Risk factors associated with the mortality of COVID‑19 in patients with type 2 diabetes mellitus
- Authors:
- Published online on: September 3, 2024 https://doi.org/10.3892/wasj.2024.277
- Article Number: 62
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Copyright : © Carbajal et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY 4.0].
Abstract
Introduction
The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to devastating consequences worldwide. Until the end of October, 2023, >771 million individuals were infected and >6 million succumbed to the disease globally (1). The COVID-19 pandemic had an even more detrimental effect in countries with limited resources. Therefore, Peru was one of the countries with the highest COVID-19-related mortality worldwide, with >220,000 related deaths (2).
The most common risk factors associated with COVID-19 severity and mortality include an older age, lower oxygen saturations and the presence of comorbidities, including diabetes. Diabetes is a prevalent comorbidity among patients with COVID-19 and is associated with an increased mortality rate in these patients. More particularly, patients with diabetes mellitus (DM) can have a 2-3-fold higher mortality rate compared with those without the condition (3-5). To date, several factors are known to be associated with worse outcomes in patients with COVID-19 and type 2 DM (T2DM) (6,7), including angiotensin converting enzyme 2 upregulation, poor glycemic control, higher levels of inflammatory markers, as well as several conditions, such as obesity, hypertension, cardiovascular diseases and dyslipidemia (8,9).
Although several studies on the risk factors associated with high mortality rates of patients with COVID-19 and T2DM have been conducted, relevant studies form Latin American countries are lacking. A better understanding of these risk factors in resource-constrained hospitals could be significant for preparedness against global emergencies, such as the recent COVID-19 pandemic. Therefore, in the present study, the data from patients with COVID-19 and T2DM hospitalized in the Hospital Regional de Ica, Ica, Peru were collected to explore the clinical features and the risk factors associated with in-hospital mortality.
Patients and methods
Study design and participants
The present cross-sectional study included the medical records of adult patients (≥18 years of age) with T2DM and COVID-19 hospitalized at the Hospital Regional de Ica (Ica, Peru) between April, 2020 and February, 2021. All patients were diagnosed with COVID-19 upon hospital admission based on serological or antigenic tests of nasopharyngeal samples, according to the World Health Organization interim guidelines (10). Patients were diagnosed with T2DM based on their medical records (11) or random serum glucose levels of ≥200 mg/dl (newly diagnosed with T2DM) (12,13). It was mandatory that the medical records of the included patients included information of the analyzed variables, such as demographic, history, clinical, laboratory and treatment data. Pregnant women, and patients with no information about the outcome variable (deceased or discharged) were excluded from the study.
Data collection and variables
The exposure and outcome data for each patient were extracted from their medical records at the time of admission. The data were reviewed by one physician (LGSH) and one researcher (CBS). Disagreements between the two lead reviewers were resolved by a third researcher (JC). The medical records included the following data: The demographic characteristics of the patients, such as age and sex; their medical history, including smoking status, alcoholism, dyslipidemia, obesity, hypertension, cerebrovascular diseases, cancer, HIV, immunosuppressive disease, chronic kidney disease (CKD), hemodialysis and asthma; their vital signs, such as respiratory rate, heart rate, systolic blood pressure (SBP) and diastolic blood pressure (DBP); signs and symptoms, including fever, cough, sore throat, general malaise, headache, tachypnea, dyspnea, anosmia, dyspnea, dysgeusia, lung crackles, diarrhea, vomiting, asthenia, abdominal pain, weight loss, polyuria, polydipsia, polyphagia and sensory; other laboratory blood findings, such as white blood cell (WBC) count, neutrophil count, lymphocyte count, platelet count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), hemoglobin levels, hematocrit, serum creatinine levels, blood urea nitrogen (BUN) levels, glucose levels, pH, anion Gap, and sodium, potassium, chlorine and calcium levels; and arterial blood gas findings, such as oxygen saturation (SaO2), fraction of inspired oxygen (FiO2), PaO2/FiO2 ratio, partial pressure of oxygen (PaO2), partial pressure of carbon dioxide (PCO2) and bicarbonate (HCO3). These variables and their categorization were considered according to previous studies (6,7). The outcome variable was in-hospital COVID-19-related mortality, which was assessed by the death certificate included in the medical records.
