Cardiovascular Disease Risk Prediction Models

To develop 10-year cardiovascular disease (CVD) risk prediction models in Chinese patients with type 2 diabetes mellitus (T2DM) managed in primary care using machine learning (ML) methods. In this 10-year population-based retrospective cohort study, 141 516 Chinese T2DM patients aged 18 years or above, without history of CVD or end-stage renal disease and managed in public primary care clinics in 2008, were included and followed up until December 2017. Two-thirds of the patients were randomly selected to develop sex-specific CVD risk prediction models. The remaining one-third of patients were used as the validation sample to evaluate the discrimination and calibration of the models. ML-based methods were applied to missing data imputation, predictor selection, risk prediction modelling, model interpretation, and model evaluation. Cox regression was used to develop the statistical models in parallel for comparison. During a median follow-up of 9.75 years, 32 445 patients (22.9%) developed CVD. Age, T2DM duration, urine albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), systolic blood pressure variability and glycated haemoglobin (HbA1c) variability were the most important predictors. ML models also identified nonlinear effects of several predictors, particularly the U-shaped effects of eGFR and body mass index. The ML models showed a Harrell's C statistic of >0.80 and good calibration. The ML models performed significantly better than the Cox regression models in CVD risk prediction and achieved better risk stratification for individual patients. Using routinely available predictors and ML-based algorithms, this study established 10-year CVD risk prediction models for Chinese T2DM patients in primary care. The findings highlight the importance of renal function indicators, and variability in both blood pressure and HbA1c as CVD predictors, which deserve more clinical attention. The derived risk prediction tools have the potential to support clinical decision making and encourage patients towards self-care, subject to further research confirming the models' feasibility, acceptability and applicability at the point of care.

Traditional cardiovascular disease risk prediction models generate a combined risk assessment for myocardial infarction (MI) and ischemic stroke (IS), which may inadequately reflect the distinct etiologies and disparate risk factors of MI and IS. We aim to develop prediction models that separately estimate the risks of MI and IS. Our analysis included 6,242,404 individuals over 40 years old who participated in a cardiovascular health screening examination in 2009. Potential predictors were selected based on a literature review and the available data. Cox proportional hazards models were used to construct 5-year risk prediction models for MI, and IS. Model performance was assessed through discrimination and calibration. During a follow-up of 39,322,434.39 person-years, 89,140 individuals were diagnosed with MI and 116,259 with IS. Both models included age, sex, body mass index, smoking, alcohol consumption, physical activity, diabetes, hypertension, dyslipidemia, chronic kidney disease, and family history. Statin use was factored into the classification of dyslipidemia. The c-indices for the prediction models were 0.709 (0.707-0.712) for MI, and 0.770 (0.768-0.772) for IS. Age and hypertension exhibited a more pronounced effect on IS risk prediction than MI, whereas smoking, body mass index, dyslipidemia, and chronic kidney disease showed the opposite effect. The models calibrated well for low-risk individuals. Our findings underscore the necessity of tailored risk assessments for MI and IS to facilitate the early detection and accurate identification of heterogeneous at-risk populations for atherosclerotic cardiovascular disease.

Aims Several risk prediction models are available for determining the 10-year risk of cardiovascular disease (CVD), including the Norwegian NORRISK 2 model. However, the existing models explain only a modest proportion of the incidence. Therefore, this study aimed to develop improved models for predicting the 10-year risk of myocardial infarction (MI) for both sexes. Methods Data from 31,946 participants without prior CVD were analyzed. The data set was divided into a training set (for estimation) and a test set (for model evaluation). Prediction models were developed on the training set for each sex using XGBoost and logistic regression, using 96 (men) and 100 (women) variables. The models were evaluated on the test set using Receiver-Operating-Characteristic (ROC) and Precision-Recall (PR) curves. Age and sex-specific thresholds for intervention were explored through cross-validation on the training set. Model performance was compared to the NORRISK 2 model using the test set. Results The XGBoost model improved CVD risk prediction for men across all age groups (AUCROC for XGBoost and NORRISK 2, respectively, 0.72 and 0.65 (age 45-54), 0.63 and 0.62 (age 55-64), 0.69 and 0.62 (age 65-74)). For women, NORRISK2 performed better than XGBoost in ROC curve evaluation. However, XGBoost were superior to NORRISK2 when evaluated by PR-curves in women aged 55-64 (PR-AUC for XGBoost and NORRISK 2, respectively, 0.20 and 0.12 compared to 0.06 for a no-skills model). Potential new risk predictors, such as alkaline phosphatase (ALP) for men and thyroid stimulation hormone (TSH) for women, were identified. The results also indicate that the thresholds for intervention should be sex specific. Conclusion By employing machine learning and incorporating sex-specific risk factors, we propose improved risk prediction models for CVD, particularly in men. Introducing sex-specific thresholds for intervention could enhance CVD prevention for both women and men.

