Jones Ceri, Taylor Marcus, Sperrin Matthew, Grant Stuart W
Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
Department of Clinical Perfusion, University Hospital Southampton NHS Foundation Trust, Southampton General Hospital, Southampton, UK.
Perfusion. 2025 Mar;40(2):328-342. doi: 10.1177/02676591241237758. Epub 2024 Apr 22.
Most cardiac surgery clinical prediction models (CPMs) are developed using pre-operative variables to predict post-operative outcomes. Some CPMs are developed with intra-operative variables, but none are widely used. The objective of this systematic review was to identify CPMs with intra-operative variables that predict short-term outcomes following adult cardiac surgery.
Ovid MEDLINE and EMBASE databases were searched from inception to December 2022, for studies developing a CPM with at least one intra-operative variable. Data were extracted using a critical appraisal framework and bias assessment tool. Model performance was analysed using discrimination and calibration measures.
A total of 24 models were identified. Frequent predicted outcomes were acute kidney injury (9/24 studies) and peri-operative mortality (6/24 studies). Frequent pre-operative variables were age (18/24 studies) and creatinine/eGFR (18/24 studies). Common intra-operative variables were cardiopulmonary bypass time (16/24 studies) and transfusion (13/24 studies). Model discrimination was acceptable for all internally validated models (AUC 0.69-0.91). Calibration was poor (15/24 studies) or unreported (8/24 studies). Most CPMs were at a high or indeterminate risk of bias (23/24 models). The added value of intra-operative variables was assessed in six studies with statistically significantly improved discrimination demonstrated in two.
Weak reporting and methodological limitations may restrict wider applicability and adoption of existing CPMs that include intra-operative variables. There is some evidence that CPM discrimination is improved with the addition of intra-operative variables. Further work is required to understand the role of intra-operative CPMs in the management of cardiac surgery patients.
大多数心脏手术临床预测模型（CPM）是使用术前变量来预测术后结果的。一些CPM是利用术中变量开发的，但没有一个被广泛使用。本系统评价的目的是识别包含术中变量且能预测成人心脏手术后短期结果的CPM。
检索Ovid MEDLINE和EMBASE数据库，检索时间从建库至2022年12月，查找开发包含至少一个术中变量的CPM的研究。使用关键评估框架和偏倚评估工具提取数据。使用区分度和校准指标分析模型性能。
共识别出24个模型。常见的预测结果是急性肾损伤（9/24项研究）和围手术期死亡率（6/24项研究）。常见的术前变量是年龄（18/24项研究）和肌酐/估算肾小球滤过率（eGFR）（18/24项研究）。常见的术中变量是体外循环时间（16/24项研究）和输血（13/24项研究）。所有内部验证模型的区分度均可接受（曲线下面积[AUC]为0.69 - 0.91）。校准情况较差（15/24项研究）或未报告（8/24项研究）。大多数CPM存在高偏倚风险或偏倚风险不确定（23/24个模型）。六项研究评估了术中变量的附加值，其中两项研究显示区分度有统计学显著改善。
报告薄弱和方法学局限性可能会限制现有包含术中变量的CPM的更广泛应用和采用。有一些证据表明，增加术中变量可改善CPM的区分度。需要进一步开展工作以了解术中CPM在心脏手术患者管理中的作用。
