Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has presented a significant global health challenge since its emergence in late 2019 [1][2]. The infection exhibits a broad clinical spectrum ranging from asymptomatic cases to severe pneumonia, multi-organ failure, and death [3]. As of mid-2021, millions of infections and deaths have been reported worldwide, prompting the urgent need for a deeper understanding of disease risk factors, particularly those influencing disease severity and outcomes [4].

Several studies have identified comorbidities such as diabetes mellitus, hypertension, obesity, cardiovascular disease, and chronic kidney disease as important determinants of adverse clinical outcomes in COVID-19 [5][6]. These comorbidities have been associated with increased risk of hospitalization, mechanical ventilation, intensive care admission, and death [7][8]. However, the strength and consistency of these associations have varied across geographic regions and populations, likely due to differences in genetic factors, healthcare infrastructure, comorbidity prevalence, and study design [9]. Despite the abundance of data from Western countries and East Asia, there remains a relative paucity of data from the Indian subcontinent, where demographic and epidemiologic characteristics may alter the risk landscape.

Hyderabad, the capital of Telangana, is one of the largest metropolitan cities in South India with an estimated population exceeding 10 million [10]. During the second wave of COVID-19 in India (February-November 2021), Telangana reported more than 660,000 confirmed cases [11]. The surge was characterized by widespread community transmission, limited ICU bed availability, and shortages of oxygen and critical-care resources. The prevalence of metabolic disorders such as diabetes and hypertension in urban Telangana populations likely contributed to the higher disease burden observed in hospitalized patients [12][13]. Given the growing burden of non-communicable diseases in India and the evolving understanding of COVID-19 pathophysiology, our study sought to evaluate the association between pre-existing comorbidities and COVID-19 severity in a cohort of hospitalized individuals in Hyderabad, India. We hypothesized that patients with diabetes, hypertension, and other chronic conditions would have significantly higher severity scores and worse outcomes compared to those without such comorbidities. This study aims to fill existing knowledge gaps by providing region-specific evidence that may inform risk stratification and clinical decision-making in similar low- and middle-income settings.

The primary outcome of interest was the severity of COVID-19 pneumonia, as assessed by CT severity scores. Secondary outcomes included CORADS scores, duration of hospital stay (in days), and in-hospital mortality.

The data were entered into a pre-coded Microsoft Excel spreadsheet and exported to IBM SPSS Statistics software version 20.0 (IBM Corp., Armonk, NY, USA) for analysis. Descriptive statistics were computed for all study variables. Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables were reported as frequencies and percentages. To assess the association between comorbidities and COVID-19 severity, independent sample t-tests were used to compare mean CT severity scores and mean duration of hospital stay between groups (e.g., diabetic vs. non-diabetic, hypertensive vs. non-hypertensive). Chi-square tests were employed to analyze associations between categorical variables such as comorbidity status and mortality. A p-value of <0.05 was considered statistically significant. Quality control measures included double-entry of a subset of the dataset and cross-validation by a second reviewer to ensure data reliability and internal consistency. All statistical analyses were unadjusted. Therefore, the observed associations may have been influenced by potential confounding factors, including age, sex, and the coexistence of multiple comorbidities.

Of the 234 hospital records initially screened, 202 met the eligibility criteria and were included in the final analysis, while 32 records were excluded because of missing disease severity data, undocumented primary outcomes, or incomplete medical records. A total of 202 individuals with laboratory-confirmed COVID-19 infection were included in the final analysis. Of these, 107 (53%) were male and 95 (47%) were female. The majority of the study population was between 41 and 60 years of age (n = 98, 49%). Among the included individuals, 29 (14.4%) died during hospitalization due to complications related to COVID-19. Comorbidities were commonly observed in this cohort. Diabetes mellitus was the most prevalent comorbidity (n = 84, 42%), followed by hypertension (n = 68, 34%). Demographics of the study cohort have been detailed in Table 1.

Table 1. Characteristics of the study cohort.

