Prognostic significance of immune-nutritional parameters for surgically resected elderly lung cancer patients: a multicentre retrospective study

Prognostic significance of immune-nutritional parameters for surgically resected elderly lung... Abstract OBJECTIVES The world’s population is rapidly ageing, and the age of patients with lung cancer will increase as well. The prognostic nutritional index, controlling nutritional status and the geriatric nutritional risk index (GNRI) are useful parameters for evaluating immune-nutritional status. We aimed to perform a multicentre retrospective study to investigate the correlations of these immune-nutritional parameters with postoperative comorbidities or surgical outcomes of elderly patients with non-small-cell lung cancer (NSCLC). METHODS We selected 272 consecutive patients with NSCLC aged >75 years treated from January 2005 to December 2012 and evaluated 3 preoperative immune-nutritional parameters as potential predictive factors of postoperative comorbidities or as prognostic factors for surgically resected elderly patients with NSCLC. RESULTS Prognostic nutritional index, GNRI, sex and preoperative respiratory comorbidities were significantly associated with postoperative comorbidities. Multivariate analyses revealed that preoperative GNRI, sex, preoperative serum carcinoembryonic antigen levels, preoperative serum cytokeratin 19 fragment levels, pathological N factor and pleural invasion were significantly associated with overall survival (OS). Abnormal GNRI was significantly associated with histology and outcomes. The Kaplan–Meier analysis of OS as a function of preoperative GNRI revealed that patients with an abnormal GNRI experienced significantly shorter OS compared with those with normal GNRI (5-year OS, 45.15% vs 64.10%, respectively; P = 0.0007, log-rank test). The controlling nutritional status score was not significantly associated with postoperative comorbidities or surgical outcomes. CONCLUSIONS Preoperative GNRI is a novel preoperative predictor of postoperative comorbidities and a prognostic factor that may identify high-risk elderly patients with NSCLC. Elderly patients, Non-small-cell lung cancer, Immune-nutritional parameters, Postoperative comorbidities and outcome INTRODUCTION The number of people worldwide aged ≥60 years will rise from 900 million to 2 billion between 2015 and 2050 [1]. Therefore, the age of patients with malignancies will increase as well. Lung cancer is the leading cause of cancer-related death worldwide [2]. Recent advances in less invasive surgery, anaesthesia and perioperative management allow patients with preoperative comorbidities and those of advanced age to undergo surgery, which offers acceptable long-term survival [3]. Ageing causes physiological changes in pulmonary, cardiovascular, metabolic and renal functions. Thus, thoracic surgeons occasionally cannot judge whether surgery will achieve acceptable outcomes for elderly patients with lung cancer. Therefore, reliable preoperative parameters must be identified to objectively assess general physical status to select elderly patients with lung cancer who may benefit from surgery. The correlation between the nutritional status and the survival of patients with organ malignancies, including lung cancer, is an important topic. Several studies [4–6] report that preoperative nutritional status is associated with postoperative comorbidities and with long-term outcomes of patients with malignant tumours. For example, immune-nutritional parameters such as the prognostic nutritional index (PNI), controlling nutritional status (CONUT) and geriatric nutritional risk index (GNRI) are predictive and prognostic factors for certain malignancies and can be calculated according to haematological and anthropometric data. For example, we reported that immune-nutritional parameters such as PNI [7] and CONUT [8] serve as prognostic markers for early-stage lung cancer. However, the utility of immune-nutritional parameters for surgically resected elderly patients with lung cancer is unclear. To address this question, we hypothesized that PNI, CONUT and GNRI can be used to predict perioperative comorbidities or postoperative outcomes of elderly patients with non-small-cell lung cancer (NSCLC). For this purpose, we performed a multicentre retrospective study to investigate the correlation of each immune-nutritional parameter and postoperative comorbidities with surgical outcomes of elderly patients with NSCLC. MATERIALS AND METHODS Patients The ethics committees of Kyushu University Hospital and National Hospital Organization Kyushu Medical Center approved this study. Between January 2005 and December 2012, 1663 consecutive patients with primary lung cancer had undergone complete surgical resection at the Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University and the National Hospital Organization Kyushu Medical Center. For the purpose of this study, we analysed 272 (16.4%) of those patients aged >75 years with NSCLC who had undergone preoperative measurement of nutritional parameters, including peripheral serum lymphocyte count, serum albumin concentration, serum total cholesterol concentration and body height and weight. The data were acquired from follow-up examinations conducted over a median period of 51 months (range 0–132 months) after surgical resection. To categorize surgical outcome, we used the Clavien–Dindo classification for grading the severity of morbidity [9]. Postoperative follow-up and definition of postoperative local and distant recurrences were followed by a previous report [7]. Preoperative calculation and cut-off values of immune-nutritional parameters Preoperative prognostic nutritional index Preoperative blood samples were obtained within 2 weeks before surgery. Preoperative PNI was calculated using the following formula: 10 × serum albumin levels (g/dl) + 0.005 × total peripheral blood lymphocyte count (per mm3) [10]. The receiver operating characteristic (ROC) curve of preoperative PNI levels was analysed, and postoperative survival was predicted by comparing the area under the curve (AUC). We determined that the optimum cut-off value for preoperative PNI levels was 49.6 (sensitivity = 50.34%, specificity = 58.54% and AUC of the ROC curve = 0.532). One hundred twenty-six (46.3%) patients had preoperative PNI levels >49.6, and 146 (53.7%) patients had lower preoperative PNI levels. Preoperative controlling nutritional status score The preoperative CONUT score was calculated from serum albumin concentrations, peripheral lymphocyte counts and total cholesterol concentrations [11]. Briefly, parameters were scored as follows: albumin concentration (g/dl) ≥3.5 (0 points), 3.0–3.49 (2 points), 2.5–2.99 (4 points) and <2.5 (6 points); total lymphocyte count (per mm3) ≥1600 (0 points), 1200–1599 (1 point), 800–1199 (2 points) and <800 (3 points) and total cholesterol concentration (mg/dl) ≥180 (0 points), 140–179 (1 point), 100–139 (2 points) and <100 (3 points). The sum of the 3 parameters was defined as the CONUT score. The ROC curve of the preoperative CONUT score was analysed, and postoperative survival was predicted by comparing the AUCs. We determined that the optimum cut-off value of the preoperative CONUT score was 0 (sensitivity = 34.69%; specificity = 73.98%; AUC of the ROC curve = 0.558). One hundred eight (39.7%) patients had a preoperative CONUT score ≥ 1 (abnormal CONUT), and the remaining 164 (60.3%) patients had a preoperative CONUT score = 0 (normal CONUT). Preoperative geriatric nutritional risk index Preoperative GNRI was calculated from the serum albumin concentration and body weight as described previously [12, 13]. The GNRI formula was as follows: GNRI = 14.87 × serum albumin concentration (g/l) + 41.7 × preoperative weight/ideal weight (kg). Ideal body weight was calculated as follows: ideal body weight = 22 × height (m2). According to previous reports [13, 14], patients with GNRI >98 were considered normal, whereas those with GNRI ≤98 were at risk of malnutrition. The preoperative GNRIs of 69 (25.4%) and 203 (74.6%) patients were ≤98 (abnormal GNRI) and >98 (normal GNRI), respectively. Histopathological evaluation Histological evaluation including pleural invasion, intratumoural blood vessel invasion and lymphatic vessel invasion was also followed by a previous report [7]. Pathological staging was based on the seventh edition TNM classification of the International Union Against Cancer. Definition of outcome Overall survival (OS) was defined as the time from resection to the date of death from any cause. Statistical analysis Clinical profiles, pathological profiles and other characteristics of patients were summarized using descriptive statistics or contingency tables. The Fisher’s exact test was used to analyse categorical data. We analysed survival using the Kaplan–Meier method and compared groups using the log-rank test. We used the Cox hazard model to identify independent predictive and prognostic factors. A P-value of <0.05 was considered significant. All statistical analyses were performed using JMP software, version 11.0 (SAS Institute Inc, Cary, NC, USA). RESULTS Clinical profiles During follow-up (median 51 months, range 0–132 months), 123 (45.2%) patients