HbA1c, lipid profiles and risk of incident type 2 Diabetes in United States Veterans

United States Veterans are at excess risk for type 2 diabetes, but population differentials in risk have not been characterized. We determined risk of type 2 diabetes in relation to prediabetes and dyslipidemic profiles in Veterans at the VA New York Harbor (VA NYHHS) during 2004-2014. Prediabetes was based on American Diabetes Association hemoglobin A1c (HbA1c) testing cut-points, one of several possible criteria used to define prediabetes. We evaluated transition to type 2 diabetes in 4,297 normoglycemic Veterans and 7,060 Veterans with prediabetes. Cox proportional hazards regression was used to relate HbA1c levels, lipid profiles, demographic, anthropometric and comorbid cardiovascular factors to incident diabetes (Hazard Ratio [HR] and 95% confidence intervals). Compared to normoglycemic Veterans (HbA1c: 5.0-5.6%; 31-38 mmol/mol), risks for diabetes were >2-fold in the moderate prediabetes risk group (HbA1c: 5.7-5.9%; 39-41 mmol/mol) (HR 2.37 [1.98-2.85]) and >5-fold in the high risk prediabetes group (HbA1c: 6.0-6.4%; 42-46 mmol/mol) (HR 5.59 [4.75-6.58]). Risks for diabetes were increased with elevated VLDL (≥40mg/dl; HR 1.31 [1.09-1.58]) and TG/HDL (≥1.5mg/dl; HR 1.34 [1.12-1.59]), and decreased with elevated HDL (≥35mg/dl; HR 0.80 [0.67-0.96]). Transition to diabetes in Veterans was related in age-stratified risk score analyses to HbA1c, VLDL, HDL and TG/HDL, BMI, hypertension and race, with 5-year risk differentials of 62% for the lowest (5-year risk, 13.5%) vs. the highest quartile (5-year risk, 21.9%) of the risk score. This investigation identified substantial differentials in risk of diabetes in Veterans, based on a readily-derived risk score suitable for risk stratification for type 2 diabetes prevention.

Electronic Health Record-Based Screening for Substance Abuse

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC = 0.884), outpatient (AROC = 0.825), and combined inpatient and outpatient (AROC = 0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.

Abstract 3290: The Side-Out Foundation Metastatic Breast Cancer Database, an open-access portal for multi-omic molecular data and more

Introduction: With the high volume of molecular data being generated daily from human malignancies, there is an increasing need to create novel internet-based portals where this information is readily and easily accessible to physicians, scientists, and the general public. Although numerous databases have been created to capture the molecular characteristics of primary tumors, limited resources are available when it comes to the metastatic lesions. We have developed a novel open-access database to capture demographic, clinical, and pathological information, outcome data, and multi-omic based molecular profiles from metastatic breast cancer (MBC) patients.

Materials and Methods: The portal was created using the open-source relational database management system MySQL and the custom-codes were written using the PHP server-side scripting language. User interface, management, and authentication were created in WordPress. The database is primarily used to record information collected through the Side-Out clinical trials, a series of prospective Phase II trials targeting refractory MBC (NCT01074814, NCT01919749, NCT03195192). De-identified patients' information includes patients' demographics, treatment history, pathological characteristics of the primary tumor, outcome data, and multi-omic molecular profiles. Molecular information collected from each lesion include genomic (NGS-based whole/targeted exome sequencing), transcriptomic (RNA microarray/RNA Seq), proteomic (protein expression by IHC), and phosphoproteomic (protein pathway activation mapping by Reverse Phase Protein Microarray) data. Over 700 data fields are collected for each patient. A higher level of security for the recorded information is achieved by using a secondary database along with custom-codes during the data entry process. Investigators are exploring how Block-chain database design can be used to make portion of the data public while encrypting patient identifiers and key variables.

Results: We present an overview of the portal, its usability, how access can be requested by interested third party, the user-friendly interface for downloading clinical, pathological and molecular data, and a few examples of how these data can be used to explore different aspects of metastatic breast cancers.

Conclusions: To our knowledge, this is the first web-based publicly accessible database portal where broad multi-omic profiles are captured from the MBC patients along with demographic, clinical and pathological data. This open-access portal represents a unique and highly valuable tool as it integrates different aspects of the disease and can be used for correlative analyses and hypothesis-generating studies. Finally, this web-based portal allows free-of-charge dissemination of data from existing or upcoming clinical and translational studies targeting breast cancer patients.

