A Multiple-Center Nomogram to Predict Pneumonectomy Complication Risk for Non-Small Cell Lung Cancer Patients

Prediction modeling of postoperative pulmonary complications following lung resection based on random forest algorithm

Postoperative pulmonary complications (PPCs) are a significant concern following lung resection due to prolonged hospital stays and increased morbidity and mortality among patients. This study aims to develop and validate a risk prediction model for PPCs after lung resection using the random forest (RF) algorithm to enhance early detection and intervention. Data from 180 patients who underwent lung resections at the Third Affiliated Hospital of the Naval Medical University between September 2022 and February 2024 were retrospectively analyzed. The patients were randomly allocated into a training set and a test set in an 8:2 ratio. An RF model was constructed using Python, with feature importance ranked based on the mean Gini index. The predictive performance of the model was evaluated through analyses of the receiver operating characteristic curve, calibration curve, and decision curve. Among the 180 patients included, 47 (26.1%) developed PPCs. The top 5 predictive factors identified by the RF model were blood loss, maximal length of resection, number of lymph nodes removed, forced expiratory volume in the first second as a percentage of predicted value, and age. The receiver operating characteristic curve and calibration curve analyses demonstrated favorable discrimination and calibration capabilities of the model, while decision curve analysis indicated its clinical applicability. The RF algorithm is effective in predicting PPCs following lung resection and holds promise for clinical application.

Development and Validation of a Clinical Nomogram for Predicting Complications From Pediatric Multiple Magnet Ingestion: A Large Retrospective Study

INTRODUCTION

This study aimed to develop and validate a reliable nomogram based on clinical factors to predict complications associated with pediatric multiple magnet ingestion, addressing the urgency and controversy surrounding its management.

METHODS

Patients aged 0-18 years with multiple magnet ingestion diagnosed at the Shenzhen Children's Hospital between January 2017 and December 2023 were enrolled. Clinical data were analyzed using least absolute shrinkage and selection operator regression and multifactor logistic regression analyses to screen for risk factors. A model was constructed, and a nomogram was plotted. Model performance was evaluated and internally validated using the area under the curve (AUC), Hosmer-Lemeshow test, calibration curve, decision curve analysis, and 1,000 bootstraps. We calculated the optimal cutoff value, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the prediction model.

RESULTS

Of the 146 patients, 57 (39.0%) experienced complications. The nomogram included age, multiple ingestions, vomiting, abdominal pain, and abdominal tenderness. The AUC was 0.941, and the internally validated AUC was 0.930. The optimal cutoff value selected as a predictive value was 0.534, with a sensitivity of 82.5%, specificity of 93.3%, positive predictive value of 88.7%, negative predictive value of 89.3%, and accuracy of 89.0%. The Hosmer-Lemeshow test yielded a P value of 0.750. The calibration plot exhibited high consistency in prediction, and decision curve analysis showed excellent net benefits.

DISCUSSION

Our nomogram demonstrates excellent discrimination, calibration, and clinical utility and may thus help clinicians accurately assess the risk of complications from pediatric multiple magnet ingestion.

The Impact of Postoperative Pulmonary Complications on Perioperative Outcomes in Patients Undergoing Pneumonectomy: A Multicenter Retrospective Cohort Study of the German Thorax Registry

Postoperative pulmonary complications have a deleterious impact in regards to thoracic surgery. Pneumonectomy is associated with the highest perioperative risk in elective thoracic surgery. The data from 152 patients undergoing pneumonectomy in this multicenter retrospective study were extracted from the German Thorax Registry database and presented after univariate and multivariate statistical processing. This retrospective study investigated the incidence of postoperative pulmonary complications (PPCs) and their impact on perioperative morbidity and mortality. Patient-specific, preoperative, procedural, and postoperative risk factors for PPCs and in-hospital mortality were analyzed. A total of 32 (21%) patients exhibited one or more PPCs, and 11 (7%) died during the hospital stay. Multivariate stepwise logistic regression identified a preoperative FEV1 < 50% (OR 9.1, 95% CI 1.9-67), the presence of medical complications (OR 7.4, 95% CI 2.7-16.2), and an ICU stay of more than 2 days (OR 14, 95% CI 3.9-59) as independent factors associated with PPCs. PPCs (OR 13, 95% CI 3.2-52), a preoperative FEV1 < 60% in patients with previous pulmonary infection (OR 21, 95% CI 3.2-52), and continued postoperative mechanical ventilation (OR 8.4, 95% CI 2-34) were independent factors for in-hospital mortality. Our data emphasizes that PPCs are a significant risk factor for morbidity and mortality after pneumonectomy. Intensified perioperative care targeting the underlying risk factors and effects of PPCs, postoperative ventilation, and preoperative respiratory infections, especially in patients with reduced pulmonary reserve, could improve patient outcomes.

