scholarly journals Cardiovascular Disease Risk Prediction Models in Haiti: Implications for Primary Prevention in Low-Middle Income Countries

2021 ◽  
Author(s):  
Lily D Yan ◽  
Jean Lookens Pierre ◽  
Vanessa Rouzier ◽  
Michel Theard ◽  
Alexandra Apollon ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Groups ◽  
Cvd Risk ◽  
Middle Income ◽  
Risk Prediction Models ◽  
Middle Income Countries ◽  
Low Middle Income Countries

Background Cardiovascular diseases (CVD) are rapidly increasing in low-middle income countries (LMICs). Accurate risk assessment is essential to reduce premature CVD by targeting primary prevention and risk factor treatment among high-risk groups. Available CVD risk prediction models are built on predominantly Caucasian, high-income country populations, and have not been evaluated in LMIC populations. Objective To compare the predicted 10-year risk of CVD and identify high-risk groups for targeted prevention and treatment in Haiti. Methods We used cross-sectional data within the Haiti CVD Cohort Study, including 653 adults ≥ 40 years without known history of CVD and with complete data. Six CVD risk prediction models were compared: pooled cohort equations (PCE), adjusted PCE with updated cohorts, Framingham CVD Lipids, Framingham CVD Body Mass Index (BMI), WHO Lipids, and WHO BMI. Risk factors were measured during clinical exams. Primary outcome was continuous and categorical predicted 10-year CVD risk. Secondary outcome was statin eligibility. Results Seventy percent were female, 65.5% lived on a daily income of ≤1 USD, 57.0% had hypertension, 14.5% had hypercholesterolemia, 9.3% had diabetes mellitus, 5.5% were current smokers, and 2.0% had HIV. Predicted 10-year CVD risk ranged from 3.9% in adjusted PCE (IQR 1.7-8.4) to 9.8% in Framingham-BMI (IQR 5.0-17.8), and Spearman rank correlation coefficients ranged from 0.87 to 0.98. The percent of the cohort categorized as high risk using the uniform threshold of 10-year CVD risk ≥ 7.5% ranged from 28.8% in the adjusted PCE model to 62.0% in the Framingham-BMI model (χ2 = 331, p value < 0.001). Statin eligibility also varied widely. Conclusions In the Haiti CVD Cohort, there was substantial variation in the proportion identified as high-risk and statin eligible using existing models, leading to very different treatment recommendations and public health implications depending on which prediction model is chosen. There is a need to design and validate CVD risk prediction tools for low-middle income countries that include locally relevant risk factors.

scholarly journals Long-term maintenance rituximab for ANCA-associated vasculitis: relapse and infection prediction models

Rheumatology ◽  
2020 ◽  
Author(s):  
Mark E McClure ◽  
Yajing Zhu ◽  
Rona M Smith ◽  
Seerapani Gopaluni ◽  
Joanna Tieu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Groups ◽  
Risk Of Infection ◽  
Risk Prediction Models ◽  
Anca Associated Vasculitis ◽  
Term Maintenance

Abstract Objectives Following a maintenance course of rituximab (RTX) for ANCA-associated vasculitis (AAV), relapses occur on cessation of therapy, and further dosing is considered. This study aimed to develop relapse and infection risk prediction models to help guide decision making regarding extended RTX maintenance therapy. Methods Patients with a diagnosis of AAV who received 4–8 grams of RTX as maintenance treatment between 2002 and 2018 were included. Both induction and maintenance doses were included; most patients received standard departmental protocol consisting of 2× 1000 mg 2 weeks apart, followed by 1000 mg every 6 months for 2 years. Patients who continued on repeat RTX dosing long-term were excluded. Separate risk prediction models were derived for the outcomes of relapse and infection. Results A total of 147 patients were included in this study with a median follow-up of 63 months [interquartile range (IQR): 34–93]. Relapse: At time of last RTX, the model comprised seven predictors, with a corresponding C-index of 0.54. Discrimination between individuals using this model was not possible; however, discrimination could be achieved by grouping patients into low- and high-risk groups. When the model was applied 12 months post last RTX, the ability to discriminate relapse risk between individuals improved (C-index 0.65), and once again, clear discrimination was observed between patients from low- and high-risk groups. Infection: At time of last RTX, five predictors were retained in the model. The C-index was 0.64 allowing discrimination between low and high risk of infection groups. At 12 months post RTX, the C-index for the model was 0.63. Again, clear separation of patients from two risk groups was observed. Conclusion While our models had insufficient power to discriminate risk between individual patients they were able to assign patients into risk groups for both relapse and infection. The ability to identify risk groups may help in decisions regarding the potential benefit of ongoing RTX treatment. However, we caution the use of these prediction models until prospective multi-centre validation studies have been performed.

