Department of Hematology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China.
Department of Pharmacy, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
BMC Infect Dis. 2021 Apr 16;21(1):356. doi: 10.1186/s12879-021-06065-z.
COVID-19 pandemic has forced physicians to quickly determine the patient's condition and choose treatment strategies. This study aimed to build and validate a simple tool that can quickly predict the deterioration and survival of COVID-19 patients.
A total of 351 COVID-19 patients admitted to the Third People's Hospital of Yichang between 9 January to 25 March 2020 were retrospectively analyzed. Patients were randomly grouped into training (n = 246) or a validation (n = 105) dataset. Risk factors associated with deterioration were identified using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. The factors were then incorporated into the nomogram. Kaplan-Meier analysis was used to compare the survival of patients between the low- and high-risk groups divided by the cut-off point.
The least absolute shrinkage and selection operator (LASSO) regression was used to construct the nomogram via four parameters (white blood cells, C-reactive protein, lymphocyte≥0.8 × 10/L, and lactate dehydrogenase ≥400 U/L). The nomogram showed good discriminative performance with the area under the receiver operating characteristic (AUROC) of 0.945 (95% confidence interval: 0.91-0.98), and good calibration (P = 0.539). Besides, the nomogram showed good discrimination performance and good calibration in the validation and total cohorts (AUROC = 0.979 and AUROC = 0.954, respectively). Decision curve analysis demonstrated that the model had clinical application value. Kaplan-Meier analysis illustrated that low-risk patients had a significantly higher 8-week survival rate than those in the high-risk group (100% vs 71.41% and P < 0.0001).
A simple-to-use nomogram with excellent performance in predicting deterioration risk and survival of COVID-19 patients was developed and validated. However, it is necessary to verify this nomogram using a large-scale multicenter study.
COVID-19 大流行迫使医生迅速确定患者的病情并选择治疗策略。本研究旨在构建和验证一种简单的工具，以便快速预测 COVID-19 患者的恶化和生存情况。
回顾性分析 2020 年 1 月 9 日至 3 月 25 日期间宜昌市第三人民医院收治的 351 例 COVID-19 患者。患者被随机分为训练集（n=246）和验证集（n=105）。使用单因素逻辑回归和最小绝对收缩和选择算子（LASSO）回归识别与恶化相关的危险因素。然后将这些因素纳入列线图。通过截断值将患者分为低危组和高危组，采用 Kaplan-Meier 分析比较两组患者的生存情况。
使用最小绝对收缩和选择算子（LASSO）回归通过四个参数（白细胞、C 反应蛋白、淋巴细胞≥0.8×10/L 和乳酸脱氢酶≥400 U/L）构建列线图。该列线图具有良好的判别性能，受试者工作特征曲线下面积（AUROC）为 0.945（95%置信区间：0.91-0.98），校准度良好（P=0.539）。此外，该列线图在验证集和总队列中均具有良好的判别性能和校准度（AUROC=0.979 和 AUROC=0.954）。决策曲线分析表明该模型具有临床应用价值。Kaplan-Meier 分析表明，低危患者 8 周的生存率明显高于高危患者（100%比 71.41%，P<0.0001）。
本研究构建并验证了一种用于预测 COVID-19 患者恶化风险和生存情况的简单易用的列线图，具有良好的性能。但是，还需要使用大规模多中心研究来验证该列线图。
