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Predictive Effects of Lung function test on Postoperative Pneumonia in Squamous Esophageal Cancer.

ABSTRACT: Pulmonary function tests had prospective implications for postoperative pneumonia, which occurred frequently after esophagectomy. Understanding factors that were associated with pulmonary infection may help in patient selection and postoperative management. We performed a retrospective review of 2 independent cohorts including 216 patients who underwent esophagectomy between November 2011 and May 2014, aiming at identifying predictors of primary pneumonia. Univariate analysis was used to identify potential covariates for the development of primary pneumonia. Adjustments for multiple comparisons were made using False Discovery Rate (FDR) (Holm-Bonferroni method). Multivariable logistic regression analysis was used to identify independent predictors and construct a regression model based on a training cohort (n?=?166) and then the regression model was validated using an independent cohort (n?=?50). It showed that low PEF (hazard ratio 0.97, P?=?0.009) was independent risk factors for the development of primary pneumonia in multivariate analyses and had a predictive effect for primary pneumonia (AUC?=?0.691 and 0.851 for training and validation data set, respectively). Therefore, PEF has clinical value in predicting postoperative pneumonia after esophagectomy and it may serve as an indicator of preoperative lung function training.

SUBMITTER: Wei R

PROVIDER: S-EPMC4804297 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Predictive Effects of Lung function test on Postoperative Pneumonia in Squamous Esophageal Cancer.

Wei Ran R Dong Wei W Shen Hongchang H Ni Yang Y Zhang Tiehong T Wang Yibing Y Du Jiajun J

Scientific reports 20160323

Pulmonary function tests had prospective implications for postoperative pneumonia, which occurred frequently after esophagectomy. Understanding factors that were associated with pulmonary infection may help in patient selection and postoperative management. We performed a retrospective review of 2 independent cohorts including 216 patients who underwent esophagectomy between November 2011 and May 2014, aiming at identifying predictors of primary pneumonia. Univariate analysis was used to identif ...[more]

PMID: 27004739

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Project description:BackgroundPostoperative pneumonia (POP) is a hospital acquired pneumonia that occurs >48 hours after tracheal intubation. The diagnosis of POP should be based on clinical and radiological findings within 30 days after surgery. It is a common complication after thoracoscopic surgery for lung cancer patients. However, the specific impact of preoperative comorbidities on the incidence of POP remains unclear. This study aimed to analyze the preoperative data of patients with lung cancer to help surgeons predict the risk of incidence of POP after thoracoscopic lung resection.MethodsThis study is a prospective study that included patients with lung cancer who were scheduled for thoracoscopic surgery in 1 year. All cases came from two medical centers. Preoperative demographic information, tumor information, preoperative comorbidities, quality of life scores, and incidence of POP were collected. Variables were screened by univariate analysis and multivariate regression. Finally, a prediction model was constructed. A total of 53 preoperative factors were included as candidate predictors. The binary outcome variable was defined as the presence or absence of POP. The incidence of POP was the primary outcome variable. The predictive performance of the model was verified internally through 1,000 iterations of bootstrap resampling.ResultsA total of 1,229 patients with lung cancer who underwent thoracoscopic surgery were enrolled. In addition, 196 cases (15.95%) had POP; 1,025 (83.40%) patients had comorbid conditions. The total number of comorbidity diagnosed in all samples was 2,929. The prediction model suggested that patients with advanced age, high body mass index (BMI), smoking, poor physical condition, respiratory diseases, diabetes, and neurological diseases were more likely to develop POP. The area under the curve (AUC) and Brier scores were 0.851 and 0.091, respectively. The expected and observed results were in strong agreement, according to the likelihood of POP calibration curve.ConclusionsThe constructed model is capable of evaluating the probability of POP occurrence in patients with lung cancer. Seven preoperative factors in patients with lung cancer were found to be associated with increased probability of having pneumonia after thoracoscopic lung resection. This model can help predict the incidence of POP after surgery.

