对 COVID-19 临床和实验室数据的回顾性分析：构建不同合并症的多变量模型。

IF 4.7 3区医学 Q1 INFECTIOUS DISEASES

Journal of Infection and Public Health Pub Date : 2024-10-18 DOI:10.1016/j.jiph.2024.102566

Mahdieh Shokrollahi Barough , Mohammad Darzi , Masoud Yunesian , Danesh Amini Panah , Yekta Ghane , Sam Mottahedan , Sohrab Sakinehpour , Tahereh Kowsarirad , Zahra Hosseini-Farjam , Mohammad Reza Amirzargar , Samaneh Dehghani , Fahimeh Shahriyary , Mohammad Mahdi Kabiri , Marzieh Nojomi , Neda Saraygord-Afshari , Seyedeh Ghazal Mostofi , Zeynab Yassin , Nazanin Mojtabavi

{"title":"对 COVID-19 临床和实验室数据的回顾性分析：构建不同合并症的多变量模型。","authors":"Mahdieh Shokrollahi Barough , Mohammad Darzi , Masoud Yunesian , Danesh Amini Panah , Yekta Ghane , Sam Mottahedan , Sohrab Sakinehpour , Tahereh Kowsarirad , Zahra Hosseini-Farjam , Mohammad Reza Amirzargar , Samaneh Dehghani , Fahimeh Shahriyary , Mohammad Mahdi Kabiri , Marzieh Nojomi , Neda Saraygord-Afshari , Seyedeh Ghazal Mostofi , Zeynab Yassin , Nazanin Mojtabavi","doi":"10.1016/j.jiph.2024.102566","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>The clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model.</div></div><div><h3>Method</h3><div>A retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores.</div></div><div><h3>Results</h3><div>The frequency and mortality rates of COVID-19 among males (19.3 %) were higher than those among females (17 %) (p = 0.01, OR = 0.85, 95 % CI = 0.76–0.96). Cancer (p < 0.05, OR = 1.9, 95 % CI = 1.48–2.4) and Alzheimer's (p < 0.05, OR = 2.36, 95 % CI = 1.89–2.9) were the two most common comorbidities associated with long-term hospitalization (LTH). Kidney disease (KD) was identified as the most lethal comorbidity (45 % of KD patients) (OR = 5.6, 95 % CI = 5.05–6.04, p < 0.001). Age > 55 was the most predictive parameter for mortality (p < 0.001, OR = 6.5, 95 % CI = 1.03–1.04), and the CT scan score showed no predictive value for death (p > 0.05). WBC, Cr, CRP, ALP, and VBG-HCO3 were the most significant critical data associated with death prediction across all comorbidities (p < 0.05).</div></div><div><h3>Conclusion</h3><div>COVID-19 is particularly lethal for elderly adults; thus, age plays a crucial role in disease prognosis. Regarding death prediction, various comorbidities rank differently, with KD having a significant impact on mortality outcomes.</div></div>","PeriodicalId":16087,"journal":{"name":"Journal of Infection and Public Health","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities\",\"authors\":\"Mahdieh Shokrollahi Barough , Mohammad Darzi , Masoud Yunesian , Danesh Amini Panah , Yekta Ghane , Sam Mottahedan , Sohrab Sakinehpour , Tahereh Kowsarirad , Zahra Hosseini-Farjam , Mohammad Reza Amirzargar , Samaneh Dehghani , Fahimeh Shahriyary , Mohammad Mahdi Kabiri , Marzieh Nojomi , Neda Saraygord-Afshari , Seyedeh Ghazal Mostofi , Zeynab Yassin , Nazanin Mojtabavi\",\"doi\":\"10.1016/j.jiph.2024.102566\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>The clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model.</div></div><div><h3>Method</h3><div>A retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores.</div></div><div><h3>Results</h3><div>The frequency and mortality rates of COVID-19 among males (19.3 %) were higher than those among females (17 %) (p = 0.01, OR = 0.85, 95 % CI = 0.76–0.96). Cancer (p < 0.05, OR = 1.9, 95 % CI = 1.48–2.4) and Alzheimer's (p < 0.05, OR = 2.36, 95 % CI = 1.89–2.9) were the two most common comorbidities associated with long-term hospitalization (LTH). Kidney disease (KD) was identified as the most lethal comorbidity (45 % of KD patients) (OR = 5.6, 95 % CI = 5.05–6.04, p < 0.001). Age > 55 was the most predictive parameter for mortality (p < 0.001, OR = 6.5, 95 % CI = 1.03–1.04), and the CT scan score showed no predictive value for death (p > 0.05). WBC, Cr, CRP, ALP, and VBG-HCO3 were the most significant critical data associated with death prediction across all comorbidities (p < 0.05).</div></div><div><h3>Conclusion</h3><div>COVID-19 is particularly lethal for elderly adults; thus, age plays a crucial role in disease prognosis. Regarding death prediction, various comorbidities rank differently, with KD having a significant impact on mortality outcomes.</div></div>\",\"PeriodicalId\":16087,\"journal\":{\"name\":\"Journal of Infection and Public Health\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Infection and Public Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1876034124003009\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFECTIOUS DISEASES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Infection and Public Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876034124003009","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}

