Comparative analysis of multiple nutritional scoring tools to predict the incidence of adverse events in elderly patients with acute coronary syndrome after PCI

doi:10.21203/rs.3.rs-3325175/v1

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Comparative analysis of multiple nutritional scoring tools to predict the incidence of adverse events in elderly patients with acute coronary syndrome after PCI

https://doi.org/10.21203/rs.3.rs-3325175/v1

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published 21 Nov, 2023

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To determine the most appropriate nutritional assessment tool for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI from four nutritional assessment tools including PNI, GNRI, CONUT, and BMI. Consecutive cases diagnosed with acute coronary syndrome (ACS) and underwent percutaneous coronary intervention (PCI) in the Department of Cardiovascular Medicine of the Air Force Specialty Medical Centre from 1 January 2020 to 1 April 2022 were retrospectively collected. The basic clinical characteristics and relevant test and examination indexes were collected uniformly, and the cases were divided into the MACE group (174 cases) and the non-MACE group (372 cases) according to whether a major adverse cardiovascular event (MACE) had occurred within 1 year. Predictive models were constructed to assess the nutritional status of patients with the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling nutritional status (CONUT) scores, and Body Mass Index (BMI), respectively, and to analyze their relationship with prognosis. The incremental value of the four nutritional assessment tools in predicting risk was compared using the Integrated Discriminant Improvement (IDI) and the net reclassification improvement (NRI). The predictive effect of each model on the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI was assessed using area under the ROC curve (AUC), calibration curves, decision analysis curves, and clinical impact curves; comparative analyses were performed. Among the four nutritional assessment tools, the area under the curve (AUC) was significantly higher for the PNI (AUC: 0.798, 95%CI: 0.755-0.840 P < 0.001) and GNRI (AUC: 0.760, 95%CI .0.715-0.804 P < 0.001) than for the CONUT (AUC: 0.719,95%CI. 0.673-0.765 P < 0.001) and BMI (AUC:0.576, 95%CI:0.522-0.630 P < 0.001). The positive predictive value (PPV) of PNI: 67.67% was better than GNRI, CONUT, and BMI, and the negative predictive value (NPV): of 83.90% was better than CONUT and BMI and similar to the NPV of GNRI. The PNI, GNRI, and CONUT were compared with BMI, respectively. The PNI had the most significant improvement in the Integrated Discriminant Improvement Index (IDI) (IDI: 0.1732, P < 0.001); the PNI also had the most significant improvement in the Net Reclassification Index (NRI) (NRI: 0.8185, P < 0.001). In addition, of the four nutritional assessment tools used in this study, the PNI was more appropriate for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI.

Health sciences/Cardiology/Interventional cardiology

Health sciences/Health care/Nutrition

As the global population continues to age, the over-65s and over-80s will be the fastest-growing segments of the population^[1]. As a result, the elderly population will continue to grow and there will be a further increase in the number of elderly patients with acute coronary syndromes. Acute coronary syndrome (ACS) is an acute ischemic syndrome caused by rupture of unstable atherosclerotic plaques or fresh thrombosis secondary to erosion in the coronary artery.^[2]. ACS is an important cause of disability and death in patients^[3]. With the rapid development of percutaneous coronary intervention (PCI), PCI has become the mainstay of treatment for patients with ACS^{[4, 5]}. Despite medical advances such as PCI, ACS still has high mortality, and MACE rates^{[6, 7]}. Along with the increasing number of elderly ACS patients treated with PCI, the regression after PCI has also become a widespread concern. Recent evidence suggests that malnutrition is an important factor in the prognosis of cardiovascular (CV) disease^[8]. However, the nutritional status of patients is often overlooked, despite its association with poor outcomes in patients with cardiovascular disease^{[9, 10]}. Nutritional status affects patient regression after surgery, so early identification of patient nutritional status facilitates clinicians to make early clinical decisions and interventions to optimize clinical management to improve patient prognosis^[11]. The Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling Nutritional Status (CONUT) score, and Body Mass Index (BMI) can be quickly calculated based on blood parameters and height and weight and can indicate the nutritional status of the patient simply and objectively, which is widely used in patients with cardio-cerebral vascular diseases^{[12, 13]}. However, to date, it remains elusive which score is more effective in predicting the incidence of MACE in elderly ACS patients undergoing PCI. Therefore, we used four objective nutritional status assessment tools to predict the prognosis of elderly patients undergoing PCI for ACS and explored which nutritional assessment tool is more suitable for nutritional assessment of elderly patients undergoing PCI for ACS, to provide clinicians with a reference for early clinical decision-making and intervention.

