Mou Hongyi, Ergashev Akmal, Zhou Bingqi, Ye Na, Li Xueyan
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China.
J Patient Saf. 2025 Sep 1;21(6):386-392. doi: 10.1097/PTS.0000000000001365. Epub 2025 May 27.
Unplanned extubation (UEX) represents a significant risk event in hospitalized patients and is considered one of the most serious safety concerns. Prevention and early detection of these events have become essential components of high-quality nursing care.
To compare random forest and logistic regression models for the prediction of UEX.
In total, 775 UEX events were selected from the adverse nursing events database of a hospital in Zhejiang Province between January 2021 and December 2022 as the observation group. In addition, 775 planned extubation events were included from the database of hospitalized patients during the same period through 1:1 propensity score matching across various inpatient departments. Subsequently, patients were randomly allocated in a 7:3 ratio to form the development group and the validation group. Both random forest and logistic regression models were constructed. Their performances were compared using metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the receiver operating characteristic curve (AUC).
In addition, multivariate logistic regression analysis identified individuals aged 65 years and over (OR = 3.34, 95% CI: 2.43-4.59), male (OR = 1.64, 95% CI: 1.18-2.27), impaired awareness (OR = 2.56, 95% CI: 1.44-4.56), concurrent dual catheters (OR = 4.18, 95% CI: 2.77-6.32), presence of 3 or more catheters (OR = 5.55, 95% CI: 3.44-8.97), catheter indwelling time exceeding 1 week but <1 month (OR = 3.32, 95% CI: 2.04-5.41) or more than 1 month (OR = 4.51, 95% CI: 1.55-13.10), and the presence of medium-risk (OR = 0.22, 95% CI: 0.12-0.41) or high-risk catheters (OR = 0.08, 95% CI: 0.04-0.17) with secondary fixation (OR = 0.07, 95% CI: 0.04-0.12) as influential factors for UEX events in inpatients. Several variables, including catheter indwelling time, number of coexisting catheters, age, secondary fixation, and catheter grade, were selected for predicting UEX events using the random forest model. The AUC of the random forest prediction model was 0.812, while the AUC of the logistic regression prediction model was slightly lower at 0.793.
The random forest model outperforms the logistic regression model in predicting inpatient UEX events. However, the logistic regression model remains valuable for its ability to provide intuitive explanations of the results.
非计划拔管（UEX）是住院患者的重大风险事件，被视为最严重的安全问题之一。预防和早期发现这些事件已成为高质量护理的重要组成部分。
比较随机森林模型和逻辑回归模型对非计划拔管的预测效果。
选取2021年1月至2022年12月浙江省某医院不良护理事件数据库中的775例非计划拔管事件作为观察组。此外，通过对各住院科室进行1:1倾向得分匹配，从同期住院患者数据库中纳入775例计划拔管事件。随后，将患者按7:3的比例随机分配，形成开发组和验证组。构建随机森林模型和逻辑回归模型。使用包括准确率、灵敏度、特异度、阳性预测值、阴性预测值和受试者工作特征曲线下面积（AUC）等指标比较它们的性能。
此外，多因素逻辑回归分析确定年龄65岁及以上（OR = 3.34，95%CI：2.43 - 4.59）、男性（OR = 1.64，95%CI：1.18 - 2.27）、意识障碍（OR = 2.56，95%CI：1.44 - 4.56）、同时存在双导管（OR = 4.18，95%CI：2.77 - 6.32）、存在3根或更多导管（OR = 5.55，95%CI：3.44 - 8.97）、导管留置时间超过1周但<1个月（OR = 3.32，95%CI：2.04 - 5.41）或超过1个月（OR = 4.51，95%CI：1.55 - 13.10），以及存在中危（OR = 0.22，95%CI：0.12 - 0.41）或高危导管（OR = 0.08，95%CI：0.04 - 0.17）并进行二次固定（OR = 0.07，95%CI：0.04 - 0.12）为住院患者非计划拔管事件的影响因素。使用随机森林模型选择了几个变量，包括导管留置时间、并存导管数量、年龄、二次固定和导管等级，用于预测非计划拔管事件。随机森林预测模型的AUC为0.812，而逻辑回归预测模型的AUC略低，为0.793。
随机森林模型在预测住院患者非计划拔管事件方面优于逻辑回归模型。然而，逻辑回归模型因其能够直观解释结果而仍然具有价值。
