Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population

Background Information: Falls are associated with high direct and indirect costs, and significant morbidity and mortality for patients. Pathological falls are usually a result of a compromised motor system, and/or cognition. Very little research has been conducted on predicting falls based on this premise. Aims: To demonstrate that cognitive and motor tests can be used to create a robust predictive tool for falls. Methods: Three tests of attention and executive function (Stroop, Trail Making, and Semantic Fluency), a measure of physical function (Walk-12), a series of questions (concerning recent falls, surgery and physical function) and demographic information were collected from a cohort of 323 patients at a tertiary neurological center. The principal outcome was a fall during the in-patient stay (n = 54). Data-driven, predictive modelling was employed to identify the statistical modelling strategies which are most accurate in predicting falls, and which yield the most parsimonious models of clinical relevance. Results: The Trail test was identified as the best predictor of falls. Moreover, addition of any others variables, to the results of the Trail test did not improve the prediction (Wilcoxon signed-rank p < .001). The best statistical strategy for predicting falls was the random forest (Wilcoxon signed-rank p < .001), based solely on results of the Trail test. Tuning of the model results in the following optimized values: 68% (+- 7.7) sensitivity, 90% (+- 2.3) specificity, with a positive predictive value of 60%, when the relevant data is available. Conclusion: Predictive modelling has identified a simple yet powerful machine learning prediction strategy based on a single clinical test, the Trail test. Predictive evaluation shows this strategy to be robust, suggesting predictive modelling and machine learning as the standard for future predictive tools

Developing vocational rehabilitation services for people with long-term neurological conditions : identifying facilitators and barriers to service provision

Purpose: This study aimed to understand existing vocational rehabilitation (VR) 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 VR within NHS services. Data were analysed thematically to build a ‘rich picture’ and develop a conceptual model of VR service delivery. Findings were then ratified with participants at an engagement event. Results: The findings indicate a spectrum of VR service provision for long - term neurological conditions with differing levels of funding in place. VR often takes place ‘under the radar’ and therefore the true VR 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 VR and outcomes are not routinely measured. Conclusion: For VR services to develop they require appropriate funding, driven by Government policy to commissioners. Clear definitions of VR, collecting and sharing outcome data and effective communication across services are needed at a local level. This is expressed in a conceptual model of VR service delivery

DNA Methylation: Basic Biology and Application to Traumatic Brain Injury.

This article reviews the literature pertinent to epigenetic changes, and in particular, DNA methylation following traumatic brain injury (TBI). TBI is a heterogeneous disease that is a major cause of death and long-term disability. The links between TBI and epigenetics, the process by which environmental factors alter gene expression without changing the underlying DNA sequence, is an expanding area of research that may have profound consequences for understanding the disease, and for clinical care. There are various epigenetic changes that may occur as a direct result of TBI, including DNA methylation, histone modification, and changes in the levels of non-coding RNA. This review focuses on DNA methylation, its potential to alter the degree of injury, and the extent of recovery, including development of post-traumatic neurodegeneration, response to therapies, and the hereditable consequences of injury. The functional consequences of non-coding RNA and histone modifications are well described in the literature; however, the mechanism by which these three mechanisms interact are often overlooked. Here, we briefly describe the interaction of DNA methylation with the two other key epigenetic changes, and highlight key work being performed to understand the functional relevance of those mechanisms. The field of epigenetics is rapidly advancing as a result of the advent of less invasive and more versatile methods for measuring epigenetic proteins and their functional impact on cells; however, the evidence specific to TBI is limited. This review identifies several important outstanding questions that remain from the work already conducted, and highlights directions for the future

Model updating after interventions paradoxically introduces bias

Machine learning is increasingly being used to generate prediction models for use in a number of real-world settings, from credit risk assessment to clinical decision support. Recent discussions have highlighted potential problems in the updating of a predictive score for a binary outcome when an existing predictive score forms part of the standard workflow, driving interventions. In this setting, the existing score induces an additional causative pathway which leads to miscalibration when the original score is replaced. We propose a general causal framework to describe and address this problem, and demonstrate an equivalent formulation as a partially observed Markov decision process. We use this model to demonstrate the impact of such `naive updating' when performed repeatedly. Namely, we show that successive predictive scores may converge to a point where they predict their own effect, or may eventually tend toward a stable oscillation between two values, and we argue that neither outcome is desirable. Furthermore, we demonstrate that even if model-fitting procedures improve, actual performance may worsen. We complement these findings with a discussion of several potential routes to overcome these issues.Comment: Sections of this preprint on 'Successive adjuvancy' (section 4, theorem 2, figures 4,5, and associated discussions) were not included in the originally submitted version of this paper due to length. This material does not appear in the published version of this manuscript, and the reader should be aware that these sections did not undergo peer revie

Hospital admissions linked to SARS-CoV-2 infection in children and adolescents: cohort study of 3.2 million first ascertained infections in England

