Neoadjuvant Immunochemotherapy and Postoperative Acute Hypoxemic Respiratory Failure in Thoracic Surgery: A Multicenter Cohort Study

Neoadjuvant Immunochemotherapy and Postoperative Acute Hypoxemic Respiratory Failure in Thoracic Surgery: A Multicenter Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Neoadjuvant Immunochemotherapy and Postoperative Acute Hypoxemic Respiratory Failure in Thoracic Surgery: A Multicenter Cohort Study Shenglan Tan, Yixin Peng, Yang Sun, Yongkang Liu, Xue He, Hengxing Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8867323/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 Objective: While immune checkpoint inhibitors (ICIs) continue to transform the neoadjuvant treatment, its association with postoperative acute hypoxemic respiratory failure (AHRF) remains unexplored. This study aimed to assess the association between neoadjuvant immunochemotherapy (nICT) and postoperative AHRF risk following thoracic tumor surgeries and identify the risk subgroups. Methods: This retrospective two-center cohort study included 327 patients receiving nICT (n=167) or nCT (n=160) before thoracic tumor surgeries from December 2017 to June 2023. Data were analyzed by using the propensity score matching (PSM) and multivariable logistic regressions. Subgroup and sensitivity analyses were performed to test the stability of the conclusions. Results: The nICT group demonstrated significantly higher postoperative AHRF incidence than the nCT group (19.8% vs. 8.1%, p=0.002). The inverse probability-weighting model (IPTW) confirmed elevated AHRF risk associated with nICT compared to nCT (OR=2.41, 95% CI: 1.2-4.82). In patients with non-small cell lung cancer (NSCLC), the binary logistic regression analysis showed that the history of nICT was significantly associated with postoperative AHRF (OR=4.12, 95% CI: 1.15-14.8) in patients with non-small cell lung cancer (NSCLC). Subgroup analyses revealed elevated AHRF risks with nICT versus nCT in patients with time interval between neoadjuvant therapy and surgery within 42 days (OR=6.68, 95% CI: 1.24-35.98), those with squamous cell carcinoma (SCC) (OR=3.64, 95% CI: 1.41-9.44), and those who did not achieve pathologic complete response (non-pCR) (OR=2.82, 95% CI: 1.14-6.98). Conclusions: nICT was associated with increased postoperative AHRF risk in thoracic surgical patients, necessitating rigorous perioperative monitoring. Biological sciences/Cancer Health sciences/Oncology neoadjuvant chemotherapy neoadjuvant immunochemotherapy acute hypoxemic respiratory failure thoracic surgery non-small cell lung cancer Figures Figure 1 Figure 2 Figure 3 Introduction Non-small cell lung cancer (NSCLC), esophageal carcinoma, and mediastinal malignancies constitute the principal indications for thoracic surgical interventions. Lung cancer is the most common malignancy in China 1 and worldwide 2 , with NSCLC accounting for 80.0% to 85.0% of newly diagnosed lung cancer cases annually 3 . Neoadjuvant therapy for thoracic malignancies aims to downstage tumors, improve resectability, and enhance survival outcomes 4 , 5 . While neoadjuvant chemotherapy (nCT) offers a modest benefit over surgery alone, emerging evidence suggests that combining immune checkpoint inhibitors (ICIs) with chemotherapy improves event-free survival (EFS) and pathological response rates 4 , 6 . The impact of neoadjuvant therapy on the development of postoperative AHRF remains controversial. Evidence is limited, and existing studies have reported inconsistent findings 7 – 10 . While Siegenthaler et al. observed no significant difference in major respiratory complications between patients undergoing lung resection with or without induction chemotherapy 11 , Leo et al. reported a significantly higher incidence of respiratory complications following pneumonectomy in patients who received induction chemotherapy 12 . As ICIs continue to transform the neoadjuvant treatment, they may introduce new challenges requiring clinical vigilance. In the CheckMate-816 and KEYNOTE-671 trials, postoperative mortality was higher in the nICT group than in the nCT group (1.65–4.4% vs. 0.75–2.23%), with fatal pulmonary complications occurring more frequently after nICT (1.1–2% vs. ~0.5%) 13, 14 . Consistently, pulmonary complications have been one of those major safety concerns across other nICT studies 15 – 17 . In patients with esophageal cancer, the incidence of postoperative pulmonary complications, such as pleural effusion, increased following nICT 18 , 19 . With the rapid advances of nICT in thoracic neoplasms, perioperative adverse events are receiving increasing attention. Notably, most reports of immunotherapy-related lung toxicity arise from medical oncology cohorts, whereas the potential interaction between neoadjuvant ICIs and postoperative AHRF remains underexplored. We conducted a two-center retrospective cohort of patients undergoing thoracic tumor resection to assess whether nICT increases postoperative AHRF risk compared with chemotherapy alone. We hypothesized that nICT is associated with a higher risk of postoperative AHRF. Methods Data sources and setting This was a two-center, retrospective cohort study. The dissemination of the findings adheres to the guidelines established by the Revised Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery (STROCSS) Guideline 20 . This study was approved by the institutional review boards (IRB) of the Second Xiangya Hospital of Central South University (No. LYF2023105) and Guilin Hospital of the Second Xiangya Hospital (No. LLkt-034) in Aug. 2023. In addition, this study was registered in the ClinicalTrials.gov (NCT06566053). Study population This retrospective study analyzed medical records from the Second Xiangya Hospital of Central South University and Guilin Hospital of the Second Xiangya Hospital, from December 2017 to June 2023. Eligible patients were adults (≥ 18 years) with pathologically confirmed thoracic malignancies (NSCLC, esophageal carcinoma, or mediastinal tumors) who underwent thoracic surgery following nICT or chemotherapy alone. Patients were excluded if they had incomplete neoadjuvant treatment documentation, concurrent malignancies, preoperative targeted therapy, did not undergo thoracic surgery following admission, received PD-L1 inhibitors, interstitial pneumonia history, grade ≥ 3 treatment-related adverse events (trAEs), pulmonary embolism, or perioperative heart failure. All patients received care from a multidisciplinary thoracic oncology team. Both centers implemented thoracic enhanced recovery after surgery (ERAS) in mid-2020 (Center A: June 2020; Center B: August 2020), and perioperative management criteria adhered to comparable institutional stand AHRF. The study flowchart is presented in Fig. 1 . Data extraction Clinical data were collected from medical records, including age, gender, smoking history, alcohol history, comorbidities, disease type, preoperative body mass index (BMI), preoperative serum albumin (ALB), preoperative creatinine, pulmonary function, neoadjuvant cycle number, time interval between neoadjuvant therapy and surgery (TTS), surgical approach, pathology type, and pathological response. In lung cancer patients, the extent of resection was also collected. Outcomes The primary outcome was the rate of postoperative AHRF. Postoperative AHRF was defined as the acute onset of hypoxemia within one week postoperatively, indicated by a ratio of arterial partial pressure of oxygen to inspired oxygen fraction (PaO₂/FiO₂) ≤ 200 mmHg, requiring high-flow nasal cannula (HFNC), noninvasive positive pressure ventilation, or invasive mechanical ventilation for a minimum of 24 hours, and exclude fluid overload as the causes 7 . We conducted a retrospective chart review with medical records. Each AHRF diagnosis was independently assessed by at least two attending physicians. Secondary outcomes encompassed the length of hospital stay, length of ICU stay, and length of postoperative stay. Statistical analysis Continuous variables were expressed as mean ± standard deviation for normal distributed data, and as median and interquartile range (IQR) for skewed distributions, while categorical variables were presented as frequency (percentage). Continuous variables were compared using Student’s t-test when normally distributed and the Wilcoxon rank-sum test otherwise. Pearson’s chi-squared test or Fisher’s exact test was used for categorical data, as appropriate. Missing data were handled using K-nearest neighbor (KNN) imputation, a widely accepted nonparametric approach 21 . A sensitivity analysis was performed after excluding patients with missing data to confirm the robustness of the results. Binary and multivariable logistic regression analyses were utilized to explore the association between nICT and the primary and secondary outcomes, using an extended logistic model to adjust for various covariates. To balance baseline characteristics between the nICT and nCT groups, we employed propensity score matching (PSM) with a 1:1 nearest neighbor matching algorithm and a caliper width of 0.2 22 . All variables listed in Table 1 were used to estimate the propensity score. Covariate balance after matching was assessed using the standardized mean difference (SMD), with SMD < 0.10 indicating adequate balance. The primary outcome was further verified using the inverse probability of treatment weighting (IPTW), with estimated propensity scores as weights 23 , 24 . Subgroup analyses were conducted based on relevant covariates. These analyses were executed using R version 3.3.2 and Free Statistics software version 1.9. Statistical significance was determined by a two-tailed test with a p-value threshold of less than 0.05. Table 1 Baseline characteristics of the included patients Before PSM After PSM Variables Total (n = 327) NCT (n = 160) NICT (n = 167) SMD Total (n = 200) NCT (n = 100) NICT (n = 100) SMD Age, years, Mean ± SD 57.9 ± 9.3 56.0 ± 10.5 59.8 ± 7.6 0.416 58.5 ± 7.8 58.6 ± 8.1 58.5 ± 7.6 0.014 Sex, n (%) 0.132 0.031 Male 291 (89.0) 139 (86.9) 152 (91) 177 (88.5) 88 (88) 89 (89) Female 36 (11.0) 21 (13.1) 15 (9) 23 (11.5) 12 (12) 11 (11) Smoking history, n (%) 193 (59.0) 84 (52.5) 109 (65.3) 0.262 120 (60.0) 61 (61) 59 (59) 0.041 Alcohol history, n (%) 120 (36.7) 50 (31.2) 70 (41.9) 0.223 57 (28.5) 29 (29) 28 (28) 0.039 Comorbidity Diabetes, n (%) 30 ( 9.2) 11 (6.9) 19 (11.4) 0.157 16 ( 8.0) 8 (8) 8 (8) < 0.001 Hypertention, n (%) 71 (21.7) 28 (17.5) 43 (25.7) 0.201 40 (20.0) 19 (19) 21 (21) 0.05 Coronary artery disease, n (%) 13 ( 4.0) 4 (2.5) 9 (5.4) 0.149 7 ( 3.5) 4 (4) 3 (3) 0.054 Disease type, n (%) 0.644 0.085 Lung 209 (63.9) 81 (50.6) 128 (76.6) 132 (66.0) 64 (64) 68 (68) Esophagus 99 (30.3) 61 (38.1) 38 (22.8) 66 (33.0) 35 (35) 31 (31) Mediastinum 19 ( 5.8) 18 (11.2) 1 (0.6) 2 ( 1.0) 1 (1) 1 (1) BMI, kg/m², Mean ± SD 23.6 ± 3.0 23.3 ± 3.1 23.9 ± 2.9 0.179 23.6 ± 3.1 23.7 ± 3.5 23.5 ± 2.7 0.042 ALB, g/L, Mean ± SD 39.2 ± 3.4 38.8 ± 3.4 39.7 ± 3.3 0.256 39.3 ± 3.5 39.3 ± 3.4 39.3 ± 3.6 0.004 Creatinine, mmol/L, Mean ± SD 78.0 ± 18.1 74.0 ± 15.7 81.8 ± 19.4 0.441 76.4 ± 15.0 76.3 ± 16.3 76.6 ± 13.7 0.021 Pulmonary function FEV1/FVC%, Mean ± SD 75.4 ± 10.0 76.0 ± 9.1 74.8 ± 10.7 0.12 74.9 ± 9.8 75.0 ± 9.0 74.8 ± 10.6 0.017 FEV1%pre, Mean ± SD 93.6 ± 18.1 96.0 ± 17.3 91.4 ± 18.6 0.256 94.4 ± 18.4 94.8 ± 17.1 93.9 ± 19.7 0.049 Cycle number*, Mean ± SD 2.9 ± 1.2 2.7 ± 1.4 3.0 ± 1.0 0.239 2.8 ± 0.9 2.7 ± 1.0 2.8 ± 0.8 0.096 TTS # , days, Median (IQR) 44.0 (37.0, 55.0) 42.5 (37.0, 52.0) 45.0 (37.0, 59.0) 0.08 43.0 (37.0, 54.0) 42.0 (37.0, 50.0) 44.0 (36.8, 56.0) 0.089 Surgical approach, n (%) 0.342 0.045 Open 95 (29.1) 59 (36.9) 36 (21.6) 56 (28.0) 29 (29) 27 (27) MIS 232 (70.9) 101 (63.1) 131 (78.4) 144 (72.0) 71 (71) 73 (73) Pathology type, n (%) 0.136 0.066 SCC 233 (71.3) 109 (68.1) 124 (74.3) 141 (70.5) 72 (72) 69 (69) Non-SCC 94 (28.7) 51 (31.9) 43 (25.7) 59 (29.5) 28 (28) 31 (31) ALB, serum albumin; BMI, body mass index; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; FEV1%pre, FEV1% predicted; MIS, minimally invasive surgery; nCT, neoadjuvant chemotherapy; nICT, neoadjuvant immunochemotherapy; SCC, squamous cell carcinoma * Cycle number refers to neoadjuvant cycles administered prior to surgery: in the chemoimmunotherapy group this denotes combined concurrent cycles of chemotherapy + PD-1 inhibitor; in the chemotherapy group, chemotherapy cycles only. # TTS refers to calendar days from the last neoadjuvant treatment to the date of surgery—for the chemoimmunotherapy group, the last PD-1 inhibitor dose; for the chemotherapy group, the last chemotherapy cycle. $ The distribution of PD-1 agents was: pembrolizumab (n = 62), tislelizumab (n = 43), sintilimab (n = 28), camrelizumab (n = 16), nivolumab (n = 8), and toripalimab (n = 10). Results Patient characteristics A total of 327 patients with thoracic cancers were finally included in our analysis, comprising 167 patients (51.1%) with a history of nICT and 160 (48.9%) with a history of nCT (Table 1 ). After PSM, 200 patients were successfully matched. The mean age at surgery was 57.9 ± 9.3 years, with the vast majority (89.0%) being male. The disease type distribution included lung (209 patients, 63.9%), esophagus (99 patients, 30.3%), and mediastinum (19 patients, 5.8%) cancers. Patients who received nICT predominantly had lung cancer, higher smoking and alcohol history, higher serum albumin (ALB) and creatinine levels before surgery, lower FEV1% per, less open surgery compared to those receiving nCT. The baseline characteristics of the two groups after PSM were balanced (Table 1 ). Primary outcome The total postoperative AHRF rate within the cohort was 14.1% (Supplemental Table 1). Among thoracic cancer patients, those with a history of nICT had a significantly higher postoperative AHRF rate of 19.8% compared to that of 8.1% for those with a history of nCT ( p = 0.002). Initial univariable logistic regression analysis yielded an odds ratio (OR) of 2.78 (95% Confidence Interval [CI]: 1.41–5.51, p = 0.003, Table 2 ). Upon adjusting for clinically pertinent covariates, OR was still as high as 2.5 (95% CI: 1.13–5.51, p = 0.024) across multiple extended multivariable logistic regression models (Table 2 ). Similar to the results in the pre-matched cohort, both PSM (OR = 5.22, 95% CI: 2.04–13.39, p = 0.001) and IPTW (OR = 2.41, 95% CI: 1.2–4.82, p = 0.013) indicated that neoadjuvant immunotherapy exposure was independently associated with an increased postoperative AHRF rate. Table 2 Association between neoadjuvant immunotherapy and postoperative AHRF rate using PSM Models OR (95%CI) p value Unmatched crude 2.78 (1.41 ~ 5.51) 0.003 Multivariable adjusted a 2.5 (1.13 ~ 5.51) 0.024 Propensity Score matched b 5.22 (2.04 ~ 13.39) 0.001 Propensity Score adjusted c 2.34 (1.1 ~ 5) 0.028 Weighted IPTW d 2.41 (1.2 ~ 4.82) 0.013 a, odds ratio from the multivariable logistic proportional model adjusted for all covariates (Table 1 ). b, odds ratio from a multivariate logistic proportional hazards model with the same strata and covariates matched according to the propensity score. The analysis included 116 patients (58 who received nCT and 58 who received nICT). c, odds ratio from a multivariable logistic proportional hazards model with the same strata and covariates, with additional adjustment for the propensity score. d, Primary analysis with a hazard ratio from the multivariable logistic proportional hazards model with the same strata and covariates with inverse probability weighting according to the propensity score. Secondary outcomes The secondary outcomes assessed included length of hospital stay, ICU stay, and postoperative stay, as presented in Supplemental Table 1. Among patients with a history of nICT, the length of hospital stay was shorter compared to those who had undergone nCT. In the propensity score–matched cohort, there were no statistically significant differences in overall hospital length of stay, ICU length of stay, or postoperative length of stay between patients receiving nICT and those receiving chemotherapy alone. Multivariable logistic regression analysis for all secondary outcomes was detailed in Supplemental Table 2. Upon thorough model adjustment, we observed no significant differences in secondary outcomes among patients with a history of nICT compared to nCT group, including length of hospital stay, length of ICU stay, or length of postoperative stay. Subgroup analyses Subgroup analyses, accounting for various confounders, consistently demonstrated that, compared to nCT, patients who received their final chemoimmunotherapy within 42 days prior to thoracic surgery had a relatively higher risk of postoperative AHRF (OR = 6.68, 95% CI: 1.24–35.98) than those with an interval of ≥ 42 days (Fig. 2 ). Similarly, squamous cell carcinoma (SCC) patients and non-pCR thoracic cancer cases constituted a significantly high-risk subgroup for postoperative AHRF (OR = 3.64, 95% CI: 1.41–9.44; OR = 2.82, 95% CI: 1.14–6.98; respectively). No significant interactions were observed between subgroups ( p for interaction > 0.05). Factorial analysis We further compared patients with (n = 46) and without AHRF (n = 281) with the aim of identifying factors related to postoperative AHRF in patients undergoing thoracic surgeries. As shown in Table 3 , patients who developed postoperative AHRF had a higher history of receiving nICT, and higher rate of MIS surgery. The cycle number and TTS between the last therapy and surgery were not significantly different between the two groups. Patients experiencing postoperative AHRF typically had longer length of hospital stay, longer length of ICU stay, and longer length of postoperative stay (all p < 0.001) compared to patients without AHRF. Table 3 Characteristics of postoperative AHRF and non-AHRF patients Variables Total (n = 327) Non-AHRF (n = 281) AHRF (n = 46) p value Age, years, Mean ± SD 57.9 ± 9.3 57.5 ± 9.4 60.1 ± 8.7 0.079 Sex, n (%) 0.119 Male 291 (89.0) 247 (87.9) 44 (95.7) Female 36 (11.0) 34 (12.1) 2 (4.3) Smoking history, n (%) 193 (59.0) 160 (56.9) 33 (71.7) 0.058 Alcohol history, n (%) 120 (36.7) 98 (34.9) 22 (47.8) 0.091 Comorbidity Diabetes, n (%) 30 ( 9.2) 22 (7.8) 8 (17.4) 0.051 Hypertention, n (%) 71 (21.7) 59 (21) 12 (26.1) 0.438 Coronary artery disease, n (%) 13 ( 4.0) 12 (4.3) 1 (2.2) 1 Disease type, n (%) 0.352 Lung 209 (63.9) 183 (65.1) 26 (56.5) Esophagus 99 (30.3) 81 (28.8) 18 (39.1) Mediastinum 19 ( 5.8) 17 (6) 2 (4.3) BMI, kg/m², Mean ± SD 23.6 ± 3.0 23.5 ± 3.1 23.9 ± 2.6 0.433 ALB, g/L, Mean ± SD 39.2 ± 3.4 39.2 ± 3.3 39.7 ± 3.6 0.357 Creatinine, mmol/L, Mean ± SD 78.0 ± 18.1 77.2 ± 18.0 82.3 ± 18.3 0.078 Pulmonary function FEV1/FVC%, Mean ± SD 75.4 ± 10.0 75.7 ± 9.9 73.9 ± 10.3 0.258 FEV1%pre, Mean ± SD 93.6 ± 18.1 94.4 ± 17.7 89.0 ± 19.8 0.057 nICT, n (%) 167 (51.1) 134 (47.7) 33 (71.7) 0.002 Cycle number, Mean ± SD 2.9 ± 1.2 2.9 ± 1.2 2.9 ± 0.9 0.83 TTS, days, Median (IQR) 44.0 (37.0, 55.0) 44.0 (37.0, 55.0) 44.0 (37.2, 55.8) 0.535 Surgical approach, n (%) 0.026 Open 95 (29.1) 88 (31.3) 7 (15.2) MIS 232 (70.9) 193 (68.7) 39 (84.8) Pathology type, n (%) 0.138 SCC 233 (71.3) 196 (69.8) 37 (80.4) Non-SCC 94 (28.7) 85 (30.2) 9 (19.6) pCR, n (%) 72 (22.0) 58 (20.6) 14 (30.4) 0.137 Outcomes Length of hospital stay, Mean ± SD 12.6 ± 5.5 12.1 ± 5.1 15.4 ± 7.0 < 0.001 Length of ICU stay, Median (IQR) 2.0 (1.0, 3.0) 2.0 (1.0, 2.0) 4.0 (3.0, 6.0) < 0.001 Length of postoperative stay, Median (IQR) 7.0 (4.0, 8.0) 6.0 (4.0, 8.0) 8.0 (7.0, 10.8) < 0.001 AHRF, acute hypoxemic respiratory failure; ALB, serum albumin; BMI, body mass index; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; FEV1%pre, FEV1% predicted; MIS, minimally invasive surgery; nICT, neoadjuvant immunochemotherapy; SCC, squamous cell carcinoma; TTS, time interval between neoadjuvant therapy and surgery; pCR, pathological complete response Analysis of NSCLC patients Because NSCLC patients constituted the majority of participants, we further conducted a sensitivity analysis in NSCLC patients. The baseline characteristics and outcomes of these patients are shown in Supplemental Table 3. A total of 209 patients with NSCLC were enrolled, of whom 128 (61.2%) had a history of nICT, and 81 (38.8%) had a history of nCT. The mean age at surgery was 58.3 ± 7.9 years, with the vast majority (89%) being male. Patients with NSCLC who received nICT were older, had more alcohol history, higher creatinine levels before surgery, longer TTS, more MIS, and higher postoperative AHRF rate compared to those receiving nCT (all p < 0.05). Variables which exhibited a p -value of less than 0.1 in univariate analyses were chosen for multivariate adjustment (Supplemental Table 4). The binary logistic regression analysis showed that the history of nICT remained associated with postoperative AHRF (OR = 4.12, 95% CI: 1.15–14.8, p = 0.03) in patients with NSCLC (Table 4 ). However, for the secondary outcomes, including length of hospital stay, length of ICU stay, and length of postoperative stay, no significant association between a history of nICT and the secondary outcome were observed in patients with NSCLC. Table 4 The relationship between neoadjuvant immunotherapy and outcomes in patients with NSCLC Outcome Crude Coefficient (95%CI) Crude p value Adjusted Coefficient (95%CI) Adjusted p value Primary Outcome AHRF, n (%) 5.7 (1.65 ~ 19.65) 0.006 4.12 (1.15 ~ 14.8) 0.03 Secondary Outcomes Length of hospital stay, d -0.48 (-1.97 ~ 1.02) 0.533 -0.3 (-1.87 ~ 1.27) 0.711 Length of ICU stay, d 0.54 (-0.12 ~ 1.21) 0.111 0.43 (-0.26 ~ 1.11) 0.227 Length of postoperative stay, d 0.69 (-0.39 ~ 1.77) 0.213 0.6 (-0.51 ~ 1.72) 0.289 AHRF, acute hypoxemic respiratory failure; ICU, intensive care unit Adjust variables from univariate analyses where the p -value was less than 0.1 Supplemental Fig. 1. Subgroup analysis of the associations between nICT and postoperative AHRF in NSCLC patients. An increased risk of AHRF associated with nICT exposure in patients with NSCLC was consistently observed across key subgroups, including those younger than 65 years, those with an