Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study

Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective 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 Research Article Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study Peigen Gao, Chongwu Li, Junqi Wu, Ye Ning, Pei Zhang, Xiucheng Liu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2089786/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2023 Read the published version in BMC Pulmonary Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Background Prolonged mechanical ventilation (PMV), mostly defined as mechanical ventilation > 72 hours after lung transplantation (LuTx) with or without tracheostomy, is associated with increased mortality. Nevertheless, the predictive factors of PMV after LuTx remain unclear. The present study aimed to develop a novel scoring system to identify PMV after LuTx. Methods A total of 141 patients who underwent lung transplantation were investigated in this study. The patients were divided into PMV and non-prolonged ventilation (NPMV) groups. Univariate and multivariate logistic regression analyses were performed to assess factors associated with PMV. A risk nomogram was then established based on the multivariate analysis, and model performance was further examined regarding its calibration, discrimination, and clinical usefulness. Results Eight factors were finally identified to be significantly associated with PMV by the multivariate analysis and therefore were included as risk factors in the nomogram as follows: the body mass index (BMI, P = 0.036);primary diagnosis as idiopathic pulmonary fibrosis (IPF, P = 0.038); pulmonary hypertension (PAH, P = 0.034); primary graft dysfunction grading (PGD, P = 0.011) at T 0 ; cold ischemia time (CIT P = 0.012); and three ventilation parameters (peak inspiratory pressure [PIP, P < 0.001], dynamic compliance [Cdyn, P = 0.001], and P/F ratio [ P = 0.015] ) at T 0 . The nomogram exhibited superior discrimination ability with an area under the curve (AUC) of 0.895. Furthermore, both calibration curve and decision-curve analysis (DCA) indicated satisfactory performance. Conclusion A novel nomogram to predict individual risk of receiving PMV for patients after LuTx was established, which may guide preventative measures for tackling this adverse event. prolonged mechanical ventilation cold ischemia time primary graft dysfunction ventilation parameters prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background More than 4,000 lung transplantations (LuTx) are currently performed worldwide per year [ 1 ]. However, the mortality and morbidity of LuTx remain at relatively high levels compared with other solid organ transplantations [2] . Prolonged mechanical ventilation (PMV) is a prognostic marker for short-term adverse outcomes in patients after LuTx [ 3 , 4 ]. Previous reports also show that PMV is associated with impaired long-term survival[ 5 ]. Thus, the discovery of predictors for PMV may assist in developing precautionary measures to ameliorate the high morbidity and mortality. Primary graft dysfunction (PGD) is a form of acute lung injury that occurs in about 30% of patients after LuTx within 72 hours, which can be characterized by hypoxemia and alveolar infiltrates in the allograft(s)[ 6 ]. PGD is reported as the most frequent cause of early death after lung transplantation and is also strongly correlated with other late outcomes[ 7 – 9 ]. Although the presence of PGD is associated with an increased duration of mechanical ventilation[ 9 ], recently, Schwarz and colleagues found that the value of PGD in predicting PMV was limited[ 3 ]. Instead, a model combing three ventilation parameters better predicted PMV. However, the predictive value of this model was still moderate, with an area under the curve (AUC) of 0.727[ 3 ]. Thus, a more precise model is urgently required. Apart from PGD and ventilation parameters, other factors, such as cold ischemia time (CIT), may also provide valuable information for predicting PMV, and CIT is closely associated with ischemia-reperfusion injury (IRI)[ 9 , 10 ]. A recent study revealed that CIT is a risk factor for developing airway complications after lung transplantation[ 11 ], even though the correlation between CIT and PMV remains unknown. According to the latest guidelines, idiopathic pulmonary fibrosis (IPF) and idiopathic pulmonary arterial hypertension (IPAH) are strongly associated with risk for PGD in primary diagnoses[ 9 ]. However, to date, no research has considered the role of different primary diagnoses in predicting the early adverse events after LuTx except PGD. Therefore, our research aimed to develop a nomogram combing clinical variables (including primary diagnosis), CIT, PGD grading, and ventilation parameters to improve the predictive accuracy of PMV. 2. Methods 2.1. Study design and participants With the Research Ethics Commission of Shanghai Pulmonary Hospital (Shanghai, China) approval (No. L20-352), we conducted a single-center, retrospective observational cohort study. Data from 146 patients who underwent lung transplantation at Shanghai Pulmonary Hospital were retrospectively extracted from electronic medical records between January 1, 2018, and February 1, 2022. The exclusion criteria included: patients with missing data; re-transplantation; postoperatively extended extracorporeal membrane oxygenation (ECMO) with clear chest radiographs (PGD ungradable)[ 6 ]. The use of postoperative extended ECMO was defined as the use of ECMO or re-use of ECMO to maintain life after arriving in the intensive care unit (ICU) after surgery[ 12 ]. According to the exclusion criteria, 141 patients who underwent LuTx were included in our study cohort ( Figure S1 ). 2.2. Data acquisition The baseline characteristics and demographics of the patients (age, gender, BMI, smoking history, and documented pulmonary hypertension), primary diagnosis before the operation, the CIT, and length of mechanical ventilation (LMV) were retrospectively collected from the medical case database of Shanghai Pulmonary Hospital. In this study, LMV was classified as PMV or non-prolonged mechanical ventilation (NPMV), with the threshold to be LMV > 72h. The extubation criteria were as follows in our clinical practice as modified from the consensus on weaning from mechanical ventilation[ 13 ]: (1) successful spontaneous breathing trial lasting for 120 minutes; (2) hemodynamic stability; (3) No sedation or adequate mentation on sedation; (4) P/F ratio > 150 mm Hg with FiO 2 ≤ 0.4, positive end-expiratory pressure ≤ 8 cm H 2 O. Patients who were extubated but needed reintubation within 72 hours after LuTx were also included in the PMV group. The ventilation parameters of T 0 , T 24 , T 48 , and T 72 were also obtained. T 0 , T 24 , T 48 , and T 72 were defined as the 2nd, 24th, 48th, and 72nd hours after the arrival at the ICU after transplantation, respectively. The ventilation parameters mainly included inhaled oxygen concentration fraction, arterial oxygen partial pressure, tidal volume (TV), peak inspiratory pressure (PIP), and positive end-expiratory pressure (PEEP). Dynamic compliance was calculated as tidal volume/(peak inspiratory pressure-positive end-expiratory pressure), while partial pressure of the oxygen fraction of inspired oxygen (P/F) ratio was calculated as arterial oxygen partial pressure (PaO 2 )/inhaled oxygen concentration fraction (FiO 2 ). The PGD diagnosis method in this study refers to the standard judgment of the International Society for Heart and Lung Transplantation (ISHLT) on PGD in 2016[ 6 ]. Notably, patients receiving mechanical ventilation with FiO 2 > 0.5 on nitric oxide > 48 hours from lung transplant or using extracorporeal lung support (ECLS) with bilateral pulmonary edema on chest X-ray, which indicated ECLS is primarily hypoxemia, were classified as grade 3. In addition, using atomized prostacyclin or other drugs that may improve oxygenation did not affect PGD classification[ 14 ]. 2.3. Statistical analysis The categorical variables were summarized as the absolute frequency and percentage, while the continuous variables were presented in the median and interquartile range (IQR). Fisher’s exact test and a non-parametric Mann-Whitney U test were performed to compare the categorical and continuous data, respectively. Subsequently, univariate and multivariate binary logistic regressions were calculated to test the effect of the PGD grading, CIT, and the ventilation parameters for predicting PMV[ 15 ]. Candidate factors with a univariate significance of P < 0.1 were selected for the multivariate analysis. The final multivariate model was displayed in the nomogram format to illustrate all the selected predictors of the individual risk of PMV. The linear relationship between the nomogram score and LMV was estimated by calculating Pearson’s correlation coefficient. 2.4. Performance assessments Bootstrapped calibration curves were used to assess the predictive probability of this model. The assessment determines whether the model is biased as a result of the overfitting of the model. The receiver operating characteristic curve (ROC) analysis was then performed to quantify the discrimination ability of the nomogram and the subjects included in it. The Bootstrap test was used to compare the area under the curve (AUC) of the different smoothed ROCs. The clinical utility was determined using decision-curve analysis (DCA), assessing the clinical net benefit associated with the use of the model[ 16 ]. The vertical axis, namely the net benefit (NB), was defined as the true positive rate minus the false positive rate over a range of threshold probability defining high risk. Each decision curve graphically illustrated the NB of the model and every indicator through a range of threshold probabilities of the outcome[ 17 – 19 ]. This study used R software (R-4.1.0) and SPSS v26.0 for the data analysis. The graphics were made with R or GraphPad Prism 9.0.0. Two-sided p-values < 0.05 were used to declare statistical significance. 2.5. Organ procurement statement Voluntary organ donation by citizens has become the only legal source of deceased donor organ transplantation in China since starting on January 1, 2015, and the origins of all organs were registered in the Chinese organ donation system and have been traceable since that date. All the donation procedures were approved by The Institutional Ethics Committees of the Organ Procurement Organization (OPO). Donated lungs were prioritized to the listed candidates following the national organ allocation principles while considering the priority based on lung allocation score (LAS), a comprehensive measure of transplantation urgency and utility. Organ procurement was performed according to the standard protocol through the China Organ Transplant Response System (COTRS) ( https://www.cot.org.cn/ )[ 20 ]. Hence, it can be guaranteed that no organ used for lung transplantation during the study period was procured from executed prisoners. 3. Results 3.1. Patient characteristics The demographical characteristics of our study cohort are presented in Table 1 . Of the 141 patients in the cohort, the median age [interquartile range (IQR)] was 62 (56–66) years, with 103 (73.0%) male patients. Sixty-four (45.4%) patients received bilateral LuTx, and seventy-seven (54.6%) patients received unilateral LuTx. The most frequent diagnosis was idiopathic pulmonary fibrosis (IPF), followed by chronic obstructive pulmonary diseases (COPD) and interstitial lung disease (ILD). The median length of mechanical ventilation was 49 hours, and 45 (31.9%) patients underwent PMV in the ICU after LuTx. Other baseline characteristics of this retrospective cohort are listed in Table 1 . Table 1 Clinical characteristics of patients according to length of mechanical ventilation Variables Total (n = 141) NPMV (n = 96) PMV (n = 45) p value Age (years) 62 (56–66) 60 (56–66) 65 (57–69) 0.041 Gender 0.066 Male 103 (73.0) 75 (55.1) 28 (62.2) Female 38 (27.0) 21 (21.9) 17 (37.8) Smoking history 0.231 Never 40 (28.4) 24 (25.0) 16 (35.6) Ever 101 (71.6) 72 (75.0) 29 (64.4) BMI 21.5 (20.1–23.0) 20.5 (17.9–22.2) 22.7 (20.6–23.6) 0.014 Pulmonary hypertension 57 (40.4) 32 (33.3) 25 (55.5) 0.010 Pretransplant diagnosis LAM 4 (2.9) 4 (4.1) 0 (0) 0.320 COPD 37 (26.2) 28 (29.2) 9 (20.0) 0.385 IPF 56 (39.7) 29 (30.2) 27 (60.0) 0.003 ILD 23 (16.9) 18 (18.8) 5 (12.5) 0.076 Bronchiectasis 7 (5.1) 6 (6.3) 1 (2.5) 0.365 Pneumoconiosis 11 (10.6) 8 (8.3) 3 (7.5) 0.723 Others 3 (2.1) 3 (3.9) 0 (0) 0.320 Type of transplant 0.858 Unilateral 77 (54.6) 53 (55.2) 24 (53.3) Bilateral 64 (45.4) 43 (44.8) 21 (46.7) Length of MV (hours) 49 (36–81) 38 (12–54) 94 (78–120) < 0.001 CIT (hours) 7.0 (5.7–8.6) 4.0 (3.7–6.5) 8.5 (7.6–10.0) < 0.001 Note: Continuous data are summarized as median and interquartile range (IQR). Categorical data are summarized as numbers and percentages. Abbreviations: BMI, body mass index; LAM, lymphangioleiomyomatosis; COPD, chronic