Naples prognostic score as a predictor of mortality in surgical aortic valve replacement

Abstract

Aim: Investigating the impact of nutritional and inflammatory status, assessed by the Naples-Prognostic-Score (NPS), on postoperative mortality in 173 older adults undergoing surgical aortic valve replacement(SAVR) for aortic stenosis(AS).

Methods: Retrospective study calculating NPS from neutrophils/lymphocytes, lymphocytes/monocytes, total cholesterol and serum albumin.

Results: Mean age was 69.39 ± 6.153 with 45.1% females. The post-operative mortality was 23.7% over a follow-up period of 50 ± 31 months. The 1-month mortality rate is 2.89%. High NPS significantly associated with increased mortality; multivariate logistic regression confirmed its independence (odds-ratio:3.494, 95% confidence-interval:1.555–7.849, p = 0.002). NPS cutoff of 2 showed 73.2% sensitivity, 56.8% specificity and area-under-the-curve of 0.758 for predicting all-cause mortality. Kaplan-Meier analysis supported lower NPS correlating with better survival.

Conclusion: NPS independently predicts postoperative mortality in SAVR patients.

Plain language summary

Article highlights.

Introduction

• AS is a prevalent heart valve disease, particularly among older adults, with the global aging population expected to increase its prevalence. SAVR is the primary treatment for severe AS, yet poses significant risks, especially in older and comorbid patients.

Background

• EUROSCORE2 and STS scores are used to predict operative mortality risk in cardiac surgery, but they may not encompass all relevant factors. The Naples Prognostic Score (NPS), incorporating nutritional and inflammation markers, may offer additional insights into mortality risk.

Study objectives

• This study aimed to investigate the relationship between NPS and postoperative mortality in AS patients undergoing SAVR.

Methods

• Retrospective study calculating NPS from neutrophils/lymphocytes, lymphocytes/monocytes, total cholesterol and serum albumin.

Results

• NPS showed a significant association with mortality, with higher scores correlating with increased mortality risk.

• NPS outperformed Log EuroScore in predicting one-year mortality, indicating its potential utility in risk assessment.

Discussion

• Integrating NPS into preoperative risk assessment may enhance risk stratification and guide interventions to improve patient outcomes.

• Despite its promise, further prospective studies with larger cohorts are needed to validate NPS's utility in AS patients undergoing SAVR.

1. Introduction

Aortic stenosis (AS) is one of the most common types of heart valve diseases, particularly prevalent among older adults [1,2]. Given the global rise in the older adult population, an increase in the prevalence of aortic valve diseases is anticipated in the near future. The primary and effective treatment for this progressive valve disease is aortic valve replacement. Surgical aortic valve replacement (SAVR) is a frequently performed surgical intervention in the treatment of AS. However, in older patients and those with comorbidities, such surgical procedures pose significant complications and a risk of mortality [3,4].

Scoring systems like EUROSCORE2 and the Society of Thoracic Surgeons (STS) score are employed in clinical practice to predict operative mortality risk related to cardiac surgery in adult patients [5–9]. These scoring systems evaluate well-known risk factors such as advanced age, multiple comorbidities, preoperative status and cardiac-related conditions. However, recent studies have reported that factors such as patient's nutritional status, inflammation status and frailty are also associated with mortality and outcomes [10–12]. Incorporating scoring systems that evaluate nutritional status and inflammation alongside existing ones may yield more comprehensive results for assessing high-risk patients.

Naples prognostic score (NPS), initially established as a prognostic indicator in colorectal cancer surgery [13], has demonstrated associations with mortality and short- and long-term outcomes in heart failure patients and those with ST-segment elevation myocardial infarction [14–16]. Moreover, recent evidence indicates its correlation with one-year mortality in patients undergoing Transcatheter Aortic Valve Implantation (TAVI) [17].

The aim of this study is to explore the relationship between nutritional and inflammation status, as assessed by the NPS and postoperative mortality in patients undergoing surgical aortic valve replacement due to AS. Understanding this relationship may pave the way for potential improvements to reduce postoperative complications and improve patient outcomes.

