Abstract
Background: The Naples prognostic score (NPS), which reflects the inflammatory and nutritional status of patients, is often used to determine prognosis in cancer patients. The aim of this study was to determine the long-term prognostic value of the NPS in acute pulmonary embolism (APE) patients. Methods: Two hundred thirty-nine patients diagnosed with APE were divided into two groups according to their NPS, and long-term mortality was compared. Results: The long-term mortality was observed in 38 patients out of 293 patients in the mean follow-up of 24 months. Multivariate analysis showed that NPS as a categorical parameter and NPS as a numeric parameter were independent predictors of long-term mortality. Conclusion: This study highlights that NPS may have the potential to predict long-term mortality in APE patients.
Keywords: : inflammation, long-term mortality, Naples prognostic score, nutrition, predictive value, pulmonary embolism
Plain language summary
Summary points.
Despite advancements in diagnosis and treatment methods, the mortality rates for acute pulmonary embolism (APE) are still unsatisfactory; therefore, effective risk classification is crucial.
The Naples prognostic score (NPS) is a newly developed metric based on inflammatory and nutritional indicators, frequently employed for predicting long-term prognosis in cancer patients.
Nevertheless, the association between the prognostic score provided by NPS and the long-term mortality of patients with APE is not yet well established.
The NPS as a numeric parameter (hazard ratio: 1.651; 95% CI: 1.217–2.241; p = 0.001) and the simplified pulmonary embolism severity index (sPESI; hazard ratio: 1.887; 95% CI: 1.223–2.911; p = 0.004) score were independent predictors of long-term mortality.
The receiver operating characteristic curve analysis showed that model 2 with NPS as continuous had higher discriminative ability than the baseline model for detecting patients with mortality (area under the curve values = 0.791 vs 0.723, respectively; p = 0.04).
The long-term predictive capability of the NPS was noninferior to that of the sPESI score.
NPS, which reflects systemic inflammation and nutritional status, has prognostic potential in APE patients.
NPS provides accurate, rapid and cost-effective risk assessment in predicting long-term mortality in APE patients.
More frequent follow-up of APE patients with high NPS scores may be considered.
Acute pulmonary embolism (APE), with an annual incidence rate of 0.1–0.2% in European countries, ranks as the third most common cause of death among patients with cardiovascular disease [1,2]. The incidence of APE is anticipated to rise further due to the heightened sensitivity of diagnostic imaging, an aging population and the growing prevalence of risk factors such as obesity and cancer associated with venous thromboembolism (VTE). The mortality rates for APE are still not satisfactory despite new diagnosis and treatment methods [3]. Therefore, risk classification is crucial due to the wide range of clinical presentations. Several risk stratifications have been developed, such as the simplified pulmonary embolism severity index (sPESI), and these indices consist of several parameters such as blood pressure, echocardiographic evidence of right ventricular overload, computed tomography pulmonary angiography and right ventricular ischemia [4]. Nonetheless, there remains a need for an optimal indicator that can be conveniently measured with a high degree of precision to predict clinical outcomes.
With increasing understanding of APE-related inflammation, multiple systemic inflammation-based indices have been shown to predict prognosis in patients with APE [5]. The Naples prognostic score (NPS) is a novel score developed according to inflammatory and nutritional status that is often used to predict long-term outcome in cancer patients [6]. However, the relationship between NPS and long-term mortality in patients with APE remains unclear. The aim of this study was to investigate the long-term prognostic value of the Naples score in patients with APE.
Methods
Data collection
The study population consisted of 293 patients diagnosed with APE between November 2016 and February 2022. The clinical diagnosis of APE was made by demonstrating acute embolism by computed tomography angiography or by ventilation/perfusion scintigraphy. APE was diagnosed when computed tomography angiography showed complete or partial lumen filling defect in the main pulmonary artery and its branches. The diagnosis of deep vein thrombosis was made by lower extremity venous Doppler ultrasonography. Patients with sepsis, active infection, chronic inflammatory conditions, cancer and use of immunosuppressive therapy at the time of diagnosis of APE were excluded from the analysis. Figure 1 shows the patient inclusion chart. All patients received standard medical treatment in accordance with current guidelines [3].
