To determine the cost-utility of a multi-professional simulation training programme for obstetric emergencies-Practical Obstetric Multi-Professional Training (PROMPT)-with a particular focus on its ...impact on permanent obstetric brachial plexus injuries (OBPIs).
A model-based cost-utility analysis.
Maternity units in England.
Simulated cohorts of individuals affected by permanent OBPIs.
A decision tree model was developed to estimate the cost-utility of adopting annual, PROMPT training (scenario 1a) or standalone shoulder dystocia training (scenario 1b) in all maternity units in England compared to current practice, where only a proportion of English units use the training programme (scenario 2). The time horizon was 30 years and the analysis was conducted from an English National Health Service (NHS) and Personal Social Services perspective. A probabilistic sensitivity analysis was performed to account for uncertainties in the model parameters.
Outcomes for the entire simulated period included the following: total costs for PROMPT or shoulder dystocia training (including costs of OBPIs), number of OBPIs averted, number of affected adult/parental/dyadic quality adjusted life years (QALYs) gained and the incremental cost per QALY gained.
Nationwide PROMPT or shoulder dystocia training conferred significant savings (in excess of £1 billion ($1.5 billion)) compared to current practice, resulting in cost-savings of at least £1 million ($1.5 million) per any type of QALY gained. The probabilistic sensitivity analysis demonstrated similar findings.
In this model, national implementation of multi-professional simulation training for obstetric emergencies (or standalone shoulder dystocia training) in England appeared to both be cost-saving when evaluating their impact on permanent OBPIs.
Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ...'pseudotime' where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.
Introduction
We aim to outline the annual cost of setting up and running a standard, local, multi‐professional obstetric emergencies training course, PROMPT (PRactical Obstetric Multi‐Professional ...Training), at Southmead Hospital, Bristol, UK – a unit caring for approximately 6500 births per year.
Material and methods
A retrospective, micro‐costing analysis was performed. Start‐up costs included purchasing training mannequins and teaching props, printing of training materials and assembly of emergency boxes (real and training). The variable costs included administration time, room hire, additional printing and the cost of releasing all maternity staff in the unit, either as attendees or trainers. Potential, extra start‐up costs for maternity units without established training were also included.
Results
The start‐up costs were €5574 and the variable costs for 1 year were €143 232. The total cost of establishing and running training at Southmead for 1 year was €148 806. Releasing staff as attendees or trainers accounted for 89% of the total first year costs, and 92% of the variable costs. The cost of running training in a maternity unit with around 6500 births per year was approximately €23 000 per 1000 births for the first year and around €22 000 per 1000 births in subsequent years.
Conclusions
The cost of local, multi‐professional obstetric emergencies training is not cheap, with staff costs potentially representing over 90% of the total expenditure. It is therefore vital that organizations consider the clinical effectiveness of local training packages before implementing them, to ensure the optimal allocation of finite healthcare budgets.
The inter-differentiation between cell states promotes cancer cell survival under stress and fosters non-genetic heterogeneity (NGH). NGH is, therefore, a surrogate of tumor resilience but its ...quantification is confounded by genetic heterogeneity. Here we show that NGH in serous ovarian cancer (SOC) can be accurately measured when informed by the molecular signatures of the normal fallopian tube epithelium (FTE) cells, the cells of origin of SOC. Surveying the transcriptomes of ∼6,000 FTE cells, predominantly from non-ovarian cancer patients, identified 6 FTE subtypes. We used subtype signatures to deconvolute SOC expression data and found substantial intra-tumor NGH. Importantly, NGH-based stratification of ∼1,700 tumors robustly correlated with survival. Our findings lay the foundation for accurate prognostic and therapeutic stratification of SOC.
•The projection of FTE subtypes refines the molecular classification of SOC•Comprehensive single-cell profiling of FTE cells identifies six molecular subtypes•Substantial non-genetic heterogeneity of HGSOC identified in 1,700 tumors•A mesenchymal-high HGSOC subtype is robustly correlated with poor prognosis
Using single-cell RNA sequencing, Hu et al. identify six subtypes of fallopian tube epithelium (FTE) cells in normal human fallopian tube tissues. The FTE cellular subtypes reveal intra-tumoral heterogeneity in serous ovarian cancer (SOC) and define SOC subtypes that correlate with patient prognosis.
