the presence or absence of methyl groups at specific rungs on the DNA ladder.

They also devised a simple measure called PDR—percent discordant reads—for quantifying the extent of irregular methylation within a tissue sample. The higher the PDR, the more variability in how the methyl groups are arranged.

They measured the PDR and the amount of genetic diversity in 104 CLL samples and 27 samples of normal B cells.

“We thought the epigenetic structure would map right onto the genetic structure,” said study author Alexander Meissner, PhD, of the Broad Institute of MIT and Harvard in Cambridge, Massachusetts.

“That is, the degree of genetic diversity in each sample would match the variation in methylation marks in an organized fashion.”

To the researchers’ surprise, the methylation patterns showed a tremendous degree of random disarray.

“We know that individual tumors are checkered with genetically distinct groups of cells,” Dr Meissner explained. “Bisulfite sequencing enabled us to see that the placement of methyl groups across tumor cell DNA also varies substantially among cells in the same tumor. In fact, disorderly methylation pervades the entire tumor.”

The results revealed that the diversity within individual tumors apparently proceeds along two independent, yet interrelated tracks: one resulting in a genetic hodgepodge of cell groups, the other resulting in haphazard methylation.

The methylation irregularities, technically known as “local methylation disorder,” were highly evident in CLL and other types of cancer.

Because methyl groups control the expression of genes, disorderly methylation might be expected to cause wildly inconsistent gene activity even within a single tumor. This, in fact, is what the researchers found.

The disruption of methylation machinery might seem hazardous to tumor survival, but the researchers theorize that tumors can turn the disorderliness to their own advantage.

“Just as in the case of genetic heterogeneity within tumors, increased random variation of the epigenetic profile may augment the diversity of malignant cells,” said study author Dan Landau, MD, PhD, of Dana-Farber and the Broad Institute.

“The ability of cancers to maintain high levels of diversity is an effective hedging strategy, enabling them to better adapt to therapy, as well as enhancing the ‘trial and error’ process in search of better evolutionary trajectories.”

“Cancer survives through some wildly inventive ways,” Dr Wu added. “Methylation disorder is one of the ways it creates the conditions that enable it to adapt.”

DNA methylation

Credit: Christoph Bock

New research suggests disordered methylation is one of the defining characteristics of cancer and helps tumors adapt to changing circumstances.

The study, published in Cancer Cell, showed that disordered methylation has a direct bearing on the effectiveness of cancer therapy.

In patients with chronic lymphocytic leukemia (CLL), researchers found that treatment produced shorter remissions if the tumor tissue showed signs of highly disordered methylation.

The findings indicate that such disorganization can actually benefit tumors and render them less vulnerable to anticancer drugs.

“The behavior of a cancer cell is dictated not only by genetics . . . but also by epigenetics,” said study author Catherine Wu, MD, of the Dana-Farber Cancer Institute in Boston.

“We know that tumors are composed of many subgroups of cells, each with its own array of gene mutations. In this study, we wanted to see if that type of genetic diversity coincides with epigenetic diversity. In other words, does the range of methylation patterns mirror the genetic variety we find in tumors?”

To find out, the researchers used bisulfite sequencing, which allows scientists to track the presence or absence of methyl groups at specific rungs on the DNA ladder.

They also devised a simple measure called PDR—percent discordant reads—for quantifying the extent of irregular methylation within a tissue sample. The higher the PDR, the more variability in how the methyl groups are arranged.

They measured the PDR and the amount of genetic diversity in 104 CLL samples and 27 samples of normal B cells.

“We thought the epigenetic structure would map right onto the genetic structure,” said study author Alexander Meissner, PhD, of the Broad Institute of MIT and Harvard in Cambridge, Massachusetts.

“That is, the degree of genetic diversity in each sample would match the variation in methylation marks in an organized fashion.”

To the researchers’ surprise, the methylation patterns showed a tremendous degree of random disarray.

“We know that individual tumors are checkered with genetically distinct groups of cells,” Dr Meissner explained. “Bisulfite sequencing enabled us to see that the placement of methyl groups across tumor cell DNA also varies substantially among cells in the same tumor. In fact, disorderly methylation pervades the entire tumor.”

The results revealed that the diversity within individual tumors apparently proceeds along two independent, yet interrelated tracks: one resulting in a genetic hodgepodge of cell groups, the other resulting in haphazard methylation.

The methylation irregularities, technically known as “local methylation disorder,” were highly evident in CLL and other types of cancer.

Because methyl groups control the expression of genes, disorderly methylation might be expected to cause wildly inconsistent gene activity even within a single tumor. This, in fact, is what the researchers found.

The disruption of methylation machinery might seem hazardous to tumor survival, but the researchers theorize that tumors can turn the disorderliness to their own advantage.

“Just as in the case of genetic heterogeneity within tumors, increased random variation of the epigenetic profile may augment the diversity of malignant cells,” said study author Dan Landau, MD, PhD, of Dana-Farber and the Broad Institute.

“The ability of cancers to maintain high levels of diversity is an effective hedging strategy, enabling them to better adapt to therapy, as well as enhancing the ‘trial and error’ process in search of better evolutionary trajectories.”

“Cancer survives through some wildly inventive ways,” Dr Wu added. “Methylation disorder is one of the ways it creates the conditions that enable it to adapt.”

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CLL drug can fight AML too, study suggests

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SAN FRANCISCO—A BCL2 inhibitor that previously proved active against chronic lymphocytic leukemia has shown activity in certain patients with acute myelogenous leukemia (AML) as well.

This phase 2 trial was the first use of the inhibitor, ABT-199 (or venetoclax), in patients with relapsed or refractory AML.

Five of 32 patients treated with ABT-199 achieved a complete response (CR) or CR with incomplete blood count recovery (CRi), and several more had stable disease.

The drug appeared to be particularly active in patients with IDH mutations.

Marina Konopleva, MD, PhD, of the University of Texas MD Anderson Cancer Center in Houston, presented these results at the 2014 ASH Annual Meeting (abstract 118). The research was funded by AbbVie, Inc., the company developing ABT-199.

The trial was launched on the basis of preclinical studies showing that ABT-199 could kill AML cell lines, patients’ AML cells, and patient-derived AML cells implanted in mice.

The researchers enrolled 32 AML patients, 30 of whom had relapsed or refractory disease. Patients had a median age of 71 (range, 19 to 84), and half were male.

The overall response rate was 15.5%, with 1 patient achieving a CR and 4 patients achieving a CRi. The researchers noted that 3 of the patients who had a CR/CRi had IDH mutations. Two of these patients also achieved minimal residual disease negativity.

The team said these results suggest single-agent ABT-199 can have considerable clinical activity in patients with relapsed or refractory AML, and patients with mutations in IDH genes may be particularly sensitive to the drug.

The researchers also found the median bone marrow blast count in evaluable patients decreased 36% after treatment with ABT-199. And 6 patients (19%) had at least a 50% reduction in bone marrow blasts.

Common adverse events following treatment (occurring in at least 25% of patients) included nausea, diarrhea, fatigue, neutropenia, and vomiting. Grade 3 and 4 adverse events (occurring in 3 or more patients) included febrile neutropenia, anemia, and pneumonia.

No patients died as a result of treatment-related adverse events.

Furthermore, the maximum-tolerated dose was not reached, leaving open the possibility of higher doses in further trials. The next step is to carry out trials combining ABT-199 with other agents. These trials are currently opening at several sites.

AbbVie said ABT-199 will be studied in combination with common AML treatments, and the company is developing ABT-199 for, and evaluating the drug in, several hematologic malignancies.

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SAN FRANCISCO—A BCL2 inhibitor that previously proved active against chronic lymphocytic leukemia has shown activity in certain patients with acute myelogenous leukemia (AML) as well.

This phase 2 trial was the first use of the inhibitor, ABT-199 (or venetoclax), in patients with relapsed or refractory AML.

Five of 32 patients treated with ABT-199 achieved a complete response (CR) or CR with incomplete blood count recovery (CRi), and several more had stable disease.

The drug appeared to be particularly active in patients with IDH mutations.

Marina Konopleva, MD, PhD, of the University of Texas MD Anderson Cancer Center in Houston, presented these results at the 2014 ASH Annual Meeting (abstract 118). The research was funded by AbbVie, Inc., the company developing ABT-199.

The trial was launched on the basis of preclinical studies showing that ABT-199 could kill AML cell lines, patients’ AML cells, and patient-derived AML cells implanted in mice.

The researchers enrolled 32 AML patients, 30 of whom had relapsed or refractory disease. Patients had a median age of 71 (range, 19 to 84), and half were male.

The overall response rate was 15.5%, with 1 patient achieving a CR and 4 patients achieving a CRi. The researchers noted that 3 of the patients who had a CR/CRi had IDH mutations. Two of these patients also achieved minimal residual disease negativity.

The team said these results suggest single-agent ABT-199 can have considerable clinical activity in patients with relapsed or refractory AML, and patients with mutations in IDH genes may be particularly sensitive to the drug.

The researchers also found the median bone marrow blast count in evaluable patients decreased 36% after treatment with ABT-199. And 6 patients (19%) had at least a 50% reduction in bone marrow blasts.

Common adverse events following treatment (occurring in at least 25% of patients) included nausea, diarrhea, fatigue, neutropenia, and vomiting. Grade 3 and 4 adverse events (occurring in 3 or more patients) included febrile neutropenia, anemia, and pneumonia.

No patients died as a result of treatment-related adverse events.

Furthermore, the maximum-tolerated dose was not reached, leaving open the possibility of higher doses in further trials. The next step is to carry out trials combining ABT-199 with other agents. These trials are currently opening at several sites.

AbbVie said ABT-199 will be studied in combination with common AML treatments, and the company is developing ABT-199 for, and evaluating the drug in, several hematologic malignancies.

Pills

Credit: FDA

SAN FRANCISCO—A BCL2 inhibitor that previously proved active against chronic lymphocytic leukemia has shown activity in certain patients with acute myelogenous leukemia (AML) as well.

This phase 2 trial was the first use of the inhibitor, ABT-199 (or venetoclax), in patients with relapsed or refractory AML.

Five of 32 patients treated with ABT-199 achieved a complete response (CR) or CR with incomplete blood count recovery (CRi), and several more had stable disease.

The drug appeared to be particularly active in patients with IDH mutations.

Marina Konopleva, MD, PhD, of the University of Texas MD Anderson Cancer Center in Houston, presented these results at the 2014 ASH Annual Meeting (abstract 118). The research was funded by AbbVie, Inc., the company developing ABT-199.

The trial was launched on the basis of preclinical studies showing that ABT-199 could kill AML cell lines, patients’ AML cells, and patient-derived AML cells implanted in mice.

The researchers enrolled 32 AML patients, 30 of whom had relapsed or refractory disease. Patients had a median age of 71 (range, 19 to 84), and half were male.

The overall response rate was 15.5%, with 1 patient achieving a CR and 4 patients achieving a CRi. The researchers noted that 3 of the patients who had a CR/CRi had IDH mutations. Two of these patients also achieved minimal residual disease negativity.

The team said these results suggest single-agent ABT-199 can have considerable clinical activity in patients with relapsed or refractory AML, and patients with mutations in IDH genes may be particularly sensitive to the drug.

The researchers also found the median bone marrow blast count in evaluable patients decreased 36% after treatment with ABT-199. And 6 patients (19%) had at least a 50% reduction in bone marrow blasts.

Common adverse events following treatment (occurring in at least 25% of patients) included nausea, diarrhea, fatigue, neutropenia, and vomiting. Grade 3 and 4 adverse events (occurring in 3 or more patients) included febrile neutropenia, anemia, and pneumonia.

No patients died as a result of treatment-related adverse events.

Furthermore, the maximum-tolerated dose was not reached, leaving open the possibility of higher doses in further trials. The next step is to carry out trials combining ABT-199 with other agents. These trials are currently opening at several sites.

AbbVie said ABT-199 will be studied in combination with common AML treatments, and the company is developing ABT-199 for, and evaluating the drug in, several hematologic malignancies.

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Crisis Mode and Information Exchange

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Associations between perceived crisis mode work climate and poor information exchange within hospitals

Using electronic health records to improve the continuity of care between hospital units does not replace the need for interpersonal communication to improve transitions of care. Hospital personnel play a critical role in accurately exchanging patient information during patient transfers, a process requiring accurate communication between hospital units to prevent system failures.[1] Because poor communication contributes to preventable adverse events,[2] and effective communication during handoffs decreases medical errors and readmissions,[3] hospitals need to ensure their work environments are conducive to effective communication.

Individuals working under time constraints and heavy workloads could potentially be at high risk of misinterpreting or delivering inaccurate information,[4] partially due to limited ability to accurately process and communicate information under stressful circumstances. Furthermore, because time‐constrained decision makers tend to use less information and less rigorous decision strategies,[5] work climates characterized by staff members doing too many things too quickly could cause patient health information to be lost during transitions of care across hospital units.

Current studies illustrate scenarios in which demanding or time‐constrained work environments caused information exchange errors. One study found that the increased rate of prescribing errors was partially attributed to a high‐demand work environment characterized by working after hours and multitasking.[6] Other studies found that clinicians' limited time to relay and respond to information and ask clarifying questions during patient handoffs was partially attributed to the fast‐faced and chaotic environment of the emergency room.[7, 8] These studies are consistent with another study that found patient handoffs between emergency departments and inpatient wards were inadequate, partially due to less interactive and more rushed communication.[9] The fact that communication breakdowns are widely cited as barriers to patient handoffs[7, 8, 10] and facilitators of medical errors,[7, 8] further underscores the detrimental effect that crisis mode work climates could have on exchanging patient information during transitions of care.

The objective of this analysis was to evaluate the extent to which a crisis mode work climate impacts the occurrence of patient information exchange problems. Estimating associations between hospital staff members' perceptions of crisis mode work climates and perceptions of information exchange problems provide insights as to whether high‐demand and time‐constrained work climates negatively impact the exchange of patient information. Because hospital staff members working under time constraints and heavy workloads could potentially be at high risk of misinterpreting or delivering inaccurate information, we hypothesized that higher levels of a perceived crisis mode work climate would be associated with higher levels of perceived problems with information exchange across hospital units.

METHODS

Data Source

Data originated from the Agency of Healthcare Research and Quality 2010 Hospital Survey on Patient Safety Culture. This validated survey, designed to assess the safety climate within acute‐care settings, remains an important annual survey deployed each year to track changes and factors impacting patient safety.[11] We included only those respondents who self‐reported their position as a nurse, physician, pharmacist, dietician, therapist, technician, patient care assistant, or hospital unit secretary, all of whom are likely responsible for exchanging patient information across hospital units. For this reason, we excluded respondents who self‐reported their position as administrative or miscellaneous. Applying these exclusion criteria resulted in 247,104 respondents across 884 hospitals.

Conceptual Framework

The relationship between perceived crisis mode work climates and patient information exchange problems is likely influenced by staff skill levels, work climate, and infrastructure (Figure 1). With respect to skill levels, hospital staff members with many years of experience compared to those with fewer years may be relatively desensitized to chaotic work environments and consequently have higher thresholds for perceiving crisis modes. Number of hours worked per week likely impacts perceived crisis mode as illustrated in 1 study finding that full‐time nurses reported a significantly lower work pace compared to part‐time nurses.[12] Years of experience likely impacts perception of information exchange problems, particularly if staff members with many years of experience are familiar enough with hospital systems or protocols to easily detect exchange errors or mishaps.

Figure 1
Conceptual framework.

With respect to work climates, workers' perception of cooperation, coordination and patient safety, and specific hospital unit likely impact perceptions of crisis work mode and information exchange problems. For example, hospital staff members reporting high levels of cooperation, coordination, and patient safety likely perceive fewer crisis modes and information exchange problems compared to those in less‐cooperative hospital units. Furthermore, the heterogeneity of work cultures across departments within a hospital results in department‐specific perceptions of crisis mode climates and information exchange problems. Infrastructure factors, such as hospital size, teaching and ownership status, and census region, likely impact the amount of resources available for staffing and infrastructure, which in turn could impact work pace and information exchange accuracy.

Variable Definitions

We defined our predictor as the perceived presence of a crisis mode work climate as captured from the survey questionnaire item: We work in crisis mode trying to do too much, too quickly. This question item had a Likert response scale comprised of the following 5 answer choices: (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, (5) strongly agree. We created a 3‐level response variable by aggregating the agree and disagree responses, respectively, as the first 2 levels, and retaining the neutral response as the third level. Consequently, those responding strongly disagree or disagree were classified as working in lowcrisis mode work climates and those responding strongly agree or agree were classified as working in high crisis mode work climates. We defined our outcome measure as the presence of patient information exchange problems as captured from the survey questionnaire item: Problems often occur in the exchange of information across hospital units. Because this question item had a Likert response scale similar to the crisis mode question predictor, we also created a 3‐level categorical variable in the same fashion. Consequently, those responding strongly disagree or disagree were classified as perceiving no problems exchanging patient information, and those responding strongly agree or agree were classified as perceiving problems exchanging patient information. For the fewer than 10% of the respondents with missing data on either the predictor our outcome variables, the mode measure of central tendency was imputed, a methodology validated in a previous study.[13]

We also included questionnaire items that captured staff skill levels, work climate, and infrastructure as covariates to account for potential confounders (Figure 1). The staff skill levels domain included years of experience working in the hospital, specialty, and unit; current staff position; and extent of patient contact. The work climate domain included respondent perceptions of coordination and cooperation, patient safety, and primary work area or unit in which the provider reported working. The hospital infrastructure domain included bed size, census region, teaching status, and government ownership status. For the fewer than 10% of the respondents with missing data on any of the categorical variables, the mode measure of central tendency was imputed, a methodology validated in a previous study.[13]

Analytic Approach

We used multivariable ordinal regressions to estimate the likelihood of perceived problems in patient information exchange conditional upon perceptions of a crisis mode work climate, controlling for staff skill levels, work climate, and hospital infrastructure. Our estimates therefore reflect the likelihood of hospital staff responding strongly agree or agree to the question Problems often occur in the exchange of information across hospital units conditional upon responding strongly agree or agree to the question We work in crisis mode trying to do too much, too quickly. In addition to controlling for hospital‐specific response rates, we also adjusted our standard errors to account for the clustering of respondents within hospitals. All analyses were conducted in SAS version 9.2 (SAS Institute Inc., Cary, NC).

RESULTS

The hospital sample averaged 279 respondents per hospital with a 56% response rate. Most hospitals were located in the Central region of the United States, and 32% and 19% were teaching and government‐owned hospitals, respectively. Forty‐three percent and 44% of the hospitals in the sample were designated as small and medium hospitals, respectively (Table 1).

Hospital and Survey Respondent Characteristics
Characteristics%
  • NOTE: Abbreviations: ECG, electrocardiography; LPN, licensed practical nurse; LVN, licensed vocational nurse; SD, standard deviation.

Hospital characteristics, N=884 
Bed size 
Small, 19943.5
Medium, 10039943.8
Large, 400 plus12.7
Teaching status 
Yes32.2
No67.8
Government ownership 
Yes19.5
No80.5
Census region 
Mid‐Atlantic and New England8.7
South Atlantic14.8
Central57.2
Mountain7.7
Pacific11.5
Response rate, mean (SD)0.56 (0.28)
Respondents per hospital, mean (SD)279 (358)
Respondent characteristics, N=274,140 
How long have you worked in your current specialty or profession? 
<1 year5.8
15 years32.8
610 years16.2
1115 years12.0
1620 years10.6
21 years22.7
How long have you worked in this hospital? 
<1 year9.8
15 years42.8
610 years17.8
1115 years9.0
1620 years8.2
21 years12.4
How long have you worked in your current hospital work area/unit? 
<1 year13.1
15 years48.0
610 years18.1
1115 years8.1
1620 years6.0
21 years6.7
Typically, how many hours per week do you work in this hospital? 
<20 hours4.8
2039 hours39.9
4059 hours48.8
6079 hours4.2
8099 hours2.1
100 hours0.11
What is your staff position in this hospital? 
Registered nurse51.2
Technician (eg, ECG, lab, radiology)14.1
Unit assistant/clerk/secretary8.5
Patient care assistant/hospital aide/care partner7.4
Physical, occupational, or speech therapist3.7
Attending/staff physician3.5
LVN/LPN3.0
Respiratory therapist2.9
Pharmacist2.2
Physician assistant/nurse practitioner1.4
Resident physician/physician in training1.2
Dietician0.83
In your staff position, do you typically have direct interaction or contact with patients? 
Yes86.6
No13.4
What is your primary work area or unit in this hospital? 
Other27.7
Medicine (nonsurgical)11.1
Surgery10.0
Intensive care unit (any type)8.6
Many different hospital units/no specific unit6.8
Radiology6.2
Emergency department5.8
Obstetrics4.9
Laboratory4.9
Rehabilitation4.2
Pediatrics3.8
Pharmacy3.2
Psychiatry/mental health2.1
Anesthesiology0.55

Thirty‐seven percent of the respondents have worked in their current specialty or profession for 5 years or less (Table 1). Over half of the respondents have worked in their current hospital for 5 years or less, whereas 61% have worked in their current unit within the hospital for 5 years or less. Forty‐nine percent work at least 40 hours per week. Registered nurses and technicians represented the 2 largest subgroups of staff positions, comprising 51% and 14% of the sample, respectively. Dieticians and resident physicians, on the other hand, represented the 2 smallest subgroups of staff positions, comprising 0.83% and 1.2% of the sample, respectively. Eighty‐seven percent of the respondents have direct interaction or contact with patients. Apart from those responding other as their hospital unit, nonsurgical medicine and surgery represented the largest subgroup primary work areas, comprising 11% and 10% of the sample, respectively. In contrast, psychiatry and anesthesiology represented the 2 smallest subgroups of primary work areas, comprising 2.1% and 0.55% of the sample, respectively (Table 1).

Respondents scored relatively high with regard to teamwork and helping each other out under hurried or busy circumstances. For example, 85% agreed or strongly agreed that their unit worked together as a team to get work done when a lot of work needed to be completed quickly, and 68% agreed or strongly agreed that individuals within their unit helped out when an area in their unit became busy (Table 1). Despite this cooperation, 31% agreed or strongly agreed that hospital units did not coordinate well together. Paradoxically, 57% agreed or strongly agreed that there was good cooperation among hospital units that needed to work together. Seventy‐five percent of the respondents reported excellent or very good patient safety levels within their unit, although 53% agreed or strongly agreed that staff worked longer hours than was best for patient care (Table 1).

With regard to perceived crisis mode work climate, 32% and 47% reported agreeing and disagreeing, respectively, that their work unit worked in crisis mode trying to do too much too quickly (Table 2). With regard to perceived problems with patient information exchange, 27% and 36% reported agreeing and disagreeing, respectively, that information exchange problems occurred across hospital units (Table 2).

Survey Respondent Work Environment Perceptions (N=247,140)
Perceptions%
We work in crisis mode trying to do too much, too quickly 
Strongly disagree8.1
Disagree39.2
Neutral21.0
Agree24.3
Strongly agree7.5
Problems often occur in the exchange of information across hospital units 
Strongly disagree4.6
Disagree31.3
Neutral37.3
Agree24.0
Strongly agree2.7
When a lot of work needs to be done quickly, we work together as a team to get the work done. 
Strongly disagree1.5
Disagree6.1
Neutral7.5
Agree53.6
Strongly agree31.2
When one area in this unit gets really busy, others help out. 
Strongly disagree3.9
Disagree13.9
Neutral13.7
Agree52.6
Strongly agree15.8
Hospital units do not coordinate well with each other. 
Strongly disagree5.6
Disagree38.8
Neutral23.7
Agree25.3
Strongly agree6.6
There is good cooperation among hospital units that need to work together. 
Strongly disagree2.7
Disagree15.1
Neutral24.7
Agree51.1
Strongly agree6.3
Please give your work area/unit in this hospital an overall grade on patient safety. 
Excellent23.0
Very good49.8
Acceptable21.8
Poor4.6
Failing0.76
Staff in this unit work longer hours than is best for patient care. 
Strongly disagree11.5
Disagree42.2
Neutral23.6
Agree18.4
Strongly agree6.3

In the unadjusted analyses, crisis mode perceptions and information exchange problem perceptions were significantly associated. Among those who agreed that their work unit worked in crisis mode, a larger proportion of respondents agreed (41%) versus disagreed (24%) that problems often occurred in exchanging patient information across units (Table 3). In contrast, among those who disagreed that their work unit worked in crisis mode, a larger proportion of respondents disagreed (47%) versus agreed (19%) that problems often occurred in exchanging patient information across units (Table 3).

Bivariate Frequency Distribution of Respondents' Perceptions of Crisis Mode Work Climate and Patient Information Exchange Problems Between Hospital Units
 Problems Often Occur in Exchange of Information Across Hospital Units
 Agree (N=66,115), Row %*Neutral (N=92,228), Row %Disagree (N=88,797), Row %
  • NOTE: *Agree or strongly agree that problems often occur in exchange of information across hospital units. Neutral response that problems often occur in exchange of information across hospital units. Disagree or strongly disagree that problems often occur in exchange of information across hospital units. Agree or strongly agree that we work in crisis mode trying to do too much too quickly. ‖Neutral response that we work in crisis mode trying to do too much too quickly. Disagree or strongly disagree that we work in crisis mode trying to do too much too quickly.

Crisis Mode Work Climate   
Agree (N=78,253)40.835.423.8
Neutral (N=51,836)22.948.928.2
Disagree (N=116,781)19.033.547.5

In the multivariable ordinal regression, compared to those who disagreed that their unit worked in crisis mode, those who agreed were 1.6 times more likely to report that problems often occurred in exchanging patient information across units (odds ratio [OR]: 1.6, 95% confidence interval [CI]: 1.58‐1.65) (Table 4). Additionally, some key covariates were independently associated with perceptions of information exchange problems. Two of these covariates measured workplace coordination. Those who reported that hospital units did not cooperate well together were more likely to report problematic information exchange compared to those who reported that hospital units did cooperate well (OR: 4.7, 95% CI: 4.35.0). Relatedly, those who reported that hospital units did coordinate well were less likely to report problematic information exchange compared to those who reported that hospital units did not coordinate well (OR: 0.10, 95% CI: 0.10‐0.11). Two other covariates measured patient contact and perceptions about long working hours. Those who reported having direct interaction or contact with patients were less likely to report problematic information exchange compared to those who reported not having direct interaction or contact with patients (OR: 0.85, 95% CI: 0.83‐0.87). Those who reported that staff did not work longer hours than was better for patient care were less likely to report problematic information exchange compared to those who did report working longer hours than was better for patient care (OR: 0.76, 95% CI: 0.73 0.79). One covariate measured hospital size. Those who reported working in smaller hospitals were less likely to report problematic information exchange compared to those reporting working in large hospitals (OR: 0.66, 95% CI 0.59‐0.75) (Table 4).