Citation Format: Elisa Baldelli, Shiva Ghaemi, Sanja Avramovic, Janusz Wojtusiak, Lance A. Liotta, Farrokh Alemi, Emanuel F. Petricoin, Bryant Dunetz, Mariaelena Pierobon. The Side-Out Foundation Metastatic Breast Cancer Database, an open-access portal for multi-omic molecular data and more [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3290.

Abstract P453: Hypertension Mortality Risk May be Fake News for Nonagenarians

Background: Little is known regarding BP control and mortality risk in subjects greater than 90 years of age.

Objective: This paper assesses the association between systolic blood pressure and mortality risk for nonagenarians.

Methods: Data from the Veterans Administration Informatics and Computing Infrastructure were used to analyze the survival of 193,651 nonagenarians in 130 Veteran Administration medical centers. Following clinical guidelines, for each day, we selected the lowest Systolic reading of the day exceeding 90 mmHg and defined sustained pressure as the average of two consecutive readings at least one month apart. Kaplan Meier curves and Cox regression was used to analyze survival.

Results: Odds of mortality from hypertension declined with age. When patients were over 90 years old, the odds of mortality for hypertensives was below 1 to 1, suggesting a protective effect. Patients whose sustained systolic pressures exceeded 140 mmHg survived longer than patients whose highest sustained pressure was between 90 mmHg to 140 mmHg (p<.0001). Furthermore, strict control of hypertension of 90 year old patients (SBP 120) was associated with lower days of survival than hypertensives, raising questions about value of strict control of hypertension among 90 year olds.

Conclusions: For nonagenarians, mortality from hypertension may be a lower concern than mortality from other causes. Randomized clinical trials are needed to examine the impact of control of hypertension of patient over 90 year olds.

A Comprehensive Index for Predicting Risk of Anemia from Patients' Diagnoses

This article demonstrates how time-dependent, interacting, and repeating risk factors can be used to create more accurate predictive medicine. In particular, we show how emergence of anemia can be predicted from medical history within electronic health records. We used the Veterans Affairs Informatics and Computing Infrastructure database to examine a retrospective cohort of 9,738,838 veterans over an 11-year period. Using International Clinical Diagnoses Version 9 codes organized into 25 major diagnostic categories, we measured progression of disease by examining changes in risk over time, interactions in risk of combination of diseases, and elevated risk associated with repeated hospitalization for the same diagnostic category. The maximum risk associated with each diagnostic category was used to predict anemia. The accuracy of the model was assessed using a validation cohort. Age and several diagnostic categories significantly contributed to the prediction of anemia. The largest contributors were health status ([Formula: see text] = -1075, t = -92, p < 0.000), diseases of the endocrine ([Formula: see text] = -1046, t = -87, p < 0.000), hepatobiliary ([Formula: see text] = -1043, t = -72, p < 0.000), kidney ([Formula: see text] = -1125, t = -111, p < 0.000), and respiratory systems ([Formula: see text] = -1151, t = -89, p < 0.000). The AUC for the additive model was 0.751 (confidence interval 74.95%-75.26%). The magnitude of AUC suggests that the model may assist clinicians in determining which patients are likely to develop anemia. The procedures used for examining changes in risk factors over time may also be helpful in other predictive medicine projects.

Covariate Balancing through Naturally Occurring Strata

OBJECTIVE

To provide an alternative to propensity scoring (PS) for the common situation where there are interacting covariates.

SETTING

We used 1.3 million assessments of residents of the United States Veterans Affairs nursing homes, collected from January 1, 2000, through October 9, 2012.

DESIGN

In stratified covariate balancing (SCB), data are divided into naturally occurring strata, where each stratum is an observed combination of the covariates. Within each stratum, cases with, and controls without, the target event are counted; controls are weighted to be as frequent as cases. This weighting procedure guarantees that covariates, or combination of covariates, are balanced, meaning they occur at the same rate among cases and controls. Finally, impact of the target event is calculated in the weighted data. We compare the performance of SCB, logistic regression (LR), and propensity scoring (PS) in simulated and real data. We examined the calibration of SCB and PS in predicting 6-month mortality from inability to eat, controlling for age, gender, and nine other disabilities for 296,051 residents in Veterans Affairs nursing homes. We also performed a simulation study, where outcomes were randomly generated from treatment, 10 covariates, and increasing number of covariate interactions. The accuracy of SCB, PS, and LR in recovering the simulated treatment effect was reported.

