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Myeloid malignancies exhibit considerable heterogeneity with overlapping clinical and genetic features among subtypes. We present a data-driven approach that integrates mutational features and clinical covariates at diagnosis within networks of their probabilistic relationships, enabling the discovery of patient subgroups. A key strength is its ability to include presumed causal directions in the edges linking clinical and mutational features, and account for them aptly in the clustering. In a cohort of 1323 patients, we identify subgroups that outperform established risk classifications in prognostic accuracy. Our approach generalises well to unseen cohorts with classification based on our subgroups similarly offering advantages in predicting prognosis. Our findings suggest that mutational patterns are often shared across myeloid malignancies, with distinct subtypes potentially representing evolutionary stages en route to leukemia. With pancancer TCGA data, we observe that our modelling framework extends naturally to other cancer types while still offering improvements in subgroup discovery. Myeloid malignancies vary significantly in their clinical outcomes and their genetic background. Here, the authors develop a network-based clustering method to predict subgroups of malignancies across disease subtypes.

Background Patient safety and quality of care depend on well-trained, motivated staff. Competitive wages are critical for staff satisfaction and retention. Understanding the factors that affect nurses’ and physicians’ wages is the first step to tackling these factors and improving retention and recruitment while reducing shortages. The aim of this study was i) to describe the distribution of nurses’ and physicians’ wages and potential drivers; and ii) to investigate which drivers are most strongly associated with nurses’ and physicians’ wages in Swiss acute care hospitals. Methods We used de-identified routine data from the Federal Statistical Office, covering Swiss acute care and specialized hospitals from 2014 to 2020. We conducted descriptive analysis and examined potential wage drivers, including gender and nationality, among nurses and physicians using mixed-effects models. We included an average of 164 (161–173) hospitals annually and a total of 524,263 nurses and 176,896 physicians over seven years. Results Descriptive findings revealed variations in wages, workforce demographics, and hospital characteristics. Nurses’ mean monthly wages ranged from 5,920 CHF − 7,720 CHF per FTE, and their mean ages varied from 38.6–42.3 years depending on hospital type. Registered nurses (RNs) were the largest nursing group, with university hospitals employing the highest proportion (75.7%) and the smallest hospitals the lowest (64.6%). For physicians, mean monthly wages ranged from 13,900 CHF − 17,300 CHF, and their mean ages varied from 37.7–44.3 years depending on hospital type. For role distributions university hospitals had more residents and medical students (52.6%), while the smallest hospitals had more senior physicians (32.9%). Inferential analysis showed that nurses’ age, RNs’ proportion, and the physicians’ wages were associated with nurses’ wages. Resident physicians’ and medical students’ proportion, as well as nurses’ wages, were associated with physicians’ wages. Conclusions Findings on age as a nurses’ wages driver suggest that careful allocation of resources and implementing remuneration policies, such as merit-based systems that reward factors beyond years of experience, may help in retaining and recruiting staff. Further research using individual wage information is essential to gain deeper insights into the health workforce wage landscape and its drivers in Swiss hospitals.

Aberrant regulation of the Wnt/β-catenin pathway has an important role during the onset and progression of colorectal cancer, with over 90% of cases of sporadic colon cancer featuring mutations in APC or β-catenin. However, it has remained a point of controversy whether these mutations are sufficient to activate the pathway or require additional upstream signals. Here we show that colorectal tumours express elevated levels of Wnt3 and Evi/Wls/GPR177. We found that in colon cancer cells, even in the presence of mutations in APC or β-catenin, downstream signalling remains responsive to Wnt ligands and receptor proximal signalling. Furthermore, we demonstrate that truncated APC proteins bind β-catenin and key components of the destruction complex. These results indicate that cells with mutations in APC or β-catenin depend on Wnt ligands and their secretion for a sufficient level of β-catenin signalling, which potentially opens new avenues for therapeutic interventions by targeting Wnt secretion via Evi/Wls. Activating mutations in the Wnt signalling pathway are associated with colon cancer. Here the authors show that tumour cells carrying mutations in APC and β-catenin are still regulated by Wnt ligands, suggesting that Wnt secretion and receptor signalling remains important to control downstream signalling.

