scholarly journals External validation of the European risk assessment tool for Chronic Cardio-metabolic disorders in a Middle Eastern population

2020 ◽  
Author(s):  
Samaneh Asgari ◽  
Fatemeh Moosaie ◽  
Davood Khalili ◽  
Fereidoun Azizi ◽  
Farzad Hadaegh
Keyword(s):  
Cardiovascular Disease ◽  
Risk Assessment ◽  
Metabolic Disorders ◽  
Assessment Tool ◽  
External Validation ◽  
Multivariable Analysis ◽  
Iranian Population ◽  
Risk Assessment Tool ◽  
Goodness Of Fit Test

Abstract Background: High burden of chronic cardio-metabolic disorders including type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and cardiovascular disease (CVD) have been reported in the Middle East and North Africa region. We aimed to externally validate a non-laboratory risk assessment tool for the prediction of the chronic cardio-metabolic disorders in the Iranian population. Methods: The predictors included age, body mass index, waist circumference, use of antihypertensive, current smoking, and family history of cardiovascular disease and/or diabetes. For external validation of the model in the Tehran lipids and glucose study (TLGS), the Area under the curve (AUC) and the Hosmer-Lemeshow (HL) goodness of fit test were performed for discrimination and calibration, respectively. Results: Among 1310 men and 1960 women aged 28-85 years, 29.5% and 47.4% experienced chronic cardio-metabolic disorders during the 6 and 9-year follow-up, respectively. The model showed acceptable discrimination, with an AUC of 0.72(95% CI: 0.69-0.75) for men and 0.73(95% CI: 0.71-0.76) for women. The calibration of the model was good for both genders (min HL P=0.5). Considering separate outcomes, AUC was highest for CKD (0.76(95% CI: 0.72-0.79)) and lowest for T2DM (0.65(95% CI: 0.61-0.69)), in men. As for women, AUC was highest for CVD (0.82(95% CI: 0.78-0.86)) and lowest for T2DM (0.69(95% CI: 0.66-0.73)). The 9-year follow-up demonstrated almost similar performances compared to the 6-year follow-up. Using Cox regression in place of logistic multivariable analysis, model’s discrimination and calibration were reduced for prediction of chronic cardio-metabolic disorders; the issue which had more effect on the prediction of incident CKD among women. Moreover, adding data of educational levels and marital status did not improve, the discrimination and calibration in the enhanced model.Conclusion: This model showed acceptable discrimination and good calibration for risk prediction of chronic cardio-metabolic disorders in short and long-term follow-up in the Iranian population.

scholarly journals External validation of the European risk assessment tool for cardio metabolic disease in a Middle Eastern population

2020 ◽  
Author(s):  
Samaneh Asgari ◽  
Fatemeh Moosaie ◽  
Davood Khalili ◽  
Fereidoun Azizi ◽  
Farzad Hadaegh
Keyword(s):  
Metabolic Disease ◽  
Cardiovascular Disease ◽  
Risk Assessment ◽  
Goodness Of Fit ◽  
Assessment Tool ◽  
External Validation ◽  
Iranian Population ◽  
Risk Assessment Tool ◽  
Goodness Of Fit Test

Abstract Background: High burden of chronic cardio-metabolic disease (CCD) including type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), and cardiovascular disease (CVD) have been reported in the Middle East and North Africa region. We aimed to externally validate a Europoid risk assessment tool designed by Alssema et al, including non-laboratory measures, for the prediction of the CCD in the Iranian population. Methods: The predictors included age, body mass index, waist circumference, use of antihypertensive, current smoking, and family history of cardiovascular disease and or diabetes. For external validation of the model in the Tehran lipids and glucose study (TLGS), the Area under the curve (AUC) and the Hosmer-Lemeshow (HL) goodness of fit test were performed for discrimination and calibration, respectively. Results: Among 1310 men and 1960 women aged 28-85 years, 29.5% and 47.4% experienced CCD during the 6 and 9-year follow-up, respectively. The model showed acceptable discrimination, with an AUC of 0.72(95% CI: 0.69-0.75) for men and 0.73(95% CI: 0.71-0.76) for women. The calibration of the model was good for both genders (min HL P=0.5). Considering separate outcomes, AUC was highest for CKD (0.76(95% CI: 0.72-0.79)) and lowest for T2DM (0.65(95% CI: 0.61-0.69)), in men. As for women, AUC was highest for CVD (0.82(95% CI: 0.78-0.86)) and lowest for T2DM (0.69(95% CI: 0.66-0.73)). The 9-year follow-up demonstrated almost similar performances compared to the 6-year follow-up. Conclusion: This model showed acceptable discrimination and good calibration for risk prediction of CCD in short and long-term follow-up in the Iranian population.

