Center for Value-Based Care Research, Community Care, Cleveland Clinic, Cleveland, Ohio, USA; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA.
Center for Value-Based Care Research, Community Care, Cleveland Clinic, Cleveland, Ohio, USA.
J Thromb Haemost. 2024 Oct;22(10):2855-2863. doi: 10.1016/j.jtha.2024.06.025. Epub 2024 Jul 11.
Guidelines recommend pharmacologic VTE prophylaxis for acutely ill medical patients at acceptable bleeding risk, but only the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model has been validated for bleeding risk assessment.
We developed and internally validated a risk assessment model (RAM) to predict major in-hospital bleeding using risk factors at admission and compared our model with IMPROVE.
We selected patients admitted to medical services at 10 hospitals in the Cleveland Clinic Health System from 2017 to 2020. We identified major bleeding according to the International Society on Thrombosis and Haemostasis criteria, using a combination of diagnostic codes and laboratory values, and confirmed events with chart review. We fit a least absolute shrinkage selection operator logistic regression model in the training set and compared the discrimination and calibration of our model with the IMPROVE model in the validation set.
Among 46 314 admissions, 268 (0.58%) had a major bleed. The final RAM included 16 risk factors, of which prior bleeding (odds ratio [OR] = 4.83), peptic ulcer (OR = 3.82), history of sepsis (OR = 3.26), and steroid use (OR = 2.59) were the strongest. The Cleveland Clinic Bleeding Model had better discrimination than IMPROVE (area under the receiver operating characteristics curve = 0.85 vs 0.70; P < .001) and, at equivalent sensitivity (52%), categorized fewer patients as high risk (7.2% vs 11.8%; P < .001). Calibration was adequate (Brier score = 0.0057).
Using a large population of medical inpatients with verified major bleeding events, we developed and internally validated a RAM for major bleeding whose performance surpassed the IMPROVE model.
指南建议对可接受出血风险的急性重病患者进行药物性静脉血栓栓塞症（VTE）预防，但只有国际静脉血栓栓塞症预防登记处（IMPROVE）模型经过了出血风险评估验证。
我们开发了一种风险评估模型（RAM），并对其进行了内部验证，以预测主要住院期间出血，使用入院时的危险因素，并将我们的模型与 IMPROVE 进行比较。
我们选择了 2017 年至 2020 年期间克利夫兰诊所医疗系统 10 家医院内科患者进行研究。根据国际血栓与止血协会标准，通过诊断代码和实验室值的组合，确定主要出血，并通过图表审查确认事件。我们在训练集中拟合最小绝对收缩选择算子逻辑回归模型，并在验证集中比较我们的模型和 IMPROVE 模型的判别和校准能力。
在 46314 例住院患者中，有 268 例（0.58%）发生了大出血。最终的 RAM 包括 16 个危险因素，其中既往出血（比值比 [OR] = 4.83）、消化性溃疡（OR = 3.82）、脓毒症史（OR = 3.26）和类固醇使用（OR = 2.59）是最强的。克利夫兰诊所出血模型的判别能力优于 IMPROVE（接受者操作特征曲线下面积为 0.85 比 0.70；P <.001），且在等效灵敏度（52%）下，将较少的患者归类为高危（7.2%比 11.8%；P <.001）。校准能力适中（Brier 评分= 0.0057）。
我们使用大量经证实发生大出血事件的内科住院患者，开发并内部验证了一种用于大出血的 RAM，其性能优于 IMPROVE 模型。
