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Mutant B-cell progenitor causes leukemia, group finds
Credit: Aaron Logan
Researchers have identified a cell that appears to be responsible for a particularly aggressive type of leukemia in mice.
The cell is a renin-expressing B-cell progenitor found in the bone marrow.
Renin cells, which are also present in the kidney, have traditionally been associated with the control of blood pressure and fluid balance in the body.
But investigators discovered renin progenitors in the bone marrow of mice with aggressive B-cell leukemia.
And they found evidence to suggest the leukemia originated from a mutation in these renin progenitors—specifically, deletion of RBP-J.
“We would now like to see if this is a relevant model of human disease,” said study author Brian C. Belyea, MD, of the University of Virginia (UVA) School of Medicine in Charlottesville.
“Our long-term goal is to identify cells at increased risk for leukemia in humans and, ultimately, develop strategies to monitor and eliminate these cells.”
Dr Belyea and his colleagues described their initial steps toward this goal in Nature Communications.
In a previous study, the researchers were investigating the effects of RBP-J deletion in mice. And they were surprised to find that, as the mice aged beyond 6 months, they developed signs of an aggressive form of precursor B-lymphoblastic leukemia.
So with the current study, the team wanted to characterize this leukemia. They set out to identify which cells in the bone marrow are capable of producing renin under normal circumstances and whether those cells might be the origin of the leukemia.
The investigators found that renin is expressed by a subset of B-cell progenitors in the mouse bone marrow, and these cells need RBP-J to differentiate.
Deleting RBP-J restrains lymphocyte differentiation, and the mutant cells undergo neoplastic transformation. The mice develop a B-cell leukemia characterized by multi-organ infiltration and resulting in early death.
Experiments showed the leukemia to be particularly hardy. The researchers placed the leukemic cells in a lab dish and found they continued to survive, and even thrive, without any assistance.
“People have been trying to grow leukemia cells in culture, even from patients, and they require other factors to survive, but not these,” said study author Maria Luisa S. Sequeira-Lopez, MD, of UVA.
“These are extremely aggressive in that they have developed a system to grow and survive no matter what,” added author Ariel Gomez, MD, also of UVA. “They have immortalized themselves.”
The researchers now want to determine if these findings will translate to humans. They believe it’s possible, as they were able to identify RBP-J mutations in 10 patients (of 44 screened) with hematologic malignancies. In fact, 5 of the patients had the same frameshift deletion.
Credit: Aaron Logan
Researchers have identified a cell that appears to be responsible for a particularly aggressive type of leukemia in mice.
The cell is a renin-expressing B-cell progenitor found in the bone marrow.
Renin cells, which are also present in the kidney, have traditionally been associated with the control of blood pressure and fluid balance in the body.
But investigators discovered renin progenitors in the bone marrow of mice with aggressive B-cell leukemia.
And they found evidence to suggest the leukemia originated from a mutation in these renin progenitors—specifically, deletion of RBP-J.
“We would now like to see if this is a relevant model of human disease,” said study author Brian C. Belyea, MD, of the University of Virginia (UVA) School of Medicine in Charlottesville.
“Our long-term goal is to identify cells at increased risk for leukemia in humans and, ultimately, develop strategies to monitor and eliminate these cells.”
Dr Belyea and his colleagues described their initial steps toward this goal in Nature Communications.
In a previous study, the researchers were investigating the effects of RBP-J deletion in mice. And they were surprised to find that, as the mice aged beyond 6 months, they developed signs of an aggressive form of precursor B-lymphoblastic leukemia.
So with the current study, the team wanted to characterize this leukemia. They set out to identify which cells in the bone marrow are capable of producing renin under normal circumstances and whether those cells might be the origin of the leukemia.
The investigators found that renin is expressed by a subset of B-cell progenitors in the mouse bone marrow, and these cells need RBP-J to differentiate.
Deleting RBP-J restrains lymphocyte differentiation, and the mutant cells undergo neoplastic transformation. The mice develop a B-cell leukemia characterized by multi-organ infiltration and resulting in early death.
Experiments showed the leukemia to be particularly hardy. The researchers placed the leukemic cells in a lab dish and found they continued to survive, and even thrive, without any assistance.
“People have been trying to grow leukemia cells in culture, even from patients, and they require other factors to survive, but not these,” said study author Maria Luisa S. Sequeira-Lopez, MD, of UVA.
“These are extremely aggressive in that they have developed a system to grow and survive no matter what,” added author Ariel Gomez, MD, also of UVA. “They have immortalized themselves.”
The researchers now want to determine if these findings will translate to humans. They believe it’s possible, as they were able to identify RBP-J mutations in 10 patients (of 44 screened) with hematologic malignancies. In fact, 5 of the patients had the same frameshift deletion.
Credit: Aaron Logan
Researchers have identified a cell that appears to be responsible for a particularly aggressive type of leukemia in mice.
The cell is a renin-expressing B-cell progenitor found in the bone marrow.
Renin cells, which are also present in the kidney, have traditionally been associated with the control of blood pressure and fluid balance in the body.
But investigators discovered renin progenitors in the bone marrow of mice with aggressive B-cell leukemia.
And they found evidence to suggest the leukemia originated from a mutation in these renin progenitors—specifically, deletion of RBP-J.
“We would now like to see if this is a relevant model of human disease,” said study author Brian C. Belyea, MD, of the University of Virginia (UVA) School of Medicine in Charlottesville.
“Our long-term goal is to identify cells at increased risk for leukemia in humans and, ultimately, develop strategies to monitor and eliminate these cells.”
Dr Belyea and his colleagues described their initial steps toward this goal in Nature Communications.
In a previous study, the researchers were investigating the effects of RBP-J deletion in mice. And they were surprised to find that, as the mice aged beyond 6 months, they developed signs of an aggressive form of precursor B-lymphoblastic leukemia.
So with the current study, the team wanted to characterize this leukemia. They set out to identify which cells in the bone marrow are capable of producing renin under normal circumstances and whether those cells might be the origin of the leukemia.
The investigators found that renin is expressed by a subset of B-cell progenitors in the mouse bone marrow, and these cells need RBP-J to differentiate.
Deleting RBP-J restrains lymphocyte differentiation, and the mutant cells undergo neoplastic transformation. The mice develop a B-cell leukemia characterized by multi-organ infiltration and resulting in early death.
Experiments showed the leukemia to be particularly hardy. The researchers placed the leukemic cells in a lab dish and found they continued to survive, and even thrive, without any assistance.
“People have been trying to grow leukemia cells in culture, even from patients, and they require other factors to survive, but not these,” said study author Maria Luisa S. Sequeira-Lopez, MD, of UVA.
“These are extremely aggressive in that they have developed a system to grow and survive no matter what,” added author Ariel Gomez, MD, also of UVA. “They have immortalized themselves.”
The researchers now want to determine if these findings will translate to humans. They believe it’s possible, as they were able to identify RBP-J mutations in 10 patients (of 44 screened) with hematologic malignancies. In fact, 5 of the patients had the same frameshift deletion.
Why genetic screening isn’t preventing SCD
and a normal one
Credit: Betty Pace
There may be a simple reason why genetic screening has failed to fulfill the promise of preventing sickle cell disease (SCD).
According to an article published in JAMA, it’s a lack of communication.
We’ve long had the technical capacity to screen individuals for the sickle cell trait (SCT). Yet few individuals of child-bearing age who were born in the US actually know their SCT status.
So they aren’t aware that they might pass SCT or SCD down to their children.
And this may boil down to a lack of communication among healthcare professionals, patients, and family members.
“[P]arents are routinely notified by NBS [newborn screening] programs if their child has SCD, but only 37% are notified if their child has SCT,” said author Barry Zuckerman, MD, of Boston Medical Center in Massachusetts.
Even if parents do receive SCT screening results, we don’t know whether they understand the implications or share them with their child. And counseling or referrals to genetic counsellors are not provided in a standard fashion.
Furthermore, although NBS programs notify primary care physicians of screening results at the time of birth, results may not be readily available during routine clinic visits, and patients may not have the same physician throughout their childhood.
The lack of knowledge regarding SCT status represents a missed opportunity to provide appropriate health and prenatal counseling and testing, according to Dr Zuckerman and his colleagues.
They said that timely knowledge of genetic vulnerability and genetic counseling are necessary for informed decision-making with regard to reproduction. It is important to increase the number of adolescents and young adults who know their SCT status to decrease the number of individuals inheriting SCD.
To increase awareness of SCT status and facilitate informed decision-making about reproductive options, we must do 2 things, according to the authors.
First, the results of positive screens for SCT must be communicated to primary care clinicians, recorded in the patient’s medical record as part of a problem list, and shared with parents and the individual.
And second, we must provide effective communication and information through genetic counseling on reproductive options for those with SCT.
The authors also stressed that schools and community organizations have potentially important roles in communicating the importance of SCT status to adolescents and young adults. And by working together, the healthcare system, schools, and community organizations may be able to improve SCT knowledge and awareness.
and a normal one
Credit: Betty Pace
There may be a simple reason why genetic screening has failed to fulfill the promise of preventing sickle cell disease (SCD).
According to an article published in JAMA, it’s a lack of communication.
We’ve long had the technical capacity to screen individuals for the sickle cell trait (SCT). Yet few individuals of child-bearing age who were born in the US actually know their SCT status.
So they aren’t aware that they might pass SCT or SCD down to their children.
And this may boil down to a lack of communication among healthcare professionals, patients, and family members.
“[P]arents are routinely notified by NBS [newborn screening] programs if their child has SCD, but only 37% are notified if their child has SCT,” said author Barry Zuckerman, MD, of Boston Medical Center in Massachusetts.
Even if parents do receive SCT screening results, we don’t know whether they understand the implications or share them with their child. And counseling or referrals to genetic counsellors are not provided in a standard fashion.
Furthermore, although NBS programs notify primary care physicians of screening results at the time of birth, results may not be readily available during routine clinic visits, and patients may not have the same physician throughout their childhood.
The lack of knowledge regarding SCT status represents a missed opportunity to provide appropriate health and prenatal counseling and testing, according to Dr Zuckerman and his colleagues.
They said that timely knowledge of genetic vulnerability and genetic counseling are necessary for informed decision-making with regard to reproduction. It is important to increase the number of adolescents and young adults who know their SCT status to decrease the number of individuals inheriting SCD.
To increase awareness of SCT status and facilitate informed decision-making about reproductive options, we must do 2 things, according to the authors.
First, the results of positive screens for SCT must be communicated to primary care clinicians, recorded in the patient’s medical record as part of a problem list, and shared with parents and the individual.
And second, we must provide effective communication and information through genetic counseling on reproductive options for those with SCT.
The authors also stressed that schools and community organizations have potentially important roles in communicating the importance of SCT status to adolescents and young adults. And by working together, the healthcare system, schools, and community organizations may be able to improve SCT knowledge and awareness.
and a normal one
Credit: Betty Pace
There may be a simple reason why genetic screening has failed to fulfill the promise of preventing sickle cell disease (SCD).
According to an article published in JAMA, it’s a lack of communication.
We’ve long had the technical capacity to screen individuals for the sickle cell trait (SCT). Yet few individuals of child-bearing age who were born in the US actually know their SCT status.
So they aren’t aware that they might pass SCT or SCD down to their children.
And this may boil down to a lack of communication among healthcare professionals, patients, and family members.
“[P]arents are routinely notified by NBS [newborn screening] programs if their child has SCD, but only 37% are notified if their child has SCT,” said author Barry Zuckerman, MD, of Boston Medical Center in Massachusetts.
Even if parents do receive SCT screening results, we don’t know whether they understand the implications or share them with their child. And counseling or referrals to genetic counsellors are not provided in a standard fashion.
Furthermore, although NBS programs notify primary care physicians of screening results at the time of birth, results may not be readily available during routine clinic visits, and patients may not have the same physician throughout their childhood.
The lack of knowledge regarding SCT status represents a missed opportunity to provide appropriate health and prenatal counseling and testing, according to Dr Zuckerman and his colleagues.
They said that timely knowledge of genetic vulnerability and genetic counseling are necessary for informed decision-making with regard to reproduction. It is important to increase the number of adolescents and young adults who know their SCT status to decrease the number of individuals inheriting SCD.
To increase awareness of SCT status and facilitate informed decision-making about reproductive options, we must do 2 things, according to the authors.
First, the results of positive screens for SCT must be communicated to primary care clinicians, recorded in the patient’s medical record as part of a problem list, and shared with parents and the individual.
And second, we must provide effective communication and information through genetic counseling on reproductive options for those with SCT.
The authors also stressed that schools and community organizations have potentially important roles in communicating the importance of SCT status to adolescents and young adults. And by working together, the healthcare system, schools, and community organizations may be able to improve SCT knowledge and awareness.
Warmer temperatures push malaria to higher elevations
Credit: Asnakew Yeshiwondim
Researchers say they have the first hard evidence that malaria creeps to higher elevations during warmer years and retreats to lower altitudes when temperatures cool.
The evidence comes from an analysis of highland regions in Ethiopia and Colombia.
It suggests that future climate warming will prompt a rise in malaria incidence in densely populated regions of Africa and
South America, unless efforts to monitor and control malaria are increased.
“We saw an upward expansion of malaria cases to higher altitudes in warmer years, which is a clear signal of a response by highland malaria to changes in climate,” said study author Mercedes Pascual, PhD, of the University of Michigan in Ann Arbor.
“This is indisputable evidence of a climate effect. The main implication is that, with warmer temperatures, we expect to see a higher number of people exposed to the risk of malaria in tropical highland areas like these.”
Dr Pascual and her colleagues reported these findings in Science.
It was more than 20 years ago that malaria was first identified as a disease that might be especially sensitive to climate change, because both the Plasmodium parasites that cause it and the Anopheles mosquitoes that spread it thrive as temperatures warm.
Some early studies concluded that climate change would lead to an increase in malaria cases as the disease expanded its range into higher elevations. But some of the assumptions behind those predictions were later criticized.
More recently, researchers have argued that improved socioeconomic conditions and more aggressive mosquito-control efforts will likely exert a far greater influence than climatic factors over the extent and intensity of malaria worldwide.
What’s been missing in this debate is an analysis of regional records with sufficient resolution to determine how the spatial distribution of malaria cases has changed in response to year-to-year temperature variations, especially in densely populated highlands that have historically provided havens from the disease.
So Dr Pascual and her colleagues looked for evidence of a changing spatial distribution of malaria with varying temperature in the highlands of Ethiopia and Colombia. They examined malaria case records from the Antioquia region of western Colombia from 1990 to 2005 and from the Debre Zeit area of central Ethiopia from 1993 to 2005.
By focusing solely on the altitudinal response to year-to-year temperature changes, the researchers were able to exclude other variables that can influence malaria case numbers, such as mosquito-control programs, resistance to antimalarial drugs, and fluctuations in rainfall amounts.
The team found that the median altitude of malaria cases shifted to higher elevations in warmer years and back to lower elevations in cooler years. This relatively simple analysis yielded a clear signal that can only be explained by temperature changes, the group said.
“Our latest research suggests that, with progressive global warming, malaria will creep up the mountains and spread to new high-altitude areas,” said study author Menno Bouma, MD, of the London School of Hygiene & Tropical Medicine in the UK.
“And because these populations lack protective immunity, they will be particularly vulnerable to severe morbidity and mortality.”
In addition, the study results suggest that climate change can explain malaria trends in both the highland regions in recent decades.
In the Debre Zeit region of Ethiopia, at an elevation range of between 5280 feet and 7920 feet, about 37 million people (roughly 43% of the country’s population) live in rural areas at risk of higher malaria exposure under a warming climate.
In a previous study, researchers estimated that a 1-degree temperature increase could result in an additional 3 million malaria cases annually in Ethiopia in the under-15 population, unless control efforts are strengthened.
“Our findings here underscore the size of the problem,” Dr Pascual said, “and emphasize the need for sustained intervention efforts in these regions, especially in Africa.”
Credit: Asnakew Yeshiwondim
Researchers say they have the first hard evidence that malaria creeps to higher elevations during warmer years and retreats to lower altitudes when temperatures cool.
The evidence comes from an analysis of highland regions in Ethiopia and Colombia.
It suggests that future climate warming will prompt a rise in malaria incidence in densely populated regions of Africa and
South America, unless efforts to monitor and control malaria are increased.
“We saw an upward expansion of malaria cases to higher altitudes in warmer years, which is a clear signal of a response by highland malaria to changes in climate,” said study author Mercedes Pascual, PhD, of the University of Michigan in Ann Arbor.
“This is indisputable evidence of a climate effect. The main implication is that, with warmer temperatures, we expect to see a higher number of people exposed to the risk of malaria in tropical highland areas like these.”
Dr Pascual and her colleagues reported these findings in Science.
It was more than 20 years ago that malaria was first identified as a disease that might be especially sensitive to climate change, because both the Plasmodium parasites that cause it and the Anopheles mosquitoes that spread it thrive as temperatures warm.
Some early studies concluded that climate change would lead to an increase in malaria cases as the disease expanded its range into higher elevations. But some of the assumptions behind those predictions were later criticized.
More recently, researchers have argued that improved socioeconomic conditions and more aggressive mosquito-control efforts will likely exert a far greater influence than climatic factors over the extent and intensity of malaria worldwide.
What’s been missing in this debate is an analysis of regional records with sufficient resolution to determine how the spatial distribution of malaria cases has changed in response to year-to-year temperature variations, especially in densely populated highlands that have historically provided havens from the disease.
So Dr Pascual and her colleagues looked for evidence of a changing spatial distribution of malaria with varying temperature in the highlands of Ethiopia and Colombia. They examined malaria case records from the Antioquia region of western Colombia from 1990 to 2005 and from the Debre Zeit area of central Ethiopia from 1993 to 2005.
By focusing solely on the altitudinal response to year-to-year temperature changes, the researchers were able to exclude other variables that can influence malaria case numbers, such as mosquito-control programs, resistance to antimalarial drugs, and fluctuations in rainfall amounts.
The team found that the median altitude of malaria cases shifted to higher elevations in warmer years and back to lower elevations in cooler years. This relatively simple analysis yielded a clear signal that can only be explained by temperature changes, the group said.
“Our latest research suggests that, with progressive global warming, malaria will creep up the mountains and spread to new high-altitude areas,” said study author Menno Bouma, MD, of the London School of Hygiene & Tropical Medicine in the UK.
“And because these populations lack protective immunity, they will be particularly vulnerable to severe morbidity and mortality.”
In addition, the study results suggest that climate change can explain malaria trends in both the highland regions in recent decades.
In the Debre Zeit region of Ethiopia, at an elevation range of between 5280 feet and 7920 feet, about 37 million people (roughly 43% of the country’s population) live in rural areas at risk of higher malaria exposure under a warming climate.
In a previous study, researchers estimated that a 1-degree temperature increase could result in an additional 3 million malaria cases annually in Ethiopia in the under-15 population, unless control efforts are strengthened.
“Our findings here underscore the size of the problem,” Dr Pascual said, “and emphasize the need for sustained intervention efforts in these regions, especially in Africa.”
Credit: Asnakew Yeshiwondim
Researchers say they have the first hard evidence that malaria creeps to higher elevations during warmer years and retreats to lower altitudes when temperatures cool.
The evidence comes from an analysis of highland regions in Ethiopia and Colombia.
It suggests that future climate warming will prompt a rise in malaria incidence in densely populated regions of Africa and
South America, unless efforts to monitor and control malaria are increased.
“We saw an upward expansion of malaria cases to higher altitudes in warmer years, which is a clear signal of a response by highland malaria to changes in climate,” said study author Mercedes Pascual, PhD, of the University of Michigan in Ann Arbor.
“This is indisputable evidence of a climate effect. The main implication is that, with warmer temperatures, we expect to see a higher number of people exposed to the risk of malaria in tropical highland areas like these.”
Dr Pascual and her colleagues reported these findings in Science.
