Predictive Value of the Advanced Lung Cancer Inflammation Index for the Postoperative Complications of Lung Resections in Patients with Bronchiectasis: A Retrospective Study Short Title : Study on the Correlation between the Advanced Lung Cancer Inflammation Index and Patients with Bronchiectasis

Predictive Value of the Advanced Lung Cancer Inflammation Index for the Postoperative Complications of Lung Resections in Patients with Bronchiectasis: A Retrospective Study Short Title : Study on the Correlation between the Advanced Lung Cancer Inflammation Index and Patients with Bronchiectasis | 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 Research Article Predictive Value of the Advanced Lung Cancer Inflammation Index for the Postoperative Complications of Lung Resections in Patients with Bronchiectasis: A Retrospective Study Short Title : Study on the Correlation between the Advanced Lung Cancer Inflammation Index and Patients with Bronchiectasis Yang Gu, Jin-Bai Miao, Hang Zheng, Xin Li, Bin Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4408951/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 Background: Bronchiectasis patients often suffer from systemic inflammation and malnutrition, which negatively affect their prognosis. The advanced lung cancer inflammation index (ALI) has emerged as a novel biomarker that reflects systemic inflammation and malnutrition. However, its utility in predicting postoperative complications in bronchiectasis patients undergoing localized surgical resection remains to be clarified. Method: This retrospective study included 160 patients with localized bronchiectasis who underwent a single lobectomy at our center from April 2012 to December 2022. The optimal ALI cutoff point was established using the receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for postoperative complications. Results: The optimal cutoff value for the ALI was determined to be 38.37. Compared to the high ALI group, the low ALI group exhibited a significantly greater incidence of open chest surgeries (P=0.001), increased duration of surgeries (P=0.024), greater intraoperative blood loss (P=0.016), prolonged postoperative chest tube drainage (P=0.001), extended hospital stays after the operation (P=0.001), and a greater rate of complications (P=0.006). Multivariate logistic regression analysis revealed that prolonged surgical duration, low body mass index (BMI), and low preoperative ALI were independent risk factors for postoperative complications. To predict the likelihood of these complications, we developed a nomogram incorporating these independent factors, which demonstrated predictive accuracy with an area under the curve (AUC) of 0.792. Conclusion: The preoperative ALI serves as an independent predictor of postoperative complications in patients with localized bronchiectasis who underwent a single lobectomy. Bronchiectasis Advanced Lung Cancer Inflammation Index Postoperative Complications Systemic Inflammation Malnutrition Figures Figure 1 Figure 2 Figure 3 1. Introduction Bronchiectasis is a significant chronic pulmonary condition characterized by permanent and irreversible enlargement of the bronchi. This condition often leads to chronic respiratory symptoms, including cough, sputum production, and hemoptysis [ 1 , 2 ]. Recent data suggest that the incidence of bronchiectasis varies widely, ranging from 67 to 1,200 cases per 100,000 individuals. Surgical resection is recommended for localized bronchiectasis when conservative treatments fail or when patients experience life-threatening hemoptysis [ 1 , 3 – 5 ]. Despite the majority of patients receiving standardized perioperative care, the rate of complications following lung resection for bronchiectasis has been reported to range from 9.4–53% [ 6 – 10 ]. Postoperative complications not only lead to prolonged hospital stays and increased treatment costs but also signify a poorer prognosis [ 11 ]. Previous research has identified multiple independent risk factors for postoperative complications in patients with bronchiectasis, including a history of tuberculosis, restricted expiratory capacity, incomplete resection, lower body mass index (BMI), and poor nutritional status [ 8 , 12 , 13 ]. Despite these insights, a comprehensive index for predicting the prognosis of bronchiectasis patients remains elusive. The Advanced Lung Cancer Inflammation Index (ALI), which integrates BMI, the serum ALB concentration, and the neutrophil-to-lymphocyte ratio (NLR), was initially developed by Jafri et al. to evaluate the prognosis of patients with metastatic non-small cell lung cancer (NSCLC) [ 14 ]. Subsequent studies have demonstrated that the ALI is effective in predicting the outcomes of various cancers, including hepatocellular carcinoma, esophageal squamous cell carcinoma, gastric cancer, colorectal cancer, and even benign conditions such as hypertension, acute coronary syndrome, and diabetes mellitus [ 15 – 21 ]. Additionally, the ALI has been utilized as a predictor of postoperative complications in colon cancer patients [ 22 ]. Despite its broad range of applications, to our knowledge, no studies have explored the relationship between the ALI and pulmonary postoperative complications, particularly under conditions with high complication rates such as bronchiectasis. This study aimed to investigate the predictive value of the preoperative ALI for postoperative complications in patients who underwent lung resection for bronchiectasis. 2. Methods 2.1. Study Design and Population This was a single-center, retrospective study that included 160 patients with localized bronchiectasis at Beijing Chao-Yang Hospital, Capital Medical University, between April 2012 and December 2022. All of the patients achieved complete resection of the lesion via surgical treatment. Complete resection was defined as surgical resection of all involved segments diagnosed preoperatively by high-resolution computed tomography (HRCT). The study received approval from the Ethics Committee of Beijing Chao-Yang Hospital, Capital Medical University, and was conducted in accordance with the principles outlined in the Declaration of Helsinki. Given the retrospective nature of the study, the requirement for written informed consent was waived. Inclusion criteria 1) Age ≥ 18 years; 2) Availability of complete clinical data; 3) Single lung lobe resection (lobectomy, segmentectomy, or wedge resection) was performed. Exclusion criteria 1) Presence of preoperative fever; 2) Patients with nonsurgical management of bronchiectasis; 3) History of previous thoracic surgery; 4) History of any type of malignancy or hematological disease. 2.2. Data collection We reviewed and collected patient data through the electronic medical records system, including patient demographics such as BMI, presence of hemoptysis, comorbidities (hypertension, diabetes and tuberculosis), smoking history, surgical status, extent of surgical resection, surgical modality (thoracotomy or video-assisted thoracoscopic surgery), operation time, intraoperative blood loss, drainage tube indwelling time and postoperative complications, length of hospital stay after the operation, total length of hospital stay, and results of routine preoperative blood tests, including leukocyte count, neutrophil count, lymphocyte count and ALB. The biomarkers were calculated as follows: ALI = BMI (kg/m 2 ) × ALB (g/dL)/NLR, BMI = weight (kg)/height (m) 2 , NLR = neutrophil count/lymphocyte count, LLR = leukocyte count/lymphocyte count, NLRAR = NLR/ALB (g/L). 2.3. Definition of complications In this study, postoperative complications were defined as adverse events occurring during hospitalization after surgery or within 30 days after discharge. The Clavien‒Dindo classification was applied to assess the severity of complications [ 23 ], and any complication that reached grade II or higher was recorded. Common complications observed in patients included prolonged air leakage (air leakage persisting for more than 7 days), pneumonia, pulmonary atelectasis, empyema, hemorrhage, wound infection, and cardiac arrhythmia [ 6 ]. 2.4. Statistical analysis The optimal cutoff value for the ALI was determined through the receiver operating characteristic (ROC) curve and the Youden index, and the ALI was subsequently converted into categorical variables. All continuous variables were first tested for normality. Variables conforming to a normal distribution are expressed as the mean ± standard deviation (M ± SD), and comparisons between two groups were conducted using the independent samples t test. Variables not normally distributed are presented as medians (interquartile ranges) and were compared using the Mann–Whitney U test. Categorical variables are expressed as numbers (percentages) and were analyzed using either the Pearson χ 2 test or Fisher’s exact test. