PHILADELPHIA –  The safety of sentinel lymph node biopsy (SLNB) alone without axillary lymph node dissection (ALND) has been established for patients with cT1-2N0 cancer that are found to have one or two metastatic sentinel lymph nodes who undergo breast conservation therapy, but questions regarding the role of regional radiation have persisted. 
This issue is addressed by the results of a large, prospective, 5+ year study at Memorial Sloan Kettering Cancer Center which confirmed the safety of omitting axillary lymph node dissection and suggested that regional radiation provides minimal benefit. 

PHILADELPHIA –  The safety of sentinel lymph node biopsy (SLNB) alone without axillary lymph node dissection (ALND) has been established for patients with cT1-2N0 cancer that are found to have one or two metastatic sentinel lymph nodes who undergo breast conservation therapy, but questions regarding the role of regional radiation have persisted. 
This issue is addressed by the results of a large, prospective, 5+ year study at Memorial Sloan Kettering Cancer Center which confirmed the safety of omitting axillary lymph node dissection and suggested that regional radiation provides minimal benefit. 

PHILADELPHIA –  The safety of sentinel lymph node biopsy (SLNB) alone without axillary lymph node dissection (ALND) has been established for patients with cT1-2N0 cancer that are found to have one or two metastatic sentinel lymph nodes who undergo breast conservation therapy, but questions regarding the role of regional radiation have persisted. 
This issue is addressed by the results of a large, prospective, 5+ year study at Memorial Sloan Kettering Cancer Center which confirmed the safety of omitting axillary lymph node dissection and suggested that regional radiation provides minimal benefit. 

Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,¹ patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit² and those with acute myocardial infarction (AMI),^3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.⁶ Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,⁷ indicating ambiguity in this care process.

Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.^8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,¹¹ and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources ^12-13 and experience worse outcomes compared to nontransferred patients,¹¹ although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.^14-16

To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.¹⁷ A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.¹⁸ This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.

To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.

We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,¹⁹ which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.

Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.²⁰

Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.

Patient Characteristics

Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;²¹ presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.^11,22

Hospital Characteristics

Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.^23-24

Analysis

Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.

To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.²⁵ Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.

To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25^th and 75^th percentiles, illustrating the range of IHT rates for the middle half of hospitals.

Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).

Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).

After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).

In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.

Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,^3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.¹¹ Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.^5,26 Thus, it is reasonable to presume that many of the beneficiaries

In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.

Disclosure

Nothing to report.

Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,¹ patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit² and those with acute myocardial infarction (AMI),^3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.⁶ Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,⁷ indicating ambiguity in this care process.

Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.^8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,¹¹ and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources ^12-13 and experience worse outcomes compared to nontransferred patients,¹¹ although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.^14-16

To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.¹⁷ A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.¹⁸ This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.

To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.

We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,¹⁹ which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.

Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.²⁰

Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.

Patient Characteristics

Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;²¹ presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.^11,22

Hospital Characteristics

Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.^23-24

Analysis

Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.

To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.²⁵ Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.

To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25^th and 75^th percentiles, illustrating the range of IHT rates for the middle half of hospitals.

Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).

Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).

After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).

In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.

Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,^3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.¹¹ Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.^5,26 Thus, it is reasonable to presume that many of the beneficiaries

In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.

Disclosure

Nothing to report.

Interhospital transfer (IHT) is defined as the transfer of hospitalized patients between acute care hospitals. Although cited reasons for transfer include providing patients access to unique specialty services,¹ patterns and practices of IHT remain largely unstudied. Interhospital transfer is known to be common in certain patient populations, including selected patients presenting to the intensive care unit² and those with acute myocardial infarction (AMI),^3-5 but no recent studies have looked at frequency of IHT among a broader group of hospitalized patients nationally. Little is known about which patients are selected for transfer and why.⁶ Limited evidence suggests poor concordance between cited reason for transfer among patients, transferring physicians, and receiving physicians,⁷ indicating ambiguity in this care process.

Interhospital transfer exposes patients to the potential risks associated with discontinuity of care. Communication is particularly vulnerable to error during times of transition.^8-10 Patients transferred between acute care hospitals are especially vulnerable, given the severity of illness in this patient population,¹¹ and the absence of other factors to fill in gaps in communication, such as common electronic health records. Limited existing literature suggests transferred patients use more resources ^12-13 and experience worse outcomes compared to nontransferred patients,¹¹ although these data involved limited patient populations, and adjustment for illness severity and other factors was variably addressed.^14-16

To improve the quality and safety of IHT, therefore, it is necessary to understand which patients benefit from IHT and identify best practices in the IHT process.¹⁷ A fundamental first step is to study patterns and practices of IHT, in particular with an eye towards identifying unwarranted variation.¹⁸ This is important to understand the prevalence of the issue, provide possible evidence of lack of standardization, and natural experiments with which to identify best practices.

To address this, we conducted a foundational study examining a national sample of Medicare patients to determine the nationwide frequency of IHT among elderly patients, patient and hospital-level predictors of transfer, and hospital variability in IHT practices.

We performed a cross-sectional analysis using 2 nationally representative datasets: (1) Center for Medicare and Medicaid Services (CMS) 2013 100% Master Beneficiary Summary and Inpatient claims files, which contains data on all fee-for-service program Medicare enrollees’ demographic information, date of death, and hospitalization claims, including ICD-9 codes for diagnoses, diagnosis-related group (DRG), and dates of service; merged with (2) 2013 American Hospital Association (AHA) data,¹⁹ which contains hospital-level characteristics for all acute care hospitals in the U.S. Our study protocol was approved by the Partners Healthcare Human Subjects Review Committee.

Beneficiaries were eligible for inclusion if they were 65 years or older, continuously enrolled in Medicare A and B, with an acute care hospitalization claim in 2013, excluding Medicare managed care and end-stage renal disease (ESRD) beneficiaries. We additionally excluded beneficiaries hospitalized at federal or nonacute care hospitals, or critical access hospitals given their mission to stabilize and transfer patients to referral hospitals.²⁰

Transferred patients were defined as: (1) beneficiaries with a “transfer out” claim and a corresponding “transfer in” claim at a different hospital; as well as (2) beneficiaries with a “transfer out” claim and a corresponding date of admission to another hospital within 1 day following the date of claim; and (3) beneficiaries with a “transfer in” claim and a corresponding date of discharge from another hospital within 1 day preceding the date of claim. Beneficiaries transferred to the same hospital, or cared for at hospitals with “outlier” transfer in rates equal to 100% or transfer out rates greater than 35%, were excluded from analysis given the suggestion of nonstandard claims practices. Beneficiaries with greater than 1 transfer within the same hospitalization were additionally excluded.

Patient Characteristics

Patient characteristics were obtained from the CMS data files and included: demographics (age, sex, race); DRG-weight, categorized into quartiles; primary diagnosis for the index hospitalization using ICD-9 codes; patient comorbidity using ICD-9 codes compiled into a CMS-Hierarchical Condition Category (HCC) risk score;²¹ presence of Medicaid co-insurance; number of hospitalizations in the past 12 months, categorized into 0, 1, 2-3, and 4 or more; season, defined as calendar quarters; and median income per household by census tract. These characteristics were chosen a priori given expert opinion in combination with prior research demonstrating association with IHT.^11,22

Hospital Characteristics

Hospital characteristics were obtained from AHA data files and included hospitals’ size, categorized into small, medium, and large (less than 100, 100 to 399, 400 or more beds); geographic location; ownership; teaching status; setting (urban vs. rural); case mix index (CMI) for all patients cared for at the hospital; and presence of selected specialty services, including certified trauma center, medical intensive care unit, cardiac intensive care unit, cardiac surgery services, adult interventional cardiac catheterization, adult cardiac electrophysiology, and composite score of presence of 55 other specialty services (complete list in Appendix A). All characteristics were chosen a priori given expert opinion or relationship of characteristics with IHT, and prior research utilizing AHA data.^23-24

Analysis

Descriptive statistics were used to evaluate the frequency of IHT, characteristics of transferred patients, and number of days to transfer. Patient and hospital characteristics of transferred vs. nontransferred patients were compared using chi-square analyses.

To analyze the effects of each patient and hospital characteristic on the odds of transfer, we used logistic regression models incorporating all patient and hospital characteristics, accounting for fixed effects for diagnosis, and utilizing generalized estimating equations (the GENMOD procedure in SAS statistical software, v 9.4; SAS Institute Inc., Cary, North Carolina) to account for the clustering of patients within hospitals.²⁵ Indicator variables were created for missing covariate data and included in analyses when missing data accounted for greater than 10% of the total cohort.

To measure the variability in transfer rates between hospitals, we used a sequence of random effects logistic regression models. We first ran a model with no covariates, representing the unadjusted differences in transfer rates between hospitals. We then added patient characteristics to see if the unadjusted differences in IHT rates were explained by differences in patient characteristics between hospitals. Lastly, we added hospital characteristics to determine if these explained the remaining differences in transfer rates. Each of the 3 models provided a measure of between-hospital variability, reflecting the degree to which IHT rates differed between hospitals. Additionally, we used the intercept from the unadjusted model and the measure of between-hospital variability from each model to calculate the 95% confidence intervals, illustrating the range of IHT rates spanning 95% of all hospitals. We used those same numbers to calculate the 25^th and 75^th percentiles, illustrating the range of IHT rates for the middle half of hospitals.

Among 28 million eligible beneficiaries, 6.6 million had an acute care hospitalization to nonfederal, noncritical access hospitals, and 107,741 met our defined criteria for IHT. An additional 3790 beneficiaries were excluded for being transferred to the same facility, 416 beneficiaries (115 transferred, 301 nontransferred) were excluded as they were cared for at 1 of the 11 hospitals with “outlier” transfer in/out rates, and 2329 were excluded because they had more than 1 transfer during hospitalization. Thus, the final cohort consisted of 101,507 transferred (1.5%) and 6,625,474 nontransferred beneficiaries (Figure 1). Of the 101,507 transferred beneficiaries, 2799 (2.8%) were included more than once (ie, experienced more than 1 IHT on separate hospitalizations throughout the study period; the vast majority of these had 2 separate hospitalizations resulting in IHT). Characteristics of transferred and nontransferred beneficiaries are shown (Table 1).

Among transferred patients, the top 5 primary diagnoses at time of transfer included AMI (12.2%), congestive heart failure (CHF) (7.2%), sepsis (6.6%), arrhythmia (6.6%), and pneumonia (3.4%). Comorbid conditions most commonly present in transferred patients included CHF (52.6%), renal failure (51.8%), arrhythmia (49.8%), and chronic obstructive pulmonary disease (COPD; 37.0%). The most common day of transfer was day after admission (hospital day 2, 24.7%), with 75% of transferred patients transferred before hospital day 6 (Appendix B).

After adjusting for all other patient and hospital characteristics and clustering by hospital, the following variables were associated with greater odds of transfer: older age, male sex, nonblack race, non-Medicaid co-insurance, higher comorbidity (HCC score), lower DRG-weight, and fewer hospitalizations in the prior 12 months. Beneficiaries also had greater odds of transfer if initially hospitalized at smaller hospitals, nonteaching hospitals, public hospitals, at hospitals in the Northeast, those with fewer specialty services, and those with a low CMI (Table 2).