Figure 1.
Patient enrollment chart.
All patients underwent transthoracic echocardiographic examination using the Vivid 7 (GE Vingmed Ultrasound AS, Horten, Norway) system by a cardiovascular imagining specialist. A modified Simpson's method was used to evaluate left ventricular ejection fraction, and left ventricular end-diastolic and end-systolic volumes were evaluated. Right ventricular dimensions were measured from the midventricular level during diastole from the apical four-chamber view. Pulmonary artery peak systolic pressure was calculated by the simplified Bernoulli equation. Medical records of patients, follow-up data and mortality data of patients were collected via the electronic medical record.
NPS consists of four parameters: serum albumin, total cholesterol (TC) concentration, neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR). Naples scores of all patients were calculated and the patients were divided into two groups, according to the scores, as group 1 and group 2. NPS was calculated with parameters in all patients obtained at the time of admission according to the predetermined method [6]. Patients received 0 points for NLR ≤2.96, LMR ≥4.44, albumin ≥40 g/l and TC >180 mg/dl, and they received 1 point for NLR >2.96, LMR <4.44, albumin <40 g/l and TC ≤180 mg/dl (Table 1). The study's participants were divided into two groups: group 1 for those with NPS scores of 0–2 and group 2 for those with NPS scores of 3 or 4.
Table 1.
Calculation of the Naples prognostic score.
| NPS | ||
|---|---|---|
| Criterion | Cutoff values | Partial scores |
| Serum albumin (mg/dl) | Albumin ≥40 g/l Albumin <40 g/l |
0 points 1 point |
| TC (mg/dl) | TC >180 mg/dl TC ≤180 mg/dl |
0 points 1 point |
| NLR | NLR ≤2.96 NLR >2.96 |
0 points 1 point |
| LMR | LMR ≥4.44 LMR <4.44 |
0 points 1 point |
NPS group 1: 0-2 points; NPS group 2: 3-4 points.
LMR: Lymphocyte-to-monocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; NPS: Naples prognostic score; TC: Total cholesterol.
The sPESI score for all patients age >80, cancer history, history of chronic cardiopulmonary disease, heart rate 110/min, systolic blood pressure <100 mmHg and arterial oxygen saturation <90% were evaluated [7]. Patients were considered low risk if their sPESI scores were 0 and high risk if their sPESI scores were ≥1. The study protocol was approved by the local ethics committee.
Study outcomes
The primary end point of this study was long-term mortality. Each patient's long-term survival status was determined using the National Death Notification System.
Statistical analyses
All statistical analyses were performed using R software v. 4.2.2 (R Statistical Software, Institute for Statistics and Mathematics, Vienna, Austria). The Kolmogorov–Smirnov test was used to evaluate the normal distribution assumption. Mean (standard deviation) and median (interquartile range) were used to express normally distributed variables and non-normally distributed data, respectively. The numbers and percentages were used to illustrate categorical data. To compare categorical variables, the Fisher's exact test or the χ2 test was employed. The independent Student's t-test or Mann–Whitney U tests were applied to examine continuous variables between the study groups. Univariable Cox proportional regression analysis was used to recognize the association of variables with mortality. Age, chronic obstructive pulmonary disease, chronic kidney disease and oral anticoagulant usage, which were observed as significantly correlated with a p < 0.05 in univariable Cox regression analysis or clinically linked with mortality, were used to build a baseline multivariable model. Models 1 and 2 were produced by adding the NPS as continuous and categorical parameters, respectively, to the baseline model. Also, model 3 was created by adding sPESI score to model 2. With a variance inflation factor >3 and tolerance (0.1) value, multicollinearity was assessed. The comparisons of discriminative abilities between model 1 and the baseline model and the NPS and sPESI score were done using receiver operating characteristic curve analysis. Decision curve analyses were conducted to evaluate the net benefits of model 1 over the baseline model to gain the additive effect of the NPS by adding to the baseline model, as well as to distinguish the benefit effect of NPS from sPESI score. Comparisons of mortality rates between Naples Prognostic Score (NPS) groups were made using Kaplan–Meier survival curves. The 95 % CI and a two-sided p < 0.05 significance level were used to assess the results.