scholarly journals Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment

2021 ◽  
Author(s):  
Zhe Xu ◽  
Matthew Arnold ◽  
David Stevens ◽  
Stephen Kaptoge ◽  
Lisa Pennells ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Predictive Performance ◽  
Statin Treatment ◽  
Clinical Practice Research Datalink ◽  
Cvd Risk ◽  
Risk Prediction Models

Abstract Cardiovascular disease (CVD) risk prediction models are used to identify high-risk individuals and guide statin-initiation. However, these models are usually derived from individuals who may initiate statins during follow-up. We present a simple approach to address statin-initiation to predict “statin-naïve” CVD risk. We analyzed primary care data (2004-2017) from the UK Clinical Practice Research Datalink for 1,678,727 individuals (40-85 years) without CVD or statin treatment history at study entry. We derived age- and sex-specific prediction models including conventional risk factors and a time-dependent effect of statin-initiation constrained to 25% risk reduction (from trial results). We compared predictive performance and measures of public-health impact (e.g., numbers-needed-to-screen to prevent one case) against models ignoring statin-initiation. During a median follow-up of 8.9 years, 103,163 individuals developed CVD. In models accounting for versus ignoring statin initiation, 10-year CVD risk predictions were slightly higher; predictive performance was moderately improved. However, few individuals were reclassified to a high-risk threshold, resulting in negligible improvements in numbers-needed-to-screen to prevent one case. In conclusion, incorporating statin effects from trial results into risk prediction models enables statin-naïve CVD risk estimation, provides moderate gains in predictive ability, but had a limited impact on treatment decision-making under current guidelines in this population.

scholarly journals Development of Nonlaboratory-Based Risk Prediction Models for Cardiovascular Diseases Using Conventional and Machine Learning Approaches

2021 ◽  
Vol 18 (23) ◽  
pp. 12586
Author(s):  
Mirza Rizwan Sajid ◽  
Bader A. Almehmadi ◽  
Waqas Sami ◽  
Mansour K. Alzahrani ◽  
Noryanti Muhammad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Risk Prediction ◽  
Prediction Models ◽  
Good Choice ◽  
Support Vector ◽  
Learning Approaches ◽  
Middle Income ◽  
Risk Prediction Models ◽  
Low Middle Income Countries

Criticism of the implementation of existing risk prediction models (RPMs) for cardiovascular diseases (CVDs) in new populations motivates researchers to develop regional models. The predominant usage of laboratory features in these RPMs is also causing reproducibility issues in low–middle-income countries (LMICs). Further, conventional logistic regression analysis (LRA) does not consider non-linear associations and interaction terms in developing these RPMs, which might oversimplify the phenomenon. This study aims to develop alternative machine learning (ML)-based RPMs that may perform better at predicting CVD status using nonlaboratory features in comparison to conventional RPMs. The data was based on a case–control study conducted at the Punjab Institute of Cardiology, Pakistan. Data from 460 subjects, aged between 30 and 76 years, with (1:1) gender-based matching, was collected. We tested various ML models to identify the best model/models considering LRA as a baseline RPM. An artificial neural network and a linear support vector machine outperformed the conventional RPM in the majority of performance matrices. The predictive accuracies of the best performed ML-based RPMs were between 80.86 and 81.09% and were found to be higher than 79.56% for the baseline RPM. The discriminating capabilities of the ML-based RPMs were also comparable to baseline RPMs. Further, ML-based RPMs identified substantially different orders of features as compared to baseline RPM. This study concludes that nonlaboratory feature-based RPMs can be a good choice for early risk assessment of CVDs in LMICs. ML-based RPMs can identify better order of features as compared to the conventional approach, which subsequently provided models with improved prognostic capabilities.

