Impacts of Preoperative Smoking and Smoking Cessation Time on Preoperative Peripheral Blood Inflammatory Indexes and Postoperative Hospitalization Outcome in Male Patients with Lung Cancer and Surgery Treatment

Development and validation of a prediction model for postoperative pneumonia in patients who received spinal surgery: A retrospective study

Objectives

To develop and validate a risk prediction model by identifying the preoperative factors associated with an increased risk of pneumonia after spinal surgery.

Methods

This study included patients with spinal disease from two hospitals between January 2021 and June 2023. The patients were divided into the training and validation sets, which were categorized as postoperative pneumonia (POP) or non-POP, respectively. This study identified the independent risk variables for POP using a multivariate logistic regression analysis. A nomogram prediction model was developed and validated using risk factors, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) to assess predictive performance.

Results

Following exclusion, 2223 patients from Changzheng Hospital were enrolled in the training set and 357 patients from the No. 905 Hospital of PLA Navy were enrolled in the validation set. Univariate and multivariate logistic regression analyses revealed that operation time, American Society of Anesthesiologists (ASA) grade, smoking, non-wearing of medical masks, lack of preoperative respiratory training, chronic obstructive pulmonary disease (COPD), underlying diseases, and spinal section were risk factors for POP development in patients with spinal diseases. The area under the ROC curve of the training set was 0.950, whereas that of the validation set was 0.879. The model calibration curves demonstrated good agreement, and the DCA indicated a high expected net benefit value.

Conclusion

The POP risk prediction model has high accuracy and efficiency in predicting POP in patients with spinal diseases. POP development is influenced by factors such as operation length, ASA grade, smoking, non-wearing of medical masks, lack of preoperative respiratory training, COPD, underlying diseases, and lumbar surgery.

Impact of smoking cessation duration on lung cancer mortality: A systematic review and meta-analysis

BACKGROUND

Smoking history is a heterogeneous situation for different populations, and numerous studies suggest that smoking cessation is conducive to reduce the mortality of lung cancer. However, no quantitative meta-analysis regarding smoking cessation duration based on different populations has demonstrated it clearly.

METHODS

We systematically searched four electronic databases (PubMed, Embase, the Cochrane Central Register of Controlled Trials, and Scoups) till February 2023. Eligible studies reported the association between lung cancer survival and duration of smoking cessation. Additionally, we stratified the study population according to whether they had lung cancer at the time they quit smoking. Studies were pooled with the random-effects model.

RESULTS

Out of the 11,361 potential studies initially identified, we included 24 studies involving 969,560 individuals in our analysis. Lung cancer mortality varied across two groups: general quitters and peri-diagnosis quitters. For general quitters, those who had quit smoking for less than 10 years exhibited an RR of 0.64 (95% CI [0.55-0.76]), while those who quit for 10-20 years had an RR of 0.33 (0.25-0.43), over 20 years had an RR of 0.16 (0.11-0.24), and never-smokers had an RR at 0.11 (0.07-0.15). Among peri-diagnosis quitters, the 1-year Overall Survival (OS) showed an RR of 0.80 (0.67-0.96), the 2-year OS had an RR of 0.89 (0.80-0.98), the 3-year OS had an RR of 0.93 (0.84-1.03), and the 5-year OS had an RR of 0.85 (0.76-0.96).

CONCLUSIONS

Earlier and longer smoking cessation is associated with reduced lung cancer mortality, no matter in which cessation stage for two different populations.