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Monitoring survival of cancer patients using data collected by population‐based cancer registries is an important component of cancer control. In this setting, patient survival is often summarized using net survival, that is survival from cancer if there were no other possible causes of death. Although net survival is the gold standard for comparing survival between groups or over time, it is less relevant for understanding the anticipated real‐world prognosis of patients. In this review, we explain statistical concepts targeted towards patients, clinicians and healthcare professionals that summarize cancer patient survival under the assumption that other causes of death exist. Specifically, we explain the appropriate use, interpretation and assumptions behind statistical methods for competing risks, loss in life expectancy due to cancer and conditional survival. These concepts are relevant when producing statistics for risk communication between physicians and patients, planning for use of healthcare resources, or other applications when consideration of both cancer outcomes and the competing risks of death is required. To reinforce the concepts, we use Swedish population‐based data of patients diagnosed with cancer of the breast, prostate, colon and chronic myeloid leukaemia. We conclude that when choosing between summary measures of survival it is critical to characterize the purpose of the study and to determine the nature of the hypothesis under investigation. The choice of terminology and style of reporting should be carefully adapted to the target audience and may range from summaries for specialist readers of scientific publications to interactive online tools aimed towards lay persons. [ABSTRACT FROM AUTHOR]

B Introduction: b With modern treatments, mantle cell lymphoma (MCL) patients may experience long-lasting remission resulting in a growing population of long-term survivors. Higher rates in patients were observed across all primary treatment groups: Nordic-MCL2 protocol (250 patients, HR SB adj sb = 1.4(1.0-2.0)), R-CHOP single (144 patients, HR SB adj sb = 2.0(1.3-2.9)), R-bendamustine (302 patients, HR SB adj sb = 2.2(1.7-2.7)), lenalidomide (16 patients, HR SB adj sb = 4.3(2.0-9.0)) (Figure 1), R-CHOP/Cytarabine (145 patients, HR SB adj sb = 1.6(1.0-2.5)), and ibrutinib (9 patients, HR SB adj sb = 4.8(1.2-18.7)) contrasted with comparators. B Conclusions: b MCL survivors have an increased risk of SM, particularly patients given primary treatment with R-bendamustine, but also patients treated with other chemotherapies or novel targeted drugs (lenalidomide, ibrutinib), though only few patients were included in the latter group and the results should be interpreted accordingly. [Extracted from the article]

We speculate that the higher survival rate among young high-risk patients may be driven by the introduction of CAR-T in the last few years, whereas the more general and successive improvement among older patients could be due to intensified treatment schemes in the primary as well as relapsed settings. B Introduction: b Most patients diagnosed with diffuse large B-cell lymphoma (DLBCL) are cured with primary immunochemotherapy, but about one in four experience relapsed/refractory (R/R) disease with worsened outcome. In young high-risk patients, the 2-year RS increased from 71% among patients diagnosed 2007-2009, to 83% among patients diagnosed 2019-2021 ( I p i SB overall sb < 0.001). [Extracted from the article]

This study describes the personal resources of older (>/= 75 years) home care clients in Finland and their perceptions of factors that enhance and constrain their ability to live independently at home. The data were collected by unstructured interviews with 21 older home care clients. Inductive content analysis were used to analyse the data. The resources of older people consisted of a sense of control over one's life and a determination to remain active. Factors enhancing older people's resources were their involvement in leisure activities and social networks, factors undermining their resources were conditions on living imposed by outsiders, declining health and loneliness. The results show that home care professionals do not yet have sufficient skills and abilities to identify and support older people's existing resources. As well as having access to necessary resources, it is also crucial that older people know how to use them. [ABSTRACT FROM AUTHOR]

Summary Insomnia is a common feature of depression; however, depression treatment guidelines provide limited recommendations regarding hypnotic drugs. Few studies have thoroughly investigated the use of hypnotic drugs in depression. In this cohort study using national Swedish registers, we included all patients ≥18 years with incident unipolar depression during 2007–2017. Patients were followed for 3 years, noting the annual and quarterly prevalence of hypnotic drug use from prescription fills. Prevalence ratios (PR) comparing 2017 to 2007 were calculated with 95% confidence intervals (CI). A total of 222,077 patients with depression were included (mean age 41 years, 59% women). In the year following diagnosis, 44.1% used any hypnotic drug in 2017, compared with 46.7% in 2007 (PR 0.94, 95% CI 0.92–0.97). The most commonly used drugs were Z‐drugs (zopiclone, zolpidem, and zaleplon) with a prevalence of 27.6% in 2017 and 35.6% in 2007 (PR 0.78, 95% CI 0.75–0.80). Melatonin use increased sharply to 12.0% in 2017 from 0.4% in 2007 (PR 28.9, 95% CI 23.5–35.7). Hypnotic drug use was most prevalent in the first two quarters after diagnosis; however, after 3 years, the quarterly prevalence was still 19.2%. Hypnotics were more common among women, older patients, those with somatic comorbidities, more severe depression, or a history of suicide attempt. Evidence from this large register‐based study demonstrates that hypnotics were used to a large extent in depression in Sweden 2007–2017. Z‐drugs use declined and melatonin use increased dramatically. Hypnotic drug use remained high even 3 years after diagnosis. [ABSTRACT FROM AUTHOR]