Results
A total of 293 pulmonary embolism patients were included in this study. Long-term mortality was observed in 38 patients during a mean follow-up period of 24 (16–42) months.
A comparison of the basic clinical, demographic and laboratory features of the study groups is shown in Table 2. Group 1 consisted of 215 patients and group 2 consisted of 78 patients. The mean age of group 2 was statistically higher than that of group 1. In group 2, C-reactive protein, D-dimer and pro-brain natriuretic peptide values were found to be higher.
Table 2.
Baseline demographic and laboratory features of study groups.
| Parameter | Group 1 (N = 215) | Group 2 (N = 78) | p-value |
|---|---|---|---|
| Age (years) | 60.0 (48.0–71.0) | 65.5 (57.2–76.8) | 0.003 |
| Male gender | 103 (47.9) | 35 (44.9) | 0.743 |
| Hypertension | 95 (44.2) | 44 (56.4) | 0.085 |
| Diabetes mellitus | 43 (20.0) | 15 (19.2) | 1.000 |
| Hyperlipidemia | 54 (25.1) | 11 (14.1) | 0.065 |
| Chronic obstructive pulmonary disease | 20 (9.30) | 12 (15.4) | 0.206 |
| Cerebral vascular accident | 8 (3.72) | 2 (2.56) | 1.000 |
| Atrial fibrillation | 11 (5.12) | 2 (2.56) | 0.525 |
| Coronary artery disease | 26 (12.1) | 12 (15.4) | 0.586 |
| Chronic kidney disease | 10 (4.65) | 4 (5.13) | 1.000 |
| Congestive heart failure | 5 (2.33) | 3 (3.85) | 0.443 |
| Cancer | 13 (6.05) | 8 (10.3) | 0.328 |
| Cigarette smoking | 61 (28.4) | 22 (28.2) | 1.000 |
| Systolic blood pressure | 125 (116–140) | 127 (114–140) | 0.844 |
| White blood cells | 9.58 (7.36–11.3) | 9.70 (7.53–13.8) | 0.217 |
| Hemoglobin | 13.1 (12.2–14.6) | 12.8 (11.2–14.4) | 0.128 |
| Platelets | 326 (233–425) | 374 (241–422) | 0.661 |
| Glucose | 107 (95.0–132) | 113 (98.8–149) | 0.083 |
| Estimated glomerular filtration rate | 85.0 (70.9–100) | 83.0 (68.6–92.3) | 0.105 |
| C-reactive protein | 2.30 (0.80–7.20) | 3.38 (1.83–8.47) | 0.023 |
| Sodium | 139 (137–141) | 139 (137–141) | 0.762 |
| Potassium | 4.40 (4.10–4.70) | 4.30 (4.03–4.77) | 0.573 |
| Lactate dehydrogenase | 247 (204–297) | 270 (211–336) | 0.065 |
| Hs-Troponin T | 0.07 (0.01–0.31) | 0.06 (0.02–0.28) | 0.681 |
| Creatine kinase-myocardial band | 12.0 (8.00–19.7) | 12.1 (8.60–19.8) | 0.785 |
| Total cholesterol | 199 (169–232) | 165 (136–179) | <0.001 |
| Triglycerides | 142 (112–189) | 136 (95.0–173) | 0.118 |
| High-density lipoprotein cholesterol | 41.0 (33.0–50.0) | 36.0 (29.0–42.8) | 0.001 |
| Low-density lipoprotein cholesterol | 122 (97.0–149) | 102 (81.5–113) | <0.001 |
| D-dimer | 2924 (1525–6665) | 3945 (2210–8600) | 0.027 |
| Pro-brain natriuretic peptide | 121 (59.3–334) | 314 (58.7–693) | 0.014 |
| Hemoglobin-A1c | 6.00 (5.70–6.75) | 6.10 (5.70–7.05) | 0.536 |
