scholarly journals SARS-CoV-2 infection and cardiovascular or pulmonary complications in ambulatory care: A risk assessment based on routine data

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258914
Author(s):  
Siranush Karapetyan ◽  
Antonius Schneider ◽  
Klaus Linde ◽  
Ewan Donnachie ◽  
Alexander Hapfelmeier
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Ambulatory Care ◽  
Decision Rules ◽  
Significant Risk ◽  
Pulmonary Complications ◽  
Prognostic Modelling ◽  
Pcr Test

Background Risk factors of severe COVID-19 have mainly been investigated in the hospital setting. We investigated pre-defined risk factors for testing positive for SARS-CoV-2 infection and cardiovascular or pulmonary complications in the outpatient setting. Methods The present cohort study makes use of ambulatory claims data of statutory health insurance physicians in Bavaria, Germany, with polymerase chain reaction (PCR) test confirmed or excluded SARS-CoV-2 infection in first three quarters of 2020. Statistical modelling and machine learning were used for effect estimation and for hypothesis testing of risk factors, and for prognostic modelling of cardiovascular or pulmonary complications. Results A cohort of 99 811 participants with PCR test was identified. In a fully adjusted multivariable regression model, dementia (odds ratio (OR) = 1.36), type 2 diabetes (OR = 1.14) and obesity (OR = 1.08) were identified as significantly associated with a positive PCR test result. Significant risk factors for cardiovascular or pulmonary complications were coronary heart disease (CHD) (OR = 2.58), hypertension (OR = 1.65), tobacco consumption (OR = 1.56), chronic obstructive pulmonary disease (COPD) (OR = 1.53), previous pneumonia (OR = 1.53), chronic kidney disease (CKD) (OR = 1.25) and type 2 diabetes (OR = 1.23). Three simple decision rules derived from prognostic modelling based on age, hypertension, CKD, COPD and CHD were able to identify high risk patients with a sensitivity of 74.8% and a specificity of 80.0%. Conclusions The decision rules achieved a high prognostic accuracy non-inferior to complex machine learning methods. They might help to identify patients at risk, who should receive special attention and intensified protection in ambulatory care.

scholarly journals Prevalence and risk factors of Type 2 diabetes in an urbanizing rural community of Bangladesh

1970 ◽  
Vol 33 (2) ◽  
pp. 48-54 ◽  
Author(s):  
Md. Mafuzar Rahman ◽  
Md. Abdur Rahim ◽  
Quamrun Nahar
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Rural Community ◽  
Rural Areas ◽  
Fasting Blood Glucose ◽  
Significant Risk ◽  
Oral Glucose ◽  
Total Prevalence

This cross-sectional study was carried out to estimate the prevalence of type 2 diabetes mellitus and its’ risk factors in an urbanizing rural community of Bangladesh. Two villages were randomly selected from the rural areas of Gazipur district and total 975 subjects (>20 years), were included following simple random procedure. Capillary blood glucose levels, fasting blood glucose (FBG) levels and 2-hour after 75 g oral glucose load (OGTT) were measured. Height, weight, waist and hip circumferences and blood pressure were measured. The study population was lean with mean body mass index (BMI) of 20.48. The total prevalence of type 2 diabetes was 8.5%, men showed higher prevalence (9.4%) compare to women (8.0%). Increasing age and higher BMI were found to be significant risk factors following both FBG and OGTT. The study has shown that prevalence of diabetes has increased in the populations who are in transitional stage of urbanization, and may indicate an epidemiological transition due to fast expanding urbanization. Keywords: Bangladesh; Diabetes; RuralDOI: 10.3329/bmrcb.v33i2.1204Bangladesh Med Res Counc Bull 2007; 33: 48-54

scholarly journals Prevalence of chronic kidney disease stages 3-5 among patients with type 2 diabetes mellitus in Bangladesh

2017 ◽  
Vol 11 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Muhammad Abdur Rahim ◽  
Palash Mitra ◽  
Hasna Fahmima Haque ◽  
Tasrina Shamnaz Samdani ◽  
Shahana Zaman ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Chronic Kidney Disease ◽  
Type 2 Diabetes Mellitus ◽  
Kidney Disease ◽  
Glycemic Control ◽  
Significant Risk ◽  
Duration Of Diabetes

