PDF(Pubmed)
Abstract：
UNASSIGNED: Based on the high prevalence and fatality rates associated with severe community-acquired pneumonia (SCAP), this study endeavored to construct an innovative nomogram for early identification of individuals at high risk of all-cause death within a 30-day period among SCAP patients receiving intensive care units (ICU) treatment.
UNASSIGNED: In this single-center, retrospective study, 718 SCAP patients were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for the development of a predictive model. A total of 97 patients eligible for inclusion were included from Chongqing General Hospital, China between January 2020 and July 2023 for external validation. Clinical data and short-term prognosis were collected. Risk factors were determined using the least absolute shrinkage and selection operator (LASSO) and multiple logistic regression analysis. The model\'s performance was evaluated through area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
UNASSIGNED: Eight risk predictors, including age, presence of malignant cancer, heart rate, mean arterial pressure, albumin, blood urea nitrogen, prothrombin time, and lactate levels were adopted in a nomogram. The nomogram exhibited high predictive accuracy, with an AUC of 0.803 (95% CI: 0.756-0.845) in the training set, 0.756 (95% CI: 0.693-0.816) in the internal validation set, 0.778 (95% CI: 0.594-0.893) in the external validation set concerning 30-day mortality. Meanwhile, the nomogram demonstrated effective calibration through well-fitted calibration curves. DCA confirmed the clinical application value of the nomogram.
UNASSIGNED: This simple and reliable nomogram can help physicians assess the short-term prognosis of patients with SCAP quickly and effectively, and could potentially be adopted widely in clinical settings after more external validations.
UNASSIGNED: In this single-center, retrospective study, 718 SCAP patients were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database for the development of a predictive model. A total of 97 patients eligible for inclusion were included from Chongqing General Hospital, China between January 2020 and July 2023 for external validation. Clinical data and short-term prognosis were collected. Risk factors were determined using the least absolute shrinkage and selection operator (LASSO) and multiple logistic regression analysis. The model\'s performance was evaluated through area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
UNASSIGNED: Eight risk predictors, including age, presence of malignant cancer, heart rate, mean arterial pressure, albumin, blood urea nitrogen, prothrombin time, and lactate levels were adopted in a nomogram. The nomogram exhibited high predictive accuracy, with an AUC of 0.803 (95% CI: 0.756-0.845) in the training set, 0.756 (95% CI: 0.693-0.816) in the internal validation set, 0.778 (95% CI: 0.594-0.893) in the external validation set concerning 30-day mortality. Meanwhile, the nomogram demonstrated effective calibration through well-fitted calibration curves. DCA confirmed the clinical application value of the nomogram.
UNASSIGNED: This simple and reliable nomogram can help physicians assess the short-term prognosis of patients with SCAP quickly and effectively, and could potentially be adopted widely in clinical settings after more external validations.
摘要：
■基于与严重社区获得性肺炎（SCAP）相关的高患病率和死亡率，本研究试图构建一个创新的列线图,用于在接受重症监护病房(ICU)治疗的SCAP患者中,在30天内早期识别全因死亡高危人群.
■在这个单中心，回顾性研究,从医学信息集市重症监护IV（MIMIC-IV）数据库中筛选了718名SCAP患者，以开发预测模型。重庆总医院共纳入97例符合纳入条件的患者,中国在2020年1月至2023年7月之间进行外部验证。收集临床资料和短期预后。使用最小绝对收缩和选择算子（LASSO）和多元逻辑回归分析确定危险因素。通过曲线下面积(AUC)评估模型的性能，校正曲线,和决策曲线分析(DCA)。
■八个风险预测因子，包括年龄,恶性肿瘤的存在，心率,平均动脉压,白蛋白,血尿素氮,凝血酶原时间,和乳酸水平在列线图中采用。列线图表现出很高的预测准确性，训练集中的AUC为0.803（95%CI：0.756-0.845），内部验证集中为0.756（95%CI：0.693-0.816），关于30天死亡率的外部验证集中为0.778（95％CI：0.594-0.893）。同时,列线图通过拟合良好的校准曲线证明了有效的校准。DCA证实了列线图的临床应用价值。
■这个简单可靠的列线图可以帮助医生快速有效地评估SCAP患者的短期预后，在更多的外部验证后，可能会在临床环境中广泛采用。
■在这个单中心，回顾性研究,从医学信息集市重症监护IV（MIMIC-IV）数据库中筛选了718名SCAP患者，以开发预测模型。重庆总医院共纳入97例符合纳入条件的患者,中国在2020年1月至2023年7月之间进行外部验证。收集临床资料和短期预后。使用最小绝对收缩和选择算子（LASSO）和多元逻辑回归分析确定危险因素。通过曲线下面积(AUC)评估模型的性能，校正曲线,和决策曲线分析(DCA)。
■八个风险预测因子，包括年龄,恶性肿瘤的存在，心率,平均动脉压,白蛋白,血尿素氮,凝血酶原时间,和乳酸水平在列线图中采用。列线图表现出很高的预测准确性，训练集中的AUC为0.803（95%CI：0.756-0.845），内部验证集中为0.756（95%CI：0.693-0.816），关于30天死亡率的外部验证集中为0.778（95％CI：0.594-0.893）。同时,列线图通过拟合良好的校准曲线证明了有效的校准。DCA证实了列线图的临床应用价值。
■这个简单可靠的列线图可以帮助医生快速有效地评估SCAP患者的短期预后，在更多的外部验证后，可能会在临床环境中广泛采用。