| Serum lactate | 1.50 (1.10–1.80) | 1.55 (1.30–1.87) | 0.184 |
| pH | 7.44 (7.40–7.47) | 7.45 (7.42–7.47) | 0.441 |
| Saturation | 96.8 (94.0–98.0) | 97.0 (95.2–98.0) | 0.220 |
| pO2 | 84.0 (70.0–112) | 84.0 (68.5–125) | 0.999 |
| pCO2 | 34.6 (30.5–38.0) | 34.1 (31.0–36.8) | 0.427 |
In total, 63 patients were treated with thrombolytic treatment, with a higher rate observed in group 2 (35; 16.3%) compared with group 1 (28; 35.9%), p = 0.001. Chronic thromboembolic pulmonary hypertension developed in 38 patients during long-term follow-up, but no statistically significant difference was found between the groups (group 1, 26 [12.1%] vs group 2, 12 [15.4%]; p = 0.586). Bleeding complications were observed in 14 (6.51%) patients in group 1 and in eight (10.3%) patients in group 2, with no statistically significant difference (p = 0.410). Deep vein thrombosis was detected in 98 patients in total, and no significant difference was found between the groups (group 1, 69 [32.1%] vs group 2, 29 [37.2%]; p = 0.499).
In group 2, serum albumin and lymphocyte levels were lower, while serum neutrophil and monocyte levels were higher. Compared with group 1, group 2 exhibited a higher NLR but a lower LMR. Mortality was statistically significantly higher in group 2 (17; 7.91%) compared with group 1 (21; 26.9%), p < 0.001. Detailed comparisons of treatment options, follow-up characteristics and components of the NPS between study groups are summarized in Table 3.
Table 3.
Comparisons of therapy options, follow-up properties and components of Naples score between study groups.
| Parameters | Group 1 (N = 215) | Group 2 (N = 78) | p-value |
|---|---|---|---|
| Tissue plasminogen activator use | 35 (16.3) | 28 (35.9) | 0.001 |
| Oral anticoagulant use | 164 (76.3) | 63 (80.8) | 0.512 |
| Non-vitamin K antagonist oral anticoagulant use | 72 (33.5) | 24 (30.8) | 0.766 |
| Follow-up time | 29.0 (18.0–42.5) | 21.0 (6.00–36.0) | 0.010 |
| Development of atrial fibrillation | 15 (6.98) | 5 (6.41) | 1.000 |
| Thromboembolic event | 18 (8.37) | 8 (10.3) | 0.788 |
| Occurrence of chronic thromboembolic pulmonary hypertension | 26 (12.1) | 12 (15.4) | 0.586 |
| Bleeding | 14 (6.51) | 8 (10.3) | 0.410 |
| Deep vein thrombosis positive on Doppler | 69 (32.1) | 29 (37.2) | 0.499 |
| Computed tomography angiography use | 207 (96.3) | 76 (97.4) | 1.000 |
| Serum albumin | 4.10 (3.80–4.30) | 3.60 (3.40–3.88) | <0.001 |
| Neutrophils | 4.60 (3.80–5.70) | 5.75 (4.55–7.90) | <0.001 |
| Lymphocytes | 2.40 (1.90–3.00) | 1.60 (1.30–2.00) | <0.001 |
| Monocytes | 0.50 (0.40–0.70) | 0.56 (0.41–0.76) | 0.063 |
| Neutrophil-to-lymphocyte ratio | 2.00 (1.49–2.54) | 3.51 (3.01–5.00) | <0.001 |