interval of less than 42 days between the last dose of neoadjuvant immunotherapy and surgery, and those with SCC (Supplemental Fig. 1). Sensitive analysis Approximately 5% of relevant variables in the dataset were missing, which were pulmonary function variables. Patients with incomplete data were excluded from both the overall cohort and the NSCLC subgroup. A sensitivity analysis was subsequently performed. Consistently, nICT was significantly associated with an increased risk of postoperative AHRF in both the overall cohort and the NSCLC subgroup after exclusion of missing data (OR = 2.67, 95% CI: 1.16–6.13, p = 0.021; OR = 6.08, 95% CI: 1.33–27.8, p = 0.02, respectively; Supplemental Table 5). No statistically significant differences were observed between groups for any of the secondary outcomes. Discussion This retrospective cohort study revealed that patients with thoracic neoplasms receiving nICT had significantly higher postoperative AHRF rates than those receiving nCT alone, a difference persisting before and after PSM. Similar patterns emerged in NSCLC surgical patients, where chemoimmunotherapy were associated with elevated postoperative AHRF incidence. Moreover, subgroup analyses revealed elevated AHRF risks with nICT versus chemotherapy alone in patients receiving preoperative immunotherapy within 42 days, SCC, and non-pCR thoracic cancer cases. Within NSCLC, age < 65 years further delineated a high-risk subgroup associated with increased risk of postoperative ARDS after nICT. Over the past 5 years, more than 10 randomized controlled trials (RCTs) have investigated the impact of neoadjuvant immunotherapy or chemoimmunotherapy on prognosis 6 , 13 – 16 , 25 – 38 . As ICIs continue to transform the neoadjuvant treatment, the investigation of perioperative complications such as AHRF is both timely and highly relevant. Perioperative trials such as CheckMate-816 and KEYNOTE-671 primarily report mortality rather than AHRF. In our study, mortality was 1/327 (0.3%, date not shown), whereas the nICT arms of CheckMate-816 and KEYNOTE-671 reported 5/179 and 4/97 deaths 13 , 14 , respectively, including two pulmonary-related deaths in each trial. This indicates a high level of surgical expertise and perioperative management across the participating centers in this study. In real-world studies, perioperative complications remain a key issue for thoracic surgeons. These differences likely reflect endpoint definition and trial versus real-world selection, underscoring the complementary nature of our AHRF-focused analysis. Postoperative hypoxemic respiratory failure is variably defined across studies, which likely contributes to the wide range of reported incidences. In a 211-patient NSCLC cohort undergoing thoracic surgery after neoadjuvant chemotherapy, hARF was defined by PaO₂/FiO₂ ≤200 mmHg requiring noninvasive positive pressure ventilation or invasive mechanical ventilation for ≥ 24 h, yielding an incidence of 5.2% 7 . In a minimally invasive esophagectomy cohort, postoperative respiratory failure was defined using a more stringent endpoint—unplanned reintubation or tracheostomy, or delayed extubation (≥ 48 h)—resulting in a reported incidence of 2.4% 39 . Some studies have identified postoperative respiratory failure using discharge diagnosis codes from administrative databases, an approach that may lead to underascertainment of true positive cases 40 . In contrast, in our study, all cases were retrospectively adjudicated by two independent physicians based on detailed clinical records. This strategy allows for more accurate capture of clinically relevant events, reduces misclassification bias, and ensures greater diagnostic consistency, particularly for cases that may not be fully reflected by diagnostic coding alone. In this study, the incidence of AHRF in the nCT group was 8.1%, which reduced to 6% after PSM. Adoption of the updated AHRF definition, which included patients receiving HFNC oxygen therapy 41 , was one reason for elevated total AHRF rates in our study. While prior studies identified advanced age, male sex, and impaired pulmonary function as risk factors for postoperative respiratory failure 39 , 40 , our analysis revealed a markedly higher AHRF incidence of 19.8% in thoracic tumor patients receiving nICT. Both IPTW and multivariate regression analyses confirmed a significant association between nICT and increased postoperative AHRF risk. Postoperative stress and compromised innate and adaptive immune responses may be potential factors contributing to perioperative complications 36 , 42 . Crucially, this comprehensive evaluation of thoracic neoadjuvant therapies identified immunotherapy itself as the predominant independent risk factor for postoperative AHRF, though its specific pathophysiological mechanisms warrant further investigation. Existing studies have demonstrated that postoperative AHRF increases mortality and hospital stay duration 40 , 43 , 44 . Intriguingly, our findings revealed that patients who received nICT had a significantly higher incidence of AHRF, but markedly shorter hospital length of stay compared to the nCT group. This temporal disparity likely reflects the study's retrospective design, with chemoimmunotherapy implemented more recently compared to earlier chemotherapy cases. Advancing perioperative care, including ERAS 45 , 46 protocols and surgical expertise, contributed to both reduced hospitalization in later period chemoimmunotherapy patients. The higher MIS proportion among AHRF cases likely reflects calendar-time, as chemoimmunotherapy—where AHRF clustered—was performed during periods of greater MIS uptake and ERAS implementation. After adjusting for surgical approach (and extent of resection in NSCLC), the nICT–AHRF association remained higher than nCT. The persistently elevated AHRF incidence in the nICT group despite these improvements suggests an intrinsic association between immunotherapy and postoperative AHRF in thoracic oncology. Factorial and subgroup analyses revealed no association between postoperative AHRF incidence and neoadjuvant therapy cycles. While prior studies report 1 to 4 neoadjuvant immunotherapy exposure cycles (typically 3 to 4 cycles) 13 , 16 , 33 , 34 , our subgroup analysis similarly demonstrated no cycle-dependent AHRF risk variation. However, intervals less than 42 days between last immunotherapy and surgery correlated with higher AHRF incidence, suggesting a 6-week minimum preoperative window. Current literature on nICT presents limited data concerning the interval between last therapy and surgery, with durations reported as 2 to 6 weeks 13 , 16 , 47 . Interestingly, subgroup analysis revealed that non-pCR patients exhibited increased AHRF risk. These findings emphasize the need to balance immunotherapy efficacy against its perioperative complication profile, particularly regarding treatment-surgery intervals and tumor response patterns. Subgroup analyses identified consistently elevated postoperative AHRF risk in patients those with TTS < 42 days, SCC, and non-pCR cases receiving chemoimmunotherapy. In the NSCLC population, age below 65 years is also considered a high-risk subgroup. These signals may inform preoperative risk assessment. Although immune biomarkers (e.g., PD-L1 expression, circulating cytokines) were not available in our dataset now, they are promising candidates to refine risk prediction and warrant prospective evaluation. For patients at elevated risk, individualized surgical timing, conservative fluid management, and lung-protective ventilation should be prioritized perioperatively 46 , whereas routine prophylactic corticosteroids are not supported by current evidence. Clinically, AHRF should remain on the differential for early postoperative hypoxemia even when overt infection or cardiogenic fluid overload is absent. These considerations enhance the patient-care relevance of our findings and outline directions for prospective, biomarker-informed optimization of perioperative management in the setting of nICT. The underlying mechanism may involve PD-1 inhibitor-mediated preoperative immune activation induces bystander lung injury via immune checkpoints derepression, CD8⁺ effector T cells activation, and elevated secretion of pro-inflammatory cytokines such as IL-2 and CXCL10 48–50 . Surgical stressors, including mechanical ventilation, ischemia, amplify pulmonary infiltration of activated T-cells and inflammatory mediators, triggering systemic inflammatory response syndrome (SIRS) and compounding AHRF risk 51 . This hypothesis-generating dual-hit model posits that preoperative PD-1 blockade primes pulmonary immune responses (the “first hit”), and surgical trauma amplifies this activation (the “second hit”), potentially precipitating AHRF (Fig. 3 ). Studies show elderly patients (≥ 65 years) receiving ICIs exhibit lower naïve cytotoxic (TcN) and helper T cells (ThN), B cells, and double-negative T cells (DNT), with attenuated cytokine responses and upregulated PD-1 expression 52 , potentially mitigating systemic inflammation and thus lowering their susceptibility to AHRF. Shorter immunotherapy-to-surgery intervals (< 42 days) correlated with higher AHRF rates, likely reflecting residual PD-1 monoclonal antibodies activity from prolonged half-life 53 . Thus, these patients might still be in an actively heightened or subclinically inflamed immunologic state during surgery. Additionally, Non-pCR patients showed elevated AHRF risk despite residual tumors, possibly associated with elevated baseline T-lymphocyte levels and immune activation-related receptors 54 , 55 , thereby predisposing them to exaggerated postoperative inflammatory cytokine release and enhanced AHRF susceptibility. Combined mechanisms, including preoperative immune activation, inflammatory mediator accumulation, lymphocyte subset imbalance, and distinctive immune profiles in certain subgroups (shorter therapeutic interval, SCC, non-pCR, and patients with NSCLC who are younger than 65 years), contribute to increased AHRF risk following nICT. Our study has several strengths. The findings are significant for perioperative practice. The retrospective two-center design encompassing a relatively large sample size with application of multiple statistical methodologies, reflects considerable effort to reduce confounding in observational data. Furthermore, the inclusion of sensitivity analyses, subgroup analyses, and a focused assessment on NSCLC subpopulations strengthens the manuscript’s internal validity. However, several limitations warrant consideration. Firstly, its retrospective nature introduces potential selection and ascertainment biases. Secondly, this was a two-center study with a modest sample size, limiting the generalizability of the results. Thirdly, this study included patients with NSCLC, esophageal cancer, and malignant mediastinal tumors. While we pooled thoracic tumors in the main analysis, we justified this by their shared exposure to ICIs, common surgical setting, and AHRF as a unified inflammatory endpoint. Although