FINDINGS

In simulated environment, as the number of interactions among the covariates increased, SCB and properly specified LR remained accurate but pairwise LR and pairwise PS, the most common applications of these tools, performed poorly. In real data, application of SCB was practical. SCB was better calibrated than linear PS, the most common method of PS.

CONCLUSIONS

In environments where covariates interact, SCB is practical and more accurate than common methods of applying LR and PS.

A Comprehensive Multimorbidity Index for Predicting Mortality in Intensive Care Unit Patients

BACKGROUND

Accurate prediction of mortality for patients admitted to the intensive care units (ICUs) is an important component of medical care. However, little is known about the role of multimorbidity in predicting end of life for high-risk and vulnerable patients.

OBJECTIVE

The aim of the study was to derive and validate a multimorbidity risk model in an attempt to predict all-cause mortality at 6 and 12 months posthospital discharge.

METHODS

This is a retrospective, observational, clinical cohort study. Data were collected on 442,692 ICU patients who received care through the Veterans Administration between January 2003 and December 2013. The primary outcome was all-cause mortality at 6 and 12 months posthospital discharge. We divided the data into derivation (80%) and validation (20%) sets. Using multivariable logistic regression models, we compared prognostic models based on age, principal diagnosis groups, physiological markers, immunosuppressants, comorbidity categories, and a newly developed multimorbidity index (MMI) based on 5695 comorbidities. The cross-validated area under the receiver operating characteristic curve (AUC) was used to report the accuracy of predicting all-cause mortality at 6 and 12 months of hospital discharge.

RESULTS

The average age of patients was 68.87 years (standard deviation = 12.1), 95.9% were males, 44.9% were widowed, divorced, or separated. The relative order of accuracy in predicting mortality was the MMI (AUC = 0.84, CI = 0.83-0.84), VA Inpatient Evaluation Center index (AUC = 0.80, CI = 0.79-0.81), principal diagnosis groups (AUC = 0.74, CI = 0.73-0.74), comorbidities (AUC = 0.69, CI = 0.68-0.70), physiological markers (AUC = 0.65, CI = 0.64-0.65), age (AUC = 0.60, CI = 0.60-0.61),and immunosuppressant use (AUC = 0.59, CI = 0.58-0.59).

CONCLUSIONS

The MMI improved the accuracy of predicting short- and long-term all-cause mortality for ICU patients. Further prospective studies are needed to validate the index in different clinical settings and test generalizability of results in patients outside the VA system of care.

The VA Medical Foster Home Program, Ambulatory Care Sensitive Conditions, and Avoidable Hospitalizations

This quality control study analyzes whether the Veterans Administration Medical Foster Home (VA MFH) program has been successful in improving access and effectiveness of ambulatory care. Individuals hospitalized for one or more of 22 adult ambulatory care sensitive conditions were identified. Pre and post comparisons of a specified population of participants in the program were conducted to determine rates of avoidable hospitalizations for 6 months prior to and following MFH enrollment. The overall rate of avoidable hospitalizations declined from 18.5 to 14.9 per 100 enrollees following enrollment. The number of bed days used declined by 39%, as did the cost associated with avoidable hospitalizations. Enrollment in the VA MFH program resulted in an overall reduction in the rate of avoidable hospitalizations, resource utilization, and costs. Studies are needed comparing these results with other matched cohorts of nursing home eligible veterans.

The Multimorbidity Index: A Tool for Assessing the Prognosis of Patients from Their History of Illness

BACKGROUND

The Multimorbidity (MM) Index predicts the prognosis of patients from their diagnostic history. In contrast to existing approaches with broad diagnostic categories, it treats each diagnosis as a separate independent variable using individual International Classification of Disease, Revision 9 (ICD-9) codes.

OBJECTIVE

This paper describes the MM Index, reviews the published data on its accuracy, and provides procedures for implementing the Index within electronic health record (EHR) systems. Methods: The MM Index was tested on various patient populations by using data from the United States Department of Veterans Affairs data warehouse and claims data within the Healthcare Cost and Utilization Project of the Agency for Health Care Research and Quality.