| Lymphocyte-to-monocyte ratio | 4.76 (3.71–6.25) | 3.10 (2.21–3.63) | <0.001 |
| Simplified pulmonary embolism severity index score: | 0.098 | ||
| 0 | 123 (57.2) | 38 (48.7) | |
| 1 | 78 (36.3) | 28 (35.9) | |
| 2 | 12 (5.58) | 10 (12.8) | |
| 3 | 2 (0.93) | 2 (2.56) | |
| Mortality | 17 (7.91) | 21 (26.9) | <0.001 |
| Drug use | |||
| Acetylsalicylic acid | 22 (10.2) | 11 (14.1) | 0.473 |
| Antiplatelets | 14 (6.51) | 6 (7.69) | 0.927 |
| Beta-blocker | 51 (23.7) | 25 (32.1) | 0.198 |
| Angiotensin-converting enzyme inhibitors/angiotensin 2 receptor blockers | 52 (24.2) | 21 (26.9) | 0.744 |
| Aldactone | 7 (3.26) | 5 (6.41) | 0.314 |
| Furosemide | 17 (7.91) | 10 (12.8) | 0.291 |
| Oral anticoagulant | 141 (65.6) | 52 (66.7) | 0.973 |
| Non-vitamin K antagonist oral anticoagulant | 40 (18.6) | 10 (12.8) | 0.323 |
| Calcium channel blockers | 31 (14.4) | 11 (14.1) | 1.000 |
| Statins | 19 (8.84) | 5 (6.41) | 0.668 |
| Low-molecular-weight heparin | 183 (85.1) | 72 (92.3) | 0.155 |
Model 1 indicates that NPS as a categorical parameter is independently associated with mortality (hazard ratio = 3.458; 95% CI: 1.734–6.896; p < 0.001). Model 2 demonstrates that the NPS as a continuous parameter is independently related to mortality (hazard ratio = 1.651; 95% CI: 1.217–2.241; p = 0.001). Finally, model 3 shows that the sPESI score is independently associated with mortality (hazard ratio = 1.887; 95% CI: 1.223–2.911; p = 0.004). Multivariate Cox regression models predicting mortality are summarized in Table 4.
Table 4.
Multivariable Cox regression analysis for predicting mortality.
| Variable | Baseline model | Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | p-value | HR (95% CI) | p-value | HR (95% CI) | p-value | HR (95% CI) | p-value | |
| Age (years) | 1.040 (1.013–1.067) | 0.003 | 1.031 (1.004–1.058) | 0.023 | 1.031 (1.004–1.058) | 0.023 | 1.018 (0.992–1.045) | 0.169 |
| Chronic obstructive pulmonary disease | 2.263 (1.058–4.842) | 0.036 | 1.631 (0.747–3.561) | 0.219 | 1.591 (0.717–3.533) | 0.254 | 0.943 (0.394–2.257) | 0.895 |
| Chronic kidney disease | 2.257 (0.758–6.716) | 0.144 | 1.871 (0.619–5.651) | 0.266 | 1.829 (0.601–5.569) | 0.288 | 2.080 (0.697–6.205) | 0.189 |
| Oral anticoagulant use | 0.484 (0.242–0.968) | 0.041 | 0.429 (0.213–0.861) | 0.017 | 0.421 (0.209–0.848) | 0.015 | 0.510 (0.248–1.047) | 0.067 |
| Naples as categoric | – | – | 3.458 (1.734–6.896) | <0.001 | – | – | – | – |
| Naples as numeric | – | – | – | – | 1.651 (1.217–2.241) | 0.001 | 1.625 (1.187–2.225) | 0.003 |
| Simplified pulmonary embolism severity index score | – | – | – | – | – | – | 1.887 (1.223–2.911) | 0.004 |
HR: Hazard ratio.
