The preoperative prognostic nutritional index is a prognostic indicator of postoperative pulmonary complications in patients with gynecological cancer

The preoperative prognostic nutritional index is a prognostic indicator of postoperative pulmonary complications in patients with gynecological cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The preoperative prognostic nutritional index is a prognostic indicator of postoperative pulmonary complications in patients with gynecological cancer Woo-Young Jo, Jeong-Hwa Seo, Seungeun Choi, Hee-Soo Kim, Yoon Jung Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4082172/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The prognostic nutritional index (PNI) reflects systemic inflammation and nutritional status. This study aimed to evaluate the predicting ability of the preoperative PNI regarding postoperative pulmonary complications (PPCs) in patients with gynecological cancer. Demographic, laboratory, and clinical data were retrospectively collected from 4887 patients who underwent gynecological cancer surgery. Preoperative PNIs were calculated from serum albumin levels and total lymphocyte counts. Primary outcome was PPCs, a composite outcome including acute respiratory distress syndrome, pneumonia, atelectasis, pleural effusion, and pneumothorax within postoperative day 15. An optimal cut-off value of 49.4 for the PNI score was determined using the receiver operating characteristic curve. The study used the stabilized inverse probability of treatment weighting (IPTW) method to reduce bias and compare between the high (n = 2898.5) and low PNI groups (n = 1290.4). The incidence of PPCs is significantly higher in the low PNI group than in the high PNI group (8.9% vs 17.0%, p < 0.001). In the multivariate logistic regression model after IPTW adjustment, a low preoperative PNI was an independent predictor of PPCs (odds ratio [95% confidence interval] 1.93 [1.53, 2.43], p < 0.001). Low preoperative PNIs are associated with PPCs in patients undergoing surgery for gynecologic cancer. Biological sciences/Cancer Health sciences/Risk factors Health sciences/Health care/Nutrition prognostic nutritional index gynecological cancer postoperative pulmonary complications Figures Figure 1 Figure 2 Figure 3 Introduction Patients with gynecologic cancer are at risk of malnutrition, and the risk increases with co-existing chronic diseases [ 1 , 2 ]. Previous studies reported that 20–53% of patients with gynecologic cancer presented with malnutrition at the time of diagnosis [ 3 – 5 ]. Preexisting malnutrition can increase the risk of postoperative adverse events and compromise long-term outcomes [ 6 , 7 ]. The primary treatment for gynecological cancers is radical resection surgery [ 8 ]. General anesthesia with mechanical ventilation is indispensable for such surgeries, but it impacts the respiratory system. Anesthetic agents reduce the sensitivity of the central respiratory drive [ 9 ], and neuromuscular-blocking agents lead to the paralysis of respiratory muscles and increase vulnerability to atelectasis [ 10 ]. During manipulation of the abdominal organs, the vital capacity, tidal volume, and functional residual capacity of the lungs are decreased [ 11 ]. Postoperative pulmonary complications (PPCs) occur in 4.2–13% of major organ surgeries and are associated with in-hospital, 30-day, and 90-day mortality rates [ 12 – 16 ]. PPCs can also prolong the length of hospital stays, increase intensive care unit (ICU) admissions, and raise health care costs [ 17 , 18 ]. Therefore, predicting PPCs prior to surgery is beneficial. The prognostic nutritional index (PNI) was developed as a prognostic index for gastrointestinal cancers, representing the immune-nutritional status of patients [ 19 , 20 ]. The PNI can be easily calculated based on the serum albumin level and the peripheral blood lymphocyte count. A low preoperative PNI is associated with shorter progression-free survival and overall survival in gynecological cancer [ 21 ]. Postoperative outcomes such as hemorrhages, infection, and pulmonary complications are also associated with the preoperative PNI [ 22 ]. In addition, the PNI is associated with the incidence of PPCs in patients undergoing radical cystectomy [ 23 ]. However, no studies have evaluated its association with PPCs in gynecologic cancers. The study aimed to investigate the association between the PNI and PPCs in patients with gynecological cancer and identify the associated risk factors of PPCs. Results 4887 patients who underwent gynecological cancer surgery under general anesthesia at Seoul National University Hospital, South Korea, from January 2005 to October 2021, were enrolled for the study. After excluding any patients who did not fulfill the study criteria, a final total of 4285 patients were analyzed (Fig. 1 ). PPCs occurred in 534 (12.5%) patients (Supplemental Table 1). In the ROC analysis for the prediction of PPCs, the area under the curve (AUC) of the PNI was significantly higher compared to that of the ARISCAT risk index (AUC [95% CI] 0.707 [0.681–0.733], p < 0.001 vs. 0.676 [0.565–0.696], p < 0.001; DeLong’s test Z = 2.066, p = 0.039; Fig. 2 ). The optimal cut-off value for the preoperative PNI was calculated as 49.4, and patients were divided into low (≤ 49.4) and high (≥ 49.4) preoperative PNI groups, based on this value. After IPTW adjustment (matched factors: age, weight, height, surgical modalities [robot or laparoscopy assisted], primary cancer site, American society of anesthesiologists–physical status classification, the international federation of gynecology and obstetrics staging, underlying diseases [hypertension, diabetes mellitus, history of heart disease, lung disease, renal disease, and liver disease]; Supplemental Table 1), all matched factors were balanced (high PNI [n = 2898.5] and low PNI [n = 1290.4] groups; Table 1 ). Table 1 Patient characteristics, cancer related data, preoperative laboratory findings, and postoperative treatment protocol in unadjusted and IPTW adjusted patient cohorts. Before adjustment After IPTW adjustment Low PNI High PNI SMD Low PNI High PNI SMD (n = 1333) (n = 2952) (n = 1290.4) (n = 2898.5) Demographics Age, yr 53 [45, 64] 52 [ 43 , 59 ] 0.242 52 [44, 61] 52 [44, 60] 0.040 Weight, kg 55.3 [49.6, 61.7] 58.0 [52.8, 64.9] 0.358 57.3 [51.4, 64.0] 57.2 [51.7, 64.00] 0.020 Height, cm 156.2 [152.2, 160.4] 157.0 [153.1, 161.1] 0.131 156.8 [153.0, 160.7] 156.9 [152.9, 161.1] 0.019 BMI, kg/m 2 22.5 [20.3, 25.1] 23.7 [21.4, 26.4] 0.321 23.4 [20.9, 26.0] 23.5 [21.1, 26.0] 0.012 Primary site 0.584 0.043 Cervix 379 (28.4%) 1147 (38.9%) 452.0 (35.0%) 1036.6 (35.8%) Endometrium 330 (24.8%) 1199 (40.6%) 451.7 (35.0%) 1049.1 (36.2%) Ovary 624 (46.8%) 606 (20.5%) 386.7 (30.0%) 812.8 (28.0%) FIGO stage 0.091 0.040 1 718 (53.9%) 1648 (55.8%) 704.2 (54.6%) 1610.6 (55.6%) 2 138 (10.4%) 354 (12.0%) 156.8 (12.2%) 334.2 (11.5%) 3 311 (23.3%) 647 (21.9%) 278.8 (21.6%) 645.1 (22.3%) 4 166 (12.5%) 303 (10.3%) 150.5 (11.7%) 308.6 (10.6%) ARISCAT risk index 23 [ 16 , 23 ] 23 [ 16 , 23 ] 0.371 23 [ 16 , 23 ] 23 [ 16 , 23 ] 0.029 ARISCAT grade * 0.400 0.091 High 9 (0.7%) 2 (0.1%) 5.2 (0.4%) 2.8 (0.1%) Intermediate 301 (22.6%) 262 (8.9%) 186.0 (14.4%) 352.8 (12.2%) Low 1023 (76.7%) 2688 (91.1%) 1099.1 (85.2%) 2542.9 (87.7%) ASA physical status 0.298 0.036 1 479 (35.9%) 1320 (44.7%) 537.8 (41.7%) 1234.5 (42.6%) 2 720 (54.0%) 1532 (51.9%) 679.7 (52.7%) 1517.8 (52.4%) 3 127 (9.5%) 98 (3.3%) 68.7 (5.3%) 140.4 (4.8%) 4 7 (0.5%) 2 (0.1%) 4.2 (0.3%) 5.9 (0.2%) Comorbidity Hypertension 330 (24.8%) 635 (21.5%) 0.077 299.8 (23.2%) 647.9 (22.4%) 0.021 Diabetes mellitus 130 (9.8%) 301 (10.2%) 0.015 124.1 (9.6%) 288.8 (10.0%) 0.012 Heart disease 113 (8.5%) 149 (5.0%) 0.137 78.1 (6.0%) 173.0 (6.0%) 0.003 Thyroid disease 135 (10.1%) 327 (11.1%) 0.031 142.7 (11.1%) 312.9 (10.8%) 0.008 Liver disease 147 (11.0%) 276 (9.3%) 0.055 122.4 (9.5%) 280.1 (9.7%) 0.006 Chronic obstructive pulmonary disease 5 (0.4%) 2 (0.1%) 0.065 2.4 (0.2%) 5.5 (0.2%) < 0.001 Asthma 12 (0.9%) 32 (1.1%) 0.019 11.9 (0.9%) 31.1 (1.1%) 0.015 Restrictive lung disease 124 (9.3%) 67 (2.3%) 0.305 58.5 (4.5%) 113.9 (3.9%) 0.030 Renal disease 63 (4.7%) 75 (2.5%) 0.117 42.9 (3.3%) 93.8 (3.2%) 0.005 Current smoking 31 (2.3%) 59 (2.0%) 0.022 29.8 (2.3%) 64.0 (2.2%) 0.007 Preoperative laboratory findings PNI 46.0 [42.4, 48.0] 54.1 [51.9, 56.7] 2.397 46.9 [44.5, 48.4] 53.7 [51.6, 56.3] 2.251 Hemoglobin, g/dL 11.7 [10.5, 12.6] 12.9 [12.1, 13.6] 0.850 12.5 [11.5, 13.4] 12.6 [11.7, 13.4] 0.089 WBC, 10 3 /µL 5.8 [4.6, 7.5] 6.4 [5.4, 7.7] 0.116 5.8 [4.7, 7.4] 6.3 [5.2, 7.6] 0.059 Lymphocyte, % 23.5 [17.3, 30.7] 33.0 [27.0, 38.8] 0.909 24.3 [18.4, 30.8] 33.0 [27.1, 39.0] 0.876 Platelet, 10 3 /µL 262 [210, 334] 262 [220, 309] 0.118 252 [208, 312] 262 [219, 311] 0.013 Albumin, g/dL 3.9 [3.6, 4.1] 4.4 [4.2, 4.6] 1.745 3.9 [3.7, 4.1] 4.4 [4.2, 4.6] 1.577 Creatinine, mg/dL 0.70 [0.61, 0.80] 0.73 [0.66, 0.81] 0.019 0.70 [0.62, 0.80] 0.73 [0.65, 0.81] 0.017 Intraoperative findings Robot or laparoscopic surgery 332 (24.9%) 1446 (49.0%) 0.515 539.5 (41.8%) 1232.9 (42.5%) 0.015 Operation duration, hr 3.9 [2.8, 5.5] 3.3 [2.3, 4.5] 0.399 3.6 [2.4, 5.0] 3.4 [2.4, 4.7] 0.109 Anesthesia duration, hr 4.8 [3.7, 6.4] 4.3 [3.3, 5.4] 0.412 4.5 [3.4, 6.0] 4.4 [3.4, 5.7] 0.111 Estimated blood loss, mL 550 [300, 1150] 300 [178, 600] 0.424 400 [200, 900] 350 [200, 650] 0.199 Data are presented as median [interquartile range] or number (proportion). * : ARISCAT grades were categorized into high (≥ 45), intermediate (26–44), and low (< 26) grades based on the ARISCAT risk index. IPTW, inverse probability treatment weighting; PNI, prognostic nutritional index; SMD, standardized mean difference; BMI, body mass index; FIGO, International Federation of Gynecology and Obstetrics; ARISCAT, assess respiratory risk in surgical patients in Catalonia; ASA, American Society of Anesthesiologists; WBC, white blood cell The incidence of PPCs is significantly lower in the high PNI group for the unadjusted cohort and IPTW-adjusted cohort (unadjusted cohort: number [proportions]; 206 [7.0%] vs 328 [24.6%], proportional difference [95% CI] − 17.62% [− 20.12%, − 15.14%], p < 0.001; IPTW-adjusted cohort: 257.2 [8.9%] vs 219.5 [17.0%], − 8.13% [− 10.43%, − 5.83%], p < 0.001; Table 2 ). In the IPTW-adjusted cohort, the incidences of pleural effusion (5.3% vs. 12.1%, p < 0.001), atelectasis (4.9% vs. 9.1%, p < 0.001), hypoxemia (0.9% vs. 1.8%, p = 0.022), and pneumonia (0.2% vs. 0.7%; p = 0.036) were significantly higher in the low PNI group (Table 2 ). The ICU admission