Imputed dataset.

ObjectiveTo develop and validate a scoring system to predict mortality among hospitalized patients with COVID-19.MethodsRetrospective cohort study. We analyzed 5,062 analyzed hospitalized patients with COVID-19 treated at two hospitals; one each in Quito and Guayaquil, from February to July 2020. We assessed predictors of mortality using survival analyses and Cox models. We randomly divided the database into two sets: (i) the derivation cohort (n = 2497) to identify predictors of mortality, and (ii) the validation cohort (n = 2565) to test the discriminative ability of a scoring system. After multivariate analyses, we used the final model’s β-coefficients to build the score. Statistical analyses involved the development of a Cox proportional hazards regression model, assessment of goodness of fit, discrimination, and calibration.ResultsThere was a higher mortality risk for these factors: male sex [(hazard ratio (HR) = 1.32, 95% confidence interval (95% CI): 1.03–1.69], per each increase in a quartile of ages (HR = 1.44, 95% CI: 1.24–1.67) considering the younger group (17–44 years old) as the reference, presence of hypoxemia (HR = 1.40, 95% CI: 1.01–1.95), hypoglycemia and hospital hyperglycemia (HR = 1.99, 95% CI: 1.01–3.91, and HR = 1.27, 95% CI: 0.99–1.62, respectively) when compared with normoglycemia, an AST–ALT ratio >1 (HR = 1.55, 95% CI: 1.25–1.92), C-reactive protein level (CRP) of >10 mg/dL (HR = 1.49, 95% CI: 1.07–2.08), arterial pH 10 × 103 per μL (HR = 1.76, 95% CI: 1.35–2.29). We found a strong discriminative ability in the proposed score in the validation cohort [AUC of 0.876 (95% CI: 0.822–0.930)], moreover, a cutoff score ≥39 points demonstrates superior performance with a sensitivity of 93.10%, a specificity of 70.28%, and a correct classification rate of 72.66%. The LR+ (3.1328) and LR- (0.0981) values further support its efficacy in identifying high-risk patients.ConclusionMale sex, increasing age, hypoxemia, hypoglycemia or hospital hyperglycemia, AST–ALT ratio >1, elevated CRP, altered arterial pH, and leucocytosis were factors significantly associated with higher mortality in hospitalized patients with COVID-19. A statistically significant Cox regression model with strong discriminatory power and good calibration was developed to predict mortality in hospitalized patients with COVID-19, highlighting its potential clinical utility.

研究目的：开发并验证一款评分系统，用以预测新型冠状病毒肺炎（COVID-19）住院患者的死亡风险。
研究方法：本研究为回顾性队列研究。我们分析了2020年2月至7月期间，分别位于厄瓜多尔基多和瓜亚基尔的两家医院收治的5062例COVID-19住院患者数据。通过生存分析与Cox模型评估患者死亡的预测因素。将数据库随机划分为两个队列：（1）推导队列（n=2497），用于筛选死亡预测因素；（2）验证队列（n=2565），用于检验评分系统的区分能力。经多因素分析后，利用最终模型的β系数构建该评分。统计学分析包括构建Cox比例风险回归模型、评估模型拟合优度、区分度与校准度。
研究结果：以下因素与更高死亡风险相关：男性[风险比（hazard ratio, HR）=1.32，95%置信区间（95% confidence interval, 95% CI）：1.03~1.69]；以17~44岁的年轻组为参照，年龄每升高一个四分位组（HR=1.44，95%CI：1.24~1.67）；低氧血症（HR=1.40，95%CI：1.01~1.95）；与血糖正常者相比，低血糖与住院期间高血糖（分别对应HR=1.99，95%CI：1.01~3.91；HR=1.27，95%CI：0.99~1.62）；天冬氨酸氨基转移酶（AST）-丙氨酸氨基转移酶（ALT）比值>1（HR=1.55，95%CI：1.25~1.92）；C反应蛋白（C-reactive protein, CRP）水平>10mg/dL（HR=1.49，95%CI：1.07~2.08）；动脉pH异常以及白细胞计数>10×10^3/μL（HR=1.76，95%CI：1.35~2.29）。本研究构建的评分在验证队列中展现出优异的区分能力[AUC为0.876（95%CI：0.822~0.930）]，当截断值（cutoff）≥39分时，模型表现最优，灵敏度为93.10%，特异度为70.28%，分类准确率为72.66%。阳性似然比（LR+）为3.1328，阴性似然比（LR-）为0.0981，进一步证实该评分在识别高危患者中的效能。
研究结论：男性、年龄增长、低氧血症、低血糖或住院期间高血糖、AST–ALT比值>1、CRP水平升高、动脉pH异常以及白细胞增多，均为COVID-19住院患者死亡风险升高的显著相关因素。本研究构建的Cox回归模型具有统计学意义，区分能力优异且校准度良好，可用于预测COVID-19住院患者的死亡风险，凸显了其潜在的临床应用价值。