Self-reported health change in haemodialysis recipients modulates the effect of frailty upon mortality and hospital admissions: outcomes from a large prospective UK cohort.

BACKGROUND: Frailty among haemodialysis patients is associated with hospitalization and mortality, but high frailty prevalence suggests further discrimination of risk is required. We hypothesized that incorporation of self-reported health with frailty measurement may aid risk stratification. METHODS: Prospective cohort study of 485 prevalent haemodialysis recipients linked to English national datasets. Frailty Phenotype (FP), Frailty Index (FI), Edmonton Frail Scale (EFS), Clinical Frailty Scale (CFS) and self-reported health change were assessed. Mortality was explored using Fine and Gray regression, and admissions by negative binomial regression. RESULTS: Over a median 678 (interquartile range 531-812) days, there were 111 deaths, and 1241 hospitalizations. Increasing frailty was associated with mortality on adjusted analyses for FP [subdistribution hazard ratio (SHR) 1.26, 95% confidence interval (CI) 1.05-1.53, P = .01], FI (SHR 1.21, 95% CI 1.09-1.35, P = .001) and CFS (SHR 1.32, 95% CI 1.11-1.58, P = .002), but not EFS (HR 1.08, 95% CI 0.99-1.18, P = .1). Health change interacted with frailty tools to modify association with mortality; only those who rated their health as the same or worse experienced increased mortality hazard associated with frailty by FP (Pinteraction = .001 and 0.035, respectively), FI (Pinteraction = .002 and .007, respectively) and CFS (Pinteraction = .009 and 0.02, respectively). CFS was the only frailty tool associated with hospitalization (incidence rate ratio 1.12, 95% CI 1.02-1.23, P = .02). CONCLUSIONS: We confirm the high burden of hospitalization and mortality associated with haemodialysis patients regardless of frailty tool utilized and introduce the discriminatory ability of self-reported health to identify the most at-risk frail individuals.

Temporal trends in admissions for atrial fibrillation and severe bleeding in England: an 18-year longitudinal analysis.

Objective. Temporal trends in admissions for atrial fibrillation (AF) and severe bleeding associated with AF vary worldwide. We aimed to explore their temporal trends in England and their relation to the introduction of DOACs in 2014 in the UK. Design. This longitudinal ecological study utilised aggregated data that was extracted from the Hospital Episode Statistics database, which captured annual admissions for AF and severe bleeding associated with AF between 2001 and 2018. Trends in admissions over the study period and across age groups, gender and regions in England were assessed. Results. In total, there were 11,292,177 admissions for AF and 324,851 admissions for severe bleeding associated with AF. There was a steady rise in admissions for AF from 2001 to 2017 (204,808 to 1,109,295; p for trend<.001). A similar trend was observed for severe bleeding (4940 to 30,169; p for trend <.001), but the increase dropped slightly between 2013 and 2014 and continued thereafter. Conclusions. There was a rise in admissions for AF and severe bleeding in England between 2001 and 2018. There is little evidence that the slight drop in admissions for severe bleeding between 2013 and 2014 may have been caused by the introduction of DOACs in 2014. Contributors to these trends need urgent exploration.

Does acute kidney injury alerting improve patient outcomes?

BACKGROUND: Electronic alerts (e-alerts) for Acute Kidney Injury (AKI) have been implemented into a variety of different Electronic Health Records (EHR) systems worldwide in order to improve recognition and encourage early appropriate management of AKI. We were interested in the impact on patient safety, specialist referral and clinical management. METHODS: All patients admitted to our institution with AKI were included in the study. We studied AKI progression, dialysis dependency, length of hospital stay, emergency readmission, ICU readmission, and death, before and after the introduction of electronic alerts. The impact on prescription of high risk drugs, fluid administration, and referral to renal services was also analysed. RESULTS: After the introduction of the e-alert, progression to higher AKI stage, emergency readmission to hospital and death during admission were significantly reduced. More prescriptions were stopped for drugs that adversely affect renal function in AKI and there was a significant increase in the ICU admissions and in the number of patients having dialysis, especially in earlier stages. Longer term mortality, renal referrals, and fluid alteration did not change significantly after the AKI e-alert introduction. CONCLUSIONS: AKI e-alerts can improve clinical outcomes in hospitalised patients.

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.

Ultrasound quadriceps muscle thickness is variably associated with frailty in haemodialysis recipients.

