患有救护车导致的肺炎:住院治疗应该考虑到人体因素

H. Frohnhofen
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摘要

背景:研究人员将心血管疾病(CVD)与年龄增长联系起来;然而,它如何推动老年重症社区获得性肺炎(SCAP)患者的疾病进展仍不清楚。本研究旨在确定老年SCAP合并CVD患者住院死亡率的主要风险预测因素,并构建综合nomogram以提供个性化预测。患者和方法:本研究回顾性纳入了2365例确诊为SCAP的老年患者。其中413例患者存在心血管疾病。采用LASSO回归和多因素logistic回归分析选择老年SCAP合并CVD患者住院死亡率的潜在预测因素。通过合并这些特征,然后开发出一个nomogram,并进行内部验证。通过c指数、校准曲线分析和决策图评估nomogram鉴别、校准和临床应用。结果:与无CVD患者相比,老年SCAP合并CVD患者预后明显较差。CVD人群的进一步分析确定了老年SCAP患者住院死亡的7个独立危险因素,包括年龄、血管加压药的使用、主要症状的数量、体温、单核细胞、CRP和NLR。纳入这7个预测因子的nomogram模型具有足够的预测精度,C-index为0.800 (95% CI 0.758 ~ 0.842)。内部验证采用自举验证,C-index值较高,为0.781。此外,校准曲线表明风险预测具有良好的一致性,决策曲线表明nomogram具有良好的总体净效益。结论:我们开发了一种综合nomogram方法,可以对老年SCAP合并CVD患者的住院死亡率进行个性化预测。
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Ambulant erworbene Pneumonie: Entscheidung zu stationärer Aufnahme sollte geriatrische Probleme berücksichtigen
Background: Researchers have linked cardiovascular disease (CVD) with advancing age; however, how it drives disease progression in elderly severe community acquired pneumonia (SCAP) patients is still unclear. This study aims to identify leading risk predictors of in-hospital mortality in elderly SCAP patients with CVD, and construct a comprehensive nomogram for providing personalized prediction. Patients and methods: The study retrospectively enrolled 2365 elderly patients identified SCAP. Among them, 413 patients were found to have CVD. The LASSO regression and multivariate logistic regression analysis were utilized to select potential predictors of in-hospital mortality in elderly SCAP patients with CVD. By incorporating these features, a nomogram was then developed and subjected to internal validations. Discrimination, calibration, and clinical use of the nomogram were assessed via C-index, calibration curve analysis, and decision plot. Results: Compared with patients without CVD, elderly SCAP patients with CVD had a significant poor outcome. Further analysis of the CVD population identified 7 independent risk factors for in-hospital mortality in elderly SCAP patients, including age, the use of vasopressor, numbers of primary symptoms, body temperature, monocyte, CRP and NLR. The nomogram model incorporated these 7 predictors showed sufficient predictive accuracy, with the C-index of 0.800 (95% CI 0.758–0.842). High C-index value of 0.781 was obtained in the internal validation via bootstrapping validation. Moreover, the calibration curve indicative a good consistency of risk prediction, and the decision curve manifested that the nomogram had good overall net benefits. Conclusion: An integrated nomogram was developed to facilitate the personalized prediction of in-hospital mortality in elderly SCAP patients with CVD.
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