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for postoperative complications. Statistical analyses were performed using SPSS (version 26.0), GraphPad Prism (version 10.2), and R software (version 3.6.2). A two-sided P value less than 0.05 was considered to indicate statistical significance. 3. Results 3.1. Baseline characteristics A total of 160 patients with a pathological diagnosis of bronchiectasis were included in the study, comprising 68 males and 92 females with a median age of 51 years. Table 1 presents the baseline characteristics of these patients. Among them, 88 (55.0%) experienced hemoptysis, and 34 (21.3%) had a history of cigarette smoking. In terms of surgical intervention, 127 (79.4%) patients underwent video-assisted thoracoscopic surgery (VATS), while 33 (20.6%) patients underwent thoracotomy. Surgical procedures included lobectomy in 145 patients and segmental or wedge resection in 15 patients. The classification of postoperative complications is detailed in Table 2 . Overall, 29 (18.1%) patients experienced a total of 36 complications, which included 13 cases of prolonged air leakage, 8 cases of pneumonia, 4 cases of pulmonary atelectasis, 3 cases each of hemorrhage, empyema, and cardiac arrhythmia, and 2 cases of wound infection. Table 1 Baseline characteristics of patients Characteristic All (n = 160) Age, median (IQR), years 51 (37–60) Sex, n(%) Male 68 (42.5) Female 92 (57.5) BMI, mean ± SD, kg/m2 23.13 ± 3.18 Smoking history, n(%) 34 (21.3) Hemoptysis , n (%) 88 (55.0) Hypertension, n(%) 30 (18.8) Diabetes, (n%) 19 (11.9) Tuberculosis, n(%) 21 (13.1) ALB, mean ± SD, g/dL 4.07 ± 0.46 NLR, median (IQR) 1.80 (1.32–2.73) LLR, median (IQR) 3.11 (2.58–4.11) NLRAR, median (IQR) 0.04 (0.03–0.07) ALI, median (IQR) 50.67 (35.01–70.15) Surgical approach, n (%) VATS 127 (79.4) Open 33 (20.6) Types of operation, n (%) Segmentectomy/Wedge resection 15 (9.4) Lobectomy 145 (90.6) Operation time, median (IQR), min 135 (100–174) Blood loss, median (IQR), ml 100 (50–200) Duration of chest drain, median (IQR), days 4 (3–7) LOS after surgery, median (IQR), days 5 (4–8) LOS, median (IQR), days 12 (9–14) Postoperative complication, n(%) 29 (18.1) IQR, interquartile range; BMI, body mass index; ALB, albumin; NLR, neutrophil-to‐lymphocyte ratio; LLR, leukocyte-to-lymphocyte ratio; NLRAR, NLR-to-albumin ratio; ALI, advanced lung cancer inflammation index; VATS, video-assisted thoracoscopic surgery; LOS, length of hospital stay. Table 2 Types of postoperative complications in patients with bronchiectasis Postoperative Complications Cases Prolonged air leak pneumonia 13 Pneumonia 8 Pulmonary atelectasis 4 Hemorrhage 3 Empyema 3 Cardiac arrhythmia 3 Wound infection 2 3.2. Comparison of the ALI and other hematological biomarkers As depicted in Table 3 , we assessed the correlation between the ALI, NLR, LLR and NLRAR and postoperative complications in bronchiectasis patients. Our findings revealed a significant correlation between these four biomarkers and postoperative complications (P < 0.05). Subsequently, we plotted ROC curves for each indicator (Fig. 1 ). By analyzing the area under the curve for the four indicators, we found that the ALI had a better ability to predict postoperative complications than did the NLR (AUC 0.663 [95% CI 0.547–0.780] vs. 0.620 [95% CI 0.495–0.746]; P = 0.021), LLR (AUC 0.663 [95% CI 0.547–0.780] vs. AUC 0.620 [95% CI 0.494–0.747]; P = 0.022) or NLRAR (AUC 0.663 [95% CI 0.547–0.780] vs. AUC 0.629 [95% CI 0.507–0.751]; P = 0.017) (Table 4 ). According to the ROC curve analysis, the critical cutoff value for the ALI was determined to be 38.37. Based on this threshold, patients were categorized into two groups: a high ALI group (ALI ≥ 38.37, n = 112) and a low ALI group (ALI < 37.69, n = 48). Table 3 Correlation between four biological markers and postoperative complication s No Complications Complications P-value NLR, median (IQR) 1.77 (1.31–2.58) 2.62 (1.37–3.56) 0.043 LLR, median (IQR) 3.03 (2.58–3.91) 3.82 (2.55–4.91) 0.043 NLRAR, median (IQR) 0.04 (0.03–0.06) 0.06 (0.04–0.08) 0.030 ALI, median (IQR) 52.82 (39.05–71.62) 35.63 (25.76–59.38) 0.006 IQR, interquartile range; NLR, neutrophil-to-lymphocyte ratio. LLR, leukocyte-to-lymphocyte ratio; NLRAR, NLR-to-albumin ratio; ALI, advanced lung cancer inflammation index. The bold values indicate the value of p < 0.05, which is statistically significant. Table 4 Comparison of the area under the curve values of the ALI and other biological markers AUC 95%CI P-value ALI 0.663 0.547–0.780 NLR 0.620 0.495–0.746 0.021 LLR 0.620 0.494–0.747 0.022 NLRAR 0.629 0.507–0.751 0.017 ALI, advanced lung cancer inflammation index; AUC, area under the curve; NLR, neutrophil-to -lymphocyte ratio. LLR, leukocyte-to-lymphocyte ratio; NLRAR, NLR-to-albumin ratio; The AUC values between the advanced lung cancer inammation index and other factors were compared using the Z-test method. The bold values indicate the value of p < 0.05, which is statistically significant. 3.3. Relationships between the ALI subgroups and clinical variables The correlations between the ALI and clinical characteristics are shown in Table 5 . Compared with the high ALI group, the low ALI group had a significantly lower BMI (p = 0.020), greater smoking history (p = 0.014), greater NLR (p < 0.001), greater LLR (p < 0.001) and greater NLRAR (p < 0.001). In addition, a low preoperative ALI was associated with a greater percentage of thoracotomy (P = 0.001), longer operative time (P = 0.024), greater volume of intraoperative blood loss (P = 0.016), longer postoperative chest tube drainage times (P = 0.001), longer postoperative hospital stays (P = 0.001) and longer length of hospital stay (P = 0.001). Nevertheless, there was no significant difference between the two groups in terms of age, sex, hemoptysis, comorbidities or serum ALB concentration. Table 5 Relationship Between Patient Characteristics and ALI High ALI (n = 112) Low ALI (n = 48) P-value Age, median (IQR), years 50 (37–59) 53 (39–62) 0.316 Sex, n(%) 0.108 Male 43 (38.4) 25 (52.1) Female 69 (61.6) 23 (47.9) BMI, mean ± SD, kg/m2 23.51 ± 3.27 22.24 ± 2.79 0.020 Smoking history, n(%) 18 (16.1) 16 (33.3) 0.014 Hemoptysis , n (%) 57 (50.9) 31 (64.6) 0.111 Hypertension, n(%) 22 (19.6) 8 (16.7) 0.658 Diabetes, (n%) 14 (12.5) 5 (10.4) 0.709 Tuberculosis, n(%) 15 (13.4) 6 (12.5) 0.878 ALB, mean ± SD, g/dL 4.10 ± 0.44 4.01 ± 0.48 0.275 NLR, median (IQR) 1.60 (1.22–1.86) 3.49 (2.89–4.57) < 0.001 LLR, median (IQR) 2.84 (2.45–3.19) 4.76 (4.19–6.15) < 0.001 NLRAR, median (IQR) 0.04 (0.03–0.05) 0.09 (0.07–0.10) < 0.001 Surgical approach, n (%) 0.001 VATS 97 (86.6) 30 (62.5) Open 15 (13.4) 18 (3.5) Operation time, median (IQR), min 128 (90–165) 148 (120–180) 0.024 Blood loss, median (IQR), ml 100 (50–200) 125 (53–338) 0.016 Duration of chest drain, median (IQR), days 4 (3–5) 6 (3–10) 0.001 LOS after surgery, median (IQR), days 5 (4–7) 7 (4–12) 0.001 LOS, median (IQR), days 11 (9–13) 14 (10–19) 0.001 IQR, interquartile range; ALI, advanced lung cancer inflammation index; BMI, body mass index; ALB, albumin; NLR, neutrophil-to-lymphocyte ratio; LLR, leukocyte-to-lymphocyte ratio; NLRAR, NLR-to-albumin ratio; VATS, video-assisted thoracoscopic surgery; LOS, length of hospital stay. The bold values indicate the value of p < 0.05, which is statistically significant. 3.4. Risk factors for postoperative complications in bronchiectasis patients According to the univariate logistic analysis, low BMI, smoking history, low ALI, thoracotomy, extended operative time and increased intraoperative blood loss were correlated with postoperative complications. After adjustment for confounding factors, multivariate analysis revealed that BMI (OR = 0.839, 95% CI = 0.697–0.968, P = 0.031), ALI < 38.37 (OR = 3.029, 95% CI = 1.161–7.902, P = 0.023) and operation time (OR = 1.013, 95% CI = 1.002–1.023, P = 0.017) were found to be independent prognostic factors for postoperative complications (Table 6 ). Table 6 Univariate and multivariate analysis of postoperative complications in patients with bronchiectasis Variable Univariate Multivariate HR(95% CI) P-value HR (95% CI) P-value Age, years 1.010 (0.981–1.040) 0.501 Sex, n(%) Male Reference Female 0.450 (0.199–1.020) 0.056 BMI, kg/m2 0.853 (0.741–0.983) 0.028 0.839 (0.716–0.984) 0.031 Smoking history(Yes vs. No) 2.870 (1.196–6.882) 0.018 2.201 (0.798–6.069) 0.127 Haemoptysis(Yes vs. No) 2.059 (0.873–4.856) 0.099 Hypertension(Yes vs. No) 0.646 (0.207–2.019) 0.453 Diabetes(Yes vs. No) 0.829 (0.225–3.057) 0.779 Tuberculosis (Yes vs. No) 1.073 (0.332–3.464) 0.906 ALB, g/dL 0.557 (0.227–1.369) 0.202 ALI ≥38.37 Reference Reference <38.37 5.509 (2.346–12.936) < 0.001 3.029 (1.161–7.902) 0.023 Surgical approach, n (%) VATS Reference Reference Open 4.509 (1.883–10.797) 0.001 2.489 (0.846–7.325) 0.098 Types of operation, n(%) Seg/Wed Reference Lobectomy 3.350 (0.423–26.558) 0.252 Operation time, median (IQR), min 1.015 (1.007–1.023) < 0.001 1.013 (1.002–1.023) 0.017 Blood loss, median (IQR), ml 1.001 (1.000–1.002) 0.036 1.000 (0.999–1.001) 0.644 IQR, interquartile range; BMI, body mass index; ALB, albumin; ALI, advanced lung cancer inflammation index; VATS, video-assisted thoracoscopic surgery; Seg, segmentectomy; Wed, wedge resection. The bold values indicate the value of p < 0.05, which is statistically significant. 