Multivariate Ordinal Regression Results Illustrating Likelihood of Perceiving Information Exchange Problems Across Hospital Units Conditional Upon Crisis Mode Work Climate
CharacteristicUnadjusted OR (95% CI)Adjusted OR* (95% CI)
  • NOTE: Abbreviations: CI, confidence interval; ECG, electrocardiography; LPN, licensed practical nurse; LVN, licensed vocational nurse; OR, odds ratio. *Controlling for hospital characteristics, respondent characteristics, and respondent perceptions as measured by covariates listed within the table. Agree or strongly agree that we work in crisis mode trying to do too much too quickly. Neutral response that we work in crisis mode trying to do too much too quickly. Disagree or strongly disagree that we work in crisis mode trying to do too much too quickly.

Primary predictor of interest
Crisis mode work climate
Agree3.0 (2.9‐3.1)1.6 (1.5‐1.6)
Neutral1.8 (1.7‐1.8)1.3 (1.2‐1.3)
DisagreeReferenceReference
Hospital characteristics
Bed Size
Small, 6240.51 (0.44‐0.59)0.66 (0.59‐0.75)
Small, 2490.59 (0.53‐0.66)0.77 (0.70‐0.84)
Small, 50990.65 (0.58‐0.73)0.78 (0.71‐0.84)
Medium, 1001990.85 (0.77‐0.95)0.92 (0.86‐1.0)
Medium, 2002991.0 (0.98‐1.1)0.97 (0.90‐1.0)
Medium, 3003990.96 (0.85‐1.1)1.0 (0.92‐1.1)
Large, 4004990.99 (0.86‐1.1)0.96 (0.87‐1.0)
Large, 500 plusReferenceReference
Teaching status
No0.81 (0.76‐0.87)1.0 (0.95‐1.0)
YesReferenceReference
Government ownership
No1.1 (1.01.2)1.0 (0.98‐1.1)
YesReferenceReference
Census region
Mid‐Atlantic and New England1.0 (0.88‐1.1)0.91 (0.84‐0.99)
South Atlantic0.95 (0.85‐1.1)1.0 (0.95‐1.1)
Central 10.95 (0.85‐1.0)0.95 (0.89‐1.0)
Central 20.71 (0.62‐0.81)0.91 (0.83‐0.99)
Central 30.80 (0.71‐0.91)0.97 (0.90‐1.0)
Central 40.76 (0.68‐0.86)0.93 (0.85‐1.0)
Mountain0.84 (0.73‐0.96)0.98 (0.90‐1.1)
PacificReferenceReference
Average survey response rate within hospital0.65 (0.58‐0.72)0.93 (0.82‐1.0)
Respondent characteristics
How long have you worked in your current specialty or profession?
<1 year0.75 (0.73‐0.78)1.03 (0.99‐1.1)
15 years0.99 (0.97‐1.0)1.1 (1.1‐1.1)
610 years1.0 (1.01.1)0.99 (0.96‐1.0)
1115 years1.0 (1.01.1)1.0 (0.97‐1.0)
1620 years1.0 (0.98‐1.0)0.97 (0.94‐1.0)
21 yearsReferenceReference
How long have you worked in this hospital?
<1 year0.75 (0.73‐0.77)0.90 (0.85‐0.90)
15 years1.03 (1.001.05)0.99 (0.95‐1.0)
610 years1.1 (1.1‐1.1)0.99 (0.95‐1.0)
1115 years1.1 (1. 01.1)1.0 (0.96‐1.0)
1620 years1.1 (1.01.1)0.98 (0.94‐1.0)
21 yearsReferenceReference
How long have you worked in your current hospital work area/unit?
<1 year0.79 (0.76‐0.82)0.98 (0.93‐1.0)
15 years1.0 (1.01.1)1.0 (0.99‐1.1)
610 years1.1 (1.1‐1.1)1.0 (1.01.1)
1115 years1.1 (1.01.1)1.0 (0.99‐1.1)
1620 years1.1 (1.01.1)1.1 (1.01.1)
21 yearsReferenceReference
Typically, how many hours per week do you work in this hospital?
<200.63 (0.50‐0.79)0.91 (0.72‐1.2)
20390.75 (0.59‐0.94)0.90 (0.71‐1.1)
40590.87 (0.69‐1.1)1.1 (0.85‐1.4)
60790.95 (0.75‐1.2)1.0 (0.82‐1.3)
80990.99 (0.78‐1.2)1.1 (0.86‐1.4)
100ReferenceReference
What is your staff position in this hospital?
Registered nurse0.92 (0.90‐0.94)1.1 (0.98‐1.0)
Technician (eg, ECG, lab, radiology)ReferenceReference
Unit assistant/clerk/secretary0.79 (0.76‐0.81)0.94 (0.80‐0.96)
Patient care assistant/hospital aide/care partner0.78 (0.75‐0.81)0.96 (0.90‐0.98)
Physical, occupational, or speech therapist0.88 (0.84‐0.92)1.2 (1.1‐1.2)
Attending/staff physician1.0 (0.97‐1.1)1.3 (1.2‐1.3)
LVN/LPN0.89 (0.85‐0.94)1.0 (0.92‐1.0)
Respiratory therapist0.84 (0.80‐0.88)0.97 (0.89‐1.0)
Pharmacist1.5 (1.4‐1.6)1.3 (1.1‐1.3)
Physician assistant/nurse practitioner0.93 (0.87‐1.0)1.2 (1.1‐1.2)
Resident physician/physician in training0.96 (0.89‐1.0)1.3 (1.2‐1.4)
Dietician0.86 (0.79‐0.94)1.2 (1.1‐1.3)
In your staff position, do you typically have direct interaction or contact with patients?
Yes0.83 (0.82‐0.85)0.85 (0.83‐0.87)
NoReferenceReference
What is your primary work area or unit in this hospital?
OtherReferenceReference
Medicine (nonsurgical)1.1 (1.01.1)0.84 (0.82‐0.89)
Surgery1.1 (1.1‐1.2)0.88 (0.86‐0.91)
Intensive care unit (any type)0.93 (0.90‐0.96)0.78 (0.76‐0.81)
Many different hospital units/no specific unit1.2 (1.1‐1.2)1.0 (0.98‐ 1.0)
Radiology1.1 (1.1‐1.1)1.0 (1.01.1)
Emergency department1.0 (0.97‐1.0)0.57 (0.55‐0.60)
Obstetrics0.76 (0.73‐0.79)0.66 (0.63‐0.69)
Laboratory1.2 (1.2‐1.3)1.0 (1.01.1)
Rehabilitation1.0 (0.97‐1.0)1.0 (0.98‐1.1)
Pediatrics0.90 (0.86‐0.94)0.83 (0.80‐0.87)
Pharmacy1.6 (1.5‐1.7)1.1 (1.01.2)
Psychiatry/mental health1.2 (1.1‐1.2)0.96 (0.90‐1.0)
Anesthesiology1.1 (1.01.3)0.93 (0.83‐1.0)
Respondent perceptions
When a lot of work needs to be done quickly, we work together as a team to get the work done.
Strongly disagree3.2 (3.03.4)1.0 (0.98‐1.1)
Disagree3.2 (3.13.3)1.0 (1.01.1)
Neutral2.3 (2.2‐2.4)0.98 (0.94‐1.0)
Agree2.3 (2.2‐2.4)1.0 (1.002‐1.04)
Strongly agreeReferenceReference
Staff in this unit work longer hours than is best for patient care.
Strongly disagree0.51 (0.48‐0.53)0.76 (0.73‐0.79)
Disagree0.68 (0.67‐0.70)0.81 (0.78‐0.84)
Neutral0.94 (0.91‐0.97)0.93 (0.90‐0.97)
Agree1.0 (0.99‐1.1)0.94 (0.91‐0.98)
Strongly agreeReferenceReference
When 1 area in this unit gets really busy, others help out.
Strongly disagree3.8 (3.7 ‐ 4.0)1.0 (0.96‐1.1)
Disagree3.0 (2.9‐3.1)1.0 (0.99‐1.1)
Neutral2.2 (2.12.3)1.0 (0.97‐1.0)
Agree1.5 (1.5‐1.6)0.99 (0.96‐1.0)
Strongly agreeReferenceReference
Hospital units do not coordinate well with each other.
Strongly disagree0.03 (0.03‐0.04)0.10 (0.10‐0.11)
Disagree0.08 (0.08‐0.08)0.18 (0.17‐0.19)
Neutral0.21 (0.20‐0.22)0.32 (0.30‐0.33)
Agree0.50 (0.48‐0.52)0.61 (0.58‐0.63)
Strongly agreeReferenceReference
There is good cooperation among hospital units that need to work together.
Strongly disagree20.1 (18.921.5)4.7 (4.35.0)
Disagree14.2 (13.614.9)4.2 (4.14.5)
Neutral6.7 (6.47.0)2.7 (2.6‐2.8)
Agree2.4 (2.3‐2.5)1.6 (1.6‐1.7)
Strongly agreeReferenceReference
Please give your work area/unit in this hospital an overall grade on patient safety
Excellent0.13 (0.12‐0.14)0.47 (0.42‐0.52)
Very good0.24 (0.21‐0.26)0.63 (0.57‐0.70)
Acceptable0.49 (0.45‐0.54)0.79 (0.72‐0.88)
Poor0.83 (0.75‐0.92)0.92 (0.83‐1.03)
FailingReferenceReference

DISCUSSION

Our results illustrate that when hospital staff agree that their hospital works in crisis mode, they are more likely to agree that their hospital unit had frequent problems exchanging patient information across units. Because hospital staff working under time constraints and heavy workloads could potentially be at risk of misinterpreting or delivering inaccurate information, these results imply that crisis mode work climates increase the risk of problematic health information exchange. An equally plausible interpretation could be that problematic patient health information exchange increases the risk of hospital staff perceiving crisis mode work climates. Given that information gaps are associated with patient handoff errors,[14] and that patient handoff errors are associated with adverse events,[2, 3, 6, 8] an urgent need exists to implement information exchange systems that prevent information gaps from harming patients. Consequently, hospitals need to implement workflow strategies that prevent information gaps from undermining patient safety during transitions of care.

Other factors affect information exchange apart from crisis mode work climate, as illustrated by the significant associations of key covariates in the multivariate model. The effect found between perceived coordination and information exchange implies that improving information exchange requires good cooperation and coordination. The effect found between patient contact and information exchange implies that working directly with patients improves either the accuracy or the perception of information exchange. Finally, the effect found between hospital size and information exchange suggests that small hospitals are less likely than large hospitals to have information exchange problem. The geographical dispersion and the complexity of larger institutions could result in information exchange problems due to more confusion and less in‐person communication.

Because problematic patient information exchanges are associated with hospital size, coordination, and patient contact, in addition to crisis mode work climate, multifaceted solutions are necessary to resolve the problem. For example, hospital interventions designed to improve coordination could in turn attenuate perceived crisis modes. Furthermore, tailoring these interventions to hospitals that belong to complex geographically dispersed provider networks would likely decrease errors during transitions of care. Because multiple factors cause information exchange problems, implementing interventions that improve both coordination and crisis mode work climates would likely result in a greater net improvement compared to interventions focused solely on decreasing crisis mode work climates.

Some limitations of our paper are worth noting. First, we did not have information on the volume of data exchanged or the functionality levels of the electronic health record systems, both of which likely impact the accuracy of patient information exchange. For example, hospitals with smaller versus larger amounts of data exchanged could be less prone to error. On the other hand, this risk of error could be reduced even further by implementing robust health information technology (IT) systems that improve the accuracy of information transfer. This is consistent with studies showing that hospitals without computerized provider order entry (CPOE) systems have been shown to have higher medication error rates compared to those hospitals with CPOE systems.[15] Therefore, omitting data volume and health IT capabilities from the multivariate model could introduce unobserved heterogeneity, resulting in biased associations between perceived crisis mode work climate and perceived information exchange problems. Second, the cross‐sectional design limits our ability to infer causality because we are not certain whether the perceived crisis mode occurred before, after, or simultaneously to perceived information exchange problems. Third, the self‐reported nature of the questionnaire items does not provide information on observed levels of crisis mode and exchange problems, which could be inconsistent with perceived levels. Fourth, the relatively low within‐hospital response rate decreases the external validity of our findings. For example, if responders' perceptions of crisis mode or information exchange problems significantly differed from nonresponders, our results would not be generalizable to the larger population of acute‐care hospitals across the United States. Therefore, conclusions should be viewed with caution if applying these results to hospitals with respondents significantly differing from those contained within our sample.

Despite these limitations, the large sample size in conjunction with the use of data from a survey having acceptable psychometric properties[16] strengthens the external and internal validity of our findings. Although questionnaire items measuring perceptions are relatively subjective in nature compared to using metrics that capture observed problems or crisis modes, we argue that staff perception data are equally informative, as they guide organization leaders on how to improve workplace performance. Because a core concept of high reliability organizations (HROs) is to preserve constant awareness by key leaders and staff of the state of the systems and processes that affect patient care,[17] HROs could benefit from knowing the extent to which staff perceptions impact patient care. From a methods perspective, the multivariable ordinal regressions enabled us to control for potential confounders that if omitted could have resulted in biased estimates. Furthermore, low levels of multicollinearity as illustrated by low variation inflation factors enabled us to isolate the independent effect of crisis mode perceptions. Including hospital size and hospital work unit as covariates was an additional methodological strength helping account for the unobserved heterogeneity caused by excluding volume of data exchanged or health IT system capability. For example, because larger compared to smaller hospitals usually have more sophisticated health IT systems,[15] including bed size in the model theoretically captures some of the variation that would have been captured if we were able to include a covariate measuring health IT capability. Last, using ordinal regression facilitates interpretation of the findings because the questionnaire items for the predictor and outcome were originally captured on a Likert scale.

Our findings underscore the significant impact that work climate has on accurate information exchange, and ultimately patient safety. Improving patient safety is imperative for hospitals, especially within the context of recent regulations stemming from the Affordable Care Act that incentivize hospitals to reduce readmissions[18] and improve transitions of care.[19] Because accurate health information exchange is a critical component of patient care, resolving barriers that decrease the accuracy of this exchange is essential. Therefore, future studies need to continue examining these associations within the context of study designs that incorporate longitudinal data and datasets that include objective measures capturing crisis mode work climates and information exchange problems. Because effective communication during handoffs is associated with decreases in medical errors and readmissions, hospitals need to continually ensure that work environments are conducive to effective patient information exchange.

Disclosures

Nothing to report

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References
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  3. Starmer AJ, Sectish TC, Simon DW, et al. Rates of medical errors and preventable adverse events among hospitalized children following implementation of a resident handoff bundle. JAMA. 2013;310(21):22622270.
  4. Frey LR, Gouran D, Poole MS. The Handbook of Group Communication Theory and Research. Thousand Oaks, CA: Sage Publications; 1999.
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  6. Nichols P, Copeland TS, Craib IA, Hopkins P, Bruce DG. Learning from error: identifying contributory causes of medication errors in an Australian hospital. Med J Aust. 2008;188(5):276279.
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  8. Singer JI, Dean J. Emergency physician intershift handovers: an analysis of our transitional care. Pediatr Emerg Care. 2006;22(10):751754.
  9. Horwitz LI, Meredith T, Schuur JD, Shah NR, Kulkarni RG, Jenq GY. Dropping the baton: a qualitative analysis of failures during the transition from emergency department to inpatient care. Ann Emerg Med. 2009;53(6):701710.
  10. Solet DJ, Norvell JM, Rutan GH, Frankel RM. Lost in translation: challenges and opportunities in physician‐to‐physician communication during patient handoffs. Acad Med. 2005;80(12):10941099.
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Using electronic health records to improve the continuity of care between hospital units does not replace the need for interpersonal communication to improve transitions of care. Hospital personnel play a critical role in accurately exchanging patient information during patient transfers, a process requiring accurate communication between hospital units to prevent system failures.[1] Because poor communication contributes to preventable adverse events,[2] and effective communication during handoffs decreases medical errors and readmissions,[3] hospitals need to ensure their work environments are conducive to effective communication.

Individuals working under time constraints and heavy workloads could potentially be at high risk of misinterpreting or delivering inaccurate information,[4] partially due to limited ability to accurately process and communicate information under stressful circumstances. Furthermore, because time‐constrained decision makers tend to use less information and less rigorous decision strategies,[5] work climates characterized by staff members doing too many things too quickly could cause patient health information to be lost during transitions of care across hospital units.

Current studies illustrate scenarios in which demanding or time‐constrained work environments caused information exchange errors. One study found that the increased rate of prescribing errors was partially attributed to a high‐demand work environment characterized by working after hours and multitasking.[6] Other studies found that clinicians' limited time to relay and respond to information and ask clarifying questions during patient handoffs was partially attributed to the fast‐faced and chaotic environment of the emergency room.[7, 8] These studies are consistent with another study that found patient handoffs between emergency departments and inpatient wards were inadequate, partially due to less interactive and more rushed communication.[9] The fact that communication breakdowns are widely cited as barriers to patient handoffs[7, 8, 10] and facilitators of medical errors,[7, 8] further underscores the detrimental effect that crisis mode work climates could have on exchanging patient information during transitions of care.

The objective of this analysis was to evaluate the extent to which a crisis mode work climate impacts the occurrence of patient information exchange problems. Estimating associations between hospital staff members' perceptions of crisis mode work climates and perceptions of information exchange problems provide insights as to whether high‐demand and time‐constrained work climates negatively impact the exchange of patient information. Because hospital staff members working under time constraints and heavy workloads could potentially be at high risk of misinterpreting or delivering inaccurate information, we hypothesized that higher levels of a perceived crisis mode work climate would be associated with higher levels of perceived problems with information exchange across hospital units.

METHODS

Data Source

Data originated from the Agency of Healthcare Research and Quality 2010 Hospital Survey on Patient Safety Culture. This validated survey, designed to assess the safety climate within acute‐care settings, remains an important annual survey deployed each year to track changes and factors impacting patient safety.[11] We included only those respondents who self‐reported their position as a nurse, physician, pharmacist, dietician, therapist, technician, patient care assistant, or hospital unit secretary, all of whom are likely responsible for exchanging patient information across hospital units. For this reason, we excluded respondents who self‐reported their position as administrative or miscellaneous. Applying these exclusion criteria resulted in 247,104 respondents across 884 hospitals.

Conceptual Framework

The relationship between perceived crisis mode work climates and patient information exchange problems is likely influenced by staff skill levels, work climate, and infrastructure (Figure 1). With respect to skill levels, hospital staff members with many years of experience compared to those with fewer years may be relatively desensitized to chaotic work environments and consequently have higher thresholds for perceiving crisis modes. Number of hours worked per week likely impacts perceived crisis mode as illustrated in 1 study finding that full‐time nurses reported a significantly lower work pace compared to part‐time nurses.[12] Years of experience likely impacts perception of information exchange problems, particularly if staff members with many years of experience are familiar enough with hospital systems or protocols to easily detect exchange errors or mishaps.

Figure 1
Conceptual framework.

With respect to work climates, workers' perception of cooperation, coordination and patient safety, and specific hospital unit likely impact perceptions of crisis work mode and information exchange problems. For example, hospital staff members reporting high levels of cooperation, coordination, and patient safety likely perceive fewer crisis modes and information exchange problems compared to those in less‐cooperative hospital units. Furthermore, the heterogeneity of work cultures across departments within a hospital results in department‐specific perceptions of crisis mode climates and information exchange problems. Infrastructure factors, such as hospital size, teaching and ownership status, and census region, likely impact the amount of resources available for staffing and infrastructure, which in turn could impact work pace and information exchange accuracy.

Variable Definitions

We defined our predictor as the perceived presence of a crisis mode work climate as captured from the survey questionnaire item: We work in crisis mode trying to do too much, too quickly. This question item had a Likert response scale comprised of the following 5 answer choices: (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, (5) strongly agree. We created a 3‐level response variable by aggregating the agree and disagree responses, respectively, as the first 2 levels, and retaining the neutral response as the third level. Consequently, those responding strongly disagree or disagree were classified as working in lowcrisis mode work climates and those responding strongly agree or agree were classified as working in high crisis mode work climates. We defined our outcome measure as the presence of patient information exchange problems as captured from the survey questionnaire item: Problems often occur in the exchange of information across hospital units. Because this question item had a Likert response scale similar to the crisis mode question predictor, we also created a 3‐level categorical variable in the same fashion. Consequently, those responding strongly disagree or disagree were classified as perceiving no problems exchanging patient information, and those responding strongly agree or agree were classified as perceiving problems exchanging patient information. For the fewer than 10% of the respondents with missing data on either the predictor our outcome variables, the mode measure of central tendency was imputed, a methodology validated in a previous study.[13]

We also included questionnaire items that captured staff skill levels, work climate, and infrastructure as covariates to account for potential confounders (Figure 1). The staff skill levels domain included years of experience working in the hospital, specialty, and unit; current staff position; and extent of patient contact. The work climate domain included respondent perceptions of coordination and cooperation, patient safety, and primary work area or unit in which the provider reported working. The hospital infrastructure domain included bed size, census region, teaching status, and government ownership status. For the fewer than 10% of the respondents with missing data on any of the categorical variables, the mode measure of central tendency was imputed, a methodology validated in a previous study.[13]

Analytic Approach

We used multivariable ordinal regressions to estimate the likelihood of perceived problems in patient information exchange conditional upon perceptions of a crisis mode work climate, controlling for staff skill levels, work climate, and hospital infrastructure. Our estimates therefore reflect the likelihood of hospital staff responding strongly agree or agree to the question Problems often occur in the exchange of information across hospital units conditional upon responding strongly agree or agree to the question We work in crisis mode trying to do too much, too quickly. In addition to controlling for hospital‐specific response rates, we also adjusted our standard errors to account for the clustering of respondents within hospitals. All analyses were conducted in SAS version 9.2 (SAS Institute Inc., Cary, NC).

RESULTS

The hospital sample averaged 279 respondents per hospital with a 56% response rate. Most hospitals were located in the Central region of the United States, and 32% and 19% were teaching and government‐owned hospitals, respectively. Forty‐three percent and 44% of the hospitals in the sample were designated as small and medium hospitals, respectively (Table 1).

Hospital and Survey Respondent Characteristics
Characteristics%
  • NOTE: Abbreviations: ECG, electrocardiography; LPN, licensed practical nurse; LVN, licensed vocational nurse; SD, standard deviation.

Hospital characteristics, N=884 
Bed size 
Small, 19943.5
Medium, 10039943.8
Large, 400 plus12.7
Teaching status 
Yes32.2
No67.8
Government ownership 
Yes19.5
No80.5
Census region 
Mid‐Atlantic and New England8.7
South Atlantic14.8
Central57.2
Mountain7.7
Pacific11.5
Response rate, mean (SD)0.56 (0.28)
Respondents per hospital, mean (SD)279 (358)
Respondent characteristics, N=274,140 
How long have you worked in your current specialty or profession? 
<1 year5.8
15 years32.8
610 years16.2
1115 years12.0
1620 years10.6
21 years22.7
How long have you worked in this hospital? 
<1 year9.8
15 years42.8
610 years17.8
1115 years9.0
1620 years8.2
21 years12.4
How long have you worked in your current hospital work area/unit? 
<1 year13.1
15 years48.0
610 years18.1
1115 years8.1
1620 years6.0
21 years6.7
Typically, how many hours per week do you work in this hospital? 
<20 hours4.8
2039 hours39.9
4059 hours48.8
6079 hours4.2
8099 hours2.1
100 hours0.11
What is your staff position in this hospital? 
Registered nurse51.2
Technician (eg, ECG, lab, radiology)14.1
Unit assistant/clerk/secretary8.5
Patient care assistant/hospital aide/care partner7.4
Physical, occupational, or speech therapist3.7
Attending/staff physician3.5
LVN/LPN3.0
Respiratory therapist2.9
Pharmacist2.2
Physician assistant/nurse practitioner1.4
Resident physician/physician in training1.2
Dietician0.83
In your staff position, do you typically have direct interaction or contact with patients? 
Yes86.6
No13.4
What is your primary work area or unit in this hospital? 
Other27.7
Medicine (nonsurgical)11.1
Surgery10.0
Intensive care unit (any type)8.6
Many different hospital units/no specific unit6.8
Radiology6.2
Emergency department5.8
Obstetrics4.9
Laboratory4.9
Rehabilitation4.2
Pediatrics3.8
Pharmacy3.2
Psychiatry/mental health2.1
Anesthesiology0.55

Thirty‐seven percent of the respondents have worked in their current specialty or profession for 5 years or less (Table 1). Over half of the respondents have worked in their current hospital for 5 years or less, whereas 61% have worked in their current unit within the hospital for 5 years or less. Forty‐nine percent work at least 40 hours per week. Registered nurses and technicians represented the 2 largest subgroups of staff positions, comprising 51% and 14% of the sample, respectively. Dieticians and resident physicians, on the other hand, represented the 2 smallest subgroups of staff positions, comprising 0.83% and 1.2% of the sample, respectively. Eighty‐seven percent of the respondents have direct interaction or contact with patients. Apart from those responding other as their hospital unit, nonsurgical medicine and surgery represented the largest subgroup primary work areas, comprising 11% and 10% of the sample, respectively. In contrast, psychiatry and anesthesiology represented the 2 smallest subgroups of primary work areas, comprising 2.1% and 0.55% of the sample, respectively (Table 1).

