Predicting the risks of kidney failure and death in adults with moderate to severe chronic kidney disease: multinational, longitudinal, population based, cohort study.

OBJECTIVE: To train and test a super learner strategy for risk prediction of kidney failure and mortality in people with incident moderate to severe chronic kidney disease (stage G3b to G4). DESIGN: Multinational, longitudinal, population based, cohort study. SETTINGS: Linked population health data from Canada (training and temporal testing), and Denmark and Scotland (geographical testing). PARTICIPANTS: People with newly recorded chronic kidney disease at stage G3b-G4, estimated glomerular filtration rate (eGFR) 15-44 mL/min/1.73 m2. MODELLING: The super learner algorithm selected the best performing regression models or machine learning algorithms (learners) based on their ability to predict kidney failure and mortality with minimised cross-validated prediction error (Brier score, the lower the better). Prespecified learners included age, sex, eGFR, albuminuria, with or without diabetes, and cardiovascular disease. The index of prediction accuracy, a measure of calibration and discrimination calculated from the Brier score (the higher the better) was used to compare KDpredict with the benchmark, kidney failure risk equation, which does not account for the competing risk of death, and to evaluate the performance of KDpredict mortality models. RESULTS: 67 942 Canadians, 17 528 Danish, and 7740 Scottish residents with chronic kidney disease at stage G3b to G4 were included (median age 77-80 years; median eGFR 39 mL/min/1.73 m2). Median follow-up times were five to six years in all cohorts. Rates were 0.8-1.1 per 100 person years for kidney failure and 10-12 per 100 person years for death. KDpredict was more accurate than kidney failure risk equation in prediction of kidney failure risk: five year index of prediction accuracy 27.8% (95% confidence interval 25.2% to 30.6%) versus 18.1% (15.7% to 20.4%) in Denmark and 30.5% (27.8% to 33.5%) versus 14.2% (12.0% to 16.5%) in Scotland. Predictions from kidney failure risk equation and KDpredict differed substantially, potentially leading to diverging treatment decisions. An 80-year-old man with an eGFR of 30 mL/min/1.73 m2 and an albumin-to-creatinine ratio of 100 mg/g (11 mg/mmol) would receive a five year kidney failure risk prediction of 10% from kidney failure risk equation (above the current nephrology referral threshold of 5%). The same man would receive five year risk predictions of 2% for kidney failure and 57% for mortality from KDpredict. Individual risk predictions from KDpredict with four or six variables were accurate for both outcomes. The KDpredict models retrained using older data provided accurate predictions when tested in temporally distinct, more recent data. CONCLUSIONS: KDpredict could be incorporated into electronic medical records or accessed online to accurately predict the risks of kidney failure and death in people with moderate to severe CKD. The KDpredict learning strategy is designed to be adapted to local needs and regularly revised over time to account for changes in the underlying health system and care processes.

Recovery of kidney function after acute kidney disease-a multi-cohort analysis.

BACKGROUND: There are no consensus definitions for evaluating kidney function recovery after acute kidney injury (AKI) and acute kidney disease (AKD), nor is it clear how recovery varies across populations and clinical subsets. We present a federated analysis of four population-based cohorts from Canada, Denmark and Scotland, 2011-18. METHODS: We identified incident AKD defined by serum creatinine changes within 48 h, 7 days and 90 days based on KDIGO AKI and AKD criteria. Separately, we applied changes up to 365 days to address widely used e-alert implementations that extend beyond the KDIGO AKI and AKD timeframes. Kidney recovery was based on resolution of AKD and a subsequent creatinine measurement below 1.2× baseline. We evaluated transitions between non-recovery, recovery and death up to 1 year; within age, sex and comorbidity subgroups; between subset AKD definitions; and across cohorts. RESULTS: There were 464 868 incident cases, median age 67-75 years. At 1 year, results were consistent across cohorts, with pooled mortalities for creatinine changes within 48 h, 7 days, 90 days and 365 days (and 95% confidence interval) of 40% (34%-45%), 40% (34%-46%), 37% (31%-42%) and 22% (16%-29%) respectively, and non-recovery of kidney function of 19% (15%-23%), 30% (24%-35%), 25% (21%-29%) and 37% (30%-43%), respectively. Recovery by 14 and 90 days was frequently not sustained at 1 year. Older males and those with heart failure or cancer were more likely to die than to experience sustained non-recovery, whereas the converse was true for younger females and those with diabetes. CONCLUSION: Consistently across multiple cohorts, based on 1-year mortality and non-recovery, KDIGO AKD (up to 90 days) is at least prognostically similar to KDIGO AKI (7 days), and covers more people. Outcomes associated with AKD vary by age, sex and comorbidities such that older males are more likely to die, and younger females are less likely to recover.

Regional variation in incidence and prognosis of acute kidney injury.