3.5. Nomograms constructed based on independent related factors Based on the independent factors identified in the multifactor logistic regression analysis, we established a nomogram to predict the risk of postoperative complications in bronchiectasis patients (Fig. 2 ). The predictive value of the nomogram for short-term complications in patients with bronchiectasis after lung resection was evaluated using receiver operating characteristic (ROC) curves (Fig. 3 ). The AUC in the analysis was 0.792. 4. Discussion Bronchiectasis is a prevalent chronic respiratory disease globally, and inflammation plays a pivotal role in its onset and progression. The infiltration of innate immune cells, primarily neutrophils, into lung tissue is a key factor initiating cascades that lead to the pathological changes observed in bronchiectasis [ 24 ]. Moreover, inflammation can contribute to malnutrition, as evidenced by decreases in the serum ALB concentration and BMI [ 25 , 26 ]. There is growing evidence that inflammatory and nutritional biomarkers in peripheral blood can predict the progression and overall prognosis of bronchiectasis patients. A high NLR is indicative of worsening conditions in these patients [ 27 ]. A study from a nationwide population in South Korea highlighted that being underweight (BMI < 18.5 kg/m 2 ) is a risk factor for bronchiectasis in young individuals [ 28 ]. Research by Qi and colleagues demonstrated that patients with a low BMI experience more severe bronchiectasis, increasing their risk of hospitalization and mortality [ 29 ]. Additionally, low serum ALB levels are significantly associated with the Bronchiectasis Severity Index (BSI) and FACED score [ 30 , 31 ]. However, these biomarkers alone do not provide a comprehensive assessment of the inflammation and nutritional status of patients. Recently, Jafri et al. proposed a simple yet effective screening tool, the ALI, based on BMI, the serum ALB concentration, and the NLR. This index can simultaneously reflect the nutritional and inflammatory status of patients [ 14 ]. he correlation between a lower ALI and poorer prognosis has been established in various cancers and inflammatory diseases [ 15 – 21 ]. Nonetheless, research on the clinical significance of the ALI in bronchiectasis patients remains limited. In our study, we found that a low preoperative ALI is independently associated with postoperative complications in bronchiectasis patients who underwent single-lung resection. A low ALI, indicating a reduced BMI and serum ALB levels along with an increased serum NLR, suggests malnutrition and a heightened inflammatory response. Therefore, mitigating excessive inflammation preoperatively and providing nutritional support may effectively reduce the incidence of postoperative complications. In our research, we observed that a greater preoperative NLR was associated with an increased incidence of postoperative complications following lung resection, which is consistent with the findings of previous studies [ 32 , 33 ]. Furthermore, we identified high preoperative LLRs and NLRARs as adverse predictors of postoperative complications in patients with bronchiectasis. However, the ROC curve analysis revealed that the ALI possesses superior predictive ability for postoperative complications compared to the aforementioned indicators, possibly because it incorporates a more comprehensive set of parameters. BMI serves as a measure of body composition. A lower BMI often indicates inadequate nutrition, which correlates with early lung inflammation and increased activity of free neutrophil elastase, thereby linking poor nutritional status with exacerbated lung disease [ 34 ]. Previous findings suggest that a low BMI is indicative of a poor prognosis in patients with bronchiectasis [ 29 ]. Our group recently reported that a low BMI is an independent risk factor for postoperative complications following bronchial dilation surgery [ 12 ]. ALB levels, another marker of nutritional status, also play a significant role. Patients with hypoalbuminemia typically exhibit more severe forms of bronchiectasis [ 30 , 31 ]. This may be due to hypoalbuminemia leading to impaired immune responses in the host [ 35 ]. The NLR represents the severity of the innate immune response, i.e., the severity of inflammation caused mainly by bacterial infection. Patients with bronchiectasis have reduced levels of lymphocytes, and lower lymphocyte counts may be associated with an unlimited innate immune response [ 24 , 27 , 36 , 37 ]. Therefore, the ALI has emerged as a comprehensive indicator, encapsulating the anthropometric, nutritional, and inflammatory factors crucial for assessing patients with bronchial dilation. We identified prolonged duration of surgery as another independent risk factor for postoperative complications, consistent with findings from previous research [ 12 , 38 , 39 ]. The specific mechanisms by which extended surgical times contribute to complications remain unclear; however, several potential explanations exist. For example, longer surgeries increase the exposure of the wound to microbes, thereby increasing the risk of wound contamination. Additionally, prolonged operations can cause tissue retraction, leading to tissue ischemia and necrosis. Furthermore, extended durations of surgery often reflect the complexity of the procedures, which can increase surgeon fatigue and potentially result in a greater incidence of postoperative complications [ 39 – 41 ]. There are several potential limitations of our study. First, this was a single-center study with a relatively small sample size. A multicenter study is needed to verify our results. Second, as a retrospective study, some selection bias might exist. A prospective study with a larger population and relative intervention focused on improving the nutritional and inflammatory status of patients might further expand the application of our findings. Additionally, only short-term complications were considered in the current study. A longer follow-up might help clinicians use the ALI as an index to predict more complications, such as the reoccurrence of bronchiectasis or the long-term recovery of pulmonary function. 5. Discussion This study is the first to establish that the preoperative ALI serves as an independent risk factor for predicting postoperative complications in patients with bronchiectasis. These insights could aid clinicians in identifying the most effective biomarkers, which encompass both inflammatory and nutritional parameters, to inform therapeutic strategies and diminish the rate of postoperative complications. Declarations Conflict of interest statement The authors declare no conflicts of interest. Funding statement This research was funded by grants from the Beijing Chaoyang Hospital 2022 Science and Technology Innovation Fund (No. 22kcjjzd-6). Author Contribution Conceptualization: Y.G., X.L. and B.H.; Methodology: B.-J.M. and H.Z.; Data collection and analysis: Y.G.; Writing - original draft preparation: Y.G.; Writing - review and editing: B.H. and X.L.. All the authors have read and approved the final manuscript. Data availability statement The authors will make the raw data supporting the conclusions of this article available without undue reservation. 