RESULTS

In cross-validated studies, the MM Index outperformed prognostic indices based on physiological markers, such as CD4 cell counts in HIV/AIDS, HbAlc levels in diabetes, ejection fractions in heart failure, or the 13 physiological markers commonly used for patients in intensive care units. When predicting the prognosis of nursing home patients by using the cross-validated area under a receiver operating characteristic (ROC) curve, the MM Index was 15 percent outperformed the Quan variant of the Charlson Index, 27 percent more accurate than the Deyo variant of the Charlson Index, and 22 percent more accurate than the von Walraven variant of the Elixhauser Index. For patients in intensive care units, the MM Index was 13 percent outperformed the cross-validated area under ROC associated with Elixhauser's categories. The MM Index also demonstrated greater accuracy than a number of commercially available measures of illness severity; including a fivefold greater accuracy than the All Patient Refined Diagnosis-Related Groups and a threefold greater accuracy than All Payer Severity-Adjusted Diagnosis-Related Groups.

CONCLUSION

The MM Index is statistically more accurate than many existing measures of prognosis. The magnitude of improvement is large and may lead to a clinically meaningful difference in patient care. Given the large improvements in accuracy, the use of the MM Index for policy and comparative effectiveness analysis is recommended.

Improving Prognostic Web Calculators: Violation of Preferential Risk Independence

BACKGROUND

Web-based applications are available for prognostication of individual patients. These prognostic models were developed for groups of patients. No one is the average patient, and using these calculators to inform individual patients could provide misleading results.

OBJECTIVE

This article gives an example of paradoxical results that may emerge when indices used for prognosis of the average person are used for care of an individual patient.

METHODS

We calculated the expected mortality risks of stomach cancer and its associated comorbidities. Mortality risks were calculated using data from 140,699 Veterans Administration nursing home residents.

RESULTS

On average, a patient with hypertension has a higher risk of mortality than one without hypertension. Surprisingly, among patients with lung cancer, hypertension is protective and reduces risk of mortality. This paradoxical result is explained by how group-level, average prognosis could mislead individual patients. In particular, average prognosis of lung cancer patients reflects the impact of various comorbidities that co-occur in lung cancer patients. The presence of hypertension, a relatively mild comorbidity of lung cancer, indicates that more serious comorbidities have not occurred. It is not that hypertension is protective; it is the absence of more serious comorbidities that is protective. The article shows how the presence of these anomalies can be checked through the mathematical concept of preferential risk independence.

CONCLUSION

Instead of reporting average risk scores, web-based calculators may improve accuracy of predictions by reporting the unconfounded risks.

Using observed sequence to orient causal networks

In learning causal networks, typically cross-sectional data are used and the sequence among the network nodes is learned through conditional independence. Sequence is inherently a longitudinal concept. We propose to learn sequence of events in longitudinal data and use it to orient arc directions in a network learned from cross-sectional data. The network is learned from cross-sectional data using various established algorithms, with one modification. Arc directions that do not agree with the longitudinal sequence were prohibited. We established longitudinal sequence through two methods: Probabilistic Contrast, and Goodman and Kruskal error reduction methods. In simulated data, the error reduction method was used to learn the sequence in the data. The procedure reduced the number of arc direction errors and larger improvements were observed with increasing number of events in the network. In real data, different algorithms were used to learn the network from cross-sectional data, while prohibiting arc directions not supported by longitudinal information. The agreement among learned networks increased significantly. It is possible to combine sequence information learned from longitudinal data with algorithms organized for learning network models from cross-sectional data. Such models may have additional causal interpretation as they more explicitly take into account observed sequence of events.

Family self-tailoring: Applying a systems approach to improving family healthy living behaviors

The adoption and maintenance of healthy living behaviors by individuals and families is a major challenge. We describe a new model of health behavior change, SystemCHANGE (SC), which focuses on the redesign of family daily routines using system improvement methods. In the SC intervention, families are taught a set of skills to engage in a series of small, family self-designed experiments to test ideas to change their daily routines. The family system-oriented changes brought about by these experiments build healthy living behaviors into family daily routines so that these new behaviors happen as a matter of course, despite wavering motivation, willpower, or personal effort on the part of individuals. Case stories of the use of SC to improve family healthy living behaviors are provided. Results of several pilot tests of SC indicate its potential effectiveness to change health living behaviors across numerous populations.

