Shalini Santhakumaran

Background: In the UK, nearly half of patients with end stage kidney disease (ESKD) who initiate dialysis do so in an unplanned manner. A reduced quality of life, increased number of hospitalisations, and higher rates of mortality are just a few of the consequences of unplanned dialysis initiation (UDI). Our study examined factors associated with UDI in patients with ESKD in England, focusing on the effects of ethnicity and late presentation (LP). Patients and methods: We conducted a retrospective cohort study using data from the UK Renal Registry on 26 512 adult patients initiating dialysis between 2019 and 2023. UDI was defined as starting dialysis via a tunnelled or non-tunnelled line. We ran univariable and multivariable (multilevel) logistic regression analyses to examine risk factors of UDI, then tested for interactions between ethnicity and other key risk factors. Results: Just under half of the population had a UDI (49%), with Black and ‘Other’ patients having increased odds (1.36 and 1.29, respectively) of having a UDI than White patients, after adjusting for age. Unsurprisingly, the age-adjusted analyses also showed that LP and UDI were strongly associated (odds ratio (OR) 8.03, 95% confidence interval (CI) 7.38–8.74). Among late presenters, Asians had lower odds of UDI (OR 0.77, 95% CI 0.61–0.99) than Whites. For non-late presenters, the odds of UDI were higher in Asians (OR 1.18, 95% CI 1.09–1.28), Blacks (OR 1.47, 95% CI 1.32–1.64), and ‘Other’ (OR 1.30, 95% CI 1.08–1.57) compared to Whites (ethnicity and LP interaction P < 0.001). UDI was also associated with being more deprived, having an acute kidney injury, diabetes, higher comorbidity burden and history of cancer, heart failure, myocardial infarction, and minimal engagement with outpatient services before starting dialysis. Conclusions: Our study highlighted ethnicity as an important risk factor for UDI. Further, timing of presentation appears to alter ethnic differences in UDI.

Background: Failing transplant management is an international research priority. The Failing Kidney Transplant Outcomes Registry Analysis (FAKTOR) is investigating management and outcomes of kidney transplant failure in the UK. Methods: A retrospective cohort analysis using UK Renal Registry data on adults (≥18 years) with kidney transplant failure (eGFR < 15ml/min/1.73m ² ) between 01/2012-12/2021. Treatment status was determined each year post-failure as the first status recorded of: i) re-transplantation, ii) haemodialysis (HD), iii) peritoneal dialysis (PD), iv) death; those alive without changing Kidney Replacement Therapy (KRT) modality classed as v) alive without new KRT, subclassified as vi) managed conservatively if eGFR < 7.5ml/min/1.73 m ² with no future dialysis. Treatment status was determined by one-year post-failure and reported by: age, sex, ethnicity, socioeconomic group, primary renal disease (PRD) and centre type (transplanting/non-transplanting). When applicable, last eGFR pre-dialysis was recorded. Regression models investigated the relationship between patient characteristics and centre type with outcomes: i) treatment status by one-year and ii) pre-dialysis eGFR. Results: We included 5447 patients. By one-year, 47.8% first received HD and 5.2% were pre-emptively re-transplanted. Black and Asian people were less likely to be re-transplanted than White people (Black Relative Risk Ratio (RRR) 0.41, 95% CI 0.20–0.87. Asian RRR 0.57, 0.34–0.96). Males were more likely to be re-transplanted (RRR 1.39, 1.06–1.81) and to receive HD (RRR 1.68, 1.47–1.92). Each quintile increase in deprivation was associated with a 22% lower re-transplantation likelihood (RRR 0.78, 0.71–0.86). Diabetic PRD was associated with higher mortality (RRR 1.97, 1.34–2.88) relative to glomerulonephritis. Median pre-dialysis eGFR was 9.6 ml/min/1.73 m ² (IQR 7.7–11.7) and 7.1% higher in non-transplanting compared with transplanting centres (+7.1%, + 2.6% to +11.9%). Conclusions: This first nationwide description of outcomes following kidney transplant failure in the UK highlights strong evidence of pre-emptive re-transplantation inequalities, favouring White people, those in less deprived areas and males.