In this nationally representative study of 6.6 million Medicare beneficiaries, we found that 1.5% of patients were transferred between acute care facilities and were most often transferred prior to hospital day 6. Older age, male sex, nonblack race, higher medical comorbidity, lower DRG weight, and fewer recent hospitalizations were associated with greater odds of transfer. Initial hospitalization at smaller, nonteaching, public hospitals, with fewer specialty services were associated with greater odds of transfer, while higher CMI was associated with a lower odds of transfer. The most common comorbid conditions among transferred patients included CHF, renal failure, arrhythmia, and COPD; particularly notable was the very high prevalence of these conditions among transferred as compared with nontransferred patients. Importantly, we found significant variation in IHT by region and a large variation in transfer practices by hospital, with significant variability in transfer rates even after accounting for known patient and hospital characteristics.

Among our examined population, we found that a sizable number of patients undergo IHT—more than 100,000 per year. Primary diagnoses at time of transfer consist of common inpatient conditions, including AMI, CHF, sepsis, arrhythmia, and pneumonia. Limited prior data support our findings, with up to 50% of AMI patients reportedly undergoing IHT,^3-5 and severe sepsis and respiratory illness reported as common diagnoses at transfer.¹¹ Although knowledge of these primary diagnoses does not directly confer an understanding of reason for transfer, one can speculate based on our findings. For example, research demonstrates the majority of AMI patients who undergo IHT had further intervention, including stress testing, cardiac catheterization, and/or coronary artery bypass graft surgery.^5,26 Thus, it is reasonable to presume that many of the beneficiaries

In this national study of IHT, we found that a sizable number of patients admitted to the hospital undergo transfer to another acute care facility. Patients are transferred with common medical conditions, including those requiring specialized care such as AMI, and a high rate of comorbid clinical conditions, and certain patient and hospital characteristics are associated with greater odds of transfer. Although many of the observed associations between characteristics and odds of transfer were expected based on limited existing literature, we found several unexpected findings, eg, suggesting the possibility of a threshold beyond which sicker patients are not transferred. Additionally, we found that black and Medicaid patients had lower odds of transfer, which warrants further investigation for potential health care disparity. Importantly, we found much variability in the practice of IHT, as evidenced by the inexplicable differences in transfer by hospital region, and by residual unexplained variability in hospital transfer rates after accounting for patient and hospital characteristics, which may be due to lack of standard guidelines to direct IHT practices. In conclusion, this study of hospitalized Medicare patients provides important foundational information regarding rates and predictors of IHT nationally, as well as unexplained variability that exists within this complex care transition. Further investigation will be essential to understand reasons for, processes related to, and outcomes of transferred patients, to help guide standardization in best practices in care.

Disclosure

Nothing to report.

WAILEA, HAWAII – Nicotinamide is one of the rare proposed agents for skin cancer chemoprevention distinguished by dirt cheap cost combined with a highly reassuring safety profile plus evidence of efficacy – which, together, make it a reasonable option in high risk patients, according to Daniel M. Siegel, MD.

Other agents that fit into that category include the tropical rainforest fern Polypodium leucotomos and milk thistle, added Dr. Siegel, a dermatologist at the State University of New York, Brooklyn.

Bruce Jancin/Frontline Medical News

Nicotinamide

In the case of nicotinamide, the efficacy is actually supported by published level 1 evidence in the form of a highly positive 1-year, double-blind, randomized, placebo-controlled phase III clinical trial.

“You can Google ‘nicotinamide’ and find it at places like Costco and Trader Joe’s for less than 6 cents per day. That makes for a really good risk/benefit ratio. A nickel a day: That’s a cheap one. That’s one where I’d say, ‘Why not?’ It seems to be safe,” Dr. Siegel said.

In the phase III ONTRAC trial, Australian investigators randomized 386 patients who averaged roughly eight nonmelanoma skin cancers in the past 5 years to either 500 mg of oral nicotinamide twice daily or matched placebo for 12 months. During the study period, the nicotinamide group had a statistically significant and clinically meaningful 23% reduction in new nonmelanoma skin cancers, compared with the control group. They also had 13% fewer actinic keratoses at 12 months than controls. And the side effect profile mirrored that of placebo (N Engl J Med. 2015 Oct 22;373[17]:1618-26).

“Nicotinamide is vitamin B₃. It’s not niacin. It doesn’t cause flushing and other vasodilatory effects. It’s actually pretty innocuous,” Dr. Siegel said.

In laboratory studies, nicotinamide has been shown to enhance DNA repair following UV exposure, as well as curb UV-induced immunosuppression.

Polypodium leucotomos Samambaia

This plant, commonly known as calaguala in the Spanish-speaking tropics and samambaia in Brazil, has a centuries-long tradition of safe medicinal use. It is commercially available over-the-counter (OTC) as a standardized product called Heliocare, designed to avoid the guesswork involved in topical sunscreen application. Each capsule contains 240 mg of an extract of P. leucotomos. Dr. Siegel said he takes it daily when he’s in a sunny locale, such as Hawaii.

Milk thistle

This plant, known as Silybum marianum, has silymarin as its bioactive compound. Dermatologist Haines Ely, MD, of the University of California, Davis, has reported therapeutic success using it in porphyria cutanea tarda and other conditions. It has been shown to inhibit photocarcinogenesis in animal studies.

Dr. Siegel said that, while Dr. Ely has told him his preferred preparation is a German OTC product, milk thistle seeds can be found in health food stores, ground to a powder using a coffee bean grinder, and used as a food supplement. Like Polypodium leucotomos and nicotinamide, milk thistle is nontoxic.

Rapamycin

This macrolide compound is produced by the bacterium Streptomyces hygroscopicus. Rapamycin is an immunosuppressant used to coat coronary stents and prevent rejection of transplanted organs. It is an mechanistic target of rapamycin signaling pathway inhibitor being studied as a cancer prevention and antiaging agent.

Science magazine called the discovery that rapamycin increased the lifespan of mice one of the top scientific breakthroughs of 2009. Subsequent animal studies have established that the extended lifespan wasn’t solely the result of rapamycin’s antineoplastic effects but of across-the-board delayed onset of all the major age-related diseases. Thus, rapamycin could turn out to be a true antiaging agent, in Dr. Siegel’s view.

Studies in humans are underway. Researchers at Novartis have reported that a rapamycin-related compound curbed the typical decline in immune function that accompanies aging as reflected in a 20% enhancement in the response to influenza vaccine in elderly volunteers (Sci Transl Med. 2014 Dec 24;6[268]:268ra179).

Dr. Siegel reported serving as a consultant to Ferndale, which markets Heliocare. The SDEF and this news organization are owned by the same parent company.

[email protected]

WAILEA, HAWAII – Nicotinamide is one of the rare proposed agents for skin cancer chemoprevention distinguished by dirt cheap cost combined with a highly reassuring safety profile plus evidence of efficacy – which, together, make it a reasonable option in high risk patients, according to Daniel M. Siegel, MD.

Other agents that fit into that category include the tropical rainforest fern Polypodium leucotomos and milk thistle, added Dr. Siegel, a dermatologist at the State University of New York, Brooklyn.

Bruce Jancin/Frontline Medical News

Nicotinamide

In the case of nicotinamide, the efficacy is actually supported by published level 1 evidence in the form of a highly positive 1-year, double-blind, randomized, placebo-controlled phase III clinical trial.

“You can Google ‘nicotinamide’ and find it at places like Costco and Trader Joe’s for less than 6 cents per day. That makes for a really good risk/benefit ratio. A nickel a day: That’s a cheap one. That’s one where I’d say, ‘Why not?’ It seems to be safe,” Dr. Siegel said.

In the phase III ONTRAC trial, Australian investigators randomized 386 patients who averaged roughly eight nonmelanoma skin cancers in the past 5 years to either 500 mg of oral nicotinamide twice daily or matched placebo for 12 months. During the study period, the nicotinamide group had a statistically significant and clinically meaningful 23% reduction in new nonmelanoma skin cancers, compared with the control group. They also had 13% fewer actinic keratoses at 12 months than controls. And the side effect profile mirrored that of placebo (N Engl J Med. 2015 Oct 22;373[17]:1618-26).

“Nicotinamide is vitamin B₃. It’s not niacin. It doesn’t cause flushing and other vasodilatory effects. It’s actually pretty innocuous,” Dr. Siegel said.

In laboratory studies, nicotinamide has been shown to enhance DNA repair following UV exposure, as well as curb UV-induced immunosuppression.

Polypodium leucotomos Samambaia

This plant, commonly known as calaguala in the Spanish-speaking tropics and samambaia in Brazil, has a centuries-long tradition of safe medicinal use. It is commercially available over-the-counter (OTC) as a standardized product called Heliocare, designed to avoid the guesswork involved in topical sunscreen application. Each capsule contains 240 mg of an extract of P. leucotomos. Dr. Siegel said he takes it daily when he’s in a sunny locale, such as Hawaii.

Milk thistle

This plant, known as Silybum marianum, has silymarin as its bioactive compound. Dermatologist Haines Ely, MD, of the University of California, Davis, has reported therapeutic success using it in porphyria cutanea tarda and other conditions. It has been shown to inhibit photocarcinogenesis in animal studies.

Dr. Siegel said that, while Dr. Ely has told him his preferred preparation is a German OTC product, milk thistle seeds can be found in health food stores, ground to a powder using a coffee bean grinder, and used as a food supplement. Like Polypodium leucotomos and nicotinamide, milk thistle is nontoxic.

Rapamycin

This macrolide compound is produced by the bacterium Streptomyces hygroscopicus. Rapamycin is an immunosuppressant used to coat coronary stents and prevent rejection of transplanted organs. It is an mechanistic target of rapamycin signaling pathway inhibitor being studied as a cancer prevention and antiaging agent.

Science magazine called the discovery that rapamycin increased the lifespan of mice one of the top scientific breakthroughs of 2009. Subsequent animal studies have established that the extended lifespan wasn’t solely the result of rapamycin’s antineoplastic effects but of across-the-board delayed onset of all the major age-related diseases. Thus, rapamycin could turn out to be a true antiaging agent, in Dr. Siegel’s view.

Studies in humans are underway. Researchers at Novartis have reported that a rapamycin-related compound curbed the typical decline in immune function that accompanies aging as reflected in a 20% enhancement in the response to influenza vaccine in elderly volunteers (Sci Transl Med. 2014 Dec 24;6[268]:268ra179).

Dr. Siegel reported serving as a consultant to Ferndale, which markets Heliocare. The SDEF and this news organization are owned by the same parent company.

[email protected]

WAILEA, HAWAII – Nicotinamide is one of the rare proposed agents for skin cancer chemoprevention distinguished by dirt cheap cost combined with a highly reassuring safety profile plus evidence of efficacy – which, together, make it a reasonable option in high risk patients, according to Daniel M. Siegel, MD.

