Adoption of high-sensitivity cardiac troponin for risk stratification of patients with suspected myocardial infarction: a multicentre cohort study.

Background: Guidelines recommend high-sensitivity cardiac troponin to risk stratify patients with possible myocardial infarction and identify those eligible for discharge. Our aim was to evaluate adoption of this approach in practice and to determine whether effectiveness and safety varies by age, sex, ethnicity, or socioeconomic deprivation status. Methods: A multi-centre cohort study was conducted in 13 hospitals across the United Kingdom from November 1st, 2021, to October 31st, 2022. Routinely collected data including high-sensitivity cardiac troponin I or T measurements were linked to outcomes. The primary effectiveness and safety outcomes were the proportion discharged from the Emergency Department, and the proportion dead or with a subsequent myocardial infarction at 30 days, respectively. Patients were stratified using peak troponin concentration as low (<5 ng/L), intermediate (5 ng/L to sex-specific 99th percentile), or high-risk (>sex-specific 99th percentile). Findings: In total 137,881 patients (49% [67,709/137,881] female) were included of whom 60,707 (44%), 42,727 (31%), and 34,447 (25%) were stratified as low-, intermediate- and high-risk, respectively. Overall, 65.8% (39,918/60,707) of low-risk patients were discharged from the Emergency Department, but this varied from 26.8% [2200/8216] to 93.5% [918/982] by site. The safety outcome occurred in 0.5% (277/60,707) and 11.4% (3917/34,447) of patients classified as low- or high-risk, of whom 0.03% (18/60,707) and 1% (304/34,447) had a subsequent myocardial infarction at 30 days, respectively. A similar proportion of male and female patients were discharged (52% [36,838/70,759] versus 54% [36,113/67,109]), but discharge was more likely if patients were <70 years old (61% [58,533/95,227] versus 34% [14,428/42,654]), from areas of low socioeconomic deprivation (48% [6697/14,087] versus 43% [12,090/28,116]) or were black or asian compared to caucasian (62% [5458/8877] and 55% [10,026/18,231] versus 46% [35,138/75,820]). Interpretation: Despite high-sensitivity cardiac troponin correctly identifying half of all patients with possible myocardial infarction as being at low risk, only two-thirds of these patients were discharged. Substantial variation in the discharge of patients by age, ethnicity, socioeconomic deprivation, and site was observed identifying important opportunities to improve care. Funding: UK Research and Innovation.

Adoption by clinicians of electronic order communications in NHS secondary care: a descriptive account.

BACKGROUND: Due to the rapid advancement in information technology, changes to communication modalities are increasingly implemented in healthcare. One such modality is Computerised Provider Order Entry (CPOE) systems which replace paper, verbal or telephone orders with electronic booking of requests. We aimed to understand the uptake, and user acceptability, of CPOE in a large National Health Service hospital system. METHODS: This retrospective single-centre study investigates the longitudinal uptake of communications through the Prescribing, Information and Communication System (PICS). The development and configuration of PICS are led by the doctors, nurses and allied health professionals that use it and requests for CPOE driven by clinical need have been described.Records of every request (imaging, specialty review, procedure, laboratory) made through PICS were collected between October 2008 and July 2019 and resulting counts were presented. An estimate of the proportion of completed requests made through the system has been provided for three example requests. User surveys were completed. RESULTS: In the first 6 months of implementation, a total of 832 new request types (imaging types and specialty referrals) were added to the system. Subsequently, an average of 6.6 new request types were added monthly. In total, 8 035 132 orders were requested through PICS. In three example request types (imaging, endoscopy and full blood count), increases in the proportion of requests being made via PICS were seen. User feedback at 6 months reported improved communications using the electronic system. CONCLUSION: CPOE was popular, rapidly adopted and diversified across specialties encompassing wide-ranging requests.

How far back do we need to look to capture diagnoses in electronic health records? A retrospective observational study of hospital electronic health record data.

