Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
BMC Pulm Med. 2022 Nov 7;22(1):403. doi: 10.1186/s12890-022-02183-9.
Diabetic patients with community-acquired pneumonia (CAP) have an increased risk of progressing to severe CAP. It is essential to develop predictive tools at the onset of the disease for early identification and intervention. This study aimed to develop and validate a clinical feature-based nomogram to identify diabetic patients with CAP at risk of developing severe CAP.
A retrospective cohort study was conducted between January 2019 to December 2020. 1026 patients with CAP admitted in 48 hospitals in Shanghai were enrolled. All included patients were randomly divided into the training and validation samples with a ratio of 7:3. The nomogram for the prediction of severe CAP development was established based on the results of the multivariate logistic regression analysis and other predictors with clinical relevance. The nomogram was then assessed using receiver operating characteristic curves (ROC), calibration curve, and decision curve analysis (DCA).
Multivariate analysis showed that chronic kidney dysfunction, malignant tumor, abnormal neutrophil count, abnormal lymphocyte count, decreased serum albumin level, and increased HbA1c level at admission was independently associated with progression to severe CAP in diabetic patients. A nomogram was established based on these above risk factors and other predictors with clinical relevance. The area under the curve (AUC) of the nomogram was 0.87 (95% CI 0.83-0.90) in the training set and 0.84 (95% CI 0.78-0.90). The calibration curve showed excellent agreement between the predicted possibility by the nomogram and the actual observation. The decision curve analysis indicated that the nomogram was applicable with a wide range of threshold probabilities due to the net benefit.
Our nomogram can be applied to estimate early the probabilities of severe CAP development in diabetic patients with CAP, which has good prediction accuracy and discrimination abilities. Since included biomarkers are common, our findings may be performed well in clinical practice and improve the early management of diabetic patients with CAP.
患有社区获得性肺炎（CAP）的糖尿病患者发生重症 CAP 的风险增加。在疾病发作时开发预测工具对于早期识别和干预至关重要。本研究旨在开发和验证一种基于临床特征的列线图，以识别患有 CAP 的糖尿病患者中发生重症 CAP 的风险。
这是一项回顾性队列研究，时间为 2019 年 1 月至 2020 年 12 月。共纳入上海 48 家医院收治的 1026 例 CAP 患者。所有纳入的患者被随机分为训练和验证样本，比例为 7:3。根据多变量逻辑回归分析和其他具有临床相关性的预测因子的结果，建立了用于预测重症 CAP 发生的列线图。然后使用接受者操作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）评估该列线图。
多变量分析显示，慢性肾功能不全、恶性肿瘤、中性粒细胞计数异常、淋巴细胞计数异常、血清白蛋白水平降低和入院时 HbA1c 水平升高与糖尿病患者向重症 CAP 进展独立相关。基于这些危险因素和其他具有临床相关性的预测因子，建立了一个列线图。该列线图在训练集中的曲线下面积（AUC）为 0.87（95%CI 0.83-0.90），在验证集中为 0.84（95%CI 0.78-0.90）。校准曲线显示，列线图预测的可能性与实际观察结果之间具有良好的一致性。决策曲线分析表明，由于净效益，该列线图在广泛的阈值概率下均适用。
我们的列线图可用于估计患有 CAP 的糖尿病患者发生重症 CAP 的概率，具有良好的预测准确性和区分能力。由于纳入的生物标志物很常见，因此我们的研究结果可能在临床实践中表现良好，并改善对 CAP 糖尿病患者的早期管理。
