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Background and Aims: Inadequate bowel preparation during colonoscopy is associated with decreased adenoma detection, increased costs, and patient procedural risks. This study aimed to develop a prediction model for identifying patients at high risk of inadequate bowel preparation for potential clinical integration into the EMR. Methods: A retrospective study was conducted using outpatient screening/surveillance colonoscopies at the University of North Carolina (UNC) from 2017 to 2022. Data were extracted from the EMRs of Epic and ProVation, including demographic, socioeconomic, and clinical variables. Logistic regression, LASSO regression, and gradient boosting machine (GBM) models were evaluated and validated in a held-out testing set. Results: The dataset included 23,456 colonoscopies, of which 6.25% had inadequate bowel preparation. The reduced LASSO regression model demonstrated an area under the curve (AUC) of 0.65 [95% CI 0.63-0.67] in the held-out testing set. The relative risk of inadequate bowel prep in the high-risk group determined by the model was 2.42 (95% CI 2.07-2.82), compared to patients identified as low risk. The model calibration in the testing set revealed that among patients categorized as having 0-11%, 11-22%, and 22-33% predicted risk of inadequate prep, the respective proportions of patients with inadequate prep were 5.5%, 19.3%, and 33.3%. Using the reduced LASSO model, a rudimentary code for a potential Epic FHIR application called PrepPredict was developed. Conclusions: This study developed a prediction model for inadequate bowel preparation with the potential to integrate into the EMR for clinical use and optimize bowel preparation to improve patient care.
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OBJECTIVE: One aim of the Back Pain Consortium (BACPAC) Research Program is to develop an integrated model of chronic low back pain that is informed by combined data from translational research and clinical trials. We describe efforts to maximize data harmonization and accessibility to facilitate Consortium-wide analyses. METHODS: Consortium-wide working groups established harmonized data elements to be collected in all studies and developed standards for tabular and nontabular data (eg, imaging and omics). The BACPAC Data Portal was developed to facilitate research collaboration across the Consortium. RESULTS: Clinical experts developed the BACPAC Minimum Dataset with required domains and outcome measures to be collected by use of questionnaires across projects. Other nonrequired domain-specific measures are collected by multiple studies. To optimize cross-study analyses, a modified data standard was developed on the basis of the Clinical Data Interchange Standards Consortium Study Data Tabulation Model to harmonize data structures and facilitate integration of baseline characteristics, participant-reported outcomes, chronic low back pain treatments, clinical exam, functional performance, psychosocial characteristics, quantitative sensory testing, imaging, and biomechanical data. Standards to accommodate the unique features of chronic low back pain data were adopted. Research units submit standardized study data to the BACPAC Data Portal, developed as a secure cloud-based central data repository and computing infrastructure for researchers to access and conduct analyses on data collected by or acquired for BACPAC. CONCLUSIONS: BACPAC harmonization efforts and data standards serve as an innovative model for data integration that could be used as a framework for other consortia with multiple, decentralized research programs.