Other agents that fit into that category include the tropical rainforest fern Polypodium leucotomos and milk thistle, added Dr. Siegel, a dermatologist at the State University of New York, Brooklyn.

Bruce Jancin/Frontline Medical News

Nicotinamide

In the case of nicotinamide, the efficacy is actually supported by published level 1 evidence in the form of a highly positive 1-year, double-blind, randomized, placebo-controlled phase III clinical trial.

“You can Google ‘nicotinamide’ and find it at places like Costco and Trader Joe’s for less than 6 cents per day. That makes for a really good risk/benefit ratio. A nickel a day: That’s a cheap one. That’s one where I’d say, ‘Why not?’ It seems to be safe,” Dr. Siegel said.

In the phase III ONTRAC trial, Australian investigators randomized 386 patients who averaged roughly eight nonmelanoma skin cancers in the past 5 years to either 500 mg of oral nicotinamide twice daily or matched placebo for 12 months. During the study period, the nicotinamide group had a statistically significant and clinically meaningful 23% reduction in new nonmelanoma skin cancers, compared with the control group. They also had 13% fewer actinic keratoses at 12 months than controls. And the side effect profile mirrored that of placebo (N Engl J Med. 2015 Oct 22;373[17]:1618-26).

“Nicotinamide is vitamin B₃. It’s not niacin. It doesn’t cause flushing and other vasodilatory effects. It’s actually pretty innocuous,” Dr. Siegel said.

In laboratory studies, nicotinamide has been shown to enhance DNA repair following UV exposure, as well as curb UV-induced immunosuppression.

Polypodium leucotomos Samambaia

This plant, commonly known as calaguala in the Spanish-speaking tropics and samambaia in Brazil, has a centuries-long tradition of safe medicinal use. It is commercially available over-the-counter (OTC) as a standardized product called Heliocare, designed to avoid the guesswork involved in topical sunscreen application. Each capsule contains 240 mg of an extract of P. leucotomos. Dr. Siegel said he takes it daily when he’s in a sunny locale, such as Hawaii.

Milk thistle

This plant, known as Silybum marianum, has silymarin as its bioactive compound. Dermatologist Haines Ely, MD, of the University of California, Davis, has reported therapeutic success using it in porphyria cutanea tarda and other conditions. It has been shown to inhibit photocarcinogenesis in animal studies.

Dr. Siegel said that, while Dr. Ely has told him his preferred preparation is a German OTC product, milk thistle seeds can be found in health food stores, ground to a powder using a coffee bean grinder, and used as a food supplement. Like Polypodium leucotomos and nicotinamide, milk thistle is nontoxic.

Rapamycin

This macrolide compound is produced by the bacterium Streptomyces hygroscopicus. Rapamycin is an immunosuppressant used to coat coronary stents and prevent rejection of transplanted organs. It is an mechanistic target of rapamycin signaling pathway inhibitor being studied as a cancer prevention and antiaging agent.

Science magazine called the discovery that rapamycin increased the lifespan of mice one of the top scientific breakthroughs of 2009. Subsequent animal studies have established that the extended lifespan wasn’t solely the result of rapamycin’s antineoplastic effects but of across-the-board delayed onset of all the major age-related diseases. Thus, rapamycin could turn out to be a true antiaging agent, in Dr. Siegel’s view.

Studies in humans are underway. Researchers at Novartis have reported that a rapamycin-related compound curbed the typical decline in immune function that accompanies aging as reflected in a 20% enhancement in the response to influenza vaccine in elderly volunteers (Sci Transl Med. 2014 Dec 24;6[268]:268ra179).

Dr. Siegel reported serving as a consultant to Ferndale, which markets Heliocare. The SDEF and this news organization are owned by the same parent company.

[email protected]

The targeted agent gefitinib is superior to the standard of care chemotherapy for treating resected early non–small cell lung cancer (NSCLC) harboring an epidermal growth factor receptor (EGFR) activating mutation, finds the phase III randomized Chinese ADJUVANT trial.

Gefitinib, an oral tyrosine kinase inhibitor that targets the EGFR kinase among others, is already approved by the Food and Drug Administration for treatment of locally advanced or metastatic disease having mutations in the gene for this receptor.

Trial results reported in a presscast leading up to the annual meeting of the American Society of Clinical Oncology showed that compared with vinorelbine and cisplatin combination chemotherapy, 2 years of gefitinib prolonged the time to recurrence or death by more than 10 months, reducing risk of these events by a significant 40%. Gefitinib also was better tolerated: The rate of grade 3 or worse adverse events with the targeted agent was one-fourth that seen with the chemotherapy.

“Targeted therapy can delay recurrence of intermediate-stage lung cancer after surgery. Two-year treatment duration of gefitinib is efficacious and tolerated well,” said lead study author Yi-Long Wu, MD, director of the Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangzhou, China. “Adjuvant gefitinib should be considered as an important option for stage II to IIIA lung cancer patients with an activating EGFR mutation.”

Clinical implications

The improved disease-free survival seen with gefitinib in ADJUVANT is “encouraging,” according to ASCO President-Elect Bruce E. Johnson, MD, chief clinical research officer and an Institute Physician at the Dana-Farber Cancer Institute in Boston.

Longer follow-up will be needed to obtain a full picture as the horizon for events in the adjuvant setting is more on the order of years, and the disease-free survival curves began converging over time, he noted. “We will ultimately be interested in seeing whether this actually prolongs survival in a longer follow-up study, which Dr. Wu’s group is planning to do.

“I haven’t changed my approach yet for the patients with EGFR-mutant lung cancer,” Dr. Johnson concluded. “But I will be following this [trial] very closely to see what happens to the survival.”

The new data from ADJUVANT will likely have several effects on the clinical management of NSCLC, according to presscast moderator and ASCO Chief Medical Officer Richard L. Schilsky, MD.

“I suspect that many doctors will begin testing these lung cancer tumors right after surgery, to see if they actually have an EGFR mutation. That is not currently standard of care in the U.S.; typically the testing doesn’t take place until the cancer recurs or becomes metastatic,” he said. “So that way, doctors and patients will know whether or not treatment with an EGFR inhibitor is even an option.

“If it is an option, then many factors will likely come into play, and most importantly we will be waiting for the survival data,” said Dr. Schilsky, professor emeritus at the University of Chicago. Another consideration is that the trial compared 12 weeks of chemotherapy with 2 years of continuous gefitinib therapy, the latter of which requires a long-term commitment to adherence by patients and carries much greater cost.

“At the end of the day, I think that once the survival data is known in particular, doctors and patients are going to have to have very thoughtful discussions about what is the magnitude of the survival benefit; what is the burden on the patient to take either cytotoxic chemotherapy for 12 weeks or 2 years of an oral treatment, which, while it is less toxic, is not without toxicity; and what’s the financial burden of that treatment choice going to be for the patient,” he concluded.

Study details

Eligibility for the ADJUVANT trial required completely resected pathological stage II-IIIA (N1-N2) NSCLC with an EGFR-activating mutation. In all, 220 patients were randomized evenly to receive gefitinib (Iressa) once daily for 24 months or vinorelbine plus cisplatin every 3 weeks for 4 cycles.

Results showed that median disease-free survival – the trial’s primary endpoint—was 28.7 months with gefitinib compared with 18.0 months with chemotherapy (hazard ratio, 0.60; P = .005), Dr. Wu reported in the presscast. Corresponding 3-year disease-free survival rates were 34% and 27%.

The rate of grade 3 or worse adverse events was 12.3% in the gefitinib group, compared with 48.3% in the chemotherapy group. Most types of events were less common with the tyrosine kinase inhibitor, with the exception of rash, diarrhea, and elevation of liver enzymes.

Dr. Wu disclosed ties with AstraZeneca, Roche, Merck, Boehringer Ingelheim; Lilly, Pierre Fabre, Pfizer, and Sanofi. The Chinese Thoracic Oncology Group and AstraZeneca Chin funded the trial.

The targeted agent gefitinib is superior to the standard of care chemotherapy for treating resected early non–small cell lung cancer (NSCLC) harboring an epidermal growth factor receptor (EGFR) activating mutation, finds the phase III randomized Chinese ADJUVANT trial.

Gefitinib, an oral tyrosine kinase inhibitor that targets the EGFR kinase among others, is already approved by the Food and Drug Administration for treatment of locally advanced or metastatic disease having mutations in the gene for this receptor.

Trial results reported in a presscast leading up to the annual meeting of the American Society of Clinical Oncology showed that compared with vinorelbine and cisplatin combination chemotherapy, 2 years of gefitinib prolonged the time to recurrence or death by more than 10 months, reducing risk of these events by a significant 40%. Gefitinib also was better tolerated: The rate of grade 3 or worse adverse events with the targeted agent was one-fourth that seen with the chemotherapy.

“Targeted therapy can delay recurrence of intermediate-stage lung cancer after surgery. Two-year treatment duration of gefitinib is efficacious and tolerated well,” said lead study author Yi-Long Wu, MD, director of the Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangzhou, China. “Adjuvant gefitinib should be considered as an important option for stage II to IIIA lung cancer patients with an activating EGFR mutation.”

Clinical implications

The improved disease-free survival seen with gefitinib in ADJUVANT is “encouraging,” according to ASCO President-Elect Bruce E. Johnson, MD, chief clinical research officer and an Institute Physician at the Dana-Farber Cancer Institute in Boston.

Longer follow-up will be needed to obtain a full picture as the horizon for events in the adjuvant setting is more on the order of years, and the disease-free survival curves began converging over time, he noted. “We will ultimately be interested in seeing whether this actually prolongs survival in a longer follow-up study, which Dr. Wu’s group is planning to do.

“I haven’t changed my approach yet for the patients with EGFR-mutant lung cancer,” Dr. Johnson concluded. “But I will be following this [trial] very closely to see what happens to the survival.”

The new data from ADJUVANT will likely have several effects on the clinical management of NSCLC, according to presscast moderator and ASCO Chief Medical Officer Richard L. Schilsky, MD.

“I suspect that many doctors will begin testing these lung cancer tumors right after surgery, to see if they actually have an EGFR mutation. That is not currently standard of care in the U.S.; typically the testing doesn’t take place until the cancer recurs or becomes metastatic,” he said. “So that way, doctors and patients will know whether or not treatment with an EGFR inhibitor is even an option.

“If it is an option, then many factors will likely come into play, and most importantly we will be waiting for the survival data,” said Dr. Schilsky, professor emeritus at the University of Chicago. Another consideration is that the trial compared 12 weeks of chemotherapy with 2 years of continuous gefitinib therapy, the latter of which requires a long-term commitment to adherence by patients and carries much greater cost.

“At the end of the day, I think that once the survival data is known in particular, doctors and patients are going to have to have very thoughtful discussions about what is the magnitude of the survival benefit; what is the burden on the patient to take either cytotoxic chemotherapy for 12 weeks or 2 years of an oral treatment, which, while it is less toxic, is not without toxicity; and what’s the financial burden of that treatment choice going to be for the patient,” he concluded.

