Predicting recovery from acute kidney injury in critically ill patients: development and validation of a prediction model

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Predicting recovery from acute kidney injury in critically ill patients : development and validation of a prediction model. / Itenov, Theis S.; Berthelsen, Rasmus Ehrenfried; Jensen, Jens-Ulrik; Gerds, Thomas A.; Pedersen, Lars M.; Strange, Ditte; Thormar, Katrin; Loken, Jesper; Andersen, Mads H.; Tousi, Hamid; Reiter, Nanna; Lundgren, Jens D.; Bestle, Morten H.

In: Critical Care and Resuscitation, Vol. 20, No. 1, 2018, p. 54-60.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Itenov, TS, Berthelsen, RE, Jensen, J-U, Gerds, TA, Pedersen, LM, Strange, D, Thormar, K, Loken, J, Andersen, MH, Tousi, H, Reiter, N, Lundgren, JD & Bestle, MH 2018, 'Predicting recovery from acute kidney injury in critically ill patients: development and validation of a prediction model', Critical Care and Resuscitation, vol. 20, no. 1, pp. 54-60.

APA

Itenov, T. S., Berthelsen, R. E., Jensen, J-U., Gerds, T. A., Pedersen, L. M., Strange, D., Thormar, K., Loken, J., Andersen, M. H., Tousi, H., Reiter, N., Lundgren, J. D., & Bestle, M. H. (2018). Predicting recovery from acute kidney injury in critically ill patients: development and validation of a prediction model. Critical Care and Resuscitation, 20(1), 54-60.

Vancouver

Itenov TS, Berthelsen RE, Jensen J-U, Gerds TA, Pedersen LM, Strange D et al. Predicting recovery from acute kidney injury in critically ill patients: development and validation of a prediction model. Critical Care and Resuscitation. 2018;20(1):54-60.

Author

Itenov, Theis S. ; Berthelsen, Rasmus Ehrenfried ; Jensen, Jens-Ulrik ; Gerds, Thomas A. ; Pedersen, Lars M. ; Strange, Ditte ; Thormar, Katrin ; Loken, Jesper ; Andersen, Mads H. ; Tousi, Hamid ; Reiter, Nanna ; Lundgren, Jens D. ; Bestle, Morten H. / Predicting recovery from acute kidney injury in critically ill patients : development and validation of a prediction model. In: Critical Care and Resuscitation. 2018 ; Vol. 20, No. 1. pp. 54-60.

Bibtex

@article{7bf859b7133848bea99b6459e269a304,
title = "Predicting recovery from acute kidney injury in critically ill patients: development and validation of a prediction model",
abstract = "OBJECTIVE: Intensive care unit (ICU) patients with acute kidney injury (AKI) who recover kidney function within 28 days experience less severe chronic kidney impairment and have increased long term survival. The aims of this study were to develop and validate a risk prediction model to identify these patients. DESIGN: Observational study with development and validation of a risk prediction model. SETTING: Nine academic ICUs in Denmark. PARTICIPANTS: Development cohort of critically ill patients with AKI at ICU admission from the Procalcitonin and Survival Study cohort (n = 568), validation cohort of adult patients with AKI admitted to two university hospitals in Denmark in 2012-13 (n = 766). INTERVENTIONS: None. MAIN OUTCOME MEASURES: Recovery of kidney function was defined as living for 5 consecutive days with no renal replacement therapy and with creatinine plasma levels below 1.5-fold the levels determined before ICU admission. RESULTS: A total of 266 patients (46.8%) recovered prior kidney function in the development cohort, and 453 patients (59.1%) in the validation cohort. The prediction model included elevation in creatinine, urinary output, sex and age. In the validation cohort, 69 patients (9.0%) had a predicted chance of recovery < 25%, and their observed rate of recovery was 21.5%. This observed rate of recovery was 81.7% among the 325 patients who had a predicted chance > 75%. The area under the receiver operations curves for predicting recovery in the validation cohort was 73.1%. CONCLUSION: We constructed and validated a simple model that can predict the chance of recovery from AKI in critically ill patients.",
author = "Itenov, {Theis S.} and Berthelsen, {Rasmus Ehrenfried} and Jens-Ulrik Jensen and Gerds, {Thomas A.} and Pedersen, {Lars M.} and Ditte Strange and Katrin Thormar and Jesper Loken and Andersen, {Mads H.} and Hamid Tousi and Nanna Reiter and Lundgren, {Jens D.} and Bestle, {Morten H.}",
year = "2018",
language = "English",
volume = "20",
pages = "54--60",
journal = "Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.",
issn = "1441-2772",
publisher = "Australasian Medical Publishing Company Pty. Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Predicting recovery from acute kidney injury in critically ill patients

T2 - development and validation of a prediction model

AU - Itenov, Theis S.

