scholarly journals A clinical diabetes risk prediction model for prediabetic women with prior gestational diabetes

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252501
Author(s):  
Bernice Man ◽  
Alan Schwartz ◽  
Oksana Pugach ◽  
Yinglin Xia ◽  
Ben Gerber
Keyword(s):  
Prediction Model ◽  
Lifestyle Intervention ◽  
Risk Prediction ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Risk Prediction Model ◽  
Clinical Variables ◽  
Intensive Lifestyle Intervention ◽  
History Of ◽  
Main Model

Introduction Without treatment, prediabetic women with a history of gestational diabetes mellitus (GDM) are at greater risk for developing type 2 diabetes compared with women without a history of GDM. Both intensive lifestyle intervention and metformin can reduce risk. To predict risk and treatment response, we developed a risk prediction model specifically for women with prior GDM. Methods The Diabetes Prevention Program was a randomized controlled trial to evaluate the effectiveness of intensive lifestyle intervention, metformin (850mg twice daily), and placebo in preventing diabetes. Data from the Diabetes Prevention Program (DPP) was used to conduct a secondary analysis to evaluate 11 baseline clinical variables of 317 women with prediabetes and a self-reported history of GDM to develop a 3-year diabetes risk prediction model using Cox proportional hazards regression. Reduced models were explored and compared with the main model. Results Within three years, 82 (25.9%) women developed diabetes. In our parsimonious model using 4 of 11 clinical variables, higher fasting glucose and hemoglobin A1C were each associated with greater risk for diabetes (each hazard ratio approximately 1.4), and there was an interaction between treatment arm and BMI suggesting that metformin was more effective relative to no treatment for BMI ≥ 35kg/m2 than BMI < 30kg/m2. The model had fair discrimination (bias corrected C index = 0.68) and was not significantly different from our main model using 11 clinical variables. The estimated incidence of diabetes without treatment was 37.4%, compared to 20.0% with intensive lifestyle intervention or metformin treatment for women with a prior GDM. Conclusions A clinical prediction model was developed for individualized decision making for prediabetes treatment in women with prior GDM.

Association of Intensive Lifestyle Intervention and Metformin with Frailty in the Diabetes Prevention Program (DPP) Outcomes Study (DPPOS)

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 134-OR ◽  
Author(s):  
HELEN P. HAZUDA ◽  
QING PAN ◽  
JILL P. CRANDALL ◽  
HERMES FLOREZ ◽  
JOSE A. LUCHSINGER ◽  
...  
Keyword(s):  
Lifestyle Intervention ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Intensive Lifestyle Intervention ◽  
Outcomes Study

Risk prediction model for heart disease among endometrial cancer survivors.

2018 ◽  
Vol 36 (7_suppl) ◽  
pp. 120-120
Author(s):  
Mia Hashibe ◽  
Brenna Blackburn ◽  
Jihye Park ◽  
Kerry G. Rowe ◽  
John Snyder ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Prediction Model ◽  
Cancer Survivors ◽  
Risk Prediction ◽  
Prediction Models ◽  
Population Based ◽  
Risk Prediction Model ◽  
High Cholesterol ◽  
Risk Prediction Models ◽  
History Of

120 Background: There are an estimated 760,000 endometrial cancer survivors alive in the US today. We previously reported on increased heart disease (HD) risk among endometrial cancer survivors from our population-based cohort study. Although there are many risk prediction models for the risk of endometrial cancer, there are none to our knowledge for endometrial cancer survivors. Methods: We identified 2,994 endometrial cancer patients in the Utah Population Database, which links data from multiple statewide sources. We estimated hazard ratios with the Cox proportional hazards model for predictors of five-, ten- and fifteen-year risks. The Harrell’s C statistic was used to evaluate the model performance. We used 70% of the data randomly selected to develop the model and the rest of the data to validate the model. Results: A total of 1,591 patients were diagnosed with HD. Increased risks of HD among endometrial cancer patients were observed for older age, obesity at baseline, family history of HD, previous disease diagnosis (hypertension, diabetes, high cholesterol, COPD), distant stage, grade, histology, chemotherapy, and radiation therapy. The C-statistics for the risk prediction model were 0.69 for the hypothesized risk factors for HD, 0.56 for clinical factors, and 0.71 when statistically significant risk factors were included. With the final model selected, as one example, the absolute risks of HD were 17.6% at 5-years, 24.0% at 10-years and 32.0% at 15 years for a woman diagnosed with regional stage, grade I endometrial cancer in her fifties, was white, was obese at cancer diagnosis, had a family history of HD but no previous history of HD herself, had hypertension, but no history of diabetes or high cholesterol or COPD, and had radiation therapy treatment but no chemotherapy. The AUCs were 0.79 for the 5-year, 0.78 for the 10-year and 0.78 for the 15-year predictions. Conclusions: We developed the first risk prediction model for HD among endometrial cancer survivors within a population-based cohort study. Risk prediction models for cancer survivors are important in understanding long-term disease risks after cancer treatment is complete. Such models may contribute to management plans for treatment and individualized prevention efforts.