rate, length of ICU stay, and length of hospital stay were also greater in the low PNI group, before and after IPTW adjustment (Table 2 ). Table 2 Postoperative outcomes in patients with gynecological cancer Before adjustment After IPTW adjustment High PNI (n = 2952) Low PNI (n = 1333) Proportion difference or Mean difference (95% CI) P value High PNI (n = 2898.5) Low PNI (n = 1290.4) Proportion difference or Mean difference (95% CI) P value PPCs (%) 206 (7.0%) 328 (24.6%) -17.6% (-20.1%, -15.1%) < 0.001 257.4 (8.9%) 219.5 (17.0%) -8.1% (-10.4%, -5.8%) < 0.001 Pneumothorax (%) 6 (0.2%) 10 (0.8%) -0.54% (-1.04%, -0.06%) 0.014 9.8 (0.3%) 5.1 (0.4%) -0.06% (-0.46%, 0.35%) 0.801 Pleural effusion (%) 109 (3.7%) 240 (18.0%) -14.3% (-16.5%, -12.1%) < 0.001 155.0 (5.3%) 156.2 (12.1%) -6.8% (-8.7%, -4.8%) < 0.001 Atelectasis (%) 120 (4.1%) 171 (12.8%) -8.8% (-10.7%, -6.8%) < 0.001 140.7 (4.9%) 117.4 (9.1%) -4.2% (-6.0%, -2.5%) < 0.001 Hypoxemia (%) 26 (0.9%) 29 (2.2%) -1.3% (-2.2%, -0.4%) 0.001 25.2 (0.9%) 23.1 (1.8%) -0.9% (-1.7%, -0.12%) 0.022 Pneumonia (%) 4 (0.1%) 10 (0.8%) -0.61% (-1.10%, -0.13%) 0.003 4.9 (0.2%) 8.7 (0.7%) -0.51% (-0.98%, -0.03%) 0.036 Count of PPCs* 1.3 ± 0.5 1.4 ± 0.6 -0.1 (-0.2, 0.0) 0.014 1.3 ± 0.5 1.4 ± 0.6 -0.1 (-0.2, 0.0) 0.036 2 ≤ count* 55 (26.7%) 117 (35.7%) -9.0% (-16.9%, -0.1%) 0.027 72 (28.0%) 77 (35.0%) -7.0% (-15.3%, -1.4%) 0.102 ICU admission (%) 92 (3.1%) 197 (14.8%) -11.7% (-13.7%, -9.7%) < 0.001 133.0 (4.6%) 126.1 (9.8%) -5.18% (-6.97%, -3.39%) < 0.001 Length of stay at ICU (day) 0.1 ± 0.3 0.4 ± 1.8 -0.3 (-0.4, -0.2) < 0.001 0.1 ± 0.3 0.4 ± 2.1 -0.3 (-0.4, -0.1) < 0.001 Hospital length of stay (day) 10.8 ± 7.4 16.4 ± 15.0 -5.7 (-6.5, -4.8) < 0.001 11.4 ± 7.7 14.0 ± 12.4 -2.6 (-3.4, -1.9) < 0.001 Data are presented as mean ± standard deviation or number (proportion). IPTW, inverse probability of treatment weighting; PNI, prognostic nutritional index; CI, confidence interval; PPCs, postoperative pulmonary complications; ICU, intensive care unit; *Calculated only in the patients with postoperative pulmonary complications. In the multivariate logistic regression analysis, a low PNI was a significant determinant in both the unadjusted and IPTW-adjusted cohorts (unadjusted cohort: odds ratio [95% CI]; 1.99 [1.59, 2.49], p < 0.001; IPTW-adjusted cohort: 1.93 [1.53, 2.43], p < 0.001) (Table 3 ). In the IPTW-adjusted cohort, the independent determinants of PPCs were old age, ovarian cancer, preoperative hypertension, restrictive lung disease, a low PNI, a high white blood cell count, intraoperative blood transfusion, robotic or laparoscopic surgery, long operation duration, and the infused crystalloid and colloid amount (Table 3 ). Table 3 Univariate and multivariate logistic regression models for postoperative pulmonary complications in patients with gynecological cancer Before adjustment After IPTW adjustment Univariate Multivariate* Univariate Multivariate † OR [95% CI] P value OR [95% CI] P value OR [95% CI] P value OR [95% CI] P value Age, yr 1.04 [1.03, 1.05] < 0.001 1.02 [1.01, 1.03] < 0.001 1.04 [1.03, 1.05] < 0.001 1.03 [1.02, 1.04] < 0.001 Weight, kg 0.98 [0.97, 0.99] < 0.001 0.99 [0.98, 1.00] 0.004 Ovary cancer‡ 5.58 [4.61, 6.76] < 0.001 2.32 [1.85, 2.93] < 0.001 4.73 [3.88, 5.76] < 0.001 2.23 [1.75, 2.84] < 0.001 FIGO Stage ≥ 3 1.06 [0.88, 1.28] 0.545 0.99 [0.81, 1.21] 0.921 Hypertension 1.77 [1.45, 2.16] < 0.001 1.45 [1.11, 1.88] 0.006 1.85 [1.51, 2.27] < 0.001 1.52 [1.17, 1.99] 0.002 Diabetes mellitus 1.36 [1.03, 1.80] 0.029 1.52 [1.15, 2.02] 0.004 Heart disease 1.59 [1.14, 2.21] 0.006 1.53 [1.07, 2.17] 0.018 Liver disease 1.29 [0.97, 1.71] 0.081 1.14 [0.84, 1.56] 0.406 Asthma 1.11 [0.47, 2.64] 0.813 1.95 [0.92, 4.15] 0.082 Restrictive lung disease 5.92 [4.37, 8.01] < 0.001 2.81 [1.92, 4.11] < 0.001 5.76 [4.18, 7.94] < 0.001 3.39 [2.19, 5.26] < 0.001 Chronic obstructive pulmonary disease 9.43 [2.10, 42.25] 0.003 15.59 [3.60, 67.60] < 0.001 Current smoking 0.78 [0.39, 1.56] 0.476 0.68 [0.32, 1.43] 0.308 ARISCAT risk index ≥ 26 0.04 [0.00, 0.32] 0.003 0.05 [0.00, 1.00] 0.050 PNI < 49.4 4.35 [3.60, 5.25] < 0.001 1.99 [1.59, 2.49] < 0.001 2.09 [1.72, 2.54] < 0.001 1.93 [1.53, 2.43] < 0.001 WBC count, 10 3 /µL 1.11 [1.07, 1.15] < 0.001 1.07 [1.03, 1.12] < 0.001 1.12 [1.08, 1.16] < 0.001 1.08 [1.04, 1.13] < 0.001 Hb, g/dL 0.77 [0.73, 0.82] < 0.001 0.87 [0.81, 0.92] < 0.001 Platelet count, 10 3 /µL 1.00 [1.00, 1.01] < 0.001 1.00 [1.00, 1.01] < 0.001 Intraoperative red blood cell transfusion 4.12 [3.42, 4.97] < 0.001 3.40 [2.79, 4.14] < 0.001 0.61 [0.46, 0.82] 0.001 Robot or laparoscopic surgery 0.15 [0.11, 0.19] < 0.001 0.51 [0.37, 0.71] < 0.001 0.19 [0.14, 0.24] < 0.001 0.57 [0.41, 0.77] < 0.001 Operation duration, hr 1.69 [1.60, 1.77] < 0.001 1.19 [1.10, 1.28] < 0.001 1.70 [1.61, 1.80] < 0.001 1.19 [1.10, 1.29] < 0.001 Estimated blood loss, mL 1.00 [1.00, 1.00] < 0.001 1.00 [1.00, 1.00] < 0.001 Infused crystalloid amount, mL 1.00 [1.00, 1.00] < 0.001 1.00 [1.00, 1.00] < 0.001 1.00 [1.00, 1.00] < 0.001 1.00 [1.00, 1.00] < 0.001 Infused colloid amount, mL 1.00 [1.00, 1.00] < 0.001 1.00 [1.00, 1.00] < 0.001 1.00 [1.00, 1.00] 0.042 *: In multivariate logistic regression analysis with stepwise forward conditional method. †: In multivariate logistic regression analysis with stepwise forward conditional method. ‡: Compared to other types of gynecological cancer. IPTW, inverse probability of treatment weighting; OR, odds ratio; CI, confidence interval; FIGO, International Federation of Gynecology and Obstetrics; ARISCAT, assess respiratory risk in surgical patients in Catalonia; PNI, prognostic nutritional index; WBC, white blood cell; Hb, hemoglobin The multivariate-adjusted restricted cubic spline curve demonstrated that the log-odds ratio of PPCs tended to decrease with an increasing preoperative PNI (unadjusted cohort: p value for preoperative PNI < 0.001, p overall < 0.001, p nonlinear = 0.010; IPTW adjusted cohort: p value for preoperative PNI < 0.001, p overall < 0.001, p nonlinear = 0.007; Fig. 3 ). In the ovarian cancer subgroup analysis, a low PNI was a robust determinant of PPCs (Supplemental Table 2). Discussion This study proposes that the preoperative PNI is an independent predictive factor for PPCs in gynecological cancer surgery patients. The findings highlight the importance of the nutritional status in optimizing surgical outcomes for gynecological cancer patients. PPCs are common following curative surgery for major organ cancers, PPCs are associated with early postoperative mortality, ICU admission, and prolonged hospital stays [ 24 – 27 ]. The prediction of PPCs can minimize medical costs and reduce morbidity and mortality [ 18 ]. The ARISCAT risk index is a generally utilized and recognized risk stratification tool [ 28 ]. After comparing the predicting ability between the PNI and ARISCAT risk index through ROC curve analysis, this study found that the PNI exhibited better performance. Furthermore, a low PNI was identified as an independent predictor of PPCs, but a high ARISCAT risk index (≥ 26) was not. This discriminative performance suggests potential clinical value in forthcoming applications. The PNI was developed to evaluate the immune-nutritional status [ 20 ]. The association between a low PNI and adverse prognostic outcomes has been identified in a wide range of cancers, such as gastric cancer [ 29 ], colorectal cancer [ 30 ], and gynecological cancer [ 21 ]. Especially, in gynecological cancers, a low PNI is associated with unfavorable overall survival and progression-free survival [ 31 – 33 ]. In addition to the long-term outcomes, the occurrence of PPCs is associated with the preoperative PNI in patients undergoing radical cystectomy [ 23 ]. This is consistent with the findings of our study, which showed that as the preoperative PNI decreased, the odds ratio of PPCs tended to increase. Notably, the PNI can be easily calculated based on serum albumin levels and the lymphocyte count in a peripheral blood test, making it a cost-effective tool. Thus, risk stratification using the PNI would be valuable in a clinical setting [ 29 ]. Malnutrition affects the respiratory system by reducing respiratory muscle mass and strength [ 34 ], worsening respiratory muscle function, and decreasing respiratory drive [ 35 ]. Furthermore, malnutrition compromises host defense mechanisms against infections [ 36 ] and inhibits lymphocyte transformation [ 37 ]. Consequently, malnutrition can make patients vulnerable to PPCs. Patients with gynecological cancer are predisposed to the risk of malnutrition. Previous studies conducted in developed countries have reported that more than one in five patients with gynecological cancer present with malnutrition at diagnosis [ 5 , 38 ]. Hence, addressing malnutrition preoperatively can improve the clinical course in patients with gynecologic cancer. While the PNI is a proven prediction parameter for various outcomes, its optimal cut-off value remains relatively unclear. Although a cut-off value of 45 has been suggested in many previous studies [ 23 , 39 – 43 ], our results propose a higher value. In this study, the cut-off value was calculated for PPCs, and the results may differ from those of previous studies regarding the long-term outcomes. To evaluate the PNI, the study examined all gynecologic cancers together. However, the progression rates also depend on the origin of the gynecological cancer, i.e., the immuno-nutritional conditions of the patients were variable [ 44 ]. This cut-off discrepancy poses a barrier to the widespread clinical applicability of the PNI. However, based on the finding that the risk of PPCs tended to decrease with higher PNI values, PNI-guided nutrition management might be beneficial, and further study is required to verify this. Furthermore, since the PNI cut-off value remains inconclusive, further studies are needed to determine its optimal value. The study investigated PPC risk factors in patients undergoing gynecological cancer surgery. In the IPTW-adjusted cohort, the independent risk factors were ovarian cancer, old age, lung disease, hypertension, preoperative white blood cell count, preoperative platelet count, crystalloid infused volume, operation duration, and laparotomy, and a favor factor was intraoperative red blood cell transfusion. Aging is a well-established risk factor for PPCs [ 45 – 48 ]. Declining swallowing and respiratory function, as well as the prevalence of sarcopenia, make the elderly vulnerable to PPCs [ 34 ]. The duration of surgery, underlying lung diseases, and infused fluid volume are also well-known