The European Renal Association (ERA) Registry collects data on patients with kidney failure receiving kidney replacement therapy (KRT). This paper presents a summary of the ERA Registry Annual Report 2023, and focuses specifically on comparisons by age. The complete ERA Registry Annual Report 2023 is available in the Supplementary information. For 2023, data were collected from 34 countries in Europe and countries bordering the Mediterranean Sea. Using these data, incidence and prevalence of KRT, kidney transplantation rates, survival probabilities, and expected remaining lifetimes were calculated. In 2023, the ERA Registry covered 519 million people in the participating countries. The incidence of KRT was 151 per million population (pmp). Among incident patients, 29% were aged ≥75 years, 64% were male, and the most common primary renal disease (PRD) was diabetes mellitus (22%). Most patients (83%) started KRT with haemodialysis (HD), 11% started with peritoneal dialysis (PD), and 6% underwent pre-emptive kidney transplantation. On 31 December 2023, the prevalence of KRT was 1101 pmp. Among prevalent patients, 24% were aged ≥75 years, 62% were male, and the most common PRD was of miscellaneous origin (18%). Moreover, 56% of prevalent patients received HD, 5% received PD, and 39% were living with a functioning graft. In 2023, the kidney transplantation rate was 43 pmp, with 69% of kidneys coming from deceased donors. For patients starting KRT between 2014 and 2018, 5-year survival probability was 51%. The proportions of incident and prevalent patients aged ≥75 varied considerably across European countries. In addition, incident patients aged ≥75 were more often male, and had more often hypertension as PRD compared with younger patients. Only 1% of incident patients aged ≥75 received a pre-emptive kidney transplant, while among prevalent patients of the same age, 22% was living with a functioning graft.

The European Renal Association (ERA) Registry collects data on kidney replacement therapy (KRT) in patients with end-stage kidney disease (ESKD). This paper summarizes the ERA Registry Annual Report 2022, with a special focus on comparisons by sex. The supplement of this paper contains the complete ERA Registry Annual Report 2022. Data was collected from 53 national and regional KRT registries from 35 countries. Using this data, incidence, and prevalence of KRT, kidney transplantation rates, survival probabilities, and expected remaining lifetimes were calculated. In 2022, 530 million people of the European general population were covered by the ERA Registry. The incidence of KRT was 152 per million population (pmp). In incident patients, 54% were 65 years or older, 64% were male, and the most common primary renal disease (PRD) was diabetes mellitus (22%). At KRT initiation, 83% of patients received haemodialysis, 12% received peritoneal dialysis, and 5% underwent pre-emptive kidney transplantation. On 31 December 2022, the prevalence of KRT was 1074 pmp. In prevalent patients, 48% were 65 years or older, 62% were male, the most common PRD was of miscellaneous origin (18%), 56% of patients received haemodialysis, 5% received peritoneal dialysis, and 39% were living with a functioning graft. In 2022, the kidney transplantation rate was 40 pmp, with most kidneys coming from deceased donors (66%). For patients starting KRT between 2013 to 2017, 5-year survival probability was 52%. Compared with the general population, the expected remaining lifetime was 66% and 68% shorter for males and females, respectively, receiving dialysis, and 46% and 49% shorter for males and females, respectively, living with a functioning graft.

Background: Previously, a comprehensive review of the risk factors for unplanned dialysis initiation (UDI) was conducted by Hassan et al. (2019), based on studies published up to the end of 2017. They demonstrated that high-quality data and well-designed studies on the subject are lacking. Thus we updated their review to establish the modifiable factors associated with UDI. Methods: MEDLINE and Embase were searched from January 2018 to August 2023. Following several rounds of screening, we identified 17 international studies (the majority of which were based in Europe) that met the eligibility criteria. Results: Many of the included studies were well designed, utilised large datasets and adopted properly adjusted analyses to examine associations between patient characteristics and UDI. Definitions of UDI varied across studies, i.e. timeliness of presentation, vascular access type, initiating dialysis as an inpatient/outpatient or for life-threatening indications. The most common risk factors reported were cardiovascular disease, older age, lower body mass index, cause of kidney disease, cancer, diabetes, lower serum albumin, faster decline in kidney function and fewer number of nephrology visits prior to dialysis start. These were in line with those reported by Hassan et al., however, our updated review revealed several other important predictors of UDI, e.g. worse coding of chronic kidney disease in the general practitioner health record, lower health literacy and having acute kidney injury. Conclusions: Our review provides new insights into reasons why people start dialysis in an unplanned manner, many of which are modifiable, thus contributing to efforts in reducing the rate of UDI.