2. Materials & methods

2.1. Study population

This retrospective study included a total of 211 older adults who underwent SAVR due to degenerative severe AS between the dates of January 2011 and December 2019 Patient's demographic information, clinical history, physical examination findings, laboratory test results, imaging, surgical data, follow-up information, information about post-op complications and survival were obtained from hospital records. The excluded groups from the study comprised non-cooperative patients and those with emergency medical conditions, individuals who underwent multivalve surgery, emergent surgery or aortic replacement, patients with hepatic insufficiency, those diagnosed with cancer, cases of endocarditis, as well as patients with a diagnosis of paravalvular leakage or prosthesis mismatch. Patients with missing data required for assessing the NPS were also excluded from the study.

The study was conducted in accordance with the principles of the Helsinki Declaration and was approved from the Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital ethical board (2023.09–86). Written Informed consent was obtained from participants.

2.2. Data collection

All laboratory data were retrospectively obtained from the hospital record system. The results of blood samples taken on the procedure day as per the standard protocol were utilized for assessing the NPS. The logEuroSCORE II was calculated for each patient by the researchers. Furthermore, patients were screened for postoperative complications, including major bleeding, postoperative stroke, pneumothorax, tamponade, acute coronary syndrome, acute kidney failure, the need for hemodialysis, postoperative atrial fibrillation (AF) and the requirement for a permanent pacemaker during the index hospitalization. Mortality data were confirmed through the National Social Security Administration's death notification records.

2.3. Echocardiography

The echocardiography findings were obtained from hospital records before the operation. All patients were evaluated using 2-dimensional and M-mode transthoracic echocardiography. Interventricular septal thickness, left ventricular systolic and diastolic diameters were noted. Left ventricular ejection fraction calculated using the biplane Simpson method was recorded. Aortic stenosis and aortic insufficiency, pulmonary hypertension, aortic valve area, aortic valve area index, mean and maximum transvalvular aortic gradients and left ventricular mass index were also noted.

2.4. Anthropometry

The patient's height was measured using a fixed stadiometer with a vertical backboard and a movable headboard. Body mass index (BMI) was calculated as weight (kg) divided by height2 (m2). Du Bois formula was used for estimating the body surface area.

2.5. Naples prognostic score

The NPS has four components: 1. Neutrophils/lymphocytes (NLR); 2. Lymphocytes/monocytes (LMR); 3. Total cholesterol (TC) level; and 4. Serum albumin level. Albumin<4 mg/dl, TC≤180 mg/dl, NLR≥2.96, or LMR≤4.44 was scored as 1. Albumin≥4 mg/dl, TC>180 mg/dl, NLR<2.96, or LMR>4.44 was scored as 0. The NPS was evaluated as the sum of the above scores [13] (Table 1).

Table 1.

Calculation of Naples prognostic score.

Variables	Cut-off value	Points
Serum albümin (mg/dl)	≥4	0
	<4	1
Total Cholestrol(mg/dl)	>180	0
	≤180	1
NLR	≤2.96	0
	>2.96	1
LMR	>4.44	0
	≤4.44	1

LMR: Lymphocyte-to-monocyte ratio; NLR: Neutrophil-to-lymphocyte ratio.

Afterward, these patients were divided into two groups based on the cut-off NPS values for predicting all-cause mortality: patients with a score of 0 and 1 were assigned to low NPS group; patients with a score of 2,3 or 4 were assigned to high NPS group.

2.6. Outcomes

The primary outcome was all-cause mortality after SAVR. One-month mortality referred to any death within the first month after SAVR, including deaths during the index hospital stay. Postoperative complications included major bleeding, postoperative stroke, pneumothorax, tamponade, acute coronary syndrome, acute kidney failure, the need for hemodialysis, postoperative AF and the requirement for a permanent pacemaker during the index hospitalization.

