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In the article, the authors present their study on the feasibility of patient inclusion and targeted next-generation sequencing (NGS) for the testing of newly diagnosed lymphoma patients in Stockholm, Sweden using precision diagnostic tools and personalized medicine techniques in a population-based setting. Also cited are the subtypes of mature B-cell lymphomas like chronic lymprocytic leukemia (CLL), follicular lymphoma (FL), and B-cell lymphoma (DLBCL).

Background: The outcome for diffuse large B‐cell lymphoma (DLBCL) patients has improved with the immunochemotherapy combination R‐CHOP. An increased rate of heart failure is well documented following this treatment, whereas incidence and outcome of other cardiac complications, for example myocardial infarction, are less well known. Method: We identified 3548 curatively treated DLBCL patients in Sweden diagnosed between 2007 and 2014, and 35474 matched lymphoma‐free general population comparators. The incidence, characteristics and outcome of acute myocardial infarctions (AMIs) were assessed using population‐based registers up to 11 years after diagnosis. The rate of AMI was estimated using flexible parametric models. Results: Overall, a 33% excess rate of AMI was observed among DLBCL patients compared with the general population (HR: 1.33, 95% CI: 1.14–1.55). The excess rate was highest during the first year after diagnosis and diminished after 2 years. High age, male sex and comorbidity were the strongest risk factors for AMI. Older patients (>70 years) with mild comorbidities (i.e. hypertension or diabetes) had a 61% higher AMI rate than comparators (HR: 1.61, 95% CI: 1.10–2.35), whereas the corresponding excess rate was 28% for patients with severe comorbidities (HR: 1.28, 95% CI: 1.01–1.64). Among younger patients (≤70), a short‐term excess rate of AMI was limited to those with severe comorbidities. There was no difference in AMI characteristics, pharmacological treatment or 30‐day survival among patients and comparators. Conclusion: DLBCL patients have an increased risk of AMI, especially during the first 2 years, which calls for improved cardiac monitoring guided by age and comorbidities. Importantly, DLBCL was not associated with differential AMI management or survival. [ABSTRACT FROM AUTHOR]

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]

Objectives: A family history of colorectal cancer (CRC) is an established risk factor for developing CRC, whilst the impact of family history on prognosis is unclear. The present study assessed the association between family history and prognosis and, based on current evidence, explored whether this association was modified by age at diagnosis.Methods: Using data from the Swedish Colorectal Cancer Registry (SCRCR) linked with the Multigeneration Register and the National Cancer Register, we identified 31 801 patients with a CRC diagnosed between 2007 and 2016. The SCRCR is a clinically rich database which includes information on the cancer stage, grade, location, treatment, complications and postoperative follow-up.Results: We estimated excess mortality rate ratios (EMRR) for relative survival and hazard ratios (HR) for disease-free survival with 95% confidence intervals (CIs) using flexible parametric models. We found no association between family history and relative survival (EMRR = 0.96, 95% CIs: 0.89-1.03, P = 0.21) or disease-free survival (HR = 0.98, 95% CIs: 0.91-1.06, P = 0.64). However, age was found to modify the impact of family history on prognosis. Young patients (<50 at diagnosis) with a positive family history had less advanced (i.e. stages I and II) cancers than those with no family history (OR = 0.71, 95% CI: 0.56-0.89, P = 0.004) and lower excess mortality even after adjusting for cancer stage (EMMR = 0.63, 95% CIs: 0.47-0.84, P = 0.002).Conclusions: Our results suggest that young individuals with a family history of CRC may have greater health awareness, attend opportunistic screening and adopt lifestyle changes, leading to earlier diagnosis and better prognosis. [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]

Background: Comorbidity impacts overall survival amongst patients with diffuse large B-cell lymphoma (DLBCL). However, associations of comorbidity with lymphoma characteristics, treatment selection and lymphoma-specific mortality are less well known.Objective: To examine the impact of comorbidity on DLBCL characteristics, treatment intent and cause of death.Methods: We identified 3905 adult patients diagnosed with DLBCL 2007-2013 through the Swedish Lymphoma Register. We assessed comorbid disease history according to the Charlson comorbidity index (CCI). Comorbidity data and causes of death were collected through register linkage. Associations were estimated using multinomial regression and flexible parametric survival models.Results: Overall, 45% of the patients (n = 1737) had a history of at least one comorbidity at DLBCL diagnosis (cardiovascular disease, diabetes and solid cancer were most frequent), and 997 (26%) had a CCI score of ≥2. The relative probability of presenting with poor performance status (PS > 2) was higher amongst comorbid patients [Relative Risk Ratio (RRR)PS>2 : 2.02, 95% CI: 1.63-2.51]. Comorbid patients had a substantially lower relative probability of receiving curative treatment (RRR: 0.48, 95% CI: 0.38-0.61). Amongst all patients, CCI ≥ 1 was associated with a significantly increased risk of all-cause and lymphoma-specific death after adjustments. Amongst patients selected for curative treatment, comorbidity was associated with an increased risk of all-cause death (HRCCI>1 : 1.54, 95% CI: 1.32-1.80), but not with lymphoma-specific death (HRCCI>1 : 1.05, 95% CI: 0.86-1.28).Conclusion: Comorbidity is associated with inferior DLBCL outcome, mainly due to a lower likelihood of receiving treatment with curative intent. Possibly, more comorbid DLBCL patients could be treated with curative intent if comorbid conditions were optimized in parallel. [ABSTRACT FROM AUTHOR]

Abstract: Aim of study: Differences in the survival of colon cancer patients by socioeconomic status have been demonstrated in several populations, but the underlying reasons for the differences are not well understood. By simultaneously estimating the proportion of patients cured from colon cancer and the survival times of the ‘uncured’ we hope to increase understanding of how socioeconomic status affects survival following a diagnosis of colon cancer. Methods: We conducted a population-based cohort study of 58,873 patients diagnosed with colon cancer in Sweden 1965–2000. Socioeconomic status was classified based on occupation. We fitted mixture cure models and Poisson regression models adjusted for age, sex and calendar period. Results: We observed higher excess mortality, lower proportion cured and shorter survival times among the uncured in patients from lower socioeconomic groups compared to the highest socioeconomic group. There was no evidence that the gap between the socioeconomic groups reduced over time. Farmers had the lowest odds of cure (odds ratio (OR) 0.85, 95% confidence interval (CI) 0.75–0.95) compared to higher non-manual workers followed by self-employed (0.91, 0.81–1.03), manual workers (0.93, 0.85–1.03) and lower non-manual workers (0.98, 0.89–1.08). Conclusion: Patients from lower socioeconomic groups in Sweden experience worse survival following a diagnosis of colon cancer. Differences exist in both the cure proportion and the survival time of the uncured, suggesting that socioeconomic differences cannot be attributed solely to lead time bias.Although this study has furthered our understanding of socioeconomic differences in survival, more detailed studies are required in order to identify, and subsequently remove, the underlying reasons for the differences. [ABSTRACT FROM AUTHOR]

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.