Background: Whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging. Objectives: To develop a machine-learning algorithm to detect normal organs and cancer lesions. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation). Design and participants: Retrospective analysis of (1) prospective single-centre study in healthy volunteers > 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438). Tests: Index: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation. Reference standard: Previously established expert panel consensus reference at 12 months from diagnosis. Outcome measures: Primary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement. Methods: Phase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded. Results: Phases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation). Limitations: Patient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity. Conclusions: There was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning. Future work: Failure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities. Study registration: This study is registered as ISRCTN23068310. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information.

Rationale Heterogeneity of sepsis limits discovery and targeting of treatments. Clustering approaches in critical illness have identified patient groups who may respond differently to therapies. These include in acute respiratory distress syndrome (ARDS) two inflammatory sub-phenotypes, using latent class analysis (LCA), and in sepsis two Sepsis Response Signatures (SRS), based on transcriptome profiling. It is unknown if inflammatory sub-phenotypes such as those identified in ARDS are present in sepsis and how sub-phenotypes defined with different techniques compare. Objectives To identify inflammatory sub-phenotypes in sepsis using LCA and assess if these show differential treatment responses. These sub-phenotypes were compared to hierarchical clusters based on inflammatory mediators and to SRS sub-phenotypes. Methods LCA was applied to clinical and biomarker data from two septic shock randomized trials. VANISH compared norepinephrine to vasopressin and hydrocortisone to placebo and LeoPARDS compared levosimendan to placebo. Hierarchical cluster analysis (HCA) was applied to 65, 21 and 11 inflammatory mediators measured in patients from the GAinS (n=124), VANISH (n=155) and LeoPARDS (n=484) studies. Measurements and Main Results LCA and HCA identified a sub-phenotype of patients with high cytokine levels and worse organ dysfunction and survival, with no interaction between LCA classes and trial treatment responses. Comparison of inflammatory and transcriptomic sub-phenotypes revealed some similarities but without sufficient overlap that they are interchangeable. Conclusions A sub-phenotype with high levels of inflammation and increased disease severity is consistently identifiable in sepsis, with similarities to that described in ARDS. There was limited overlap with the transcriptomic sub-phenotypes.

