One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites.

OBJECTIVE: To characterize the interplay between multiple medical conditions across sites and account for the heterogeneity in patient population characteristics across sites within a distributed research network, we develop a one-shot algorithm that can efficiently utilize summary-level data from various institutions. By applying our proposed algorithm to a large pediatric cohort across four national Children's hospitals, we replicated a recently published prospective cohort, the RISK study, and quantified the impact of the risk factors associated with the penetrating or stricturing behaviors of pediatric Crohn's disease (PCD). METHODS: In this study, we introduce the ODACoRH algorithm, a one-shot distributed algorithm designed for the competing risks model with heterogeneity. Our approach considers the variability in baseline hazard functions of multiple endpoints of interest across different sites. To accomplish this, we build a surrogate likelihood function by combining patient-level data from the local site with aggregated data from other external sites. We validated our method through extensive simulation studies and replication of the RISK study to investigate the impact of risk factors on the PCD for adolescents and children from four children's hospitals within the PEDSnet, A National Pediatric Learning Health System. To evaluate our ODACoRH algorithm, we compared results from the ODACoRH algorithms with those from meta-analysis as well as those derived from the pooled data. RESULTS: The ODACoRH algorithm had the smallest relative bias to the gold standard method (-0.2%), outperforming the meta-analysis method (-11.4%). In the PCD association study, the estimated subdistribution hazard ratios obtained through the ODACoRH algorithms are identical on par with the results derived from pooled data, which demonstrates the high reliability of our federated learning algorithms. From a clinical standpoint, the identified risk factors for PCD align well with the RISK study published in the Lancet in 2017 and other published studies, supporting the validity of our findings. CONCLUSION: With the ODACoRH algorithm, we demonstrate the capability of effectively integrating data from multiple sites in a decentralized data setting while accounting for between-site heterogeneity. Importantly, our study reveals several crucial clinical risk factors for PCD that merit further investigations.

Design and Evaluation of a Postpartum Depression Ontology.

OBJECTIVE: Postpartum depression (PPD) remains an understudied research area despite its high prevalence. The goal of this study is to develop an ontology to aid in the identification of patients with PPD and to enable future analyses with electronic health record (EHR) data. METHODS: We used Protégé-OWL to construct a postpartum depression ontology (PDO) of relevant comorbidities, symptoms, treatments, and other items pertinent to the study and treatment of PPD. RESULTS: The PDO identifies and visualizes the risk factor status of variables for PPD, including comorbidities, confounders, symptoms, and treatments. The PDO includes 734 classes, 13 object properties, and 4,844 individuals. We also linked known and potential risk factors to their respective codes in the International Classification of Diseases versions 9 and 10 that would be useful in structured EHR data analyses. The representation and usefulness of the PDO was assessed using a task-based patient case study approach, involving 10 PPD case studies. Final evaluation of the ontology yielded 86.4% coverage of PPD symptoms, treatments, and risk factors. This demonstrates strong coverage of the PDO for the PPD domain. CONCLUSION: The PDO will enable future researchers to study PPD using EHR data as it contains important information with regard to structured (e.g., billing codes) and unstructured data (e.g., synonyms of symptoms not coded in EHRs). The PDO is publicly available through the National Center for Biomedical Ontology (NCBO) BioPortal ( https://bioportal.bioontology.org/ontologies/PARTUMDO ) which will enable other informaticists to utilize the PDO to study PPD in other populations.

Why Is the Electronic Health Record So Challenging for Research and Clinical Care?

BACKGROUND: The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinical data with others are large number of challenges that arguably limit the optimal use of the EHR OBJECTIVES: Our goal was to provide an exhaustive reference for those who use the EHR in clinical and research contexts, but also for health information systems professionals as they design, implement, and maintain EHR systems. METHODS: This study includes a panel of 24 biomedical informatics researchers, information technology professionals, and clinicians, all of whom have extensive experience in design, implementation, and maintenance of EHR systems, or in using the EHR as clinicians or researchers. All members of the panel are affiliated with Penn Medicine at the University of Pennsylvania and have experience with a variety of different EHR platforms and systems and how they have evolved over time. RESULTS: Each of the authors has shared their knowledge and experience in using the EHR in a suite of 20 short essays, each representing a specific challenge and classified according to a functional hierarchy of interlocking facets such as usability and usefulness, data quality, standards, governance, data integration, clinical care, and clinical research. CONCLUSION: We provide here a set of perspectives on the challenges posed by the EHR to clinical and research users.

Mapping the effects of drugs on the immune system.

Understanding how drugs affect the immune system has consequences for treating disease and minimizing unwanted side effects. Here we present an integrative computational approach for predicting interactions between drugs and immune cells in a system-wide manner. The approach matches gene sets between transcriptional signatures to determine their similarity. We apply the method to model the interactions between 1,309 drugs and 221 immune cell types and predict 69,995 interactions. The resulting immune-cell pharmacology map is used to predict how five drugs influence four immune cell types in humans and mice. To validate the predictions, we analyzed patient records and examined cell population changes from in vivo experiments. Our method offers a tool for screening thousands of interactions to identify relationships between drugs and the immune system.