Background High bed-occupancy (capacity utilization) rates are commonly thought to increase in-hospital mortality; however, little evidence supports a causal relationship between the two. This observational study aimed to assess three time-varying covariates—capacity utilization, patient turnover and clinical complexity level— and to estimate causal effect of time-varying high capacity utilization on 14 day in-hospital mortality. Methods This retrospective population-based analysis was based on routine administrative data ( n  = 1,152,506 inpatient cases) of 102 Swiss general hospitals. Considering the longitudinal nature of the problem from available literature and expert knowledge, we represented the underlying data generating mechanism as a directed acyclic graph. To adjust for patient turnover and patient clinical complexity levels as time-varying confounders, we fitted a marginal structure model (MSM) that used inverse probability of treatment weights (IPTWs) for high and low capacity utilization. We also adjusted for patient age and sex, weekdays-vs-weekend, comorbidity weight, and hospital type. Results For each participating hospital, our analyses evaluated the ≥85th percentile as a threshold for high capacity utilization for the higher risk of mortality. The mean bed-occupancy threshold was 83.1% (SD 8.6) across hospitals and ranged from 42.1 to 95.9% between hospitals. For each additional day of exposure to high capacity utilization, our MSM incorporating IPTWs showed a 2% increase in the odds of 14-day in-hospital mortality (OR 1.02, 95% CI: 1.01 to 1.03). Conclusions Exposure to high capacity utilization increases the mortality risk of inpatients. Accurate monitoring of capacity utilization and flexible human resource planning are key strategies for hospitals to lower the exposure to high capacity utilization.

Large-scale genomic data highlight the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues can guide drug repositioning as well as the design of targeted treatments. Here, we develop an improved Bayesian network model for tumour mutational profiles and apply it to 8198 patient samples across 22 cancer types from TCGA. For each cancer type, we identify the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we find genes which interact both within and across cancer types. To detach cancer classification from the tissue type we perform de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We find 22 novel clusters which significantly improve survival prediction beyond clinical information. The models highlight key gene interactions for each cluster potentially allowing genomic stratification for clinical trials and identifying drug targets. Tumour heterogeneity hinders translation of large-scale genomic data into the clinic. Here the authors develop a method for the stratification of cancer patients based on the molecular gene status, including genetic interactions, rather than clinico-histological data, and apply it to TCGA data for over 8000 cases across 22 cancer types.

Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights about the structural properties of a domain. Structure learning forms one of the inference challenges of statistical graphical models. Markov chain Monte Carlo (MCMC) methods, notably structure MCMC, to sample graphs from the posterior distribution given the data are probably the only viable option for Bayesian model averaging. Score modularity and restrictions on the number of parents of each node allow the graphs to be grouped into larger collections, which can be scored as a whole to improve the chain's convergence. Current examples of algorithms taking advantage of grouping are the biased order MCMC, which acts on the alternative space of permuted triangular matrices, and nonergodic edge reversal moves. Here, we propose a novel algorithm, which employs the underlying combinatorial structure of DAGs to define a new grouping. As a result convergence is improved compared to structure MCMC, while still retaining the property of producing an unbiased sample. Finally, the method can be combined with edge reversal moves to improve the sampler further. Supplementary materials for this article are available online.

Background Centralization of care is an established concept in complex visceral surgery. Switzerland introduced case load requirements (CR) in 2013 in five areas of cancer surgery. The current study investigates the effects of CR on indication and mortality in liver surgery. Methods This is a retrospective analysis of a complete national in-hospital data set including all admissions between January 1, 2005, and December 31, 2015. Primary outcome variables were the incidence proportion and the 60-day in-hospital mortality of liver resections. Incidence proportion was calculated as the overall yearly number of liver resections performed in relation to the population living in Switzerland before and after the introduction of CR. Results Our analysis shows an increase number of liver resections compared to the period before introduction of CR from 2005–2012 (4.67 resections/100,000) to 2013–2015 (5.32 resections/100,000) after CR introduction. Age-adjusted incidence proportion increased by 14% (OR 1.14 95 CI [1.07–1.22]). National in-hospital mortality remained stable before and after CR (4.1 vs 3.7%), but increased in high-volume institutions (3.6 vs 5.6%). The number of hospitals performing liver resections decreased after the introduction of CR from 86 to 43. Half of the resections were performed in institutions reaching the stipulated numbers (53% before vs 49% after introduction of CR). After implementation of CR, patients undergoing liver surgery had more comorbidities (88 vs 92%). Conclusion The introduction of CR for liver surgery in Switzerland in 2013 was accompanied by an increase in operative volume with limited effects on centralization of care.