Study design and selection of patients. This study was approved by the Ethics Committee of the Air force characteristic medical center. All our research methods are by the Helsinki Declaration and relevant guidelines/regulations and all participants informed consent to the study. Informed consent was obtained from all participants in the study. In this retrospective, observational, single-center study, we retrospectively collected 618 consecutive cases diagnosed with acute coronary syndrome (ACS) and underwent percutaneous coronary intervention (PCI) in the Department of Cardiovascular Medicine at the Air Force Specialty Medical Center from 1 January 2020 to 11 April 2022, and 546 cases were finally included according to the inclusion and exclusion criteria, of which 426 were male 120 females, aged 60-95 years. They were followed up for 1 year and divided into MACE group (174 cases) and non-MACE group (372 cases) according to the occurrence of major adverse cardiovascular events (MACE) within 1 year.

Inclusion criteria: (1) age ≥60 years; (2) patients with ACS treated with PCI; refer to “Acute coronary syndromes” for diagnostic criteria of ACS^[2]; (3) MACE criteria: included recurrent angina, restenosis, cardiac death, acute myocardial infarction, and hospital readmission for cardiovascular reasons (unstable angina/severe arrhythmia/heart failure, etc.); and (4) complete clinical case information.

Exclusion criteria: (1) Insufficient relevant case information; (2) Patients with a combination of severe hepatic/renal dysfunction, hematological disease, infectious disease, malignancy, autoimmune or inflammatory disease. Patients with one or more of the above conditions are excluded.

Methods Basic data collection ① Patient's basic clinical characteristics: collect the patient's age, gender, height, weight, admission blood pressure, resting heart rate, smoking history, and drinking history. ② The patient's previous medical history: history of hyperlipidemia, hypertension, diabetes mellitus, peripheral vascular disease, cerebrovascular disease, and so on. ③Auxiliary examination: coronary angiography: location of diseased coronary vessels, degree of stenosis of diseased vessels, number of diseased vessel branches, etc. Cardiac ultrasound: left ventricular ejection fraction (LVEF). Blood routine (white blood cells, neutrophils, lymphocytes, hemoglobin, platelets, ultrasensitive C-reactive protein); coagulation function, liver function, renal function, cardiac infarction three, blood lipids, electrolytes, and so on.

Telephone follow-up: The follow-up endpoint event was the occurrence of a major adverse cardiovascular event within 1 year; follow-up began on 1 January 2020 and ended on 31 April 2023; the occurrence of a major adverse event after the patient's discharge was recorded.

Nutritional status assessment tools: Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling Nutritional Status (CONUT) score, and Body Mass Index (BMI). The formulae for PNI, GNRI, and BMI were based on previous literature and were as follows: PNI = albumin (g/L) + 5 × lymphocytes (×10⁹/L); GNRI = [1.489 × serum albumin (g/L)] + [41.7 × (body weight (kg)/ideal body weight (kg)); and BMI = body weight (Kg)/height (cm)²; The CONUT score was developed by Ulibarri et al ^[14]in 2005 as a screening tool for nutritional status of hospitalized patients. It is a composite index based on total lymphocyte count, total cholesterol, and serum albumin proposed to assess the nutritional status of patients.

