Ren Yinlong, Zhang Luming, Xu Fengshuo, Han Didi, Zheng Shuai, Zhang Feng, Li Longzhu, Wang Zichen, Lyu Jun, Yin Haiyan
Intensive Care Unit, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, Guangdong Province, People's Republic of China.
Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, Guangdong Province, People's Republic of China.
BMC Pulm Med. 2022 Jan 7;22(1):17. doi: 10.1186/s12890-021-01809-8.
Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection.
Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis.
The risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713-0.773) and 0.746 (95% C.I.: 0.699-0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets.
Our new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.
肺部感染是脓毒症的常见病因，与无肺部感染的脓毒症患者相比，合并肺部感染的脓毒症患者病情更重，死亡率更高。我们构建了一个列线图预测模型，以准确评估脓毒症合并肺部感染患者的预后并提供治疗建议。
数据从重症监护医学信息数据库（MIMIC-III）开源临床数据库中进行回顾性提取。采用脓毒症3.0的定义[10]，包括因宿主对感染的失控反应导致危及生命的器官功能障碍且序贯器官衰竭评估（SOFA）评分≥2的患者。使用逻辑回归分析从训练集中构建列线图预测模型，然后进行内部验证并进行敏感性分析。
列线图纳入了年龄、乳酸、体温、氧合指数、血尿素氮、乳酸、格拉斯哥昏迷评分（GCS）、肝病、癌症、器官移植、肌钙蛋白T（TnT）、中性粒细胞与淋巴细胞比值（NLR）以及连续性肾脏替代治疗（CRRT）、机械通气（MV）和血管活性药物使用等危险因素。我们将我们的列线图与序贯器官衰竭评估（SOFA）评分和简化急性生理学评分II（SAPSII）进行比较，列线图具有更好的区分能力，在训练集和验证集中，受试者操作特征曲线下面积（AUROC）分别为0.743（95%可信区间：0.713 - 0.773）和0.746（95%可信区间：0.699 - 0.790）。校准图表明列线图足以预测两组患者的院内死亡风险。列线图的决策曲线分析（DCA）显示，在两组中，与使用SOFA评分和SAPSII相比，它在临床应用中提供了净效益。
我们新的列线图是准确预测ICU中脓毒症合并肺部感染患者院内死亡率的便捷工具。改善模型中相关因素的治疗策略可提高这些ICU患者的院内生存率。
Zotero 插件