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World J Surg Oncol 20(1):246. http://dx.doi.org/10.1186/s12957-022-02712-0 Dindo D, Demartines N, Clavien PA (2004) Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg 240(2):205–213. http://dx.doi.org/10.1097/01.sla.0000133083.54934.ae Chalmers JD, Elborn S, Greene CM (2023) Basic, translational and clinical aspects of bronchiectasis in adults. Eur Respir Rev 32(168). http://dx.doi.org/10.1183/16000617.0015-2023 Sheinenzon A, Shehadeh M, Michelis R, Shaoul E, Ronen O (2021) Serum albumin levels and inflammation. Int J Biol Macromol 184:857–862. http://dx.doi.org/10.1016/j.ijbiomac.2021.06.140 Lennie TA (1998) Relationship of body energy status to inflammation-induced anorexia and weight loss. Physiol Behav 64(4):475–481. .http://dx.doi.org/10.1016/s0031-9384(98)00103-6 Martinez-Garcia MA, Olveira C, Giron R, Garcia-Clemente M, Maiz-Carro L, Sibila O, Golpe R, Mendez R, Rodriguez Hermosa JL, Barreiro E et al (2022) Peripheral Neutrophil-to-Lymphocyte Ratio in Bronchiectasis: A Marker of Disease Severity. Biomolecules 12(10). http://dx.doi.org/10.3390/biom12101399 Yang B, Han K, Kim SH, Lee DH, Park SH, Yoo JE, Shin DW, Choi H, Lee H (2021) Being Underweight Increases the Risk of Non-Cystic Fibrosis Bronchiectasis in the Young Population: A Nationwide Population-Based Study. Nutrients 13(9). http://dx.doi.org/10.3390/nu13093206 Qi Q, Li T, Li JC, Li Y (2015) Association of body mass index with disease severity and prognosis in patients with non-cystic fibrosis bronchiectasis. Braz J Med Biol Res 48(8):715–724. http://dx.doi.org/10.1590/1414-431X20154135 Ju S, Jeong JH, Heo M, Heo IR, Kim TH, Kim HC, Yoo JW, Cho YJ, Jeong YY, Lee JD et al (2021) Serum albumin is a predictor of respiratory hospitalization in patients with bronchiectasis. Chron Respir Dis 1814799731211017548. http://dx.doi.org/10.1177/14799731211017548 Li L, Li Z, Bi J, Li H, Wang S, Shao C, Song Y (2020) The association between serum albumin/prealbumin level and disease severity in non-CF bronchiectasis. Clin Exp Pharmacol Physiol 47(9):1537–1544. http://dx.doi.org/10.1111/1440-1681.13331 Moreno C, Urena A, Macia I, Rivas F, Deniz C, Munoz A, Serratosa I, Poltorak V, Moya-Guerola M, Masuet-Aumatell C et al (2023) The Influence of Preoperative Nutritional and Systemic Inflammatory Status on Perioperative Outcomes following Da Vinci Robot-Assisted Thoracic Lung Cancer Surgery. J Clin Med 12(2). http://dx.doi.org/10.3390/jcm12020554 Lan H, Zhou L, Chi D, Zhou Q, Tang X, Zhu D, Yue J, Liu B (2017) Preoperative platelet to lymphocyte and neutrophil to lymphocyte ratios are independent prognostic factors for patients undergoing lung cancer radical surgery: A single institutional cohort study. Oncotarget 8(21):35301–35310. http://dx.doi.org/10.18632/oncotarget.13312 Ranganathan SC, Parsons F, Gangell C, Brennan S, Stick SM, Sly PD, Australian Respiratory Early Surveillance Team for Cystic F (2011) Evolution of pulmonary inflammation and nutritional status in infants and young children with cystic fibrosis. Thorax 66(5):408–413. http://dx.doi.org/10.1136/thx.2010.139493 Rivadeneira DE, Grobmyer SR, Naama HA, Mackrell PJ, Mestre JR, Stapleton PP, Daly JM (2001) Malnutrition-induced macrophage apoptosis. Surgery 129(5):617–625. http://dx.doi.org/10.1067/msy.2001.112963 Chalmers JD, Hill AT (2013) Mechanisms of immune dysfunction and bacterial persistence in non-cystic fibrosis bronchiectasis. Mol Immunol 55(1):27–34. http://dx.doi.org/10.1016/j.molimm.2012.09.011 Bekir M, Karakoc Aydiner E, Yildizeli SO, Ogulur I, Kocakaya D, Baris S, Eryuksel E, Ozen A, Ceyhan BB (2021) Primary Immun Deficiency in Patients with Non-Cystic Fibrosis Bronchiectasis and Its Relationship with Clinical Parameters. Turk Thorac J 22(1):37–44. http://dx.doi.org/10.5152/TurkThoracJ.2020.19077 Hino H, Karasaki T, Yoshida Y, Fukami T, Sano A, Tanaka M, Furuhata Y, Ichinose J, Kawashima M, Nakajima J (2018) Risk factors for postoperative complications and long-term survival in lung cancer patients older than 80 years. Eur J Cardiothorac Surg 53(5):980–986. http://dx.doi.org/10.1093/ejcts/ezx437 Ji D, Sun R, Wu Z (2023) Effects of uniportal thoracoscopic pulmonary segmentectomy and lobectomy on patients with early-stage non-small-cell lung cancer and risk factors of postoperative complications. Am J Transl Res 15(6):4369–4379 Song KB, Hong S, Kim HJ, Park Y, Kwon J, Lee W, Jun E, Lee JH, Hwang DW, Kim SC (2020) Predictive Factors Associated with Complications after Laparoscopic Distal Pancreatectomy. J Clin Med 9(9). http://dx.doi.org/10.3390/jcm9092766 Wang FT, Lin Y, Yuan XQ, Gao RY, Wu XC, Xu WW, Wu TQ, Xia K, Jiao YR, Yin L et al (2024) Predicting short-term major postoperative complications in intestinal resection for Crohn's disease: A machine learning-based study. World J Gastrointest Surg 16(3):717–730. http://dx.doi.org/10.4240/wjgs.v16.i3.717 Additional Declarations No competing interests reported. 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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-4408951","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309228892,"identity":"7e9a46cc-f7cc-4b68-9307-2444b2f1f4ca","order_by":0,"name":"Yang Gu","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Gu","suffix":""},{"id":309228893,"identity":"860315b4-3a0f-4fe6-ac75-4bf8818bd01b","order_by":1,"name":"Jin-Bai Miao","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jin-Bai","middleName":"","lastName":"Miao","suffix":""},{"id":309228894,"identity":"ddc24ada-926b-45b4-ac4d-e97534028d57","order_by":2,"name":"Hang Zheng","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Zheng","suffix":""},{"id":309228896,"identity":"e9ad7358-f67c-458c-9706-caab72b49211","order_by":3,"name":"Xin Li","email":"","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""},{"id":309228897,"identity":"a2df26a9-09b8-4aff-809d-a503e070765f","order_by":4,"name":"Bin Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACAwYehgOM/yTq+RmYGw4Qr4WBzSZBsoGRBC0MDGxpCQYHGBuIc5i5RO7BwwU8h/OMjx9sPMzbdoeBv707Aa8Wyxl5CYdnSBwuNjuT2ADU8oxB4szZDfgddiPH4DCPwWHGbQfAWg4zGEjkEqMl4TDj5v6HJGk5kJa4QYJoW868SzjM22BjLHHjYcPBOecO8xD2y/Hcw595GyTk+PuTD394U3ZYjr+9F78WFMAEjCMe4pWDAOMP0tSPglEwCkbBCAEAwA5R/u7BVb4AAAAASUVORK5CYII=","orcid":"","institution":"Beijing Chao-Yang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-05-12 15:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4408951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4408951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57941357,"identity":"f7295538-40cc-4aea-9926-0cd2825b46b7","added_by":"auto","created_at":"2024-06-07 18:56:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17160,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves for NLR, LLR, NLRAR and ALI for bronchiectasis patients undergoing lung resection.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4408951/v1/6021258f77525c6fad1a055d.png"},{"id":57941358,"identity":"fa621313-86cb-410e-80e2-a0903354b515","added_by":"auto","created_at":"2024-06-07 18:56:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6067,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for postoperative complications in bronchiectasis patients undergoing lung resection surgery.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4408951/v1/5ed707dddb6ae7a0d05d95aa.png"},{"id":57941356,"identity":"e642473a-2a11-4282-87a9-2008717fca43","added_by":"auto","created_at":"2024-06-07 18:56:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13888,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the nomogram for postoperative complications in bronchiectasis patients undergoing lung resection surgery.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4408951/v1/4f9e2d60d24ad901f65cb19b.png"},{"id":72814214,"identity":"51574441-94fc-4ced-834c-2d495a002485","added_by":"auto","created_at":"2025-01-02 11:54:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":854290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4408951/v1/8264bc7c-6828-4b46-a95f-d8ee292c2e0e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of the Advanced Lung Cancer Inflammation Index for the Postoperative Complications of Lung Resections in Patients with Bronchiectasis: A Retrospective Study Short Title : Study on the Correlation between the Advanced Lung Cancer Inflammation Index and Patients with Bronchiectasis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBronchiectasis is a significant chronic pulmonary condition characterized by permanent and irreversible enlargement of the bronchi. This condition often leads to chronic respiratory symptoms, including cough, sputum production, and hemoptysis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent data suggest that the incidence of bronchiectasis varies widely, ranging from 67 to 1,200 cases per 100,000 individuals. Surgical resection is recommended for localized bronchiectasis when conservative treatments fail or when patients experience life-threatening hemoptysis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite the majority of patients receiving standardized perioperative care, the rate of complications following lung resection for bronchiectasis has been reported to range from 9.4\u0026ndash;53% [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Postoperative complications not only lead to prolonged hospital stays and increased treatment costs but also signify a poorer prognosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Previous research has identified multiple independent risk factors for postoperative complications in patients with bronchiectasis, including a history of tuberculosis, restricted expiratory capacity, incomplete resection, lower body mass index (BMI), and poor nutritional status [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite these insights, a comprehensive index for predicting the prognosis of bronchiectasis patients remains elusive.