BACKGROUND: Examining regional variation in acute kidney injury (AKI) and associated outcomes may reveal inequalities and possibilities for optimization of the quality of care. Using the Danish medical databases, we examined regional variation in the incidence, follow-up, and prognosis of AKI in Denmark. METHODS: Patients with one or more AKI episodes in 2017 were identified using population-based creatinine measurements covering all Danish residents. Crude and sex-and-age-standardized incidence rates of AKI were estimated using census statistics for each municipality. Adjusted hazard ratios (aHR) of chronic kidney disease (CKD), all-cause death, biochemical follow-up, and outpatient contact with a nephrology department after AKI were estimated across geographical regions and categories of municipalities, accounting for differences in demographics, comorbidities, medication use, lifestyle and social factors, and baseline kidney function. RESULTS: We identified 63 382 AKI episodes in 58 356 adults in 2017. The regional standardized AKI incidence rates ranged from 12.9 to 14.9 per 1 000 person-years. Compared with the Capital Region of Denmark, the aHRs across regions ranged from 1.04 to 1.25 for CKD, from 0.97 to 1.04 for all-cause death, from 1.09 to 1.15 for biochemical follow-up, and from 1.08 to 1.49 for outpatient contact with a nephrology department after AKI. Similar variations were found across municipality categories. CONCLUSIONS: Within the uniform Danish healthcare system, we found modest regional variation in AKI incidence. The mortality after AKI was similar; however, CKD, biochemical follow-up, and nephrology follow-up after AKI varied across regions and municipality categories.

Inequities in kidney health and kidney care.

Health inequity refers to the existence of unnecessary and unfair differences in the ability of an individual or community to achieve optimal health and access appropriate care. Kidney diseases, including acute kidney injury and chronic kidney disease, are the epitome of health inequity. Kidney disease risk and outcomes are strongly associated with inequities that occur across the entire clinical course of disease. Insufficient investment across the spectrum of kidney health and kidney care is a fundamental source of inequity. In addition, social and structural inequities, including inequities in access to primary health care, education and preventative strategies, are major risk factors for, and contribute to, poorer outcomes for individuals living with kidney diseases. Access to affordable kidney care is also highly inequitable, resulting in financial hardship and catastrophic health expenditure for the most vulnerable. Solutions to these injustices require leadership and political will. The nephrology community has an important role in advocacy and in identifying and implementing solutions to dismantle inequities that affect kidney health.

Prediction of major postoperative events after non-cardiac surgery for people with kidney failure: derivation and internal validation of risk models.

BACKGROUND: People with kidney failure often require surgery and experience worse postoperative outcomes compared to the general population, but existing risk prediction tools have excluded those with kidney failure during development or exhibit poor performance. Our objective was to derive, internally validate, and estimate the clinical utility of risk prediction models for people with kidney failure undergoing non-cardiac surgery. DESIGN, SETTING, PARTICIPANTS, AND MEASURES: This study involved derivation and internal validation of prognostic risk prediction models using a retrospective, population-based cohort. We identified adults from Alberta, Canada with pre-existing kidney failure (estimated glomerular filtration rate [eGFR] < 15 mL/min/1.73m2 or receipt of maintenance dialysis) undergoing non-cardiac surgery between 2005-2019. Three nested prognostic risk prediction models were assembled using clinical and logistical rationale. Model 1 included age, sex, dialysis modality, surgery type and setting. Model 2 added comorbidities, and Model 3 added preoperative hemoglobin and albumin. Death or major cardiac events (acute myocardial infarction or nonfatal ventricular arrhythmia) within 30 days after surgery were modelled using logistic regression models. RESULTS: The development cohort included 38,541 surgeries, with 1,204 outcomes (after 3.1% of surgeries); 61% were performed in males, the median age was 64 years (interquartile range [IQR]: 53, 73), and 61% were receiving hemodialysis at the time of surgery. All three internally validated models performed well, with c-statistics ranging from 0.783 (95% Confidence Interval [CI]: 0.770, 0.797) for Model 1 to 0.818 (95%CI: 0.803, 0.826) for Model 3. Calibration slopes and intercepts were excellent for all models, though Models 2 and 3 demonstrated improvement in net reclassification. Decision curve analysis estimated that use of any model to guide perioperative interventions such as cardiac monitoring would result in potential net benefit over default strategies. CONCLUSIONS: We developed and internally validated three novel models to predict major clinical events for people with kidney failure having surgery. Models including comorbidities and laboratory variables showed improved accuracy of risk stratification and provided the greatest potential net benefit for guiding perioperative decisions. Once externally validated, these models may inform perioperative shared decision making and risk-guided strategies for this population.

The Kidney Failure Risk Equation: Evaluation of Novel Input Variables including eGFR Estimated Using the CKD-EPI 2021 Equation in 59 Cohorts.