Ethical aspects
Informed consents from patients were not necessary, since the study solely included retrospective data from their clinical records. The personal information of patients was anonymized prior to storage in the local hospital database. The present study was conducted according to the tenet of the Declaration of Helsinki and was approved by The Research Ethics Committees of the Hospital Regional de Ica (approval no. RD N 561-2021-HRI/DE) and the Universidad Privada San Juan Bautista (approval no. 429-2022-CIEI-UPSJB).
Statistical analysis
Categorical and continuous variables are expressed as frequencies with percentages (n, %) and medians with interquartile range (IQR), respectively. Where appropriate, a Pearson's Chi-squared (χ² test), Fisher's exact or Wilcoxon-Mann-Whitney U tests were employed for the bivariate analysis (survivors vs non-survivors). The risk factors associated with in-hospital COVID-19-related mortality were assessed using univariate and multivariate logistic regression models, and the unadjusted and adjusted odds ratios (OR) with their respective 95% confidence intervals (95% CIs) were determined. Self-reported and low-frequency variables and variables without significant differences between groups were excluded from the univariate analysis. To prevent biased estimates caused by overfitting and multicollinearity among predictor variables in the multivariate logistic regression analysis, the variables were selected based on scientific evidence, significance from univariate analysis (Wald test <0.05), the stepwise modeling method and checking for collinearity. Therefore, correlation analysis was performed for numerical variables and the Spearman's correlation coefficients (Rho) were estimated. Additionally, the generalized variance inflation factor (GVIF) and adjusted generalized standard error inflation factor (aGSIF) values were determined. Principal component analysis (PCA) was carried out to reduce dimensionality and visualize numerical variables (vital signs, laboratory findings and arterial blood gas findings) between the outcome groups. All statistical analyses were performed using R Statistical Software (v4.3.2; R Core Team 2023). The R collection tidyverse v2.0.0 package was used to prepare data (14). Statistical analyses were performed using the gtsummary package v1.7.2(15), while the logistic regression models and PCA were evaluated using the performance package v0.10.8(16) and factoextra package v1.0.7(17), respectively. A P-value <0.05 was considered to indicate a statistically significant difference between groups.
Results
Demographic characteristics
In the present study, a total of 287 adult patient with T2DM and COVID-19, who were admitted to the Hospital Regional de Ica, Peru between April, 2020 and February, 2021, were included. Among these patients, 132 (46%) succumbed during hospitalization (Fig. 1). The median age of the patients was 60 years (IQR, 51-68) and 189 (65.9%) were males (Table I).
Comorbidities and symptoms
The most frequent comorbidities identified were hypertension, followed by obesity and CKD. Approximately half of the patients had abnormal respiratory or heart rates, while the majority of them (>80%) displayed normal blood pressure (Table I). Furthermore, the most common symptoms reported upon admission were dyspnea (84.3%), dry cough (76.0%), general malaise (71.8%) and fever (59.2%). The frequency of the remaining symptoms was <50% (Table I).
Blood and biochemical tests
Blood tests revealed leukocytosis in 146 (54.3%) patients, neutrophilia in 196 (68.3%) and lymphopenia in 146 (50.9%) patients. The majority of the patients (92.7%) had a platelet count of >125x109/l, while ~80% of them had normal hemoglobin and hematocrit levels (Table II). In addition, the biochemical tests revealed that the serum creatinine and BUN levels differed significantly between the two groups of patients. Among the 287 patients included, 278 (96.9%) received antibiotics, 232 (80.8%) anticoagulants, 208 (72.5%) corticosteroids, 125 (43.6%) hydroxychloroquine, 115 (44.1%) pronation and 56 (19.5%) ivermectin (Table III). The median time from the onset of illness to either patient discharge or death was 7 days (IQR, 5-10; Table I).