Shared Homes as an Alternative to Nursing Home Care: Impact of VA's Medical Foster Home Program on Hospitalization

PURPOSE OF THE STUDY

This study compares hospitalization rates for common conditions in the Veteran Affairs (VA) Medical Foster Home (MFH) program to VA nursing homes, known as Community Living Centers (CLCs).

DESIGN AND METHODS

We used a nested, matched, case control design. We examined 817 MFH residents and matched each to 3 CLC residents selected from a pool of 325,031. CLC and MFH cases were matched on (a) baseline time period, (b) follow-up time period, (c) age, (d) gender, (e) race, (f) risk of mortality calculated from comorbidities, and (g) history of hospitalization for the selected condition during the baseline period. Odds ratio (OR) and related confidence interval (CI) were calculated to contrast MFH cases and matched CLC controls.

RESULTS

Compared with matched CLC cases, MFH residents were less likely to be hospitalized for adverse care events, (OR = 0.13, 95% CI = 0.03-0.53), anxiety disorders (OR = 0.52, 95% CI = 0.33-0.80), mood disorders (OR = 0.57, 95% CI = 0.42-0.79), skin infections (OR = 0.22, 95% CI = 0.10-0.51), pressure ulcers (OR = 0.22, 95% CI = 0.09-0.50) and bacterial infections other than tuberculosis or septicemia (OR = 0.54, 95% CI = 0.31-0.92). MFH cases and matched CLC controls did not differ in rates of urinary tract infections, pneumonia, septicemia, suicide/self-injury, falls, other injury besides falls, history of injury, delirium/dementia/cognitive impairments, or adverse drug events. Hospitalization rates were not higher for any conditions studied in the MFH cohort compared with the CLC cohort.

IMPLICATIONS

MFH participants had the same or lower rates of hospitalizations for conditions examined compared with CLC controls suggesting that noninstitutional care by a nonfamilial caregiver does not increase hospitalization rates for common medical conditions.

Sequence of Functional Loss and Recovery in Nursing Homes

PURPOSE OF THE STUDY

This study provides benchmarks for likelihood, number of days until, and sequence of functional decline and recovery.

DESIGN AND METHODS

We analyzed activities of daily living (ADLs) of 296,051 residents in Veteran Affairs nursing homes between January 1, 2000 and October 9, 2012. ADLs were extracted from standard minimum data set assessments. Because of significant overlap between short- and long-stay residents, we did not distinguish between these populations. Twenty-five combinations of ADL deficits described the experience of 84.3% of all residents. A network model described transitions among these 25 combinations. The network was used to calculate the shortest, longest, and maximum likelihood paths using backward induction methodology. Longitudinal data were used to derive a Bayesian network that preserved the sequence of occurrence of 9 ADL deficits.

RESULTS

The majority of residents (57%) followed 4 pathways in loss of function. The most likely sequence, in order of occurrence, was bathing, grooming, walking, dressing, toileting, bowel continence, urinary continence, transferring, and feeding. The other three paths occurred with reversals in the order of dressing/toileting and bowel/urinary continence. ADL impairments persisted without any change for an average of 164 days (SD = 62). Residents recovered partially or completely from a single impairment in 57% of cases over an average of 119 days (SD = 41). Recovery rates declined as residents developed more than 4 impairments.

IMPLICATIONS

Recovery of deficits among those studied followed a relatively predictable path, and although more than half recovered from a single functional deficit, recovery exceeded 100 days suggesting time to recover often occurs over many months.

The development and application of an oncology Therapy-Related Symptom Checklist for Adults (TRSC) and Children (TRSC-C) and e-health applications

Background

Studies found that treatment symptoms of concern to oncology/hematology patients were greatly under-identified in medical records. On average, 11.0 symptoms were reported of concern to patients compared to 1.5 symptoms identified in their medical records. A solution to this problem is use of an electronic symptom checklist that can be easily accessed by patients prior to clinical consultations. Purpose: Describe the oncology Therapy-Related Symptom Checklists for Adults (TRSC) and Children (TRSC-C), which are validated bases for e-Health symptom documentation and management. The TRSC has 25 items/symptoms; the TRSC-C has 30 items/symptoms. These items capture up to 80% of the variance of patient symptoms. Measurement properties and applications with outpatients are presented. E-Health applications are indicated.

