Comparative Effect of Loop Diuretic Prescription on Mortality and Heart Failure Readmission.

Loop diuretics are a standard pharmacologic therapy in heart failure (HF) management. Although furosemide is most frequently used, torsemide and bumetanide are increasingly prescribed in clinical practice, possibly because of superior bioavailability. Few real-world comparative effectiveness studies have examined outcomes across all 3 loop diuretics. The study goal was to compare the effects of loop diuretic prescribing at HF hospitalization discharge on mortality and HF readmission. We identified patients in Medicare claims data initiating furosemide, torsemide, or bumetanide after an index HF hospitalization from 2007 to 2017. We estimated 6-month risks of all-cause mortality and a composite outcome (HF readmission or all-cause mortality) using inverse probability of treatment weighting to adjust for relevant confounders. We identified 62,632 furosemide, 1,720 torsemide, and 2,389 bumetanide initiators. The 6-month adjusted all-cause mortality risk was lowest for torsemide (13.2%), followed by furosemide (14.5%) and bumetanide (15.6%). The 6-month composite outcome risk was 21.4% for torsemide, 24.7% for furosemide, and 24.9% for bumetanide. Compared with furosemide, the 6-month all-cause mortality risk was 1.3% (95% confidence interval [CI]: -3.7, 1.0) lower for torsemide and 1.0% (95% CI: -1.2, 3.2) higher for bumetanide, and the 6-month composite outcome risk was 3.3% (95% CI: -6.3, -0.3) lower for torsemide and 0.2% (95% CI: -2.5, 2.9) higher for bumetanide. In conclusion, the findings suggested that the first prescribed loop diuretic following HF hospitalization is associated with clinically important differences in morbidity in older patients receiving torsemide, bumetanide, or furosemide. These differences were consistent for the effect of all-cause mortality alone, but were not statistically significant.

Translation of a Claims-Based Frailty Index From the International Classification of Diseases, Ninth Revision, Clinical Modification to the Tenth Revision.

The Faurot frailty index (FFI) is a validated algorithm that uses enrollment and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-based billing information from Medicare claims data as a proxy for frailty. In October 2015, the US health-care system transitioned from the ICD-9-CM to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Applying the Centers for Medicare and Medicaid Services General Equivalence Mappings, we translated diagnosis-based frailty indicator codes from the ICD-9-CM to the ICD-10-CM, followed by manual review. We used interrupted time-series analysis of Medicare data to assess the comparability of the pre- and posttransition FFI scores. In cohorts of beneficiaries enrolled in January 2015-2017 with 8-month frailty look-back periods, we estimated associations between the FFI and 1-year risk of aging-related outcomes (mortality, hospitalization, and admission to a skilled nursing facility). Updated indicators had similar prevalences as pretransition definitions. The median FFI scores and interquartile ranges (IQRs) for the predicted probability of frailty were similar before and after the International Classification of Diseases transition (pretransition: median, 0.034 (IQR, 0.02-0.07); posttransition: median, 0.038 (IQR, 0.02-0.09)). The updated FFI was associated with increased risks of mortality, hospitalization, and skilled nursing facility admission, similar to findings from the ICD-9-CM era. Studies of medical interventions in older adults using administrative claims should use validated indices, like the FFI, to mitigate confounding or assess effect-measure modification by frailty.

Comparison of Approaches for Measuring Adherence and Persistence to Oral Oncologic Therapies in Patients Diagnosed with Metastatic Renal Cell Carcinoma.

BACKGROUND: Adherence and persistence studies face several methodologic difficulties, including short-term mortality. We compared approaches to quantify adherence and persistence to first line (1L) oral targeted therapy (TT) in patients diagnosed with metastatic renal cell carcinoma (mRCC). METHODS: Patients with mRCC ages 66 years or more who initiated TTs within 4 months of diagnosis were identified in the Surveillance, Epidemiology, and End Results Medicare-linked database (2007-2015). Adherence [proportion of days covered (PDC) >80%] was calculated using (i) PDC with a fixed 6-month denominator including then excluding patients who died within the 6 months and (ii) PDC with a denominator measuring time on treatment. Risk of nonpersistence was obtained by censoring death or treating death as a competing risk using cumulative incidence functions. RESULTS: Among 485 patients with mRCC initiating a 1L oral TT (sunitinib, 64%; pazopanib, 25%; other, 11%), 40% died within 6 months. Adherence was higher after restricting to patients who survived (60%) compared with including those patients and assigning zero days covered after death (47%). Risk of nonpersistence was higher when censoring patients at death, 0.91 [95% confidence interval (CI), 0.88-0.94], compared with treating death as a competing risk, 0.75 (95% CI, 0.71-0.79). CONCLUSIONS: Different approaches to handling death resulted in different adherence and persistence estimates in the metastatic setting. Future studies should explicitly report the proportion of patient deaths over time and explore appropriate methods to account for death as competing risk. IMPACT: Use of several approaches can provide a more comprehensive picture of medication-taking behavior in the metastatic setting where death is a major competing risk.

