Hospitalized COVID‐19 patients with diabetes have an increased risk for pneumonia, intensive care unit requirement, intubation, and death: A cross‐sectional cohort study in Mexico in 2020
Abstract:Background: Diabetes mellitus is a chronic health condition that has been linked with an increased risk of severe illness and mortality from COVID-19. In Mexico, the impact of diabetes on COVID-19 outcomes in hospitalized patients has not been fully quantified. Understanding the increased risk posed by diabetes in this patient population can help healthcare providers better allocate resources and improve patient outcomes. Objective: The objective of this study was to quantify the extent outcomes (pneumonia, in… Show more
“…Community-Acquired Pneumonia (CAP) is an acute lung infection that causes 1.5 million hospitalizations in the United States each year [ 13 ]. A Recent study showed that the hospitalized COVID-19 patients with diabetes have an increased risk for pneumonia, intensive care unit requirement, intubation, and death [ 14 ]. As the COVID pandemic continues to evolve, health systems could see subsequent waves with large numbers of patients admitted to the inpatient setting with COVID-19 pneumonia, consuming valuable resources.…”
Section: Discussionmentioning
confidence: 99%
“…After adjusting for the 11 risk factors, we identified previously [ 3 ], the AUC’s of the PSI-17 and PSI-20 models both increased to above 0.85. The PSI is not comprehensive and other model features could be added, such as diabetes, which is a known risk factor for poor outcomes in COVID-19 [ 14 ] and is not one of the listed co-morbidities in PSI ( S1 Table ). Pneumonia predictive scores have been studied in COVID-19 and PSI was shown as one of the better performing scores for 30-day COVID mortality along with CURB-65, and covid specific scores: 4C and COVID GRAM among 11 different scores for mortality assessment [ 21 ].…”
Background
The Pneumonia Score Index (PSI) was developed to estimate the risk of dying within 30 days of presentation for community-acquired pneumonia patients and is a strong predictor of 30-day mortality after COVID-19. However, three of its required 20 variables (skilled nursing home, altered mental status and pleural effusion) are not discreetly available in the electronic medical record (EMR), resulting in manual chart review for these 3 factors. The goal of this study is to compare a simplified 17-factor version (PSI-17) to the original (denoted PSI-20) in terms of prediction of 30-day mortality in COVID-19.
Methods
In this retrospective cohort study, the hospitalized patients with confirmed SARS-CoV-2 infection between 2/28/20–5/28/20 were identified to compare the predictive performance between PSI-17 and PSI-20. Correlation was assessed between PSI-17 and PSI-20, and logistic regressions were performed for 30-day mortality. The predictive abilities were compared by discrimination, calibration, and overall performance.
Results
Based on 1,138 COVID-19 patients, the correlation between PSI-17 and PSI-20 was 0.95. Univariate logistic regression showed that PSI-17 had performance similar to PSI-20, based on AUC, ICI and Brier Score. After adjusting for confounding variables by multivariable logistic regression, PSI-17 and PSI-20 had AUCs (95% CI) of 0.85 (0.83–0.88) and 0.86 (0.84–0.89), respectively, indicating no significant difference in AUC at significance level of 0.05.
Conclusion
PSI-17 and PSI-20 are equally effective predictors of 30-day mortality in terms of several performance metrics. PSI-17 can be obtained without the manual chart review, which allows for automated risk calculations within an EMR. PSI-17 can be easily obtained and may be a comparable alternative to PSI-20.
“…Community-Acquired Pneumonia (CAP) is an acute lung infection that causes 1.5 million hospitalizations in the United States each year [ 13 ]. A Recent study showed that the hospitalized COVID-19 patients with diabetes have an increased risk for pneumonia, intensive care unit requirement, intubation, and death [ 14 ]. As the COVID pandemic continues to evolve, health systems could see subsequent waves with large numbers of patients admitted to the inpatient setting with COVID-19 pneumonia, consuming valuable resources.…”
Section: Discussionmentioning
confidence: 99%
“…After adjusting for the 11 risk factors, we identified previously [ 3 ], the AUC’s of the PSI-17 and PSI-20 models both increased to above 0.85. The PSI is not comprehensive and other model features could be added, such as diabetes, which is a known risk factor for poor outcomes in COVID-19 [ 14 ] and is not one of the listed co-morbidities in PSI ( S1 Table ). Pneumonia predictive scores have been studied in COVID-19 and PSI was shown as one of the better performing scores for 30-day COVID mortality along with CURB-65, and covid specific scores: 4C and COVID GRAM among 11 different scores for mortality assessment [ 21 ].…”
Background
The Pneumonia Score Index (PSI) was developed to estimate the risk of dying within 30 days of presentation for community-acquired pneumonia patients and is a strong predictor of 30-day mortality after COVID-19. However, three of its required 20 variables (skilled nursing home, altered mental status and pleural effusion) are not discreetly available in the electronic medical record (EMR), resulting in manual chart review for these 3 factors. The goal of this study is to compare a simplified 17-factor version (PSI-17) to the original (denoted PSI-20) in terms of prediction of 30-day mortality in COVID-19.
Methods
In this retrospective cohort study, the hospitalized patients with confirmed SARS-CoV-2 infection between 2/28/20–5/28/20 were identified to compare the predictive performance between PSI-17 and PSI-20. Correlation was assessed between PSI-17 and PSI-20, and logistic regressions were performed for 30-day mortality. The predictive abilities were compared by discrimination, calibration, and overall performance.