Methods

The TRSC was developed for adults (N = 282) then modified for children (N = 385). Statistical analyses have been done using correlational, epidemiologic, and qualitative methods. Extensive validation of measurement properties has been reported.

Results

Research has found high levels of patient/clinician satisfaction, no increase in clinic costs, and strong correlations of TRSC/TRSC-C with medical outcomes. A recently published sequential cohort trial with adult outpatients at a Mayo Clinic community cancer center found TRSC use produced a 7.2% higher patient quality of life, 116% more symptoms identified/managed, and higher functional status.

Discussion, implications, and follow-up

An electronic system has been built to collect TRSC symptoms, reassure patients, and enhance patient-clinician communications. This report discusses system design and efforts made to provide an electronic system comfortable to patients. Methods used by clinicians to promote comfort and patient engagement were examined and incorporated into system design. These methods included (a) conversational data collection as opposed to survey style or standardized questionnaires, (b) short response phrases indicating understanding of the reported symptom, (c) use of open-ended questions to reduce long lists of symptoms, (d) directed questions that ask for confirmation of expected symptoms, (e) review of symptoms at designated stages, and (d) alerting patients when the computer has informed clinicians about patient-reported symptoms.

Conclusions

An e-Health symptom checklist (TRSC/TRSC-C) can facilitate identification, monitoring, and management of symptoms; enhance patient-clinician communications; and contribute to improved patient outcomes.

Predicting Functional Decline and Recovery for Residents in Veterans Affairs Nursing Homes

PURPOSE OF THE STUDY

This article describes methods and accuracy of predicting change in activities of daily living (ADLs) for nursing home patients following hospitalization.

DESIGN AND METHODS

Electronic Health Record data for 5,595 residents of Veterans Affairs' (VAs') Community Living Centers (CLCs) aged 70 years and older were analyzed within the VA Informatics and Computing Infrastructure. Data included diagnoses from 7,106 inpatient records, 21,318 functional status evaluations, and 69,140 inpatient diagnoses. The Barthel Index extracted from CLC's Minimum Data Set was used to assess ADLs loss and recovery. Patients' diagnoses on hospital admission, ADL status prior to hospitalization, age, and gender were used alone or in combination to predict ADL loss/gain following hospitalization. Area under the Receiver-Operator Curve (AUC) was used to report accuracy of predictions in short (14 days) and long-term (15-365 days) follow-up post-hospitalization.

RESULTS

Admissions fell into 7 distinct patterns of recovery and loss: early recovery 19%, delayed recovery 9%, delayed recovery after temporary decline 9%, early decline 29%, delayed decline 10%, delayed decline after temporary recovery 6%, and no change 18%. Models accurately predicted ADL's 14-day post-hospitalization (AUC for bathing 0.917, bladder 0.842, bowels 0.875, dressing 0.871, eating 0.867, grooming 0.902, toileting 0.882, transfer 0.852, and walking deficits was 0.882). Accuracy declined but remained relatively high when predicting 14-365 days post-hospitalization (AUC ranging from 0.798 to 0.875).

IMPLICATIONS

Predictive modeling may allow development of more personalized predictions of functional loss and recovery after hospitalization among nursing home patients.

Predictors of six-month mortality among nursing home residents: diagnoses may be more predictive than functional disability

OBJECTIVE

Loss of daily living functions can be a marker for end of life and possible hospice eligibility. Unfortunately, data on patient's functional abilities is not available in all settings. In this study we compare predictive accuracy of two indices designed to predict 6-month mortality among nursing home residents. One is based on traditional measures of functional deterioration and the other on patients' diagnoses and demography.

METHODS

We created the Hospice ELigibility Prediction (HELP) Index by examining mortality of 140,699 Veterans Administration (VA) nursing home residents. For these nursing home residents, the available data on history of hospital admissions were divided into training (112,897 cases) and validation (27,832 cases) sets. The training data were used to estimate the parameters of the HELP Index based on (1) diagnoses, (2) age on admission, and (3) number of diagnoses at admission. The validation data were used to assess the accuracy of predictions of the HELP Index. The cross-validated accuracy of the HELP Index was compared with the Barthel Index (BI) of functional ability obtained from 296,052 VA nursing home residents. A receiver operating characteristic curve was used to examine sensitivity and specificity of the predicted odds of mortality.

