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Research Paper Volume 13, Issue 4 pp 5332-5341
A novel risk model to predict first-ever ischemic stroke in heart failure with reduced ejection fraction
Relevance score: 5.9426513Xiaodong Zhou, Lingfang Yu, Weizhen Hu, Ruiyu Shi, Yinan Ji, Changzuan Zhou, Chenglong Xue, Guojia Yu, Weijian Huang, Peiren Shan
Keywords: predictors, score model, heart failure with reduced ejection fraction, ischemic stroke
Published in Aging on February 1, 2021
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Research Paper Volume 12, Issue 11 pp 10317-10336
Nomogram for the prediction of diabetic nephropathy risk among patients with type 2 diabetes mellitus based on a questionnaire and biochemical indicators: a retrospective study
Relevance score: 6.1983614Yuhong Hu, Rong Shi, Ruohui Mo, Fan Hu
Keywords: diabetic nephropathy, predictors, nomogram, type 2 diabetes mellitus, risk factors
Published in Aging on June 2, 2020
Feature selection using the LASSO binary logistic regression model. (A) Features selection by LASSO binary logistic regression model. By verifying the optimal parameter (lambda) in the LASSO model, the partial likelihood deviance (binomial deviance) curve was plotted versus log(lambda). Dotted vertical lines were drawn based on 1 SE of the minimum criteria (the 1-SE criteria). (B) Features selection by LASSO binary logistic regression model. A coefficient profile plot was produced against the log(lambda) sequence in figure 1(A). 16 features with nonzero coefficients were selected by optimal lambda.
The forest plot of the OR of the selected feature. Use of forest plot for outcome in LASSO regression model and logistic regression analysis.
Developed DN incidence risk nomogram. The DN incidence risk nomogram was developed in the array, with SBP, DBP, FBG, HbA1c, TG, SCR, BUN and BMI incorporated.
Calibration curves of the DN incidence risk nomogram prediction in the cohort. The x-axis represents the predicted DN incidence risk. The y-axis represents the actual diagnosed DN. The diagonal dotted line represents a perfect prediction by an ideal model. The solid line represents the performance of the nomogram, of which a closer fit to the diagonal dotted line represents a better prediction.
The pooled AUC of the ROC curve. The y-axis means the true positive rate of the risk prediction. The x-axis means the false positive rate of the risk prediction. The blue line represents the performance of the nomogram.
Decision curve analysis for the DN incidence risk nomogram. The y-axis measures the net benefit. The dotted line represents the DN incidence risk nomogram. The thin solid line represents the assumption that all patients are diagnosed as DN. Thin thick solid line represents the assumption that no patients are diagnosed as DN. The decision curve showed that if the threshold probability of a patient and a doctor is >20%, respectively, using this DN incidence risk nomogram in the current study to predict DN incidence risk adds more benefit than the intervention-all-patients scheme.
Model comparison based on NRI. The value of cutoff is 0.14, the value of NRI is 0.131 (0.086, 0.168).
Dynamic Nomogram. A T2DM patient was randomly selected from the population, and the DN incidence of the patient was predicted based on the 8 characteristic indicators of the nomogram.
Schematic diagram of research flow. The research design, research object, research method and results are presented simply by flow chart.
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Research Paper Volume 9, Issue 3 pp 769-777
Individualizing treatment targets for elderly patients with type 2 diabetes: factors influencing clinical decision making in the 24-week, randomized INTERVAL study
Relevance score: 8.053938W. David Strain, Abhijit S. Agarwal, Päivi M. Paldánius
Keywords: elderly, individualization, predictors, type 2 diabetes
Published in Aging on March 5, 2017
Summary of individualized HbA1c targets set by investigators (by country).
(A) Baseline factors affecting target setting (overall and by country). *For categorical covariates, the estimate is the difference between the adjusted means of comparison-reference in the corresponding category. For continuous covariates, the estimate is the change in adjusted means per unit. **Patients from Finland were identified by a single investigator. The figure estimates the difference between adjusted means for different factors potentially driving the individualized target setting and thus no reliable statistics for such a low sample size (n=2) could be generated. Hence, Finland has been removed. (B) Baseline HbA1c versus target reduction HbA1c. (C) Sex status versus target reduction HbA1c. (D) Baseline weight versus targeted individualized HbA1c by frailty status.
Summary of individualized HbA1c target response (overall and by country). *Patients from Finland were identified by a single investigator. The figure estimates the difference between adjusted means for different factors potentially driving the individualized target setting and thus no reliable statistics for such a low sample size (n=2) could be generated. Hence, Finland has been removed.