Literature DB >> 10803768

Prediction of acute renal failure by "bedside formula" in medical and surgical intensive care patients.

G N Coritsidis1, K Guru, L Ward, R Bashir, D A Feinfeld, C P Carvounis.   

Abstract

BACKGROUND: Prediction of which intensive care unit (ICU) patients are likely to develop acute renal failure (ARF) would be useful. However, scoring systems such as APACHE have been disappointing in this regard. We previously developed a bedside formula to predict ARF using only 3 parameters: serum albumin, urine osmolality, and presence of sepsis.
METHODS: We prospectively evaluated 115 consecutive medical ICU (MICU) patients, comparing the bedside formula to APACHE II AND APACHE III as predictors of ARF or death and looking at nutritional parameters such as iron binding capacity, triceps skin fold, mid-arm circumference, and total lymphocyte count. We then evaluated 123 additional consecutive MICU and 98 consecutive surgical ICU (SICU) patients, comparing the bedside formula to APACHE II.
RESULTS: The bedside formula was consistently more accurate than APACHE II in predicting ARF or in-hospital death in MICU patients. However, in SICU neither formula predicted ARF, and APACHE II predicted in-hospital death slightly better. No nutritional parameter other than albumin correlated with ARF.
CONCLUSION: The bedside formula appears superior to APACHE II in predicting ARF or death in MICU but not SICU. This suggests that these two ICU populations are different.

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Year:  2000        PMID: 10803768     DOI: 10.1081/jdi-100100868

Source DB:  PubMed          Journal:  Ren Fail        ISSN: 0886-022X            Impact factor:   2.606


  5 in total

1.  Risk Factors for Acute Kidney Injury in Hospitalized Non-Critically Ill Patients: A Population-Based Study.

Authors:  Sami Safadi; Musab S Hommos; Felicity T Enders; John C Lieske; Kianoush B Kashani
Journal:  Mayo Clin Proc       Date:  2020-01-31       Impact factor: 7.616

2.  Massive and disproportionate elevation of blood urea nitrogen in acute azotemia.

Authors:  Donald A Feinfeld; Hiba Bargouthi; Qaiser Niaz; Christos P Carvounis
Journal:  Int Urol Nephrol       Date:  2002       Impact factor: 2.370

3.  Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.

Authors:  Khaled Shawwa; Erina Ghosh; Stephanie Lanius; Emma Schwager; Larry Eshelman; Kianoush B Kashani
Journal:  Clin Kidney J       Date:  2020-09-30

4.  Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model.

Authors:  Daniel Murphy; Scott Reule; David Vock; Paul Drawz
Journal:  Kidney Med       Date:  2021-10-16

5.  Acute Kidney Injury in Trauma Patients Admitted to Critical Care: Development and Validation of a Diagnostic Prediction Model.

Authors:  Ryan W Haines; Shih-Pin Lin; Russell Hewson; Christopher J Kirwan; Hew D Torrance; Michael J O'Dwyer; Anita West; Karim Brohi; Rupert M Pearse; Parjam Zolfaghari; John R Prowle
Journal:  Sci Rep       Date:  2018-02-26       Impact factor: 4.379

  5 in total

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