2.7. Statistical analysis

All data entered the database were verified by a second researcher. Statistical analyses were performed using statistical Package for Social Sciences (IBM SPSS v.21.0). We assessed normality of the variables by Kolmogorov-Smirnov test and visual histograms. Continuous variables were given as mean ± standard deviation for normally distributed variables and median for skew-distributed variables. Categorical variables were given as frequencies. Depending on whether the continuous variables were normally distributed, two groups were compared with independent sample t test or Mann-Whitney U test. Chi-square test with Yates correction and Fisher's exact test were used for 2 × 2 contingency tables as appropriate for non-numerical data. Correlations between numerical parameters were analyzed with Pearson's correlation test. The factors found significantly associated with all-cause mortality in univariate analysis were further evaluated by logistic regression analysis. The multicollinearity among the possible regression analyses independent variables were checked with Pearson, Spearman, or Kendall's tau-b correlation analyses. The variables that were detected having multicollinearity in between with an r coefficient value >0.7 were not included in the same regression analyses. p values less than 0.05 were accepted as significant. Results were given as odds ratio (OR) and 95% confidence interval (CI).

A receiver operating characteristic (ROC) curve was applied to determine the cut-off NPS values for predicting all-cause mortality. The predictive ability of NPS to indicate all-cause mortality was measured using two indicators: sensitivity, specificity. The ROC analysis were performed to determine the area under the curve (AUC). An AUC value greater than 0.9 indicates high accuracy, 0.7–0.9 moderate accuracy, 0.5–0.7 low accuracy and 0.5 a chance result [18]. Kaplan–Meier method and log-rank test were applied to compare the survival difference between NPS groups. p values less than 0.05 were accepted as significant. We used IBM SPSS v.21.0 and and MedCalc Statistical Software to perform statistical analysis.

3. Results

We screened 211 patients, of which 38 excluded due to exclusion criteria. Data were analyzed for 173 (45.1% female) older adults who underwent SAVR due to degenerative severe AS. The mean age was 69.39 ± 6.153 years. The post-operative mortality rate of 23.7% over a follow-up period of 50 ± 31 months. The 1-month mortality rate is 2.89%, and the in-hospital mortality rate is 2.31%. Patients were divided into two groups: survivors and non-survivors. Among these patients, 132 (76.3%) survived and 41(23.7%) died. The clinical and demographic characteristics of the participants were shown in Table 2.

Table 2.

The baseline demographic, clinical and laboratory characteristics of the patients.

Parameters	All patients	Mortality (-)	Mortality (+)	p-value
Age, years (mean±SD)	69.39 ± 6.153	69.04 ± 6.16	70.51 ± 6.06	0.180
Gender, n(%)				0.372
Male	95(54.9%)	70(53%)	25 (61%)
Female	78(45.1%)	62(47%)	16(39%)
Height	162.92 ± 9.39	162.71 ± 8.96	163.61 ± 10.74	0.629
Weight	78.02 ± 15.40	78.91 ± 15.42	75.15 ± 15.15	0.171
BMI	29.44 ± 5.54	29.88 ± 5.72	28.04 ± 4.69	0.042
BSA	1.871 ± 0.21	1.880 ± 0.20	1.841 ± 0.22	0.316
HT, n(%)	131(75.7%)	101(76.5%)	30(73.2%)	0.663
DM, n(%)	70(40.5%)	52(39.4%)	18(43.9%)	0.607
Hyperlipidemia, n(%)	108(62.4%)	84(63.6%)	24(58.5%)	0.556
CRF, n(%)	23(13.3%)	17(12.9%)	6(14.6%)	0.772
CHF, c	15(8.7%)	9(6.8%)	6(14.6%)	0.199
COPD n(%)	43(24.9%)	32(24.2%)	11(26.8%	0.738
AF, n(%)	14(8.1%)	10(7.6%)	4(9.8%)	0.743
PAD, n(%)	21(12.1%)	13(9.8%)	8(19.5%)	0.107
CVA, n(%)	17(9.8%)	8(6.1%).	9(22%)	0.006
Anemi, n(%)	56(32.4%)	39(29.5%)	14(41.5%)	0.154
Smoking, n(%)	41(23.7%)	29(22%)	12(29.3%)	0.337
Logistic EuroScore	2.16 ± 1.34	1.97 ± 1.06	1.84 ± 0.22	0.069
LVEF, %	59.16 ± 7.7	59.58 ± 7.23	57.83 ± 9.01	0.262
LVEDD, mm	47.92 ± 5.5	47.98 ± 5.48	47.76 ± 5.62	0.826
Septum, mm	13,27 ± 2.09	13.53 ± 1.99	13.71 ± 2.14	0.641
Posterior wall, mm	12,34 ± 1.83	12.52 ± 1.78	12.90 ± 1.99	0.280
LVMI, g/m2	136.80 ± 35.78	135.67 ± 36.70.	140.43 ± 32.83	0.433
MAG, mmHg	53.90 ± 13.43	53.45 ± 12.96	55.34 ± 14.93	0.469
Max AG, mmHg	85.79 ± 20.62	84.73 ± 19.02	89.17 ± 25.06	0.301
AVA, cm2	0.73 ± 0.14	0.73 ± 0.14	0.73 ± 0.15	0.946
sPAP >30, n (%).	54(31.2%)	36(27.3%)	18(43.9%)	0.045
LVEF<50, n(%)	16(9.2%)	11(8.3%)	5(12.2)	0.537
Hemoglobin	12.94 ± 1.74	13.08 ± 1.68	12.50 ± 1.86	0.066
Total Cholesterol	193 ± 51	197 ± 50	179 ± 52	0.025
Albumin g/dl	4.17 ± 0.44	4.26 ± 0.38	3.88 ± 0.53	<0.001
Lymphocyte (×1000/μl)	2.171 ± 0.818	2.276 ± 0.838	1.833 ± 0.654	0.001
Neutrophil (×1000/μl)	5.156 ± 2.210	5.100 ± 2.212	5.336 ± 2.221	0.564
Monocyte (×1000/μl)	0.650 ± 0.248	0.646 ± 0.254.	0.661 ± 0.232	0.451
NAPLES (median)	2(0–4)	1(0–4)	3(0–4)	<0.001
Follow-up period, months	50 ± 31	59 ± 28	23 ± 22	<0.001