risk factors of PPCs [ 17 , 24 , 49 – 51 ]. PPCs frequently occur in patients with ovarian cancer due to its rapid progression and wide surgical cover area. Studies have shown that the incidence of PPCs in ovarian cancer patients undergoing cytoreductive surgery ranged from 26.9–32.3% [ 52 , 53 ]. Intraoperative diaphragmatic evaluation is required for patients undergoing cytoreductive surgery for advanced ovarian cancer [ 54 , 55 ]. Diaphragmatic surgery is associated with a high risk of developing postoperative pleural effusion [ 56 , 57 ]. In the IPTW analysis, primary cancer sites were initially matched, yet a robust association between ovarian cancer and PPCs was identified. Consequently, a subgroup analysis was conducted in patients with ovarian cancer, and the preoperative PNI was an independent predictive factor. The low odds ratio of PPCs regarding intraoperative red blood cell transfusion contradicts findings from previous studies [ 58 , 59 ]. Although no significant variable was identified in the co-linearity test and all matching variables were balanced, confounding variables might be masked. Another plausible explanation is focused management. Intraoperative blood transfusions are usually performed when there is a large amount of blood loss and insufficient circulating blood volume. In these situations, the human resources might be focused; the anesthetic management might be more attentive, and lung care might be more aggressive. Moreover, the surgery department might provide closer observation and management to patients transfused with blood products intraoperatively. However, due to the retrospective nature of the study, the criteria for transfusion, which was not investigated, were applied differently depending on the anesthesiologist in charge at the time. Therefore, a controlled study is needed to investigate the effects of transfusion. This study has several limitations. First, although IPTW analysis was performed, unexpected selection bias may exist between the high and low PNI groups. Additionally, a cautious interpretation of the study results is advised since the data were collected from a single tertiary institution. Second, the focus on PPCs within 15 days after surgery limits insights into long-term respiratory effects. Long-term outcomes, including overall and progression-free survival, were regretfully not investigated in this study, despite their significant clinical relevance. Third, the study contributes to the literature but does not directly improve postoperative outcomes. Further prospective trials are needed to assess the impact of preoperative nutritional status on PPCs. Lastly, information regarding intraoperative pulmonary interventions, such as recruitment maneuvers and positive end-expiratory pressure, was not collected. Therefore, hidden effects from such interventions may exist that could not be determined during this study. In conclusion, the preoperative PNI, reflecting both systemic inflammation and nutritional status, has the potential to predict PPCs in patients undergoing gynecological cancer surgery. The preoperative PNI can be a practical predictor, comparable to the ARISCAT risk index. Methods Ethics This retrospective study was approved by the Institutional Review Board of the Seoul National University College of Medicine/Seoul National University Hospital, South Korea (number: 2111-150-1276), prior to data collection. The requirement for written informed consent was waived due to the retrospective study design. The study cohort included adult patients who underwent resection surgery to cure gynecological cancers (cervical cancer, endometrial cancer, and ovarian cancer) under general anesthesia at Seoul National University Hospital between January 2005 and October 2021. Patients with missing laboratory data for calculating the preoperative PNI, a preoperative infectious disease or immune disorder, one-lung ventilation, a preoperative poor lung condition requiring ventilation support, and an operation time of less than one hour were excluded from the study. Data collection Demographics (age, body mass index, and comorbidities), preoperative laboratory findings, intraoperative data (operation and anesthesia time, infused crystalloid and colloid volume, estimated blood loss, red blood cell transfusion, surgical modalities [laparoscopy or robot-assisted and open method]), and oncologic data (primary site and stage) were retrospectively collected using electronic medical records. The PNI was calculated as 10 x serum albumin level (g/dL) + 0.005 x total lymphocyte count (10 9 /L). The assess respiratory risk in surgical patients in Catalonia (ARISCAT) risk index score was also calculated [ 12 ]. Outcome measures The primary outcome was PPCs, which defined as a composite outcome including acute respiratory distress syndrome, pneumonia, atelectasis, pleural effusion, hypoxemia (peripheral oxygen saturation < 90% or arterial oxygen pressure < 60mmHg), and pneumothorax within 15 days after surgery. Information including ICU admissions, ICU length of stays, and hospital length of stays were collected as secondary outcomes. Statistical analysis Statistical analyses were conducted using statistical software (R software version 4.1.3, R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were presented as the numbers (proportions) and continuous variables were presented as means (standard deviations) or medians (interquartile ranges). The Chi-square test or Fisher’s exact test were used to compare categorical variables according to cell sparsity. For continuous variables, the Kolmogorov–Smirnov test was first conducted to determine the normality of the data distribution, and the Student’s t-test and Mann–Whitney U-test were used to compare the normally distributed and skewed distributed variables, respectively. To obtain the cut-off value and discriminating ability of the PNI for predicting PPCs, the receiver operating characteristic (ROC) curve was depicted. The study divided the patients into two groups according to the cut-off value of PNI and used the inverse probability of treatment weighting (IPTW) method to correct for any imbalance between the two groups. Among the preoperative variables, the matched factors included significantly different variables when the PPCs and no PPCs groups were compared. The successful balance of covariates after adjustment for IPTW was affirmed using standardized mean difference < 0.1. To identify risk factors of PPCs, multivariate logistic regression analyses were conducted in the unadjusted and IPTW-adjusted cohorts. Variables with a p-value < 0.05 in the univariable logistic regression analyses were entered into multivariable logistic regression analysis. The multivariate-adjusted restricted cubic spline curve for logistic regression demonstrated a relative association between PPCs and the preoperative PNI in the unadjusted and IPTW-adjusted cohorts. Declarations Author Contribution W-Y.J. and S.C. analyzed the data and contributed to writing of the manuscript. YJ.K analyzed the data, oversaw the analysis and interpretation, and editing the manuscript. J-H.S. and H-S.K. contributed to the data interpretation and critical revision of this manuscript. Data availability statement Data is provided within the supplementary information file. Additional Information The authors declare no competing interests. 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Xu, M., Zhang, W., Gao, C., Zhou, Y. & Xie, Y. Postoperative pulmonary complications and outcomes in cytoreductive surgery for ovarian cancer: a propensity-matched analysis. BMC Anesthesiol 22 , 120, doi:10.1186/s12871-022-01660-2 (2022). Bogani, G. et al. Surgical Techniques for Diaphragmatic Resection During Cytoreduction in Advanced or Recurrent Ovarian Carcinoma: A Systematic Review and Meta-analysis. Int J Gynecol Cancer 26 , 371-380, doi:10.1097/IGC.0000000000000597 (2016). Ferrari, F. et al. Diaphragmatic peritonectomy vs. full thickness resection with pleurectomy during Visceral-Peritoneal Debulking (VPD) in 100 consecutive patients with stage IIIC–IV ovarian cancer: a surgical-histological analysis. Gynecol Oncol 140 , 430-435 (2016). Cliby, W., Dowdy, S., Feitoza, S. S., Gostout, B. S. & Podratz, K. C. Diaphragm resection for ovarian cancer: technique and short-term complications. Gynecol Oncol 94 , 655-660 (2004). Ye, S. et al. Diaphragmatic surgery and related complications in primary cytoreduction for advanced ovarian, tubal, and peritoneal carcinoma. BMC cancer 17 , 1-9 (2017). Panici, P. B. et al. Predictors of postoperative morbidity after cytoreduction for advanced ovarian cancer: Analysis and management of complications in upper abdominal surgery. Gynecol Oncol 137 , 406-411 (2015). Arozullah, A. M., Khuri, S. F., Henderson, W. G., Daley, J. & Participants in the National Veterans Affairs Surgical Quality Improvement, P. Development and validation of a multifactorial risk index for predicting postoperative pneumonia after major noncardiac surgery. Ann Intern Med 135 , 847-857, doi:10.7326/0003-4819-135-10-200111200-00005 (2001). Cata, J. P., Wang, H., Gottumukkala, V., Reuben, J. & Sessler, D. I. Inflammatory response, immunosuppression, and cancer recurrence after perioperative blood transfusions. Br J Anaesth 110 , 690-701, doi:10.1093/bja/aet068 (2013). Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.docx supptable2.docx thesupplementaryinformationfile.