It was more than 20 years ago that malaria was first identified as a disease that might be especially sensitive to climate change, because both the Plasmodium parasites that cause it and the Anopheles mosquitoes that spread it thrive as temperatures warm.
Some early studies concluded that climate change would lead to an increase in malaria cases as the disease expanded its range into higher elevations. But some of the assumptions behind those predictions were later criticized.
More recently, researchers have argued that improved socioeconomic conditions and more aggressive mosquito-control efforts will likely exert a far greater influence than climatic factors over the extent and intensity of malaria worldwide.
What’s been missing in this debate is an analysis of regional records with sufficient resolution to determine how the spatial distribution of malaria cases has changed in response to year-to-year temperature variations, especially in densely populated highlands that have historically provided havens from the disease.
So Dr Pascual and her colleagues looked for evidence of a changing spatial distribution of malaria with varying temperature in the highlands of Ethiopia and Colombia. They examined malaria case records from the Antioquia region of western Colombia from 1990 to 2005 and from the Debre Zeit area of central Ethiopia from 1993 to 2005.
By focusing solely on the altitudinal response to year-to-year temperature changes, the researchers were able to exclude other variables that can influence malaria case numbers, such as mosquito-control programs, resistance to antimalarial drugs, and fluctuations in rainfall amounts.
The team found that the median altitude of malaria cases shifted to higher elevations in warmer years and back to lower elevations in cooler years. This relatively simple analysis yielded a clear signal that can only be explained by temperature changes, the group said.
“Our latest research suggests that, with progressive global warming, malaria will creep up the mountains and spread to new high-altitude areas,” said study author Menno Bouma, MD, of the London School of Hygiene & Tropical Medicine in the UK.
“And because these populations lack protective immunity, they will be particularly vulnerable to severe morbidity and mortality.”
In addition, the study results suggest that climate change can explain malaria trends in both the highland regions in recent decades.
In the Debre Zeit region of Ethiopia, at an elevation range of between 5280 feet and 7920 feet, about 37 million people (roughly 43% of the country’s population) live in rural areas at risk of higher malaria exposure under a warming climate.
In a previous study, researchers estimated that a 1-degree temperature increase could result in an additional 3 million malaria cases annually in Ethiopia in the under-15 population, unless control efforts are strengthened.
“Our findings here underscore the size of the problem,” Dr Pascual said, “and emphasize the need for sustained intervention efforts in these regions, especially in Africa.”
Pathway may drive treatment resistance in T-ALL
Experiments in zebrafish have revealed a mechanism that may drive relapse in human T-cell acute lymphoblastic leukemia (T-ALL).
Researchers identified a subset of T-ALL cells that spontaneously acquired activation of the Akt pathway.
This increased the frequency of leukemia-propagating cells (LPCs) and mediated resistance to treatment with dexamethasone. However, adding an Akt inhibitor to treatment overcame this resistance.
“The Akt pathway appears to be a major driver of treatment resistance,” said study author David Langenau, PhD, of the Harvard Stem Cell Institute in Boston.
“We also show that this same pathway increases overall growth of leukemic cells and increases the fraction of cells capable of driving relapse.”
Dr Langenau and his colleagues described these findings in Cancer Cell.
Previous research had shown that, if LPCs are retained following treatment, they will initiate disease relapse. And LPC frequency can increase over time. However, it was not clear if this was the result of continued clonal evolution or if a clone with high LPC frequency out-competed other cells.
So Dr Langenau and his colleagues used zebrafish models to study T-ALL clones. The team observed functional variation within individual clones and identified clones that enhanced growth rate and leukemia-propagating potential with time.
A subset of these clones acquired Akt pathway activation, which increased the number of LPCs by activating mTORC1. The cells also exhibited an elevated growth rate, which may have resulted from stabilizing the Myc protein.
Furthermore, the LPCs proved resistant to treatment with dexamethasone. But the researchers were able to reverse this resistance by combining dexamethasone with the Akt inhibitor MK2206. This approach proved effective both in zebrafish models and in human T-ALL cells.
“Our work will likely help in identifying patients that are prone to relapse and would benefit from co-treatment with inhibitors of the Akt pathway and typical front-line cancer therapy,” said Jessica Blackburn, PhD, a member of Dr Langenau’s lab.
She and her colleagues are now hoping to identify other mutations that lead to relapse, thereby pinpointing potential drug targets for patients with aggressive leukemia.
Experiments in zebrafish have revealed a mechanism that may drive relapse in human T-cell acute lymphoblastic leukemia (T-ALL).
Researchers identified a subset of T-ALL cells that spontaneously acquired activation of the Akt pathway.
This increased the frequency of leukemia-propagating cells (LPCs) and mediated resistance to treatment with dexamethasone. However, adding an Akt inhibitor to treatment overcame this resistance.
“The Akt pathway appears to be a major driver of treatment resistance,” said study author David Langenau, PhD, of the Harvard Stem Cell Institute in Boston.
“We also show that this same pathway increases overall growth of leukemic cells and increases the fraction of cells capable of driving relapse.”
Dr Langenau and his colleagues described these findings in Cancer Cell.
Previous research had shown that, if LPCs are retained following treatment, they will initiate disease relapse. And LPC frequency can increase over time. However, it was not clear if this was the result of continued clonal evolution or if a clone with high LPC frequency out-competed other cells.
So Dr Langenau and his colleagues used zebrafish models to study T-ALL clones. The team observed functional variation within individual clones and identified clones that enhanced growth rate and leukemia-propagating potential with time.
A subset of these clones acquired Akt pathway activation, which increased the number of LPCs by activating mTORC1. The cells also exhibited an elevated growth rate, which may have resulted from stabilizing the Myc protein.
Furthermore, the LPCs proved resistant to treatment with dexamethasone. But the researchers were able to reverse this resistance by combining dexamethasone with the Akt inhibitor MK2206. This approach proved effective both in zebrafish models and in human T-ALL cells.
“Our work will likely help in identifying patients that are prone to relapse and would benefit from co-treatment with inhibitors of the Akt pathway and typical front-line cancer therapy,” said Jessica Blackburn, PhD, a member of Dr Langenau’s lab.
She and her colleagues are now hoping to identify other mutations that lead to relapse, thereby pinpointing potential drug targets for patients with aggressive leukemia.
Experiments in zebrafish have revealed a mechanism that may drive relapse in human T-cell acute lymphoblastic leukemia (T-ALL).
Researchers identified a subset of T-ALL cells that spontaneously acquired activation of the Akt pathway.
This increased the frequency of leukemia-propagating cells (LPCs) and mediated resistance to treatment with dexamethasone. However, adding an Akt inhibitor to treatment overcame this resistance.
“The Akt pathway appears to be a major driver of treatment resistance,” said study author David Langenau, PhD, of the Harvard Stem Cell Institute in Boston.
“We also show that this same pathway increases overall growth of leukemic cells and increases the fraction of cells capable of driving relapse.”
Dr Langenau and his colleagues described these findings in Cancer Cell.
Previous research had shown that, if LPCs are retained following treatment, they will initiate disease relapse. And LPC frequency can increase over time. However, it was not clear if this was the result of continued clonal evolution or if a clone with high LPC frequency out-competed other cells.
So Dr Langenau and his colleagues used zebrafish models to study T-ALL clones. The team observed functional variation within individual clones and identified clones that enhanced growth rate and leukemia-propagating potential with time.
A subset of these clones acquired Akt pathway activation, which increased the number of LPCs by activating mTORC1. The cells also exhibited an elevated growth rate, which may have resulted from stabilizing the Myc protein.
Furthermore, the LPCs proved resistant to treatment with dexamethasone. But the researchers were able to reverse this resistance by combining dexamethasone with the Akt inhibitor MK2206. This approach proved effective both in zebrafish models and in human T-ALL cells.
“Our work will likely help in identifying patients that are prone to relapse and would benefit from co-treatment with inhibitors of the Akt pathway and typical front-line cancer therapy,” said Jessica Blackburn, PhD, a member of Dr Langenau’s lab.
She and her colleagues are now hoping to identify other mutations that lead to relapse, thereby pinpointing potential drug targets for patients with aggressive leukemia.
RIT can improve transplant outcomes in NHL, CLL
GRAPEVINE, TEXAS—Administering radioimmunotherapy (RIT) prior to non-myeloablative allogeneic transplant (NMAT) can improve survival in patients with persistent disease, according to a speaker at the 2014 BMT Tandem Meetings.
Ryan Cassaday, MD, of the University of Washington in Seattle, noted that RIT-augmented NMAT can produce long-term remissions in patients with relapsed or refractory B-cell non-Hodgkin lymphoma (B-NHL) or chronic lymphocytic leukemia (CLL).
But outcomes for patients with persistent disease are “underdescribed.”
So he and his colleagues set out to describe outcomes of NMAT for patients with persistent indolent B-NHL or CLL and estimate the impact of RIT in these patients.
Treatment details
The researchers retrospectively analyzed data from 89 patients who underwent NMAT from December 1998 to April 2009 and were followed until September 2013. Eighteen of the patients had received RIT as part of a prospective study (AK Gopal et al, Blood 2011).
The remaining 71 patients did not receive RIT but met eligibility criteria for that study. Specifically, they had a CD20+ B-cell malignancy, an HLA-matched peripheral blood stem cell donor, and persistent disease at NMAT. These control subjects received fludarabine (30 mg/m2 on days -7, -6, and -5) and 2 Gy of total body irradiation prior to NMAT.
Patients in the RIT group received the same treatment following RIT. On day -21, they received 250 mg/m2 of rituximab before an imaging dose of 111In-ibritumomab tiuxetan. And on day -14, they received 250 mg/m2 of rituximab and 0.4 mCi/kg of 90Y-ibritumomab tiuxetan.
Patient characteristics
In the RIT group, 10 patients had CLL/small lymphocytic lymphoma (SLL), 6 had follicular lymphoma (FL), 1 had marginal zone lymphoma (MZL), and 1 had hairy cell leukemia. As for controls, 52 had CLL/SLL, 18 had FL, and 1 had MZL.
“The majority of patients were male [74%] and a relatively young age, given the diseases being treated [median of 56 years],” Dr Cassaday said. “The majority of patients had previously received rituximab [88%], and patients were heavily pretreated [median of 4 prior therapies, range 1-12].”
There were no significant differences between the 2 treatment groups with regard to the aforementioned characteristics. However, there were some “striking differences” between the 2 groups, Dr Cassaday said, including characteristics that portend worse prognosis.
Specifically, RIT-treated patients had more bulky disease (> 5 cm) than controls (61% vs 15%, P<0.001) and more chemoresistant disease (81% vs 39%, P=0.003). And RIT patients were more likely to have HCT comorbidity index scores of 3 or higher (72% vs 37%, P=0.006), as well as pre-NMAT platelet counts less than 25k/μL (33% vs 7%, P=0.002).
RIT improves PFS, OS
The researchers conducted a multivariate analysis including the factors that differed significantly between the 2 treatment groups. And they found that only RIT was significantly associated with both progression-free survival (PFS) and overall survival (OS).
When calculating survival curves, the researchers adjusted for the imbalance in covariates between the treatment groups.
“[The adjusted survival rate] is essentially what one might expect had the RIT group had similar baseline characteristics as the control group,” Dr Cassaday explained.
Control subjects had a 3-year OS of 55%. For the RIT-treated patients, the unadjusted 3-year OS was 78% (P=0.20), and the adjusted OS was 87% (P=0.008).
The 3-year PFS was 44% for controls. For the RIT group, the unadjusted 3-year PFS was 56% (P=0.36), and the adjusted PFS was 71% (P=0.02).
The researchers also found that RIT did not increase the rate of non-relapse mortality. The unadjusted hazard ratio was 0.5 (P=0.32), and the adjusted hazard ratio was 0.4 (P=0.18).
“This analysis does have some limitations,” Dr Cassaday conceded. “Clearly, it does not replace the strength of evidence that would come from a randomized, controlled trial. And the relatively small sample size does limit our ability to look at a lot of different subgroups.”
In addition, the findings may not apply to other non-myeloablative regimens. And, due to the time frame of the study, the researchers could not account for the potential impact of newer agents.
Nevertheless, Dr Cassaday said the data suggest that RIT can improve the outcome of NMAT in patients with persistent indolent B-NHL or CLL. And a prospective, randomized study evaluating this approach is warranted.
Dr Cassaday presented this research at the 2014 BMT Tandem Meetings as abstract 75. Information in the abstract differs from that presented.
GRAPEVINE, TEXAS—Administering radioimmunotherapy (RIT) prior to non-myeloablative allogeneic transplant (NMAT) can improve survival in patients with persistent disease, according to a speaker at the 2014 BMT Tandem Meetings.
Ryan Cassaday, MD, of the University of Washington in Seattle, noted that RIT-augmented NMAT can produce long-term remissions in patients with relapsed or refractory B-cell non-Hodgkin lymphoma (B-NHL) or chronic lymphocytic leukemia (CLL).
But outcomes for patients with persistent disease are “underdescribed.”
So he and his colleagues set out to describe outcomes of NMAT for patients with persistent indolent B-NHL or CLL and estimate the impact of RIT in these patients.
Treatment details
The researchers retrospectively analyzed data from 89 patients who underwent NMAT from December 1998 to April 2009 and were followed until September 2013. Eighteen of the patients had received RIT as part of a prospective study (AK Gopal et al, Blood 2011).
The remaining 71 patients did not receive RIT but met eligibility criteria for that study. Specifically, they had a CD20+ B-cell malignancy, an HLA-matched peripheral blood stem cell donor, and persistent disease at NMAT. These control subjects received fludarabine (30 mg/m2 on days -7, -6, and -5) and 2 Gy of total body irradiation prior to NMAT.
Patients in the RIT group received the same treatment following RIT. On day -21, they received 250 mg/m2 of rituximab before an imaging dose of 111In-ibritumomab tiuxetan. And on day -14, they received 250 mg/m2 of rituximab and 0.4 mCi/kg of 90Y-ibritumomab tiuxetan.
Patient characteristics
In the RIT group, 10 patients had CLL/small lymphocytic lymphoma (SLL), 6 had follicular lymphoma (FL), 1 had marginal zone lymphoma (MZL), and 1 had hairy cell leukemia. As for controls, 52 had CLL/SLL, 18 had FL, and 1 had MZL.
“The majority of patients were male [74%] and a relatively young age, given the diseases being treated [median of 56 years],” Dr Cassaday said. “The majority of patients had previously received rituximab [88%], and patients were heavily pretreated [median of 4 prior therapies, range 1-12].”
There were no significant differences between the 2 treatment groups with regard to the aforementioned characteristics. However, there were some “striking differences” between the 2 groups, Dr Cassaday said, including characteristics that portend worse prognosis.
Specifically, RIT-treated patients had more bulky disease (> 5 cm) than controls (61% vs 15%, P<0.001) and more chemoresistant disease (81% vs 39%, P=0.003). And RIT patients were more likely to have HCT comorbidity index scores of 3 or higher (72% vs 37%, P=0.006), as well as pre-NMAT platelet counts less than 25k/μL (33% vs 7%, P=0.002).
RIT improves PFS, OS
The researchers conducted a multivariate analysis including the factors that differed significantly between the 2 treatment groups. And they found that only RIT was significantly associated with both progression-free survival (PFS) and overall survival (OS).
When calculating survival curves, the researchers adjusted for the imbalance in covariates between the treatment groups.
“[The adjusted survival rate] is essentially what one might expect had the RIT group had similar baseline characteristics as the control group,” Dr Cassaday explained.
Control subjects had a 3-year OS of 55%. For the RIT-treated patients, the unadjusted 3-year OS was 78% (P=0.20), and the adjusted OS was 87% (P=0.008).
The 3-year PFS was 44% for controls. For the RIT group, the unadjusted 3-year PFS was 56% (P=0.36), and the adjusted PFS was 71% (P=0.02).
The researchers also found that RIT did not increase the rate of non-relapse mortality. The unadjusted hazard ratio was 0.5 (P=0.32), and the adjusted hazard ratio was 0.4 (P=0.18).
“This analysis does have some limitations,” Dr Cassaday conceded. “Clearly, it does not replace the strength of evidence that would come from a randomized, controlled trial. And the relatively small sample size does limit our ability to look at a lot of different subgroups.”
In addition, the findings may not apply to other non-myeloablative regimens. And, due to the time frame of the study, the researchers could not account for the potential impact of newer agents.
Nevertheless, Dr Cassaday said the data suggest that RIT can improve the outcome of NMAT in patients with persistent indolent B-NHL or CLL. And a prospective, randomized study evaluating this approach is warranted.
Dr Cassaday presented this research at the 2014 BMT Tandem Meetings as abstract 75. Information in the abstract differs from that presented.
GRAPEVINE, TEXAS—Administering radioimmunotherapy (RIT) prior to non-myeloablative allogeneic transplant (NMAT) can improve survival in patients with persistent disease, according to a speaker at the 2014 BMT Tandem Meetings.
Ryan Cassaday, MD, of the University of Washington in Seattle, noted that RIT-augmented NMAT can produce long-term remissions in patients with relapsed or refractory B-cell non-Hodgkin lymphoma (B-NHL) or chronic lymphocytic leukemia (CLL).
But outcomes for patients with persistent disease are “underdescribed.”
So he and his colleagues set out to describe outcomes of NMAT for patients with persistent indolent B-NHL or CLL and estimate the impact of RIT in these patients.
Treatment details
The researchers retrospectively analyzed data from 89 patients who underwent NMAT from December 1998 to April 2009 and were followed until September 2013. Eighteen of the patients had received RIT as part of a prospective study (AK Gopal et al, Blood 2011).
The remaining 71 patients did not receive RIT but met eligibility criteria for that study. Specifically, they had a CD20+ B-cell malignancy, an HLA-matched peripheral blood stem cell donor, and persistent disease at NMAT. These control subjects received fludarabine (30 mg/m2 on days -7, -6, and -5) and 2 Gy of total body irradiation prior to NMAT.
Patients in the RIT group received the same treatment following RIT. On day -21, they received 250 mg/m2 of rituximab before an imaging dose of 111In-ibritumomab tiuxetan. And on day -14, they received 250 mg/m2 of rituximab and 0.4 mCi/kg of 90Y-ibritumomab tiuxetan.
Patient characteristics
In the RIT group, 10 patients had CLL/small lymphocytic lymphoma (SLL), 6 had follicular lymphoma (FL), 1 had marginal zone lymphoma (MZL), and 1 had hairy cell leukemia. As for controls, 52 had CLL/SLL, 18 had FL, and 1 had MZL.
“The majority of patients were male [74%] and a relatively young age, given the diseases being treated [median of 56 years],” Dr Cassaday said. “The majority of patients had previously received rituximab [88%], and patients were heavily pretreated [median of 4 prior therapies, range 1-12].”
There were no significant differences between the 2 treatment groups with regard to the aforementioned characteristics. However, there were some “striking differences” between the 2 groups, Dr Cassaday said, including characteristics that portend worse prognosis.
Specifically, RIT-treated patients had more bulky disease (> 5 cm) than controls (61% vs 15%, P<0.001) and more chemoresistant disease (81% vs 39%, P=0.003). And RIT patients were more likely to have HCT comorbidity index scores of 3 or higher (72% vs 37%, P=0.006), as well as pre-NMAT platelet counts less than 25k/μL (33% vs 7%, P=0.002).
RIT improves PFS, OS
The researchers conducted a multivariate analysis including the factors that differed significantly between the 2 treatment groups. And they found that only RIT was significantly associated with both progression-free survival (PFS) and overall survival (OS).
When calculating survival curves, the researchers adjusted for the imbalance in covariates between the treatment groups.
“[The adjusted survival rate] is essentially what one might expect had the RIT group had similar baseline characteristics as the control group,” Dr Cassaday explained.
Control subjects had a 3-year OS of 55%. For the RIT-treated patients, the unadjusted 3-year OS was 78% (P=0.20), and the adjusted OS was 87% (P=0.008).