Respondents scored relatively high with regard to teamwork and helping each other out under hurried or busy circumstances. For example, 85% agreed or strongly agreed that their unit worked together as a team to get work done when a lot of work needed to be completed quickly, and 68% agreed or strongly agreed that individuals within their unit helped out when an area in their unit became busy (Table 1). Despite this cooperation, 31% agreed or strongly agreed that hospital units did not coordinate well together. Paradoxically, 57% agreed or strongly agreed that there was good cooperation among hospital units that needed to work together. Seventy‐five percent of the respondents reported excellent or very good patient safety levels within their unit, although 53% agreed or strongly agreed that staff worked longer hours than was best for patient care (Table 1).

With regard to perceived crisis mode work climate, 32% and 47% reported agreeing and disagreeing, respectively, that their work unit worked in crisis mode trying to do too much too quickly (Table 2). With regard to perceived problems with patient information exchange, 27% and 36% reported agreeing and disagreeing, respectively, that information exchange problems occurred across hospital units (Table 2).

Survey Respondent Work Environment Perceptions (N=247,140)
Perceptions%
We work in crisis mode trying to do too much, too quickly 
Strongly disagree8.1
Disagree39.2
Neutral21.0
Agree24.3
Strongly agree7.5
Problems often occur in the exchange of information across hospital units 
Strongly disagree4.6
Disagree31.3
Neutral37.3
Agree24.0
Strongly agree2.7
When a lot of work needs to be done quickly, we work together as a team to get the work done. 
Strongly disagree1.5
Disagree6.1
Neutral7.5
Agree53.6
Strongly agree31.2
When one area in this unit gets really busy, others help out. 
Strongly disagree3.9
Disagree13.9
Neutral13.7
Agree52.6
Strongly agree15.8
Hospital units do not coordinate well with each other. 
Strongly disagree5.6
Disagree38.8
Neutral23.7
Agree25.3
Strongly agree6.6
There is good cooperation among hospital units that need to work together. 
Strongly disagree2.7
Disagree15.1
Neutral24.7
Agree51.1
Strongly agree6.3
Please give your work area/unit in this hospital an overall grade on patient safety. 
Excellent23.0
Very good49.8
Acceptable21.8
Poor4.6
Failing0.76
Staff in this unit work longer hours than is best for patient care. 
Strongly disagree11.5
Disagree42.2
Neutral23.6
Agree18.4
Strongly agree6.3

In the unadjusted analyses, crisis mode perceptions and information exchange problem perceptions were significantly associated. Among those who agreed that their work unit worked in crisis mode, a larger proportion of respondents agreed (41%) versus disagreed (24%) that problems often occurred in exchanging patient information across units (Table 3). In contrast, among those who disagreed that their work unit worked in crisis mode, a larger proportion of respondents disagreed (47%) versus agreed (19%) that problems often occurred in exchanging patient information across units (Table 3).

Bivariate Frequency Distribution of Respondents' Perceptions of Crisis Mode Work Climate and Patient Information Exchange Problems Between Hospital Units
 Problems Often Occur in Exchange of Information Across Hospital Units
 Agree (N=66,115), Row %*Neutral (N=92,228), Row %Disagree (N=88,797), Row %
  • NOTE: *Agree or strongly agree that problems often occur in exchange of information across hospital units. Neutral response that problems often occur in exchange of information across hospital units. Disagree or strongly disagree that problems often occur in exchange of information across hospital units. Agree or strongly agree that we work in crisis mode trying to do too much too quickly. ‖Neutral response that we work in crisis mode trying to do too much too quickly. Disagree or strongly disagree that we work in crisis mode trying to do too much too quickly.

Crisis Mode Work Climate   
Agree (N=78,253)40.835.423.8
Neutral (N=51,836)22.948.928.2
Disagree (N=116,781)19.033.547.5

In the multivariable ordinal regression, compared to those who disagreed that their unit worked in crisis mode, those who agreed were 1.6 times more likely to report that problems often occurred in exchanging patient information across units (odds ratio [OR]: 1.6, 95% confidence interval [CI]: 1.58‐1.65) (Table 4). Additionally, some key covariates were independently associated with perceptions of information exchange problems. Two of these covariates measured workplace coordination. Those who reported that hospital units did not cooperate well together were more likely to report problematic information exchange compared to those who reported that hospital units did cooperate well (OR: 4.7, 95% CI: 4.35.0). Relatedly, those who reported that hospital units did coordinate well were less likely to report problematic information exchange compared to those who reported that hospital units did not coordinate well (OR: 0.10, 95% CI: 0.10‐0.11). Two other covariates measured patient contact and perceptions about long working hours. Those who reported having direct interaction or contact with patients were less likely to report problematic information exchange compared to those who reported not having direct interaction or contact with patients (OR: 0.85, 95% CI: 0.83‐0.87). Those who reported that staff did not work longer hours than was better for patient care were less likely to report problematic information exchange compared to those who did report working longer hours than was better for patient care (OR: 0.76, 95% CI: 0.73 0.79). One covariate measured hospital size. Those who reported working in smaller hospitals were less likely to report problematic information exchange compared to those reporting working in large hospitals (OR: 0.66, 95% CI 0.59‐0.75) (Table 4).

Multivariate Ordinal Regression Results Illustrating Likelihood of Perceiving Information Exchange Problems Across Hospital Units Conditional Upon Crisis Mode Work Climate
CharacteristicUnadjusted OR (95% CI)Adjusted OR* (95% CI)
  • NOTE: Abbreviations: CI, confidence interval; ECG, electrocardiography; LPN, licensed practical nurse; LVN, licensed vocational nurse; OR, odds ratio. *Controlling for hospital characteristics, respondent characteristics, and respondent perceptions as measured by covariates listed within the table. Agree or strongly agree that we work in crisis mode trying to do too much too quickly. Neutral response that we work in crisis mode trying to do too much too quickly. Disagree or strongly disagree that we work in crisis mode trying to do too much too quickly.

Primary predictor of interest
Crisis mode work climate
Agree3.0 (2.9‐3.1)1.6 (1.5‐1.6)
Neutral1.8 (1.7‐1.8)1.3 (1.2‐1.3)
DisagreeReferenceReference
Hospital characteristics
Bed Size
Small, 6240.51 (0.44‐0.59)0.66 (0.59‐0.75)
Small, 2490.59 (0.53‐0.66)0.77 (0.70‐0.84)
Small, 50990.65 (0.58‐0.73)0.78 (0.71‐0.84)
Medium, 1001990.85 (0.77‐0.95)0.92 (0.86‐1.0)
Medium, 2002991.0 (0.98‐1.1)0.97 (0.90‐1.0)
Medium, 3003990.96 (0.85‐1.1)1.0 (0.92‐1.1)
Large, 4004990.99 (0.86‐1.1)0.96 (0.87‐1.0)
Large, 500 plusReferenceReference
Teaching status
No0.81 (0.76‐0.87)1.0 (0.95‐1.0)
YesReferenceReference
Government ownership
No1.1 (1.01.2)1.0 (0.98‐1.1)
YesReferenceReference
Census region
Mid‐Atlantic and New England1.0 (0.88‐1.1)0.91 (0.84‐0.99)
South Atlantic0.95 (0.85‐1.1)1.0 (0.95‐1.1)
Central 10.95 (0.85‐1.0)0.95 (0.89‐1.0)
Central 20.71 (0.62‐0.81)0.91 (0.83‐0.99)
Central 30.80 (0.71‐0.91)0.97 (0.90‐1.0)
Central 40.76 (0.68‐0.86)0.93 (0.85‐1.0)
Mountain0.84 (0.73‐0.96)0.98 (0.90‐1.1)
PacificReferenceReference
Average survey response rate within hospital0.65 (0.58‐0.72)0.93 (0.82‐1.0)
Respondent characteristics
How long have you worked in your current specialty or profession?
<1 year0.75 (0.73‐0.78)1.03 (0.99‐1.1)
15 years0.99 (0.97‐1.0)1.1 (1.1‐1.1)
610 years1.0 (1.01.1)0.99 (0.96‐1.0)
1115 years1.0 (1.01.1)1.0 (0.97‐1.0)
1620 years1.0 (0.98‐1.0)0.97 (0.94‐1.0)
21 yearsReferenceReference
How long have you worked in this hospital?
<1 year0.75 (0.73‐0.77)0.90 (0.85‐0.90)
15 years1.03 (1.001.05)0.99 (0.95‐1.0)
610 years1.1 (1.1‐1.1)0.99 (0.95‐1.0)
1115 years1.1 (1. 01.1)1.0 (0.96‐1.0)
1620 years1.1 (1.01.1)0.98 (0.94‐1.0)
21 yearsReferenceReference
How long have you worked in your current hospital work area/unit?
<1 year0.79 (0.76‐0.82)0.98 (0.93‐1.0)
15 years1.0 (1.01.1)1.0 (0.99‐1.1)
610 years1.1 (1.1‐1.1)1.0 (1.01.1)
1115 years1.1 (1.01.1)1.0 (0.99‐1.1)
1620 years1.1 (1.01.1)1.1 (1.01.1)
21 yearsReferenceReference
Typically, how many hours per week do you work in this hospital?
<200.63 (0.50‐0.79)0.91 (0.72‐1.2)
20390.75 (0.59‐0.94)0.90 (0.71‐1.1)
40590.87 (0.69‐1.1)1.1 (0.85‐1.4)
60790.95 (0.75‐1.2)1.0 (0.82‐1.3)
80990.99 (0.78‐1.2)1.1 (0.86‐1.4)
100ReferenceReference
What is your staff position in this hospital?
Registered nurse0.92 (0.90‐0.94)1.1 (0.98‐1.0)
Technician (eg, ECG, lab, radiology)ReferenceReference
Unit assistant/clerk/secretary0.79 (0.76‐0.81)0.94 (0.80‐0.96)
Patient care assistant/hospital aide/care partner0.78 (0.75‐0.81)0.96 (0.90‐0.98)
Physical, occupational, or speech therapist0.88 (0.84‐0.92)1.2 (1.1‐1.2)
Attending/staff physician1.0 (0.97‐1.1)1.3 (1.2‐1.3)
LVN/LPN0.89 (0.85‐0.94)1.0 (0.92‐1.0)
Respiratory therapist0.84 (0.80‐0.88)0.97 (0.89‐1.0)
Pharmacist1.5 (1.4‐1.6)1.3 (1.1‐1.3)
Physician assistant/nurse practitioner0.93 (0.87‐1.0)1.2 (1.1‐1.2)
Resident physician/physician in training0.96 (0.89‐1.0)1.3 (1.2‐1.4)
Dietician0.86 (0.79‐0.94)1.2 (1.1‐1.3)
In your staff position, do you typically have direct interaction or contact with patients?
Yes0.83 (0.82‐0.85)0.85 (0.83‐0.87)
NoReferenceReference
What is your primary work area or unit in this hospital?
OtherReferenceReference
Medicine (nonsurgical)1.1 (1.01.1)0.84 (0.82‐0.89)
Surgery1.1 (1.1‐1.2)0.88 (0.86‐0.91)
Intensive care unit (any type)0.93 (0.90‐0.96)0.78 (0.76‐0.81)
Many different hospital units/no specific unit1.2 (1.1‐1.2)1.0 (0.98‐ 1.0)
Radiology1.1 (1.1‐1.1)1.0 (1.01.1)
Emergency department1.0 (0.97‐1.0)0.57 (0.55‐0.60)
Obstetrics0.76 (0.73‐0.79)0.66 (0.63‐0.69)
Laboratory1.2 (1.2‐1.3)1.0 (1.01.1)
Rehabilitation1.0 (0.97‐1.0)1.0 (0.98‐1.1)
Pediatrics0.90 (0.86‐0.94)0.83 (0.80‐0.87)
Pharmacy1.6 (1.5‐1.7)1.1 (1.01.2)
Psychiatry/mental health1.2 (1.1‐1.2)0.96 (0.90‐1.0)
Anesthesiology1.1 (1.01.3)0.93 (0.83‐1.0)
Respondent perceptions
When a lot of work needs to be done quickly, we work together as a team to get the work done.
Strongly disagree3.2 (3.03.4)1.0 (0.98‐1.1)
Disagree3.2 (3.13.3)1.0 (1.01.1)
Neutral2.3 (2.2‐2.4)0.98 (0.94‐1.0)
Agree2.3 (2.2‐2.4)1.0 (1.002‐1.04)
Strongly agreeReferenceReference
Staff in this unit work longer hours than is best for patient care.
Strongly disagree0.51 (0.48‐0.53)0.76 (0.73‐0.79)
Disagree0.68 (0.67‐0.70)0.81 (0.78‐0.84)
Neutral0.94 (0.91‐0.97)0.93 (0.90‐0.97)
Agree1.0 (0.99‐1.1)0.94 (0.91‐0.98)
Strongly agreeReferenceReference
When 1 area in this unit gets really busy, others help out.
Strongly disagree3.8 (3.7 ‐ 4.0)1.0 (0.96‐1.1)
Disagree3.0 (2.9‐3.1)1.0 (0.99‐1.1)
Neutral2.2 (2.12.3)1.0 (0.97‐1.0)
Agree1.5 (1.5‐1.6)0.99 (0.96‐1.0)
Strongly agreeReferenceReference
Hospital units do not coordinate well with each other.
Strongly disagree0.03 (0.03‐0.04)0.10 (0.10‐0.11)
Disagree0.08 (0.08‐0.08)0.18 (0.17‐0.19)
Neutral0.21 (0.20‐0.22)0.32 (0.30‐0.33)
Agree0.50 (0.48‐0.52)0.61 (0.58‐0.63)
Strongly agreeReferenceReference
There is good cooperation among hospital units that need to work together.
Strongly disagree20.1 (18.921.5)4.7 (4.35.0)
Disagree14.2 (13.614.9)4.2 (4.14.5)
Neutral6.7 (6.47.0)2.7 (2.6‐2.8)
Agree2.4 (2.3‐2.5)1.6 (1.6‐1.7)
Strongly agreeReferenceReference
Please give your work area/unit in this hospital an overall grade on patient safety
Excellent0.13 (0.12‐0.14)0.47 (0.42‐0.52)
Very good0.24 (0.21‐0.26)0.63 (0.57‐0.70)
Acceptable0.49 (0.45‐0.54)0.79 (0.72‐0.88)
Poor0.83 (0.75‐0.92)0.92 (0.83‐1.03)
FailingReferenceReference

DISCUSSION

Our results illustrate that when hospital staff agree that their hospital works in crisis mode, they are more likely to agree that their hospital unit had frequent problems exchanging patient information across units. Because hospital staff working under time constraints and heavy workloads could potentially be at risk of misinterpreting or delivering inaccurate information, these results imply that crisis mode work climates increase the risk of problematic health information exchange. An equally plausible interpretation could be that problematic patient health information exchange increases the risk of hospital staff perceiving crisis mode work climates. Given that information gaps are associated with patient handoff errors,[14] and that patient handoff errors are associated with adverse events,[2, 3, 6, 8] an urgent need exists to implement information exchange systems that prevent information gaps from harming patients. Consequently, hospitals need to implement workflow strategies that prevent information gaps from undermining patient safety during transitions of care.

Other factors affect information exchange apart from crisis mode work climate, as illustrated by the significant associations of key covariates in the multivariate model. The effect found between perceived coordination and information exchange implies that improving information exchange requires good cooperation and coordination. The effect found between patient contact and information exchange implies that working directly with patients improves either the accuracy or the perception of information exchange. Finally, the effect found between hospital size and information exchange suggests that small hospitals are less likely than large hospitals to have information exchange problem. The geographical dispersion and the complexity of larger institutions could result in information exchange problems due to more confusion and less in‐person communication.

Because problematic patient information exchanges are associated with hospital size, coordination, and patient contact, in addition to crisis mode work climate, multifaceted solutions are necessary to resolve the problem. For example, hospital interventions designed to improve coordination could in turn attenuate perceived crisis modes. Furthermore, tailoring these interventions to hospitals that belong to complex geographically dispersed provider networks would likely decrease errors during transitions of care. Because multiple factors cause information exchange problems, implementing interventions that improve both coordination and crisis mode work climates would likely result in a greater net improvement compared to interventions focused solely on decreasing crisis mode work climates.

Some limitations of our paper are worth noting. First, we did not have information on the volume of data exchanged or the functionality levels of the electronic health record systems, both of which likely impact the accuracy of patient information exchange. For example, hospitals with smaller versus larger amounts of data exchanged could be less prone to error. On the other hand, this risk of error could be reduced even further by implementing robust health information technology (IT) systems that improve the accuracy of information transfer. This is consistent with studies showing that hospitals without computerized provider order entry (CPOE) systems have been shown to have higher medication error rates compared to those hospitals with CPOE systems.[15] Therefore, omitting data volume and health IT capabilities from the multivariate model could introduce unobserved heterogeneity, resulting in biased associations between perceived crisis mode work climate and perceived information exchange problems. Second, the cross‐sectional design limits our ability to infer causality because we are not certain whether the perceived crisis mode occurred before, after, or simultaneously to perceived information exchange problems. Third, the self‐reported nature of the questionnaire items does not provide information on observed levels of crisis mode and exchange problems, which could be inconsistent with perceived levels. Fourth, the relatively low within‐hospital response rate decreases the external validity of our findings. For example, if responders' perceptions of crisis mode or information exchange problems significantly differed from nonresponders, our results would not be generalizable to the larger population of acute‐care hospitals across the United States. Therefore, conclusions should be viewed with caution if applying these results to hospitals with respondents significantly differing from those contained within our sample.

Despite these limitations, the large sample size in conjunction with the use of data from a survey having acceptable psychometric properties[16] strengthens the external and internal validity of our findings. Although questionnaire items measuring perceptions are relatively subjective in nature compared to using metrics that capture observed problems or crisis modes, we argue that staff perception data are equally informative, as they guide organization leaders on how to improve workplace performance. Because a core concept of high reliability organizations (HROs) is to preserve constant awareness by key leaders and staff of the state of the systems and processes that affect patient care,[17] HROs could benefit from knowing the extent to which staff perceptions impact patient care. From a methods perspective, the multivariable ordinal regressions enabled us to control for potential confounders that if omitted could have resulted in biased estimates. Furthermore, low levels of multicollinearity as illustrated by low variation inflation factors enabled us to isolate the independent effect of crisis mode perceptions. Including hospital size and hospital work unit as covariates was an additional methodological strength helping account for the unobserved heterogeneity caused by excluding volume of data exchanged or health IT system capability. For example, because larger compared to smaller hospitals usually have more sophisticated health IT systems,[15] including bed size in the model theoretically captures some of the variation that would have been captured if we were able to include a covariate measuring health IT capability. Last, using ordinal regression facilitates interpretation of the findings because the questionnaire items for the predictor and outcome were originally captured on a Likert scale.

Our findings underscore the significant impact that work climate has on accurate information exchange, and ultimately patient safety. Improving patient safety is imperative for hospitals, especially within the context of recent regulations stemming from the Affordable Care Act that incentivize hospitals to reduce readmissions[18] and improve transitions of care.[19] Because accurate health information exchange is a critical component of patient care, resolving barriers that decrease the accuracy of this exchange is essential. Therefore, future studies need to continue examining these associations within the context of study designs that incorporate longitudinal data and datasets that include objective measures capturing crisis mode work climates and information exchange problems. Because effective communication during handoffs is associated with decreases in medical errors and readmissions, hospitals need to continually ensure that work environments are conducive to effective patient information exchange.

Disclosures

Nothing to report

Using electronic health records to improve the continuity of care between hospital units does not replace the need for interpersonal communication to improve transitions of care. Hospital personnel play a critical role in accurately exchanging patient information during patient transfers, a process requiring accurate communication between hospital units to prevent system failures.[1] Because poor communication contributes to preventable adverse events,[2] and effective communication during handoffs decreases medical errors and readmissions,[3] hospitals need to ensure their work environments are conducive to effective communication.

Individuals working under time constraints and heavy workloads could potentially be at high risk of misinterpreting or delivering inaccurate information,[4] partially due to limited ability to accurately process and communicate information under stressful circumstances. Furthermore, because time‐constrained decision makers tend to use less information and less rigorous decision strategies,[5] work climates characterized by staff members doing too many things too quickly could cause patient health information to be lost during transitions of care across hospital units.

Current studies illustrate scenarios in which demanding or time‐constrained work environments caused information exchange errors. One study found that the increased rate of prescribing errors was partially attributed to a high‐demand work environment characterized by working after hours and multitasking.[6] Other studies found that clinicians' limited time to relay and respond to information and ask clarifying questions during patient handoffs was partially attributed to the fast‐faced and chaotic environment of the emergency room.[7, 8] These studies are consistent with another study that found patient handoffs between emergency departments and inpatient wards were inadequate, partially due to less interactive and more rushed communication.[9] The fact that communication breakdowns are widely cited as barriers to patient handoffs[7, 8, 10] and facilitators of medical errors,[7, 8] further underscores the detrimental effect that crisis mode work climates could have on exchanging patient information during transitions of care.

The objective of this analysis was to evaluate the extent to which a crisis mode work climate impacts the occurrence of patient information exchange problems. Estimating associations between hospital staff members' perceptions of crisis mode work climates and perceptions of information exchange problems provide insights as to whether high‐demand and time‐constrained work climates negatively impact the exchange of patient information. Because hospital staff members working under time constraints and heavy workloads could potentially be at high risk of misinterpreting or delivering inaccurate information, we hypothesized that higher levels of a perceived crisis mode work climate would be associated with higher levels of perceived problems with information exchange across hospital units.

METHODS

Data Source

Data originated from the Agency of Healthcare Research and Quality 2010 Hospital Survey on Patient Safety Culture. This validated survey, designed to assess the safety climate within acute‐care settings, remains an important annual survey deployed each year to track changes and factors impacting patient safety.[11] We included only those respondents who self‐reported their position as a nurse, physician, pharmacist, dietician, therapist, technician, patient care assistant, or hospital unit secretary, all of whom are likely responsible for exchanging patient information across hospital units. For this reason, we excluded respondents who self‐reported their position as administrative or miscellaneous. Applying these exclusion criteria resulted in 247,104 respondents across 884 hospitals.

Conceptual Framework

The relationship between perceived crisis mode work climates and patient information exchange problems is likely influenced by staff skill levels, work climate, and infrastructure (Figure 1). With respect to skill levels, hospital staff members with many years of experience compared to those with fewer years may be relatively desensitized to chaotic work environments and consequently have higher thresholds for perceiving crisis modes. Number of hours worked per week likely impacts perceived crisis mode as illustrated in 1 study finding that full‐time nurses reported a significantly lower work pace compared to part‐time nurses.[12] Years of experience likely impacts perception of information exchange problems, particularly if staff members with many years of experience are familiar enough with hospital systems or protocols to easily detect exchange errors or mishaps.

Figure 1
Conceptual framework.

With respect to work climates, workers' perception of cooperation, coordination and patient safety, and specific hospital unit likely impact perceptions of crisis work mode and information exchange problems. For example, hospital staff members reporting high levels of cooperation, coordination, and patient safety likely perceive fewer crisis modes and information exchange problems compared to those in less‐cooperative hospital units. Furthermore, the heterogeneity of work cultures across departments within a hospital results in department‐specific perceptions of crisis mode climates and information exchange problems. Infrastructure factors, such as hospital size, teaching and ownership status, and census region, likely impact the amount of resources available for staffing and infrastructure, which in turn could impact work pace and information exchange accuracy.

Variable Definitions

We defined our predictor as the perceived presence of a crisis mode work climate as captured from the survey questionnaire item: We work in crisis mode trying to do too much, too quickly. This question item had a Likert response scale comprised of the following 5 answer choices: (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, (5) strongly agree. We created a 3‐level response variable by aggregating the agree and disagree responses, respectively, as the first 2 levels, and retaining the neutral response as the third level. Consequently, those responding strongly disagree or disagree were classified as working in lowcrisis mode work climates and those responding strongly agree or agree were classified as working in high crisis mode work climates. We defined our outcome measure as the presence of patient information exchange problems as captured from the survey questionnaire item: Problems often occur in the exchange of information across hospital units. Because this question item had a Likert response scale similar to the crisis mode question predictor, we also created a 3‐level categorical variable in the same fashion. Consequently, those responding strongly disagree or disagree were classified as perceiving no problems exchanging patient information, and those responding strongly agree or agree were classified as perceiving problems exchanging patient information. For the fewer than 10% of the respondents with missing data on either the predictor our outcome variables, the mode measure of central tendency was imputed, a methodology validated in a previous study.[13]

We also included questionnaire items that captured staff skill levels, work climate, and infrastructure as covariates to account for potential confounders (Figure 1). The staff skill levels domain included years of experience working in the hospital, specialty, and unit; current staff position; and extent of patient contact. The work climate domain included respondent perceptions of coordination and cooperation, patient safety, and primary work area or unit in which the provider reported working. The hospital infrastructure domain included bed size, census region, teaching status, and government ownership status. For the fewer than 10% of the respondents with missing data on any of the categorical variables, the mode measure of central tendency was imputed, a methodology validated in a previous study.[13]

Analytic Approach

We used multivariable ordinal regressions to estimate the likelihood of perceived problems in patient information exchange conditional upon perceptions of a crisis mode work climate, controlling for staff skill levels, work climate, and hospital infrastructure. Our estimates therefore reflect the likelihood of hospital staff responding strongly agree or agree to the question Problems often occur in the exchange of information across hospital units conditional upon responding strongly agree or agree to the question We work in crisis mode trying to do too much, too quickly. In addition to controlling for hospital‐specific response rates, we also adjusted our standard errors to account for the clustering of respondents within hospitals. All analyses were conducted in SAS version 9.2 (SAS Institute Inc., Cary, NC).

RESULTS

The hospital sample averaged 279 respondents per hospital with a 56% response rate. Most hospitals were located in the Central region of the United States, and 32% and 19% were teaching and government‐owned hospitals, respectively. Forty‐three percent and 44% of the hospitals in the sample were designated as small and medium hospitals, respectively (Table 1).

Hospital and Survey Respondent Characteristics
Characteristics%
  • NOTE: Abbreviations: ECG, electrocardiography; LPN, licensed practical nurse; LVN, licensed vocational nurse; SD, standard deviation.

