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Aims/hypothesis: A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium–glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA). Methods: We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA1c) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events. Results: Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications. Conclusions/interpretation: Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries. [ABSTRACT FROM AUTHOR]

Objective: Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model. Methods: Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink). Results: Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0–14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5–10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7–8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4–10.1). Conclusions: Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior. [ABSTRACT FROM AUTHOR]

Vaccination rates against SARS-CoV-2 in children aged five to eleven years remain low in many countries. The current benefit of vaccination in this age group has been questioned given that the large majority of children have now experienced at least one SARS-CoV-2 infection. However, protection from infection, vaccination or both wanes over time. National decisions on offering vaccines to this age group have tended to be made without considering time since infection. There is an urgent need to evaluate the additional benefits of vaccination in previously infected children and under what circumstances those benefits accrue. We present a novel methodological framework for estimating the potential benefits of COVID-19 vaccination in previously infected children aged five to eleven, accounting for waning. We apply this framework to the UK context and for two adverse outcomes: hospitalisation related to SARS-CoV-2 infection and Long Covid. We show that the most important drivers of benefit are: the degree of protection provided by previous infection; the protection provided by vaccination; the time since previous infection; and future attack rates. Vaccination can be very beneficial for previously infected children if future attack rates are high and several months have elapsed since the previous major wave in this group. Benefits are generally larger for Long Covid than hospitalisation, because Long Covid is both more common than hospitalisation and previous infection offers less protection against it. Our framework provides a structure for policy makers to explore the additional benefit of vaccination across a range of adverse outcomes and different parameter assumptions. It can be easily updated as new evidence emerges. [ABSTRACT FROM AUTHOR]

Background: Beneficial response to first-line immunosuppressive azathioprine in patients with inflammatory bowel disease (IBD) is low due to high rates of adverse events. Co-administrating allopurinol has been shown to improve tolerability. However, data on this co-therapy as first-line treatment are scarce. Aim: Retrospective comparison of long-term effectiveness and safety of first-line low-dose azathioprine-allopurinol co-therapy (LDAA) with first-line azathioprine monotherapy (AZAm) in patients with IBD without metabolite monitoring. Methods: Clinical benefit was defined as ongoing therapy without initiation of steroids, biologics or surgery. Secondary outcomes included CRP, HBI/SCCAI, steroid withdrawal and adverse events. Results: In total, 166 LDAA and 118 AZAm patients (median follow-up 25 and 27 months) were evaluated. Clinical benefit was more frequently observed in LDAA patients at 6 months (74% vs. 53%, p = 0.0003), 12 months (54% vs. 37%, p = 0.01) and in the long-term (median 36 months; 37% vs. 24%, p = 0.04). Throughout follow-up, AZAm patients were 60% more likely to fail therapy, due to a higher intolerance rate (45% vs. 26%, p = 0.001). Only 73% of the effective AZA dose was tolerated in AZAm patients, while LDAA could be initiated and maintained at its target dose. Incidence of myelotoxicity and elevated liver enzymes was similar in both cohorts, and both conditions led to LDAA withdrawal in only 2%. Increasing allopurinol from 100 to 200–300 mg/day significantly lowered liver enzymes in 5/6 LDAA patients with hepatotoxicity. Conclusions: Our poor AZAm outcomes emphasize that optimization of azathioprine is needed. We demonstrated a long-term safe and more effective profile of first-line LDAA. This co-therapy may therefore be considered standard first-line immunosuppressive. [ABSTRACT FROM AUTHOR]

Objectives: To determine the psychometric validity, using Rasch analysis, of summing the three constituent parts of the Glasgow Coma Scale (GCS). Design: National (registry-based) retrospective study. Setting: England and Wales. Patients: All individuals who sustained a traumatic injury and were: admitted for more than three days; required critical care resources; transferred for specialist management; or who died from their injuries. Main outcomes and measures: Demographic information (i.e., age at time of injury, and sex), item sub-scores of the first available GCS (either completed by the attending paramedics or on arrival to hospital), injury severity as denoted by the Injury Severity Scale (ISS), and outcome (survival to hospital discharge or 30-days post-injury, whichever is earliest). Results: 321,203 cases between 2008 and 2017. 55.9% were male, the median age was 62.7 years (IQR 44.2–80.8), the median ISS was 9 (IQR 9 to 17), and 6.6% were deceased at 30 days. The reliability statistics suggest that when the extreme scores (i.e. 3 and 15) are accounted for, that there is only sufficient consistency to support the separation of injuries into 3 broad categories, e.g. mild, moderate and severe. As extreme scores don't impact Rasch item calibrations, subsequent analysis was restricted to the 48,417 non-extreme unique cases. Overall fit to the Rasch model was poor across all analyses (p < 0.0001). Through a combination of empirical evidence and clinical reasoning, item response categories were collapsed to provide a post-hoc scoring amendment. Whilst the modifications improved the function of the individual items, there is little evidence to support them meaningfully contributing to a total score that can be interpreted on an interval scale. Conclusion and relevance: The GCS does not perform in a psychometrically robust manner in a national retrospective cohort of individuals who have experienced a traumatic injury, even after post-hoc correction. [ABSTRACT FROM AUTHOR]