Differences between groups
Data analysis demonstrated that the non-survivors were significantly older than the survivors (P<0.001). In addition, hypertension (P=0.003), leukocytosis (P<0.001), neutrophilia (P<0.001) and lymphopenia (P=0.010) were significantly more common among the non-survivors compared with the survivors (Table I and II). Furthermore, compared with the survivors, the non-survivors exhibited higher levels of serum creatinine and BUN (both P<0.001; Table II). The median FiO2 value in patients who underwent O2 therapy was higher in the non-survivors than in the survivors (80 vs. 38%), while the opposite was recorded for the PaO2/FiO2 ratio, PaO2 and PCO2. In terms of therapy, the administration of anticoagulants, corticosteroids and antimalarials were more common among non-survivors (Table III).
Logistic regression analysis
The univariate logistic regression analysis (unadjusted) revealed that the odds of in-hospital mortality were higher in patients aged >60 years and in those with hypertension, obesity, tachypnea, dyspnea, lung crackles, an increased heart rate, increased SBP, leukocytosis, neutrophilia, anemia and increased serum creatinine, and BUN levels. The decreased levels of gas parameters (SaO2< 94%, PaO2<60, PaO2/FiO2 ratio ≤200 and HCO3 < 21) and the administration of corticosteroids were also associated with increased mortality. In the adjusted multivariate regression analysis, a total of 217 patients were included, and in particular, 114 in the survivor group and 103 in the non-survivor group. For this analysis, we excluded highly correlated variables (Rho >0.6) were excluded (Fig. S1). Thus, WBC, diastolic blood pressure, MCV, hematocrit, PaO2, FiO2 and serum creatinine were excluded from the analysis. The analysis revealed that the presence of dyspnea (OR, 7.68; 95% CI, 1.77-40.89; P=0.010), neutrophilia (OR, 8.17; 95% CI, 3.16-23.62; P<0.001), anemia (OR, 3.24; 95% CI, 1.30-8.56; P<0.014) and PaO2 <60 mmHg (OR, 7.46; 95% CI, 2.83-21.86; P<0.001) were associated with increased odds of mortality. By contrast, lung crackles were associated with reduced mortality rates (OR, 0.21; 95% CI, 0.08-0.55; P<0.002; Table IV). The aGSIF value of the adjusted model was <1.4, thus indicating that the control for highly associated variables that provided the same information was effective in avoiding collinearity.
PCA
The biplots of PCA revealed that the PaO2/FiO2 ratio, BUN levels, hematocrit, hemoglobin, serum creatinine, FiO2, potassium, SaO2, neutrophils and PaO2 were the variables with the highest representation and contribution in PC1 and PC2. In addition, chlorine, calcium, sodium, age, HCO3, WBC, PCO2 and neutrophils were the variables with the highest representation and contribution in PC3 and PC4. PC1 and PC2 contributed to 23.3% of total variance, while PC3 and PC4 to 17.8%. For these principal components, no clear separation between the survivors and non-survivors was observed (Fig. 2). However, based on the results of the bivariate analysis and PCA, in the non-survivor group, the high values of FiO2, BUN, serum creatinine, neutrophils and PaO2/FiO2 ratio considerably contributed to PC1 and PC2 (Fig. 2A), while WBC, neutrophils, age and PCO2 to PC3 and PC4 (Fig. 2B).
Discussion
Once diagnosed with COVID-19, patients with diabetes are more likely to develop severe illness and can be at higher risk of mortality (18). The results of the present study reported a mortality rate of 46%, which was higher compared with that reported in other regions. Additionally, dyspnea, the absence of lung crackles, neutrophilia, anemia and PaO2 of <60 mmHg were identified as significant risk factors for COVID-19-related mortality in patients diagnosed with T2DM. Although the PCA results revealed a substantial contribution of multiple numerical variables, neutrophilia remained constant in non-survivors.
The mortality rate in the present study (46%) was among the highest reported in Peruvian populations, which ranged between 33-50% (19-21). A previous meta-analysis, including 42 studies from several regions worldwide, revealed that the prevalence of COVID-19-related mortality ranged from 3.14 (95% CI, 2.34-4.14%) to 61.51% (95% CI, 55.02-67.71%) (18). The mortality rate was higher in Latin American countries. In fact, regionally, Andean Latin America had the highest mortality rate compared with other regions worldwide (22).
