Optimal sampling in derivation studies was associated with improved discrimination in external validation for heart failure prognostic models

Risk Stratification Towards Precision Medicine in Heart Failure - Current Progress and Future Perspectives

Clinical risk stratification is a key strategy used to identify low- and high-risk subjects to optimize the management, ranging from pharmacological treatment to palliative care, of patients with heart failure (HF). Using statistical modeling techniques, many HF risk prediction models that combine predictors to assess the risk of specific endpoints, including death or worsening HF, have been developed. However, most risk prediction models have not been well-integrated into the clinical setting because of their inadequacy and diverse predictive performance. To improve the performance of such models, several factors, including optimal sampling and biomarkers, need to be considered when deriving the models; however, given the large heterogeneity of HF, the currently advocated one-size-fits-all approach is not appropriate for every patient. Recent advances in techniques to analyze biological "omics" information could allow for the development of a personalized medicine platform, and there is growing awareness that an integrated approach based on the concept of system biology may be an excessively naïve view of the multiple contributors and complexity of an individual's HF phenotype. This review article describes the progress in risk stratification strategies and perspectives of emerging precision medicine in the field of HF management.

The Global Impact of the COVID-19 Pandemic on STEMI care: A Systematic Review and Meta-Analysis

BACKGROUND

The coronavirus disease-2019 (COVID-19) pandemic has affected patients with ST-segment elevation myocardial infarction (STEMI) requiring primary percutaneous coronary intervention (PCI) worldwide. This review examines the global impact of COVID-19 pandemic on incidence of STEMI admissions, and relationship between the pandemic and door-to-balloon time (D2B), all-cause mortality and other secondary STEMI outcomes.

METHODS

We performed a systematic review and meta-analysis to primarily compare D2B time and in-hospital mortality of STEMI patients who underwent primary PCI during and before the pandemic. Subgroup analyses were performed to investigate the influence of geographical region and income status of a country on STEMI care. An online database search included studies comparing the aforementioned outcomes between STEMI patients during and before the pandemic.

RESULTS

In total, 32 articles were analyzed. Overall, 19,140 and 68,662 STEMI patients underwent primary PCI during and before the pandemic respectively. Significant delay in D2B was observed during the pandemic (WMD=8.10mins; CI:3.90-12.30mins; p=0.0002, I²=90%). In-hospital mortality was higher during the pandemic (OR=1.27; CI:1.09-1.49; p=0.002, I²=36%), however this varied with factors such as geographical location and income status of a country. Subgroup analysis found that low-middle income countries observed a higher rate of mortality during the pandemic (OR=1.52; CI:1.13-2.05; p=0.006), with a similar but insignificant trend seen among the high income countries (OR=1.17; CI:0.95-1.44; p=0.13).

CONCLUSION

The COVID-19 pandemic is associated with worse STEMI performance metrics and clinical outcome, particularly in the Eastern low-middle income status countries. Better strategies are needed to address these global trends in STEMI care during the pandemic.

Predicting treatment effects in unipolar depression: A meta-review

There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.