Background: Sepsis and acute respiratory distress syndrome are two heterogeneous acute illnesses with high risk of death and for which there are many ‘statistically negative’ randomised controlled trials. We hypothesised that negative randomised controlled trials occur because of between-participant differences in response to treatment, illness manifestation (phenotype) and risk of outcomes (heterogeneity). Objectives: To assess (1) heterogeneity of treatment effect, which tests whether or not treatment effect varies with a patient’s pre-randomisation risk of outcome; and (2) whether or not subphenotypes explain the treatment response differences in sepsis and acute respiratory distress syndrome demonstrated in randomised controlled trials. Study population: We performed secondary analysis of two randomised controlled trials in patients with sepsis [i.e. the Vasopressin vs Noradrenaline as Initial Therapy in Septic Shock (VANISH) trial and the Levosimendan for the Prevention of Acute oRgan Dysfunction in Sepsis (LeoPARDS) trial] and one acute respiratory distress syndrome multicentre randomised controlled trial [i.e. the Hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in Acute lung injury to Reduce Pulmonary dysfunction (HARP-2) trial], conducted in the UK. The VANISH trial is a 2 × 2 factorial randomised controlled trial of vasopressin (Pressyn AR^®; Ferring Pharmaceuticals, Saint-Prex, Switzerland) and hydrocortisone sodium phosphate (hereafter referred to as hydrocortisone) (Efcortesol^TM; Amdipharm plc, St Helier, Jersey) compared with placebo. The LeoPARDS trial is a two-arm-parallel-group randomised controlled trial of levosimendan (Simdax^®; Orion Pharma, Espoo, Finland) compared with placebo. The HARP-2 trial is a parallel-group randomised controlled trial of simvastatin compared with placebo. Methods: To test for heterogeneity of the effect on 28-day mortality of vasopressin, hydrocortisone and levosimendan in patients with sepsis and of simvastatin in patients with acute respiratory distress syndrome. We used the total Acute Physiology And Chronic Health Evaluation II (APACHE II) score as the baseline risk measurement, comparing treatment effects in patients with baseline APACHE II scores above (high) and below (low) the median using regression models with an interaction between treatment and baseline risk. To identify subphenotypes, we performed latent class analysis using only baseline clinical and biomarker data, and compared clinical outcomes across subphenotypes and treatment groups. Results: The odds of death in the highest APACHE II quartile compared with the lowest quartile ranged from 4.9 to 7.4, across the three trials. We did not observe heterogeneity of treatment effect for vasopressin, hydrocortisone and levosimendan. In the HARP-2 trial, simvastatin reduced mortality in the low-APACHE II group and increased mortality in the high-APACHE II group. In the VANISH trial, a two-subphenotype model provided the best fit for the data. Subphenotype 2 individuals had more inflammation and shorter survival. There were no treatment effect differences between the two subphenotypes. In the LeoPARDS trial, a three-subphenotype model provided the best fit for the data. Subphenotype 3 individuals had the greatest inflammation and lowest survival. There were no treatment effect differences between the three subphenotypes, although survival was lowest in the levosimendan group for all subphenotypes. In the HARP-2 trial, a two-subphenotype model provided the best fit for the data. The inflammatory subphenotype was associated with fewer ventilator-free days and higher 28-day mortality. Limitations: The lack of heterogeneity of treatment effect and any treatment effect differences between sepsis subphenotypes may be secondary to the lack of statistical power to detect such effects, if they truly exist. Conclusions: We highlight lack of heterogeneity of treatment effect in all three trial populations. We report three subphenotypes in sepsis and two subphenotypes in acute respiratory distress syndrome, with an inflammatory phenotype with greater risk of death as a consistent finding in both sepsis and acute respiratory distress syndrome. Future work: Our analysis highlights the need to identify key discriminant markers to characterise subphenotypes in sepsis and acute respiratory distress syndrome with an observational cohort study. Funding: This project was funded by the Efficacy and Mechanism Evaluation (EME) programme, a MRC and National Institute for Health Research (NIHR) partnership. This will be published in full in Efficacy and Mechanism Evaluation; Vol. 8, No. 10. See the NIHR Journals Library website for further project information.