The receiver operating characteristic curve analysis revealed that model 2, with NPS as continuous parameter, has higher discriminative ability than the baseline model in detecting patients with mortality (area under the curve values = 0.791 vs 0.723, respectively; p = 0.04; Figure 2A). No significant difference in discriminative ability was observed between sPESI and NPS, as shown in Figure 2B (area under the curve values = 0.716 vs 0.691, respectively; p = 0.698).
Figure 2.
Receiver operating characteristic curve.
(A) Receiver operating characteristic curve illustrating the sensitivity and specificity of the baseline model and model 2 for predicting mortality. (B) Receiver operating characteristic curve illustrating the sensitivity and specificity of the simplified pulmonary embolism severity index and Naples prognostic score for predicting mortality.
Decision curve analysis (Figure 3A) demonstrated that above a threshold of 5% risk, model 2 has a higher net clinical benefit over the baseline model for detecting mortality in patients with APE. No significant difference was observed between sPESI and NPS regarding a net clinical benefit effect for detecting APE patients with mortality, except for the 25–35% threshold risk, as shown in Figure 3B.
Figure 3.
Decision curve analysis.
(A) Decision curve analysis showing that model 2 has a higher net clinical utility than the base model for detecting mortality in patients with pulmonary embolism. (B) Decision curve analysis showing no difference between the simplified pulmonary embolism severity index and Naples prognostic score in terms of the effect of net clinical benefit in detecting pulmonary embolism patients with mortality beyond the 25–35% threshold risk.
The Kaplan–Meier curve illustrated that group 2 has a higher long-term risk of mortality (log-rank p < 0.0001; Figure 4).
Figure 4.
Kaplan–Meier curve showing survival for group 1 and group 2 patients according to Naples prognostic score.
Discussion
The current study revealed that NPS predicted long-term mortality in APE patients. To the authors' knowledge, this is the first study to show the relationship between long-term mortality and NPS in APE patients.
In cases of APE, signs of neutrophil and macrophage infiltration are present in both the pulmonary arterial wall and the right ventricle [8]. In vivo studies have shown that several cytokines elevated during the inflammatory response are associated with a high risk of VTE [9]. The relationship between inflammation and APE may be linked to the increased release of various proinflammatory cytokines that promote the procoagulant state and play an important role in the progression of VTE. Various studies have been conducted on systemic inflammation-based indices in the prognosis of APE patients. Systemic inflammatory index, which can be calculated by complete blood count, has been shown to be an independent predictor of massive APE [10]. Another study showed that serum C-reactive protein-to-albumin ratio, known as an inflammation-based prognostic marker, may be a useful prognostic marker of APE [11].
Neutrophils, serving as pivotal agents within the immune system, play a crucial role in innate immune defense [12]. In patients with APE, the acute inflammatory response leads to increased neutrophil recruitment and is associated with a poor prognosis [13]. On the other hand, during the disease, the number of lymphocytes decreases as a result of the secretion of adrenaline and glucocorticoids due to the stress response [14]. The NLR at hospital admission has been shown to predict long-term mortality in patients with APE [15].
Monocytes constitute the main source of blood tissue factor, which is the basic element of the extrinsic coagulation cascade [16]. Monocytes play a significant role in the development of complications associated with inflammatory processes and the pathophysiology of vascular diseases [17]. Moreover, high monocyte counts at admission have been shown to be associated with the development of VTE in hospitalized cancer patients [18]. The present research illustrated the potential of inflammatory cells, such as neutrophils, lymphocytes and monocytes, in evaluating the prognosis of APE.
Low serum TC levels have been shown to be independently associated with short-term mortality in patients with APE [19]. Inflammation also leads to oxidative changes that can reduce cholesterol synthesis [20]. High levels of acute phase reactants and cytokines such as IL-6 may explain the decrease in cholesterol levels [21]. Considering that liver function and body fluid volume changes can influence albumin levels [22], it is a logical approach to include plasma cholesterol levels for an enhanced assessment of nutritional status.
