Tofacitinib real-world experience in ulcerative colitis in Finland (FinTofUC): a retrospective non-interventional multicenter patient chart data study.

OBJECTIVES: The aim was to define the effectiveness of tofacitinib and to characterize the patient population receiving tofacitinib in a real-world cohort clinical setting for ulcerative colitis (UC) in Finland. METHODS: This is a retrospective non-interventional multicenter patient chart data study conducted in 23 Finnish Inflammatory Bowel Disease (IBD) centers. Baseline demographic and clinical data, clinical remission, steroid-free remission rate and time to tofacitinib discontinuation, colectomy or UC-related hospitalization were studied. RESULTS: The study included 252 UC patients of which 69% were male. Most patients had extensive disease (71%) and were bio-experienced (81%). Tofacitinib demonstrated positive treatment outcomes with clinical response, clinical remission, and steroid-free clinical remission at one year in 33%, 34% and 31% of patients, respectively. Moreover, 64% of patients in pMayo remission at week 16 from the start of tofacitinib were still in remission at one year. Only no or mild disease activity compared to moderate activity at baseline was associated with a higher probability of achieving remission according to pMayo at six months, p = .008. Hospitalizations and/or colectomies during the study period (before treatment discontinuation/end of follow-up) were low (n = 24), with less than 5 colectomies. CONCLUSIONS: In this real-world cohort, including a majority of bio-experienced UC patients, tofacitinib was effective in achieving steroid-free clinical remission in a third of the population at one year. A majority of patients in remission at week 16 were also in remission at one year. Results are in line with earlier published real-world studies. Registration: ClinicalTrials.gov NCT05082428.

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.

Observational study on the evolution of systemic treatments for advanced renal cell carcinoma in Southwest Finland between 2010 and 2021 The aim of the study was to evaluate the use of novel medical treatments for advanced kidney cancer in routine clinical practice in Southwest Finland from 2010 to 2021 and to study the impact of IMDC risk factors on patients' survival and healthcare resource utilization. Before 2018, sunitinib (79%) was the most common first-line treatment for advanced kidney cancer, and pazopanib (17%), axitinib (17%), and cabozantinib (5%) were frequently used in the second-line. After 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 treatment. The IMDC risk category predicted the patient's prognosis accurately as the median overall survival times for patients with favorable, intermediate, and poor risk were 61.9 months, 28.6 months, and 8.1 months, respectively. 73­74% of the patients with favorable and intermediate risk had received second-line medical treatment for advanced disease, whereas only 35% of the patients with poor risk had received second-line treatment after disease progression on the first-line treatment. Among patients with poor risk, the number of hospital inpatient days was twofold higher compared to intermediate and fourfold higher compared to favorable-risk patients. This study demonstrated that new treatment options for advanced kidney cancer were readily adopted into clinical practice and IMDC risk scoring was a valuable tool in determining patient prognosis and healthcare resource utilization.

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.