\u003c/p\u003e \u003cp\u003eThe Advanced Lung Cancer Inflammation Index (ALI), which integrates BMI, the serum ALB concentration, and the neutrophil-to-lymphocyte ratio (NLR), was initially developed by Jafri et al. to evaluate the prognosis of patients with metastatic non-small cell lung cancer (NSCLC) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Subsequent studies have demonstrated that the ALI is effective in predicting the outcomes of various cancers, including hepatocellular carcinoma, esophageal squamous cell carcinoma, gastric cancer, colorectal cancer, and even benign conditions such as hypertension, acute coronary syndrome, and diabetes mellitus [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, the ALI has been utilized as a predictor of postoperative complications in colon cancer patients [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite its broad range of applications, to our knowledge, no studies have explored the relationship between the ALI and pulmonary postoperative complications, particularly under conditions with high complication rates such as bronchiectasis. This study aimed to investigate the predictive value of the preoperative ALI for postoperative complications in patients who underwent lung resection for bronchiectasis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. Study Design and Population\u003c/h2\u003e\n\u003cp\u003eThis was a single-center, retrospective study that included 160 patients with localized bronchiectasis at Beijing Chao-Yang Hospital, Capital Medical University, between April 2012 and December 2022. All of the patients achieved complete resection of the lesion via surgical treatment. Complete resection was defined as surgical resection of all involved segments diagnosed preoperatively by high-resolution computed tomography (HRCT). The study received approval from the Ethics Committee of Beijing Chao-Yang Hospital, Capital Medical University, and was conducted in accordance with the principles outlined in the Declaration of Helsinki. Given the retrospective nature of the study, the requirement for written informed consent was waived.\u003c/p\u003e\n\u003cp\u003eInclusion criteria\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e1) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years;\u003c/p\u003e\n\u003cp\u003e2) Availability of complete clinical data;\u003c/p\u003e\n\u003cp\u003e3) Single lung lobe resection (lobectomy, segmentectomy, or wedge resection) was performed.\u003c/p\u003e\n\u003cp\u003eExclusion criteria\u003c/p\u003e\n\u003cp\u003e1) Presence of preoperative fever;\u003c/p\u003e\n\u003cp\u003e2) Patients with nonsurgical management of bronchiectasis;\u003c/p\u003e\n\u003cp\u003e3) History of previous thoracic surgery;\u003c/p\u003e\n\u003cp\u003e4) History of any type of malignancy or hematological disease.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. Data collection\u003c/h2\u003e\n\u003cp\u003eWe reviewed and collected patient data through the electronic medical records system, including patient demographics such as BMI, presence of hemoptysis, comorbidities (hypertension, diabetes and tuberculosis), smoking history, surgical status, extent of surgical resection, surgical modality (thoracotomy or video-assisted thoracoscopic surgery), operation time, intraoperative blood loss, drainage tube indwelling time and postoperative complications, length of hospital stay after the operation, total length of hospital stay, and results of routine preoperative blood tests, including leukocyte count, neutrophil count, lymphocyte count and ALB. The biomarkers were calculated as follows: ALI\u0026thinsp;=\u0026thinsp;BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) \u0026times; ALB (g/dL)/NLR, BMI\u0026thinsp;=\u0026thinsp;weight (kg)/height (m)\u003csup\u003e2\u003c/sup\u003e, NLR\u0026thinsp;=\u0026thinsp;neutrophil count/lymphocyte count, LLR\u0026thinsp;=\u0026thinsp;leukocyte count/lymphocyte count, NLRAR\u0026thinsp;=\u0026thinsp;NLR/ALB (g/L).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. Definition of complications\u003c/h2\u003e\n\u003cp\u003eIn this study, postoperative complications were defined as adverse events occurring during hospitalization after surgery or within 30 days after discharge. The Clavien‒Dindo classification was applied to assess the severity of complications [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], and any complication that reached grade II or higher was recorded. Common complications observed in patients included prolonged air leakage (air leakage persisting for more than 7 days), pneumonia, pulmonary atelectasis, empyema, hemorrhage, wound infection, and cardiac arrhythmia [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e\n\u003cp\u003eThe optimal cutoff value for the ALI was determined through the receiver operating characteristic (ROC) curve and the Youden index, and the ALI was subsequently converted into categorical variables. All continuous variables were first tested for normality. Variables conforming to a normal distribution are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD), and comparisons between two groups were conducted using the independent samples t test. Variables not normally distributed are presented as medians (interquartile ranges) and were compared using the Mann\u0026ndash;Whitney U test. Categorical variables are expressed as numbers (percentages) and were analyzed using either the Pearson \u0026chi;\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for postoperative complications. Statistical analyses were performed using SPSS (version 26.0), GraphPad Prism (version 10.2), and R software (version 3.6.2). A two-sided P value less than 0.05 was considered to indicate statistical significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 160 patients with a pathological diagnosis of bronchiectasis were included in the study, comprising 68 males and 92 females with a median age of 51 years. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of these patients. Among them, 88 (55.0%) experienced hemoptysis, and 34 (21.3%) had a history of cigarette smoking. In terms of surgical intervention, 127 (79.4%) patients underwent video-assisted thoracoscopic surgery (VATS), while 33 (20.6%) patients underwent thoracotomy. Surgical procedures included lobectomy in 145 patients and segmental or wedge resection in 15 patients. The classification of postoperative complications is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, 29 (18.1%) patients experienced a total of 36 complications, which included 13 cases of prolonged air leakage, 8 cases of pneumonia, 4 cases of pulmonary atelectasis, 3 cases each of hemorrhage, empyema, and cardiac arrhythmia, and 2 cases of wound infection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;160)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (37\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (42.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (57.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.13\u0026nbsp;\u0026plusmn;\u0026nbsp;3.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (21.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoptysis ,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (55.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (18.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, (n%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (11.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuberculosis, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (13.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.07\u0026nbsp;\u0026plusmn;\u0026nbsp;0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80 (1.32\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.11 (2.58\u0026ndash;4.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRAR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.03\u0026ndash;0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.67 (35.01\u0026ndash;70.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical approach, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127 (79.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (20.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of operation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmentectomy/Wedge resection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 (90.