Background: Executive dysregulation and impulsivity can both predispose individuals to risk‐prone actions. Although the risk of falls is well established in people with poor executive function, its association to impulsivity is less clear. Purpose: To describe and assess the prognostic capabilities of the relationship between impulsivity, executive function, functional capability, and falls in the in‐patient neurorehabilitation population. Materials and Methods: A prospective cohort study in a 26‐bed neurorehabilitation unit in London, recruiting 121 patients, of whom 94 were deemed eligible for inclusion. Cognitive‐behavioural assessment was undertaken using the short (16‐item) version of the Urgency‐Premeditation‐Perseverance‐Sensation Seeking‐Positive Urgency (UPPS) impulsive behaviour scale, and the Trail Making Test (TMT). Patients also underwent a functional assessment at admission and discharge using the UK Functional Independence and Assessment Measure tool (FIM + FAM). The main outcome of interest was falling during an in‐patient episode, which are routinely recorded in a computerized registry of adverse incidents. Results: Measurements of impulsivity (based on the UPPS‐Short form) and executive function (based on the Trail Making Test) were not found to be significantly associated with functional improvement, or risk of falling. Predictive modelling experiments demonstrated that neither of the aforementioned results were capable of identifying individuals at risk of falling more accurately than an informed guess. Conclusion: Where impulsivity is present, measurement using structured tools such as the UPPS may be informative to guide individualized rehabilitation programmes; however, its usefulness as the basis of risk prediction models for falls is less likely given the results of this study. [ABSTRACT FROM AUTHOR]

Background: The literature paints a complex picture of the association between mortality risk and ICU strain. In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain.Methods: A national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease).Results: One hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)].Conclusion: Increasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation. [ABSTRACT FROM AUTHOR]

Objectives: To describe the relationship between reported serious operational problems (SOPs), and mortality for patients with COVID-19 admitted to intensive care units (ICUs). Design: English national retrospective cohort study. Setting: 89 English hospital trusts (i.e. small groups of hospitals functioning as single operational units). Patients: All adults with COVID-19 admitted to ICU between 2nd April and 1st December, 2020 (n = 6,737). Interventions: N/A Main outcomes and measures: Hospital trusts routinely submit declarations of whether they have experienced 'serious operational problems' in the last 24 hours (e.g. due to staffing issues, adverse weather conditions, etc.). Bayesian hierarchical models were used to estimate the association between in-hospital mortality (binary outcome) and: 1) an indicator for whether a SOP occurred on the date of a patient's admission, and; 2) the proportion of the days in a patient's stay that had a SOP occur within their trust. These models were adjusted for individual demographic characteristics (age, sex, ethnicity), and recorded comorbidities. Results: Serious operational problems (SOPs) were common; reported in 47 trusts (52.8%) and were present for 2,701 (of 21,716; 12.4%) trust days. Overall mortality was 37.7% (2,539 deaths). Admission during a period of SOPs was associated with a substantially increased mortality; adjusted odds ratio (OR) 1.34 (95% posterior credible interval (PCI): 1.07 to 1.68). Mortality was also associated with the proportion of a patient's admission duration that had concurrent SOPs; OR 1.47 (95% PCI: 1.10 to 1.96) for mortality where SOPs were present for 100% compared to 0% of the stay. Conclusion and relevance: Serious operational problems at the trust-level are associated with a significant increase in mortality in patients with COVID-19 admitted to critical care. The link isn't necessarily causal, but this observation justifies further research to determine if a binary indicator might be a valid prognostic marker for deteriorating quality of care. [ABSTRACT FROM AUTHOR]

