Dynamic logistic state space prediction model for clinical decision making.

Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time-varying and time-invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying binomial density function. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply the method to predict 1 year survival after lung transplantation using the United Network for Organ Sharing data.

Mitigating selection bias in organ allocation models.

BACKGROUND: The lung allocation system in the U.S. prioritizes lung transplant candidates based on estimated pre- and post-transplant survival via the Lung Allocation Scores (LAS). However, these models do not account for selection bias, which results from individuals being removed from the waitlist due to receipt of transplant, as well as transplanted individuals necessarily having survived long enough to receive a transplant. Such selection biases lead to inaccurate predictions. METHODS: We used a weighted estimation strategy to account for selection bias in the pre- and post-transplant models used to calculate the LAS. We then created a modified LAS using these weights, and compared its performance to that of the existing LAS via time-dependent receiver operating characteristic (ROC) curves, calibration curves, and Bland-Altman plots. RESULTS: The modified LAS exhibited better discrimination and calibration than the existing LAS, and led to changes in patient prioritization. CONCLUSIONS: Our approach to addressing selection bias is intuitive and can be applied to any organ allocation system that prioritizes patients based on estimated pre- and post-transplant survival. This work is especially relevant to current efforts to ensure more equitable distribution of organs.

Comparing medication adherence using a smartphone application and electronic monitoring among patients with acute coronary syndrome.

AIM: The purpose of this study was to determine the extent of agreement between adherence measures obtained using two technological interventions, electronic monitoring (EM) and a smartphone application (App). BACKGROUND: Clinicians, patients, and researchers depend on valid measurements of medication adherence to inform the delivery of preemptive care when needed. Technology is routinely used for monitoring medication adherence in both clinical practice and research, yet there is a dearth of research comparing novel App based approaches to traditional approaches used for assessing medication adherence. METHODS: Adherence rates were captured on both the EM and the App for 3697 daily observations from 44 participants with acute coronary syndrome over 90 days immediately following discharge from acute care. For EM, adherence was measured using EM equipped pill bottles. For the App, adherence was measured by having participants upload daily photos to the App prior to taking their daily aspirin. Agreement was assessed using a Bland-Altman analysis. RESULTS: The mean adherence rate was higher on the App, 92%, than the EM, 78% (p < 0.001). The mean difference in adherence rates between these methods was 14% (95% Confidence Interval: -23%, -5%). CONCLUSIONS: These findings illustrate a lack of agreement between technological interventions used for measuring adherence in cardiovascular patient populations, with higher adherence rates observed with the App compared to EM. These findings are salient given the increased reliance on telehealth due to the ongoing COVID-19 pandemic.

The Galaxy Plot: A New Visualization Tool for Bivariate Meta-Analysis Studies.

Funnel plots have been widely used to detect small-study effects in the results of univariate meta-analyses. However, there is no existing visualization tool that is the counterpart of the funnel plot in the multivariate setting. We propose a new visualization method, the galaxy plot, which can simultaneously present the effect sizes of bivariate outcomes and their standard errors in a 2-dimensional space. We illustrate the use of the galaxy plot with 2 case studies, including a meta-analysis of hypertension trials with studies from 1979-1991 (Hypertension. 2005;45(5):907-913) and a meta-analysis of structured telephone support or noninvasive telemonitoring with studies from 1966-2015 (Heart. 2017;103(4):255-257). The galaxy plot is an intuitive visualization tool that can aid in interpreting results of multivariate meta-analysis. It preserves all of the information presented by separate funnel plots for each outcome while elucidating more complex features that may only be revealed by examining the joint distribution of the bivariate outcomes.

Correction: Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The risk of sudden cardiac arrest and ventricular arrhythmia with rosiglitazone versus pioglitazone: real-world evidence on thiazolidinedione safety.