Study details

Eligibility for the ADJUVANT trial required completely resected pathological stage II-IIIA (N1-N2) NSCLC with an EGFR-activating mutation. In all, 220 patients were randomized evenly to receive gefitinib (Iressa) once daily for 24 months or vinorelbine plus cisplatin every 3 weeks for 4 cycles.

Results showed that median disease-free survival – the trial’s primary endpoint—was 28.7 months with gefitinib compared with 18.0 months with chemotherapy (hazard ratio, 0.60; P = .005), Dr. Wu reported in the presscast. Corresponding 3-year disease-free survival rates were 34% and 27%.

The rate of grade 3 or worse adverse events was 12.3% in the gefitinib group, compared with 48.3% in the chemotherapy group. Most types of events were less common with the tyrosine kinase inhibitor, with the exception of rash, diarrhea, and elevation of liver enzymes.

Dr. Wu disclosed ties with AstraZeneca, Roche, Merck, Boehringer Ingelheim; Lilly, Pierre Fabre, Pfizer, and Sanofi. The Chinese Thoracic Oncology Group and AstraZeneca Chin funded the trial.

The targeted agent gefitinib is superior to the standard of care chemotherapy for treating resected early non–small cell lung cancer (NSCLC) harboring an epidermal growth factor receptor (EGFR) activating mutation, finds the phase III randomized Chinese ADJUVANT trial.

Gefitinib, an oral tyrosine kinase inhibitor that targets the EGFR kinase among others, is already approved by the Food and Drug Administration for treatment of locally advanced or metastatic disease having mutations in the gene for this receptor.

Trial results reported in a presscast leading up to the annual meeting of the American Society of Clinical Oncology showed that compared with vinorelbine and cisplatin combination chemotherapy, 2 years of gefitinib prolonged the time to recurrence or death by more than 10 months, reducing risk of these events by a significant 40%. Gefitinib also was better tolerated: The rate of grade 3 or worse adverse events with the targeted agent was one-fourth that seen with the chemotherapy.

“Targeted therapy can delay recurrence of intermediate-stage lung cancer after surgery. Two-year treatment duration of gefitinib is efficacious and tolerated well,” said lead study author Yi-Long Wu, MD, director of the Guangdong Lung Cancer Institute, Guangdong General Hospital, Guangzhou, China. “Adjuvant gefitinib should be considered as an important option for stage II to IIIA lung cancer patients with an activating EGFR mutation.”

Clinical implications

The improved disease-free survival seen with gefitinib in ADJUVANT is “encouraging,” according to ASCO President-Elect Bruce E. Johnson, MD, chief clinical research officer and an Institute Physician at the Dana-Farber Cancer Institute in Boston.

Longer follow-up will be needed to obtain a full picture as the horizon for events in the adjuvant setting is more on the order of years, and the disease-free survival curves began converging over time, he noted. “We will ultimately be interested in seeing whether this actually prolongs survival in a longer follow-up study, which Dr. Wu’s group is planning to do.

“I haven’t changed my approach yet for the patients with EGFR-mutant lung cancer,” Dr. Johnson concluded. “But I will be following this [trial] very closely to see what happens to the survival.”

The new data from ADJUVANT will likely have several effects on the clinical management of NSCLC, according to presscast moderator and ASCO Chief Medical Officer Richard L. Schilsky, MD.

“I suspect that many doctors will begin testing these lung cancer tumors right after surgery, to see if they actually have an EGFR mutation. That is not currently standard of care in the U.S.; typically the testing doesn’t take place until the cancer recurs or becomes metastatic,” he said. “So that way, doctors and patients will know whether or not treatment with an EGFR inhibitor is even an option.

“If it is an option, then many factors will likely come into play, and most importantly we will be waiting for the survival data,” said Dr. Schilsky, professor emeritus at the University of Chicago. Another consideration is that the trial compared 12 weeks of chemotherapy with 2 years of continuous gefitinib therapy, the latter of which requires a long-term commitment to adherence by patients and carries much greater cost.

“At the end of the day, I think that once the survival data is known in particular, doctors and patients are going to have to have very thoughtful discussions about what is the magnitude of the survival benefit; what is the burden on the patient to take either cytotoxic chemotherapy for 12 weeks or 2 years of an oral treatment, which, while it is less toxic, is not without toxicity; and what’s the financial burden of that treatment choice going to be for the patient,” he concluded.

Study details

Eligibility for the ADJUVANT trial required completely resected pathological stage II-IIIA (N1-N2) NSCLC with an EGFR-activating mutation. In all, 220 patients were randomized evenly to receive gefitinib (Iressa) once daily for 24 months or vinorelbine plus cisplatin every 3 weeks for 4 cycles.

Results showed that median disease-free survival – the trial’s primary endpoint—was 28.7 months with gefitinib compared with 18.0 months with chemotherapy (hazard ratio, 0.60; P = .005), Dr. Wu reported in the presscast. Corresponding 3-year disease-free survival rates were 34% and 27%.

The rate of grade 3 or worse adverse events was 12.3% in the gefitinib group, compared with 48.3% in the chemotherapy group. Most types of events were less common with the tyrosine kinase inhibitor, with the exception of rash, diarrhea, and elevation of liver enzymes.

Dr. Wu disclosed ties with AstraZeneca, Roche, Merck, Boehringer Ingelheim; Lilly, Pierre Fabre, Pfizer, and Sanofi. The Chinese Thoracic Oncology Group and AstraZeneca Chin funded the trial.

Dramatic changes could be on the horizon for women’s health care should the controversial American Health Care Act of 2017 (AHCA) become law.

In May, the House of Representatives passed the AHCA, a bill that would replace many elements of the Affordable Care Act (ACA). The legislation is now being considered by the Senate, where it’s future is uncertain.

From contraceptive coverage to maternity care to abortion services, women have much at stake under the bill, said Kandice A. Kapinos, PhD, an economist who specializes in maternal health care at the nonpartisan RAND Corporation.

1. Tax credits change

Under the ACA, individuals receive tax credits based on income, which means higher subsidies for patients who are lower income, older, and who live in areas with more expensive coverage. The AHCA would calculate tax credit assistance based primarily on age, and the bill would repeal the ACA’s cost-sharing protections for low-income individuals.

“How these credits are calculated [under the AHCA] will really affect lower-income women,” Dr. Kapinos said. “They will pay more under these calculations because their credits will be lowered. The other women who will be negatively affected by those changes will be women in rural or high-cost areas where care is on average more expensive.”

2. Essential health benefits waiver

The ACA required that marketplace plans and Medicaid expansion plans cover 10 benefit categories, including maternity care, preventive services, mental health, and hospitalizations and emergency care. Under the AHCA, states could apply for a waiver to define their own essential health benefits starting in 2020, leaving states free to exclude certain benefits such as maternity care or pregnancy-related services.

In addition, the AHCA would rescind the essential health benefit requirement for Medicaid expansion programs, meaning that patients in expansion plans would not be entitled to coverage for all 10 categories.

3. Medicaid changes

To reduce federal spending, the AHCA would shift the Medicaid program from an open-ended matching system to an annual fixed amount of federal funds. To this end, states would get to choose between a per capita cap funding approach or a block grant structure. Under a block grant, states would receive a fixed amount of funding for Medicaid that would increase by a specified amount each year. Under a per capita cap, federal funding would be capped based on the number of beneficiaries, or separate caps could be applied per Medicaid coverage groups such as children, adults, seniors, and disabled individuals.

Both capped approaches would limit a state’s ability to respond to rising costs, new and costly treatments, or public health emergencies such as Zika, according to a summary of the AHCA by the Kaiser Family Foundation. Women could get the short end of the stick if states decide to limit the number of women enrollees or if they limit certain benefits, Dr. Kapinos said. For instance, states could decide to cover only lower-cost contraception services, such as birth control pills, rather than more expensive methods such as an IUD, she said. A per capita cap approach would still require states to cover family planning services, but there would no longer be an enhanced federal matching rate for family planning services provided to most beneficiaries, the Kaiser summary notes. Under the block grant option, family planning services would no longer be a mandatory benefit for nondisabled women on Medicaid.

4. Preexisting conditions

The AHCA would retain the current ban on coverage denials for preexisting conditions. However, the bill would charge patients a penalty if they did not maintain continuous insurance coverage and then tried to regain insurance. These patients could pay higher premiums for 1 year or states could obtain a waiver that allows insurers to consider an individual’s health status for 1 year, enabling them to charge higher rates for prior health conditions.

“They could charge you more if you’re pregnant and you haven’t had insurance for more than 2 months,” Ms. Kapinos said. “They could only charge you a higher amount for 1 year and they can’t deny you coverage, but effectively what happens, the woman who’s pregnant would be sent to a higher-risk pool and be charged higher premiums. That could be a big deal for women, especially lower-income women.”

5. Planned Parenthood gets defunded

Although federal law already bans federal funds from paying for most abortions, the AHCA would stop Planned Parenthood from receiving federal Medicaid funding for 1 year. The AHCA would provide additional funds to other community health centers, but the bill does not require the health centers to use the money to provide women’s health services.

An AHCA analysis by the Congressional Budget Office found that withholding Medicaid payments to Planned Parenthood for 1 year would reduce access to care for women in some low-income communities and would result in thousands of unintended pregnancies that would ultimately be financed by Medicaid.

“That means a woman who’s covered by Medicaid and would go to Planned Parenthood to say, get a Pap smear, couldn’t go there and do that because they’re not going to let Medicaid reimburse Planned Parenthood,” Ms. Kapinos said. “That has real implications for access in areas where there aren’t a lot choices to get this kind of preventive care.”

[email protected]

On Twitter @legal_med

Dramatic changes could be on the horizon for women’s health care should the controversial American Health Care Act of 2017 (AHCA) become law.

In May, the House of Representatives passed the AHCA, a bill that would replace many elements of the Affordable Care Act (ACA). The legislation is now being considered by the Senate, where it’s future is uncertain.

From contraceptive coverage to maternity care to abortion services, women have much at stake under the bill, said Kandice A. Kapinos, PhD, an economist who specializes in maternal health care at the nonpartisan RAND Corporation.

1. Tax credits change

Under the ACA, individuals receive tax credits based on income, which means higher subsidies for patients who are lower income, older, and who live in areas with more expensive coverage. The AHCA would calculate tax credit assistance based primarily on age, and the bill would repeal the ACA’s cost-sharing protections for low-income individuals.

“How these credits are calculated [under the AHCA] will really affect lower-income women,” Dr. Kapinos said. “They will pay more under these calculations because their credits will be lowered. The other women who will be negatively affected by those changes will be women in rural or high-cost areas where care is on average more expensive.”

2. Essential health benefits waiver

The ACA required that marketplace plans and Medicaid expansion plans cover 10 benefit categories, including maternity care, preventive services, mental health, and hospitalizations and emergency care. Under the AHCA, states could apply for a waiver to define their own essential health benefits starting in 2020, leaving states free to exclude certain benefits such as maternity care or pregnancy-related services.

In addition, the AHCA would rescind the essential health benefit requirement for Medicaid expansion programs, meaning that patients in expansion plans would not be entitled to coverage for all 10 categories.

