Scholarlink

Background <p>The 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.</p> Methods <p>We 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).</p> Results <p>Among 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.</p> Conclusions <p>EMR-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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Accuracy of Electronic Medical Records for Quantifying Rates of Sedative and Analgesic Infusions for Acute Disorders of Consciousness in Big Data Research

  • 期刊论文

  • 发表日期：

    2026年3月17日

查看全文
  • Joel Neves Briard,
  • Vedant Kansara,
  • Roxana Dumitru,
  • Qi Shen,
  • Sweta Patel,
  • You Lim Song,
  • Samvrit Vasudev,
  • Alex J. Klein,
  • Bert Vancura,
  • Angela Velazquez,
  • Shivani Ghoshal,
  • David Roh,
  • Sachin Agarwal,
  • Soojin Park,
  • E. Sander Connolly,
  • Jan Claassen

摘要

Background

The 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.

Methods

We 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).

Results

Among 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.

Conclusions

EMR-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.