Effect Of In-Hospital Stroke Alert On Thrombolytic Therapy In Women

Background/Purpose. Stroke is the leading cause of disability in the United States. Women who have experienced a stroke have greater disability than men. Thrombolytic agents decrease adverse side effects of stroke by dissolving blood clots. Yet, women have 8% higher odds against being treated with a thrombolytic agent. Also, about 17% of stroke cases occur in-hospital. Therefore, the purpose of this study was to investigate the effects and associated variables of having an in-hospital stroke alert activation on outcomes in women admitted to the hospital for a separate condition. Methods. Guided by the Model for Nursing Effectiveness Research, a retrospective observational study of 149 women participants was completed for a 4 year period. Study measures based on empirical evidence included the primary independent variable of in-hospital stroke alert, and confounding variables (patient characteristics, clinical conditions, and context of care) that are conceptually related to the primary outcome of thrombolytic therapy and secondary outcome of discharge status. Analysis included regression models and propensity score matching to isolate the treatment (in-hospital stroke alert) and outcome (thrombolytic therapy) while controlling the effects of other influential variables. Results. In-hospital stroke alert was activated in 46 of 149 or 30.9% women and 15 of 149 or 10.1% of women received thrombolytic therapy. In-hospital stroke alert was significant (p \u3c .001) for women receiving thrombolytic therapy and significant to a home discharge status (p = .014). Age (p \u3c .001), marital status (p = .067), ethnicity (p \u3c .001), common (p = \u3c.001) and unique symptoms (p = .012), stroke risk factors were present (p \u3c.001), comorbid conditions were present (p \u3c.001), Time Last Known Well (the time that the patient was without stroke symptoms) (p = .041), diagnostic imaging (p \u3c.001) were all significantly related to in-hospital stroke alert. Discussion/Conclusions. Results from this study suggest that younger married women from non-Caucasian ethnic groups and women with risk factors or comorbid conditions are all at higher levels of late stroke symptom detection and no in-hospital stroke alert activation. Improved stroke detection in women with attention to barriers may improve in-hospital stroke alert activation and early treatment

Clinical Treatment Human Disease Networks and Comparative Effectiveness Research: Analyses of the Medicare Administrative Data

As the nation’s largest healthcare payer, the Medicare program generates an unimaginable vast volume of medical data. With an increasing emphasis on evidence-based care, how to effectively handle and make inferences from the heterogeneous and noisy healthcare data remains an important question. High-quality analysis could improve the quality, planning, and administrations of health services, evaluate comparative therapies, and forward research on epidemiology and disease etiology. This is especially true for older adults since this population’s health condition is generally complicated with multimorbidity, and the healthcare system for older adults is riddled with administrative and regulatory complexities. Taking advantage of the scaled and comprehensive Medicare data, this dissertation focuses on outcome research, human disease networks, and comparative effectiveness research for older adults. Healthcare outcome measures such as mortality, readmission, length of stay (LOS), and medical costs have been extensively studied. However, existing analysis generally focuses on one single disease (or at most a few pre-selected and closely related diseases) or all diseases combined. It is increasingly evident that human diseases are interconnected with each other. Motivated by the emerging human disease network (HDN) analysis, we conduct network analysis of disease interconnections on healthcare outcomes measures. First, we propose a clinical treatment HDN that analyzes inpatient LOS data. In the network graph, one node represents one disease, and two nodes are linked with an edge if their disease-specific LOS are correlated (conditional on LOS of all other diseases). To accommodate zero-inflated LOS data, we propose a network construction approach based on the multivariate Hurdle model. We analyze the Medicare inpatient data for the period of January 2008 to December 2018. Based on the constructed network, key network properties such as connectivity, module/hub, and temporal variation are analyzed. The results are found to be biomedically sensible, especially from a treatment perspective. A closer examination also reveals novel findings that are less/not investigated in the individual-disease studies. This work has been published in Statistics in Medicine. Second, considering that many healthcare outcomes are closely related to each other, we propose a high-dimensional clinical treatment HDN that can incorporate multiple outcomes. We construct a clinical treatment HDN on LOS and readmission and note that the proposed method can be easily generalized to other outcomes of different data types. To deal with uniquely challenging data distributions (high-dimensionality and zero-inflation), a new network construction approach is developed based on the integrative analysis of generalized linear models. Data analysis is conducted using the Medicare inpatient data from January 2010 to December 2018. Network structure and properties are found to be similar to that of the LOS HDN (in Chapter 2) but provide additional insights into disease interconnections considering both LOS and readmission. The proposed clinical treatment of HDNs can promote a better understanding of human diseases and their interconnections, guide a more efficient disease management and healthcare resources allocation, and foster complex network analysis. The manuscript of this work has been drafted and is ready for submission. Comparative effectiveness research aims to directly compare the outcomes of two or more healthcare strategies to address a particular medical condition. Such analysis can provide information about the risks, benefits, and costs of different treatment options, thus guide better clinical decisions. While conducting a randomized controlled trial is the gold-standard approach, there are several limitations. Efforts have been made to utilize healthcare record data in comparative effectiveness research. To estimate and compare causal effects of treatments/interventions, we use the Medicare data to emulate target clinical trials and develop a deep learning-based analysis approach. Under emulation, target clinical trials are explicitly “assembled” using the Medicare data. As such, statistical methods for clinical trials can be directly applied to estimate causal effects. With emulation analysis, we evaluate the effectiveness and safety outcomes of rivaroxaban versus dabigatran for Medicare patients with atrial fibrillation. The results show that dabigatran is superior in terms of time to any primary event (including ischemic stroke, other thromboembolic events, major bleeding, and death), major bleeding, and mortality. This work has been submitted to Clinical Epidemiology. Considering that many regression-based statistical methods (e.g., Cox proportional hazards model for survival data) have too strict data assumptions, we further develop an innovative deep learning-based analysis strategy. With the “emulation + deep learning” approach, we study the survival outcomes of endovascular repair versus open aortic repair for Medicare patients with abdominal aortic aneurysms. It is found that endovascular repair has survival advantages in both short- and long-term mortality. This work has been published in Entropy. Significantly different and advancing from the existing literature, this dissertation extends the scope of outcome research, human disease networks, and comparative effectiveness research. The findings in this dissertation are shown to have scientific merits, and the methodological developments may have other applications and serve as prototypes for future analysis