3. Medicaid changes

To reduce federal spending, the AHCA would shift the Medicaid program from an open-ended matching system to an annual fixed amount of federal funds. To this end, states would get to choose between a per capita cap funding approach or a block grant structure. Under a block grant, states would receive a fixed amount of funding for Medicaid that would increase by a specified amount each year. Under a per capita cap, federal funding would be capped based on the number of beneficiaries, or separate caps could be applied per Medicaid coverage groups such as children, adults, seniors, and disabled individuals.

Both capped approaches would limit a state’s ability to respond to rising costs, new and costly treatments, or public health emergencies such as Zika, according to a summary of the AHCA by the Kaiser Family Foundation. Women could get the short end of the stick if states decide to limit the number of women enrollees or if they limit certain benefits, Dr. Kapinos said. For instance, states could decide to cover only lower-cost contraception services, such as birth control pills, rather than more expensive methods such as an IUD, she said. A per capita cap approach would still require states to cover family planning services, but there would no longer be an enhanced federal matching rate for family planning services provided to most beneficiaries, the Kaiser summary notes. Under the block grant option, family planning services would no longer be a mandatory benefit for nondisabled women on Medicaid.

4. Preexisting conditions

The AHCA would retain the current ban on coverage denials for preexisting conditions. However, the bill would charge patients a penalty if they did not maintain continuous insurance coverage and then tried to regain insurance. These patients could pay higher premiums for 1 year or states could obtain a waiver that allows insurers to consider an individual’s health status for 1 year, enabling them to charge higher rates for prior health conditions.

“They could charge you more if you’re pregnant and you haven’t had insurance for more than 2 months,” Ms. Kapinos said. “They could only charge you a higher amount for 1 year and they can’t deny you coverage, but effectively what happens, the woman who’s pregnant would be sent to a higher-risk pool and be charged higher premiums. That could be a big deal for women, especially lower-income women.”

5. Planned Parenthood gets defunded

Although federal law already bans federal funds from paying for most abortions, the AHCA would stop Planned Parenthood from receiving federal Medicaid funding for 1 year. The AHCA would provide additional funds to other community health centers, but the bill does not require the health centers to use the money to provide women’s health services.

An AHCA analysis by the Congressional Budget Office found that withholding Medicaid payments to Planned Parenthood for 1 year would reduce access to care for women in some low-income communities and would result in thousands of unintended pregnancies that would ultimately be financed by Medicaid.

“That means a woman who’s covered by Medicaid and would go to Planned Parenthood to say, get a Pap smear, couldn’t go there and do that because they’re not going to let Medicaid reimburse Planned Parenthood,” Ms. Kapinos said. “That has real implications for access in areas where there aren’t a lot choices to get this kind of preventive care.”

[email protected]

On Twitter @legal_med

Dramatic changes could be on the horizon for women’s health care should the controversial American Health Care Act of 2017 (AHCA) become law.

In May, the House of Representatives passed the AHCA, a bill that would replace many elements of the Affordable Care Act (ACA). The legislation is now being considered by the Senate, where it’s future is uncertain.

From contraceptive coverage to maternity care to abortion services, women have much at stake under the bill, said Kandice A. Kapinos, PhD, an economist who specializes in maternal health care at the nonpartisan RAND Corporation.

1. Tax credits change

Under the ACA, individuals receive tax credits based on income, which means higher subsidies for patients who are lower income, older, and who live in areas with more expensive coverage. The AHCA would calculate tax credit assistance based primarily on age, and the bill would repeal the ACA’s cost-sharing protections for low-income individuals.

“How these credits are calculated [under the AHCA] will really affect lower-income women,” Dr. Kapinos said. “They will pay more under these calculations because their credits will be lowered. The other women who will be negatively affected by those changes will be women in rural or high-cost areas where care is on average more expensive.”

2. Essential health benefits waiver

The ACA required that marketplace plans and Medicaid expansion plans cover 10 benefit categories, including maternity care, preventive services, mental health, and hospitalizations and emergency care. Under the AHCA, states could apply for a waiver to define their own essential health benefits starting in 2020, leaving states free to exclude certain benefits such as maternity care or pregnancy-related services.

In addition, the AHCA would rescind the essential health benefit requirement for Medicaid expansion programs, meaning that patients in expansion plans would not be entitled to coverage for all 10 categories.

3. Medicaid changes

To reduce federal spending, the AHCA would shift the Medicaid program from an open-ended matching system to an annual fixed amount of federal funds. To this end, states would get to choose between a per capita cap funding approach or a block grant structure. Under a block grant, states would receive a fixed amount of funding for Medicaid that would increase by a specified amount each year. Under a per capita cap, federal funding would be capped based on the number of beneficiaries, or separate caps could be applied per Medicaid coverage groups such as children, adults, seniors, and disabled individuals.

Both capped approaches would limit a state’s ability to respond to rising costs, new and costly treatments, or public health emergencies such as Zika, according to a summary of the AHCA by the Kaiser Family Foundation. Women could get the short end of the stick if states decide to limit the number of women enrollees or if they limit certain benefits, Dr. Kapinos said. For instance, states could decide to cover only lower-cost contraception services, such as birth control pills, rather than more expensive methods such as an IUD, she said. A per capita cap approach would still require states to cover family planning services, but there would no longer be an enhanced federal matching rate for family planning services provided to most beneficiaries, the Kaiser summary notes. Under the block grant option, family planning services would no longer be a mandatory benefit for nondisabled women on Medicaid.

4. Preexisting conditions

The AHCA would retain the current ban on coverage denials for preexisting conditions. However, the bill would charge patients a penalty if they did not maintain continuous insurance coverage and then tried to regain insurance. These patients could pay higher premiums for 1 year or states could obtain a waiver that allows insurers to consider an individual’s health status for 1 year, enabling them to charge higher rates for prior health conditions.

“They could charge you more if you’re pregnant and you haven’t had insurance for more than 2 months,” Ms. Kapinos said. “They could only charge you a higher amount for 1 year and they can’t deny you coverage, but effectively what happens, the woman who’s pregnant would be sent to a higher-risk pool and be charged higher premiums. That could be a big deal for women, especially lower-income women.”

5. Planned Parenthood gets defunded

Although federal law already bans federal funds from paying for most abortions, the AHCA would stop Planned Parenthood from receiving federal Medicaid funding for 1 year. The AHCA would provide additional funds to other community health centers, but the bill does not require the health centers to use the money to provide women’s health services.

An AHCA analysis by the Congressional Budget Office found that withholding Medicaid payments to Planned Parenthood for 1 year would reduce access to care for women in some low-income communities and would result in thousands of unintended pregnancies that would ultimately be financed by Medicaid.

“That means a woman who’s covered by Medicaid and would go to Planned Parenthood to say, get a Pap smear, couldn’t go there and do that because they’re not going to let Medicaid reimburse Planned Parenthood,” Ms. Kapinos said. “That has real implications for access in areas where there aren’t a lot choices to get this kind of preventive care.”

[email protected]

On Twitter @legal_med

Inpatient hyperglycemia is a very common condition, affecting approximately 38% of patients in the non–intensive care unit setting.

Enhance the efficiency and reliability of your quality improvement efforts to close the gap between best practices and methods for caring for inpatients with hyperglycemia with SHM’s Glycemic Control (GC) Electronic Quality Improvement Program (eQUIPS). The GC eQUIPS program supports the development and implementation of GC programs at hospitals nationwide.

iStock/ThinkStock

• Gaining understanding in the principles of glycemic control
• Improving glycemic control data collection/analysis/and reporting
• Building and obtaining approval for protocols/policies for glycemic control
• Creating a culture for change and change management

When you enroll in the Glycemic Control eQUIPS, you’ll receive:

• Data center for performance tracking. Helps track performance on project milestones and outcomes, and benchmark performance against comparison groups at your institution and other participating facilities.
• Implementation toolkit. Provides stepwise instruction for improving glycemic control, preventing hypoglycemia and optimizing care of the inpatient with hyperglycemia and diabetes.
• Online glycemic control toolkit. Includes clinical tools and interventions, research materials and literature review, informational papers and case studies, teaching slide sets, and more.
• Online community and collaborative:

– Glycemic Control Library of site-created tools and documents allows you to view sample order sets and protocols, awareness campaigns, patient education materials, and various articles.

– National Discussion Forum lets you share professional questions and discuss topics related to the planning, implementation and evaluation of glycemic control interventions.

– Access to on-demand webinar, facilitated by national experts, topics include IV Insulin Management Strategies, Change Management and Introduction to Glycemic Control.



Join the webinar on June 28 from 1–2 p.m., ET, to receive additional information about SHM’s GC programs. Visit hospitalmedicine.org/gc to register or learn more. If you have questions on the program, please email Sara Platt at [email protected].
 

Brett Radler is communications specialist at the Society of Hospital Medicine.

Inpatient hyperglycemia is a very common condition, affecting approximately 38% of patients in the non–intensive care unit setting.

Enhance the efficiency and reliability of your quality improvement efforts to close the gap between best practices and methods for caring for inpatients with hyperglycemia with SHM’s Glycemic Control (GC) Electronic Quality Improvement Program (eQUIPS). The GC eQUIPS program supports the development and implementation of GC programs at hospitals nationwide.

iStock/ThinkStock

• Gaining understanding in the principles of glycemic control
• Improving glycemic control data collection/analysis/and reporting
• Building and obtaining approval for protocols/policies for glycemic control
• Creating a culture for change and change management

When you enroll in the Glycemic Control eQUIPS, you’ll receive:

• Data center for performance tracking. Helps track performance on project milestones and outcomes, and benchmark performance against comparison groups at your institution and other participating facilities.
• Implementation toolkit. Provides stepwise instruction for improving glycemic control, preventing hypoglycemia and optimizing care of the inpatient with hyperglycemia and diabetes.
• Online glycemic control toolkit. Includes clinical tools and interventions, research materials and literature review, informational papers and case studies, teaching slide sets, and more.
• Online community and collaborative:

– Glycemic Control Library of site-created tools and documents allows you to view sample order sets and protocols, awareness campaigns, patient education materials, and various articles.

– National Discussion Forum lets you share professional questions and discuss topics related to the planning, implementation and evaluation of glycemic control interventions.

– Access to on-demand webinar, facilitated by national experts, topics include IV Insulin Management Strategies, Change Management and Introduction to Glycemic Control.



Join the webinar on June 28 from 1–2 p.m., ET, to receive additional information about SHM’s GC programs. Visit hospitalmedicine.org/gc to register or learn more. If you have questions on the program, please email Sara Platt at [email protected].
 

Brett Radler is communications specialist at the Society of Hospital Medicine.

Inpatient hyperglycemia is a very common condition, affecting approximately 38% of patients in the non–intensive care unit setting.