We present a case study on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units ...(GPUs), are self-contained parallel computational devices that can be housed in conventional desktop and laptop computers and can be thought of as prototypes of the next generation of many-core processors. For certain classes of population-based Monte Carlo algorithms they offer massively parallel simulation, with the added advantage over conventional distributed multicore processors that they are cheap, easily accessible, easy to maintain, easy to code, dedicated local devices with low power consumption. On a canonical set of stochastic simulation examples including population-based Markov chain Monte Carlo methods and Sequential Monte Carlo methods, we find speedups from 35- to 500-fold over conventional single-threaded computer code. Our findings suggest that GPUs have the potential to facilitate the growth of statistical modeling into complex data-rich domains through the availability of cheap and accessible many-core computation. We believe the speedup we observe should motivate wider use of parallelizable simulation methods and greater methodological attention to their design. This article has supplementary material online.
Genomic insights in settings where tumour sample sizes are limited to just hundreds or even tens of cells hold great clinical potential, but also present significant technical challenges. We ...previously developed the DigiPico sequencing platform to accurately identify somatic mutations from such samples.
Here, we complete this genomic characterisation with copy number. We present a novel protocol, PicoCNV, to call allele-specific somatic copy number alterations from picogram quantities of tumour DNA. We find that PicoCNV provides exactly accurate copy number in 84% of the genome for even the smallest samples, and demonstrate its clinical potential in maintenance therapy.
PicoCNV complements our existing platform, allowing for accurate and comprehensive genomic characterisations of cancers in settings where only microscopic samples are available.
Multimorbidity, characterised by the coexistence of multiple chronic conditions in an individual, is a rising public health concern. While much of the existing research has focused on cross-sectional ...patterns of multimorbidity, there remains a need to better understand the longitudinal accumulation of diseases. This includes examining the associations between important sociodemographic characteristics and the rate of progression of chronic conditions.
We utilised electronic primary care records from 13.48 million participants in England, drawn from the Clinical Practice Research Datalink (CPRD Aurum), spanning from 2005 to 2020 with a median follow-up of 4.71 years (IQR: 1.78, 11.28). The study focused on 5 important chronic conditions: cardiovascular disease (CVD), type 2 diabetes (T2D), chronic kidney disease (CKD), heart failure (HF), and mental health (MH) conditions. Key sociodemographic characteristics considered include ethnicity, social and material deprivation, gender, and age. We employed a flexible spline-based parametric multistate model to investigate the associations between these sociodemographic characteristics and the rate of different disease transitions throughout multimorbidity development. Our findings reveal distinct association patterns across different disease transition types. Deprivation, gender, and age generally demonstrated stronger associations with disease diagnosis compared to ethnic group differences. Notably, the impact of these factors tended to attenuate with an increase in the number of preexisting conditions, especially for deprivation, gender, and age. For example, the hazard ratio (HR) (95% CI; p-value) for the association of deprivation with T2D diagnosis (comparing the most deprived quintile to the least deprived) is 1.76 (1.74, 1.78; p < 0.001) for those with no preexisting conditions and decreases to 0.95 (0.75, 1.21; p = 0.69) with 4 preexisting conditions. Furthermore, the impact of deprivation, gender, and age was typically more pronounced when transitioning from an MH condition. For instance, the HR (95% CI; p-value) for the association of deprivation with T2D diagnosis when transitioning from MH is 2.03 (1.95, 2.12, p < 0.001), compared to transitions from CVD 1.50 (1.43, 1.58, p < 0.001), CKD 1.37 (1.30, 1.44, p < 0.001), and HF 1.55 (1.34, 1.79, p < 0.001). A primary limitation of our study is that potential diagnostic inaccuracies in primary care records, such as underdiagnosis, overdiagnosis, or ascertainment bias of chronic conditions, could influence our results.
Our results indicate that early phases of multimorbidity development could warrant increased attention. The potential importance of earlier detection and intervention of chronic conditions is underscored, particularly for MH conditions and higher-risk populations. These insights may have important implications for the management of multimorbidity.
The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance ...has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial.
The CONSORT 2010 statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More ...recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders), and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
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