Current cardiovascular disease (CVD) risk prediction models tailored for older adults are inadequate. This study aimed to validate, update and assess the utility of widely used CVD risk prediction models including American College of Cardiology/American Heart Association, 2008 Framingham, GloboRisk, National Vascular Disease Prevention Alliance and Predict1 originally developed for middle-aged population, as well as an age-specific Systematic COronary Risk Evaluation 2-Older Person model, in Australian and the US community-dwelling older adults. Participants, without history of CVD events, dementia or physical disability, enrolled in the ASPREE (ASPirin in Reducing Events in the Elderly) clinical trial and ASPREE-eXTention observational post-trial follow-up, were considered for CVD risk prediction. The main outcome was predicted CVD risk from adjudicated CVD events. The performance of the original, recalibrated (adjusting models' intercept and slope) and updated (adjusting models' coefficients) models was evaluated by discrimination (C statistic), calibration (calibration plots) and clinical utility (decision curves). Models were extended by incorporating predictors including serum creatinine, depression and socioeconomic status index (Index of Relative Socio-economic Advantage and Disadvantage, IRSAD) into models' equation, and the changes in discrimination were evaluated. Among 15 618 adults (mean age 75 (4.4) years), 520 men and 498 women experienced CVD events over a median follow-up of 6.3 (IQR: 5.2-7.7) years. Following updating, the discrimination power of models increased for both sexes (C statistics ranged 0.62-0.64 for men and 0.68-0.69 for women). Updated models indicated good calibration, with an added net benefit at the risk thresholds ranging from 4%-10% for women to 5%-12% for men. Incorporating IRSAD, depression and serum creatinine did not improve CVD risk discrimination of updated models. Updating models, by adjusting model coefficients to better reflect the characteristics and risk factors of older adults, improves CVD risk prediction in a large cohort of relatively healthy Caucasian population aged 70+. Further external validation in diverse older populations including those with frailty and multimorbidity is recommended before clinical implementation.

Table of ContentsI. IntroductionA. Development of GuidelinesB. General ApproachC. Preoperative Clinical EvaluationII. Further Preoperative Testing to Assess Coronary RiskA. Clinical MarkersB. Functional CapacityC. Surgery-Specific RiskIII. Management of Specific Preoperative Cardiovascular Condition

All applications in current trends need to use Machine Learning and Big Data in this huge data world.Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.Machine learning use historical data as input to predict new output values.Small Scale and even large developers cannot maintain Machine Learning Algorithms and other big data processing techniques all by themselves.To harvest, process and analyze the data we need a data processing engine.Our project is to provide them with a machine learning engine which runs on.a specified port on their servers which collects the data sets in real time.This which can be queried using a simple Query Language and analyze the database on the query.The purpose of our project is to create a Data Processing Engine for Big Data Analytics. Machine learning and Big Data plays a major role in the current huge data world. Our project is to maintain a dedicated machine learning and data processing engine, so that even small developers without the knowledge of machine learning and big data analysis can produce expected results based on Machine Learning to make their application smooth. All applications in current trends need to use Machine Learning and Big Data in this huge data world. Machine learning is a subset of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning uses historical data as input to predict new output values. Small Scale and even large developers cannot maintain Machine Learning Algorithms and other big data processing techniques all by themselves. To harvest, process and analyze the data we need a data processing engine. This project is to provide them a data processing engine which runs on a specified port on their servers which collects the data sets in real time. This can be queried using a simple Query Language and analyze the data and produce respected results using ML based on the query

Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models. Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic. We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.14, P <0.001). The simplified diet-containing model also showed a decrease in AIC (delta AIC=14), a 38% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable. We suggest that dietary information may be useful in constructing CVD risk prediction models.