Trends in Opioid and Psychotropic Prescription in Pregnancy in the United States From 2001 to 2015 in a Privately Insured Population : A Cross-sectional Study.

BACKGROUND: Opioid and psychotropic prescriptions are common during pregnancy. Little is known about coprescriptions of both medications in this setting. OBJECTIVE: To describe opioid prescription among women who are prescribed psychotropics compared with women who are not. DESIGN: Cross-sectional study. SETTING: U.S. commercial insurance beneficiaries from MarketScan (2001 to 2015). PARTICIPANTS: Pregnant women at 22 weeks' gestation or greater who were insured continuously for 3 months or more before pregnancy through delivery. MEASUREMENTS: Opioid prescription, dosage thresholds (morphine milligram equivalents [MME] of ≥50/day and ≥90/day), number of opioid agents (≥2), and duration (≥30 days) among those with and without prescription of psychotropics, from 2011 to 2015. RESULTS: Among 958 980 pregnant women, 10% received opioids only, 6% psychotropics only, and 2% opioids with coprescription of psychotropics. Opioid prescription was higher among women prescribed psychotropics versus those who were not (26.5% vs. 10.7%). From 2001 to 2015, psychotropic prescription overall increased from 4.4% to 7.6%, opioid prescription without coprescription of psychotropics decreased from 11.9% to 8.4%, and opioids with coprescription decreased from 28.1% to 22.0%. Morphine milligram equivalents of 50 or greater per day decreased for women with and without coprescription (29.6% to 17.3% and 22.8% to 18.5%, respectively); MME of 90 or greater per day also decreased in both groups (15.0% to 4.7% and 11.5% to 4.2%, respectively). Women prescribed opioids only were more likely to have an antepartum hospitalization compared with those with neither prescription, as were women with coprescription versus those prescribed psychotropics only. Compared with those prescribed opioids only, women with coprescriptions were more likely to exceed MME of 90 or greater per day and to be prescribed 2 or more opioid agents and for 30 days or longer. Number and duration of opioids increased with benzodiazepine and gabapentin coprescription. LIMITATION: Inability to determine appropriateness of prescribing or overdose events. CONCLUSION: Opioids are frequently coprescribed with psychotropic medication during pregnancy and are associated with antepartum hospitalization. A substantial proportion of pregnant women are prescribed opioids at doses that increase overdose risk and exceed daily recommendations. PRIMARY FUNDING SOURCE: None.

Predictors of prevalent statin use among older adults identified as statin initiators based on Medicare claims data.

PURPOSE: Few studies have evaluated the degree to which prescription drug initiators are correctly identified using claims data. We examine the prevalence and predictors of recent statin possession in statin initiators identified using claims data. METHODS: Among Medicare Current Beneficiary Survey (MCBS) respondents, we used Medicare Part D claims from 2006 to 2011 to identify statin initiators using a 12-month baseline period of no prior statin claims. Using MCBS interview data, we identified those with self-reported statins obtained during the baseline period. We used log-binomial regression to estimate adjusted prevalence ratios (adjPR) and 95% confidence intervals (CI) for predictors of recent statin possession. RESULTS: Among 766 statin initiators identified in prescription claims, 155 (20%) reported recent statin possession during baseline. Beneficiaries with no Part D claims in the past 30 days (adjPR = 1.49, 95%CI: 1.13, 1.96), those with no inpatient, outpatient or physician visits in the past 30 days (adjPR = 1.50, 95%CI: 1.11, 2.03), those with a brand name statin index claim (adjPR = 1.55, 95%CI: 1.19, 2.02), and those with an index claim in January or February (adjPR = 1.50, 95%CI: 1.00, 2.26) had an increased probability of recent statin possession. CONCLUSIONS: In a cohort of statin initiators identified using prescription claims, 20% had evidence of statin possession during the baseline period. Pharmacoepidemiologic new user studies may benefit from including sensitivity analyses within subgroups less likely to include prevalent users to assess the robustness of key findings to misidentification of the time of treatment initiation. Copyright © 2016 John Wiley & Sons, Ltd.