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation time, median (IQR), min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (100\u0026ndash;174)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood loss, median (IQR), ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (50\u0026ndash;200)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of chest drain, median (IQR), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS after surgery, median (IQR), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (4\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS, median (IQR), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (9\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative complication, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eIQR, interquartile range; BMI, body mass index; ALB, albumin; NLR, neutrophil-to‐lymphocyte ratio; LLR, leukocyte-to-lymphocyte ratio; NLRAR, NLR-to-albumin ratio; ALI, advanced lung cancer inflammation index; VATS, video-assisted thoracoscopic surgery; LOS, length of hospital stay.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTypes of postoperative complications in patients with bronchiectasis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative Complications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProlonged air leak pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary atelectasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmpyema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac arrhythmia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWound infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Comparison of the ALI and other hematological biomarkers\u003c/h2\u003e \u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we assessed the correlation between the ALI, NLR, LLR and NLRAR and postoperative complications in bronchiectasis patients. Our findings revealed a significant correlation between these four biomarkers and postoperative complications (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, we plotted ROC curves for each indicator (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By analyzing the area under the curve for the four indicators, we found that the ALI had a better ability to predict postoperative complications than did the NLR (AUC 0.663 [95% CI 0.547\u0026ndash;0.780] vs. 0.620 [95% CI 0.495\u0026ndash;0.746]; P\u0026thinsp;=\u0026thinsp;0.021), LLR (AUC 0.663 [95% CI 0.547\u0026ndash;0.780] vs. AUC 0.620 [95% CI 0.494\u0026ndash;0.747]; P\u0026thinsp;=\u0026thinsp;0.022) or NLRAR (AUC 0.663 [95% CI 0.547\u0026ndash;0.780] vs. AUC 0.629 [95% CI 0.507\u0026ndash;0.751]; P\u0026thinsp;=\u0026thinsp;0.017) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the ROC curve analysis, the critical cutoff value for the ALI was determined to be 38.37. Based on this threshold, patients were categorized into two groups: a high ALI group (ALI\u0026thinsp;\u0026ge;\u0026thinsp;38.37, n\u0026thinsp;=\u0026thinsp;112) and a low ALI group (ALI\u0026thinsp;\u0026lt;\u0026thinsp;37.69, n\u0026thinsp;=\u0026thinsp;48).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCorrelation between four biological markers and postoperative complication\u003c/b\u003es\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Complications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.77 (1.31\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.62 (1.37\u0026ndash;3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.03 (2.58\u0026ndash;3.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.82 (2.55\u0026ndash;4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.043\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRAR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.03\u0026ndash;0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06 (0.04\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.82 (39.05\u0026ndash;71.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.63 (25.76\u0026ndash;59.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eIQR, interquartile range; NLR, neutrophil-to-lymphocyte ratio. LLR, leukocyte-to-lymphocyte ratio; NLRAR, NLR-to-albumin ratio; ALI, advanced lung cancer inflammation index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe bold values indicate the value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which is statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the area under the curve values of the ALI and other biological markers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.547\u0026ndash;0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.495\u0026ndash;0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.494\u0026ndash;0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.507\u0026ndash;0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eALI, advanced lung cancer inflammation index; AUC, area under the curve; NLR, neutrophil-to -lymphocyte ratio. LLR, leukocyte-to-lymphocyte ratio; NLRAR, NLR-to-albumin ratio; The AUC values between the advanced lung cancer inammation index and other factors were compared using the Z-test method.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe bold values indicate the value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which is statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Relationships between the ALI subgroups and clinical variables\u003c/h2\u003e \u003cp\u003eThe correlations between the ALI and clinical characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Compared with the high ALI group, the low ALI group had a significantly lower BMI (p\u0026thinsp;=\u0026thinsp;0.020), greater smoking history (p\u0026thinsp;=\u0026thinsp;0.014), greater NLR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), greater LLR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and greater NLRAR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, a low preoperative ALI was associated with a greater percentage of thoracotomy (P\u0026thinsp;=\u0026thinsp;0.001), longer operative time (P\u0026thinsp;=\u0026thinsp;0.024), greater volume of intraoperative blood loss (P\u0026thinsp;=\u0026thinsp;0.016), longer postoperative chest tube drainage times (P\u0026thinsp;=\u0026thinsp;0.001), longer postoperative hospital stays (P\u0026thinsp;=\u0026thinsp;0.001) and longer length of hospital stay (P\u0026thinsp;=\u0026thinsp;0.001). Nevertheless, there was no significant difference between the two groups in terms of age, sex, hemoptysis, comorbidities or serum ALB concentration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship Between Patient Characteristics and ALI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh ALI (n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow ALI (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (37\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (39\u0026ndash;62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.51\u0026nbsp;\u0026plusmn;\u0026nbsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.24\u0026nbsp;\u0026plusmn;\u0026nbsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoptysis ,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (64.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, (n%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuberculosis, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, mean\u0026nbsp;\u0026plusmn;\u0026nbsp;SD, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10\u0026nbsp;\u0026plusmn;\u0026nbsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.01\u0026nbsp;\u0026plusmn;\u0026nbsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.22\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49 (2.89\u0026ndash;4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLLR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84 (2.45\u0026ndash;3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.76 (4.19\u0026ndash;6.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLRAR, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04 (0.03\u0026ndash;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.07\u0026ndash;0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical approach, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (86.