BACKGROUND: The low cost of thiazolidinediones makes them a potentially valuable therapeutic option for the > 300 million economically disadvantaged persons worldwide with type 2 diabetes mellitus. Differential selectivity of thiazolidinediones for peroxisome proliferator-activated receptors in the myocardium may lead to disparate arrhythmogenic effects. We examined real-world effects of thiazolidinediones on outpatient-originating sudden cardiac arrest (SCA) and ventricular arrhythmia (VA). METHODS: We conducted population-based high-dimensional propensity score-matched cohort studies in five Medicaid programs (California, Florida, New York, Ohio, Pennsylvania | 1999-2012) and a commercial health insurance plan (Optum Clinformatics | 2000-2016). We defined exposure based on incident rosiglitazone or pioglitazone dispensings; the latter served as an active comparator. We controlled for confounding by matching exposure groups on propensity score, informed by baseline covariates identified via a data adaptive approach. We ascertained SCA/VA outcomes precipitating hospital presentation using a validated, diagnosis-based algorithm. We generated marginal hazard ratios (HRs) via Cox proportional hazards regression that accounted for clustering within matched pairs. We prespecified Medicaid and Optum findings as primary and secondary, respectively; the latter served as a conceptual replication dataset. RESULTS: The adjusted HR for SCA/VA among rosiglitazone (vs. pioglitazone) users was 0.91 (0.75-1.10) in Medicaid and 0.88 (0.61-1.28) in Optum. Among Medicaid but not Optum enrollees, we found treatment effect heterogeneity by sex (adjusted HRs = 0.71 [0.54-0.93] and 1.16 [0.89-1.52] in men and women respectively, interaction term p-value = 0.01). CONCLUSIONS: Rosiglitazone and pioglitazone appear to be associated with similar risks of SCA/VA.

Testing small study effects in multivariate meta-analysis.

Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects include publication bias, outcome reporting bias, and clinical heterogeneity. Methods to account for small study effects in univariate meta-analysis have been extensively studied. However, detecting small study effects in a multivariate meta-analysis setting remains an untouched research area. One of the complications is that different types of selection processes can be involved in the reporting of multivariate outcomes. For example, some studies may be completely unpublished while others may selectively report multiple outcomes. In this paper, we propose a score test as an overall test of small study effects in multivariate meta-analysis. Two detailed case studies are given to demonstrate the advantage of the proposed test over various naive applications of univariate tests in practice. Through simulation studies, the proposed test is found to retain nominal Type I error rates with considerable power in moderate sample size settings. Finally, we also evaluate the concordance between the proposed tests with the naive application of univariate tests by evaluating 44 systematic reviews with multiple outcomes from the Cochrane Database.

A behavioral economics-based telehealth intervention to improve aspirin adherence following hospitalization for acute coronary syndrome.

PURPOSE: A significant number of patients with acute coronary syndrome (ACS) are nonadherent to aspirin after hospital discharge, with an associated increased risk of subsequent cardiovascular events. The purpose of this pilot study was to test the efficacy of a telehealth intervention based on behavioral economics to improve aspirin adherence following hospitalization for ACS. METHODS: We enrolled 130 participants (c¯X = 58 ± 10.7 years of age, 38% female, 45% black) from two hospitals. Patients were eligible if they owned a smartphone and were admitted to the hospital for ACS, prescribed aspirin at discharge, and responsible for administering their own medications. Consenting participants were randomized to the intervention or usual care group. The intervention group was eligible to receive up to $50 per month if they took their medicine daily, with $2 per day deducted if a dose was missed. All participants received an electronic monitoring (EM) pill bottle containing a 90-day supply of aspirin, which was used to measure adherence calculated as the proportion of prescribed drug taken using the EM device. Based on the skewness in the adherence distribution, quantile regression was used to evaluate the effect of the intervention on median adherence over time. RESULTS: After 90 days, adherence fell in the control group but remained high in the intervention group (median adherence 81% vs 90%, P = .18). Rehospitalization was higher in the control group (24% vs 13%, P = .17). CONCLUSION: A loss aversion behavioral economics-based telehealth intervention is a promising approach to improving aspirin adherence following hospitalization for ACS.

Qualitative study of system-level factors related to genomic implementation.

PURPOSE: Research on genomic medicine integration has focused on applications at the individual level, with less attention paid to implementation within clinical settings. Therefore, we conducted a qualitative study using the Consolidated Framework for Implementation Research (CFIR) to identify system-level factors that played a role in implementation of genomic medicine within Implementing GeNomics In PracTicE (IGNITE) Network projects. METHODS: Up to four study personnel, including principal investigators and study coordinators from each of six IGNITE projects, were interviewed using a semistructured interview guide that asked interviewees to describe study site(s), progress at each site, and factors facilitating or impeding project implementation. Interviews were coded following CFIR inner-setting constructs. RESULTS: Key barriers included (1) limitations in integrating genomic data and clinical decision support tools into electronic health records, (2) physician reluctance toward genomic research participation and clinical implementation due to a limited evidence base, (3) inadequate reimbursement for genomic medicine, (4) communication among and between investigators and clinicians, and (5) lack of clinical and leadership engagement. CONCLUSION: Implementation of genomic medicine is hindered by several system-level barriers to both research and practice. Addressing these barriers may serve as important facilitators for studying and implementing genomics in practice.

Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study.

BACKGROUND: Capturing and incorporating patient-centered factors into 30-day readmission risk prediction after hospitalized heart failure (HF) could improve the modest performance of current models. METHODS: Using a mixed-methods approach, we developed a patient-centered survey and evaluated the additional predictive utility of the survey compared to a traditional readmission risk model (the Krumholz et al. model). Area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit statistic quantified the performance of both models. We measured the amount of model improvement with the addition of patient-centered factors to the Krumholz et al. model with the integrated discrimination improvement (IDI). In an exploratory analysis, we used hierarchical clustering algorithms to identify groups with similar survey responses and tested for differences between clusters using standard descriptive statistics. RESULTS: From 3/24/2014 to 3/12/2015, 183 patients hospitalized with HF were enrolled from an urban, academic health system and followed for 30days after discharge. The Krumholz et al. plus patient-centered factors model had similar-to-slightly lower performance (AUC [95%CI]:0.62 [0.52, 0.71]; goodness-of-fit P=.10) than the Krumholz et al. model (AUC [95%CI]:0.66 [0.57, 0.76]; goodness-of-fit P=.19). The IDI (95%CI) was 0.003 (-0.014,0.020). We identified three patient clusters based on patient-centered survey responses. The clusters differed with respect to gender, self-rated health, employment status, and prior hospitalization frequency (all P<.05). CONCLUSIONS: The addition of patient-centered factors did not improve 30-day readmission model performance. Rather than designing interventions based on predicted readmission risk, tailoring interventions to all patients, based on their characteristics, could inform the design of targeted, readmission reduction strategies.

Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group.

PurposeImplementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing Genomics in Practice (IGNITE) Network's efforts to promote (i) a broader understanding of genomic medicine implementation research and (ii) the sharing of knowledge generated in the network.MethodsTo facilitate this goal, the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide its approach to identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross-network analyses.ResultsCMG identified 10 high-priority CFIR constructs as important for genomic medicine. Of those, eight did not have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model.ConclusionWe developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field.

Heart Failure Home Management Challenges and Reasons for Readmission: a Qualitative Study to Understand the Patient's Perspective.

BACKGROUND: Heart failure patients have high 30-day hospital readmission rates. Interventions designed to prevent readmissions have had mixed success. Understanding heart failure home management through the patient's experience may reframe the readmission "problem" and, ultimately, inform alternative strategies. OBJECTIVE: To understand patient and caregiver challenges to heart failure home management and perceived reasons for readmission. DESIGN: Observational qualitative study. PARTICIPANTS: Heart failure patients were recruited from two hospitals and included those who were hospitalized for heart failure at least twice within 30 days and those who had been recently discharged after their first heart failure admission. APPROACH: Open-ended, semi-structured interviews. Conclusions vetted using focus groups. KEY RESULTS: Semi-structured interviews with 31 patients revealed a combination of physical and socio-emotional influences on patients' home heart failure management. Major themes identified were home management as a struggle between adherence and adaptation, and hospital readmission as a rational choice in response to distressing symptoms. Patients identified uncertainty regarding recommendations, caused by unclear instructions and temporal incongruence between behavior and symptom onset. This uncertainty impaired their competence in making routine management decisions, resulting in a cycle of limit testing and decreasing adherence. Patients reported experiencing hopelessness and frustration in response to perceiving a deteriorating functional status. This led some to a cycle of despair characterized by worsening adherence and negative emotions. As these cycles progressed and distressing symptoms worsened, patients viewed the hospital as the safest place for recovery and not a "negative" outcome. CONCLUSION: Cycles of limit testing and despair represent important patient-centered struggles in managing heart failure. The resulting distress and fear make readmission a rational choice for patients rather than a negative outcome. Interventions (e.g., palliative care) that focus on methods to address these patient-centered factors should be further studied rather than methods to reduce hospital readmissions.

A systematic analysis and comparison of warfarin initiation strategies.

