Dataset Information

Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chinese rural population.

ABSTRACT: This research aims to assess application of different cardiovascular disease (CVD) mortality risk prediction models in Chinese rural population. Data was collected from a 6-year follow-up survey in rural area of Henan Province, China. 10338 participants aged 40 to 65 years were included. Baseline study was conducted between 2007 and 2008, and followed up from 2013 to 2014. Seven models: general Framingham risk score (general-FRS), simplified-FRS, Systematic Coronary Risk Evaluation for high (SCORE-high), SCORE-low, Chinese ischemic CVD (CN-ICVD), Pooled Cohort Risk Equation for white (PCE-white) and for African-American (PCE-AA) were assessed and recalibrated. The model performance was evaluated by C-statistics and modified Nam-D'Agostino test. 168 CVD deaths occurred during follow-up. All seven models showed moderate C-statics ranging from 0.727 to 0.744. Following recalibration, general-FRS, simplified-FRS, CN-ICVD, PCE-white and PCE-AA had improved C-statistics of 0.776, 0.795, 0.793, 0.779, and 0.776 for men and 0.756, 0.753, 0.755, 0.758 and 0.760 for women, respectively. Calibrations ?2 of general-FRS, simplified-FRS, SCORE-high, CN-ICVD and PCE-AA model for men, and general-FRS, CN-ICVD and PCE-white model for women were statistically acceptable, indicating these models predicts CVD mortality risk more accurately than others and could be recommended in Chinese rural population.

SUBMITTER: Sun C

PROVIDER: S-EPMC5364500 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chinese rural population.

Sun Changqing C Xu Fei F Liu Xiaotian X Fang Mingwang M Zhou Hao H Lian Yixiao Y Xie Chen C Sun Nan N Wang Chongjian C

Scientific reports 20170324

This research aims to assess application of different cardiovascular disease (CVD) mortality risk prediction models in Chinese rural population. Data was collected from a 6-year follow-up survey in rural area of Henan Province, China. 10338 participants aged 40 to 65 years were included. Baseline study was conducted between 2007 and 2008, and followed up from 2013 to 2014. Seven models: general Framingham risk score (general-FRS), simplified-FRS, Systematic Coronary Risk Evaluation for high (SCO ...[more]

PMID: 28337999

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Project description:BackgroundThe effect of bariatric surgery on type 2 diabetes mellitus (T2DM) control can be assessed based on predictive models of T2DM remission. Various models have been externally verified internationally. However, long-term validated results after laparoscopic sleeve gastrectomy (LSG) surgery are lacking. The best model for the Chinese population is also unknown.MethodsWe retrospectively analyzed Chinese population data 5 years after LSG at Beijing Shijitan Hospital in China between March 2009 and December 2016. The independent t -test, Mann-Whitney U test, and chi-squared test were used to compare characteristics between T2DM remission and non-remission groups. We evaluated the predictive efficacy of each model for long-term T2DM remission after LSG by calculating the area under the curve (AUC), sensitivity, specificity, Youden index, positive predictive value (PPV), negative predictive value (NPV), and predicted-to-observed ratio, and performed calibration using Hosmer-Lemeshow test for 11 prediction models.ResultsWe enrolled 108 patients, including 44 (40.7%) men, with a mean age of 35.5 years. The mean body mass index was 40.3 ± 9.1 kg/m 2 , the percentage of excess weight loss (%EWL) was (75.9 ± 30.4)%, and the percentage of total weight loss (%TWL) was (29.1± 10.6)%. The mean glycated hemoglobin A1c (HbA1c) level was (7.3 ± 1.8)% preoperatively and decreased to (5.9 ± 1.0)% 5 years after LSG. The 5-year postoperative complete and partial remission rates of T2DM were 50.9% [55/108] and 27.8% [30/108], respectively. Six models, i.e., "ABCD", individualized metabolic surgery (IMS), advanced-DiaRem, DiaBetter, Dixon et al' s regression model, and Panunzi et al 's regression model, showed a good discrimination ability (all AUC >0.8). The "ABCD" (sensitivity, 74%; specificity, 80%; AUC, 0.82 [95% confidence interval [CI]: 0.74-0.89]), IMS (sensitivity, 78%; specificity, 84%; AUC, 0.82 [95% CI: 0.73-0.89]), and Panunzi et al' s regression models (sensitivity, 78%; specificity, 91%; AUC, 0.86 [95% CI: 0.78-0.92]) showed good discernibility. In the Hosmer-Lemeshow goodness-of-fit test, except for DiaRem ( P <0.01), DiaBetter ( P <0.01), Hayes et al ( P = 0.03), Park et al ( P = 0.02), and Ramos-Levi et al' s ( P <0.01) models, all models had a satifactory fit results ( P >0.05). The P values of calibration results of the "ABCD" and IMS were 0.07 and 0.14, respectively. The predicted-to-observed ratios of the "ABCD" and IMS were 0.87 and 0.89, respectively.ConclusionThe prediction model IMS was recommended for clinical use because of excellent predictive performance, good statistical test results, and simple and practical design features.