SIGNIFICANCE STATEMENT: The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict 2- and 5-year risk of kidney failure in populations with eGFR <60 ml/min per 1.73 m 2 . However, the CKD-EPI 2021 creatinine equation for eGFR is now recommended for use but has not been fully tested in the context of KFRE. In 59 cohorts comprising 312,424 patients with CKD, the authors assessed the predictive performance and calibration associated with the use of the CKD-EPI 2021 equation and whether additional variables and accounting for the competing risk of death improves the KFRE's performance. The KFRE generally performed well using the CKD-EPI 2021 eGFR in populations with eGFR <45 ml/min per 1.73 m 2 and was not improved by adding the 2-year prior eGFR slope and cardiovascular comorbidities. BACKGROUND: The kidney failure risk equation (KFRE) uses age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to predict kidney failure risk in people with GFR <60 ml/min per 1.73 m 2 . METHODS: Using 59 cohorts with 312,424 patients with CKD, we tested several modifications to the KFRE for their potential to improve the KFRE: using the CKD-EPI 2021 creatinine equation for eGFR, substituting 1-year average ACR for single-measure ACR and 1-year average eGFR in participants with high eGFR variability, and adding 2-year prior eGFR slope and cardiovascular comorbidities. We also assessed calibration of the KFRE in subgroups of eGFR and age before and after accounting for the competing risk of death. RESULTS: The KFRE remained accurate and well calibrated overall using the CKD-EPI 2021 eGFR equation. The other modifications did not improve KFRE performance. In subgroups of eGFR 45-59 ml/min per 1.73 m 2 and in older adults using the 5-year time horizon, the KFRE demonstrated systematic underprediction and overprediction, respectively. We developed and tested a new model with a spline term in eGFR and incorporating the competing risk of mortality, resulting in more accurate calibration in those specific subgroups but not overall. CONCLUSIONS: The original KFRE is generally accurate for eGFR <45 ml/min per 1.73 m 2 when using the CKD-EPI 2021 equation. Incorporating competing risk methodology and splines for eGFR may improve calibration in low-risk settings with longer time horizons. Including historical averages, eGFR slopes, or a competing risk design did not meaningfully alter KFRE performance in most circumstances.

Major cardiovascular events and subsequent risk of kidney failure with replacement therapy: a CKD Prognosis Consortium study.

AIMS: Chronic kidney disease (CKD) increases risk of cardiovascular disease (CVD). Less is known about how CVD associates with future risk of kidney failure with replacement therapy (KFRT). METHODS AND RESULTS: The study included 25 903 761 individuals from the CKD Prognosis Consortium with known baseline estimated glomerular filtration rate (eGFR) and evaluated the impact of prevalent and incident coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF) events as time-varying exposures on KFRT outcomes. Mean age was 53 (standard deviation 17) years and mean eGFR was 89 mL/min/1.73 m2, 15% had diabetes and 8.4% had urinary albumin-to-creatinine ratio (ACR) available (median 13 mg/g); 9.5% had prevalent CHD, 3.2% prior stroke, 3.3% HF, and 4.4% prior AF. During follow-up, there were 269 142 CHD, 311 021 stroke, 712 556 HF, and 605 596 AF incident events and 101 044 (0.4%) patients experienced KFRT. Both prevalent and incident CVD were associated with subsequent KFRT with adjusted hazard ratios (HRs) of 3.1 [95% confidence interval (CI): 2.9-3.3], 2.0 (1.9-2.1), 4.5 (4.2-4.9), 2.8 (2.7-3.1) after incident CHD, stroke, HF and AF, respectively. HRs were highest in first 3 months post-CVD incidence declining to baseline after 3 years. Incident HF hospitalizations showed the strongest association with KFRT [HR 46 (95% CI: 43-50) within 3 months] after adjustment for other CVD subtype incidence. CONCLUSION: Incident CVD events strongly and independently associate with future KFRT risk, most notably after HF, then CHD, stroke, and AF. Optimal strategies for addressing the dramatic risk of KFRT following CVD events are needed.

Care processes and outcomes of deprivation across the clinical course of kidney disease: findings from a high-income country with universal healthcare.

BACKGROUND: No single study contrasts the extent and consequences of inequity of kidney care across the clinical course of kidney disease. METHODS: This population study of Grampian (UK) followed incident presentations of acute kidney injury (AKI) and incident estimated glomerular filtration rate (eGFR) thresholds of <60, <45 and <30 mL/min/1.73 m2 in separate cohorts (2011-2021). The key exposure was area-level deprivation (lowest quintile of the Scottish Index of Multiple Deprivation). Outcomes were care processes (monitoring, prescribing, appointments, unscheduled care), long-term mortality and kidney failure. Modelling involved multivariable logistic regression, negative binomial regression and cause-specific Cox models with and without adjustment of comorbidities. RESULTS: There were 41 313, 51 190, 32 171 and 17 781 new presentations of AKI and eGFR thresholds <60, <45 and <30  mL/min/1.73 m2. A total of 6.1-7.8% of the population was from deprived areas and (versus all others) presented on average 5 years younger, with more diabetes and pulmonary and liver disease. Those from deprived areas were more likely to present initially in hospital, less likely to receive community monitoring, less likely to attend appointments and more likely to have an unplanned emergency department or hospital admission episode. Deprivation had the greatest association with long-term kidney failure at the eGFR <60 mL/min/1.73 m2 threshold {adjusted hazard ratio [HR] 1.48 [95% confidence interval (CI) 1.17-1.87]} and this association decreased with advancing disease severity [HR 1.09 (95% CI 0.93-1.28) at eGFR <30 mL/min/1.73 m2), with a similar pattern for mortality. Across all analyses the most detrimental associations of deprivation were an eGFR threshold <60 mL/min/1.73 m2, AKI, males and those <65 years of age. CONCLUSIONS: Even in a high-income country with universal healthcare, serious and consistent inequities in kidney care exist. The poorer care and outcomes with area-level deprivation were greater earlier in the disease course.