Estimating Oral Anticoagulant Comparative Effectiveness in the Setting of Effect Heterogeneity: Comparing Clinical Trial Transport and Non-experimental Epidemiologic Methods

Oral anticoagulation is vital to the health of patients with atrial fibrillation at elevated risk of stroke. The first treatment for these patients, warfarin, was approved in the 1990s. Since 2010, dabigatran has been available for use after demonstrating non-inferiority to warfarin in a randomized controlled trial. Non-experimental studies comparing dabigatran to warfarin and censoring at treatment discontinuation have shown greater benefits than the original trial for all-cause mortality and attenuated harms for gastrointestinal bleeding. The goals of this dissertation, then, were to compute and compare 1) estimates of the absolute-scale effects of dabigatran vs warfarin initiation on ischemic stroke (IS), death, and gastrointestinal bleeding (GIB) in trial-eligible older adults using non-experimental Medicare data and 2) estimates of those effects in the same populations using inverse odds of sampling weights to transport results from the Randomized Evaluation of Long-Term Anticoagulation (RE-LY) trial. First, we conducted a propensity score weighted non-experimental study with the new user active comparator design in a 20% random sample of Medicare beneficiares. We estimated on-treatment two-year risk differences for IS (RD for dabigatran users, RDdabi: -0.67%, 95% CI -1.10%, -0.24%), mortality (RDdabi: -2.98%, 95% CI -3.97%, -1.95%) and GIB (RDdabi: 0.51%, 95% CI -0.30%, 1.31%). Intention-to-treat estimates showed attenuation for mortality (RDdabi: -1.65%, 95% CI -2.32%, -0.98%) and reversal for IS (RDdabi: 0.16%, 95% CI -0.20%, 0.52%). Next, we reweighted RE-LY to resemble the Medicare new users of warfarin or dabigatran (restricted to those with less than 15% predicted probability of frailty). After weighting, we estimated on-treatment two-year risk differences for IS (RDdabi: -0.77%, 95% CI -1.69%, 0.14%), death (RDdabi: -0.57%, 95% CI -1.83%, 0.68%) and GIB (RDdabi: 1.75%, 95% CI 0.76%, 2.74%). These twin studies show non-experimental and weighted trial analyses comparing dabigatran to warfarin agree much better for IS than they do for mortality or GIB. This could be due to confounding in the non-experimental estimates, missing treatment effect modifiers, or outcome misclassification. Researchers should be cautious about comparing studies without considering treatment effect heterogeneity and differences in adherence across study populations.Doctor of Philosoph

Design and Analysis of Randomized and Non-randomized Studies: Improving Validity and Reliability