Diabetic individuals exhibited significantly higher CT severity scores than non-diabetics (20.04 ± 8.29 vs. 16.13 ± 8.99; p = 0.0008) and required longer hospitalization (19.76 ± 6.10 vs. 12.34 ± 7.00 days; p = 0.003). Similarly, hypertensive individuals had higher CT scores (20.77 ± 9.36 vs. 16.22 ± 8.22; p = 0.0002) and prolonged hospital stays (20.4 ± 7.30 vs. 13.29 ± 6.69 days; p = 0.002). CORADS scores were comparable between these groups (p > 0.05) (Table 2). Individuals with other comorbidities experienced significantly longer recovery times compared to those without (19.38 ± 6.77 vs. 14.93 ± 7.55 days; p = 0.015) and had lower mean CORADS scores (4.67 ± 0.61 vs. 4.95 ± 0.24; p = 0.007). CT severity scores were not significantly different between these groups (Table 2).

Table 2. Comparison of clinical outcomes according to comorbidity status.

Out of 202 individuals, 29 (14.4%) died during hospitalization. Non-survivors had significantly higher CT severity scores compared to survivors (23.90 ± 9.95 vs. 16.72 ± 8.26; p = 0.0003). Mortality was higher in individuals with comorbidities. However, the CORADS score was comparable between the non-survivor and survivor groups (4.79 ± 0.49 vs. 4.93 ± 0.30; p = 0.07).

Of the 29 deceased individuals, 17 (59%) were male and 12 (41%) were female. The highest proportion of deaths occurred in the 41 - 60 age group (n = 18, 62%). Mortality increased with the number of comorbidities: 13.4% in those with one comorbidity, 15.5% with two, 37.5% with three, and 87.5% with more than three. The mean CT severity score was highest in individuals with more than three comorbidities (Table 3) (25.25 ± 10.5), and recovery duration was longest among individuals with three comorbidities (Table 3). Demographics of non survivors have been summarized in Supplementary Table S1 along with their demographics in Table S2.

Table 3. Distribution according to the number of comorbidities.

Older individuals (61 - 80 years) had higher CT severity scores (19.68 ± 8.48) and longer recovery durations (17.43 ± 6.4 days) compared to younger groups. Female participants had slightly higher CT scores (18.04 ± 8.76) and longer hospital stays (15.88 ± 7.76 days) than males (Supplementary Table S3).

This study demonstrates that individuals with pre-existing comorbidities, especially diabetes and hypertension, exhibited significantly higher COVID-19 severity as indicated by CT severity scores, prolonged hospitalization, and increased mortality. Our findings support an association between pre-existing comorbidities and worse clinical outcomes in SARS-CoV-2 infection while highlighting the prognostic utility of CT severity scoring in hospitalized patients in a low- and middle-income setting.

While multiple international studies have established associations between comorbidities and COVID-19 mortality or ICU admission rates, region-specific studies with radiological correlation remain limited [5]-[7]. The results from our Hyderabad-based cohort mirror global patterns but provide critical local data. Notably, the CT severity score consistently differentiated survivors from non-survivors (23.90 ± 9.95 vs. 16.72 ± 8.26; p = 0.0003), whereas CORADS did not, reinforcing findings from Hadad and Afzelius (2023) that CT severity score is superior to CORADS for severity stratification [16]. It was also observed that individuals with three or more comorbidities had markedly higher mortality rates, peaking at 87.5% in those with >3 conditions. This is consistent with the findings of Marušić et al. (2024), who reported a dose-dependent relationship between multimorbidity and poor COVID-19 outcomes [9]. The observed increase in CT severity score and hospital stay with rising age also supports well-documented associations between aging, immunosenescence, and poorer viral clearance [17][18]. Several region-specific factors likely accentuate these effects in South India. Hyderabad and other metropolitan areas in Telangana report a high prevalence of type 2 diabetes (12% - 15%) and hypertension (30%) substantially higher than national averages, creating a population with heightened baseline vulnerability to severe infection [19]-[21]. During the second wave (February-November 2021), health systems in Hyderabad experienced unprecedented strain, with shortages of oxygen supplies, ICU beds, and critical-care staff, which may have contributed to delayed admission and poorer outcomes compared to cohorts in high-resource settings [22]. Differences in healthcare access between public and private facilities, socioeconomic disparities, and a large proportion of unvaccinated individuals during early 2021 could have further exacerbated disease severity and mortality. These region-specific challenges underline the importance of local epidemiological data to inform preparedness and management strategies in future respiratory pandemics.