Enhance the efficiency and reliability of your quality improvement efforts to close the gap between best practices and methods for caring for inpatients with hyperglycemia with SHM’s Glycemic Control (GC) Electronic Quality Improvement Program (eQUIPS). The GC eQUIPS program supports the development and implementation of GC programs at hospitals nationwide.

iStock/ThinkStock

• Gaining understanding in the principles of glycemic control
• Improving glycemic control data collection/analysis/and reporting
• Building and obtaining approval for protocols/policies for glycemic control
• Creating a culture for change and change management

When you enroll in the Glycemic Control eQUIPS, you’ll receive:

• Data center for performance tracking. Helps track performance on project milestones and outcomes, and benchmark performance against comparison groups at your institution and other participating facilities.
• Implementation toolkit. Provides stepwise instruction for improving glycemic control, preventing hypoglycemia and optimizing care of the inpatient with hyperglycemia and diabetes.
• Online glycemic control toolkit. Includes clinical tools and interventions, research materials and literature review, informational papers and case studies, teaching slide sets, and more.
• Online community and collaborative:

– Glycemic Control Library of site-created tools and documents allows you to view sample order sets and protocols, awareness campaigns, patient education materials, and various articles.

– National Discussion Forum lets you share professional questions and discuss topics related to the planning, implementation and evaluation of glycemic control interventions.

– Access to on-demand webinar, facilitated by national experts, topics include IV Insulin Management Strategies, Change Management and Introduction to Glycemic Control.



Join the webinar on June 28 from 1–2 p.m., ET, to receive additional information about SHM’s GC programs. Visit hospitalmedicine.org/gc to register or learn more. If you have questions on the program, please email Sara Platt at [email protected].
 

Brett Radler is communications specialist at the Society of Hospital Medicine.

Lenalidomide maintenance therapy further improved depth of response in newly diagnosed, transplant-eligible patients with multiple myeloma in the EMN02/HO95 trial, based on the abstract of a poster to be presented at the annual meeting of the American Society of Clinical Oncology.

The study results also show that using multiparameter flow cytometry to monitor minimal residual disease (MRD) was predictive of patient outcome. A high-risk cytogenetic profile – defined as having del17, translocation (4;14), or translocation (14;16) – was the most important prognostic factor in MRD-positive patients, according to Stefania Oliva, MD, of the University of Torino [Italy] and her colleagues.

Peter Anderson/ Pathology Education Informational Resource (PEIR) Digital Library/ © University of Alabama at Birmingham, Department of Pathology

Citation: Minimal residual disease (MRD) monitoring by multiparameter flow cytometry (MFC) in newly diagnosed transplant eligible multiple myeloma (MM) patients: Results from the EMN02/HO95 phase 3 trial. 2017 ASCO annual meeting. Abstract No: 8011

[email protected]

On Twitter @maryjodales

Lenalidomide maintenance therapy further improved depth of response in newly diagnosed, transplant-eligible patients with multiple myeloma in the EMN02/HO95 trial, based on the abstract of a poster to be presented at the annual meeting of the American Society of Clinical Oncology.

The study results also show that using multiparameter flow cytometry to monitor minimal residual disease (MRD) was predictive of patient outcome. A high-risk cytogenetic profile – defined as having del17, translocation (4;14), or translocation (14;16) – was the most important prognostic factor in MRD-positive patients, according to Stefania Oliva, MD, of the University of Torino [Italy] and her colleagues.

Peter Anderson/ Pathology Education Informational Resource (PEIR) Digital Library/ © University of Alabama at Birmingham, Department of Pathology

Citation: Minimal residual disease (MRD) monitoring by multiparameter flow cytometry (MFC) in newly diagnosed transplant eligible multiple myeloma (MM) patients: Results from the EMN02/HO95 phase 3 trial. 2017 ASCO annual meeting. Abstract No: 8011

[email protected]

On Twitter @maryjodales

Lenalidomide maintenance therapy further improved depth of response in newly diagnosed, transplant-eligible patients with multiple myeloma in the EMN02/HO95 trial, based on the abstract of a poster to be presented at the annual meeting of the American Society of Clinical Oncology.

The study results also show that using multiparameter flow cytometry to monitor minimal residual disease (MRD) was predictive of patient outcome. A high-risk cytogenetic profile – defined as having del17, translocation (4;14), or translocation (14;16) – was the most important prognostic factor in MRD-positive patients, according to Stefania Oliva, MD, of the University of Torino [Italy] and her colleagues.

Peter Anderson/ Pathology Education Informational Resource (PEIR) Digital Library/ © University of Alabama at Birmingham, Department of Pathology

Citation: Minimal residual disease (MRD) monitoring by multiparameter flow cytometry (MFC) in newly diagnosed transplant eligible multiple myeloma (MM) patients: Results from the EMN02/HO95 phase 3 trial. 2017 ASCO annual meeting. Abstract No: 8011

[email protected]

On Twitter @maryjodales

PORTLAND – Twice-daily topical therapy with the Janus kinase (JAK) inhibitor ruxolitinib led to significant improvements in facial vitiligo in a small, uncontrolled, open-label, proof-of-concept study.

Four patients with significant baseline facial involvement improved by an average of 76% on the facial Vitiligo Area Scoring Index, or VASI (95% confidence interval, 53%-99%, P = .001), Brooke Rothstein reported at the annual meeting of the Society for Investigative Dermatology. The results suggest that topical JAK inhibition might help treat facial vitiligo, while potentially sparing patients from the side effects of oral therapy, said Ms. Rothstein, a medical student at Tufts University, Boston, who conducted the study under the mentorship of David Rosmarin, MD, of the department of dermatology at Tufts.

The study included 11 patients with vitiligo affecting at least 1% of body surface area. In all, 54% were male and the average age was 52 years. Patients applied ruxolitinib 1.5% phosphate cream to affected areas twice daily for 20 weeks. The primary outcome was percent improvement in VASI from baseline, Ms. Rothstein said.

By week 20, eight (73%) patients responded to treatment. Overall VASI scores improved by 23% (95% CI, 4%-43%; P = .02) when considering all patients and affected body regions. Three of eight patients responded on the body, and one of these eight patients also improved on acral surfaces, but these improvements were modest – less than 10%, compared with baseline, which was statistically insignificant.

Adverse events were generally mild and included erythema, hyperpigmentation, and transient acne, Ms. Rothstein reported. Despite the small sample size and open-label design of this study, the findings support further studies of topical JAK inhibition in vitiligo and add to mounting evidence that targeting interferon-gamma and its associated chemokines might stimulate repigmentation of skin in affected patients, she concluded.

This study also was published online in the Journal of the American Academy of Dermatology (J Am Acad Dermatol. 2017 Apr 5. doi: 10.1016/j.jaad.2017.02.049). The work was partially supported by Incyte, manufacturer of ruxolitinib (Jakafi), which supplied the study drug and reviewed the manuscript, but did not have final approval or control over the decision to submit for publication. An Alpha Omega Alpha Carolyn L. Kuckein Student Research Fellowship also helped support the work. Ms. Rothstein and her coinvestigators reported having no financial conflicts of interest.

Ruxolitinib, in a tablet formulation, is approved by the Food and Drug Administration for treating myelofibrosis and polycythemia vera.

PORTLAND – Twice-daily topical therapy with the Janus kinase (JAK) inhibitor ruxolitinib led to significant improvements in facial vitiligo in a small, uncontrolled, open-label, proof-of-concept study.

Four patients with significant baseline facial involvement improved by an average of 76% on the facial Vitiligo Area Scoring Index, or VASI (95% confidence interval, 53%-99%, P = .001), Brooke Rothstein reported at the annual meeting of the Society for Investigative Dermatology. The results suggest that topical JAK inhibition might help treat facial vitiligo, while potentially sparing patients from the side effects of oral therapy, said Ms. Rothstein, a medical student at Tufts University, Boston, who conducted the study under the mentorship of David Rosmarin, MD, of the department of dermatology at Tufts.

The study included 11 patients with vitiligo affecting at least 1% of body surface area. In all, 54% were male and the average age was 52 years. Patients applied ruxolitinib 1.5% phosphate cream to affected areas twice daily for 20 weeks. The primary outcome was percent improvement in VASI from baseline, Ms. Rothstein said.

By week 20, eight (73%) patients responded to treatment. Overall VASI scores improved by 23% (95% CI, 4%-43%; P = .02) when considering all patients and affected body regions. Three of eight patients responded on the body, and one of these eight patients also improved on acral surfaces, but these improvements were modest – less than 10%, compared with baseline, which was statistically insignificant.

Adverse events were generally mild and included erythema, hyperpigmentation, and transient acne, Ms. Rothstein reported. Despite the small sample size and open-label design of this study, the findings support further studies of topical JAK inhibition in vitiligo and add to mounting evidence that targeting interferon-gamma and its associated chemokines might stimulate repigmentation of skin in affected patients, she concluded.

This study also was published online in the Journal of the American Academy of Dermatology (J Am Acad Dermatol. 2017 Apr 5. doi: 10.1016/j.jaad.2017.02.049). The work was partially supported by Incyte, manufacturer of ruxolitinib (Jakafi), which supplied the study drug and reviewed the manuscript, but did not have final approval or control over the decision to submit for publication. An Alpha Omega Alpha Carolyn L. Kuckein Student Research Fellowship also helped support the work. Ms. Rothstein and her coinvestigators reported having no financial conflicts of interest.

Ruxolitinib, in a tablet formulation, is approved by the Food and Drug Administration for treating myelofibrosis and polycythemia vera.

PORTLAND – Twice-daily topical therapy with the Janus kinase (JAK) inhibitor ruxolitinib led to significant improvements in facial vitiligo in a small, uncontrolled, open-label, proof-of-concept study.

Four patients with significant baseline facial involvement improved by an average of 76% on the facial Vitiligo Area Scoring Index, or VASI (95% confidence interval, 53%-99%, P = .001), Brooke Rothstein reported at the annual meeting of the Society for Investigative Dermatology. The results suggest that topical JAK inhibition might help treat facial vitiligo, while potentially sparing patients from the side effects of oral therapy, said Ms. Rothstein, a medical student at Tufts University, Boston, who conducted the study under the mentorship of David Rosmarin, MD, of the department of dermatology at Tufts.

The study included 11 patients with vitiligo affecting at least 1% of body surface area. In all, 54% were male and the average age was 52 years. Patients applied ruxolitinib 1.5% phosphate cream to affected areas twice daily for 20 weeks. The primary outcome was percent improvement in VASI from baseline, Ms. Rothstein said.

By week 20, eight (73%) patients responded to treatment. Overall VASI scores improved by 23% (95% CI, 4%-43%; P = .02) when considering all patients and affected body regions. Three of eight patients responded on the body, and one of these eight patients also improved on acral surfaces, but these improvements were modest – less than 10%, compared with baseline, which was statistically insignificant.

Adverse events were generally mild and included erythema, hyperpigmentation, and transient acne, Ms. Rothstein reported. Despite the small sample size and open-label design of this study, the findings support further studies of topical JAK inhibition in vitiligo and add to mounting evidence that targeting interferon-gamma and its associated chemokines might stimulate repigmentation of skin in affected patients, she concluded.

