Abstract

Background

Early identification of patients with COVID-19 who may develop critical illness is of great importance.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

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开发和验证基于实验室的风险评分，以预测住院 COVID-19 患者危重疾病的发生

摘要

背景

早期识别可能发展为危重病的 COVID-19 患者非常重要。

方法

在这项研究中，马卡雷纳大学录取的 264 例 COVID-19 病例的回顾性队列被用于开发和内部验证风险评分，以预测住院 COVID-19 患者的危重疾病的发生。向后逐步逻辑回归用于推导模型，包括预测危重疾病的临床和实验室变量。最终模型的内部验证使用自举样本和从系数得出的模型评分。在 Valme 和 Virgen del Rocio 大学医院收治的 154 例病例队列中进行了外部验证。

结果

住院期间共有62例（23.5%）患者出现危重症，21例（8.0%）患者需要有创通气，34例（12.9%）入住ICU，总死亡率为14.8%（39例） . 最终模型中包含 5 个变量：年龄 >59.5 岁（OR：3.11；95%CI 1.39–6.97）、CRP 结果异常（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 结果异常（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，敏感性为 65%，特异性为 87%，IDI 和 NRI 分别为 0.1744 和 0.2785。验证表明对外部人群的良好区分。

结论

生物标志物增加了 COVID-19 患者的预后信息。我们的风险评分提供了一种易于使用的工具来识别可能在住院期间患上危重疾病的患者。