The prolonged hospital stay and increased severity in diabetics may be explained by several mechanisms, including impaired neutrophil and lymphocyte function, chronic hyperglycemia-induced oxidative stress, and upregulation of ACE2 receptors facilitating viral entry [23][24]. Hypertension is associated with endothelial dysfunction, heightened cytokine activity, and increased thrombotic tendency, factors known to exacerbate COVID-19 [25][26]. These pathophysiological pathways support our findings and the observed radiologic burden in these patient subsets. Another important finding was that patients with other comorbidities such as malignancy and chronic kidney disease also had prolonged hospitalizations, even though their CT severity scores were not significantly elevated. This discrepancy may be due to delayed immune recovery or treatment-related immunosuppression in malignancy and CKD patients [8][27]. Although gender-based differences were not statistically significant, females had marginally higher CT scores and recovery durations, which could be due to estrogen and progesterone modulating immune response [28][29].

This study benefits from a robust sample size for a single-center analysis, radiological correlation using standardized CT scoring, and inclusion of multiple clinically relevant comorbidities. However, several limitations should be acknowledged. The retrospective design may introduce selection and reporting biases. While imaging was performed using standardized protocols, inter-observer variability in CT scoring was not formally quantified. As a single-center study from South India, the generalizability of these findings may be limited to similar demographic settings. HbA1c data were not uniformly available in medical records; hence, correlation of glycemic control with COVID-19 severity could not be performed. Future prospective studies should include HbA1c measurements to better characterize this association. We also lacked data on inflammatory markers (e.g., CRP, IL-6, D-dimer) and vaccination status, both of which are known to influence disease trajectory. Additionally, the subgroup of patients with three or more comorbidities was relatively small; therefore, the corresponding mortality findings should be interpreted with caution and confirmed in larger multicenter studies.

Our findings underscore the critical need for early triage and intensive monitoring of COVID-19 patients with diabetes, hypertension, and multimorbidity. CT severity score showed a stronger prognostic association with mortality than the CORADS score in this cohort. Given the high mortality in patients with >3 comorbidities, these individuals should be prioritized for aggressive care and possibly early therapeutic interventions. Although our dataset could not support derivation of a formal prognostic score due to limited sample size and absence of key biomarkers, future prospective, multicenter studies integrating clinical, radiological, biochemical, and immunologic parameters are needed to expand upon our findings and to develop validated region-specific severity prediction models. Follow-up studies evaluating post-discharge outcomes and long COVID manifestations in high-risk groups would further inform long-term management strategies.

The authors are grateful to the medical and administrative staff at Princess Esra Hospital, Hyderabad, for their support in facilitating data collection during the study period.

Mohammed Shariq Siddiqui contributed to the study conception. All authors contributed to the study design. Mohammed Shariq Siddiqui, Numeera Arjuman, Sadia Sarwath, Ruhina Mirza, Aamena Fathima, Mustafa Kagalwala and Mohammad Faizuddin collected and curated the data. Mohammed Shariq Siddiqui supervised data analysis, and critically revised the manuscript. All authors contributed to manuscript writing and approved the final version for submission.

Table S1. Demographics of non-survivors.

Table S2. Age-wise distribution of non-survivors along with cause of mortality.

Table S3. Relationship between CT severity scores, CORAD scores, demographic variables, and rate of recovery.