The ICH E9 (R1) guidance and the related estimand framework propose to clearly define and separate the clinical question of interest formulated as estimand from the estimation method. With that it becomes important to assess the validity of the estimation method and the assumptions that must be made. When going beyond the intention to treat analyses that can rely on randomization, causal inference is usually used to discuss the validity of estimation methods for the estimand of interest. In pharmacometrics, mixed‐effects models are routinely used to analyze longitudinal clinical trial data; however, they are rarely discussed as a method for causal inference. Here, we evaluate nonlinear mixed‐effects modeling and simulation (NLME M&S) in the context of causal inference as a standardization method for longitudinal data in the presence of confounders. Standardization is a well‐known method in causal inference to correct for confounding by analyzing and combining results from subgroups of patients. We show that nonlinear mixed‐effects modeling is a particular implementation of standardization that conditions on individual parameters described by the random effects of the mixed‐effects model. As an example, we use a simulated clinical trial with within‐subject dose titration. Being interested in the outcome of the hypothetical situation that patients adhere to the planned treatment schedule, we put assumptions in a causal diagram. From the causal diagram, conditional independence assumptions are derived either by conditioning on the individual parameters or on earlier outcomes. With both conditional independencies unbiased estimates can be obtained.

IntroductionThe emphasis on aesthetic outcomes and quality of life (QoL) has motivated surgeons to develop skin-sparing or nipple-sparing mastectomy (SSM/ NSM) for breast cancer treatment or prevention. During the same operation, a so-called immediate breast reconstruction is performed. The breast can be reconstructed by positioning of a breast implant above (prepectoral) or below (subpectoral) the pectoralis major muscle or by using the patients’ own tissue (autologous reconstruction). The optimal positioning of the implant prepectoral or subpectoral is currently not clear. Subpectoral implant-based breast reconstruction (IBBR) is still standard care in many countries, but prepectoral IBBR is increasingly performed. This heterogeneity in breast reconstruction practice is calling for randomised clinical trials (RCTs) to guide treatment decisions.Methods and analysisInternational, pragmatic, multicentre, randomised, superiority trial. The primary objective of this trial is to test whether prepectoral IBBR provides better QoL with respect to long-term (24 months) physical well-being (chest) compared with subpectoral IBBR for patients undergoing SSM or NSM for prevention or treatment of breast cancer. Secondary objectives will compare prepectoral versus subpectoral IBBR in terms of safety, QoL and patient satisfaction, aesthetic outcomes and burden on patients. Total number of patients to be included: 372 (186 per arm).Ethics and disseminationThis study will be conducted in compliance with the Declaration of Helsinki. Ethical approval has been obtained for the lead investigator’s site by the Ethics Committee ‘Ethikkommission Nordwest- und Zentralschweiz‘ (2020–00256, 26 March 2020). The results of this study will be published in a peer-reviewed medical journal, independent of the results, following the Consolidated Standards of Reporting Trials standards for RCTs and good publication practice. Metadata describing the type, size and content of the datasets will be shared along with the study protocol and case report forms on public repositories adhering to the FAIR (Findability, Accessibility, Interoperability, and Reuse) principles.Trial registration number NCT04293146.

ObjectiveTo describe the characteristics and the survival of patients with cancer with intended off-label use (OLU) cancer treatment and reimbursement request.DesignCohort study using medical record data.SettingThree major cancer centres in Switzerland.Participants519 patients with cancer and a reimbursement request for OLU between January 2015 and July 2018.Main outcomesCharacteristics of patients with cancer with and without access to intended OLU. Characteristics included the Glasgow prognostic score (GPS) which includes C reactive protein and albumin and discriminates prognostic groups.ResultsOLU was intended for 519 (17%) of 3046 patients with cancer, as first-line treatment in 51% (n=264) and second-line in 31% (n=162). Of the 519 patients, 63% (n=328) were male, 63% (n=329) had solid cancer and 21% (n=111) had a haematological malignancy. Their median overall survival was 23.6 months (95% CI: 19.0 to 32.5). Access to OLU had 389 (75%) patients who were compared with patients without access on average 4.9 years younger (mean; 95% CI: 1.9 to 7.9 years), had a better overall prognosis according to the GPS (51% with GPS of 0 vs 39%; OR: 1.62 (95% CI: 1.01 to 2.59)), had less frequently solid cancer (62% vs 71%; OR: 0.66 (95% CI: 0.41 to 1.05)) and advanced stage cancer (53% vs 70%; OR: 0.48 (95% CI: 0.30 to 0.75)), were more frequently treatment-naive (53% vs 43%; OR: 1.55 (95% CI 1.01 to 2.39)) and were more frequently in an adjuvant/neoadjuvant treatment setting (14% vs 5%; OR: 3.39 (95% CI: 1.45 to 9.93)). Patients with access to OLU had a median OS of 31.1 months versus 8.7 months for patients without access (unadjusted HR: 0.54; (95% CI: 0.41 to 0.70)).ConclusionContrary to the common assumption, OLU in oncology is typically not primarily intended for patients with exhausted treatment options. Patient characteristics largely differ between patients with and without access to intended OLU. More systematic evaluations of the benefits and harms of OLU in cancer care and the regulation of its access is warranted.