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation time, median (IQR), min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (90\u0026ndash;165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (120\u0026ndash;180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood loss, median (IQR), ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (50\u0026ndash;200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (53\u0026ndash;338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of chest drain, median (IQR), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS after surgery, median (IQR), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (4\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS, median (IQR), days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (9\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (10\u0026ndash;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eIQR, interquartile range; ALI, advanced lung cancer inflammation index; BMI, body mass index; ALB, albumin; NLR, neutrophil-to-lymphocyte ratio; LLR, leukocyte-to-lymphocyte ratio; NLRAR, NLR-to-albumin ratio; VATS, video-assisted thoracoscopic surgery; LOS, length of hospital stay.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe bold values indicate the value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which is statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Risk factors for postoperative complications in bronchiectasis patients\u003c/h2\u003e \u003cp\u003eAccording to the univariate logistic analysis, low BMI, smoking history, low ALI, thoracotomy, extended operative time and increased intraoperative blood loss were correlated with postoperative complications. After adjustment for confounding factors, multivariate analysis revealed that BMI (OR\u0026thinsp;=\u0026thinsp;0.839, 95% CI\u0026thinsp;=\u0026thinsp;0.697\u0026ndash;0.968, P\u0026thinsp;=\u0026thinsp;0.031), ALI\u0026thinsp;\u0026lt;\u0026thinsp;38.37 (OR\u0026thinsp;=\u0026thinsp;3.029, 95% CI\u0026thinsp;=\u0026thinsp;1.161\u0026ndash;7.902, P\u0026thinsp;=\u0026thinsp;0.023) and operation time (OR\u0026thinsp;=\u0026thinsp;1.013, 95% CI\u0026thinsp;=\u0026thinsp;1.002\u0026ndash;1.023, P\u0026thinsp;=\u0026thinsp;0.017) were found to be independent prognostic factors for postoperative complications (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of postoperative complications in patients with bronchiectasis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.010 (0.981\u0026ndash;1.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.450 (0.199\u0026ndash;1.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.853 (0.741\u0026ndash;0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.839 (0.716\u0026ndash;0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history(Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.870 (1.196\u0026ndash;6.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.201 (0.798\u0026ndash;6.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoptysis(Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.059 (0.873\u0026ndash;4.856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension(Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.646 (0.207\u0026ndash;2.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes(Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.829 (0.225\u0026ndash;3.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuberculosis (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.073 (0.332\u0026ndash;3.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.557 (0.227\u0026ndash;1.369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;38.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;38.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.509 (2.346\u0026ndash;12.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.029 (1.161\u0026ndash;7.902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical approach, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVATS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.509 (1.883\u0026ndash;10.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.489 (0.846\u0026ndash;7.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypes of operation, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeg/Wed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLobectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.350 (0.423\u0026ndash;26.558)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation time, median (IQR), min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.015 (1.007\u0026ndash;1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.013 (1.002\u0026ndash;1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood loss, median (IQR), ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.001 (1.000\u0026ndash;1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (0.999\u0026ndash;1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eIQR, interquartile range; BMI, body mass index; ALB, albumin; ALI, advanced lung cancer inflammation index; VATS, video-assisted thoracoscopic surgery; Seg, segmentectomy; Wed, wedge resection.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe bold values indicate the value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which is statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Nomograms constructed based on independent related factors\u003c/h2\u003e \u003cp\u003eBased on the independent factors identified in the multifactor logistic regression analysis, we established a nomogram to predict the risk of postoperative complications in bronchiectasis patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The predictive value of the nomogram for short-term complications in patients with bronchiectasis after lung resection was evaluated using receiver operating characteristic (ROC) curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The AUC in the analysis was 0.792.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBronchiectasis is a prevalent chronic respiratory disease globally, and inflammation plays a pivotal role in its onset and progression. The infiltration of innate immune cells, primarily neutrophils, into lung tissue is a key factor initiating cascades that lead to the pathological changes observed in bronchiectasis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, inflammation can contribute to malnutrition, as evidenced by decreases in the serum ALB concentration and BMI [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. There is growing evidence that inflammatory and nutritional biomarkers in peripheral blood can predict the progression and overall prognosis of bronchiectasis patients. A high NLR is indicative of worsening conditions in these patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A study from a nationwide population in South Korea highlighted that being underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e) is a risk factor for bronchiectasis in young individuals [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Research by Qi and colleagues demonstrated that patients with a low BMI experience more severe bronchiectasis, increasing their risk of hospitalization and mortality [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, low serum ALB levels are significantly associated with the Bronchiectasis Severity Index (BSI) and FACED score [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, these biomarkers alone do not provide a comprehensive assessment of the inflammation and nutritional status of patients. Recently, Jafri et al. proposed a simple yet effective screening tool, the ALI, based on BMI, the serum ALB concentration, and the NLR. This index can simultaneously reflect the nutritional and inflammatory status of patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. he correlation between a lower ALI and poorer prognosis has been established in various cancers and inflammatory diseases [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nonetheless, research on the clinical significance of the ALI in bronchiectasis patients remains limited. In our study, we found that a low preoperative ALI is independently associated with postoperative complications in bronchiectasis patients who underwent single-lung resection. A low ALI, indicating a reduced BMI and serum ALB levels along with an increased serum NLR, suggests malnutrition and a heightened inflammatory response. Therefore, mitigating excessive inflammation preoperatively and providing nutritional support may effectively reduce the incidence of postoperative complications.