Serum albumin, the main protein of intravascular spaces in the human body, is also a negative acute phase reactant. Albumin levels in the blood decrease in inflammatory clinical conditions due to decreased hepatic output and increased catabolism [23]. Low serum albumin levels have been shown to be associated with massive APE [24]. In addition, it has been shown that low serum albumin level is a strong and independent predictor for long-term mortality in APE patients [25]. Albumin has antioxidant properties due to its ligand-binding and free radical-trapping activities [26]. Additionally, it has been shown that reactive oxygen species and myeloperoxidase enzyme levels are high in acute PE patients [27]. As a result, low albumin levels may cause a lack of antioxidant function, leading to high mortality. Finally, considering that albumin is an important inhibitor of platelet activation and aggregation [28], low albumin levels may increase aggregation and platelet activation and may contribute to poor outcome in patients with APE. Serum albumin and TC, crucial markers for assessing human nutritional well-being, exhibit a strong association with the extent of malnutrition. The relationship between malnutrition and in-hospital mortality after APE has been demonstrated by various studies [29].
Galizia et al. evaluated the NPS for the first time in a group of patients undergoing colorectal cancer surgery [6]. In addition, NPS has been shown to have prognostic value for various cancers as well as heart diseases such as myocardial infarction and heart failure [30,31]. However, there is a lack of evidence for its prognostic value in patients with APE. The score reflects the patient's systemic inflammatory response and nutritional status. These inflammatory and nutritional response biomarkers are readily available from routine laboratory studies and provide important information about inflammation/nutritional status. The authors consider that as a simple index, NPS could be used for risk stratification to assist clinicians in making decisions for the management of APE patients in the long-term follow-up.
There is a study in the literature that evaluated the short-term mortality of APE patients with the NPS [32]. The study showed that the patient group with high NPS scores had high 30-day all-cause mortality rates. Unlike this study in the literature, the current study compared the long-term mortality and outcomes of the NPS in APE patients. In addition, unlike the previous study, the current study compared the NPS with the sPESI score and showed that the NPS was noninferior to sPESI. The current study showed that the NPS has the potential to predict long-term mortality, as well as being associated with short-term mortality, as shown in the literature.
The following are some of the study's limitations. First, a causal association between the NPS and long-term mortality in APE patients could not be effectively proven due to the cross-sectional and retrospective study design. Second, despite the availability of multivariable regression models, there may be unmeasured confounding effects. Another disadvantage of the study was that it only assessed long-term, all-cause mortality. The dynamic changes of NPS over time was not evaluated in this study. The study goal was to determine a widely accessible and identifiable index that could be driven at the time of admission and used to accurately predict long-term mortality in APE patients.
Conclusion
To summarize, NPS, which reflects systemic inflammation and nutritional status, has prognostic value in APE patients. NPS provides accurate, rapid and cost-effective risk assessment in predicting long-term mortality in APE patients. It may be reasonable to closely follow up patients diagnosed with APE and with high NPS scores in the long term. The long-term predictive capacity of NPS is not lower than that of the sPESI score, indicating its potential in risk stratification of APE patients. Nevertheless, the current results need to be evaluated in prospective studies involving larger cohorts of patients.
Author contributions
L Pay: conceptualization, investigation, writing – original draft; T Çetin: writing – review and editing, methodology; K Keskin: data curation, software; Ş Dereli: data curation, software; O Tezen: methodology, software; AÇ Yumurtaş: data curation, validation; Z Kolak: data curation; S Eren: data curation; F Şaylık: investigation, formal analysis; T Çınar: investigation, formal analysis; Mİ Hayıroğlu: supervision, writing – review and editing.
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, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.
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 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.
References
Papers of special note have been highlighted as: • of interest; •• of considerable interest
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