A streamlined, machine learning-derived approach to risk-stratification in heart failure patients with secondary tricuspid regurgitation

Abstract

Aims: Secondary tricuspid regurgitation (sTR) is the most frequent valvular heart disease and has a significant impact on mortality. A high burden of comorbidities often worsens the already dismal prognosis of sTR, while tricuspid interventions remain underused and initiated too late. The aim was to examine the most powerful predictors of all-cause mortality in moderate and severe sTR using machine learning techniques and to provide a streamlined approach to risk-stratification using readily available clinical, echocardiographic and laboratory parameters.

Methods and results: This large-scale, long-term observational study included 3359 moderate and 1509 severe sTR patients encompassing the entire heart failure spectrum (preserved, mid-range and reduced ejection fraction). A random survival forest was applied to investigate the most important predictors and group patients according to their number of adverse features.The identified predictors and thresholds, that were associated with significantly worse mortality were lower glomerular filtration rate (<60 mL/min/1.73m2), higher NT-proBNP, increased high sensitivity C-reactive protein, serum albumin < 40 g/L and hemoglobin < 13 g/dL. Additionally, grouping patients according to the number of adverse features yielded important prognostic information, as patients with 4 or 5 adverse features had a fourfold risk increase in moderate sTR [4.81(3.56-6.50) HR 95%CI, P < 0.001] and fivefold risk increase in severe sTR [5.33 (3.28-8.66) HR 95%CI, P < 0.001].

Conclusion: This study presents a streamlined, machine learning-derived and internally validated approach to risk-stratification in patients with moderate and severe sTR, that adds important prognostic information to aid clinical-decision-making.

Keywords: HFmrEF; HFpEF; HFrEF; machine learning; secondary tricuspid regurgitation.