scholarly journals Diabetes mellitus as a risk factor for poor early outcomes in patients hospitalized with COVID-19

2020 ◽  
Author(s):  
Jacqueline Seiglie ◽  
Jesse Platt ◽  
Sara Jane Cromer ◽  
Bridget Bunda ◽  
Andrea S. Foulkes ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Mechanical Ventilation ◽  
Logistic Regression ◽  
Risk Factor ◽  
Regression Models ◽  
Massachusetts General Hospital ◽  
Hospitalized Patients ◽  
Patient Centered ◽  
Logistic Regression Models ◽  
Icu Admission

<b>OBJECTIVE</b> <p>Diabetes mellitus and obesity are highly prevalent among hospitalized patients with COVID-19, but little is known about their contributions to early COVID-19 outcomes. We tested the hypothesis that diabetes is a risk factor for poor early outcomes, after adjustment for obesity, among a cohort of patients hospitalized with COVID-19. <b></b></p> <p><b> </b></p> <p><b>RESEARCH DESIGN AND METHODS </b>We used data from the Massachusetts General Hospital (MGH) COVID-19 Data Registry of patients hospitalized with COVID-19 between March 11, 2020 and April 30, 2020. Primary outcomes were admission to the intensive care unit (ICU), need for mechanical ventilation, and death within 14 days of presentation to care. Logistic regression models were adjusted for demographic characteristics, obesity, and relevant comorbidities. </p> <p> </p> <p><b>RESULTS</b></p> <p>Among 450 patients, 178 (39.6%) had diabetes, mostly type 2 diabetes. A higher proportion of patients with diabetes were admitted to the ICU (42.1% vs. 29.8%, p=0.007), required mechanical ventilation (37.1% vs. 23.2%, p=0.001), and died (15.9% vs. 7.9%, p=0.009), compared with patients without diabetes. In multivariable logistic regression models, diabetes was associated with greater odds of ICU admission (OR 1.59 [95% CI 1.01-2.52]), mechanical ventilation (1.97 [1.21-3.20]), and death (2.02 [1.01-4.03]) at 14-days. Obesity was associated with higher odds of ICU admission (2.16 [1.20-3.88]) and mechanical ventilation (2.13 [1.14-4.00]) but not with death. </p> <p> </p> <p><b>CONCLUSIONS</b></p> <p>Among hospitalized patients with COVID-19, diabetes was associated with poor early outcomes, after adjusting for obesity. These findings can help inform patient-centered care decision making for people with diabetes at risk of COVID-19.</p>

scholarly journals Diabetes mellitus as a risk factor for poor early outcomes in patients hospitalized with COVID-19

2020 ◽  
Author(s):  
Jacqueline Seiglie ◽  
Jesse Platt ◽  
Sara Jane Cromer ◽  
Bridget Bunda ◽  
Andrea S. Foulkes ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Mechanical Ventilation ◽  
Logistic Regression ◽  
Risk Factor ◽  
Regression Models ◽  
Massachusetts General Hospital ◽  
Hospitalized Patients ◽  
Patient Centered ◽  
Logistic Regression Models ◽  
Icu Admission

<b>OBJECTIVE</b> <p>Diabetes mellitus and obesity are highly prevalent among hospitalized patients with COVID-19, but little is known about their contributions to early COVID-19 outcomes. We tested the hypothesis that diabetes is a risk factor for poor early outcomes, after adjustment for obesity, among a cohort of patients hospitalized with COVID-19. <b></b></p> <p><b> </b></p> <p><b>RESEARCH DESIGN AND METHODS </b>We used data from the Massachusetts General Hospital (MGH) COVID-19 Data Registry of patients hospitalized with COVID-19 between March 11, 2020 and April 30, 2020. Primary outcomes were admission to the intensive care unit (ICU), need for mechanical ventilation, and death within 14 days of presentation to care. Logistic regression models were adjusted for demographic characteristics, obesity, and relevant comorbidities. </p> <p> </p> <p><b>RESULTS</b></p> <p>Among 450 patients, 178 (39.6%) had diabetes, mostly type 2 diabetes. A higher proportion of patients with diabetes were admitted to the ICU (42.1% vs. 29.8%, p=0.007), required mechanical ventilation (37.1% vs. 23.2%, p=0.001), and died (15.9% vs. 7.9%, p=0.009), compared with patients without diabetes. In multivariable logistic regression models, diabetes was associated with greater odds of ICU admission (OR 1.59 [95% CI 1.01-2.52]), mechanical ventilation (1.97 [1.21-3.20]), and death (2.02 [1.01-4.03]) at 14-days. Obesity was associated with higher odds of ICU admission (2.16 [1.20-3.88]) and mechanical ventilation (2.13 [1.14-4.00]) but not with death. </p> <p> </p> <p><b>CONCLUSIONS</b></p> <p>Among hospitalized patients with COVID-19, diabetes was associated with poor early outcomes, after adjusting for obesity. These findings can help inform patient-centered care decision making for people with diabetes at risk of COVID-19.</p>

