External validation of five predictive models for postoperative cardiopulmonary morbidity in a Chinese population receiving lung resection

Impact of surgical approach on 90-day mortality after lung resection for nonsmall cell lung cancer in high-risk operable patients

Introduction

Non-small cell lung cancer (NSCLC) is often associated with compromised lung function. Real-world data on the impact of surgical approach in NSCLC patients with compromised lung function are still lacking. The objective of this study is to assess the potential impact of minimally invasive surgery (MIS) on 90-day post-operative mortality after anatomic lung resection in high-risk operable NSCLC patients.

Methods

We conducted a retrospective multicentre study including all patients who underwent anatomic lung resection between January 2010 and October 2021 and registered in the Epithor database. High-risk patients were defined as those with a forced expiratory volume in 1 s (FEV1) or diffusing capacity of the lung for carbon monoxide (DLCO) value below 50%. Co-primary end-points were the impact of risk status on 90-day mortality and the impact of MIS on 90-day mortality in high-risk patients.

Results

Of the 46 909 patients who met the inclusion criteria, 42 214 patients (90%) with both preoperative FEV1 and DLCO above 50% were included in the low-risk group, and 4695 patients (10%) with preoperative FEV1 and/or preoperative DLCO below 50% were included in the high-risk group. The 90-day mortality rate was significantly higher in the high-risk group compared to the low-risk group (280 (5.96%) versus 1301 (3.18%); p<0.0001). In high-risk patients, MIS was associated with lower 90-day mortality compared to open surgery in univariate analysis (OR=0.04 (0.02-0.05), p<0.001) and in multivariable analysis after propensity score matching (OR=0.46 (0.30-0.69), p<0.001). High-risk patients operated through MIS had a similar 90-day mortality rate compared to low-risk patients in general (3.10% versus 3.18% respectively).

Conclusion

By examining the impact of surgical approaches on 90-day mortality using a nationwide database, we found that either preoperative FEV1 or DLCO below 50% is associated with higher 90-day mortality, which can be reduced by using minimally invasive surgical approaches. High-risk patients operated through MIS have a similar 90-day mortality rate as low-risk patients.

Interpretable prediction of cardiopulmonary complications after non-small cell lung cancer surgery based on machine learning and SHapley additive exPlanations

Introduction

Lung cancer is a prevalent malignancy globally, with approximately 20% of patients developing cardiopulmonary complications after lobectomy. In order to prevent complications, an accurate and personalized method based on machine learning (ML) is required.

Methods

During the period of 2017-2021, a retrospective analysis was conducted on the medical records of patients who had undergone lobectomy for non-small cell lung cancer (NSCLC). We performed logical regression, decision tree (DT), random forest (RF), gradient boost DT, and eXtreme gradient boosting analyses to establish an ML model. The ten-fold cross-validation was used to evaluate the performance of multiple ML models based on various evaluation metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating (AUC). Additionally, we also calculated the Kappa value of these model. Each model used grid search to optimize hyper-parameters and then used the interpretability method to provide explanations for the model's Decisions.

Results

The study included 718 eligible patients, among whom the incidence of postoperative cardiopulmonary complications was 20.89%. The RF model showed the best comprehensive performance among all models, and its ten-fold cross-validation accuracy, precision, recall, F1 score, and AUC were (OR and 95% confidence interval [CI]) 0.786 (0.738-0.834), 0.803 (0.735-0.872), 0.738 (0.678-0.797), 0.766 (0.714-0.818), 0.856 (0.815-0.898), respectively. The kappa value of the RF model was 0.696 (0.617-0.768). The SHAP method showed that gender, age, and intraoperative blood loss were closely associated with postoperative cardiopulmonary complications.

Conclusion

The application of ML methods for predicting postoperative cardiopulmonary complications based on clinical data of patients with NSCLC showed a good performance. The results indicate that ML combined with the SHAP individualized interpretation method has practical clinical value.

Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: A machine learning-based study

Background

Approximately 20% of patients with lung cancer would experience postoperative cardiopulmonary complications after anatomic lung resection. Current prediction models for postoperative complications were not suitable for Chinese patients. This study aimed to develop and validate novel prediction models based on machine learning algorithms in a Chinese population.

Methods

Patients with lung cancer receiving anatomic lung resection and no neoadjuvant therapies from September 1, 2018 to August 31, 2019 were enrolled. The dataset was split into two cohorts at a 7:3 ratio. The logistic regression, random forest, and extreme gradient boosting were applied to construct models in the derivation cohort with 5-fold cross validation. The validation cohort accessed the model performance. The area under the curves measured the model discrimination, while the Spiegelhalter z test evaluated the model calibration.

Results

A total of 1085 patients were included, and 760 were assigned to the derivation cohort. 8.4% and 8.0% of patients experienced postoperative cardiopulmonary complications in the two cohorts. All baseline characteristics were balanced. The values of the area under the curve were 0.728, 0.721, and 0.767 for the logistic, random forest and extreme gradient boosting models, respectively. No significant differences existed among them. They all showed good calibration (p > 0.05). The logistic model consisted of male, arrhythmia, cerebrovascular disease, the percentage of predicted postoperative forced expiratory volume in one second, and the ratio of forced expiratory volume in one second to forced vital capacity. The last two variables, the percentage of forced vital capacity and age ranked in the top five important variables for novel machine learning models. A nomogram was plotted for the logistic model.

Conclusion

Three models were developed and validated for predicting postoperative cardiopulmonary complications among Chinese patients with lung cancer. They all exerted good discrimination and calibration. The percentage of predicted postoperative forced expiratory volume in one second and the ratio of forced expiratory volume in one second to forced vital capacity might be the most important variables. Further validation in different scenarios is still warranted.