Results
Based on 1,138 COVID-19 patients, the correlation between PSI-17 and PSI-20 was 0.95. Univariate logistic regression showed that PSI-17 had performance similar to PSI-20, based on AUC, ICI and Brier Score. After adjusting for confounding variables by multivariable logistic regression, PSI-17 and PSI-20 had AUCs (95% CI) of 0.85 (0.83–0.88) and 0.86 (0.84–0.89), respectively, indicating no significant difference in AUC at significance level of 0.05.
Conclusion
PSI-17 and PSI-20 are equally effective predictors of 30-day mortality in terms of several performance metrics. PSI-17 can be obtained without the manual chart review, which allows for automated risk calculations within an EMR. PSI-17 can be easily obtained and may be a comparable alternative to PSI-20.
“…Khan et al 12 provided a comprehensive overview of the global epidemiology of type 2 diabetes, emphasizing its increasing prevalence and the urgent need for effective management strategies. This perspective is particularly relevant in the context of recent health crises, such as the COVID‐19 pandemic, where Huang et al 13 identified a significantly increased risk of severe outcomes and mortality among hospitalized patients with diabetes in Mexico. These findings emphasize the critical need for effective diabetes management and preventive strategies.…”
Background and AimsWith the global rise in type 2 diabetes, predictive modeling has become crucial for early detection, particularly in populations with low routine medical checkup profiles. This study aimed to develop a predictive model for type 2 diabetes using health check‐up data focusing on clinical details, demographic features, biochemical markers, and diabetes knowledge.MethodsData from 444 Nigerian patients were collected and analysed. We used 80% of this data set for training, and the remaining 20% for testing. Multivariable penalized logistic regression was employed to predict the disease onset, incorporating waist‐hip ratio (WHR), triglycerides (TG), catalase, and atherogenic indices of plasma (AIP).ResultsThe predictive model demonstrated high accuracy, with an area under the curve of 99% (95% CI = 97%–100%) for the training set and 94% (95% CI = 89%–99%) for the test set. Notably, an increase in WHR (adjusted odds ratio [AOR] = 70.35; 95% CI = 10.04–493.1, p‐value < 0.001) and elevated AIP (AOR = 4.55; 95% CI = 1.48–13.95, p‐value = 0.008) levels were significantly associated with a higher risk of type 2 diabetes, while higher catalase levels (AOR = 0.33; 95% CI = 0.22–0.49, p < 0.001) correlated with a decreased risk. In contrast, TG levels (AOR = 1.04; 95% CI = 0.40–2.71, p‐value = 0.94) were not associated with the disease.ConclusionThis study emphasizes the importance of using distinct clinical and biochemical markers for early type 2 diabetes detection in Nigeria, reflecting global trends in diabetes modeling, and highlighting the need for context‐specific methods. The development of a web application based on these results aims to facilitate the early identification of individuals at risk, potentially reducing health complications, and improving diabetes management strategies in diverse settings.
“…To address these gaps and limitations, the present study focuses on the Mexican population and seeks to investigate the relationship between diabetes and COVID‐19 mortality. Patients that contract COVID‐19 have a range of symptoms ranging from asymptomatic to death 27 . Previous studies have looked at mortality in COVID‐19, but have not looked at the specific subset of individuals in Mexico with comorbid diabetes with the level of statistical analyses utilizing both Cox proportional hazards and restricted mean survival time 11,28,29 .…”
Section: Introductionmentioning
confidence: 99%
“…Patients that contract COVID-19 have a range of symptoms ranging from asymptomatic to death. 27 Previous studies have looked at mortality in COVID-19, but have not looked at the specific subset of individuals in Mexico with comorbid diabetes with the level of statistical analyses utilizing both Cox proportional hazards and restricted mean survival time. 11,28,29…”
Background and Aim
The COVID‐19 disease course can be thought of as a function of prior risk factors consisting of comorbidities and outcomes. Survival analysis data for diabetic patients with COVID‐19 from an up to date and representative sample can increase efficiency in resource allocation. The study aimed to quantify mortality in Mexico for individuals with diabetes in the setting of COVID‐19 hospitalization.
Methods
This retrospective cohort study utilized publicly available data from the Mexican Federal Government, covering the period from April 14, 2020, to December 20, 2020 (last accessed). Survival analysis techniques were applied, including Kaplan–Meier curves to estimate survival probabilities, log‐rank tests to compare survival between groups, Cox proportional hazard models to assess the association between diabetes and mortality risk, and restricted mean survival time (RMST) analyses to measure the average survival time.
Results
A total of 402,388 adults age greater than 18 with COVID‐19 were used in the analysis. Mean age = 16.16 (SD = 15.55), 214,161 males (53%). Twenty‐day Kaplan–Meier estimates of mortality were 32% for COVID‐19 patients with diabetes and 10.2% for those without diabetes with log‐rank p < 0.01. Univariable analysis showed increased mortality in diabetic patients (hazard ratio [HR]: 3.61, 95% confidence interval [CI]: 3.54–3.67, p < 0.01) showing a 254% increase in death. After controlling for confounding variables, multivariate analysis continued to show increased mortality in diabetics (HR: 1.37, 95% CI: 1.29–1.44, p < 0.01) indicating a 37% increase in death. Multivariable RMST at Day 20 showed in Mexico, hospitalized COVID‐19 patients were associated with less mean survival time by 2.01 days (p < 0.01) and a 10% increased mortality (p < 0.01).
Conclusions
In the present analysis, COVID‐19 patients with diabetes in Mexico had shorter survival times. Further interventions aimed at improving comorbidities in the population, particularly in individuals with diabetes, may contribute to better outcomes in COVID‐19 patients.
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