The 3-year PFS was 44% for controls. For the RIT group, the unadjusted 3-year PFS was 56% (P=0.36), and the adjusted PFS was 71% (P=0.02).
The researchers also found that RIT did not increase the rate of non-relapse mortality. The unadjusted hazard ratio was 0.5 (P=0.32), and the adjusted hazard ratio was 0.4 (P=0.18).
“This analysis does have some limitations,” Dr Cassaday conceded. “Clearly, it does not replace the strength of evidence that would come from a randomized, controlled trial. And the relatively small sample size does limit our ability to look at a lot of different subgroups.”
In addition, the findings may not apply to other non-myeloablative regimens. And, due to the time frame of the study, the researchers could not account for the potential impact of newer agents.
Nevertheless, Dr Cassaday said the data suggest that RIT can improve the outcome of NMAT in patients with persistent indolent B-NHL or CLL. And a prospective, randomized study evaluating this approach is warranted.
Dr Cassaday presented this research at the 2014 BMT Tandem Meetings as abstract 75. Information in the abstract differs from that presented.
The demise of renal artery stenting
The announcement from Medtronic in January has apparently brought down the curtain on the much-heralded approach to the treatment of refractory hypertension using radio-frequency renal artery sympathetic nerve ablation (RASNA) applied through the tip of the Symplicity catheter.
This technology is being used widely around the world for the treatment of refractory hypertensive patients. The enthusiasm for RASNA was generated by a series of reports suggesting that an amazing decrease in systolic blood pressure of more than 30 mm Hg can be obtained in patients with resistant hypertension taking three or more antihypertensive drugs. However, the SYMPLICITY HTN-3 trial (Clin. Cardiol. 2012;35:528-35), which enrolled 535 patients with refractory hypertension, failed to achieve the primary endpoint of a significant decrease in systolic pressure in the radio-frequency (RF)-treated patients compared with the sham-operated patients. In the study, blinding was rigorously managed by renal artery catheterization of all 535 patients, with RF ablation instituted in two-thirds of the patients, and a sham operation conducted in one-third.
As a result of the observations in SYMPLICITY HTN-3, Medtronic is suspending enrollment in current trials using the Symplicity device throughout the world, and will "continue to ensure patients access to the Symplicity technology at the discretion of their physicians in countries where the device is approved," according to its statement.
Enthusiasm for RF ablation of the sympathetic nerves accompanying the renal artery was generated by a series of publications describing the physiologic and therapeutic effects. The first publications in this series described the metabolic changes that occurred after RF ablation carried out in one patient who experienced a decrease in systolic pressure of 20 mm Hg associated with modulation of sympathetic activity 30 days and 12 months after the procedure (N. Engl. J. Med. 2009;361:932-4). This study was followed by two subsequent reports of patients in whom RASNA was carried out. A proof-of-concept trial (SYMPLICITY HTN-1) in 153 patients reported a substantial decrease in blood pressure over a 2-year period (Hypertension 2011;57:911-7). A second trial (SYMPLICITY HTN-2) randomized 106 patients to either RASNA or standard therapy. That trial reported that 84% of the patients receiving RASNA had a reduction of blood pressure greater than 10 mm Hg within 6 months, compared with 35% of the control group (Lancet 2010;376:1903-9). Both studies reported a profound decrease in blood pressure that ensued over a 6-month period in 80%-90% of patients undergoing the therapy. In light of these reports, it is difficult to explain the fact that SYMPLICITY HTN-3 was a negative study.
Modulation of the sympathetic nervous system for the treatment of hypertension is not new. More than 60 years ago, Smithwick and colleagues carried out both surgical lumbar and sympathetic splanchnicectomy for its treatment with uncertain results (JAMA 1952;153:1501-4). In an era when all that we could offer hypertensive patients was a low-salt diet, the procedure became rather popular. However, the surgical risks, adverse side effects, and uncertainty of benefit led to both procedures being discontinued. Recently, there have been studies of the effect of stimulation of the carotid sinus nerve for the treatment of hypertension.
The potential benefit of modulation of the arterial sympathetic nerves, and particularly those located in the renal artery, became the focus of this recent interest. Nevertheless, a number of questions have arisen in regard to the mechanism of RASNA. And why does it take 6 months to achieve the blood pressure response? In addition, there is very little published data in regard to the changes in the renal artery and its adjacent tissue as a result of the RF ablation.
At the present, Medtronic has not provided any information beyond its indicating the lack of benefit. Further information will be reported at the upcoming American College of Cardiology scientific sessions. In the meantime, speculation is rampant as to whether the initial reports were purely placebo effects or if there is something intrinsically flawed in the SYMPLICITY HTN -3 trial.
Dr. Goldstein, medical editor of Cardiology News, is professor of medicine at Wayne State University and division head emeritus of cardiovascular medicine at Henry Ford Hospital, both in Detroit. He is on data safety monitoring committees for the National Institutes of Health and several pharmaceutical companies.
The announcement from Medtronic in January has apparently brought down the curtain on the much-heralded approach to the treatment of refractory hypertension using radio-frequency renal artery sympathetic nerve ablation (RASNA) applied through the tip of the Symplicity catheter.
This technology is being used widely around the world for the treatment of refractory hypertensive patients. The enthusiasm for RASNA was generated by a series of reports suggesting that an amazing decrease in systolic blood pressure of more than 30 mm Hg can be obtained in patients with resistant hypertension taking three or more antihypertensive drugs. However, the SYMPLICITY HTN-3 trial (Clin. Cardiol. 2012;35:528-35), which enrolled 535 patients with refractory hypertension, failed to achieve the primary endpoint of a significant decrease in systolic pressure in the radio-frequency (RF)-treated patients compared with the sham-operated patients. In the study, blinding was rigorously managed by renal artery catheterization of all 535 patients, with RF ablation instituted in two-thirds of the patients, and a sham operation conducted in one-third.
As a result of the observations in SYMPLICITY HTN-3, Medtronic is suspending enrollment in current trials using the Symplicity device throughout the world, and will "continue to ensure patients access to the Symplicity technology at the discretion of their physicians in countries where the device is approved," according to its statement.
Enthusiasm for RF ablation of the sympathetic nerves accompanying the renal artery was generated by a series of publications describing the physiologic and therapeutic effects. The first publications in this series described the metabolic changes that occurred after RF ablation carried out in one patient who experienced a decrease in systolic pressure of 20 mm Hg associated with modulation of sympathetic activity 30 days and 12 months after the procedure (N. Engl. J. Med. 2009;361:932-4). This study was followed by two subsequent reports of patients in whom RASNA was carried out. A proof-of-concept trial (SYMPLICITY HTN-1) in 153 patients reported a substantial decrease in blood pressure over a 2-year period (Hypertension 2011;57:911-7). A second trial (SYMPLICITY HTN-2) randomized 106 patients to either RASNA or standard therapy. That trial reported that 84% of the patients receiving RASNA had a reduction of blood pressure greater than 10 mm Hg within 6 months, compared with 35% of the control group (Lancet 2010;376:1903-9). Both studies reported a profound decrease in blood pressure that ensued over a 6-month period in 80%-90% of patients undergoing the therapy. In light of these reports, it is difficult to explain the fact that SYMPLICITY HTN-3 was a negative study.
Modulation of the sympathetic nervous system for the treatment of hypertension is not new. More than 60 years ago, Smithwick and colleagues carried out both surgical lumbar and sympathetic splanchnicectomy for its treatment with uncertain results (JAMA 1952;153:1501-4). In an era when all that we could offer hypertensive patients was a low-salt diet, the procedure became rather popular. However, the surgical risks, adverse side effects, and uncertainty of benefit led to both procedures being discontinued. Recently, there have been studies of the effect of stimulation of the carotid sinus nerve for the treatment of hypertension.
The potential benefit of modulation of the arterial sympathetic nerves, and particularly those located in the renal artery, became the focus of this recent interest. Nevertheless, a number of questions have arisen in regard to the mechanism of RASNA. And why does it take 6 months to achieve the blood pressure response? In addition, there is very little published data in regard to the changes in the renal artery and its adjacent tissue as a result of the RF ablation.
At the present, Medtronic has not provided any information beyond its indicating the lack of benefit. Further information will be reported at the upcoming American College of Cardiology scientific sessions. In the meantime, speculation is rampant as to whether the initial reports were purely placebo effects or if there is something intrinsically flawed in the SYMPLICITY HTN -3 trial.
Dr. Goldstein, medical editor of Cardiology News, is professor of medicine at Wayne State University and division head emeritus of cardiovascular medicine at Henry Ford Hospital, both in Detroit. He is on data safety monitoring committees for the National Institutes of Health and several pharmaceutical companies.
The announcement from Medtronic in January has apparently brought down the curtain on the much-heralded approach to the treatment of refractory hypertension using radio-frequency renal artery sympathetic nerve ablation (RASNA) applied through the tip of the Symplicity catheter.
This technology is being used widely around the world for the treatment of refractory hypertensive patients. The enthusiasm for RASNA was generated by a series of reports suggesting that an amazing decrease in systolic blood pressure of more than 30 mm Hg can be obtained in patients with resistant hypertension taking three or more antihypertensive drugs. However, the SYMPLICITY HTN-3 trial (Clin. Cardiol. 2012;35:528-35), which enrolled 535 patients with refractory hypertension, failed to achieve the primary endpoint of a significant decrease in systolic pressure in the radio-frequency (RF)-treated patients compared with the sham-operated patients. In the study, blinding was rigorously managed by renal artery catheterization of all 535 patients, with RF ablation instituted in two-thirds of the patients, and a sham operation conducted in one-third.
As a result of the observations in SYMPLICITY HTN-3, Medtronic is suspending enrollment in current trials using the Symplicity device throughout the world, and will "continue to ensure patients access to the Symplicity technology at the discretion of their physicians in countries where the device is approved," according to its statement.
Enthusiasm for RF ablation of the sympathetic nerves accompanying the renal artery was generated by a series of publications describing the physiologic and therapeutic effects. The first publications in this series described the metabolic changes that occurred after RF ablation carried out in one patient who experienced a decrease in systolic pressure of 20 mm Hg associated with modulation of sympathetic activity 30 days and 12 months after the procedure (N. Engl. J. Med. 2009;361:932-4). This study was followed by two subsequent reports of patients in whom RASNA was carried out. A proof-of-concept trial (SYMPLICITY HTN-1) in 153 patients reported a substantial decrease in blood pressure over a 2-year period (Hypertension 2011;57:911-7). A second trial (SYMPLICITY HTN-2) randomized 106 patients to either RASNA or standard therapy. That trial reported that 84% of the patients receiving RASNA had a reduction of blood pressure greater than 10 mm Hg within 6 months, compared with 35% of the control group (Lancet 2010;376:1903-9). Both studies reported a profound decrease in blood pressure that ensued over a 6-month period in 80%-90% of patients undergoing the therapy. In light of these reports, it is difficult to explain the fact that SYMPLICITY HTN-3 was a negative study.
Modulation of the sympathetic nervous system for the treatment of hypertension is not new. More than 60 years ago, Smithwick and colleagues carried out both surgical lumbar and sympathetic splanchnicectomy for its treatment with uncertain results (JAMA 1952;153:1501-4). In an era when all that we could offer hypertensive patients was a low-salt diet, the procedure became rather popular. However, the surgical risks, adverse side effects, and uncertainty of benefit led to both procedures being discontinued. Recently, there have been studies of the effect of stimulation of the carotid sinus nerve for the treatment of hypertension.
The potential benefit of modulation of the arterial sympathetic nerves, and particularly those located in the renal artery, became the focus of this recent interest. Nevertheless, a number of questions have arisen in regard to the mechanism of RASNA. And why does it take 6 months to achieve the blood pressure response? In addition, there is very little published data in regard to the changes in the renal artery and its adjacent tissue as a result of the RF ablation.
At the present, Medtronic has not provided any information beyond its indicating the lack of benefit. Further information will be reported at the upcoming American College of Cardiology scientific sessions. In the meantime, speculation is rampant as to whether the initial reports were purely placebo effects or if there is something intrinsically flawed in the SYMPLICITY HTN -3 trial.
Dr. Goldstein, medical editor of Cardiology News, is professor of medicine at Wayne State University and division head emeritus of cardiovascular medicine at Henry Ford Hospital, both in Detroit. He is on data safety monitoring committees for the National Institutes of Health and several pharmaceutical companies.
Hyperglycemia and CV Complications
Hyperglycemia in hospitalized patients is frequently observed and is recognized as an important threat to the health of patients with varying levels of illness, independent of diabetes status.[1, 2, 3, 4] Previous studies have found that in‐hospital hyperglycemia is associated with higher short‐term mortality rates,[5, 6, 7] longer lengths of stay, and higher rates of admission to intensive care units.[4]
In‐hospital hyperglycemia may be the result of diabetes mellitus or stress associated with hospitalization. The mechanism of stress hyperglycemia differs from diabetic hyperglycemia and can occur independently of diabetes status.[8] Stress hyperglycemia is characterized by rapid onset of insulin resistance, which normally takes months or years in diabetes mellitus. It develops in association with or because of other stressor such as infection or inflammatory processes.[9]
To our knowledge, no long‐term follow‐up study on hospitalized patients with elevated glucose levels but without a diabetes diagnosis has ever been conducted. In this study, we measured the 1‐ and 5‐year risk of mortality and diabetes‐related diseases for hospitalized patients with a diabetes diagnosis compared with patients grouped according to their peak in‐hospital serum glucose level. Our primary comparison was between patients with diagnosed diabetes and those with peak serum glucose >200 mg/dL (11.1 mmol/L), but we also examined 4 other categories of peak serum glucose: 140 to 200 mg/dL (7.811.1 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), and <108 mg/dL (<6.0 mmol/L).
METHODS
Study Base
The study included all adult patients (excluding patients admitted to psychiatry and obstetrics) admitted to The Ottawa Hospital (TOH) between January 1, 1996 and March 31, 2008 and discharged alive. Included patients were 18 years or older and had complete medical record abstracts. TOH is the primary hospital in Ottawa, Ontario, Canada, and the main tertiary hospital in the Champlain Local Health Integration Network, the public healthcare authority for the Ottawa region. This study was approved by The Ottawa Hospital Research Ethics Board.
Data Sources
The Ottawa Hospital Data Warehouse (OHDW) is a repository for data from the hospital's patient information systems. These systems include patient registration information, discharge abstract, and laboratory, pharmacy, and radiology results. OHDW was used to determine each patient's age, gender, diagnoses, pharmacy orders, laboratory test results, in‐hospital comorbidities, most responsible hospital service, and admission urgency.
This dataset was linked to 3 population‐based administrative datasets and 1 derived cohort. Ontario's Registered Persons Database (RPDB) is a population‐based registry containing date of death (if applicable) as well as eligibility status for the provincial universal health insurance program, the Ontario Health Insurance Plan (OHIP).[10] The OHIP database records billing claims submitted by approximately 95% of Ontario physicians. Each claim contains a fee code describing the type of service provided and a location denoting where the service had taken place. The Canadian Institute for Health Information Discharge Abstract Database (DAD) records clinical, demographic, and administrative data for all hospital admissions and same‐day surgeries for all Ontario acute care hospitals since April 1, 1988. The Ontario Myocardial Infarction Database (OMID) contains records of all patients with a most responsible diagnosis of acute myocardial infarction (AMI) (International Classification of Diseases, 9th Revision [ICD‐9] code 410 or International Classification of Diseases, 10th Revision [ICD‐10] code I21) identified from the DAD. Details on the creation of the OMID are provided in an earlier study.[10]
Variable Definitions
We identified the index admission of the TOH cohort in the DAD using a unique encrypted health insurance number, admission and discharge dates, and the institution number. To avoid double counting, patients with the same encrypted health insurance number who were discharged from an institution and admitted to another within 2 days were classified as transfers and counted as 1 hospitalization.
Diabetes and cardiovascular (CV) complications (acute myocardial infarction [AMI], congestive heart failure [CHF], cardiovascular disease [CVD], peripheral vascular disease [PVD], and end‐stage renal disease [ESRD]) were identified in the discharge abstract as the most responsible diagnosis for the admission as well as postadmission comorbidities. The discharge abstract records diagnostic codes according to ICD‐9 (April 1, 1988March 31, 2002) or ICD‐10 (April 1, 2002March 1, 2009).
Each hospitalization was classified into 1 of 6 mutually exclusive diabetes status categories based on diagnostic codes, serum glucose test results, and pharmacy records. The diagnosed diabetes group included hospitalizations with any diagnostic code of diabetes in the discharge abstract or any order for a diabetes medication during hospitalization. Eligible medications included acarbose, acetohexamide, chlorpropamide, glibenclamide, gliclazide, glimepiride, glipizide, glyburide, insulin, metformin, nateglinide, pioglitazone, repaglinide, rosiglitazone, and tolbutamide. A chart validation study showed excellent ascertainment of diabetes status using these methods (correct classification 88.8%; 95% confidence interval [CI]: 85.791.3 with weighted kappa=0.89; 95% CI: 0.85‐0.92).[11, 12] Patients without diagnosed diabetes were classified according to their peak serum glucose value during the index hospitalization: <108 mg/dL (<6.0 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), 140 to 200 mg/dL (7.811.1 mmol/L), >200 mg/dL (>11.1 mmol/L); hospitalizations in which glucose levels were not obtained were classified as unknown. These peak serum glucose categories are based on the World Health Organization's definition of diabetes and poor glucose tolerance.[12]
Outcomes
The primary outcome was all‐cause postdischarge mortality determined by linking the index admission to the RPDB. Secondary outcomes were CV complications, including: AMI (determined by linking to the OMID); hospitalization for CHF (determined by linking to the DAD for primary diagnosis of 425, 428, 514, 518.4 or I50, I42.0, I42.6, I42.7, I42.8, I42.9, I43, J81), CVD (determined by linking to the DAD for primary diagnosis of 430, 431, 432, 434, 436 or I60, I61, I62, I63, I64, G46), PVD (determined by primary diagnosis of 96.11, 96.12, 96.13, 96.14, 96.15 [excluded if in conjunction with 170, 171, 213, 730, 740759, 800900, 901904, 940950], 50.18, 51.25, 51.29 [excluded if in conjunction with 414.1, 441, 44] or 1VC93, 1VG93, 1VQ93, 1WA93, 1WE93, 1WJ93, 1WL93, 1WM93 [excluded if in conjunction with C40, C41, C46.1, C47, C49, D160, M46.2, M86, M87, M89.6, M90.0‐M90.5, Q00, Q38‐Q40, S02.0, S09.0, S04.0, S15, S25, T26), and 1KG50, 1KG57, 1KG76, 1KG35HAC1, 1KG35HHC1 [excluded if in conjunction with I60, I67.1, I71, I72, 177.0, 179.0]), and ESRD (determined by 403.9, 404.9,584, 585, 586, 788.5 or I12, I13, N17, N18, N19, R34).
Analysis
We identified all encounters of cohort patients in any Ontario acute‐care hospital within 5 years following the index discharge. Patients not covered by provincial health insurance (OHIP) were excluded.
We first measured crude mortality and morbidity rates by patient category (diagnosed diabetes and glucose levels). Next, we compared the unadjusted outcomes and baseline characteristics (age, sex, previous inpatient and emergency admissions, and disease at admission) between groups using a [2] test for categorical variables and analysis of variance for continuous variables.
