Moon Min Kyong, Ham Hyeonjung, Song Soo Min, Lee Chanhee, Goo Taewan, Oh Bumjo, Lee Seungyeoun, Kim Shin-Woo, Park Taesung
Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea.
Front Med (Lausanne). 2024 Jan 4;10:1239789. doi: 10.3389/fmed.2023.1239789. eCollection 2023.
Understanding the clinical course and pivotal time points of COVID-19 aggravation is critical for enhancing patient monitoring. This retrospective, multi-center cohort study aims to identify these significant time points and associate them with potential risk factors, leveraging data from a sizable cohort with mild-to-moderate symptoms upon admission.
This study included data from 1,696 COVID-19 patients with mild-to-moderate clinical severity upon admission across multiple hospitals in Daegu-Kyungpook Province (Daegu dataset) between February 18 and early March 2020 and 321 COVID-19 patients at Seoul Boramae Hospital (Boramae dataset) collected from February to July 2020. The approach involved: (1) identifying the optimal time point for aggravation using survival analyses with maximally selected rank statistics; (2) investigating the relationship between comorbidities and time to aggravation; and (3) developing prediction models through machine learning techniques. The models were validated internally among patients from the Daegu dataset and externally among patients from the Boramae dataset.
The Daegu dataset showed a mean age of 51.0 ± 19.6 years, with 8 days for aggravation and day 5 being identified as the pivotal point for survival. Contrary to previous findings, specific comorbidities had no notable impact on aggravation patterns. Prediction models utilizing factors including age and chest X-ray infiltration demonstrated promising performance, with the top model achieving an AUC of 0.827 in external validation for 5 days aggravation prediction.
Our study highlights the crucial significance of the initial 5 days period post-admission in managing COVID-19 patients. The identification of this pivotal time frame, combined with our robust predictive models, provides valuable insights for early intervention strategies. This research underscores the potential of proactive monitoring and timely interventions in enhancing patient outcomes, particularly for those at risk of rapid aggravation. Our findings offer a meaningful contribution to understanding the COVID-19 clinical course and supporting healthcare providers in optimizing patient care and resource allocation.
了解新型冠状病毒肺炎(COVID-19)病情加重的临床过程和关键时间点对于加强患者监测至关重要。这项回顾性多中心队列研究旨在确定这些重要时间点,并将其与潜在风险因素相关联,利用来自入院时症状为轻至中度的大量队列的数据。
本研究纳入了2020年2月18日至3月初大邱-庆尚北道省多家医院1696例入院时临床严重程度为轻至中度的COVID-19患者的数据(大邱数据集),以及2020年2月至7月在首尔博拉梅医院收集的321例COVID-19患者的数据(博拉梅数据集)。研究方法包括:(1)使用最大选择秩统计的生存分析确定病情加重的最佳时间点;(2)研究合并症与病情加重时间之间的关系;(3)通过机器学习技术开发预测模型。这些模型在大邱数据集的患者内部进行了验证,并在博拉梅数据集的患者外部进行了验证。
大邱数据集的患者平均年龄为51.0±19.6岁,病情加重时间为8天,第5天被确定为生存的关键点。与之前的研究结果相反,特定合并症对病情加重模式没有显著影响。利用年龄和胸部X线浸润等因素的预测模型表现出良好的性能,顶级模型在5天病情加重预测的外部验证中AUC达到0.827。
我们的研究强调了入院后最初5天在管理COVID-19患者中的关键意义。这一关键时间框架的确定,结合我们强大的预测模型,为早期干预策略提供了有价值的见解。这项研究强调了主动监测和及时干预在改善患者预后方面的潜力,特别是对于那些有快速病情加重风险的患者。我们的研究结果为理解COVID-19临床过程以及支持医疗保健提供者优化患者护理和资源分配做出了有意义的贡献。