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4082172","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":285599443,"identity":"2477cd58-0d10-49c2-86d7-e4330022ce5a","order_by":0,"name":"Woo-Young Jo","email":"","orcid":"","institution":"Seoul National University Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Woo-Young","middleName":"","lastName":"Jo","suffix":""},{"id":285599444,"identity":"7278bffd-115e-4a3f-bbe2-31a1e1cedafb","order_by":1,"name":"Jeong-Hwa Seo","email":"","orcid":"","institution":"Seoul National University Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jeong-Hwa","middleName":"","lastName":"Seo","suffix":""},{"id":285599445,"identity":"fb8f74e1-60d1-4f78-ab83-dd77955ea808","order_by":2,"name":"Seungeun Choi","email":"","orcid":"","institution":"Seoul National University Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Seungeun","middleName":"","lastName":"Choi","suffix":""},{"id":285599446,"identity":"aced2f41-a764-4c1b-a57e-0d32b815b295","order_by":3,"name":"Hee-Soo Kim","email":"","orcid":"","institution":"Seoul National University Hospital, Seoul National University College of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hee-Soo","middleName":"","lastName":"Kim","suffix":""},{"id":285599447,"identity":"aa258c2a-b475-4552-abd1-9549810b0dc5","order_by":4,"name":"Yoon Jung Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYBACNiglR7IWPmOSLZNLbCBaLZ/Y4WMSP9vM0reznzF8XMFgJ6dLSDObdFqaZM+ZtNydPTnGhmcYko3NDhDUkmNswFNxLHfDAaDeBoYDiduI0WL4x+B/usH5Z+k/idVi+Jingi3B4EbyMUYitaQlPpY5w2a44cbjw5INBkT4RX528oGDb9vY5A3OJzZ+bKiwkyOoBQ0YkKZ8FIyCUTAKRgEOAACJYTzEbzkRLQAAAABJRU5ErkJggg==","orcid":"","institution":"Seoul National University Hospital, Seoul National University College of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yoon","middleName":"Jung","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2024-03-12 08:47:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4082172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4082172/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53876767,"identity":"030b871c-c982-4115-ac6b-a4016cd30564","added_by":"auto","created_at":"2024-04-01 16:42:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":161666,"visible":true,"origin":"","legend":"\u003cp\u003ePatient flow chart\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4082172/v1/8755ed2fddf621a30b7340db.jpeg"},{"id":53876763,"identity":"a1c46aff-90d7-4a17-ad3c-6100c5982545","added_by":"auto","created_at":"2024-04-01 16:42:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135780,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves for the preoperative PNI (red line) and ARISCAT (blue line) for postoperative pulmonary complications. PNI, prognostic nutritional index; ARISCAT, assess respiratory risk in surgical patients in Catalonia\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4082172/v1/41055941fbd65ebbdfb74a9c.jpeg"},{"id":53876768,"identity":"9dad099c-3ebc-4a1a-b40c-799d52801c41","added_by":"auto","created_at":"2024-04-01 16:42:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":224280,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate-adjustedrestricted cubic spline curves for the association between the odds ratio for postoperative pulmonary complications and preoperative prognostic nutritional index in patients with gynecological cancer in the (A) unadjusted cohort, and (B) IPTW-adjusted cohort. The shaded area represents the 95% confidence interval. The adjusted variables included age, ovarian cancer, underlying hypertension, restrictive lung disease, white blood cell count, operation duration, intraoperative red blood cell transfusion, robot or laparoscopic surgery, and crystalloid and colloid volumes. 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Previous studies reported that 20\u0026ndash;53% of patients with gynecologic cancer presented with malnutrition at the time of diagnosis [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Preexisting malnutrition can increase the risk of postoperative adverse events and compromise long-term outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary treatment for gynecological cancers is radical resection surgery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. General anesthesia with mechanical ventilation is indispensable for such surgeries, but it impacts the respiratory system. Anesthetic agents reduce the sensitivity of the central respiratory drive [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and neuromuscular-blocking agents lead to the paralysis of respiratory muscles and increase vulnerability to atelectasis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. During manipulation of the abdominal organs, the vital capacity, tidal volume, and functional residual capacity of the lungs are decreased [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePostoperative pulmonary complications (PPCs) occur in 4.2\u0026ndash;13% of major organ surgeries and are associated with in-hospital, 30-day, and 90-day mortality rates [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. PPCs can also prolong the length of hospital stays, increase intensive care unit (ICU) admissions, and raise health care costs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, predicting PPCs prior to surgery is beneficial.\u003c/p\u003e \u003cp\u003eThe prognostic nutritional index (PNI) was developed as a prognostic index for gastrointestinal cancers, representing the immune-nutritional status of patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The PNI can be easily calculated based on the serum albumin level and the peripheral blood lymphocyte count. A low preoperative PNI is associated with shorter progression-free survival and overall survival in gynecological cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Postoperative outcomes such as hemorrhages, infection, and pulmonary complications are also associated with the preoperative PNI [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, the PNI is associated with the incidence of PPCs in patients undergoing radical cystectomy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, no studies have evaluated its association with PPCs in gynecologic cancers.\u003c/p\u003e \u003cp\u003eThe study aimed to investigate the association between the PNI and PPCs in patients with gynecological cancer and identify the associated risk factors of PPCs.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e4887 patients who underwent gynecological cancer surgery under general anesthesia at Seoul National University Hospital, South Korea, from January 2005 to October 2021, were enrolled for the study. After excluding any patients who did not fulfill the study criteria, a final total of 4285 patients were analyzed (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePPCs occurred in 534 (12.5%) patients (Supplemental Table\u0026nbsp;1). In the ROC analysis for the prediction of PPCs, the area under the curve (AUC) of the PNI was significantly higher compared to that of the ARISCAT risk index (AUC [95% CI] 0.707 [0.681\u0026ndash;0.733], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. 0.676 [0.565\u0026ndash;0.696], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; DeLong\u0026rsquo;s test Z\u0026thinsp;=\u0026thinsp;2.066, p\u0026thinsp;=\u0026thinsp;0.039; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The optimal cut-off value for the preoperative PNI was calculated as 49.4, and patients were divided into low (\u0026le;\u0026thinsp;49.4) and high (\u0026ge;\u0026thinsp;49.4) preoperative PNI groups, based on this value.\u003c/p\u003e\n\u003cp\u003eAfter IPTW adjustment (matched factors: age, weight, height, surgical modalities [robot or laparoscopy assisted], primary cancer site, American society of anesthesiologists\u0026ndash;physical status classification, the international federation of gynecology and obstetrics staging, underlying diseases [hypertension, diabetes mellitus, history of heart disease, lung disease, renal disease, and liver disease]; Supplemental Table\u0026nbsp;1), all matched factors were balanced (high PNI [n\u0026thinsp;=\u0026thinsp;2898.5] and low PNI [n\u0026thinsp;=\u0026thinsp;1290.4] groups; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePatient characteristics, cancer related data, preoperative laboratory findings, and postoperative treatment protocol in unadjusted and IPTW adjusted patient cohorts.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eBefore adjustment\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eAfter IPTW adjustment\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow PNI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh PNI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSMD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow PNI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh PNI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSMD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1333)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2952)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1290.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2898.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDemographics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, yr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53 [45, 64]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.242\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 [44, 61]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52 [44, 60]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight, kg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.3 [49.6, 61.7]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.0 [52.8, 64.9]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.3 [51.4, 64.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.2 [51.7, 64.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeight, cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156.2 [152.2, 160.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157.0 [153.1, 161.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156.8 [153.0, 160.7]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156.9 [152.9, 161.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.5 [20.3, 25.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.7 [21.4, 26.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.4 [20.9, 26.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.5 [21.1, 26.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary site\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.584\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.043\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCervix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e379 (28.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1147 (38.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e452.0 (35.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1036.6 (35.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEndometrium\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e330 (24.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1199 (40.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e451.7 (35.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1049.1 (36.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOvary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e624 (46.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e606 (20.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e386.7 (30.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e812.8 (28.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFIGO stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.040\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e718 (53.