died, including 2 in-hospital deaths after thoracic surgery. The clinical profiles of patients are listed in Table 1. The study group included 117 women and 155 men, with a median age 78 years (range 75–91 years) at the time of surgery. All patients had Eastern Cooperative Oncology Group Performance Status (ECOG PS) 0–1. One hundred fifteen (42.3%) patients never smoked, and the remaining 157 patients were former or current smokers. Two hundred sixteen (79.4%) patients had preoperative comorbidities such as hypertension, hyperlipidaemia, respiratory functional disorder, history of the other organ cancers, cardiovascular disease, cerebrovascular disease, diabetes mellitus or liver dysfunction. In particular, 67 (24.7%) patients had preoperative respiratory comorbidities. The details of preoperative respiratory comorbidities are listed in Supplementary Material, Table S1. The most frequent disease was chronic obstructive pulmonary disease. One hundred four (38.2%) patients had abnormal preoperative carcinoembryonic antigen levels (normal values <3.4 ng/ml or 5.0 ng/ml), and 41 (15.1%) patients had abnormal preoperative cytokeratin 19 fragment levels (normal value <3.5 ng/ml). Three (1.1%), 177 (65.1%) and 92 (33.2%) patients underwent pneumonectomy with systemic lymphadenectomies; lobectomies with systemic lymphadenectomies and limited resections, including segmentectomy or wedge resections, in those with peripheral lesions or poor pulmonary function, respectively. Thirty-three (12.2%) and 77 (28.3%) patients received adjuvant chemotherapy and experienced postoperative recurrence, respectively. Table 1: Patient clinical profiles Variables/characteristics  n = 272  Follow-up (months), median (range)  51 (0–132)  Age (years), median (range)  78 (75–91)  Sex   Female  117 (43.0)   Male  155 (57.0)  ECOG performance status   0–1  272 (100)   2–4  0 (0)  Smoking status   Never  115 (42.3)   Former/current  157 (57.7)  Preoperative comorbidities (all)   No  56 (20.6)   Yes  216 (79.4)  Preoperative comorbidities (respiratory)   No  205 (75.4)   Yes  67 (24.6)  Preoperative CEA level (mg/ml)   Normal  104 (38.2)   Abnormal  168 (61.8)  Preoperative CYFRA level (mg/ml)   Normal  231 (84.9)   Abnormal  41 (15.1)  Surgical procedure   Pneumonectomy  3 (1.1)   Lobectomy  177 (65.1)   Limited resection  92 (33.8)  Adjuvant chemotherapy   No  239 (87.8)   Yes  33 (12.2)  Postoperative recurrence   No  195 (71.7)   Yes  77 (28.3)  Variables/characteristics  n = 272  Follow-up (months), median (range)  51 (0–132)  Age (years), median (range)  78 (75–91)  Sex   Female  117 (43.0)   Male  155 (57.0)  ECOG performance status   0–1  272 (100)   2–4  0 (0)  Smoking status   Never  115 (42.3)   Former/current  157 (57.7)  Preoperative comorbidities (all)   No  56 (20.6)   Yes  216 (79.4)  Preoperative comorbidities (respiratory)   No  205 (75.4)   Yes  67 (24.6)  Preoperative CEA level (mg/ml)   Normal  104 (38.2)   Abnormal  168 (61.8)  Preoperative CYFRA level (mg/ml)   Normal  231 (84.9)   Abnormal  41 (15.1)  Surgical procedure   Pneumonectomy  3 (1.1)   Lobectomy  177 (65.1)   Limited resection  92 (33.8)  Adjuvant chemotherapy   No  239 (87.8)   Yes  33 (12.2)  Postoperative recurrence   No  195 (71.7)   Yes  77 (28.3)  Data are represented as n (%), unless otherwise specified. CEA: carcinoembryonic antigen; CYFRA: cytokeratin 19 fragment; ECOG: Eastern Cooperative Oncology Group. Pathological profiles The histological types were adenocarcinoma, squamous cell carcinoma and others in 197 (72.4%), 63 (23.2%) and 12 (4.4%) patients, respectively. Of the 272 patients, 202 (74.2%), 45 (16.6%) and 25 (9.2%) had pathological Stages I, II and III, respectively. Seventy-four (27.5%), 58 (28.9%) and 52 (27.1%) patients had visceral pleural invasion, blood vessel invasion and lymphatic vessel invasion, respectively (Table 2). Table 2: Patient pathological profiles Characteristics  n (%)  Histological type   Adenocarcinoma  197 (72.4)   Squamous cell carcinoma  63 (23.2)   Others  12 (4.4)  Pathological stage   IA  126 (46.3)   IB  76 (27.9)   IIA  26 (9.6)   IIB  19 (7.0)   IIIA  22 (8.1)   IIIB  3 (1.1)  Pleural invasion (pl)   pl 0  198 (72.3)   pl 1–3  74 (27.7)  Intratumoural blood vessel invasion   No  214 (71.1)   Yes  58 (28.9)  Intratumoural lymphatic vessel invasion   No  220 (72.9)   Yes  52 (27.1)  Characteristics  n (%)  Histological type   Adenocarcinoma  197 (72.4)   Squamous cell carcinoma  63 (23.2)   Others  12 (4.4)  Pathological stage   IA  126 (46.3)   IB  76 (27.9)   IIA  26 (9.6)   IIB  19 (7.0)   IIIA  22 (8.1)   IIIB  3 (1.1)  Pleural invasion (pl)   pl 0  198 (72.3)   pl 1–3  74 (27.7)  Intratumoural blood vessel invasion   No  214 (71.1)   Yes  58 (28.9)  Intratumoural lymphatic vessel invasion   No  220 (72.9)   Yes  52 (27.1)  Correlation between postoperative comorbidities and preoperative immune-nutritional parameters Seventy-four (27.2%) patients had postoperative comorbidities (Supplementary Material, Table S2). All postoperative comorbidities in this study were graded as I or II according to the Clavien–Dindo classification [9]. The most frequent (18.6%) complication was prolonged alveolar air leakage (PAAL) for >7 days. We evaluated the correlations between postoperative comorbidities and 3 immune-nutritional parameters and body mass index (BMI). Low BMI (<18.5 kg/m2) was defined according to the World Health Organization (WHO) classification [15]. Preoperative BMI, PNI and GNRI were significantly associated with postoperative complications (P = 0.0170, 0.0287 and 0.0443, respectively) (Table 3). Table 3: Correlations between postoperative comorbidities and immune-nutritional parameters Variables  Postoperative comorbidities (−) (n = 198)  Postoperative comorbidities (+) (n = 74)  P-value  Preoperative BMI      0.0170   ≥18.5 (n = 241)  181  60     <18.5 (n = 31)  17  14    Preoperative PNI      0.0287   Normal (n = 126)  100  26     Abnormal (n = 146)  98  48    Preoperative CONUT      0.8837   Normal (n = 189)  138  51     Abnormal (n = 83)  60  23    Preoperative GNRI      0.0443   Normal (n = 201)  153  48     Abnormal (n = 71)  45  26    Variables  Postoperative comorbidities (−) (n = 198)  Postoperative comorbidities (+) (n = 74)  P-value  Preoperative BMI      0.0170   ≥18.5 (n = 241)  181  60     <18.5 (n = 31)  17  14    Preoperative PNI      0.0287   Normal (n = 126)  100  26     Abnormal (n = 146)  98  48    Preoperative CONUT      0.8837   Normal (n = 189)  138  51     Abnormal (n = 83)  60  23    Preoperative GNRI      0.0443   Normal (n = 201)  153  48     Abnormal (n = 71)  45  26    BMI: body mass index; CONUT: controlling nutritional status; GNRI: geriatric nutritional risk index; PNI: prognostic nutritional index. Prognostic factors of surgically resected elderly patients with non-small-cell lung cancer We analysed the associations between preoperative BMI and 3 immune-nutritional parameters. CONUT and GNRI were significantly associated with BMI (Supplementary Material, Table S3). Further, we compared OS with BMI and the immune-nutritional parameters such as PNI, CONUT and GNRI. Multivariate analyses revealed that only preoperative GNRI (P = 0.0216) significantly affected OS (Table 4). Table 4: Univariate and multivariate analyses of overall survival of surgically resected elderly patients with non-small cell lung cancer Preoperative variables  Univariate analysis  P-value  Multivariate analysis  P-value  HR (95% CI)  HR (95% CI)  BMI (<18.5 vs ≥ 18.5)  1.332 (0.757–2.190)  0.3030      PNI (abnormal vs normal)  1.541 (1.077–2.222)  0.0179  1.147 (0.734–1.781)  0.5449  CONUT (abnormal vs normal)  1.448 (1.007–2.072)  0.0456  1.225 (0.822–1.819)  0.3172  GNRI (abnormal vs normal)  1.897 (1.290–2.748)  0.0014  1.672 (1.079–2.581)  0.0216  Preoperative variables  Univariate analysis  P-value  Multivariate analysis  P-value  HR (95% CI)  HR (95% CI)  BMI (<18.5 vs ≥ 18.5)  1.332 (0.757–2.190)  0.3030      PNI (abnormal vs normal)  1.541 (1.077–2.222)  0.0179  1.147 (0.734–1.781)  0.5449  CONUT (abnormal vs normal)  1.448 (1.007–2.072)  0.0456  1.225 (0.822–1.819)  0.3172  GNRI (abnormal vs normal)  1.897 (1.290–2.748)  0.0014  1.672 (1.079–2.581)  0.0216  BMI: body mass index; CI: confidence interval; CONUT: controlling nutritional status; GNRI: geriatric nutritional risk index; HR: hazard ratio; PNI: prognostic nutritional index. Correlation between patient characteristics and preoperative geriatric nutritional risk index On the basis of the results presented in Table 3, we analysed the correlations between patient characteristics and preoperative GNRI. Abnormal GNRI was significantly associated with histology (P = 0.0419) and outcome (P =0.0077) but with no other factor (Table 5). Table 5: Correlations of preoperative GNRI with patient characteristics Variables  Normal GNRI (n = 203)  Abnormal GNRI (n = 69)  P-value  Sex      0.7787   Male  117  38   Female  86  31    Age      0.3138   ≥80  71  29     75–79  132  40    Preoperative comorbidities (all)      0.1205   Yes  166  50     No  37  19    Preoperative comorbidities (respiratory)      0.0743   Yes  44  23     No  159  46    Smoking history      0.7792   Current/former  116  41     Never  87  28    Histology      0.0419   Non-adenocarcinoma  49  26     