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Kidney disease is a recognised risk factor for poor COVID-19 outcomes. Up to 30 June 2020, the UK Renal Registry (UKRR) collected data for 2,385 in-centre haemodialysis (ICHD) patients with COVID-19 in England and Wales. Overall unadjusted survival at 1 week after date of positive COVID-19 test was 87.5% (95% CI 86.1-88.8%); mortality increased with age, treatment vintage and there was borderline evidence of Asian ethnicity (HR 1.16, 95% CI 0.94-1.44) being associated with higher mortality. Compared to the general population, the relative risk of mortality for ICHD patients with COVID-19 was 45.4 and highest in younger adults. This retrospective cohort study based on UKRR data supports efforts to protect this vulnerable patient group.

Mortality prediction is critical on long-term kidney replacement therapy (KRT), both for individual treatment decisions and resource planning. Many mortality prediction models already exist, but as a major shortcoming most of them have only been validated internally. This leaves reliability and usefulness of these models in other KRT populations, especially foreign, unknown. Previously two models were constructed for one- and two-year mortality prediction of Finnish patients starting long-term dialysis. These models are here internationally validated in KRT populations of the Dutch NECOSAD Study and the UK Renal Registry (UKRR). We validated the models externally on 2051 NECOSAD patients and on two UKRR patient cohorts (5328 and 45493 patients). We performed multiple imputation for missing data, used c-statistic (AUC) to assess discrimination, and evaluated calibration by plotting average estimated probability of death against observed risk of death. Both prediction models performed well in the NECOSAD population (AUC 0.79 for the one-year model and 0.78 for the two-year model). In the UKRR populations, performance was slightly weaker (AUCs: 0.73 and 0.74). These are to be compared to the earlier external validation in a Finnish cohort (AUCs: 0.77 and 0.74). In all tested populations, our models performed better for PD than HD patients. Level of death risk (i.e., calibration) was well estimated by the one-year model in all cohorts but was somewhat overestimated by the two-year model. Our prediction models showed good performance not only in the Finnish but in foreign KRT populations as well. Compared to the other existing models, the current models have equal or better performance and fewer variables, thus increasing models' usability. The models are easily accessible on the web. These results encourage implementing the models into clinical decision-making widely among European KRT populations.

Background Routine healthcare datasets capturing clinical and administrative information are increasingly being used to examine health outcomes. The accuracy of such data is not clearly defined. We examine the accuracy of diagnosis recording in individuals with advanced chronic kidney disease using a routine healthcare dataset in England with comparison to information collected by trained research nurses. Methods We linked records from the Access to Transplant and Transplant Outcome Measures study to the Hospital Episode Statistics dataset. International Classification of Diseases (ICD-10) and Office for Population Censuses and Surveys Classification of Interventions and Procedures (OPCS-4) codes were used to identify medical conditions from hospital data. The sensitivity, specificity, positive and negative predictive values were calculated for a range of diagnoses. Results Comorbidity information was available in 96% of individuals prior to starting kidney replacement therapy. There was variation in the accuracy of individual medical conditions identified from the routine healthcare dataset. Sensitivity and positive predictive values ranged from 97.7 and 90.4% for diabetes and 82.6 and 82.9% for ischaemic heart disease to 44.2 and 28.4% for liver disease. Conclusions Routine healthcare datasets accurately capture certain conditions in an advanced chronic kidney disease population. They have potential for use within clinical and epidemiological research studies but are unlikely to be sufficient as a single resource for identifying a full spectrum of comorbidities.