Hospital characteristics, N=884 
Bed size 
Small, 19943.5
Medium, 10039943.8
Large, 400 plus12.7
Teaching status 
Yes32.2
No67.8
Government ownership 
Yes19.5
No80.5
Census region 
Mid‐Atlantic and New England8.7
South Atlantic14.8
Central57.2
Mountain7.7
Pacific11.5
Response rate, mean (SD)0.56 (0.28)
Respondents per hospital, mean (SD)279 (358)
Respondent characteristics, N=274,140 
How long have you worked in your current specialty or profession? 
<1 year5.8
15 years32.8
610 years16.2
1115 years12.0
1620 years10.6
21 years22.7
How long have you worked in this hospital? 
<1 year9.8
15 years42.8
610 years17.8
1115 years9.0
1620 years8.2
21 years12.4
How long have you worked in your current hospital work area/unit? 
<1 year13.1
15 years48.0
610 years18.1
1115 years8.1
1620 years6.0
21 years6.7
Typically, how many hours per week do you work in this hospital? 
<20 hours4.8
2039 hours39.9
4059 hours48.8
6079 hours4.2
8099 hours2.1
100 hours0.11
What is your staff position in this hospital? 
Registered nurse51.2
Technician (eg, ECG, lab, radiology)14.1
Unit assistant/clerk/secretary8.5
Patient care assistant/hospital aide/care partner7.4
Physical, occupational, or speech therapist3.7
Attending/staff physician3.5
LVN/LPN3.0
Respiratory therapist2.9
Pharmacist2.2
Physician assistant/nurse practitioner1.4
Resident physician/physician in training1.2
Dietician0.83
In your staff position, do you typically have direct interaction or contact with patients? 
Yes86.6
No13.4
What is your primary work area or unit in this hospital? 
Other27.7
Medicine (nonsurgical)11.1
Surgery10.0
Intensive care unit (any type)8.6
Many different hospital units/no specific unit6.8
Radiology6.2
Emergency department5.8
Obstetrics4.9
Laboratory4.9
Rehabilitation4.2
Pediatrics3.8
Pharmacy3.2
Psychiatry/mental health2.1
Anesthesiology0.55

Thirty‐seven percent of the respondents have worked in their current specialty or profession for 5 years or less (Table 1). Over half of the respondents have worked in their current hospital for 5 years or less, whereas 61% have worked in their current unit within the hospital for 5 years or less. Forty‐nine percent work at least 40 hours per week. Registered nurses and technicians represented the 2 largest subgroups of staff positions, comprising 51% and 14% of the sample, respectively. Dieticians and resident physicians, on the other hand, represented the 2 smallest subgroups of staff positions, comprising 0.83% and 1.2% of the sample, respectively. Eighty‐seven percent of the respondents have direct interaction or contact with patients. Apart from those responding other as their hospital unit, nonsurgical medicine and surgery represented the largest subgroup primary work areas, comprising 11% and 10% of the sample, respectively. In contrast, psychiatry and anesthesiology represented the 2 smallest subgroups of primary work areas, comprising 2.1% and 0.55% of the sample, respectively (Table 1).

Respondents scored relatively high with regard to teamwork and helping each other out under hurried or busy circumstances. For example, 85% agreed or strongly agreed that their unit worked together as a team to get work done when a lot of work needed to be completed quickly, and 68% agreed or strongly agreed that individuals within their unit helped out when an area in their unit became busy (Table 1). Despite this cooperation, 31% agreed or strongly agreed that hospital units did not coordinate well together. Paradoxically, 57% agreed or strongly agreed that there was good cooperation among hospital units that needed to work together. Seventy‐five percent of the respondents reported excellent or very good patient safety levels within their unit, although 53% agreed or strongly agreed that staff worked longer hours than was best for patient care (Table 1).

With regard to perceived crisis mode work climate, 32% and 47% reported agreeing and disagreeing, respectively, that their work unit worked in crisis mode trying to do too much too quickly (Table 2). With regard to perceived problems with patient information exchange, 27% and 36% reported agreeing and disagreeing, respectively, that information exchange problems occurred across hospital units (Table 2).

Survey Respondent Work Environment Perceptions (N=247,140)
Perceptions%
We work in crisis mode trying to do too much, too quickly 
Strongly disagree8.1
Disagree39.2
Neutral21.0
Agree24.3
Strongly agree7.5
Problems often occur in the exchange of information across hospital units 
Strongly disagree4.6
Disagree31.3
Neutral37.3
Agree24.0
Strongly agree2.7
When a lot of work needs to be done quickly, we work together as a team to get the work done. 
Strongly disagree1.5
Disagree6.1
Neutral7.5
Agree53.6
Strongly agree31.2
When one area in this unit gets really busy, others help out. 
Strongly disagree3.9
Disagree13.9
Neutral13.7
Agree52.6
Strongly agree15.8
Hospital units do not coordinate well with each other. 
Strongly disagree5.6
Disagree38.8
Neutral23.7
Agree25.3
Strongly agree6.6
There is good cooperation among hospital units that need to work together. 
Strongly disagree2.7
Disagree15.1
Neutral24.7
Agree51.1
Strongly agree6.3
Please give your work area/unit in this hospital an overall grade on patient safety. 
Excellent23.0
Very good49.8
Acceptable21.8
Poor4.6
Failing0.76
Staff in this unit work longer hours than is best for patient care. 
Strongly disagree11.5
Disagree42.2
Neutral23.6
Agree18.4
Strongly agree6.3

In the unadjusted analyses, crisis mode perceptions and information exchange problem perceptions were significantly associated. Among those who agreed that their work unit worked in crisis mode, a larger proportion of respondents agreed (41%) versus disagreed (24%) that problems often occurred in exchanging patient information across units (Table 3). In contrast, among those who disagreed that their work unit worked in crisis mode, a larger proportion of respondents disagreed (47%) versus agreed (19%) that problems often occurred in exchanging patient information across units (Table 3).

Bivariate Frequency Distribution of Respondents' Perceptions of Crisis Mode Work Climate and Patient Information Exchange Problems Between Hospital Units
 Problems Often Occur in Exchange of Information Across Hospital Units
 Agree (N=66,115), Row %*Neutral (N=92,228), Row %Disagree (N=88,797), Row %
  • NOTE: *Agree or strongly agree that problems often occur in exchange of information across hospital units. Neutral response that problems often occur in exchange of information across hospital units. Disagree or strongly disagree that problems often occur in exchange of information across hospital units. Agree or strongly agree that we work in crisis mode trying to do too much too quickly. ‖Neutral response that we work in crisis mode trying to do too much too quickly. Disagree or strongly disagree that we work in crisis mode trying to do too much too quickly.

Crisis Mode Work Climate   
Agree (N=78,253)40.835.423.8
Neutral (N=51,836)22.948.928.2
Disagree (N=116,781)19.033.547.5

In the multivariable ordinal regression, compared to those who disagreed that their unit worked in crisis mode, those who agreed were 1.6 times more likely to report that problems often occurred in exchanging patient information across units (odds ratio [OR]: 1.6, 95% confidence interval [CI]: 1.58‐1.65) (Table 4). Additionally, some key covariates were independently associated with perceptions of information exchange problems. Two of these covariates measured workplace coordination. Those who reported that hospital units did not cooperate well together were more likely to report problematic information exchange compared to those who reported that hospital units did cooperate well (OR: 4.7, 95% CI: 4.35.0). Relatedly, those who reported that hospital units did coordinate well were less likely to report problematic information exchange compared to those who reported that hospital units did not coordinate well (OR: 0.10, 95% CI: 0.10‐0.11). Two other covariates measured patient contact and perceptions about long working hours. Those who reported having direct interaction or contact with patients were less likely to report problematic information exchange compared to those who reported not having direct interaction or contact with patients (OR: 0.85, 95% CI: 0.83‐0.87). Those who reported that staff did not work longer hours than was better for patient care were less likely to report problematic information exchange compared to those who did report working longer hours than was better for patient care (OR: 0.76, 95% CI: 0.73 0.79). One covariate measured hospital size. Those who reported working in smaller hospitals were less likely to report problematic information exchange compared to those reporting working in large hospitals (OR: 0.66, 95% CI 0.59‐0.75) (Table 4).

Multivariate Ordinal Regression Results Illustrating Likelihood of Perceiving Information Exchange Problems Across Hospital Units Conditional Upon Crisis Mode Work Climate
CharacteristicUnadjusted OR (95% CI)Adjusted OR* (95% CI)
  • NOTE: Abbreviations: CI, confidence interval; ECG, electrocardiography; LPN, licensed practical nurse; LVN, licensed vocational nurse; OR, odds ratio. *Controlling for hospital characteristics, respondent characteristics, and respondent perceptions as measured by covariates listed within the table. Agree or strongly agree that we work in crisis mode trying to do too much too quickly. Neutral response that we work in crisis mode trying to do too much too quickly. Disagree or strongly disagree that we work in crisis mode trying to do too much too quickly.

Primary predictor of interest
Crisis mode work climate
Agree3.0 (2.9‐3.1)1.6 (1.5‐1.6)
Neutral1.8 (1.7‐1.8)1.3 (1.2‐1.3)
DisagreeReferenceReference
Hospital characteristics
Bed Size
Small, 6240.51 (0.44‐0.59)0.66 (0.59‐0.75)
Small, 2490.59 (0.53‐0.66)0.77 (0.70‐0.84)
Small, 50990.65 (0.58‐0.73)0.78 (0.71‐0.84)
Medium, 1001990.85 (0.77‐0.95)0.92 (0.86‐1.0)
Medium, 2002991.0 (0.98‐1.1)0.97 (0.90‐1.0)
Medium, 3003990.96 (0.85‐1.1)1.0 (0.92‐1.1)
Large, 4004990.99 (0.86‐1.1)0.96 (0.87‐1.0)
Large, 500 plusReferenceReference
Teaching status
No0.81 (0.76‐0.87)1.0 (0.95‐1.0)
YesReferenceReference
Government ownership
No1.1 (1.01.2)1.0 (0.98‐1.1)
YesReferenceReference
Census region
Mid‐Atlantic and New England1.0 (0.88‐1.1)0.91 (0.84‐0.99)
South Atlantic0.95 (0.85‐1.1)1.0 (0.95‐1.1)
Central 10.95 (0.85‐1.0)0.95 (0.89‐1.0)
Central 20.71 (0.62‐0.81)0.91 (0.83‐0.99)
Central 30.80 (0.71‐0.91)0.97 (0.90‐1.0)
Central 40.76 (0.68‐0.86)0.93 (0.85‐1.0)
Mountain0.84 (0.73‐0.96)0.98 (0.90‐1.1)
PacificReferenceReference
Average survey response rate within hospital0.65 (0.58‐0.72)0.93 (0.82‐1.0)
Respondent characteristics
How long have you worked in your current specialty or profession?
<1 year0.75 (0.73‐0.78)1.03 (0.99‐1.1)
15 years0.99 (0.97‐1.0)1.1 (1.1‐1.1)
610 years1.0 (1.01.1)0.99 (0.96‐1.0)
1115 years1.0 (1.01.1)1.0 (0.97‐1.0)
1620 years1.0 (0.98‐1.0)0.97 (0.94‐1.0)
21 yearsReferenceReference
How long have you worked in this hospital?
<1 year0.75 (0.73‐0.77)0.90 (0.85‐0.90)
15 years1.03 (1.001.05)0.99 (0.95‐1.0)
610 years1.1 (1.1‐1.1)0.99 (0.95‐1.0)
1115 years1.1 (1. 01.1)1.0 (0.96‐1.0)
1620 years1.1 (1.01.1)0.98 (0.94‐1.0)
21 yearsReferenceReference
How long have you worked in your current hospital work area/unit?
<1 year0.79 (0.76‐0.82)0.98 (0.93‐1.0)
15 years1.0 (1.01.1)1.0 (0.99‐1.1)
610 years1.1 (1.1‐1.1)1.0 (1.01.1)
1115 years1.1 (1.01.1)1.0 (0.99‐1.1)
1620 years1.1 (1.01.1)1.1 (1.01.1)
21 yearsReferenceReference
Typically, how many hours per week do you work in this hospital?
<200.63 (0.50‐0.79)0.91 (0.72‐1.2)
20390.75 (0.59‐0.94)0.90 (0.71‐1.1)
40590.87 (0.69‐1.1)1.1 (0.85‐1.4)
60790.95 (0.75‐1.2)1.0 (0.82‐1.3)
80990.99 (0.78‐1.2)1.1 (0.86‐1.4)
100ReferenceReference
What is your staff position in this hospital?
Registered nurse0.92 (0.90‐0.94)1.1 (0.98‐1.0)
Technician (eg, ECG, lab, radiology)ReferenceReference
Unit assistant/clerk/secretary0.79 (0.76‐0.81)0.94 (0.80‐0.96)
Patient care assistant/hospital aide/care partner0.78 (0.75‐0.81)0.96 (0.90‐0.98)
Physical, occupational, or speech therapist0.88 (0.84‐0.92)1.2 (1.1‐1.2)
Attending/staff physician1.0 (0.97‐1.1)1.3 (1.2‐1.3)
LVN/LPN0.89 (0.85‐0.94)1.0 (0.92‐1.0)
Respiratory therapist0.84 (0.80‐0.88)0.97 (0.89‐1.0)
Pharmacist1.5 (1.4‐1.6)1.3 (1.1‐1.3)
Physician assistant/nurse practitioner0.93 (0.87‐1.0)1.2 (1.1‐1.2)
Resident physician/physician in training0.96 (0.89‐1.0)1.3 (1.2‐1.4)
Dietician0.86 (0.79‐0.94)1.2 (1.1‐1.3)
In your staff position, do you typically have direct interaction or contact with patients?
Yes0.83 (0.82‐0.85)0.85 (0.83‐0.87)
NoReferenceReference
What is your primary work area or unit in this hospital?
OtherReferenceReference
Medicine (nonsurgical)1.1 (1.01.1)0.84 (0.82‐0.89)
Surgery1.1 (1.1‐1.2)0.88 (0.86‐0.91)
Intensive care unit (any type)0.93 (0.90‐0.96)0.78 (0.76‐0.81)
Many different hospital units/no specific unit1.2 (1.1‐1.2)1.0 (0.98‐ 1.0)
Radiology1.1 (1.1‐1.1)1.0 (1.01.1)
Emergency department1.0 (0.97‐1.0)0.57 (0.55‐0.60)
Obstetrics0.76 (0.73‐0.79)0.66 (0.63‐0.69)
Laboratory1.2 (1.2‐1.3)1.0 (1.01.1)
Rehabilitation1.0 (0.97‐1.0)1.0 (0.98‐1.1)
Pediatrics0.90 (0.86‐0.94)0.83 (0.80‐0.87)
Pharmacy1.6 (1.5‐1.7)1.1 (1.01.2)
Psychiatry/mental health1.2 (1.1‐1.2)0.96 (0.90‐1.0)
Anesthesiology1.1 (1.01.3)0.93 (0.83‐1.0)
Respondent perceptions
When a lot of work needs to be done quickly, we work together as a team to get the work done.
Strongly disagree3.2 (3.03.4)1.0 (0.98‐1.1)
Disagree3.2 (3.13.3)1.0 (1.01.1)
Neutral2.3 (2.2‐2.4)0.98 (0.94‐1.0)
Agree2.3 (2.2‐2.4)1.0 (1.002‐1.04)
Strongly agreeReferenceReference
Staff in this unit work longer hours than is best for patient care.
Strongly disagree0.51 (0.48‐0.53)0.76 (0.73‐0.79)
Disagree0.68 (0.67‐0.70)0.81 (0.78‐0.84)
Neutral0.94 (0.91‐0.97)0.93 (0.90‐0.97)
Agree1.0 (0.99‐1.1)0.94 (0.91‐0.98)
Strongly agreeReferenceReference
When 1 area in this unit gets really busy, others help out.
Strongly disagree3.8 (3.7 ‐ 4.0)1.0 (0.96‐1.1)
Disagree3.0 (2.9‐3.1)1.0 (0.99‐1.1)
Neutral2.2 (2.12.3)1.0 (0.97‐1.0)
Agree1.5 (1.5‐1.6)0.99 (0.96‐1.0)
Strongly agreeReferenceReference
Hospital units do not coordinate well with each other.
Strongly disagree0.03 (0.03‐0.04)0.10 (0.10‐0.11)
Disagree0.08 (0.08‐0.08)0.18 (0.17‐0.19)
Neutral0.21 (0.20‐0.22)0.32 (0.30‐0.33)
Agree0.50 (0.48‐0.52)0.61 (0.58‐0.63)
Strongly agreeReferenceReference
There is good cooperation among hospital units that need to work together.
Strongly disagree20.1 (18.921.5)4.7 (4.35.0)
Disagree14.2 (13.614.9)4.2 (4.14.5)
Neutral6.7 (6.47.0)2.7 (2.6‐2.8)
Agree2.4 (2.3‐2.5)1.6 (1.6‐1.7)
Strongly agreeReferenceReference
Please give your work area/unit in this hospital an overall grade on patient safety
Excellent0.13 (0.12‐0.14)0.47 (0.42‐0.52)
Very good0.24 (0.21‐0.26)0.63 (0.57‐0.70)
Acceptable0.49 (0.45‐0.54)0.79 (0.72‐0.88)
Poor0.83 (0.75‐0.92)0.92 (0.83‐1.03)
FailingReferenceReference

DISCUSSION

Our results illustrate that when hospital staff agree that their hospital works in crisis mode, they are more likely to agree that their hospital unit had frequent problems exchanging patient information across units. Because hospital staff working under time constraints and heavy workloads could potentially be at risk of misinterpreting or delivering inaccurate information, these results imply that crisis mode work climates increase the risk of problematic health information exchange. An equally plausible interpretation could be that problematic patient health information exchange increases the risk of hospital staff perceiving crisis mode work climates. Given that information gaps are associated with patient handoff errors,[14] and that patient handoff errors are associated with adverse events,[2, 3, 6, 8] an urgent need exists to implement information exchange systems that prevent information gaps from harming patients. Consequently, hospitals need to implement workflow strategies that prevent information gaps from undermining patient safety during transitions of care.

Other factors affect information exchange apart from crisis mode work climate, as illustrated by the significant associations of key covariates in the multivariate model. The effect found between perceived coordination and information exchange implies that improving information exchange requires good cooperation and coordination. The effect found between patient contact and information exchange implies that working directly with patients improves either the accuracy or the perception of information exchange. Finally, the effect found between hospital size and information exchange suggests that small hospitals are less likely than large hospitals to have information exchange problem. The geographical dispersion and the complexity of larger institutions could result in information exchange problems due to more confusion and less in‐person communication.

Because problematic patient information exchanges are associated with hospital size, coordination, and patient contact, in addition to crisis mode work climate, multifaceted solutions are necessary to resolve the problem. For example, hospital interventions designed to improve coordination could in turn attenuate perceived crisis modes. Furthermore, tailoring these interventions to hospitals that belong to complex geographically dispersed provider networks would likely decrease errors during transitions of care. Because multiple factors cause information exchange problems, implementing interventions that improve both coordination and crisis mode work climates would likely result in a greater net improvement compared to interventions focused solely on decreasing crisis mode work climates.

Some limitations of our paper are worth noting. First, we did not have information on the volume of data exchanged or the functionality levels of the electronic health record systems, both of which likely impact the accuracy of patient information exchange. For example, hospitals with smaller versus larger amounts of data exchanged could be less prone to error. On the other hand, this risk of error could be reduced even further by implementing robust health information technology (IT) systems that improve the accuracy of information transfer. This is consistent with studies showing that hospitals without computerized provider order entry (CPOE) systems have been shown to have higher medication error rates compared to those hospitals with CPOE systems.[15] Therefore, omitting data volume and health IT capabilities from the multivariate model could introduce unobserved heterogeneity, resulting in biased associations between perceived crisis mode work climate and perceived information exchange problems. Second, the cross‐sectional design limits our ability to infer causality because we are not certain whether the perceived crisis mode occurred before, after, or simultaneously to perceived information exchange problems. Third, the self‐reported nature of the questionnaire items does not provide information on observed levels of crisis mode and exchange problems, which could be inconsistent with perceived levels. Fourth, the relatively low within‐hospital response rate decreases the external validity of our findings. For example, if responders' perceptions of crisis mode or information exchange problems significantly differed from nonresponders, our results would not be generalizable to the larger population of acute‐care hospitals across the United States. Therefore, conclusions should be viewed with caution if applying these results to hospitals with respondents significantly differing from those contained within our sample.

Despite these limitations, the large sample size in conjunction with the use of data from a survey having acceptable psychometric properties[16] strengthens the external and internal validity of our findings. Although questionnaire items measuring perceptions are relatively subjective in nature compared to using metrics that capture observed problems or crisis modes, we argue that staff perception data are equally informative, as they guide organization leaders on how to improve workplace performance. Because a core concept of high reliability organizations (HROs) is to preserve constant awareness by key leaders and staff of the state of the systems and processes that affect patient care,[17] HROs could benefit from knowing the extent to which staff perceptions impact patient care. From a methods perspective, the multivariable ordinal regressions enabled us to control for potential confounders that if omitted could have resulted in biased estimates. Furthermore, low levels of multicollinearity as illustrated by low variation inflation factors enabled us to isolate the independent effect of crisis mode perceptions. Including hospital size and hospital work unit as covariates was an additional methodological strength helping account for the unobserved heterogeneity caused by excluding volume of data exchanged or health IT system capability. For example, because larger compared to smaller hospitals usually have more sophisticated health IT systems,[15] including bed size in the model theoretically captures some of the variation that would have been captured if we were able to include a covariate measuring health IT capability. Last, using ordinal regression facilitates interpretation of the findings because the questionnaire items for the predictor and outcome were originally captured on a Likert scale.

Our findings underscore the significant impact that work climate has on accurate information exchange, and ultimately patient safety. Improving patient safety is imperative for hospitals, especially within the context of recent regulations stemming from the Affordable Care Act that incentivize hospitals to reduce readmissions[18] and improve transitions of care.[19] Because accurate health information exchange is a critical component of patient care, resolving barriers that decrease the accuracy of this exchange is essential. Therefore, future studies need to continue examining these associations within the context of study designs that incorporate longitudinal data and datasets that include objective measures capturing crisis mode work climates and information exchange problems. Because effective communication during handoffs is associated with decreases in medical errors and readmissions, hospitals need to continually ensure that work environments are conducive to effective patient information exchange.