obstructive pulmonary dysfunction; ILD, interstitial lung disease; IPF, idiopathic pulmonary fibrosis; MV, mechanical ventilation; CIT, cold ischemia time. 3.2. Comparison between the PMV and NPMV patients Patients in the PMV group tended to be older (65 vs. 60 years, P = 0.041) and were more likely to have a higher BMI (22.7 vs. 20.5, P = 0.011) and longer CIT ( P < 0.001) compared with the NPMV group. In addition, patients with primary diagnoses as IPF were more likely to undergo PMV than those diagnosed with other diseases (60.0% vs 30.2%, P = 0.003). A similar trend was found for the presence of pulmonary hypertension (55.5% vs 33.3%, P = 0.010, Table 1 ), which was considered a complication of primary diagnoses. However, no statistically significant difference was found between the two groups regarding gender, smoking history, other diagnoses, and type of transplant. As for the mechanical ventilation parameters at T 0 , more patients in the PMV group had controlled ventilation status than those in the NPMV group (95.0% vs 79.1%, P = 0.015). Nevertheless, there was no significance in the detailed ventilation modes between the PMV and NPMV groups. Furthermore, patients who underwent PMV had a significantly higher peak inspiratory pressure (PIP, 19 vs. 16 cmH2O, P = 0.039) and lower dynamic compliance (Cdyn, 27.80 vs. 32.92, P = 0.018) and PaO 2 /FiO 2 ratio (P/F ratio, 222 vs. 306, P = 0.041, Table 2 ). More detailed ventilation parameters are presented in the supplementary materials ( Table S1 ). We also investigated the difference in PGD grading between the subgroups. PGD grading was significantly higher in the PMV group, whereas the difference decreased over time (all P < 0.05, Table 3 ). Additionally, no statistically significant differences were found in donor characteristics between the PMV and NPMV groups ( Table S3 ). Table 2 Detailed Ventilation Parameters at T0 according to mechanical ventilation Parameters NPMV (n = 96) PMV (n = 45) p value Ventilation status 0.015 Control ventilation 76 (79.1) 43 (95.6) Assisted ventilation 20 (20.8) 2 (4.4) Ventilation mode 0.763 Pressure controlled/assisted mode 18 (18.8) 3 (6.7) Pressure controlled ventilation mode 8 (8.3) 2 (4.4) Pressure assisted ventilation mode 10 (10.4) 1 (2.2) Volume controlled/assisted mode 78 (81.3) 42 (93.3) Volume controlled ventilation mode 68 (70.8) 41 (91.1) Volume assisted ventilation mode 10 (10.4) 1 (2.2) Ventilation parameters FiO 2 0.60 (0.53–0.80) 0.40 (0.40–0.45) 0.095 PEEP (cmH 2 O) 5 (3–7) 5 (3–8) 0.125 Peak inspiratory pressure (cmH2O) 16 (14–20) 19 (14–22) 0.039 Tidal volume (ml) 394 (360–420) 360 (320–445) 0.116 Dynamic compliance (ml/cmH2O) 32.92 (15.66–41.31) 27.80 (21.12–43.56) 0.018 PaO2/FiO2 ratio 306 (282–390) 222 (150–332) 0.041 Note: Continuous data are summarized as median and interquartile range (IQR). Categorical and other data are summarized as numbers and percentages. Table 3 PGD grading of patients at T0, T24, T48, T72 after transplantation PGD Grades NPMV n = 96 PMV n = 45 p value T 0 hours < 0.001 PGD 0 58 (60.4) 11 (24.4) PGD 1 7 (7.3) 2 (4.4) PGD 2 24 (25.0) 3 (6.7) PGD 3 7 (7.3) 29 (64.4) T 24 hours 0.012 PGD 0 60 (64.9) 14 (31.1) PGD 1 12 (12.5) 5 (11.1) PGD 2 18 (18.8) 5 (11.1) PGD 3 6 (6.3) 21 (46.6) T 48 hours 0.020 PGD 0 66 (68.8) 20 (44.4) PGD 1 14 (14.6) 5 (11.1) PGD 2 12 (12.5) 5 (11.1) PGD 3 4 (4.2) 15 (33.3) T 72 hours 0.032 PGD 0 81 (84.4) 28 (62.2) PGD 1 12 (12.5) 3 (6.7) PGD 2 3 (3.1) 2 (4.4) PGD 3 0 (0) 12 (17.5) Note: Categorical data are summarized as numbers and percentages. Prophylactic NIV after extubation was applied in 32 (22.7%) transplant recipients and the percentage of patients receiving NIV were similar between the NPMV and PMV groups (30.2% vs 44.4%; P = 0.272). Twenty-five (17.7%) patients underwent reintubation and the majority of patients underwent reintubation were in the PMV group (37.8% vs 8.3%, P < 0.01, Table S4 ). 3.3. Logistic regression analyses Possible correlations between PMV and thirteen parameters for the patients in this cohort were evaluated by univariate logistic regression. BMI, CIT, PGD grading at all times, pulmonary hypertension as a complication, primary diagnosis as IPF, and four ventilation parameters at T 0 (ventilation status, PIP, P/F ratio and Cdyn) were identified as potential predictors for PMV (all P < 0.05), while age, gender and smoking history were considered not predictive. Further multivariate logistic regression identified 8 independent variables. BMI (odds ratio [OR] with 95% confidence interval [CI], 1.425[1.323–1.767]; P = 0.032), CIT (OR with 95%CI, 1.777[1.065–2.889]; P = 0.012), PGD grading at T 0 (OR with 95%CI, 1.557[1.331–1.899]; P = 0.011), primary hypertension (OR with 95%CI, 1.894[1.243–3.001]; P = 0.034), primary diagnosis as IPF (OR with 95%CI, 1.788[1.245–3.634]; P = 0.038), PIP (OR with 95%CI, 1.961[1.211–2.747]; P < 0.001), P/F ratio (OR with 95%CI, 0.991[0,980-0.996]; P = 0.015) and Cydn (OR with 95%CI, 1.266[1.121–1.473]; P = 0.001) remained independent predictors of PMV (Table 4 ). Table 4 Univariate and multivariate logistic regression analyses testing effects of perioperatively assessable variables on predicting PMV in 141 patients after LuTx Characteristic Univariable Multivariable OR 95% CI p value OR 95% CI p value Age, y 0.965 0.927–0.991 0.213 NA NA NA BMI 1.213 1.219–1.444 0.030 1.425 1.323–1.767 0.032 Gender 0.957 0.927–0.994 0.445 NA NA NA Smoking history Nonsmoker vs Smoker 1.204 1.162–1.231 0.145 NA NA NA Pulmonary hypertension Normal vs High 2.706 1.278–5.845 0.011 1.894 1.243–3.001 0.034 Primary diagnose as IPF 3.001 1.643–6.153 0.002 1.788 1.245–2.634 0.038 PGDatT 0 2.231 1.601–3.110 < 0.001 1.557 1.331–1.899 0.011 PGDatT 24 1.599 1.082–2.361 0.017 0.205 0.044–0.958 0.054 PGDatT 48 1.628 1.080–2.454 0.024 0.626 0.099–3.956 0.619 PGDatT 72 1.510 1.518–2.262 0.041 2.007 0.427–9.430 0.378 CIT, h 2.068 1.537–2.783 < 0.001 1.777 1.065–2.889 0.012 Ventilation status CV vs AV 2.323 1.621–3.011 0.003 2.007 1.117–3.444 0.138 PIP 1.362 1.203–1.542 < 0.001 1.961 1.211–2.747 < 0.001 Cdyn 1.645 1.200-1.962 0.001 1.266 1.121–1.473 0.001 P/F ratio 0.993 0.989–0.998 0.002 0.991 0.981–0.996 0.015 Note: BMI, body mass index; IPF, idiopathic pulmonary fibrosis; CIT, cold ischemia time; CV, controlled ventilation; AV, assisted ventilation; TV, tidal volume; PIP, peak airway pressure; PEEP, positive end expiratory pressure; Cdyn, pulmonary dynamic compliance. Confidence interval; P/F ratio, PaO 2 /FiO 2 ratio. In contrast, ventilation status and PGD grading at other times were not appropriate for inclusion in the final nomogram (all P > 0.05). We further investigated the prediction value of the donor factors using the univariate logistic regression analysis and we found no statistically significant differences in our results ( Table S5 ). 3.4. Predictive Nomogram for PMV Based on the multivariate logistic regression, a nomogram incorporating BMI, CIT, PGD grading at T 0 , PIP, and Cdyn for predicting PMV after LuTx was established (Fig. 1 ). The model demonstrated excellent discrimination, with an AUC of 0.895 (95%CI, 0.852–0.955, Fig. 2 ) and an accuracy of 0.90 ( Table S2 ). A bootstrapped calibration curve was further established to estimate the predictive ability of the model, which demonstrated a superior ability with a preserved calibration. (Fig. 3 ). The Bootstrap test for the different ROC curves demonstrated significant differences between the nomogram and each variable included in it ( P < 0.001). Other performance metrics are listed in the supplementary materials ( Table S2) . As the DCA depicted in Fig. 4 , the nomogram added clinical risk prediction within the range of the PMV threshold probability < 0.80, which presented satisfactory clinical usefulness. 4. Discussion LuTx is the ultimate treatment option for selected patients with end-stage lung diseases. However, the risks associated with LuTx remain considerable. One of the most important risk factors after LuTx is PMV, which leads to an increased cost of care and a greater risk of death for the patient[ 21 ]. Predicting patients at risk of PMV helps clinicians devise personalized care plans to mitigate the risk of PMV and timely decide on tracheostomy if ventilatory support is still required. However, tools to accurately predict PMV after LuTx are limited. In the present study, we established a nomogram incorporating patients’ BMI, pulmonary hypertension, primary diagnosis as IPF, three ventilation parameters, CIT, and PGD grading at T 0 to predict PMV. Compared with ventilation parameters alone, this nomogram achieved a better predictive value. Since the variables included in this nomogram are easily obtainable, the utility of this nomogram to predict the risk of PMV and guide treatment decisions may be considered routine clinical practice shortly. In more detail, lung-protective ventilation, fluid restriction, prophylactic use of ECLS, and pulmonary vasodilators may be viable options for preventing PMV in high-risk individuals from this model. Although a series of studies have confirmed the negative prognostic impact of PMV[ 22 , 23 ], the definition of PMV is still controversial, ranging from 5 hours to 21 days[ 24 ]. In 2005, a report by the National Association for Medical Direction of Respiratory Care (NAMDRC) consensus conference defined PMV as mechanical ventilation for \(\ge\) 21 consecutive days[ 25 ]. However, the definitional criteria may not fit all studies due to subject cohort variations. For LuTx, most patients undergo extubation within the first 72 hours. Two previous studies defined PMV as mechanical ventilation > 72 hours based on their finding that most patients (77.1% and 80.6%, respectively) were already extubated at T 72 [ 3 , 26 ]. They thus referred to > 72 hours as the threshold to define PMV. A similar extubation rate (96/141, 68.1%) within the first 72 hours after transplantation was observed in the present study. Therefore, we used the same criteria as in the two previously mentioned studies to define PMV. In our study, BMI, pulmonary hypertension, primary diagnosis as IPF, PGD grading at T 0 , relevant ventilation parameters, and cold ischemia time were included in the nomogram. Obesity has long been considered an independent predictor of the length of mechanical ventilation in mechanically ventilated patients in the ICU setting[ 27 ]. Obesity is also a risk factor for PGD and mortality after LuTx[ 28 , 29 ]. Thus, obese recipients should be given particular caution regarding perioperative management. Despite previous studies that have reported that IPF and IPAH were independent predictors of increased PGD[ 9 , 30 ], our findings are the first study to implicate the predictive ability of IPF as a primary diagnosis and pulmonary hypertension as a complication for early adverse events after LuTx instead of PGD. As for PGD grading at T 0 , we demonstrated that patients with NPMV were more likely to be PGD grade 0 than patients with PMV (60.4% vs. 24.4%). A previous report also revealed that patients with PGD grade 0 at T 0 had a shorter LMV than those with PGD grade 1–3[ 3 ]. However, the AUC of PGD grading for predicting PMV was only 0.634, slightly smaller than our study (AUC = 0.747). Thus, the predictive value of PGD grading at T0 alone for PMV was limited. Although PGD grading at a later time point is reported to be more closely related to long-term outcomes after LuTx[ 31 ], only PGD grading at T 0 remained statistically significant in the multivariate logistic regression analysis ( P = 0.011). A likely reason for this result is that what led to long-term outcomes did not necessarily generalize to some early outcomes, such as PMV. The length of mechanical ventilation is closely related to the ventilation parameters[ 32 ]. Three ventilation parameters, P/F ratio, PIP, and Cdyn, were included in the nomogram in our study. Similarly, Schwarz and colleagues[ 3 ] also found these three ventilation parameters were predictors of PMV after LuTx. According to Ripoll et al., elevated PIP is associated with the development of acute respiratory distress syndrome (ARDS) in liver transplant recipients[ 33 ]. Moreover, Laffey et al[ 34 ] demonstrated that higher PIP and lower P/F ratio contribute to increased hospital mortality in patients with ARDS. Cdyn was reported as a critical parameter for evaluating graft function after ex vivo lung perfusion in a previous study[ 35 ]. However, we show that in our multivariate logistic regression analysis, PIP was the strongest predictor of PMV. Only mechanical ventilation parameters at T 0 were included in our study. This is because only ventilation parameters in the immediate postoperative period were thought to have predictive value while ventilation parameters at later times hold value for assessing the status of those patients after LuTx rather than being predictive. Among these variables included in the nomogram, CIT outperformed other individual factors for predicting PMV. Since the pathological basis of PGD is consistent with IRI[ 36 ], CIT is closely related to early allograft function[ 37 ]. Recently, CIT was also reported to have a significant correlation with postoperative complications of lung transplantation[ 38 ]. However, whether CIT could be used to predict PMV remains unknown. In the present study, we demonstrated for the first time that longer CIT was an independent risk factor for PMV. Our study has several limitations. First, one major limitation in this single-center study is that the absence of external validation may limit the application of the nomogram. Regrettably, despite repeated attempts to add a validation cohort, we ultimately failed to establish such a cohort because there are so few lung transplantation centers in China. However, both the lung transplantation centers and the annual number of lung transplants has markedly increased in recent years in China[ 20 ]. Hopefully, this preliminary result will be validated in multicenter studies in the future. Second, the sample size was relatively small. Third, the majority of the patients in our study cohort underwent a unilateral LuTx, which may influence the estimation of the PGD grading’s impact on LMV. Although the Report of the ISHLT Working Group does not recommend separately grading PGD for bilateral and single LuTx recipients routinely[ 6 ], previous publications do show that single LuTx may have an elevated overall incidence of PGD[ 28 , 39 ]. In addition, the residual pulmonary function of the contralateral lung may influence the LMV, which could not be evaluated in our study. Finally, this model can only be applied post-operatively to evaluate the risk for PMV after LuTx. This may limit the interventions available to reduce the incidence of PMV and hence restricts potential applications. 