scholarly journals Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8232
Author(s):  
Amalia Hosein ◽  
Valerie Stoute ◽  
Samantha Chadee ◽  
Natasha Ramroop Singh
Keyword(s):  
Cardiovascular Disease ◽  
High Risk ◽  
Trinidad And Tobago ◽  
Risk Prediction ◽  
Prediction Models ◽  
Time Frame ◽  
Risk Scores ◽  
Cvd Risk ◽  
Biometric Data ◽  
Risk Prediction Models

Background Cardiovascular Disease (CVD) risk prediction models have been useful in estimating if individuals are at low, intermediate, or high risk, of experiencing a CVD event within some established time frame, usually 10 years. Central to this is the concern in Trinidad and Tobago of using pre-existing CVD risk prediction methods, based on populations in the developed world (e.g. ASSIGN, Framingham and QRISK®2), to establish risk for its multiracial/ethnic Caribbean population. The aim of this study was to determine which pre-existing CVD risk method is best suited for predicting CVD risk for individuals in this population. Method A survey was completed by 778 participants, 526 persons with no prior CVD, and 252 who previously reported a CVD event. Lifestyle and biometric data was collected from non-CVD participants, while for CVD participants, medical records were used to collect data at the first instance of CVD. The performances of three CVD risk prediction models (ASSIGN, Framingham and QRISK®2) were evaluated using their calculated risk scores. Results All three models (ASSIGN, Framingham and QRISK®2) identified less than 62% of cases (CVD participants) with a high proportion of false-positive predictions to true predictions as can be seen by positive predictabilities ranging from 78% (ASSIGN and Framingham) to 87% (QRISK®2). Further, for all three models, individuals whose scores fell into the misclassification range were 2X more likely to be individuals who had experienced a prior CVD event as opposed to healthy individuals. Conclusion The ASSIGN, Framingham and QRISK®2 models should be utilised with caution on a Trinidad and Tobago population of intermediate and high risk for CVD since these models were found to have underestimated the risk for individuals with CVD up to 2.5 times more often than they overestimated the risk for healthy persons.

Risk Factors of Mortality Among Patients with Stroke in Low Middle-Income Countries:  A Series of 180 Patients

2020 ◽  
Author(s):  
Larrey Kamabu ◽  
Hervé Monka Lekuya ◽  
Bienvenu Muhindo Kasusula ◽  
Nicole Kavugho Mutimani ◽  
Louange Maha Kathaka ◽  
...  
Keyword(s):  
Risk Factors ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low Middle Income Countries

scholarly journals Finding Children with High Risk of Non-Vaccination in 92 Low- and Middle-Income Countries: A Decision Tree Approach

Vaccines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 646
Author(s):  
Thiago M. Santos ◽  
Bianca O. Cata-Preta ◽  
Cesar G. Victora ◽  
Aluisio J. D. Barros
Keyword(s):  
High Risk ◽  
Decision Tree ◽  
Immunization Coverage ◽  
Risk Groups ◽  
Demographic And Health Surveys ◽  
Middle Income ◽  
Agenda 2030 ◽  
Middle Income Countries ◽  
Tree Approach ◽  
Low And Middle Income

Reducing vaccination inequalities is a key goal of the Immunization Agenda 2030. Our main objective was to identify high-risk groups of children who received no vaccines (zero-dose children). A decision tree approach was used for 92 low- and middle-income countries using data from Demographic and Health Surveys and Multiple Indicator Cluster Surveys, allowing the identification of groups of children aged 12–23 months at high risk of being zero dose (no doses of the four basic vaccines—BCG, polio, DPT and measles). Three high-risk groups were identified in the analysis combining all countries. The group with the highest zero-dose prevalence (42%) included 4% of all children, but almost one in every four zero-dose children in the sample. It included children whose mothers did not receive the tetanus vaccine during and before the pregnancy, who had no antenatal care visits and who did not deliver in a health facility. Separate analyses by country presented similar results. Children who have been missed by vaccination services were also left out by other primary health care interventions, especially those related to antenatal and delivery care. There is an opportunity for better integration among services in order to achieve high and equitable immunization coverage.