Adenocarcinoma  154  43    Preoperative CEA level      0.1990   Abnormal  130  38     Normal  73  31    Preoperative CYFRA level      0.0815   Abnormal  26  15     Normal  177  54    Procedures      0.0563   Limited  62  30     Others  141  39    Pathological stage      1.0000   I  151  51     II–III  52  18    Pleural invasion      0.1213   pl 1–2  52  25     pl 0  151  44    Intratumoural blood vessel invasion  0.5951   Yes  37  15     No  166  54    Intratumoural lymphatic vessel invasion  0.7339   Yes  42  16     No  161  53    Prognosis      0.0077   Alive  121  28     Dead  82  41    Variables  Normal GNRI (n = 203)  Abnormal GNRI (n = 69)  P-value  Sex      0.7787   Male  117  38   Female  86  31    Age      0.3138   ≥80  71  29     75–79  132  40    Preoperative comorbidities (all)      0.1205   Yes  166  50     No  37  19    Preoperative comorbidities (respiratory)      0.0743   Yes  44  23     No  159  46    Smoking history      0.7792   Current/former  116  41     Never  87  28    Histology      0.0419   Non-adenocarcinoma  49  26     Adenocarcinoma  154  43    Preoperative CEA level      0.1990   Abnormal  130  38     Normal  73  31    Preoperative CYFRA level      0.0815   Abnormal  26  15     Normal  177  54    Procedures      0.0563   Limited  62  30     Others  141  39    Pathological stage      1.0000   I  151  51     II–III  52  18    Pleural invasion      0.1213   pl 1–2  52  25     pl 0  151  44    Intratumoural blood vessel invasion  0.5951   Yes  37  15     No  166  54    Intratumoural lymphatic vessel invasion  0.7339   Yes  42  16     No  161  53    Prognosis      0.0077   Alive  121  28     Dead  82  41    Data in the table represent the number of patients in each category. CEA: carcinoembryonic antigen; CYFRA: cytokeratin 19 fragment; GNRI: geriatric nutritional risk index. Preoperative geriatric nutritional risk index and overall survival The Kaplan–Meier analysis revealed that the preoperative GNRI of patients with abnormal GNRI was significantly associated with shorter OS compared with those with normal GNRI (5-year OS 45.15% vs 64.10%; P = 0.0007, log-rank test) (Fig. 1). Figure 1 View largeDownload slide The Kaplan–Meier analysis of overall survival (OS) of patients with non-small-cell lung cancer as a function of preoperative GNRI. Bold line, abnormal GNRI group; thin line, normal GNRI group. The abnormal GNRI group experienced significantly shorter OS compared with the normal GNRI group (5-year OS 45.15% vs 64.10%; P = 0.0007, log-rank test). GNRI: geriatric nutritional risk index. Figure 1 View largeDownload slide The Kaplan–Meier analysis of overall survival (OS) of patients with non-small-cell lung cancer as a function of preoperative GNRI. Bold line, abnormal GNRI group; thin line, normal GNRI group. The abnormal GNRI group experienced significantly shorter OS compared with the normal GNRI group (5-year OS 45.15% vs 64.10%; P = 0.0007, log-rank test). GNRI: geriatric nutritional risk index. DISCUSSION Here, we evaluated 3 preoperative immune-nutritional parameters as potential predictive factors of postoperative comorbidities or as prognostic factors for surgically resected elderly patients with NSCLC. We found that PNI and GNRI were useful for predicting postoperative comorbidities. Comprehensive geriatric assessment is used to perform systematic multidimensional evaluations of the elderly, which focuses on somatic, functional, psychological and social features [16]. comprehensive geriatric assessment (CGA) and conventional cardiopulmonary functional assessment are required to predict postoperative complications of elderly patients who undergo thoracic surgery [17]. Mini-nutritional assessment (MNA) or the MNA-Short Form is widely used to detect malnutrition among elderly patients; however, these tools require long-term routine screening or lack biological parameters [18]. In contrast, PNI and GNRI are calculated using only blood test and anthropometric data. For example, PNI is a useful predictor of postoperative comorbidities of patients with malignancies of the digestive tract [10]. Moreover, GNRI is a clinically useful marker that predicts postoperative respiratory complications experienced by surgically resected patients with oesophageal cancer [19]. In this study, PAAL after pulmonary resection was the most frequent postoperative comorbidity, and postoperative comorbidities that significantly correlated with preoperative respiratory comorbidities included chronic obstructive pulmonary disease. Matsumura et al. [20] found that GNRI is useful for evaluating the physical performance of elderly patients with chronic obstructive pulmonary disease, and Fukuse et al. [17] reported that malnourished patients were more likely to experience PAAL after pulmonary resection. Poor healing rates with less collagen formation or wound dehiscence may explain why PAAL occurs in malnourished patients with protein deficiency [21]. Thus, this study supports these results and shows that PAAL and GNRI are simple and valuable predictors of the outcomes of surgically resected elderly patients with NSCLC. We show here that among 3 immune-nutritional parameters, only GNRI was a prognostic factor for surgically resected elderly patients with NSCLC. The GNRI, which was developed as a new index for evaluating at-risk elderly patients [22], is calculated using the serum albumin concentration and ideal body weight (calculated using the Lorentz equations) or BMI. Previous studies support the use of GNRI because of its significant associations with most nutritional parameters and short- and long-term outcomes of patients [12]. Moreover, GNRI may account for the acute and chronic causes of malnutrition associated with underlying disease and age-related factors. Thus, the GNRI serves as an efficient tool for early detection and continuous control of malnutrition in hospitalized patients as well as for predicting mortality and morbidity of elderly inpatients [22]. Moreover, the GNRI, which serves as an indicator of the severity of systemic disease and protein-calorie stores, is widely used to objectively assess the nutritional status of elderly patients with chronic diseases [20]. However, few studies validate the clinical application of GNRI for organ malignancies [14, 18]. For example, Bo et al. [14] addressed the GNRI as an independent prognostic factor for the OS in elderly oesophageal cancer patients who received radiotherapy, and Gu et al. [18] found that GNRI could identify patients with metastatic renal cell carcinoma at risk of poor survival outcomes. In this study, multivariate analysis identified abnormal preoperative GNRI as an independent prognostic factor for surgically resected elderly patients with NSCLC. Further, patients with an abnormal preoperative GNRI experienced significantly shorter OS compared to those with a normal preoperative GNRI. Therefore, the preoperative GNRI may be useful for predicting the outcomes of high-risk elderly patients with NSCLC. Moreover, approximately 25% of patients had an abnormal preoperative GNRI, although the ECOG PS of all patients ranged from 0 to 1, and according to their appearance and the results of preoperative functional tests, these patients seemed to be in suitable condition to undergo surgical resection. Therefore, preoperative GNRI may serve to identify malnourished elderly patients with acceptable organ function who can withstand surgery. Moreover, GNRI might serve as the most significant parameter, because it is a definitive predictive factor of postoperative comorbidities and an independent prognostic factor. In contrast, the CONUT score was not identified here as a significant predictive or prognostic factor. The rates of surgical complications and mortality associated with thoracic surgery are significantly higher among patients with low BMI compared to patients with normal values [23]. The study cited recommends that patients with low BMI with lung cancer should receive preoperative nutritional support to reduce these risks. Moreover, Kaya et al. [24] found that a 25% reduction in postoperative albumin levels occurs in surgically resected lung cancer patients who are not malnourished and concluded that preoperative nutritional intervention can reduce albumin loss and less serious complications that occur following lung cancer surgery. Thus, preoperative and postoperative immune-nutritional normality is important to reduce postoperative comorbidities. However, there are no definite data, to our knowledge, which support the conclusion that nutritional intervention reduces the risk of mortality. Moreover, the detailed mechanism of the effect of nutritional intervention on OS is unclear. Further investigations are therefore required to establish the prognostic benefit conferred by nutritional intervention and to explore this mechanism. Limitations This study was limited because of its retrospective design. Therefore, a large prospective study will be required to further evaluate the predictive and prognostic significance of the GNRI. Finally, a review of surgery of cancers of the head and neck presents evidence that patients who receive perioperative immune nutrition have improved clinical outcomes [25]. In this review, most studies used arginine or omega-3 