Prediction models for risk classification in cardiovascular disease

Recommendations for prediction models in clinical practice guidelines for cardiovascular diseases are over-optimistic: a global survey utilizing a systematic literature search

Background

This study aimed to synthesize the recommendations for prediction models in cardiovascular clinical practice guidelines (CPGs) and assess the methodological quality of the relevant primary modeling studies.

Methods

We performed a systematic literature search of all available cardiovascular CPGs published between 2018 and 2023 that presented specific recommendations (whether in support or non-support) for at least one multivariable clinical prediction model. For the guideline-recommended models, the assessment of the methodological quality of their primary modeling studies was conducted using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

Results

In total, 46 qualified cardiovascular CPGs were included, with 69 prediction models and 80 specific recommendations. Of the 80 specific recommendations, 74 supported 57 models (53 were fully recommended and 4 were conditionally recommended) in cardiovascular practice with moderate to strong strength. Most of the guideline-recommended models were focused on predicting prognosis outcomes (53/57, 93%) in primary and tertiary prevention, focusing primarily on long-term risk stratification and prognosis management. A total of 10 conditions and 7 types of target population were involved in the 57 models, while heart failure (14/57, 25%) and a general population with or without cardiovascular risk factor(s) (12/57, 21%) received the most attention from the guidelines. The assessment of the methodological quality of 57 primary studies on the development of the guideline-recommended models revealed that only 40% of the modeling studies had a low risk of bias (ROB). The causes of high ROB were mainly in the analysis and participant domains.

Conclusions

Global cardiovascular CPGs presented an unduly positive appraisal of the existing prediction models in terms of ROB, leading to stronger recommendations than were warranted. Future cardiovascular practice may benefit from well-established clinical prediction models with better methodological quality and extensive external validation.

Ceramides and Phosphatidylcholines Associate with Cardiovascular Diseases in the Elderly

Background

The ceramide- and phospholipid-based cardiovascular risk score (CERT2) has been found to predict the risk for cardiovascular disease (CVD) events, especially cardiovascular mortality. In the present study, our aim was to estimate the predictive ability of CERT2 for mortality of CVD, coronary artery disease (CAD), and stroke in the elderly and to compare these results with those of conventional lipids.

Methods

We conducted a prospective study with an 18-year follow-up period that included a total of 1260 participants ages ≥64 years. Ceramides and phosphatidylcholines were analyzed using a LC-MS. Total cholesterol and triglycerides were performed by enzymatic methods and HDL cholesterol was determined by a direct enzymatic method. Concentrations of LDL-cholesterol were calculated according to the Friedewald formula.

Results

A higher score of CERT2 was significantly associated with higher CVD, CAD, and stroke mortality during the 18-year follow-up both in unadjusted and adjusted Cox regression models. The unadjusted hazard ratios (HRs) of CERT2 (95% CI) per SD for CVD, CAD, and stroke were 1.72 (1.52–1.96), 1.76 (1.52–2.04), and 1.63 (1.27–2.10), respectively, and the corresponding adjusted HRs (95% CI) per SD for CERT2 were 1.48 (1.29–1.69), 1.50 (1.28–1.75), and 1.41 (1.09–1.83). For conventional lipids, HRs per SD were lower than for CERT2.

Conclusions

The risk score CERT2 associated strongly with CVD, CAD, and stroke mortality in the elderly, while the association between these events and conventional lipids was weak.

Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury

Individual biomarkers of renal injury are only modestly predictive of acute kidney injury (AKI). Using multiple biomarkers has the potential to improve predictive capacity. In this systematic review, statistical methods of articles developing biomarker combinations to predict acute kidney injury were assessed. We identified and described three potential sources of bias (resubstitution bias, model selection bias and bias due to center differences) that may compromise the development of biomarker combinations. Fifteen studies reported developing kidney injury biomarker combinations for the prediction of AKI after cardiac surgery (8 articles), in the intensive care unit (4 articles) or other settings (3 articles). All studies were susceptible to at least one source of bias and did not account for or acknowledge the bias. Inadequate reporting often hindered our assessment of the articles. We then evaluated, when possible (7 articles), the performance of published biomarker combinations in the TRIBE-AKI cardiac surgery cohort. Predictive performance was markedly attenuated in six out of seven cases. Thus, deficiencies in analysis and reporting are avoidable and care should be taken to provide accurate estimates of risk prediction model performance. Hence, rigorous design, analysis and reporting of biomarker combination studies are essential to realizing the promise of biomarkers in clinical practice.

Circulating microRNAs predict future fatal myocardial infarction in healthy individuals - The HUNT study

Coronary heart disease is the most common cause of death, and the number of individuals at risk is increasing. To better manage this pandemic, improved tool for risk prediction, including more accurate biomarkers are needed. The objective of this study was to assess the utility of circulating microRNAs (miRs) to predict future fatal acute myocardial infarction (AMI) in healthy participants. We performed a prospective nested case-control study with 10-year observation period and fatal AMI as endpoint. In total, 179 miRs were quantified by real-time polymerase chain reaction in serum of 112 healthy participants (40-70years) that either (1) suffered from fatal AMI within 10years [n=56], or (2) remained healthy [n=56, risk factor-matched controls]. Candidate miRs were validated in a separate cohort of healthy individuals (n=100). Twelve miRs were differently expressed in cases and controls in the derivation cohort (p<0.05). Among these, 10 miRs differed significantly between cases and controls in the validation cohort (p<0.05). We identified gender dimorphisms, as miR-424-5p and miR-26a-5p were associated exclusively with risk in men and women, respectively. The best model for predicting future AMI consisted of miR-106a-5p, miR-424-5p, let-7g-5p, miR-144-3p and miR-660-5p, providing 77.6% correct classification for both genders, and 74.1% and 81.8% for men and women, respectively. Adding these 5 miRs to the Framingham Risk Score, increased the AUC from 0.72 to 0.91 (p<0.001). In conclusion, we identified several miRs associated with future AMI, revealed gender-specific associations, and proposed a panel of 5 miRs to enhance AMI risk prediction in healthy individuals.

