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Validation of 10-Year Stroke Prediction Scores in a Community-Based Cohort of Chinese Older Adults.

ABSTRACT: A stroke prediction model based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project was developed. We compared its predictive ability with the revised Framingham Stroke Risk Score (R-FSRS) for 5-year stroke incidence in a community cohort of Chinese adults, namely the Beijing Longitudinal Study of Aging (BLSA). Calibration, discrimination, and recalibration were used to compare the predictive ability between the two prediction models. Category-less net reclassification improvement (NRI) and integrated discrimination improvement (IDI) values were also assessed. During a mean follow-up duration of 5.1 years, 106 incidents of fatal or non-fatal strokes occurred among 1,203 participants aged 55-84 years. The R-FSRS applied to our cohort underestimated the 5-year risk for stroke in men and women. China-PAR performed better than the R-FSRS in terms of calibration (men, R-FSRS: ?2-value 144.2 [P < 0.001], China-PAR: 10.4 [P = 0.238]; women, R-FSRS: 280.1 [P < 0.001], China-PAR: 12.5 [P = 0.129]). In terms of discrimination, R-FSRS and China-PAR models performed modestly in our cohort (C-statistic 0.603 [95% CI: 0.560-0.644] for men using China-PAR and 0.568 [95% CI: 0.524-0.610] using the R-FSRS; the corresponding numbers for women were 0.602 [95% CI: 0.564-0.639] and 0.575 [95% CI: 0.537-0.613). The recalibrated China-PAR model significantly improved the discrimination in C statistics and produced higher category-less NRI and IDI for stroke incidence than the R-FSRS. Although China-PAR fairly estimated stroke risk in our cohort, it did not sufficiently identify adults at high risk of stroke. Caution would be exercised by practitioners in applying the original China-PAR to Chinese older adults. Further studies are needed to develop an adequate prediction model based on the recalibrated China-PAR or to find new risk markers which could upgrade this model.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7642878 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Project description:Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with balanced data. Thus, this study aimed to develop machine learning models for predicting stroke with imbalanced data in an elderly population in China. Data were obtained from a prospective cohort that included 1131 participants (56 stroke patients and 1075 non-stroke participants) in 2012 and 2014, respectively. Data balancing techniques including random over-sampling (ROS), random under-sampling (RUS), and synthetic minority over-sampling technique (SMOTE) were used to process the imbalanced data in this study. Machine learning methods such as regularized logistic regression (RLR), support vector machine (SVM), and random forest (RF) were used to predict stroke with demographic, lifestyle, and clinical variables. Accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves (AUCs) were used for performance comparison. The top five variables for stroke prediction were selected for each machine learning method based on the SMOTE-balanced data set. The total prevalence of stroke was high in 2014 (4.95%), with men experiencing much higher prevalence than women (6.76% vs. 3.25%). The three machine learning methods performed poorly in the imbalanced data set with extremely low sensitivity (approximately 0.00) and AUC (approximately 0.50). After using data balancing techniques, the sensitivity and AUC considerably improved with moderate accuracy and specificity, and the maximum values for sensitivity and AUC reached 0.78 (95% CI, 0.73-0.83) for RF and 0.72 (95% CI, 0.71-0.73) for RLR. Using AUCs for RLR, SVM, and RF in the imbalanced data set as references, a significant improvement was observed in the AUCs of all three machine learning methods (p < 0.05) in the balanced data sets. Considering RLR in each data set as a reference, only RF in the imbalanced data set and SVM in the ROS-balanced data set were superior to RLR in terms of AUC. Sex, hypertension, and uric acid were common predictors in all three machine learning methods. Blood glucose level was included in both RLR and RF. Drinking, age and high-sensitivity C-reactive protein level, and low-density lipoprotein cholesterol level were also included in RLR, SVM, and RF, respectively. Our study suggests that machine learning methods with data balancing techniques are effective tools for stroke prediction with imbalanced data.

Project description:ObjectiveThe inter-individual variability in cognitive changes may be early indicators of major health events. We aimed to determine whether late-life cognitive trajectories were associated with incident dementia, persistent physical disability and all-cause mortality.MethodsData came from a cohort of older community-dwelling individuals aged 70 years or above in Australia and the United States. Global cognition, verbal fluency, episodic memory and psychomotor speed were assessed regularly at up to seven waves between 2010 and 2017. Dementia, disability in activities of daily living, and death were adjudicated between 2017 and 2020. Latent classes of cognitive trajectories over seven years were determined using group-based trajectory modeling. Multivariable logistic regression was used for the prospective associations between cognitive trajectories and these outcomes.ResultsCognitive trajectories were defined for 16,174 participants (mean age: 78.9 years; 56.7% female) who were alive and without incident dementia or disability by 2017, among which 14,655 participants were included in the association analysis. Between three and five trajectory classes were identified depending on the cognitive test. Cognitive trajectories were strongly associated with the risk of dementia. For example, compared to those in the highest-functioning trajectory, the worst performers of episodic memory had a 37-fold increased risk of dementia (95% CI: 17.23-82.64). The lowest trajectories of both global cognition and episodic memory also predicted increased mortality risk (OR: 1.80, 95% CI: 1.28-2.52; OR: 1.61, 95% CI: 1.09-2.36, respectively), while only slow psychomotor speed was marginally associated with physical disability (OR: 2.39, 95% CI: 0.99-5.77).ConclusionsIn older individuals, cognitive trajectories appear to be early indicators of clinically relevant health outcomes. Systematic cognitive assessments as part of routine geriatric evaluation may facilitate early identification and interventions for those individuals at highest risk.

Project description:ObjectiveAlthough some people with mild cognitive impairment may not suffer from dementia lifelong, about 5% of them will progress to dementia within 1 year in community settings. However, a general tool for predicting the risk of cognitive impairment was not adequately studied among older adults.DesignProspective cohort study.SettingCommunity-living, older adults from 22 provinces in China.ParticipantsWe included 10,066 older adults aged 65 years and above (mean age, 83.2 ± 11.1 years), with normal cognition at baseline in the 2002-2008 cohort and 9354 older adults (mean age, 83.5 ± 10.8 years) in the 2008-2014 cohort of the Chinese Longitudinal Healthy Longevity Survey.MethodsWe measured cognitive function using the Chinese version of the Mini-Mental State Examination. Demographic, medical, and lifestyle information was used to develop the nomogram via a Lasso selection procedure using a Cox proportional hazards regression model. We validated the nomogram internally with 2000 bootstrap resamples and externally in a later cohort. The predictive accuracy and discriminative ability of the nomogram were measured by area-under-the-curves and calibration curves, respectively.ResultsEight factors were identified with which to construct the nomogram: age, baseline of the Mini-Mental State Examination, activities of daily living and instrumental activities of daily living score, chewing ability, visual function, history of stroke, watching TV or listening to the radio, and growing flowers or raising pets. The area-under-the-curves for internal and external validation were 0.891 and 0.867, respectively, for predicting incident cognitive impairment. The calibration curves showed good consistency between nomogram-based predictions and observations.Conclusions and implicationsThe nomogram-based prediction yielded consistent results in 2 separate large cohorts. This feasible prognostic nomogram constructed using readily ascertained information may assist public health practitioners or physicians to provide preventive interventions of cognitive impairment.

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Validation of 10-Year Stroke Prediction Scores in a Community-Based Cohort of Chinese Older Adults.

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