Due to the violation of proportional hazards, we used the Weibull accelerated failure time model to calculate the hazard of death associated with diabetes status or serum glucose level. Consistency of the probability plots confirmed appropriateness of using the Weibull function. Competing risk is defined as a type of failure that prevents the observation of the event of interest or fundamentally alters the probability of its occurrence.[13, 14] In the comorbidity analyses, death is a competing risk for all other outcomes. We calculated the multivariate competing risk hazard for each outcome of interest except death. Each model was adjusted for potential confounders: baseline risk of in‐hospital mortality, common comorbidities, most responsible hospital service, and the number of previous inpatient admissions and emergency department visits to TOH in the previous 6 months. The probability of dying during the admission was calculated using the Escobar model, which predicts the in‐hospital probability of dying using data available at the time of admission to the hospital.[15] The Escobar model has been validated in the study population.[16] The baseline probability of dying was based on age, sex, acuity of admission, primary condition, Charlson comorbidity score,[17] and the laboratory‐based acute physiology score.[15] The Charlson comorbidity score was calculated using weights from Schneeweiss et al.[17] Kaplan‐Meier survival curves were created for both adjusted and unadjusted models. Death was used as the main censoring variable when analyzing nonfatal outcomes at 1‐ and 5‐year follow‐up.
Adjusted models were constructed using a stepwise selection technique and compared with Akaike and Bayesian information criteria values. For all tests, significance was defined as a P value of 0.05 or less.
We recognize that the cohort may contain more than 1 index hospitalization per patient. However, repeated analyses using only the first or last encounter per patient produced nearly identical hazard ratios (HRs) and CIs.
RESULTS
Between January 1, 1996 and March 31, 2008, 194,641 nonpsychiatric and nonobstetric adults were admitted to TOH. Seventeen patients were excluded because they were ineligible for healthcare coverage and had no encounter with the Ontario healthcare system following discharge from hospital, and 11,175 of the admissions ended in death. The final cohort consisted of 114,764 unique individuals representing 183,449 encounters.
Patients had a mean age of 59.5 years (standard deviation: 18.0) and 48.9% were male. The baseline risk of dying during hospitalization was 4.8%.
Table 1 describes patients by diabetes and peak serum glucose status. Patients with diagnosed diabetes were more likely to be older and male. Patients with elevated peak serum glucose (>200 mg/dL; >11.1 mmol/L) were younger than the diagnosed diabetes group and had a higher baseline probability of in‐hospital death (9.4%, 95% CI: 9.09.7). Patients in these 2 groups had more inpatient admissions within the previous 6 months compared to the groups with other peak serum glucose values.
Serum Glucose Level, mg/dL | ||||||
---|---|---|---|---|---|---|
Diagnosed Diabetes, n=32,774, 17.9% | >200, n=5,082, 2.8% | 140200, n=25,857, 14.1% | 108140, n=38,741, 21.1% | <108, n=27,603, 15.0% | Unknown, n=53,392, 29.1% | |
| ||||||
Age, y, mean (SD) | 65.8 (14.1) | 64.3 (17.0)a | 63.9 (17.3)a | 60.9 (18.6)a | 54.3 (19.7)a | 54.8 (16.9)a |
Risk‐adjusted mortality at admission, mean (SD) | 0.08 (0.12) | 0.09 (0.12)a | 0.07 (0.10)a | 0.05 (0.09)a | 0.04 (0.07)a | 0.01 (0.04)a |
Sex, male, no. (%) | 18,200 (55.5) | 2,610 (51.4)a | 13,477 (52.1)a | 19,495 (50.3)a | 12,907 (46.8)a | 22,951 (43.0)a |
No. of previous inpatient admissions, 6 months (%) | ||||||
0 | 22,780 (69.5) | 3,576 (70.4) | 19,118 (73.9)a | 28,827 (74.4)a | 20,354 (73.7)a | 43,317 (81.1)a |
1 | 6,470 (19.7) | 962 (18.9) | 4,484 (17.3)a | 6,526 (16.9)a | 4,744 (17.2)a | 7,360 (13.8)a |
2+ | 3,524 (10.8) | 544 (10.7) | 2,255 (8.7)a | 3,388 (9.1)a | 2,505 (9.1)a | 2,715 (5.1)a |
No. of previous emergency admissions (6 months) (%) | ||||||
0 | 15,518 (47.4) | 2,638 (51.9)a | 12,681 (49.0)a | 16,584 (42.8)a | 13,039 (47.2) | 43,112 (80.8)a |
1 | 9,665 (29.5) | 1,490 (29.3)a | 8,339 (32.3)a | 13,829 (35.7)a | 8,709 (31.6) | 6,533 (12.2)a |
2+ | 7,591 (23.2) | 954 (18.8)a | 4,837 (18.7)a | 8,328 (21.5)a | 5,855 (21.2) | 3,747 (7.0)a |
Emergency admission at index hospitalization (%) | 25,420 (77.6) | 4,178 (82.2)a | 20,284 (78.5)a | 33,282 (85.9)a | 23,598 (85.5)a | 15,039 (28.2)a |
Length of stay, d (mean/SD) | 12.4 (22.3) | 15.8 (30.0)a | 11.9 (17.1)a | 8.5 (12.2)a | 6.3 (9.5)a | 3.4 (4.9)a |
Disease at index hospitalization (%) | ||||||
PVD | 2,783 (8.5) | 257 (5.1)a | 1,387 (5.4)a | 1,372 (3.5)a | 908 (3.3)a | 965 (1.8)a |
Pneumonia | 3,308 (10.1) | 658 (13.0)a | 2,577 (10.0) | 2,809 (7.3)a | 1,220 (4.4)a | 399 (0.8)a |
UTI | 3,549 (10.8) | 665 (13.1)a | 2,660 (10.3)a | 3,336 (8.6)a | 1,559 (5.7)a | 929 (1.7)a |
IHD | 8,957 (27.3) | 1,086 (21.4)a | 4,714 (18.2)a | 5,306 (13.7)a | 3,100 (11.2)a | 2,842 (5.3)a |
Hypertension | 10,864 (33.2) | 1,115 (21.9)a | 5,155 (19.9)a | 5,880 (15.2)a | 3,045 (11.0)a | 3,211 (6.0)a |
Arrhythmia | 5,010 (15.3) | 823 (16.2) | 3,768 (14.6)a | 3,718 (9.6)a | 2,046 (7.4)a | 1,172 (2.2)a |
CHF | 570 (17.5) | 763 (15.0)a | 2,761 (10.7)a | 2,449 (6.3)a | 1,121 (4.1)a | 386 (0.7)a |
Diagnoses of diabetes in the next year (%) | 126 (2.5) | 368 (1.4) | 370 (1.0) | 215 (0.8) | 540 (1.0) | |
Diagnoses of diabetes in the next 5 years (%) | 310 (6.1) | 981 (3.8) | 916 (2.4) | 434 (1.7) | 1,317 (2.5) | |
First 3 most common primary discharge codes | AHD I251 (5.7%), HF I50 (3.1%), NSTEMI I214 (1.6%) | AHD I251 (2.7%), COPD J441 (2.1%), HF I50 (2.0%) | AHD I251 (4.0%), Pneumonia J189 (1.6%), HF I50 (1.5%) | AHD I251 (1.6%), Pneumonia J189 (1.5%), CA D700 (1.3%) | AHD I251 (1.8%), AA K359 (1.7%), UA I200 (1.2%) | OA M171 (2.3%), AHD I251 (2.2%), OA M170 (1.7%) |
Of the 5082 patients classified with a peak serum glucose measurement >200 mg/dL (11.1 mmol/L), 15% had 2 and 8% had more than 2 serum glucose measurements that exceeded this threshold. For the remaining patients (with 1 peak serum glucose measurement >200 mg/dL), 52% had additional serum glucose measurements over 140 mg/dL (7.8 mmol/L).
Table 2 presents crude 1‐ and 5‐year mortality and morbidity rates by patient group. The mortality rate is the percentage of patients who died within 1 year and 5 years of their index admission, with or without developing a CV complication. Morbidity rate describes the percentage who developed the complication among patients who survived after discharge or developed the complication prior to death within a 1‐year and 5‐year period. During the 1‐year follow‐up period, the crude mortality rate among patients with elevated peak serum glucose was higher than all other groups (25.4% vs 20.6% for diagnosed diabetes and 7.4% to 22.5% for other peak serum glucose levels; all P<0.0001). For the 5‐year follow‐up, the gap between patients with elevated peak serum glucose and those with diagnosed diabetes lessened but remained significant (45.1% vs 41.7%; P<0.0001). In the 1‐year follow‐up, patients with diagnosed diabetes had significantly higher morbidity rates than all other groups except for AMI. The difference in the rate of AMI for diagnosed diabetes and patients with elevated glucose was not statistically significant.
Diagnosed Diabetes, n=32,774 | Serum Glucose Level (mg/dL) | |||||
---|---|---|---|---|---|---|
>200, n=5,082 | 140200, n=25,857 | 108140, n=38,741 | <108, n=27,603 | Unknown, n=53,392 | ||
| ||||||
All‐cause death | ||||||
1 year | 6,762 (20.6) | 1,293 (25.4)a | 5,309 (20.5) | 7,212 (18.6)a | 4,178 (15)a | 3,969 (7.4)a |
5 years | 13,659 (41.7) | 2,292 (45.1)a | 9,871 (38.2)a | 13,256 (34.2)a | 7,606 (27.6)a | 98,614 (16.1)a |
AMI | ||||||
1 year | 728 (2.8) | 104 (2.7) | 326 (1.6)a | 386 (1.2)a | 172 (0.7)a | 218 (0.4)a |
5 years | 1,687 (8.4) | 182 (6.3)a | 687 (4.2)a | 837 (3.2)a | 436 (2.2)a | 633 (1.4)a |
CVD | ||||||
1 year | 582 (2.2) | 71 (1.9) | 306 (1.5)a | 403 (1.3)a | 251 (1.1)a | 195 (0.4)a |
5 years | 1,153 (5.8) | 143 (5.0) | 623 (3.8)a | 872 (3.4)a | 470 (2.3)a | 534 (1.2)a |
CHF | ||||||
1 year | 2,260 (8.4) | 245 (6.3)a | 870 (4.2)a | 1,023 (3.2)a | 516 (2.2)a | 362 (0.7)a |
5 years | 3,830 (17.7) | 395 (12.9)a | 1,529 (9.0)a | 1,802 (6.8)a | 960 (4.7)a | 884 (2.0)a |
PVD | ||||||
1 year | 795 (3.0) | 24 (0.6)a | 114 (0.6)a | 154 (0.5)a | 97 (0.4)a | 141 (0.3)a |
5 years | 1,547 (7.8) | 50 (1.8)a | 220 (1.4)a | 275 (1.1)a | 192 (1.0)a | 295 (0.7)a |
ESRD | ||||||
1 year | 953 (3.6) | 104 (2.7)a | 403 (1.9)a | 457 (1.4)a | 311 (1.3)a | 329 (0.7)a |
5 years | 1,938 (9.5) | 183 (6.3)a | 807 (4.9)a | 995 (3.8)a | 611 (3.0)a | 771 (1.7)a |
The 1‐year adjusted hazard ratios for mortality and CV complications are presented in Figure 1. After adjustment for baseline demographics and clinical and hospital factors, having a peak serum glucose level above 200 mg/dL (11.1 mmol/L) was an independent predictor of death. The mortality risk for this group in the year following discharge was 31% higher than patients with diagnosed diabetes (adjusted HR: 1.31, 95% CI: 1.20‐1.43). There was no mortality risk difference between the group with diagnosed diabetes and the lower peak serum glucose levels (adjusted HR: 1.05, 95% CI: 0.99‐1.11), 108160 mg/dL (6.07.8 mmol/L) (adjusted HR: 1.02, 95% CI: 0.971.07); and <108 mg/dL (<6.0 mmol/L) (adjusted HR: 0.97, 95% CI: 0.921.03). Adjusted HRs, with 95% CIs and P values for 5‐year follow‐up are presented in Figure 2.


Adjusted hazard ratios for morbidity showed a different pattern. Patients with diagnosed diabetes had a higher risk of developing complications in the year following discharge from the hospital (Figure 1). After adjusting for potential confounders, we found that patients with diagnosed diabetes had the same risk of AMI and CVD as patients with peak serum glucose levels above 200 mg/dL (11.1 mmol/L) (AMI, adjusted HR: 0.96; 95% CI: 0.78‐1.18; CVD, adjusted HR: 0.79; 95% CI: 0.61‐1.00) but a 36% to 57% higher AMI risk and 29% to 34% higher CVD risk than other peak serum glucose level groups. Compared to all peak serum glucose groups, patients with diagnosed diabetes had a significantly higher risk of developing CHF, PVD, and ESRD (Figure 1). Similarly, the 5‐year risk of AMI, CVD, CHF, PVD, and ESRD was higher for patients with diagnosed diabetes compared to all peak serum glucose groups (Figure 2).
CONCLUSION/DISCUSSION
The study found that in‐hospital hyperglycemia was a strong predictor of mortality at 1‐ and 5‐years follow‐up, even after adjustment using well‐established and discriminating comorbidity measures. Extreme in‐hospital hyperglycemia was a stronger predictor of mortality than diagnosed diabetes.
Previous studies have shown that diabetes and stress hyperglycemia among hospitalized patients are important markers for poor clinical outcomes and in‐hospital mortality.[1, 2, 4] This study indicates that hospitalized patients with extreme hyperglycemia (peak serum glucose >200 mg/dL) were at a high risk of death for at least 5 years following discharge.
These findings, based on a large general sample of hospitalized patients, indicate that the extreme elevations in peak serum glucose convey a risk for all hospitalized patients, not only those with critical illness.[3, 18] Hyperglycemia appears to be an independent indicator of mortality risk and should be evaluated as a potential component within risk prediction tools. Further study is required to determine mechanisms for this risk association to identify what therapies, if any, might be used to minimize this risk.
The diagnosed diabetes group, which likely included some patients who also had extreme in‐hospital hyperglycemia, had a lower 1‐year risk of death than patients with hyperglycemia who did not have diabetes diagnosis. This may be an indication of the protective effect of blood glucose control, because patients with diabetes are more likely to receive therapy for hyperglycemia during and after hospitalization.
Classification of patients by several serum glucose levels (as opposed to a dichotomous classification, where hyperglycemia is either present or absent), showed that hyperglycemia constitutes a graded risk[5] for almost all outcomes examined, particularly mortality, AMI, and CVD.
Previous studies indicate that diabetes is observed in only 23% to 35% of hospitalized patients with hyperglycemia.[18, 19] We would expect higher risk for CV complications for patients with elevated glucose if the proportion of these patients who had undiagnosed diabetes was higher than the proportion estimated in the literature. However, we observed lower risk of CV complications (especially PVD and ESRD) for the elevated glucose group in the 1 and 5 years following discharge. In‐hospital hyperglycemia is not equivalent to undiagnosed diabetes.
There are several potential limitations in this study. The first is our method for ascertaining CV complications. Diverse disease definitions are used in medical literature, and similar studies using different definitions may yield different results, though we would not expect to find a wide range of variation. Second, our study did not include information about severity of diabetes and the persistence of elevated glucose; if available, this knowledge may provide better insight into patient experiences, especially in long‐term follow‐up. Third, results are also limited by the absence of data on cause of death, a potentially helpful means of identifying posthospitalization difficulties experienced by patients with hyperglycemia. Fourth, this study mainly compares experiences of patients with elevated peak serum glucose level to diabetes patients; it would be worthwhile to explore the impact of lower gradations of glucose levels.
We would like to emphasize that we did not confirm diabetes diagnosis following discharge for patients with hyperglycemia. However, we did not observe a high rate of complications at 5‐year follow‐up, particularly for ESRD. This may be because most patients with in‐hospital elevated glucose had early diabetes or transient hyperglycemia and therefore lower risk of long‐term diabetes‐specific consultations.
Hyperglycemia is an important independent indicator, carrying a greater risk for 1‐ and 5‐year mortality than diagnosed diabetes. However, it is unclear whether hospitalized patients with elevated peak serum glucose have early diabetes or their hyperglycemia reflects hospital stress or another comorbidity concept.
Acknowledgements
The authors are grateful to Amy Zierler and Allison Whalen O'Connor for their editorial assistance. This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long‐Term Care (MOHLTC). The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred.
Disclosure: Nothing to report.
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- American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Diabetes Care. 2009;32(6):1119–1131. , , , et al.
- The NICE‐SUGAR Study Investigators. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. 2009;360(13):1283–1297.
- Hyperglycemia: an independent marker of in‐hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol Metab. 2002;87(3):978–982. , , , , , .
- Hyperglycemia‐related mortality in critically ill patients varies with admission diagnosis. Crit Care Med. 2009;37(12):3001–3009. , , , , .
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- Does Stress‐induced hyperglycemia increase the risk of perioperative infectious complications in orthopaedic trauma patients? J Orthop Trauma. 2010;24(12):752–756. , .
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- Temporal changes in the outcomes of acute myocardial infarction in Ontario, 1992–1996. CMAJ. 1999;161(10):1257–1261. , , .
- The accuracy of using integrated electronic health care data to identify patients with undiagnosed diabetes mellitus. J Eval Clin Pract. 2012;18(3):606–611. , , , et al.
- American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2006;29(suppl 1):s43–s48.
- Dealing with competing risks: testing covariates and calculating sample size. Stat Med. 2002;21(22):3317–3324. .
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Hyperglycemia in hospitalized patients is frequently observed and is recognized as an important threat to the health of patients with varying levels of illness, independent of diabetes status.[1, 2, 3, 4] Previous studies have found that in‐hospital hyperglycemia is associated with higher short‐term mortality rates,[5, 6, 7] longer lengths of stay, and higher rates of admission to intensive care units.[4]
In‐hospital hyperglycemia may be the result of diabetes mellitus or stress associated with hospitalization. The mechanism of stress hyperglycemia differs from diabetic hyperglycemia and can occur independently of diabetes status.[8] Stress hyperglycemia is characterized by rapid onset of insulin resistance, which normally takes months or years in diabetes mellitus. It develops in association with or because of other stressor such as infection or inflammatory processes.[9]
To our knowledge, no long‐term follow‐up study on hospitalized patients with elevated glucose levels but without a diabetes diagnosis has ever been conducted. In this study, we measured the 1‐ and 5‐year risk of mortality and diabetes‐related diseases for hospitalized patients with a diabetes diagnosis compared with patients grouped according to their peak in‐hospital serum glucose level. Our primary comparison was between patients with diagnosed diabetes and those with peak serum glucose >200 mg/dL (11.1 mmol/L), but we also examined 4 other categories of peak serum glucose: 140 to 200 mg/dL (7.811.1 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), and <108 mg/dL (<6.0 mmol/L).
METHODS
Study Base
The study included all adult patients (excluding patients admitted to psychiatry and obstetrics) admitted to The Ottawa Hospital (TOH) between January 1, 1996 and March 31, 2008 and discharged alive. Included patients were 18 years or older and had complete medical record abstracts. TOH is the primary hospital in Ottawa, Ontario, Canada, and the main tertiary hospital in the Champlain Local Health Integration Network, the public healthcare authority for the Ottawa region. This study was approved by The Ottawa Hospital Research Ethics Board.
Data Sources
The Ottawa Hospital Data Warehouse (OHDW) is a repository for data from the hospital's patient information systems. These systems include patient registration information, discharge abstract, and laboratory, pharmacy, and radiology results. OHDW was used to determine each patient's age, gender, diagnoses, pharmacy orders, laboratory test results, in‐hospital comorbidities, most responsible hospital service, and admission urgency.
This dataset was linked to 3 population‐based administrative datasets and 1 derived cohort. Ontario's Registered Persons Database (RPDB) is a population‐based registry containing date of death (if applicable) as well as eligibility status for the provincial universal health insurance program, the Ontario Health Insurance Plan (OHIP).[10] The OHIP database records billing claims submitted by approximately 95% of Ontario physicians. Each claim contains a fee code describing the type of service provided and a location denoting where the service had taken place. The Canadian Institute for Health Information Discharge Abstract Database (DAD) records clinical, demographic, and administrative data for all hospital admissions and same‐day surgeries for all Ontario acute care hospitals since April 1, 1988. The Ontario Myocardial Infarction Database (OMID) contains records of all patients with a most responsible diagnosis of acute myocardial infarction (AMI) (International Classification of Diseases, 9th Revision [ICD‐9] code 410 or International Classification of Diseases, 10th Revision [ICD‐10] code I21) identified from the DAD. Details on the creation of the OMID are provided in an earlier study.[10]
Variable Definitions
We identified the index admission of the TOH cohort in the DAD using a unique encrypted health insurance number, admission and discharge dates, and the institution number. To avoid double counting, patients with the same encrypted health insurance number who were discharged from an institution and admitted to another within 2 days were classified as transfers and counted as 1 hospitalization.