Herein, the univariate analysis revealed that an advanced age, hypertension, and obesity were associated with an increased risk of COVID-19-related mortality in patients with T2DM. Consistently, the aforementioned factors have been extensively reported by previous studies as risk factors for COVID-19-related mortality (7,18,23). In hospitals in Peru, an older age, low oxygen saturation and different drugs, such as ivermectin, hydroxychloroquine and corticosteroids, were all associated with COVID-19-related mortality. However, the comorbidities were not significant (19,21,24). Another study including patients from three hospitals of Peru, demonstrated that patients aged ≥60 years, those with low oxygen saturation and with two or more comorbidities were at an increased risk of COVID-19-related mortality (25). Similar findings were reported in other populations, including patients with COVID-19, T1DM and T2DM (26). In the present study, an age >60 years, hypertension, and obesity did not reach significance in the multivariate analysis. This finding may be due to the fact that the patients included in the present study were already older when they were diagnosed with T2DM or other diseases. Although herein, hepatic damage was not assessed, previous studies have demonstrated that chronic liver disease and hepatic fibrosis are associated with an increased risk of in-hospital COVID-19-related mortality (27,28).
Dyspnea was the most prevalent symptom in the present study, and it was associated with COVID-19-related mortality in the multivariate regression analysis. Fever, dry cough, fatigue and dyspnea are the most common symptoms of COVID-19 (7,29), with dyspnea being present in most severe cases (29). Furthermore, a previous systematic review and meta-analysis revealed that dyspnea, fatigue, myalgia and a low respiratory rate were the optimal predictors of mortality (7). Other studies have also reported that dyspnea is an independent risk factor associated with COVID-19-associated mortality (30-32). In addition, in patients with diabetes, dyspnea upon admission was associated with a reduced risk of hospital discharge and an increased mortality at day 28(6). Aksel et al (33) demonstrated that dyspnea was an early predictor of mortality in patients with moderate or severe SARS-CoV-2 infection (hazard ratio, 4.26; 95% CI, 1.19-15.28). Lung damage caused by inflammatory responses that compromise gas exchange could explain dyspnea as an early predictor of mortality. The early recognition of this symptom could help to recognize patients who are at high risk.
The utility of vital signs as predictors of COVID-19-related mortality is inconclusive. In the present study, an elevated heart rate (≥100) upon admission was identified as a risk factor associated with an increased mortality. In several studies, an increased heart rate was found to be significantly more common among non-survivors of COVID-19 (34,35). In previous a study including 8,770 patients with COVID-19 from 53 health centers in New York City found that a higher heart rate was associated with an increased risk of mortality (OR, 1.00; 95% CI, 1.00-1.01), thus suggesting that the higher heart rate and other clinical risk factors could serve as effective predictors of COVID-19-related mortality, regardless of the patient's level of consciousness (34). Notably, a higher heart rate variability could predict improved survival in elderly patients with COVID-19(35).
Furthermore, the results of the present study revealed that neutrophilia and anemia were also independent predictors of COVID-19-related mortality in patients with diabetes. An increased neutrophil count (≥8.0x109/l), hemoglobin levels of <12.5 g/dl, creatinine levels of ≥1.36 mg/dl, an age ≥65 years and CKD have been shown to be associated with the need for intensive care unit (ICU) admission, invasive mechanical ventilation and in-hospital mortality (36). Additionally, three previous meta-analyses of early studies revealed that an increased neutrophil count, and decreased lymphocyte and platelet counts could increase the risk of progression and mortality in patients with COVID-19; however, there was high heterogeneity and publication bias (7,37,38). During SARS-CoV-2 infection, neutrophils release a web-like structures to kill viruses. However, neutrophil overactivation and the subsequent enhanced formation of neutrophil extracellular traps can also lead to lung injury and thrombosis (39). Emerging evidence has also indicated that the neutrophil to lymphocyte ratio (NLR), a known indicator of systemic inflammation, is a valuable prognostic marker in patients with COVID-19 with or without diabetes (40,41). Therefore, a study including 201 patients with COVID-19 and T2DM demonstrated that a NLR of ≥7.36 was an independent predictor of disease severity (42). Although a previous study indicated that renal impairment and respiratory frequency were associated with higher COVID-19-related mortality (43), the results of the present study demonstrated that low CDK frequencies (n=10 patients) and respiratory frequency were significant predictors in the descriptive analysis and PCA, but not in the univariate logistic regression analysis.