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1648 (55.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e704.2 (54.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1610.6 (55.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e138 (10.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e354 (12.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e156.8 (12.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e334.2 (11.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e311 (23.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e647 (21.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e278.8 (21.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e645.1 (22.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e166 (12.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e303 (10.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e150.5 (11.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e308.6 (10.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARISCAT risk index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARISCAT grade\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.400\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.2 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.8 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntermediate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e301 (22.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e262 (8.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e186.0 (14.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e352.8 (12.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1023 (76.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2688 (91.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1099.1 (85.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2542.9 (87.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eASA physical status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.298\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e479 (35.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1320 (44.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e537.8 (41.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1234.5 (42.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e720 (54.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1532 (51.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e679.7 (52.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1517.8 (52.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127 (9.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.7 (5.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140.4 (4.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (0.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.9 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComorbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e330 (24.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e635 (21.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e299.8 (23.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e647.9 (22.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130 (9.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e301 (10.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124.1 (9.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e288.8 (10.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeart disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113 (8.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149 (5.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.1 (6.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173.0 (6.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThyroid disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135 (10.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e327 (11.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.031\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142.7 (11.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e312.9 (10.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiver disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147 (11.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e276 (9.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e122.4 (9.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e280.1 (9.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.4 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.5 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.9 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.1 (1.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRestrictive lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124 (9.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.305\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58.5 (4.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113.9 (3.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.030\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRenal disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63 (4.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75 (2.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.9 (3.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.8 (3.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 (2.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.8 (2.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.0 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePreoperative laboratory findings\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePNI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.0 [42.4, 48.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.1 [51.9, 56.7]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.397\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.9 [44.5, 48.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.7 [51.6, 56.3]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.251\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.7 [10.5, 12.6]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.9 [12.1, 13.6]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.5 [11.5, 13.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.6 [11.7, 13.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.089\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.8 [4.6, 7.5]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.4 [5.4, 7.7]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.8 [4.7, 7.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.3 [5.2, 7.6]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLymphocyte, %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.5 [17.3, 30.7]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.0 [27.0, 38.8]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.909\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.3 [18.4, 30.8]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.0 [27.1, 39.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.876\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlatelet, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e262 [210, 334]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e262 [220, 309]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e252 [208, 312]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e262 [219, 311]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlbumin, g/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9 [3.6, 4.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.4 [4.2, 4.6]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.745\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9 [3.7, 4.1]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.4 [4.2, 4.6]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.577\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70 [0.61, 0.80]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73 [0.66, 0.81]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70 [0.62, 0.80]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73 [0.65, 0.81]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntraoperative findings\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRobot or laparoscopic surgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e332 (24.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1446 (49.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e539.5 (41.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1232.9 (42.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOperation duration, hr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9 [2.8, 5.5]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.3 [2.3, 4.5]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.6 [2.4, 5.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.4 [2.4, 4.7]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.109\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnesthesia duration, hr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.8 [3.7, 6.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3 [3.3, 5.4]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.412\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.5 [3.4, 6.0]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.4 [3.4, 5.7]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.111\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimated blood loss, mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e550 [300, 1150]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e300 [178, 600]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.424\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e400 [200, 900]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e350 [200, 650]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.199\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003eData are presented as median [interquartile range] or number (proportion). \u003csup\u003e*\u003c/sup\u003e: ARISCAT grades were categorized into high (\u0026ge;\u0026thinsp;45), intermediate (26\u0026ndash;44), and low (\u0026lt;\u0026thinsp;26) grades based on the ARISCAT risk index. IPTW, inverse probability treatment weighting; PNI, prognostic nutritional index; SMD, standardized mean difference; BMI, body mass index; FIGO, International Federation of Gynecology and Obstetrics; ARISCAT, assess respiratory risk in surgical patients in Catalonia; ASA, American Society of Anesthesiologists; WBC, white blood cell\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe incidence of PPCs is significantly lower in the high PNI group for the unadjusted cohort and IPTW-adjusted cohort (unadjusted cohort: number [proportions]; 206 [7.0%] vs 328 [24.6%], proportional difference [95% CI]\u0026thinsp;\u0026minus;\u0026thinsp;17.62% [\u0026minus;\u0026thinsp;20.12%, \u0026minus;\u0026thinsp;15.14%], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; IPTW-adjusted cohort: 257.2 [8.9%] vs 219.5 [17.0%], \u0026minus;\u0026thinsp;8.13% [\u0026minus;\u0026thinsp;10.43%, \u0026minus;\u0026thinsp;5.83%], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In the IPTW-adjusted cohort, the incidences of pleural effusion (5.3% vs. 12.