Alfred Hospital, Melbourne, Australia.
Monash Alfred Psychiatry Research Centre (MAPRc), Melbourne, Australia.
BMC Health Serv Res. 2020 Feb 27;20(1):151. doi: 10.1186/s12913-020-5005-1.
Delirium is a frequent diagnosis made by Consultation-Liaison Psychiatry (CLP). Numerous studies have demonstrated misdiagnosis prior to referral to CLP. Few studies have considered the factors underlying misdiagnosis using multivariate approaches.
To determine the number of cases referred to CLP, which are misdiagnosed at time of referral, to build an accurate predictive classifier algorithm, using input variables related to delirium misdiagnosis.
A retrospective observational study was conducted at Alfred Hospital in Melbourne, collecting data from a record of all patients seen by CLP for a period of 5 months. Data was collected pertaining to putative factors underlying misdiagnosis. A Machine Learning-Logistic Regression classifier model was built, to classify cases of accurate delirium diagnosis vs. misdiagnosis.
Thirty five of 74 new cases referred were misdiagnosed. The proposed predictive algorithm achieved a mean Receiver Operating Characteristic (ROC) Area under the curve (AUC) of 79%, an average 72% classification accuracy, 77% sensitivity and 67% specificity.
Delirium is commonly misdiagnosed in hospital settings. Our findings support the potential application of Machine Leaning-logistic predictive classifier in health care settings.
谵妄是联络精神病学（CLP）经常做出的诊断。许多研究表明，在转介到 CLP 之前存在误诊。很少有研究使用多元方法考虑导致误诊的因素。
确定转介到 CLP 的病例数量，这些病例在转介时被误诊，以使用与谵妄误诊相关的输入变量构建准确的预测分类器算法。
在墨尔本的阿尔弗雷德医院进行了一项回顾性观察性研究，收集了 CLP 为 5 个月期间就诊的所有患者的记录数据。收集了与误诊有关的潜在因素的数据。建立了机器学习-逻辑回归分类器模型，以对准确的谵妄诊断与误诊病例进行分类。
74 例新转介病例中有 35 例被误诊。该预测算法的平均受试者工作特征（ROC）曲线下面积（AUC）为 79%，平均分类准确率为 72%，灵敏度为 77%，特异性为 67%。
谵妄在医院环境中经常被误诊。我们的研究结果支持机器学习-逻辑预测分类器在医疗保健环境中的潜在应用。