OBJECTIVES: Analysis of routinely collected electronic health data is a key tool for long-term condition research and practice for hospitalised patients. This requires accurate and complete ascertainment of a broad range of diagnoses, something not always recorded on an admission document at a single point in time. This study aimed to ascertain how far back in time electronic hospital records need to be interrogated to capture long-term condition diagnoses. DESIGN: Retrospective observational study of routinely collected hospital electronic health record data. SETTING: Queen Elizabeth Hospital Birmingham (UK)-linked data held by the PIONEER acute care data hub. PARTICIPANTS: Patients whose first recorded admission for chronic obstructive pulmonary disease (COPD) exacerbation (n=560) or acute stroke (n=2142) was between January and December 2018 and who had a minimum of 10 years of data prior to the index date. OUTCOME MEASURES: We identified the most common International Classification of Diseases version 10-coded diagnoses received by patients with COPD and acute stroke separately. For each diagnosis, we derived the number of patients with the diagnosis recorded at least once over the full 10-year lookback period, and then compared this with shorter lookback periods from 1 year to 9 years prior to the index admission. RESULTS: Seven of the top 10 most common diagnoses in the COPD dataset reached >90% completeness by 6 years of lookback. Atrial fibrillation and diabetes were >90% coded with 2-3 years of lookback, but hypertension and asthma completeness continued to rise all the way out to 10 years of lookback. For stroke, 4 of the top 10 reached 90% completeness by 5 years of lookback; angina pectoris was >90% coded at 7 years and previous transient ischaemic attack completeness continued to rise out to 10 years of lookback. CONCLUSION: A 7-year lookback captures most, but not all, common diagnoses. Lookback duration should be tailored to the conditions being studied.

Mapping inpatient care pathways for patients with COPD: an observational study using routinely collected electronic hospital record data.

Introduction: Respiratory specialist ward care is associated with better outcomes for patients with COPD exacerbations. We assessed patient pathways and associated factors for people admitted to hospital with COPD exacerbations. Methods: We analysed routinely collected electronic health data for patients admitted with COPD exacerbation in 2018 to Queen Elizabeth Hospital, Birmingham, UK. We extracted data on demographics, deprivation index, Elixhauser comorbidities, ward moves, length of stay, and in-hospital and 1-year mortality. We compared care pathways with recommended care pathways (transition from initial assessment area to respiratory wards or discharge). We used Markov state transition models to derive probabilities of following recommended pathways for patient subgroups. Results: Of 42 555 patients with unplanned admissions during 2018, 571 patients were admitted at least once with an exacerbation of COPD. The mean±sd age was 51±11 years; 313 (55%) were women, 337 (59%) lived in the most deprived neighbourhoods and 45 (9%) were from non-white ethnic backgrounds. 428 (75.0%) had ≥4 comorbidities. Age >70 years was associated with higher in-hospital and 1-year mortality, more places of care (wards) and longer length of stay; having ≥4 comorbidities was associated with higher mortality and longer length of stay. Older age was associated with a significantly lower probability of following a recommended pathway (>70 years: 0.514, 95% CI 0.458-0.571; ≤70 years: 0.636, 95% CI 0.572-0.696; p=0.004). Conclusions: Only older age was associated with a lower chance of following recommended hospital pathways of care. Such analyses could help refine appropriate care pathways for patients with COPD exacerbations.

Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making.

BACKGROUND: Predictive models have been used in clinical care for decades. They can determine the risk of a patient developing a particular condition or complication and inform the shared decision-making process. Developing artificial intelligence (AI) predictive models for use in clinical practice is challenging; even if they have good predictive performance, this does not guarantee that they will be used or enhance decision-making. We describe nine stages of developing and evaluating a predictive AI model, recognising the challenges that clinicians might face at each stage and providing practical tips to help manage them. FINDINGS: The nine stages included clarifying the clinical question or outcome(s) of interest (output), identifying appropriate predictors (features selection), choosing relevant datasets, developing the AI predictive model, validating and testing the developed model, presenting and interpreting the model prediction(s), licensing and maintaining the AI predictive model and evaluating the impact of the AI predictive model. The introduction of an AI prediction model into clinical practice usually consists of multiple interacting components, including the accuracy of the model predictions, physician and patient understanding and use of these probabilities, expected effectiveness of subsequent actions or interventions and adherence to these. Much of the difference in whether benefits are realised relates to whether the predictions are given to clinicians in a timely way that enables them to take an appropriate action. CONCLUSION: The downstream effects on processes and outcomes of AI prediction models vary widely, and it is essential to evaluate the use in clinical practice using an appropriate study design.

Somatic Symptoms of Depression Lose Association with Mortality upon Adjustment for Frailty: Analysis from the Fitness Haemodialysis Cohort.