separate analysis within the NSCLC patient cohort yielded consistent results, the inclusion of patients with NSCLC, esophageal carcinoma, and mediastinal malignancies in the overall study population may constitute a potential source of heterogeneity. Further studies are needed within disease-specific or tumor-type–specific or tumor-staging-matched contexts to validate these findings. Fourthly, while our data suggests immune activation may contribute to AHRF, the absence of biomarker measurements (e.g., IL-6, CD8 + T cells) limits mechanistic conclusions. Future studies should correlate immunological profiles with AHRF risk. Fifthly, our male-predominant, Chinese cohort may limit generalizability across sexes and regions. Despite adjustment for sex, potential sex-specific and regional differences warrant external validation in multi-center, multi-ethnic, more female-enriched cohorts. Conclusion nICT was independently associated with increased postoperative AHRF risk compared to nCT in thoracic oncology patients, necessitating rigorous perioperative monitoring. Notably, Patients who received preoperative immunotherapy within 42 days, SCC, and individuals with non-pCR thoracic malignancies, and patients with NSCLC who are younger than 65 years represented subgroups with a significantly increased risk of AHRF following nICT. Abbreviations AHRF, acute hypoxemic respiratory failure; ALB, serum albumin; BMI, body mass index; FEV1, forced expiratory volume in one second; FEV1%pre, FEV1% predicted; FVC, forced vital capacity; HFNC, high-flow nasal cannula; ICIs, immune checkpoint inhibitors; ICU, intensive care unit; IQR, interquartile range; KNN, K-nearest neighbor MIS, minimally invasive surgery; nCT, neoadjuvant chemotherapy; nICT, neoadjuvant immunochemotherapy; NSCLC, non-small cell lung cancer; pCR, pathological complete response; SCC, squamous cell carcinoma; trAE, treatment-related adverse event; TTS, time interval between neoadjuvant therapy and surgery Declarations CONFLICTS OF INTEREST The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none was reported. FUNDING This study was supported by the National Natural Science Foundation of China (No. 82302449), the Guangxi Natural Science Foundation (No. 2024GXNSFAA010047), the Scientific Research Launch Project for New Employees of the Second Xiangya Hospital of Central South University, the Health Research Project of Hunan Provincial Health Commission (No. W20243115), the Natural Science Foundation of Hunan Province (No. 2023JJ60081, 2024JJ9204), and the Project of Hunan Provincial Administration of Traditional Chinese Medicine (No. B2023062). ETHICAL APPROVAL This study was approved by the institutional review boards (IRB) of the Second Xiangya Hospital of Central South University (No. LYF2023105) and Guilin Hospital of the Second Xiangya Hospital (No. LLkt-034) in Aug. 2023. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki of the World Medical Association. Given the retrospective nature of the study, the requirement for informed consent was waived by the IRB. Central Picture nICT associated with postoperative AHRF risk after thoracic surgery Central Message nICT is independently associated with a higher risk of postoperative AHRF, particularly in patients with short treatment-to-surgery intervals, squamous histology, and non-pCR tumors. Perspective Statement As nICT becomes standard for resectable thoracic malignancies, attention must extend beyond oncologic efficacy to perioperative safety. This multicenter cohort highlights postoperative acute hypoxemic respiratory failure as a clinically relevant complication, identifies high-risk subgroups, and underscores the need for refined perioperative monitoring and optimized surgical timing. References Xia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl) . 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Pathological complete response to neoadjuvant chemoimmunotherapy correlates with peripheral blood immune cell subsets and metastatic status of mediastinal lymph nodes (N2 lymph nodes) in non-small cell lung cancer. Lung Cancer . Oct 2022;172:43-52. doi:10.1016/j.lungcan.2022.08.002 Ji G, Yang Q, Wang S, et al. Single-cell profiling of response to neoadjuvant chemo-immunotherapy in surgically resectable esophageal squamous cell carcinoma. Genome Med . Apr 2 2024;16(1):49. doi:10.1186/s13073-024-01320-9 Additional Declarations No competing interests reported. Supplementary Files SupplementalFigure1.jpg Supplemental Figure 1. Subgroup analysis of the associations between nICT and postoperative AHRF in NSCLC patients. 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Each stratification was adjusted for all confounders shown in Table 1, except for the stratification factor itself.\u003c/p\u003e","description":"","filename":"Figure2new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8867323/v1/81d1fd8341bceda390ebc1ec.jpg"},{"id":103323939,"identity":"a0a08baf-4a60-413e-8bb6-e3c8b800636e","added_by":"auto","created_at":"2026-02-24 12:25:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":497653,"visible":true,"origin":"","legend":"\u003cp\u003eProposed mechanism linking nICT to postoperative AHRF.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8867323/v1/dfc79bd7e674e67f1b8ea7b6.jpg"},{"id":103979362,"identity":"91a72a9d-fbd4-4df1-90d4-7f2fd29449e3","added_by":"auto","created_at":"2026-03-05 09:12:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2413757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8867323/v1/afc08576-c826-4c84-a507-0b73e4e76205.pdf"},{"id":103323933,"identity":"df46b6e2-efdb-4802-bad9-b0afd40f1cc4","added_by":"auto","created_at":"2026-02-24 12:25:48","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":700993,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Figure 1. Subgroup analysis of the associations between nICT and postoperative AHRF in NSCLC patients.\u003c/p\u003e","description":"","filename":"SupplementalFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8867323/v1/808a2cacc71c4aa08c1386a1.jpg"},{"id":103323936,"identity":"9e7b30ee-eabe-46c0-b136-3fdf485ca747","added_by":"auto","created_at":"2026-02-24 12:25:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34836,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaltables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8867323/v1/c131b4dd6572aa53b856da90.docx"},{"id":103323938,"identity":"005d6d55-91bd-4ad2-924b-2fe2a4ca8b60","added_by":"auto","created_at":"2026-02-24 12:25:49","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":497653,"visible":true,"origin":"","legend":"","description":"","filename":"GA.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8867323/v1/588b05c4be6b8ce42e943e13.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neoadjuvant Immunochemotherapy and Postoperative Acute Hypoxemic Respiratory Failure in Thoracic Surgery: A Multicenter Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-small cell lung cancer (NSCLC), esophageal carcinoma, and mediastinal malignancies constitute the principal indications for thoracic surgical interventions. Lung cancer is the most common malignancy in China \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and worldwide \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, with NSCLC accounting for 80.0% to 85.0% of newly diagnosed lung cancer cases annually \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Neoadjuvant therapy for thoracic malignancies aims to downstage tumors, improve resectability, and enhance survival outcomes \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. While neoadjuvant chemotherapy (nCT) offers a modest benefit over surgery alone, emerging evidence suggests that combining immune checkpoint inhibitors (ICIs) with chemotherapy improves event-free survival (EFS) and pathological response rates \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe impact of neoadjuvant therapy on the development of postoperative AHRF remains controversial. Evidence is limited, and existing studies have reported inconsistent findings \u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. While Siegenthaler et al. observed no significant difference in major respiratory complications between patients undergoing lung resection with or without induction chemotherapy \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, Leo et al. reported a significantly higher incidence of respiratory complications following pneumonectomy in patients who received induction chemotherapy \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs ICIs continue to transform the neoadjuvant treatment, they may introduce new challenges requiring clinical vigilance. In the CheckMate-816 and KEYNOTE-671 trials, postoperative mortality was higher in the nICT group than in the nCT group (1.65\u0026ndash;4.4% vs. 0.75\u0026ndash;2.23%), with fatal pulmonary complications occurring more frequently after nICT (1.1\u0026ndash;2% vs. ~0.5%) \u003csup\u003e13, 14\u003c/sup\u003e. Consistently, pulmonary complications have been one of those major safety concerns across other nICT studies \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In patients with esophageal cancer, the incidence of postoperative pulmonary complications, such as pleural effusion, increased following nICT \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. With the rapid advances of nICT in thoracic neoplasms, perioperative adverse events are receiving increasing attention. Notably, most reports of immunotherapy-related lung toxicity arise from medical oncology cohorts, whereas the potential interaction between neoadjuvant ICIs and postoperative AHRF remains underexplored.\u003c/p\u003e \u003cp\u003eWe conducted a two-center retrospective cohort of patients undergoing thoracic tumor resection to assess whether nICT increases postoperative AHRF risk compared with chemotherapy alone. We hypothesized that nICT is associated with a higher risk of postoperative AHRF.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources and setting\u003c/h2\u003e \u003cp\u003eThis was a two-center, retrospective cohort study. The dissemination of the findings adheres to the guidelines established by the Revised Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery (STROCSS) Guideline \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This study was approved by the institutional review boards (IRB) of the Second Xiangya Hospital of Central South University (No. LYF2023105) and Guilin Hospital of the Second Xiangya Hospital (No. LLkt-034) in Aug. 2023. In addition, this study was registered in the ClinicalTrials.gov (NCT06566053).