Background: As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care. Objective: In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed. Methods: We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio. Results: Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm3. The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days). Conclusions: The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic.

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中文翻译：

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用于预测 COVID-19 病例生存率的预后评分系统：韩国全国队列研究


背景：随着 COVID-19 大流行的持续，最初的风险适应分配对于管理医疗资源和提供重症监护至关重要。目的：在本研究中，我们旨在确定预测 COVID-19 病例总体生存率的因素，并根据这些因素开发 COVID-19 预后评分 (COPS) 系统。此外，还分析了 COVID-19 患者的疾病严重程度和住院时间。方法：我们回顾性分析了 2020 年 1 月至 4 月韩国全国范围内实验室确诊的 COVID-19 病例。该队列被随机分为开发队列和验证队列，比例为 2:1。在开发队列 (n=3729) 中，我们尝试确定与总体生存相关的因素，并开发一个评分系统，通过使用 Cox 比例风险回归模型和自举方法确定的参数来预测总体生存率。在验证队列 (n=1865) 中，我们使用接受者操作特征曲线下的面积评估了预测准确性。 COPS 系统中每个变量的分数在调整后的风险比进行对数尺度转换后进行四舍五入。结果：在本次分析中纳入的 5594 名患者中，234 名患者 (4.2%) 在接受 COVID-19 诊断后死亡。在开发队列中，有六个参数与较差的总生存率显着相关：年龄较大、痴呆、慢性肾衰竭、呼吸困难、精神障碍和绝对淋巴细胞计数<1000/mm3。形成以下风险组：低风险（分数0-2）、中风险（分数3）、高风险（分数4）和极高风险（分数5-7）组。 COPS 系统得出的曲线下面积值为 0。在验证队列中，预测 14 天生存率为 918，预测 28 天生存率为 0.896。使用 COPS 系统，低风险、中风险、高风险和极高风险组的 28 天生存率分别估计为 99.8%、95.4%、82.3% 和 55.1%。总队列 (P<.001)。住院时间和疾病严重程度与总生存率直接相关 (P<.001)，幸存者的住院时间（平均 26.1 天，标准差 10.7 天）明显长于非幸存者的住院时间（平均 15.6 天，标准差 13.3 天） 。结论：新开发的预测性 COPS 系统可能有助于在 COVID-19 大流行期间做出适合风险的医疗资源分配决策，包括重症监护。


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