Diabetes and cardiovascular (CV) complications (acute myocardial infarction [AMI], congestive heart failure [CHF], cardiovascular disease [CVD], peripheral vascular disease [PVD], and end‐stage renal disease [ESRD]) were identified in the discharge abstract as the most responsible diagnosis for the admission as well as postadmission comorbidities. The discharge abstract records diagnostic codes according to ICD‐9 (April 1, 1988March 31, 2002) or ICD‐10 (April 1, 2002March 1, 2009).
Each hospitalization was classified into 1 of 6 mutually exclusive diabetes status categories based on diagnostic codes, serum glucose test results, and pharmacy records. The diagnosed diabetes group included hospitalizations with any diagnostic code of diabetes in the discharge abstract or any order for a diabetes medication during hospitalization. Eligible medications included acarbose, acetohexamide, chlorpropamide, glibenclamide, gliclazide, glimepiride, glipizide, glyburide, insulin, metformin, nateglinide, pioglitazone, repaglinide, rosiglitazone, and tolbutamide. A chart validation study showed excellent ascertainment of diabetes status using these methods (correct classification 88.8%; 95% confidence interval [CI]: 85.791.3 with weighted kappa=0.89; 95% CI: 0.85‐0.92).[11, 12] Patients without diagnosed diabetes were classified according to their peak serum glucose value during the index hospitalization: <108 mg/dL (<6.0 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), 140 to 200 mg/dL (7.811.1 mmol/L), >200 mg/dL (>11.1 mmol/L); hospitalizations in which glucose levels were not obtained were classified as unknown. These peak serum glucose categories are based on the World Health Organization's definition of diabetes and poor glucose tolerance.[12]
Outcomes
The primary outcome was all‐cause postdischarge mortality determined by linking the index admission to the RPDB. Secondary outcomes were CV complications, including: AMI (determined by linking to the OMID); hospitalization for CHF (determined by linking to the DAD for primary diagnosis of 425, 428, 514, 518.4 or I50, I42.0, I42.6, I42.7, I42.8, I42.9, I43, J81), CVD (determined by linking to the DAD for primary diagnosis of 430, 431, 432, 434, 436 or I60, I61, I62, I63, I64, G46), PVD (determined by primary diagnosis of 96.11, 96.12, 96.13, 96.14, 96.15 [excluded if in conjunction with 170, 171, 213, 730, 740759, 800900, 901904, 940950], 50.18, 51.25, 51.29 [excluded if in conjunction with 414.1, 441, 44] or 1VC93, 1VG93, 1VQ93, 1WA93, 1WE93, 1WJ93, 1WL93, 1WM93 [excluded if in conjunction with C40, C41, C46.1, C47, C49, D160, M46.2, M86, M87, M89.6, M90.0‐M90.5, Q00, Q38‐Q40, S02.0, S09.0, S04.0, S15, S25, T26), and 1KG50, 1KG57, 1KG76, 1KG35HAC1, 1KG35HHC1 [excluded if in conjunction with I60, I67.1, I71, I72, 177.0, 179.0]), and ESRD (determined by 403.9, 404.9,584, 585, 586, 788.5 or I12, I13, N17, N18, N19, R34).
Analysis
We identified all encounters of cohort patients in any Ontario acute‐care hospital within 5 years following the index discharge. Patients not covered by provincial health insurance (OHIP) were excluded.
We first measured crude mortality and morbidity rates by patient category (diagnosed diabetes and glucose levels). Next, we compared the unadjusted outcomes and baseline characteristics (age, sex, previous inpatient and emergency admissions, and disease at admission) between groups using a [2] test for categorical variables and analysis of variance for continuous variables.
Due to the violation of proportional hazards, we used the Weibull accelerated failure time model to calculate the hazard of death associated with diabetes status or serum glucose level. Consistency of the probability plots confirmed appropriateness of using the Weibull function. Competing risk is defined as a type of failure that prevents the observation of the event of interest or fundamentally alters the probability of its occurrence.[13, 14] In the comorbidity analyses, death is a competing risk for all other outcomes. We calculated the multivariate competing risk hazard for each outcome of interest except death. Each model was adjusted for potential confounders: baseline risk of in‐hospital mortality, common comorbidities, most responsible hospital service, and the number of previous inpatient admissions and emergency department visits to TOH in the previous 6 months. The probability of dying during the admission was calculated using the Escobar model, which predicts the in‐hospital probability of dying using data available at the time of admission to the hospital.[15] The Escobar model has been validated in the study population.[16] The baseline probability of dying was based on age, sex, acuity of admission, primary condition, Charlson comorbidity score,[17] and the laboratory‐based acute physiology score.[15] The Charlson comorbidity score was calculated using weights from Schneeweiss et al.[17] Kaplan‐Meier survival curves were created for both adjusted and unadjusted models. Death was used as the main censoring variable when analyzing nonfatal outcomes at 1‐ and 5‐year follow‐up.
Adjusted models were constructed using a stepwise selection technique and compared with Akaike and Bayesian information criteria values. For all tests, significance was defined as a P value of 0.05 or less.
We recognize that the cohort may contain more than 1 index hospitalization per patient. However, repeated analyses using only the first or last encounter per patient produced nearly identical hazard ratios (HRs) and CIs.
RESULTS
Between January 1, 1996 and March 31, 2008, 194,641 nonpsychiatric and nonobstetric adults were admitted to TOH. Seventeen patients were excluded because they were ineligible for healthcare coverage and had no encounter with the Ontario healthcare system following discharge from hospital, and 11,175 of the admissions ended in death. The final cohort consisted of 114,764 unique individuals representing 183,449 encounters.
Patients had a mean age of 59.5 years (standard deviation: 18.0) and 48.9% were male. The baseline risk of dying during hospitalization was 4.8%.
Table 1 describes patients by diabetes and peak serum glucose status. Patients with diagnosed diabetes were more likely to be older and male. Patients with elevated peak serum glucose (>200 mg/dL; >11.1 mmol/L) were younger than the diagnosed diabetes group and had a higher baseline probability of in‐hospital death (9.4%, 95% CI: 9.09.7). Patients in these 2 groups had more inpatient admissions within the previous 6 months compared to the groups with other peak serum glucose values.
Serum Glucose Level, mg/dL | ||||||
---|---|---|---|---|---|---|
Diagnosed Diabetes, n=32,774, 17.9% | >200, n=5,082, 2.8% | 140200, n=25,857, 14.1% | 108140, n=38,741, 21.1% | <108, n=27,603, 15.0% | Unknown, n=53,392, 29.1% | |
| ||||||
Age, y, mean (SD) | 65.8 (14.1) | 64.3 (17.0)a | 63.9 (17.3)a | 60.9 (18.6)a | 54.3 (19.7)a | 54.8 (16.9)a |
Risk‐adjusted mortality at admission, mean (SD) | 0.08 (0.12) | 0.09 (0.12)a | 0.07 (0.10)a | 0.05 (0.09)a | 0.04 (0.07)a | 0.01 (0.04)a |
Sex, male, no. (%) | 18,200 (55.5) | 2,610 (51.4)a | 13,477 (52.1)a | 19,495 (50.3)a | 12,907 (46.8)a | 22,951 (43.0)a |
No. of previous inpatient admissions, 6 months (%) | ||||||
0 | 22,780 (69.5) | 3,576 (70.4) | 19,118 (73.9)a | 28,827 (74.4)a | 20,354 (73.7)a | 43,317 (81.1)a |
1 | 6,470 (19.7) | 962 (18.9) | 4,484 (17.3)a | 6,526 (16.9)a | 4,744 (17.2)a | 7,360 (13.8)a |
2+ | 3,524 (10.8) | 544 (10.7) | 2,255 (8.7)a | 3,388 (9.1)a | 2,505 (9.1)a | 2,715 (5.1)a |
No. of previous emergency admissions (6 months) (%) | ||||||
0 | 15,518 (47.4) | 2,638 (51.9)a | 12,681 (49.0)a | 16,584 (42.8)a | 13,039 (47.2) | 43,112 (80.8)a |
1 | 9,665 (29.5) | 1,490 (29.3)a | 8,339 (32.3)a | 13,829 (35.7)a | 8,709 (31.6) | 6,533 (12.2)a |
2+ | 7,591 (23.2) | 954 (18.8)a | 4,837 (18.7)a | 8,328 (21.5)a | 5,855 (21.2) | 3,747 (7.0)a |
Emergency admission at index hospitalization (%) | 25,420 (77.6) | 4,178 (82.2)a | 20,284 (78.5)a | 33,282 (85.9)a | 23,598 (85.5)a | 15,039 (28.2)a |
Length of stay, d (mean/SD) | 12.4 (22.3) | 15.8 (30.0)a | 11.9 (17.1)a | 8.5 (12.2)a | 6.3 (9.5)a | 3.4 (4.9)a |
Disease at index hospitalization (%) | ||||||
PVD | 2,783 (8.5) | 257 (5.1)a | 1,387 (5.4)a | 1,372 (3.5)a | 908 (3.3)a | 965 (1.8)a |
Pneumonia | 3,308 (10.1) | 658 (13.0)a | 2,577 (10.0) | 2,809 (7.3)a | 1,220 (4.4)a | 399 (0.8)a |
UTI | 3,549 (10.8) | 665 (13.1)a | 2,660 (10.3)a | 3,336 (8.6)a | 1,559 (5.7)a | 929 (1.7)a |
IHD | 8,957 (27.3) | 1,086 (21.4)a | 4,714 (18.2)a | 5,306 (13.7)a | 3,100 (11.2)a | 2,842 (5.3)a |
Hypertension | 10,864 (33.2) | 1,115 (21.9)a | 5,155 (19.9)a | 5,880 (15.2)a | 3,045 (11.0)a | 3,211 (6.0)a |
Arrhythmia | 5,010 (15.3) | 823 (16.2) | 3,768 (14.6)a | 3,718 (9.6)a | 2,046 (7.4)a | 1,172 (2.2)a |
CHF | 570 (17.5) | 763 (15.0)a | 2,761 (10.7)a | 2,449 (6.3)a | 1,121 (4.1)a | 386 (0.7)a |
Diagnoses of diabetes in the next year (%) | 126 (2.5) | 368 (1.4) | 370 (1.0) | 215 (0.8) | 540 (1.0) | |
Diagnoses of diabetes in the next 5 years (%) | 310 (6.1) | 981 (3.8) | 916 (2.4) | 434 (1.7) | 1,317 (2.5) | |
First 3 most common primary discharge codes | AHD I251 (5.7%), HF I50 (3.1%), NSTEMI I214 (1.6%) | AHD I251 (2.7%), COPD J441 (2.1%), HF I50 (2.0%) | AHD I251 (4.0%), Pneumonia J189 (1.6%), HF I50 (1.5%) | AHD I251 (1.6%), Pneumonia J189 (1.5%), CA D700 (1.3%) | AHD I251 (1.8%), AA K359 (1.7%), UA I200 (1.2%) | OA M171 (2.3%), AHD I251 (2.2%), OA M170 (1.7%) |
Of the 5082 patients classified with a peak serum glucose measurement >200 mg/dL (11.1 mmol/L), 15% had 2 and 8% had more than 2 serum glucose measurements that exceeded this threshold. For the remaining patients (with 1 peak serum glucose measurement >200 mg/dL), 52% had additional serum glucose measurements over 140 mg/dL (7.8 mmol/L).
Table 2 presents crude 1‐ and 5‐year mortality and morbidity rates by patient group. The mortality rate is the percentage of patients who died within 1 year and 5 years of their index admission, with or without developing a CV complication. Morbidity rate describes the percentage who developed the complication among patients who survived after discharge or developed the complication prior to death within a 1‐year and 5‐year period. During the 1‐year follow‐up period, the crude mortality rate among patients with elevated peak serum glucose was higher than all other groups (25.4% vs 20.6% for diagnosed diabetes and 7.4% to 22.5% for other peak serum glucose levels; all P<0.0001). For the 5‐year follow‐up, the gap between patients with elevated peak serum glucose and those with diagnosed diabetes lessened but remained significant (45.1% vs 41.7%; P<0.0001). In the 1‐year follow‐up, patients with diagnosed diabetes had significantly higher morbidity rates than all other groups except for AMI. The difference in the rate of AMI for diagnosed diabetes and patients with elevated glucose was not statistically significant.
Diagnosed Diabetes, n=32,774 | Serum Glucose Level (mg/dL) | |||||
---|---|---|---|---|---|---|
>200, n=5,082 | 140200, n=25,857 | 108140, n=38,741 | <108, n=27,603 | Unknown, n=53,392 | ||
| ||||||
All‐cause death | ||||||
1 year | 6,762 (20.6) | 1,293 (25.4)a | 5,309 (20.5) | 7,212 (18.6)a | 4,178 (15)a | 3,969 (7.4)a |
5 years | 13,659 (41.7) | 2,292 (45.1)a | 9,871 (38.2)a | 13,256 (34.2)a | 7,606 (27.6)a | 98,614 (16.1)a |
AMI | ||||||
1 year | 728 (2.8) | 104 (2.7) | 326 (1.6)a | 386 (1.2)a | 172 (0.7)a | 218 (0.4)a |
5 years | 1,687 (8.4) | 182 (6.3)a | 687 (4.2)a | 837 (3.2)a | 436 (2.2)a | 633 (1.4)a |
CVD | ||||||
1 year | 582 (2.2) | 71 (1.9) | 306 (1.5)a | 403 (1.3)a | 251 (1.1)a | 195 (0.4)a |
5 years | 1,153 (5.8) | 143 (5.0) | 623 (3.8)a | 872 (3.4)a | 470 (2.3)a | 534 (1.2)a |
CHF | ||||||
1 year | 2,260 (8.4) | 245 (6.3)a | 870 (4.2)a | 1,023 (3.2)a | 516 (2.2)a | 362 (0.7)a |
5 years | 3,830 (17.7) | 395 (12.9)a | 1,529 (9.0)a | 1,802 (6.8)a | 960 (4.7)a | 884 (2.0)a |
PVD | ||||||
1 year | 795 (3.0) | 24 (0.6)a | 114 (0.6)a | 154 (0.5)a | 97 (0.4)a | 141 (0.3)a |
5 years | 1,547 (7.8) | 50 (1.8)a | 220 (1.4)a | 275 (1.1)a | 192 (1.0)a | 295 (0.7)a |
ESRD | ||||||
1 year | 953 (3.6) | 104 (2.7)a | 403 (1.9)a | 457 (1.4)a | 311 (1.3)a | 329 (0.7)a |
5 years | 1,938 (9.5) | 183 (6.3)a | 807 (4.9)a | 995 (3.8)a | 611 (3.0)a | 771 (1.7)a |
The 1‐year adjusted hazard ratios for mortality and CV complications are presented in Figure 1. After adjustment for baseline demographics and clinical and hospital factors, having a peak serum glucose level above 200 mg/dL (11.1 mmol/L) was an independent predictor of death. The mortality risk for this group in the year following discharge was 31% higher than patients with diagnosed diabetes (adjusted HR: 1.31, 95% CI: 1.20‐1.43). There was no mortality risk difference between the group with diagnosed diabetes and the lower peak serum glucose levels (adjusted HR: 1.05, 95% CI: 0.99‐1.11), 108160 mg/dL (6.07.8 mmol/L) (adjusted HR: 1.02, 95% CI: 0.971.07); and <108 mg/dL (<6.0 mmol/L) (adjusted HR: 0.97, 95% CI: 0.921.03). Adjusted HRs, with 95% CIs and P values for 5‐year follow‐up are presented in Figure 2.


Adjusted hazard ratios for morbidity showed a different pattern. Patients with diagnosed diabetes had a higher risk of developing complications in the year following discharge from the hospital (Figure 1). After adjusting for potential confounders, we found that patients with diagnosed diabetes had the same risk of AMI and CVD as patients with peak serum glucose levels above 200 mg/dL (11.1 mmol/L) (AMI, adjusted HR: 0.96; 95% CI: 0.78‐1.18; CVD, adjusted HR: 0.79; 95% CI: 0.61‐1.00) but a 36% to 57% higher AMI risk and 29% to 34% higher CVD risk than other peak serum glucose level groups. Compared to all peak serum glucose groups, patients with diagnosed diabetes had a significantly higher risk of developing CHF, PVD, and ESRD (Figure 1). Similarly, the 5‐year risk of AMI, CVD, CHF, PVD, and ESRD was higher for patients with diagnosed diabetes compared to all peak serum glucose groups (Figure 2).
CONCLUSION/DISCUSSION
The study found that in‐hospital hyperglycemia was a strong predictor of mortality at 1‐ and 5‐years follow‐up, even after adjustment using well‐established and discriminating comorbidity measures. Extreme in‐hospital hyperglycemia was a stronger predictor of mortality than diagnosed diabetes.
Previous studies have shown that diabetes and stress hyperglycemia among hospitalized patients are important markers for poor clinical outcomes and in‐hospital mortality.[1, 2, 4] This study indicates that hospitalized patients with extreme hyperglycemia (peak serum glucose >200 mg/dL) were at a high risk of death for at least 5 years following discharge.
These findings, based on a large general sample of hospitalized patients, indicate that the extreme elevations in peak serum glucose convey a risk for all hospitalized patients, not only those with critical illness.[3, 18] Hyperglycemia appears to be an independent indicator of mortality risk and should be evaluated as a potential component within risk prediction tools. Further study is required to determine mechanisms for this risk association to identify what therapies, if any, might be used to minimize this risk.
The diagnosed diabetes group, which likely included some patients who also had extreme in‐hospital hyperglycemia, had a lower 1‐year risk of death than patients with hyperglycemia who did not have diabetes diagnosis. This may be an indication of the protective effect of blood glucose control, because patients with diabetes are more likely to receive therapy for hyperglycemia during and after hospitalization.
Classification of patients by several serum glucose levels (as opposed to a dichotomous classification, where hyperglycemia is either present or absent), showed that hyperglycemia constitutes a graded risk[5] for almost all outcomes examined, particularly mortality, AMI, and CVD.
Previous studies indicate that diabetes is observed in only 23% to 35% of hospitalized patients with hyperglycemia.[18, 19] We would expect higher risk for CV complications for patients with elevated glucose if the proportion of these patients who had undiagnosed diabetes was higher than the proportion estimated in the literature. However, we observed lower risk of CV complications (especially PVD and ESRD) for the elevated glucose group in the 1 and 5 years following discharge. In‐hospital hyperglycemia is not equivalent to undiagnosed diabetes.
There are several potential limitations in this study. The first is our method for ascertaining CV complications. Diverse disease definitions are used in medical literature, and similar studies using different definitions may yield different results, though we would not expect to find a wide range of variation. Second, our study did not include information about severity of diabetes and the persistence of elevated glucose; if available, this knowledge may provide better insight into patient experiences, especially in long‐term follow‐up. Third, results are also limited by the absence of data on cause of death, a potentially helpful means of identifying posthospitalization difficulties experienced by patients with hyperglycemia. Fourth, this study mainly compares experiences of patients with elevated peak serum glucose level to diabetes patients; it would be worthwhile to explore the impact of lower gradations of glucose levels.
We would like to emphasize that we did not confirm diabetes diagnosis following discharge for patients with hyperglycemia. However, we did not observe a high rate of complications at 5‐year follow‐up, particularly for ESRD. This may be because most patients with in‐hospital elevated glucose had early diabetes or transient hyperglycemia and therefore lower risk of long‐term diabetes‐specific consultations.