Disclosures

Nothing to report

References
  1. Waring J, McDonald R, Harrison S. Safety and complexity: inter‐departmental relationships as a threat to patient safety in the operating department. J Health Organ Manag. 2006;20(2–3):227242.
  2. Coiera RA, Jayasuria J, Hardy A, Bannan A, Thorpe ME. Communication loads on clinical staff in the emergency department. Med J Aust. 2002;176(9):415418.
  3. Starmer AJ, Sectish TC, Simon DW, et al. Rates of medical errors and preventable adverse events among hospitalized children following implementation of a resident handoff bundle. JAMA. 2013;310(21):22622270.
  4. Frey LR, Gouran D, Poole MS. The Handbook of Group Communication Theory and Research. Thousand Oaks, CA: Sage Publications; 1999.
  5. Higgins A, Edland A, Svenson O. Judgment and decision making under time pressure. In: Svenson O, Maule AJ, eds. Time Pressure and Stress in Human Judgment and Decision Making. New York, NY: Plenum Press; 1993:2740.
  6. Nichols P, Copeland TS, Craib IA, Hopkins P, Bruce DG. Learning from error: identifying contributory causes of medication errors in an Australian hospital. Med J Aust. 2008;188(5):276279.
  7. Apker J, Mallak LA, Gibson SC. Communicating in the “gray zone”: perceptions about emergency physician hospitalist handoffs and patient safety. Acad Emerg Med. 2007;14(10):884894.
  8. Singer JI, Dean J. Emergency physician intershift handovers: an analysis of our transitional care. Pediatr Emerg Care. 2006;22(10):751754.
  9. Horwitz LI, Meredith T, Schuur JD, Shah NR, Kulkarni RG, Jenq GY. Dropping the baton: a qualitative analysis of failures during the transition from emergency department to inpatient care. Ann Emerg Med. 2009;53(6):701710.
  10. Solet DJ, Norvell JM, Rutan GH, Frankel RM. Lost in translation: challenges and opportunities in physician‐to‐physician communication during patient handoffs. Acad Med. 2005;80(12):10941099.
  11. Sorra J, Famolaro T, Dyer N, et al. Hospital Survey on Patient Safety Culture: 2010 user comparative database report. (Prepared by Westat, Rockville, MD, under Contract No. HHSA 290200710024C). Rockville, MD: Agency for Healthcare Research and Quality; February 2010. AHRQ Publication No. 10‐0026.
  12. Chen J, Davis LS, Davis KG, Pan W, Daraiseh NM. Physiological and behavioural response patterns at work among hospital nurses. J Nurs Manag. 2011;19(1):5768.
  13. Acuna E, Rodriguez C. The treatment of missing values and its effect in the classifier accuracy. In: Banks D, House L, McMorris FR, Arabie P, Gaul W, eds. Classification, Clustering and Data Mining Applications. Berlin, Germany: Springer‐Verlag; 2004.
  14. Ong MS, Coiera E. A systematic review of failures in handoff communication during intrahospital transfers. Jt Comm J Qual Patient Saf. 2011;37(6):274284.
  15. Radley DC, Wasserman MR, Olsho LE, et al. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc. 2013;20(3):470476.
  16. Sorra JS, Dyer N. Multilevel psychometric properties of the AHRQ hospital survey on patient safety culture. BMC Health Serv Res. 2010;10:199.
  17. Hines S, Luna K, Lofthus J, et al. Becoming a High Reliability Organization: Operational Advice for Hospital Leaders. Prepared by the Lewin Group under contract no. 290‐04‐0011. AHRQ publication no. 08–0022. Rockville, MD: Agency for Healthcare Research and Quality; 2008.
  18. Health policy brief: Medicare hospital readmissions reduction program. Health Affairs. November 12, 2013. Available at: http://www.healthaffairs.org/healthpolicybriefs/brief.php?brief_id=102. Accessed August 11, 2014.
  19. Naylor MD, Aiken LH, Kurtzman ET, Olds DM, Hirschman KB. The care span: the importance of transitional care in achieving health reform. Health Aff (Millwood). 2011;30(4):746754.
References
  1. Waring J, McDonald R, Harrison S. Safety and complexity: inter‐departmental relationships as a threat to patient safety in the operating department. J Health Organ Manag. 2006;20(2–3):227242.
  2. Coiera RA, Jayasuria J, Hardy A, Bannan A, Thorpe ME. Communication loads on clinical staff in the emergency department. Med J Aust. 2002;176(9):415418.
  3. Starmer AJ, Sectish TC, Simon DW, et al. Rates of medical errors and preventable adverse events among hospitalized children following implementation of a resident handoff bundle. JAMA. 2013;310(21):22622270.
  4. Frey LR, Gouran D, Poole MS. The Handbook of Group Communication Theory and Research. Thousand Oaks, CA: Sage Publications; 1999.
  5. Higgins A, Edland A, Svenson O. Judgment and decision making under time pressure. In: Svenson O, Maule AJ, eds. Time Pressure and Stress in Human Judgment and Decision Making. New York, NY: Plenum Press; 1993:2740.
  6. Nichols P, Copeland TS, Craib IA, Hopkins P, Bruce DG. Learning from error: identifying contributory causes of medication errors in an Australian hospital. Med J Aust. 2008;188(5):276279.
  7. Apker J, Mallak LA, Gibson SC. Communicating in the “gray zone”: perceptions about emergency physician hospitalist handoffs and patient safety. Acad Emerg Med. 2007;14(10):884894.
  8. Singer JI, Dean J. Emergency physician intershift handovers: an analysis of our transitional care. Pediatr Emerg Care. 2006;22(10):751754.
  9. Horwitz LI, Meredith T, Schuur JD, Shah NR, Kulkarni RG, Jenq GY. Dropping the baton: a qualitative analysis of failures during the transition from emergency department to inpatient care. Ann Emerg Med. 2009;53(6):701710.
  10. Solet DJ, Norvell JM, Rutan GH, Frankel RM. Lost in translation: challenges and opportunities in physician‐to‐physician communication during patient handoffs. Acad Med. 2005;80(12):10941099.
  11. Sorra J, Famolaro T, Dyer N, et al. Hospital Survey on Patient Safety Culture: 2010 user comparative database report. (Prepared by Westat, Rockville, MD, under Contract No. HHSA 290200710024C). Rockville, MD: Agency for Healthcare Research and Quality; February 2010. AHRQ Publication No. 10‐0026.
  12. Chen J, Davis LS, Davis KG, Pan W, Daraiseh NM. Physiological and behavioural response patterns at work among hospital nurses. J Nurs Manag. 2011;19(1):5768.
  13. Acuna E, Rodriguez C. The treatment of missing values and its effect in the classifier accuracy. In: Banks D, House L, McMorris FR, Arabie P, Gaul W, eds. Classification, Clustering and Data Mining Applications. Berlin, Germany: Springer‐Verlag; 2004.
  14. Ong MS, Coiera E. A systematic review of failures in handoff communication during intrahospital transfers. Jt Comm J Qual Patient Saf. 2011;37(6):274284.
  15. Radley DC, Wasserman MR, Olsho LE, et al. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc. 2013;20(3):470476.
  16. Sorra JS, Dyer N. Multilevel psychometric properties of the AHRQ hospital survey on patient safety culture. BMC Health Serv Res. 2010;10:199.
  17. Hines S, Luna K, Lofthus J, et al. Becoming a High Reliability Organization: Operational Advice for Hospital Leaders. Prepared by the Lewin Group under contract no. 290‐04‐0011. AHRQ publication no. 08–0022. Rockville, MD: Agency for Healthcare Research and Quality; 2008.
  18. Health policy brief: Medicare hospital readmissions reduction program. Health Affairs. November 12, 2013. Available at: http://www.healthaffairs.org/healthpolicybriefs/brief.php?brief_id=102. Accessed August 11, 2014.
  19. Naylor MD, Aiken LH, Kurtzman ET, Olds DM, Hirschman KB. The care span: the importance of transitional care in achieving health reform. Health Aff (Millwood). 2011;30(4):746754.
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Address for correspondence and reprint requests: Mark E. Patterson, PhD, Division of Pharmacy Practice and Administration, University of Missouri–Kansas City School of Pharmacy, 4245 Health Sciences Building, 2464 Charlotte Street, Kansas City, MO 64108‐2718; Telephone: 816‐235‐6320; Fax: 816‐235‐6008; E‐mail: [email protected]
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Furuncular Myiasis in 2 American Travelers Returning From Senegal

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Furuncular Myiasis in 2 American Travelers Returning From Senegal

Case Reports

Patient 1

A 16-year-old adolescent boy presented to the emergency department with painful, pruritic, erythematous nodules on the bilateral legs of 1 week’s duration. The lesions had developed 1 week after returning from a monthlong trip to Senegal with a volunteer youth group. He did not recall sustaining any painful insect bites or illnesses while traveling in Africa and only noticed the erythematous papules on the legs when he returned home to the United States. After consulting with his primary care physician and a local dermatologist, the patient began taking oral cephalexin for suspected bacterial furunculosis with no considerable improvement. Over the course of 1 week, the lesions became increasingly painful and pruritic, prompting a visit to the emergency department. Prior to his arrival, the patient reported squeezing a live worm from one of the lesions on the right ankle.

On presentation, the patient was afebrile (temperature, 36.7°C) and his vital signs revealed no abnormalities. Physical examination revealed tender erythematous nodules on the bilateral heels, ankles, and shins with pinpoint puncta noted at the center of many of the lesions (Figure 1). The nodules were warm and indurated and no pulsatile movement was appreciated. The legs appeared to be well perfused with intact sensation and motor function. The patient brought in the live mobile larva that he extruded from the lesion on the right ankle. Both the departments of infectious diseases and dermatology were consulted and a preliminary diagnosis of furuncular myiasis was made.

Figure 1. Tender erythematous nodules on the bilateral heels, ankles, and shins.

The lesions were occluded with petroleum jelly and the patient was instructed to follow-up with the dermatology department later that same day. On follow-up in the dermatology clinic, the tips of intact larvae were appreciated at the central puncta of some of the lesions (Figure 2). Lidocaine adrenaline tetracaine gel was applied to lesions on the legs for 40 minutes, then lidocaine gel 1% was injected into each lesion. On injection, immobile larvae were ejected from the central puncta of most of the lesions; the remaining lesions were treated via 3-mm punch biopsy as a means of extraction. Each nodule contained only a single larva, all of which were dead at the time of removal (Figure 3). The wounds were left open and the patient was instructed to continue treatment with cephalexin with leg elevation and rest. Pathologic examination of deep dermal skin sections revealed larval fragments encased by a thick chitinous cuticle with spines that were consistent with furuncular myiasis (Figures 4 and 5). Given the patient’s recent history of travel to Africa along with the morphology of the extracted specimens, the larvae were identified as Cordylobia anthropophaga, a common cause of furuncular myiasis in that region.

Patient 2

The next week, a 17-year-old adolescent girl who had been on the same trip to Senegal as patient 1 presented with 2 similar erythematous nodules with central crusts on the left inner thigh and buttock. On noticing the lesions approximately 3 days prior to presentation, the patient applied topical antibiotic ointment to each nodule, which incited the evacuation of white tube-shaped structures that were presented for examination. On presentation, the nodules were healing well. Given the patient’s travel history and physical examination, a presumptive diagnosis of furuncular myiasis from C anthropophaga also was made.

Figure 2. The tips of intact larvae were appreciated at the central puncta of some of the lesions following occlusion with petroleum jelly.

Figure 3. Dead larva extracted by lidocaine injection and punch biopsy.

Comment

The term myiasis stems from the Greek term for fly and is used to describe the infestation of fly larvae in living vertebrates.1 Myiasis has many classifications, the 3 most common being furuncular, migratory, and wound myiasis, which are differentiated by the different fly species found in distinct regions of the world. Furuncular myiasis is the most benign form, usually affecting only a localized region of the skin; migratory myiasis is characterized by larvae traveling substantial distances from one anatomic site to another within the lower layers of the epidermis; and wound myiasis involves rapid reproduction of larvae in necrotic tissue with subsequent tissue destruction.2

The clinical presentation of the lesions noted in our patients suggested a diagnosis of furuncular myiasis, which commonly is caused by Dermatobia hominis, C anthropophaga, Cuterebra species, Wohlfahrtia vigil, and Wohlfahrtia opaca larvae.3Dermatobia hominis is the most common cause of furuncular myiasis and usually is found in Central and South America. Our patients likely developed an infestation of C anthropophaga (also known as the tumbu fly), a yellow-brown, 7- to 12-mm blowfly commonly found throughout tropical Africa.3 Although C anthropophaga is historically limited to sub-Saharan Africa, there has been a report of a case acquired in Portugal.4

 

 

In a review of the literature, C anthropophaga myiasis was documented in Italian travelers returning from Senegal5-7; our cases are unique because they represent North American travelers returning from Senegal with furuncular myiasis. Furuncular myiasis from C anthropophaga has been reported in travelers returning to North America from other African countries, including Angola,8 Tanzania,9-11 Kenya,9 Sierra Leone,12 and Ivory Coast.13 Several cases of ocular myiasis from D hominis and Oestrus ovis have been reported in European travelers returning from Tunisia.14,15

Tumbu fly infestations typically affect dogs and rodents but can arise in human hosts.3 Children may be affected by C anthropophaga furuncular myiasis more often than adults because they have thinner skin and less immunity to the larvae.2

Figure 4. Deep dermal cavity containing larval fragments encased by a thick chitinous cuticle with spines surrounded by mixed dermal inflammation (H&E, original magnification ×40).

Figure 5. Larval intestinal components were visualized as well as
striated muscle (H&E, original magnification ×200).

There are 2 mechanisms by which infestation of human hosts by C anthropophaga can occur. Most commonly, female flies lay eggs in shady areas in soil that is contaminated by feces or urine. The hatched larvae can survive in the ground for up to 2 weeks and later attach to a host when prompted by heat or movement.3 Therefore, clothing set out to dry may be contaminated by this soil. Alternatively, female flies can lay eggs directly onto clothing that is contaminated by feces or urine and the larvae subsequently hatch outside the soil with easy access to human skin once the clothing is worn.2

Common penetration sites are the head, neck, and back, as well as areas covered by contaminated or infested clothing.2,3 Penetration of the human skin occurs instantly and is a painless process that is rarely noticed by the human host.3 The larvae burrow into the skin for 8 to 12 days, resulting in a furuncle that occasionally secretes a serous fluid.2 Within the first 2 days of infestation, the host may experience symptoms ranging from local pruritus to severe pain. Six days following initial onset, an intense inflammatory response may result in local lymphadenopathy along with fever and fatigue.2 The larvae use their posterior spiracles to create openings in the skin to create air holes that allow them to breathe.3 On physical examination, the spiracles generally appear as 1- to 3-mm dark linear streaks within furuncles, which is important in the diagnosis of C anthropophaga furuncular myiasis.1,3 If spiracles are not appreciated on initial examination, diagnosis can be made by submerging the affected areas in water or saliva to look for air bubbles arising from the central puncta of the lesions.1

All causes of furuncular myiasis are characterized by a ratio of 1 larva to 1 furuncle.16 Although most of these types of larvae that can cause furuncular myiasis result in single lesions, C anthropophaga infestation often produces several furuncles that may coalesce into plaques.1,2 The differential diagnosis for C anthropophaga furuncular myiasis includes pyoderma, impetigo, staphylococcal furunculosis, cutaneous leishmaniasis, infected cyst, retained foreign body, and facticial disease.2,3 Dracunculiasis also may be considered, which occurs after ingestion of contaminated water.2 Ultrasonography may be helpful for the diagnosis of furuncular myiasis, as it can facilitate identification of foreign bodies, abscesses, and even larvae in some cases.17 Definitive diagnosis of any type of myiasis involves extraction of the larva and identification of the family, genus, and species by a parasitologist.1 Some experts suggest rearing preserved live larvae with raw meat after extraction because adult specimens are more reliable than larvae for species diagnosis.1

Treatment of furuncular myiasis involves occlusion and extraction of the larvae from the skin. Suffocation of the larvae by occlusion of air holes with petroleum jelly, paraffin oil, bacon fat, glue, and other obstructing substances forces the larvae to emerge in search of oxygen, though immature larvae may be more reluctant than mature ones.2,3 Definitive treatment involves the direct removal of the larvae by surgery or expulsion by pressure, though it is recommended that lesions are pretreated with occlusive techniques.1,3 Other reported methods of extraction include injection of lidocaine and the use of a commercial venom extractor.1 It should be noted that rupture and incomplete extraction of larvae can lead to secondary infections and allergic reactions. Lesions can be pretreated with lidocaine gel prior to extraction, and antibiotics should be used in cases of secondary bacterial infection. Ivermectin also has been reported as a treatment of furuncular myiasis and other types of myiasis.1 Prevention of infestation by C anthropophaga includes avoidance of endemic areas, maintaining good hygiene, and ironing clothing or drying it in sunny locations.1,2 Overall, furuncular myiasis has a good prognosis with rapid recovery and a low incidence of complications.1

 

 

Conclusion

We present 2 cases of travelers returning to North America from Senegal with C anthropophaga furuncular myiasis. Careful review of travel history, physical examination, and identification of fly larvae are important for diagnosis. Individuals traveling to sub-Saharan Africa should avoid drying clothes in shady places and lying on the ground. They also are urged to iron their clothing before wearing it.

References

1. Caissie R, Beaulieu F, Giroux M, et al. Cutaneous myiasis: diagnosis, treatment, and prevention. J Oral Maxillofac Surg. 2008;66:560-568.

2. McGraw TA, Turiansky GW. Cutaneous myiasis. J Am Acad Dermatol. 2008;58:907-926.

3. Robbins K, Khachemoune A. Cutaneous myiasis: a review of the common types of myiasis. Int J Dermatol. 2010;49:1092-1098.

4. Curtis SJ, Edwards C, Athulathmuda C, et al. Case of the month: cutaneous myiasis in a returning traveller from the Algarve: first report of tumbu maggots, Cordylobia anthropophaga, acquired in Portugal. Emerg Med J. 2006;23:236-237.

5. Veraldi S, Brusasco A, Süss L. Cutaneous myiasis caused by larvae of Cordylobia anthropophaga (Blanchard). Int J Dermatol. 1993;32:184-187.

6. Cultrera R, Dettori G, Calderaro A, et al. Cutaneous myiasis caused by Cordylobia anthropophaga (Blanchard 1872): description of 5 cases from costal regions of Senegal [in Italian]. Parassitologia. 1993;35:47-49.

7. Fusco FM, Nardiello S, Brancaccio G, et al. Cutaneous myiasis from Cordylobia anthropophaga in a traveller returning from Senegal: a case study [in Italian]. Infez Med. 2005;13:109-111.

8. Lee EJ, Robinson F. Furuncular myiasis of the face caused by larva of the tumbu fly (Cordylobia anthropophaga)[published online ahead of print July 21, 2006]. Eye (Lond). 2007;21:268-269.

9. Rice PL, Gleason N. Two cases of myiasis in the United States by the African tumbu fly, Cordylobia anthropophaga (Diptera, Calliphoridae). Am J Trop Med Hyg. 1972;21:62-65.

10. March CH. A case of “ver du Cayor” in Manhattan. Arch Dermatol. 1964;90:32-33.

11. Schorr WF. Tumbu-fly myiasis in Marshfield, Wis. Arch Dermatol. 1967;95:61-62.

12. Potter TS, Dorman MA, Ghaemi M, et al. Inflammatory papules on the back of a traveling businessman. tumbu
fly myiasis. Arch Dermatol. 1995;131:951, 954.

13. Ockenhouse CF, Samlaska CP, Benson PM, et al. Cutaneous myiasis caused by the African tumbu fly (Cordylobia anthropophaga). Arch Dermatol. 1990;126:199-202.

14. Kaouech E, Kallel K, Belhadj S, et al. Dermatobia hominis furuncular myiasis in a man returning from Latin America: first imported case in Tunisia [in French]. Med Trop (Mars). 2010;70:135-136.

15. Zayani A, Chaabouni M, Gouiaa R, et al. Conjuctival myiasis. 23 cases in the Tunisian Sahel [in French]. Arch Inst Pasteur Tunis. 1989;66:289-292.

16. Latorre M, Ullate JV, Sanchez J, et al. A case of myiasis due to Dermatobia hominis. Eur J Clin Microbiol Infect Dis. 1993;12:968-969.

17. Mahal JJ, Sperling JD. Furuncular myiasis from Dermatobia hominis: a case of human botfly infestation [published online ahead of print February 1, 2010]. J Emerg Med. 2012;43:618-621.

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Author and Disclosure Information

Lauren Rimoin, MD; Julie Jackson, MD; Aparche Yang, MD; Carolyn Goh, MD; Teresa Soriano, MD

Dr. Rimoin is from the Department of Dermatology, Emory University, Atlanta, Georgia. Drs. Jackson, Yang, Goh, and Soriano are from the Division of Dermatology, David Geffen School of Medicine, University of California, Los Angeles.

The authors report no conflict of interest.

Correspondence: Lauren Rimoin, MD, 1525 Clifton Rd, Atlanta, GA 30329 ([email protected]).

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281-284
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myiasis, furuncle, Botfly, environmental dermatology, infestation, fly larvae, occlusion,
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Lauren Rimoin, MD; Julie Jackson, MD; Aparche Yang, MD; Carolyn Goh, MD; Teresa Soriano, MD

Dr. Rimoin is from the Department of Dermatology, Emory University, Atlanta, Georgia. Drs. Jackson, Yang, Goh, and Soriano are from the Division of Dermatology, David Geffen School of Medicine, University of California, Los Angeles.

The authors report no conflict of interest.

Correspondence: Lauren Rimoin, MD, 1525 Clifton Rd, Atlanta, GA 30329 ([email protected]).

Author and Disclosure Information

Lauren Rimoin, MD; Julie Jackson, MD; Aparche Yang, MD; Carolyn Goh, MD; Teresa Soriano, MD

Dr. Rimoin is from the Department of Dermatology, Emory University, Atlanta, Georgia. Drs. Jackson, Yang, Goh, and Soriano are from the Division of Dermatology, David Geffen School of Medicine, University of California, Los Angeles.

The authors report no conflict of interest.

Correspondence: Lauren Rimoin, MD, 1525 Clifton Rd, Atlanta, GA 30329 ([email protected]).

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Case Reports

Patient 1

A 16-year-old adolescent boy presented to the emergency department with painful, pruritic, erythematous nodules on the bilateral legs of 1 week’s duration. The lesions had developed 1 week after returning from a monthlong trip to Senegal with a volunteer youth group. He did not recall sustaining any painful insect bites or illnesses while traveling in Africa and only noticed the erythematous papules on the legs when he returned home to the United States. After consulting with his primary care physician and a local dermatologist, the patient began taking oral cephalexin for suspected bacterial furunculosis with no considerable improvement. Over the course of 1 week, the lesions became increasingly painful and pruritic, prompting a visit to the emergency department. Prior to his arrival, the patient reported squeezing a live worm from one of the lesions on the right ankle.

On presentation, the patient was afebrile (temperature, 36.7°C) and his vital signs revealed no abnormalities. Physical examination revealed tender erythematous nodules on the bilateral heels, ankles, and shins with pinpoint puncta noted at the center of many of the lesions (Figure 1). The nodules were warm and indurated and no pulsatile movement was appreciated. The legs appeared to be well perfused with intact sensation and motor function. The patient brought in the live mobile larva that he extruded from the lesion on the right ankle. Both the departments of infectious diseases and dermatology were consulted and a preliminary diagnosis of furuncular myiasis was made.

Figure 1. Tender erythematous nodules on the bilateral heels, ankles, and shins.

The lesions were occluded with petroleum jelly and the patient was instructed to follow-up with the dermatology department later that same day. On follow-up in the dermatology clinic, the tips of intact larvae were appreciated at the central puncta of some of the lesions (Figure 2). Lidocaine adrenaline tetracaine gel was applied to lesions on the legs for 40 minutes, then lidocaine gel 1% was injected into each lesion. On injection, immobile larvae were ejected from the central puncta of most of the lesions; the remaining lesions were treated via 3-mm punch biopsy as a means of extraction. Each nodule contained only a single larva, all of which were dead at the time of removal (Figure 3). The wounds were left open and the patient was instructed to continue treatment with cephalexin with leg elevation and rest. Pathologic examination of deep dermal skin sections revealed larval fragments encased by a thick chitinous cuticle with spines that were consistent with furuncular myiasis (Figures 4 and 5). Given the patient’s recent history of travel to Africa along with the morphology of the extracted specimens, the larvae were identified as Cordylobia anthropophaga, a common cause of furuncular myiasis in that region.

Patient 2

The next week, a 17-year-old adolescent girl who had been on the same trip to Senegal as patient 1 presented with 2 similar erythematous nodules with central crusts on the left inner thigh and buttock. On noticing the lesions approximately 3 days prior to presentation, the patient applied topical antibiotic ointment to each nodule, which incited the evacuation of white tube-shaped structures that were presented for examination. On presentation, the nodules were healing well. Given the patient’s travel history and physical examination, a presumptive diagnosis of furuncular myiasis from C anthropophaga also was made.

Figure 2. The tips of intact larvae were appreciated at the central puncta of some of the lesions following occlusion with petroleum jelly.

Figure 3. Dead larva extracted by lidocaine injection and punch biopsy.

Comment

The term myiasis stems from the Greek term for fly and is used to describe the infestation of fly larvae in living vertebrates.1 Myiasis has many classifications, the 3 most common being furuncular, migratory, and wound myiasis, which are differentiated by the different fly species found in distinct regions of the world. Furuncular myiasis is the most benign form, usually affecting only a localized region of the skin; migratory myiasis is characterized by larvae traveling substantial distances from one anatomic site to another within the lower layers of the epidermis; and wound myiasis involves rapid reproduction of larvae in necrotic tissue with subsequent tissue destruction.2

The clinical presentation of the lesions noted in our patients suggested a diagnosis of furuncular myiasis, which commonly is caused by Dermatobia hominis, C anthropophaga, Cuterebra species, Wohlfahrtia vigil, and Wohlfahrtia opaca larvae.3Dermatobia hominis is the most common cause of furuncular myiasis and usually is found in Central and South America. Our patients likely developed an infestation of C anthropophaga (also known as the tumbu fly), a yellow-brown, 7- to 12-mm blowfly commonly found throughout tropical Africa.3 Although C anthropophaga is historically limited to sub-Saharan Africa, there has been a report of a case acquired in Portugal.4

 

 

In a review of the literature, C anthropophaga myiasis was documented in Italian travelers returning from Senegal5-7; our cases are unique because they represent North American travelers returning from Senegal with furuncular myiasis. Furuncular myiasis from C anthropophaga has been reported in travelers returning to North America from other African countries, including Angola,8 Tanzania,9-11 Kenya,9 Sierra Leone,12 and Ivory Coast.13 Several cases of ocular myiasis from D hominis and Oestrus ovis have been reported in European travelers returning from Tunisia.14,15

Tumbu fly infestations typically affect dogs and rodents but can arise in human hosts.3 Children may be affected by C anthropophaga furuncular myiasis more often than adults because they have thinner skin and less immunity to the larvae.2

Figure 4. Deep dermal cavity containing larval fragments encased by a thick chitinous cuticle with spines surrounded by mixed dermal inflammation (H&E, original magnification ×40).

Figure 5. Larval intestinal components were visualized as well as
striated muscle (H&E, original magnification ×200).

There are 2 mechanisms by which infestation of human hosts by C anthropophaga can occur. Most commonly, female flies lay eggs in shady areas in soil that is contaminated by feces or urine. The hatched larvae can survive in the ground for up to 2 weeks and later attach to a host when prompted by heat or movement.3 Therefore, clothing set out to dry may be contaminated by this soil. Alternatively, female flies can lay eggs directly onto clothing that is contaminated by feces or urine and the larvae subsequently hatch outside the soil with easy access to human skin once the clothing is worn.2

Common penetration sites are the head, neck, and back, as well as areas covered by contaminated or infested clothing.2,3 Penetration of the human skin occurs instantly and is a painless process that is rarely noticed by the human host.3 The larvae burrow into the skin for 8 to 12 days, resulting in a furuncle that occasionally secretes a serous fluid.2 Within the first 2 days of infestation, the host may experience symptoms ranging from local pruritus to severe pain. Six days following initial onset, an intense inflammatory response may result in local lymphadenopathy along with fever and fatigue.2 The larvae use their posterior spiracles to create openings in the skin to create air holes that allow them to breathe.3 On physical examination, the spiracles generally appear as 1- to 3-mm dark linear streaks within furuncles, which is important in the diagnosis of C anthropophaga furuncular myiasis.1,3 If spiracles are not appreciated on initial examination, diagnosis can be made by submerging the affected areas in water or saliva to look for air bubbles arising from the central puncta of the lesions.1

All causes of furuncular myiasis are characterized by a ratio of 1 larva to 1 furuncle.16 Although most of these types of larvae that can cause furuncular myiasis result in single lesions, C anthropophaga infestation often produces several furuncles that may coalesce into plaques.1,2 The differential diagnosis for C anthropophaga furuncular myiasis includes pyoderma, impetigo, staphylococcal furunculosis, cutaneous leishmaniasis, infected cyst, retained foreign body, and facticial disease.2,3 Dracunculiasis also may be considered, which occurs after ingestion of contaminated water.2 Ultrasonography may be helpful for the diagnosis of furuncular myiasis, as it can facilitate identification of foreign bodies, abscesses, and even larvae in some cases.17 Definitive diagnosis of any type of myiasis involves extraction of the larva and identification of the family, genus, and species by a parasitologist.1 Some experts suggest rearing preserved live larvae with raw meat after extraction because adult specimens are more reliable than larvae for species diagnosis.1

Treatment of furuncular myiasis involves occlusion and extraction of the larvae from the skin. Suffocation of the larvae by occlusion of air holes with petroleum jelly, paraffin oil, bacon fat, glue, and other obstructing substances forces the larvae to emerge in search of oxygen, though immature larvae may be more reluctant than mature ones.2,3 Definitive treatment involves the direct removal of the larvae by surgery or expulsion by pressure, though it is recommended that lesions are pretreated with occlusive techniques.1,3 Other reported methods of extraction include injection of lidocaine and the use of a commercial venom extractor.1 It should be noted that rupture and incomplete extraction of larvae can lead to secondary infections and allergic reactions. Lesions can be pretreated with lidocaine gel prior to extraction, and antibiotics should be used in cases of secondary bacterial infection. Ivermectin also has been reported as a treatment of furuncular myiasis and other types of myiasis.1 Prevention of infestation by C anthropophaga includes avoidance of endemic areas, maintaining good hygiene, and ironing clothing or drying it in sunny locations.1,2 Overall, furuncular myiasis has a good prognosis with rapid recovery and a low incidence of complications.1

 

 

Conclusion

We present 2 cases of travelers returning to North America from Senegal with C anthropophaga furuncular myiasis. Careful review of travel history, physical examination, and identification of fly larvae are important for diagnosis. Individuals traveling to sub-Saharan Africa should avoid drying clothes in shady places and lying on the ground. They also are urged to iron their clothing before wearing it.