Reclassification of cardiovascular risk by myocardial perfusion imaging in diabetic patients with abnormal resting electrocardiogram

BACKGROUND AND AIMS

Despite an extensive use of stress myocardial perfusion single-photon emission computed tomography (MPS), no study addressed the role of perfusion imaging in diabetic patients with abnormal resting electrocardiogram (ECG). We compared analytical approaches to assess the added value of stress MPS variables in estimating coronary heart disease outcomes in diabetic patients with abnormal resting ECG.

METHODS AND RESULTS

A total of 416 patients with diabetes and abnormal resting ECG who underwent stress MPS were prospectively followed up after the index study. The end point was the occurrence of a major cardiac event, including cardiac death and nonfatal myocardial infarction. At the end of follow-up (median 58 months), 42 patients experienced events. MPS data increased the predictive value of a model including traditional cardiovascular risk factors and left ventricular (LV) ejection fraction (likelihood ratio χ² from 17.54 to 24.15, p < 0.05, with a C statistic of 0.72, 95% confidence interval: 0.65-0.79). The addition of MPS data resulted in reclassification of 25% of the sample with a net reclassification improvement of 0.20 (95% confidence interval: 0.05-0.36). Overall, 63 patients were reclassified to a lower risk category, with a 5-year event rate of 3.5%, and 40 patients were reclassified to a higher risk category, with a 5-year event rate of 20%.

CONCLUSION

The addition of MPS findings to a model based on traditional cardiovascular risk factors and LV ejection fraction improves risk classification for incident cardiac events in diabetic patients with abnormal resting ECG.

Cardiovascular health and cognitive function: the Maine-Syracuse Longitudinal Study

BACKGROUND

Smoking, physical inactivity, and poor diet, along with obesity, fasting glucose and blood pressure have been independently associated with poorer cognitive performance. Few studies have related scales representing a combination of these variables to multiple domains of cognitive performance. The aim of this study was to investigate the association between overall cardiovascular health, incorporating seven components, and cognitive function.

METHODS

A cross-sectional analysis employing 972 participants, from the Maine-Syracuse Longitudinal Study was undertaken. Four health behaviors (body mass index, physical activity, diet, smoking) and three health factors (total cholesterol, blood pressure, and fasting plasma glucose) were measured. Each was categorized according to the American Heart Association definitions for ideal cardiovascular health, except diet, for which two food scores were calculated. A Cardiovascular Health Score was determined by summing the number of cardiovascular metrics at ideal levels. Cognitive function was assessed using a thorough neuropsychological test battery.

RESULTS

Cardiovascular Health Score was positively associated with seven out of eight measures of cognitive function, with adjustment for age, education, and gender. With further adjustment for cardiovascular and psychological variables, these associations remained significant for Visual-Spatial Memory, Working Memory, Scanning and Tracking, Executive Function and the Global Composite score (p<0.05 for all). Ideal levels of a number of health factors and behaviors were positively associated with global cognitive performance.

CONCLUSION

Increasing cardiovascular health, indexed by a higher number of metrics at ideal levels, is associated with greater cognitive performance. Smoking, physical activity, and diet are important components of cardiovascular health that impact upon cognition.

Role of nuclear cardiology for guiding device therapy in patients with heart failure

Heart failure is a dynamic condition with high morbidity and mortality and its prognosis should be reassessed frequently, particularly in patients for whom critical treatment decisions may depend on the results of prognostication. In patients with heart failure, nuclear cardiology techniques are useful to establish the etiology and the severity of the disease, while fewer studies have explored the potential capability of nuclear cardiology to guide cardiac resynchronization therapy (CRT) and to select patients for implantable cardioverter defibrillators (ICD). Left ventricular synchrony may be assessed by radionuclide angiography or gated single-photon emission computed tomography myocardial perfusion scintigraphy. These modalities have shown promise as predictors of CRT outcome using phase analysis. Combined assessment of myocardial viability and left ventricular dyssynchrony is feasible using positron emission tomography and could improve conventional response prediction criteria for CRT. Preliminary data also exists on integrated positron emission tomography/computed tomography approach for assessing myocardial viability, identifying the location of biventricular pacemaker leads, and obtaining left ventricular functional data, including contractile phase analysis. Finally, cardiac imaging with autonomic radiotracers may be useful in predicting CRT response and for identifying patients at risk for sudden cardiac death, therefore potentially offering a way to select patients for both CRT and ICD therapy. Prospective trials where imaging is combined with image-test driven therapy are needed to better define the role of nuclear cardiology for guiding device therapy in patients with heart failure.