Development of an innovative technology for predicting the course of COVID-19 based on the analysis of clinical and anamnestic data

O. Shumakov, O. Parkhomenko, O. Golubovska
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Abstract

The aim – to determine a prognostically significant set of anamnestic (primarily cardiovascular) risk factors and indicators of the initial clinical condition in the population of patients with COVID-19, on the basis of which to develop a scale for assessing the clinical condition to identify patients with a more severe subsequent course of the disease for the individualization of treatment tactics.Materials and methods. The retrospective analysis included data on 104 patients with COVID-19 (50 men and 54 women, aged 24 to 84 years), who during 2020-2021 underwent treatment (16 days) in clinics of Ukraine within the framework of the program for studying the effectiveness of treatment COVID-19. Risk factors (advanced age, inflammatory diseases, hypertension, obesity, diabetes, coronary heart disease, heart failure (HF)), dynamics of the clinical state (heart rate, body temperature, blood pressure, SpO2, respiratory rate (RR), clinical symptoms and signs from all systems of the body) were assessed. Based on the dynamics of the clinical condition (according to a specially developed scale), all patients were divided into subgroup A (66 patients, more severe hospital course of COVID-19, ≥ 7 points) and subgroup B (38 patients, milder course of COVID-19, < 7) points).Results and discussion. Among the anamnestic risk factors (RF) of a more severe hospitalization for COVID-19, the following were more informative than others: age > 53 years (HR 1.8 (1.11–3.02)), history of coronary artery disease (HR 1.42 (1.09–1.85)) and SN (HR 1.67 (1.41–1.96)), as well as a model built taking into account all the estimated RFs according to their significance (HR = 1.88 (1.37–2.74), area under the ROC curve (ROC) 0.73). Among the clinical markers (CM) of the first day, the most informative were: RR > 20/min (HR 1.74 (1.10–2.74)), body temperature > 37.8 °C (HR 1.48 (1.13–1.94)) and a model with eight KM (HR 2.45 (1.55–3.87), ROC 0.80). The obtained scales were additive: the combined scale of RF and CM had ROC 0.84, value > 21 units on the first day of COVID-19 had a sensitivity of 76 % and a specificity of 76 % (HR 2.38 (1.58–3.58)) in predicting the adverse course of the disease during the next 16 days.Conclusions. The risk assessment system developed by us, based on clinical and anamnestic data, on the first day of treatment for COVID-19 allows predicting a more severe course of the disease. The data obtained by us require further study in a prospective study.
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开发基于临床和记忆数据分析的新冠肺炎病程预测创新技术
目的是确定一组具有预后意义的遗忘(主要是心血管)风险因素和COVID-19患者人群初始临床状况指标,在此基础上制定评估临床状况的量表,以识别具有更严重后续病程的患者,从而制定个性化治疗策略。材料和方法。回顾性分析包括104名COVID-19患者(50名男性和54名女性,年龄24至84岁)的数据,这些患者在2020-2021年期间在乌克兰诊所接受了COVID-19治疗有效性研究方案框架内的治疗(16天)。评估危险因素(高龄、炎症性疾病、高血压、肥胖、糖尿病、冠心病、心力衰竭)、临床状态动态(心率、体温、血压、SpO2、呼吸频率、身体各系统的临床症状和体征)。根据临床情况的动态(根据专门制定的量表)将所有患者分为a亚组(66例,病程较重,≥7分)和B亚组(38例,病程较轻,< 7分)。结果和讨论。在COVID-19重症住院的遗忘危险因素(RF)中,年龄> 53岁(HR 1.8(1.11-3.02))、冠状动脉病史(HR 1.42(1.09-1.85))和SN (HR 1.67(1.41-1.96))以及根据其显著性考虑所有估计RF的模型(HR = 1.88 (1.37-2.74), ROC曲线下面积(ROC) 0.73)比其他因素更具信息。在第一天的临床指标(CM)中,最有信息的是:RR > 20/min (HR 1.74(1.10-2.74)),体温> 37.8°C (HR 1.48(1.13-1.94))和8 KM模型(HR 2.45 (1.55-3.87), ROC 0.80)。所得量表具有可加性:RF和CM联合量表的ROC为0.84,在COVID-19发病第一天值> 21个单位预测未来16天疾病不良病程的敏感性为76%,特异性为76% (HR 2.38(1.58-3.58))。我们在COVID-19治疗第一天根据临床和记忆数据开发的风险评估系统可以预测更严重的病程。我们获得的数据需要在前瞻性研究中进一步研究。
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