ArticleKidney international reports2019
Predicting Renal Recovery After Dialysis-Requiring Acute Kidney Injury.
Article in Kidney international reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Validated risk prediction models for outcomes of acute kidney injury: a systematic review.BMC nephrology · 2023Pooled it
- Prospective model for predicting renal recovery in cardiac surgery patients with acute kidney injury requiring renal replacement therapy.Nephrology (Carlton, Vic.) · 2021Trial
- Machine learning-based prediction of a high-risk kidney function trajectory class after acute kidney injury.BMJ health & care informatics · 2026Article
- Predicting and Modeling Recovery Dynamics Post-Acute Kidney Injury: A Multicenter Study.Kidney360 · 2026Article
- Discharge-Based Recovery After Dialysis-Requiring Acute Kidney Injury: A National U.S. Analysis of Sociodemographic and Hospital Factors.International journal of nephrology and renovascular disease · 2026Article
- Construction and evaluation of prediction model for renal function recovery in acute kidney injury patients undergoing continuous renal replacement therapy based on machine learning algorithms.Annals of medicine · 2025Article
- Outcomes In Acute Kidney Injury Requiring Haemodialysis - A Retrospective Cohort Study.The Ulster medical journal · 2025Article
- ASN Kidney Health Guidance on the Outpatient Management of Patients with Dialysis-Requiring Acute Kidney Injury.Journal of the American Society of Nephrology : JASN · 2025Article
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
- Renal recovery after acute kidney injury requiring dialysis: Predictors and long-term outcomes.Qatar medical journal · 2025Article
- Outcomes of Intermittent Hemodialysis versus Continuous Kidney Replacement Therapy in Hemodynamically Stable Patients with Acute Kidney Injury: A Prospective, Observational, Multicenter Study.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2025Observational
- Cystatin C and Kidney Function Recovery in Patients Requiring Continuous KRT for Acute Kidney Injury.Clinical journal of the American Society of Nephrology : CJASN · 2024Article
- A clinical score to predict recovery in end-stage kidney disease due to acute kidney injury.Clinical kidney journal · 2024Article
- Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis.Journal of multidisciplinary healthcare · 2024Article
- Article
- Evaluation of simple diagnostic parameters in acute kidney injury in hospitalized patients-diagnostic recommendations for non-nephrologists.Internal and emergency medicine · 2023Article
- Prediction of Mortality and Major Adverse Kidney Events in Critically Ill Patients With Acute Kidney Injury.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2023Article
- Kidney Function Trajectory within Six Months after Acute Kidney Injury Inpatient Care and Subsequent Adverse Kidney Outcomes: A Retrospective Cohort Study.Journal of personalized medicine · 2022Article
- Patient outcomes following AKI and AKD: a population-based cohort study.BMC medicine · 2022Article
- Association of Intradialytic Hypotension and Ultrafiltration with AKI-D Outcomes in the Outpatient Dialysis Setting.Journal of clinical medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
introductionAfter dialysis-requiring acute kidney injury (AKI-D), recovery of sufficient kidney function to discontinue dialysis is an important clinical and patient-oriented outcome. Predicting the probability of recovery in individual patients is a common dilemma.
methodsThis cohort study examined all adult members of Kaiser Permanente Northern California who experienced AKI-D between January 2009 and September 2015 and had predicted inpatient mortality of <20%. Candidate predictors included demographic characteristics, comorbidities, laboratory values, and medication use. We used logistic regression and classification and regression tree (CART) approaches to develop and cross-validate prediction models for recovery.
resultsAmong 2214 patients with AKI-D, mean age was 67.1 years, 40.8% were women, and 54.0% were white; 40.9% of patients recovered. Patients who recovered were younger, had higher baseline estimated glomerular filtration rates (eGFR) and preadmission hemoglobin levels, and were less likely to have prior heart failure or chronic liver disease. Stepwise logistic regression applied to bootstrapped samples identified baseline eGFR, preadmission hemoglobin level, chronic liver disease, and age as the predictors most commonly associated with coming off dialysis within 90 days. Our final logistic regression model including these predictors had a correlation coefficient between observed and predicted probabilities of 0.97, with a c-index of 0.64. An alternate CART approach did not outperform the logistic regression model (c-index 0.61).
conclusionWe developed and cross-validated a parsimonious prediction model for recovery after AKI-D with excellent calibration using routinely available clinical data. However, the model's modest discrimination limits its clinical utility. Further research is needed to develop better prediction tools.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.