Using the Revised Cardiac Risk Index to Predict Major Postoperative Events for People With Kidney Failure: An External Validation and Update.

Background: People with kidney failure have high risk of postoperative morbidity and mortality. Although the revised cardiac risk index (RCRI) is used to estimate the risk of major postoperative events, it has not been validated in this population. We aimed to externally validate the RCRI and determine whether updating the model improved predictions for people with kidney failure. Methods: We derived a retrospective, population-based cohort of adults with kidney failure (maintenance dialysis or sustained estimated glomerular filtration rate < 15 mL/min per 1.73 m2) who had surgery in Alberta, Canada between 2005 and 2019. We categorized participants based on RCRI variables and assigned risk estimates of death or major cardiac events, and then estimated predictive performance. We re-estimated the coefficients for each RCRI variable and internally validated the updated model. Net benefit was estimated with decision curve analysis. Results: After 38,541 surgeries, 1204 events (3.1%) occurred. The estimated C-statistic for the original RCRI was 0.64 (95% confidence interval: 0.62, 0.65). Examination of calibration revealed significant risk overestimation. In the re-estimated RCRI model, discrimination was marginally different (C-statistic 0.67 [95% confidence interval: 0.66, 0.69]), though calibration was improved. No net benefit was observed when the data were examined with decision curve analysis, whereas the original RCRI was associated with harm. Conclusions: The RCRI performed poorly in a Canadian kidney failure cohort and significantly overestimated risk, suggesting that RCRI use in similar kidney failure populations should be limited. A re-estimated kidney failure-specific RCRI may be promising but needs external validation. Novel perioperative models for this population are urgently needed.

Contexte: Les personnes atteintes d'insuffisance rénale présentent un risque élevé de mortalité et de morbidité postopératoires. L'indice de risque cardiaque révisé (IRCR) est utilisé pour estimer le risque d'événements postopératoires majeurs, mais il n'a pas été validé au sein de cette po-pulation. Nous avons cherché à réaliser une validation externe de l'IRCR et à déterminer si une modification du modèle pourrait permettre une meilleure valeur prédictive pour les patients atteints d'insuffisance rénale. Méthodologie: Nous avons étudié rétrospectivement une cohorte populationnelle d'adultes atteints d'insuffisance rénale (sous dialyse d'entretien ou avec un débit de filtration glomérulaire estimé < 15 ml/min/1,73 m2, de façon soutenue) ayant subi une intervention chirurgicale en Alberta (Canada) entre 2005 et 2019. Les participants ont été classifiés selon les variables de l'IRCR, et une estimation du risque de décès ou d'événement cardiovasculaire majeur leur a été attribuée; la performance prédictive a ensuite été évaluée. Nous avons réestimé les coefficients pour chacune des variables de l'IRCR et nous avons validé de manière interne le modèle modifié. Le bénéfice net a été estimé avec une analyse de la courbe décisionnelle. Résultats: Après 38 541 interventions chirurgicales, des événements cardiovasculaires sont survenus dans 1 204 cas (3,1 %). La statistique C estimée obtenue avec l'IRCR initial était de 0,64 (intervalle de confiance [IC] à 95 %, de 0,62 à 0,65). Un examen de la calibration de l'indice a révélé une surestimation significative du risque. Avec le modèle d'IRCR modifié, la discrimination présentait une légère différence (statistique C de 0,67 [IC à 95 %, de 0,66 à 0,69]), bien que la calibration ait été améliorée. Pour l'indice modifié, aucun bénéfice net n'a été observé lors de l'examen des données par une analyse décisionnelle, alors qu'un préjudice était associé à l'IRCR initial. Conclusions: L'IRCR s'est révélé peu concluant dans une cohorte populationnelle de patients canadiens atteints d'insuffisance rénale et il a significativement surestimé les risques pour ces patients, ce qui suggère que l'utilisation de l'IRCR dans des populations similaires atteintes d'insuffisance rénale devrait être limitée. Un IRCR réestimé, propre à la population des patients atteints d'insuffisance rénale, pourrait être prometteur, mais requiert une validation externe. De nouveaux modèles périopératoires sont indispensables pour cette population.

Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes.