AU - Berthelsen, Rasmus Ehrenfried

AU - Jensen, Jens-Ulrik

AU - Gerds, Thomas A.

AU - Pedersen, Lars M.

AU - Strange, Ditte

AU - Thormar, Katrin

AU - Loken, Jesper

AU - Andersen, Mads H.

AU - Tousi, Hamid

AU - Reiter, Nanna

AU - Lundgren, Jens D.

AU - Bestle, Morten H.

PY - 2018

Y1 - 2018

N2 - OBJECTIVE: Intensive care unit (ICU) patients with acute kidney injury (AKI) who recover kidney function within 28 days experience less severe chronic kidney impairment and have increased long term survival. The aims of this study were to develop and validate a risk prediction model to identify these patients. DESIGN: Observational study with development and validation of a risk prediction model. SETTING: Nine academic ICUs in Denmark. PARTICIPANTS: Development cohort of critically ill patients with AKI at ICU admission from the Procalcitonin and Survival Study cohort (n = 568), validation cohort of adult patients with AKI admitted to two university hospitals in Denmark in 2012-13 (n = 766). INTERVENTIONS: None. MAIN OUTCOME MEASURES: Recovery of kidney function was defined as living for 5 consecutive days with no renal replacement therapy and with creatinine plasma levels below 1.5-fold the levels determined before ICU admission. RESULTS: A total of 266 patients (46.8%) recovered prior kidney function in the development cohort, and 453 patients (59.1%) in the validation cohort. The prediction model included elevation in creatinine, urinary output, sex and age. In the validation cohort, 69 patients (9.0%) had a predicted chance of recovery < 25%, and their observed rate of recovery was 21.5%. This observed rate of recovery was 81.7% among the 325 patients who had a predicted chance > 75%. The area under the receiver operations curves for predicting recovery in the validation cohort was 73.1%. CONCLUSION: We constructed and validated a simple model that can predict the chance of recovery from AKI in critically ill patients.

AB - OBJECTIVE: Intensive care unit (ICU) patients with acute kidney injury (AKI) who recover kidney function within 28 days experience less severe chronic kidney impairment and have increased long term survival. The aims of this study were to develop and validate a risk prediction model to identify these patients. DESIGN: Observational study with development and validation of a risk prediction model. SETTING: Nine academic ICUs in Denmark. PARTICIPANTS: Development cohort of critically ill patients with AKI at ICU admission from the Procalcitonin and Survival Study cohort (n = 568), validation cohort of adult patients with AKI admitted to two university hospitals in Denmark in 2012-13 (n = 766). INTERVENTIONS: None. MAIN OUTCOME MEASURES: Recovery of kidney function was defined as living for 5 consecutive days with no renal replacement therapy and with creatinine plasma levels below 1.5-fold the levels determined before ICU admission. RESULTS: A total of 266 patients (46.8%) recovered prior kidney function in the development cohort, and 453 patients (59.1%) in the validation cohort. The prediction model included elevation in creatinine, urinary output, sex and age. In the validation cohort, 69 patients (9.0%) had a predicted chance of recovery < 25%, and their observed rate of recovery was 21.5%. This observed rate of recovery was 81.7% among the 325 patients who had a predicted chance > 75%. The area under the receiver operations curves for predicting recovery in the validation cohort was 73.1%. CONCLUSION: We constructed and validated a simple model that can predict the chance of recovery from AKI in critically ill patients.

M3 - Journal article

VL - 20

SP - 54

EP - 60

JO - Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

JF - Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine.

SN - 1441-2772

IS - 1

ER -

ID: 210005502