Association Between Opioid Use and Diabetes in Patients With Ankle Fracture Repair

2021 ◽  
pp. 107110072110581
Author(s):  
Wenye Song ◽  
Naohiro Shibuya ◽  
Daniel C. Jupiter
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Ankle Fracture ◽  
Regression Models ◽  
Fracture Repair ◽  
Opioid Use ◽  
Data Set ◽  
Opioid Prescription ◽  
Logistic Regression Models ◽  
Patients With Diabetes

Background: Ankle fractures in patients with diabetes mellitus have long been recognized as a challenge to practicing clinicians. Ankle fracture patients with diabetes may experience prolonged healing, higher risk of hardware failure, an increased risk of wound dehiscence and infection, and higher pain scores pre- and postoperatively, compared to patients without diabetes. However, the duration of opioid use among this patient cohort has not been previously evaluated. The purpose of this study is to retrospectively compare the time span of opioid utilization between ankle fracture patients with and without diabetes mellitus. Methods: We conducted a retrospective cohort study using our institution’s TriNetX database. A total of 640 ankle fracture patients were included in the analysis, of whom 73 had diabetes. All dates of opioid use for each patient were extracted from the data set, including the first and last date of opioid prescription. Descriptive analysis and logistic regression models were employed to explore the differences in opioid use between patients with and without diabetes after ankle fracture repair. A 2-tailed P value of .05 was set as the threshold for statistical significance. Results: Logistic regression models revealed that patients with diabetes are less likely to stop using opioids within 90 days, or within 180 days, after repair compared to patients without diabetes. Female sex, neuropathy, and prefracture opioid use are also associated with prolonged opioid use after ankle fracture repair. Conclusion: In our study cohort, ankle fracture patients with diabetes were more likely to require prolonged opioid use after fracture repair. Level of Evidence: Level III, prognostic.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Mengyang Tang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.Methods: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Wenqian Lu ◽  
Mingjuan Luo ◽  
Xiangnan Fang ◽  
Rong Zhang ◽  
Shanshan Li ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Regression Models ◽  
Second Trimester ◽  
Third Trimester ◽  
Logistic Regression Models ◽  
The Third ◽  
Clinical Indices

Abstract Background Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. Methods Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. Results This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. Conclusions Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

scholarly journals Inflammation and mortality in COVID-19 hospitalized patients with and without type 2 diabetes

2022 ◽  
Author(s):  
Jia Guo ◽  
Wen-Hsuan W Lin ◽  
Jason E Zucker ◽  
Renu Nandakumar ◽  
Anne-Catrin Uhlemann ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Logistic Regression ◽  
Inflammatory Markers ◽  
Regression Models ◽  
Serum Levels ◽  
Hospitalized Patients ◽  
Logistic Regression Models ◽  
Diabetes Status ◽  
The Relationship

Abstract Background The aim of this study was to determine the relationship of inflammation with mortality in COVID-19 hospitalized patients and to assess if the relationship differed by strata of type 2 diabetes status. We hypothesized that the association of inflammation with mortality was different by type 2 diabetes status. Methods A case-control (died-survived) study of 538 COVID-19 hospitalized patients, stratified by diabetes status, was conducted at Columbia University Irving Medical Center. We quantified the levels of eight cytokines and chemokines in serum, including interferon(IFN)-α2, IFN-γ, Interleukin(IL)-1α, IL-1β, IL-6, IL-8/CXCL8, IFNγ-induced protein 10 (IP10)/CXCL10 and tumor necrosis factor α (TNF-α) using immunoassays. Logistic regression models were used to model the relationships of log-transformed inflammatory markers (or their principal components) and mortality. Results In multiple logistic regression models, higher serum levels of IL-6 (adjusted odds ratio (aOR):1.74, 95% confidence intervals (CI): (1.48, 2.06)), IL-8 (aOR: 1.75 (1.41, 2.19)) and IP10 (aOR: 1.36 (1.24, 1.51)), were significantly associated with mortality. This association was also seen in second principal component (PC) with loadings reflecting similarities among these three markers (aOR: 1.88 (1.54-2.31)). Significant positive association of these same inflammatory markers with mortality was also observed within each strata of diabetes. Conclusions We show that mortality in COVID19 patients is associated with elevated serum levels of innate inflammatory cytokine IL-6 and inflammatory chemokines IL-8 and IP10. This relationship is consistent across strata of diabetes, suggesting interventions targeting these innate immune pathways could potentially also benefit patients with diabetes.

