A Clinical Score to Predict Mortality in Septic Acute Kidney Injury Patients Requiring Continuous Renal Replacement Therapy: the HELENICC Score

Overview

Journal BMC Anesthesiol

Publisher Biomed Central

Specialty Anesthesiology

Date 2017 Feb 9

PMID 28173756

Citations 35

Authors

Rogerio da Hora Passos

Joao Gabriel Rosa Ramos

Evandro Jose Bulhoes Mendonca

Eva Alves Miranda

Fabio Ricardo Dantas Dutra

Maria Fernanda R Coelho

Andrea C Pedroza

Luis Claudio L Correia

Paulo Benigno Pena Batista

Etienne Macedo

Margarida M D Dutra

Affiliations

Soon will be listed here.

Abstract

Background: This 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).

Methods: Prospective cohort of 186 septic AKI patients undergoing CRRT at a tertiary hospital, from October 2005 to November 2010.

Results: After 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 = 4.3; p = 0.83). SAPS 3, APACHE II and SOFA scores demonstrated poor performance in this population.

Conclusions: The HEpatic failure, LactatE, NorepInephrine, medical Condition, and Creatinine (HELENICC) score outperformed tested generic models. Future studies should further validate this score in different cohorts.

Citing Articles

Use of vasopressors in patients with acute kidney injury on continuous kidney replacement therapy.

Ramesh A, Doddi A, Abbasi A, Al-Mamun M, Sakhuja A, Shawwa K PLoS One. 2024; 19(12):e0315643.

PMID: 39700078 PMC: 11658609. DOI: 10.1371/journal.pone.0315643.

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.

Li C, Zhao K, Ren Q, Chen L, Zhang Y, Wang G Front Cell Infect Microbiol. 2024; 14:1488505.

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Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms.

Wang Y, Sun X, Lu J, Zhong L, Yang Z Ann Med. 2024; 56(1):2388709.

PMID: 39155811 PMC: 11334739. DOI: 10.1080/07853890.2024.2388709.

Risk prediction models for successful discontinuation in acute kidney injury undergoing continuous renal replacement therapy.

Zhong L, Min J, Zhang J, Hu B, Qian C iScience. 2024; 27(8):110397.

PMID: 39108713 PMC: 11301094. DOI: 10.1016/j.isci.2024.110397.

Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review.

Li J, Zhu M, Yan L Ren Fail. 2024; 46(2):2380748.

PMID: 39082758 PMC: 11293267. DOI: 10.1080/0886022X.2024.2380748.

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