A Hierarchy-driven Multi-label Network with Label Constraints for Post-operative Complication Prediction of Lung Cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-6. [PMID: 38083421 DOI: 10.1109/embc40787.2023.10339943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Lung cancer is one of the most dangerous cancers all over the world. Surgical resection remains the only potentially curative option for patients with lung cancer. However, this invasive treatment often causes various complications, which seriously endanger patient health. In this study, we proposed a novel multi-label network, namely a hierarchy-driven multi-label network with label constraints (HDMN-LC), to predict the risk of complications of lung cancer patients. In this method, we first divided all complications into pulmonary and cardiovascular complication groups and employed the hierarchical cluster algorithm to analyze the hierarchies between these complications. After that, we employed the hierarchies to drive the network architecture design so that related complications could share more hidden features. And then, we combined all complications and employed an auxiliary task to predict whether any complications would occur to impose the bottom layer to learn general features. Finally, we proposed a regularization term to constrain the relationship between specific and combined complication labels to improve performance. We conducted extensive experiments on real clinical data of 593 patients. Experimental results indicate that the proposed method outperforms the single-label, multi-label baseline methods, with an average AUC value of 0.653. And the results also prove the effectiveness of hierarchy-driven network architecture and label constraints. We conclude that the proposed method can predict complications for lung cancer patients more effectively than the baseline methods.Clinical relevance-This study presents a novel multi-label network that can more accurately predict the risk of specific postoperative complications for lung cancer patients. The method can help clinicians identify high-risk patients more accurately and timely so that interventions can be implemented in advance to ensure patient safety.

A Clinical Prediction Model for Postoperative Pneumonia After Lung Cancer Surgery

INTRODUCTION

Postoperative pneumonia (POP) is a common complication following lung cancer surgery and is associated with increased hospitalization costs and mortalities. We aimed to identify risk factors associated with POP and to develop a reliable predictive model.

METHODS

Patients who underwent lung cancer surgery between January 2015 and December 2021 in our hospital were enrolled. Least absolute shrinkage and selection operator regression analysis was used to select predictors of POP. Multivariable logistic regression was performed to construct the nomogram. Bootstrap resampling was conducted for internal validation. The performance of the model was evaluated by discrimination and calibration.

RESULTS

A total of 5269 consecutive patients were enrolled. POP occurred in 1.7% of patients (92/5269). Five independent predictors were identified: age, predicted forced expiratory volume in 1 s, predicted diffusing capacity of the lungs for carbon monoxide, tuberculosis history, and surgery duration. The multivariable regression model showed good discrimination (C-index: 0.821, 95% confidence interval, 0.783-0.859), which was well validated by internal validation. The calibration curve illustrated good agreement between the predicted probability and observed probability of POP.

CONCLUSIONS

Based on the easily available risk factors, our nomogram could predict the risk of POP with good discrimination and calibration. The model has good clinical practicability, enabling precise and targeted interventions to reduce the incidence of POP in high-risk patients.

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.

Clinical characteristics and survival outcomes of patients with pneumonectomies: A population-based study

Background

Prognostic factors in a pneumonectomy (PN) are not yet fully defined. This study sought to analyze and evaluate long-term survival after pneumonectomies (PNs) for patients with non-small cell lung cancer (NSCLC).

Methods

We obtained data from the Surveillance, Epidemiology, and End Results (SEER) database for patients who underwent PNs between 2004 and 2015. Propensity score matching (PSM) analysis and Kaplan–Meier curves were used to estimate overall survival (OS), while univariate and multivariable Cox proportional hazards regression analyses were applied to create a forest plot.

Results

In total, 1,376 patients were grouped according to right/left PNs. Before matching, OS was worse after a right PN [hazard ratio (HR): 1.459; 95% CI 1.254–1.697; P &lt; 0.001] and after matching, survival differences between groups were not significant (HR: 1.060; 95% CI 0.906–1.240; P = 0.465). Regression analysis revealed that age, gender, grade, lymph node dissection, N-stage, and chemotherapy were independent predictors of OS (P &lt; 0.05). Chemotherapy was associated with improved OS (P &lt; 0.001).

Conclusions

Laterality was not a significant prognostic factor for long-term survival after a PN for NSCLC. Chemotherapy was a significant independent predictor of improved OS. Long-term survival and outcomes analyses should be conducted on larger numbers of patients.