AF: Atrial fibrillation; AVA: Aortic valve area; BMI: Body mass index; BSA: Body surface area; CHF: Congestive heart failure; COPD: Chronic obstructive pulmonary disease; CRF: Chronic renal failure; CVA: Cerebrovascular accident; DM: Diabetes mellitus; LVEF: Left ventricle ejection fraction; LVEDD: Left ventricle end-diastolic diameter; LVMI: Left ventricular mass index; MAG: Mean aortic gradient; Max AG: Maximum aortic gradient; PAD: Peripheral arterial disease; sPAP: Systolic pulmonary artery pressure.

There were no statistically significant differences between the two groups in terms of age, gender, presence of hypertension, diabetes mellitus, hyperlipidemia, chronic renal failure, congestive heart failure, chronic obstructive pulmonary disease, atrial fibrillation, peripheral arterial disease, anemia, current smoking status or log EuroScore.

The non-survivors group was older (69.04 ± 6.16 vs. 70.51 ± 6.06; p: 0.180) and had significantly lower BMI (29.88 ± 5.72 vs 28.04 ± 4.69; p: 0.042). The incidence of preoperative systolic pulmonary pressure >30 mmHg was higher (27.3 vs. 43.9%; p: 0.045) in deceased patients. The non-survivors group had a higher prevalence of previous cerebrovascular accident (6.1% vs 22%, p: 0.006).

Lymphocyte count (2276 ± 838 vs. 1833 ± 654; p: 0.001), and serum albumin (4.26 ± 0.38 vs 3.88 ± 0.53; p: < 0.001), total cholesterol (197 ± 50 vs. 179 ± 52; p: 0.025) were significantly lower in non-survivors' group.

The mean hemoglobin level was 12.9 mg/dl, but was lower in the high-NPS group. Additionally, the high-NPS group had a higher basal creatinine level Across the full study population, where the average total cholesterol level was 193.1 ± 51.5 mg/dl and the average LDL level was 116.7 ± 42.6 mg/dl, it's notable that 49.1% of patients were on statins. Comparatively, within the low-NPS group, with an average total cholesterol level of 211.2 ± 45 mg/dl and an average LDL level of 128.2 ± 39.9 mg/dl, 52.3% of patients were on statins. In contrast, the high-NPS group exhibited lower total cholesterol levels (175.1± 51.4 mg/dl) and LDL levels (105.4 ± 42.5 mg/dl), with 46% of patients on statins. Despite the lower rate of statin use in the high-NPS group, there was no significant difference between the two groups (p: 0.448).