5. Conclusions As shown in Visual Abstract, we established a novel nomogram that could efficiently predict individual risk of receiving PMV for patients after LuTx, which facilitates early diagnosis and rational intervention. Still, additional prospective validation cohorts from more clinical centers will be needed to confirm the practical utility of the newly established nomogram before its translation to wide-accepted clinical practice. Abbreviations AUC, area under the curve, AV, assisted ventilation., BMI, body mass index, Cdyn, dynamic compliance, CI, confidence interval, CIT, cold ischemia time, CV, controlled ventilation, DBD, Donation after brain death, DCA, decision-curve analysis, DCD, Donation after circulatory death, ECLS, extracorporeal life support, ECMO, extended extracorporeal membrane oxygenation, FiO 2 , the fraction of inspiration O 2 , HFNC, High-flow nasal cannula oxygen therapy, ICU, intensive care unit, IRI, ischemia-reperfusion injury, ISHLT, the International Society for Heart and Lung Transplantation, LMV, length of mechanical ventilation, LuTx, lung transplantation, NB, net benefit, NIV, Noninvasive ventilation, NPMV, non-prolonged mechanical ventilation, PaO 2 , partial pressure of oxygen, PEEP, positive end-expiratory pressure, PGD, primary graft dysfunction, PIP, peak inspiratory pressure, PMV, prolonged mechanical ventilation, ROC, receiver operating characteristic curve, SaO 2 , arterial oxygen saturation, TV, tidal volume. Declarations Ethics approval and consent to participate The study was approved by the Research Ethics Commission of Shanghai Pulmonary Hospital (No. L20-352). The requirement for informed consent was waived by the Research Ethics Commission of Shanghai Pulmonary Hospital, Tongji University School of Medicine because of the retrospective nature of the study. All procedures were in accordance with relevant guidelines and regulations (Declaration of Helsinki). We confirm that our retrospective data collection didn’t subject the patients to any additional experimental protocols. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication. Funding This research was supported by the scientific and technological innovation action plan of Science and Technology Commission of Shanghai Municipality (No.20DZ2253700). Author contributions PG and CL analyzed the data and wrote the paper, YZ, YN and JW collected the data, XL, PZ and JD checked the integrity of the data and the accuracy of the data analysis, CC, YS and WH designed the study and revised the paper. All authors read and approved the final manuscript. Acknowledgements We would like to thank the thoracic surgery and ICU staff of the Shanghai Pulmonary Hospital for making this research possible and all patients and their family members for participating in this study. Author details 1 Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. 2 Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China. References Hachem RR. Advancing Lung Transplantation. Clin Transpl 2015, 31 : 239-47 Raskin J, Vanstapel A, Verbeken EK, et al. Mortality after lung transplantation: a single-centre cohort analysis. Transpl Int 2020, 33 (2): 130-4110.1111/tri.13540. Schwarz S, Benazzo A, Dunkler D, et al. Ventilation parameters and early graft function in double lung transplantation. J Heart Lung Transplant 2021, 40 (1): 4-1110.1016/j.healun.2020.10.003. Van Herck A, Frick AE, Schaevers V, et al. Azithromycin and early allograft function after lung transplantation: A randomized, controlled trial. J Heart Lung Transplant 2019, 38 (3): 252-910.1016/j.healun.2018.12.006. Efrati O, Bylin I, Segal E, et al. Outcome of patients with cystic fibrosis admitted to the intensive care unit: is invasive mechanical ventilation a risk factor for death in patients waiting lung transplantation? Heart Lung 2010, 39 (2): 153-910.1016/j.hrtlng.2009.06.014. Snell GI, Yusen RD, Weill D, et al. Report of the ISHLT Working Group on Primary Lung Graft Dysfunction, part I: Definition and grading-A 2016 Consensus Group statement of the International Society for Heart and Lung Transplantation. J Heart Lung Transplant 2017, 36 (10): 1097-10310.1016/j.healun.2017.07.021. Christie JD, Kotloff RM, Ahya VN, et al. The effect of primary graft dysfunction on survival after lung transplantation. Am J Respir Crit Care Med 2005, 171 (11): 1312-610.1164/rccm.200409-1243OC. Daud SA, Yusen RD, Meyers BF, et al. Impact of immediate primary lung allograft dysfunction on bronchiolitis obliterans syndrome. Am J Respir Crit Care Med 2007, 175 (5): 507-1310.1164/rccm.200608-1079OC. Diamond JM, Arcasoy S, Kennedy CC, et al. Report of the International Society for Heart and Lung Transplantation Working Group on Primary Lung Graft Dysfunction, part II: Epidemiology, risk factors, and outcomes-A 2016 Consensus Group statement of the International Society for Heart and Lung Transplantation. J Heart Lung Transplant 2017, 36 (10): 1104-1310.1016/j.healun.2017.07.020. Calfee CS, Budev MM, Matthay MA, et al. Plasma receptor for advanced glycation end-products predicts duration of ICU stay and mechanical ventilation in patients after lung transplantation. J Heart Lung Transplant 2007, 26 (7): 675-8010.1016/j.healun.2007.04.002. Mendogni P, Pieropan S, Rosso L, et al. Impact of Cold Ischemic Time on Airway Complications After Lung Transplantation: A Single-center Cohort Study. Transplant Proc 2019, 51 (9): 2981-510.1016/j.transproceed.2019.04.092. Hoetzenecker K, Schwarz S, Muckenhuber M, et al. Intraoperative extracorporeal membrane oxygenation and the possibility of postoperative prolongation improve survival in bilateral lung transplantation. J Thorac Cardiovasc Surg 2018, 155 (5): 2193-206 e310.1016/j.jtcvs.2017.10.144. Boles JM, Bion J, Connors A, et al. Weaning from mechanical ventilation. Eur Respir J 2007, 29 (5): 1033-5610.1183/09031936.00010206. Van Raemdonck D, Hartwig MG, Hertz MI, et al. Report of the ISHLT Working Group on primary lung graft dysfunction Part IV: Prevention and treatment: A 2016 Consensus Group statement of the International Society for Heart and Lung Transplantation. J Heart Lung Transplant 2017, 36 (10): 1121-3610.1016/j.healun.2017.07.013. Aziz A, May M, Burger M, et al. Prediction of 90-day mortality after radical cystectomy for bladder cancer in a prospective European multicenter cohort. Eur Urol 2014, 66 (1): 156-6310.1016/j.eururo.2013.12.018. Gandaglia G, Fossati N, Zaffuto E, et al. Development and Internal Validation of a Novel Model to Identify the Candidates for Extended Pelvic Lymph Node Dissection in Prostate Cancer. Eur Urol 2017, 72 (4): 632-4010.1016/j.eururo.2017.03.049. Van Calster B, Wynants L, Verbeek JFM, et al. Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators. Eur Urol 2018, 74 (6): 796-80410.1016/j.eururo.2018.08.038. Fitzgerald M, Saville BR, Lewis RJ. Decision curve analysis. JAMA 2015, 313 (4): 409-1010.1001/jama.2015.37. Steyerberg EW, Vickers AJ. Decision curve analysis: a discussion. Med Decis Making 2008, 28 (1): 146-910.1177/0272989X07312725. Dong NG, Hu XJ, Wang HB, Chen JY, Wan S. Should we tolerate biased critiques in cardiothoracic surgery journals? J Thorac Cardiovasc Surg 2022, 10.1016/j.jtcvs.2022.03.033: 10.1016/j.jtcvs.2022.03.033. Hadem J, Gottlieb J, Seifert D, et al. Prolonged Mechanical Ventilation After Lung Transplantation-A Single-Center Study. Am J Transplant 2016, 16 (5): 1579-8710.1111/ajt.13632. Fernandez-Zamora MD, Gordillo-Brenes A, Banderas-Bravo E, et al. Prolonged Mechanical Ventilation as a Predictor of Mortality After Cardiac Surgery. Respiratory care 2018, 63 (5): 550-710.4187/respcare.04915. Damuth E, Mitchell JA, Bartock JL, Roberts BW, Trzeciak S. Long-term survival of critically ill patients treated with prolonged mechanical ventilation: a systematic review and meta-analysis. The Lancet Respiratory medicine 2015, 3 (7): 544-5310.1016/s2213-2600(15)00150-2. Rose L, McGinlay M, Amin R, et al. Variation in Definition of Prolonged Mechanical Ventilation. Respiratory care 2017, 62 (10): 1324-3210.4187/respcare.05485. MacIntyre NR, Epstein SK, Carson S, Scheinhorn D, Christopher K, Muldoon S. Management of patients requiring prolonged mechanical ventilation: report of a NAMDRC consensus conference. Chest 2005, 128 (6): 3937-5410.1378/chest.128.6.3937. Pilcher DV, Scheinkestel CD, Snell GI, Davey-Quinn A, Bailey MJ, Williams TJ. High central venous pressure is associated with prolonged mechanical ventilation and increased mortality after lung transplantation. J Thorac Cardiovasc Surg 2005, 129 (4): 912-810.1016/j.jtcvs.2004.07.006. Akinnusi ME, Pineda LA, El Solh AA. Effect of obesity on intensive care morbidity and mortality: a meta-analysis. Critical care medicine 2008, 36 (1): 151-810.1097/01.ccm.0000297885.60037.6e. Diamond JM, Lee JC, Kawut SM, et al. Clinical risk factors for primary graft dysfunction after lung transplantation. American journal of respiratory and critical care medicine 2013, 187 (5): 527-3410.1164/rccm.201210-1865OC. Upala S, Panichsillapakit T, Wijarnpreecha K, Jaruvongvanich V, Sanguankeo A. Underweight and obesity increase the risk of mortality after lung transplantation: a systematic review and meta-analysis. Transplant international : official journal of the European Society for Organ Transplantation 2016, 29 (3): 285-9610.1111/tri.12721. Diamond JM, Lee JC, Kawut SM, et al. Clinical risk factors for primary graft dysfunction after lung transplantation. Am J Respir Crit Care Med 2013, 187 (5): 527-3410.1164/rccm.201210-1865OC. Whitson BA, Prekker ME, Herrington CS, et al. Primary graft dysfunction and long-term pulmonary function after lung transplantation. J Heart Lung Transplant 2007, 26 (10): 1004-1110.1016/j.healun.2007.07.018. Figueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. Journal of critical care 2015, 30 (3): 502-510.1016/j.jcrc.2015.01.020. Ripoll JG, Wanta BT, Wetzel DR, Frank RD, Findlay JY, Vogt MNP. Association of Perioperative Variables and the Acute Respiratory Distress Syndrome in Liver Transplant Recipients. Transplantation direct 2020, 6 (1): e52010.1097/txd.0000000000000965. Laffey JG, Bellani G, Pham T, et al. Potentially modifiable factors contributing to outcome from acute respiratory distress syndrome: the LUNG SAFE study. Intensive care medicine 2016, 42 (12): 1865-7610.1007/s00134-016-4571-5. Arni S, Maeyashiki T, Citak N, Opitz I, Inci I. Subnormothermic Ex Vivo Lung Perfusion Temperature Improves Graft Preservation in Lung Transplantation. Cells 2021, 10 (4): 10.3390/cells10040748. Wang X, O'Brien ME, Yu J, et al. Prolonged Cold Ischemia Induces Necroptotic Cell Death in Ischemia-Reperfusion Injury and Contributes to Primary Graft Dysfunction after Lung Transplantation. Am J Respir Cell Mol Biol 2019, 61 (2): 244-5610.1165/rcmb.2018-0207OC. Kuntz CL, Hadjiliadis D, Ahya VN, et al. Risk factors for early primary graft dysfunction after lung transplantation: a registry study. Clinical transplantation 2009, 23 (6): 819-3010.1111/j.1399-0012.2008.00951.x. Necki M, Antonczyk R, Pandel A, et al. Impact of Cold Ischemia Time on Frequency of Airway Complications Among Lung Transplant Recipients. Transplant Proc 2020, 52 (7): 2160-410.1016/j.transproceed.2020.03.047. Prekker ME, Nath DS, Walker AR, et al. Validation of the proposed International Society for Heart and Lung Transplantation grading system for primary graft dysfunction after lung transplantation. The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation 2006, 25 (4): 371-810.1016/j.healun.2005.11.436. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1.tiff SupplementaryStudyTableS1.docx SupplementaryStudyTableS2.docx SupplementaryStudyTableS3.docx SupplementaryStudyTableS4.docx SupplementaryStudyTableS5.docx VisualAbstract.pdf Visual Abstract: Strong multivariate baseline and perioperative predictors were factored into a novel nomogram to predict PMV after LuTx. Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2023 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Major revision 16 Nov, 2022 Reviews received at journal 13 Nov, 2022 Reviewers agreed at journal 03 Nov, 2022 Reviewers agreed at journal 24 Oct, 2022 Reviewers agreed at journal 22 Oct, 2022 Reviewers invited by journal 17 Oct, 2022 Editor assigned by journal 17 Oct, 2022 Editor invited by journal 26 Sep, 2022 Submission checks completed at journal 26 Sep, 2022 First submitted to journal 21 Sep, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2089786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":139638896,"identity":"5aa41481-0730-4642-aa31-c8b9ee74eefc","order_by":0,"name":"Peigen Gao","email":"","orcid":"","institution":"Tongji University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peigen","middleName":"","lastName":"Gao","suffix":""},{"id":139638897,"identity":"a5f21902-a651-4599-b676-6bd8bba00c22","order_by":1,"name":"Chongwu Li","email":"","orcid":"","institution":"Tongji University School of 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16:44:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2089786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2089786/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-023-02307-9","type":"published","date":"2023-01-10T18:16:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":27106021,"identity":"9f2052e4-aa69-4c7d-8bbe-6710bac51b0c","added_by":"auto","created_at":"2022-09-28 21:27:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11684,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk prediction nomogram of logistic regression.\u003c/strong\u003e Nomogram constructed to predict prolonged mechanical ventilation in LuTX recipients after surgery. The included variables were cold ischemia time, ventilation parameters at T\u003csub\u003e0\u003c/sub\u003e (including peak inspiratory pressure, tidal volume, dynamic compliance and oxygenation index), and PGD grade at T\u003csub\u003e0\u003c/sub\u003e. The full point density and risk density plots show their distribution. For category variables, their distribution is reflected by the size of the box. Rank the importance of each variable according to the standard deviation on the Nomogram scale. When using the Nomogram image, specific points (black spots) for each patient are located on each variable axis. Draw lines to determine the points received by each variable, The sum of these points is placed on the total point line and a line drawn down the risk line to obtain the total predicted risk of prolonged ventilation after surgery. CIT, cold ischemia time, PGDT\u003csub\u003e0\u003c/sub\u003e, primary graft dysfunction at T\u003csub\u003e0\u003c/sub\u003e, BMI, body mass index, Cydn, dynamic compliance, PIP, peak inspiratory pressure, PAH pulmonary hypertension, IPF, idiopathic pulmonary fibrosis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/947adc7246dd5af4d2e340d9.png"},{"id":27107112,"identity":"62206dda-f7ff-4cda-972c-0330293e676e","added_by":"auto","created_at":"2022-09-28 21:37:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29812,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis for the nomogram of the prediction model of recipients with prolonged mechanical ventilation after LuTx based on all indicators and all variables. \u003c/strong\u003eROC curve summation of various factors, including cold ischemia time, ventilation parameters, and PGD grade at T0. The final integrated model in the figure has an area under the ROC curve of 0.895. Among the indicators, the area under ROC curve of cold ischemia time was the largest, reaching 0.789.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/63bad43c7521f92945d27bdd.png"},{"id":27106515,"identity":"887e48e1-c061-4cfa-be22-f972931afde8","added_by":"auto","created_at":"2022-09-28 21:32:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA calibration curve of this risk prediction nomogram.\u003c/strong\u003e The model calibration has been depicted by bootstrapped calibration curve showing ideal (dotted line), apparent (purple line), and bias-corrected (green line) model.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/7923e281ab7e630d74b6cedf.png"},{"id":27106022,"identity":"b695e6fe-9faf-436f-8b3c-edc361cffca2","added_by":"auto","created_at":"2022-09-28 21:27:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe decision curve analysis (DCA) of the prediction model of recipients with prolonged mechanical ventilation after LuTx based on all indicators and all variables.\u003c/strong\u003e The prediction model or index with the largest net benefit has the best clinical guidance efficiency. Net benefit is defined as the true positive rate minus the weighted false positive rate under a given threshold probability, which defines the high risk of prolonged mechanical ventilation after LuTx.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/a4d2da77bbfdfdf7736fa05b.png"},{"id":44716552,"identity":"3e99f407-4189-49c6-abaa-8cdae54f690f","added_by":"auto","created_at":"2023-10-16 18:26:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":739928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/cb8e6ee6-97d0-4c33-a92d-841f9600d26d.pdf"},{"id":27106023,"identity":"4d30c671-9785-478c-975d-1eeb558bf666","added_by":"auto","created_at":"2022-09-28 21:27:42","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":274414,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/8f43bab02764663c4e046558.tiff"},{"id":27106517,"identity":"e1d2dd31-73db-4a4f-af3d-76161ee24d60","added_by":"auto","created_at":"2022-09-28 21:32:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19925,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryStudyTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/09301d7b7cae983cf0d0a6f9.docx"},{"id":27106516,"identity":"c2743e12-1ade-4d68-b1a3-1d264cbeca15","added_by":"auto","created_at":"2022-09-28 21:32:42","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17894,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryStudyTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/cef918416e247040f9745c91.docx"},{"id":27106029,"identity":"059e6f7d-0157-47f4-88d3-eec9d9281ca0","added_by":"auto","created_at":"2022-09-28 21:27:42","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21801,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryStudyTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/73291dce7051c589fa029c6f.docx"},{"id":27106024,"identity":"9ff5b141-4b0c-464c-b013-6eb546b879b0","added_by":"auto","created_at":"2022-09-28 21:27:42","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17788,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryStudyTableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/235f53ce50411e3162f83e32.docx"},{"id":27106028,"identity":"73a97dcc-b0ad-47f6-ba43-7802f205f39a","added_by":"auto","created_at":"2022-09-28 21:27:42","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":20832,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryStudyTableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/2d6e13fe5e4b03fcec01507c.docx"},{"id":27107113,"identity":"577ed261-5e87-4559-95f9-7383e4818e08","added_by":"auto","created_at":"2022-09-28 21:37:43","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":5313625,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisual Abstract: \u003c/strong\u003eStrong multivariate baseline and perioperative predictors were factored into a novel nomogram to predict PMV after LuTx.\u003c/p\u003e","description":"","filename":"VisualAbstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2089786/v1/c4503592dc833270b1401b67.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eMore than 4,000 lung transplantations (LuTx) are currently performed worldwide per year [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the mortality and morbidity of LuTx remain at relatively high levels compared with other solid organ transplantations\u003csup\u003e[2]\u003c/sup\u003e. Prolonged mechanical ventilation (PMV) is a prognostic marker for short-term adverse outcomes in patients after LuTx [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Previous reports also show that PMV is associated with impaired long-term survival[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, the discovery of predictors for PMV may assist in developing precautionary measures to ameliorate the high morbidity and mortality.\u003c/p\u003e \u003cp\u003ePrimary graft dysfunction (PGD) is a form of acute lung injury that occurs in about 30% of patients after LuTx within 72 hours, which can be characterized by hypoxemia and alveolar infiltrates in the allograft(s)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. PGD is reported as the most frequent cause of early death after lung transplantation and is also strongly correlated with other late outcomes[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although the presence of PGD is associated with an increased duration of mechanical ventilation[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], recently, Schwarz and colleagues found that the value of PGD in predicting PMV was limited[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Instead, a model combing three ventilation parameters better predicted PMV. However, the predictive value of this model was still moderate, with an area under the curve (AUC) of 0.727[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, a more precise model is urgently required.\u003c/p\u003e \u003cp\u003eApart from PGD and ventilation parameters, other factors, such as cold ischemia time (CIT), may also provide valuable information for predicting PMV, and CIT is closely associated with ischemia-reperfusion injury (IRI)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A recent study revealed that CIT is a risk factor for developing airway complications after lung transplantation[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], even though the correlation between CIT and PMV remains unknown. According to the latest guidelines, idiopathic pulmonary fibrosis (IPF) and idiopathic pulmonary arterial hypertension (IPAH) are strongly associated with risk for PGD in primary diagnoses[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, to date, no research has considered the role of different primary diagnoses in predicting the early adverse events after LuTx except PGD. Therefore, our research aimed to develop a nomogram combing clinical variables (including primary diagnosis), CIT, PGD grading, and ventilation parameters to improve the predictive accuracy of PMV.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design and participants\u003c/h2\u003e \u003cp\u003e With the Research Ethics Commission of Shanghai Pulmonary Hospital (Shanghai, China) approval (No. L20-352), we conducted a single-center, retrospective observational cohort study. Data from 146 patients who underwent lung transplantation at Shanghai Pulmonary Hospital were retrospectively extracted from electronic medical records between January 1, 2018, and February 1, 2022. The exclusion criteria included: patients with missing data; re-transplantation; postoperatively extended extracorporeal membrane oxygenation (ECMO) with clear chest radiographs (PGD ungradable)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The use of postoperative extended ECMO was defined as the use of ECMO or re-use of ECMO to maintain life after arriving in the intensive care unit (ICU) after surgery[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. According to the exclusion criteria, 141 patients who underwent LuTx were included in our study cohort (\u003cb\u003eFigure S1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data acquisition\u003c/h2\u003e \u003cp\u003eThe baseline characteristics and demographics of the patients (age, gender, BMI, smoking history, and documented pulmonary hypertension), primary diagnosis before the operation, the CIT, and length of mechanical ventilation (LMV) were retrospectively collected from the medical case database of Shanghai Pulmonary Hospital. In this study, LMV was classified as PMV or non-prolonged mechanical ventilation (NPMV), with the threshold to be LMV\u0026thinsp;\u0026gt;\u0026thinsp;72h. The extubation criteria were as follows in our clinical practice as modified from the consensus on weaning from mechanical ventilation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]: (1) successful spontaneous breathing trial lasting for 120 minutes; (2) hemodynamic stability; (3) No sedation or adequate mentation on sedation; (4) P/F ratio\u0026thinsp;\u0026gt;\u0026thinsp;150 mm Hg with FiO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.4, positive end-expiratory pressure\u0026thinsp;\u0026le;\u0026thinsp;8 cm H\u003csub\u003e2\u003c/sub\u003eO. Patients who were extubated but needed reintubation within 72 hours after LuTx were also included in the PMV group.