Project description:BackgroundThe machine learning models with dose factors and the deep learning models with dose distribution matrix have been used to building lung toxics models for radiotherapy and achieve promising results. However, few studies have integrated clinical features into deep learning models. This study aimed to explore the role of three-dimension dose distribution and clinical features in predicting radiation pneumonitis (RP) in esophageal cancer patients after radiotherapy and designed a new hybrid deep learning network to predict the incidence of RP.MethodsA total of 105 esophageal cancer patients previously treated with radiotherapy were enrolled in this study. The three-dimension (3D) dose distributions within the lung were extracted from the treatment planning system, converted into 3D matrixes and used as inputs to predict RP with ResNet. In total, 15 clinical factors were normalized and converted into one-dimension (1D) matrixes. A new prediction model (HybridNet) was then built based on a hybrid deep learning network, which combined 3D ResNet18 and 1D convolution layers. Machine learning-based prediction models, which use the traditional dosiomic factors with and without the clinical factors as inputs, were also constructed and their predictive performance compared with that of HybridNet using tenfold cross validation. Accuracy and area under the receiver operator characteristic curve (AUC) were used to evaluate the model effect. DeLong test was used to compare the prediction results of the models.ResultsThe deep learning-based model achieved superior prediction results compared with machine learning-based models. ResNet performed best in the group that only considered dose factors (accuracy, 0.78 ± 0.05; AUC, 0.82 ± 0.25), whereas HybridNet performed best in the group that considered both dose factors and clinical factors (accuracy, 0.85 ± 0.13; AUC, 0.91 ± 0.09). HybridNet had higher accuracy than that of Resnet (p = 0.009).ConclusionBased on prediction results, the proposed HybridNet model could predict RP in esophageal cancer patients after radiotherapy with significantly higher accuracy, suggesting its potential as a useful tool for clinical decision-making. This study demonstrated that the information in dose distribution is worth further exploration, and combining multiple types of features contributes to predict radiotherapy response.

Project description:BackgroundPostoperative recovery in lung cancer patients is a complex process, where breathing exercises may play a crucial role in enhancing pulmonary function and quality of life (QoL). This study systematically reviews and meta-analyzes the impact of breathing exercises on post-surgical lung function and QoL in lung cancer patients.MethodsAn extensive literature search was conducted across PubMed, Cochrane, Web of Science, and Embase databases using terms like "Lung Neoplasms", "breathing exercises", and "randomized controlled trial", supplemented by Medical Subject Headings (MeSH) and free words. The Cochrane risk of bias tool was used for quality assessment. A systematic review and meta-analysis on the effects of breathing exercises post-lung cancer surgery followed by data extraction and quality evaluation.ResultsFrom 384 retrieved studies, 10 met the inclusion criteria and were selected for detailed analysis. The main outcomes assessed were postoperative pulmonary function indices and QoL measures. The majority of studies were deemed 'low risk' for random sequence generation and allocation concealment. However, due to the nature of the interventions, blinding was a 'high risk' in most cases. The meta-analysis revealed significant improvements in key pulmonary function indices: forced vital capacity (FVC%) increased by an average of 1.73%, maximal voluntary ventilation (MVV) improved by 7.58 L/min, and maximal inspiratory pressure (MIP) enhanced by 0.95 cmH2O. Additionally, there was a notable alleviation of postoperative dyspnea and an enhancement in QoL, with anxiety scores decreasing by an average of 3.42 points and complication rates reducing correspondingly. However, the interventions did not significantly affect physical activity levels or performance on the 6-minute walk test (6WMT), with effect sizes for these outcomes being non-significant.ConclusionsThis study indicates that breathing exercises significantly improve postoperative pulmonary function and QoL in lung cancer patients. Future research should delve into the mechanisms behind these exercises and evaluate their long-term rehabilitation effects. Customized programs could further optimize recovery and enhance patient QoL.

Dataset Information

Predictive Effects of Lung function test on Postoperative Pneumonia in Squamous Esophageal Cancer.

Publications

Predictive Effects of Lung function test on Postoperative Pneumonia in Squamous Esophageal Cancer.

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