The median NPS was higher in the non-survivors group than in the survivors group (3(0–4) vs 1(0–4), p < 0.001). Four patients died during the index hospitalization. Of these patients, one were in the low-NPS group and three were in the high-NPS group. During the 30 days of follow-up, there were five deaths, one were in the low-NPS group, and four were in the high-NPS group which was statistically similar (p = 0.368) (Table 3). During follow-up period, a total of 41 patients died. All-cause mortality was significantly higher in high Naples group (p = 0.001) The incidence of post-procedural cerebrovascular events was significantly higher in the high-NPS group. There was no significant difference in terms of the other postoperative complications (Table 3).

Table 3.

Postprocedural clinical outcomes of the groups.

Parameters	ALL	Low-Naples	High Naples	p-value
	(n:173)	(n:86)	(n:87)
Age	69.39 ± 6.153	69.36 ± 6.129	69.36 ± 6.211	0.947
Gender, n(%).				0.001
Male	95 (54.9%)	36 (41.9%)	59 (67.8%)
Female	78 (45.1%)	50 (58.1%)	28 (32.2%)
hemoglobin, g/dl	12.9 ± 1.7	13.1 ± 1.5	12.7 ± 1.9	0.104
Lymphocyte, (×1000/μl)	2.171 ± 0.818	2.224 ± 0.834	1.793 ± 0.601	<0.001
Neutrophil (×1000/μl)	5.156 ± 2.210	4.506 ± 1.874	5.799 ± 2.336	<0.001
Monocyte (×1000/μl)	0.650 ± 0.248	0.576 ± 0.223	0.723 ± 0.252	<0.001
Creatinine, mg/dl	0.8 ± 0.2	0.8 ± 0.2	0.9 ± 0.2	0.001
GFR	87.4 ± 34.0	89.1 ± 31.2	85.8 ± 36.6	0.520
Total cholesterol, mg/dl	193.1 ± 51.5	211.2 ± 45	175.1 ± 51.4	<0.001
LDL, mg/dl	116.7 ± 42.6	128.2 ± 39.9	105.4 ± 42.5	<0.001
HDL, mg/dl	47.8 ± 13.5	50.7 ± 12.05	44.9 ± 14.3	0.005
Albumin g/dl	4.17 ± 0.4	4.34 ± 0.33	3.99 ± 0.47	<0.001
Statin	85(49.1%)	45(52.3%)	40(46%)	0.448
Postop and in-hospital outcomes
Tamponade	4(2.3%)	1(1.2%)	3(3.4%)	0.621
Permanent pace	4(2.3%)	3(3.5%)	1(1.1%)	0.368
Pneumothorax	4(2.3%)	1(1.2%)	3(3.4%)	0.621
Hemodialyses	7(4%)	1(1.2%)	6(6.9%)	0.117
CVA	6(3.5%)	0(0%)	6(6.9%)	0.029
Major bleeding	7(4%)	2(2.3%)	5(5.7%)	0.443
Post-op AF	58(33.5%)	24(27.9)	34(39.1)	0.120
Post-op MI	0	0	0
Length of hospital stay, days		7(0–18)	7(4–50)	0.214
In-hospital mortality	4(2.3%)	1(1.2%)	3(3.4%)	0.621
30 day mortality	5(2.9%)	1(1.2%)	4(4.6%)	0.368
All-cause mortality	41(23.7%)	11(12.8%)	30(34.5%)	0.001

AF: Atrial fibrillation; CVA: Cerebrovascular accident; MI: Myocardial infarct.

Multivariate logistic regression analyses were performed to establish independent factors associated with mortality in the univariate analysis. Two models were generated (1) adjusted for age and gender; (2) adjusted for BMI, cerebrovascular accident, preoperative systolic pulmonary pressure >30 mmHg which are statistically different between mortality groups. High NPS (score 2–4) were found as the independent predictors of total mortality even after the adjustments (OR: 3.494, 95% CI (1.555–7.849), p = 0.002) (Table 4).

Table 4.

Results of multivariate logistic regression analyses analyses for all-cause mortality.