\u003c/p\u003e \u003cp\u003eThe ventilation parameters of T\u003csub\u003e0\u003c/sub\u003e, T\u003csub\u003e24\u003c/sub\u003e, T\u003csub\u003e48\u003c/sub\u003e, and T\u003csub\u003e72\u003c/sub\u003e were also obtained. T\u003csub\u003e0\u003c/sub\u003e, T\u003csub\u003e24\u003c/sub\u003e, T\u003csub\u003e48\u003c/sub\u003e, and T\u003csub\u003e72\u003c/sub\u003e were defined as the 2nd, 24th, 48th, and 72nd hours after the arrival at the ICU after transplantation, respectively. The ventilation parameters mainly included inhaled oxygen concentration fraction, arterial oxygen partial pressure, tidal volume (TV), peak inspiratory pressure (PIP), and positive end-expiratory pressure (PEEP). Dynamic compliance was calculated as tidal volume/(peak inspiratory pressure-positive end-expiratory pressure), while partial pressure of the oxygen fraction of inspired oxygen (P/F) ratio was calculated as arterial oxygen partial pressure (PaO\u003csub\u003e2\u003c/sub\u003e)/inhaled oxygen concentration fraction (FiO\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eThe PGD diagnosis method in this study refers to the standard judgment of the International Society for Heart and Lung Transplantation (ISHLT) on PGD in 2016[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, patients receiving mechanical ventilation with FiO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.5 on nitric oxide\u0026thinsp;\u0026gt;\u0026thinsp;48 hours from lung transplant or using extracorporeal lung support (ECLS) with bilateral pulmonary edema on chest X-ray, which indicated ECLS is primarily hypoxemia, were classified as grade 3. In addition, using atomized prostacyclin or other drugs that may improve oxygenation did not affect PGD classification[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eThe categorical variables were summarized as the absolute frequency and percentage, while the continuous variables were presented in the median and interquartile range (IQR). Fisher\u0026rsquo;s exact test and a non-parametric Mann-Whitney U test were performed to compare the categorical and continuous data, respectively. Subsequently, univariate and multivariate binary logistic regressions were calculated to test the effect of the PGD grading, CIT, and the ventilation parameters for predicting PMV[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Candidate factors with a univariate significance of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 were selected for the multivariate analysis. The final multivariate model was displayed in the nomogram format to illustrate all the selected predictors of the individual risk of PMV. The linear relationship between the nomogram score and LMV was estimated by calculating Pearson\u0026rsquo;s correlation coefficient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Performance assessments\u003c/h2\u003e \u003cp\u003eBootstrapped calibration curves were used to assess the predictive probability of this model. The assessment determines whether the model is biased as a result of the overfitting of the model. The receiver operating characteristic curve (ROC) analysis was then performed to quantify the discrimination ability of the nomogram and the subjects included in it. The Bootstrap test was used to compare the area under the curve (AUC) of the different smoothed ROCs. The clinical utility was determined using decision-curve analysis (DCA), assessing the clinical net benefit associated with the use of the model[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The vertical axis, namely the net benefit (NB), was defined as the true positive rate minus the false positive rate over a range of threshold probability defining high risk. Each decision curve graphically illustrated the NB of the model and every indicator through a range of threshold probabilities of the outcome[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This study used R software (R-4.1.0) and SPSS v26.0 for the data analysis. The graphics were made with R or GraphPad Prism 9.0.0. Two-sided p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used to declare statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Organ procurement statement\u003c/h2\u003e \u003cp\u003eVoluntary organ donation by citizens has become the only legal source of deceased donor organ transplantation in China since starting on January 1, 2015, and the origins of all organs were registered in the Chinese organ donation system and have been traceable since that date. All the donation procedures were approved by The Institutional Ethics Committees of the Organ Procurement Organization (OPO). Donated lungs were prioritized to the listed candidates following the national organ allocation principles while considering the priority based on lung allocation score (LAS), a comprehensive measure of transplantation urgency and utility. Organ procurement was performed according to the standard protocol through the China Organ Transplant Response System (COTRS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cot.org.cn/\u003c/span\u003e\u003cspan address=\"https://www.cot.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Hence, it can be guaranteed that no organ used for lung transplantation during the study period was procured from executed prisoners.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Patient characteristics\u003c/h2\u003e \u003cp\u003eThe demographical characteristics of our study cohort are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the 141 patients in the cohort, the median age [interquartile range (IQR)] was 62 (56\u0026ndash;66) years, with 103 (73.0%) male patients. Sixty-four (45.4%) patients received bilateral LuTx, and seventy-seven (54.6%) patients received unilateral LuTx. The most frequent diagnosis was idiopathic pulmonary fibrosis (IPF), followed by chronic obstructive pulmonary diseases (COPD) and interstitial lung disease (ILD). The median length of mechanical ventilation was 49 hours, and 45 (31.9%) patients underwent PMV in the ICU after LuTx. Other baseline characteristics of this retrospective cohort are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eClinical characteristics of patients according to length of mechanical ventilation\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\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;141)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNPMV\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePMV\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;45)\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)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (56\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (56\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (57\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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\u003e0.066\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\u003e103 (73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (62.2)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (37.8)\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\u003eSmoking history\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\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (35.6)\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\u003eEver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (64.4)\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\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.5 (20.1\u0026ndash;23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5 (17.9\u0026ndash;22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7 (20.6\u0026ndash;23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretransplant diagnosis\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\u003eLAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBronchiectasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumoconiosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of transplant\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\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (53.3)\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\u003eBilateral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (46.7)\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\u003eLength of MV (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (36\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (12\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (78\u0026ndash;120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIT (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0 (5.7\u0026ndash;8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.7\u0026ndash;6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5 (7.6\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: Continuous data are summarized as median and interquartile range (IQR). Categorical data are summarized as numbers and percentages. Abbreviations: BMI, body mass index; LAM, lymphangioleiomyomatosis; COPD, chronic obstructive pulmonary dysfunction; ILD, interstitial lung disease; IPF, idiopathic pulmonary fibrosis; MV, mechanical ventilation; CIT, cold ischemia time.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Comparison between the PMV and NPMV patients\u003c/h2\u003e \u003cp\u003ePatients in the PMV group tended to be older (65 vs. 60 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.041) and were more likely to have a higher BMI (22.7 vs. 20.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) and longer CIT (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with the NPMV group. In addition, patients with primary diagnoses as IPF were more likely to undergo PMV than those diagnosed with other diseases (60.0% vs 30.2%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). A similar trend was found for the presence of pulmonary hypertension (55.5% vs 33.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which was considered a complication of primary diagnoses. However, no statistically significant difference was found between the two groups regarding gender, smoking history, other diagnoses, and type of transplant.\u003c/p\u003e \u003cp\u003eAs for the mechanical ventilation parameters at T\u003csub\u003e0\u003c/sub\u003e, more patients in the PMV group had controlled ventilation status than those in the NPMV group (95.0% vs 79.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). Nevertheless, there was no significance in the detailed ventilation modes between the PMV and NPMV groups. Furthermore, patients who underwent PMV had a significantly higher peak inspiratory pressure (PIP, 19 vs. 16 cmH2O, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039) and lower dynamic compliance (Cdyn, 27.80 vs. 32.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio (P/F ratio, 222 vs. 306, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.041, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). More detailed ventilation parameters are presented in the supplementary materials (\u003cb\u003eTable S1\u003c/b\u003e). We also investigated the difference in PGD grading between the subgroups. PGD grading was significantly higher in the PMV group, whereas the difference decreased over time (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, no statistically significant differences were found in donor characteristics between the PMV and NPMV groups (\u003cb\u003eTable S3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed Ventilation Parameters at T0 according to mechanical ventilation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPMV\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMV\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eVentilation status\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 \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssisted ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation mode\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 \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure controlled/assisted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure controlled ventilation mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure assisted ventilation mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume controlled/assisted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume controlled ventilation mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolume assisted ventilation mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation parameters\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.53\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.40\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEEP (cmH\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak inspiratory pressure (cmH2O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (14\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (14\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTidal volume (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e394 (360\u0026ndash;420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e360 (320\u0026ndash;445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDynamic compliance (ml/cmH2O)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.92 (15.66\u0026ndash;41.