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), atelectasis (4.9% vs. 9.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hypoxemia (0.9% vs. 1.8%, p\u0026thinsp;=\u0026thinsp;0.022), and pneumonia (0.2% vs. 0.7%; p\u0026thinsp;=\u0026thinsp;0.036) were significantly higher in the low PNI group (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The ICU admission rate, length of ICU stay, and length of hospital stay were also greater in the low PNI group, before and after IPTW adjustment (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePostoperative outcomes in patients with gynecological cancer\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eBefore adjustment\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAfter IPTW adjustment\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh PNI (n\u0026thinsp;=\u0026thinsp;2952)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLow PNI (n\u0026thinsp;=\u0026thinsp;1333)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eProportion difference\u003c/p\u003e\n\u003cp\u003eor Mean difference (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh PNI (n\u0026thinsp;=\u0026thinsp;2898.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLow PNI (n\u0026thinsp;=\u0026thinsp;1290.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eProportion difference\u003c/p\u003e\n\u003cp\u003eor Mean difference (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePPCs (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206 (7.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e328 (24.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-17.6% (-20.1%, -15.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e257.4 (8.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e219.5 (17.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-8.1% (-10.4%, -5.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePneumothorax (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e10 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.54% (-1.04%, -0.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.8 (0.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e5.1 (0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.06% (-0.46%, 0.35%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.801\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePleural effusion (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e109 (3.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e240 (18.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-14.3% (-16.5%, -12.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e155.0 (5.3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e156.2 (12.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-6.8% (-8.7%, -4.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAtelectasis (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e120 (4.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e171 (12.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-8.8% (-10.7%, -6.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140.7 (4.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e117.4 (9.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-4.2% (-6.0%, -2.5%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypoxemia (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e29 (2.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-1.3% (-2.2%, -0.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.2 (0.9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e23.1 (1.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.9% (-1.7%, -0.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.022\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePneumonia (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e10 (0.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.61% (-1.10%, -0.13%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.9 (0.2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8.7 (0.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.51% (-0.98%, -0.03%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCount of PPCs*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.1 (-0.2, 0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.1 (-0.2, 0.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u0026thinsp;\u0026le;\u0026thinsp;count*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55 (26.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e117 (35.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-9.0% (-16.9%, -0.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72 (28.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e77 (35.0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-7.0% (-15.3%, -1.4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.102\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eICU admission (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92 (3.1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e197 (14.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-11.7% (-13.7%, -9.7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e133.0 (4.6%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e126.1 (9.8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-5.18% (-6.97%, -3.39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLength of stay at ICU (day)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.3 (-0.4, -0.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-0.3 (-0.4, -0.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital length of stay (day)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-5.7 (-6.5, -4.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e-2.6 (-3.4, -1.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or number (proportion). IPTW, inverse probability of treatment weighting; PNI, prognostic nutritional index; CI, confidence interval; PPCs, postoperative pulmonary complications; ICU, intensive care unit; *Calculated only in the patients with postoperative pulmonary complications.\u003c/p\u003e\n\u003cp\u003eIn the multivariate logistic regression analysis, a low PNI was a significant determinant in both the unadjusted and IPTW-adjusted cohorts (unadjusted cohort: odds ratio [95% CI]; 1.99 [1.59, 2.49], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; IPTW-adjusted cohort: 1.93 [1.53, 2.43], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In the IPTW-adjusted cohort, the independent determinants of PPCs were old age, ovarian cancer, preoperative hypertension, restrictive lung disease, a low PNI, a high white blood cell count, intraoperative blood transfusion, robotic or laparoscopic surgery, long operation duration, and the infused crystalloid and colloid amount (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eUnivariate and multivariate logistic regression models for postoperative pulmonary complications in patients with gynecological cancer\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBefore adjustment\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAfter IPTW adjustment\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnivariate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultivariate*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnivariate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMultivariate\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR [95% CI]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR [95% CI]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR [95% CI]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOR [95% CI]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, yr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04 [1.03, 1.05]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02 [1.01, 1.03]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04 [1.03, 1.05]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 [1.02, 1.04]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWeight, kg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 [0.97, 0.99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99 [0.98, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOvary cancer\u0026Dagger;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.58 [4.61, 6.76]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.32 [1.85, 2.93]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.73 [3.88, 5.76]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.23 [1.75, 2.84]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFIGO Stage\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06 [0.88, 1.28]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99 [0.81, 1.21]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77 [1.45, 2.16]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.45 [1.11, 1.88]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.85 [1.51, 2.27]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.52 [1.17, 1.99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36 [1.03, 1.80]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.52 [1.15, 2.02]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeart disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59 [1.14, 2.21]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.53 [1.07, 2.17]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiver disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.29 [0.97, 1.71]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14 [0.84, 1.56]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.406\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsthma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11 [0.47, 2.64]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.813\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.95 [0.92, 4.15]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.082\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRestrictive lung disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.92 [4.37, 8.01]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.81 [1.92, 4.11]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.76 [4.18, 7.94]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.39 [2.19, 5.26]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic obstructive pulmonary disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.43 [2.10, 42.25]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.59 [3.60, 67.60]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78 [0.39, 1.56]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.68 [0.32, 1.43]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.308\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eARISCAT risk index\u0026thinsp;\u0026ge;\u0026thinsp;26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 [0.00, 0.32]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05 [0.