Objective To describe hospital admissions associated with SARS-CoV-2 infection in children and adolescents. Design Cohort study of 3.2 million first ascertained SARS-CoV-2 infections using electronic health care record data. Setting England, July 2020 to February 2022. Participants About 12 million children and adolescents (age &lt;18 years) who were resident in England. Main outcome measures Ascertainment of a first SARS-CoV-2 associated hospital admissions: due to SARS-CoV-2, with SARS-CoV-2 as a contributory factor, incidental to SARS-CoV-2 infection, and hospital acquired SARS-CoV-2. Results 3 226 535 children and adolescents had a recorded first SARS-CoV-2 infection during the observation period, and 29 230 (0.9%) infections involved a SARS-CoV-2 associated hospital admission. The median length of stay was 2 (interquartile range 1-4) days) and 1710 of 29 230 (5.9%) SARS-CoV-2 associated admissions involved paediatric critical care. 70 deaths occurred in which covid-19 or paediatric inflammatory multisystem syndrome was listed as a cause, of which 55 (78.6%) were in participants with a SARS-CoV-2 associated hospital admission. SARS-CoV-2 was the cause or a contributory factor in 21 000 of 29 230 (71.8%) participants who were admitted to hospital and only 380 (1.3%) participants acquired infection as an inpatient and 7855 (26.9%) participants were admitted with incidental SARS-CoV-2 infection. Boys, younger children (&lt;5 years), and those from ethnic minority groups or areas of high deprivation were more likely to be admitted to hospital (all P&lt;0.001). The covid-19 vaccination programme in England has identified certain conditions as representing a higher risk of admission to hospital with SARS-CoV-2: 11 085 (37.9%) of participants admitted to hospital had evidence of such a condition, and a further 4765 (16.3%) of participants admitted to hospital had a medical or developmental health condition not included in the vaccination programme’s list. Conclusions Most SARS-CoV-2 associated hospital admissions in children and adolescents in England were due to SARS-CoV-2 or SARS-CoV-2 was a contributory factor. These results should inform future public health initiatives and research

Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes:a retrospective cohort study

Background: Current treatment guidelines do not provide recommendations to support the selection of treatment for most people with type 2 diabetes. We aimed to develop and validate an algorithm to allow selection of optimal treatment based on glycaemic response, weight change, and tolerability outcomes when choosing between SGLT2 inhibitor or DPP-4 inhibitor therapies. Methods: In this retrospective cohort study, we identified patients initiating SGLT2 and DPP-4 inhibitor therapies after Jan 1, 2013, from the UK Clinical Practice Research Datalink (CPRD). We excluded those who received SGLT2 or DPP-4 inhibitors as first-line treatment or insulin at the same time, had estimated glomerular filtration rate (eGFR) of less than 45 mL/min per 1·73 m2, or did not have a valid baseline glycated haemoglobin (HbA1c) measure (&lt;53 or ≥120 mmol/mol). The primary efficacy outcome was the HbA1c value reached 6 months after drug initiation, adjusted for baseline HbA1c. Clinical features associated with differential HbA1c outcome on the two therapies were identified in CPRD (n=26 877), and replicated in reanalysis of 14 clinical trials (n=10 414). An algorithm to predict individual-level differential HbA1c outcome on the two therapies was developed in CPRD (derivation; n=14 069) and validated in head-to-head trials (n=2499) and CPRD (independent validation; n=9376). In CPRD, we further explored heterogeneity in 6-month weight change and treatment discontinuation. Findings: Among 10 253 patients initiating SGLT2 inhibitors and 16 624 patients initiating DPP-4 inhibitors in CPRD, baseline HbA1c, age, BMI, eGFR, and alanine aminotransferase were associated with differential HbA1c outcome with SGLT2 inhibitor and DPP-4 inhibitor therapies. The median age of participants was 62·0 years (IQR 55·0–70·0). 10 016 (37·3%) were women and 16 861 (62·7%) were men. An algorithm based on these five features identified a subgroup, representing around four in ten CPRD patients, with a 5 mmol/mol or greater observed benefit with SGLT2 inhibitors in all validation cohorts (CPRD 8·8 mmol/mol [95% CI 7·8–9·8]; CANTATA-D and CANTATA-D2 trials 5·8 mmol/mol [3·9–7·7]; BI1245.20 trial 6·6 mmol/mol [2·2–11·0]). In CPRD, predicted differential HbA1c response with SGLT2 inhibitor and DPP-4 inhibitor therapies was not associated with weight change. Overall treatment discontinuation within 6 months was similar in patients predicted to have an HbA1c benefit with SGLT2 inhibitors over DPP-4 inhibitors (median 15·2% [13·2–20·3] vs 14·4% [12·9–16·7]). A smaller subgroup predicted to have greater HbA1c reduction with DPP-4 inhibitors were twice as likely to discontinue SGLT2 inhibitors than DPP-4 inhibitors (median 26·8% [23·4–31·0] vs 14·8% [12·9–16·8]). Interpretation: A validated treatment selection algorithm for SGLT2 inhibitor and DPP-4 inhibitor therapies can support decisions on optimal treatment for people with type 2 diabetes. Funding: BHF-Turing Cardiovascular Data Science Award and the UK Medical Research Council

Rasch Analysis of the Upper-Limb Sub-scale of the STREAM Tool in an Acute Stroke Population

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 sub-scale of the STREAM in an acute stroke population. Methods: Rasch Analysis, including unidimensionality assumption testing, determining model fit, and analysis of: reliability, residual correlations, & differential item functioning. Results - 125 individuals were assessed using the upper limb sub-scale 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 & 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 sub-scale of the STREAM outcome measure in the acute stroke population.Stroke Associatio

Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI (Nature Medicine, (2022), 28, 5, (924-933), 10.1038/s41591-022-01772-9)

In the version of this article initially published, a list of the DECIDE-AI expert group members and their affiliations was omitted and has now been included in the HTML and PDF versions of the article. *A list of authors and their affiliations appears online