	Multivariate	Model 1		Multivariate	Model 2
	OR (95% CI)	p-value		OR (95% CI)	p-value
Age	1.044 (0.984–1.108)	0.156	BMI	0.939 (0.872–1.011)	0.094
Gender	1.105 (0.509–2.398)	0.801	CVA	4.645 (1.498–14.401)	0.008
High NAPLES	3.562 (1.601–7.924)	0.002	sPAP >30,n(%)	1.916 (0.874–4.198)	0.104
			High NAPLES	3.494 (1.555–7.849)	0.002

BMI: Body mass index; CI: Confidence interval; CVA: Cerebrovascular accident; OR: Odds ratio; sPAP: Pulmonary arterial pressure.

Cut-off values for NPS and log EuroScore were determined using ROC curve analysis. The performance-including sensitivity, specificity and AUC of the NPS for all-cause mortality were shown in Table 2. For all-cause mortality, NPS demonstrated a sensitivity of 73.2% and specificity of 56.8% with an AUC of 0.758 (95% CI 0.670–0.847; p < 0.001) using a cut-off value of 2. On the other hand, Log EuroSccore predicted one-year mortality with a sensitivity and specificity of 71.4 and 50%, respectively, and an AUC of 0.617 (95% CI: 0.494–0.739, p 0.069), using a cut-off value of 1.58. The ROC curves illustrating these results were provided in Figure 1.

Figure 1.

ROC curves for the detection of all-cause mortality in SAVR patients.

Kaplan-Meier survival analysis showed that lower NPS had better survival rates (the log rank value <0.0012; Figure 2).

Figure 2.

Kaplan–Meier curves for all-cause mortality in SAVR patients.

4. Discussion

This study represents the first attempt to comprehensively examine the combined impact of malnutrition and inflammation on the survival of patients with severe degenerative aortic stenosis undergoing surgery. Our research findings have revealed a significant correlation between the preoperative NPS and overall mortality.

It is widely accepted that the active mechanisms involved in the pathogenesis of aortic stenosis are the result of a highly complex active process influenced by inflammation. This process involves multifactorial pathological mechanisms driven by inflammation, which promote valvular calcification and valvular bone deposition. Monocytes and T lymphocytes are the primary inflammatory cells playing a crucial role in the pathogenesis and progression of AS. These cells adhere and infiltrate the valvular subendothelium, where they differentiate into macrophages and activated T cells, releasing growth factors and proinflammatory cytokines such as IL-1 and TNF-α, thereby promoting fibrosis and calcification [19–21].

Numerous studies have consistently shown that the preoperative inflammatory state of patients undergoing cardiovascular surgery is closely associated with the incidence of postoperative complications, including bleeding, deep wound infection and mortality [22–27]. Numerous studies assess inflammation using scores like NLR and LMR. Studies in patients with AS have also been conducted. Shvartz at all. reported that NLR, as an indicator of systemic inflammation, is associated with in-hospital mortality after aortic valve replacement in patients with AS [28]. Cho et al. reported NLR to be a significant independent predictor of major adverse cardiovascular events in severe calcific aortic stenosis. Additionally, integrating NLR into a model with EuroSCORE-I improved the model's performance for long-term clinical outcomes [29]. Nishibe et al. demonstrated that a low preoperative LMR (<3.21) is an independent predictor of overall mortality after Endovascular Aortic Repair for Abdominal Aortic Aneurysm [30]. Inflammation has been identified as a central driver of disease-related malnutrition, resulting in anorexia, reduced food intake, muscle catabolism and insulin resistance, all contributing to a catabolic state [31]. AS is a chronic condition characterized by prolonged inflammation, which can lead to malnutrition and sarcopenia in affected patients. Moreover, the decline in physical capacity caused by AS contributes to a reduction in muscle mass and strength, ultimately culminating in sarcopenia. Furthermore, it is well-established that frail older adults face a heightened risk of morbidity and mortality following cardiac surgery [32]. Frailty is characterized by a reduced physiological reserve, leading to an increased risk of dependency, institutionalization and mortality [33]. Frailty, especially its sarcopenic component, shares common risk factors with malnutrition, such as socioeconomic deprivation, chronic diseases and polypharmacy. Inflammation and protein-energy metabolic imbalances are major biological factors that underlie both frailty and malnutrition. Considering these interconnected factors, the preoperative assessment of these patients with respect to frailty, inflammation and malnutrition, as well as the implementation of necessary interventions, have become crucial for preventing and mitigating adverse postoperative outcomes and reducing mortality.