Background Patients on kidney replacement therapy (KRT) are vulnerable to severe illness from COVID-19. Timely, accurate surveillance is essential for planning and implementing infection control at local, regional and national levels. Our aim was to compare two methods of data collection for COVID-19 infections amongst KRT patients in England. Methods Adults receiving KRT in England were linked to two sources of data on positive COVID-19 tests recorded March-August 2020: (1) submissions from renal centres to the UK Renal Registry (UKRR) and (2) Public Health England (PHE) laboratory data. Patient characteristics, cumulative incidence by modality (in-centre haemodialysis (ICHD), home HD, peritoneal dialysis (PD) and transplant), and 28-day survival were compared between the two sources. Results 2,783/54,795 patients (5.1%) had a positive test in the combined UKRR-PHE dataset. Of these 2,783, 87% had positive tests in both datasets. Capture was consistently high for PHE (> 95% across modalities) but varied for UKRR (ranging from ICHD 95% to transplant 78%, p  < 0.0001). Patients captured only by PHE were more likely to be on transplant or home therapies (OR 3.5 95% CI [2.3–5.2] vs. ICHD) and to be infected in later months (OR 3.3 95%CI [2.4–4.6] for May-June, OR 6.5 95%CI [3.8–11.3] for July-August, vs. March-April), compared to patients in both datasets. Stratified by modality, patient characteristics and 28-day survival were similar between datasets. Conclusions For patients undergoing ICHD treatment the collection of data submitted directly by renal centres allows constant monitoring in real time. For other KRT modalities, using a national swab test dataset through frequent linkage may be the most effective method. Optimising central surveillance can improve patient care by informing interventions and assisting planning at local, regional and national levels.

Background Incidence of acute kidney injury (AKI) is known to peak in winter months. This is likely influenced by seasonality of commonly associated acute illnesses. We set out to assess seasonal mortality trends for patients who develop AKI across the English National Health Service (NHS) and to better understand associations with patient ‘case-mix’. Methods The study cohort included all hospitalised adult patients in England who triggered a biochemical AKI alert in 2017. We modelled the impact of season on 30-day mortality using multivariable logistic regression; adjusting for age, sex, ethnicity, index of multiple deprivation (IMD), primary diagnosis, comorbidity (RCCI), elective/emergency admission, peak AKI stage and community/hospital acquired AKI. Seasonal odds ratios for AKI mortality were then calculated and compared across individual NHS hospital trusts. Results The crude 30-day mortality for hospitalised AKI patients was 33% higher in winter compared to summer. Case-mix adjustment for a wide range of clinical and demographic factors did not fully explain excess winter mortality. The adjusted odds ratio of patients dying in winter vs. summer was 1.25 (1.22–1.29), this was higher than for Autumn and Spring vs. Summer, 1.09 (1.06–1.12) and 1.07 (1.04–1.11) respectively and varied across different NHS trusts (9 out of 90 centres outliers). Conclusion We have demonstrated an excess winter mortality risk for hospitalised patients with AKI across the English NHS, which could not be fully explained by seasonal variation in patient case-mix. Whilst the explanation for worse winter outcomes is not clear, unaccounted differences including ‘winter-pressures’ merit further investigation.

Background Acute Kidney Injury (AKI) is a common and serious clinical syndrome. There is increasing recognition of heterogeneity in observed AKI across different clinical settings. In this analysis we have utilised a large national dataset to outline, for the first time, differences in burden of hospital acquired AKI (H-AKI) and mortality risk across different treatment specialities in the English National Health Service (NHS). Methods A retrospective observational study was conducted using a large national dataset of patients who triggered a biochemical AKI alert in England during 2019. This dataset was enriched through linkage with NHS hospitals administrative and mortality data. Episodes of H-AKI were identified and attributed to the speciality of the supervising consultant during the hospitalisation episode in which the H-AKI alert was generated. Associations between speciality and death in hospital or within 30 days of discharge (30-day mortality) was modelled using logistic regression, adjusting for patient age, sex, ethnicity, socioeconomic status, AKI severity, season and method of admission. Results In total, 93,196 episodes of H-AKI were studied. The largest number of patients with H-AKI were observed under general medicine (21.9%), care of the elderly (18.9%) and general surgery (11.2%). Despite adjusting for differences in patient case-mix, 30-day mortality risk was consistently lower for patients in surgical specialities compared to general medicine, including general surgery (OR 0.65, 95% CI 0.61 to 0.7) and trauma and orthopaedics (OR 0.52, 95% CI 0.48 to 0.56). Mortality risk was highest in critical care (OR 1.78, 95% CI 1.56 to 2.03) and oncology (OR 1.74, CI 1.54 to 1.96). Conclusions Significant differences were identified in the burden of H-AKI and associated mortality risk for patients across different specialities in the English NHS. This work can help inform future service delivery and quality improvement activity for patients with AKI across the NHS.

