A study of unplanned 30-day hospital readmissions in the United States : early prediction and potentially modifiable risk factor identification

Unplanned hospital readmissions greatly impair patients' quality of life and have imposed a significant economic burden on American society. The pressure to reduce costs and improve healthcare quality has triggered the development of readmission reduction interventions. However, existing solutions focus on complementing inpatient care with enhanced care transition and post-discharge interventions, which are initiated near or after discharge when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an under-explored area, which holds the potential for reducing readmission risk. Nevertheless, it is challenging for clinicians to predict readmission risk at the early stage of inpatient care because little data is available. Existing readmission predictive models tend to incorporate variables whose values are only available near or after discharge. As a result, these models cannot be used for the early prediction of readmission. Another challenge is that there is no universal solution to reduce readmissions during hospitalization. Patients can be readmitted for any reason, and their heterogeneous social and clinical factors can further complicate the planning of interventions. The objective of this project was to improve the timeliness of readmission preventive intervention through a data-driven approach. A systematic review of the literature was performed to collect reported risk factors for unplanned 30-day hospital readmission. Using various predictive modeling and exploratory analysis methods, we have developed an early prediction model of readmission and have identified potentially modifiable readmission risk factors, which may be used to guide the development of readmission preventive interventions during hospitalization for different patients

Complex Care Management Program Overview

This report includes brief updates on various forms of complex care management including: Aetna - Medicare Advantage Embedded Case Management ProgramBrigham and Women's Hospital - Care Management ProgramIndependent Health - Care PartnersIntermountain Healthcare and Oregon Health and Science University - Care Management PlusJohns Hopkins University - Hospital at HomeMount Sinai Medical Center -- New York - Mount Sinai Visiting Doctors Program/ Chelsea-Village House Calls ProgramsPartners in Care Foundation - HomeMeds ProgramPrinceton HealthCare System - Partnerships for PIECEQuality Improvement for Complex Chronic Conditions - CarePartner ProgramSenior Services - Project Enhance/EnhanceWellnessSenior Whole Health - Complex Care Management ProgramSumma Health/Ohio Department of Aging - PASSPORT Medicaid Waiver ProgramSutter Health - Sutter Care Coordination ProgramUniversity of Washington School of Medicine - TEAMcar

Development of the Assessment of Clinical Prediction Model Transportability (APT) Checklist

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and tools. However, naïve implementations of external CPMs are prone to failure due to the incompatibilities between the environments of the development and implementation sites. Although prior research has described methods for estimating the external validity of predictive models, quantifying dataset shift, updating models, as well as numerous CPM-specific frameworks for guiding the development, evaluation, reporting, and systematic reviews of CPMs, there are no frameworks for assessing the compatibility between a CPM and the target environment. This dissertation addresses this critical gap by proposing a novel CPM transportability checklist for guiding the adoption of externally developed CPMs.To guide the development of the checklist, four extant CPM-relevant frameworks (TRIPOD, CHARMS, PROBAST, and GRASP) were reviewed and synthesized, thereby identifying the key domains of CPMs. Then, four individual studies were conducted, each identifying, assessing the impact of, and/or proposing solutions for the disparity between CPM and environment in those domains. The first two studies target disparities in features, with the first characterizing the non-generalizability impact of a particular class of commonly used, EHR-idiosyncratic features. The second study was conducted to identify and propose a solution for the semantic discrepancy in features across sites caused by the insufficient coverage of EHR data by standards. The third study focused on the prediction target of CPMs, identifying significant heterogeneity in disease understanding, phenotyping algorithms, and cohort characteristics of the same clinical condition. In the fourth study investigating CPM evaluation, the gap between typical CPM evaluation design and expected implemented behavior was identified, and a novel evaluative framework was proposed to bridge that gap. Finally, the APT checklist was developed using the synthesis of the aforementioned CPM frameworks as the foundation, enriched through the incorporation of innovations and findings from these four conducted studies. While rigorous meta-evaluation remains, the APT checklist shows promise as a tool for assessing CPM transportability thereby reducing the risk of failure of externally implemented CPMs. The key contributions to informatics include: the discovery of healthcare process (HCP) variables as a driver of CPM non-transportability, the fragility of clinical phenotyping used to identify CPM targets, a novel classification system and meta-heuristics for an aspect of EHR data previously lacking in standards, a novel CPM evaluation design termed the pseudo-prospective trial, and the APT checklist. Overall, this work contributes to the body of biomedical informatics literature guiding the success of informatics interventions

Exploring the association between traumatic brain injury and psychotic-like experiences in children