scholarly journals Circulating sex hormone binding globulin levels are modified with intensive lifestyle intervention, but their changes did not independently predict diabetes risk in the Diabetes Prevention Program

2020 ◽  
Vol 8 (2) ◽  
pp. e001841
Author(s):  
Vanita R Aroda ◽  
Costas A Christophi ◽  
Sharon L Edelstein ◽  
Leigh Perreault ◽  
Catherine Kim ◽  
...  
Keyword(s):  
Postmenopausal Women ◽  
Lifestyle Intervention ◽  
Prevention Program ◽  
Diabetes Prevention ◽  
Diabetes Prevention Program ◽  
Diabetes Risk ◽  
Sex Hormone ◽  
Sex Hormone Binding Globulin ◽  
Hormone Binding ◽  
Intensive Lifestyle Intervention

IntroductionSex hormone binding globulin (SHBG) levels are reported to be inversely associated with diabetes risk. It is unknown whether diabetes prevention interventions increase SHBG and whether resultant changes in SHBG affect diabetes risk. The purpose of this analysis was to determine whether intensive lifestyle intervention (ILS) or metformin changed circulating SHBG and if resultant changes influenced diabetes risk in the Diabetes Prevention Program (DPP).Research design and methodsThis is a secondary analysis from the DPP (1996–2001), a randomized trial of ILS or metformin versus placebo on diabetes risk over a mean follow-up of 3.2 years. The DPP was conducted across 27 academic study centers in the USA. Men, premenopausal and postmenopausal women without hormone use in the DPP were evaluated. The DPP included overweight/obese persons with elevated fasting glucose and impaired glucose tolerance. Main outcomes measures were changes in SHBG levels at 1 year and risk of diabetes over 3 years.ResultsILS resulted in significantly higher increases (postmenopausal women: p<0.01) or smaller decrements (men: p<0.05; premenopausal women: p<0.01) in SHBG compared with placebo or metformin. Changes in SHBG were primarily attributable to changes in adiposity. There were no consistent associations of change in SHBG with the risk of diabetes by treatment arm or participant group.ConclusionsLifestyle intervention may be associated with favorable changes in circulating SHBG, which is largely due to changes in adiposity. Changes in circulating SHBG do not independently predict reductions in diabetes incidence.

scholarly journals Re-Examination of Classic Risk Factors for Suicidal Behavior in the Psychiatric Population

Crisis ◽  
2015 ◽  
Vol 36 (4) ◽  
pp. 231-240
Author(s):  
Brittany B. Dennis ◽  
Pavel S. Roshanov ◽  
Monica Bawor ◽  
Wala ElSheikh ◽  
Sue Garton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Psychiatric Hospital ◽  
Risk Prediction ◽  
Risk Score ◽  
Suicidal Behavior ◽  
Psychiatric Patients ◽  
Risk Prediction Model ◽  
Predicted Probability ◽  
History Of

Abstract. Background: For decades we have understood the risk factors for suicide in the general population but have fallen short in understanding what distinguishes the risk for suicide among patients with serious psychiatric conditions. Aims: This prompted us to investigate risk factors for suicidal behavior among psychiatric inpatients. Method: We reviewed all psychiatric hospital admissions (2008–2011) to a centralized psychiatric hospital in Ontario, Canada. Using multivariable logistic regression we evaluated the association between potential risk factors and lifetime history of suicidal behavior, and constructed a model and clinical risk score to predict a history of this behavior. Results: The final risk prediction model for suicidal behavior among psychiatric patients (n = 2,597) included age (in three categories: 60–69 [OR = 0.74, 95% CI = 0.73–0.76], 70–79 [OR = 0.45, 95% CI = 0.44–0.46], 80+ [OR = 0.31, 95% CI = 0.30–.31]), substance use disorder (OR = 1.30, 95% CI = 1.27–1.32), mood disorder (OR = 1.49, 95% CI = 1.47–1.52), personality disorder (OR = 2.30, 95% CI = 2.25–2.36), psychiatric disorders due to general medical condition (OR = 0.52, 95% CI = 0.50–0.55), and schizophrenia (OR = 0.42, 95% CI = 0.41–0.43). The risk score constructed from the risk prediction model ranges from −9 (lowest risk, 0% predicted probability of suicidal behavior) to +5 (highest risk, 97% predicted probability). Conclusion: Risk estimation may help guide intensive screening and treatment efforts of psychiatric patients with high risk of suicidal behavior.

Development of Neurobehavioral Deterioration Risk Prediction Model for Welder: A Proposed Study

2018 ◽  
Vol 10 (5) ◽  
Author(s):  
Nuur Azreen Paiman ◽  
◽  
Azian Hariri ◽  
Ibrahim Masood ◽  
Arma Noor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model
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