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.80 (21.12\u0026ndash;43.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO2/FiO2 ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e306 (282\u0026ndash;390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222 (150\u0026ndash;332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: Continuous data are summarized as median and interquartile range (IQR). Categorical and other data are summarized as numbers and percentages.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003ePGD grading of patients at T0, T24, T48, T72 after transplantation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD Grades\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNPMV\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;96\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMV\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;45\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\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\u003eT\u003csub\u003e0\u003c/sub\u003e hours\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 \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e24\u003c/sub\u003e hours\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 \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e48\u003c/sub\u003e hours\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 \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003e72\u003c/sub\u003e hours\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 \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGD 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: Categorical data are summarized as numbers and percentages.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eProphylactic NIV after extubation was applied in 32 (22.7%) transplant recipients and the percentage of patients receiving NIV were similar between the NPMV and PMV groups (30.2% vs 44.4%; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.272). Twenty-five (17.7%) patients underwent reintubation and the majority of patients underwent reintubation were in the PMV group (37.8% vs 8.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cb\u003eTable S4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Logistic regression analyses\u003c/h2\u003e \u003cp\u003ePossible correlations between PMV and thirteen parameters for the patients in this cohort were evaluated by univariate logistic regression. BMI, CIT, PGD grading at all times, pulmonary hypertension as a complication, primary diagnosis as IPF, and four ventilation parameters at T\u003csub\u003e0\u003c/sub\u003e (ventilation status, PIP, P/F ratio and Cdyn) were identified as potential predictors for PMV (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while age, gender and smoking history were considered not predictive. Further multivariate logistic regression identified 8 independent variables. BMI (odds ratio [OR] with 95% confidence interval [CI], 1.425[1.323\u0026ndash;1.767]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), CIT (OR with 95%CI, 1.777[1.065\u0026ndash;2.889]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), PGD grading at T\u003csub\u003e0\u003c/sub\u003e (OR with 95%CI, 1.557[1.331\u0026ndash;1.899]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), primary hypertension (OR with 95%CI, 1.894[1.243\u0026ndash;3.001]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), primary diagnosis as IPF (OR with 95%CI, 1.788[1.245\u0026ndash;3.634]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038), PIP (OR with 95%CI, 1.961[1.211\u0026ndash;2.747]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), P/F ratio (OR with 95%CI, 0.991[0,980-0.996]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and Cydn (OR with 95%CI, 1.266[1.121\u0026ndash;1.473]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) remained independent predictors of PMV (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses testing effects of perioperatively assessable variables on predicting PMV in 141 patients after LuTx\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.927\u0026ndash;0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.219\u0026ndash;1.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.323\u0026ndash;1.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.927\u0026ndash;0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003cp\u003eNonsmoker vs Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.162\u0026ndash;1.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary hypertension\u003c/p\u003e \u003cp\u003eNormal vs High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.278\u0026ndash;5.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.243\u0026ndash;3.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnose as IPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.643\u0026ndash;6.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.245\u0026ndash;2.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGDatT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.601\u0026ndash;3.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.331\u0026ndash;1.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGDatT\u003csub\u003e24\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.082\u0026ndash;2.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u0026ndash;0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGDatT\u003csub\u003e48\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.080\u0026ndash;2.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.099\u0026ndash;3.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePGDatT\u003csub\u003e72\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.518\u0026ndash;2.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.427\u0026ndash;9.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIT, h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.537\u0026ndash;2.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.065\u0026ndash;2.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation status\u003c/p\u003e \u003cp\u003eCV vs AV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.621\u0026ndash;3.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.117\u0026ndash;3.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.203\u0026ndash;1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.211\u0026ndash;2.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCdyn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.200-1.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.121\u0026ndash;1.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP/F ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.989\u0026ndash;0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.981\u0026ndash;0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNote: BMI, body mass index; IPF, idiopathic pulmonary fibrosis; CIT, cold ischemia time; CV, controlled ventilation; AV, assisted ventilation; TV, tidal volume; PIP, peak airway pressure; PEEP, positive end expiratory pressure; Cdyn, pulmonary dynamic compliance. Confidence interval; P/F ratio, PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, ventilation status and PGD grading at other times were not appropriate for inclusion in the final nomogram (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We further investigated the prediction value of the donor factors using the univariate logistic regression analysis and we found no statistically significant differences in our results (\u003cb\u003eTable S5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Predictive Nomogram for PMV\u003c/h2\u003e \u003cp\u003eBased on the multivariate logistic regression, a nomogram incorporating BMI, CIT, PGD grading at T\u003csub\u003e0\u003c/sub\u003e, PIP, and Cdyn for predicting PMV after LuTx was established (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The model demonstrated excellent discrimination, with an AUC of 0.895 (95%CI, 0.852\u0026ndash;0.955, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and an accuracy of 0.90 (\u003cb\u003eTable S2\u003c/b\u003e). A bootstrapped calibration curve was further established to estimate the predictive ability of the model, which demonstrated a superior ability with a preserved calibration. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Bootstrap test for the different ROC curves demonstrated significant differences between the nomogram and each variable included in it (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other performance metrics are listed in the supplementary materials (\u003cb\u003eTable S2)\u003c/b\u003e. As the DCA depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the nomogram added clinical risk prediction within the range of the PMV threshold probability\u0026thinsp;\u0026lt;\u0026thinsp;0.80, which presented satisfactory clinical usefulness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLuTx is the ultimate treatment option for selected patients with end-stage lung diseases. However, the risks associated with LuTx remain considerable. One of the most important risk factors after LuTx is PMV, which leads to an increased cost of care and a greater risk of death for the patient[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Predicting patients at risk of PMV helps clinicians devise personalized care plans to mitigate the risk of PMV and timely decide on tracheostomy if ventilatory support is still required. However, tools to accurately predict PMV after LuTx are limited. In the present study, we established a nomogram incorporating patients\u0026rsquo; BMI, pulmonary hypertension, primary diagnosis as IPF, three ventilation parameters, CIT, and PGD grading at T\u003csub\u003e0\u003c/sub\u003e to predict PMV. Compared with ventilation parameters alone, this nomogram achieved a better predictive value. Since the variables included in this nomogram are easily obtainable, the utility of this nomogram to predict the risk of PMV and guide treatment decisions may be considered routine clinical practice shortly. In more detail, lung-protective ventilation, fluid restriction, prophylactic use of ECLS, and pulmonary vasodilators may be viable options for preventing PMV in high-risk individuals from this model.\u003c/p\u003e \u003cp\u003eAlthough a series of studies have confirmed the negative prognostic impact of PMV[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the definition of PMV is still controversial, ranging from 5 hours to 21 days[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In 2005, a report by the National Association for Medical Direction of Respiratory Care (NAMDRC) consensus conference defined PMV as mechanical ventilation for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e 21 consecutive days[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the definitional criteria may not fit all studies due to subject cohort variations. For LuTx, most patients undergo extubation within the first 72 hours. Two previous studies defined PMV as mechanical ventilation \u0026gt; 72 hours based on their finding that most patients (77.1% and 80.6%, respectively) were already extubated at T\u003csub\u003e72\u003c/sub\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. They thus referred to \u0026gt;\u0026thinsp;72 hours as the threshold to define PMV. A similar extubation rate (96/141, 68.1%) within the first 72 hours after transplantation was observed in the present study. Therefore, we used the same criteria as in the two previously mentioned studies to define PMV.