Introduction: The somatic symptom component of depression is associated with increased hospitalisation and mortality and poorer health-related quality of life (HRQOL). However, the relationship of subsets of depression symptoms with frailty and outcomes is not known. This study aimed to (1) explore the relationship between the Clinical Frailty Scale (CFS) and components of depression and (2) their association with mortality, hospitalisation, and HRQOL in haemodialysis recipients. Methods: We conducted a prospective cohort study of prevalent haemodialysis recipients, with deep bio-clinical phenotyping including CFS and PHQ-9 somatic (fatigue, poor appetite, and poor sleep) and cognitive component scores. EuroQol EQ-5D summary index assessed HRQOL at the baseline. Electronic linkage to English national administration datasets ensured robust follow-up data for hospitalisation and mortality events. Findings. Somatic (ß = 0.067; 95% C.I. 0.029 to 0.104; P < 0.001) and cognitive (ß = 0.062; 95% C.I. 0.034 to 0.089; P<0.001) components were associated with increased CFS scores. Both somatic (ß = -0.062; 95% C.I. -0.104 to -0.021; P<0.001) and cognitive (ß = 0.052; 95% C.I. -0.081 to -0.024; P < 0.001) scores were associated with lower HRQOL. Somatic scores lost mortality association on addition of CFS to the multivariable model (HR1.06; 95% C.I. 0.977 to 1.14; P=0.173). Cognitive symptoms were not associated with mortality. Neither the component score was associated with hospitalisation on multivariable analyses. Conclusions: Both somatic and cognitive depression symptoms are associated with frailty and poorer HRQOL in haemodialysis recipients but were not associated with mortality or hospitalisation when adjusted for frailty. The risk profile of depression somatic scores may be related to overlap with symptoms of frailty.

Gender Disparity in Expression of Sarcopenia in Haemodialysis Recipients: Analysis from the FITNESS Cohort.

Background: There has been little exploration of the interplay between sarcopenia and frailty in haemodialysis, particularly regarding gender difference. We aimed to (1) assess whether ultrasound-derived low muscle mass (LMM) and sarcopenia are more common in male or female haemodialysis recipients; (2) assess whether age influences any observed gender difference, and (3) explore the interplay between sarcopenia, frailty, and gender in haemodialysis recipients. Methods: This was an exploratory analysis of a subgroup of adult prevalent (≥3 months) haemodialysis with frailty phenotype (FP) scores. Bilateral anterior thigh thickness (BATT) was obtained according to an established ultrasound protocol. Associations with frailty were explored via both linear and logistic regressions for BATT, LMM, and sarcopenia with a priori covariables, stratified by gender. Results: In total of 223 studies, participants had ultrasound measurements. Males showed greater prevalence of LMM. On adjusted analyses, LMM was associated with lower hand grip strength in males (ß = -4.17; 95% C.I. -7.57 to -0.77; P=0.02), but not females (ß = -1.88; 95% C.I. -5.41 to 1.64; P=0.29). LMM was also associated with slower walking speed in both males (ß = -0.115; 95% C.I. -0.258 to -0.013; P=0.03) and females (ß = -0.152; 95% C.I. -0.300 to -0.005; P=0.04). Sarcopenia was associated with greater odds of frailty on adjusted models in males (OR = 9.86; 95% C.I. 1.8 to 54.0; P=0.01), but not females (OR = 5.16; 95% C.I. 0.22 to 124; P=0.31). Conclusions: The clinical expression and significance of sarcopenia differ substantially between males and females on haemodialysis. Further work is required to elucidate underlying mechanisms and guide tailored treatment.

A Datasheet for the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Screening Dataset.

Purpose: Diabetic retinopathy (DR) is the most common microvascular complication associated with diabetes mellitus (DM), affecting approximately 40% of this patient population. Early detection of DR is vital to ensure monitoring of disease progression and prompt sight saving treatments as required. This article describes the data contained within the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Dataset. Design: Dataset descriptor for routinely collected eye screening data. Participants: All diabetic patients aged 12 years and older, attending annual digital retinal photography-based screening within the Birmingham, Solihull, and Black Country Eye Screening Programme. Methods: The INSIGHT Health Data Research Hub for Eye Health is a National Health Service (NHS)-led ophthalmic bioresource that provides researchers with safe access to anonymized, routinely collected data from contributing NHS hospitals to advance research for patient benefit. This report describes the INSIGHT Birmingham, Solihull, and Black Country DR Screening Dataset, a dataset of anonymized images and linked screening data derived from the United Kingdom's largest regional DR screening program. Main Outcome Measures: This dataset consists of routinely collected data from the eye screening program. The data primarily include retinal photographs with the associated DR grading data. Additional data such as corresponding demographic details, information regarding patients' diabetic status, and visual acuity data are also available. Further details regarding available data points are available in the supplementary information, in addition to the INSIGHT webpage included below. Results: At the time point of this analysis (December 31, 2019), the dataset comprised 6 202 161 images from 246 180 patients, with a dataset inception date of January 1, 2007. The dataset includes 1 360 547 grading episodes between R0M0 and R3M1. Conclusions: This dataset descriptor article summarizes the content of the dataset, how it has been curated, and what its potential uses are. Data are available through a structured application process for research studies that support discovery, clinical evidence analyses, and innovation in artificial intelligence technologies for patient benefit. Further information regarding the data repository and contact details can be found at https://www.insight.hdrhub.org/. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

A clinical frailty scale obtained from MDT discussion performs poorly in assessing frailty in haemodialysis recipients.

