Pulmonary and Critical Care Medicine, Yantai Yuhuangding Hospital, Yantai, Shandong, China.
Department of Critical Care Medicine, Yantai Yuhuangding Hospital, Yantai, Shandong, China.
Front Public Health. 2022 Nov 24;10:1047073. doi: 10.3389/fpubh.2022.1047073. eCollection 2022.
INTRODUCTION: Acute kidney injury (AKI) is a prevalent complication of coronavirus disease 2019 (COVID-19) and is closely linked with a poorer prognosis. The aim of this study was to develop and validate an easy-to-use and accurate early prediction model for AKI in hospitalized COVID-19 patients. METHODS: Data from 480 COVID-19-positive patients (336 in the training set and 144 in the validation set) were obtained from the public database of the Cancer Imaging Archive (TCIA). The least absolute shrinkage and selection operator (LASSO) regression method and multivariate logistic regression were used to screen potential predictive factors to construct the prediction nomogram. Receiver operating curves (ROC), calibration curves, as well as decision curve analysis (DCA) were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined. RESULTS: A predictive nomogram for AKI was developed based on arterial oxygen saturation, procalcitonin, C-reactive protein, glomerular filtration rate, and the history of coronary artery disease. In the training set, the nomogram produced an AUC of 0.831 (95% confidence interval [CI]: 0.774-0.889) with a sensitivity of 85.2% and a specificity of 69.9%. In the validation set, the nomogram produced an AUC of 0.810 (95% CI: 0.737-0.871) with a sensitivity of 77.4% and a specificity of 78.8%. The calibration curve shows that the nomogram exhibited excellent calibration and fit in both the training and validation sets. DCA suggested that the nomogram has promising clinical effectiveness. In addition, the median length of stay (m-LS) for patients in the high-risk group for AKI (risk score ≥ 0.122) was 14.0 days (95% CI: 11.3-16.7 days), which was significantly longer than 8.0 days (95% CI: 7.1-8.9 days) for patients in the low-risk group (risk score <0.122) (hazard ratio (HR): 1.98, 95% CI: 1.55-2.53, < 0.001). Moreover, the mortality rate was also significantly higher in the high-risk group than that in the low-risk group (20.6 vs. 2.9%, odd ratio (OR):8.61, 95%CI: 3.45-21.52). CONCLUSIONS: The newly constructed nomogram model could accurately identify potential COVID-19 patients who may experience AKI during hospitalization at the very beginning of their admission and may be useful for informing clinical prognosis.
简介：急性肾损伤（AKI）是 2019 年冠状病毒病（COVID-19）的一种普遍并发症，与预后较差密切相关。本研究旨在开发和验证一种用于住院 COVID-19 患者 AKI 的易于使用且准确的早期预测模型。 方法：从公共癌症成像档案（TCIA）数据库中获取 480 例 COVID-19 阳性患者（训练集 336 例，验证集 144 例）的数据。使用最小绝对收缩和选择算子（LASSO）回归方法和多变量逻辑回归筛选潜在的预测因素来构建预测列线图。采用受试者工作特征曲线（ROC）、校准曲线以及决策曲线分析（DCA）来评估列线图的有效性。还检查了列线图的预后价值。 结果：基于动脉血氧饱和度、降钙素原、C 反应蛋白、肾小球滤过率和冠状动脉疾病史，开发了一种 AKI 预测列线图。在训练集中，该列线图的 AUC 为 0.831（95%置信区间[CI]：0.774-0.889），灵敏度为 85.2%，特异性为 69.9%。在验证集中，该列线图的 AUC 为 0.810（95%CI：0.737-0.871），灵敏度为 77.4%，特异性为 78.8%。校准曲线表明，该列线图在训练集和验证集中均具有出色的校准和拟合度。DCA 表明该列线图具有有前景的临床效果。此外，AKI 高危组（风险评分≥0.122）患者的中位住院时间（m-LS）为 14.0 天（95%CI：11.3-16.7 天），显著长于低危组（风险评分<0.122）的 8.0 天（95%CI：7.1-8.9 天）（风险比（HR）：1.98，95%CI：1.55-2.53，<0.001）。此外，高危组的死亡率也明显高于低危组（20.6%比 2.9%，比值比（OR）：8.61，95%CI：3.45-21.52）。 结论：新构建的列线图模型可以在患者入院的早期准确识别可能发生 AKI 的潜在 COVID-19 患者，可能有助于告知临床预后。
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