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Objectives Few studies have evaluated the prevalence of psychiatric disorders among treatment-seeking elite athletes (EA) or high-performance coaches (HPC) in psychiatric outpatient settings. Methods Descriptive overview of EA and HPC with psychiatric disorders at two publicly funded psychiatric outpatient treatment clinics in Stockholm and Malmo, Sweden. Co-occurring psychiatric disorders were illustrated using Venn diagrams for EA and HPC, and male and female EA separately, among patients from the Stockholm clinic (SC) that used standardised diagnostic interviews. Results Overall, most patients were EA (n=221) compared with HPC (n=34). The mean age was 23.5 (+/- 5.9) years for EA and 42.8 (+/- 8.8) for HPC. Anxiety disorders were most common at the SC in EA and HPC (69% vs 91%, respectively). Stress-related disorders were found in 72% of HPC compared with 25% of EA. Affective disorders were found in 51% of EA and 52% of HPC. Eating disorders were common among EA (26%), especially females (37%). Substance use disorders were found in 17% of HPC. Comorbidity was generally common between affective and anxiety disorders. Conclusion Stress and adjustment disorders were found in nearly three of the four HPC compared with one in four EA. Eating disorders were prevalent in around one in four athletes and about one in six HPC had a substance use disorder.

Several recently published trials investigate novel therapies for relapsed/refractory diffuse large B-cell lymphoma (R/R DLBCL). To estimate the benefit of these therapies in the real-world setting, comprehensive data on patients treated in clinical routine are needed. We report outcomes for 736 R/R DLBCL patients identified among all curatively treated DLBCL patients in Sweden in the period 2007-2014. Survival and associations with disease characteristics, second-line treatment and fulfilment of chimaeric antigen receptor (CAR) T-cell trial criteria were assessed. Median overall survival (OS) was 6.6 months (<= 70 years 9.6 months, >70 years 4.9 months). Early relapse (<= 12 months) was strongly associated with selection of less intensive treatment and poor survival. Among patients of at most 70 years of age, 63% started intensive second-line treatment and 34% received autologous stem cell transplantation (ASCT). Two-year OS among transplanted patients was 56% (early relapse <= 12 months 40%, late relapse >12 months 66%). A minority of patients 76 years (n = 178/506, 35%) fitted CAR T trial criteria. Median progression-free survival (PFS) for patients with early relapse fitting trial criteria was 4.8 months. In conclusion, most R/R DLBCL manifest early and are often ineligible for or cannot complete intensive regimens resulting in dismal survival. Real-world patients eligible for CAR T trials also did poorly, providing a benchmark for efficacy of novel therapies.

The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted a review of the recent literature that explains standard taxonomies, terminology and central concepts to a broad clinical readership. Articles aimed at readers with little or no prior experience of commonly used methods or typical workflows were summarised and key references are highlighted. Continual interdisciplinary developments in data science, biostatistics and epidemiology also motivated us to further discuss emerging topics in predictive and data-driven (hypothesis-less) analytics with machine learning. Through two methodological deep dives using examples from precision psychiatry and outcome prediction after lymphoma, we highlight how the use of, for example, natural language processing can outperform established clinical risk scores and aid dynamic prediction and adaptive care strategies. Such realistic and detailed examples allow for critical analysis of the importance of new technological advances in artificial intelligence for clinical decision-making. New clinical decision support systems can assist in prevention and care by leveraging precision medicine., QC 20230124

Background: The increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process. Purpose: To design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders. Methods: Design science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system. Results: The analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations. Impact: Our work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system t, QC 20230109

There is a lack of data regarding treatment and prognosis for the growing group of oldest old patients with lymphoma. Therefore, we studied 2347 patients aged >= 85 years from the Danish and Swedish lymphoma registers 2000-2016 (Denmark) and 2007-2013 (Sweden). Outcome was assessed using relative survival (RS). The 2-year RS overall for patients with aggressive lymphomas was 38% [95% confidence interval (CI) 35-42%], of whom 845 (66%) patients received active treatment (chemotherapy, radiotherapy, immunotherapy, other). For aggressive lymphomas, not receiving active treatment was associated with an inferior 2-year RS of 12% (95% CI 9-17%) compared to 49% (95% CI 45-53%) for patients who received active treatment (excess mortality rate ratio 2 center dot 84, 95% CI 2 center dot 3-3 center dot 5; P < 0 center dot 0001). For patients with indolent lymphoma, the 2-year RS was 77% (95% CI 72-82%). Here, 383 (46%) patients received active treatment at diagnosis, but did not have better 2-year RS (75%, 95% CI 67-81%) compared to those who did not receive active treatment (83%, 95% CI 74-89%). We conclude that outcomes for the oldest old patients with lymphoma are encouraging for several subtypes and that active treatment is associated with improved outcome amongst the oldest old patients with aggressive lymphomas, indicating that age itself should not be a contraindication to treatment.

PURPOSE The majority of young adults with Hodgkin lymphoma (HL) are cured, but chemotherapy-induced infertility can have profound psychosocial consequences. Providing data on parenthood rates and use of assisted reproductive techniques (ARTs) after contemporary HL treatment is important for patient counseling and survivorship care. MATERIALS AND METHODS All Danish patients with HL diagnosed during 2000-2015 at the ages 18-40 years who achieved remission after first-line therapy were included and matched on age, sex, and parenthood status to five random persons from the general population. Parenthood rates were defined as the rate of first live birth per 1,000 person years, starting 9 months after HL diagnosis. Nationwide birth and patient registers were used to capture parenthood outcomes and ARTs use. RESULTS A total of 793 HL survivors and 3,965 comparators were included (median follow-up 8.7 years). Similar parenthood rates were observed for male and female HL survivors when compared with matched comparators (56.2 v 57.1; P = .871 for males and 63.8 v 61.2; P = .672 for females). For male HL survivors, BEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone) therapy was associated with lower parenthood rates as compared to the matched comparators (28.1 v 60.8; P = .020). Live birth after ARTs were more common for HL survivors than for comparators (males 21.6% v 6.3%; P < .001; females 13.6% v 5.5%; P = .001). There were no differences in gestational age, Apgar score, or newborn measurements between HL survivors and matched comparators. CONCLUSION The parenthood rates for HL survivors who have not experienced relapse were generally similar to the general population. However, ARTs were used more often before the first live birth in HL survivors, which is relevant information when discussing possible long-term side effects and fertility-preserving treatment options.