RESULTS

The area under the curve (AUC) for the HELP Index was 0.838. This was significantly (α <0.01) higher than the AUC for the BI of 0.692.

CONCLUSIONS

For nursing home residents, comorbid diagnoses predict 6-month mortality more accurately than functional status. The HELP Index can be used to estimate 6-month mortality from hospital data and can guide prognostic discussions prior to and following nursing home admission.

Comparative effectiveness of hypoglycemic medications among veterans

BACKGROUND

The efficacy of diabetic medications among patients with multiple comorbidities is not tested in randomized clinical studies. It is important to monitor the performance of these medications after marketing approvals.

OBJECTIVE

To investigate the risk of all-cause mortality associated with prescription of hypoglycemic agents.

METHODS

We retrospectively examined data from 17,773 type 2 diabetic patients seen from March 2, 1998, to December 13, 2010, in 3 Veterans Administration medical centers. Severity was measured using patients' inpatient and outpatient comorbidities during the last year of visits. Severity-adjusted logistic regression was used to measure the odds ratio for mortality within the study period.

RESULTS

Patients' severity of illness correctly classified mortality for 89.8% of the patients (P less than 0.0001). Being younger, married, and white decreased severity adjusted risk of mortality. Exposure to the following medications increased severity adjusted risk of mortality: glyburide (odds ratio [OR] = 1.804, 95% CI from 1.518 to 2.145), glipizide (OR = 1.566, 95% CI from 1.333 to 1.839), rosiglitazone (OR = 1.805, 95% CI from 1.378 to 2.365), chlorpropamide (OR = 3.026, 95% CI from 1.096 to 8.351), insulin (OR = 2.382, 95% CI from 2.112 to 2.686). None of the other medications (metformin, acarbose, glimepiride, pioglitazone, repaglinide, troglitazone, or dipeptidyl peptidase-4) were associated with excess mortality beyond what could be expected from the patients' severity of illness or demographic characteristics. The reported excess mortality could not be explained away by use of other concurrent, nondiabetic classes of medications.

CONCLUSION

Our findings suggest chlorpropamide, glipizide, glyburide, insulin, and rosiglitazone increased severity-adjusted mortality in veterans with type 2 diabetes. A decision aid that could optimize selection of hypoglycemic medications based on patients' comorbidities might increase patients' survival.

Continuously Rethinking the Definition of Influenza for Surveillance Systems

Objective. In the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), influenza was originally defined by a list of 29 and later by a list of 12 diagnosis codes. This article describes a dependent Bayesian procedure designed to improve the ESSENCE system and exploit multiple sources of information without being biased by redundancy. Methods. We obtained 13,096 cases within the Armed Forces Health Longitudinal Technological Application electronic medical records that included an influenza laboratory test. A Dependent Bayesian Expert System (D-BESt) was used to predict influenza from diagnoses, symptoms, reason for visit, temperature, month of visit, category of enrollment, and demographics. For each case, D-BESt sequentially selects the most discriminating piece of information, calculates its likelihood ratio conditioned on previously selected information, and updates the case’s probability of influenza. Results. When the analysis was limited to definitions based on diagnoses and was applied to a sample of patients for whom laboratory tests had been ordered, the areas under the receiver operating characteristic curve (AUCs) for the previous (29-diagnosis) and current (12-diagnosis) ESSENCE lists and the D-BESt algorithm were, respectively, 0.47, 0.36, and 0.77. Including other sources of information further improved the AUC for D-BESt to 0.79. At the best cutoff point for D-BESt, where the receiver operating characteristic curve for D-BESt is farthest from the diagonal line, the D-BESt algorithm correctly classified 84% of cases (specificity = 88%, sensitivity = 62%). In comparison, the current ESSENCE approach of using a list of 12 diagnoses correctly classified only 31% of this sample of cases (specificity = 29%, sensitivity = 42%). Conclusions. False alarms in ESSENCE surveillance systems can be reduced if a probabilistic dynamic learning system is used.