Case Reports

Patient 1

A 16-year-old adolescent boy presented to the emergency department with painful, pruritic, erythematous nodules on the bilateral legs of 1 week’s duration. The lesions had developed 1 week after returning from a monthlong trip to Senegal with a volunteer youth group. He did not recall sustaining any painful insect bites or illnesses while traveling in Africa and only noticed the erythematous papules on the legs when he returned home to the United States. After consulting with his primary care physician and a local dermatologist, the patient began taking oral cephalexin for suspected bacterial furunculosis with no considerable improvement. Over the course of 1 week, the lesions became increasingly painful and pruritic, prompting a visit to the emergency department. Prior to his arrival, the patient reported squeezing a live worm from one of the lesions on the right ankle.

On presentation, the patient was afebrile (temperature, 36.7°C) and his vital signs revealed no abnormalities. Physical examination revealed tender erythematous nodules on the bilateral heels, ankles, and shins with pinpoint puncta noted at the center of many of the lesions (Figure 1). The nodules were warm and indurated and no pulsatile movement was appreciated. The legs appeared to be well perfused with intact sensation and motor function. The patient brought in the live mobile larva that he extruded from the lesion on the right ankle. Both the departments of infectious diseases and dermatology were consulted and a preliminary diagnosis of furuncular myiasis was made.

Figure 1. Tender erythematous nodules on the bilateral heels, ankles, and shins.

The lesions were occluded with petroleum jelly and the patient was instructed to follow-up with the dermatology department later that same day. On follow-up in the dermatology clinic, the tips of intact larvae were appreciated at the central puncta of some of the lesions (Figure 2). Lidocaine adrenaline tetracaine gel was applied to lesions on the legs for 40 minutes, then lidocaine gel 1% was injected into each lesion. On injection, immobile larvae were ejected from the central puncta of most of the lesions; the remaining lesions were treated via 3-mm punch biopsy as a means of extraction. Each nodule contained only a single larva, all of which were dead at the time of removal (Figure 3). The wounds were left open and the patient was instructed to continue treatment with cephalexin with leg elevation and rest. Pathologic examination of deep dermal skin sections revealed larval fragments encased by a thick chitinous cuticle with spines that were consistent with furuncular myiasis (Figures 4 and 5). Given the patient’s recent history of travel to Africa along with the morphology of the extracted specimens, the larvae were identified as Cordylobia anthropophaga, a common cause of furuncular myiasis in that region.

Patient 2

The next week, a 17-year-old adolescent girl who had been on the same trip to Senegal as patient 1 presented with 2 similar erythematous nodules with central crusts on the left inner thigh and buttock. On noticing the lesions approximately 3 days prior to presentation, the patient applied topical antibiotic ointment to each nodule, which incited the evacuation of white tube-shaped structures that were presented for examination. On presentation, the nodules were healing well. Given the patient’s travel history and physical examination, a presumptive diagnosis of furuncular myiasis from C anthropophaga also was made.

Figure 2. The tips of intact larvae were appreciated at the central puncta of some of the lesions following occlusion with petroleum jelly.

Figure 3. Dead larva extracted by lidocaine injection and punch biopsy.

Comment

The term myiasis stems from the Greek term for fly and is used to describe the infestation of fly larvae in living vertebrates.1 Myiasis has many classifications, the 3 most common being furuncular, migratory, and wound myiasis, which are differentiated by the different fly species found in distinct regions of the world. Furuncular myiasis is the most benign form, usually affecting only a localized region of the skin; migratory myiasis is characterized by larvae traveling substantial distances from one anatomic site to another within the lower layers of the epidermis; and wound myiasis involves rapid reproduction of larvae in necrotic tissue with subsequent tissue destruction.2

The clinical presentation of the lesions noted in our patients suggested a diagnosis of furuncular myiasis, which commonly is caused by Dermatobia hominis, C anthropophaga, Cuterebra species, Wohlfahrtia vigil, and Wohlfahrtia opaca larvae.3Dermatobia hominis is the most common cause of furuncular myiasis and usually is found in Central and South America. Our patients likely developed an infestation of C anthropophaga (also known as the tumbu fly), a yellow-brown, 7- to 12-mm blowfly commonly found throughout tropical Africa.3 Although C anthropophaga is historically limited to sub-Saharan Africa, there has been a report of a case acquired in Portugal.4

 

 

In a review of the literature, C anthropophaga myiasis was documented in Italian travelers returning from Senegal5-7; our cases are unique because they represent North American travelers returning from Senegal with furuncular myiasis. Furuncular myiasis from C anthropophaga has been reported in travelers returning to North America from other African countries, including Angola,8 Tanzania,9-11 Kenya,9 Sierra Leone,12 and Ivory Coast.13 Several cases of ocular myiasis from D hominis and Oestrus ovis have been reported in European travelers returning from Tunisia.14,15

Tumbu fly infestations typically affect dogs and rodents but can arise in human hosts.3 Children may be affected by C anthropophaga furuncular myiasis more often than adults because they have thinner skin and less immunity to the larvae.2

Figure 4. Deep dermal cavity containing larval fragments encased by a thick chitinous cuticle with spines surrounded by mixed dermal inflammation (H&E, original magnification ×40).

Figure 5. Larval intestinal components were visualized as well as
striated muscle (H&E, original magnification ×200).

There are 2 mechanisms by which infestation of human hosts by C anthropophaga can occur. Most commonly, female flies lay eggs in shady areas in soil that is contaminated by feces or urine. The hatched larvae can survive in the ground for up to 2 weeks and later attach to a host when prompted by heat or movement.3 Therefore, clothing set out to dry may be contaminated by this soil. Alternatively, female flies can lay eggs directly onto clothing that is contaminated by feces or urine and the larvae subsequently hatch outside the soil with easy access to human skin once the clothing is worn.2

Common penetration sites are the head, neck, and back, as well as areas covered by contaminated or infested clothing.2,3 Penetration of the human skin occurs instantly and is a painless process that is rarely noticed by the human host.3 The larvae burrow into the skin for 8 to 12 days, resulting in a furuncle that occasionally secretes a serous fluid.2 Within the first 2 days of infestation, the host may experience symptoms ranging from local pruritus to severe pain. Six days following initial onset, an intense inflammatory response may result in local lymphadenopathy along with fever and fatigue.2 The larvae use their posterior spiracles to create openings in the skin to create air holes that allow them to breathe.3 On physical examination, the spiracles generally appear as 1- to 3-mm dark linear streaks within furuncles, which is important in the diagnosis of C anthropophaga furuncular myiasis.1,3 If spiracles are not appreciated on initial examination, diagnosis can be made by submerging the affected areas in water or saliva to look for air bubbles arising from the central puncta of the lesions.1

All causes of furuncular myiasis are characterized by a ratio of 1 larva to 1 furuncle.16 Although most of these types of larvae that can cause furuncular myiasis result in single lesions, C anthropophaga infestation often produces several furuncles that may coalesce into plaques.1,2 The differential diagnosis for C anthropophaga furuncular myiasis includes pyoderma, impetigo, staphylococcal furunculosis, cutaneous leishmaniasis, infected cyst, retained foreign body, and facticial disease.2,3 Dracunculiasis also may be considered, which occurs after ingestion of contaminated water.2 Ultrasonography may be helpful for the diagnosis of furuncular myiasis, as it can facilitate identification of foreign bodies, abscesses, and even larvae in some cases.17 Definitive diagnosis of any type of myiasis involves extraction of the larva and identification of the family, genus, and species by a parasitologist.1 Some experts suggest rearing preserved live larvae with raw meat after extraction because adult specimens are more reliable than larvae for species diagnosis.1

Treatment of furuncular myiasis involves occlusion and extraction of the larvae from the skin. Suffocation of the larvae by occlusion of air holes with petroleum jelly, paraffin oil, bacon fat, glue, and other obstructing substances forces the larvae to emerge in search of oxygen, though immature larvae may be more reluctant than mature ones.2,3 Definitive treatment involves the direct removal of the larvae by surgery or expulsion by pressure, though it is recommended that lesions are pretreated with occlusive techniques.1,3 Other reported methods of extraction include injection of lidocaine and the use of a commercial venom extractor.1 It should be noted that rupture and incomplete extraction of larvae can lead to secondary infections and allergic reactions. Lesions can be pretreated with lidocaine gel prior to extraction, and antibiotics should be used in cases of secondary bacterial infection. Ivermectin also has been reported as a treatment of furuncular myiasis and other types of myiasis.1 Prevention of infestation by C anthropophaga includes avoidance of endemic areas, maintaining good hygiene, and ironing clothing or drying it in sunny locations.1,2 Overall, furuncular myiasis has a good prognosis with rapid recovery and a low incidence of complications.1

 

 

Conclusion

We present 2 cases of travelers returning to North America from Senegal with C anthropophaga furuncular myiasis. Careful review of travel history, physical examination, and identification of fly larvae are important for diagnosis. Individuals traveling to sub-Saharan Africa should avoid drying clothes in shady places and lying on the ground. They also are urged to iron their clothing before wearing it.

References

1. Caissie R, Beaulieu F, Giroux M, et al. Cutaneous myiasis: diagnosis, treatment, and prevention. J Oral Maxillofac Surg. 2008;66:560-568.

2. McGraw TA, Turiansky GW. Cutaneous myiasis. J Am Acad Dermatol. 2008;58:907-926.

3. Robbins K, Khachemoune A. Cutaneous myiasis: a review of the common types of myiasis. Int J Dermatol. 2010;49:1092-1098.

4. Curtis SJ, Edwards C, Athulathmuda C, et al. Case of the month: cutaneous myiasis in a returning traveller from the Algarve: first report of tumbu maggots, Cordylobia anthropophaga, acquired in Portugal. Emerg Med J. 2006;23:236-237.

5. Veraldi S, Brusasco A, Süss L. Cutaneous myiasis caused by larvae of Cordylobia anthropophaga (Blanchard). Int J Dermatol. 1993;32:184-187.

6. Cultrera R, Dettori G, Calderaro A, et al. Cutaneous myiasis caused by Cordylobia anthropophaga (Blanchard 1872): description of 5 cases from costal regions of Senegal [in Italian]. Parassitologia. 1993;35:47-49.

7. Fusco FM, Nardiello S, Brancaccio G, et al. Cutaneous myiasis from Cordylobia anthropophaga in a traveller returning from Senegal: a case study [in Italian]. Infez Med. 2005;13:109-111.

8. Lee EJ, Robinson F. Furuncular myiasis of the face caused by larva of the tumbu fly (Cordylobia anthropophaga)[published online ahead of print July 21, 2006]. Eye (Lond). 2007;21:268-269.

9. Rice PL, Gleason N. Two cases of myiasis in the United States by the African tumbu fly, Cordylobia anthropophaga (Diptera, Calliphoridae). Am J Trop Med Hyg. 1972;21:62-65.

10. March CH. A case of “ver du Cayor” in Manhattan. Arch Dermatol. 1964;90:32-33.

11. Schorr WF. Tumbu-fly myiasis in Marshfield, Wis. Arch Dermatol. 1967;95:61-62.

12. Potter TS, Dorman MA, Ghaemi M, et al. Inflammatory papules on the back of a traveling businessman. tumbu
fly myiasis. Arch Dermatol. 1995;131:951, 954.

13. Ockenhouse CF, Samlaska CP, Benson PM, et al. Cutaneous myiasis caused by the African tumbu fly (Cordylobia anthropophaga). Arch Dermatol. 1990;126:199-202.

14. Kaouech E, Kallel K, Belhadj S, et al. Dermatobia hominis furuncular myiasis in a man returning from Latin America: first imported case in Tunisia [in French]. Med Trop (Mars). 2010;70:135-136.

15. Zayani A, Chaabouni M, Gouiaa R, et al. Conjuctival myiasis. 23 cases in the Tunisian Sahel [in French]. Arch Inst Pasteur Tunis. 1989;66:289-292.

16. Latorre M, Ullate JV, Sanchez J, et al. A case of myiasis due to Dermatobia hominis. Eur J Clin Microbiol Infect Dis. 1993;12:968-969.

17. Mahal JJ, Sperling JD. Furuncular myiasis from Dermatobia hominis: a case of human botfly infestation [published online ahead of print February 1, 2010]. J Emerg Med. 2012;43:618-621.

References

1. Caissie R, Beaulieu F, Giroux M, et al. Cutaneous myiasis: diagnosis, treatment, and prevention. J Oral Maxillofac Surg. 2008;66:560-568.

2. McGraw TA, Turiansky GW. Cutaneous myiasis. J Am Acad Dermatol. 2008;58:907-926.

3. Robbins K, Khachemoune A. Cutaneous myiasis: a review of the common types of myiasis. Int J Dermatol. 2010;49:1092-1098.

4. Curtis SJ, Edwards C, Athulathmuda C, et al. Case of the month: cutaneous myiasis in a returning traveller from the Algarve: first report of tumbu maggots, Cordylobia anthropophaga, acquired in Portugal. Emerg Med J. 2006;23:236-237.

5. Veraldi S, Brusasco A, Süss L. Cutaneous myiasis caused by larvae of Cordylobia anthropophaga (Blanchard). Int J Dermatol. 1993;32:184-187.

6. Cultrera R, Dettori G, Calderaro A, et al. Cutaneous myiasis caused by Cordylobia anthropophaga (Blanchard 1872): description of 5 cases from costal regions of Senegal [in Italian]. Parassitologia. 1993;35:47-49.

7. Fusco FM, Nardiello S, Brancaccio G, et al. Cutaneous myiasis from Cordylobia anthropophaga in a traveller returning from Senegal: a case study [in Italian]. Infez Med. 2005;13:109-111.

8. Lee EJ, Robinson F. Furuncular myiasis of the face caused by larva of the tumbu fly (Cordylobia anthropophaga)[published online ahead of print July 21, 2006]. Eye (Lond). 2007;21:268-269.

9. Rice PL, Gleason N. Two cases of myiasis in the United States by the African tumbu fly, Cordylobia anthropophaga (Diptera, Calliphoridae). Am J Trop Med Hyg. 1972;21:62-65.

10. March CH. A case of “ver du Cayor” in Manhattan. Arch Dermatol. 1964;90:32-33.

11. Schorr WF. Tumbu-fly myiasis in Marshfield, Wis. Arch Dermatol. 1967;95:61-62.

12. Potter TS, Dorman MA, Ghaemi M, et al. Inflammatory papules on the back of a traveling businessman. tumbu
fly myiasis. Arch Dermatol. 1995;131:951, 954.

13. Ockenhouse CF, Samlaska CP, Benson PM, et al. Cutaneous myiasis caused by the African tumbu fly (Cordylobia anthropophaga). Arch Dermatol. 1990;126:199-202.

14. Kaouech E, Kallel K, Belhadj S, et al. Dermatobia hominis furuncular myiasis in a man returning from Latin America: first imported case in Tunisia [in French]. Med Trop (Mars). 2010;70:135-136.

15. Zayani A, Chaabouni M, Gouiaa R, et al. Conjuctival myiasis. 23 cases in the Tunisian Sahel [in French]. Arch Inst Pasteur Tunis. 1989;66:289-292.

16. Latorre M, Ullate JV, Sanchez J, et al. A case of myiasis due to Dermatobia hominis. Eur J Clin Microbiol Infect Dis. 1993;12:968-969.

17. Mahal JJ, Sperling JD. Furuncular myiasis from Dermatobia hominis: a case of human botfly infestation [published online ahead of print February 1, 2010]. J Emerg Med. 2012;43:618-621.

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      Practice Points

  • ­Cutaneous myiasis is caused by an infestation of fly larvae and can present as furuncles (furuncular myiasis), migratory inflammatory linear plaques (migratory myiasis), and worsening tissue destruction in existing wounds (wound myiasis).
  • Furuncular myiasis should be included in the differential diagnosis in patients with furuncular skin lesions who have recently traveled to Central America, South America, or sub-Saharan Africa.
  • Furuncular myiasis may be treated by both occlusive and extraction techniques.
Article PDF Media

Reduced resident duty hours haven’t changed patient outcomes

Major benefits lacking
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Display Headline
Reduced resident duty hours haven’t changed patient outcomes

Patient mortality and morbidity outcomes have not changed since the most recent round of reforms to medical residents’ duty hours in 2011, according to two of the first nationwide studies to assess these “improvements,” which both were published online Dec. 9 in JAMA.

In addition, one of the studies found no difference between pre-reform and post-reform scores or on pass rates for oral or written national in-training and board certification examinations.

©Thinglass/thinkstockphotos.com

Thus, two separate studies involving millions of hospitalized patients across the country have both found that these reforms had no discernible effect on patient care. However, both groups of researchers cautioned that their studies were observational and therefore subject to potential biases and that they covered only the first 2 years that the duty-hours reforms have been in place.

The 2011 requirements expanded on those enacted in 2003 by further restricting residents’ duty hours, in the hope of reducing medical errors attributed to exhausted residents. The hours of continuous in-hospital duty were reduced from 30 to 16 for first-year residents and to 24 for upper-year residents, and the interval between shifts was increased to at least 8 hours off for first-year residents and at least 14 hours off for upper-year residents.

“Duty hour reform is arguably one of the largest efforts ever undertaken to improve the quality and safety of patient care in teaching hospitals,” said Dr. Mitesh S. Patel of the University of Pennsylvania and the Veterans Affairs Hospital Center for Health Equity Research and Promotion, both in Philadelphia, and his associates.

They assessed 30-day mortality and readmissions among 2,790,356 Medicare patients who were treated either for acute MI, stroke, gastrointestinal bleeding, or heart failure, or who underwent general, orthopedic, or vascular surgery, at 3,104 hospitals between 2009 and 2012. The investigators found no significant associations, either positive or negative, between the reforms to residents’ duty hours and any patient outcomes. Sensitivity analyses confirmed the results of the primary data analyses.

“Our findings suggest that ... the goals of improving the quality and safety of patient care ... were not being achieved. Conversely, concerns that outcomes might actually worsen because of decreased continuity of care have not been borne out,” Dr. Patel and his associates said (JAMA 2014 Dec. 9 [doi:10.1001/jama.2014.15273]).

The investigators noted that their study was limited in that it could not take into account hospitals’ adherence to the new requirements. Their study also did not assess other outcomes such as patient safety indicators or complication rates, which “may better elucidate the relative effects of decreased resident fatigue and increased patient hand offs.” And their study couldn’t address any possible confounding effects from other concurrent policy initiatives aimed at improving care for Medicare beneficiaries, such as the Hospital Readmissions Reduction Program.

In the other study, a separate group of researchers used data from the American College of Surgeons National Surgical Quality Improvement Program to assess outcomes for 535,499 patients who underwent general surgery at 131 hospitals during the 2 years before and the 2 years after the reforms to residents’ duty hours were implemented. This included 23 teaching hospitals in which residents were involved in at least 95% of general surgeries, said Dr. Ravi Rajaram of the division of research and optimal patient care, American College of Surgeons, and the Institute for Public Health and Medicine at Northwestern University, both in Chicago, and his associates.

The reforms were not associated with any change in rates of patient mortality or serious morbidity, either in the study population as a whole or in the subgroups of high-risk and low-risk patients. They also had no effect on secondary outcomes such as surgical-site infection or sepsis. These results remained consistent across several sensitivity analyses.

Neither mean scores for in-training, written board, and oral board examinations nor pass rates for those examinations showed any significant changes during the study period.

“Moreover, first-year trainees, who were most directly affected by the 2011 reforms, did not improve their ABSITE [American Board of Surgery In-Training Examination] scores, despite presumably more free time to prepare,” Dr. Rajaram and his associates said (JAMA 2014 Dec. 9 [doi:10.1001/JAMA.2014.15277]).

They cautioned that their study assessed only the first 2 years following duty-hour reform, and “there may be differences in patient care or resident examination performance that are evident only several years after implementation and adoption of new duty-hour requirements.” In addition, a retrospective observational study such as this one could not produce the high-level evidence needed to guide policy decisions. “To that end, a national multicenter cluster-randomized trial is being conducted (the Flexibility In duty hour Requirements for Surgical Trainees [FIRST] trial), comparing current duty-hour requirements with flexible duty hours to assess the effects of this intervention on patient outcomes and resident well-being. This trial may further inform the debate of how to optimally structure postgraduate training,” they said.

References

Body

The results of these two large studies are aligned with those of most previous research into the effects of duty hour requirements on patient outcomes. There is a consistent theme: a lack of a major beneficial effect.

Complex problems often demand complex answers. The goal is for the medical profession to move forward with more comprehensive and nuanced approaches to help fulfill its responsibility to provide trainees with the necessary skills to manage fatigue and allow the safest environment for quality care.

Dr. James A. Arrigh is chair of the Accreditation Council for Graduate Medical Education (ACGME) residency review committee for internal medicine. Dr. James C. Hebert is chair of the ACGME Council of Review Committee Chairs. They made these remarks in an editorial accompanying the studies.

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The results of these two large studies are aligned with those of most previous research into the effects of duty hour requirements on patient outcomes. There is a consistent theme: a lack of a major beneficial effect.

Complex problems often demand complex answers. The goal is for the medical profession to move forward with more comprehensive and nuanced approaches to help fulfill its responsibility to provide trainees with the necessary skills to manage fatigue and allow the safest environment for quality care.

Dr. James A. Arrigh is chair of the Accreditation Council for Graduate Medical Education (ACGME) residency review committee for internal medicine. Dr. James C. Hebert is chair of the ACGME Council of Review Committee Chairs. They made these remarks in an editorial accompanying the studies.

Body

The results of these two large studies are aligned with those of most previous research into the effects of duty hour requirements on patient outcomes. There is a consistent theme: a lack of a major beneficial effect.

Complex problems often demand complex answers. The goal is for the medical profession to move forward with more comprehensive and nuanced approaches to help fulfill its responsibility to provide trainees with the necessary skills to manage fatigue and allow the safest environment for quality care.

Dr. James A. Arrigh is chair of the Accreditation Council for Graduate Medical Education (ACGME) residency review committee for internal medicine. Dr. James C. Hebert is chair of the ACGME Council of Review Committee Chairs. They made these remarks in an editorial accompanying the studies.

Title
Major benefits lacking
Major benefits lacking

Patient mortality and morbidity outcomes have not changed since the most recent round of reforms to medical residents’ duty hours in 2011, according to two of the first nationwide studies to assess these “improvements,” which both were published online Dec. 9 in JAMA.

In addition, one of the studies found no difference between pre-reform and post-reform scores or on pass rates for oral or written national in-training and board certification examinations.

©Thinglass/thinkstockphotos.com

Thus, two separate studies involving millions of hospitalized patients across the country have both found that these reforms had no discernible effect on patient care. However, both groups of researchers cautioned that their studies were observational and therefore subject to potential biases and that they covered only the first 2 years that the duty-hours reforms have been in place.

The 2011 requirements expanded on those enacted in 2003 by further restricting residents’ duty hours, in the hope of reducing medical errors attributed to exhausted residents. The hours of continuous in-hospital duty were reduced from 30 to 16 for first-year residents and to 24 for upper-year residents, and the interval between shifts was increased to at least 8 hours off for first-year residents and at least 14 hours off for upper-year residents.

“Duty hour reform is arguably one of the largest efforts ever undertaken to improve the quality and safety of patient care in teaching hospitals,” said Dr. Mitesh S. Patel of the University of Pennsylvania and the Veterans Affairs Hospital Center for Health Equity Research and Promotion, both in Philadelphia, and his associates.

They assessed 30-day mortality and readmissions among 2,790,356 Medicare patients who were treated either for acute MI, stroke, gastrointestinal bleeding, or heart failure, or who underwent general, orthopedic, or vascular surgery, at 3,104 hospitals between 2009 and 2012. The investigators found no significant associations, either positive or negative, between the reforms to residents’ duty hours and any patient outcomes. Sensitivity analyses confirmed the results of the primary data analyses.

“Our findings suggest that ... the goals of improving the quality and safety of patient care ... were not being achieved. Conversely, concerns that outcomes might actually worsen because of decreased continuity of care have not been borne out,” Dr. Patel and his associates said (JAMA 2014 Dec. 9 [doi:10.1001/jama.2014.15273]).

The investigators noted that their study was limited in that it could not take into account hospitals’ adherence to the new requirements. Their study also did not assess other outcomes such as patient safety indicators or complication rates, which “may better elucidate the relative effects of decreased resident fatigue and increased patient hand offs.” And their study couldn’t address any possible confounding effects from other concurrent policy initiatives aimed at improving care for Medicare beneficiaries, such as the Hospital Readmissions Reduction Program.

In the other study, a separate group of researchers used data from the American College of Surgeons National Surgical Quality Improvement Program to assess outcomes for 535,499 patients who underwent general surgery at 131 hospitals during the 2 years before and the 2 years after the reforms to residents’ duty hours were implemented. This included 23 teaching hospitals in which residents were involved in at least 95% of general surgeries, said Dr. Ravi Rajaram of the division of research and optimal patient care, American College of Surgeons, and the Institute for Public Health and Medicine at Northwestern University, both in Chicago, and his associates.

The reforms were not associated with any change in rates of patient mortality or serious morbidity, either in the study population as a whole or in the subgroups of high-risk and low-risk patients. They also had no effect on secondary outcomes such as surgical-site infection or sepsis. These results remained consistent across several sensitivity analyses.

