Estimation of Missing Baseline Serum Creatinine for Acute Kidney Injury Diagnosis in Hospitalized Patients

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Laszczynska, O (Autor) Outra; Severo M (Autor) FMUP; Correia, S (Autor) FMUP; Azevedo A (Autor) FMUP

Revista

Título: Nephron

Páginas: 1-10

ISSN: 1660-8151

Editora: Karger

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00T-8VP

DOI: 10.1159/000512080

Abstract (EN): Introduction: In hospitalized patients, information on preadmission kidney function is often missing, impeding timely and accurate acute kidney injury (AKI) detection and affecting results of AKI-related studies. Objective: In this study, we provided estimates of preadmission serum creatinine (SCr), based on a multivariate linear regression (Model 1) and random forest model (Model 2) built with different parametrizations. Their accuracy for AKI diagnosis was compared with the accuracy of commonly used surrogate methods: (i) SCr at hospital admission (first SCr) and (ii) SCr back-calculated from the assumed estimated glomerular filtration rate of 75 mL/min/1.73 m(2) (eGFR 75). Methods: From 44,670 unique adult admissions to a tertiary referral centre between 2013 and 2015, we analysed 8,540 patients with preadmission SCr available. To control for differences in characteristics of patients with and without SCr, we used an inverse probability weighting technique. Results: Estimates of SCr were likely to be higher than true preadmission SCr in a low Cr concentration and undervalued in high concentrations although for Model 2 Complete-SCr these differences were smallest. The true cumulative incidence of AKI was 14.8%. Model 2 Complete-SCr had the best agreement for AKI diagnosis (kappa 0.811, 95% CI 0.787-0.835), while surrogate methods resulted in the lowest agreement: (kappa 0.553, 0.516-0.590) and (0.648, 0.620-0.676) for first SCr and eGFR 75, respectively. Conclusions: Multivariable imputation of preadmission SCr, taking into account elementary admission data, improved accuracy in AKI diagnosis over commonly used surrogate methods. Random forest-based models can serve as an effective tool in research.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10