One approach to verifying the quality of research data obtained from EHRs is auditing how complete and correct the data are in comparison with those collected by manual and controlled methods. This study analyzed data quality of an EHR-derived dataset for COVID-19 research, obtained during the pandemic at Hospital Universitario 12 de Octubre. Data were extracted from EHRs and a manually collected research database, and then transformed into the ISARIC-WHO COVID-19 CRF model. Subsequently, a data analysis was performed, comparing both sources through this convergence model. More concepts and records were obtained from EHRs, and PPV (95% CI) was above 85% in most sections. In future studies, a more detailed analysis of data quality will be carried out.
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http://dx.doi.org/10.3233/SHTI220430 | DOI Listing |
Syst Rev
January 2025
Department of Epidemiology, Mailman School of Public Health, Columbia University Irving Medical Center, New York, NY, USA.
Background: Impaired intrauterine growth, a significant global health problem, contributes to a higher burden of infant morbidity and mortality, mainly in resource-poor settings. Maternal anemia and undernutrition, two important causes of impaired intrauterine growth, are prioritized by global nutrition targets of 2030. We synthesized the evidence on the role of preconception nutrition supplements in reducing maternal anemia and improving intrauterine growth.
View Article and Find Full Text PDFDiagn Progn Res
January 2025
Department of Applied Health Sciences, College of Medicine and Health, University of Birmingham, Edgbaston, Birmingham, UK.
Background: Pressure injuries (PIs) place a substantial burden on healthcare systems worldwide. Risk stratification of those who are at risk of developing PIs allows preventive interventions to be focused on patients who are at the highest risk. The considerable number of risk assessment scales and prediction models available underscores the need for a thorough evaluation of their development, validation, and clinical utility.
View Article and Find Full Text PDFKnee Surg Relat Res
January 2025
Bioengineering Laboratory, Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Background: Unplanned readmission, a measure of surgical quality, occurs after 4.8% of primary total knee arthroplasties (TKA). Although the prediction of individualized readmission risk may inform appropriate preoperative interventions, current predictive models, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) surgical risk calculator (SRC), have limited utility.
View Article and Find Full Text PDFBMC Res Notes
January 2025
Department of Computer Engineering, Chungbuk National University, Chungdae-ro 1, Cheongju, 28644, Republic of Korea.
Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.
View Article and Find Full Text PDFInt J Retina Vitreous
January 2025
Department of Retina and Vitreous, Narayana Nethralaya, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, 560010, India.
Purpose: To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters.
Methods: This retrospective study included 158 eyes (151 patients) with full-thickness MHs treated between 2017 and 2023 by the same surgeon and using the same intraoperative surgical technique. Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded.
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