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eThis retrospective study analyzed medical records from the Second Xiangya Hospital of Central South University and Guilin Hospital of the Second Xiangya Hospital, from December 2017 to June 2023. Eligible patients were adults (\u0026ge;\u0026thinsp;18 years) with pathologically confirmed thoracic malignancies (NSCLC, esophageal carcinoma, or mediastinal tumors) who underwent thoracic surgery following nICT or chemotherapy alone. Patients were excluded if they had incomplete neoadjuvant treatment documentation, concurrent malignancies, preoperative targeted therapy, did not undergo thoracic surgery following admission, received PD-L1 inhibitors, interstitial pneumonia history, grade\u0026thinsp;\u0026ge;\u0026thinsp;3 treatment-related adverse events (trAEs), pulmonary embolism, or perioperative heart failure. All patients received care from a multidisciplinary thoracic oncology team. Both centers implemented thoracic enhanced recovery after surgery (ERAS) in mid-2020 (Center A: June 2020; Center B: August 2020), and perioperative management criteria adhered to comparable institutional stand AHRF. The study flowchart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData extraction\u003c/h3\u003e\n\u003cp\u003eClinical data were collected from medical records, including age, gender, smoking history, alcohol history, comorbidities, disease type, preoperative body mass index (BMI), preoperative serum albumin (ALB), preoperative creatinine, pulmonary function, neoadjuvant cycle number, time interval between neoadjuvant therapy and surgery (TTS), surgical approach, pathology type, and pathological response. In lung cancer patients, the extent of resection was also collected.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the rate of postoperative AHRF. Postoperative AHRF was defined as the acute onset of hypoxemia within one week postoperatively, indicated by a ratio of arterial partial pressure of oxygen to inspired oxygen fraction (PaO₂/FiO₂)\u0026thinsp;\u0026le;\u0026thinsp;200 mmHg, requiring high-flow nasal cannula (HFNC), noninvasive positive pressure ventilation, or invasive mechanical ventilation for a minimum of 24 hours, and exclude fluid overload as the causes \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. We conducted a retrospective chart review with medical records. Each AHRF diagnosis was independently assessed by at least two attending physicians. Secondary outcomes encompassed the length of hospital stay, length of ICU stay, and length of postoperative stay.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normal distributed data, and as median and interquartile range (IQR) for skewed distributions, while categorical variables were presented as frequency (percentage). Continuous variables were compared using Student\u0026rsquo;s t-test when normally distributed and the Wilcoxon rank-sum test otherwise. Pearson\u0026rsquo;s chi-squared test or Fisher\u0026rsquo;s exact test was used for categorical data, as appropriate. Missing data were handled using K-nearest neighbor (KNN) imputation, a widely accepted nonparametric approach \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A sensitivity analysis was performed after excluding patients with missing data to confirm the robustness of the results. Binary and multivariable logistic regression analyses were utilized to explore the association between nICT and the primary and secondary outcomes, using an extended logistic model to adjust for various covariates. To balance baseline characteristics between the nICT and nCT groups, we employed propensity score matching (PSM) with a 1:1 nearest neighbor matching algorithm and a caliper width of 0.2 \u003csup\u003e22\u003c/sup\u003e. All variables listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were used to estimate the propensity score. Covariate balance after matching was assessed using the standardized mean difference (SMD), with SMD\u0026thinsp;\u0026lt;\u0026thinsp;0.10 indicating adequate balance. The primary outcome was further verified using the inverse probability of treatment weighting (IPTW), with estimated propensity scores as weights \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Subgroup analyses were conducted based on relevant covariates. These analyses were executed using R version 3.3.2 and Free Statistics software version 1.9. Statistical significance was determined by a two-tailed test with a p-value threshold of less than 0.05.\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 the included patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eBefore PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAfter PSM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;327)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNICT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNCT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNICT\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.031\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\u003e291 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (86.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e177 (88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e89 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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\u003e36 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e193 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (65.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e30 ( 9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 ( 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertention, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 ( 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 ( 3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease type, 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 \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e132 (66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEsophagus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediastinum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 ( 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 ( 1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u0026sup2;, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.3\u0026thinsp;\u0026plusmn;\u0026thinsp;16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary function\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1/FVC%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1%pre, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.0\u0026thinsp;\u0026plusmn;\u0026thinsp;17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.9\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCycle number*, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTS\u003csup\u003e#\u003c/sup\u003e, days, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.0 (37.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.5 (37.0, 52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.0 (37.0, 59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.0 (37.0, 54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.0 (37.0, 50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.0 (36.8, 56.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.089\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 \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.045\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\u003e95 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (78.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144 (72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology type, 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 \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e141 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-SCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eALB, serum albumin; BMI, body mass index; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; FEV1%pre, FEV1% predicted; MIS, minimally invasive surgery; nCT, neoadjuvant chemotherapy; nICT, neoadjuvant immunochemotherapy; SCC, squamous cell carcinoma\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e* Cycle number refers to neoadjuvant cycles administered prior to surgery: in the chemoimmunotherapy group this denotes combined concurrent cycles of chemotherapy\u0026thinsp;+\u0026thinsp;PD-1 inhibitor; in the chemotherapy group, chemotherapy cycles only.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e#\u003c/sup\u003e TTS refers to calendar days from the last neoadjuvant treatment to the date of surgery\u0026mdash;for the chemoimmunotherapy group, the last PD-1 inhibitor dose; for the chemotherapy group, the last chemotherapy cycle.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e$\u003c/sup\u003eThe distribution of PD-1 agents was: pembrolizumab (n\u0026thinsp;=\u0026thinsp;62), tislelizumab (n\u0026thinsp;=\u0026thinsp;43), sintilimab (n\u0026thinsp;=\u0026thinsp;28), camrelizumab (n\u0026thinsp;=\u0026thinsp;16), nivolumab (n\u0026thinsp;=\u0026thinsp;8), and toripalimab (n\u0026thinsp;=\u0026thinsp;10).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics\u003c/p\u003e \u003cp\u003eA total of 327 patients with thoracic cancers were finally included in our analysis, comprising 167 patients (51.1%) with a history of nICT and 160 (48.9%) with a history of nCT (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After PSM, 200 patients were successfully matched. The mean age at surgery was 57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 years, with the vast majority (89.0%) being male. The disease type distribution included lung (209 patients, 63.9%), esophagus (99 patients, 30.3%), and mediastinum (19 patients, 5.8%) cancers. Patients who received nICT predominantly had lung cancer, higher smoking and alcohol history, higher serum albumin (ALB) and creatinine levels before surgery, lower FEV1% per, less open surgery compared to those receiving nCT. The baseline characteristics of the two groups after PSM were balanced (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrimary outcome\u003c/p\u003e \u003cp\u003eThe total postoperative AHRF rate within the cohort was 14.1% (Supplemental Table\u0026nbsp;1). Among thoracic cancer patients, those with a history of nICT had a significantly higher postoperative AHRF rate of 19.8% compared to that of 8.1% for those with a history of nCT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Initial univariable logistic regression analysis yielded an odds ratio (OR) of 2.78 (95% Confidence Interval [CI]: 1.41\u0026ndash;5.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Upon adjusting for clinically pertinent covariates, OR was still as high as 2.5 (95% CI: 1.13\u0026ndash;5.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) across multiple extended multivariable logistic regression models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar to the results in the pre-matched cohort, both PSM (OR\u0026thinsp;=\u0026thinsp;5.22, 95% CI: 2.04\u0026ndash;13.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and IPTW (OR\u0026thinsp;=\u0026thinsp;2.41, 95% CI: 1.2\u0026ndash;4.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) indicated that neoadjuvant immunotherapy exposure was independently associated with an increased postoperative AHRF rate.\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\u003eAssociation between neoadjuvant immunotherapy and postoperative AHRF rate using PSM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmatched crude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.78 (1.41\u0026thinsp;~\u0026thinsp;5.