Background: A specific risk-stratification tool is needed to facilitate safe and cost-effective approaches to the prophylaxis of acute pulmonary thromboembolism (PTE) in lung cancer surgery patients. This study aimed to develop and validate a simple nomogram model for the prediction of PTE after lung cancer surgery using readily obtainable clinical characteristics. Methods: A total of 14,427 consecutive adult patients who underwent lung cancer surgery between January 2015 and July 2018 in our institution were retrospectively reviewed. Included in the cohort were 136 patients who developed PTE and 544 non-PTE patients. The patients were randomly divided into the derivation group (70%, 95 PTE patients and 380 non-PTE patients) and the validation group (30%, 41 PTE patients and 164 non-PTE patients). A nomogram model was developed based on the results of multivariate logistic analysis in the derivation group. The cut-off values were defined using Youden's index. The prognostic accuracy was measured by area under the curve (AUC) values. Results: In the derivation group, multivariate logistic analysis was carried out to evaluate the risk score. The risk assessment model contained five variables: age [95% confidence interval (CI): 1.008-1.083, P=0.016], body mass index (95% CI: 1.077-1.319, P=0.001), operation time (95% CI: 1.002-1.014, P=0.008), the serum level of cancer antigen 15-3 (CA15-3) before surgery (95% CI: 1.019-1.111, P=0.005), and the abnormal results of compression venous ultrasonography before surgery (95% CI: 2.819-18.838, P<0.001). All of them were independent risk factors of PTE. To simplify the risk assessment model, a nomogram model was established, which showed a good predictive performance in the derivation group (AUC 0.792, 95% CI: 0.734-0.853) and in the validation group (AUC 0.813, 95% CI: 0.737-0.890). Conclusions: A high-performance nomogram was established on the risk factors for PTE in patients undergoing lung cancer surgery. The nomogram could be used to provide an individual risk assessment and guide prophylaxis decisions for patients. Further external validation of the model is needed in lung cancer surgery patients in other clinical centers.

Background Prolonged mechanical ventilation (PMV), mostly defined as mechanical ventilation > 72 h after lung transplantation with or without tracheostomy, is associated with increased mortality. Nevertheless, the predictive factors of PMV after lung transplant remain unclear. The present study aimed to develop a novel scoring system to identify PMV after lung transplantation. 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 T0; 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 T0. The nomogram exhibited superior discrimination ability with an area under the curve of 0.895. Furthermore, both calibration curve and decision-curve analysis indicated satisfactory performance. Conclusion A novel nomogram to predict individual risk of receiving PMV for patients after lung transplantation was established, which may guide preventative measures for tackling this adverse event. Graphic Abstract

Background: Isolated distal deep vein thrombosis (IDDVT) accounts for ~50% of all patients diagnosed with deep venous thrombosis (DVT), but the diagnosis and optimal management of IDDVT remains unclear and controversial. The aim of this study was to explore potential risk factors and predictors of IDDVT, and to evaluate different strategies of anticoagulation therapy.Methods: A total of 310 consecutive patients after thoracic surgery, who underwent whole-leg ultrasonography as well as routine measurements of D-dimer levels before and after surgery were evaluated. The general clinical data, anticoagulant therapy, pre- and postoperative D-dimer levels were collected. Differences between IDDVT, DVT and non-DVT groups were calculated. Logistic regression analysis was used to analyze risk factors of postoperative IDDVT.Results: Age and postoperative D-dimer levels were significantly higher in IDDVT group than in non DVT group (p = 0.0053 and p &lt; 0.001, respectively). Logistic regression analysis showed that postoperative D-dimer level was a significant independent predictor of IDDVT even when adjusted for age and operation method (p = 0.0003). There were no significant side effects associated with both full-dose and half-dose anticoagulation regimens. Half-dose therapy was associated with a significant decrease in the requirement for anticoagulation medications after discharge (p = 0.0002).Conclusion: Age and D-dimer levels after surgery are strong predictors of IDDVT following thoracic surgery. Half-dose therapeutic anticoagulation has the same efficiency in preventing IDDVT progression, is not associated with any additional risks of adverse effects compared to a full-dose regimen, and may be adopted for treating IDDVT patients after thoracic surgery.