A clinical risk prediction model to identify patients with hepatorenal syndrome at hospital admission.
Acute Kidney Injury
/ diagnosis
Adult
Area Under Curve
Cohort Studies
Female
Hepatorenal Syndrome
/ diagnosis
Hospitalization
/ statistics & numerical data
Humans
Intensive Care Units
Liver Cirrhosis
/ diagnosis
Logistic Models
Male
Middle Aged
Patient Admission
/ statistics & numerical data
Retrospective Studies
Severity of Illness Index
Journal
International journal of clinical practice
ISSN: 1742-1241
Titre abrégé: Int J Clin Pract
Pays: India
ID NLM: 9712381
Informations de publication
Date de publication:
Nov 2019
Nov 2019
Historique:
received:
16
05
2019
revised:
02
07
2019
accepted:
17
07
2019
pubmed:
28
7
2019
medline:
8
1
2020
entrez:
27
7
2019
Statut:
ppublish
Résumé
Hepatorenal syndrome (HRS) is a life-threatening complication of cirrhosis and early detection of evolving HRS may provide opportunities for early intervention. We developed a HRS risk model to assist early recognition of inpatient HRS. We analysed a retrospective cohort of patients hospitalised from among 122 medical centres in the US Department of Veterans Affairs between 1 January 2005 and 31 December 2013. We included cirrhotic patients who had Kidney Disease Improving Global Outcomes criteria based acute kidney injury on admission. We developed a logistic regression risk prediction model to detect HRS on admission using 10 variables. We calculated 95% confidence intervals on the model building dataset and, subsequently, calculated performance on a 1000 sample holdout test set. We report model performance with area under the curve (AUC) for discrimination and several calibration measures. The cohort included 19 368 patients comprising 32 047 inpatient admissions. The event rate for hospitalised HRS was 2810/31 047 (9.1%) and 79/1000 (7.9%) in the model building and validation datasets, respectively. The variable selection procedure designed a parsimonious model involving ten predictor variables. Final model performance in the validation dataset had an AUC of 0.87, Brier score of 0.05, slope of 1.10 and intercept of 0.04. We developed a probabilistic risk model to diagnose HRS within 24 hours of hospital admission using routine clinical variables in the largest ever published HRS cohort. The performance was excellent and this model may help identify high-risk patients for HRS and promote early intervention.
Sections du résumé
BACKGROUND
BACKGROUND
Hepatorenal syndrome (HRS) is a life-threatening complication of cirrhosis and early detection of evolving HRS may provide opportunities for early intervention. We developed a HRS risk model to assist early recognition of inpatient HRS.
METHODS
METHODS
We analysed a retrospective cohort of patients hospitalised from among 122 medical centres in the US Department of Veterans Affairs between 1 January 2005 and 31 December 2013. We included cirrhotic patients who had Kidney Disease Improving Global Outcomes criteria based acute kidney injury on admission. We developed a logistic regression risk prediction model to detect HRS on admission using 10 variables. We calculated 95% confidence intervals on the model building dataset and, subsequently, calculated performance on a 1000 sample holdout test set. We report model performance with area under the curve (AUC) for discrimination and several calibration measures.
RESULTS
RESULTS
The cohort included 19 368 patients comprising 32 047 inpatient admissions. The event rate for hospitalised HRS was 2810/31 047 (9.1%) and 79/1000 (7.9%) in the model building and validation datasets, respectively. The variable selection procedure designed a parsimonious model involving ten predictor variables. Final model performance in the validation dataset had an AUC of 0.87, Brier score of 0.05, slope of 1.10 and intercept of 0.04.
CONCLUSIONS
CONCLUSIONS
We developed a probabilistic risk model to diagnose HRS within 24 hours of hospital admission using routine clinical variables in the largest ever published HRS cohort. The performance was excellent and this model may help identify high-risk patients for HRS and promote early intervention.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e13393Subventions
Organisme : NCATS NIH HHS
ID : UL1 TR000445
Pays : United States
Organisme : U.S. Department of Veterans Affairs
ID : HSR&D IIR 13-073
Organisme : HSRD VA
ID : I01 HX001280
Pays : United States
Organisme : National Science Foundation
ID : IIS1418504
Organisme : U.S. Department of Veterans Affairs
ID : HSR&D IIR 13-457
Organisme : NIH HHS
ID : 1U2COD023196
Pays : United States
Organisme : U.S. Department of Veterans Affairs
ID : HSR&D IIR 13-052
Organisme : HSRD VA
ID : I01 HX001284
Pays : United States
Informations de copyright
© 2019 John Wiley & Sons Ltd.
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