scholarly journals Seroprevalence of SARS CoV-2 infection in Low and Middle-Income Countries and risk classification in the context of rolling out vaccine candidate across these countries : Protocol for a systematic review and meta-analysis

2021 ◽  
Author(s):  
Martin Ackah ◽  
Louise Ameyaw ◽  
Kwadwo Owusu Akuffo ◽  
Cynthia Osei Yeboah ◽  
Nana Esi Wood ◽  
...  
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Vaccine Candidate ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Research Database ◽  
Middle Income ◽  
Middle Income Countries ◽  
Low Middle Income Countries ◽  
Low And Middle Income

Abstract Background Seroprevalence of SARS Cov-2 provides a good indication of the extent of exposure and spread in the population, as well as those likely to benefit from a vaccine candidate. To date, there is no published or ongoing systematic review on the seroprevalence of COVID-19 in Low- and Middle-Income Countries (LMICs). This systematic review and meta-analysis will estimate SARS Cov-2 seroprevalence and the risk factors for SARS Cov-2 infection in LMICs.Methods We will search PubMed, EMBASE, WHO COVID-19 Global research database, Google Scholar, the African Journals Online, LILAC, HINARI, medRxiv, bioRxiv and Cochrane Library for potentially useful studies on seroprevalence of COVID-19 in LMICs from December 2019 to December 2020 without language restriction. Two authors will independently screen all the articles, select studies based on pre-specified eligibility criteria and extract data using a pre-tested data extraction form. Any disagreements will be resolved through discussion between the authors. The pooled seroprevalence of SARS CoV-2 for people from LMICs will be calculated. Random effects model will be used in case of substantial heterogeneity in the included studies, otherwise fixed-effect model will be used. A planned subgroup, sensitivity and meta-regression analyses will be performed. For comparative studies, the analyses will be performed using Review Manager v 5.4; otherwise, STATA 16 will be used. All effect estimates will be presented with their confidence intervals.Discussion The study will explore and systematically review empirical evidence on SARS Cov-2 seroprevalence in LMICs, and to assess the risk factors for SARS Cov-2 infection in Low Middle Income Countries in the context of rolling out vaccines in these countries. Finally, explore risk classifications to help with the rolling out of vaccines in LMICs.Systematic review registration: The protocol for this review has been registered in PROSPERO (CRD422020221548).

scholarly journals Polygenic risk prediction models for colorectal cancer: a systematic review

BMC Cancer ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Michele Sassano ◽  
Marco Mariani ◽  
Gianluigi Quaranta ◽  
Roberta Pastorino ◽  
Stefania Boccia
Keyword(s):  
Colorectal Cancer ◽  
Risk Factors ◽  
Systematic Review ◽  
Prediction Model ◽  
Risk Prediction ◽  
Genetic Variants ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Discriminatory Accuracy ◽  
Traditional Risk Factors

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

scholarly journals Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts

Stroke ◽  
2020 ◽  
Vol 51 (7) ◽  
pp. 2095-2102
Author(s):  
Eugene Y.H. Tang ◽  
Christopher I. Price ◽  
Louise Robinson ◽  
Catherine Exley ◽  
David W. Desmond ◽  
...  
Keyword(s):  
Risk Factors ◽  
General Population ◽  
Risk Prediction ◽  
Prediction Models ◽  
Disease Risk ◽  
Risk Index ◽  
Area Under The Curve ◽  
Risk Prediction Models ◽  
C Statistic ◽  
Dementia Risk

Background and Purpose: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. Methods: Data were harmonized from 4 stroke studies (follow-up range, ≈12–18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. Results: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). Conclusions: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

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