The aim of the thesis is to investigate how to optimize the design and analysis of randomized and non-randomized therapeutic studies, in order to increase the validity and reliability of causal treatment effect estimates, specifically in heterogeneous diseases. The following research questions will be addressed: __1)__ What are the benefits of more advanced statistical analyses to estimate treatment effects from RTCs in heterogeneous diseases? a. What is the heterogeneity in acute neurological diseases with regard to baseline severity and further course of the disease? b. What is the potential gain in efficiency of covariate adjustment and proportional odds analysis in RCTs in Guillain-Barré syndrome (GBS)? __2)__ What is the validity and reliability of the RD design compared to an RCT to estimate causal treatment effects? a. What are threats to the validity of the RD design to estimate treatment effects compared to an RCT? b. How efficient is the RD design to estimate treatment effects compared to an RCT? c. What are the potential benefits of an alternative assignment approach in an RD design

Variation in Clinical Practice of PCI and Its Impact on Patient Outcomes

In contemporary clinical practice, percutaneous coronary intervention (PCI) is one of the most common methods to treat ischemic heart disease. It has proven to be very effective in appropriately selected patients. However, clinical discretion among interventional clinicians in the absence of definitive evidence-based guidelines results in significant variation in clinical practice of PCI. The objective of this dissertation research is to study the effect of such variation in three aspects on patient outcomes following PCI: (i) post-discharge statin prescription versus no prescription in the setting of otherwise aggressive medical therapy; (ii) use of multiple stents versus a single stent when either approach is clinically feasible; (iii) use versus no use of stent postdilation. Patients were evaluated from multiple data sources. The first source included the multi-center National Heart, Lung, and Blood Institute (NHLBI) Dynamic registry recruitment Wave 4 (2004) and Wave 5 (2006). For Aim 1 (post-discharge statin vs. no post-discharge statin), patient eligibility criteria included receipt of aspirin, thienopyridines and at least one type of cardiovascular protective medication (angiotensin-converting enzyme inhibitors, Beta blockers, or Calcium channel blockers) after the PCI procedure, and no in-hospital death. Risk of adverse events was compared between post-discharge statin recipients and non-recipients at one-year follow up. Results showed that post-discharge statin use was associated with a reduced risk of mortality and the composite endpoint of death/MI, death/MI/CABG. These data support the routine use of post-PCI statin therapy in the presence of otherwise aggressive medical therapy.For Aim 2 (multiple versus single stents), the DEScover Registry, a prospective, multicenter, observational study among 140 clinical centers in the United States, was used. The eligibility criteria for this analysis included: receipt of at least one stent for a lesion treated with PCI and the following characteristics: lesion not previously treated; lesion length of 10 to 32mm (i.e. able to be treated with either a single or multiple stents); and an angiographically successful procedure. Survival analysis over 1-year post-PCI showed that patients who received multiple stents had a similar risk of adverse events compared to patients who received a single long stent for each lesion treated. Thus, this analysis was unable to provide definitive evidence for a preference of single versus multiple stents for lesions in the range of 10 to 32 mm.For Aim 3 (postdilation versus no postdilation), the Dynamic registry recruitment Wave 4 (2004) and Wave 5 (2006) were used. Patient eligibility criteria for this analysis included receipt of greater than or equal to 1 stent and an angiographically successful PCI procedure. Survival analysis over 1-year post-PCI showed that among PCI patients who presented with acute MI, postdilation appears to significantly increase the risk of death by as much as 3-fold. However, because this finding was observed only among patents with one lesion treated but not among patients with multiple lesions treated, the possibility of a chance finding exists. Moreover, among PCI patients who had no acute MI, lesion postdilation did not appear to be associated with either a benefit or increased risk of adverse cardiac events. Thus, this analysis indicated no obvious clinical benefit associated with postdilation in the setting of PCI patients who had no acute MI, and a potential hazardous effect in the setting of acute MI.Our study has significant public health importance. Heart disease is the leading cause of mortality in nearly every region of the world, accounting for an estimated 30% of all deaths. Coronary heart disease (CHD) is the principal type of heart disease. The public health significance of our study is that investigating the effect of variation in clinical PCI practice can be a benefit to numerous CHD patients all over the world

Electronic Health Record-Derived Phenotyping Models to Improve Genomic Research in Stroke

