Laboratory Medicine Department, Virgen Macarena University Hospital, Seville, Spain.
Emergency Department, Virgen Macarena University Hospital, Seville, Spain.
Scand J Clin Lab Invest. 2021 Jul;81(4):282-289. doi: 10.1080/00365513.2020.1847313. Epub 2021 May 11.
Early identification of patients with COVID-19 who may develop critical illness is of great importance.
In this study a retrospective cohort of 264 COVID-19 cases admitted at Macarena University was used for development and internal validation of a risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. Backward stepwise logistic regression was used to derive the model, including clinical and laboratory variables predictive of critical illness. Internal validation of the final model used bootstrapped samples and the model scoring derived from the coefficients. External validation was performed in a cohort of 154 cases admitted at Valme and Virgen del Rocio University Hospital.
A total of 62 (23.5%) patients developed a critical illness during their hospitalization stay, 21 (8.0%) patients needed invasive ventilation, 34 (12.9%) were admitted at the ICU and the overall mortality was of 14.8% (39 cases). 5 variables were included in the final model: age >59.5 years (OR: 3.11;95%CI 1.39-6.97), abnormal CRP results (OR: 5.76;95%CI 2.32-14.30), abnormal lymphocytes count (OR: 3.252;95%CI 1.56-6.77), abnormal CK results (OR: 3.38;95%CI 1.59-7.20) and abnormal creatinine (OR: 3.30;95%CI 1.42-7.68). The AUC of this model was 0.850 with sensitivity of 65% and specificity of 87% and the IDI and NRI were 0.1744 and 0.2785, respectively. The validation indicated a good discrimination for the external population.
Biomarkers add prognostic information in COVID-19 patients. Our risk-score provides an easy to use tool to identify patients who are likely to develop critical illness during their hospital stay.
早期识别可能发展为危重症的 COVID-19 患者非常重要。
本研究使用了 264 例在麦卡雷纳大学住院的 COVID-19 患者的回顾性队列，用于开发和内部验证预测 COVID-19 住院患者发生危重症的风险评分。使用向后逐步逻辑回归推导模型，包括预测危重症的临床和实验室变量。使用 bootstrap 样本和从系数中得出的模型评分对最终模型进行内部验证。在 154 例在瓦尔梅和罗西奥圣母大学医院住院的患者队列中进行外部验证。
共有 62 例（23.5%）患者在住院期间发生危重症，21 例（8.0%）患者需要有创通气，34 例（12.9%）患者入住 ICU，总死亡率为 14.8%（39 例）。最终模型纳入 5 个变量：年龄>59.5 岁（OR：3.11；95%CI 1.39-6.97）、CRP 异常（OR：5.76；95%CI 2.32-14.30）、淋巴细胞计数异常（OR：3.252；95%CI 1.56-6.77）、CK 异常（OR：3.38；95%CI 1.59-7.20）和肌酐异常（OR：3.30；95%CI 1.42-7.68）。该模型的 AUC 为 0.850，灵敏度为 65%，特异性为 87%，IDI 和 NRI 分别为 0.1744 和 0.2785。验证表明该模型对外部人群具有良好的区分度。
生物标志物为 COVID-19 患者提供了预后信息。我们的风险评分提供了一种简单易用的工具，可以识别出在住院期间可能发展为危重症的患者。