Hyperglycemia is an important independent indicator, carrying a greater risk for 1‐ and 5‐year mortality than diagnosed diabetes. However, it is unclear whether hospitalized patients with elevated peak serum glucose have early diabetes or their hyperglycemia reflects hospital stress or another comorbidity concept.
Acknowledgements
The authors are grateful to Amy Zierler and Allison Whalen O'Connor for their editorial assistance. This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long‐Term Care (MOHLTC). The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred.
Disclosure: Nothing to report.
Hyperglycemia in hospitalized patients is frequently observed and is recognized as an important threat to the health of patients with varying levels of illness, independent of diabetes status.[1, 2, 3, 4] Previous studies have found that in‐hospital hyperglycemia is associated with higher short‐term mortality rates,[5, 6, 7] longer lengths of stay, and higher rates of admission to intensive care units.[4]
In‐hospital hyperglycemia may be the result of diabetes mellitus or stress associated with hospitalization. The mechanism of stress hyperglycemia differs from diabetic hyperglycemia and can occur independently of diabetes status.[8] Stress hyperglycemia is characterized by rapid onset of insulin resistance, which normally takes months or years in diabetes mellitus. It develops in association with or because of other stressor such as infection or inflammatory processes.[9]
To our knowledge, no long‐term follow‐up study on hospitalized patients with elevated glucose levels but without a diabetes diagnosis has ever been conducted. In this study, we measured the 1‐ and 5‐year risk of mortality and diabetes‐related diseases for hospitalized patients with a diabetes diagnosis compared with patients grouped according to their peak in‐hospital serum glucose level. Our primary comparison was between patients with diagnosed diabetes and those with peak serum glucose >200 mg/dL (11.1 mmol/L), but we also examined 4 other categories of peak serum glucose: 140 to 200 mg/dL (7.811.1 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), and <108 mg/dL (<6.0 mmol/L).
METHODS
Study Base
The study included all adult patients (excluding patients admitted to psychiatry and obstetrics) admitted to The Ottawa Hospital (TOH) between January 1, 1996 and March 31, 2008 and discharged alive. Included patients were 18 years or older and had complete medical record abstracts. TOH is the primary hospital in Ottawa, Ontario, Canada, and the main tertiary hospital in the Champlain Local Health Integration Network, the public healthcare authority for the Ottawa region. This study was approved by The Ottawa Hospital Research Ethics Board.
Data Sources
The Ottawa Hospital Data Warehouse (OHDW) is a repository for data from the hospital's patient information systems. These systems include patient registration information, discharge abstract, and laboratory, pharmacy, and radiology results. OHDW was used to determine each patient's age, gender, diagnoses, pharmacy orders, laboratory test results, in‐hospital comorbidities, most responsible hospital service, and admission urgency.
This dataset was linked to 3 population‐based administrative datasets and 1 derived cohort. Ontario's Registered Persons Database (RPDB) is a population‐based registry containing date of death (if applicable) as well as eligibility status for the provincial universal health insurance program, the Ontario Health Insurance Plan (OHIP).[10] The OHIP database records billing claims submitted by approximately 95% of Ontario physicians. Each claim contains a fee code describing the type of service provided and a location denoting where the service had taken place. The Canadian Institute for Health Information Discharge Abstract Database (DAD) records clinical, demographic, and administrative data for all hospital admissions and same‐day surgeries for all Ontario acute care hospitals since April 1, 1988. The Ontario Myocardial Infarction Database (OMID) contains records of all patients with a most responsible diagnosis of acute myocardial infarction (AMI) (International Classification of Diseases, 9th Revision [ICD‐9] code 410 or International Classification of Diseases, 10th Revision [ICD‐10] code I21) identified from the DAD. Details on the creation of the OMID are provided in an earlier study.[10]
Variable Definitions
We identified the index admission of the TOH cohort in the DAD using a unique encrypted health insurance number, admission and discharge dates, and the institution number. To avoid double counting, patients with the same encrypted health insurance number who were discharged from an institution and admitted to another within 2 days were classified as transfers and counted as 1 hospitalization.
Diabetes and cardiovascular (CV) complications (acute myocardial infarction [AMI], congestive heart failure [CHF], cardiovascular disease [CVD], peripheral vascular disease [PVD], and end‐stage renal disease [ESRD]) were identified in the discharge abstract as the most responsible diagnosis for the admission as well as postadmission comorbidities. The discharge abstract records diagnostic codes according to ICD‐9 (April 1, 1988March 31, 2002) or ICD‐10 (April 1, 2002March 1, 2009).
Each hospitalization was classified into 1 of 6 mutually exclusive diabetes status categories based on diagnostic codes, serum glucose test results, and pharmacy records. The diagnosed diabetes group included hospitalizations with any diagnostic code of diabetes in the discharge abstract or any order for a diabetes medication during hospitalization. Eligible medications included acarbose, acetohexamide, chlorpropamide, glibenclamide, gliclazide, glimepiride, glipizide, glyburide, insulin, metformin, nateglinide, pioglitazone, repaglinide, rosiglitazone, and tolbutamide. A chart validation study showed excellent ascertainment of diabetes status using these methods (correct classification 88.8%; 95% confidence interval [CI]: 85.791.3 with weighted kappa=0.89; 95% CI: 0.85‐0.92).[11, 12] Patients without diagnosed diabetes were classified according to their peak serum glucose value during the index hospitalization: <108 mg/dL (<6.0 mmol/L), 108 to 140 mg/dL (6.07.8 mmol/L), 140 to 200 mg/dL (7.811.1 mmol/L), >200 mg/dL (>11.1 mmol/L); hospitalizations in which glucose levels were not obtained were classified as unknown. These peak serum glucose categories are based on the World Health Organization's definition of diabetes and poor glucose tolerance.[12]
Outcomes
The primary outcome was all‐cause postdischarge mortality determined by linking the index admission to the RPDB. Secondary outcomes were CV complications, including: AMI (determined by linking to the OMID); hospitalization for CHF (determined by linking to the DAD for primary diagnosis of 425, 428, 514, 518.4 or I50, I42.0, I42.6, I42.7, I42.8, I42.9, I43, J81), CVD (determined by linking to the DAD for primary diagnosis of 430, 431, 432, 434, 436 or I60, I61, I62, I63, I64, G46), PVD (determined by primary diagnosis of 96.11, 96.12, 96.13, 96.14, 96.15 [excluded if in conjunction with 170, 171, 213, 730, 740759, 800900, 901904, 940950], 50.18, 51.25, 51.29 [excluded if in conjunction with 414.1, 441, 44] or 1VC93, 1VG93, 1VQ93, 1WA93, 1WE93, 1WJ93, 1WL93, 1WM93 [excluded if in conjunction with C40, C41, C46.1, C47, C49, D160, M46.2, M86, M87, M89.6, M90.0‐M90.5, Q00, Q38‐Q40, S02.0, S09.0, S04.0, S15, S25, T26), and 1KG50, 1KG57, 1KG76, 1KG35HAC1, 1KG35HHC1 [excluded if in conjunction with I60, I67.1, I71, I72, 177.0, 179.0]), and ESRD (determined by 403.9, 404.9,584, 585, 586, 788.5 or I12, I13, N17, N18, N19, R34).
Analysis
We identified all encounters of cohort patients in any Ontario acute‐care hospital within 5 years following the index discharge. Patients not covered by provincial health insurance (OHIP) were excluded.
We first measured crude mortality and morbidity rates by patient category (diagnosed diabetes and glucose levels). Next, we compared the unadjusted outcomes and baseline characteristics (age, sex, previous inpatient and emergency admissions, and disease at admission) between groups using a [2] test for categorical variables and analysis of variance for continuous variables.
Due to the violation of proportional hazards, we used the Weibull accelerated failure time model to calculate the hazard of death associated with diabetes status or serum glucose level. Consistency of the probability plots confirmed appropriateness of using the Weibull function. Competing risk is defined as a type of failure that prevents the observation of the event of interest or fundamentally alters the probability of its occurrence.[13, 14] In the comorbidity analyses, death is a competing risk for all other outcomes. We calculated the multivariate competing risk hazard for each outcome of interest except death. Each model was adjusted for potential confounders: baseline risk of in‐hospital mortality, common comorbidities, most responsible hospital service, and the number of previous inpatient admissions and emergency department visits to TOH in the previous 6 months. The probability of dying during the admission was calculated using the Escobar model, which predicts the in‐hospital probability of dying using data available at the time of admission to the hospital.[15] The Escobar model has been validated in the study population.[16] The baseline probability of dying was based on age, sex, acuity of admission, primary condition, Charlson comorbidity score,[17] and the laboratory‐based acute physiology score.[15] The Charlson comorbidity score was calculated using weights from Schneeweiss et al.[17] Kaplan‐Meier survival curves were created for both adjusted and unadjusted models. Death was used as the main censoring variable when analyzing nonfatal outcomes at 1‐ and 5‐year follow‐up.
Adjusted models were constructed using a stepwise selection technique and compared with Akaike and Bayesian information criteria values. For all tests, significance was defined as a P value of 0.05 or less.
We recognize that the cohort may contain more than 1 index hospitalization per patient. However, repeated analyses using only the first or last encounter per patient produced nearly identical hazard ratios (HRs) and CIs.
RESULTS
Between January 1, 1996 and March 31, 2008, 194,641 nonpsychiatric and nonobstetric adults were admitted to TOH. Seventeen patients were excluded because they were ineligible for healthcare coverage and had no encounter with the Ontario healthcare system following discharge from hospital, and 11,175 of the admissions ended in death. The final cohort consisted of 114,764 unique individuals representing 183,449 encounters.
Patients had a mean age of 59.5 years (standard deviation: 18.0) and 48.9% were male. The baseline risk of dying during hospitalization was 4.8%.
Table 1 describes patients by diabetes and peak serum glucose status. Patients with diagnosed diabetes were more likely to be older and male. Patients with elevated peak serum glucose (>200 mg/dL; >11.1 mmol/L) were younger than the diagnosed diabetes group and had a higher baseline probability of in‐hospital death (9.4%, 95% CI: 9.09.7). Patients in these 2 groups had more inpatient admissions within the previous 6 months compared to the groups with other peak serum glucose values.
Serum Glucose Level, mg/dL | ||||||
---|---|---|---|---|---|---|
Diagnosed Diabetes, n=32,774, 17.9% | >200, n=5,082, 2.8% | 140200, n=25,857, 14.1% | 108140, n=38,741, 21.1% | <108, n=27,603, 15.0% | Unknown, n=53,392, 29.1% | |
| ||||||
Age, y, mean (SD) | 65.8 (14.1) | 64.3 (17.0)a | 63.9 (17.3)a | 60.9 (18.6)a | 54.3 (19.7)a | 54.8 (16.9)a |
Risk‐adjusted mortality at admission, mean (SD) | 0.08 (0.12) | 0.09 (0.12)a | 0.07 (0.10)a | 0.05 (0.09)a | 0.04 (0.07)a | 0.01 (0.04)a |
Sex, male, no. (%) | 18,200 (55.5) | 2,610 (51.4)a | 13,477 (52.1)a | 19,495 (50.3)a | 12,907 (46.8)a | 22,951 (43.0)a |
No. of previous inpatient admissions, 6 months (%) | ||||||
0 | 22,780 (69.5) | 3,576 (70.4) | 19,118 (73.9)a | 28,827 (74.4)a | 20,354 (73.7)a | 43,317 (81.1)a |
1 | 6,470 (19.7) | 962 (18.9) | 4,484 (17.3)a | 6,526 (16.9)a | 4,744 (17.2)a | 7,360 (13.8)a |
2+ | 3,524 (10.8) | 544 (10.7) | 2,255 (8.7)a | 3,388 (9.1)a | 2,505 (9.1)a | 2,715 (5.1)a |
No. of previous emergency admissions (6 months) (%) | ||||||
0 | 15,518 (47.4) | 2,638 (51.9)a | 12,681 (49.0)a | 16,584 (42.8)a | 13,039 (47.2) | 43,112 (80.8)a |
1 | 9,665 (29.5) | 1,490 (29.3)a | 8,339 (32.3)a | 13,829 (35.7)a | 8,709 (31.6) | 6,533 (12.2)a |
2+ | 7,591 (23.2) | 954 (18.8)a | 4,837 (18.7)a | 8,328 (21.5)a | 5,855 (21.2) | 3,747 (7.0)a |
Emergency admission at index hospitalization (%) | 25,420 (77.6) | 4,178 (82.2)a | 20,284 (78.5)a | 33,282 (85.9)a | 23,598 (85.5)a | 15,039 (28.2)a |
Length of stay, d (mean/SD) | 12.4 (22.3) | 15.8 (30.0)a | 11.9 (17.1)a | 8.5 (12.2)a | 6.3 (9.5)a | 3.4 (4.9)a |
Disease at index hospitalization (%) | ||||||
PVD | 2,783 (8.5) | 257 (5.1)a | 1,387 (5.4)a | 1,372 (3.5)a | 908 (3.3)a | 965 (1.8)a |
Pneumonia | 3,308 (10.1) | 658 (13.0)a | 2,577 (10.0) | 2,809 (7.3)a | 1,220 (4.4)a | 399 (0.8)a |
UTI | 3,549 (10.8) | 665 (13.1)a | 2,660 (10.3)a | 3,336 (8.6)a | 1,559 (5.7)a | 929 (1.7)a |
IHD | 8,957 (27.3) | 1,086 (21.4)a | 4,714 (18.2)a | 5,306 (13.7)a | 3,100 (11.2)a | 2,842 (5.3)a |
Hypertension | 10,864 (33.2) | 1,115 (21.9)a | 5,155 (19.9)a | 5,880 (15.2)a | 3,045 (11.0)a | 3,211 (6.0)a |
Arrhythmia | 5,010 (15.3) | 823 (16.2) | 3,768 (14.6)a | 3,718 (9.6)a | 2,046 (7.4)a | 1,172 (2.2)a |
CHF | 570 (17.5) | 763 (15.0)a | 2,761 (10.7)a | 2,449 (6.3)a | 1,121 (4.1)a | 386 (0.7)a |
Diagnoses of diabetes in the next year (%) | 126 (2.5) | 368 (1.4) | 370 (1.0) | 215 (0.8) | 540 (1.0) | |
Diagnoses of diabetes in the next 5 years (%) | 310 (6.1) | 981 (3.8) | 916 (2.4) | 434 (1.7) | 1,317 (2.5) | |
First 3 most common primary discharge codes | AHD I251 (5.7%), HF I50 (3.1%), NSTEMI I214 (1.6%) | AHD I251 (2.7%), COPD J441 (2.1%), HF I50 (2.0%) | AHD I251 (4.0%), Pneumonia J189 (1.6%), HF I50 (1.5%) | AHD I251 (1.6%), Pneumonia J189 (1.5%), CA D700 (1.3%) | AHD I251 (1.8%), AA K359 (1.7%), UA I200 (1.2%) | OA M171 (2.3%), AHD I251 (2.2%), OA M170 (1.7%) |
Of the 5082 patients classified with a peak serum glucose measurement >200 mg/dL (11.1 mmol/L), 15% had 2 and 8% had more than 2 serum glucose measurements that exceeded this threshold. For the remaining patients (with 1 peak serum glucose measurement >200 mg/dL), 52% had additional serum glucose measurements over 140 mg/dL (7.8 mmol/L).
Table 2 presents crude 1‐ and 5‐year mortality and morbidity rates by patient group. The mortality rate is the percentage of patients who died within 1 year and 5 years of their index admission, with or without developing a CV complication. Morbidity rate describes the percentage who developed the complication among patients who survived after discharge or developed the complication prior to death within a 1‐year and 5‐year period. During the 1‐year follow‐up period, the crude mortality rate among patients with elevated peak serum glucose was higher than all other groups (25.4% vs 20.6% for diagnosed diabetes and 7.4% to 22.5% for other peak serum glucose levels; all P<0.0001). For the 5‐year follow‐up, the gap between patients with elevated peak serum glucose and those with diagnosed diabetes lessened but remained significant (45.1% vs 41.7%; P<0.0001). In the 1‐year follow‐up, patients with diagnosed diabetes had significantly higher morbidity rates than all other groups except for AMI. The difference in the rate of AMI for diagnosed diabetes and patients with elevated glucose was not statistically significant.
Diagnosed Diabetes, n=32,774 | Serum Glucose Level (mg/dL) | |||||
---|---|---|---|---|---|---|
>200, n=5,082 | 140200, n=25,857 | 108140, n=38,741 | <108, n=27,603 | Unknown, n=53,392 | ||
| ||||||
All‐cause death | ||||||
1 year | 6,762 (20.6) | 1,293 (25.4)a | 5,309 (20.5) | 7,212 (18.6)a | 4,178 (15)a | 3,969 (7.4)a |
5 years | 13,659 (41.7) | 2,292 (45.1)a | 9,871 (38.2)a | 13,256 (34.2)a | 7,606 (27.6)a | 98,614 (16.1)a |
AMI | ||||||
1 year | 728 (2.8) | 104 (2.7) | 326 (1.6)a | 386 (1.2)a | 172 (0.7)a | 218 (0.4)a |
5 years | 1,687 (8.4) | 182 (6.3)a | 687 (4.2)a | 837 (3.2)a | 436 (2.2)a | 633 (1.4)a |
CVD | ||||||
1 year | 582 (2.2) | 71 (1.9) | 306 (1.5)a | 403 (1.3)a | 251 (1.1)a | 195 (0.4)a |
5 years | 1,153 (5.8) | 143 (5.0) | 623 (3.8)a | 872 (3.4)a | 470 (2.3)a | 534 (1.2)a |
CHF | ||||||
1 year | 2,260 (8.4) | 245 (6.3)a | 870 (4.2)a | 1,023 (3.2)a | 516 (2.2)a | 362 (0.7)a |
5 years | 3,830 (17.7) | 395 (12.9)a | 1,529 (9.0)a | 1,802 (6.8)a | 960 (4.7)a | 884 (2.0)a |
PVD | ||||||
1 year | 795 (3.0) | 24 (0.6)a | 114 (0.6)a | 154 (0.5)a | 97 (0.4)a | 141 (0.3)a |
5 years | 1,547 (7.8) | 50 (1.8)a | 220 (1.4)a | 275 (1.1)a | 192 (1.0)a | 295 (0.7)a |
ESRD | ||||||
1 year | 953 (3.6) | 104 (2.7)a | 403 (1.9)a | 457 (1.4)a | 311 (1.3)a | 329 (0.7)a |
5 years | 1,938 (9.5) | 183 (6.3)a | 807 (4.9)a | 995 (3.8)a | 611 (3.0)a | 771 (1.7)a |
The 1‐year adjusted hazard ratios for mortality and CV complications are presented in Figure 1. After adjustment for baseline demographics and clinical and hospital factors, having a peak serum glucose level above 200 mg/dL (11.1 mmol/L) was an independent predictor of death. The mortality risk for this group in the year following discharge was 31% higher than patients with diagnosed diabetes (adjusted HR: 1.31, 95% CI: 1.20‐1.43). There was no mortality risk difference between the group with diagnosed diabetes and the lower peak serum glucose levels (adjusted HR: 1.05, 95% CI: 0.99‐1.11), 108160 mg/dL (6.07.8 mmol/L) (adjusted HR: 1.02, 95% CI: 0.971.07); and <108 mg/dL (<6.0 mmol/L) (adjusted HR: 0.97, 95% CI: 0.921.03). Adjusted HRs, with 95% CIs and P values for 5‐year follow‐up are presented in Figure 2.