Statistical analysis. Statistical analyses were performed using R (4.2.3) and SPSS 25.0 software, normally distributed measures were expressed as mean ± standard deviation (𝑥̅ ± 𝑠) and comparisons were made using a t-test; Non-normally distributed measures were expressed as median (M(Q1, Q3)) and comparisons were made using the Mann-Whitney U test; counts were expressed as percentages using the c2 test; and analyses were performed using Spearman's correlation analysis. To assess the utility of each nutritional status assessment tool on the occurrence of adverse cardiovascular events within 1 year after PCI in elderly ACS patients, we plotted the area under the curve (AUC) of subjects' work characteristics (ROC) to assess the sensitivity and specificity of the four nutritional status assessment tools on the occurrence of MACE within 1 year after PCI in elderly ACS patients. The difference was considered statistically significant at P < 0.05. We also calculated the integrated discrimination improvement (IDI) and the net reclassification improvement (NRI) with a category-free option in our models. Associations between nutritional scores and outcomes were examined using one-way logistic regression analyses. The nutritional status assessment tool was used to construct the column-line diagram model, and the calibration curve the clinical decision analysis curve, and the clinical impact curve were used to judge the differentiation, calibration, and clinical impact of the model, and the difference of P＜0.05 was regarded as statistically significant.

Baseline characteristics. In 546 patients, the incidence of MACE within 1 year was 31.9%. The comparison of the proportions of males, hypertension, peripheral vascular disease, cerebrovascular disease, diabetes mellitus, and platelet count between the 2 groups showed no statistically significant difference (P > 0.05). The MACE group had significantly higher proportions of age, smoking, heart rate, hyperlipidemia, leukocyte count, neutrophils, high-sensitivity C-reactive protein, creatinine, uric acid, and myoglobin than the non-MACE group, Creatine kinase isoenzyme, troponin, LDL, B-type natriuretic peptide, CONUT score, and GRACE score were significantly higher than those in the non-MACE group, and systolic blood pressure, lymphocytes, hemoglobin, albumin, body mass index, LVEF, PNI and GNRI were significantly lower than those in the non-MACE group, with statistically significant differences (P < 0.05, Table 1).

Table 1

Comparison of general clinical data between MACE and non-MACE groups
Characteristic	Non-MACE(n = 372)	MACE(n = 174)	P value
Age [years old, M (Q1, Q3)]	65.00(62.00,70.00)	69.00(65.00,77.00)	0.001
Gender (male, %)	290(78.0)	136(78.2)	0.957
Smoking, n (%)	119(32.0)	93(53.4)	0.001
Heart rate[n/min,M(Q1,Q3)]	72.00(67.00,80.00)	77.00(68.00,90.00)	0.001
Past history n(%)
Hyperlipidemia	138(37.1)	84(48.3)	0.013
High blood pressure	253(68.0)	122(70.1)	0.621
Peripheral Vascular Disease	26(7.0)	8(4.6)	0.281
Cerebral vascular disease	61(16.4)	34(19.5)	0.367
Diabetes	140(37.6)	79(45.4)	0.084
Systolic blood pressure (mmHg,𝑥̅±𝑠)	133.00(121.00, 143.00)	122.50(113.50, 137.00)	0.001
White blood cell count [×10⁹/L,M(Q1,Q3)]	6.70(5.60,8.23)	7.50(6.16,10.80)	0.001
Neutral particle count [×10⁹/L,M(Q1,Q3)]	4.21(3.30,5.29)	6.30(4.79,8.99)	0.001
Lymphocyte [×10⁹/L,M(Q1,Q3)]	1.63(1.28,2.02)	1.25(0.97,1.71)	0.001
Hemoglobin [g/L,M(Q1,Q3)]	142.00(134.00,152.00)	136.00(116.50, 147.50)	0.001
Platelet count [×109/L,M(Q1,Q3)]	210.00(176.00, 241.00)	220.50(178.00, 245.00)	0.223
Hs-CRP[mg/L,M(Q1,Q3)]	1.20(0.50, 5.10)	3.74(0.67, 30.59)	0.001
Creatinine [µmol/L,M(Q1,Q3)]	74.00(65.00, 84.00)	88.00(72.50, 108.50)	0.001
Uric acid [µmol/L,M(Q1,Q3)]	341.00(286.50, 389.50)	357.50(294.00, 426.00)	0.022
Albumen[g/L,M(Q1,Q3)]	44.20(42.80, 46.00)	40.20(38.60, 41.80)	0.001
Myoglobin[µg/L,M(Q1,Q3)]	56.00(36.00, 74.50)	82.50(54.50, 263.45)	0.001
Creatine kinase isoenzyme [µg/L,M(Q1,Q3)]	2.00(2.00, 3.35)	4.35(2.00, 40.70)	0.003
Troponin[µg/L,M(Q1,Q3)]	0.01(0.01, 0.91)	0.13(0.01, 5.25)	0.001
TC [mmol/L,M(Q1,Q3)]	3.99(3.34, 5.24)	3.80(3.17, 4.25)	0.001
TG [mmol/L,M(Q1,Q3)]	1.39(1.06,2.03)	1.31(1.02,1.72)	0.047
HDL [mmol/L,M(Q1,Q3)]	1.03(0.91,1.20)	1.00(0.85,1.15)	0.042
LDL [mmol/L,M(Q1,Q3)]	2.04(1.57,2.62)	2.49(2.02,2.90)	0.001
NT-proBNP [ng/L,M(Q1,Q3)]	35.90(17.75,88.90)	153.45(56.60,378.80)	0.001
BMI (kg/m2,𝑥̅±𝑠)	25.71(24.03, 27.73)	25.05(22.49, 27.41)	0.004
Ejection fraction [%,M(Q1,Q3)]	61.00(58.00,64.00)	56.00(51.50,59.00)	0.001
PNI [M(Q1,Q3)]	52.70(49.75, 55.80)	46.35(43.45, 49.72)	0.001
GNRI [M(Q1,Q3)]	115.60(110.53,119.68)	107.49(101.10,112.56)	0.001
CONUT [M(Q1,Q3)]	3.00(3.00,4.00)	4.00(3.00,5.00)	0.001
GRACE score[M(Q1,Q3)]	94.00(83.00,106.00)	113.50(99.50,126.00)	0.001