ObjectiveTo examine the association between practice percentage coding of chronic kidney disease (CKD) in primary care with risk of subsequent hospitalisations and death.DesignRetrospective cohort study using linked electronic healthcare records.Setting637 general practitioner (GP) practices in England.Participants167 208 patients with CKD stages 3–5 identified by 2 measures of estimated glomerular filtration rate <60 mL/min/1.73 m2, separated by at least 90 days, excluding those with coded initiation of renal replacement therapy.Main outcome measuresHospitalisations with cardiovascular (CV) events, heart failure (HF), acute kidney injury (AKI) and all-cause mortalityResultsParticipants were followed for (median) 3.8 years for hospital outcomes and 4.3 years for deaths. Rates of hospitalisations with CV events and HF were lower in practices with higher percentage CKD coding. Trends of a small reduction in AKI but no substantial change in rate of deaths were also observed as CKD coding increased. Compared with patients in the median performing practice (74% coded), patients in practices coding 55% of CKD cases had a higher rate of CV hospitalisations (HR 1.061 (95% CI 1.015 to 1.109)) and HF hospitalisations (HR 1.097 (95% CI 1.013 to 1.187)) and patients in practices coding 88% of CKD cases had a reduced rate of CV hospitalisations (HR 0.957 (95% CI 0.920 to 0.996)) and HF hospitalisations (HR 0.918 (95% CI 0.855 to 0.985)). We estimate that 9.0% of CV hospitalisations and 16.0% of HF hospitalisations could be prevented by improving practice CKD coding from 55% to 88%. Prescription of antihypertensives was the most dominant predictor of a reduction in hospitalisation rates for patients with CKD, followed by albuminuria testing and use of statins.ConclusionsHigher levels of CKD coding by GP practices were associated with lower rates of CV and HF events, which may be driven by increased use of antihypertensives and regular albuminuria testing, although residual confounding cannot be ruled out.

ObjectiveTo validate primary and secondary care codes in electronic health records to identify people receiving chronic kidney replacement therapy based on gold standard registry data.DesignValidation study using data from OpenSAFELY and the UK Renal Registry, with the approval of NHS England.SettingPrimary and secondary care electronic health records from people registered at 45% of general practices in England on 1 January 2020, linked to data from the UK Renal Registry (UKRR) within the OpenSAFELY-TPP platform, part of the NHS England OpenSAFELY covid-19 service.Participants38 745 prevalent patients (recorded as receiving kidney replacement therapy on 1 January 2020 in UKRR data, or primary or secondary care data) and 10 730 incident patients (starting kidney replacement therapy during 2020), from a population of 19 million people alive and registered with a general practice in England on 1 January 2020.Main outcome measuresSensitivity and positive predictive values of primary and secondary care code lists for identifying prevalent and incident kidney replacement therapy cohorts compared with the gold standard UKRR data on chronic kidney replacement therapy. Agreement across the data sources overall, and by treatment modality (transplantation or dialysis) and personal characteristics.ResultsPrimary and secondary care code lists were sensitive for identifying the UKRR prevalent cohort (91.2% (95% confidence interval (CI) 90.8% to 91.6%) and 92.0% (91.6% to 92.4%), respectively), but not the incident cohort (52.3% (50.3% to 54.3%) and 67.9% (66.1% to 69.7%)). Positive predictive values were low (77.7% (77.2% to 78.2%) for primary care data and 64.7% (64.1% to 65.3%) for secondary care data), particularly for chronic dialysis (53.7% (52.9% to 54.5%) for primary care data and 49.1% (48.0% to 50.2%) for secondary care data). Sensitivity decreased with age and index of multiple deprivation in primary care data, but the opposite was true in secondary care data. Agreement was lower in children, with 30% (295/980) featuring in all three datasets. Half (1165/2315) of the incident patients receiving dialysis in UKRR data had a kidney replacement therapy code in the primary care data within three months of the start date of the kidney replacement therapy. No codes existed whose exclusion would substantially improve the positive predictive value without a decrease in sensitivity.ConclusionsCodes used in primary and secondary care data failed to identify a small proportion of prevalent patients receiving kidney replacement therapy. Codes also identified many patients who were not recipients of chronic kidney replacement therapy in UKRR data, particularly dialysis codes. Linkage with UKRR kidney replacement therapy data facilitated more accurate identification of incident and prevalent kidney replacement therapy cohorts for research into this vulnerable population. Poor coding has implications for any patient care (including eligibility for vaccination, resourcing, and health policy responses in future pandemics) that relies on accurate reporting of kidney replacement therapy in primary and secondary care data.