scholarly journals Initial Experience in Predicting the Risk of Hospitalization of 496 Outpatients with COVID-19 Using a Telemedicine Risk Assessment Tool

2020 ◽  
Author(s):  
James B O'Keefe ◽  
Elizabeth J Tong ◽  
Thomas H Taylor ◽  
Ghazala D Datoo O'Keefe ◽  
David C Tong
Keyword(s):  
Risk Assessment ◽  
Assessment Tool ◽  
Cox Regression ◽  
Risk Assessment Tool ◽  
Individual Risk ◽  
Tier 2 ◽  
Tier 1 ◽  
Risk Patients ◽  
Tier 3

Objective: To determine whether a risk prediction tool developed and implemented in March 2020 accurately predicts subsequent hospitalizations. Design: Retrospective cohort study, enrollment from March 24 to May 26, 2020 with follow-up calls until hospitalization or clinical improvement (final calls until June 19, 2020) Setting: Single center telemedicine program managing outpatients from a large medical system in Atlanta, Georgia Participants: 496 patients with laboratory-confirmed COVID-19 in isolation at home. Exclusion criteria included: (1) hospitalization prior to telemedicine program enrollment, (2) immediate discharge with no follow-up calls due to resolution. Exposure: Acute COVID-19 illness Main Outcome and Measures: Hospitalization was the outcome. Days to hospitalization was the metric. Survival analysis using Cox regression was used to determine factors associated with hospitalization. Results: The risk-assessment rubric assigned 496 outpatients to risk tiers as follows: Tier 1, 237 (47.8%); Tier 2, 185 (37.3%); Tier 3, 74 (14.9%). Subsequent hospitalizations numbered 3 (1%), 15 (7%), and 17 (23%) and for Tiers 1-3, respectively. From a Cox regression model with age ≥ 60, gender, and self-reported obesity as covariates, the adjusted hazard ratios using Tier 1 as reference were: Tier 2 HR=3.74 (95% CI, 1.06-13.27; P=0.041); Tier 3 HR=10.87 (95% CI, 3.09-38.27; P<0.001). Tier was the strongest predictor of time to hospitalization. Conclusions and Relevance: A telemedicine risk assessment tool prospectively applied to an outpatient population with COVID-19 identified both low-risk and high-risk patients with better performance than individual risk factors alone. This approach may be appropriate for optimum allocation of resources.

Validation of the NCI colorectal cancer risk assessment tool in the CSP 380 veterans cohort.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15135-e15135
Author(s):  
Laura W. Musselwhite ◽  
Thomas S. Redding ◽  
Kellie J. Sims ◽  
Meghan O'Leary ◽  
Elizabeth R. Hauser ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Assessment ◽  
Predictive Value ◽  
Assessment Tool ◽  
Risk Assessment Tool ◽  
Sensitivity Analyses ◽  
Screening Colonoscopy ◽  
Average Risk ◽  
Male Veterans

e15135 Background: Refining screening to colorectal cancer (CRC) risk may promote screening effectiveness. We applied the National Cancer Institute (NCI) CRC Risk Assessment Tool to estimate 5- and 10-year CRC risk in an average-risk Veterans cohort undergoing screening colonoscopy with follow-up. Methods: This was a prospective evaluation of predicted to actual risk of CRC using the NCI CRC Risk Assessment Tool in male Veterans undergoing screening colonoscopy with a median follow-up of 10 years.Family, medical, dietary and physical activity histories were collected at enrollment and used to calculate absolute 5- and 10-year CRC risk, and to compare tertiles of expected to observed CRC risk. Sensitivity analyses were performed. Results: For 2,934 male Veterans with complete data (average age 62.4 years, 15% minorities), 1.3% (N=30) and 1.7% (N=50) were diagnosed with CRC within 5 and 10 years of survey completion. The area under the curve for predicting CRC was 0.69 (95% CI; 0.61-0.78) at 5 years and 0.67 (95% CI, 0.59-0.75) at 10 years. We calculated the sensitivity (0.60, 95% CI; 0.45-0.73), specificity (0.67, 95% CI; 0.65-0.69) positive predictive value (0.031, 95% CI; 0.02-0.04) and negative predictive value (0.99, 95% CI; 0.98-0.99). Conclusions: The NCI CRC Risk Assessment Tool was well-calibrated at 5 years and overestimated CRC risk at 10 years, had modest discriminatory function, and a high NPV in a cohort of ethnically diverse male Veterans. This tool reliably excludes 10-year CRC in low-scoring individuals and may inform patient-provider decision making when the benefit of screening is uncertain. [Table: see text]

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