Abstract Background Relative survival is commonly used for studying survival of cancer patients as it captures both the direct and indirect contribution of a cancer diagnosis on mortality by comparing the observed survival of the patients to the expected survival in a comparable cancer-free population. However, existing methods do not allow estimation of the impact of isolated conditions (e.g., excess cardiovascular mortality) on the total excess mortality. For this purpose we extend flexible parametric survival models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects. Methods In the extended model we partition the excess mortality associated with a diagnosis of cancer through estimating a separate baseline excess hazard function for the outcomes under investigation. This is done by incorporating mutually exclusive background mortality rates, stratified by the underlying causes of death reported in the Swedish population, and by introducing cause of death as a time-dependent effect in the extended model. This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously. Furthermore, we illustrate how the results from the proposed model can be used to derive crude probabilities of death due to the component parts, i.e., probabilities estimated in the presence of competing causes of death. Results The method is illustrated with examples where the total excess mortality experienced by patients diagnosed with breast cancer is partitioned into excess cardiovascular mortality and remaining cancer excess mortality. Conclusions The proposed method can be used to simultaneously study disease patterns and temporal trends for various causes of cancer-consequent deaths. Such information should be of interest for patients and clinicians as one way of improving prognosis after cancer is through adapting treatment strategies and follow-up of patients towards reducing the excess mortality caused by side effects of the treatment.

Abstract Background When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models. Methods Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified. Results We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates. Conclusions Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models.

Background: The aim of this study was to compare relative survival in non-metastatic rectal cancer clinically staged as T3-T4 requiring beyond total mesorectal excision (TME) to that after standard TME., Methods: This population-based study included all patients operated with anterior resection, abdominoperineal excision or Hartmann's procedure for non-metastatic rectal cancer clinically staged as T3-T4 in Sweden between 2009 and 2018. Relative survival was analysed in relation to surgery beyond TME (bTME), which was subcategorized as bTME- and bTME + to account for extent of resection. In all survival analyses, follow-up started at 90 days after surgery. Based on a causal model defined a priori excess mortality rate ratios (EMRR) were estimated using Poisson regression., Results: Of 8272 included patients 1220 (14.7 %) were operated bTME. In a model adjusted for age and sex bTME was associated with higher excess mortality compared to standard TME (EMRR: 1.76, 95%CI:1.52-2.04). This association persisted after additional adjustment for tumour characteristics, neoadjuvant therapy and hospital volume (EMRR: 1.32, 95%CI:1.11-1.56) and was mainly attributable to restricted relative survival after bTME- (EMRR: 1.42, 95%CI:1.18-1.72) as EMRR after bTME+ was 1.07 (95%CI:0.80-1.44)., Conclusion: This national population-based study showed inferior relative survival after bTME compared to standard TME in non-metastasized rectal cancer cT3-cT4. Unexpectedly this difference was mainly seen after bTME of limited extent., (Copyright © 2024. Published by Elsevier Ltd.)

Purpose: Etoposide to standard R-CHOP is used for high-risk diffuse large B-cell lymphoma (DLBCL) in some countries. Due to the lack of randomized trials, a real-world data study using matching methods was used to test the potential effectiveness of R-CHOEP over R-CHOP., Patients and Methods: This study included patients from the Danish Lymphoma Register diagnosed between 2006 and 2020 at the age of 18-60 years with de novo DLBCL and age-adjusted IPI ≥2. R-CHOEP treated patients were matched 1:1 without replacement to R-CHOP treated patients using a hybrid exact and genetic matching technique. Primary endpoints were progression-free survival (PFS) and overall survival (OS)., Results: In total, 396 patients were included; 213 received R-CHOEP and 183 received R-CHOP. Unadjusted 5-year PFS and OS for R-CHOEP were 69% (95% Confidence intervals [CI]; 63%-76%) and 79% (CI;73%-85%) versus 62% (CI;55%-70%) and 76% (CI;69%-82%) for R-CHOP (log-rank test, PFS p = .25 and OS p = .31). A total of 127 patients treated with R-CHOEP were matched to 127 patients treated with R-CHOP. Matching-adjusted 5-year PFS and OS were 65% (CI; 57%-74%) and 79% (CI; 72%-84%) for R-CHOEP versus 63% (CI; 55%-73%) and 79% (CI;72%-87%) for R-CHOP (log-rank test, PFS p = .90 and OS p = .63)., Conclusion: The present study did not confirm superiority of R-CHOEP over R-CHOP for young patients with high-risk DLBCL., (© 2024 The Author(s). European Journal of Haematology published by John Wiley & Sons Ltd.)

Background: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited., Method: We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders., Results: We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models., Discussion: Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs . olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs . olanzapine responders., Competing Interests: Joseph F. Hayes has received consultancy fees from juli Health and Wellcome Trust. No other authors declare competing interests., (© 2024 Hayes et al.)