Project description:Objective:To assess the extent to which maternal histories of newborn danger signs independently or combined with birth weight and/or gestational age (GA) can capture and/or predict postsecond day (age>48?hours) neonatal death. Methods:Data from a cluster-randomised trial conducted in rural Bangladesh were split into development and validation sets. The prompted recall of danger signs and birth weight measurements were collected within 48?hours postchildbirth. Maternally recalled danger signs included cyanosis (any part of the infant's body was blue at birth), non-cephalic presentation (part other than head came out first at birth), lethargy (weak or no arm/leg movement and/or cry at birth), trouble suckling (infant unable to suckle/feed normally in the 2?days after birth or before death, collected 1-month postpartum or from verbal autopsy). Last menstrual period was collected at maternal enrolment early in pregnancy. Singleton newborns surviving 2?days past childbirth were eligible for analysis. Prognostic multivariable models were developed and internally validated. Results:Recalling ?1?sign of lethargy, cyanosis, non-cephalic presentation or trouble suckling identified postsecond day neonatal death with 65.3% sensitivity, 60.8% specificity, 2.1% positive predictive value (PPV) and 99.3% negative predictive value (NPV) in the development set. Requiring either lethargy or weight <2.5 kg identified 89.1% of deaths (at 39.7% specificity, 1.9% PPV and 99.6% NPV) while lethargy or preterm birth (<37 weeks) captured 81.0% of deaths (at 53.6% specificity, 2.3% PPV and 99.5% NPV). A simplified model (birth weight, GA, lethargy, cyanosis, non-cephalic presentation and trouble suckling) predicted death with good discrimination (validation area under the receiver-operator characteristic curve (AUC) 0.80, 95%?CI 0.73 to 0.87). A further simplified model (GA, non-cephalic presentation, lethargy, trouble suckling) predicted death with moderate discrimination (validation AUC 0.74, 95%?CI 0.66 to 0.81). Conclusion:Maternally recalled danger signs, coupled to either birth weight or GA, can predict and capture postsecond day neonatal death with high discrimination and sensitivity.

Project description:Accurate risk assessment of an individuals' propensity to develop cardiovascular diseases (CVDs) is crucial for the prevention of these conditions. Numerous published risk prediction models used for CVD risk assessment are based on conventional risk factors and include only a limited number of biomarkers. The addition of novel biomarkers can boost the discriminative ability of risk prediction models for CVDs with different pathogenesis. The present study reports the development of risk prediction models for a range of heterogeneous CVDs, including coronary artery disease (CAD), stroke, deep vein thrombosis (DVT), and abdominal aortic aneurysm (AAA), as well as for Type 2 diabetes mellitus (DM2), a major CVD risk factor. In addition to conventional risk factors, the models incorporate various blood biomarkers and comorbidities to improve both individual and population stratification. An automatic variable selection approach was developed to generate the best set of explanatory variables for each model from the initial panel of risk factors. In total, up to 254,220 UK Biobank participants (ranging from 215,269 to 254,220 for different CVDs and DM2) were included in the analyses. The derived prediction models utilizing Cox proportional hazards regression achieved consistent discrimination performance (C-index) for all diseases: CAD, 0.794 (95% CI, 0.787-0.801); DM2, 0.909 (95% CI, 0.903-0.916); stroke, 0.778 (95% CI, 0.756-0.801); DVT, 0.743 (95% CI, 0.737-0.749); and AAA, 0.893 (95% CI, 0.874-0.912). When validated on various subpopulations, they demonstrated higher discrimination in healthier and middle-age individuals. In general, calibration of a five-year risk of developing the CVDs and DM2 demonstrated incremental overestimation of disease-related conditions amongst the highest decile of risk probabilities. In summary, the risk prediction models described were validated with high discrimination and good calibration for several CVDs and DM2. These models incorporate multiple shared predictor variables and may be integrated into a single platform to enhance clinical stratification to impact health outcomes.