BACKGROUND: Ultrasonographic quantitation of quadriceps muscle mass is increasingly used for assessment of sarcopenia, but its relationship with frailty in haemodialysis recipients is not known. This study explores the relationship between ultrasound-derived bilateral anterior thigh thickness (BATT), sarcopenia, and frailty by common frailty tools (Frailty Phenotype [FP], Frailty Index [FI], Edmonton Frailty [EFS], and Clinical Frailty Scale [CFS]). METHODS: This was an exploratory analysis of a subgroup of adult prevalent (≥3 months) haemodialysis recipients deeply phenotyped for frailty. Ultrasound assessment of BATT was obtained with participants at an angle of ≤45°, with legs outstretched and knees resting at 10°-20°, according to an established protocol. Associations with frailty were explored via both linear and logistic regressions for BATT, Low Muscle Mass (LMM), and sarcopenia with stepwise adjustment for a priori covariables. RESULTS: In total 223 study participants had ultrasound measurements. Frailty ranged from 34% for FP to 58% for FI. BATT was associated with increasing frailty on simple linear regression by all frailty tools, but lost significance on addition of covariables. Upon dichotomising frailty tools into Frail/Not Frail, BATT was associated with frailty by all tools on univariable analyses, but only retained association for EFS on the fully adjusted model (OR 0.97, 95% C.I. 0.94-1.00, P = 0.05). CONCLUSIONS: Ultrasound measures of quadriceps thickness is variably associated with frailty in prevalent haemodialysis recipients, dependent upon the frailty tool used, but not independent of other variables. Further work is required to establish the added value of sarcopenia measurement in frail haemodialysis patients. TRIAL REGISTRATION: Clinicaltrials.gov : NCT03071107 registered 06/03/2017.

A population cohort analysis of English transplant centers indicates major adverse cardiovascular events after kidney transplantation.

Major adverse cardiovascular event (MACE) rates immediately after kidney transplantation remain uncertain due to heterogeneous reporting in the literature. To clarify this, we retrospectively studied every eligible kidney transplant procedure performed in England between April 1, 2002 and March 31, 2018, with follow-up through August 31, 2019. The primary outcome of interest was MACE broadly defined as any hospital admission with myocardial infarction, stroke, unstable angina, heart failure, any coronary revascularisation procedure and/or any cardiovascular death. Among 30,325 kidney transplant recipients, MACE occurred in 781 within the first year after transplantation (2.6% of all kidney transplant procedures). Of these 781 events, 201 occurred during the index admission for kidney transplantation surgery representing 25.7% of all first-year MACE and 0.7% of all kidney transplant procedures. Kidney transplant recipients who suffered a non-fatal MACE within the first year had significantly decreased 1-, 3-, 5- and 10-year patient survival of 80.5%, 70.2%, 59.5% and 38.6% respectively, compared to 97.4%, 94.4%, 90.7% and 78.4% for kidney transplant recipients not developing MACE. In an adjusted Cox proportional hazard model, non-fatal MACE within the first-year post-transplant was associated with significant long-term mortality risk (hazard ratio 2.59; 95% confidence interval 2.34-2.88). Kidney transplant recipients experiencing MACE during the index admission compared to subsequent admissions were differentiated by age, sex and previous cardiac history but had similar patient survival. These rates are significantly lower than those reported in North America. Thus, our data confirm MACE is not a benign post-transplant event and has a strong association with long-term mortality risk.

Comparing survival outcomes for kidney transplant recipients with pre-existing diabetes versus those who develop post-transplantation diabetes.

INTRODUCTION: The aim of this study was to compare the management strategy and clinical outcomes for kidney transplant recipients with pre-transplant versus post-transplantation diabetes (PTDM) in a contemporary cohort. METHODS: This is a single-centre, retrospective. observational study of kidney transplant recipients between 2007 and 2018 with follow-up to 31 December 2020. Data were extracted from hospital electronic patient records, with clinical outcomes linked to national data sets. PTDM was diagnosed by international consensus guidelines. Unadjusted and adjusted survival outcomes were assessed with Kaplan-Meier curves and Cox regression models, respectively, with PTDM handled as a time-varying covariate. RESULTS: Data were analysed for 1,757 kidney transplant recipients, of whom 11.8% (n = 207) had pre-transplant diabetes, and 13.8% (n = 243) developed PTDM with median time to onset 108 days (IQR 46-549 days). Median follow-up was 1,839 days (IQR 928-2985 days). Disparate management strategies were observed, although insulin was the commonest glucose-lowering therapy for all patients with diabetes. In adjusted models, PTDM was associated with lower mortality (HR 0.663, 95% CI 0.543-0.810) and pre-diabetes with higher mortality (HR 1.675, 95% CI 1.396-2.011). However, if analyses are restricted to those with at least 5-year follow-up, then PTDM has no association with mortality (HR 0.771, 95% CI 0.419-1.096), but pre-transplant diabetes remains associated with higher mortality (HR 2.029, 95% CI 1.367-3.012). CONCLUSIONS: Pre-transplant diabetes remains associated with increased mortality risk after kidney transplantation, but PTDM effects are time dependent. Development of PTDM should be encouraged as a mandated registry return to study the long-term impact on survival outcomes.