ROC curves for MACE

In this study, ROC curves were analyzed for PNI, GNRI, CONUT, and BMI for the prediction model of MACE within 1 year in elderly ACS patients who underwent PCI (Fig. 1 and Table 2).In terms of AUC, the area under the curve (AUC) was significantly higher for PNI (AUC: 0.798, 95%CI: 0.755–0.840 P < 0.001) and GNRI (AUC: 0.760, 95%CI:0.715–0.804 P < 0.001) than for CONUT (AUC:0.719, 95%CI:0.673–0.765 P < 0.001) and BMI (AUC:0.576, 95%CI:0.522–0.630 P < 0.001). The established Jordon's index was used to determine the cut-off values for PNI, GNRI, CONUT, and BMI, respectively; and to calculate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively (Table 2). PPV of PNI: 67.67% was better than GNRI, CONUT, and BMI, and NPV: 83.90% was better than CONUT and BMI and similar to NPV of GNRI. (Table 2).

Table 2

Comparison of AUCs, PPV, and NPV of nutritional assessment tools. PPV Positive Predictive Value,
Models	AUC	95%CI	P value for AUCs	cut-off value	sensitivity	specificity	PPV	NPV	P value
PNI	0.798	0.755–0.840	<0.001	47.98	0.649	0.855	67.67%	83.90%	<0.001
GNRI	0.760	0.715–0.804	<0.001	112.33	0.736	0.683	52.03%	84.67%	<0.001
CONUT	0.719	0.673–0.765	<0.001	3.50	0.713	0.608	45.93%	81.88%	<0.001
BMI	0.576	0.522–0.630	0.004	23.00	0.316	0.866	48.14%	70.97%	0.001

NPV Negative Predictive Value, AUC Area Under the Curve. CI Confidence Intervals

In addition, to assess the incremental value of the Nutrition Assessment Tool (NAT) in predicting the occurrence of MACE within 1 year, we analyzed the four indicators using the Integrated Discriminant Improvement Index (IDI) and the Net Reclassification Index (NRI). The PNI, GNRI, and CONUT were used to compare with BMI, respectively. The most significant improvement in IDI was found for PNI (IDI: 0.1732, p < 0.001); the most significant improvement in NRI was also found for PNI (NRI: 0.8185, p < 0.001) (Table 3).