Background Services for patients with kidney disease underwent radical adaptations in response to the COVID-19 pandemic. We undertook an online national survey of UK kidney centres to understand the nature, range, and degree of variation in these changes and to explore factors contributing to differing practice. Methods The survey was designed by a multidisciplinary team of kidney professionals, service users and researchers. It enquired about centre services and staffing, including psychosocial provision, and changes to these in response to the COVID-19 pandemic. Links to the survey were sent to all 68 UK kidney centres and remained active from December 2021 to April 2022, and a revised version to nurses in late 2022 for additional data. Quantitative data were analysed descriptively. Content analysis on free-text responses identified common themes. Results Analysable responses were received from 41 out of the 68 UK centres (60%), with partial data from an additional 7 (11%). Adaptations were system-wide and affected all aspects of service provision. Some changes were almost universal such as virtual consultations for outpatient appointments, with significant variation in others. Outpatient activity varied from fully maintained to suspended. Many centres reduced peritoneal dialysis access provision but in some this was increased. Centres considered that changes to transplant surgical services and for patients with advanced CKD approaching end-stage kidney disease had the greatest impact on patients. Few centres implemented adjustments aimed at vulnerable and underrepresented groups, including the frail elderly, people with language and communication needs, and those with mental health needs. Communication issues were attributed to rapid evolution of the pandemic, changing planning guidance and lack of resources. Staffing shortages, involving all staff groups particularly nurses, mainly due to COVID-19 infection and redeployment, were compounded by deficiencies in staffing establishments and high vacancy levels. Centres cited three main lessons influencing future service delivery, the need for service redesign, improvements in communication, and better support for staff. Conclusion Kidney centre responses to the pandemic involved adaptations across the whole service. Though some changes were almost universal, there was wide variation in other areas. Exploring the role of centre characteristics may help planning for potential future severe service disruptions.

ObjectivesTo examine the relative importance of patient and centre level factors in determining self-reported experience of care in patients with advanced kidney disease treated by maintenance haemodialysis (HD).DesignAnalysis of data from a cross sectional national survey; the UK Renal Registry (UKRR) national Kidney patient-reported experience measure (PREM) survey (2018). Centre-level data were obtained from the UKRR report (2018).SettingNational survey of patients with advanced kidney disease receiving treatment with maintenance HD in UK renal centres in 2018.ParticipantsThe Kidney PREM was distributed to all UK renal centres by the UKRR in May 2018. Each centre invited patients receiving outpatient treatment for kidney disease to complete the PREM. These included patients with chronic kidney disease, those receiving dialysis—both HD and peritoneal dialysis, and those with a functioning kidney transplant. There were no formal inclusion/exclusion criteria.Main outcome measuresThe Kidney PREM has 38 questions in 13 subscales. Responses were captured using a 7-point Likert scale (never 1, always 7). The primary outcome of interest was the mean PREM score calculated across all questions. Multilevel modelling was used to determine the proportion of variation of the mean PREM score across centres due to patient-related and centre-related factors.ResultsThere were records for 8253 HD patients (61% men, 77% white) from 69 renal centres (9–710 patients per centre). There was significant variation in mean PREM score across centres (5.35–6.53). In the multivariable analysis there was some variation in relation to both patient- and centre-level factors but these contributed little to explaining the overall variation. However, multilevel modelling showed that the overwhelming proportion of the explained variance (45%) was explained by variation between centres (40%), only a small proportion of which is identified by measured factors. Only 5% of the variation was related to patient-level factors.ConclusionsCentre rather than patient characteristics determine the experience of care of patients receiving HD. Further work is required to define the characteristics of the treating centre which determine patient experience.