OBJECTIVE: To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design. RESEARCH DESIGN AND METHODS: In this meta-analysis of individual participant data, 43 cohorts (N = 1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and estimated glomerular filtration rate (eGFR; ≥60 or <60 mL/min/1.73 m2) to predict a composite of ≥40% decline in eGFR or kidney failure (i.e., receipt of kidney replacement therapy) over 2-3 years. RESULTS: There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts. CONCLUSIONS: Novel prediction equations for a decline of ≥40% in eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.

Harmonization of epidemiology of acute kidney injury and acute kidney disease produces comparable findings across four geographic populations.

There is substantial variability in the reported incidence and outcomes of acute kidney injury (AKI). The extent to which this is attributable to differences in source populations versus methodological differences between studies is uncertain. We used 4 population-based datasets from Canada, Denmark, and the United Kingdom to measure the annual incidence and prognosis of AKI and acute kidney disease (AKD), using a homogenous analytical approach that incorporated KDIGO creatinine-based definitions and subsets of the AKI/AKD criteria. The cohorts included 7 million adults ≥18 years of age between 2011 and 2014; median age 59-68 years, 51.9-54.4% female sex. Age- and sex-standardised incidence rates for AKI or AKD were similar between regions and years; range 134.3-162.4 events/10,000 person years. Among patients who met either KDIGO 48-hour or 7-day AKI creatinine criteria, the standardised 1-year mortality was similar (30.4%-38.5%) across the cohorts, which was comparable to standardised 1-year mortality among patients who met AKI/AKD criteria using a baseline creatinine within 8-90 days prior (32.0%-37.4%). Standardised 1-year mortality was lower (21.0%-25.5% across cohorts) among patients with AKI/AKD ascertained using a baseline creatinine >90 days prior. These findings illustrate that the incidence and prognosis of AKI and AKD based on KDIGO criteria are consistent across 3 high-income countries when capture of laboratory tests is complete, creatinine-based definitions are implemented consistently within but not beyond a 90-day period, and adjustment is made for population age and sex. These approaches should be consistently applied to improve the generalizability and comparability of AKI research and clinical reporting.

Identifying multimorbidity clusters in an unselected population of hospitalised patients.

Multimorbidity (multiple coexisting chronic health conditions) is common and increasing worldwide, and makes care challenging for both patients and healthcare systems. To ensure care is patient-centred rather than specialty-centred, it is important to know which conditions commonly occur together and identify the corresponding patient profile. To date, no studies have described multimorbidity clusters within an unselected hospital population. Our aim was to identify and characterise multimorbidity clusters, in a large, unselected hospitalised patient population. Linked inpatient hospital episode data were used to identify adults admitted to hospital in Grampian, Scotland in 2014 who had ≥ 2 of 30 chronic conditions diagnosed in the 5 years prior. Cluster analysis (Gower distance and Partitioning around Medoids) was used to identify groups of patients with similar conditions. Clusters of conditions were defined based on clinical review and assessment of prevalence within patient groups and labelled according to the most prevalent condition. Patient profiles for each group were described by age, sex, admission type, deprivation and urban-rural area of residence. 11,389 of 41,545 hospitalised patients (27%) had ≥ 2 conditions. Ten clusters of conditions were identified: hypertension; asthma; alcohol misuse; chronic kidney disease and diabetes; chronic kidney disease; chronic pain; cancer; chronic heart failure; diabetes; hypothyroidism. Age ranged from 51 (alcohol misuse) to 79 (chronic heart failure). Women were a higher proportion in the chronic pain and hypothyroidism clusters. The proportion of patients from the most deprived quintile of the population ranged from 6% (hypertension) to 14% (alcohol misuse). Identifying clusters of conditions in hospital patients is a first step towards identifying opportunities to target patient-centred care towards people with unmet needs, leading to improved outcomes and increased efficiency. Here we have demonstrated the face validity of cluster analysis as an exploratory method for identifying clusters of conditions in hospitalised patients with multimorbidity.

Biomarkers for assessing acute kidney injury for people who are being considered for admission to critical care: a systematic review and cost-effectiveness analysis.