This study also was published online in the Journal of the American Academy of Dermatology (J Am Acad Dermatol. 2017 Apr 5. doi: 10.1016/j.jaad.2017.02.049). The work was partially supported by Incyte, manufacturer of ruxolitinib (Jakafi), which supplied the study drug and reviewed the manuscript, but did not have final approval or control over the decision to submit for publication. An Alpha Omega Alpha Carolyn L. Kuckein Student Research Fellowship also helped support the work. Ms. Rothstein and her coinvestigators reported having no financial conflicts of interest.

Ruxolitinib, in a tablet formulation, is approved by the Food and Drug Administration for treating myelofibrosis and polycythemia vera.

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

A primary care physician in the VA San Diego Healthcare System (VASDHS) clinically observed an unexpected rate of elevated eosinophil levels on routine blood tests of patients residing in inland areas of San Diego County and Imperial County. The majority of the affected patients did not present with symptoms or associated pathology, leaving the significance of these laboratory results unclear and creating question of what intervention, if any, might be most appropriate for these patients. A preliminary chart review of clinic visits at community-based clinic sites confirmed higher rates of elevated eosinophil levels compared with those of patients seen at the San Diego-based medical center. Based on this finding, a more formal investigation was initiated.

Eosinophils are leukocyte components of the cell-mediated immune response and may be elevated in conditions that include hypersensitivity reactions, adrenal insufficiency, neoplastic disorders, and parasitic infections, among others.¹ An elevated percentage of eosinophils can be attributed to a variety of causes, and isolated elevations in a particular individual may not necessarily reflect an underlying pathology. Furthermore, elevated eosinophil levels alone do not necessarily indicate eosinophilia, as the latter is defined by absolute eosinophil counts. However, the occurrence of elevated eosinophil levels that remain unexplained at the population level raises the possibility of a common exposure and warrants further investigation. If such a phenomenon appears to be geographically distributed, as was noted by VA physicians in San Diego and Imperial County, it becomes important to consider what exposures might be unique to a particular site.

Coccidioides immitis

The soil fungus Coccidioides immitis (C immitis) is a growing public health concern for inland areas of San Diego County and Imperial County. While its presence in the northern California San Joaquin Valley has been of particular research interest and has gained traction in public discourse, the organism also is endemic to much of southern California, Arizona, New Mexico, and Texas, with its range extending as far north as parts of Nevada and Utah.² Although C immitis has been identified as endemic to the dry climate of Imperial County, the precise degree of its endemicity and clinical significance are less clear.

From 2006 to 2010, Imperial County reported a comparatively low incidence rate of coccidioidomycosis (C immitis infection) compared with that of similar adjacent climates, such as Yuma, Arizona. A 2011 Imperial County survey found that only 23% of clinicians considered coccidioidomycosis a problem in California, and only 43% would consider the diagnosis in a patient presenting with respiratory problems.³ These findings have raised the concern that cases are being missed either from failure to diagnose or from underreporting. Furthermore, in light of a 1997 study that found intestinal parasites in about 28% of the population in Mexico, there is concern that given the close proximity to northern Mexico (where C immitis also is found), rates of Strongyloides stercoralis, Giardia lamblia, Entamoeba histolytica, Cryptosporidium, Ascaris lumbricoides, and other parasitic infections might be higher in border counties, such as Imperial County, compared with other sites in California.⁴

While coccidioidomycosis and parasitic infections are potential causes of the elevated eosinophil levels at VASDHS, recent studies have demonstrated an association between cardiovascular risk factors, such as dyslipidemia and diabetes mellitus, and eosinophil count.⁵ The association between dyslipidemia and elevated eosinophil levels is not well understood, although recent studies have described it as likely multifactorial with contributing mechanisms involving oxidative stress, endothelial dysfunction, and inflammatory changes.⁶ Consideration of these cardiovascular risk factors is of particular importance in this population because of its high rate of overweight and obesity. According to the 2011-2012 California Health Interview Survey, 71% of Imperial Valley adults were found to be either overweight or obese compared with the California state average of 55% and the San Diego County average of 57%.^7,8

This investigation aimed to identify whether geographically distributed elevated eosinophil levels can be identified using population-level data, whether eosinophil levels are found to be elevated at a particular site, and whether such observations might be explained by known characteristics of the patient population based on existing patient data.

Methods

The percentage of eosinophils on complete blood counts (CBCs) were acquired for all VASDHS patients who had laboratory visits from May 1 to June 30, 2010, based on patient records. For patients with multiple laboratory visits during the period, only data from the earliest visit were included for this investigation. Initially, patients were sorted according to the site of their laboratory blood draw: Chula Vista, Escondido, Imperial Valley, La Jolla, Mission Valley, and Oceanside. Descriptive statistical analyses were carried out for each specific site as well as with patients from all sites pooled.

Sites With Elevated Eosinophil Levels

In addition to descriptive statistics, Pearson χ² tests were initially performed to determine whether the proportions of elevated eosinophil levels at inland VASDHS sites in San Diego and Imperial counties deviated significantly from the expected levels at the coastal La Jolla hospital comparison site. Additional Pearson χ² tests were performed subsequently to compare all sites involved in the study against all other sites. The goal of these Pearson χ² tests was to identify potential sites for further investigation with no adjustment made for multiple testing. Sites with eosinophil levels significantly higher or lower than the expected levels when compared with the other sites included in the study were investigated further with a chart review.

Based on the VA Clinical Laboratory standards, a peripheral eosinophil percentage > 3% was considered elevated. Absolute eosinophil levels also were calculated to determine whether elevated eosinophil levels were associated with absolute counts reflective of eosinophilia. Counts of 500 to 1,499 eosinophils/mL were considered mild eosinophilia, 1,500 to 4,999 eosinophils/mL considered moderate eosinophilia, and ≥ 5,000 considered severe eosinophilia.⁹

Site-Specific Subgroup Analysis

A structured chart review was conducted for all patient notes, laboratory findings, studies, and communications for sites identified with elevated eosinophil levels. Demographic information was collected for all subjects, including age, race, occupation, and gender. Each record was systematically evaluated for information relating to possible causes of eosinophilia, including recent or prior data on the following: CBC, eosinophil percentage; HIV, C immitis, or Strongyloides stercoralis serology, stool ova and parasites, diagnoses of dyslipidemia, diabetes mellitus, malignancy, or adrenal insufficiency; and histories of atopy, allergies, and/or allergic rhinitis. In addition, given the unique exposures of the veteran population, data on service history and potential exposures during service, such as to Agent Orange, also were collected.

A multivariate analysis using logistic regression was conducted to determine whether conditions or exposures often associated with eosinophilia might explain any observed elevations in eosinophil levels. For the logistic regression model, the response variable was eosinophil levels > 3%. Explanatory variables included parasitic infection diagnosis, including C immitis, dyslipidemia diagnosis, malignancy diagnosis, allergy and/or atopy diagnosis, and HIV diagnosis. In addition, the analysis controlled for demographic variables, such as age, sex, race, period of service, and Agent Orange exposure and were included as explanatory variables in the model. Categorical variables were coded as 0 for negative results and 1 for positive results and were identified as missing if no data were recorded for that variable. Statistics were performed using Stata 13 (College Station, TX).

Results

A total of 6,777 VASDHS patient records were acquired. Two records included CBC without differentials and were omitted from the study. Among those included, the median eosinophil percentage was 2.3% (SD 2.51). Eosinophil percentages ranged from 0% to 39.3%. The 25th percentile and 75th percentile eosinophil levels were 1.3% and 3.6%, respectively. Nine percent of patients had percentages below 11.6%, and 4 patients had eosinophil percentages ranging from 30% to 39% (Figure 1).

Grouping the records by clinic, 30% to 40% of patients had elevated eosinophil levels at all sites except for Imperial Valley (Figure 2). At the Imperial Valley site, 50.5% of patients had elevated eosinophil levels, which was statistically higher than those of all other sites (Figure 3).

The authors tested the null hypothesis that there is no association between geographic location and the proportion of the population with elevated eosinophil levels. A Pearson χ² test of the proportion of elevated eosinophil level (P < .001) indicated that the observed differences in elevated eosinophil levels were unlikely due to chance. Further sets of exploratory χ² tests comparing only 2 sites at a time identified Imperial Valley as differing significantly from all other sites at α = .05. Eosinophil proportions at the Mission Valley (P = .003) and Oceanside (P < .001) sites also were found to differ significantly from the La Jolla site. In contrast, eosinophil proportions at the Escondido (P = .199) and Chula Vista (P = .237) sites did not differ significantly from those of the La Jolla site using χ² testing.

Imperial Valley Clinic

Records were acquired for 109 patients at the Imperial Valley clinic (107 male and 2 female). Fifty-five patients (50.5%) were identified as having elevated eosinophil levels. However, only 5 patients were classified as having mild eosinophilia. No patients were found to have moderate or severe eosinophilia (Table 1).

On review of the data for Imperial Valley patients, 68 had a diagnosis of dyslipidemia and 17 had asthma, atopic dermatitis, allergic rhinitis, and/or atopy not otherwise specified diagnoses. Three patients were identified with diagnoses of malignancies or premalignant conditions, including 1 patient with chronic lymphocytic leukemia, 1 patient with renal cell carcinoma with metastasis to the lungs, and 1 patient with myelodysplastic syndrome. No patients were identified with a diagnosis of HIV. There were no diagnostic laboratory tests on record for C immitis serology, stool ova and parasites, Strongyloides stercoralis serology, or clinical diagnoses of related conditions.

Logistic regressions assessed whether elevated eosinophil levels > 3% might be explained by predictor variables, such as a history of dyslipidemia, malignancy, or asthma/allergies/atopy (Table 2). As no parasitic infections or HIV diagnoses were identified in the patient population, they were noncontributory in the model. The probability of obtaining the χ² statistic given the assumption that the null hypothesis is true equals .027 for the model, suggesting that the overall model was statistically significant at the α = .05 level.

Of the key predictor variables of interest, only dyslipidemia was found to predict elevated eosinophil levels. Patients with a diagnosis of dyslipidemia were found to have nearly 4 times greater likelihood of having elevated eosinophil levels compared with patients without dyslipidemia (odds ratio 3.88, 95% confidence interval: 1.04-14.43). Patients with malignancy or a history of asthma, allergy, or atopy were not found to have significantly different odds of having elevated eosinophil levels compared with baseline within the study population.

Discussion

High proportions of elevated eosinophil levels among VASDHS patients were found to be geographically concentrated at sites that included Imperial Valley, Oceanside, and Mission Valley. Although initial exploratory Pearson χ² tests did not accommodate for multiple comparisons, a particularly consistent finding was that the proportion of patients with elevated eosinophil levels seemed to be notably high at the Imperial Valley site in particular, which corresponded with the clinical observations made by physicians.

It was initially thought that the elevated eosinophil levels might be due to exposure to geographically distributed pathogens, such as C immitis, but there were no clinically diagnosed cases in the population studied. However, it also is true that no C immitis serologies or other parasitic serologies were ordered for the patients during the study period. In the context of possible undertesting and underdiagnosis of coccidioidomycosis, it may be possible that these cases were simply missed.