Symptomatic AS is often observed in the older adults, and they should be considered to be potentially at risk of malnutrition due to various factors. These factors can be associated with the normal aging process and comorbidities. Furthermore, symptoms linked to the obstruction of blood flow across the aortic valve can lead to appetite disturbances, reduced physical activity and consequently, a propensity for lean mass loss [34]. Therefore, it is crucial to include the screening for signs of malnutrition as an integral part of evaluating patients with chronic heart disease, especially before considering surgical intervention. Previous research has emphasized the notable prevalence of malnutrition within this patient group [35]. In cardiac surgery units, the prevalence of malnutrition ranges from approximately 10% to 25% [36,37]. Studies conducted in Europe and the United States report an overall malnutrition prevalence of 30–60%, with significant underdiagnosis [38]. Surprisingly, between 70 and 80% of patients are discharged without any measures taken regarding nutrition assessment and treatment [39,40].

In patients with severe AS, preoperative risk assessment often utilizes scoring systems such as EUROSCORE and STS. However, these scoring systems lack parameters for assessing malnutrition or inflammation. Building upon this, several studies have been conducted in this patient group to evaluate the predictive capability of mortality using scoring systems like PNI (Prognostic Nutritional Index) and Controlling Nutritional Status (CONUT), which are created based on values such as albumin, cholesterol and lymphocytes. In Lee and colleagues' study, it was shown that the PNI score is associated with short-term adverse events, including morbidity, mortality and intensive care unit stay, in patients undergoing coronary artery bypass surgery [41]. In 2019, Okuno and colleagues demonstrated that having low PNI scores is a significant predictor of long-term mortality in patients undergoing TAVI [42]. It has been indicated that the PNI score is related to all-cause mortality in patients following SAVR [43]. In a recently published study, as in our study, it was shown that the NPS is a good predictor of mortality in patients undergoing TAVI [17].

5. Limitations

Despite the valuable insights provided by this study, certain limitations should be acknowledged. The retrospective design introduces inherent biases and the study's single-center nature might limit the generalizability of the findings. The exclusion criteria, while necessary for the study's focus, could impact the representation of certain patient groups. Additionally, the relatively modest sample size might influence the robustness of the results. Furthermore, the study does not explore interventions based on the identified risk factors, and the impact of such interventions on patient outcomes remains an avenue for future research. The long-term follow-up and inclusion of additional outcome measures beyond mortality could provide a more comprehensive understanding of the implications of nutritional and inflammation status. There is no information available regarding the cause of death in the National Social Security Administration's death notification records. Therefore, it was not possible to examine the relationship between causes of death and NPS. Another important point is that the impact of statins on total cholesterol levels appears to be significant based on the provided data. Although the evaluation of total cholesterol levels during statin treatment may be subject to some uncertainty, it is important to note that the proportion of patients on statin therapy was comparable in both groups.

In conclusion, while the NPS shows promise in predicting mortality, further prospective studies with larger, diverse cohorts are warranted to validate its utility and explore its potential for guiding interventions aimed at improving patient outcomes in the context of AS and SAVR.

6. Conclusion

This study underscores the significance of considering nutritional and inflammation status in older adults undergoing SAVR for AS. The NPS, which incorporates parameters related to nutritional and inflammatory status, emerged as an independent predictor of all-cause mortality in this patient population. The findings suggest that a comprehensive preoperative assessment, including factors beyond traditional risk scores, could contribute to better risk stratification and patient management. The study adds to the growing body of evidence highlighting the complex interplay between inflammation, malnutrition and adverse outcomes in cardiac surgery. The NPS demonstrated promising predictive ability, and its integration into existing risk assessment tools may enhance their accuracy.

Author contributions

All authors contributed to conception and design, acquisition of data, or analysis and interpretation of data; drafting the article or revising it critically for important intellectual content; and final approval of the version to be published.

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate institutional review board approval (Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital ethical board (2023.09-86)) and/or have followed the principles outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investigations involving human subjects, informed consent has been obtained from the participants involved.