GENDER DIFFERENCES IN THE ASSOCIATION BETWEEN MARITAL STATUS AND HYPERTENSION IN GHANA

2018 ◽  
Vol 51 (3) ◽  
pp. 313-334 ◽  
Author(s):  
Derek Anamaale Tuoyire ◽  
Harold Ayetey
Keyword(s):  
Logistic Regression ◽  
Risk Factor ◽  
Marital Status ◽  
Regression Models ◽  
Cardiovascular Health ◽  
Binary Logistic Regression ◽  
Sub Saharan Africa ◽  
Wealth Status ◽  
Social Characteristic ◽  
Logistic Regression Models

SummaryHypertension is a significant contributor to the global burden of cardiovascular and related target organ diseases such as heart failure, coronary heart disease, stroke and kidney failure, and their associated premature morbidity, mortality and disability. Marital status is an important social characteristic known to predict a range of health outcomes including cardiovascular disease. However, little is known about its impact on hypertension in sub-Saharan Africa. This study explored the relationship between marital status and hypertension among women and men in Ghana. Drawing on data from the 2014 Ghana Demographic and Health Survey (GDHS), descriptive statistics and binary logistic regression models were used to analyse the link between marital status and hypertension. About 13% of women aged 15–49 and 15% of men aged 15–59 were found to be hypertensive. After controlling for lifestyle and socio-demographic covariates, the logistic regression models showed significantly higher odds of hypertension for married (OR=2.14, 95% CI=1.30–3.53), cohabiting (OR=1.94, 95% CI=1.16–3.23) and previously married (OR=2.23, 95% CI=1.29–3.84) women. In contrast, no significant association was found between any of the marital status cohorts and hypertension for men. Other significant predictors of hypertension were age, body mass index and wealth status. The results demonstrate that marital status is an independent risk factor for hypertension in Ghana for women, rather than men. This could have immediate and far-reaching consequences for cardiovascular health policy in Ghana. In particular, the findings could lead to better targeted public health interventions, including more effective risk factor assessment and patient education in clinical settings, which could lead to more effective patient management and improved cardiovascular outcomes.

scholarly journals 55. Diabetes, Obesity and COVID-19 Disease: An Observational Study of Outcomes Among Hospitalized Patients in Boston, Massachusetts

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S158-S159
Author(s):  
Jacqueline Seiglie ◽  
Jesse Platt ◽  
Sara Jane Cromer ◽  
Bridget Bunda ◽  
Andrea Foulkes ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Risk Mitigation ◽  
Massachusetts General Hospital ◽  
Data Monitoring Committee ◽  
The United States ◽  
Patient Centered ◽  
Obese People ◽  
Material Support ◽  
Icu Admission ◽  
Diabetes Status