It has been suggested that integrated cardiovascular risk management guidelines and absolute cardiovascular risk prediction scores should be used routinely in aeromedical risk assessment. In this study a cardiovascular disease (CVD) risk prediction model has been applied to UK commercial pilots as an occupational group. This retrospective cross-sectional study measured the variables age, sex, body mass index (BMI), blood pressure, use of antihypertensive medication, current smoking, and diabetes status of commercial pilots. Individual 10-yr absolute CVD risk scores (also referred to as 10-yr global CVD risk) were calculated using a non-laboratory based Framingham Heart Study developed model. Of the 14,379 subjects eligible for the study, none had missing values for risk factors. None of the female pilots and 9.7% of all male pilots were found to be high risk. The mean 10-yr absolute CVD risk for the entire pilot population was 8.41% (median 5.6). High-risk pilots are concentrated around 60 yr of age, (mean 59, median 60 yr) with an age range of 40-81 yr. A sub-analysis of high-risk pilots younger than 65 revealed 1137 pilots in this group. The application of a 10-yr absolute CVD risk prediction model identified a group of pilots, previously unidentified, who may require a more comprehensive risk assessment. Pilots are continuing to fly commercially beyond the age of 60, which results in substantial increase in the CVD risk burden of the pilot population as a whole.

Background and ObjectivesContemporary cardiovascular disease (CVD) risk prediction models are rarely applied in routine clinical practice in China due to substantial regional differences in absolute risks of major CVD types within China. Moreover, the inclusion of blood lipids in most risk prediction models also limits their use in the Chinese population. We developed 10-year CVD risk prediction models excluding blood lipids that may be applicable to diverse regions of China.MethodsWe derived sex-specific models separately for ischemic heart disease (IHD), ischemic stroke (IS), and hemorrhagic stroke (HS) in addition to total CVD in the China Kadoorie Biobank. Participants were age 30–79 years without CVD at baseline. Predictors included age, systolic and diastolic blood pressure, use of blood pressure–lowering treatment, current daily smoking, diabetes, and waist circumference. Total CVD risks were combined in terms of conditional probability using the predicted risks of 3 submodels. Risk models were recalibrated in each region by 2 methods (practical and ideal) and risk prediction was estimated before and after recalibration.ResultsModel derivation involved 489,596 individuals, including 45,947 IHD, 43,647 IS, and 11,168 HS cases during 11 years of follow-up. In women, the Harrell C was 0.732 (95% CI 0.706–0.758), 0.759 (0.738–0.779), and 0.803 (0.778–0.827) for IHD, IS, and HS, respectively. The Harrell C for total CVD was 0.734 (0.732–0.736), 0.754 (0.752–0.756), and 0.774 (0.772–0.776) for models before recalibration, after practical recalibration, and after ideal recalibration. The calibration performances improved after recalibration, with models after ideal recalibration showing the best model performances. The results for men were comparable to those for women.DiscussionOur CVD risk prediction models yielded good discrimination of IHD and stroke subtypes in addition to total CVD without including blood lipids. Flexible recalibration of our models for different regions could enable more widespread use using resident health records covering the overall Chinese population.Classification of EvidenceThis study provides Class I evidence that a prediction model incorporating accessible clinical variables predicts 10-year risk of IHD, IS, and HS in the Chinese population age 30–79 years.

BackgroundCardiovascular disease (CVD) risk prediction models are often used to identify individuals at high risk of CVD events. Providing preventive treatment to these individuals may then reduce the CVD burden at population level. However, different prediction models may predict different (sets of) CVD outcomes which may lead to variation in selection of high risk individuals. Here, it is investigated if the use of different prediction models may actually lead to different treatment recommendations in clinical practice.MethodThe exact definition of and the event types included in the predicted outcomes of four widely used CVD risk prediction models (ATP-III, Framingham (FRS), Pooled Cohort Equations (PCE) and SCORE) was determined according to ICD-10 codes. The models were applied to a Dutch population cohort (n = 18,137) to predict the 10-year CVD risks. Finally, treatment recommendations, based on predicted risks and the treatment threshold associated with each model, were investigated and compared across models.ResultsDue to the different definitions of predicted outcomes, the predicted risks varied widely, with an average 10-year CVD risk of 1.2% (ATP), 5.2% (FRS), 1.9% (PCE), and 0.7% (SCORE). Given the variation in predicted risks and recommended treatment thresholds, preventive drugs would be prescribed for 0.2%, 14.9%, 4.4%, and 2.0% of all individuals when using ATP, FRS, PCE and SCORE, respectively.ConclusionWidely used CVD prediction models vary substantially regarding their outcomes and associated absolute risk estimates. Consequently, absolute predicted 10-year risks from different prediction models cannot be compared directly. Furthermore, treatment decisions often depend on which prediction model is applied and its recommended risk threshold, introducing unwanted practice variation into risk-based preventive strategies for CVD.