Adjusted hazard ratios for morbidity showed a different pattern. Patients with diagnosed diabetes had a higher risk of developing complications in the year following discharge from the hospital (Figure 1). After adjusting for potential confounders, we found that patients with diagnosed diabetes had the same risk of AMI and CVD as patients with peak serum glucose levels above 200 mg/dL (11.1 mmol/L) (AMI, adjusted HR: 0.96; 95% CI: 0.78‐1.18; CVD, adjusted HR: 0.79; 95% CI: 0.61‐1.00) but a 36% to 57% higher AMI risk and 29% to 34% higher CVD risk than other peak serum glucose level groups. Compared to all peak serum glucose groups, patients with diagnosed diabetes had a significantly higher risk of developing CHF, PVD, and ESRD (Figure 1). Similarly, the 5‐year risk of AMI, CVD, CHF, PVD, and ESRD was higher for patients with diagnosed diabetes compared to all peak serum glucose groups (Figure 2).
CONCLUSION/DISCUSSION
The study found that in‐hospital hyperglycemia was a strong predictor of mortality at 1‐ and 5‐years follow‐up, even after adjustment using well‐established and discriminating comorbidity measures. Extreme in‐hospital hyperglycemia was a stronger predictor of mortality than diagnosed diabetes.
Previous studies have shown that diabetes and stress hyperglycemia among hospitalized patients are important markers for poor clinical outcomes and in‐hospital mortality.[1, 2, 4] This study indicates that hospitalized patients with extreme hyperglycemia (peak serum glucose >200 mg/dL) were at a high risk of death for at least 5 years following discharge.
These findings, based on a large general sample of hospitalized patients, indicate that the extreme elevations in peak serum glucose convey a risk for all hospitalized patients, not only those with critical illness.[3, 18] Hyperglycemia appears to be an independent indicator of mortality risk and should be evaluated as a potential component within risk prediction tools. Further study is required to determine mechanisms for this risk association to identify what therapies, if any, might be used to minimize this risk.
The diagnosed diabetes group, which likely included some patients who also had extreme in‐hospital hyperglycemia, had a lower 1‐year risk of death than patients with hyperglycemia who did not have diabetes diagnosis. This may be an indication of the protective effect of blood glucose control, because patients with diabetes are more likely to receive therapy for hyperglycemia during and after hospitalization.
Classification of patients by several serum glucose levels (as opposed to a dichotomous classification, where hyperglycemia is either present or absent), showed that hyperglycemia constitutes a graded risk[5] for almost all outcomes examined, particularly mortality, AMI, and CVD.
Previous studies indicate that diabetes is observed in only 23% to 35% of hospitalized patients with hyperglycemia.[18, 19] We would expect higher risk for CV complications for patients with elevated glucose if the proportion of these patients who had undiagnosed diabetes was higher than the proportion estimated in the literature. However, we observed lower risk of CV complications (especially PVD and ESRD) for the elevated glucose group in the 1 and 5 years following discharge. In‐hospital hyperglycemia is not equivalent to undiagnosed diabetes.
There are several potential limitations in this study. The first is our method for ascertaining CV complications. Diverse disease definitions are used in medical literature, and similar studies using different definitions may yield different results, though we would not expect to find a wide range of variation. Second, our study did not include information about severity of diabetes and the persistence of elevated glucose; if available, this knowledge may provide better insight into patient experiences, especially in long‐term follow‐up. Third, results are also limited by the absence of data on cause of death, a potentially helpful means of identifying posthospitalization difficulties experienced by patients with hyperglycemia. Fourth, this study mainly compares experiences of patients with elevated peak serum glucose level to diabetes patients; it would be worthwhile to explore the impact of lower gradations of glucose levels.
We would like to emphasize that we did not confirm diabetes diagnosis following discharge for patients with hyperglycemia. However, we did not observe a high rate of complications at 5‐year follow‐up, particularly for ESRD. This may be because most patients with in‐hospital elevated glucose had early diabetes or transient hyperglycemia and therefore lower risk of long‐term diabetes‐specific consultations.
Hyperglycemia is an important independent indicator, carrying a greater risk for 1‐ and 5‐year mortality than diagnosed diabetes. However, it is unclear whether hospitalized patients with elevated peak serum glucose have early diabetes or their hyperglycemia reflects hospital stress or another comorbidity concept.
Acknowledgements
The authors are grateful to Amy Zierler and Allison Whalen O'Connor for their editorial assistance. This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long‐Term Care (MOHLTC). The opinions, results and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred.
Disclosure: Nothing to report.
- Management of diabetes and hyperglycemia in hospitals. Diabetes Care. 2004;27(2):553–591. , , , et al.
- American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Diabetes Care. 2009;32(6):1119–1131. , , , et al.
- The NICE‐SUGAR Study Investigators. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. 2009;360(13):1283–1297.
- Hyperglycemia: an independent marker of in‐hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol Metab. 2002;87(3):978–982. , , , , , .
- Hyperglycemia‐related mortality in critically ill patients varies with admission diagnosis. Crit Care Med. 2009;37(12):3001–3009. , , , , .
- Hospital management of hyperglycemia. Curr Opin Endocrinol Diabetes Obes. 2011;18(2):110–118. , .
- IScore: a risk score to predict death early after hospitalization for an acute ischemic stroke. Circulation. 2011;123(7):739–749. , , , et al.
- Does Stress‐induced hyperglycemia increase the risk of perioperative infectious complications in orthopaedic trauma patients? J Orthop Trauma. 2010;24(12):752–756. , .
- Metabolic mechanisms of stress hyperglycemia. J Parenter Enteral Nutr. 2006;30(2):157–163. .
- Temporal changes in the outcomes of acute myocardial infarction in Ontario, 1992–1996. CMAJ. 1999;161(10):1257–1261. , , .
- The accuracy of using integrated electronic health care data to identify patients with undiagnosed diabetes mellitus. J Eval Clin Pract. 2012;18(3):606–611. , , , et al.
- American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2006;29(suppl 1):s43–s48.
- Dealing with competing risks: testing covariates and calculating sample size. Stat Med. 2002;21(22):3317–3324. .
- Competing risk analysis of events 10 years after revascularization. Scand Cardiovasc J. 2010;44(5):279–288. , , , .
- Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases. Med Care. 2008;46(3):232–239. , , , , , .
- The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. J Clin Epidemiol. 2010;63(7):798–803. , , , .
- Improved comorbidity adjustment for predicting mortality in medicare populations. Health Serv Res. 2003;38(4):1103–1120. , , , .
- Glucose metabolism in patients with acute myocardial infarction and no previous diagnosis of diabetes mellitus: a prospective study. Lancet. 2002;359(9324):2140–2144. , , , et al.
- Disorders of glucose metabolism in acute stroke patients: an underrecognized problem. Diabetes Care. 2006;29(4):792–797. , , , et al.
- Management of diabetes and hyperglycemia in hospitals. Diabetes Care. 2004;27(2):553–591. , , , et al.
- American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Diabetes Care. 2009;32(6):1119–1131. , , , et al.
- The NICE‐SUGAR Study Investigators. Intensive versus conventional glucose control in critically ill patients. N Engl J Med. 2009;360(13):1283–1297.
- Hyperglycemia: an independent marker of in‐hospital mortality in patients with undiagnosed diabetes. J Clin Endocrinol Metab. 2002;87(3):978–982. , , , , , .
- Hyperglycemia‐related mortality in critically ill patients varies with admission diagnosis. Crit Care Med. 2009;37(12):3001–3009. , , , , .
- Hospital management of hyperglycemia. Curr Opin Endocrinol Diabetes Obes. 2011;18(2):110–118. , .
- IScore: a risk score to predict death early after hospitalization for an acute ischemic stroke. Circulation. 2011;123(7):739–749. , , , et al.
- Does Stress‐induced hyperglycemia increase the risk of perioperative infectious complications in orthopaedic trauma patients? J Orthop Trauma. 2010;24(12):752–756. , .
- Metabolic mechanisms of stress hyperglycemia. J Parenter Enteral Nutr. 2006;30(2):157–163. .
- Temporal changes in the outcomes of acute myocardial infarction in Ontario, 1992–1996. CMAJ. 1999;161(10):1257–1261. , , .
- The accuracy of using integrated electronic health care data to identify patients with undiagnosed diabetes mellitus. J Eval Clin Pract. 2012;18(3):606–611. , , , et al.
- American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2006;29(suppl 1):s43–s48.
- Dealing with competing risks: testing covariates and calculating sample size. Stat Med. 2002;21(22):3317–3324. .
- Competing risk analysis of events 10 years after revascularization. Scand Cardiovasc J. 2010;44(5):279–288. , , , .
- Risk‐adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases. Med Care. 2008;46(3):232–239. , , , , , .
- The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. J Clin Epidemiol. 2010;63(7):798–803. , , , .
- Improved comorbidity adjustment for predicting mortality in medicare populations. Health Serv Res. 2003;38(4):1103–1120. , , , .
- Glucose metabolism in patients with acute myocardial infarction and no previous diagnosis of diabetes mellitus: a prospective study. Lancet. 2002;359(9324):2140–2144. , , , et al.
- Disorders of glucose metabolism in acute stroke patients: an underrecognized problem. Diabetes Care. 2006;29(4):792–797. , , , et al.
© 2014 Society of Hospital Medicine
Europe’s latest sarcoma guidelines increase emphasis on genetic profiling
MILAN – The 2014 update to the European Society of Medical Oncology’s sarcoma-management guidelines put unprecedented emphasis on genetic assessment and using the data to guide treatment.
"Molecular diagnosis is recommended as standard care" for patients with gastrointestinal stromal tumors (GIST), Dr. Jean-Yves Blay said at Sarcoma and GIST 2014, hosted by the European Society for Medical Oncology (ESMO). Identifying the genetic profile of a newly diagnosed GIST "is predictive and can guide treatment in the adjuvant setting and possibly also in the advanced phase," said Dr. Blay, professor and head of medical oncology at Claude Bernard University in Lyon, France. "Molecular characterization is increasingly an important diagnostic and prognostic tool and also helps select treatment."
The new update also strengthened the advice from past updates to centralize GIST and sarcoma management at reference centers. "What is clear from looking at past guidelines is that since 2008 we have moved toward increasingly stronger recommendations to centralize," although the panel remained unable to settle on a definition of a GIST and sarcoma reference center, Dr. Blay said.
The 2014 revision to ESMO’s guidelines for managing GIST and sarcomas is the fourth biannual revision since these guidelines first appeared in 2008. The new update will soon appear on ESMO’s website. A majority of the guidelines remain based on consensus opinion and not evidence, because the number of randomized controlled trials that have tested various aspects of management remains limited, Dr. Blay said. The lack of trials also leaves many questions unanswered, such as the best treatment for GIST that carry the D842V mutation in their PDGFRA gene or for the "wild-type" GIST that don’t have any of the described GIST mutations. "Everyone agrees we need more studies," he said.
For managing patients with advanced GIST, the new update noted that surgical removal of recurrent lesions has not been proven beneficial to patients, and that the benefit from monitoring trough levels of a tyrosine-kinase inhibitor drug also remains unproven. The new update also acknowledged a possible role for continued imatinib (Gleevec) treatment after relapse, based on results from the RIGHT (Resumption of Imatinib to Control Metastatic or Unresectable GIST After Failure of Imatinib and Sunitinib) trial (Lancet Oncol. 2013;14:1175-82). In addition, the update recognized the potential benefit of trying regorafenib (Stivarga) after patients progress on imatinib or sunitinib (Sutent) treatment, and strongly advised against treating patients with two or more tyrosine-kinase inhibitor drugs simultaneously, as this approach needs further study.
One other change to the GIST guidelines this year was a suggestion to increase the frequency of follow-up examinations during the 1-3 years following the end of adjuvant therapy, also based on consensus opinion and without firm evidence, Dr. Blay said.
The revised soft-tissue sarcoma (STS) guidelines included adoption of the bone and STS classification scheme issued by the World Health Organization last year, and endorsement of genetic analyses when the histologic diagnosis is uncertain or the tumor has an unusual presentation. The revision added stronger language promoting the need to individualize radiotherapy based on factors such as clinical presentation of the sarcoma, patient history, tumor site, and patient’s age. The revision panel could not reach a consensus on a recommended, standard adjuvant regimen.
This inability to recommend adjuvant therapies should be "no surprise because there are no data. We need to look at larger numbers of patients to identify those who would benefit from adjuvant therapy," Dr. Blay said.
For treatment of advanced STSs, the panel added pazopanib (Votrient) as a second-line treatment option, but not for patients with liposarcomas, who were not included in the trial that established pazopanib’s efficacy for metastatic STS (Lancet 2012;379:1879-86). The update also recommended identifying sarcoma subtypes genetically and matching drugs to these types using agents such as sunitinib, crizotinib (Xalkori), and cediranib (Recentin). In addition, the new update highlighted that a role for intensified follow-up of metastatic STS using computed tomography was not supported by the results of a recent randomized, controlled trial.
The 2014 guidelines also include sections for four specific STSs: retroperitoneal, uterine, desmoids, and breast. "The guidelines are expanding to organ-specific locations, something we will probably see more of" in future updates, Dr. Blay said.
Dr. Blay disclosed that he has received honoraria as a consultant to PharmaMar, and that he has received research grants from Roche, GlaxoSmithKline, and Novartis.
On Twitter @mitchelzoler
MILAN – The 2014 update to the European Society of Medical Oncology’s sarcoma-management guidelines put unprecedented emphasis on genetic assessment and using the data to guide treatment.
"Molecular diagnosis is recommended as standard care" for patients with gastrointestinal stromal tumors (GIST), Dr. Jean-Yves Blay said at Sarcoma and GIST 2014, hosted by the European Society for Medical Oncology (ESMO). Identifying the genetic profile of a newly diagnosed GIST "is predictive and can guide treatment in the adjuvant setting and possibly also in the advanced phase," said Dr. Blay, professor and head of medical oncology at Claude Bernard University in Lyon, France. "Molecular characterization is increasingly an important diagnostic and prognostic tool and also helps select treatment."
The new update also strengthened the advice from past updates to centralize GIST and sarcoma management at reference centers. "What is clear from looking at past guidelines is that since 2008 we have moved toward increasingly stronger recommendations to centralize," although the panel remained unable to settle on a definition of a GIST and sarcoma reference center, Dr. Blay said.
The 2014 revision to ESMO’s guidelines for managing GIST and sarcomas is the fourth biannual revision since these guidelines first appeared in 2008. The new update will soon appear on ESMO’s website. A majority of the guidelines remain based on consensus opinion and not evidence, because the number of randomized controlled trials that have tested various aspects of management remains limited, Dr. Blay said. The lack of trials also leaves many questions unanswered, such as the best treatment for GIST that carry the D842V mutation in their PDGFRA gene or for the "wild-type" GIST that don’t have any of the described GIST mutations. "Everyone agrees we need more studies," he said.
For managing patients with advanced GIST, the new update noted that surgical removal of recurrent lesions has not been proven beneficial to patients, and that the benefit from monitoring trough levels of a tyrosine-kinase inhibitor drug also remains unproven. The new update also acknowledged a possible role for continued imatinib (Gleevec) treatment after relapse, based on results from the RIGHT (Resumption of Imatinib to Control Metastatic or Unresectable GIST After Failure of Imatinib and Sunitinib) trial (Lancet Oncol. 2013;14:1175-82). In addition, the update recognized the potential benefit of trying regorafenib (Stivarga) after patients progress on imatinib or sunitinib (Sutent) treatment, and strongly advised against treating patients with two or more tyrosine-kinase inhibitor drugs simultaneously, as this approach needs further study.
One other change to the GIST guidelines this year was a suggestion to increase the frequency of follow-up examinations during the 1-3 years following the end of adjuvant therapy, also based on consensus opinion and without firm evidence, Dr. Blay said.
The revised soft-tissue sarcoma (STS) guidelines included adoption of the bone and STS classification scheme issued by the World Health Organization last year, and endorsement of genetic analyses when the histologic diagnosis is uncertain or the tumor has an unusual presentation. The revision added stronger language promoting the need to individualize radiotherapy based on factors such as clinical presentation of the sarcoma, patient history, tumor site, and patient’s age. The revision panel could not reach a consensus on a recommended, standard adjuvant regimen.
This inability to recommend adjuvant therapies should be "no surprise because there are no data. We need to look at larger numbers of patients to identify those who would benefit from adjuvant therapy," Dr. Blay said.
For treatment of advanced STSs, the panel added pazopanib (Votrient) as a second-line treatment option, but not for patients with liposarcomas, who were not included in the trial that established pazopanib’s efficacy for metastatic STS (Lancet 2012;379:1879-86). The update also recommended identifying sarcoma subtypes genetically and matching drugs to these types using agents such as sunitinib, crizotinib (Xalkori), and cediranib (Recentin). In addition, the new update highlighted that a role for intensified follow-up of metastatic STS using computed tomography was not supported by the results of a recent randomized, controlled trial.
The 2014 guidelines also include sections for four specific STSs: retroperitoneal, uterine, desmoids, and breast. "The guidelines are expanding to organ-specific locations, something we will probably see more of" in future updates, Dr. Blay said.
Dr. Blay disclosed that he has received honoraria as a consultant to PharmaMar, and that he has received research grants from Roche, GlaxoSmithKline, and Novartis.
On Twitter @mitchelzoler
MILAN – The 2014 update to the European Society of Medical Oncology’s sarcoma-management guidelines put unprecedented emphasis on genetic assessment and using the data to guide treatment.
"Molecular diagnosis is recommended as standard care" for patients with gastrointestinal stromal tumors (GIST), Dr. Jean-Yves Blay said at Sarcoma and GIST 2014, hosted by the European Society for Medical Oncology (ESMO). Identifying the genetic profile of a newly diagnosed GIST "is predictive and can guide treatment in the adjuvant setting and possibly also in the advanced phase," said Dr. Blay, professor and head of medical oncology at Claude Bernard University in Lyon, France. "Molecular characterization is increasingly an important diagnostic and prognostic tool and also helps select treatment."
The new update also strengthened the advice from past updates to centralize GIST and sarcoma management at reference centers. "What is clear from looking at past guidelines is that since 2008 we have moved toward increasingly stronger recommendations to centralize," although the panel remained unable to settle on a definition of a GIST and sarcoma reference center, Dr. Blay said.
The 2014 revision to ESMO’s guidelines for managing GIST and sarcomas is the fourth biannual revision since these guidelines first appeared in 2008. The new update will soon appear on ESMO’s website. A majority of the guidelines remain based on consensus opinion and not evidence, because the number of randomized controlled trials that have tested various aspects of management remains limited, Dr. Blay said. The lack of trials also leaves many questions unanswered, such as the best treatment for GIST that carry the D842V mutation in their PDGFRA gene or for the "wild-type" GIST that don’t have any of the described GIST mutations. "Everyone agrees we need more studies," he said.
For managing patients with advanced GIST, the new update noted that surgical removal of recurrent lesions has not been proven beneficial to patients, and that the benefit from monitoring trough levels of a tyrosine-kinase inhibitor drug also remains unproven. The new update also acknowledged a possible role for continued imatinib (Gleevec) treatment after relapse, based on results from the RIGHT (Resumption of Imatinib to Control Metastatic or Unresectable GIST After Failure of Imatinib and Sunitinib) trial (Lancet Oncol. 2013;14:1175-82). In addition, the update recognized the potential benefit of trying regorafenib (Stivarga) after patients progress on imatinib or sunitinib (Sutent) treatment, and strongly advised against treating patients with two or more tyrosine-kinase inhibitor drugs simultaneously, as this approach needs further study.
One other change to the GIST guidelines this year was a suggestion to increase the frequency of follow-up examinations during the 1-3 years following the end of adjuvant therapy, also based on consensus opinion and without firm evidence, Dr. Blay said.
The revised soft-tissue sarcoma (STS) guidelines included adoption of the bone and STS classification scheme issued by the World Health Organization last year, and endorsement of genetic analyses when the histologic diagnosis is uncertain or the tumor has an unusual presentation. The revision added stronger language promoting the need to individualize radiotherapy based on factors such as clinical presentation of the sarcoma, patient history, tumor site, and patient’s age. The revision panel could not reach a consensus on a recommended, standard adjuvant regimen.