Table 3

Comparison of IDI and NRI for PNI, GNRI, CONUT and BMI. IDI, Integrated Discrimination Improvement; NRI, Net Reclassification Improvement.
Models	IDI			NRI
Models	Absolute IDI	95% CI	P value	Total NRI	95% CI	P value
PNI	0.1732	0.1402–0.2062	<0.001	0.8185	0.6342–1.0146	<0.001
GNRI	0.126	0.0982–0.1537	<0.001	0.7879	0.4716–0.9745	<0.001
CONUT	0.1041	0.0756–0.1327	<0.001	0.5471	0.3420–0.7215	<0.001
BMI

Single-factor logistic regression analysis

With the occurrence of MACE in elderly ACS patients 1 year after PCI as the dependent variable, and four nutritional assessment tools, including PNI, GNRI, CONUT, and BMI, as the independent variables, one-way logistic regression analyses were used to calculate the ratio of ratio (OR) and 95% confidence intervals (CI). When the regression coefficient in the regression analysis was positive and the OR was greater than 1, the adjudicated factor was a risk factor affecting the outcome; conversely, it was a protective factor. The results showed that PNI, GNRI, and BMI were protective factors CONUT was a risk factor; and all four nutritional assessment tools were independent influences (Table 4).

Table 4

Single-factor logistic regression analysis. SE Standard Error, OR Odds Ratios
Models	ß	SE	OR	95%CI	P value
PNI	-0.219	0.023	0.804	0.768–0.841	<0.001
GNRI	-0.127	0.014	0.881	0.857–0.906	<0.001
CONUT	0.763	0.094	2.145	1.785–2.579	<0.001
BMI	-0.094	0.030	0.910	0.858–0.965	0.002

Predictive modeling and evaluation

Four nutritional assessment tools, including PNI, GNRI, CONUT, and BMI, were each used to construct a column-line diagram of clinical prediction models for the occurrence of MACE in elderly ACS patients 1 year after PCI (Fig 2); A more intuitive understanding of MACE incidence can be obtained based on the total score in the graph. According to the calibration curves, the three prediction models constructed by PNI, GNRI, and CONUT showed good calibration ability (Fig 3); Decision-analysis curves (DCA) suggested that PNI and GNRI at threshold probabilities greater than 15% and CONUT at threshold probabilities greater than 20% were more favourable for predicting the risk of MACE 1 year after PCI in elderly ACS patients using this prediction model than implementing an intervention programme for all patients, with the net benefit of the prediction model being significantly higher than that of all or no intervention. Predictive models constructed from BMI, on the other hand, have poor clinical validity (Fig 4). Clinical impact curves (CICs) were further plotted based on DCA to assess the clinical impact of each model, showing the estimated number of people predicted to have MACE and the actual number of people with the disease at each risk threshold; the PNI and GNRI constructed models had a lower rate of misdiagnosis than the CONUT and BMI constructed models (Fig 5).

This study aimed to identify the most appropriate nutritional assessment tool for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI from four nutritional assessment tools including PNI, GNRI, CONUT, and BMI. Currently, the commonly used nutritional assessment tools are divided into subjective nutritional assessment tools and objective nutritional assessment tools. Subjective nutritional assessment tools are more limited in their application to this group of patients, whereas the four nutritional assessment tools, such as PNI, GNRI, CONUT, and BMI, can be quickly assessed by only requiring the results of routine blood tests in the clinic and height and weight^[15–17]. Previous studies have shown that malnutrition is common in patients with chronic heart failure, valvular disease, or coronary artery disease and is associated with a poor prognosis^[18–20]. Yuan et al ^[21] used PNI, GNRI, and CONUT to define malnutrition, which was associated with a significantly increased risk of death in old age. To our knowledge, no studies are comparing PNI, GNRI, CONUT, and BMI in ACS patients undergoing PCI. Therefore, it is imperative to find a nutritional assessment tool that is more suitable for assessing the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI.