引用次数: 0

摘要

背景：要了解 COVID-19 的临床发病机制，就必须进行全面、同质的研究。确定临床症状和实验室指标作为关键预测指标，可指导预后并为有效的治疗策略提供依据。本研究采用同质模型分析了合并症和实验室指标，以预测COVID-19的死亡率：方法：对 2022 年至 2022 年期间 Rasoul Akram 医院收治的 7500 名 COVID-19 患者进行了回顾性队列研究。研究收集了临床和实验室数据以及合并症信息，并使用高级编码、数据对齐和回归分析进行了分析。采用机器学习算法识别相关特征并计算预测概率得分：男性 COVID-19 的发病率和死亡率（19.3%）高于女性（17%）（P = 0.01，OR = 0.85，95 % CI = 0.76-0.96）。癌症（p 55）是最能预测死亡率的参数（p 0.05）。在所有合并症中，WBC、Cr、CRP、ALP 和 VBG-HCO3 是与死亡预测相关的最重要的关键数据（p 结论：COVID-19 对老年人的致死率特别高；因此，年龄在疾病预后中起着至关重要的作用。在死亡预测方面，各种合并症的排名不同，KD对死亡结果有显著影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities

Background

The clinical pathogenesis of COVID-19 necessitates a comprehensive and homogeneous study to understand the disease mechanisms. Identifying clinical symptoms and laboratory parameters as key predictors can guide prognosis and inform effective treatment strategies. This study analyzed comorbidities and laboratory metrics to predict COVID-19 mortality using a homogeneous model.

Method

A retrospective cohort study was conducted on 7500 COVID-19 patients admitted to Rasoul Akram Hospital between 2022 and 2022. Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores.

Results

The frequency and mortality rates of COVID-19 among males (19.3 %) were higher than those among females (17 %) (p = 0.01, OR = 0.85, 95 % CI = 0.76–0.96). Cancer (p < 0.05, OR = 1.9, 95 % CI = 1.48–2.4) and Alzheimer's (p < 0.05, OR = 2.36, 95 % CI = 1.89–2.9) were the two most common comorbidities associated with long-term hospitalization (LTH). Kidney disease (KD) was identified as the most lethal comorbidity (45 % of KD patients) (OR = 5.6, 95 % CI = 5.05–6.04, p < 0.001). Age > 55 was the most predictive parameter for mortality (p < 0.001, OR = 6.5, 95 % CI = 1.03–1.04), and the CT scan score showed no predictive value for death (p > 0.05). WBC, Cr, CRP, ALP, and VBG-HCO3 were the most significant critical data associated with death prediction across all comorbidities (p < 0.05).

Conclusion

COVID-19 is particularly lethal for elderly adults; thus, age plays a crucial role in disease prognosis. Regarding death prediction, various comorbidities rank differently, with KD having a significant impact on mortality outcomes.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Infection and Public Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -INFECTIOUS DISEASES

CiteScore

13.10

自引率

1.50%

发文量

203

审稿时长

96 days

期刊介绍： The Journal of Infection and Public Health, first official journal of the Saudi Arabian Ministry of National Guard Health Affairs, King Saud Bin Abdulaziz University for Health Sciences and the Saudi Association for Public Health, aims to be the foremost scientific, peer-reviewed journal encompassing infection prevention and control, microbiology, infectious diseases, public health and the application of healthcare epidemiology to the evaluation of health outcomes. The point of view of the journal is that infection and public health are closely intertwined and that advances in one area will have positive consequences on the other. The journal will be useful to all health professionals who are partners in the management of patients with communicable diseases, keeping them up to date. The journal is proud to have an international and diverse editorial board that will assist and facilitate the publication of articles that reflect a global view on infection control and public health, as well as emphasizing our focus on supporting the needs of public health practitioners. It is our aim to improve healthcare by reducing risk of infection and related adverse outcomes by critical review, selection, and dissemination of new and relevant information in the field of infection control, public health and infectious diseases in all healthcare settings and the community.