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. 2023 May 9:15:127-150.
doi: 10.1016/j.xjon.2023.03.017. eCollection 2023 Sep.

Predictors of outcomes in patients with obesity following mitral valve surgery

Ahmed Alnajar  1   2 , Kelley N Benck  1 , Tawseef Dar  1 , Sameer A Hirji  3 , Walid Ibrahim  1 , Brian Detweiler  2 , Venkat Vuddanda  3 , Raymond Balise  2 , J Sunil Rao  2 , Min Lu  2 , Joseph Lamelas  1
Affiliations

Predictors of outcomes in patients with obesity following mitral valve surgery

Ahmed Alnajar et al. JTCVS Open. .

Abstract

Objective: Few studies have assessed the outcomes of mitral valve surgery in patients with obesity. We sought to study factors that determine the in-hospital outcomes of this population to help clinicians provide optimal care.

Methods: A retrospective analysis of adult patients with obesity who underwent open mitral valve replacement or repair between January 1, 2012, and December 31, 2020, was conducted using the National Inpatient Sample. Weighted logistic regression and random forest analyses were performed to assess factors associated with mortality and the interaction of each variable.

Results: Of the 48,775 patients with obesity, 34% had morbid obesity (body mass index ≥40), 55% were women, 66% underwent elective surgery, and 55% received isolated open mitral valve replacement or repair. In-hospital mortality was 5.0% (n = 2430). After adjusting for important covariates, a greater risk of mortality was associated with older patients (adjusted odds ratio [aOR], 1.24; 95% CI, 1.08-1.43), higher Elixhauser comorbidity score (aOR, 2.10; 95% CI, 1.87-2.36), prior valve surgery (aOR, 1.63; 95% CI, 1.01-2.63), and more than 2 concomitant procedures (aOR, 2.83; 95% CI, 2.07-3.85). Lower mortality was associated with elective admissions (aOR, 0.70; 95% CI, 0.56-0.87) and valve repair (aOR, 0.58; 95% CI, 0.46-0.73). Machine learning identified several interactions associated with early mortality, such as Elixhauser score, female sex, body mass index ≥40, and kidney failure.

Conclusions: The complexity of presentation, comorbidities in older and female patients, and morbid obesity are independently associated with an increased risk of mortality in patients undergoing open mitral valve replacement or repair. Morbid obesity and sex disparity should be recognized in this population, and physicians should consider older patients and females with multiple comorbidities for earlier and more opportune treatment windows.

Keywords: adult cardiac; machine learning; mitral valve; mortality; obesity; random forest.

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Figures

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Graphical abstract
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National trends of mitral valve surgery and obesity (2012-2020).
Figure 1
Figure 1
Graphical abstract. NE, National estimate; BMI, body mass index.
Figure 2
Figure 2
Temporal trend of patients with obesity underwent mitral valve surgery in the United States between 2012 and 2020. MVR, Mitral valve surgery; BMI, body mass index.
Figure 3
Figure 3
Minimal depth and variable importance (VIMP) rankings for random forest variables. The top independent variables were ordered according to their minimal depth, where variables with the lowest minimal depth are the most important. TIA, Transient ischemic attack; TVR, transcatheter valve replacement; CABG, coronary artery bypass graft; MI, myocardial infarction; CHF, congestive heart failure; FPL, federal poverty level; PCI, percutaneous coronary intervention; COPD, chronic obstructive pulmonary disease; PAD, peripheral artery disease; CAD, coronary artery disease; AVR, aortic valve replacement; DM, diabetes mellitus; LAA, left atrial appendage; HTN, hypertension; Afib, atrial fibrillation; BMI, body mass index; CKD, chronic kidney disease.
Figure 4
Figure 4
Density distributions in patients with absent and present complications in terms of hospital length of stay and hospitalization cost.
Figure E1
Figure E1
Random forest interactions among a few important preoperative variables associated with in-hospital mortality. Variables with the lowest minimal depth denote strong interactions. CKD, Chronic kidney disease; BMI, body mass index; MV, mitral valve; Afib, atrial fibrillation; HTN, hypertension; LAAL, left atrial appendage ligation; DM, diabetes mellitus; AVR, aortic valve replacement; CAD, coronary artery disease; PAD, peripheral artery disease; COPD, chronic obstructive pulmonary disease; CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention; FPL, federal poverty level; CHF, congestive heart failure; MI, myocardial infarction; TVR, transcatheter valve replacement; TIA, transient ischemic attack.
Figure E1
Figure E1
Random forest interactions among a few important preoperative variables associated with in-hospital mortality. Variables with the lowest minimal depth denote strong interactions. CKD, Chronic kidney disease; BMI, body mass index; MV, mitral valve; Afib, atrial fibrillation; HTN, hypertension; LAAL, left atrial appendage ligation; DM, diabetes mellitus; AVR, aortic valve replacement; CAD, coronary artery disease; PAD, peripheral artery disease; COPD, chronic obstructive pulmonary disease; CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention; FPL, federal poverty level; CHF, congestive heart failure; MI, myocardial infarction; TVR, transcatheter valve replacement; TIA, transient ischemic attack.
Figure E1
Figure E1
Random forest interactions among a few important preoperative variables associated with in-hospital mortality. Variables with the lowest minimal depth denote strong interactions. CKD, Chronic kidney disease; BMI, body mass index; MV, mitral valve; Afib, atrial fibrillation; HTN, hypertension; LAAL, left atrial appendage ligation; DM, diabetes mellitus; AVR, aortic valve replacement; CAD, coronary artery disease; PAD, peripheral artery disease; COPD, chronic obstructive pulmonary disease; CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention; FPL, federal poverty level; CHF, congestive heart failure; MI, myocardial infarction; TVR, transcatheter valve replacement; TIA, transient ischemic attack.
Figure E1
Figure E1
Random forest interactions among a few important preoperative variables associated with in-hospital mortality. Variables with the lowest minimal depth denote strong interactions. CKD, Chronic kidney disease; BMI, body mass index; MV, mitral valve; Afib, atrial fibrillation; HTN, hypertension; LAAL, left atrial appendage ligation; DM, diabetes mellitus; AVR, aortic valve replacement; CAD, coronary artery disease; PAD, peripheral artery disease; COPD, chronic obstructive pulmonary disease; CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention; FPL, federal poverty level; CHF, congestive heart failure; MI, myocardial infarction; TVR, transcatheter valve replacement; TIA, transient ischemic attack.
Figure E2
Figure E2
Pairwise interactions between all variables. CKD, Chronic kidney disease; BMI, body mass index; Afib, atrial fibrillation; MV, mitral valve; HTN, hypertension; LAAL, left atrial appendage ligation; DM, diabetes mellitus; AVR, aortic valve replacement; CAD, coronary artery disease; PAD, peripheral artery disease; COPD, chronic obstructive pulmonary disease; CABG, coronary artery bypass grafting; PCI, percutaneous coronary intervention; FPL, federal poverty level; CHF, congestive heart failure; MI, myocardial infarction; TVR, transcatheter valve replacement; TIA, transient ischemic attack; NA, not available.
Figure E3
Figure E3
Partial plots for (A) age (z score), (B) Elixhauser (z score), and (C) surgical years (z score). The vertical axis displays the ensemble predicted value, whereas × variables are plotted on the horizontal axis as red points and blue dashed line for the partial values and dashed red lines to indicate a smoothed error bar of ±2 SE.
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