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePNI\u0026thinsp;\u0026lt;\u0026thinsp;49.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.35 [3.60, 5.25]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.99 [1.59, 2.49]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.09 [1.72, 2.54]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.93 [1.53, 2.43]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC count, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11 [1.07, 1.15]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07 [1.03, 1.12]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.12 [1.08, 1.16]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08 [1.04, 1.13]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHb, g/dL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77 [0.73, 0.82]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87 [0.81, 0.92]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlatelet count, 10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.01]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.01]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntraoperative red blood cell transfusion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.12 [3.42, 4.97]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.40 [2.79, 4.14]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.61 [0.46, 0.82]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRobot or laparoscopic surgery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15 [0.11, 0.19]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51 [0.37, 0.71]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.19 [0.14, 0.24]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.57 [0.41, 0.77]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOperation duration, hr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.69 [1.60, 1.77]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19 [1.10, 1.28]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.70 [1.61, 1.80]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.19 [1.10, 1.29]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimated blood loss, mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInfused crystalloid amount, mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInfused colloid amount, mL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 [1.00, 1.00]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.042\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003e*: In multivariate logistic regression analysis with stepwise forward conditional method. \u0026dagger;: In multivariate logistic regression analysis with stepwise forward conditional method. \u0026Dagger;: Compared to other types of gynecological cancer. IPTW, inverse probability of treatment weighting; OR, odds ratio; CI, confidence interval; FIGO, International Federation of Gynecology and Obstetrics; ARISCAT, assess respiratory risk in surgical patients in Catalonia; PNI, prognostic nutritional index; WBC, white blood cell; Hb, hemoglobin\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe multivariate-adjusted restricted cubic spline curve demonstrated that the log-odds ratio of PPCs tended to decrease with an increasing preoperative PNI (unadjusted cohort: p value for preoperative PNI\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p nonlinear\u0026thinsp;=\u0026thinsp;0.010; IPTW adjusted cohort: p value for preoperative PNI\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p nonlinear\u0026thinsp;=\u0026thinsp;0.007; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the ovarian cancer subgroup analysis, a low PNI was a robust determinant of PPCs (Supplemental Table\u0026nbsp;2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study proposes that the preoperative PNI is an independent predictive factor for PPCs in gynecological cancer surgery patients. The findings highlight the importance of the nutritional status in optimizing surgical outcomes for gynecological cancer patients.\u003c/p\u003e \u003cp\u003ePPCs are common following curative surgery for major organ cancers, PPCs are associated with early postoperative mortality, ICU admission, and prolonged hospital stays [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The prediction of PPCs can minimize medical costs and reduce morbidity and mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The ARISCAT risk index is a generally utilized and recognized risk stratification tool [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. After comparing the predicting ability between the PNI and ARISCAT risk index through ROC curve analysis, this study found that the PNI exhibited better performance. Furthermore, a low PNI was identified as an independent predictor of PPCs, but a high ARISCAT risk index (\u0026ge;\u0026thinsp;26) was not. This discriminative performance suggests potential clinical value in forthcoming applications.\u003c/p\u003e \u003cp\u003eThe PNI was developed to evaluate the immune-nutritional status [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The association between a low PNI and adverse prognostic outcomes has been identified in a wide range of cancers, such as gastric cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], colorectal cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and gynecological cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Especially, in gynecological cancers, a low PNI is associated with unfavorable overall survival and progression-free survival [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition to the long-term outcomes, the occurrence of PPCs is associated with the preoperative PNI in patients undergoing radical cystectomy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This is consistent with the findings of our study, which showed that as the preoperative PNI decreased, the odds ratio of PPCs tended to increase. Notably, the PNI can be easily calculated based on serum albumin levels and the lymphocyte count in a peripheral blood test, making it a cost-effective tool. Thus, risk stratification using the PNI would be valuable in a clinical setting [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMalnutrition affects the respiratory system by reducing respiratory muscle mass and strength [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], worsening respiratory muscle function, and decreasing respiratory drive [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, malnutrition compromises host defense mechanisms against infections [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and inhibits lymphocyte transformation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Consequently, malnutrition can make patients vulnerable to PPCs. Patients with gynecological cancer are predisposed to the risk of malnutrition. Previous studies conducted in developed countries have reported that more than one in five patients with gynecological cancer present with malnutrition at diagnosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Hence, addressing malnutrition preoperatively can improve the clinical course in patients with gynecologic cancer.\u003c/p\u003e \u003cp\u003eWhile the PNI is a proven prediction parameter for various outcomes, its optimal cut-off value remains relatively unclear. Although a cut-off value of 45 has been suggested in many previous studies [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], our results propose a higher value. In this study, the cut-off value was calculated for PPCs, and the results may differ from those of previous studies regarding the long-term outcomes. To evaluate the PNI, the study examined all gynecologic cancers together. However, the progression rates also depend on the origin of the gynecological cancer, i.e., the immuno-nutritional conditions of the patients were variable [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This cut-off discrepancy poses a barrier to the widespread clinical applicability of the PNI. However, based on the finding that the risk of PPCs tended to decrease with higher PNI values, PNI-guided nutrition management might be beneficial, and further study is required to verify this. Furthermore, since the PNI cut-off value remains inconclusive, further studies are needed to determine its optimal value.\u003c/p\u003e \u003cp\u003eThe study investigated PPC risk factors in patients undergoing gynecological cancer surgery. In the IPTW-adjusted cohort, the independent risk factors were ovarian cancer, old age, lung disease, hypertension, preoperative white blood cell count, preoperative platelet count, crystalloid infused volume, operation duration, and laparotomy, and a favor factor was intraoperative red blood cell transfusion. Aging is a well-established risk factor for PPCs [\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Declining swallowing and respiratory function, as well as the prevalence of sarcopenia, make the elderly vulnerable to PPCs [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The duration of surgery, underlying lung diseases, and infused fluid volume are also well-known risk factors of PPCs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. PPCs frequently occur in patients with ovarian cancer due to its rapid progression and wide surgical cover area. Studies have shown that the incidence of PPCs in ovarian cancer patients undergoing cytoreductive surgery ranged from 26.9\u0026ndash;32.3% [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Intraoperative diaphragmatic evaluation is required for patients undergoing cytoreductive surgery for advanced ovarian cancer [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Diaphragmatic surgery is associated with a high risk of developing postoperative pleural effusion [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In the IPTW analysis, primary cancer sites were initially matched, yet a robust association between ovarian cancer and PPCs was identified. Consequently, a subgroup analysis was conducted in patients with ovarian cancer, and the preoperative PNI was an independent predictive factor.