基于系统性炎症指标构建的列线图可预测心力衰竭患者的不良结局

目的 基于系统性炎症指标，构建列线图评估心力衰竭患者不良结局的风险。方法 回顾性分析2017年6月~2019年6月在重庆医科大学附属第一医院心血管内科住院的430例心力衰竭患者的临床资料。将患者按照7∶3比例随机分为建模组（n=286）和验证组（n=144），采用COX回归分析筛选心力衰竭预后的危险因素，并构建列线图。使用校准曲线评估列线图的预测准确性，使用决策曲线分析（DCA）和 Kaplan-Meier 曲线评估列线图的临床实用性。结果 多因素COX回归分析结果显示，年龄（P=0.03）、体质量指数（BMI，P=0.002）、纽约心功能分级（NYHA分级，P<0.001）、高血压（P=0.004）、淋巴细胞计数（P<0.001）、血小板淋巴细胞计数（PLR，P=0.007）、中性粒细胞淋巴细胞计数（NLR，P<0.001）和系统炎症反应指数（SIRI，P<0.001）是心力衰竭的独立预后因子。利用这些预后危险因素构建列线图，结果显示其预测性能良好：在建模组和验证组中，列线图C指数分别为0.719 (95% CI: 0.680~0.758)和0.732 (95% CI: 0.693~0.771)。校准曲线显示，该模型在预测HF患者的不良结局方面（心血管再入院或全因死亡）具有较好的一致性。结论 联合系统性炎症指标和传统危险因素构建的列线图在HF患者不良结局预测方面性能良好。

A nomogram based on systemic inflammation markers can predict adverse outcomes in patients with heart failure

Objective To construct a nomogram based on systemic inflammation markers for assessing the risk of adverse outcomes in patients with heart failure (HF). Methods We retrospectively collected the clinical data from 430 patients with HF hospitalized in our hospital from June, 2017 to June, 2019. The patients were randomized into derivation group (n=286) and validation group (n=144) at a 7∶3 ratio using R software. The risk factors for adverse prognosis of HF were screened using COX regression analysis to establish the nomogram. The predictive accuracy of the nomogram was assessed using calibration curves. Decision curve analysis (DCA) and Kaplan-Meier curves were used to evaluate the clinical utility of the nomogram. Results The results of COX multivariate regression analysis showed that age (P=0.030), body mass index (BMI, P=0.002), New York Heart Association classification (NYHA, P<0.001), hypertension (P=0.004), lymphocyte count (P<0.001), platelet-to-lymphocyte ratio (PLR, P=0.007), neutrophil-to-lymphocyte ratio (NLR, P<0.001) and system inflammation response index (SIRI, P<0.001) were prognostic factors for HF patients. The nomogram was constructed using these prognostic factors. The C-indexes of the derivation and validation cohorts were 0.719 (95% CI: 0.680-0.758) and 0.732 (95% CI: 0.693-0.771), respectively. The calibration curves showed a good concordance of the nomogram for predicting adverse outcomes in patients with HF. Conclusion The nomogram constructed based on the systemic inflammation markers and the conventional risk factors can predict adverse outcomes (mortality and readmission) in patients with HF.