fatty acids as nutritional supplements and regimens. A prospective study found that preoperative nutritional support using arginine, omega-3 fatty acids and nucleotides benefits patients with NSCLC who undergo surgery, by decreasing complications and chest-tube removal time, although these patients were not malnourished [24]. Now, numerous enhanced recovery after surgery (ERAS) programmes are available for patients who undergo urological, digestive or liver surgery [26–29]. ERAS programmes comprise numerous elements, including perioperative nutritional support, and effectively reduce postoperative complications and hospitalization. Our findings strongly support the conclusion that immune-nutritional support for patients with an abnormal GNRI may be started before and continued after surgery. Thus, we will plan to conduct a prospective study of elderly patients with lung cancer with an abnormal GNRI, which employs a preoperative immune-nutrition programme that provides arginine or omega-3 fatty acids, with the aim of reducing postoperative morbidity and mortality. This effort may lead to the establishment of a novel ERAS programme that can be administered to malnourished elderly patients who undergo thoracic surgery. CONCLUSION In conclusion, we evaluated the preoperative immune-nutritional parameters PNI, CONUT and GNRI as potential predictive factors of postoperative comorbidities or as prognostic factors for surgically resected elderly patients with NSCLC. We conclude that the preoperative GNRI was a novel predictor and prognostic factor of postoperative comorbidities that may identify high-risk elderly patients with NSCLC before they undergo surgical resection. SUPPLEMENTARY MATERIAL Supplementary material is available at ICVTS online. Conflict of interest: none declared. REFERENCES 1 World Health Organization. Ageing and health. http://www.who.int/mediacentre/factsheets/fs404/en/ (March 2017, date last accessed). 2 Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin  2017; 67: 7– 30. Google Scholar CrossRef Search ADS PubMed  3 Brock MV, Kim MP, Hooker CM, Alberg AJ, Jordan MM, Roig CM et al.   Pulmonary resection in octogenarians with stage I non-small cell lung cancer: a 22-year experience. Ann Thorac Surg  2004; 77: 271– 7. Google Scholar CrossRef Search ADS PubMed  4 Schwegler I, von Holzen A, Gutzwiller JP, Schlumpf R, Mühlebach S, Stanga Z. Nutritional risk is a clinical predictor of postoperative mortality and morbidity in surgery for colorectal cancer. Br J Surg  2009; 97: 92– 7. Google Scholar CrossRef Search ADS   5 Alifano M, Mansuet-Lupo A, Lococo F, Roche N, Bobbio A, Canny E et al.   Systemic inflammation, nutritional status and tumor immune microenvironment determine outcome of resected non-small cell lung cancer. PLoS One  2014; 9: e106914. Google Scholar CrossRef Search ADS PubMed  6 Nakagawa T, Toyazaki T, Chiba N, Ueda Y, Gotoh M. Prognostic value of body mass index and change in body weight in postoperative outcomes of lung cancer surgery. Interact CardioVasc Thorac Surg  2016; 23: 560– 6. Google Scholar CrossRef Search ADS PubMed  7 Shoji F, Morodomi Y, Akamine T, Takamori S, Katsura M, Takada K et al.   Predictive impact for postoperative recurrence using the preoperative prognostic nutritional index in pathological stage I non-small cell lung cancer. Lung Cancer  2016; 98: 15– 21. Google Scholar CrossRef Search ADS PubMed  8 Shoji F, Haratake N, Akamine T, Takamori S, Katsura M, Takada K et al.   The preoperative Controlling Nutritional Status Score predicts survival after curative operation in patients with pathological stage I non-small cell lung cancer. Anticancer Res  2017; 37: 741– 7. Google Scholar CrossRef Search ADS PubMed  9 Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg  2004; 240: 205– 13. Google Scholar CrossRef Search ADS PubMed  10 Onodera T, Goseki N, Kosaki G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi  1984; 85: 1001– 5. Google Scholar PubMed  11 Ignacio de Ulíbarri J, González-Madroño A, de Villar NG, González P, González B, Mancha A et al.   CONUT: A tool for Controlling Nutritional Status. First validation in a hospital population. Nutr Hosp  2005; 20: 38– 45. Google Scholar PubMed  12 Cereda E, Pusani C, Limonta D, Vanotti A. The ability of the geriatric nutritional risk index to assess the nutritional status and predict the outcome of home care resident elderly: a comparison with the mini nutritional assessment. Br J Nutr  2009; 102: 563– 70. Google Scholar CrossRef Search ADS PubMed  13 Hasselmann M, Alix E. Tools and procedures for screening for malnutrition and its associated in risks in hospital. Nutr Clin Metabol  2003; 17: 218– 26. Google Scholar CrossRef Search ADS   14 Bo Y, Wang K, Liu Y, You J, Cui H, Zhu Y et al.   The Geriatric Nutritional Risk Index predicts survival in elderly esophageal squamous cell carcinoma patients with radiotherapy. PLoS One  2016; 11: e0155903. Google Scholar CrossRef Search ADS PubMed  15 Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser  1995; 854: 1– 452. PubMed  16 Hamaker ME, Jonker JM, de Rooij SE, Vos AG, Smorenburg CH, van Munster BC. Frailty screening methods for predicting outcome of a comprehensive geriatric assessment in elderly patients with cancer: a systematic review. Lancet Oncol  2012; 13: e437– 44. Google Scholar CrossRef Search ADS PubMed  17 Fukuse T, Satoda N, Hijiya K, Fujinaga T. Importance of a comprehensive geriatric assessment in prediction of complications following thoracic surgery in elderly patients. Chest  2005; 127: 886– 91. Google Scholar CrossRef Search ADS PubMed  18 Gu W, Zhang G, Sun L, Ma Q, Cheng Y, Zhang H et al.   Nutritional screening is strongly associated with overall survival in patients treated with targeted agents for metastatic renal cell carcinoma. J Cachexia Sarcopenia Muscle  2015; 6: 222– 30. Google Scholar CrossRef Search ADS PubMed  19 Yamana I, Takeno S, Shibata R, Shiwaku H, Maki K, Hashimoto T et al.   Is the geriatric nutritional risk index a significant predictor of postoperative complications in patients with esophageal cancer undergoing esophagectomy? Eur Surg Res  2015; 5: 35– 42. Google Scholar CrossRef Search ADS   20 Matsumura T, Mitani Y, Oki Y, Fujimoto Y, Ohira M, Kaneko H et al.   Comparison of Geriatric Nutritional Risk Index scores on physical performance among elderly patients with chronic obstructive pulmonary disease. Heart Lung  2015; 44: 534– 8. Google Scholar CrossRef Search ADS PubMed  21 McClave SA, Snider HL, Spain DA. Preoperative issues in clinical nutrition. Chest  1999; 115: 64S– 70S. Google Scholar CrossRef Search ADS PubMed  22 Bouillanne O, Morineau G, Dupont C, Coulombel I, Vincent JP, Nicolis I et al.   Geriatric Nutritional Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr  2005; 82: 7777– 83. Google Scholar CrossRef Search ADS   23 Thomas PA, Berbis J, Falcoz PE, Le Pimpec-Barthes F, Bernard A, Jougon J et al.  ; EPITHOR Group. National perioperative outcomes of pulmonary lobectomy for cancer: the influence of nutritional status. Eur J Cardiothorac Surg  2014; 45: 652– 9. Google Scholar CrossRef Search ADS PubMed  24 Kaya SO, Akcam TI, Ceylan KC, Samancılar O, Ozturk O, Usluer O. Is preoperative protein-rich nutrition effective on postoperative outcome in non-small cell lung cancer surgery? A prospective randomized study. J Cardiothorac Surg  2016; 11: 14. Google Scholar CrossRef Search ADS PubMed  25 Casas Rodera P, de Luis DA, Gómez Candela C, Culebras JM. Immunoenhanced enteral nutrition formulas in head and neck cancer surgery: a systematic review. Nutr Hosp  2012; 27: 681– 90. Google Scholar PubMed  26 Gustafsson UO, Scott MJ, Schwenk W, Demartines N, Roulin D, Francis N et al.  ; Enhanced Recovery After Surgery Society. Guidelines for perioperative care in elective colonic surgery: Enhanced Recovery After Surgery (ERAS®) Society recommendations. Clin Nutr  2012; 31: 783– 800. Google Scholar CrossRef Search ADS PubMed  27 Cerantola Y, Valerio M, Persson B, Jichlinski P, Ljungqvist O, Hubner M et al.   Guidelines for perioperative care after radical cystectomy for bladder cancer: Enhanced Recovery After Surgery (ERAS®) Society recommendations. Clin Nutr  2013; 32: 879– 87. Google Scholar CrossRef Search ADS PubMed  28 Mortensen K, Nilsson M, Slim K, Schäfer M, Mariette C, Braga M et al.  ; Enhanced Recovery After Surgery (ERAS®) Group. Consensus guidelines for enhanced recovery after gastrectomy: Enhanced Recovery After Surgery (ERAS®) Society recommendations. Br J Surg  2014; 101: 1209– 29. Google Scholar CrossRef Search ADS PubMed  29 Zhao Y, Qin H, Wu Y, Xiang B. Enhanced recovery after surgery program reduces length of hospital stay and complications in liver resection: a PRISMA-compliant systematic review and meta-analysis of randomized controlled trials. Medicine (Baltimore)  2017; 96: e7628. Google Scholar CrossRef Search ADS PubMed  © The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Interactive CardioVascular and Thoracic Surgery Oxford University Press

Prognostic significance of immune-nutritional parameters for surgically resected elderly lung cancer patients: a multicentre retrospective study

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© The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.