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariable adjusted\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.5 (1.13\u0026thinsp;~\u0026thinsp;5.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropensity Score matched\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.22 (2.04\u0026thinsp;~\u0026thinsp;13.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropensity Score adjusted\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.34 (1.1\u0026thinsp;~\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted IPTW\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.41 (1.2\u0026thinsp;~\u0026thinsp;4.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ea, odds ratio from the multivariable logistic proportional model adjusted for all covariates (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). b, odds ratio from a multivariate logistic proportional hazards model with the same strata and covariates matched according to the propensity score. The analysis included 116 patients (58 who received nCT and 58 who received nICT). c, odds ratio from a multivariable logistic proportional hazards model with the same strata and covariates, with additional adjustment for the propensity score. d, Primary analysis with a hazard ratio from the multivariable logistic proportional hazards model with the same strata and covariates with inverse probability weighting according to the propensity score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSecondary outcomes\u003c/p\u003e \u003cp\u003eThe secondary outcomes assessed included length of hospital stay, ICU stay, and postoperative stay, as presented in Supplemental Table\u0026nbsp;1. Among patients with a history of nICT, the length of hospital stay was shorter compared to those who had undergone nCT. In the propensity score\u0026ndash;matched cohort, there were no statistically significant differences in overall hospital length of stay, ICU length of stay, or postoperative length of stay between patients receiving nICT and those receiving chemotherapy alone. Multivariable logistic regression analysis for all secondary outcomes was detailed in Supplemental Table\u0026nbsp;2. Upon thorough model adjustment, we observed no significant differences in secondary outcomes among patients with a history of nICT compared to nCT group, including length of hospital stay, length of ICU stay, or length of postoperative stay.\u003c/p\u003e \u003cp\u003eSubgroup analyses\u003c/p\u003e \u003cp\u003eSubgroup analyses, accounting for various confounders, consistently demonstrated that, compared to nCT, patients who received their final chemoimmunotherapy within 42 days prior to thoracic surgery had a relatively higher risk of postoperative AHRF (OR\u0026thinsp;=\u0026thinsp;6.68, 95% CI: 1.24\u0026ndash;35.98) than those with an interval of \u0026ge;\u0026thinsp;42 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, squamous cell carcinoma (SCC) patients and non-pCR thoracic cancer cases constituted a significantly high-risk subgroup for postoperative AHRF (OR\u0026thinsp;=\u0026thinsp;3.64, 95% CI: 1.41\u0026ndash;9.44; OR\u0026thinsp;=\u0026thinsp;2.82, 95% CI: 1.14\u0026ndash;6.98; respectively). No significant interactions were observed between subgroups (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFactorial analysis\u003c/p\u003e \u003cp\u003eWe further compared patients with (n\u0026thinsp;=\u0026thinsp;46) and without AHRF (n\u0026thinsp;=\u0026thinsp;281) with the aim of identifying factors related to postoperative AHRF in patients undergoing thoracic surgeries. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, patients who developed postoperative AHRF had a higher history of receiving nICT, and higher rate of MIS surgery. The cycle number and TTS between the last therapy and surgery were not significantly different between the two groups. Patients experiencing postoperative AHRF typically had longer length of hospital stay, longer length of ICU stay, and longer length of postoperative stay (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to patients without AHRF.\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\u003eCharacteristics of postoperative AHRF and non-AHRF patients\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;327)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-AHRF\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;281)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAHRF\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e 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, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.119\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\u003e291 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (95.7)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.3)\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\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (59.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (56.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\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\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 ( 9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertention, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 ( 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease type, 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 \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (65.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (56.5)\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\u003eEsophagus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (39.1)\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\u003eMediastinum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 ( 5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.3)\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\u003eBMI, kg/m\u0026sup2;, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB, g/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mmol/L, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.2\u0026thinsp;\u0026plusmn;\u0026thinsp;18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary function\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\u003eFEV1/FVC%, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.9\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1%pre, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enICT, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCycle number, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTS, days, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.0 (37.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0 (37.0, 55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.0 (37.2, 55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.535\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 \u003cp\u003e0.026\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\u003e95 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (15.2)\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\u003eMIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (68.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (84.8)\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\u003ePathology type, 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 \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (80.4)\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\u003eNon-SCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (19.6)\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\u003epCR, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcomes\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\u003eLength of hospital stay, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of ICU stay, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.0, 3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (3.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of postoperative stay, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (4.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0 (7.0, 10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAHRF, acute hypoxemic respiratory failure; ALB, serum albumin; BMI, body mass index; FVC, forced vital capacity; FEV1, forced expiratory volume in one second; FEV1%pre, FEV1% predicted; MIS, minimally invasive surgery; nICT, neoadjuvant immunochemotherapy; SCC, squamous cell carcinoma; TTS, time interval between neoadjuvant therapy and surgery; pCR, pathological complete response\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalysis of NSCLC patients\u003c/p\u003e \u003cp\u003eBecause NSCLC patients constituted the majority of participants, we further conducted a sensitivity analysis in NSCLC patients. The baseline characteristics and outcomes of these patients are shown in Supplemental Table\u0026nbsp;3. A total of 209 patients with NSCLC were enrolled, of whom 128 (61.2%) had a history of nICT, and 81 (38.8%) had a history of nCT. The mean age at surgery was 58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 years, with the vast majority (89%) being male. Patients with NSCLC who received nICT were older, had more alcohol history, higher creatinine levels before surgery, longer TTS, more MIS, and higher postoperative AHRF rate compared to those receiving nCT (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Variables which exhibited a \u003cem\u003ep\u003c/em\u003e-value of less than 0.1 in univariate analyses were chosen for multivariate adjustment (Supplemental Table\u0026nbsp;4). The binary logistic regression analysis showed that the history of nICT remained associated with postoperative AHRF (OR\u0026thinsp;=\u0026thinsp;4.12, 95% CI: 1.15\u0026ndash;14.8, p\u0026thinsp;=\u0026thinsp;0.03) in patients with NSCLC (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, for the secondary outcomes, including length of hospital stay, length of ICU stay, and length of postoperative stay, no significant association between a history of nICT and the secondary outcome were observed in patients with NSCLC.\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\u003eThe relationship between neoadjuvant immunotherapy and outcomes in patients with NSCLC\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude Coefficient (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted Coefficient (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Outcome\u003c/b\u003e\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\u003eAHRF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.7 (1.65\u0026thinsp;~\u0026thinsp;19.