Stroke is a highly heterogeneous and complex disease that is a leading cause of death in the United States. The landscape of risk factors for stroke is vast, and its large genetic burden has yet to be fully discovered. We hypothesize that the small number of stroke variants recovered so far is due to 1) the vast phenotypic heterogeneity of stroke and 2) binary labeling of stroke genome-wide association study (GWAS) participants as cases or controls. Specifically, genome-wide association studies accumulate hundreds of thousands to millions of participants to acquire adequate signal for variant discovery. This requires time-consuming manual curation of cases and controls often involving large-scale collaborations. Genetic biobanks connected to electronic health records (EHR) can facilitate these studies by using data routinely captured during clinical care like billing diagnosis codes. These data, however, do not define adjudicated cases and controls, with many patients falling somewhere in between. There is an opportunity to use machine learning to add nuance to these definitions. We hypothesize that an expanded definition of disease by incorporating correlated diseases and risk factors from EHR data will improve GWAS power. We also hypothesize that granularly subtyping stroke using unsupervised learning methods can provide insight into stroke etiology and heterogeneity. In Chapter 1, we described the motivation for building upon current phenotyping methods for subtyping and genome-wide association studies to improve GWAS power. In Chapter 2, using patients from Columbia-New York Presbyterian (NYP) Hospital, we built and evaluated machine learning models to identify patients with acute ischemic stroke based on 75 different case-control and classifier combinations. In chapter 3, we compared two data-driven and unsupervised methods, non-negative matrix factorization (NMF) and Hierarchical Poisson Factorization, to subtype stroke patients and determined whether any of the subtypes correlate to stroke severity. In chapter 4, we estimated the heritability of acute ischemic stroke by treating the patient probabilities assigned by the machine learning phenotyping models for acute ischemic stroke in chapter 2 as a quantitative trait and mapping the probabilities to Columbia-NYP EHR-generated pedigrees. We also applied our machine learning phenotyping algorithm method, which we call QTPhenProxy, to venous thromboembolism on Columbia eMERGE Consortium patients and ran a genome-wide association study using the model probabilities as a quantitative trait. Finally, we applied QTPhenProxy to subjects in the UK Biobank for stroke and 14 other diseases and ran genome-wide association studies for each disease. We found that our machine-learned models performed well in identifying acute ischemic stroke patients in the Columbia-NYP EHR and in the UK Biobank. We also found some NMF-derived subtypes that were significantly correlated with stroke severity. We were underpowered in the eMERGE venous thromboembolism cohort GWAS and did not recover any known or new variants. Finally, we found that QTPhenProxy improved the power of GWAS of stroke and several subtypes in the UK Biobank, recovered known variants, and discovered a new variant that replicates in a previous stroke GWAS. Our results for QTPhenProxy demonstrate the promise of incorporating large but messy sets of data, such as the electronic health record, to improve signal in genome-wide association studies

Cardiovascular polypharmacy in patients with coronary heart disease and stroke

Abstract Background: There was limited evidence on the utilisation and effectiveness of cardiovascular (CV) polypharmacy (≥5 CV medications) in the secondary prevention of cardiovascular disease (CVD). Aim: To investigate the patterns of CV polypharmacy and the impact of multiple CV medications on long-term survival in patients following the incident of myocardial infarction (MI) or stroke or CVD patients with diabetes and chronic obstructive pulmonary disease (COPD). Methods: Firstly, a systematic review and meta-analysis was conducted to assess the effect of evidence-based combination pharmacotherapy on mortality and CV events in patients with CVD. Secondly, a cross-sectional study was conducted to investigate the patterns of CV medications initially prescribed after the incident CVD event. Thirdly, six retrospective cohort studies were conducted to assess the impact of multiple CV medications on long-term survival among patients with incident ischemic stroke or MI, and among those with comorbidity of type 2 diabetes or COPD. Results: There were 40.6% of patients with CV polypharmacy. Male, younger age, current smoking, high BMI, hypertension, hyperlipidaemia, higher deprivation score and multiple comorbidities were associated with an increased likelihood of CV polypharmacy. Among patients with ischemic stroke, combination therapy with four or five CV medications was associated with around 40% reduction of all-cause mortality compared to monotherapy. Combinations containing antiplatelet agents (APAs), lipid-regulating medications (LRMs), angiotensin-converting enzyme inhibitors (ACEIs)/ angiotensin receptor blockers (ARBs) and calcium channel blockers (CCBs) were associated with a significant 61% lower risk of mortality (95% CI: 53%-68%) compared with APAs alone. Among patients with MI, combination therapy with four CV medications was associated with the lowest risk of mortality compared to monotherapy (HR: 0.38, 0.32-0.45). The combination of APAs, LRMs, ACEIs/ARBs and BBs decreased the risk of mortality by 79% (70%-85%) compared with APAs alone. Conclusions: This project suggested that combination therapy (more than two CV medications) is potentially beneficial and necessary to improve long-term survival among all individuals who have had an ischemic stroke or MI regardless of the further risk of CVD