A Prediction Model for Postoperative Pulmonary Complication in Pulmonary Function-Impaired Patients Following Lung Resection

Risk factors for postoperative pulmonary complications in non-adenocarcinoma non-small cell lung cancer patients undergoing surgery after neoadjuvant therapy

Background

Neoadjuvant therapy followed by surgery is the recommended treatment for patients with locally advanced lung cancer. No studies have examined the risk factors of postoperative pulmonary complications (PPCs) in this group of patients. The addition of immune checkpoint inhibitors (ICIs) can improve the efficacy of neoadjuvant therapy; however, it is unknown whether ICIs will also increase the PPC incidence. Thus, we conducted this study to identify the predictors of PPCs.

Methods

We reviewed the database of Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University. Patients with non-adenocarcinoma non-small cell lung cancer (non-ADC NSCLC) who underwent surgery after neoadjuvant therapy were included. The clinical information was collected, the PPCs and mortality were evaluated.

Results

The cohort in this study consisted of 108 patients. Among them, 36 had PPCs, and the incidence of PPCs was 33.3% (36/108). The majority of PPCs were prolonged time to chest tube removal and pneumonia. One patient died within 30 days due to serious postoperative complications. The mortality within 30 days was 0.9%. The addition of ICIs to neoadjuvant therapy did not increase the incidence of PPCs, but the operation time was longer in the ICI group. Multivariate analysis indicated that age, blood urea nitrogen (BUN) level and N2 stage may be superior predictors of PPCs.

Conclusions

The addition of ICIs did not increase the incidence of PPCs but did prolong the operation time. Age, BUN level, and N2 stage were excellent predictors of PPCs in non-ADC NSCLC patients treated with surgery after neoadjuvant therapy.

Prediction models for postoperative pulmonary complications in intensive care unit patients after noncardiac thoracic surgery

BACKGROUND

Postoperative pulmonary complication (PPC) is a leading cause of mortality and poor outcomes in postoperative patients. No studies have enrolled intensive care unit (ICU) patients after noncardiac thoracic surgery, and effective prediction models for PPC have not been developed. This study aimed to explore the incidence and risk factors and construct prediction models for PPC in these patients.

METHODS

This study retrospectively recruited patients admitted to the ICU after noncardiac thoracic surgery at West China Hospital, Sichuan University, from July 2019 to December 2022. The patients were randomly divided into a development cohort and a validation cohort at a 70% versus 30% ratio. The preoperative, intraoperative and postoperative variables during the ICU stay were compared. Univariate and multivariate logistic regression analyses were applied to identify candidate predictors, establish prediction models, and compare the accuracy of the models with that of reported risk models.

RESULTS

A total of 475 ICU patients were enrolled after noncardiac thoracic surgery (median age, 58; 72% male). At least one PPC occurred in 171 patients (36.0%), and the most common PPC was pneumonia (153/475, 32.21%). PPC significantly increased the duration of mechanical ventilation (p < 0.001), length of ICU stay (p < 0.001), length of hospital stay (LOS) (p < 0.001), and rate of reintubation (p = 0.047) in ICU patients. Seven risk factors were identified, and then the prediction nomograms for PPC were constructed. At ICU admission, the area under the curve (AUC) was 0.766, with a sensitivity of 0.71 and specificity of 0.60; after extubation, the AUC was 0.841, with a sensitivity of 0.75 and specificity of 0.83. The models showed robust discrimination in both the development cohort and the validation cohort, and they were well calibrated and more accurate than reported risk models.

CONCLUSIONS

ICU patients who underwent noncardiac thoracic surgery were at high risk of developing PPCs. Prediction nomograms were constructed and they were more accurate than reported risk models, with excellent sensitivity and specificity. Moreover, these findings could help assess individual PPC risk and enhance postoperative management of patients.

Incidence and risk factors of pulmonary complications after lung cancer surgery: A systematic review and meta-analysis

Postoperative pulmonary complications (PPCs) are associated with high mortality rates after lung cancer surgery. Although some studies have discussed the different risk factors for PPCs, the relationship between these factors and their impact on PPCs remains unclear. Hence, this study aimed to systematically summarize the incidence and determine the risk factors for PPCs. We conducted a systematic search of five English and four Chinese databases from their inception to April 1, 2023. A total of 34 articles (8 cohort studies and 26 case-control studies) (n = 31696, 5833 with PPCs) were included in the analysis. The primary outcome was the incidence of PPC. The secondary outcome was the odds ratio (OR) of PPCs based on the identified risk factors calculated by RevMan 5.4. A narrative descriptive summary of the study results was presented when pooling the results or conducting a meta-analysis was not possible. The pooled incidence of PPCs was 18.4 %. This meta-analysis demonstrated that TNM staging (OR 4.29, 95 % CI 2.59-7.13), chronic obstructive pulmonary disease (COPD) (OR 2.47, 95 % CI 1.80-3.40), smoking history (OR 2.37, 95 % CI 1.33-4.21), poor compliance with respiratory rehabilitation (OR 1.64, 95 % CI 1.17-2.30), male sex (OR 1.62, 95 % CI 1.28-2.04), diabetes (OR 1.56, 95 % CI 1.07-2.27), intraoperative bleeding volume (OR 1.44, 95 % CI 1.02-2.04), Eastern Cooperative Oncology Group score (ECOG) > 1 (OR 1.37, 95 % CI 1.04-1.80), history of chemotherapy and/or radiotherapy (OR 1.32, 95 % CI 1.03-1.70), older age (OR 1.18, 95 % CI 1.11-1.24), and duration of surgery (OR 1.07, 95 % CI 1.04-1.10) were significantly associated with a higher risk of PPCs. In contrast, the peak expiratory flow rate (PEF) (OR 0.99, 95 % CI 0.98-0.99) was a protective factor. Clinicians should implement targeted and effective interventions to prevent the occurrence of PPCs.

Preoperative Physical Dysfunction Characteristics and Influence Factors Among Elderly Patients with Early Lung Cancer: A Latent Class Analysis

Objective

To identify latent classes of preoperative physical dysfunction in elderly patients with early lung cancer. To analyze the differences in demographic characteristics between different classes.

Methods

We invited elderly patients with early lung cancer who were scheduled for surgery at Shanghai Elderly Characteristic Hospital to participate in the study using a convenience sampling method. We took latent class analysis to divide elderly patients with early lung cancer into latent classes based on preoperative physical dysfunction features. Furthermore, we used single-factor analysis and multinomial logistic regression to investigate the influence variables of each latent class.

Results

The characteristics of preoperative physical dysfunction in elderly patients with early lung cancer can be divided into "Anxiety/depression emotion-poor sleep group" "Frailty of physical function group" "Pulmonary hypofunction-low activity tolerance group". The distribution of age, chronic disease history, COPD history, smoking history and perceived social support level of elderly patients with early lung cancer in different potential categories were not the same, and the differences were statistically significant (P<0.05). The elderly lung cancer patients with chronic disease history and age ≥75 years were more likely to be classified as "frailty of physical function group". The elderly lung cancer patients with COPD and smoking history were more likely to be classified into "pulmonary hypofunction-low activity tolerance group". Elderly lung cancer patients with moderate or low degree of perceived social support were more prone to be grouped into "anxiety/depression emotion-poor sleep group".

Conclusion

The variety of preoperative physical dysfunction seen in elderly patients with early lung cancer can be categorized into three latent classes. Medical professionals should create strategies for intervention for multiple patient populations with the goal of further enhancing their general state of life.

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.