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.12 (1.15\u0026thinsp;~\u0026thinsp;14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecondary Outcomes\u003c/b\u003e\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\u003eLength of hospital stay, d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.48 (-1.97\u0026thinsp;~\u0026thinsp;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.3 (-1.87\u0026thinsp;~\u0026thinsp;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of ICU stay, d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (-0.12\u0026thinsp;~\u0026thinsp;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43 (-0.26\u0026thinsp;~\u0026thinsp;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of postoperative stay, d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 (-0.39\u0026thinsp;~\u0026thinsp;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6 (-0.51\u0026thinsp;~\u0026thinsp;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAHRF, acute hypoxemic respiratory failure; ICU, intensive care unit\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAdjust variables from univariate analyses where the \u003cem\u003ep\u003c/em\u003e-value was less than 0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSupplemental Fig.\u0026nbsp;1. Subgroup analysis of the associations between nICT and postoperative AHRF in NSCLC patients.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAn increased risk of AHRF associated with nICT exposure in patients with NSCLC was consistently observed across key subgroups, including those younger than 65 years, those with an interval of less than 42 days between the last dose of neoadjuvant immunotherapy and surgery, and those with SCC (Supplemental Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eSensitive analysis\u003c/p\u003e \u003cp\u003eApproximately 5% of relevant variables in the dataset were missing, which were pulmonary function variables. Patients with incomplete data were excluded from both the overall cohort and the NSCLC subgroup. A sensitivity analysis was subsequently performed. Consistently, nICT was significantly associated with an increased risk of postoperative AHRF in both the overall cohort and the NSCLC subgroup after exclusion of missing data (OR\u0026thinsp;=\u0026thinsp;2.67, 95% CI: 1.16\u0026ndash;6.13, p\u0026thinsp;=\u0026thinsp;0.021; OR\u0026thinsp;=\u0026thinsp;6.08, 95% CI: 1.33\u0026ndash;27.8, p\u0026thinsp;=\u0026thinsp;0.02, respectively; Supplemental Table\u0026nbsp;5). No statistically significant differences were observed between groups for any of the secondary outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort study revealed that patients with thoracic neoplasms receiving nICT had significantly higher postoperative AHRF rates than those receiving nCT alone, a difference persisting before and after PSM. Similar patterns emerged in NSCLC surgical patients, where chemoimmunotherapy were associated with elevated postoperative AHRF incidence. Moreover, subgroup analyses revealed elevated AHRF risks with nICT versus chemotherapy alone in patients receiving preoperative immunotherapy within 42 days, SCC, and non-pCR thoracic cancer cases. Within NSCLC, age\u0026thinsp;\u0026lt;\u0026thinsp;65 years further delineated a high-risk subgroup associated with increased risk of postoperative ARDS after nICT.\u003c/p\u003e \u003cp\u003eOver the past 5 years, more than 10 randomized controlled trials (RCTs) have investigated the impact of neoadjuvant immunotherapy or chemoimmunotherapy on prognosis \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. As ICIs continue to transform the neoadjuvant treatment, the investigation of perioperative complications such as AHRF is both timely and highly relevant. Perioperative trials such as CheckMate-816 and KEYNOTE-671 primarily report mortality rather than AHRF. In our study, mortality was 1/327 (0.3%, date not shown), whereas the nICT arms of CheckMate-816 and KEYNOTE-671 reported 5/179 and 4/97 deaths \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, respectively, including two pulmonary-related deaths in each trial. This indicates a high level of surgical expertise and perioperative management across the participating centers in this study. In real-world studies, perioperative complications remain a key issue for thoracic surgeons. These differences likely reflect endpoint definition and trial versus real-world selection, underscoring the complementary nature of our AHRF-focused analysis.\u003c/p\u003e \u003cp\u003ePostoperative hypoxemic respiratory failure is variably defined across studies, which likely contributes to the wide range of reported incidences. In a 211-patient NSCLC cohort undergoing thoracic surgery after neoadjuvant chemotherapy, hARF was defined by PaO₂/FiO₂ \u0026le;200 mmHg requiring noninvasive positive pressure ventilation or invasive mechanical ventilation for \u0026ge;\u0026thinsp;24 h, yielding an incidence of 5.2% \u003csup\u003e7\u003c/sup\u003e. In a minimally invasive esophagectomy cohort, postoperative respiratory failure was defined using a more stringent endpoint\u0026mdash;unplanned reintubation or tracheostomy, or delayed extubation (\u0026ge;\u0026thinsp;48 h)\u0026mdash;resulting in a reported incidence of 2.4% \u003csup\u003e39\u003c/sup\u003e. Some studies have identified postoperative respiratory failure using discharge diagnosis codes from administrative databases, an approach that may lead to underascertainment of true positive cases \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In contrast, in our study, all cases were retrospectively adjudicated by two independent physicians based on detailed clinical records. This strategy allows for more accurate capture of clinically relevant events, reduces misclassification bias, and ensures greater diagnostic consistency, particularly for cases that may not be fully reflected by diagnostic coding alone.\u003c/p\u003e \u003cp\u003eIn this study, the incidence of AHRF in the nCT group was 8.1%, which reduced to 6% after PSM. Adoption of the updated AHRF definition, which included patients receiving HFNC oxygen therapy \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, was one reason for elevated total AHRF rates in our study. While prior studies identified advanced age, male sex, and impaired pulmonary function as risk factors for postoperative respiratory failure \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, our analysis revealed a markedly higher AHRF incidence of 19.8% in thoracic tumor patients receiving nICT. Both IPTW and multivariate regression analyses confirmed a significant association between nICT and increased postoperative AHRF risk. Postoperative stress and compromised innate and adaptive immune responses may be potential factors contributing to perioperative complications \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Crucially, this comprehensive evaluation of thoracic neoadjuvant therapies identified immunotherapy itself as the predominant independent risk factor for postoperative AHRF, though its specific pathophysiological mechanisms warrant further investigation.\u003c/p\u003e \u003cp\u003eExisting studies have demonstrated that postoperative AHRF increases mortality and hospital stay duration \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Intriguingly, our findings revealed that patients who received nICT had a significantly higher incidence of AHRF, but markedly shorter hospital length of stay compared to the nCT group. This temporal disparity likely reflects the study's retrospective design, with chemoimmunotherapy implemented more recently compared to earlier chemotherapy cases. Advancing perioperative care, including ERAS \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e protocols and surgical expertise, contributed to both reduced hospitalization in later period chemoimmunotherapy patients. The higher MIS proportion among AHRF cases likely reflects calendar-time, as chemoimmunotherapy\u0026mdash;where AHRF clustered\u0026mdash;was performed during periods of greater MIS uptake and ERAS implementation. After adjusting for surgical approach (and extent of resection in NSCLC), the nICT\u0026ndash;AHRF association remained higher than nCT. The persistently elevated AHRF incidence in the nICT group despite these improvements suggests an intrinsic association between immunotherapy and postoperative AHRF in thoracic oncology.\u003c/p\u003e \u003cp\u003eFactorial and subgroup analyses revealed no association between postoperative AHRF incidence and neoadjuvant therapy cycles. While prior studies report 1 to 4 neoadjuvant immunotherapy exposure cycles (typically 3 to 4 cycles) \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, our subgroup analysis similarly demonstrated no cycle-dependent AHRF risk variation. However, intervals less than 42 days between last immunotherapy and surgery correlated with higher AHRF incidence, suggesting a 6-week minimum preoperative window. Current literature on nICT presents limited data concerning the interval between last therapy and surgery, with durations reported as 2 to 6 weeks \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Interestingly, subgroup analysis revealed that non-pCR patients exhibited increased AHRF risk. These findings emphasize the need to balance immunotherapy efficacy against its perioperative complication profile, particularly regarding treatment-surgery intervals and tumor response patterns.\u003c/p\u003e \u003cp\u003eSubgroup analyses identified consistently elevated postoperative AHRF risk in patients those with TTS\u0026thinsp;\u0026lt;\u0026thinsp;42 days, SCC, and non-pCR cases receiving chemoimmunotherapy. In the NSCLC population, age below 65 years is also considered a high-risk subgroup. These signals may inform preoperative risk assessment. Although immune biomarkers (e.g., PD-L1 expression, circulating cytokines) were not available in our dataset now, they are promising candidates to refine risk prediction and warrant prospective evaluation. For patients at elevated risk, individualized surgical timing, conservative fluid management, and lung-protective ventilation should be prioritized perioperatively \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, whereas routine prophylactic corticosteroids are not supported by current evidence. Clinically, AHRF should remain on the differential for early postoperative hypoxemia even when overt infection or cardiogenic fluid overload is absent. These considerations enhance the patient-care relevance of our findings and outline directions for prospective, biomarker-informed optimization of perioperative management in the setting of nICT.