Abstract Background In the United States, diabetes mellitus (DM) is among the most common chronic diseases, with approximately 34.2 million people affected. DM has also emerged as a commonly reported risk factor among people hospitalized with coronavirus disease 2019 (COVID-19). In this study, we sought to evaluate whether people with DM who are hospitalized with COVID-19 were more likely to experience poor early outcomes and whether this association remained after adjustment for obesity status. Methods We analyzed data from the Massachusetts General Hospital (MGH) COVID-19 Data Registry. The sample included 450 people with PCR-confirmed SARS-CoV-2 infection who were hospitalized at MGH between March 11, 2020 and April 30, 2020. The primary outcomes were (1) admission to the intensive care unit (ICU) and (2) need for mechanical ventilation or death, both within 14 days of presentation to care. Data were obtained by manual chart review and via an EMR-associated database. Logistic regression was used to evaluate the relationship between diabetes and these outcomes. All models were adjusted for age, sex, race, BMI category and key comorbidities. Results In this study, 178 (39.6%) of 450 participants had DM and 346 (76.9%) were overweight or obese. People with DM were on average older and had a higher BMI than those without DM. A higher percentage of patients with DM were admitted to the ICU (42.1% vs 29.8%, p=0.007) and required mechanical ventilation or died (46.6% vs 27.7%, p&lt; 0.001), compared with patients without DM (Figure 1). In adjusted models, DM was associated with a greater odds of ICU admission (aOR: 1.58 [95% CI: 1.01–2.46]) and mechanical ventilation or death (2.15 [1.38–3.34). Obesity was associated with a greater odds of ICU admission (2.15 [1.20–3.86]) but not with mechanical ventilation or death (1.52 [0.87–2.67]). Table 1 provides the model results in full. Figure 1. ICU Admission and mechanical ventilation or death within 14-days by diabetes status among 450 people hospitalized with COVID-19 Conclusion Diabetes was associated with poor outcomes within 14-days of presentation to care for COVID-19. These findings remained after adjustment for obesity. Our findings can help guide risk mitigation efforts and patient-centered care decision making for people with DM and obesity, particularly in areas of the US that have a high prevalence of DM and obesity and are in early phases of the SARS-CoV-2 outbreak. Disclosures Sara Jane Cromer, MD, Depuy-Synthes (a Johnson & Johnson company) (Employee) James Meigs, MD, Quest Diagnostics (Other Financial or Material Support, Academic Associate) Deborah Wexler, MD, Novo Nordisk (Other Financial or Material Support, Data Monitoring Committee)

Factors related toCampylobacterspp. carriage in client-owned dogs visiting veterinary clinics in a region of Ontario, Canada

2011 ◽  
Vol 139 (10) ◽  
pp. 1531-1541 ◽  
Author(s):  
E. K. LEONARD ◽  
D. L. PEARL ◽  
N. JANECKO ◽  
J. S. WEESE ◽  
R. J. REID-SMITH ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Risk Factor ◽  
Regression Models ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Pet Dogs ◽  
Logistic Regression Models ◽  
High Prevalence ◽  
Study Population ◽  
Cooked Food

SUMMARYFrom July 2008 until May 2009, 240 client-owned pet dogs from seven veterinary clinics in the Region of Waterloo, Ontario, Canada participated in a study to determine pet-related management factors that may be associated with the presence ofCampylobacterspp. in dogs. The prevalence ofCampylobacterspp. carriage in our study population of pet dogs was 22%, with 19% of the dogs positive forC. upsaliensis, and 3% positive forC. jejuni. A significant risk factor from multivariable logistic regression models for bothCampylobacterspp. andC. upsaliensiscarriage was having homemade cooked food as the dog's diet or added to its diet, and a significant sparing factor for both models was treatment with antibiotics in the previous month. Increasing age of the dog decreased the odds ofCampylobacterspp. andC. upsaliensiscarriage. Based on the high prevalence ofCampylobacter, and specificallyC. upsaliensis, further research concerning pet dogs as a risk factor for campylobacteriosis in humans is warranted.

scholarly journals Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study

2020 ◽  
Vol 7 (10) ◽  
Author(s):  
◽  
S K Mallipattu ◽  
R Jawa ◽  
R Moffitt ◽  
J Hajagos ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Cohort Study ◽  
Length Of Stay ◽  
Regression Models ◽  
Kidney Injury ◽  
Hospital Length ◽  
Hospitalized Patients ◽  
Hospital Length Of Stay ◽  
Logistic Regression Models

Abstract Background The global coronavirus disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals manage the care of hospitalized patients with varying demographics and clinical presentations. The goal of this study was to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally. Methods This was a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020, to May 11, 2020. Geospatial distribution of study patients’ residences relative to population density in the region were mapped, and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients. Results The median age of the study cohort (interquartile range [IQR]) was 62 (49–75) years, and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (rs = 0.235; P = .004), with noted “hot spots” in the region. Study patients were predominantly hypertensive (MAP &gt; 90 mmHg; 670, 51%) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (estimated glomerular filtration rate &lt; 60 mL/min/1.73 m2; 381, 29%). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, the median (IQR) hospital length of stay in survivors (22 [16.5–29.5] days) was significantly longer than that of nonsurvivors (15 [10–23.75] days), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15%, and in patients receiving IMV it was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project disposition in the remaining patients on ventilators. Acute kidney injury during hospitalization (odds ratioE, 3.23) was the strongest predictor of mortality in patients requiring IMV. Conclusions This is the first study to collectively utilize the demographics, clinical characteristics, and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.

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