Discrimination and calibration are insensitive metrics for comparing the performance of cardiovascular risk prediction models

In a 1998 survey of New York metropolitan area anesthesiologists, surgeons, and cardiologists, the three specialties were in general agreement that important purposes of a cardiology consultation are to treat an inadequately treated cardiac condition before surgery (e.g., unstable angina or congestive heart failure [CHF]), to provide data to use in anesthesia management (e.g., ischemic threshold of tachycardia on stress test or left ventricular ejection fraction), and possibly to diagnose a medical condition before surgery (e.g., the cause of a new-onset atrial fibrillation). The yield of a cardiology consultation, in terms of new therapy or significant effect on patient management strategy prior to surgery, has, however, been reported to vary widely from 10% to more than 70%. The reasons why the consultation process often falls short of ideal are probably multifold, but may often originate from vague understanding of the consultation process. The physician initiating the consultation, whether an anesthesiologist or a surgeon, may not make it clear to the cardiologist why the consultation is being sought. In a retrospective review of 202 cardiology consultations at a university hospital, it was found that 108 just asked for an “evaluation,” 79 asked for a “clearance,” and 9 did not specifically request anything. Only six posed a specific question. As a result, the consultant often makes broadly inclusive, general remarks about perioperative management of the patient and may recommend preoperative diagnostic work-up that does not influence the patient’s outcome but prolongs the hospital stay. In this review are presented (1) the indications for cardiology consultations as implied in the American College of Cardiology–American Heart Association (ACC–AHA) guidelines on preoperative cardiac evaluation, and (2) suggestions on how the ACC–AHA guidelines may be critically applied to, and improve, the consultation process. Preoperative Cardiac Consultation Based on the ACC–AHA Guidelines The ACC–AHA guidelines on preoperative cardiac evaluation were published initially in 1996 and were also endorsed by the Society of Cardiovascular Anesthesiologists and the Society for Vascular Surgery. An updated version of the guidelines was published in 2002. The guidelines were based on the then-available literature as well as expert opinions from the disciplines of anesthesiology, cardiology, electrophysiology, vascular medicine, vascular surgery, and noninvasive cardiac testing. The guidelines provided an 8-step algorithm for stratifying the patient’s risks and triaging to either surgery or cardiac evaluation. The first three steps of the guidelines consider the urgency of the operation and the recency of cardiac evaluation and intervention. If the operation is not emergent and if there has not been recent cardiac evaluation and/or intervention with no significant interim changes, then the remainder of the guidelines are applied. Steps 4 through 7 deal with an assessment of the patient’s clinical predictors of cardiac risk, functional status, and the risk of the surgery proposed. Step 8, or noninvasive cardiac testing to further define the patient’s risk, is indicated (1) if the patient has a major clinical predictor, (2) if the patient has an intermediate clinical predictor and either has poor functional status or is undergoing a high-risk surgery, or (3) if the patient has poor functional status and is undergoing a high-risk surgery. As defined by the ACC–AHA, major clinical predictors are unstable coronary syndrome, decompensated congestive heart failure (CHF), significant arrhythmias, and severe valvular disease. Intermediate clinical predictors are mild angina pectoris, history of myocardial infarction (MI) or CHF, diabetes mellitus, and chronic renal failure with serum creatinine greater than 2 mg/dl. A high-risk surgery carries a greater than 5% perioperative risk of cardiac events such as MI, CHF, or death and is exemplified by emergent major operations, especially in the elderly, aortic, and other major vascular procedures, peripheral vascular bypass procedures, and anticipated prolonged surgical procedures associated with large fluid shifts and/or blood loss. An intermediate-risk surgery carries 1–5% perioperative risk of cardiac events and is exemplified by carotid endarterectomy, intraperitoneal and Director of Vascular Anesthesia, Department of Anesthesia & Critical Care, Beth Israel Deaconess Medical Center, and Associate Professor of Anaesthesia, Harvard Medical School.

