Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada.
Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada.
J Pediatr Surg. 2023 May;58(5):908-916. doi: 10.1016/j.jpedsurg.2023.01.020. Epub 2023 Jan 19.
Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artificial intelligence have resulted in a proliferation of CPTs created using machine learning (ML)-yet the clinical applicability of ML-based CPTs and their validation in clinical settings remain unclear. This systematic review aims to compare the validity and clinical efficacy of ML-based to traditional CPTs in pediatric surgery.
Nine databases were searched from 2000 until July 9, 2021 to retrieve articles reporting on CPTs and ML for pediatric surgical conditions. PRISMA standards were followed, and screening was performed by two independent reviewers in Rayyan, with a third reviewer resolving conflicts. Risk of bias was assessed using the PROBAST.
Out of 8300 studies, 48 met the inclusion criteria. The most represented surgical specialties were pediatric general (14), neurosurgery (13) and cardiac surgery (12). Prognostic (26) CPTs were the most represented type of surgical pediatric CPTs followed by diagnostic (10), interventional (9), and risk stratifying (2). One study included a CPT for diagnostic, interventional and prognostic purposes. 81% of studies compared their CPT to ML-based CPTs, statistical CPTs, or the unaided clinician, but lacked external validation and/or evidence of clinical implementation.
While most studies claim significant potential improvements by incorporating ML-based CPTs in pediatric surgical decision-making, both external validation and clinical application remains limited. Further studies must focus on validating existing instruments or developing validated tools, and incorporating them in the clinical workflow.
Systematic Review LEVEL OF EVIDENCE: Level III.
临床预测工具（CPTs）是利用患者数据预测特定临床结局、对患者进行风险分层或提供个性化诊断或治疗选择的决策工具。最近人工智能的进步导致了大量使用机器学习（ML）创建的 CPT 的出现-然而，基于 ML 的 CPT 的临床适用性及其在临床环境中的验证仍然不清楚。本系统评价旨在比较儿科手术中基于 ML 的 CPT 与传统 CPT 的有效性和临床疗效。
从 2000 年到 2021 年 7 月 9 日，检索了 9 个数据库，以检索报告儿科手术条件下 CPT 和 ML 的文章。遵循 PRISMA 标准，由两名独立审查员在 Rayyan 中进行筛选，第三名审查员解决冲突。使用 PROBAST 评估偏倚风险。
在 8300 项研究中，有 48 项符合纳入标准。代表的外科专业包括儿科普通外科（14 项）、神经外科（13 项）和心脏外科（12 项）。预后（26 项）CPT 是最具代表性的儿科外科 CPT 类型，其次是诊断（10 项）、介入（9 项）和风险分层（2 项）。一项研究包括用于诊断、介入和预后目的的 CPT。81%的研究将他们的 CPT 与基于 ML 的 CPT、统计 CPT 或未辅助的临床医生进行比较，但缺乏外部验证和/或临床实施的证据。
虽然大多数研究声称通过将基于 ML 的 CPT 纳入儿科手术决策具有显著的潜在改进，但外部验证和临床应用仍然有限。进一步的研究必须集中于验证现有工具或开发经过验证的工具，并将其纳入临床工作流程。
系统评价 证据水平：III 级
