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Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different
Mahdieh Shokrollahi Barough1, Mohammad Darzi2, Masoud Yunesian3
1Department of Immunology, School of Medicine Iran University of Medical Sciences, Tehran, Iran; Immunology research center institute of immunology and infectious diseases Iran University of Medical Sciences, Tehran, Iran; ATMP Department, Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
Insights
COVID-19 mortality is higher in males and significantly predicted by age over 55. Kidney disease is the deadliest comorbidity, while CT scans have no predictive value for death.
Area of Science:
- Medical research
- Epidemiology
- Public Health
Background:
- Understanding COVID-19 pathogenesis requires homogeneous studies on disease mechanisms.
- Identifying clinical and laboratory predictors is crucial for prognosis and treatment.
- This study aimed to predict COVID-19 mortality using comorbidities and laboratory metrics.
Purpose of the Study:
- To analyze comorbidities and laboratory parameters for predicting COVID-19 mortality.
- To identify key predictors of mortality in a homogeneous patient cohort.
- To inform effective treatment strategies and improve patient prognosis.
Main Methods:
- Retrospective cohort study of 7500 COVID-19 patients (2022-2022).
- Collected and analyzed clinical, laboratory, and comorbidity data.
- Employed machine learning for feature identification and predictive scoring.
Main Results:
- Male COVID-19 patients had higher mortality rates (19.3%) than females (17%).
- Cancer and Alzheimer's were common comorbidities linked to long-term hospitalization.
- Kidney disease (KD) was the most lethal comorbidity (45% mortality); age >55 was the strongest mortality predictor.
- WBC, Cr, CRP, ALP, and VBG-HCO3 were significant predictors across comorbidities; CT scan scores were not predictive.
Conclusions:
- Age is a critical factor in COVID-19 prognosis, especially for elderly patients.
- Kidney disease significantly impacts COVID-19 mortality outcomes.
- Predictive models incorporating comorbidities and lab data can guide clinical management.
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.
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