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Abstract

Abstract OBJECTIVES The world’s population is rapidly ageing, and the age of patients with lung cancer will increase as well. The prognostic nutritional index, controlling nutritional status and the geriatric nutritional risk index (GNRI) are useful parameters for evaluating immune-nutritional status. We aimed to perform a multicentre retrospective study to investigate the correlations of these immune-nutritional parameters with postoperative comorbidities or surgical outcomes of elderly patients with non-small-cell lung cancer (NSCLC). METHODS We selected 272 consecutive patients with NSCLC aged >75 years treated from January 2005 to December 2012 and evaluated 3 preoperative immune-nutritional parameters as potential predictive factors of postoperative comorbidities or as prognostic factors for surgically resected elderly patients with NSCLC. RESULTS Prognostic nutritional index, GNRI, sex and preoperative respiratory comorbidities were significantly associated with postoperative comorbidities. Multivariate analyses revealed that preoperative GNRI, sex, preoperative serum carcinoembryonic antigen levels, preoperative serum cytokeratin 19 fragment levels, pathological N factor and pleural invasion were significantly associated with overall survival (OS). Abnormal GNRI was significantly associated with histology and outcomes. The Kaplan–Meier analysis of OS as a function of preoperative GNRI revealed that patients with an abnormal GNRI experienced significantly shorter OS compared with those with normal GNRI (5-year OS, 45.15% vs 64.10%, respectively; P = 0.0007, log-rank test). The controlling nutritional status score was not significantly associated with postoperative comorbidities or surgical outcomes. CONCLUSIONS Preoperative GNRI is a novel preoperative predictor of postoperative comorbidities and a prognostic factor that may identify high-risk elderly patients with NSCLC. Elderly patients, Non-small-cell lung cancer, Immune-nutritional parameters, Postoperative comorbidities and outcome INTRODUCTION The number of people worldwide aged ≥60 years will rise from 900 million to 2 billion between 2015 and 2050 [1]. Therefore, the age of patients with malignancies will increase as well. Lung cancer is the leading cause of cancer-related death worldwide [2]. Recent advances in less invasive surgery, anaesthesia and perioperative management allow patients with preoperative comorbidities and those of advanced age to undergo surgery, which offers acceptable long-term survival [3]. Ageing causes physiological changes in pulmonary, cardiovascular, metabolic and renal functions. Thus, thoracic surgeons occasionally cannot judge whether surgery will achieve acceptable outcomes for elderly patients with lung cancer. Therefore, reliable preoperative parameters must be identified to objectively assess general physical status to select elderly patients with lung cancer who may benefit from surgery. The correlation between the nutritional status and the survival of patients with organ malignancies, including lung cancer, is an important topic. Several studies [4–6] report that preoperative nutritional status is associated with postoperative comorbidities and with long-term outcomes of patients with malignant tumours. For example, immune-nutritional parameters such as the prognostic nutritional index (PNI), controlling nutritional status (CONUT) and geriatric nutritional risk index (GNRI) are predictive and prognostic factors for certain malignancies and can be calculated according to haematological and anthropometric data. For example, we reported that immune-nutritional parameters such as PNI [7] and CONUT [8] serve as prognostic markers for early-stage lung cancer. However, the utility of immune-nutritional parameters for surgically resected elderly patients with lung cancer is unclear. To address this question, we hypothesized that PNI, CONUT and GNRI can be used to predict perioperative comorbidities or postoperative outcomes of elderly patients with non-small-cell lung cancer (NSCLC). For this purpose, we performed a multicentre retrospective study to investigate the correlation of each immune-nutritional parameter and postoperative comorbidities with surgical outcomes of elderly patients with NSCLC. MATERIALS AND METHODS Patients The ethics committees of Kyushu University Hospital and National Hospital Organization Kyushu Medical Center approved this study. Between January 2005 and December 2012, 1663 consecutive patients with primary lung cancer had undergone complete surgical resection at the Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University and the National Hospital Organization Kyushu Medical Center. For the purpose of this study, we analysed 272 (16.4%) of those patients aged >75 years with NSCLC who had undergone preoperative measurement of nutritional parameters, including peripheral serum lymphocyte count, serum albumin concentration, serum total cholesterol concentration and body height and weight. The data were acquired from follow-up examinations conducted over a median period of 51 months (range 0–132 months) after surgical resection. To categorize surgical outcome, we used the Clavien–Dindo classification for grading the severity of morbidity [9]. Postoperative follow-up and definition of postoperative local and distant recurrences were followed by a previous report [7]. Preoperative calculation and cut-off values of immune-nutritional parameters Preoperative prognostic nutritional index Preoperative blood samples were obtained within 2 weeks before surgery. Preoperative PNI was calculated using the following formula: 10 × serum albumin levels (g/dl) + 0.005 × total peripheral blood lymphocyte count (per mm3) [10]. The receiver operating characteristic (ROC) curve of preoperative PNI levels was analysed, and postoperative survival was predicted by comparing the area under the curve (AUC). We determined that the optimum cut-off value for preoperative PNI levels was 49.6 (sensitivity = 50.34%, specificity = 58.54% and AUC of the ROC curve = 0.532). One hundred twenty-six (46.3%) patients had preoperative PNI levels >49.6, and 146 (53.7%) patients had lower preoperative PNI levels. Preoperative controlling nutritional status score The preoperative CONUT score was calculated from serum albumin concentrations, peripheral lymphocyte counts and total cholesterol concentrations [11]. Briefly, parameters were scored as follows: albumin concentration (g/dl) ≥3.5 (0 points), 3.0–3.49 (2 points), 2.5–2.99 (4 points) and <2.5 (6 points); total lymphocyte count (per mm3) ≥1600 (0 points), 1200–1599 (1 point), 800–1199 (2 points) and <800 (3 points) and total cholesterol concentration (mg/dl) ≥180 (0 points), 140–179 (1 point), 100–139 (2 points) and <100 (3 points). The sum of the 3 parameters was defined as the CONUT score. The ROC curve of the preoperative CONUT score was analysed, and postoperative survival was predicted by comparing the AUCs. We determined that the optimum cut-off value of the preoperative CONUT score was 0 (sensitivity = 34.69%; specificity = 73.98%; AUC of the ROC curve = 0.558). One hundred eight (39.7%) patients had a preoperative CONUT score ≥ 1 (abnormal CONUT), and the remaining 164 (60.3%) patients had a preoperative CONUT score = 0 (normal CONUT). Preoperative geriatric nutritional risk index Preoperative GNRI was calculated from the serum albumin concentration and body weight as described previously [12, 13]. The GNRI formula was as follows: GNRI = 14.87 × serum albumin concentration (g/l) + 41.7 × preoperative weight/ideal weight (kg). Ideal body weight was calculated as follows: ideal body weight = 22 × height (m2). According to previous reports [13, 14], patients with GNRI >98 were considered normal, whereas those with GNRI ≤98 were at risk of malnutrition. The preoperative GNRIs of 69 (25.4%) and 203 (74.6%) patients were ≤98 (abnormal GNRI) and >98 (normal GNRI), respectively. Histopathological evaluation Histological evaluation including pleural invasion, intratumoural blood vessel invasion and lymphatic vessel invasion was also followed by a previous report [7]. Pathological staging was based on the seventh edition TNM classification of the International Union Against Cancer. Definition of outcome Overall survival (OS) was defined as the time from resection to the date of death from any cause. Statistical analysis Clinical profiles, pathological profiles and other characteristics of patients were summarized using descriptive statistics or contingency tables. The Fisher’s exact test was used to analyse categorical data. We analysed survival using the Kaplan–Meier method and compared groups using the log-rank test. We used the Cox hazard model to identify independent predictive and prognostic factors. A P-value of <0.05 was considered significant. All statistical analyses were performed using JMP software, version 11.0 (SAS Institute Inc, Cary, NC, USA). RESULTS Clinical profiles During follow-up (median 51 months, range 0–132 months), 123 (45.2%) patients died, including 2 in-hospital deaths after thoracic surgery. The clinical profiles of patients are listed in