OBJECTIVE: Randomized trials have reported inconsistent evidence on the effectiveness of algorithms that use genotypes to initiate warfarin therapy. The Clarification of Optimal Anticoagulation through Genetics (COAG) trial initiated therapy on the basis of predicted maintenance doses, with a pharmacogenetic-guided algorithm in one study group and a clinically guided algorithm in the other. The European Pharmacogenetics of Anticoagulant Therapy (EU-PACT) consortium initiated therapy on the basis of loading doses, with an algorithm-based prediction in one study group and a fixed-dose regimen in the other. To understand the differences between these trials, we compared the initial doses between alternative dosing algorithms (the pharmacogenetic-guided and clinically guided algorithms developed by Gage and colleagues and those developed by the International Warfarin Pharmacogenetics Consortium) and between the COAG and EU-PACT dose-initiation strategies. METHODS: This was a secondary analysis of the COAG trial - a double-blind, randomized-controlled trial (2009-2013) - conducted at 18 clinical centers in the USA, which included 1010 adults initiating warfarin therapy, of whom 719 achieved maintenance dose. RESULTS: Among COAG participants, the distribution of initial doses differed between algorithms, but showed similar prediction accuracy for maintenance dose. However, had the COAG trial implemented the EU-PACT strategy, the 3-day initial dose would have been 4.8 mg greater among participants randomized to pharmacogenetic-guided dosing, but only 2.5 mg greater among participants randomized to clinically guided dosing (P<0.001). CONCLUSION: Compared with the COAG trial, the EU-PACT trial used systematically larger loading doses in the pharmacogenetic-guided group and might have inadequately adjusted for clinical variability in warfarin dose requirements in the fixed-dose group.

A pharmacogenetic versus a clinical algorithm for warfarin dosing.

BACKGROUND: The clinical utility of genotype-guided (pharmacogenetically based) dosing of warfarin has been tested only in small clinical trials or observational studies, with equivocal results. METHODS: We randomly assigned 1015 patients to receive doses of warfarin during the first 5 days of therapy that were determined according to a dosing algorithm that included both clinical variables and genotype data or to one that included clinical variables only. All patients and clinicians were unaware of the dose of warfarin during the first 4 weeks of therapy. The primary outcome was the percentage of time that the international normalized ratio (INR) was in the therapeutic range from day 4 or 5 through day 28 of therapy. RESULTS: At 4 weeks, the mean percentage of time in the therapeutic range was 45.2% in the genotype-guided group and 45.4% in the clinically guided group (adjusted mean difference, [genotype-guided group minus clinically guided group], -0.2; 95% confidence interval, -3.4 to 3.1; P=0.91). There also was no significant between-group difference among patients with a predicted dose difference between the two algorithms of 1 mg per day or more. There was, however, a significant interaction between dosing strategy and race (P=0.003). Among black patients, the mean percentage of time in the therapeutic range was less in the genotype-guided group than in the clinically guided group. The rates of the combined outcome of any INR of 4 or more, major bleeding, or thromboembolism did not differ significantly according to dosing strategy. CONCLUSIONS: Genotype-guided dosing of warfarin did not improve anticoagulation control during the first 4 weeks of therapy. (Funded by the National Heart, Lung, and Blood Institute and others; COAG ClinicalTrials.gov number, NCT00839657.).

Comparing Perspectives of Patients, Caregivers, and Clinicians on Heart Failure Management.

BACKGROUND: Although substantial effort has been devoted to reducing readmissions among heart failure (HF) patients, little is known about factors identified by patients and caregivers that may contribute to readmissions. The goal of this study was to compare the perspectives of HF patients, their caregivers, and their care team on HF management and hospital admissions. Understanding these perspectives may lead to better strategies for improving care during the post-hospital transition and for reducing preventable readmissions. METHODS AND RESULTS: We performed freelisting, an anthropologic technique in which participants list items in response to a question, with hospitalized HF patients (n = 58), their caregivers (n = 32), and clinicians (n = 67). We asked about home HF management tasks, difficulties in managing HF, and perceived reasons for hospital admission. Results were analyzed with the use of Anthropac. Salience indices (measures of the most important words for defining the domain of interest) were calculated. Patients and clinicians described similar home HF management tasks, whereas caregivers described tasks related to activities of daily living. Clinicians cited socioeconomic factors as challenges to HF management, whereas patients and caregivers cited limited functional status and daily activities. When asked about reasons for hospitalization, patients and caregivers listed distressing symptoms and illness, whereas clinicians viewed patient behaviors to be primarily responsible for admission. CONCLUSIONS: These findings highlight that although some similarities exist, there are important differences among patients, caregivers, and clinicians in how they perceive the challenges of HF management and reasons for readmission. Understanding these differences may be critical to developing strategies to reduce readmissions.

Predicting prolonged dose titration in patients starting warfarin.

