Patel Anita K, Olson Taylor, Ray Christopher, Trujillo-Rivera Eduardo A, Morizono Hiroki, Pollack Murray M
Department of Pediatrics, Division of Critical Care Medicine, Children's National Health System, Washington, District of Columbia, United States of America.
George Washington University School of Medicine and Health Sciences, Washington, District of Columbia, United States of America.
PLoS One. 2025 Apr 30;20(4):e0320586. doi: 10.1371/journal.pone.0320586. eCollection 2025.
To assess patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations (ICU vs. non-ICU) by the Criticality Index-Dynamic (CI-D), with the goal of enhancing the CI-D.
Retrospective structured chart review.
All pediatric inpatients admitted from January` 1st 2018 - February 29th 2020 through the emergency department.
MAIN OUTCOME(S) AND MEASURE(S): Patient characteristics and care factors associated with correct (true positives, true negatives) and incorrect predictions (false positives, false negatives) of future care locations (ICU vs. non-ICU) by the CI-D were assessed.
Of the 3,018, patients, 139 transitioned from non-ICU locations to ICU care; 482 were transferred from the ICU to non-ICU care locations, and 2,400 remained in non-ICU care locations. For the ICU Prediction group, the false negative patients were older, more frequently male, and had longer hospital and ICU lengths of stay compared to the true positive patients. The significant differences in the ICU Prediction group for false negative compared to the true positive patients included a less frequent: primary diagnosis of respiratory failure, use of high flow nasal canula, hourly cardio-respiratory vital signs prior to transfer to the ICU, and neurologic vital signs after transfer from the ICU. For the ICU Discharge prediction group, false positive patients were more frequently: younger, had a primary diagnosis of respiratory failure, more frequently received respiratory support after discharge from the ICU, and received less frequent neurological vital signs prior to transfer from the ICU. For the Non-transfer prediction category, demographics and clinical variables did not differ between the true negative and false positive prediction groups.
We conducted the first comprehensive analysis via structured chart reviews of patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations by the machine learning algorithm, the CI-D, gaining insights into potential new predictor variables for inclusion in the model to improve future model iterations.
通过动态危急指数（CI-D）评估与未来护理地点（重症监护病房[ICU]与非ICU）的正确和错误预测相关的患者特征及护理因素，以改进CI-D。
回顾性结构化病历审查。
2018年1月1日至2020年2月29日期间通过急诊科收治的所有儿科住院患者。
评估与CI-D对未来护理地点（ICU与非ICU）的正确预测（真阳性、真阴性）和错误预测（假阳性、假阴性）相关的患者特征及护理因素。
在3018例患者中，139例从非ICU护理地点转入ICU护理；482例从ICU转入非ICU护理地点，2400例仍在非ICU护理地点。对于ICU预测组，与真阳性患者相比，假阴性患者年龄更大，男性比例更高，住院时间和ICU住院时间更长。与真阳性患者相比，ICU预测组假阴性患者的显著差异包括：呼吸衰竭的主要诊断频率较低、使用高流量鼻导管的频率较低、转入ICU前每小时的心肺生命体征监测频率较低以及从ICU转出后的神经生命体征监测频率较低。对于ICU转出预测组，假阳性患者更常见的情况是：年龄较小、主要诊断为呼吸衰竭、从ICU转出后接受呼吸支持的频率更高以及从ICU转出前接受神经生命体征监测的频率更低。对于非转出预测类别，真阴性和假阳性预测组之间的人口统计学和临床变量无差异。
我们通过结构化病历审查首次对与机器学习算法CI-D对未来护理地点的正确和错误预测相关的患者特征及护理因素进行了全面分析，深入了解了可能纳入模型以改进未来模型迭代的潜在新预测变量。