\u003c/p\u003e \u003cp\u003eIn our study, BMI, pulmonary hypertension, primary diagnosis as IPF, PGD grading at T\u003csub\u003e0\u003c/sub\u003e, relevant ventilation parameters, and cold ischemia time were included in the nomogram. Obesity has long been considered an independent predictor of the length of mechanical ventilation in mechanically ventilated patients in the ICU setting[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Obesity is also a risk factor for PGD and mortality after LuTx[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, obese recipients should be given particular caution regarding perioperative management. Despite previous studies that have reported that IPF and IPAH were independent predictors of increased PGD[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], our findings are the first study to implicate the predictive ability of IPF as a primary diagnosis and pulmonary hypertension as a complication for early adverse events after LuTx instead of PGD. As for PGD grading at T\u003csub\u003e0\u003c/sub\u003e, we demonstrated that patients with NPMV were more likely to be PGD grade 0 than patients with PMV (60.4% vs. 24.4%). A previous report also revealed that patients with PGD grade 0 at T\u003csub\u003e0\u003c/sub\u003e had a shorter LMV than those with PGD grade 1\u0026ndash;3[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the AUC of PGD grading for predicting PMV was only 0.634, slightly smaller than our study (AUC\u0026thinsp;=\u0026thinsp;0.747). Thus, the predictive value of PGD grading at T0 alone for PMV was limited. Although PGD grading at a later time point is reported to be more closely related to long-term outcomes after LuTx[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], only PGD grading at T\u003csub\u003e0\u003c/sub\u003e remained statistically significant in the multivariate logistic regression analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). A likely reason for this result is that what led to long-term outcomes did not necessarily generalize to some early outcomes, such as PMV.\u003c/p\u003e \u003cp\u003eThe length of mechanical ventilation is closely related to the ventilation parameters[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Three ventilation parameters, P/F ratio, PIP, and Cdyn, were included in the nomogram in our study. Similarly, Schwarz and colleagues[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] also found these three ventilation parameters were predictors of PMV after LuTx. According to Ripoll et al., elevated PIP is associated with the development of acute respiratory distress syndrome (ARDS) in liver transplant recipients[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, Laffey et al[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] demonstrated that higher PIP and lower P/F ratio contribute to increased hospital mortality in patients with ARDS. Cdyn was reported as a critical parameter for evaluating graft function after ex vivo lung perfusion in a previous study[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, we show that in our multivariate logistic regression analysis, PIP was the strongest predictor of PMV. Only mechanical ventilation parameters at T\u003csub\u003e0\u003c/sub\u003e were included in our study. This is because only ventilation parameters in the immediate postoperative period were thought to have predictive value while ventilation parameters at later times hold value for assessing the status of those patients after LuTx rather than being predictive.\u003c/p\u003e \u003cp\u003eAmong these variables included in the nomogram, CIT outperformed other individual factors for predicting PMV. Since the pathological basis of PGD is consistent with IRI[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], CIT is closely related to early allograft function[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Recently, CIT was also reported to have a significant correlation with postoperative complications of lung transplantation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, whether CIT could be used to predict PMV remains unknown. In the present study, we demonstrated for the first time that longer CIT was an independent risk factor for PMV.\u003c/p\u003e \u003cp\u003eOur study has several limitations. First, one major limitation in this single-center study is that the absence of external validation may limit the application of the nomogram. Regrettably, despite repeated attempts to add a validation cohort, we ultimately failed to establish such a cohort because there are so few lung transplantation centers in China. However, both the lung transplantation centers and the annual number of lung transplants has markedly increased in recent years in China[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Hopefully, this preliminary result will be validated in multicenter studies in the future. Second, the sample size was relatively small. Third, the majority of the patients in our study cohort underwent a unilateral LuTx, which may influence the estimation of the PGD grading\u0026rsquo;s impact on LMV. Although the Report of the ISHLT Working Group does not recommend separately grading PGD for bilateral and single LuTx recipients routinely[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], previous publications do show that single LuTx may have an elevated overall incidence of PGD[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In addition, the residual pulmonary function of the contralateral lung may influence the LMV, which could not be evaluated in our study. Finally, this model can only be applied post-operatively to evaluate the risk for PMV after LuTx. This may limit the interventions available to reduce the incidence of PMV and hence restricts potential applications.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eAs shown in Visual Abstract, we established a novel nomogram that could efficiently predict individual risk of receiving PMV for patients after LuTx, which facilitates early diagnosis and rational intervention. Still, additional prospective validation cohorts from more clinical centers will be needed to confirm the practical utility of the newly established nomogram before its translation to wide-accepted clinical practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC, area under the curve,\u003c/p\u003e\n\u003cp\u003eAV, assisted ventilation.,\u003c/p\u003e\n\u003cp\u003eBMI, body mass index,\u003c/p\u003e\n\u003cp\u003eCdyn,\u0026nbsp;dynamic compliance,\u003c/p\u003e\n\u003cp\u003eCI, confidence interval,\u003c/p\u003e\n\u003cp\u003eCIT, cold ischemia time,\u003c/p\u003e\n\u003cp\u003eCV,\u0026nbsp;controlled ventilation,\u003c/p\u003e\n\u003cp\u003eDBD, Donation after brain death,\u003c/p\u003e\n\u003cp\u003eDCA, decision-curve analysis,\u003c/p\u003e\n\u003cp\u003eDCD, Donation after circulatory death,\u003c/p\u003e\n\u003cp\u003eECLS, extracorporeal life support,\u003c/p\u003e\n\u003cp\u003eECMO, extended extracorporeal membrane oxygenation,\u003c/p\u003e\n\u003cp\u003eFiO\u003csub\u003e2\u003c/sub\u003e, the fraction of inspiration O\u003csub\u003e2\u003c/sub\u003e,\u003c/p\u003e\n\u003cp\u003eHFNC, High-flow nasal cannula oxygen therapy,\u003c/p\u003e\n\u003cp\u003eICU, intensive care unit,\u003c/p\u003e\n\u003cp\u003eIRI, ischemia-reperfusion injury,\u003c/p\u003e\n\u003cp\u003eISHLT, the International Society for Heart and Lung Transplantation,\u003c/p\u003e\n\u003cp\u003eLMV, length of mechanical ventilation,\u003c/p\u003e\n\u003cp\u003eLuTx, lung transplantation,\u003c/p\u003e\n\u003cp\u003eNB, net benefit,\u003c/p\u003e\n\u003cp\u003eNIV, Noninvasive ventilation,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNPMV, non-prolonged mechanical ventilation,\u003c/p\u003e\n\u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e, partial pressure of oxygen,\u003c/p\u003e\n\u003cp\u003ePEEP, positive end-expiratory pressure,\u003c/p\u003e\n\u003cp\u003ePGD, primary graft dysfunction,\u003c/p\u003e\n\u003cp\u003ePIP, peak inspiratory pressure,\u003c/p\u003e\n\u003cp\u003ePMV, prolonged mechanical ventilation,\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic curve,\u003c/p\u003e\n\u003cp\u003eSaO\u003csub\u003e2\u003c/sub\u003e, arterial oxygen saturation,\u003c/p\u003e\n\u003cp\u003eTV, tidal volume.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by\u0026nbsp;the\u0026nbsp;Research Ethics Commission of Shanghai Pulmonary Hospital\u0026nbsp;(No. L20-352). The requirement for informed consent was waived by the Research Ethics Commission of Shanghai Pulmonary Hospital, Tongji University School of Medicine\u0026nbsp;because of the retrospective nature of the study. All procedures were in accordance with relevant guidelines and regulations (Declaration of Helsinki). We confirm that our retrospective data collection didn\u0026rsquo;t subject the patients to any additional experimental protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the scientific and technological innovation action plan of Science and Technology Commission of Shanghai Municipality (No.20DZ2253700).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePG and CL analyzed the data and wrote the paper, YZ, YN and JW collected the data, XL, PZ and JD checked the integrity of the data and the accuracy of the data analysis, CC, YS and WH designed the study and revised the paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the thoracic surgery and ICU staff of the Shanghai Pulmonary Hospital for making this research possible and all patients and their family members for participating in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Shanghai Engineering Research Center of Lung Transplantation, Shanghai, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHachem RR. Advancing Lung Transplantation. \u003cem\u003eClin Transpl\u003c/em\u003e 2015, \u003cstrong\u003e31\u003c/strong\u003e: 239-47\u003c/li\u003e\n\u003cli\u003eRaskin J, Vanstapel A, Verbeken EK, et al. Mortality after lung transplantation: a single-centre cohort analysis. \u003cem\u003eTranspl Int\u003c/em\u003e 2020, \u003cstrong\u003e33\u003c/strong\u003e(2): 130-4110.1111/tri.13540.\u003c/li\u003e\n\u003cli\u003eSchwarz S, Benazzo A, Dunkler D, et al. Ventilation parameters and early graft function in double lung transplantation. \u003cem\u003eJ Heart Lung Transplant\u003c/em\u003e 2021, \u003cstrong\u003e40\u003c/strong\u003e(1): 4-1110.1016/j.healun.2020.10.003.\u003c/li\u003e\n\u003cli\u003eVan Herck A, Frick AE, Schaevers V, et al. Azithromycin and early allograft function after lung transplantation: A randomized, controlled trial. \u003cem\u003eJ Heart Lung Transplant\u003c/em\u003e 2019, \u003cstrong\u003e38\u003c/strong\u003e(3): 252-910.1016/j.healun.2018.12.006.\u003c/li\u003e\n\u003cli\u003eEfrati O, Bylin I, Segal E, et al. Outcome of patients with cystic fibrosis admitted to the intensive care unit: is invasive mechanical ventilation a risk factor for death in patients waiting lung transplantation? \u003cem\u003eHeart Lung\u003c/em\u003e 2010, \u003cstrong\u003e39\u003c/strong\u003e(2): 153-910.1016/j.hrtlng.2009.06.014.\u003c/li\u003e\n\u003cli\u003eSnell GI, Yusen RD, Weill D, et al. Report of the ISHLT Working Group on Primary Lung Graft Dysfunction, part I: Definition and grading-A 2016 Consensus Group statement of the International Society for Heart and Lung Transplantation. \u003cem\u003eJ Heart Lung Transplant\u003c/em\u003e 2017, \u003cstrong\u003e36\u003c/strong\u003e(10): 1097-10310.1016/j.healun.2017.07.021.\u003c/li\u003e\n\u003cli\u003eChristie JD, Kotloff RM, Ahya VN, et al. The effect of primary graft dysfunction on survival after lung transplantation. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e 2005, \u003cstrong\u003e171\u003c/strong\u003e(11): 1312-610.1164/rccm.200409-1243OC.\u003c/li\u003e\n\u003cli\u003eDaud SA, Yusen RD, Meyers BF, et al. Impact of immediate primary lung allograft dysfunction on bronchiolitis obliterans syndrome. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e 2007, \u003cstrong\u003e175\u003c/strong\u003e(5): 507-1310.1164/rccm.200608-1079OC.\u003c/li\u003e\n\u003cli\u003eDiamond JM, Arcasoy S, Kennedy CC, et al. Report of the International Society for Heart and Lung Transplantation Working Group on Primary Lung Graft Dysfunction, part II: Epidemiology, risk factors, and outcomes-A 2016 Consensus Group statement of the International Society for Heart and Lung Transplantation. \u003cem\u003eJ Heart Lung Transplant\u003c/em\u003e 2017, \u003cstrong\u003e36\u003c/strong\u003e(10): 1104-1310.1016/j.healun.2017.07.020.\u003c/li\u003e\n\u003cli\u003eCalfee CS, Budev MM, Matthay MA, et al. Plasma receptor for advanced glycation end-products predicts duration of ICU stay and mechanical ventilation in patients after lung transplantation. \u003cem\u003eJ Heart Lung Transplant\u003c/em\u003e 2007, \u003cstrong\u003e26\u003c/strong\u003e(7): 675-8010.1016/j.healun.2007.04.002.\u003c/li\u003e\n\u003cli\u003eMendogni P, Pieropan S, Rosso L, et al. Impact of Cold Ischemic Time on Airway Complications After Lung Transplantation: A Single-center Cohort Study. \u003cem\u003eTransplant Proc\u003c/em\u003e 2019, \u003cstrong\u003e51\u003c/strong\u003e(9): 2981-510.1016/j.transproceed.2019.04.092.\u003c/li\u003e\n\u003cli\u003eHoetzenecker K, Schwarz S, Muckenhuber M, et al. Intraoperative extracorporeal membrane oxygenation and the possibility of postoperative prolongation improve survival in bilateral lung transplantation. \u003cem\u003eJ Thorac Cardiovasc Surg\u003c/em\u003e 2018, \u003cstrong\u003e155\u003c/strong\u003e(5): 2193-206 e310.1016/j.jtcvs.2017.10.144.