Division of Gastroenterology, Western University, London, Ontario, Canada.
Department of Medicine, Grand River Hospital, Kitchener, Ontario, Canada.
JAMA Netw Open. 2022 May 2;5(5):e2214253. doi: 10.1001/jamanetworkopen.2022.14253.
Clinical prediction models, or risk scores, can be used to risk stratify patients with lower gastrointestinal bleeding (LGIB), although the most discriminative score is unknown.
To identify all LGIB risk scores available and compare their prognostic performance.
A systematic search of Ovid MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials from January 1, 1990, through August 31, 2021, was conducted. Non-English-language articles were excluded.
Observational and interventional studies deriving or validating an LGIB risk score for the prediction of a clinical outcome were included. Studies including patients younger than 16 years or limited to a specific patient population or a specific cause of bleeding were excluded. Two investigators independently screened the studies, and disagreements were resolved by consensus.
Data were abstracted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline independently by 2 investigators and pooled using random-effects models.
Summary diagnostic performance measures (sensitivity, specificity, and area under the receiver operating characteristic curve [AUROC]) determined a priori were calculated for each risk score and outcome combination.
A total of 3268 citations were identified, of which 9 studies encompassing 12 independent cohorts and 4 risk scores (Oakland, Strate, NOBLADS [nonsteroidal anti-inflammatory drug use, no diarrhea, no abdominal tenderness, blood pressure ≤100 mm Hg, antiplatelet drug use (nonaspirin), albumin <3.0 g/dL, disease score ≥2 (according to the Charlson Comorbidity Index), and syncope], and BLEED [ongoing bleeding, low systolic blood pressure, elevated prothrombin time, erratic mental status, and unstable comorbid disease]) were included in the meta-analysis. For the prediction of safe discharge, the AUROC for the Oakland score was 0.86 (95% CI, 0.82-0.88). For major bleeding, the AUROC was 0.93 (95% CI, 0.90-0.95) for the Oakland score, 0.73 (95% CI, 0.69-0.77) for the Strate score, 0.58 (95% CI, 0.53-0.62) for the NOBLADS score, and 0.65 (95% CI, 0.61-0.69) for the BLEED score. For transfusion, the AUROC was 0.99 (95% CI, 0.98-1.00) for the Oakland score and 0.88 (95% CI, 0.85-0.90) for the NOBLADS score. For hemostasis, the AUROC was 0.36 (95% CI, 0.32-0.40) for the Oakland score, 0.82 (95% CI, 0.79-0.85) for the Strate score, and 0.24 (95% CI, 0.20-0.28) for the NOBLADS score.
The Oakland score was the most discriminative LGIB risk score for predicting safe discharge, major bleeding, and need for transfusion, whereas the Strate score was best for predicting need for hemostasis. This study suggests that these scores can be used to predict outcomes from LGIB and guide clinical care accordingly.
临床预测模型(或风险评分)可用于对下消化道出血(LGIB)患者进行风险分层,但目前尚不清楚哪种评分最具区分度。
确定所有可用的 LGIB 风险评分,并比较其预后性能。
从 1990 年 1 月 1 日至 2021 年 8 月 31 日,对 Ovid MEDLINE、Embase 和 Cochrane 对照试验中心注册库进行了系统搜索。排除非英语文章。
纳入了用于预测临床结局的 LGIB 风险评分的推导或验证的观察性和干预性研究。排除了包括年龄小于 16 岁或仅限于特定患者人群或特定出血原因的研究。两名研究者独立筛选研究,意见分歧通过共识解决。
根据系统评价和荟萃分析的首选报告项目(PRISMA)指南,独立地由两名研究者提取数据,并使用随机效应模型进行汇总。
根据事先确定的综合诊断性能指标(敏感性、特异性和受试者工作特征曲线下面积 [AUROC])计算了每个风险评分和结局组合的结果。
共确定了 3268 条引文,其中 9 项研究共纳入了 12 个独立队列和 4 个风险评分(奥克兰、斯特雷特、NOBLADS[非甾体抗炎药使用、无腹泻、无腹痛、血压≤100mmHg、抗血小板药物使用(非阿司匹林)、白蛋白<3.0g/dL、疾病评分≥2(根据 Charlson 合并症指数)和晕厥]和 BLEED[持续出血、低收缩压、延长的凝血时间、精神状态不稳定和不稳定的合并症])被纳入荟萃分析。对于安全出院的预测,奥克兰评分的 AUROC 为 0.86(95%CI,0.82-0.88)。对于大出血,奥克兰评分的 AUROC 为 0.93(95%CI,0.90-0.95),斯特雷特评分的 AUROC 为 0.73(95%CI,0.69-0.77),NOBLADS 评分的 AUROC 为 0.58(95%CI,0.53-0.62),BLEED 评分的 AUROC 为 0.65(95%CI,0.61-0.69)。对于输血,奥克兰评分的 AUROC 为 0.99(95%CI,0.98-1.00),NOBLADS 评分的 AUROC 为 0.88(95%CI,0.85-0.90)。对于止血,奥克兰评分的 AUROC 为 0.36(95%CI,0.32-0.40),斯特雷特评分的 AUROC 为 0.82(95%CI,0.79-0.85),NOBLADS 评分的 AUROC 为 0.24(95%CI,0.20-0.28)。
奥克兰评分是预测 LGIB 患者安全出院、大出血和输血需求的最具区分度的 LGIB 风险评分,而斯特雷特评分则最适合预测止血需求。本研究表明,这些评分可用于预测 LGIB 的结局,并相应地指导临床护理。