The effects of preoperative alcohol, tobacco, and psychological stress on postoperative complications: a prospective observational study

Preoperative Alcohol Use, Postoperative Pain, and Opioid Use After Coronary Artery Bypass Surgery

OBJECTIVES

Chronic alcohol use is associated with chronic pain and increased opioid consumption. The association between chronic alcohol use and acute postoperative pain has been studied minimally. The authors' objective was to explore the association among preoperative alcohol use, postoperative pain, and opioid consumption after coronary artery bypass grafting (CABG).

DESIGN

A retrospective cohort study.

SETTING

At a single academic medical center.

PARTICIPANTS

Patients having isolated CABG.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Demographics, comorbidities, and baseline alcohol consumption were recorded. Primary outcomes were mean pain score and morphine milligram equivalent (MME) consumption on postoperative day 0. Among 1,338 patients, there were 764 (57.1%) who had no weekly preoperative alcohol use, 294 (22.0%) who drank ≤1 drink per week, 170 (12.7%) who drank 2-to-7 drinks per week, and 110 (8.2%) who drank 8 or more drinks per week. There was no significant difference in mean pain score on postoperative day 0 in patients who consumed different amounts of alcohol (no alcohol = 5.3 ± 2.2, ≤1 drink = 5.2 ± 2.1, 2 to 7 drinks = 5.3 ± 2.3, 8 or more drinks = 5.4 ± 1.9, p = 0.66). There was also no significant difference in median MME use on postoperative day 0 in patients who consumed different amounts of alcohol (no alcohol = 22.5 mg, ≤1 drink = 21.1 mg, 2-to-7 drinks = 24.8 mg, 8 or more drinks = 24.5 mg, p = 0.14).

CONCLUSIONS

There is no apparent association among mild-to-moderate preoperative alcohol consumption and early postoperative pain and opioid use in patients who underwent CABG.

Association between preoperative modifiable lifestyle factors and mortality after cancer surgery: a population-based cohort study in South Korea

Purpose

We aimed to examine whether preoperative lifestyle factors are associated with mortality after cancer surgery.

Methods

This study used data from the National Health Insurance Service database in South Korea. We included all adult patients who underwent major cancer surgery between January 1, 2016, and December 31, 2018. Three lifestyle factors were evaluated preoperatively: smoking status, alcohol consumption, and physical activity.

Results

A total of 48,557 patients who underwent major cancer surgery were included in the final analysis. In the multivariable logistic regression modeling, current smokers showed 1.40-fold higher odds of 90-day mortality after cancer surgery (odds ratio, 1.40; 95% confidence interval, 1.14-1.71; P = 0.001) than never smokers. However, alcohol consumption and physical activity were not associated with 90-day mortality after cancer surgery. In the multivariable Cox regression modeling, current smokers showed 1.25-fold higher odds of 1-year mortality after cancer surgery (hazard ratio, 1.25; 95% confidence interval, 1.13-1.38; P < 0.001) than never smokers. However, alcohol consumption and physical activity were not associated with 1-year mortality after cancer surgery.

Conclusion

In conclusion, current smoking was associated with worse short- and long-term survival outcomes in South Korea, though preoperative alcohol consumption and physical activity levels were not associated with mortality after cancer surgery.

Managing missing items in the Fagerström Test for Nicotine Dependence: a simulation study

Background

The Fagerström Test for Nicotine Dependence (FTND) is frequently used to assess the level of smokers’ nicotine dependence; however, it is unclear how to manage missing items. The aim of this study was to investigate different methods for managing missing items in the FTND.

Methods

We performed a simulation study using data from the Arizona Smokers’ Helpline. We randomly sampled with replacement from the complete data to simulate 1000 datasets for each parameter combination of sample size, proportion of missing data, and type of missing data (missing at random and missing not at random). Then for six methods for managing missing items on the FTND (two involving no imputation and four involving single imputation), we assessed the accuracy (via bias) and precision (via bias of standard error) of the total FTND score itself and of the regression coefficient for the total FTND score regressed on a covariate.

Results

When using the total FTND score as a descriptive statistic or in analysis for both types of missing data and for all levels of missing data, proration performed the best in terms of accuracy and precision. Proration’s accuracy decreased with the amount of missing data; for example, at 9% missing data proration’s maximum bias for the mean FTND was only − 0.3%, but at 35% missing data its maximum bias for the mean FTND increased to − 6%.

Conclusions

For managing missing items on the FTND, we recommend proration, because it was found to be accurate and precise, and it is easy to implement. However, because proration becomes less accurate with more missing data, if more than ~ 10% of data are missing, we recommend performing a sensitivity analysis with a different method of managing missing data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01637-2.