Monterde David, Carot-Sans Gerard, Cainzos-Achirica Miguel, Abilleira Sònia, Coca Marc, Vela Emili, Clèries Montse, Valero-Bover Damià, Comin-Colet Josep, García-Eroles Luis, Pérez-Sust Pol, Arrufat Miquel, Lejardi Yolanda, Piera-Jiménez Jordi
Catalan Institute of Health, Barcelona, Spain.
Digitalization for the Sustainability of the Healthcare System (DS3), Sistema de Salut de Catalunya, Barcelona, Spain.
Risk Manag Healthc Policy. 2021 Nov 23;14:4729-4737. doi: 10.2147/RMHP.S326132. eCollection 2021.
Comorbidity burden has been identified as a relevant predictor of critical illness in patients hospitalized with coronavirus disease 2019 (COVID-19). However, comorbidity burden is often represented by a simple count of few conditions that may not fully capture patients' complexity.
To evaluate the performance of a comprehensive index of the comorbidity burden (Queralt DxS), which includes all chronic conditions present on admission, as an adjustment variable in models for predicting critical illness in hospitalized COVID-19 patients and compare it with two broadly used measures of comorbidity.
We analyzed data from all COVID-19 hospitalizations reported in eight public hospitals in Catalonia (North-East Spain) between June 15 and December 8 2020. The primary outcome was a composite of critical illness that included the need for invasive mechanical ventilation, transfer to ICU, or in-hospital death. Predictors including age, sex, and comorbidities present on admission measured using three indices: the Charlson index, the Elixhauser index, and the Queralt DxS index for comorbidities on admission. The performance of different fitted models was compared using various indicators, including the area under the receiver operating characteristics curve (AUROCC).
Our analysis included 4607 hospitalized COVID-19 patients. Of them, 1315 experienced critical illness. Comorbidities significantly contributed to predicting the outcome in all summary indices used. AUC (95% CI) for prediction of critical illness was 0.641 (0.624-0.660) for the Charlson index, 0.665 (0.645-0.681) for the Elixhauser index, and 0.787 (0.773-0.801) for the Queralt DxS index. Other metrics of model performance also showed Queralt DxS being consistently superior to the other indices.
In our analysis, the ability of comorbidity indices to predict critical illness in hospitalized COVID-19 patients increased with their exhaustivity. The comprehensive Queralt DxS index may improve the accuracy of predictive models for resource allocation and clinical decision-making in the hospital setting.
合并症负担已被确定为2019冠状病毒病（COVID-19）住院患者危重症的一个相关预测因素。然而，合并症负担通常仅通过少数几种疾病的简单计数来表示，这可能无法完全反映患者的复杂性。
评估一种合并症负担综合指数（Queralt DxS）的性能，该指数包括入院时存在的所有慢性病，作为预测COVID-19住院患者危重症模型中的一个调整变量，并将其与两种广泛使用的合并症测量方法进行比较。
我们分析了2020年6月15日至12月8日期间西班牙东北部加泰罗尼亚八家公立医院报告的所有COVID-19住院病例数据。主要结局是一个危重症综合指标，包括需要有创机械通气、转入重症监护病房或院内死亡。预测因素包括年龄、性别以及入院时使用三种指数测量的合并症：查尔森指数、埃利克斯豪泽指数和入院时合并症的Queralt DxS指数。使用包括受试者工作特征曲线下面积（AUROCC）在内的各种指标比较不同拟合模型的性能。
我们的分析纳入了4607例COVID-19住院患者。其中，1315例经历了危重症。在所有使用的汇总指数中，合并症对预测结局有显著贡献。查尔森指数预测危重症的AUC（95%CI）为0.641（0.624-0.660），埃利克斯豪泽指数为0.665（0.645-0.681），Queralt DxS指数为0.787（0.773-0.801）。模型性能的其他指标也显示Queralt DxS始终优于其他指数。
在我们的分析中，合并症指数预测COVID-19住院患者危重症的能力随着其详尽程度的增加而提高。综合的Queralt DxS指数可能会提高医院环境中资源分配和临床决策预测模型的准确性。