\u003c/li\u003e\n\u003cli\u003eBoles JM, Bion J, Connors A, et al. Weaning from mechanical ventilation. \u003cem\u003eEur Respir J\u003c/em\u003e 2007, \u003cstrong\u003e29\u003c/strong\u003e(5): 1033-5610.1183/09031936.00010206.\u003c/li\u003e\n\u003cli\u003eVan Raemdonck D, Hartwig MG, Hertz MI, et al. Report of the ISHLT Working Group on primary lung graft dysfunction Part IV: Prevention and treatment: A 2016 Consensus Group statement of the International Society for Heart and Lung Transplantation. \u003cem\u003eJ Heart Lung Transplant\u003c/em\u003e 2017, \u003cstrong\u003e36\u003c/strong\u003e(10): 1121-3610.1016/j.healun.2017.07.013.\u003c/li\u003e\n\u003cli\u003eAziz A, May M, Burger M, et al. Prediction of 90-day mortality after radical cystectomy for bladder cancer in a prospective European multicenter cohort. \u003cem\u003eEur Urol\u003c/em\u003e 2014, \u003cstrong\u003e66\u003c/strong\u003e(1): 156-6310.1016/j.eururo.2013.12.018.\u003c/li\u003e\n\u003cli\u003eGandaglia G, Fossati N, Zaffuto E, et al. Development and Internal Validation of a Novel Model to Identify the Candidates for Extended Pelvic Lymph Node Dissection in Prostate Cancer. \u003cem\u003eEur Urol\u003c/em\u003e 2017, \u003cstrong\u003e72\u003c/strong\u003e(4): 632-4010.1016/j.eururo.2017.03.049.\u003c/li\u003e\n\u003cli\u003eVan Calster B, Wynants L, Verbeek JFM, et al. Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators. \u003cem\u003eEur Urol\u003c/em\u003e 2018, \u003cstrong\u003e74\u003c/strong\u003e(6): 796-80410.1016/j.eururo.2018.08.038.\u003c/li\u003e\n\u003cli\u003eFitzgerald M, Saville BR, Lewis RJ. Decision curve analysis. \u003cem\u003eJAMA\u003c/em\u003e 2015, \u003cstrong\u003e313\u003c/strong\u003e(4): 409-1010.1001/jama.2015.37.\u003c/li\u003e\n\u003cli\u003eSteyerberg EW, Vickers AJ. Decision curve analysis: a discussion. \u003cem\u003eMed Decis Making\u003c/em\u003e 2008, \u003cstrong\u003e28\u003c/strong\u003e(1): 146-910.1177/0272989X07312725.\u003c/li\u003e\n\u003cli\u003eDong NG, Hu XJ, Wang HB, Chen JY, Wan S. Should we tolerate biased critiques in cardiothoracic surgery journals? \u003cem\u003eJ Thorac Cardiovasc Surg\u003c/em\u003e 2022, 10.1016/j.jtcvs.2022.03.033: 10.1016/j.jtcvs.2022.03.033.\u003c/li\u003e\n\u003cli\u003eHadem J, Gottlieb J, Seifert D, et al. Prolonged Mechanical Ventilation After Lung Transplantation-A Single-Center Study. \u003cem\u003eAm J Transplant\u003c/em\u003e 2016, \u003cstrong\u003e16\u003c/strong\u003e(5): 1579-8710.1111/ajt.13632.\u003c/li\u003e\n\u003cli\u003eFernandez-Zamora MD, Gordillo-Brenes A, Banderas-Bravo E, et al. Prolonged Mechanical Ventilation as a Predictor of Mortality After Cardiac Surgery. \u003cem\u003eRespiratory care\u003c/em\u003e 2018, \u003cstrong\u003e63\u003c/strong\u003e(5): 550-710.4187/respcare.04915.\u003c/li\u003e\n\u003cli\u003eDamuth E, Mitchell JA, Bartock JL, Roberts BW, Trzeciak S. Long-term survival of critically ill patients treated with prolonged mechanical ventilation: a systematic review and meta-analysis. \u003cem\u003eThe Lancet Respiratory medicine\u003c/em\u003e 2015, \u003cstrong\u003e3\u003c/strong\u003e(7): 544-5310.1016/s2213-2600(15)00150-2.\u003c/li\u003e\n\u003cli\u003eRose L, McGinlay M, Amin R, et al. Variation in Definition of Prolonged Mechanical Ventilation. \u003cem\u003eRespiratory care\u003c/em\u003e 2017, \u003cstrong\u003e62\u003c/strong\u003e(10): 1324-3210.4187/respcare.05485.\u003c/li\u003e\n\u003cli\u003eMacIntyre NR, Epstein SK, Carson S, Scheinhorn D, Christopher K, Muldoon S. Management of patients requiring prolonged mechanical ventilation: report of a NAMDRC consensus conference. \u003cem\u003eChest\u003c/em\u003e 2005, \u003cstrong\u003e128\u003c/strong\u003e(6): 3937-5410.1378/chest.128.6.3937.\u003c/li\u003e\n\u003cli\u003ePilcher DV, Scheinkestel CD, Snell GI, Davey-Quinn A, Bailey MJ, Williams TJ. High central venous pressure is associated with prolonged mechanical ventilation and increased mortality after lung transplantation. \u003cem\u003eJ Thorac Cardiovasc Surg\u003c/em\u003e 2005, \u003cstrong\u003e129\u003c/strong\u003e(4): 912-810.1016/j.jtcvs.2004.07.006.\u003c/li\u003e\n\u003cli\u003eAkinnusi ME, Pineda LA, El Solh AA. Effect of obesity on intensive care morbidity and mortality: a meta-analysis. \u003cem\u003eCritical care medicine\u003c/em\u003e 2008, \u003cstrong\u003e36\u003c/strong\u003e(1): 151-810.1097/01.ccm.0000297885.60037.6e.\u003c/li\u003e\n\u003cli\u003eDiamond JM, Lee JC, Kawut SM, et al. Clinical risk factors for primary graft dysfunction after lung transplantation. \u003cem\u003eAmerican journal of respiratory and critical care medicine\u003c/em\u003e 2013, \u003cstrong\u003e187\u003c/strong\u003e(5): 527-3410.1164/rccm.201210-1865OC.\u003c/li\u003e\n\u003cli\u003eUpala S, Panichsillapakit T, Wijarnpreecha K, Jaruvongvanich V, Sanguankeo A. Underweight and obesity increase the risk of mortality after lung transplantation: a systematic review and meta-analysis. \u003cem\u003eTransplant international : official journal of the European Society for Organ Transplantation\u003c/em\u003e 2016, \u003cstrong\u003e29\u003c/strong\u003e(3): 285-9610.1111/tri.12721.\u003c/li\u003e\n\u003cli\u003eDiamond JM, Lee JC, Kawut SM, et al. Clinical risk factors for primary graft dysfunction after lung transplantation. \u003cem\u003eAm J Respir Crit Care Med\u003c/em\u003e 2013, \u003cstrong\u003e187\u003c/strong\u003e(5): 527-3410.1164/rccm.201210-1865OC.\u003c/li\u003e\n\u003cli\u003eWhitson BA, Prekker ME, Herrington CS, et al. Primary graft dysfunction and long-term pulmonary function after lung transplantation. \u003cem\u003eJ Heart Lung Transplant\u003c/em\u003e 2007, \u003cstrong\u003e26\u003c/strong\u003e(10): 1004-1110.1016/j.healun.2007.07.018.\u003c/li\u003e\n\u003cli\u003eFigueroa-Casas JB, Dwivedi AK, Connery SM, Quansah R, Ellerbrook L, Galvis J. Predictive models of prolonged mechanical ventilation yield moderate accuracy. \u003cem\u003eJournal of critical care\u003c/em\u003e 2015, \u003cstrong\u003e30\u003c/strong\u003e(3): 502-510.1016/j.jcrc.2015.01.020.\u003c/li\u003e\n\u003cli\u003eRipoll JG, Wanta BT, Wetzel DR, Frank RD, Findlay JY, Vogt MNP. Association of Perioperative Variables and the Acute Respiratory Distress Syndrome in Liver Transplant Recipients. \u003cem\u003eTransplantation direct\u003c/em\u003e 2020, \u003cstrong\u003e6\u003c/strong\u003e(1): e52010.1097/txd.0000000000000965.\u003c/li\u003e\n\u003cli\u003eLaffey JG, Bellani G, Pham T, et al. Potentially modifiable factors contributing to outcome from acute respiratory distress syndrome: the LUNG SAFE study. \u003cem\u003eIntensive care medicine\u003c/em\u003e 2016, \u003cstrong\u003e42\u003c/strong\u003e(12): 1865-7610.1007/s00134-016-4571-5.\u003c/li\u003e\n\u003cli\u003eArni S, Maeyashiki T, Citak N, Opitz I, Inci I. Subnormothermic Ex Vivo Lung Perfusion Temperature Improves Graft Preservation in Lung Transplantation. \u003cem\u003eCells\u003c/em\u003e 2021, \u003cstrong\u003e10\u003c/strong\u003e(4): 10.3390/cells10040748.\u003c/li\u003e\n\u003cli\u003eWang X, O\u0026apos;Brien ME, Yu J, et al. Prolonged Cold Ischemia Induces Necroptotic Cell Death in Ischemia-Reperfusion Injury and Contributes to Primary Graft Dysfunction after Lung Transplantation. \u003cem\u003eAm J Respir Cell Mol Biol\u003c/em\u003e 2019, \u003cstrong\u003e61\u003c/strong\u003e(2): 244-5610.1165/rcmb.2018-0207OC.\u003c/li\u003e\n\u003cli\u003eKuntz CL, Hadjiliadis D, Ahya VN, et al. Risk factors for early primary graft dysfunction after lung transplantation: a registry study. \u003cem\u003eClinical transplantation\u003c/em\u003e 2009, \u003cstrong\u003e23\u003c/strong\u003e(6): 819-3010.1111/j.1399-0012.2008.00951.x.\u003c/li\u003e\n\u003cli\u003eNecki M, Antonczyk R, Pandel A, et al. Impact of Cold Ischemia Time on Frequency of Airway Complications Among Lung Transplant Recipients. \u003cem\u003eTransplant Proc\u003c/em\u003e 2020, \u003cstrong\u003e52\u003c/strong\u003e(7): 2160-410.1016/j.transproceed.2020.03.047.\u003c/li\u003e\n\u003cli\u003ePrekker ME, Nath DS, Walker AR, et al. Validation of the proposed International Society for Heart and Lung Transplantation grading system for primary graft dysfunction after lung transplantation. \u003cem\u003eThe Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation\u003c/em\u003e 2006, \u003cstrong\u003e25\u003c/strong\u003e(4): 371-810.1016/j.healun.2005.11.436.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"prolonged mechanical ventilation, cold ischemia time, primary graft dysfunction, ventilation parameters, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-2089786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2089786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eProlonged mechanical ventilation (PMV), mostly defined as mechanical ventilation\u0026thinsp;\u0026gt;\u0026thinsp;72 hours after lung transplantation (LuTx) with or without tracheostomy, is associated with increased mortality. Nevertheless, the predictive factors of PMV after LuTx remain unclear. The present study aimed to develop a novel scoring system to identify PMV after LuTx.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 141 patients who underwent lung transplantation were investigated in this study. The patients were divided into PMV and non-prolonged ventilation (NPMV) groups. Univariate and multivariate logistic regression analyses were performed to assess factors associated with PMV. A risk nomogram was then established based on the multivariate analysis, and model performance was further examined regarding its calibration, discrimination, and clinical usefulness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEight factors were finally identified to be significantly associated with PMV by the multivariate analysis and therefore were included as risk factors in the nomogram as follows: the body mass index (BMI, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036);primary diagnosis as idiopathic pulmonary fibrosis (IPF, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038); pulmonary hypertension (PAH, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034); primary graft dysfunction grading (PGD, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) at T\u003csub\u003e0\u003c/sub\u003e; cold ischemia time (CIT \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012); and three ventilation parameters (peak inspiratory pressure [PIP, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], dynamic compliance [Cdyn, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001], and P/F ratio [\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015] ) at T\u003csub\u003e0\u003c/sub\u003e. The nomogram exhibited superior discrimination ability with an area under the curve (AUC) of 0.895. Furthermore, both calibration curve and decision-curve analysis (DCA) indicated satisfactory performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA novel nomogram to predict individual risk of receiving PMV for patients after LuTx was established, which may guide preventative measures for tackling this adverse event.\u003c/p\u003e","manuscriptTitle":"Establishment of a risk prediction model for prolonged mechanical ventilation after lung transplantation: a retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-28 21:27:40","doi":"10.21203/rs.3.rs-2089786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-11-16T06:44:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-11-13T20:58:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"db9115e4-fbdb-4639-8369-12392340d959","date":"2022-11-03T14:13:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0db81bf3-d7d8-4ece-b233-feedd728e334","date":"2022-10-24T20:57:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a99844f7-e66d-4808-aa01-fdce6d3b87cd","date":"2022-10-22T20:36:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-10-17T18:01:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-10-17T17:32:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-09-26T11:50:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-09-26T11:42:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2022-09-21T16:34:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae96158d-a131-44d3-9186-7352395a09b5","owner":[],"postedDate":"September 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:23:38+00:00","versionOfRecord":{"articleIdentity":"rs-2089786","link":"https://doi.org/10.1186/s12890-023-02307-9","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2023-01-10 18:16:43","publishedOnDateReadable":"January 10th, 2023"},"versionCreatedAt":"2022-09-28 21:27:40","video":"","vorDoi":"10.1186/s12890-023-02307-9","vorDoiUrl":"https://doi.org/10.1186/s12890-023-02307-9","workflowStages":[]},"version":"v1","identity":"rs-2089786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2089786","identity":"rs-2089786","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}