Comparison of diagnostic evaluations for cough among initiators of angiotensin converting enzyme inhibitors and angiotensin receptor blockers.

PURPOSE: Differential diagnostic evaluation associated with a drug may bias effect estimates because of an increased detection of preclinical outcomes. Persistent cough is a common side effect with angiotensin-converting enzyme inhibitors (ACEI), and we hypothesized that ACEI initiators would undergo more diagnostic evaluations, potentially leading to diagnosis of preclinical lung cancer. We compared the incidence of cough-related diagnostic evaluations and lung cancer among ACEI versus angiotensin receptor blockers (ARB) initiators. METHODS: Using a 20% sample of Medicare claims 2007-2012, we identified initiators of ACEI or ARB, age 66-99 years. Incidence of diagnostic evaluation and lung cancer were compared using adjusted Cox models. Monthly probabilities of workup were compared using proportion differences. RESULTS: There were 342 611 and 108 116 ACEI and ARB initiators, respectively. Monthly probability of chest X-rays ranged from minimum 4.7% to maximum 21.2% in the 6 months pre and post-initiation. Differences in incidence of diagnostic procedures in the 6 months after initiation were only minimal (chest X-rays hazard ratio (HR) = 1.12; 95% CI: 1.10-1.14), chest-MRI (0.86, 95% CI: 0.74-0.99), CT-scans (1.09, 95% CI: 0.99-1.18) or bronchoscopies (1.03, 95% CI: 0.83-1.29). Proportion differences for chest X-rays peaked in the month pre-initiation (8.4%, 95% CI: 8.1-8.6) but negligible thereafter. There was no difference in the incidence of lung cancer among ACEI versus ARB initiators (HR = 0.99, 95% CI: 0.84-1.16). CONCLUSION: Results indicate minimal differential chest workup after ACEI versus ARB initiation and no difference in lung cancer incidence, but suggest differential workup in the month before the first recorded prescription. The latter may reflect drug use before the first observed pharmacy claim or increased workup before initiation of ACEI therapy. Copyright © 2016 John Wiley & Sons, Ltd.

Doubly robust estimation of causal effects.

Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/~mfunk/dr/.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

OBJECTIVE: Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. STUDY DESIGN AND SETTING: We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. RESULTS: We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). CONCLUSION: Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice.

Discordance between patient-predicted and model-predicted life expectancy among ambulatory patients with heart failure.

CONTEXT: Patients with chronic heart failure have impaired long-term survival, but their own expectations regarding prognosis have not been well studied. OBJECTIVES: To quantify expectations for survival in patients with heart failure, to compare patient expectations to model predictions, and to identify factors associated with discrepancies between patient-predicted and model-predicted prognosis. DESIGN, SETTING, AND PARTICIPANTS: Prospective face-to-face survey of patients from the single-center Duke Heart Failure Disease Management Program between July and December 2004, with follow-up through February 2008. Patient-predicted life expectancy was obtained using a visual analog scale. Model-predicted life expectancy was calculated using the Seattle Heart Failure Model. Actuarial-predicted life expectancy, based on age and sex alone, was calculated using life tables. Observed survival was determined from review of medical records and search of the Social Security Death Index. MAIN OUTCOME MEASURE: Life expectancy ratio (LER), defined as the ratio of patient-predicted to model-predicted life expectancy. RESULTS: The cohort consisted of 122 patients (mean age, 62 years; 47% African American, 42% New York Heart Association [NYHA] class III or IV). On average, patients overestimated their life expectancy relative to model-predicted life expectancy (median patient-predicted life expectancy, 13.0 years; model-predicted expectancy, 10.0 years). Median LER was 1.4 (interquartile range, 0.8-2.5). Younger age, increased NYHA class, lower ejection fraction, and less depression were the most significant predictors of higher LER. During a median follow-up of 3.1 years, 29% of the original cohort died. There was no association between higher LER and improved survival (adjusted hazard ratio for overestimated compared with concordant LER, 1.05; 95% confidence interval, 0.46-2.42). CONCLUSIONS: Ambulatory patients with heart failure tended to substantially overestimate their life expectancy compared with model-based predictions for survival. Because differences in perceived survival could affect decision making regarding advanced therapies and end-of-life planning, the causes of these discordant predictions warrant further study.