Background and objectives: Diabetes mellitus is one of the most common causes of chronic kidney disease (CKD). The prevalence of CKD in type 2 diabetes mellitus (T2DM) in Bangladesh is not well described. The present study aimed to find out the prevalence of CKD stages 3-5 and its risk factors among selected Bangladeshi T2DM patients.Methods: This cross-sectional study was conducted in BIRDEM (Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders) General Hospital, Dhaka, Bangladesh from July to December 2015. Diagnosed adult T2DM patients were consecutively and purposively included in this study. Pregnant women, patients with diagnosed kidney disease due to non-diabetic etiology, acute kidney injury (AKI), AKI on CKD and patients on renal replacement therapy were excluded. Age, gender, body mass index (BMI) and laboratory parameters were recorded systematically in a predesigned data sheet. Diagnosis of CKD and its stages were determined according to Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guidelines 2012 and estimated glomerular filtration rate (eGFR). Estimated GFR was calculated by using Modification of Diet in Renal Disease (MDRD), Cockcroft-Gault (CG) and Chronic Kidney Disease Epidemiology (CKDEPI) creatinine based formula.Results: A total of 400 patients with T2DM of various durations were enrolled in the study. Out of 400 patients, 254 (63.5%), 259 (64.75%) and 218 (54.5%) cases had CKD stages 3-5 according to MDRD, C-G and CKD-EPI equations respectively. CKD was significantly more common in females (p<0.001) and in cases with long duration of diabetes (?5 years; p=0.007). CKD stages 3-5 were significantly associated with hypertension (?2=5.2125, p =0.02) and good control of diabetes (HbA1c <7%) as evidenced by higher proportion of CKD in them (73.3%) compared to those with poor glycemic control (52.1%).Conclusions: More than half of T2DM patients had CKD stages 3-5. Female gender, duration of diabetes and hypertension were significant risk factors and should be emphasized for the prevention of CKD in T2DM. Glycemic control may not reduce CKD in diabetes.IMC J Med Sci 2017; 11(1): 19-24

scholarly journals Bone fractures in patients with type 2 diabetes mellitus: prevalence and risk factors

2017 ◽  
Vol 20 (4) ◽  
pp. 4-10
Author(s):  
Tatiana Olegovna Yalochkina ◽  
Janna Evgen'evna Belaya ◽  
Lyudmila Yakovlevna Rozhinskaya ◽  
Michail Borisovich Antsiferov ◽  
Larisa Konstantinovna Dzeranova ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Outpatient Clinic ◽  
Bone Fractures ◽  
Significant Risk ◽  
History Of ◽  
Male Sex

Aim. To estimate the prevalence of and risk factors for low-traumatic fractures in patients with type 2 diabetes mellitus (T2DM).Materials and methods. We questioned 214 patients with T2DM from a single outpatient clinic located in Moscow to evaluate the prevalence of and risk factors for low-traumatic fractures, the duration of and complications from TD2M and HbA1c levels.Results. Of 214 patients, 65 reported low-traumatic bone fractures. Patients with a history of low-traumatic fractures reported falls in the previous year (28%), whereas only 13% of patients without fractures reported falls. The difference was statistically significant, with an odds ratio of 2.34 (1,144,76), P=0,022. Men reported fractures more frequently than women (43.3% vs. 24.7%, respectively, P = 0.01). Patients with bone fractures had a lower body mass index (P = 0.022); however, a multivariate analysis revealed that a history of falls and male sex were the most significant risk factors for fracture.Conclusion. Around 30% of patients with T2DM from a Moscow outpatient clinic reported bone fractures. The most significant risk factors for fracture were a history of falls in the previous year and male sex. The article is the RePrint from the original article inDiabetes Mellitus (2016); 19(5) pp. 359-365. doi: 10.14341/DM7796

Obesity, type 2 diabetes, age, and female gender: significant risk factors in the development of alcoholic liver cirrhosis

2012 ◽  
Vol 7 (1) ◽  
pp. 280-285 ◽  
Author(s):  
Yoshinori Horie ◽  
Yoshiyuki Yamagishi ◽  
Hirotoshi Ebinuma ◽  
Toshifumi Hibi
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Liver Cirrhosis ◽  
Significant Risk ◽  
Female Gender ◽  
Alcoholic Liver Cirrhosis

scholarly journals Effect of thiazolidinediones on insulin resistance and cardiovascular risk factors

2008 ◽  
Vol 5 (2) ◽  
pp. 2-5 ◽  
Author(s):  
T Yu Demidova ◽  
E N Erokhina
Keyword(s):  
Risk Factors ◽  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Cardiovascular Risk ◽  
Cardiovascular Risk Factors ◽  
Clinical Management ◽  
Significant Risk ◽  
High Prevalence ◽  
Multivariate Control

The development of new, more effective ways of multivariate control of type 2 diabetes is currently the most important problem of endocrinology. This is caused by a high prevalence of this disease in the population, as well as a significant risk of complications leading to early morbidity and mortality of patients. Clinical management of patients with type 2 diabetes should be based on a thorough study of the mechanisms of this disease in order to correct the basic pathogenetic defects.

A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740055 ◽  
Author(s):  
Jiang Xie ◽  
Yan Liu ◽  
Xu Zeng ◽  
Wu Zhang ◽  
Zhen Mei
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Electronic Health Records ◽  
Physical Examination ◽  
Risk Model ◽  
Learning Approach ◽  
Health Records ◽  
Machine Learning Approach

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

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