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

Tupakointistatuksen selvittäminen potilastietojärjestelmästä koneoppimiseen pohjautuvan potilastekstien luokittelijan avulla

Smoking is a significant factor affecting human health and development of various diseases but smoking status is usually documented in an unstructured format in the electronic health records. Therefore the information about smoking status is difficult to extract with purpose to, for example, analyse the health effects of smoking based on a real world data. This thesis was made as a part of a study where effects of smoking on postoperative surgical complications were assessed. Therefore a text classifier to identify smoking status of a patient based on clinical notes was built. Smoking-related sentences were selected by searching smoking-related regular expressions from the clinical notes. Overall 809,958 sentences were classified with a machine learning-based fastText classifier trained with 19,999 sentences into classes ex-smoker, nonsmoker, smoker and unknown smoking status. The results were improved by estimating the uncertainty of the classification results and the classifications in the classes ex-smoker, nonsmoker and smoker that were considered as uncertain results were reassigned to the class unknown. The final classifier achieved the precisions of 0.958, 0.974 and 0.95 for the classes ex-smoker, nonsmoker and smoker, respectively and the accuracy of the classifier for the sentences classified in these three classes was 0.959. Additionally, a rule-based classifier to assign smoking status for each surgery patient based on the smoking statuses of the classified sentences was introduced. The classifier outperformed prior approaches to identify smoking status from clinical notes taking into account the differences in the study settings.Tupakointi on merkittävä terveyteen ja sairauksiin vaikuttava taustatekijä. Potilaan tupakointistatus ei kuitenkaan usein ole kirjattu sähköisiin potilastietojärjestelmiin rakenteisesti, mikä hankaloittaa tupakointistatuksen saamista tutkimuskäyttöön, kun tutkitaan esimerkiksi tupakoinnin vaikutusta terveyteen tosielämän tietoon perustuen. Tämä diplomityö on tehty osana tutkimusta, jossa tutkittiin tupakoinnin vaikutusta postoperatiivisiin leikkauskomplikaatioihin ja sitä varten kehitettiin tekstiluokittelija tunnistamaan potilaan tupakointistatus potilasteksteistä. Tupakointiin viittaavat lauseet valittiin etsimällä tupakointiin liittyviä säännöllisiä lausekkeita potilasteksteistä. Tupakointiin viittaavia lauseita löytyi yhteensä 809 958, joista kliinikot antoivat 19 999 lausetta opetusaineistoksi luokittelijalle. Lauseet luokiteltiin ohjattuun koneoppimiseen pohjautuvalla fastText-luokittelijalla luokkiin entinen tupakoitsija, ei tupakoitsija, tupakoitsija ja ei tiedossa oleva tupakointistatus. Tuloksia parannettiin arvioimalla luokittelutulosten epävarmuutta ja siirtämällä epävarmoiksi arvioidut lauseet luokista entinen tupakoitsija, ei tupakoitsija ja tupakoitsija luokkaan ei tiedossa oleva tupakointistatus. Lopullisen luokittelijan täsmällisyydet luokille entinen tupakoitsija, ei tupakoitsija ja tupakoitsija olivat 0.958, 0.974 and 0.95, ja tarkkuus näihin luokkiin luokitelluille lauseille oli 0.959. Tässä työssä esitettiin myös sääntöpohjainen luokittelija määrittämään leikkauspotilaille tupakointistatus luokiteltujen lauseiden tupakointistatusten perusteella. Lausetason luokittelija suoriutui tunnistamaan tupakointistatuksen potilasteksteistä paremmin kuin aiemmissa tutkimuksissa esitetyt vastaavat luokittelijat, kun otetaan huomioon erot tutkimusasetelmissa

Observational study on the evolution of systemic treatments for advanced renal cell carcinoma in Southwest Finland between 2010 and 2021

Background: Novel receptor tyrosine kinase inhibitors and immune checkpoint inhibitors have been introduced to the treatment of advanced renal cell carcinoma (aRCC) during the past decade. However, the adoption of novel treatments into clinical practice has been unknown in Finland. Objectives: Our aim was to evaluate the use of systemic treatments and treatment outcomes of aRCC patients in Southwest Finland during 2010–2021. Design and Methods: Clinical characteristics, treatments for aRCC, healthcare resource utilization, and overall survival (OS) were retrospectively obtained from electronic medical records. Patients were stratified using the International Metastatic RCC Database Consortium (IMDC) risk classification. Results: In total, 1112 RCC patients were identified, 336 (30%) patients presented with aRCC, and 57% of them ( n  = 191) had received systemic treatment. Pre-2018, sunitinib (79%) was the most common first-line treatment, and pazopanib (17%), axitinib (17%), and cabozantinib (5%) were frequently used in the second-line. Post-2018, sunitinib (52%), cabozantinib (31%), and the combination of ipilimumab and nivolumab (10%) were most commonly used in the first-line, and cabozantinib (23%) in the second-line. Median OS for patients with favorable, intermediate, and poor risk were 61.9, 28.6, and 8.1 months, respectively. A total of 73%, 74%, and 35% of the patients with favorable, intermediate, and poor risk had received second-line systemic treatment. In poor-risk patients, the number of hospital inpatient days was twofold higher compared to intermediate and fourfold higher compared to favorable-risk patients. Conclusion: New treatment options were readily adopted into routine clinical practice after becoming reimbursed in Finland. OS and the need for hospitalization depended significantly on the IMDC risk category. Upfront combination treatments are warranted for poor-risk patients as the proportion of patients receiving second-line treatment is low. Registration: Clinical trial identifier: ClinicalTrials.gov NCT05363072

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