Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital

Sahar Kandil; Ayman I Tharwat; Sherief M Mohsen; Mai Eldeeb; Waleed Abdallah; Amr Hilal; Hala Sweed; Mohamed Mortada; Elham Arif; Tarek Ahmed; Ahmed Elshafie; Tarek Youssef; Mohamed Zaki; Yasmin El-Gendy; Essam Ebied; Safwat Hamad; Ihab Habil; Hany Dabbous; Amr El-Said; Yasser Mostafa; Samia Girgis; Ossama Mansour; Ali El-Anwar; Ashraf Omar; Ayman Saleh; Mahmoud El-Meteini

doi:10.1186/s12890-023-02345-3

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital

BMC Pulm Med. 2023 Feb 7;23(1):57. doi: 10.1186/s12890-023-02345-3.

Authors

Sahar Kandil¹, Ayman I Tharwat², Sherief M Mohsen³, Mai Eldeeb⁴, Waleed Abdallah², Amr Hilal², Hala Sweed⁵, Mohamed Mortada⁵, Elham Arif⁵, Tarek Ahmed⁴, Ahmed Elshafie⁴, Tarek Youssef³, Mohamed Zaki³, Yasmin El-Gendy⁶, Essam Ebied³, Safwat Hamad⁷, Ihab Habil⁸, Hany Dabbous⁹, Amr El-Said², Yasser Mostafa¹⁰, Samia Girgis¹¹, Ossama Mansour¹², Ali El-Anwar³, Ashraf Omar³, Ayman Saleh¹³, Mahmoud El-Meteini^{3

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Affiliations

¹ Department of Community, Environmental, and Occupational Medicine, Faculty of Medicine, Ain Shams University, 38 Ramses St., Abbassia Square, Cairo, 1156, Egypt. saharkandil@med.asu.edu.eg.
² Department of Anaesthesia, Intensive Care and Pain Management, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
³ Department of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
⁴ Department of Internal Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
⁵ Department of Geriatric Medicine and Gerontology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
⁶ Department of Paediatrics, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
⁷ Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.
⁸ Department of Community, Environmental, and Occupational Medicine, Faculty of Medicine, Ain Shams University, 38 Ramses St., Abbassia Square, Cairo, 1156, Egypt.
⁹ Department of Hepatology and Infectious Diseases, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
¹⁰ Department of Chest Diseases, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
¹¹ Department of Clinical Pathology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
¹² Department of Otolaryngology and Head and Neck Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
¹³ Department of Cardiology, Faculty of Medicine, Ain Shams University, Cairo, Egypt.
¹⁴ Department of Hepatobiliary Surgery and Liver Transplantation, Faculty of Medicine, Ain Shams Centre for Organ Transplantation (ASCOT),, Ain Shams University, Cairo, Egypt.

Abstract

Purpose: Since the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction.

Methods: COVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020-February 2021) were included in this retrospective cohort study (n = 3663). Data were retrieved from patients' records. Kaplan-Meier survival and Cox proportional hazard regression were used. Binary logistic regression was used for creating mortality prediction models.

Results: Patients were 53.6% males, 4.6% current smokers, and their median age was 58 (IQR 41-68) years. Admission to intensive care units was 41.1% and mortality was 26.5% (972/3663, 95% CI 25.1-28.0%). Independent mortality predictors-with rapid mortality onset-were age ≥ 75 years, patients' admission in critical condition, and being symptomatic. Current smoking and presence of comorbidities particularly, obesity, malignancy, and chronic haematological disorders predicted mortality too. Some biomarkers were also recognized. Two prediction models exhibited the best performance: a basic model including age, presence/absence of comorbidities, and the severity level of the condition on admission (Area Under Receiver Operating Characteristic Curve (AUC) = 0.832, 95% CI 0.816-0.847) and another model with added International Normalized Ratio (INR) value (AUC = 0.842, 95% CI 0.812-0.873).

Conclusion: Patients with the identified mortality risk factors are to be prioritized for preventive and rapid treatment measures. With the provided prediction models, clinicians can calculate mortality probability for their patients. Presenting multiple and very generic models can enable clinicians to choose the one containing the parameters available in their specific clinical setting, and also to test the applicability of such models in a non-COVID-19 respiratory infection.

Keywords: COVID-19; Comorbidities; In-hospital mortality; Mortality predictors; Obesity; Prognostic model; Smoking; Survival.

MeSH terms

Aged
COVID-19*
Egypt
Female
Hospital Mortality
Hospitals, University
Humans
Male
Middle Aged
Retrospective Studies
SARS-CoV-2