ArticleNeurocritical care2026
Accuracy of Electronic Medical Records for Quantifying Rates of Sedative and Analgesic Infusions for Acute Disorders of Consciousness in Big Data Research.
Article in Neurocritical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe reliability of electronic medical records (EMR) on exposure to sedative and analgesic medications in patients with acute disorders of consciousness is unknown. Our objective was to quantify the accuracy of sedative and analgesic infusion rates derived from the EMR to support its use in Big Data clinical research.
methodsWe conducted a secondary analysis of prospective cohort studies enrolling patients with critical illness who were unresponsive to verbal commands after acute brain injury. During standardized behavioral assessments, research coordinators documented infusing sedative and analgesic medications in case report forms (CRF; reference standard). We paired infusion rates from the CRF with corresponding infusion rates from the EMR (index). For each drug with ≥ 10 EMR-CRF infusion rate pairs, we calculated the concordance correlation coefficient and created Bland-Altman plots to estimate biases and limits of agreement (LOA).
resultsAmong 63 included patients (median [interquartile range (IQR)] age: 61 [46-72] years; 22 [35%] female), we collected 404 EMR-CRF infusion rate pairs (225 fentanyl, 86 propofol, 70 dexmedetomidine, 19 midazolam, 4 ketamine). Concordance correlation coefficients were 0.82 (95% CI: 0.69-0.91) for propofol, 0.93 (95% CI: 0.85-0.97) for fentanyl, 0.92 (95% CI: 0.81-0.98) for dexmedetomidine, and 0.94 (95% CI: 0.55-1.00) for midazolam. Biases (upper LOA, lower LOA) were -3 (17, -22) mcg/kg/min for propofol, -2 (43, -47) mcg/h for fentanyl, -0.01 (0.32, -0.33) mcg/kg/h for dexmedetomidine, and 0.02 (0.30, -0.27) mg/kg/h for midazolam.
conclusionsEMR-derived data on sedative and analgesic infusion rates have variable but overall adequate accuracy to support Big Data research. External validation is required to support its routine use in clinical research.
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