Table 1. The study group included 117 women and 155 men, with a median age 78 years (range 75–91 years) at the time of surgery. All patients had Eastern Cooperative Oncology Group Performance Status (ECOG PS) 0–1. One hundred fifteen (42.3%) patients never smoked, and the remaining 157 patients were former or current smokers. Two hundred sixteen (79.4%) patients had preoperative comorbidities such as hypertension, hyperlipidaemia, respiratory functional disorder, history of the other organ cancers, cardiovascular disease, cerebrovascular disease, diabetes mellitus or liver dysfunction. In particular, 67 (24.7%) patients had preoperative respiratory comorbidities. The details of preoperative respiratory comorbidities are listed in Supplementary Material, Table S1. The most frequent disease was chronic obstructive pulmonary disease. One hundred four (38.2%) patients had abnormal preoperative carcinoembryonic antigen levels (normal values <3.4 ng/ml or 5.0 ng/ml), and 41 (15.1%) patients had abnormal preoperative cytokeratin 19 fragment levels (normal value <3.5 ng/ml). Three (1.1%), 177 (65.1%) and 92 (33.2%) patients underwent pneumonectomy with systemic lymphadenectomies; lobectomies with systemic lymphadenectomies and limited resections, including segmentectomy or wedge resections, in those with peripheral lesions or poor pulmonary function, respectively. Thirty-three (12.2%) and 77 (28.3%) patients received adjuvant chemotherapy and experienced postoperative recurrence, respectively. Table 1: Patient clinical profiles Variables/characteristics  n = 272  Follow-up (months), median (range)  51 (0–132)  Age (years), median (range)  78 (75–91)  Sex   Female  117 (43.0)   Male  155 (57.0)  ECOG performance status   0–1  272 (100)   2–4  0 (0)  Smoking status   Never  115 (42.3)   Former/current  157 (57.7)  Preoperative comorbidities (all)   No  56 (20.6)   Yes  216 (79.4)  Preoperative comorbidities (respiratory)   No  205 (75.4)   Yes  67 (24.6)  Preoperative CEA level (mg/ml)   Normal  104 (38.2)   Abnormal  168 (61.8)  Preoperative CYFRA level (mg/ml)   Normal  231 (84.9)   Abnormal  41 (15.1)  Surgical procedure   Pneumonectomy  3 (1.1)   Lobectomy  177 (65.1)   Limited resection  92 (33.8)  Adjuvant chemotherapy   No  239 (87.8)   Yes  33 (12.2)  Postoperative recurrence   No  195 (71.7)   Yes  77 (28.3)  Variables/characteristics  n = 272  Follow-up (months), median (range)  51 (0–132)  Age (years), median (range)  78 (75–91)  Sex   Female  117 (43.0)   Male  155 (57.0)  ECOG performance status   0–1  272 (100)   2–4  0 (0)  Smoking status   Never  115 (42.3)   Former/current  157 (57.7)  Preoperative comorbidities (all)   No  56 (20.6)   Yes  216 (79.4)  Preoperative comorbidities (respiratory)   No  205 (75.4)   Yes  67 (24.6)  Preoperative CEA level (mg/ml)   Normal  104 (38.2)   Abnormal  168 (61.8)  Preoperative CYFRA level (mg/ml)   Normal  231 (84.9)   Abnormal  41 (15.1)  Surgical procedure   Pneumonectomy  3 (1.1)   Lobectomy  177 (65.1)   Limited resection  92 (33.8)  Adjuvant chemotherapy   No  239 (87.8)   Yes  33 (12.2)  Postoperative recurrence   No  195 (71.7)   Yes  77 (28.3)  Data are represented as n (%), unless otherwise specified. CEA: carcinoembryonic antigen; CYFRA: cytokeratin 19 fragment; ECOG: Eastern Cooperative Oncology Group. Pathological profiles The histological types were adenocarcinoma, squamous cell carcinoma and others in 197 (72.4%), 63 (23.2%) and 12 (4.4%) patients, respectively. Of the 272 patients, 202 (74.2%), 45 (16.6%) and 25 (9.2%) had pathological Stages I, II and III, respectively. Seventy-four (27.5%), 58 (28.9%) and 52 (27.1%) patients had visceral pleural invasion, blood vessel invasion and lymphatic vessel invasion, respectively (Table 2). Table 2: Patient pathological profiles Characteristics  n (%)  Histological type   Adenocarcinoma  197 (72.4)   Squamous cell carcinoma  63 (23.2)   Others  12 (4.4)  Pathological stage   IA  126 (46.3)   IB  76 (27.9)   IIA  26 (9.6)   IIB  19 (7.0)   IIIA  22 (8.1)   IIIB  3 (1.1)  Pleural invasion (pl)   pl 0  198 (72.3)   pl 1–3  74 (27.7)  Intratumoural blood vessel invasion   No  214 (71.1)   Yes  58 (28.9)  Intratumoural lymphatic vessel invasion   No  220 (72.9)   Yes  52 (27.1)  Characteristics  n (%)  Histological type   Adenocarcinoma  197 (72.4)   Squamous cell carcinoma  63 (23.2)   Others  12 (4.4)  Pathological stage   IA  126 (46.3)   IB  76 (27.9)   IIA  26 (9.6)   IIB  19 (7.0)   IIIA  22 (8.1)   IIIB  3 (1.1)  Pleural invasion (pl)   pl 0  198 (72.3)   pl 1–3  74 (27.7)  Intratumoural blood vessel invasion   No  214 (71.1)   Yes  58 (28.9)  Intratumoural lymphatic vessel invasion   No  220 (72.9)   Yes  52 (27.1)  Correlation between postoperative comorbidities and preoperative immune-nutritional parameters Seventy-four (27.2%) patients had postoperative comorbidities (Supplementary Material, Table S2). All postoperative comorbidities in this study were graded as I or II according to the Clavien–Dindo classification [9]. The most frequent (18.6%) complication was prolonged alveolar air leakage (PAAL) for >7 days. We evaluated the correlations between postoperative comorbidities and 3 immune-nutritional parameters and body mass index (BMI). Low BMI (<18.5 kg/m2) was defined according to the World Health Organization (WHO) classification [15]. Preoperative BMI, PNI and GNRI were significantly associated with postoperative complications (P = 0.0170, 0.0287 and 0.0443, respectively) (Table 3). Table 3: Correlations between postoperative comorbidities and immune-nutritional parameters Variables  Postoperative comorbidities (−) (n = 198)  Postoperative comorbidities (+) (n = 74)  P-value  Preoperative BMI      0.0170   ≥18.5 (n = 241)  181  60     <18.5 (n = 31)  17  14    Preoperative PNI      0.0287   Normal (n = 126)  100  26     Abnormal (n = 146)  98  48    Preoperative CONUT      0.8837   Normal (n = 189)  138  51     Abnormal (n = 83)  60  23    Preoperative GNRI      0.0443   Normal (n = 201)  153  48     Abnormal (n = 71)  45  26    Variables  Postoperative comorbidities (−) (n = 198)  Postoperative comorbidities (+) (n = 74)  P-value  Preoperative BMI      0.0170   ≥18.5 (n = 241)  181  60     <18.5 (n = 31)  17  14    Preoperative PNI      0.0287   Normal (n = 126)  100  26     Abnormal (n = 146)  98  48    Preoperative CONUT      0.8837   Normal (n = 189)  138  51     Abnormal (n = 83)  60  23    Preoperative GNRI      0.0443   Normal (n = 201)  153  48     Abnormal (n = 71)  45  26    BMI: body mass index; CONUT: controlling nutritional status; GNRI: geriatric nutritional risk index; PNI: prognostic nutritional index. Prognostic factors of surgically resected elderly patients with non-small-cell lung cancer We analysed the associations between preoperative BMI and 3 immune-nutritional parameters. CONUT and GNRI were significantly associated with BMI (Supplementary Material, Table S3). Further, we compared OS with BMI and the immune-nutritional parameters such as PNI, CONUT and GNRI. Multivariate analyses revealed that only preoperative GNRI (P = 0.0216) significantly affected OS (Table 4). Table 4: Univariate and multivariate analyses of overall survival of surgically resected elderly patients with non-small cell lung cancer Preoperative variables  Univariate analysis  P-value  Multivariate analysis  P-value  HR (95% CI)  HR (95% CI)  BMI (<18.5 vs ≥ 18.5)  1.332 (0.757–2.190)  0.3030      PNI (abnormal vs normal)  1.541 (1.077–2.222)  0.0179  1.147 (0.734–1.781)  0.5449  CONUT (abnormal vs normal)  1.448 (1.007–2.072)  0.0456  1.225 (0.822–1.819)  0.3172  GNRI (abnormal vs normal)  1.897 (1.290–2.748)  0.0014  1.672 (1.079–2.581)  0.0216  Preoperative variables  Univariate analysis  P-value  Multivariate analysis  P-value  HR (95% CI)  HR (95% CI)  BMI (<18.5 vs ≥ 18.5)  1.332 (0.757–2.190)  0.3030      PNI (abnormal vs normal)  1.541 (1.077–2.222)  0.0179  1.147 (0.734–1.781)  0.5449  CONUT (abnormal vs normal)  1.448 (1.007–2.072)  0.0456  1.225 (0.822–1.819)  0.3172  GNRI (abnormal vs normal)  1.897 (1.290–2.748)  0.0014  1.672 (1.079–2.581)  0.0216  BMI: body mass index; CI: confidence interval; CONUT: controlling nutritional status; GNRI: geriatric nutritional risk index; HR: hazard ratio; PNI: prognostic nutritional index. Correlation between patient characteristics and preoperative geriatric nutritional risk index On the basis of the results presented in Table 3, we analysed the correlations between patient characteristics and preoperative GNRI. Abnormal GNRI was significantly associated with histology (P = 0.0419) and outcome (P =0.0077) but with no other factor (Table 5). Table 5: Correlations of preoperative GNRI with patient characteristics Variables  Normal GNRI (n = 203)  Abnormal GNRI (n = 69)  P-value  Sex      0.7787   Male  117  38   Female  86  31    Age      0.3138   ≥80  71  29     75–79  132  40    Preoperative comorbidities (all)      0.1205   Yes  166  50     No  37  19    Preoperative comorbidities (respiratory)      0.0743   Yes  44  23     No  159  46    Smoking history      0.7792   Current/former  116  41     Never  87  28    Histology      0.0419   Non-adenocarcinoma  49  26     Adenocarcinoma  154  43    Preoperative CEA