\u003c/p\u003e \u003cp\u003eThe underlying mechanism may involve PD-1 inhibitor-mediated preoperative immune activation induces bystander lung injury via immune checkpoints derepression, CD8⁺ effector T cells activation, and elevated secretion of pro-inflammatory cytokines such as IL-2 and CXCL10 \u003csup\u003e48\u0026ndash;50\u003c/sup\u003e. Surgical stressors, including mechanical ventilation, ischemia, amplify pulmonary infiltration of activated T-cells and inflammatory mediators, triggering systemic inflammatory response syndrome (SIRS) and compounding AHRF risk \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. This hypothesis-generating dual-hit model posits that preoperative PD-1 blockade primes pulmonary immune responses (the \u0026ldquo;first hit\u0026rdquo;), and surgical trauma amplifies this activation (the \u0026ldquo;second hit\u0026rdquo;), potentially precipitating AHRF (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStudies show elderly patients (\u0026ge;\u0026thinsp;65 years) receiving ICIs exhibit lower na\u0026iuml;ve cytotoxic (TcN) and helper T cells (ThN), B cells, and double-negative T cells (DNT), with attenuated cytokine responses and upregulated PD-1 expression \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, potentially mitigating systemic inflammation and thus lowering their susceptibility to AHRF. Shorter immunotherapy-to-surgery intervals (\u0026lt;\u0026thinsp;42 days) correlated with higher AHRF rates, likely reflecting residual PD-1 monoclonal antibodies activity from prolonged half-life \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Thus, these patients might still be in an actively heightened or subclinically inflamed immunologic state during surgery. Additionally, Non-pCR patients showed elevated AHRF risk despite residual tumors, possibly associated with elevated baseline T-lymphocyte levels and immune activation-related receptors \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, thereby predisposing them to exaggerated postoperative inflammatory cytokine release and enhanced AHRF susceptibility. Combined mechanisms, including preoperative immune activation, inflammatory mediator accumulation, lymphocyte subset imbalance, and distinctive immune profiles in certain subgroups (shorter therapeutic interval, SCC, non-pCR, and patients with NSCLC who are younger than 65 years), contribute to increased AHRF risk following nICT.\u003c/p\u003e \u003cp\u003eOur study has several strengths. The findings are significant for perioperative practice. The retrospective two-center design encompassing a relatively large sample size with application of multiple statistical methodologies, reflects considerable effort to reduce confounding in observational data. Furthermore, the inclusion of sensitivity analyses, subgroup analyses, and a focused assessment on NSCLC subpopulations strengthens the manuscript\u0026rsquo;s internal validity.\u003c/p\u003e \u003cp\u003eHowever, several limitations warrant consideration. Firstly, its retrospective nature introduces potential selection and ascertainment biases. Secondly, this was a two-center study with a modest sample size, limiting the generalizability of the results. Thirdly, this study included patients with NSCLC, esophageal cancer, and malignant mediastinal tumors. While we pooled thoracic tumors in the main analysis, we justified this by their shared exposure to ICIs, common surgical setting, and AHRF as a unified inflammatory endpoint. Although separate analysis within the NSCLC patient cohort yielded consistent results, the inclusion of patients with NSCLC, esophageal carcinoma, and mediastinal malignancies in the overall study population may constitute a potential source of heterogeneity. Further studies are needed within disease-specific or tumor-type\u0026ndash;specific or tumor-staging-matched contexts to validate these findings. Fourthly, while our data suggests immune activation may contribute to AHRF, the absence of biomarker measurements (e.g., IL-6, CD8\u0026thinsp;+\u0026thinsp;T cells) limits mechanistic conclusions. Future studies should correlate immunological profiles with AHRF risk. Fifthly, our male-predominant, Chinese cohort may limit generalizability across sexes and regions. Despite adjustment for sex, potential sex-specific and regional differences warrant external validation in multi-center, multi-ethnic, more female-enriched cohorts.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003enICT was independently associated with increased postoperative AHRF risk compared to nCT in thoracic oncology patients, necessitating rigorous perioperative monitoring. Notably, Patients who received preoperative immunotherapy within 42 days, SCC, and individuals with non-pCR thoracic malignancies, and patients with NSCLC who are younger than 65 years represented subgroups with a significantly increased risk of AHRF following nICT.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAHRF, acute hypoxemic respiratory failure;\u003c/p\u003e\n\u003cp\u003eALB, serum albumin;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI, body mass index;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFEV1, forced expiratory volume in one second;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFEV1%pre, FEV1% predicted;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFVC, forced vital capacity;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHFNC, high-flow nasal cannula;\u003c/p\u003e\n\u003cp\u003eICIs, immune checkpoint inhibitors;\u003c/p\u003e\n\u003cp\u003eICU, intensive care unit;\u003c/p\u003e\n\u003cp\u003eIQR, interquartile range;\u003c/p\u003e\n\u003cp\u003eKNN, K-nearest neighbor\u003c/p\u003e\n\u003cp\u003eMIS, minimally invasive surgery;\u003c/p\u003e\n\u003cp\u003enCT, neoadjuvant chemotherapy;\u003c/p\u003e\n\u003cp\u003enICT, neoadjuvant immunochemotherapy;\u003c/p\u003e\n\u003cp\u003eNSCLC, non-small cell lung cancer;\u003c/p\u003e\n\u003cp\u003epCR, pathological complete response;\u003c/p\u003e\n\u003cp\u003eSCC, squamous cell carcinoma;\u003c/p\u003e\n\u003cp\u003etrAE, treatment-related adverse event;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTTS, time interval between neoadjuvant therapy and surgery\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest, and none was reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 82302449), the Guangxi Natural Science Foundation (No. 2024GXNSFAA010047), the Scientific Research Launch Project for New Employees of the Second Xiangya Hospital of Central South University, the Health Research Project of Hunan Provincial Health Commission (No. W20243115), the Natural Science Foundation of Hunan Province (No. 2023JJ60081, 2024JJ9204), and the Project of Hunan Provincial Administration of Traditional Chinese Medicine (No. B2023062).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL APPROVAL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional review boards (IRB) of the Second Xiangya Hospital of Central South University (No. LYF2023105) and Guilin Hospital of the Second Xiangya Hospital (No. LLkt-034) in Aug. 2023. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki of the World Medical Association. Given the retrospective nature of the study, the requirement for informed consent was waived by the IRB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCentral Picture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enICT associated with postoperative AHRF risk after thoracic surgery\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCentral Message\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enICT is independently associated with a higher risk of postoperative AHRF, particularly in patients with short treatment-to-surgery intervals, squamous histology, and non-pCR tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerspective Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs nICT becomes standard for resectable thoracic malignancies, attention must extend beyond oncologic efficacy to perioperative safety. This multicenter cohort highlights postoperative acute hypoxemic respiratory failure as a clinically relevant complication, identifies high-risk subgroups, and underscores the need for refined perioperative monitoring and optimized surgical timing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. \u003cem\u003eChin Med J (Engl)\u003c/em\u003e. Feb 9 2022;135(5):584-590. doi:10.1097/CM9.0000000000002108\u003c/li\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. Jan-Feb 2024;74(1):12-49. doi:10.3322/caac.21820\u003c/li\u003e\n\u003cli\u003eGoldstraw P, Chansky K, Crowley J, et al. 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Apr 2 2024;16(1):49. doi:10.1186/s13073-024-01320-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"neoadjuvant chemotherapy, neoadjuvant immunochemotherapy, acute hypoxemic respiratory failure, thoracic surgery, non-small cell lung cancer","lastPublishedDoi":"10.21203/rs.3.rs-8867323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8867323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: While immune checkpoint inhibitors (ICIs) continue to transform the neoadjuvant treatment, its association with postoperative acute hypoxemic respiratory failure (AHRF) remains unexplored. This study aimed to assess the association between neoadjuvant immunochemotherapy (nICT) and postoperative AHRF risk following thoracic tumor surgeries and identify the risk subgroups.\u003c/p\u003e\n\u003cp\u003eMethods: This retrospective two-center cohort study included 327 patients receiving nICT (n=167) or nCT (n=160) before thoracic tumor surgeries from December 2017 to June 2023. Data were analyzed by using the propensity score matching (PSM) and multivariable logistic regressions. Subgroup and sensitivity analyses were performed to test the stability of the conclusions.\u003c/p\u003e\n\u003cp\u003eResults: The nICT group demonstrated significantly higher postoperative AHRF incidence than the nCT group (19.8% vs. 8.1%, p=0.002). The inverse probability-weighting model (IPTW) confirmed elevated AHRF risk associated with nICT compared to nCT (OR=2.41, 95% CI: 1.2-4.82). \u0026nbsp;In patients with non-small cell lung cancer (NSCLC), the binary logistic regression analysis showed that the history of nICT was significantly associated with postoperative AHRF (OR=4.12, 95% CI: 1.15-14.8) in patients with non-small cell lung cancer (NSCLC). Subgroup analyses revealed elevated AHRF risks with nICT versus nCT in patients with time interval between neoadjuvant therapy and surgery within 42 days (OR=6.68, 95% CI: 1.24-35.98), those with squamous cell carcinoma (SCC) (OR=3.64, 95% CI: 1.41-9.44), and those who did not achieve pathologic complete response (non-pCR) \u0026nbsp;(OR=2.82, 95% CI: 1.14-6.98).\u003c/p\u003e\n\u003cp\u003eConclusions: nICT was associated with increased postoperative AHRF risk in thoracic surgical patients, necessitating rigorous perioperative monitoring.\u003c/p\u003e","manuscriptTitle":"Neoadjuvant Immunochemotherapy and Postoperative Acute Hypoxemic Respiratory Failure in Thoracic Surgery: A Multicenter Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 12:24:50","doi":"10.21203/rs.3.rs-8867323/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":"09381dcd-0866-4162-8620-760c5b25ab22","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63325912,"name":"Biological sciences/Cancer"},{"id":63325913,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-05T09:11:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 12:24:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8867323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8867323","identity":"rs-8867323","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}