Background: Clinical data offer the potential to advance patient care. Neonatal specialised care is a high-cost NHS service received by approximately 80,000 newborn infants each year.Objectives: (1) To develop the use of routinely recorded operational clinical data from electronic patient records (EPRs), secure national coverage, evaluate and improve the quality of clinical data, and develop their use as a national resource to improve neonatal health care and outcomes. To test the hypotheses that (2) clinical and research data are of comparable quality, (3) routine NHS clinical assessment at the age of 2 years reliably identifies children with neurodevelopmental impairment and (4) trial-based economic evaluations of neonatal interventions can be reliably conducted using clinical data. (5) To test methods to link NHS data sets and (6) to evaluate parent views of personal data in research.Design: Six inter-related workstreams; quarterly extractions of predefined data from neonatal EPRs; and approvals from the National Research Ethics Service, Health Research Authority Confidentiality Advisory Group, Caldicott Guardians and lead neonatal clinicians of participating NHS trusts.Setting: NHS neonatal units.Participants: Neonatal clinical teams; parents of babies admitted to NHS neonatal units.Interventions: In workstream 3, we employed the Bayley-III scales to evaluate neurodevelopmental status and the Quantitative Checklist of Autism in Toddlers (Q-CHAT) to evaluate social communication skills. In workstream 6, we recruited parents with previous experience of a child in neonatal care to assist in the design of a questionnaire directed at the parents of infants admitted to neonatal units.Data sources: Data were extracted from the EPR of admissions to NHS neonatal units.Main outcome measures: We created a National Neonatal Research Database (NNRD) containing a defined extract from real-time, point-of-care, clinician-entered EPRs from all NHS neonatal units in England, Wales and Scotland (n = 200), established a UK Neonatal Collaborative of all NHS trusts providing neonatal specialised care, and created a new NHS information standard: the Neonatal Data Set (ISB 1595) (seehttp://webarchive.nationalarchives.gov.uk/±/http://www.isb.nhs.uk/documents/isb-1595/amd-32–2012/index_html; accessed 25 June 2018).Results: We found low discordance between clinical (NNRD) and research data for most important infant and maternal characteristics, and higher prevalence of clinical outcomes. Compared with research assessments, NHS clinical assessment at the age of 2 years has lower sensitivity but higher specificity for identifying children with neurodevelopmental impairment. Completeness and quality are higher for clinical than for administrative NHS data; linkage is feasible and substantially enhances data quality and scope. The majority of hospital resource inputs for economic evaluations of neonatal interventions can be extracted reliably from the NNRD. In general, there is strong parent support for sharing routine clinical data for research purposes.Limitations: We were only able to include data from all English neonatal units from 2012 onwards and conduct only limited cross validation of NNRD data directly against data in paper case notes. We were unable to conduct qualitative analyses of parent perspectives. We were also only able to assess the utility of trial-based economic evaluations of neonatal interventions using a single trial. We suggest that results should be validated against other trials.Conclusions: We show that it is possible to obtain research-standard data from neonatal EPRs, and achieve complete population coverage, but we highlight the importance of implementing systematic examination of NHS data quality and completeness and testing methods to improve these measures. Currently available EPR data do not enable ascertainment of neurodevelopmental outcomes reliably in very preterm infants. Measures to maintain high quality and completeness of clinical and administrative data are important health service goals. As parent support for sharing clinical data for research is underpinned by strong altruistic motivation, improving wider public understanding of benefits may enhance informed decision-making.Future work: We aim to implement a new paradigm for newborn health care in which continuous incremental improvement is achieved efficiently and cost-effectively by close integration of evidence generation with clinical care through the use of high-quality EPR data. In future work, we aim to automate completeness and quality checks and make recording processes more ‘user friendly’ and constructed in ways that minimise the likelihood of missing or erroneous entries. The development of criteria that provide assurance that data conform to prespecified completeness and quality criteria would be an important development. The benefits of EPR data might be extended by testing their use in large pragmatic clinical trials. It would also be of value to develop methods to quality assure EPR data including involving parents, and link the NNRD to other health, social care and educational data sets to facilitate the acquisition of lifelong outcomes across multiple domains.Study registration: This study is registered as PROSPERO CRD42015017439 (workstream 1) and PROSPERO CRD42012002168 (workstream 3).Funding: The National Institute for Health Research Programme Grants for Applied Research programme (£1,641,471). Unrestricted donations were supplied by Abbott Laboratories (Maidenhead, UK: £35,000), Nutricia Research Foundation (Schiphol, the Netherlands: £15,000), GE Healthcare (Amersham, UK: £1000). A grant to support the use of routinely collected, standardised, electronic clinical data for audit, management and multidisciplinary feedback in neonatal medicine was received from the Department of Health and Social Care (£135,494).

Rationale: There remains uncertainty about the role of corticosteroids in sepsis with clear beneficial effects on shock duration, but conflicting survival effects. Two transcriptomic sepsis response signatures (SRSs) have been identified. SRS1 is relatively immunosuppressed, whereas SRS2 is relatively immunocompetent. Objectives: We aimed to categorize patients based on SRS endotypes to determine if these profiles influenced response to either norepinephrine or vasopressin, or to corticosteroids in septic shock. Methods: A post hoc analysis was performed of a double-blind, randomized clinical trial in septic shock (VANISH [Vasopressin vs. Norepinephrine as Initial Therapy in Septic Shock]). Patients were included within 6 hours of onset of shock and were randomized to receive norepinephrine or vasopressin followed by hydrocortisone or placebo. Genome-wide gene expression profiling was performed and SRS endotype was determined by a previously established model using seven discriminant genes. Measurements and Main Results: Samples were available from 176 patients: 83 SRS1 and 93 SRS2. There was no significant interaction between SRS group and vasopressor assignment (P = 0.50). However, there was an interaction between assignment to hydrocortisone or placebo, and SRS endotype (P = 0.02). Hydrocortisone use was associated with increased mortality in those with an SRS2 phenotype (odds ratio = 7.9; 95% confidence interval = 1.6–39.9). Conclusions: Transcriptomic profile at onset of septic shock was associated with response to corticosteroids. Those with the immunocompetent SRS2 endotype had significantly higher mortality when given corticosteroids compared with placebo. Clinical trial registered with www.clinicaltrials.gov (ISRCTN 20769191).