Project description:BackgroundThere is an increasing prevalence of cardiovascular disease (CVD) in China, which represents the leading cause of mortality. Precise CVD risk identification is the fundamental prevention component. This study sought to systematically review the CVD risk prediction models derived and/or validated in the Chinese population to promote primary CVD prevention.MethodsReports were included if they derived or validated one or more CVD risk prediction models in the Chinese population. PubMed, Embase, CINAHL, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), VIP database, etc., were searched. The risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed in R using the package metamisc.ResultsFrom 55,183 records, 22 studies were included. Twelve studies derived 18 CVD risk prediction models, of which seven models were derived based on a multicentre cohort including more than two provinces of mainland China, and one was a model developed based on a New Zealand cohort including Chinese individuals. The number of predictors ranged from 6 to 22. The definitions of predicted outcomes showed considerable heterogeneity. Fourteen articles described 29 validations of 8 models. The Framingham model and pooled cohort equations (PCEs) are the most frequently validated foreign tools. Discrimination was acceptable and similar for men and women among models (0.60-0.83). The calibration estimates changed substantially from one population to another. Prediction for atherosclerotic cardiovascular disease Risk in China (China-PAR) showed good calibration [observed/expected events ratio = 0.99, 95% PI (0.57,1.70)] and female sex [1.10, 95% PI (0.23,5.16)].ConclusionsSeveral models have been developed or validated in the Chinese population. The usefulness of most of the models remains unclear due to incomplete external validation and head-to-head comparison. Future research should focus on externally validating or tailoring these models to local settings.Trail registrationThis systematic review was registered at PROSPERO (International Prospective Register of Systematic Reviews, CRD42021277453).

Project description:ObjectiveThe objective is to develop and validate a predictive model for 15-month mortality using a random sample of community-dwelling Medicare beneficiaries.Data sourceThe Centres for Medicare & Medicaid Services' Limited Data Set files containing the five per cent samples for 2014 and 2015.ParticipantsThe data analysed contains de-identified administrative claims information at the beneficiary level, including diagnoses, procedures and demographics for 2.7 million beneficiaries.SettingUS national sample of Medicare beneficiaries.Study designEleven different models were used to predict 15-month mortality risk: logistic regression (using both stepwise and least absolute shrinkage and selection operator (LASSO) selection of variables as well as models using an age gender baseline, Charlson scores, Charlson conditions, Elixhauser conditions and all variables), naïve Bayes, decision tree with adaptive boosting, neural network and support vector machines (SVMs) validated by simple cross validation. Updated Charlson score weights were generated from the predictive model using only Charlson conditions.Primary outcome measureC-statistic.ResultsThe c-statistics was 0.696 for the naïve Bayes model and 0.762 for the decision tree model. For models that used the Charlson score or the Charlson variables the c-statistic was 0.713 and 0.726, respectively, similar to the model using Elixhauser conditions of 0.734. The c-statistic for the SVM model was 0.788 while the four models that performed the best were the logistic regression using all variables, logistic regression after selection of variables by the LASSO method, the logistic regression using a stepwise selection of variables and the neural network with c-statistics of 0.798, 0.798, 0.797 and 0.795, respectively.ConclusionsImproved means for identifying individuals in the last 15 months of life is needed to improve the patient experience of care and reducing the per capita cost of healthcare. This study developed and validated a predictive model for 15-month mortality with higher generalisability than previous administrative claims-based studies.

Dataset Information

Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chinese rural population.

Publications

Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chinese rural population.

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