Chronic kidney disease (CKD) is a complex disease in which renal damage is frequently associated with cardiovascular and cerebral complications. The aim of our study was to establish if kidney vascular lesions are accompanied by atherosclerotic lesions on the aorta, the coronary and cerebral arteries.Vascular lesions of 364 violent death cases were analyzed based on data provided by necropsy reports. Aortic, coronary and cerebral lesions were identified by macroscopic examination, while renal lesions were detected by light microscopy. The mean age of the subjects was 55.61+ 10.57 (42, 86). Atherosclerotic lesions on the coronary arteries were present at 65.4% of the subjects, while 62.6% had atherosclerotic lesions on the aorta and 60.2% on the arteries of the circle of Willis. Renal vascular lesions were encountered in 95 cases, representing 26.09% of all subjects. The subjects with renal vascular lesions also presented coronary, cerebral and aortic vascular lesions in 89.5% of the cases. Thus, these subjects presented significantly more such lesions than the subjects without renal vascular lesions did (p

Background Displaced intracapsular hip fractures are most commonly treated using either total hip arthroplasty (THA) or hemiarthroplasty. Aim of the Work To review systematically studies about the use of Total hip arthroplasty Versus hemiarthoplasty in management of displaced femoral neck fracture in elderly. Patients and Methods We included randomized controlled trials, including cluster RCTs, controlled (non-randomized) clinical trials or cluster trials, prospective and retrospective comparative cohort studies, and case-control or nested case-control studies. We excluded cross-sectional studies, case series, and case reports. Search results were uploaded to systematic review management software and manually screened for eligibility to be included. PRISMA flowchart were produced based on the search results and the inclusion/exclusion criteria. Results In our study there was statistically significant increase in the incidence of Reoperation in total hip arthroplasty (3.5%) than hemiarthroplasty (2.3%) with p-value &amp;lt; 0.001. Regarding Dislocation, there was statistically significant increase in the incidence of Dislocation in total hip arthroplasty (3.2%) than hemiarthroplasty (1.7%) with p-value &amp;lt; 0.001. Regarding Infection there was statistically significant increase in the incidence of Infection in total hip arthroplasty (4.5%) than hemiarthroplasty (3.4%) with p- value &amp;lt; 0.001. Regarding General Complications including: (Pneumonia – DVT- Pulmonary embolism- Myocardial infarct- Cerebral vascular lesion- Acute kidney failure- life threatening complications there was statistically no significant difference in the incidence of General complications in hemiarthroplasty (5.5%) and total hip arthroplasty (5.4%) with p-value = 0.608. Conclusions The authors concluded that DMTHA offered a better functional outcome than BA with no significant complications or mortality, and there was no significant difference between DMTHA and BA in the rate of dislocation.

Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). However, traditional CVD risk prediction equations do not work well in patients with CKD, and inclusion of kidney disease metrics such as albuminuria and estimated glomerular filtration rate have a modest to no benefit in improving prediction. As CKD progresses, the strength of traditional CVD risk factors in predicting clinical outcomes weakens. A pooled cohort equation used for CVD risk prediction is a useful tool for guiding clinicians on management of patients with CVD risk, but these equations do not calibrate well in patients with CKD, although a number of studies have developed modifications of the traditional equations to improve risk prediction. The reason for the poor calibration may be related to the fact that as CKD progresses, associations of traditional risk factors such as BMI, lipids and blood pressure with CVD outcomes are attenuated or reverse, and other risk factors may become more important. Large national cohorts such as the US Veteran cohort with many patients with evolving CKD may be useful resources for the developing CVD prediction models; however, additional considerations are needed for the unique composition of patients receiving care in these healthcare systems, including those with multiple comorbidities, as well as mental health issues, homelessness, posttraumatic stress disorders, frailty, malnutrition and polypharmacy. Machine learning over conventional risk prediction models may be better suited to handle the complexity needed for these CVD prediction models.