Department of Geriatrics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Department of Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
BMJ Open. 2022 Apr 8;12(4):e054023. doi: 10.1136/bmjopen-2021-054023.
Delirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models.
Single-site university hospital.
Secondary analysis of prospective cohort study.
CPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients.
Discrimination, calibration and clinical usefulness.
14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from -0.02 to 0.34, slopes from -0.74 to 1.96 and scaled Brier from -1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai (c-index: 0.739; (95% CI: 0.664 to 0.813); intercept: -0.018; slope: 1.96; scaled Brier: 0.049) and Litaker (c-index: 0.706 (95% CI: 0.590 to 0.823); intercept: -0.015; slope: 0.995; scaled Brier: 0.088). For the remaining CPMs, model discrimination was considered poor with corresponding c-indices <0.70.
Our head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.
谵妄与发病率、死亡率增加、住院时间延长和医疗保健费用增加有关。用于预测术后谵妄的临床预测模型（CPM）数量呈指数级增长。我们的目标是对预测非重症监护病房（非 ICU）老年患者术后谵妄的 CPM 进行头对头比较，以确定表现最佳的模型。
单站点大学医院。
前瞻性队列研究的二次分析。
检查了 1990 年 1 月 1 日至 2020 年 5 月 1 日期间发表的 CPM，以确定其是否符合条件（系统评价和荟萃分析的首选报告项目）。对于 1990 年 1 月 1 日至 2017 年 1 月 1 日的时间段，根据预先指定的纳入和排除标准，在系统评价中确定了纳入的 CPM。两位作者独立进行了原始研究的扩展文献检索，包括 2017 年 1 月 1 日至 2020 年 5 月 1 日期间发表的 CPM。使用包含 292 名非 ICU 老年患者的外科队列进行外部验证。
区分度、校准和临床实用性。
从审查的 366 篇全文中，有 14 篇 CPM 符合分析条件。8/14（57%）CPM 之前已发表过外部验证。C 指数范围为 0.52 至 0.74，截距范围为-0.02 至 0.34，斜率范围为-0.74 至 1.96，缩放 Brier 范围为-1.29 至 0.088。根据预设标准，表现最好的两个模型是 Dai 的模型（C 指数：0.739；（95%CI：0.664 至 0.813）；截距：-0.018；斜率：1.96；缩放 Brier：0.049）和 Litaker 的模型（C 指数：0.706（95%CI：0.590 至 0.823）；截距：-0.015；斜率：0.995；缩放 Brier：0.088）。对于其余 CPM，认为模型区分度较差，相应的 C 指数<0.70。
我们的头对头分析确定了 14 个 CPM 中的 2 个作为表现最佳的模型，具有良好的区分度和可接受的校准度。基于我们的发现，这些模型可能有助于医生对术后谵妄风险进行估计，并选择患者进行预防措施。