Massively parallel sequencing helps create new knowledge on genes, variants and their association with disease phenotype. This important technological advancement simultaneously makes clinical decision making, using genomic information for cancer patients, more complex. Currently, identifying actionable pathogenic variants with diagnostic, prognostic, or predictive impact requires substantial manual effort. Objective: The purpose is to design a solution for clinical diagnostics of lymphoma, specifically for systematic variant filtering and interpretation. Methods: A scoping review and demonstrations from specialists serve as a basis for a blueprint of a solution for massively parallel sequencing-based genetic diagnostics. Results: The solution uses machine learning methods to facilitate decision making in the diagnostic process. A validation round of interviews with specialists consolidated the blueprint and anchored it across all relevant expert disciplines. The scoping review identified four components of variant filtering solutions: algorithms and Artificial Intelligence (AI) applications, software, bioinformatics pipelines and variant filtering strategies. The blueprint describes the input, the AI model and the interface for dynamic browsing. Conclusion: An AI-augmented system is designed for predicting pathogenic variants. While such a system can be used to classify identified variants, diagnosticians should still evaluate the classification's accuracy, make corrections when necessary, and ultimately decide which variants are truly pathogenic., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: RR received honoraria from Abbvie, AstraZeneca, Janssen, Illumina and Roche.

Previous solid organ transplantation has been associated with worse survival among colorectal cancer (CRC) patients. This study investigates the contribution of CRC characteristics and treatment-related factors to the differential survival. Using the Swedish register-linkage CRCBaSe, all patients with solid organ transplantation before CRC diagnosis were identified and matched with non-transplanted CRC patients. Associations between transplantation history and clinical CRC factors and survival were estimated using the Kaplan-Meier estimator and logistic, multinomial, and Cox regression, respectively. Ninety-eight transplanted and 474 non-transplanted CRC patients were followed for 5 years after diagnosis. Among patients with stage I-III cancer, transplanted patients had lower odds of treatment with abdominal surgery [odds ratio (OR):0.27, 95% confidence interval (CI):0.08-0.90], than non-transplanted patients. Among those treated with surgery, transplanted colon cancer patients had lower odds of receiving adjuvant chemotherapy (OR:0.31, 95% CI:0.11-0.85), and transplanted rectal cancer patients had higher rate of relapse (hazard ratio:9.60, 95% CI:1.84-50.1), than non-transplanted patients. Five-year cancer-specific and overall survival was 56% and 35% among transplanted CRC patients, and 68% and 57% among non-transplanted. Accordingly, transplanted CRC patients were treated less intensely than non-transplanted patients, and had worse cancer-specific and overall survival. These patients might benefit from multidisciplinary evaluation including transplantation specialists., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Benoni, Nordenvall, Hellström, Dietrich, Martling, Smedby and Eloranta.)

Background and Purpose: The Swedish Lymphoma Register (SLR) was initiated in the year 2000 with the aim to monitor quality of care in diagnostics, treatment and outcome of all lymphomas diagnosed nationally among adults. Here, we present the first systematic validation of SLR records as a basis for improved register quality and patient care., Patients and Methods: We evaluated timeliness and completeness of register records among patients diagnosed with lymphoma in the SLR (n = 16,905) compared with the National Cancer Register for the period 2013-2020. Comparability was assessed through evaluation of coding routines against national and international guidelines. Accuracy of 42 variables was evaluated through re-abstraction of data from medical records among 600 randomly selected patients diagnosed in 2016-2017 and treated across all six Swedish healthcare regions.  Results: Completeness was high, >95% per year for the period 2013-2018, and >89% for 2019-2020 compared to the National Cancer Register. One in four patients was registered within 3 months, and 89.9% within 2 years of diagnosis. Registration instructions and coding procedures followed the prespecified guidelines. Missingness was generally low (<5%), but high for occasional variables, for example, those describing maintenance and consolidative treatment. Exact agreement of categorical variables was high overall (>80% for 24/34 variables), especially for treatment-related data (>80% for 17/19 variables)., Interpretation: Completeness and accuracy are high in the SLR, while timeliness could be improved. Finetuning of variable registration guided by this validation can further improve reliability of register reports and advance service to lymphoma patients and health care in the future.

Effective quality leadership is identified as a critical factor for organisational success. Leadership style has an important role in the implementation of quality management. This systematic review describes the characteristics of leadership in quality management in healthcare, and analyses their association with successful or unsuccessful quality management by using content analysis. Papers published in peer-reviewed journals between 2011 and June 2023 were selected by exploring the Abi/Inform, Business Source Complete, Cinahl, Pubmed and Web of Science electronic databases. Altogether 3014 records were found. PRISMA-protocol was used for selection of studies. 12 original and three review articles were included. Six leadership characteristics were identified that were linked to successful or unsuccessful implementation of quality management. The six categories were strategic, knowledge-oriented, value-based, supportive, participatory and communicative leadership. Supercategories and subcategories of successful and unsuccessful implementation are described. The results can be used to identify the leadership characteristics that promote successful quality management in healthcare organisations. In education for leaders and managers, attention should be in the characteristics and style of leadership and their impact on the effectiveness of quality management., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS)., Patients and Methods: This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort)., Results: In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis., Conclusion: The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.