Pre-operative Waterlow score and outcomes after kidney transplantation.

BACKGROUND: Waterlow scoring was introduced in the 1980s as a nursing tool to risk stratify for development of decubitus ulcers (pressure sores) and is commonly used in UK hospitals. Recent interest has focussed on its value as a pre-op surrogate marker for adverse surgical outcomes, but utility after kidney transplantation has never been explored. METHODS: In this single-centre observational study, data was extracted from hospital informatics systems for all kidney allograft recipients transplanted between 1st January 2007 and 30th June 2020. Waterlow scores were categorised as per national standards; 0-9 (low risk), 10-14 (at risk), 15-19 (high risk) and ≥ 20 (very high risk). Multiple imputation was used to replace missing data with substituted values. Primary outcomes of interest were post-operative length of stay, emergency re-admission within 90-days and mortality analysed by linear, logistic or Cox regression models respectively. RESULTS: Data was available for 2,041 kidney transplant patients, with baseline demographics significantly different across Waterlow categories. As a continuous variable, the median Waterlow score across the study cohort was 10 (interquartile range 8-13). As a categorical variable, Waterlow scores pre-operatively were classified as low risk (n = 557), at risk (n = 543), high risk (n = 120), very high risk (n = 27) and a large proportion of missing data (n = 794). Median length of stay in days varied significantly with pre-op Waterlow category scores, progressively getting longer with increasing severity of Waterlow category. However, no difference was observed in risk for emergency readmission within 90-days of surgery with severity of Waterlow category. Patients with 'very high risk' Waterlow scores had increased risk for mortality at 41.9% versus high risk (23.7%), at risk (17.4%) and low risk (13.4%). In adjusted analyses, 'very high risk' Waterlow group (as a categorical variable) or Waterlow score (as a continuous variable) had an independent association with increase length of stay after transplant surgery only. No association was observed between any Waterlow risk group/score with emergency 90-day readmission rates or post-transplant mortality after adjustment. CONCLUSIONS: Pre-operative Waterlow scoring is a poor surrogate marker to identify kidney transplant patients at risk of emergency readmission or death and should not be utilised outside its intended use.

Interaction between socioeconomic deprivation and ethnicity for likelihood of receiving living-donor kidney transplantation.

BACKGROUND: The interplay between ethnicity and socioeconomic deprivation for living-donor kidney transplantation (LDKT) opportunities is unclear. METHODS: Data for 2040 consecutive kidney-alone transplant recipients receiving an allograft between 1st January 2007 and 30th June 2020 at a single center were retrospectively analyzed. The associations between the proportions of transplants that were LDKT (versus deceased donation) and both ethnicity and socioeconomic deprivation were assessed, with the latter quantified by the Index of Multiple Deprivation (IMD) quintile. RESULTS: The cohort comprised recipients of White (64.7%), South Asian (21.7%), Black (7.0%) and other (6.6%) ethnic groups. Recipients tended to be from socioeconomically deprived areas, with the most deprived quintile being the most frequently observed (quintile 1: 38.6% of patients); non-White recipients were significantly more likely to live in socioeconomically deprived areas (p < 0.001). Overall, 36.5% of transplants were LDKT, with this proportion declining progressively with socioeconomic deprivation, from 50.4 to 27.6% in the least versus most deprived IMD quintile (p < 0.001). A significant difference across recipient ethnicities was also observed, with the proportion of LDKTs ranging from 43.2% in White recipients to 17.8% in Black recipients (p < 0.001). Both socioeconomic deprivation (p < 0.001) and ethnicity (p = 0.005) remained significant predictors of LDKT on multivariable analysis, with a significant interaction between these factors also being observed (p < 0.001). Further assessment of this interaction effect found that, whilst there was a marked difference in the proportions of transplants that were LDKT between White versus non-White recipients in the most socioeconomically deprived groups (39.5% versus 19.3%), no such difference was seen in the least deprived recipients (48.5% versus 51.9%). CONCLUSIONS: Whilst both socioeconomic deprivation and non-White ethnicity are independent predictors for lower proportions of LDKTs, the significant interaction between the two factors should be appreciated.

The impact of changes in coding on mortality reports using the example of sepsis.