Neither mean scores for in-training, written board, and oral board examinations nor pass rates for those examinations showed any significant changes during the study period.

“Moreover, first-year trainees, who were most directly affected by the 2011 reforms, did not improve their ABSITE [American Board of Surgery In-Training Examination] scores, despite presumably more free time to prepare,” Dr. Rajaram and his associates said (JAMA 2014 Dec. 9 [doi:10.1001/JAMA.2014.15277]).

They cautioned that their study assessed only the first 2 years following duty-hour reform, and “there may be differences in patient care or resident examination performance that are evident only several years after implementation and adoption of new duty-hour requirements.” In addition, a retrospective observational study such as this one could not produce the high-level evidence needed to guide policy decisions. “To that end, a national multicenter cluster-randomized trial is being conducted (the Flexibility In duty hour Requirements for Surgical Trainees [FIRST] trial), comparing current duty-hour requirements with flexible duty hours to assess the effects of this intervention on patient outcomes and resident well-being. This trial may further inform the debate of how to optimally structure postgraduate training,” they said.

Patient mortality and morbidity outcomes have not changed since the most recent round of reforms to medical residents’ duty hours in 2011, according to two of the first nationwide studies to assess these “improvements,” which both were published online Dec. 9 in JAMA.

In addition, one of the studies found no difference between pre-reform and post-reform scores or on pass rates for oral or written national in-training and board certification examinations.

©Thinglass/thinkstockphotos.com

Thus, two separate studies involving millions of hospitalized patients across the country have both found that these reforms had no discernible effect on patient care. However, both groups of researchers cautioned that their studies were observational and therefore subject to potential biases and that they covered only the first 2 years that the duty-hours reforms have been in place.

The 2011 requirements expanded on those enacted in 2003 by further restricting residents’ duty hours, in the hope of reducing medical errors attributed to exhausted residents. The hours of continuous in-hospital duty were reduced from 30 to 16 for first-year residents and to 24 for upper-year residents, and the interval between shifts was increased to at least 8 hours off for first-year residents and at least 14 hours off for upper-year residents.

“Duty hour reform is arguably one of the largest efforts ever undertaken to improve the quality and safety of patient care in teaching hospitals,” said Dr. Mitesh S. Patel of the University of Pennsylvania and the Veterans Affairs Hospital Center for Health Equity Research and Promotion, both in Philadelphia, and his associates.

They assessed 30-day mortality and readmissions among 2,790,356 Medicare patients who were treated either for acute MI, stroke, gastrointestinal bleeding, or heart failure, or who underwent general, orthopedic, or vascular surgery, at 3,104 hospitals between 2009 and 2012. The investigators found no significant associations, either positive or negative, between the reforms to residents’ duty hours and any patient outcomes. Sensitivity analyses confirmed the results of the primary data analyses.

“Our findings suggest that ... the goals of improving the quality and safety of patient care ... were not being achieved. Conversely, concerns that outcomes might actually worsen because of decreased continuity of care have not been borne out,” Dr. Patel and his associates said (JAMA 2014 Dec. 9 [doi:10.1001/jama.2014.15273]).

The investigators noted that their study was limited in that it could not take into account hospitals’ adherence to the new requirements. Their study also did not assess other outcomes such as patient safety indicators or complication rates, which “may better elucidate the relative effects of decreased resident fatigue and increased patient hand offs.” And their study couldn’t address any possible confounding effects from other concurrent policy initiatives aimed at improving care for Medicare beneficiaries, such as the Hospital Readmissions Reduction Program.

In the other study, a separate group of researchers used data from the American College of Surgeons National Surgical Quality Improvement Program to assess outcomes for 535,499 patients who underwent general surgery at 131 hospitals during the 2 years before and the 2 years after the reforms to residents’ duty hours were implemented. This included 23 teaching hospitals in which residents were involved in at least 95% of general surgeries, said Dr. Ravi Rajaram of the division of research and optimal patient care, American College of Surgeons, and the Institute for Public Health and Medicine at Northwestern University, both in Chicago, and his associates.

The reforms were not associated with any change in rates of patient mortality or serious morbidity, either in the study population as a whole or in the subgroups of high-risk and low-risk patients. They also had no effect on secondary outcomes such as surgical-site infection or sepsis. These results remained consistent across several sensitivity analyses.

Neither mean scores for in-training, written board, and oral board examinations nor pass rates for those examinations showed any significant changes during the study period.

“Moreover, first-year trainees, who were most directly affected by the 2011 reforms, did not improve their ABSITE [American Board of Surgery In-Training Examination] scores, despite presumably more free time to prepare,” Dr. Rajaram and his associates said (JAMA 2014 Dec. 9 [doi:10.1001/JAMA.2014.15277]).

They cautioned that their study assessed only the first 2 years following duty-hour reform, and “there may be differences in patient care or resident examination performance that are evident only several years after implementation and adoption of new duty-hour requirements.” In addition, a retrospective observational study such as this one could not produce the high-level evidence needed to guide policy decisions. “To that end, a national multicenter cluster-randomized trial is being conducted (the Flexibility In duty hour Requirements for Surgical Trainees [FIRST] trial), comparing current duty-hour requirements with flexible duty hours to assess the effects of this intervention on patient outcomes and resident well-being. This trial may further inform the debate of how to optimally structure postgraduate training,” they said.

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Key clinical point: The newest (2011) reforms to resident duty hours haven’t changed patient mortality or morbidity outcomes.

Major finding: 30-day mortality and readmissions among almost 3 million Medicare patients at 3,104 hospitals did not change between 2009 and 2012.

Data source: Two observational cohort studies of millions of hospitalized adults across the country, comparing patient outcomes before with those after the 2011 reforms in duty hours for residents.

Disclosures: Dr. Patel’s study was funded in part by the National Heart, Lung, and Blood Institute, the Department of Veterans Affairs, and the Robert Wood Johnson Foundation. Dr. Rajaram’s study was supported by the Agency for Healthcare Research and Quality, the American College of Surgeons, and Merck. All of the investigators reported having no relevant financial conflicts of interest.

Investigational sotatercept improves heme parameters in MDS

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Investigational sotatercept improves heme parameters in MDS

SAN FRANCISCO – A first-in-class investigational agent called sotatercept appears to be safe and to improve hematologic parameters in patients with lower-risk myelodysplastic syndrome or nonproliferative chronic myelomonocytic leukemia and anemia requiring transfusion, a study showed.

In the open-label phase II dose-finding study of sotatercept in patients with myelodysplastic syndrome (MDS) or nonproliferative chronic myelomonocytic leukemia (CMML), hematologic improvement according to International Working Group (IWG) 2006 criteria was seen in 24 of 53 evaluable patients, said Dr. Rami Komrokji of the Moffitt Cancer Center,Tampa.

The patients were all refractory to, or were deemed to have a low chance of responding to, an erythropoiesis-stimulating agent (ESA), Dr. Komrokji said at the annual meeting of the American Society of Hematology.

Neil Osterweil/Frontline Medical News
Dr. Rami Komrokji

“A medication like sotatercept would probably have a role in the management of anemia in lower-risk MDS patients. The treatment is administered every 3 weeks, which makes it also logistically easier for the patients to get the treatment. I don’t think we have seen any safety concern, at least at this point, about the chronic use of this medication,” he said in an interview.

Sotatercept (ACE-011) is an activin type IIA receptor fusion protein that acts on late-stage erythropoiesis to increase the release of mature erythrocytes into circulation. The mechanism of action is distinct from that of erythropoietins such as epoetin alfa (Procrit, Epogen) or darbapoietin alfa (Aranesp).

In clinical trials with healthy volunteers, sotatercept has been shown to increase hemoglobin levels, suggesting that it could help to reduce anemia and perhaps lessen dependence on transfusions among patients with lower-risk MDS, Dr. Komrokji said.

He and his colleagues at centers in the United States and France enrolled patients with low-risk or intermediate-1–risk MDS as defined by the International Prognostic Scoring System (IPSS), or nonproliferative CMML (fewer than 13,000 white blood cells per microliter). The patients had to have anemia requiring at least 2 red blood cell (RBC) transfusions in the 12 weeks before enrollment for hemoglobin levels below 9.0 g/dL, and no response, loss of response, or a low chance of response to an ESA. Those patients with serum erythropoietin levels greater than 500 mIU/mL were considered to have a low chance of responding to an ESA.

The patients received subcutaneous injections of sotatercept at doses of 0.1, 0.3, 0.5, or 1.0 mg/kg once every 3 weeks.

As noted, the rate of overall hematologic improvement by IWG 2006 criteria was 45%, occurring in 24 of 53 patients available for evaluation. Five of 44 patients with a high transfusion burden (4 or more RBC units required within 8 weeks) were able to be free of RBC transfusions for at least 8 weeks, as were 5 of 9 with a low transfusion burden (fewer than 4 RBC units over a period of 8 weeks).

Looking at the efficacy in patients with a high transfusion burden, the investigators found that 4 of 6 assigned to the 0.3-mg/kg dose group and 8 of 14 assigned to the 1-mg/kg dose group had a reduction in transfusion burden. The median duration of effect was 106 days, with the longest response lasting for 150 days.

There were no major adverse events in the study, and no apparent increase in risk for thrombosis, as had been seen in some studies of ESAs. Another theoretical risk with this type of agent is hypertension, but there was only one grade 3 case and no grade 4 cases of hypertension in the study, Dr. Komrokji said.

Sotatercept is currently in phase II trials for anemia related to hematologic malignancies and other diseases.

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SAN FRANCISCO – A first-in-class investigational agent called sotatercept appears to be safe and to improve hematologic parameters in patients with lower-risk myelodysplastic syndrome or nonproliferative chronic myelomonocytic leukemia and anemia requiring transfusion, a study showed.

In the open-label phase II dose-finding study of sotatercept in patients with myelodysplastic syndrome (MDS) or nonproliferative chronic myelomonocytic leukemia (CMML), hematologic improvement according to International Working Group (IWG) 2006 criteria was seen in 24 of 53 evaluable patients, said Dr. Rami Komrokji of the Moffitt Cancer Center,Tampa.

The patients were all refractory to, or were deemed to have a low chance of responding to, an erythropoiesis-stimulating agent (ESA), Dr. Komrokji said at the annual meeting of the American Society of Hematology.

Neil Osterweil/Frontline Medical News
Dr. Rami Komrokji

“A medication like sotatercept would probably have a role in the management of anemia in lower-risk MDS patients. The treatment is administered every 3 weeks, which makes it also logistically easier for the patients to get the treatment. I don’t think we have seen any safety concern, at least at this point, about the chronic use of this medication,” he said in an interview.

Sotatercept (ACE-011) is an activin type IIA receptor fusion protein that acts on late-stage erythropoiesis to increase the release of mature erythrocytes into circulation. The mechanism of action is distinct from that of erythropoietins such as epoetin alfa (Procrit, Epogen) or darbapoietin alfa (Aranesp).

In clinical trials with healthy volunteers, sotatercept has been shown to increase hemoglobin levels, suggesting that it could help to reduce anemia and perhaps lessen dependence on transfusions among patients with lower-risk MDS, Dr. Komrokji said.

He and his colleagues at centers in the United States and France enrolled patients with low-risk or intermediate-1–risk MDS as defined by the International Prognostic Scoring System (IPSS), or nonproliferative CMML (fewer than 13,000 white blood cells per microliter). The patients had to have anemia requiring at least 2 red blood cell (RBC) transfusions in the 12 weeks before enrollment for hemoglobin levels below 9.0 g/dL, and no response, loss of response, or a low chance of response to an ESA. Those patients with serum erythropoietin levels greater than 500 mIU/mL were considered to have a low chance of responding to an ESA.

The patients received subcutaneous injections of sotatercept at doses of 0.1, 0.3, 0.5, or 1.0 mg/kg once every 3 weeks.

As noted, the rate of overall hematologic improvement by IWG 2006 criteria was 45%, occurring in 24 of 53 patients available for evaluation. Five of 44 patients with a high transfusion burden (4 or more RBC units required within 8 weeks) were able to be free of RBC transfusions for at least 8 weeks, as were 5 of 9 with a low transfusion burden (fewer than 4 RBC units over a period of 8 weeks).

Looking at the efficacy in patients with a high transfusion burden, the investigators found that 4 of 6 assigned to the 0.3-mg/kg dose group and 8 of 14 assigned to the 1-mg/kg dose group had a reduction in transfusion burden. The median duration of effect was 106 days, with the longest response lasting for 150 days.

There were no major adverse events in the study, and no apparent increase in risk for thrombosis, as had been seen in some studies of ESAs. Another theoretical risk with this type of agent is hypertension, but there was only one grade 3 case and no grade 4 cases of hypertension in the study, Dr. Komrokji said.

Sotatercept is currently in phase II trials for anemia related to hematologic malignancies and other diseases.

SAN FRANCISCO – A first-in-class investigational agent called sotatercept appears to be safe and to improve hematologic parameters in patients with lower-risk myelodysplastic syndrome or nonproliferative chronic myelomonocytic leukemia and anemia requiring transfusion, a study showed.

In the open-label phase II dose-finding study of sotatercept in patients with myelodysplastic syndrome (MDS) or nonproliferative chronic myelomonocytic leukemia (CMML), hematologic improvement according to International Working Group (IWG) 2006 criteria was seen in 24 of 53 evaluable patients, said Dr. Rami Komrokji of the Moffitt Cancer Center,Tampa.

The patients were all refractory to, or were deemed to have a low chance of responding to, an erythropoiesis-stimulating agent (ESA), Dr. Komrokji said at the annual meeting of the American Society of Hematology.

Neil Osterweil/Frontline Medical News
Dr. Rami Komrokji

“A medication like sotatercept would probably have a role in the management of anemia in lower-risk MDS patients. The treatment is administered every 3 weeks, which makes it also logistically easier for the patients to get the treatment. I don’t think we have seen any safety concern, at least at this point, about the chronic use of this medication,” he said in an interview.

Sotatercept (ACE-011) is an activin type IIA receptor fusion protein that acts on late-stage erythropoiesis to increase the release of mature erythrocytes into circulation. The mechanism of action is distinct from that of erythropoietins such as epoetin alfa (Procrit, Epogen) or darbapoietin alfa (Aranesp).

In clinical trials with healthy volunteers, sotatercept has been shown to increase hemoglobin levels, suggesting that it could help to reduce anemia and perhaps lessen dependence on transfusions among patients with lower-risk MDS, Dr. Komrokji said.

He and his colleagues at centers in the United States and France enrolled patients with low-risk or intermediate-1–risk MDS as defined by the International Prognostic Scoring System (IPSS), or nonproliferative CMML (fewer than 13,000 white blood cells per microliter). The patients had to have anemia requiring at least 2 red blood cell (RBC) transfusions in the 12 weeks before enrollment for hemoglobin levels below 9.0 g/dL, and no response, loss of response, or a low chance of response to an ESA. Those patients with serum erythropoietin levels greater than 500 mIU/mL were considered to have a low chance of responding to an ESA.

The patients received subcutaneous injections of sotatercept at doses of 0.1, 0.3, 0.5, or 1.0 mg/kg once every 3 weeks.

As noted, the rate of overall hematologic improvement by IWG 2006 criteria was 45%, occurring in 24 of 53 patients available for evaluation. Five of 44 patients with a high transfusion burden (4 or more RBC units required within 8 weeks) were able to be free of RBC transfusions for at least 8 weeks, as were 5 of 9 with a low transfusion burden (fewer than 4 RBC units over a period of 8 weeks).

Looking at the efficacy in patients with a high transfusion burden, the investigators found that 4 of 6 assigned to the 0.3-mg/kg dose group and 8 of 14 assigned to the 1-mg/kg dose group had a reduction in transfusion burden. The median duration of effect was 106 days, with the longest response lasting for 150 days.

There were no major adverse events in the study, and no apparent increase in risk for thrombosis, as had been seen in some studies of ESAs. Another theoretical risk with this type of agent is hypertension, but there was only one grade 3 case and no grade 4 cases of hypertension in the study, Dr. Komrokji said.

Sotatercept is currently in phase II trials for anemia related to hematologic malignancies and other diseases.

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Key clinical point: Sotatercept is a first-in-its-class agent that stimulates erythropoiesis through a mechanism different from that of erythropoietins.

Major finding: The rate of overall hematologic improvement by IWG 2006 criteria was 45%, occurring in 24 of 53 patients available for evaluation.

Data source: An ongoing phase II study with data available on 53 patients with MDS or nonproliferative CMML.

Disclosures: The study is sponsored by Celgene. Dr. Komrokji reported consulting for and receiving research funding from the company.

Nilotinib plus chemotherapy pays off for older patients with Ph+ALL

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Nilotinib plus chemotherapy pays off for older patients with Ph+ALL

SAN FRANCISCO– The study was small but encouraging: Among 47 older patients with newly diagnosed acute lymphoblastic leukemia positive for the Philadelphia chromosome, 41 had a complete hematologic response to a combination of chemotherapy and the targeted agent nilotinib (Tasigna), report investigators from a European consortium.

Dr. Oliver Ottmann

“The data I have presented show that the combination of nilotinib with this age-adapted chemotherapy is highly effective. We do have quite a reasonable overall survival estimate at 2 years of just more than 70%,” said Dr. Oliver Ottmann of Goethe University in Frankfurt, on behalf of colleagues in the European Working Group for Adult ALL (EWALL).

The study also shows that although some centers are reluctant to offer allogeneic stem cell transplantation (SCT) to older patients, it is still a viable treatment option in this population, Dr. Ottmann said at a briefing at the annual meeting of the American Society of Hematology.

Although older patients with newly diagnosed Philadelphia-positive (Ph+) ALL have a high complete hematologic response rate (CHR) with imatinib (Gleevec), they generally have a poor prognosis because of a high rate of relapse.

Because nilotinib, a potent inhibitor of the ABL kinase, has good efficacy in the chronic and accelerated phase of Ph+ chronic myeloid leukemia, the EWALL investigators initiated a study to evaluate it in combination with chemotherapy in the front-line setting.

Adults aged 55 years and older with ALL positive for the Philadelphia chromosome and/or BCR-ABL1 fusion who were treatment naive or had not received therapy other than corticosteroids, single-dose vincristine, or three doses of cyclophosphamide were eligible.

Details of the combination regimen are available online.

Briefly, following a prephase with dexamethasone and optional cyclophosphamide, patients receive nilotinib 400 mg twice daily starting with induction and continuously thereafter. During induction, nilotinib is given with intravenous injections of vincristine and dexamethasone for 4 weeks, followed by consolidation with nilotinib, methotrexate, asparaginase and cytarabine. Maintenance consists of nilotinib, 6-mercaptopurine, and methotrexate once weekly for 1 month then every other month, and dexamethasone and vincristine in 2 month intervals up to 24 months.

The data Dr. Ottmann reported come from an interim analysis of the ongoing study. As of August 2014, data on 47 of 56 patients was available for an efficacy analysis, As noted before, the rate of CHR was 87%, occurring in 41 of 47 patients. The treatment evoked a partial response or no response in 2 patients, and there was one death during the induction phase. Additionally, three patients discontinued therapy early and were not included in the assessment, but at least one had a complete response later on, Dr. Ottmann noted.

The median time to a complete response (CR) was 41 days, but CRs occurred as early as 25 days and as late as 62 days after the start of therapy. The remissions at the time of data cutoff appeared to be durable, but follow-up is still early, he said.

Overall survival at a median follow-up for all patients of 8.6 months was 72.7% at 30 months for patients who did not undergo SCT (allowed under the protocol), and 67.1% at 30 months for patients who underwent SCT. This difference was not significant, but only nine patients at the time of data cutoff had undergone transplantation.

“It will be interesting to see how this will proceed if the transplant-free patients will do as well as the others,” Dr. Ottmann said.

An analysis of molecular response by minimal residual disease (MRD) time point showed a significant further increase with the consolidation chemotherapy and kinase inhibitor, emphasizing that “continuing the treatment in this form emphasizes the depth of response. If we then look at the rate of MRD negativity using high quality assays, then a quarter of the patients have undetectable polymerase chain reaction during the consolidation cycles, and approximately 80% achieves something that we call a major molecular response,” he said.

Dr. Ottmann did not provide updated safety data, but at the time of the data cutoff, there had been 34 serious adverse events reported, 11 of which occurred during induction, 16 during consolidation, 6 during maintenance, and 1 following discontinuation. The most-common adverse events were infections and neutropenic fevers. Single serious adverse events included metabolic, cardiovascular, neurologic, renal, and hepatic events.

The trial is expected to be completed in the next few months.

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SAN FRANCISCO– The study was small but encouraging: Among 47 older patients with newly diagnosed acute lymphoblastic leukemia positive for the Philadelphia chromosome, 41 had a complete hematologic response to a combination of chemotherapy and the targeted agent nilotinib (Tasigna), report investigators from a European consortium.

Dr. Oliver Ottmann

“The data I have presented show that the combination of nilotinib with this age-adapted chemotherapy is highly effective. We do have quite a reasonable overall survival estimate at 2 years of just more than 70%,” said Dr. Oliver Ottmann of Goethe University in Frankfurt, on behalf of colleagues in the European Working Group for Adult ALL (EWALL).

The study also shows that although some centers are reluctant to offer allogeneic stem cell transplantation (SCT) to older patients, it is still a viable treatment option in this population, Dr. Ottmann said at a briefing at the annual meeting of the American Society of Hematology.

Although older patients with newly diagnosed Philadelphia-positive (Ph+) ALL have a high complete hematologic response rate (CHR) with imatinib (Gleevec), they generally have a poor prognosis because of a high rate of relapse.

Because nilotinib, a potent inhibitor of the ABL kinase, has good efficacy in the chronic and accelerated phase of Ph+ chronic myeloid leukemia, the EWALL investigators initiated a study to evaluate it in combination with chemotherapy in the front-line setting.

Adults aged 55 years and older with ALL positive for the Philadelphia chromosome and/or BCR-ABL1 fusion who were treatment naive or had not received therapy other than corticosteroids, single-dose vincristine, or three doses of cyclophosphamide were eligible.

Details of the combination regimen are available online.

Briefly, following a prephase with dexamethasone and optional cyclophosphamide, patients receive nilotinib 400 mg twice daily starting with induction and continuously thereafter. During induction, nilotinib is given with intravenous injections of vincristine and dexamethasone for 4 weeks, followed by consolidation with nilotinib, methotrexate, asparaginase and cytarabine. Maintenance consists of nilotinib, 6-mercaptopurine, and methotrexate once weekly for 1 month then every other month, and dexamethasone and vincristine in 2 month intervals up to 24 months.

The data Dr. Ottmann reported come from an interim analysis of the ongoing study. As of August 2014, data on 47 of 56 patients was available for an efficacy analysis, As noted before, the rate of CHR was 87%, occurring in 41 of 47 patients. The treatment evoked a partial response or no response in 2 patients, and there was one death during the induction phase. Additionally, three patients discontinued therapy early and were not included in the assessment, but at least one had a complete response later on, Dr. Ottmann noted.

The median time to a complete response (CR) was 41 days, but CRs occurred as early as 25 days and as late as 62 days after the start of therapy. The remissions at the time of data cutoff appeared to be durable, but follow-up is still early, he said.

Overall survival at a median follow-up for all patients of 8.6 months was 72.7% at 30 months for patients who did not undergo SCT (allowed under the protocol), and 67.1% at 30 months for patients who underwent SCT. This difference was not significant, but only nine patients at the time of data cutoff had undergone transplantation.

“It will be interesting to see how this will proceed if the transplant-free patients will do as well as the others,” Dr. Ottmann said.

An analysis of molecular response by minimal residual disease (MRD) time point showed a significant further increase with the consolidation chemotherapy and kinase inhibitor, emphasizing that “continuing the treatment in this form emphasizes the depth of response. If we then look at the rate of MRD negativity using high quality assays, then a quarter of the patients have undetectable polymerase chain reaction during the consolidation cycles, and approximately 80% achieves something that we call a major molecular response,” he said.

Dr. Ottmann did not provide updated safety data, but at the time of the data cutoff, there had been 34 serious adverse events reported, 11 of which occurred during induction, 16 during consolidation, 6 during maintenance, and 1 following discontinuation. The most-common adverse events were infections and neutropenic fevers. Single serious adverse events included metabolic, cardiovascular, neurologic, renal, and hepatic events.

The trial is expected to be completed in the next few months.

SAN FRANCISCO– The study was small but encouraging: Among 47 older patients with newly diagnosed acute lymphoblastic leukemia positive for the Philadelphia chromosome, 41 had a complete hematologic response to a combination of chemotherapy and the targeted agent nilotinib (Tasigna), report investigators from a European consortium.

Dr. Oliver Ottmann

“The data I have presented show that the combination of nilotinib with this age-adapted chemotherapy is highly effective. We do have quite a reasonable overall survival estimate at 2 years of just more than 70%,” said Dr. Oliver Ottmann of Goethe University in Frankfurt, on behalf of colleagues in the European Working Group for Adult ALL (EWALL).

The study also shows that although some centers are reluctant to offer allogeneic stem cell transplantation (SCT) to older patients, it is still a viable treatment option in this population, Dr. Ottmann said at a briefing at the annual meeting of the American Society of Hematology.

Although older patients with newly diagnosed Philadelphia-positive (Ph+) ALL have a high complete hematologic response rate (CHR) with imatinib (Gleevec), they generally have a poor prognosis because of a high rate of relapse.

Because nilotinib, a potent inhibitor of the ABL kinase, has good efficacy in the chronic and accelerated phase of Ph+ chronic myeloid leukemia, the EWALL investigators initiated a study to evaluate it in combination with chemotherapy in the front-line setting.

Adults aged 55 years and older with ALL positive for the Philadelphia chromosome and/or BCR-ABL1 fusion who were treatment naive or had not received therapy other than corticosteroids, single-dose vincristine, or three doses of cyclophosphamide were eligible.

Details of the combination regimen are available online.

Briefly, following a prephase with dexamethasone and optional cyclophosphamide, patients receive nilotinib 400 mg twice daily starting with induction and continuously thereafter. During induction, nilotinib is given with intravenous injections of vincristine and dexamethasone for 4 weeks, followed by consolidation with nilotinib, methotrexate, asparaginase and cytarabine. Maintenance consists of nilotinib, 6-mercaptopurine, and methotrexate once weekly for 1 month then every other month, and dexamethasone and vincristine in 2 month intervals up to 24 months.