Herein, in terms of arterial blood gas parameters, an increased FiO2, and a decreased PaO2 and PaO2/FiO2 ratio were associated with COVID-19-related mortality. However, only PaO2 remained significant in the multivariate analysis. The PaO2/FiO2 ratio reflects the level of respiratory failure and it is an oxygenation index commonly used in the management of respiratory distress syndrome (44). Therefore, it is considered as a significant predictor of COVID-19 outcomes (45-47). Previously, two different studies demonstrated that the decreased PaO2/FiO2 ratio was an independent risk factor associated with mortality in patients with COVID-19 in the ICU (45,46). One of these studies further suggested that a 10% increase in FiO2 at admission was also associated with mortality in patients admitted to the ICU (45). In the present study, the high prevalence (53%) of patients with moderate-to-severe respiratory failure (PaO2/FiO2 ratio of ≤200) could explain why statistically significance (of of PaO2/FiO2 ratio) was not reached at the multivariate analysis. On the other hand, it has been argued that hypoxemia (SaO2 <90%) is a less useful predictor for mortality in patients with COVID-19, since it could hide hypoxia (48,49). It has been reported that the PaO2/FiO2 ratio and SaO2 are good predictors of COVID-19-related mortality. When combined with the 12-field lung ultrasound score, they could be even more useful in predicting mortality and clinical progression of patients with COVID-19(50).
The use of drugs against COVID-19 without sufficient evidence was promoted as a national policy during the so called ‘first wave’ of the COVID-19 pandemic in Peru. Subsequent studies revealed the detrimental effects of this policy. A previous study conducted in hospitals of the Seguro Social de Salud del Perú (EsSalud) revealed no benefits of hydroxychloroquine, ivermectin or azithromycin administration in preventing all-cause mortality in hospitalized patients with COVID-19. In fact, treatment with hydroxychloroquine in combination with azithromycin showed an increased risk of all-cause death (51). In a previous phase III clinical trial among Peruvian healthcare workers, hydroxychloroquine was tested as preventive drug against SARS-CoV-2 infection. However, no differences in the incidence of SARS-CoV-2 infection were recorded (52). Furthermore, another study conducted in a Peruvian hospital found that ivermectin and azithromycin were associated with a higher risk of COVID-19-related mortality (19). Herein, the analysis revealed that the drugs administrated were not associated with an increased mortality. In the univariate analysis, only corticosteroids were identified as significant predictors. A study demonstrated that low doses of corticosteroids could be beneficial in patients who were subjected to respiratory support (53). In addition, two observational studies conducted in Perú found that low or low-to-moderate doses of corticosteroids were associated with reduced mortality (19). In patients with or without diabetes, corticosteroids could further lead to severe hyperglycemia, and life-threatening ketoacidosis and hyperglycemic hyperosmolar state (54).
The present study has a cross-sectional design; therefore, it could not examine a cause-effect association between the factors assessed and in-hospital mortality. Additionally, the present study has other limitations. According to the American Diabetes Association, diabetes can be diagnosed based on the random glucose levels in combination with the presence of classic hyperglycemic symptoms/crises. For this subset of patients, herein, the data on hyperglycemic symptoms were lacking. However, it has been suggested that random plasma glucose levels can exhibit a good diagnostic value in predicting diabetes, even when glucose levels are well below the conventional ‘diagnostic’ range (12). Furthermore, the plasma glucose levels were measured upon admission. Therefore, corticosteroids, if administered, could not have any effect on glucose levels. Secondly, working conditions in a pandemic context led to resource constraints and therefore the levels of hemoglobin A1c (HbA1c) were not measured. The lack of measurements of glycemic control could constrain the findings of the present study, as elevated HbA1c could be associated with worse outcomes and poor immunological response to COVID-19 vaccines (55.) Thirdly, the majority of patients included in the present study were diagnosed with COVID-19 based on serological tests. Fourth, a control group, namely patients without T2DM, was lacking in the present study. Finally, the sample size was not sufficient to draw firm conclusions. Despite these limitations, the present study provided notable findings, while performing mixed statistical analyses (logistic regression and PCA) on patients with T2DM and COVID-19 from Peru. These issues had been poorly reported in Peru.
