Clinical and prognostic implications of phenomapping in patients with heart failure receiving cardiac resynchronization therapy

Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review

The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed according to the PRISMA guidelines. After a search of Scopus, PubMed, Cochrane Library, and Embase databases, 675 records were identified. Twenty supervised (prediction of CRT response) and 9 unsupervised (clustering and phenotyping) AI models were analyzed qualitatively (22 studies, 14,258 patients). Fifty-five percent of AI models were based on retrospective studies. Unsupervised AI models were able to identify clusters of patients with significantly different rates of primary outcome events (death, heart failure event). In comparison to the guideline-based CRT response prediction accuracy of 70%, supervised AI models trained on cohorts with > 100 patients achieved up to 85% accuracy and an AUC of 0.86 in their prediction of response to CRT for echocardiographic and clinical outcomes, respectively. AI models seem to be an accurate and clinically applicable tool in phenotyping of patients eligible for CRT implantation and predicting potential responders. In the future, AI may help to increase CRT response rates to over 80% and improve clinical decision-making and prognosis of the patients, including reduction of mortality rates. However, these findings must be validated in randomized controlled trials.

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Background

Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.

Methods

The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.

Results

Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.

Conclusion

This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

Prevalence and clinical outcome of main echocardiographic and hemodynamic heart failure phenotypes in a population of hospitalized patients 70 years old and older

BACKGROUND

Heart failure (HF) echocardiographic and hemodynamic categories are poorly characterized in the elderly. We aimed to evaluate the prevalence and clinical outcomes of echocardiographic and hemodynamic HF phenotypes in a consecutive series of hospitalized patients aged ≥ 70 years.

METHODS

All consecutive patients ≥ 70 years old discharged from the Internal Medicine Unit of our Hospital with a diagnosis of HF, between January and November 2020, entered this retrospective study. All patients underwent physical examination, complete blood tests, chest X-ray and transthoracic echocardiography. At 1-year follow-up, we evaluated the occurrence of the composite outcome of all-cause mortality and re-hospitalization.

RESULTS

Two hundred and sixty-one patients (86.3 ± 6.4 years, 60.9% women) were retrospectively analyzed. From the study group, 106 "old" (70-84 years) and 155 "oldest-old" (≥ 85 years) patients were separately analyzed. A total of 169 (64.7%) patients reported the composite outcome during follow-up: 41 (15.7%) died and 128 (49.0%) were re-hospitalized. At 1-year follow-up, survival analysis did not show any statistically significant difference between age groups (p = 0.31) and between HF echocardiographic categories (p = 0.34), whereas HF patients with "cold-dry" phenotype had significantly poorer survival compared to the other hemodynamic subtypes (p < 0.001). Male sex (HR 1.44, 95% CI 1.04-1.98), "cold-dry" phenotype (HR 3.90, 95% CI 1.73-8.77), high sodium level (HR 1.03, 95% CI 1.01-1.04) and low estimated glomerular filtration rate (eGFR) (HR 0.98, 95% CI 0.97-0.99) were independently associated with the outcome occurrence.

CONCLUSIONS

Male sex, "cold-dry" phenotype, high sodium level and low eGFR are the main adverse prognostic indicators over a mid-term follow-up in hospitalized patients aged ≥ 70 years.