level      0.1990   Abnormal  130  38     Normal  73  31    Preoperative CYFRA level      0.0815   Abnormal  26  15     Normal  177  54    Procedures      0.0563   Limited  62  30     Others  141  39    Pathological stage      1.0000   I  151  51     II–III  52  18    Pleural invasion      0.1213   pl 1–2  52  25     pl 0  151  44    Intratumoural blood vessel invasion  0.5951   Yes  37  15     No  166  54    Intratumoural lymphatic vessel invasion  0.7339   Yes  42  16     No  161  53    Prognosis      0.0077   Alive  121  28     Dead  82  41    Variables  Normal GNRI (n = 203)  Abnormal GNRI (n = 69)  P-value  Sex      0.7787   Male  117  38   Female  86  31    Age      0.3138   ≥80  71  29     75–79  132  40    Preoperative comorbidities (all)      0.1205   Yes  166  50     No  37  19    Preoperative comorbidities (respiratory)      0.0743   Yes  44  23     No  159  46    Smoking history      0.7792   Current/former  116  41     Never  87  28    Histology      0.0419   Non-adenocarcinoma  49  26     Adenocarcinoma  154  43    Preoperative CEA level      0.1990   Abnormal  130  38     Normal  73  31    Preoperative CYFRA level      0.0815   Abnormal  26  15     Normal  177  54    Procedures      0.0563   Limited  62  30     Others  141  39    Pathological stage      1.0000   I  151  51     II–III  52  18    Pleural invasion      0.1213   pl 1–2  52  25     pl 0  151  44    Intratumoural blood vessel invasion  0.5951   Yes  37  15     No  166  54    Intratumoural lymphatic vessel invasion  0.7339   Yes  42  16     No  161  53    Prognosis      0.0077   Alive  121  28     Dead  82  41    Data in the table represent the number of patients in each category. CEA: carcinoembryonic antigen; CYFRA: cytokeratin 19 fragment; GNRI: geriatric nutritional risk index. Preoperative geriatric nutritional risk index and overall survival The Kaplan–Meier analysis revealed that the preoperative GNRI of patients with abnormal GNRI was significantly associated with shorter OS compared with those with normal GNRI (5-year OS 45.15% vs 64.10%; P = 0.0007, log-rank test) (Fig. 1). Figure 1 View largeDownload slide The Kaplan–Meier analysis of overall survival (OS) of patients with non-small-cell lung cancer as a function of preoperative GNRI. Bold line, abnormal GNRI group; thin line, normal GNRI group. The abnormal GNRI group experienced significantly shorter OS compared with the normal GNRI group (5-year OS 45.15% vs 64.10%; P = 0.0007, log-rank test). GNRI: geriatric nutritional risk index. Figure 1 View largeDownload slide The Kaplan–Meier analysis of overall survival (OS) of patients with non-small-cell lung cancer as a function of preoperative GNRI. Bold line, abnormal GNRI group; thin line, normal GNRI group. The abnormal GNRI group experienced significantly shorter OS compared with the normal GNRI group (5-year OS 45.15% vs 64.10%; P = 0.0007, log-rank test). GNRI: geriatric nutritional risk index. DISCUSSION Here, we evaluated 3 preoperative immune-nutritional parameters as potential predictive factors of postoperative comorbidities or as prognostic factors for surgically resected elderly patients with NSCLC. We found that PNI and GNRI were useful for predicting postoperative comorbidities. Comprehensive geriatric assessment is used to perform systematic multidimensional evaluations of the elderly, which focuses on somatic, functional, psychological and social features [16]. comprehensive geriatric assessment (CGA) and conventional cardiopulmonary functional assessment are required to predict postoperative complications of elderly patients who undergo thoracic surgery [17]. Mini-nutritional assessment (MNA) or the MNA-Short Form is widely used to detect malnutrition among elderly patients; however, these tools require long-term routine screening or lack biological parameters [18]. In contrast, PNI and GNRI are calculated using only blood test and anthropometric data. For example, PNI is a useful predictor of postoperative comorbidities of patients with malignancies of the digestive tract [10]. Moreover, GNRI is a clinically useful marker that predicts postoperative respiratory complications experienced by surgically resected patients with oesophageal cancer [19]. In this study, PAAL after pulmonary resection was the most frequent postoperative comorbidity, and postoperative comorbidities that significantly correlated with preoperative respiratory comorbidities included chronic obstructive pulmonary disease. Matsumura et al. [20] found that GNRI is useful for evaluating the physical performance of elderly patients with chronic obstructive pulmonary disease, and Fukuse et al. [17] reported that malnourished patients were more likely to experience PAAL after pulmonary resection. Poor healing rates with less collagen formation or wound dehiscence may explain why PAAL occurs in malnourished patients with protein deficiency [21]. Thus, this study supports these results and shows that PAAL and GNRI are simple and valuable predictors of the outcomes of surgically resected elderly patients with NSCLC. We show here that among 3 immune-nutritional parameters, only GNRI was a prognostic factor for surgically resected elderly patients with NSCLC. The GNRI, which was developed as a new index for evaluating at-risk elderly patients [22], is calculated using the serum albumin concentration and ideal body weight (calculated using the Lorentz equations) or BMI. Previous studies support the use of GNRI because of its significant associations with most nutritional parameters and short- and long-term outcomes of patients [12]. Moreover, GNRI may account for the acute and chronic causes of malnutrition associated with underlying disease and age-related factors. Thus, the GNRI serves as an efficient tool for early detection and continuous control of malnutrition in hospitalized patients as well as for predicting mortality and morbidity of elderly inpatients [22]. Moreover, the GNRI, which serves as an indicator of the severity of systemic disease and protein-calorie stores, is widely used to objectively assess the nutritional status of elderly patients with chronic diseases [20]. However, few studies validate the clinical application of GNRI for organ malignancies [14, 18]. For example, Bo et al. [14] addressed the GNRI as an independent prognostic factor for the OS in elderly oesophageal cancer patients who received radiotherapy, and Gu et al. [18] found that GNRI could identify patients with metastatic renal cell carcinoma at risk of poor survival outcomes. In this study, multivariate analysis identified abnormal preoperative GNRI as an independent prognostic factor for surgically resected elderly patients with NSCLC. Further, patients with an abnormal preoperative GNRI experienced significantly shorter OS compared to those with a normal preoperative GNRI. Therefore, the preoperative GNRI may be useful for predicting the outcomes of high-risk elderly patients with NSCLC. Moreover, approximately 25% of patients had an abnormal preoperative GNRI, although the ECOG PS of all patients ranged from 0 to 1, and according to their appearance and the results of preoperative functional tests, these patients seemed to be in suitable condition to undergo surgical resection. Therefore, preoperative GNRI may serve to identify malnourished elderly patients with acceptable organ function who can withstand surgery. Moreover, GNRI might serve as the most significant parameter, because it is a definitive predictive factor of postoperative comorbidities and an independent prognostic factor. In contrast, the CONUT score was not identified here as a significant predictive or prognostic factor. The rates of surgical complications and mortality associated with thoracic surgery are significantly higher among patients with low BMI compared to patients with normal values [23]. The study cited recommends that patients with low BMI with lung cancer should receive preoperative nutritional support to reduce these risks. Moreover, Kaya et al. [24] found that a 25% reduction in postoperative albumin levels occurs in surgically resected lung cancer patients who are not malnourished and concluded that preoperative nutritional intervention can reduce albumin loss and less serious complications that occur following lung cancer surgery. Thus, preoperative and postoperative immune-nutritional normality is important to reduce postoperative comorbidities. However, there are no definite data, to our knowledge, which support the conclusion that nutritional intervention reduces the risk of mortality. Moreover, the detailed mechanism of the effect of nutritional intervention on OS is unclear. Further investigations are therefore required to establish the prognostic benefit conferred by nutritional intervention and to explore this mechanism. Limitations This study was limited because of its retrospective design. Therefore, a large prospective study will be required to further evaluate the predictive and prognostic significance of the GNRI. Finally, a review of surgery of cancers of the head and neck presents evidence that patients who receive perioperative immune nutrition have improved clinical outcomes [25]. In this review, most studies used