OBJECTIVES: NHS Digital issued new guidance on sepsis coding in April 2017 which was further modified in April 2018. During these timeframes some centres reported increased sepsis associated mortality, whilst others reported reduced mortality, in some cases coincident with specific quality improvement programmes. We hypothesised that changes in reported mortality could not be separated from changes in coding practice. METHODS: Hospital Episode Statistics from the Admitted Patient Care dataset for NHS hospitals in England, from April 2016 to March 2020 were analysed. Admissions of adults with sepsis: an International Classification of Diseases 10 (ICD-10) code associated with the Agency for Healthcare Research and Quality Clinical Classifications Software class 'Septicaemia (except in labour)', were assessed. Patient comorbidities were defined by other ICD-10 codes recorded within the admission episode. RESULTS: 1,081,565 hospital episodes with a coded diagnosis of sepsis were studied. After April 2017 there was a significant increase in admission episodes with sepsis coded as the primary reason for admission. There were significant changes in the case-mix of patients with a primary diagnosis of sepsis after April 2017. An analysis of case-mix, hospital and year treated as random effects, defined a small reduction in sepsis associated mortality across England following the first change in coding guidance. No centre specific improvement in outcome could be separated from these random-effects. CONCLUSION: Changes in sepsis coding practice altered case-mix and case selection, in ways that varied between centres. This was associated with changes in centre-specific sepsis associated mortality, over time. According to the direction of change these may be interpreted either as requiring local investigation for cause or as supporting coincident changes in clinical practice. A whole system analysis showed that centre specific changes in mortality cannot be separated from system-wide changes. Caution is therefore required when interpreting sepsis outcomes in England, particularly when using single centre studies to inform or support guidance or policy.

Effectiveness of clinical decision support in controlling inappropriate red blood cell and platelet transfusions, speciality specific responses and behavioural change.

BACKGROUND: Electronic clinical decision support (CDS) within Electronic Health Records has been used to improve patient safety, including reducing unnecessary blood product transfusions. We assessed the effectiveness of CDS in controlling inappropriate red blood cell (RBC) and platelet transfusion in a large acute hospital and how speciality specific behaviours changed in response. METHODS: We used segmented linear regression of interrupted time series models to analyse the instantaneous and long term effect of introducing blood product electronic warnings to prescribers. We studied the impact on transfusions for patients in critical care (CC), haematology/oncology (HO) and elsewhere. RESULTS: In non-CC or HO, there was significant and sustained decrease in the numbers of RBC transfusions after introduction of alerts. In CC the alerts reduced transfusions but this was not sustained, and in HO there was no impact on RBC transfusion. For platelet transfusions outside of CC and HO, the introduction of alerts stopped a rising trend of administration of platelets above recommended targets. In CC, alerts reduced platelet transfusions, but in HO alerts had little impact on clinician prescribing. CONCLUSION: The findings suggest that CDS can result in immediate change in user behaviour which is more obvious outside specialist settings of CC and HO. It is important that this is then sustained. In CC and HO, blood transfusion practices differ. CDS thus needs to take specific circumstances into account. In this case there are acceptable reasons to transfuse outside of these crude targets and CDS should take these into account.

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.

Depression is associated with frailty and lower quality of life in haemodialysis recipients, but not with mortality or hospitalization.

Background: Frailty and depression are highly prevalent in haemodialysis recipients, exhibit a reciprocal relationship, and are associated with increased mortality and hospitalization, and lower quality of life. Despite this, there has been little exploration of the relationship between depression and frailty upon patient outcomes. We aimed to explore the relationship between depression and frailty, and their associations with mortality, hospitalization and quality of life. Methods: We performed a prospective cohort study of prevalent haemodialysis recipients linked to national datasets for outcomes including mortality and hospitalization. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9), frailty using the Clinical Frailty Scale (CFS) and quality of life using the EuroQol 5-Dimension (EQ-5D) Summary Index. Results: A total of 485 prevalent haemodialysis recipients were recruited, with 111 deaths and 1241 hospitalizations during follow-up. CFS was independently associated with mortality [hazard ratio (HR) 1.31; 95% confidence interval (CI) 1.08, 1.59; P = .006], hospitalization [incidence rate ratio (IRR) 1.13; 95% CI 1.03, 1.25; P = .010] and lower quality of life (Coef. -0.401; 95% CI -0.511, -0.292; P < .001). PHQ-9 score was independently associated with lower quality of life (Coef. -0.042; 95% CI -0.063, -0.021; P < .001), but not mortality (HR 1.00; 95% CI 0.96, 1.04; P = .901) or hospitalization (IRR 0.99; 95% CI 0.97, 1.01; P = .351). In an adjusted model including CFS, moderate depression was associated with reduced hospitalization (IRR 0.72; 95% CI 0.56, 0.93; P = .013). Conclusions: With the addition of frailty, depression was associated with lower hospital admissions, but poorer quality of life. The relationship between frailty and depression, and their influence on outcomes is complex, requiring further study.

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.

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.

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.