Objective: To describe the relationship between type 2 diabetes and all-cause mortality among adults with coronavirus disease 2019 (COVID-19) in the critical care setting.Research Design and Methods: This was a nationwide retrospective cohort study in people admitted to hospital in England with COVID-19 requiring admission to a high dependency unit (HDU) or intensive care unit (ICU) between 1 March 2020 and 27 July 2020. Cox proportional hazards models were used to estimate 30-day in-hospital all-cause mortality associated with type 2 diabetes, with adjustment for age, sex, ethnicity, obesity, and other major comorbidities (chronic respiratory disease, asthma, chronic heart disease, hypertension, immunosuppression, chronic neurological disease, chronic renal disease, and chronic liver disease).Results: A total of 19,256 COVID-19-related HDU and ICU admissions were included in the primary analysis, including 13,809 HDU (mean age 70 years) and 5,447 ICU (mean age 58 years) admissions. Of those admitted, 3,524 (18.3%) had type 2 diabetes and 5,077 (26.4%) died during the study period. Patients with type 2 diabetes were at increased risk of death (adjusted hazard ratio [aHR] 1.23 [95% CI 1.14, 1.32]), and this result was consistent in HDU and ICU subsets. The relative mortality risk associated with type 2 diabetes decreased with higher age (age 18-49 years aHR 1.50 [95% CI 1.05, 2.15], age 50-64 years 1.29 [1.10, 1.51], and age ≥65 years 1.18 [1.09, 1.29]; P value for age-type 2 diabetes interaction = 0.002).Conclusions: Type 2 diabetes may be an independent prognostic factor for survival in people with severe COVID-19 requiring critical care treatment, and in this setting the risk increase associated with type 2 diabetes is greatest in younger people. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to understand existing vocational rehabilitation service provision in one locality in London (population 3.74 million), identify any gaps and explore reasons for this, to support service development. Method: Using soft systems methodology to guide the research process, semi-structured interviews were completed with nine participants, who were clinicians and managers providing vocational rehabilitation within a National Health Service context. Data were analysed thematically to build a 'rich picture' and develop a conceptual model of vocational rehabilitation service delivery. Findings were then ratified with participants at an engagement event. Results: The findings indicate a spectrum of vocational rehabilitation service provision for long-term neurological conditions with differing levels of funding in place. Vocational rehabilitation often takes place 'under the radar' and therefore the true vocational rehabilitation needs of this population, and the extent of service provision, is not known. There is inconsistency of understanding across the services as to what constitutes vocational rehabilitation and outcomes are not routinely measured. Conclusion: For vocational rehabilitation services to develop they require appropriate funding, driven by government policy to commissioners. Clear definitions of vocational rehabilitation, collecting and sharing outcome data and effective communication across services are needed at a local level. This is expressed in a conceptual model of vocational rehabilitation service delivery. [ABSTRACT FROM AUTHOR]

Background: Stroke is a leading cause of disability worldwide. The most common impairment resulting from stroke is upper-limb weakness. Objectives: To determine the usefulness and psychometric validity of the upper-limb subscale of the STREAM in an acute stroke population. Methods: Rasch Analysis, including unidimensionality assumption testing, determining model fit, and analysis of: reliability, residual correlations, and differential item functioning. Results: 125 individuals were assessed using the upper-limb subscale of the Stroke Rehabilitation Assessment of Movement (STREAM) tool. Rasch analysis suggests the STREAM is a unidimensional measure. However, when scored using the originally proposed method (0-2), or using the response pattern (0-5) neither variant fit the Rasch model (p < 0.05). Although, the reliability was good (Person-Separation Index - 0.847 and 0.903, respectively). Correcting for the disordered thresholds, and thereby producing the new scoring pattern, led to substantial improvement in the overall fit (chi-square probability of fit - 22%), however, the reliability was slightly reduced (PSI - 0.806). Conclusions: The study proposes a new scoring method for the upper-limb subscale of the STREAM outcome measure in the acute stroke population. [ABSTRACT FROM AUTHOR]

Objective: To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. Design: Prospective cohort study. Setting: Tertiary neurological and neurosurgical center. Subjects: In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. Main Measures: Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). Results: The principal outcome was a fall during the in-patient stay (n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. Conclusion: This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test. [ABSTRACT FROM AUTHOR]

Purpose: Selecting the most appropriate health-related work outcome to evaluate an intervention can be fraught with difficulty. To aid clinicians in navigating this problem we have developed a model, which illustrates how pathology can affect specific measureable quantities, such as work instability. Methods: Using a modified-Delphi procedure, a panel of experts met initially to analyze the content of 95 health-related work outcome measures and organize the identified areas of measurement into a coherent model, complemented by a narrative review of the literature. This initial model underwent two rounds of stakeholder-based feedback, the results of which were incorporated in the final expert panel meeting to produce the States-traits Work Instability Model (SWIM). Results: The States-traits Work Instability Model (SWIM) illustrates how changes to an individual's physical and psychological states and traits might affect their work-related performance, well-being and self-efficacy. Moreover, each concept utilized in the model was specifically selected as it represents a measurable quantity, for which there are tools available. Conclusion: The SWIM is arguably the first holistic model of work that is based on both the clinical realities of vocational rehabilitation, sociological research, and is born from analyzing the basis of practical measurements. [ABSTRACT FROM AUTHOR]