The data Dr. Ottmann reported come from an interim analysis of the ongoing study. As of August 2014, data on 47 of 56 patients was available for an efficacy analysis, As noted before, the rate of CHR was 87%, occurring in 41 of 47 patients. The treatment evoked a partial response or no response in 2 patients, and there was one death during the induction phase. Additionally, three patients discontinued therapy early and were not included in the assessment, but at least one had a complete response later on, Dr. Ottmann noted.

The median time to a complete response (CR) was 41 days, but CRs occurred as early as 25 days and as late as 62 days after the start of therapy. The remissions at the time of data cutoff appeared to be durable, but follow-up is still early, he said.

Overall survival at a median follow-up for all patients of 8.6 months was 72.7% at 30 months for patients who did not undergo SCT (allowed under the protocol), and 67.1% at 30 months for patients who underwent SCT. This difference was not significant, but only nine patients at the time of data cutoff had undergone transplantation.

“It will be interesting to see how this will proceed if the transplant-free patients will do as well as the others,” Dr. Ottmann said.

An analysis of molecular response by minimal residual disease (MRD) time point showed a significant further increase with the consolidation chemotherapy and kinase inhibitor, emphasizing that “continuing the treatment in this form emphasizes the depth of response. If we then look at the rate of MRD negativity using high quality assays, then a quarter of the patients have undetectable polymerase chain reaction during the consolidation cycles, and approximately 80% achieves something that we call a major molecular response,” he said.

Dr. Ottmann did not provide updated safety data, but at the time of the data cutoff, there had been 34 serious adverse events reported, 11 of which occurred during induction, 16 during consolidation, 6 during maintenance, and 1 following discontinuation. The most-common adverse events were infections and neutropenic fevers. Single serious adverse events included metabolic, cardiovascular, neurologic, renal, and hepatic events.

The trial is expected to be completed in the next few months.

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Key clinical point: Combining a kinase inhibitor with an intensive chemotherapy regimen produces a high complete response rate in patients age 55 and older who have Ph+All.

Major finding: The complete hematologic response rate for the combination of chemotherapy and nilotinib was 87%.

Data source: Investigator initiated study in 56 patients with acute lymphoblastic leukemia positive for the Philadelphia chromosome or BCR/ABL fusion.

Disclosures: The study was sponsored by participating institution. Dr. Ottmann disclosed consultancy, honoraria, and research funding from Novartis, maker of nilotinib.

PFS improvement will translate to OS, speaker says

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SAN FRANCISCO—Administering brentuximab vedotin immediately after autologous stem cell transplant can improve progression-free survival (PFS) in patients with Hodgkin lymphoma (HL), results of the phase 3 AETHERA trial suggest.

The overall survival (OS) data for this study are not yet mature, but the significant improvement in PFS will likely translate to improved OS in a few years’ time, according to Craig Moskowitz, MD, of Memorial Sloan Kettering Cancer Center in New York.

Dr Moskowitz presented results from the AETHERA trial at the 2014 ASH Annual Meeting as abstract 673. The trial was funded by Seattle Genetics, Inc., and Takeda Pharmaceutical Company Limited, the companies developing brentuximab.

The trial included HL patients with at least one risk factor for progression. Eligible patients must have had a history of refractory HL, relapsed within a year of receiving frontline chemotherapy, and/or had disease outside of the lymph nodes at the time of pre-transplant relapse.

Researchers enrolled 329 patients, and they were randomized to receive brentuximab or placebo every 3 weeks for up to about a year. Baseline characteristics were similar between the 2 arms.

Dr Moskowitz pointed out that 43% of patients in the brentuximab arm and 48% in the placebo arm had required 2 or more prior salvage therapies, and 60% and 59%, respectively, had primary refractory HL.

Patients in both arms received a median of 15 treatment cycles, with an average of 12 cycles on the brentuximab arm and 11 cycles on the placebo arm.

“Patients who progressed in the placebo arm could be unblinded and subsequently receive brentuximab on a companion study,” Dr Moskowitz noted. “So technically, this was a cross-over design, making overall survival at 24 months quite unlikely.”

Efficacy/survival results

About half of patients in each arm completed treatment—47% in the brentuximab arm and 49% in the placebo arm. The reasons for discontinuation included disease progression (15% and 42%, respectively), adverse events (33% and 6%, respectively), and patient decision (5% and 2%, respectively).

Still, the trial achieved its primary endpoint, demonstrating a significant increase in PFS, according to an independent review facility (IRF).

The median PFS per the IRF was 43 months for patients in the brentuximab arm and 24 months in the placebo arm (hazard ratio=0.57, P=0.001). The 2-year PFS rates per the IRF were 63% and 51%, respectively.

The 2-year PFS rate according to investigators was 65% in the brentuximab arm and 45% in the placebo arm. The median PFS per investigators has not yet been reached for brentuximab but was 16 months for placebo.

The PFS benefit was consistent across all pre-specified subgroups, Dr Moskowitz noted, including primary refractory patients, patients who relapsed within 12 months of frontline therapy, and patients who relapsed after 12 months with extranodal disease.

Patients who experienced disease progression received a variety of subsequent therapies.

In the brentuximab arm, 16% of patients receiving subsequent therapy were treated with brentuximab after relapse. In the placebo arm, 85% of patients receiving subsequent therapy were treated with single-agent brentuximab.

Twenty-eight percent of patients in the placebo arm and 25% in the brentuximab arm received stem cell transplant as subsequent therapy, the majority of which were allogeneic transplants. Dr Moskowitz said a second transplant could have improved survival in these patients, but whether it actually did is unclear.

He noted that the OS data are immature, but there is currently no significant difference in OS between the treatment arms (hazard ratio=1.15; P=0.62).

“The median follow-up right now is 24 months,” he said. “So one will have to wait for a survival advantage or disadvantage, but from my point of view, a PFS of 65% at 2 years will translate to an overall survival difference. We’re just going to have to wait a few more years.”

 

 

Dr Moskowitz said another analysis of OS is planned in 2016.

Safety data

The most common adverse events in the brentuximab arm were peripheral sensory neuropathy (56%), neutropenia (35%), upper respiratory tract infection (26%), fatigue (24%), and peripheral motor neuropathy (23%).

The most common adverse events in the placebo arm were upper respiratory tract infection (23%), fatigue (18%), peripheral sensory neuropathy (16%), cough (16%), and neutropenia (12%).

Eighty-five percent of patients with peripheral neuropathy in the brentuximab arm had a resolution or improvement in symptoms, with a median time to improvement of 23.4 weeks.

Grade 3 or higher adverse events in the brentuximab arm included neutropenia, peripheral sensory neuropathy, peripheral motor neuropathy, nausea, fatigue, and diarrhea.

Grade 3 or higher adverse events in the placebo arm included neutropenia, fatigue, peripheral motor neuropathy, diarrhea, and peripheral sensory neuropathy. No Grade 4 peripheral neuropathy events occurred.

One death occurred within 30 days of brentuximab treatment. The patient died from treatment-related acute respiratory distress syndrome (ARDS) associated with pneumonitis.

Another death occurred on the brentuximab arm at day 40 from ARDS following an episode of treatment-related acute pancreatitis, which had resolved at the time of death.

Nevertheless, Dr Moskowitz characterized brentuximab consolidation as “very well-tolerated” in this patient population.

He concluded, “For patients with a remission duration of less than a year, patients with primary refractory Hodgkin lymphoma, and patients with Hodgkin lymphoma with extranodal involvement, I do believe this will become standard treatment.”

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SAN FRANCISCO—Administering brentuximab vedotin immediately after autologous stem cell transplant can improve progression-free survival (PFS) in patients with Hodgkin lymphoma (HL), results of the phase 3 AETHERA trial suggest.

The overall survival (OS) data for this study are not yet mature, but the significant improvement in PFS will likely translate to improved OS in a few years’ time, according to Craig Moskowitz, MD, of Memorial Sloan Kettering Cancer Center in New York.

Dr Moskowitz presented results from the AETHERA trial at the 2014 ASH Annual Meeting as abstract 673. The trial was funded by Seattle Genetics, Inc., and Takeda Pharmaceutical Company Limited, the companies developing brentuximab.

The trial included HL patients with at least one risk factor for progression. Eligible patients must have had a history of refractory HL, relapsed within a year of receiving frontline chemotherapy, and/or had disease outside of the lymph nodes at the time of pre-transplant relapse.

Researchers enrolled 329 patients, and they were randomized to receive brentuximab or placebo every 3 weeks for up to about a year. Baseline characteristics were similar between the 2 arms.

Dr Moskowitz pointed out that 43% of patients in the brentuximab arm and 48% in the placebo arm had required 2 or more prior salvage therapies, and 60% and 59%, respectively, had primary refractory HL.

Patients in both arms received a median of 15 treatment cycles, with an average of 12 cycles on the brentuximab arm and 11 cycles on the placebo arm.

“Patients who progressed in the placebo arm could be unblinded and subsequently receive brentuximab on a companion study,” Dr Moskowitz noted. “So technically, this was a cross-over design, making overall survival at 24 months quite unlikely.”

Efficacy/survival results

About half of patients in each arm completed treatment—47% in the brentuximab arm and 49% in the placebo arm. The reasons for discontinuation included disease progression (15% and 42%, respectively), adverse events (33% and 6%, respectively), and patient decision (5% and 2%, respectively).

Still, the trial achieved its primary endpoint, demonstrating a significant increase in PFS, according to an independent review facility (IRF).

The median PFS per the IRF was 43 months for patients in the brentuximab arm and 24 months in the placebo arm (hazard ratio=0.57, P=0.001). The 2-year PFS rates per the IRF were 63% and 51%, respectively.

The 2-year PFS rate according to investigators was 65% in the brentuximab arm and 45% in the placebo arm. The median PFS per investigators has not yet been reached for brentuximab but was 16 months for placebo.

The PFS benefit was consistent across all pre-specified subgroups, Dr Moskowitz noted, including primary refractory patients, patients who relapsed within 12 months of frontline therapy, and patients who relapsed after 12 months with extranodal disease.

Patients who experienced disease progression received a variety of subsequent therapies.

In the brentuximab arm, 16% of patients receiving subsequent therapy were treated with brentuximab after relapse. In the placebo arm, 85% of patients receiving subsequent therapy were treated with single-agent brentuximab.

Twenty-eight percent of patients in the placebo arm and 25% in the brentuximab arm received stem cell transplant as subsequent therapy, the majority of which were allogeneic transplants. Dr Moskowitz said a second transplant could have improved survival in these patients, but whether it actually did is unclear.

He noted that the OS data are immature, but there is currently no significant difference in OS between the treatment arms (hazard ratio=1.15; P=0.62).

“The median follow-up right now is 24 months,” he said. “So one will have to wait for a survival advantage or disadvantage, but from my point of view, a PFS of 65% at 2 years will translate to an overall survival difference. We’re just going to have to wait a few more years.”

 

 

Dr Moskowitz said another analysis of OS is planned in 2016.

Safety data

The most common adverse events in the brentuximab arm were peripheral sensory neuropathy (56%), neutropenia (35%), upper respiratory tract infection (26%), fatigue (24%), and peripheral motor neuropathy (23%).

The most common adverse events in the placebo arm were upper respiratory tract infection (23%), fatigue (18%), peripheral sensory neuropathy (16%), cough (16%), and neutropenia (12%).

Eighty-five percent of patients with peripheral neuropathy in the brentuximab arm had a resolution or improvement in symptoms, with a median time to improvement of 23.4 weeks.

Grade 3 or higher adverse events in the brentuximab arm included neutropenia, peripheral sensory neuropathy, peripheral motor neuropathy, nausea, fatigue, and diarrhea.

Grade 3 or higher adverse events in the placebo arm included neutropenia, fatigue, peripheral motor neuropathy, diarrhea, and peripheral sensory neuropathy. No Grade 4 peripheral neuropathy events occurred.

One death occurred within 30 days of brentuximab treatment. The patient died from treatment-related acute respiratory distress syndrome (ARDS) associated with pneumonitis.

Another death occurred on the brentuximab arm at day 40 from ARDS following an episode of treatment-related acute pancreatitis, which had resolved at the time of death.

Nevertheless, Dr Moskowitz characterized brentuximab consolidation as “very well-tolerated” in this patient population.

He concluded, “For patients with a remission duration of less than a year, patients with primary refractory Hodgkin lymphoma, and patients with Hodgkin lymphoma with extranodal involvement, I do believe this will become standard treatment.”

SAN FRANCISCO—Administering brentuximab vedotin immediately after autologous stem cell transplant can improve progression-free survival (PFS) in patients with Hodgkin lymphoma (HL), results of the phase 3 AETHERA trial suggest.

The overall survival (OS) data for this study are not yet mature, but the significant improvement in PFS will likely translate to improved OS in a few years’ time, according to Craig Moskowitz, MD, of Memorial Sloan Kettering Cancer Center in New York.

Dr Moskowitz presented results from the AETHERA trial at the 2014 ASH Annual Meeting as abstract 673. The trial was funded by Seattle Genetics, Inc., and Takeda Pharmaceutical Company Limited, the companies developing brentuximab.

The trial included HL patients with at least one risk factor for progression. Eligible patients must have had a history of refractory HL, relapsed within a year of receiving frontline chemotherapy, and/or had disease outside of the lymph nodes at the time of pre-transplant relapse.

Researchers enrolled 329 patients, and they were randomized to receive brentuximab or placebo every 3 weeks for up to about a year. Baseline characteristics were similar between the 2 arms.

Dr Moskowitz pointed out that 43% of patients in the brentuximab arm and 48% in the placebo arm had required 2 or more prior salvage therapies, and 60% and 59%, respectively, had primary refractory HL.

Patients in both arms received a median of 15 treatment cycles, with an average of 12 cycles on the brentuximab arm and 11 cycles on the placebo arm.

“Patients who progressed in the placebo arm could be unblinded and subsequently receive brentuximab on a companion study,” Dr Moskowitz noted. “So technically, this was a cross-over design, making overall survival at 24 months quite unlikely.”

Efficacy/survival results

About half of patients in each arm completed treatment—47% in the brentuximab arm and 49% in the placebo arm. The reasons for discontinuation included disease progression (15% and 42%, respectively), adverse events (33% and 6%, respectively), and patient decision (5% and 2%, respectively).

Still, the trial achieved its primary endpoint, demonstrating a significant increase in PFS, according to an independent review facility (IRF).

The median PFS per the IRF was 43 months for patients in the brentuximab arm and 24 months in the placebo arm (hazard ratio=0.57, P=0.001). The 2-year PFS rates per the IRF were 63% and 51%, respectively.

The 2-year PFS rate according to investigators was 65% in the brentuximab arm and 45% in the placebo arm. The median PFS per investigators has not yet been reached for brentuximab but was 16 months for placebo.

The PFS benefit was consistent across all pre-specified subgroups, Dr Moskowitz noted, including primary refractory patients, patients who relapsed within 12 months of frontline therapy, and patients who relapsed after 12 months with extranodal disease.

Patients who experienced disease progression received a variety of subsequent therapies.

In the brentuximab arm, 16% of patients receiving subsequent therapy were treated with brentuximab after relapse. In the placebo arm, 85% of patients receiving subsequent therapy were treated with single-agent brentuximab.

Twenty-eight percent of patients in the placebo arm and 25% in the brentuximab arm received stem cell transplant as subsequent therapy, the majority of which were allogeneic transplants. Dr Moskowitz said a second transplant could have improved survival in these patients, but whether it actually did is unclear.

He noted that the OS data are immature, but there is currently no significant difference in OS between the treatment arms (hazard ratio=1.15; P=0.62).

“The median follow-up right now is 24 months,” he said. “So one will have to wait for a survival advantage or disadvantage, but from my point of view, a PFS of 65% at 2 years will translate to an overall survival difference. We’re just going to have to wait a few more years.”

 

 

Dr Moskowitz said another analysis of OS is planned in 2016.

Safety data

The most common adverse events in the brentuximab arm were peripheral sensory neuropathy (56%), neutropenia (35%), upper respiratory tract infection (26%), fatigue (24%), and peripheral motor neuropathy (23%).

The most common adverse events in the placebo arm were upper respiratory tract infection (23%), fatigue (18%), peripheral sensory neuropathy (16%), cough (16%), and neutropenia (12%).

Eighty-five percent of patients with peripheral neuropathy in the brentuximab arm had a resolution or improvement in symptoms, with a median time to improvement of 23.4 weeks.

Grade 3 or higher adverse events in the brentuximab arm included neutropenia, peripheral sensory neuropathy, peripheral motor neuropathy, nausea, fatigue, and diarrhea.

Grade 3 or higher adverse events in the placebo arm included neutropenia, fatigue, peripheral motor neuropathy, diarrhea, and peripheral sensory neuropathy. No Grade 4 peripheral neuropathy events occurred.

One death occurred within 30 days of brentuximab treatment. The patient died from treatment-related acute respiratory distress syndrome (ARDS) associated with pneumonitis.

Another death occurred on the brentuximab arm at day 40 from ARDS following an episode of treatment-related acute pancreatitis, which had resolved at the time of death.

Nevertheless, Dr Moskowitz characterized brentuximab consolidation as “very well-tolerated” in this patient population.

He concluded, “For patients with a remission duration of less than a year, patients with primary refractory Hodgkin lymphoma, and patients with Hodgkin lymphoma with extranodal involvement, I do believe this will become standard treatment.”

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Method may predict likelihood of GVHD

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Credit: Darren Baker

Researchers say that computer modeling of next-generation DNA sequencing data can help us understand the variable outcomes of stem cell transplant and provide a theoretical framework to make transplant a possibility for more patients who don’t have a related donor.

The team analyzed data obtained from whole-exome sequencing of 9 donor-recipient pairs (DRPs) and found it’s possible to predict the risk of graft-vs-host disease (GVHD).

This finding could one day help physicians tailor immunosuppressive therapies to possibly improve transplant outcomes.

The investigators say their data provide evidence that the way a patient’s immune system rebuilds itself following transplant is representative of a dynamical system, a system in which the current state determines what future state will follow.

“The immune system seems chaotic, but that is because there are so many variables involved,” said Amir Toor, MD, of the Virginia Commonwealth University in Richmond.

“We have found evidence of an underlying order. Using next-generation DNA sequencing technology, it may be possible to account for many of the molecular variables that eventually determine how well a donor’s immune system will graft to a patient.”

Dr Toor and his colleagues describe this work in two articles in Frontiers in Immunology.

In the first paper, the researchers recount how they used whole-exome sequencing to examine variation in minor histocompatibility antigens (mHAs) of transplant DRPs.

Using advanced computer-based analysis, the investigators examined potential interactions between mHAs and HLAs and discovered a high level of mHA variation in HLA-matched DRPs that could potentially contribute to GVHD.

These findings may help explain why many HLA-matched recipients experience GVHD, but why some HLA-mismatched recipients do not develop GVHD remains a mystery.

The researchers offer an explanation for this seeming paradox in a companion article. In this paper, they suggest that by inhibiting peptide generation through immunosuppressive therapies in the earliest weeks following stem cell transplant, antigen presentation to donor T cells could be diminished, which reduces the risk of GVHD as the recipients reconstitute their T-cell repertoire.

In previous research, Dr Toor and his colleagues discovered a fractal pattern in the DNA of recipients’ T-cell repertoires. (Fractals are self-similar patterns that repeat themselves at every scale.)

Based on their data, the researchers believe that the presentation of mHAs following transplant helps shape the development of T-cell clonal families.

Thus, inhibiting this antigen presentation through immunosuppressive therapies in patients who have high mHA variation can potentially reduce the risk of GVHD by influencing the development of their T-cell repertoire. This is supported by data from clinical studies showing immune suppression soon after transplant improves outcomes in unrelated DRPs.

The investigators suggest that an equation such as the logistic model of growth, a mathematical formula used to explain population growth, could be employed to predict the evolution of T-cell clones and determine a patient’s future risk of GVHD.

“Currently, we rely on population-based outcomes derived from probabilistic studies to determine the best way to perform stem cell transplants,” Dr Toor said. “The development of accurate mathematical models that account for the key variables influencing transplant outcomes may allow us to treat patients using a systematic and personalized approach.”

“We plan to keep exploring this concept in hopes that we can tailor the transplantation process to each individual in order to improve outcomes and make transplantation an option for more patients.”

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Scientist at a computer

Credit: Darren Baker

Researchers say that computer modeling of next-generation DNA sequencing data can help us understand the variable outcomes of stem cell transplant and provide a theoretical framework to make transplant a possibility for more patients who don’t have a related donor.

The team analyzed data obtained from whole-exome sequencing of 9 donor-recipient pairs (DRPs) and found it’s possible to predict the risk of graft-vs-host disease (GVHD).

This finding could one day help physicians tailor immunosuppressive therapies to possibly improve transplant outcomes.

The investigators say their data provide evidence that the way a patient’s immune system rebuilds itself following transplant is representative of a dynamical system, a system in which the current state determines what future state will follow.

“The immune system seems chaotic, but that is because there are so many variables involved,” said Amir Toor, MD, of the Virginia Commonwealth University in Richmond.

“We have found evidence of an underlying order. Using next-generation DNA sequencing technology, it may be possible to account for many of the molecular variables that eventually determine how well a donor’s immune system will graft to a patient.”

Dr Toor and his colleagues describe this work in two articles in Frontiers in Immunology.

In the first paper, the researchers recount how they used whole-exome sequencing to examine variation in minor histocompatibility antigens (mHAs) of transplant DRPs.

Using advanced computer-based analysis, the investigators examined potential interactions between mHAs and HLAs and discovered a high level of mHA variation in HLA-matched DRPs that could potentially contribute to GVHD.

These findings may help explain why many HLA-matched recipients experience GVHD, but why some HLA-mismatched recipients do not develop GVHD remains a mystery.

The researchers offer an explanation for this seeming paradox in a companion article. In this paper, they suggest that by inhibiting peptide generation through immunosuppressive therapies in the earliest weeks following stem cell transplant, antigen presentation to donor T cells could be diminished, which reduces the risk of GVHD as the recipients reconstitute their T-cell repertoire.

In previous research, Dr Toor and his colleagues discovered a fractal pattern in the DNA of recipients’ T-cell repertoires. (Fractals are self-similar patterns that repeat themselves at every scale.)

Based on their data, the researchers believe that the presentation of mHAs following transplant helps shape the development of T-cell clonal families.

Thus, inhibiting this antigen presentation through immunosuppressive therapies in patients who have high mHA variation can potentially reduce the risk of GVHD by influencing the development of their T-cell repertoire. This is supported by data from clinical studies showing immune suppression soon after transplant improves outcomes in unrelated DRPs.

The investigators suggest that an equation such as the logistic model of growth, a mathematical formula used to explain population growth, could be employed to predict the evolution of T-cell clones and determine a patient’s future risk of GVHD.

“Currently, we rely on population-based outcomes derived from probabilistic studies to determine the best way to perform stem cell transplants,” Dr Toor said. “The development of accurate mathematical models that account for the key variables influencing transplant outcomes may allow us to treat patients using a systematic and personalized approach.”

“We plan to keep exploring this concept in hopes that we can tailor the transplantation process to each individual in order to improve outcomes and make transplantation an option for more patients.”

Scientist at a computer

Credit: Darren Baker

Researchers say that computer modeling of next-generation DNA sequencing data can help us understand the variable outcomes of stem cell transplant and provide a theoretical framework to make transplant a possibility for more patients who don’t have a related donor.

The team analyzed data obtained from whole-exome sequencing of 9 donor-recipient pairs (DRPs) and found it’s possible to predict the risk of graft-vs-host disease (GVHD).

This finding could one day help physicians tailor immunosuppressive therapies to possibly improve transplant outcomes.

The investigators say their data provide evidence that the way a patient’s immune system rebuilds itself following transplant is representative of a dynamical system, a system in which the current state determines what future state will follow.

“The immune system seems chaotic, but that is because there are so many variables involved,” said Amir Toor, MD, of the Virginia Commonwealth University in Richmond.

“We have found evidence of an underlying order. Using next-generation DNA sequencing technology, it may be possible to account for many of the molecular variables that eventually determine how well a donor’s immune system will graft to a patient.”

Dr Toor and his colleagues describe this work in two articles in Frontiers in Immunology.

In the first paper, the researchers recount how they used whole-exome sequencing to examine variation in minor histocompatibility antigens (mHAs) of transplant DRPs.

Using advanced computer-based analysis, the investigators examined potential interactions between mHAs and HLAs and discovered a high level of mHA variation in HLA-matched DRPs that could potentially contribute to GVHD.

These findings may help explain why many HLA-matched recipients experience GVHD, but why some HLA-mismatched recipients do not develop GVHD remains a mystery.

The researchers offer an explanation for this seeming paradox in a companion article. In this paper, they suggest that by inhibiting peptide generation through immunosuppressive therapies in the earliest weeks following stem cell transplant, antigen presentation to donor T cells could be diminished, which reduces the risk of GVHD as the recipients reconstitute their T-cell repertoire.

In previous research, Dr Toor and his colleagues discovered a fractal pattern in the DNA of recipients’ T-cell repertoires. (Fractals are self-similar patterns that repeat themselves at every scale.)

Based on their data, the researchers believe that the presentation of mHAs following transplant helps shape the development of T-cell clonal families.

Thus, inhibiting this antigen presentation through immunosuppressive therapies in patients who have high mHA variation can potentially reduce the risk of GVHD by influencing the development of their T-cell repertoire. This is supported by data from clinical studies showing immune suppression soon after transplant improves outcomes in unrelated DRPs.

The investigators suggest that an equation such as the logistic model of growth, a mathematical formula used to explain population growth, could be employed to predict the evolution of T-cell clones and determine a patient’s future risk of GVHD.

“Currently, we rely on population-based outcomes derived from probabilistic studies to determine the best way to perform stem cell transplants,” Dr Toor said. “The development of accurate mathematical models that account for the key variables influencing transplant outcomes may allow us to treat patients using a systematic and personalized approach.”

“We plan to keep exploring this concept in hopes that we can tailor the transplantation process to each individual in order to improve outcomes and make transplantation an option for more patients.”

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