In conclusion, the present study reported that the risk of mortality was increased in patients hospitalized with COVID-19 and T2DM at the Hospital Regional de Ica. Several factors, including dyspnea, neutrophilia, anemia and low PaO2 levels could be independently associated with an increased risk of in-hospital mortality in patients with COVID-19 and T2DM. Overall, the results of the present study provided data on risk factors associated with COVID-19-related mortality, thus assisting the development of novel treatment strategies for patients with COVID-19 in Peru.
Supplementary Material
Spearman's correlation matrix. SBP, systolic blood pressure; DBP, diastolic blood pressure; MCV, median corpuscular volume; MCH, median corpuscular hemoglobin; BUN, blood urea nitrogen; SaO2, oxygen saturation; FiO2, fraction of inspired oxygen; PaO2, partial pressure of oxygen; PCO2, partial pressure of carbon dioxide; HCO3, bicarbonate.
Acknowledgements
The authors would like to thank the Statistics Department of the Hospital Regional de Ica, Ica, Peru, for facilitating the data collection for the present study.
Funding
Funding: No funding was received.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
JC was involved in the conceptualization of the study, in the study methodology, validation, investigation and data curation, as well as in the writing of the original draft, and in the writing, reviewing and editing of the manuscript. CBS was involved in the conceptualization of the study, in the study methodology, data validation, formal analysis, investigation, provision of resources, data curation, as well as in the writing of the original draft, and in the writing, reviewing and editing of the manuscript, and in the preparation of the figures. LJUC was involved in the study methodology, data validation, formal analysis, investigation, provision of resources, data curation, and in the writing of the original draft of the manuscript. GGS was involved in the study methodology, data validation, investigation, provision of resources, data curation, and in the writing of the original draft of the manuscript. SSCA was involved in the study methodology, data validation, investigation, provision of resources, data curation, and in the writing of the original draft of the manuscript. MJL was involved in the study methodology, data validation, investigation, provision of resources, data curation, and in the writing of the original draft of the manuscript. AR was involved in the study methodology, data validation, investigation, data curation, and in the writing of the original draft of the manuscript. JA was involved in the conceptualization of the study, in the study methodology, data validation, investigation, provision of resources, data curation, and in the writing of the original draft of the manuscript. JAP was involved in the conceptualization of the study, in the study methodology, validation, formal analysis, investigation, in the writing, reviewing and editing of the manuscript, in study supervision and in project administration. KJP was involved in the conceptualization of the study, in the study methodology, validation, formal analysis, investigation, data curation, in the writing of the original draft of the manuscript, in the writing, reviewing and editing of the manuscript, in the preparation of the figures, and in project administration. LGSH was involved in the conceptualization of the study, in the study methodology, data validation, formal analysis, investigation, resources, data curation, in the writing of the original draft of the manuscript, reviewing and editing of the manuscript, in study supervision and in project administration. All authors have read and approved the final manuscript. JC and LGSH confirm the authenticity of all the raw data.
Ethics approval and consent to participate
Informed consents from patients were not necessary, since the study solely included retrospective data from their clinical records. The personal information of patients was anonymized prior to storage in the local hospital database. The present study was conducted according to the tenet of the Declaration of Helsinki and was approved by The Research Ethics Committees of the Hospital Regional de Ica (approval no. RD N 561-2021-HRI/DE) and the Universidad Privada San Juan Bautista (approval no. 429-2022-CIEI-UPSJB).
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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