This thesis studies the relationships between exposure to paediatric traumatic brain injury (TBI) and psychosis outcomes, and is presented in three parts. Part 1 is a systematic review and meta-analysis of the association between paediatric TBI and subsequent psychotic disorders/symptoms. We identified 10 relevant studies, of which eight were included in the meta-analysis. Based on a pooled sample size of 479,686, the pooled odds ratio (OR) for the association between paediatric TBI and psychosis outcomes was found to be marginally significant. Part 1 reports cautious meta-analytic evidence for a positive association between paediatric TBI and future psychosis. Part 2 utilised the Adolescent Brain Cognitive Development (ABCD) large cohort data from children aged 9 to 10 years old at baseline (n = 11,875), with longitudinal and prospective 3-year follow-up to investigate the extent to which TBI at baseline predicted psychotic-like experiences (PLEs) in children, using multi-level logistic regression analyses. It was found that the presence of paediatric TBI at baseline was a significant predictor of the occurrence of PLEs at 36 months, with the relationship remaining robust after controlling for potential confounders. However, no significant association was found between the presence of TBI and the presence of PLEs at baseline. Additionally, no significant relationships were observed between the number of TBIs and the presence of PLEs at both baseline and 36 months, whilst the severity of TBI was only found to be significantly associated with the presence of PLEs at baseline, but not at 36 months. In conclusion, part 2 provides evidence indicating (i) a delayed occurrence of PLEs following TBI among children aged 9 to 10 years old; (ii) weak associations between TBI and PLEs; however, (iii) a persistence of post-TBI PLEs and (iv) a dose–response relationship could not be observed. Part 3 is a critical appraisal which presents considerations in relation to three broad topics, including (i) transparency, accessibility, and reproducibility of the conducted research; (ii) reasons for and likely impact of a lack of expert by experience (EbE) involvement in the research design, conduct, analysis, and interpretation; and (iii) an exploration of the relationships between paediatric TBI and mental health outcomes in children

USING REAL-WOLRD HEALTHCARE DATA TO DEFINE AND PREVENT COMPLICATIONS IN INFLAMMATORY BOWEL DISEASE

Inflammatory bowel disease (IBD) is a collection of chronic, immune mediated disorders of the gastrointestinal track, characterized by relapsing and remitting disease activity. Despite our growing understanding of risk factors associated with developing disease, we still lack understanding of the impact of disease complications and how to best avoid complications with preventive care. Two known complications of IBD include the increased predisposition to Clostridium difficile infection and the increased risk of non-melanoma and melanoma skin cancers. This thesis aims to (1) define the long-term impact of Clostridium difficile infection on IBD patients after accounting for patients’ inherent risk of infection, (2) evaluate the rate at which IBD patients access dermatologic preventive care for skin cancer screening, and (3) model the cost-effectiveness of melanoma screening strategies in the IBD patient population. We found that Clostridium difficile infection was significantly associated with more steroid and antibiotic exposure, elevated inflammatory markers, increased disease activity, worse quality of life, and increased healthcare utilization in the year of infection. During the year after infection, patients in the Clostridium difficile group continued to have increased exposure to Clostridium difficile targeted antibiotics and other systemic antibiotics, while having more clinic visits, telephone encounters, and a five-fold increase in healthcare charges. We determined that 21% of IBD patients utilized dermatology from 2010-2016, and 2.6% of patients had at least one total body exam for skin cancer screening. Between 8% and 11% of patients recommended by gastroenterology preventive care guidelines visited dermatology each year, suggesting only a small proportion of IBD patients recommended for screening obtain dermatologic care. Finally, we used a Markov model to estimate intervention costs and effectiveness of melanoma screening in the IBD population. We found screening for melanoma in IBD patients was more effective, but expensive. Among model variations, screening every other year was the most cost-effective strategy. In conclusion, the dissertation reveals the long-term impact of infection among IBD patients, the underutilization of dermatologic preventive care, and provides a cost effectiveness model to inform the development of skin cancer screening programs in IBD

Longitudinal multi-dimensional investigation of metabolic and endocrine genetics

Genome-wide association studies (GWASs) in recent decades have revealed the genetic landscape and shared aetiology of common, complex traits across the spectrum of human phenotypes. In this work, I develop and apply statistical tools to interrogate the genetic basis of, and relationships between, metabolic and endocrine traits. I demonstrate that under-explored primary care electronic health records (EHRs), linked to massive biobank projects across the globe, are a valuable source of longitudinal and rare biomarker data for genetics studies. Using EHRs, I find a common missense variant in the APOE gene that is associated with weight-loss in adulthood, which replicates in three global biobanking cohorts of between 125,000 to 475,000 individuals each. While the heritability of weight-change is low ( 700,000 participants across seven global biobanks), to characterise the genetic contributions to these common but poorly understood phenotypes. I find 21 unique genetic loci for infertility, of which only six colocalise with reproductive hormone levels. While there is modest correlation between female infertility and heritable diseases of the reproductive tract, such as endometriosis (rG = 58%) and polycystic ovary syndrome (PCOS) (rG = 40%), I find no evidence for metabolic conditions such as obesity in the genetic aetiology of infertility. I explore these findings further through Mendelian Randomisation analyses to reveal heterogeneity in the genetically predicted causal effects of overall and central obesity on the risk of female reproductive conditions, including infertility, endometriosis, and PCOS, which may be partly genetically mediated by hormone levels. Through a range of genetics-based investigations, I outline the shared and distinct mechanisms of metabolic and endocrine disease in humans