\u003c/p\u003e \u003cp\u003eIn our research, we observed that a greater preoperative NLR was associated with an increased incidence of postoperative complications following lung resection, which is consistent with the findings of previous studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, we identified high preoperative LLRs and NLRARs as adverse predictors of postoperative complications in patients with bronchiectasis. However, the ROC curve analysis revealed that the ALI possesses superior predictive ability for postoperative complications compared to the aforementioned indicators, possibly because it incorporates a more comprehensive set of parameters. BMI serves as a measure of body composition. A lower BMI often indicates inadequate nutrition, which correlates with early lung inflammation and increased activity of free neutrophil elastase, thereby linking poor nutritional status with exacerbated lung disease [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Previous findings suggest that a low BMI is indicative of a poor prognosis in patients with bronchiectasis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our group recently reported that a low BMI is an independent risk factor for postoperative complications following bronchial dilation surgery [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. ALB levels, another marker of nutritional status, also play a significant role. Patients with hypoalbuminemia typically exhibit more severe forms of bronchiectasis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This may be due to hypoalbuminemia leading to impaired immune responses in the host [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The NLR represents the severity of the innate immune response, i.e., the severity of inflammation caused mainly by bacterial infection. Patients with bronchiectasis have reduced levels of lymphocytes, and lower lymphocyte counts may be associated with an unlimited innate immune response [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Therefore, the ALI has emerged as a comprehensive indicator, encapsulating the anthropometric, nutritional, and inflammatory factors crucial for assessing patients with bronchial dilation.\u003c/p\u003e \u003cp\u003eWe identified prolonged duration of surgery as another independent risk factor for postoperative complications, consistent with findings from previous research [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The specific mechanisms by which extended surgical times contribute to complications remain unclear; however, several potential explanations exist. For example, longer surgeries increase the exposure of the wound to microbes, thereby increasing the risk of wound contamination. Additionally, prolonged operations can cause tissue retraction, leading to tissue ischemia and necrosis. Furthermore, extended durations of surgery often reflect the complexity of the procedures, which can increase surgeon fatigue and potentially result in a greater incidence of postoperative complications [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several potential limitations of our study. First, this was a single-center study with a relatively small sample size. A multicenter study is needed to verify our results. Second, as a retrospective study, some selection bias might exist. A prospective study with a larger population and relative intervention focused on improving the nutritional and inflammatory status of patients might further expand the application of our findings. Additionally, only short-term complications were considered in the current study. A longer follow-up might help clinicians use the ALI as an index to predict more complications, such as the reoccurrence of bronchiectasis or the long-term recovery of pulmonary function.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study is the first to establish that the preoperative ALI serves as an independent risk factor for predicting postoperative complications in patients with bronchiectasis. These insights could aid clinicians in identifying the most effective biomarkers, which encompass both inflammatory and nutritional parameters, to inform therapeutic strategies and diminish the rate of postoperative complications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest statement\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis research was funded by grants from the Beijing Chaoyang Hospital 2022 Science and Technology Innovation Fund (No. 22kcjjzd-6).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: Y.G., X.L. and B.H.; Methodology: B.-J.M. and H.Z.; Data collection and analysis: Y.G.; Writing - original draft preparation: Y.G.; Writing - review and editing: B.H. and X.L.. All the authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe authors will make the raw data supporting the conclusions of this article available without undue reservation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChandrasekaran R, Mac Aogain M, Chalmers JD, Elborn SJ, Chotirmall SH (2018) Geographic variation in the aetiology, epidemiology and microbiology of bronchiectasis. BMC Pulm Med 18(1):83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1186/s12890-018-0638-0\u003c/span\u003e\u003cspan address=\"10.1186/s12890-018-0638-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalmers JD, Chang AB, Chotirmall SH, Dhar R, McShane PJ (2018) Bronchiectasis. Nat Rev Dis Primers 4(1):45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1038/s41572-018-0042-3\u003c/span\u003e\u003cspan address=\"10.1038/s41572-018-0042-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu JF, Gao YH, Song YL, Qu JM, Guan WJ (2022) Research advances and clinical management of bronchiectasis: Chinese perspective. ERJ Open Res 8(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1183/23120541.00017-2022\u003c/span\u003e\u003cspan address=\"10.1183/23120541.00017-2022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWriting BEC, Pulmonary Infection Assembly G (2021) [Expert consensus on the diagnosis and treatment of adult bronchiectasis in China]. 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World J Gastrointest Surg 16(3):717\u0026ndash;730. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.4240/wjgs.v16.i3.717\u003c/span\u003e\u003cspan address=\"10.4240/wjgs.v16.i3.717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Bronchiectasis, Advanced Lung Cancer Inflammation Index, Postoperative Complications, Systemic Inflammation, Malnutrition","lastPublishedDoi":"10.21203/rs.3.rs-4408951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4408951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBronchiectasis patients often suffer from systemic inflammation and malnutrition, which negatively affect their prognosis. The advanced lung cancer inflammation index (ALI) has emerged as a novel biomarker that reflects systemic inflammation and malnutrition. However, its utility in predicting postoperative complications in bronchiectasis patients undergoing localized surgical resection remains to be clarified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThis retrospective study included 160 patients with localized bronchiectasis who underwent a single lobectomy at our center from April 2012 to December 2022. The optimal ALI cutoff point was established using the receiver operating characteristic (ROC) curve. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for postoperative complications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The optimal cutoff value for the ALI was determined to be 38.37. Compared to the high ALI group, the low ALI group exhibited a significantly greater incidence of open chest surgeries (P=0.001), increased duration of surgeries (P=0.024), greater intraoperative blood loss (P=0.016), prolonged postoperative chest tube drainage (P=0.001), extended hospital stays after the operation (P=0.001), and a greater rate of complications (P=0.006). Multivariate logistic regression analysis revealed that prolonged surgical duration, low body mass index (BMI), and low preoperative ALI were independent risk factors for postoperative complications. To predict the likelihood of these complications, we developed a nomogram incorporating these independent factors, which demonstrated predictive accuracy with an area under the curve (AUC) of 0.792.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe preoperative ALI serves as an independent predictor of postoperative complications in patients with localized bronchiectasis who underwent a single lobectomy.\u003c/p\u003e","manuscriptTitle":"Predictive Value of the Advanced Lung Cancer Inflammation Index for the Postoperative Complications of Lung Resections in Patients with Bronchiectasis: A Retrospective Study Short Title : Study on the Correlation between the Advanced Lung Cancer Inflammation Index and Patients with Bronchiectasis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 18:56:49","doi":"10.21203/rs.3.rs-4408951/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":"1e41ed35-2132-4357-8dd5-7fdcb9becd43","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-02T11:54:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 18:56:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4408951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4408951","identity":"rs-4408951","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}