ArticleBMC anesthesiology2017
A clinical score to predict mortality in septic acute kidney injury patients requiring continuous renal replacement therapy: the HELENICC score.
Article in BMC anesthesiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Inclusion and definition of acute renal dysfunction in critically ill patients in randomized controlled trials: a systematic review.Critical care (London, England) · 2018Pooled it
- Complications and outcomes of children under one year treated with continuous kidney replacement therapy.Pediatric nephrology (Berlin, Germany) · 2026Observational
- Development and validation of machine learning models to predict 30-day mortality in patients with cardiac arrest complicated by acute kidney injury.Clinics (Sao Paulo, Brazil) · 2026Article
- Risk factors for short-term mortality in elderly critically ill AKI patients treated with CRRT in the ICU: a retrospective cohort study.BMC nephrology · 2025Article
- Prediction of Moderate-to-Severe Sepsis-Associated Acute Kidney Injury Using a Dual-Timepoint Machine Learning Model: Development, Multiregional Validation, and Clinical Deployment Study.Journal of medical Internet research · 2025Article
- Development and validation of a prediction model for 90-day mortality among critically ill patients with AKI undergoing CRRT.Journal of nephrology · 2025Article
- Sarcopenia is independently associated with mortality and recovery from dialysis in critically ill patients with sepsis-induced acute kidney injury receiving continuous renal replacement therapy.Kidney research and clinical practice · 2025Article
- Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms.Annals of medicine · 2024Article
- Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.Renal failure · 2024Article
- Risk prediction models for successful discontinuation in acute kidney injury undergoing continuous renal replacement therapy.iScience · 2024Article
- Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury.BMC medical informatics and decision making · 2024Article
- Article
- Use of vasopressors in patients with acute kidney injury on continuous kidney replacement therapy.PloS one · 2024Article
- Development and validation of a model for predicting in-hospital mortality in patients with sepsis-associated kidney injury receiving renal replacement therapy: a retrospective cohort study based on the MIMIC-IV database.Frontiers in cellular and infection microbiology · 2024Article
- Significance of platelets in the early warning of new-onset AKI in the ICU by using supervise learning: a retrospective analysis.Renal failure · 2023Article
- Construction and validation of prognostic models in critically Ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach.Journal of translational medicine · 2023Article
- Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury.Scientific reports · 2023Article
- Article
- Development and validation of prediction model using nursing notes on sentiment scores for prognosis of patients with severe acute kidney injury receiving continuous renal replacement therapy based on computational intelligence algorithms.Annals of translational medicine · 2022Article
- Predicting Mortality Using Machine Learning Algorithms in Patients Who Require Renal Replacement Therapy in the Critical Care Unit.Journal of clinical medicine · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to identify predictors of early (7-day) mortality in patients with septic acute kidney injury (AKI) who required continuous renal replacement therapy (CRRT).
methodsProspective cohort of 186 septic AKI patients undergoing CRRT at a tertiary hospital, from October 2005 to November 2010.
resultsAfter multivariate adjustment, five variables were associated to early mortality: norepinephrine utilization, liver failure, medical condition, lactate level, and pre-dialysis creatinine level. These variables were combined in a score, which demonstrated good discrimination, with a C-statistic of 0.82 (95% CI = 0.76-0.88), and good calibration (χ
conclusionsThe HEpatic failure, LactatE, NorepInephrine, medical Condition, and Creatinine (HELENICC) score outperformed tested generic models. Future studies should further validate this score in different cohorts.
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