Weak smoking cessation awareness in primary health care before surgery : a real-world, retrospective cohort study

Objective: Tobacco smoking is a well-established risk factor for postoperative complications. Research on preoperative smoking cessation in primary health care is scarce. Design: This was a retrospective cohort study. Setting: The Stop Smoking before Surgery Project (SSSP) started in Porvoo, Finland, in May of 2016, involving both primary health care and specialized health care. The goals of the project were smoking awareness and preoperative smoking cessation. Subjects: Our study involved 1482 surgical patients operated at Porvoo Hospital between May and December of 2016. Main outcome measures: We studied the recording of smoking status in all patients, and ICD-10 diagnosis of nicotine dependency and the initiation of preoperative smoking cessation in current smokers. Variables were studied from electronic patient records, comparing primary health care referrals and surgical outpatient clinic records. Results: Smoking status was visible in 14.2% of primary health care referrals, and in 18.4% of outpatient clinic records. Corresponding rates for current smokers (n = 275) were 0.0 and 8.7% for ICD-10 diagnosis of nicotine dependence, and 2.2 and 15.3% for initiation of preoperative smoking cessation. The differences between primary health care referrals and outpatient clinic records were statistically significant for all three variables (pPeer reviewe

Smoking is a predictor of complications in all types of surgery : a machine learning-based big data study

Background: Machine learning algorithms are promising tools for smoking status classification in big patient data sets. Smoking is a risk factor for postoperative complications in major surgery. Whether this applies to all surgery is unknown. The aims of this retrospective cohort study were to develop a machine learning algorithm for clinical record-based smoking status classification and to determine whether smoking and former smoking predict complications in all surgery types. Methods: All surgeries performed in a Finnish hospital district from 1 January 2015 to 31 December 2019 were analysed. Exclusion criteria were age below 16 years, unknown smoking status, and unknown ASA class. A machine learning algorithm was developed for smoking status classification. The primary outcome was 90-day overall postoperative complications in all surgeries. Secondary outcomes were 90-day overall complications in specialties with over 10 000 surgeries and critical complications in all surgeries. Results: The machine learning algorithm had precisions of 0.958 for current smokers, 0.974 for ex-smokers, and 0.95 for never-smokers. The sample included 158 638 surgeries. In adjusted logistic regression analyses, smokers had increased odds of overall complications (odds ratio 1.17; 95 per cent c.i. 1.14 to 1.20) and critical complications (odds ratio 1.21; 95 per cent c.i. 1.14 to 1.29). Corresponding odds ratios of ex-smokers were 1.09 (95 per cent c.i. 1.06 to 1.13) and 1.09 (95 per cent c.i. 1.02 to 1.17). Smokers had increased odds of overall complications in all specialties with over 10 000 surgeries. ASA class was the most important complication predictor. Conclusion: Machine learning algorithms are feasible for smoking status classification in big surgical data sets. Current and former smoking predict complications in all surgery types.Peer reviewe

Smoking is a predictor of complications in all types of surgery : a machine learning-based big data study

Background Machine learning algorithms are promising tools for smoking status classification in big patient data sets. Smoking is a risk factor for postoperative complications in major surgery. Whether this applies to all surgery is unknown. The aims of this retrospective cohort study were to develop a machine learning algorithm for clinical record-based smoking status classification and to determine whether smoking and former smoking predict complications in all surgery types. Methods All surgeries performed in a Finnish hospital district from 1 January 2015 to 31 December 2019 were analysed. Exclusion criteria were age below 16 years, unknown smoking status, and unknown ASA class. A machine learning algorithm was developed for smoking status classification. The primary outcome was 90-day overall postoperative complications in all surgeries. Secondary outcomes were 90-day overall complications in specialties with over 10 000 surgeries and critical complications in all surgeries. Results The machine learning algorithm had precisions of 0.958 for current smokers, 0.974 for ex-smokers, and 0.95 for never-smokers. The sample included 158 638 surgeries. In adjusted logistic regression analyses, smokers had increased odds of overall complications (odds ratio 1.17; 95 per cent c.i. 1.14 to 1.20) and critical complications (odds ratio 1.21; 95 per cent c.i. 1.14 to 1.29). Corresponding odds ratios of ex-smokers were 1.09 (95 per cent c.i. 1.06 to 1.13) and 1.09 (95 per cent c.i. 1.02 to 1.17). Smokers had increased odds of overall complications in all specialties with over 10 000 surgeries. ASA class was the most important complication predictor. Conclusion Machine learning algorithms are feasible for smoking status classification in big surgical data sets. Current and former smoking predict complications in all surgery types.Machine learning algorithms are feasible for smoking status classification in large data sets. Current and former smoking associate with overall and critical complications in all types of inpatient and outpatient surgery of various invasiveness.Peer reviewe