BACKGROUND: The Clinical Frailty Scale (CFS) is a commonly utilised frailty screening tool that has been associated with hospitalisation and mortality in haemodialysis recipients, but is subject to heterogenous methodologies including subjective clinician opinion. The aims of this study were to (i) examine the accuracy of a subjective, multidisciplinary assessment of CFS at haemodialysis Quality Assurance (QA) meetings (CFS-MDT), compared with a standard CFS score via clinical interview, and (ii) ascertain the associations of these scores with hospitalisation and mortality. METHODS: We performed a prospective cohort study of prevalent haemodialysis recipients linked to national datasets for outcomes including mortality and hospitalisation. Frailty was assessed using the CFS after structured clinical interview. The CFS-MDT was derived from consensus at haemodialysis QA meetings, involving dialysis nurses, dietitians, and nephrologists. RESULTS: 453 participants were followed-up for a median of 685 days (IQR 544-812), during which there were 96 (21.2%) deaths and 1136 hospitalisations shared between 327 (72.1%) participants. Frailty was identified in 246 (54.3%) participants via CFS, but only 120 (26.5%) via CFS-MDT. There was weak correlation (Spearman Rho 0.485, P < 0.001) on raw frailty scores and minimal agreement (Cohen's κ = 0.274, P < 0.001) on categorisation of frail, vulnerable and robust between the CFS and CFS-MDT. Increasing frailty was associated with higher rates of hospitalisation for the CFS (IRR 1.26, 95% C.I. 1.17-1.36, P = 0.016) and CFS-MDT (IRR 1.10, 1.02-1.19, P = 0.02), but only the CFS-MDT was associated with nights spent in hospital (IRR 1.22, 95% C.I. 1.08-1.38, P = 0.001). Both scores were associated with mortality (CFS HR 1.31, 95% C.I. 1.09-1.57, P = 0.004; CFS-MDT HR 1.36, 95% C.I. 1.16-1.59, P < 0.001). CONCLUSIONS: Assessment of CFS is deeply affected by the underlying methodology, with the potential to profoundly affect decision-making. The CFS-MDT appears to be a weak alternative to conventional CFS. Standardisation of CFS use is of paramount importance in clinical and research practice in haemodialysis. TRIAL REGISTRATION: Clinicaltrials.gov : NCT03071107 registered 06/03/2017.

Cognitive Impairment, Frailty, and Adverse Outcomes Among Prevalent Hemodialysis Recipients: Results From a Large Prospective Cohort Study in the United Kingdom.

Rationale & Objective: Frailty and cognitive impairment are common in hemodialysis recipients and have been associated with high mortality. There is considerable heterogeneity in frailty reporting, with little comparison between commonly used frailty tools and little exploration of the interplay between cognition and frailty. The aims were to explore the relationship between frailty scores and cognition and their associations with hospitalization and mortality. Study Design: Prospective cohort study. Setting & Population: Prevalent hemodialysis recipients linked to national datasets for hospitalization and mortality. Predictors: Montreal Cognitive Assessment (MoCA), Frailty Phenotype, Frailty Index (FI), Edmonton Frailty Scale, and Clinical Frailty Scale (CFS) were performed at baseline. Cognitive impairment was defined as MoCA scores of <26, or <21 in dexterity impairment, <18 in visual impairment. Outcomes: Mortality, hospitalization. Analytical Approach: Cox proportional hazards model for mortality, censored for end of follow-up. Negative binomial regression for admission rates, censored for death/end of follow-up. Results: In total, 448 participants were recruited with valid MoCAs and followed up for a median of 685 days. There were 103 (23%) deaths and 1,120 admissions of at least one night. Cognitive impairment was identified in 346 (77.2%) participants. Increasing frailty by all definitions was associated with poorer cognition. Cognition was not associated with mortality (HR, 0.99; 95% CI, 0.95-1.03; P = 0.41) or hospitalization (IRR, 1.01; 95% CI, 0.99-1.04; P = 0.39) on multivariable analyses. There were interactions between MoCA scores and increasing frailty by FI (P = 0.002) and Clinical Frailty Scale (P = 0.005); admissions were highest when both MoCA and frailty scores were high, and when both scores were low. Limitations: As frailty is a dynamic state, a single cross-sectional assessment may not accurately reflect its year-to-year variability. In addition, these findings are in maintenance dialysis and may not be transferable to incident hemodialysis. There were small variations in application of frailty tool criteria from other studies, which may have influenced the results. Conclusions: Cognitive impairment is highly prevalent in this hemodialysis cohort. The interaction between cognition and frailty on rates of admission suggests the MoCA offers value in identifying higher risk hemodialysis populations with both high and low degrees of frailty.