BACKGROUND: Acute kidney injury is a serious complication that occurs in the context of an acute critical illness or during a postoperative period. Earlier detection of acute kidney injury may facilitate strategies to preserve renal function, prevent further disease progression and reduce mortality. Acute kidney injury diagnosis relies on a rise in serum creatinine levels and/or fall in urine output; however, creatinine is an imperfect marker of kidney function. There is interest in the performance of novel biomarkers used in conjunction with existing clinical assessment, such as NephroCheck® (Astute Medical, Inc., San Diego, CA, USA), ARCHITECT® urine neutrophil gelatinase-associated lipocalin (NGAL) (Abbott Laboratories, Abbott Park, IL, USA), and urine and plasma BioPorto NGAL (BioPorto Diagnostics A/S, Hellerup, Denmark) immunoassays. If reliable, these biomarkers may enable earlier identification of acute kidney injury and enhance management of those with a modifiable disease course. OBJECTIVE: The objective was to evaluate the role of biomarkers for assessing acute kidney injury in critically ill patients who are considered for admission to critical care. DATA SOURCES: Major electronic databases, conference abstracts and ongoing studies were searched up to June 2019, with no date restrictions. MEDLINE, EMBASE, Health Technology Assessment Database, Cumulative Index to Nursing and Allied Health Literature, Cochrane Central Register of Controlled Trials, Web of Science, World Health Organization Global Index Medicus, EU Clinical Trials Register, International Clinical Trials Registry Platform and ClinicalTrials.gov were searched. REVIEW METHODS: A systematic review and meta-analysis were conducted to evaluate the performance of novel biomarkers for the detection of acute kidney injury and prediction of other relevant clinical outcomes. Random-effects models were adopted to combine evidence. A decision tree was developed to evaluate costs and quality-adjusted life-years accrued as a result of changes in short-term outcomes (up to 90 days), and a Markov model was used to extrapolate results over a lifetime time horizon. RESULTS: A total of 56 studies (17,967 participants), mainly prospective cohort studies, were selected for inclusion. No studies addressing the clinical impact of the use of biomarkers on patient outcomes, compared with standard care, were identified. The main sources of bias across studies were a lack of information on blinding and the optimal threshold for NGAL. For prediction studies, the reporting of statistical details was limited. Although the meta-analyses results showed the potential ability of these biomarkers to detect and predict acute kidney injury, there were limited data to establish any causal link with longer-term health outcomes and there were considerable clinical differences across studies. Cost-effectiveness results were highly uncertain, largely speculative and should be interpreted with caution in the light of the limited evidence base. To illustrate the current uncertainty, 15 scenario analyses were undertaken. Incremental quality-adjusted life-years were very low across all scenarios, ranging from positive to negative increments. Incremental costs were also small, in general, with some scenarios generating cost savings with tests dominant over standard care (cost savings with quality-adjusted life-year gains). However, other scenarios generated results whereby the candidate tests were more costly with fewer quality-adjusted life-years, and were thus dominated by standard care. Therefore, it was not possible to determine a plausible base-case incremental cost-effectiveness ratio for the tests, compared with standard care. LIMITATIONS: Clinical effectiveness and cost-effectiveness results were hampered by the considerable heterogeneity across identified studies. Economic model predictions should also be interpreted cautiously because of the unknown impact of NGAL-guided treatment, and uncertain causal links between changes in acute kidney injury status and changes in health outcomes. CONCLUSIONS: Current evidence is insufficient to make a full appraisal of the role and economic value of these biomarkers and to determine whether or not they provide cost-effective improvements in the clinical outcomes of acute kidney injury patients. FUTURE WORK: Future studies should evaluate the targeted use of biomarkers among specific patient populations and the clinical impact of their routine use on patient outcomes and management. STUDY REGISTRATION: This study is registered as PROSPERO CRD42019147039. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Evidence Synthesis programme and will be published in full in Health Technology Assessment; Vol. 26, No. 7. See the NIHR Journals Library website for further project information.

Among people who are very ill or have undergone surgery, the kidneys may suddenly stop working properly. This is known as acute kidney injury. Acute kidney injury can progress to serious kidney problems and can be fatal. Currently, to decide whether or not acute kidney injury is present, doctors use the level of creatinine (a waste product filtered by the kidneys) in the blood or urine. However, creatinine levels are not a precise indicator and they can take hours or days to rise; this may lead to delays in acute kidney injury recognition. Novel biomarkers may help doctors to recognise the presence of acute kidney injury earlier and treat patients promptly. This work evaluates current evidence on the use of biomarkers for acute kidney injury with respect to clinical usefulness and costs. We reviewed the current evidence on the use of biomarkers for assessing the risk of acute kidney injury among people who are very ill, and assessed whether or not the evidence was of good value for the NHS. We assessed the ARCHITECT^® urine neutrophil gelatinase-associated lipocalin (NGAL) (Abbott Laboratories, Abbott Park, IL, USA), urine and plasma BioPorto NGAL (BioPorto Diagnostics A/S, Hellerup, Denmark) and urine NephroCheck^® (Astute Medical, Inc., San Diego, CA, USA) biomarkers. We checked studies published up to June 2019 and found 56 relevant studies (17,967 patients). Most studies were conducted outside the UK and investigated people already admitted to critical care. We combined the results of the studies and found that NephroCheck and NGAL biomarkers might be useful in identifying acute kidney injury or pre-empting acute kidney injury in some circumstances. However, studies differed in patient characteristics, clinical setting and the way in which biomarkers were used. This could explain why the number of people correctly identified and missed by the biomarkers varied across studies. Hence, we do not completely trust the pooled results. We also found that acute kidney injury is associated with substantial costs for the NHS, but there was insufficient good-quality evidence to decide which biomarker (if any) offered the best value for money.

Population Epidemiology of Hyperkalemia: Cardiac and Kidney Long-term Health Outcomes.