Nonetheless, alternative explanations for elevated eosinophil levels also must be considered. Of the possible explanatory exposures considered, only dyslipidemia was found to be statistically significant in the study population. Patients with dyslipidemia had 4 times greater odds of also having elevated eosinophil levels compared with those who did not have dyslipidemia, which is in line with recent literature identifying conditions such as dyslipidemia and diabetes mellitus as independent predictors of elevated eosinophil levels.⁶

In light of the known high rates of obesity in the Imperial Valley in comparison with rates of obesity in San Diego County from previous studies and questionnaires, the increased levels of dyslipidemia in the Imperial Valley compared with those of the other sites included in the study may help explain the geographic distribution of observed elevated eosinophil levels.^7,8 Although data on dyslipidemia rates among study participants at sites other than Imperial Valley were not collected for this study, this explanation represents a promising area of further investigation.

Furthermore, although about 50% of the population in the Imperial Valley had CBCs with eosinophil levels > 3%, only 5% of the population was found to have eosinophilia based on absolute eosinophil counts, and all such cases were mild. Although excluding infection or other causes of elevated eosinophil levels is difficult, it is reasonable to believe that such low-grade elevations that do not meet the criteria for true eosinophilia may be more consistent with chronic processes, such as dyslipidemia, as opposed to frank infection in which one might expect a morerobust response.

Limitations

The cause of this phenomenon is not yet clear, with the investigation limited by several factors. Possibly the sample size of 109 patients in the Imperial Valley was not sufficient to capture some causes of elevated eosinophil levels, particularly if the effect size of an exposure is low or the exposure infrequent. Of note, no cases of HIV, C immitis infection, or other parasitic infections were observed. Furthermore, only 3 cases of malignancy and 17 cases of asthma, allergies, and/or atopy were identified. Malignancy, asthma, and allergy and/or atopy were not statistically significant as predictors of eosinophilia at the α = .05 level, although the analysis of these variables was likely limited by the small number of patients with these conditions in the sample population. While all these exposures are known to be associated with eosinophilia in the literature, none were identified as predictors in the logistic regression model, likely due, in part, to the limited sample size.

Given the high proportion of the Imperial Valley population with elevated eosinophil levels compared with those of all other sites investigated, a rare or subtle exposure of the types noted would be less likely to explain such a large difference. It is important to look more carefully at a number of possible factors—including gathering more detailed data on dyslipidemia and C immitis infection rates among other possible contributors—to determine more precisely the cause of the notably elevated eosinophil levels in this and other sites in the region.

Conclusion

Using a convenience sample of the VA population based on routine laboratory testing, this study has established that geographically distributed elevated eosinophil levels can be identified in the San Diego region. However, it is less clear why notably elevated eosinophil levels were found at these sites. Although there was no evidence of a correlation between certain environmental factors and elevated eosinophil levels, this may have been due to insufficiently detailed consideration of environmental factors.

Logistic regression analysis associated dyslipidemia with a notably increased risk of elevated eosinophil levels in the Imperial Valley population, but it would be premature to conclude that this association is necessarily causal. Further research would help elucidate this. Increasing the investigational time frame and a chart review of additional sites could provide informative data points for analysis and would allow for a more in-depth comparison between sites. More immediately, given the possibility that dyslipidemia may be a source of the observed elevated eosinophil levels in the Imperial Valley population, it would be worth investigating the rates of dyslipidemia at comparison sites to see whether the lower rates of elevated eosinophil levels at these other sites correspond to lower rates of dyslipidemia.

In future work, it may be valuable to test the study population for C immitis, given the prevalence of the fungus in the area and the concern among many public health professionals of its undertesting and underdiagnosis. Because many cases of C immitis are subclinical, it may be worth investigating whether these are being missed and to what degree such cases might be accompanied by elevations in eosinophil levels.

Given that much remains unknown regarding the causes of elevated eosinophil levels in the Imperial Valley and other sites in the region, further study of such elevations across sites and over time—as well as careful consideration of noninfectious causes of elevated eosinophil levels, such as dyslipidemia—may be of important value to both local clinicians and public health professionals in this region. ˜

Acknowledgments
The authors thank Ms. Robin Nuspl and Mr. Ben Clark for their assistance with the data and guidance. The authors also are grateful to the staff members at the VA San Diego Healthcare System for their many contributions to this project.

Children’s Research Hospital

New research indicates that some Africans carry a gene variant that reduces the risk of severe malaria.

The study suggests this variant results from the rearrangement of 2 glycophorin receptors found on the surface of red blood cells.

The malaria parasite Plasmodium falciparum uses these receptors—GYPA and GYPB—to enter the cells.

Researchers identified a gene variant that results in altered GYPA and GYPB receptors and may reduce the risk of severe malaria by 40%.

Ellen Leffler, of the University of Oxford in the UK, and her colleagues reported these findings in Science.

The researchers performed genome sequencing of 765 individuals from 10 ethnic groups in Gambia, Burkina Faso, Cameroon, and Tanzania.

The team also conducted a study across the Gambia, Kenya, and Malawi that included 5310 individuals from the general population and 4579 people who were hospitalized with severe malaria.

These analyses revealed copy number variants affecting GYPA and GYPB.

“[W]e found strong evidence that variation in the glycophorin gene cluster influences malaria susceptibility,” Dr Leffler said.

“We found some people have a complex rearrangement of GYPA and GYPB genes, forming a hybrid glycophorin, and these people are less likely to develop severe complications of the disease.”

The rearrangement involves the loss of GYPB and gain of 2 GYPB-A hybrid genes. The hybrid GYPB-A gene is found in a rare blood group—part of the MNS blood group system—where it is known as Dantu.

DUP4, the most common Dantu gene variant, is a result of the rearrangement. And the researchers found that DUP4 reduced the risk of severe malaria by an estimated 40%.

DUP4 was only present in certain populations, particularly in individuals of East African descent.

The researchers proposed a number of reasons as to why DUP4 may not be more widespread, including the possibility that it emerged recently. Alternatively, it may only protect against certain strains of P falciparum that are specific to east Africa.

Though more research is needed, the team said these findings link the structural variation of glycophorin receptors with resistance to severe malaria.

“We are starting to find that the glycophorin region of the genome has an important role in protecting people against malaria,” said study author Dominic Kwiatkowski, MD, of the University of Oxford.

“Our discovery that a specific variant of glycophorin invasion receptors can give substantial protection against severe malaria will hopefully inspire further research on exactly how Plasmodium falciparum invade red blood cells. This could also help us discover novel parasite weaknesses that could be exploited in future interventions against this deadly disease.” 

Children’s Research Hospital

New research indicates that some Africans carry a gene variant that reduces the risk of severe malaria.

The study suggests this variant results from the rearrangement of 2 glycophorin receptors found on the surface of red blood cells.

The malaria parasite Plasmodium falciparum uses these receptors—GYPA and GYPB—to enter the cells.

Researchers identified a gene variant that results in altered GYPA and GYPB receptors and may reduce the risk of severe malaria by 40%.

Ellen Leffler, of the University of Oxford in the UK, and her colleagues reported these findings in Science.

The researchers performed genome sequencing of 765 individuals from 10 ethnic groups in Gambia, Burkina Faso, Cameroon, and Tanzania.

The team also conducted a study across the Gambia, Kenya, and Malawi that included 5310 individuals from the general population and 4579 people who were hospitalized with severe malaria.

These analyses revealed copy number variants affecting GYPA and GYPB.

“[W]e found strong evidence that variation in the glycophorin gene cluster influences malaria susceptibility,” Dr Leffler said.

“We found some people have a complex rearrangement of GYPA and GYPB genes, forming a hybrid glycophorin, and these people are less likely to develop severe complications of the disease.”

The rearrangement involves the loss of GYPB and gain of 2 GYPB-A hybrid genes. The hybrid GYPB-A gene is found in a rare blood group—part of the MNS blood group system—where it is known as Dantu.

DUP4, the most common Dantu gene variant, is a result of the rearrangement. And the researchers found that DUP4 reduced the risk of severe malaria by an estimated 40%.

DUP4 was only present in certain populations, particularly in individuals of East African descent.

The researchers proposed a number of reasons as to why DUP4 may not be more widespread, including the possibility that it emerged recently. Alternatively, it may only protect against certain strains of P falciparum that are specific to east Africa.

Though more research is needed, the team said these findings link the structural variation of glycophorin receptors with resistance to severe malaria.

“We are starting to find that the glycophorin region of the genome has an important role in protecting people against malaria,” said study author Dominic Kwiatkowski, MD, of the University of Oxford.

“Our discovery that a specific variant of glycophorin invasion receptors can give substantial protection against severe malaria will hopefully inspire further research on exactly how Plasmodium falciparum invade red blood cells. This could also help us discover novel parasite weaknesses that could be exploited in future interventions against this deadly disease.” 

Children’s Research Hospital

New research indicates that some Africans carry a gene variant that reduces the risk of severe malaria.

The study suggests this variant results from the rearrangement of 2 glycophorin receptors found on the surface of red blood cells.

The malaria parasite Plasmodium falciparum uses these receptors—GYPA and GYPB—to enter the cells.

Researchers identified a gene variant that results in altered GYPA and GYPB receptors and may reduce the risk of severe malaria by 40%.

Ellen Leffler, of the University of Oxford in the UK, and her colleagues reported these findings in Science.

The researchers performed genome sequencing of 765 individuals from 10 ethnic groups in Gambia, Burkina Faso, Cameroon, and Tanzania.

The team also conducted a study across the Gambia, Kenya, and Malawi that included 5310 individuals from the general population and 4579 people who were hospitalized with severe malaria.

These analyses revealed copy number variants affecting GYPA and GYPB.

“[W]e found strong evidence that variation in the glycophorin gene cluster influences malaria susceptibility,” Dr Leffler said.

“We found some people have a complex rearrangement of GYPA and GYPB genes, forming a hybrid glycophorin, and these people are less likely to develop severe complications of the disease.”

The rearrangement involves the loss of GYPB and gain of 2 GYPB-A hybrid genes. The hybrid GYPB-A gene is found in a rare blood group—part of the MNS blood group system—where it is known as Dantu.

DUP4, the most common Dantu gene variant, is a result of the rearrangement. And the researchers found that DUP4 reduced the risk of severe malaria by an estimated 40%.

DUP4 was only present in certain populations, particularly in individuals of East African descent.

The researchers proposed a number of reasons as to why DUP4 may not be more widespread, including the possibility that it emerged recently. Alternatively, it may only protect against certain strains of P falciparum that are specific to east Africa.

Though more research is needed, the team said these findings link the structural variation of glycophorin receptors with resistance to severe malaria.

“We are starting to find that the glycophorin region of the genome has an important role in protecting people against malaria,” said study author Dominic Kwiatkowski, MD, of the University of Oxford.

“Our discovery that a specific variant of glycophorin invasion receptors can give substantial protection against severe malaria will hopefully inspire further research on exactly how Plasmodium falciparum invade red blood cells. This could also help us discover novel parasite weaknesses that could be exploited in future interventions against this deadly disease.”