Mahmood Jawad, Ul Haque Muhammad Izhar, Gul Maria, Ayub Aliya, Ansari Fawwad A, Ahmad Wiqas
Gastroenterology and Hepatology, Hayatabad Medical Complex, Peshawar, PAK.
Department of Comparative Biomedical Sciences, College of Veterinary Medicine, University of Georgia, Athens, USA.
Cureus. 2025 Mar 15;17(3):e80611. doi: 10.7759/cureus.80611. eCollection 2025 Mar.
Background and objective Treatment in ICUs became extremely difficult due to the growing number of coronavirus disease 2019 (COVID-19) patients at the height of the pandemic. Consequently, prompt patient triage depends on the early categorization of severe cases in such scenarios. This study aimed to provide an evidence-based strategy to ensure the best use of resources by triaging patients based on objective risk factors. Methods This retrospective observational study comprised 500 inpatients (>age 18 years) who were hospitalized between March 20 and April 19, 2020, at the Khyber Teaching Hospital (KTH) and Hayatabad Medical Complex (HMC) in Peshawar, Pakistan. The clinical, laboratory, and radiological parameters were assessed. Real-time polymerase chain reaction (RT-PCR) findings were used to confirm the diagnosis of COVID-19. Results A total of 19 potential clinical and laboratory risk factors associated with ICU admissions were identified. At least one comorbidity among chronic lung disease, cardiovascular disease (CVD), and diabetes was the factor with the strongest association with ICU admission with a univariable odds ratio (OR) of over 27, followed by renal disease and other COVID-19 sequelae such as diarrhea, respiratory rate (>24 breaths/minute), and positive RT-PCR (vs. negative) with an univariable OR between 9 and 15. Furthermore, a multivariate logistic regression model was further developed with five risk factors, including comorbidity, presence of chronic lung disease, presence of diabetes, and RT-PCR (positive vs. negative), male sex (vs. female), and older age (65.0-80.5 years), suggesting a good fit of the model to the data shown by the area under the receiver operator characteristic curve (AUC) of 0.943 (95% CI: 0.917, 0.969). Additionally, a chest CT scan showed the typical COVID-19 pneumonia with pulmonary involvement of 30-40%, which was further evaluated by the COVID-19 Reporting and Data System (CO-RADS). The typical COVID-19 pneumonia was on a scale of four (15/25) or five (19/25) lung lesions. Conclusions Based on our findings, this approach could be used to screen the severe cases of COVID-19 patients and help them to be treated in ICUs on time while preventing others from unnecessarily using ICUs in the setting of limited medical resources, such as the outbreak of a pandemic.
背景与目的 在新冠疫情高峰期，由于2019冠状病毒病（COVID-19）患者数量不断增加，重症监护病房（ICU）的治疗变得极其困难。因此，在这种情况下，及时进行患者分诊取决于对重症病例的早期分类。本研究旨在提供一种基于证据的策略，通过根据客观风险因素对患者进行分诊，以确保资源的最佳利用。方法 这项回顾性观察研究纳入了2020年3月20日至4月19日期间在巴基斯坦白沙瓦的开伯尔教学医院（KTH）和哈亚塔巴德医疗中心（HMC）住院的500名成年患者（年龄>18岁）。对临床、实验室和放射学参数进行了评估。采用实时聚合酶链反应（RT-PCR）结果确诊COVID-19。结果 共确定了19个与入住ICU相关的潜在临床和实验室风险因素。慢性肺病、心血管疾病（CVD）和糖尿病中至少有一种合并症与入住ICU的关联最强，单变量比值比（OR）超过27，其次是肾病以及腹泻、呼吸频率（>24次/分钟）和RT-PCR阳性（与阴性相比）等其他COVID-19后遗症，单变量OR在9至15之间。此外，进一步建立了一个多变量逻辑回归模型，包含五个风险因素，即合并症、慢性肺病的存在、糖尿病的存在、RT-PCR（阳性与阴性）、男性（与女性相比）以及老年（65.0 - 80.5岁），提示该模型与数据拟合良好，受试者工作特征曲线下面积（AUC）为0.943（95%CI：0.917，0.969）。此外，胸部CT扫描显示典型的COVID-19肺炎，肺部受累率为30 - 40%，通过COVID-19报告和数据系统（CO-RADS）进行了进一步评估。典型的COVID-19肺炎的肺部病变在4级（15/25）或5级（19/25）。结论 根据我们的研究结果，这种方法可用于筛查COVID-19患者中的重症病例，并帮助他们及时在ICU接受治疗，同时在医疗资源有限的情况下，如疫情爆发时，防止其他患者不必要地使用ICU。
