Literature DB >> 19812539

SAPS 3 scores at the start of renal replacement therapy predict mortality in critically ill patients with acute kidney injury.

Elizabeth Maccariello1, Carla Valente, Lina Nogueira, Helio Bonomo, Marcia Ismael, Jose Eduardo Machado, Fernanda Baldotto, Marise Godinho, Ricardo Valença, Eduardo Rocha, Marcio Soares.   

Abstract

Patients can experience acute kidney injury and require renal replacement therapy at any time during their admission to intensive care units. Prognostic scores have been used to characterize and stratify patients by the severity of acute disease, but scores based on findings during the day of admission may not be reliable surrogate markers of the severity of acute illness in this population. The aim of this study was to evaluate the performance of SAPS 3 and MPM(0)-III scores, determined at the start of renal replacement therapy, in 244 patients admitted to 11 units of three hospitals in Rio de Janeiro, Brazil. Continuous renal replacement therapy was used as first indication in 84% of these patients. Discrimination by area under the receiver operating characteristic curve was significantly better for SAPS 3 than for MPM(0)-III, as was the calibration measured by the Hosmer-Lemeshow goodness-of-fit test. Mortality prediction and calibration approached those eventually found when a customized equation of SAPS 3 for Central and South America was used. After adjusting for other relevant covariates in multivariate analyses, both higher prognostic scores and length of stay in the unit prior to the start of renal replacement therapy were the main predictive factors for hospital mortality. Our study shows that a customized SAPS 3 model was accurate in predicting mortality and seems a promising algorithm to characterize and stratify patients in clinical studies.

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Year:  2010        PMID: 19812539     DOI: 10.1038/ki.2009.385

Source DB:  PubMed          Journal:  Kidney Int        ISSN: 0085-2538            Impact factor:   10.612


  11 in total

1.  Calibration strategies to validate predictive models: is new always better?

Authors:  Nicolás Serrano
Journal:  Intensive Care Med       Date:  2012-05-15       Impact factor: 17.440

2.  Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records.

Authors:  Min Hyuk Choi; Dokyun Kim; Eui Jun Choi; Yeo Jin Jung; Yong Jun Choi; Jae Hwa Cho; Seok Hoon Jeong
Journal:  Sci Rep       Date:  2022-05-03       Impact factor: 4.996

3.  Predictors of death and dialysis in severe AKI: the UPHS-AKI cohort.

Authors:  Francis Perry Wilson; Wei Yang; Harold I Feldman
Journal:  Clin J Am Soc Nephrol       Date:  2012-12-20       Impact factor: 8.237

4.  Impact of timing of renal replacement therapy initiation on outcome of septic acute kidney injury.

Authors:  Yu-Hsiang Chou; Tao-Min Huang; Vin-Cent Wu; Cheng-Yi Wang; Chih-Chung Shiao; Chun-Fu Lai; Hung-Bin Tsai; Chia-Ter Chao; Guang-Huar Young; Wei-Jei Wang; Tze-Wah Kao; Shuei-Liong Lin; Yin-Yi Han; Anne Chou; Tzu-Hsin Lin; Ya-Wen Yang; Yung-Ming Chen; Pi-Ru Tsai; Yu-Feng Lin; Jenq-Wen Huang; Wen-Chih Chiang; Nai-Kuan Chou; Wen-Je Ko; Kwan-Dun Wu; Tun-Jun Tsai
Journal:  Crit Care       Date:  2011-06-06       Impact factor: 9.097

Review 5.  Evaluation of Simplified Acute Physiology Score 3 performance: a systematic review of external validation studies.

Authors:  Antonio Paulo Nassar; Luiz Marcelo Sa Malbouisson; Rui Moreno
Journal:  Crit Care       Date:  2014-06-06       Impact factor: 9.097

6.  A clinical score to predict mortality in septic acute kidney injury patients requiring continuous renal replacement therapy: the HELENICC score.

Authors:  Rogério da Hora Passos; João Gabriel Rosa Ramos; Evandro Jose Bulhoes Mendonça; Eva Alves Miranda; Fábio Ricardo Dantas Dutra; Maria Fernanda R Coelho; Andrea C Pedroza; Luis Claudio L Correia; Paulo Benigno Pena Batista; Etienne Macedo; Margarida M D Dutra
Journal:  BMC Anesthesiol       Date:  2017-02-07       Impact factor: 2.217

7.  Performance of Simplified Acute Physiology Score 3 In Predicting Hospital Mortality In Emergency Intensive Care Unit.

Authors:  Qing-Bian Ma; Yuan-Wei Fu; Lu Feng; Qiang-Rong Zhai; Yang Liang; Meng Wu; Ya-An Zheng
Journal:  Chin Med J (Engl)       Date:  2017-07-05       Impact factor: 2.628

8.  A systematic review and meta-analysis of acute kidney injury in the intensive care units of developed and developing countries.

Authors:  Fernando de Assis Ferreira Melo; Etienne Macedo; Ana Caroline Fonseca Bezerra; Walédya Araújo Lopes de Melo; Ravindra L Mehta; Emmanuel de Almeida Burdmann; Dirce Maria Trevisan Zanetta
Journal:  PLoS One       Date:  2020-01-17       Impact factor: 3.240

9.  Heart rate is associated with mortality in patients undergoing continuous renal replacement therapy.

Authors:  Soojin Lee; Yeonhee Lee; Heejoon Jang; Hongran Moon; Dong Ki Kim; Seung Seok Han
Journal:  Kidney Res Clin Pract       Date:  2017-09-30

10.  Electronic health records accurately predict renal replacement therapy in acute kidney injury.

Authors:  Sanmay Low; Anantharaman Vathsala; Tanusya Murali Murali; Long Pang; Graeme MacLaren; Wan-Ying Ng; Sabrina Haroon; Amartya Mukhopadhyay; Shir-Lynn Lim; Bee-Hong Tan; Titus Lau; Horng-Ruey Chua
Journal:  BMC Nephrol       Date:  2019-01-31       Impact factor: 2.388

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