\u003c/p\u003e \u003cp\u003eThe low odds ratio of PPCs regarding intraoperative red blood cell transfusion contradicts findings from previous studies [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Although no significant variable was identified in the co-linearity test and all matching variables were balanced, confounding variables might be masked. Another plausible explanation is focused management. Intraoperative blood transfusions are usually performed when there is a large amount of blood loss and insufficient circulating blood volume. In these situations, the human resources might be focused; the anesthetic management might be more attentive, and lung care might be more aggressive. Moreover, the surgery department might provide closer observation and management to patients transfused with blood products intraoperatively. However, due to the retrospective nature of the study, the criteria for transfusion, which was not investigated, were applied differently depending on the anesthesiologist in charge at the time. Therefore, a controlled study is needed to investigate the effects of transfusion.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, although IPTW analysis was performed, unexpected selection bias may exist between the high and low PNI groups. Additionally, a cautious interpretation of the study results is advised since the data were collected from a single tertiary institution. Second, the focus on PPCs within 15 days after surgery limits insights into long-term respiratory effects. Long-term outcomes, including overall and progression-free survival, were regretfully not investigated in this study, despite their significant clinical relevance. Third, the study contributes to the literature but does not directly improve postoperative outcomes. Further prospective trials are needed to assess the impact of preoperative nutritional status on PPCs. Lastly, information regarding intraoperative pulmonary interventions, such as recruitment maneuvers and positive end-expiratory pressure, was not collected. Therefore, hidden effects from such interventions may exist that could not be determined during this study.\u003c/p\u003e \u003cp\u003eIn conclusion, the preoperative PNI, reflecting both systemic inflammation and nutritional status, has the potential to predict PPCs in patients undergoing gynecological cancer surgery. The preoperative PNI can be a practical predictor, comparable to the ARISCAT risk index.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the Institutional Review Board of the Seoul National University College of Medicine/Seoul National University Hospital, South Korea (number: 2111-150-1276), prior to data collection. The requirement for written informed consent was waived due to the retrospective study design. The study cohort included adult patients who underwent resection surgery to cure gynecological cancers (cervical cancer, endometrial cancer, and ovarian cancer) under general anesthesia at Seoul National University Hospital between January 2005 and October 2021. Patients with missing laboratory data for calculating the preoperative PNI, a preoperative infectious disease or immune disorder, one-lung ventilation, a preoperative poor lung condition requiring ventilation support, and an operation time of less than one hour were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eDemographics (age, body mass index, and comorbidities), preoperative laboratory findings, intraoperative data (operation and anesthesia time, infused crystalloid and colloid volume, estimated blood loss, red blood cell transfusion, surgical modalities [laparoscopy or robot-assisted and open method]), and oncologic data (primary site and stage) were retrospectively collected using electronic medical records. The PNI was calculated as 10 x serum albumin level (g/dL)\u0026thinsp;+\u0026thinsp;0.005 x total lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L). The assess respiratory risk in surgical patients in Catalonia (ARISCAT) risk index score was also calculated [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measures\u003c/h2\u003e \u003cp\u003eThe primary outcome was PPCs, which defined as a composite outcome including acute respiratory distress syndrome, pneumonia, atelectasis, pleural effusion, hypoxemia (peripheral oxygen saturation\u0026thinsp;\u0026lt;\u0026thinsp;90% or arterial oxygen pressure\u0026thinsp;\u0026lt;\u0026thinsp;60mmHg), and pneumothorax within 15 days after surgery. Information including ICU admissions, ICU length of stays, and hospital length of stays were collected as secondary outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using statistical software (R software version 4.1.3, R Foundation for Statistical Computing, Vienna, Austria). Categorical variables were presented as the numbers (proportions) and continuous variables were presented as means (standard deviations) or medians (interquartile ranges). The Chi-square test or Fisher\u0026rsquo;s exact test were used to compare categorical variables according to cell sparsity. For continuous variables, the Kolmogorov\u0026ndash;Smirnov test was first conducted to determine the normality of the data distribution, and the Student\u0026rsquo;s t-test and Mann\u0026ndash;Whitney U-test were used to compare the normally distributed and skewed distributed variables, respectively. To obtain the cut-off value and discriminating ability of the PNI for predicting PPCs, the receiver operating characteristic (ROC) curve was depicted. The study divided the patients into two groups according to the cut-off value of PNI and used the inverse probability of treatment weighting (IPTW) method to correct for any imbalance between the two groups. Among the preoperative variables, the matched factors included significantly different variables when the PPCs and no PPCs groups were compared. The successful balance of covariates after adjustment for IPTW was affirmed using standardized mean difference\u0026thinsp;\u0026lt;\u0026thinsp;0.1. To identify risk factors of PPCs, multivariate logistic regression analyses were conducted in the unadjusted and IPTW-adjusted cohorts. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariable logistic regression analyses were entered into multivariable logistic regression analysis. The multivariate-adjusted restricted cubic spline curve for logistic regression demonstrated a relative association between PPCs and the preoperative PNI in the unadjusted and IPTW-adjusted cohorts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eW-Y.J. and S.C. analyzed the data and contributed to writing of the manuscript. YJ.K analyzed the data, oversaw the analysis and interpretation, and editing the manuscript. J-H.S. and H-S.K. contributed to the data interpretation and critical revision of this manuscript.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003e Data is provided within the supplementary information file.\u003c/p\u003e\u003ch2\u003eAdditional Information\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eNo disclosure of financial support\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSantarpia, L., Contaldo, F. \u0026amp; Pasanisi, F. Nutritional screening and early treatment of malnutrition in cancer patients. \u003cem\u003eJ Cachexia Sarcopenia Muscle\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 27-35, doi:10.1007/s13539-011-0022-x (2011).\u003c/li\u003e\n\u003cli\u003eBarker, L. A., Gout, B. S. \u0026amp; Crowe, T. C. 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Inflammatory response, immunosuppression, and cancer recurrence after perioperative blood transfusions. \u003cem\u003eBr J Anaesth\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e, 690-701, doi:10.1093/bja/aet068 (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"prognostic nutritional index, gynecological cancer, postoperative pulmonary complications","lastPublishedDoi":"10.21203/rs.3.rs-4082172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4082172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prognostic nutritional index (PNI) reflects systemic inflammation and nutritional status. This study aimed to evaluate the predicting ability of the preoperative PNI regarding postoperative pulmonary complications (PPCs) in patients with gynecological cancer. Demographic, laboratory, and clinical data were retrospectively collected from 4887 patients who underwent gynecological cancer surgery. Preoperative PNIs were calculated from serum albumin levels and total lymphocyte counts. Primary outcome was PPCs, a composite outcome including acute respiratory distress syndrome, pneumonia, atelectasis, pleural effusion, and pneumothorax within postoperative day 15. An optimal cut-off value of 49.4 for the PNI score was determined using the receiver operating characteristic curve. The study used the stabilized inverse probability of treatment weighting (IPTW) method to reduce bias and compare between the high (n\u0026thinsp;=\u0026thinsp;2898.5) and low PNI groups (n\u0026thinsp;=\u0026thinsp;1290.4). The incidence of PPCs is significantly higher in the low PNI group than in the high PNI group (8.9% vs 17.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the multivariate logistic regression model after IPTW adjustment, a low preoperative PNI was an independent predictor of PPCs (odds ratio [95% confidence interval] 1.93 [1.53, 2.43], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Low preoperative PNIs are associated with PPCs in patients undergoing surgery for gynecologic cancer.\u003c/p\u003e","manuscriptTitle":"The preoperative prognostic nutritional index is a prognostic indicator of postoperative pulmonary complications in patients with gynecological cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 16:42:20","doi":"10.21203/rs.3.rs-4082172/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3de792b8-1eb6-4b92-8786-1f49788c96ce","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30066422,"name":"Biological sciences/Cancer"},{"id":30066423,"name":"Health sciences/Risk factors"},{"id":30066424,"name":"Health sciences/Health care/Nutrition"}],"tags":[],"updatedAt":"2025-10-08T16:39:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 16:42:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4082172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4082172","identity":"rs-4082172","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}