This inability to recommend adjuvant therapies should be "no surprise because there are no data. We need to look at larger numbers of patients to identify those who would benefit from adjuvant therapy," Dr. Blay said.
For treatment of advanced STSs, the panel added pazopanib (Votrient) as a second-line treatment option, but not for patients with liposarcomas, who were not included in the trial that established pazopanib’s efficacy for metastatic STS (Lancet 2012;379:1879-86). The update also recommended identifying sarcoma subtypes genetically and matching drugs to these types using agents such as sunitinib, crizotinib (Xalkori), and cediranib (Recentin). In addition, the new update highlighted that a role for intensified follow-up of metastatic STS using computed tomography was not supported by the results of a recent randomized, controlled trial.
The 2014 guidelines also include sections for four specific STSs: retroperitoneal, uterine, desmoids, and breast. "The guidelines are expanding to organ-specific locations, something we will probably see more of" in future updates, Dr. Blay said.
Dr. Blay disclosed that he has received honoraria as a consultant to PharmaMar, and that he has received research grants from Roche, GlaxoSmithKline, and Novartis.
On Twitter @mitchelzoler
EXPERT ANALYSIS FROM SARCOMA AND GIST 2014
Study links graft source to length of hospital stay
GRAPEVINE, TEXAS—Acute leukemia patients who undergo cord blood (CB) transplant have longer hospital stays than patients who receive other types of transplant, new research indicates.
The study also suggests the length of stay (LOS) is similar whether patients receive double or single CB grafts.
So it seems strategies are needed to decrease hospital stay after CB transplant, particularly as LOS drives the cost of care, said Karen K. Ballen, MD, of Massachusetts General Hospital in Boston.
Dr Ballen presented this research at the 2014 BMT Tandem Meetings as abstract 104.*
She and her colleagues studied patients diagnosed with acute leukemias who were transplanted at US centers and reported to the CIBMTR between 2008 and 2011.
Patients were eligible if they received an unrelated single or double CB transplant, an 8/8 matched unrelated donor (MUD) transplant with peripheral blood (PB) or bone marrow (BM), or a 7/8 MUD PB or BM graft.
In all, 1796 patients met these criteria. The researchers evaluated patients’ total hospital LOS in the first 100 days after transplant, compared LOS among graft sources, and looked for predictors of LOS in the first 100 days.
The team stratified patients according to age and conditioning regimen. Pediatric patients were classified as those aged 18 and younger, and they only received myeloablative conditioning (MAC). Adults received either MAC or reduced-intensity conditioning (RIC).
Pediatric patients
In a univariate analysis of the 368 pediatric patients, there was no significant difference in 100-day survival according to graft source (P=0.13).
However, patients who received single or double CB grafts had a significantly higher median total LOS by day 100 than patients who received 8/8 MUD BM, which was the only other graft source in this patient group (P=0.03).
Patients who received CB grafts also had significantly fewer days in which they were alive and not in the hospital (P=0.005).
“We wanted to account for patients whose length of stay was short because they actually died early after transplant,” Dr Ballen explained. “Therefore, we did an analysis of days alive and not in the hospital.”
In a multivariate analysis, pediatric patients who received CB grafts had significantly fewer days alive and out of the hospital than those who received 8/8 MUD BM (P=0.03).
Other factors associated with fewer days alive and out of the hospital were CMV positivity (P=0.01), black race (P=0.01), and a Karnofsky performance score of less than 80 (P=0.03).
Adults on MAC
In a univariate analysis of the 768 adults who received MAC, recipients of CB grafts had significantly worse 100-day survival than their peers (P<0.001), as well as a longer median LOS by day 100 (P<0.001) and fewer days alive and not in the hospital (P<0.001).
In a multivariate analysis, adults who received MAC had significantly fewer days alive and out of the hospital if they received CB grafts than if they received 8/8 MUD BM (P<0.001), 8/8 MUD PB (P<0.001), or 7/8 MUD PB (P=0.01), but not 7/8 MUD BM (P=0.49).
Other factors associated with fewer days alive and out of the hospital were black race (P=0.04), having acute lymphocytic leukemia rather than acute myeloid leukemia (P=0.01), and age 18-25 (P=0.01).
“We were a little surprised at these results—that the older patients actually spent more time alive and out of the hospital,” Dr Ballen said.
Adults on RIC
In a univariate analysis of the 660 adults who received RIC, recipients of CB grafts had significantly worse 100-day survival than their peers (P=0.017), as well as a longer median LOS by day 100 (P<0.001) and fewer days alive and not in the hospital (P<0.001).
In a multivariate analysis, adults who received RIC had significantly fewer days alive and out of the hospital if they received CB grafts than if they received 8/8 MUD PB (P<0.001) or 7/8 MUD PB (P<0.001).
No other factors were associated with the number of days these patients were alive and out of the hospital.
These results, when taken together, suggest that CB grafts are associated with longer hospital stays, independent of other factors.
“The majority of cost appears to be driven by the number of days in the hospital,” Dr Ballen noted. “So these data may be important for resource allocation, especially given the recent changes in the US healthcare system.”
*Information in the abstract differs from that presented at the meeting.
GRAPEVINE, TEXAS—Acute leukemia patients who undergo cord blood (CB) transplant have longer hospital stays than patients who receive other types of transplant, new research indicates.
The study also suggests the length of stay (LOS) is similar whether patients receive double or single CB grafts.
So it seems strategies are needed to decrease hospital stay after CB transplant, particularly as LOS drives the cost of care, said Karen K. Ballen, MD, of Massachusetts General Hospital in Boston.
Dr Ballen presented this research at the 2014 BMT Tandem Meetings as abstract 104.*
She and her colleagues studied patients diagnosed with acute leukemias who were transplanted at US centers and reported to the CIBMTR between 2008 and 2011.
Patients were eligible if they received an unrelated single or double CB transplant, an 8/8 matched unrelated donor (MUD) transplant with peripheral blood (PB) or bone marrow (BM), or a 7/8 MUD PB or BM graft.
In all, 1796 patients met these criteria. The researchers evaluated patients’ total hospital LOS in the first 100 days after transplant, compared LOS among graft sources, and looked for predictors of LOS in the first 100 days.
The team stratified patients according to age and conditioning regimen. Pediatric patients were classified as those aged 18 and younger, and they only received myeloablative conditioning (MAC). Adults received either MAC or reduced-intensity conditioning (RIC).
Pediatric patients
In a univariate analysis of the 368 pediatric patients, there was no significant difference in 100-day survival according to graft source (P=0.13).
However, patients who received single or double CB grafts had a significantly higher median total LOS by day 100 than patients who received 8/8 MUD BM, which was the only other graft source in this patient group (P=0.03).
Patients who received CB grafts also had significantly fewer days in which they were alive and not in the hospital (P=0.005).
“We wanted to account for patients whose length of stay was short because they actually died early after transplant,” Dr Ballen explained. “Therefore, we did an analysis of days alive and not in the hospital.”
In a multivariate analysis, pediatric patients who received CB grafts had significantly fewer days alive and out of the hospital than those who received 8/8 MUD BM (P=0.03).
Other factors associated with fewer days alive and out of the hospital were CMV positivity (P=0.01), black race (P=0.01), and a Karnofsky performance score of less than 80 (P=0.03).
Adults on MAC
In a univariate analysis of the 768 adults who received MAC, recipients of CB grafts had significantly worse 100-day survival than their peers (P<0.001), as well as a longer median LOS by day 100 (P<0.001) and fewer days alive and not in the hospital (P<0.001).
In a multivariate analysis, adults who received MAC had significantly fewer days alive and out of the hospital if they received CB grafts than if they received 8/8 MUD BM (P<0.001), 8/8 MUD PB (P<0.001), or 7/8 MUD PB (P=0.01), but not 7/8 MUD BM (P=0.49).
Other factors associated with fewer days alive and out of the hospital were black race (P=0.04), having acute lymphocytic leukemia rather than acute myeloid leukemia (P=0.01), and age 18-25 (P=0.01).
“We were a little surprised at these results—that the older patients actually spent more time alive and out of the hospital,” Dr Ballen said.
Adults on RIC
In a univariate analysis of the 660 adults who received RIC, recipients of CB grafts had significantly worse 100-day survival than their peers (P=0.017), as well as a longer median LOS by day 100 (P<0.001) and fewer days alive and not in the hospital (P<0.001).
In a multivariate analysis, adults who received RIC had significantly fewer days alive and out of the hospital if they received CB grafts than if they received 8/8 MUD PB (P<0.001) or 7/8 MUD PB (P<0.001).
No other factors were associated with the number of days these patients were alive and out of the hospital.
These results, when taken together, suggest that CB grafts are associated with longer hospital stays, independent of other factors.
“The majority of cost appears to be driven by the number of days in the hospital,” Dr Ballen noted. “So these data may be important for resource allocation, especially given the recent changes in the US healthcare system.”
*Information in the abstract differs from that presented at the meeting.
GRAPEVINE, TEXAS—Acute leukemia patients who undergo cord blood (CB) transplant have longer hospital stays than patients who receive other types of transplant, new research indicates.
The study also suggests the length of stay (LOS) is similar whether patients receive double or single CB grafts.
So it seems strategies are needed to decrease hospital stay after CB transplant, particularly as LOS drives the cost of care, said Karen K. Ballen, MD, of Massachusetts General Hospital in Boston.
Dr Ballen presented this research at the 2014 BMT Tandem Meetings as abstract 104.*
She and her colleagues studied patients diagnosed with acute leukemias who were transplanted at US centers and reported to the CIBMTR between 2008 and 2011.
Patients were eligible if they received an unrelated single or double CB transplant, an 8/8 matched unrelated donor (MUD) transplant with peripheral blood (PB) or bone marrow (BM), or a 7/8 MUD PB or BM graft.
In all, 1796 patients met these criteria. The researchers evaluated patients’ total hospital LOS in the first 100 days after transplant, compared LOS among graft sources, and looked for predictors of LOS in the first 100 days.
The team stratified patients according to age and conditioning regimen. Pediatric patients were classified as those aged 18 and younger, and they only received myeloablative conditioning (MAC). Adults received either MAC or reduced-intensity conditioning (RIC).
Pediatric patients
In a univariate analysis of the 368 pediatric patients, there was no significant difference in 100-day survival according to graft source (P=0.13).
However, patients who received single or double CB grafts had a significantly higher median total LOS by day 100 than patients who received 8/8 MUD BM, which was the only other graft source in this patient group (P=0.03).
Patients who received CB grafts also had significantly fewer days in which they were alive and not in the hospital (P=0.005).
“We wanted to account for patients whose length of stay was short because they actually died early after transplant,” Dr Ballen explained. “Therefore, we did an analysis of days alive and not in the hospital.”
In a multivariate analysis, pediatric patients who received CB grafts had significantly fewer days alive and out of the hospital than those who received 8/8 MUD BM (P=0.03).
Other factors associated with fewer days alive and out of the hospital were CMV positivity (P=0.01), black race (P=0.01), and a Karnofsky performance score of less than 80 (P=0.03).
Adults on MAC
In a univariate analysis of the 768 adults who received MAC, recipients of CB grafts had significantly worse 100-day survival than their peers (P<0.001), as well as a longer median LOS by day 100 (P<0.001) and fewer days alive and not in the hospital (P<0.001).
In a multivariate analysis, adults who received MAC had significantly fewer days alive and out of the hospital if they received CB grafts than if they received 8/8 MUD BM (P<0.001), 8/8 MUD PB (P<0.001), or 7/8 MUD PB (P=0.01), but not 7/8 MUD BM (P=0.49).
Other factors associated with fewer days alive and out of the hospital were black race (P=0.04), having acute lymphocytic leukemia rather than acute myeloid leukemia (P=0.01), and age 18-25 (P=0.01).
“We were a little surprised at these results—that the older patients actually spent more time alive and out of the hospital,” Dr Ballen said.
Adults on RIC
In a univariate analysis of the 660 adults who received RIC, recipients of CB grafts had significantly worse 100-day survival than their peers (P=0.017), as well as a longer median LOS by day 100 (P<0.001) and fewer days alive and not in the hospital (P<0.001).
In a multivariate analysis, adults who received RIC had significantly fewer days alive and out of the hospital if they received CB grafts than if they received 8/8 MUD PB (P<0.001) or 7/8 MUD PB (P<0.001).
No other factors were associated with the number of days these patients were alive and out of the hospital.
These results, when taken together, suggest that CB grafts are associated with longer hospital stays, independent of other factors.
“The majority of cost appears to be driven by the number of days in the hospital,” Dr Ballen noted. “So these data may be important for resource allocation, especially given the recent changes in the US healthcare system.”
*Information in the abstract differs from that presented at the meeting.
England’s Cancer Drugs Fund raises concerns
Credit: Rhoda Baer
Cancer patients in England are more likely to receive prescriptions for expensive drugs than patients in Wales, according to a study published in the British Journal of Cancer.
The research suggests this disparity is associated with the Cancer Drugs Fund (CDF), money set aside by the English government to pay for drugs that haven’t been approved by the National Institute for Health and Care Excellence (NICE) and aren’t available within the National Health Service (NHS).
The governments of Wales, Scotland, and Northern Ireland do not have access to the CDF or have similar programs of their own.
“There’s been much debate surrounding the Cancer Drugs Fund,” said study author Charlotte Chamberlain, MBBS, of the University of Bristol in the UK.
“The vast majority of Cancer Drugs Fund drugs do not cure the cancer but may extend life or improve symptoms in some people. The high cost of these drugs means that the NHS cannot afford other treatments, and, therefore, critics argue that public money is being spent inefficiently.”
To assess the impact of the CDF, Dr Chamberlain and her colleagues analyzed data from hospital pharmacies in England and Wales from August 2007 to December 2012. (The CDF was established in 2010, and the researchers wanted to capture data from before and after its introduction.)
The team evaluated 15 drugs that represent different categories of NICE approval—recommended, not recommended, and not yet appraised.
The results showed that, after the CDF was established, drugs recommended by NICE were not prescribed any more in England than in Wales.
However, drugs that were rejected by NICE because they were not cost-effective were prescribed up to 7 times more often in England than in Wales. For example, in the year before the CDF was introduced, prescription rates of imatinib (which is not recommended by NICE) were substantially higher in England than in Wales.
Immediately before the introduction of the CDF, following the first NICE rejection, imatinib prescribing declined in both countries. But it declined more slowly in England than in Wales, despite 2 additional NICE rejections. Regression analysis showed evidence of an association between the CDF and increased prescribing in England compared to Wales (P<0.001).
The research also revealed surprising information regarding the 3 most recently launched drugs—bendamustine, pazopanib, and abiraterone, which were awaiting NICE appraisal when the CDF was established but have since been approved.
These drugs were prescribed less often in England than in Wales. For instance, prescription rates of bendamustine were 25% lower in England.
This finding suggests that physicians in England have been slower to adopt newer drugs that are cost-effective, the researchers said.
“Our research has highlighted that the CDF has created an inequality between cancer sufferers in England and those in Wales,” Dr Chamberlain noted. “This raises ethical, moral, financial, and policy concerns.”
Credit: Rhoda Baer
Cancer patients in England are more likely to receive prescriptions for expensive drugs than patients in Wales, according to a study published in the British Journal of Cancer.
The research suggests this disparity is associated with the Cancer Drugs Fund (CDF), money set aside by the English government to pay for drugs that haven’t been approved by the National Institute for Health and Care Excellence (NICE) and aren’t available within the National Health Service (NHS).
The governments of Wales, Scotland, and Northern Ireland do not have access to the CDF or have similar programs of their own.
“There’s been much debate surrounding the Cancer Drugs Fund,” said study author Charlotte Chamberlain, MBBS, of the University of Bristol in the UK.
“The vast majority of Cancer Drugs Fund drugs do not cure the cancer but may extend life or improve symptoms in some people. The high cost of these drugs means that the NHS cannot afford other treatments, and, therefore, critics argue that public money is being spent inefficiently.”
To assess the impact of the CDF, Dr Chamberlain and her colleagues analyzed data from hospital pharmacies in England and Wales from August 2007 to December 2012. (The CDF was established in 2010, and the researchers wanted to capture data from before and after its introduction.)
The team evaluated 15 drugs that represent different categories of NICE approval—recommended, not recommended, and not yet appraised.
The results showed that, after the CDF was established, drugs recommended by NICE were not prescribed any more in England than in Wales.
However, drugs that were rejected by NICE because they were not cost-effective were prescribed up to 7 times more often in England than in Wales. For example, in the year before the CDF was introduced, prescription rates of imatinib (which is not recommended by NICE) were substantially higher in England than in Wales.
Immediately before the introduction of the CDF, following the first NICE rejection, imatinib prescribing declined in both countries. But it declined more slowly in England than in Wales, despite 2 additional NICE rejections. Regression analysis showed evidence of an association between the CDF and increased prescribing in England compared to Wales (P<0.001).
The research also revealed surprising information regarding the 3 most recently launched drugs—bendamustine, pazopanib, and abiraterone, which were awaiting NICE appraisal when the CDF was established but have since been approved.
These drugs were prescribed less often in England than in Wales. For instance, prescription rates of bendamustine were 25% lower in England.
This finding suggests that physicians in England have been slower to adopt newer drugs that are cost-effective, the researchers said.
“Our research has highlighted that the CDF has created an inequality between cancer sufferers in England and those in Wales,” Dr Chamberlain noted. “This raises ethical, moral, financial, and policy concerns.”
Credit: Rhoda Baer
Cancer patients in England are more likely to receive prescriptions for expensive drugs than patients in Wales, according to a study published in the British Journal of Cancer.
The research suggests this disparity is associated with the Cancer Drugs Fund (CDF), money set aside by the English government to pay for drugs that haven’t been approved by the National Institute for Health and Care Excellence (NICE) and aren’t available within the National Health Service (NHS).
The governments of Wales, Scotland, and Northern Ireland do not have access to the CDF or have similar programs of their own.
“There’s been much debate surrounding the Cancer Drugs Fund,” said study author Charlotte Chamberlain, MBBS, of the University of Bristol in the UK.
“The vast majority of Cancer Drugs Fund drugs do not cure the cancer but may extend life or improve symptoms in some people. The high cost of these drugs means that the NHS cannot afford other treatments, and, therefore, critics argue that public money is being spent inefficiently.”
To assess the impact of the CDF, Dr Chamberlain and her colleagues analyzed data from hospital pharmacies in England and Wales from August 2007 to December 2012. (The CDF was established in 2010, and the researchers wanted to capture data from before and after its introduction.)
The team evaluated 15 drugs that represent different categories of NICE approval—recommended, not recommended, and not yet appraised.
The results showed that, after the CDF was established, drugs recommended by NICE were not prescribed any more in England than in Wales.
However, drugs that were rejected by NICE because they were not cost-effective were prescribed up to 7 times more often in England than in Wales. For example, in the year before the CDF was introduced, prescription rates of imatinib (which is not recommended by NICE) were substantially higher in England than in Wales.
Immediately before the introduction of the CDF, following the first NICE rejection, imatinib prescribing declined in both countries. But it declined more slowly in England than in Wales, despite 2 additional NICE rejections. Regression analysis showed evidence of an association between the CDF and increased prescribing in England compared to Wales (P<0.001).
The research also revealed surprising information regarding the 3 most recently launched drugs—bendamustine, pazopanib, and abiraterone, which were awaiting NICE appraisal when the CDF was established but have since been approved.
These drugs were prescribed less often in England than in Wales. For instance, prescription rates of bendamustine were 25% lower in England.
This finding suggests that physicians in England have been slower to adopt newer drugs that are cost-effective, the researchers said.
“Our research has highlighted that the CDF has created an inequality between cancer sufferers in England and those in Wales,” Dr Chamberlain noted. “This raises ethical, moral, financial, and policy concerns.”