Validation of Globorisk in Turkish people and development of a new model for cardiovascular diseases

Abstract Background Cardiovascular diseases (CVD) is a major cause of death globally, and accurate risk assessment is important for identifying high-risk individuals. This study aimed to validate the laboratory-based Globorisk score for predicting CVD in the Turkish population and to develop a Turkish population-specific model. Methods We analyzed data from Turkey's Chronic Diseases and Risk Factors study, which examined CVD incidence from 2011 until 2017. After excluding those with prior CVD history, a total of 7239 individuals aged 40 to 80 years were included in the analysis. The performance of Globorisk in predicting CVD was assessed using the C-index. With demographic, dietary, anthropometric and Globorisk variables; we used backward stepwise logistic regression to select the final model (Turkish CVD-TCVD). Lastly, the TCVD model was internally validated and calibrated with 200 bootstrap replicates. Results Out of 7239 participants (mean age: 53.9±10.3), women: 52.3%); 766 developed CVD within six years (cumulative incidence rate: 10.6%). The C-index of the Globorisk was 0.72 with sensitivity and specificity being 68.2% and 67.3%. In the final TCVD model, backward stepwise selection identified age (odds ratio-OR: 1.06, 95% Cl: 1.05-1.07), diabetes (OR:1.83, 1.47-2.28), body mass index (OR:1.02, 1.01-1.04), high waist-hip ratio (OR:1.37, 1.13-1.66) and systolic blood pressure (OR:1.01, 1.00-1.01) as significant predictors for CVD. C-index was 0.73 with sensitivity and specificity being 72.9%, and 62.9%. Examination of the calibration plot showed signs of overprediction when the actual CVD probability was &amp;gt;20%. Conclusions Laboratory-based Globorisk score had a good fit in the Turkish population. TCVD model had better sensitivity than Globorisk. Adding of waist-hip ratio to the Globorisk score could improve predictive CVD models in the Turkish population. Key messages • External validation of laboratory-based Globorisk showed good predictive accuracy in the Turkish population. • Although more challenging to measure, adding of waist-hip ratio to the laboratory-based Globorisk score could improve predictive cardiovascular disease models in the Turkish population. </jats:sec

Validity of Finnish diabetes risk score in Turkish population and developing a predictive model for Type 2 diabetes mellitus

Background and Objectives: Diabetes is a major health problem in turkey as wellas all over the world and its prevalence is increasing. The aim of this study is toevaluate the validity of the Finnish Diabetes Risk Score (FINDRISC) in Turkishpopulation and develop a Turkish Diabetes Prediction (TDP) model.Methods: The participants of the Turkey Chronic Diseases and Risk Factorsstudy-2011 (TCDRF) were followed up through electronic health records until2017 for incident type 2 diabetes (t2dm, ICD codes: E10, E11, E13, E14). Afterexcluding 4997 people with a diabetes history and 1231 with missing data; a totalof 12249 participants older than 14 years of age were included in the analysis. Theperformance of FINDRISC in predicting t2dm was evaluated according to c-index.We identified the final variables for the TDP model using backward stepwiselogistic regression.Results: Out of 12249 participants (mean age: 40±16.7, women: 53%); 505developed t2dm within six years (cumulative incidence rate: 4.1%). The c-indexof the FINDRISC was 0.75 (95% confidence interval: 0.73-0.77) with sensitivityand specificity of 78.4% and 59%. TDP model identified age (odds ratio: 1.02, 95%CI: 1.01-1.03), female gender (OR:1.48, 95% CI:1.17-1.86), university graduation(OR:1.91, 95% CI:1.24-2.94), BMI&gt;30kg/m2 (OR:1.89, 95% CI:1.33-2.70), waistcircumference (OR:1.03, 95% CI:1.02-1.03), having hypertension (OR:1.70, 95%CI:1.34-2.15), impaired fasting glucose (OR:1.73, 95% CI:1.40-2.13) and familyhistory of diabetes (OR:1.40, 95% CI:1.15-1.71) as significant predictors fort2dm. The c-index of TDP model was 0.77 (95% ci: 0.75 to 0.79) with sensitivity,specificity, positive and negative predictive values of 66.7%, 75.3%, 10.4% and98.1%, respectively.Conclusions: FINDRISC had good validity in Turkish population. Compared with&nbsp;FINDRISC, TDP model showed similar results in terms of model performance.TDP model had a good predictive ability for the population who had a low risk ofdeveloping t2dm.</div

EVALUATION OF HOSPITALIZED YOUNGEST-OLD, MIDDLE-OLD AND OLDEST-OLD COVID-19 PATIENTS IN TERMS OF MORTALITY AND RISK FACTORS

Introduction: In the coronavirus disease 2019 (COVID-19) pandemic, severe disease is predominantly seen in advanced-aged patients. In our study, we evaluated hospitalized youngest-old (65–74 years), middle-old (75–84 years) and oldest-old (≥85 years) COVID-19 patients in terms of mortality and risk factors. Materials and Methods: This retrospective study included hospitalized COVID-19 patients aged 65 years and older. Demographic characteristics, such as age, gender and comorbid conditions, baseline blood oxygen saturation levels, the necessity of oxygen treatments (nasal cannula,oxygen mask/reservoir oxygen mask), condition of the patients(mild, moderate, severe), baseline laboratory findings as C-reactive protein, white blood cell counts, thrombocyte counts, lymphocyte counts, D-dimer, alanine aminotransferase, aspartate aminotransferase and ferritin levels, pulmonary involvement on computerized tomography, the increase in oxygen requirements, the status of going to the intensive care unit and the status of receiving corticosteroids were recorded. Factors associated with mortality were analyzed. Results: A total of 399 geriatric COVID-19 patients were included in this study: 214(53.6%) were female and 185 (46.4%) were male. The mean age of the patients was 75±7.87(min:65–max:96). In our study, the mortality rate was found to be higher in the middle-old and oldest-old groups than in the youngest-old group (p=0.01). Other factors associated with mortality were as follows: lower baseline oxygen saturation levels (p=0.03), necessity of higher oxygen treatment (p<0.01), higher pulmonary involvement on computerized tomography (p<0.01), corticosteroid use (p<0.01) and having Alzheimer’s disease (p=0.03). Conclusion: Our findings emphasize that older patients are more vulnerable to COVID-19 infection and require special attention

Cause-of-death distributions and mortality trends in Turkey between 2009 and 2017

Background: Making the right decisions in the field of public health depends on the reliable recording of statistical data such as death and birth. There have been radical changes and innovations in the death registration since 2009 in Turkey to improve reporting