arginine or omega-3 fatty acids as nutritional supplements and regimens. A prospective study found that preoperative nutritional support using arginine, omega-3 fatty acids and nucleotides benefits patients with NSCLC who undergo surgery, by decreasing complications and chest-tube removal time, although these patients were not malnourished [24]. Now, numerous enhanced recovery after surgery (ERAS) programmes are available for patients who undergo urological, digestive or liver surgery [26–29]. ERAS programmes comprise numerous elements, including perioperative nutritional support, and effectively reduce postoperative complications and hospitalization. Our findings strongly support the conclusion that immune-nutritional support for patients with an abnormal GNRI may be started before and continued after surgery. Thus, we will plan to conduct a prospective study of elderly patients with lung cancer with an abnormal GNRI, which employs a preoperative immune-nutrition programme that provides arginine or omega-3 fatty acids, with the aim of reducing postoperative morbidity and mortality. This effort may lead to the establishment of a novel ERAS programme that can be administered to malnourished elderly patients who undergo thoracic surgery. CONCLUSION In conclusion, we evaluated the preoperative immune-nutritional parameters PNI, CONUT and GNRI as potential predictive factors of postoperative comorbidities or as prognostic factors for surgically resected elderly patients with NSCLC. We conclude that the preoperative GNRI was a novel predictor and prognostic factor of postoperative comorbidities that may identify high-risk elderly patients with NSCLC before they undergo surgical resection. SUPPLEMENTARY MATERIAL Supplementary material is available at ICVTS online. Conflict of interest: none declared. REFERENCES 1 World Health Organization. Ageing and health. http://www.who.int/mediacentre/factsheets/fs404/en/ (March 2017, date last accessed). 2 Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin  2017; 67: 7– 30. Google Scholar CrossRef Search ADS PubMed  3 Brock MV, Kim MP, Hooker CM, Alberg AJ, Jordan MM, Roig CM et al.   Pulmonary resection in octogenarians with stage I non-small cell lung cancer: a 22-year experience. Ann Thorac Surg  2004; 77: 271– 7. Google Scholar CrossRef Search ADS PubMed  4 Schwegler I, von Holzen A, Gutzwiller JP, Schlumpf R, Mühlebach S, Stanga Z. Nutritional risk is a clinical predictor of postoperative mortality and morbidity in surgery for colorectal cancer. Br J Surg  2009; 97: 92– 7. Google Scholar CrossRef Search ADS   5 Alifano M, Mansuet-Lupo A, Lococo F, Roche N, Bobbio A, Canny E et al.   Systemic inflammation, nutritional status and tumor immune microenvironment determine outcome of resected non-small cell lung cancer. PLoS One  2014; 9: e106914. Google Scholar CrossRef Search ADS PubMed  6 Nakagawa T, Toyazaki T, Chiba N, Ueda Y, Gotoh M. Prognostic value of body mass index and change in body weight in postoperative outcomes of lung cancer surgery. Interact CardioVasc Thorac Surg  2016; 23: 560– 6. Google Scholar CrossRef Search ADS PubMed  7 Shoji F, Morodomi Y, Akamine T, Takamori S, Katsura M, Takada K et al.   Predictive impact for postoperative recurrence using the preoperative prognostic nutritional index in pathological stage I non-small cell lung cancer. Lung Cancer  2016; 98: 15– 21. Google Scholar CrossRef Search ADS PubMed  8 Shoji F, Haratake N, Akamine T, Takamori S, Katsura M, Takada K et al.   The preoperative Controlling Nutritional Status Score predicts survival after curative operation in patients with pathological stage I non-small cell lung cancer. Anticancer Res  2017; 37: 741– 7. Google Scholar CrossRef Search ADS PubMed  9 Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg  2004; 240: 205– 13. Google Scholar CrossRef Search ADS PubMed  10 Onodera T, Goseki N, Kosaki G. Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients. Nihon Geka Gakkai Zasshi  1984; 85: 1001– 5. Google Scholar PubMed  11 Ignacio de Ulíbarri J, González-Madroño A, de Villar NG, González P, González B, Mancha A et al.   CONUT: A tool for Controlling Nutritional Status. First validation in a hospital population. Nutr Hosp  2005; 20: 38– 45. Google Scholar PubMed  12 Cereda E, Pusani C, Limonta D, Vanotti A. The ability of the geriatric nutritional risk index to assess the nutritional status and predict the outcome of home care resident elderly: a comparison with the mini nutritional assessment. Br J Nutr  2009; 102: 563– 70. Google Scholar CrossRef Search ADS PubMed  13 Hasselmann M, Alix E. Tools and procedures for screening for malnutrition and its associated in risks in hospital. Nutr Clin Metabol  2003; 17: 218– 26. Google Scholar CrossRef Search ADS   14 Bo Y, Wang K, Liu Y, You J, Cui H, Zhu Y et al.   The Geriatric Nutritional Risk Index predicts survival in elderly esophageal squamous cell carcinoma patients with radiotherapy. PLoS One  2016; 11: e0155903. Google Scholar CrossRef Search ADS PubMed  15 Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser  1995; 854: 1– 452. PubMed  16 Hamaker ME, Jonker JM, de Rooij SE, Vos AG, Smorenburg CH, van Munster BC. Frailty screening methods for predicting outcome of a comprehensive geriatric assessment in elderly patients with cancer: a systematic review. Lancet Oncol  2012; 13: e437– 44. Google Scholar CrossRef Search ADS PubMed  17 Fukuse T, Satoda N, Hijiya K, Fujinaga T. Importance of a comprehensive geriatric assessment in prediction of complications following thoracic surgery in elderly patients. Chest  2005; 127: 886– 91. Google Scholar CrossRef Search ADS PubMed  18 Gu W, Zhang G, Sun L, Ma Q, Cheng Y, Zhang H et al.   Nutritional screening is strongly associated with overall survival in patients treated with targeted agents for metastatic renal cell carcinoma. J Cachexia Sarcopenia Muscle  2015; 6: 222– 30. Google Scholar CrossRef Search ADS PubMed  19 Yamana I, Takeno S, Shibata R, Shiwaku H, Maki K, Hashimoto T et al.   Is the geriatric nutritional risk index a significant predictor of postoperative complications in patients with esophageal cancer undergoing esophagectomy? Eur Surg Res  2015; 5: 35– 42. Google Scholar CrossRef Search ADS   20 Matsumura T, Mitani Y, Oki Y, Fujimoto Y, Ohira M, Kaneko H et al.   Comparison of Geriatric Nutritional Risk Index scores on physical performance among elderly patients with chronic obstructive pulmonary disease. Heart Lung  2015; 44: 534– 8. Google Scholar CrossRef Search ADS PubMed  21 McClave SA, Snider HL, Spain DA. Preoperative issues in clinical nutrition. Chest  1999; 115: 64S– 70S. Google Scholar CrossRef Search ADS PubMed  22 Bouillanne O, Morineau G, Dupont C, Coulombel I, Vincent JP, Nicolis I et al.   Geriatric Nutritional Index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr  2005; 82: 7777– 83. Google Scholar CrossRef Search ADS   23 Thomas PA, Berbis J, Falcoz PE, Le Pimpec-Barthes F, Bernard A, Jougon J et al.  ; EPITHOR Group. National perioperative outcomes of pulmonary lobectomy for cancer: the influence of nutritional status. Eur J Cardiothorac Surg  2014; 45: 652– 9. Google Scholar CrossRef Search ADS PubMed  24 Kaya SO, Akcam TI, Ceylan KC, Samancılar O, Ozturk O, Usluer O. Is preoperative protein-rich nutrition effective on postoperative outcome in non-small cell lung cancer surgery? A prospective randomized study. J Cardiothorac Surg  2016; 11: 14. Google Scholar CrossRef Search ADS PubMed  25 Casas Rodera P, de Luis DA, Gómez Candela C, Culebras JM. Immunoenhanced enteral nutrition formulas in head and neck cancer surgery: a systematic review. Nutr Hosp  2012; 27: 681– 90. Google Scholar PubMed  26 Gustafsson UO, Scott MJ, Schwenk W, Demartines N, Roulin D, Francis N et al.  ; Enhanced Recovery After Surgery Society. Guidelines for perioperative care in elective colonic surgery: Enhanced Recovery After Surgery (ERAS®) Society recommendations. Clin Nutr  2012; 31: 783– 800. Google Scholar CrossRef Search ADS PubMed  27 Cerantola Y, Valerio M, Persson B, Jichlinski P, Ljungqvist O, Hubner M et al.   Guidelines for perioperative care after radical cystectomy for bladder cancer: Enhanced Recovery After Surgery (ERAS®) Society recommendations. Clin Nutr  2013; 32: 879– 87. Google Scholar CrossRef Search ADS PubMed  28 Mortensen K, Nilsson M, Slim K, Schäfer M, Mariette C, Braga M et al.  ; Enhanced Recovery After Surgery (ERAS®) Group. Consensus guidelines for enhanced recovery after gastrectomy: Enhanced Recovery After Surgery (ERAS®) Society recommendations. Br J Surg  2014; 101: 1209– 29. Google Scholar CrossRef Search ADS PubMed  29 Zhao Y, Qin H, Wu Y, Xiang B. Enhanced recovery after surgery program reduces length of hospital stay and complications in liver resection: a PRISMA-compliant systematic review and meta-analysis of randomized controlled trials. Medicine (Baltimore)  2017; 96: e7628. Google Scholar CrossRef Search ADS PubMed  © The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

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Interactive CardioVascular and Thoracic SurgeryOxford University Press

Published: Mar 1, 2018

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