Development and validation of an open-source pipeline for automatic population of case report forms from electronic health records: a pediatric multi-center prospective study.

BACKGROUND: Clinical trials and registry studies are essential for advancing research and developing novel treatments. However, these studies rely on manual entry of thousands of variables for each patient. Repurposing real-world data can significantly simplify the data collection, reduce transcription errors, and make the data entry process more efficient, consistent, and cost-effective. METHODS: We developed an open-source computational pipeline to collect laboratory and medication information from the electronic health record (EHR) data and populate case report forms. The pipeline was developed and validated with data from two independent pediatric hospitals in the US as part of the Long-terM OUtcomes after Multisystem Inflammatory Syndrome In Children (MUSIC) study. Our pipeline allowed the completion of two of the most time-consuming forms. We compared automatically extracted results with manually entered values in one hospital and applied the pipeline to a second hospital, where the output served as the primary data source for case report forms. FINDINGS: We extracted and populated 51,845 laboratory and 4913 medication values for 159 patients in two hospitals participating in a prospective pediatric study. We evaluated pipeline performance against data for 104 patients manually entered by clinicians in one of the hospitals. The highest concordance was found during patient hospitalization, with 91.59% of the automatically extracted laboratory and medication values corresponding with the manually entered values. In addition to the successfully populated values, we identified an additional 13,396 laboratory and 567 medication values of interest for the study. INTERPRETATION: The automatic data entry of laboratory and medication values during admission is feasible and has a high concordance with the manually entered data. By implementing this proof of concept, we demonstrate the quality of automatic data extraction and highlight the potential of secondary use of EHR data to advance medical science by improving data entry efficiency and expediting clinical research. FUNDING: NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685.

Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Long-term Survival after Hematopoietic Cell Transplant for Sickle Cell Disease Compared to the United States Population.

Hematopoietic cell transplant for sickle cell disease is curative but is associated with life threatening complications most of which occur within the first 2 years after transplantation. In the current era with interest in gene therapy and gene editing we felt it timely to report on sickle cell disease transplant recipients who were alive for at least 2-year after transplantation, not previously reported. Our objectives were to (1) report the conditional survival rates of patients who were alive for 2 or more years after transplantation (2) identify risk factors for death beyond 2 years after transplantation and (3) compare all-cause mortality risks to those of an age-, sex- and race-matched general population in the United States. By limiting to 2-year survivors, we exclude deaths that occur as a direct consequence of the transplantation procedure. De-identified records of 1149 patients were reviewed from a publicly available data source and 950 patients were eligible (https://picsure.biodatacatalyst.nhlbi.nih.gov). All analyses were performed in this secure cloud environment using the available statistical software package(s). The validity of the public database was confirmed by reproducing results from an earlier publication. Conditional survival estimates were obtained using the Kaplan-Meier method for the sub-cohort that had survived a given length (x) of time after transplantation. Cox regression models were built to identify risk factors associated with mortality beyond 2 years after transplantation. The standardized relative mortality risk (SMR) or the ratio of observed to expected number of deaths, was used to quantify all-cause mortality risk after transplantation and compared to age, race and sex-matched general population. Person-years at risk were calculated from an anchor date (i.e., 2-, 5- and 7-years) after transplantation until date of death or last date known alive. The expected number of deaths was calculated using age, race and sex-specific US mortality rates. The median follow up was 5 years (range 2-20) and 300 (32%) patients were observed for more than 7 years. Among those who lived for at least 7 years after transplantation the 12-year probability of survival was 97% (95% CI, 92%-99%). Compared to an age-, race- and sex-matched US population, the risk for late death after transplantation was higher as late as 7 years after transplantation (hazard ratio (HR) 3.2; P= .020) but the risk receded over time. Risk factors for late death included age at transplant and donor type. For every 10-year increment in patient age, an older patient was 1.75 times more likely to die than a younger patient (P= .0004). Compared to HLA-matched siblings the use of other donors was associated with higher risk for late death (HR 3.49; P= .003). Graft failure (beyond 2-years after transplantation) was 7% (95% CI, 5%-9%) and graft failure was higher after transplantation of grafts from donors who were not HLA-matched siblings (HR 2.59, P< .0001). Long-term survival after transplantation is excellent and support this treatment as a cure for sickle cell disease. The expected risk for death recedes over time but the risk for late death is not negligible.