Dynamic prediction of the need for renal replacement therapy in intensive care unit patients using a simple and robust model.
We aimed at identifying a model that dynamically predicts future need for renal replacement therapy (RRT) in intensive care unit (ICU) patients and can easily be implemented for online monitoring at the bedside. 7290 interdisciplinary ICU admissions were investigated. Patients with <3 days of stay o...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 31; no. 1; pp. 195 - 205 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2017
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120844369&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120844369 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Feb2017 vid: 31 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 120844369 120844369 NLM26686690 10.1007/s10877-015-9814-4 NLM26686690 120844369 ppf: 195 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Dynamic prediction of the need for renal replacement therapy in intensive care unit patients using a simple and robust model. aug: au: Erdfelder, Felix Grigutsch, Daniel Zenker, Sven Hoeft, Andreas Reider, Evgeny Matot, Idit affil: Applied Mathematical Physiology (AMP) Lab, Department of Anesthesiology and Intensive Care Medicine , University of Bonn Medical Center , Sigmund-Freud-Str. 25 53127 Bonn Germany sug: subj: Renal Replacement Therapy Methods Critical Care Methods Models, Theoretical Probability Female Decision Support Systems, Clinical Online Systems Adolescence Patient Admission Child Young Adult Intensive Care Units Retrospective Design Discriminant Analysis Pharmacokinetics Calibration Reproducibility of Results Aged False Positive Results Prognosis Middle Age Child, Preschool Aged, 80 and Over Adult Kidney Failure, Acute Physiopathology Time Factors ROC Curve Creatinine Blood Male Scales Adolescent: 13-18 years Child: 6-12 years Aged: 65+ years Middle Aged: 45-64 years Child, Preschool: 2-5 years Aged, 80 & over Adult: 19-44 years Female Male ab: We aimed at identifying a model that dynamically predicts future need for renal replacement therapy (RRT) in intensive care unit (ICU) patients and can easily be implemented for online monitoring at the bedside. 7290 interdisciplinary ICU admissions were investigated. Patients with <3 days of stay or RRT in the first 2 days were excluded. 1624 of the remaining 2625 patients had a normal serum creatinine at admission. Every second of these 1624 patients was used for model calibration whereas the other half and, in addition, the 1001 patients with elevated serum creatinine were exclusively used for validation. Discriminant analysis was used to determine and validate a combination of clinical parameters that predicts the need for RRT 72 h ahead. Based on the calibration sample, stepwise discriminant analysis selected the serum values of (1) current urea, (2) current lactate, (3) the ratio of current and admission serum creatinine, and (4) the mean urine output of the previous 24 h. In the validation datasets, the model reached areas under the receiver operating characteristic curve of 0.866 and 0.833 in patients with normal and elevated serum creatinine at admission, respectively. Moreover, the model's predictive value extended to at least 5 days prior to initiation of RRT and exceeded that of the RIFLE classification at all investigated prediction intervals. We identified a robust model that dynamically predicts the future need for RRT successfully. This tool may help improve timing of therapy and prognosis in ICU patients. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|