Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.
Department of Pulmonary and Critical Care Medicine, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong Province, China.
Clin Transl Sci. 2023 Oct;16(10):1923-1934. doi: 10.1111/cts.13599. Epub 2023 Aug 1.
Acute kidney injury (AKI) is one of the common complications of pulmonary infections. However, nomograms predicting the risk of early-onset AKI in patients with pulmonary infections have not been comprehensively researched. In this study, 3278 patients with pulmonary infection were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. These patients were randomly divided into training and validation cohorts, with the training cohort used for model building and the validation cohort used for validation. Independent risk factors for patients with pulmonary infection were determined using the least absolute shrinkage and selection operator (LASSO) method and forward stepwise logistic regression, which revealed that 11 independent risk factors for AKI in patients with pulmonary infections were congestive heart failure (CHF), hypertension, diabetes, transcutaneous oxygen saturation (SpO2), 24-h urine output, white blood cells (WBC), serum creatinine (Scr), prothrombin time (PT), potential of hydrogen (PH), vasopressor use, and mechanical ventilation (MV) use. The nomogram was then constructed and validated. The area under the receiver operating characteristic curve (AUC) values of the nomogram were 0.770 (95% CI = 0.789-0.807) in the training cohort and 0.724 (95% CI = 0.754-0.784) in the validation cohort. High AUC values indicated the good discriminative ability of the nomogram, while the calibration curves and Hosmer-Lemeshow test results indicated that the nomogram was well-calibrated. Improvements in net reclassification index (NRI) and integrated discrimination improvement (IDI) values indicate that our nomogram was superior to the Simplified Acute Physiology Score (SAPS) II scoring system, and the decision-curve analysis (DCA) curves indicate that the nomogram has good clinical application. We established a risk-prediction model for AKI in patients with pulmonary infection, which has good discriminative power and is superior to the SAPS II scoring system. This model can provide clinical reference information for patients with this type of disease in the intensive care unit.
急性肾损伤（AKI）是肺部感染的常见并发症之一。然而，目前尚未全面研究预测肺部感染患者早期 AKI 风险的列线图。本研究从医疗信息监护 III （MIMIC-III）数据库中提取了 3278 名肺部感染患者。这些患者被随机分为训练队列和验证队列，其中训练队列用于构建模型，验证队列用于验证。使用最小绝对收缩和选择算子（LASSO）方法和逐步向前逻辑回归确定肺部感染患者的独立危险因素，结果显示 11 个肺部感染患者 AKI 的独立危险因素为充血性心力衰竭（CHF）、高血压、糖尿病、经皮血氧饱和度（SpO2）、24 小时尿量、白细胞（WBC）、血清肌酐（Scr）、凝血酶原时间（PT）、潜在氢（PH）、血管加压素使用和机械通气（MV）使用。然后构建并验证了列线图。列线图在训练队列中的受试者工作特征曲线（AUC）值为 0.770（95%CI=0.789-0.807），在验证队列中的 AUC 值为 0.724（95%CI=0.754-0.784）。高 AUC 值表明列线图具有良好的判别能力，而校准曲线和 Hosmer-Lemeshow 检验结果表明列线图具有良好的校准度。净重新分类指数（NRI）和综合判别改善（IDI）值的提高表明，我们的列线图优于简化急性生理学评分（SAPS） II 评分系统，决策曲线分析（DCA）曲线表明该列线图具有良好的临床应用价值。我们建立了一种预测肺部感染患者 AKI 的风险预测模型，该模型具有良好的判别能力，优于 SAPS II 评分系统。该模型可为重症监护病房此类疾病患者提供临床参考信息。
