Frailty, malnutrition, and the endocrine system impact outcome in patients undergoing aortic valve replacement

Development of a machine learning-based model to predict major adverse events after surgery for type A aortic dissection complicated by malnutrition

Objective

This study aims to develop a predictive model for the risk of major adverse events (MAEs) in type A aortic dissection (AAAD) patients with malnutrition after surgery, utilizing machine learning (ML) algorithms.

Methods

We retrospectively collected clinical data from AAAD patients with malnutrition who underwent surgical treatment at our center. Through least absolute shrinkage and selection operator (LASSO) regression analysis, we screened for preoperative and intraoperative characteristic variables. Based on the random forest (RF) algorithm, we constructed a ML predictive model, and further evaluated and interpreted this model.

Results

Through LASSO regression analysis and univariate analysis, we ultimately selected seven feature variables for modeling. After comparing six different ML models, we confirmed that the RF model demonstrated the best predictive performance in this dataset. Subsequently, we constructed a model using the RF algorithm to predict the risk of postoperative MAEs in AAAD patients with malnutrition. The test set results indicated that this model has excellent predictive efficacy and clinical applicability. Finally, we employed the Shapley additive explanations (SHAP) method to further interpret the predictions of this model.

Conclusion

We have successfully constructed a risk prediction model for postoperative MAEs in AAAD patients with malnutrition using the RF algorithm, and we have interpreted the model through the SHAP method. This model aids clinicians in early identification of high-risk patients for MAEs, thereby potentially mitigating adverse clinical outcomes associated with malnutrition.

Estimated prevalence of frailty and prefrailty in patients undergoing coronary artery or valvular surgeries/procedures: A systematic review and proportional meta-analysis

BACKGROUND

The aging population has led to an increasing number of older patients undergoing cardiac surgeries/procedures. Frailty and prefrailty have emerged as important prognostic indicators among these patients. This proportional meta-analysis estimated the prevalence of frailty and prefrailty among patients undergoing cardiac surgery.

METHODS

We searched seven electronic databases for observational studies that used validated measure(s) of frailty and reported prevalence data on frailty and/or prefrailty in older patients undergoing coronary artery or valvular surgeries or transcatheter procedures. Meta-analyses were performed using a random-effects model.

RESULTS

One hundred and one articles involving 626,863 patients were included. The pooled prevalence rates of frailty and prefrailty were 28% (95% confidence interval [CI]: 23%-33%) and 40% (95% CI: 31%-50%), respectively, for patients scheduled for open-heart surgeries and 40% (95% CI: 36%-45%) and 43% (95% CI: 34%-53%), respectively, for patients undergoing transcatheter procedures. Frailty measured using a multidimensional approach identified a higher proportion of frail patients when compared with measures solely focused on physical frailty. Older age, female sex, and lower body mass index and hemoglobin concentrations were significantly associated with higher frailty prevalence. Moreover, countries with higher gross domestic product spent on healthcare exhibited a higher frailty prevalence.

CONCLUSION

Frailty represents a considerable health challenge among patients undergoing cardiac surgeries/procedures. Routine screening for frailty should be considered during perioperative care planning.

Clinical implication of the Naples prognostic score on transcatheter aortic valve replacement in patients with severe aortic stenosis

BACKGROUND

One of the hallmarks of frailty in patients with severe aortic stenosis (AS) is malnutrition, for which one of the most up-to-date scoring systems is the Naples prognostic score (NPS). This study sought to investigate the predictive role of the NPS in determining mortality in patients undergoing transcatheter aortic valve replacement (TAVR) under long-term follow-up.

METHODS

A total of 430 consecutive patients with symptomatic severe AS who underwent TAVR were included retrospectively. The primary endpoint of the study was the long-term all-cause mortality. The study population was divided into two groups according to the NPS value, including Group 1 (NPS 0-2) and Group 2 (NPS 3-4).

RESULTS

The all-cause mortality occurred in 250 patients (62.5%) patients during a follow-up time of 40.6 (22.0-69.4) months. During the follow-up period, all-cause mortality was higher in Group 2 compared with Group 1 (87.9% vs. 42.9%, p < 0.001). Older age (p < 0.001), chronic obstructive pulmonary disease (p = 0.015), left ventricular ejection fraction (p = 0.021), and being in Group 2 (high NPS) (hazard ratio: 7.058, 95% confidence interval: 5.174-9.629, p < 0.001) were found to be independent predictors of all-cause mortality at long-term follow-up.

CONCLUSION

The NPS as a malnutrition and inflammation marker in patients with severe aortic stenosis who underwent TAVR provides valuable information for all-cause mortality under long-term follow-up.

The Pandora's Box of Frailty Assessments: Which Is the Best for Clinical Purposes in TAVI Patients? A Critical Review

Frailty assessment is recommended before elective transcatheter aortic valve implantation (TAVI) to determine post-interventional prognosis. Several studies have investigated frailty in TAVI-patients using numerous assessments; however, it remains unclear which is the most appropriate tool for clinical practice. Therefore, we evaluate which frailty assessment is mainly used and meaningful for ≤30-day and ≥1-year prognosis in TAVI patients. Randomized controlled or observational studies (prospective/retrospective) investigating all-cause mortality in older (≥70 years) TAVI patients were identified (PubMed; May 2020). In total, 79 studies investigating frailty with 49 different assessments were included. As single markers of frailty, mostly gait speed (23 studies) and serum albumin (16 studies) were used. Higher risk of 1-year mortality was predicted by slower gait speed (highest Hazard Ratios (HR): 14.71; 95% confidence interval (CI) 6.50–33.30) and lower serum albumin level (highest HR: 3.12; 95% CI 1.80–5.42). Composite indices (five items; seven studies) were associated with 30-day (highest Odds Ratio (OR): 15.30; 95% CI 2.71–86.10) and 1-year mortality (highest OR: 2.75; 95% CI 1.55–4.87). In conclusion, single markers of frailty, in particular gait speed, were widely used to predict 1-year mortality. Composite indices were appropriate, as well as a comprehensive assessment of frailty.