RATIONALE & OBJECTIVE: The population burden and long-term implications of hyperkalemia have not been comprehensively studied. We studied how often and where hyperkalemia occurs as well as its independent association with survival and long-term cardiac and kidney health. STUDY DESIGN: Population-based cohort study of adult residents of Grampian, United Kingdom. SETTING & PARTICIPANTS: Among the 468,594 adult residents (2012-2014), 302,630 people with at least 1 blood test were followed until 2019. EXPOSURE: Hyperkalemia was defined as serum potassium ≥ 5.5 mmol/L. Adjustment for comorbidities, demographics, measures of acute and chronic kidney function, and medications prescribed before measurement of serum potassium. OUTCOME: All-cause mortality, cardiac events, and kidney failure. ANALYTICAL APPROACH: Description of the annual incidence of hyperkalemia and the characteristics associated with its occurrence, and adjusted Cox proportional hazards (PH) analysis to evaluate the independent long-term association of hyperkalemia with all-cause mortality among people who survived ≥90 days after blood testing. Cause-specific PH models were fit to evaluate the association of hyperkalemia with cardiac events/death, noncardiac death, and kidney failure. Effect modification by level of estimated glomerular filtration rate (eGFR) at the time of blood testing was explored. RESULTS: The annual population incidence of hyperkalemia was 0.96 per 100 person-years. This represented 2.3%, 2.1%, and 1.9% of people with at least one blood test in 2012, 2013, and 2014, respectively. Two-thirds of episodes of hyperkalemia occurred in the community. The hyperkalemia rate was 2-fold higher for each 10-year greater age. Those with hyperkalemia were 20 times more likely to have concurrent acute kidney injury (AKI), and 17 times more likely to have an eGFR of <30 mL/min/1.73 m2. Throughout 5 years of follow-up evaluation (2,483,452 person-years), hyperkalemia was associated with poorer health outcomes. This association held across all levels of kidney function and was irrespective of concurrent AKI, but was stronger among those with a baseline eGFR of ≥60 mL/min/1.73 m2 (P for interaction < 0.001). The adjusted HRs (hyperkalemia vs no hyperkalemia) for people with eGFR ≥60 mL/min/1.73 m2 and eGFR <30 mL/min/1.73 m2 were 2.3 (95% CI, 2.2-2.5) and 1.5 (95% CI, 1.3-1.6) for mortality; 1.8 (95% CI, 1.6-1.9) and 1.4 (95% CI, 1.2-1.6) for cardiac events; and 17.0 (95% CI, 9.3-31.1) and 2.0 (95% CI, 1.5-2.8) for kidney failure, respectively. LIMITATIONS: The observational nature of this study limits evaluation of causal relationships. CONCLUSIONS: There is a substantial burden of hyperkalemia in the general population. Hyperkalemia is associated with poorer long-term health outcomes, especially kidney outcomes, that are independent of other established risk factors.

Cost-effectiveness and value of information analysis of NephroCheck and NGAL tests compared to standard care for the diagnosis of acute kidney injury.

BACKGROUND: Early and accurate acute kidney injury (AKI) detection may improve patient outcomes and reduce health service costs. This study evaluates the diagnostic accuracy and cost-effectiveness of NephroCheck and NGAL (urine and plasma) biomarker tests used alongside standard care, compared with standard care to detect AKI in hospitalised UK adults. METHODS: A 90-day decision tree and lifetime Markov cohort model predicted costs, quality adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) from a UK NHS perspective. Test accuracy was informed by a meta-analysis of diagnostic accuracy studies. Clinical trial and observational data informed the link between AKI and health outcomes, health state probabilities, costs and utilities. Value of information (VOI) analysis informed future research priorities. RESULTS: Under base case assumptions, the biomarker tests were not cost-effective with ICERs of £105,965 (NephroCheck), £539,041 (NGAL urine BioPorto), £633,846 (NGAL plasma BioPorto) and £725,061 (NGAL urine ARCHITECT) per QALY gained compared to standard care. Results were uncertain, due to limited trial data, with probabilities of cost-effectiveness at £20,000 per QALY ranging from 0 to 99% and 0 to 56% for NephroCheck and NGAL tests respectively. The expected value of perfect information (EVPI) was £66 M, which demonstrated that additional research to resolve decision uncertainty is worthwhile. CONCLUSIONS: Current evidence is inadequate to support the cost-effectiveness of general use of biomarker tests. Future research evaluating the clinical and cost-effectiveness of test guided implementation of protective care bundles is necessary. Improving the evidence base around the impact of tests on AKI staging, and of AKI staging on clinical outcomes would have the greatest impact on reducing decision uncertainty.

Validation of Risk Prediction Models to Inform Clinical Decisions After Acute Kidney Injury.