PURPOSE: Patients initiating warfarin therapy generally experience a dose-titration period of weeks to months, during which time they are at higher risk of both thromboembolic and bleeding events. Accurate prediction of prolonged dose titration could help clinicians determine which patients might be better treated by alternative anticoagulants that, while more costly, do not require dose titration. METHODS: A prediction model was derived in a prospective cohort of patients starting warfarin (n = 390), using Cox regression, and validated in an external cohort (n = 663) from a later time period. Prolonged dose titration was defined as a dose-titration period >12 weeks. Predictor variables were selected using a modified best subsets algorithm, using leave-one-out cross-validation to reduce overfitting. RESULTS: The final model had five variables: warfarin indication, insurance status, number of doctor's visits in the previous year, smoking status, and heart failure. The area under the ROC curve (AUC) in the derivation cohort was 0.66 (95%CI 0.60, 0.74) using leave-one-out cross-validation, but only 0.59 (95%CI 0.54, 0.64) in the external validation cohort, and varied across clinics. Including genetic factors in the model did not improve the area under the ROC curve (0.59; 95%CI 0.54, 0.65). Relative utility curves indicated that the model was unlikely to provide a clinically meaningful benefit compared with no prediction. CONCLUSIONS: Our results suggest that prolonged dose titration cannot be accurately predicted in warfarin patients using traditional clinical, social, and genetic predictors, and that accurate prediction will need to accommodate heterogeneities across clinical sites and over time. Copyright © 2016 John Wiley & Sons, Ltd.

A randomized trial of lottery-based incentives and reminders to improve warfarin adherence: the Warfarin Incentives (WIN2) Trial.

BACKGROUND: Previous research has suggested that daily lottery incentives could improve medication adherence. Such daily incentives include implicit reminders. However, the comparative effectiveness of reminders alone versus daily incentives has not been tested. METHODS: A total of 270 patients on warfarin were enrolled in a four-arm, multi-center, randomized controlled trial comparing a daily lottery-based incentive, a daily reminder, and a combination of the two against a control group (usual care). RESULTS: Participants in the reminder group had the lowest percentage of time out of target international normalized ratio (INR) range, the primary outcome, with an adjusted odds of an out-of-range INR 36% lower than among those in the control group, 95%CI [7%, 55%]. No other group had a statistically significant improvement in anticoagulation control relative to the control group or to each other. The only group that had significant improvement in incorrect adherence was the lottery group (incorrect adherence: 12.1% compared with 23.7% in the control group, difference of -7.4% 95%CI [-14%, -0.3%]). However, there was no relationship between changes in adherence and anticoagulation control in the lottery group. CONCLUSIONS: Automated reminders led to the largest improvements in anticoagulation control, although without impacting measured adherence. Lottery-based reminders improved measured adherence but did not lead to improved anticoagulation control. Copyright © 2016 John Wiley & Sons, Ltd.

Comparative risk of ischemic stroke among users of clopidogrel together with individual proton pump inhibitors.

BACKGROUND AND PURPOSE: There is controversy and little information about whether individual proton pump inhibitors (PPIs) differentially alter the effectiveness of clopidogrel in reducing ischemic stroke risk. We, therefore, aimed to elucidate the risk of ischemic stroke among concomitant users of clopidogrel and individual PPIs. METHODS: We conducted a propensity score-adjusted cohort study of adult new users of clopidogrel, using 1999 to 2009 Medicaid claims from 5 large states. Exposures were defined by prescriptions for esomeprazole, lansoprazole, omeprazole, rabeprazole, and pantoprazole-with pantoprazole serving as the referent. The end point was hospitalization for acute ischemic stroke, defined by International Classification of Diseases Ninth Revision Clinical Modification codes in the principal position on inpatient claims, within 180 days of concomitant therapy initiation. RESULTS: Among 325 559 concomitant users of clopidogrel and a PPI, we identified 1667 ischemic strokes for an annual incidence of 2.4% (95% confidence interval, 2.3-2.5). Adjusted hazard ratios for ischemic stroke versus pantoprazole were 0.98 (0.82-1.17) for esomeprazole; 1.06 (0.92-1.21) for lansoprazole; 0.98 (0.85-1.15) for omeprazole; and 0.85 (0.63-1.13) for rabeprazole. CONCLUSIONS: PPIs of interest did not increase the rate of ischemic stroke among clopidogrel users when compared with pantoprazole, a PPI thought to be devoid of the potential to interact with clopidogrel.