Jauk Stefanie, Kramer Diether, Sumerauer Stefan, Veeranki Sai Pavan Kumar, Schrempf Michael, Puchwein Paul
Division of Technology and IT, Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes), 8010 Graz, Austria.
PH Predicting Health GmbH, 8010 Graz, Austria.
JAMIA Open. 2024 Sep 17;7(3):ooae091. doi: 10.1093/jamiaopen/ooae091. eCollection 2024 Oct.
Delirium is a syndrome that leads to severe complications in hospitalized patients, but is considered preventable in many cases. One of the biggest challenges is to identify patients at risk in a hectic clinical routine, as most screening tools cause additional workload. The aim of this study was to validate a machine learning (ML)-based delirium prediction tool on surgical in-patients undergoing a systematic assessment of delirium.
738 in-patients of a vascular surgery, a trauma surgery and an orthopedic surgery department were screened for delirium using the DOS scale twice a day over their hospital stay. Concurrently, delirium risk was predicted by the ML algorithm in real-time for all patients at admission and evening of admission. The prediction was performed automatically based on existing EHR data and without any additional documentation needed.
103 patients (14.0%) were screened positive for delirium using the DOS scale. Out of them, 85 (82.5%) were correctly identified by the ML algorithm. Specificity was slightly lower, detecting 463 (72.9%) out of 635 patients without delirium. The AUROC of the algorithm was 0.883 (95% CI, 0.8523-0.9147).
In this prospective validation study, the implemented machine-learning algorithm was able to detect patients with delirium in surgical departments with high discriminative performance.
In future, this tool or similar decision support systems may help to replace time-intensive screening tools and enable efficient prevention of delirium.
谵妄是一种可导致住院患者出现严重并发症的综合征，但在许多情况下被认为是可预防的。最大的挑战之一是在繁忙的临床工作中识别有风险的患者，因为大多数筛查工具会增加额外工作量。本研究的目的是在对手术住院患者进行谵妄系统评估的基础上，验证一种基于机器学习（ML）的谵妄预测工具。
对血管外科、创伤外科和骨科的738名住院患者在住院期间每天使用DOS量表进行两次谵妄筛查。同时，在入院时和入院当晚对所有患者实时进行ML算法谵妄风险预测。该预测基于现有的电子健康记录数据自动进行，无需任何额外记录。
使用DOS量表筛查出103例（14.0%）谵妄阳性患者。其中，ML算法正确识别出85例（82.5%）。特异性略低，在635例无谵妄的患者中检测出463例（72.9%）。该算法的曲线下面积为0.883（95%可信区间，0.8523 - 0.9147）。
在这项前瞻性验证研究中，所实施的机器学习算法能够在外科科室中以较高的判别性能检测出谵妄患者。
未来，该工具或类似的决策支持系统可能有助于取代耗时的筛查工具，并实现对谵妄的有效预防。