RATIONALE & OBJECTIVE: There is limited evidence to guide follow-up after acute kidney injury (AKI). Knowledge gaps include which patients to prioritize, at what time point, and for mitigation of which outcomes. In this study, we sought to compare the net benefit of risk model-based clinical decisions following AKI. STUDY DESIGN: External validation of 2 risk models of AKI outcomes: the Grampian -Aberdeen (United Kingdom) AKI readmissions model and the Alberta (Canada) kidney disease risk model of chronic kidney disease (CKD) glomerular (G) filtration rate categories 4 and 5 (CKD G4 and G5). Process mining to delineate existing care pathways. SETTING & PARTICIPANTS: Validation was based on data from adult hospital survivors of AKI from Grampian, 2011-2013. PREDICTORS: KDIGO-based measures of AKI severity and comorbidities specified in the original models. OUTCOMES: Death or readmission within 90 days for all hospital survivors. Progression to new CKD G4-G5 for patients surviving at least 90 days after AKI. ANALYTICAL APPROACH: Decision curve analysis to assess the "net benefit" of use of risk models to guide clinical care compared to alternative approaches (eg, prioritizing all AKI, severe AKI, or only those without kidney recovery). RESULTS: 26,575 of 105,461 hospital survivors in Grampian (mean age, 60.9 ± 19.8 [SD] years) were included for validation of the death or readmission model, and 9,382 patients (mean age, 60.9 ± 19.8 years) for the CKD G4-G5 model. Both models discriminated well (area under the curve [AUC], 0.77 and 0.86, respectively). Decision curve analysis showed greater net benefit for follow up of all AKI than only severe AKI in most cases. Both original and refitted models provided net benefit superior to any other decision strategy. In process mining of all hospital discharges, 41% of readmissions and deaths occurred among people recovering after AKI. 1,464 of 3,776 people (39%) readmitted after AKI had received no intervening monitoring. LIMITATIONS: Both original models overstated risks, indicating a need for regular updating. CONCLUSIONS: Follow up after AKI has potential net benefit for preempting readmissions, death, and subsequent CKD progression. Decisions could be improved by using risk models and by focusing on AKI across a full spectrum of severity. The current lack of monitoring among many with poor outcomes indicates possible opportunities for implementation of decision support.

Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis : An Individual Participant-Based Meta-analysis.

BACKGROUND: Although measuring albuminuria is the preferred method for defining and staging chronic kidney disease (CKD), total urine protein or dipstick protein is often measured instead. OBJECTIVE: To develop equations for converting urine protein-creatinine ratio (PCR) and dipstick protein to urine albumin-creatinine ratio (ACR) and to test their diagnostic accuracy in CKD screening and staging. DESIGN: Individual participant-based meta-analysis. SETTING: 12 research and 21 clinical cohorts. PARTICIPANTS: 919 383 adults with same-day measures of ACR and PCR or dipstick protein. MEASUREMENTS: Equations to convert urine PCR and dipstick protein to ACR were developed and tested for purposes of CKD screening (ACR ≥30 mg/g) and staging (stage A2: ACR of 30 to 299 mg/g; stage A3: ACR ≥300 mg/g). RESULTS: Median ACR was 14 mg/g (25th to 75th percentile of cohorts, 5 to 25 mg/g). The association between PCR and ACR was inconsistent for PCR values less than 50 mg/g. For higher PCR values, the PCR conversion equations demonstrated moderate sensitivity (91%, 75%, and 87%) and specificity (87%, 89%, and 98%) for screening (ACR >30 mg/g) and classification into stages A2 and A3, respectively. Urine dipstick categories of trace or greater, trace to +, and ++ for screening for ACR values greater than 30 mg/g and classification into stages A2 and A3, respectively, had moderate sensitivity (62%, 36%, and 78%) and high specificity (88%, 88%, and 98%). For individual risk prediction, the estimated 2-year 4-variable kidney failure risk equation using predicted ACR from PCR had discrimination similar to that of using observed ACR. LIMITATION: Diverse methods of ACR and PCR quantification were used; measurements were not always performed in the same urine sample. CONCLUSION: Urine ACR is the preferred measure of albuminuria; however, if ACR is not available, predicted ACR from PCR or urine dipstick protein may help in CKD screening, staging, and prognosis. PRIMARY FUNDING SOURCE: National Institute of Diabetes and Digestive and Kidney Diseases and National Kidney Foundation.

Predicting kidney failure risk after acute kidney injury among people receiving nephrology clinic care.

BACKGROUND: Outcomes after acute kidney injury (AKI) are well described, but not for those already under nephrology clinic care. This is where discussions about kidney failure risk are commonplace. We evaluated whether the established kidney failure risk equation (KFRE) should account for previous AKI episodes when used in this setting. METHODS: This observational cohort study included 7491 people referred for nephrology clinic care in British Columbia in 2003-09 followed to 2016. Predictors were previous Kidney Disease: Improving Global Outcomes-based AKI, age, sex, proteinuria, estimated glomerular filtration rate (eGFR) and renal diagnosis. Outcomes were 5-year kidney failure and death. We developed cause-specific Cox models (AKI versus no AKI) for kidney failure and death, stratified by eGFR (