The PNI is a nutritional assessment tool based on serum albumin levels and lymphocyte counts; it was initially used to assess immunological and nutritional aspects in patients undergoing gastrointestinal surgery^[22]. It has since been used for a wide range of other diseases, such as cancer, chronic kidney disease, and cardiovascular disease^[23]. There is evidence that all of these factors are associated with the prognosis of patients with acute coronary syndrome (ACS)^[20]. The prognostic value of low albumin in ACS and stable coronary artery disease (including previous myocardial infarction and heart failure) has been reported^[24]. Another study concluded that low albumin in stable coronary artery disease (CAD) is caused by atherosclerotic systemic inflammation^[25]. However, the GNRI, which takes into account both serum albumin levels and body weight, is commonly used to assess the nutritional status of hospitalized older adults^[26]. Underweight elderly patients with ACS also have a higher risk of MACE in the year following PCI than normal-weight patients. BMI is an indirect indicator of body fat based on height and weight and has traditionally been used for nutritional assessment in adult men and women^[27]. Bucholz et al^[28] suggested that low BMI is associated with increased short- and long-term mortality after AMI. The CONUT score was developed by Ulibarri et al ^[14] in 2005 as a screening tool for nutritional status in hospitalized patients. It is a composite indicator based on total lymphocyte count, total cholesterol, and serum albumin proposed to assess the nutritional status of patients.

In this study, nutritional status was assessed by PNI, GNRI, CONUT, and BMI, and its association with the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly patients undergoing PCI for ACS was evaluated. The main results were as follows: among the four assessment tools, the AUC, PPV, and NPV of PNI and GNRI were significantly higher than those of CONUT and BMI; this finding is consistent with the results of other studies^{[18, 29]}. The PNI predicted the greatest incremental value of risk, with patients with low nutritional scores having a higher risk of MACE than those with high nutritional scores^[30]. According to the calibration curves, the three prediction models constructed from PNI, GNRI, and CONUT had good calibration ability; the decision analysis curves (DCA) suggested that the models constructed from PNI and GNRI had high clinical validity, while the prediction model constructed from BMI had poor clinical validity. The Clinical Impact Curve (CIC) suggests that the PNI and GNRI constructs have a lower rate of misdiagnosis than the CONUT and BMI constructs.

There are many screening tools for malnutrition, but there is no consensus on which screening tool to use in patients with ACS. Based on our results, we suggest using the PNI score, which uses only 2 laboratory values and is very easy to calculate even without a specific automated calculator. Screening for malnutrition in elderly ACS patients undergoing PCI may identify patients at high risk of adverse cardiovascular outcomes who may benefit from targeted secondary prevention programs with supplementation to improve their prognosis.

LIMITATIONS OF THE STUDY: This is a single-center retrospective study. The results still need further validation with large samples and multicenter data. We did not compare the prognostic value of a nutritional screening tool with a more sophisticated comprehensive nutritional assessment. This is because malnutrition is a complex problem, especially in the elderly, with diverse etiologies and a wide range of determinants. The validity of assessing nutritional status through simple screening tools (PNI, GNRI, CONUT, and BMI) alone remains uncertain because of the lack of comparison with comprehensive nutritional assessments, such as subjective holistic assessments and mini-nutritional assessments. We welcome additions to this study from other researchers and medical centers.

ACS Acute Coronary Syndromes

PCI Percutaneous Coronary Intervention

MACE Major Adverse Cardiovascular Event

PNI Prognostic Nutritional Index

GNRI Geriatric Nutritional Risk Index

CONUT Controlling nutritional status

BMI Body Mass Index

ROC Receiver Operating Characteristic

AUC Area Under the Curve

IDI Integrated Discrimination Improvement

NRI Net Reclassification Improvement

PPV Positive Predictive Value

NPV Negative Predictive Value

OR Odds Ratios

CI Confidence Intervals

SE Standard Error

GRACE Global registry of acute coronary events

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on

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No competing interests reported.

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published 21 Nov, 2023

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Comparative analysis of multiple nutritional scoring tools to predict the incidence of adverse events in elderly patients with acute coronary syndrome after PCI

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Introduction

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ROC curves for MACE

Single-factor logistic regression analysis

Predictive modeling and evaluation

Discussion

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