Pregled bibliografske jedinice broj: 1253594
Diagnosing acute kidney injury ahead of time in critically ill septic patients using kinetic estimated glomerular filtration rate
Diagnosing acute kidney injury ahead of time in critically ill septic patients using kinetic estimated glomerular filtration rate // Journal of Critical Care, 75 (2023), 154276, 7 doi:10.1016/j.jcrc.2023.154276 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1253594 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Diagnosing acute kidney injury ahead of time in
critically ill septic patients using kinetic
estimated glomerular filtration rate
Autori
Lijović, Lada ; Pelajić, Stipe ; Hawchar, Fatime ; Minev, Ivaylo ; da Silva, Beatriz Helena Cermaria Soares ; Angelucci, Alessandra ; Ercole, Ari ; de Grooth, Harm-Jan ; Thoral, Patrick ; Radočaj, Tomislav ; Elbers, Paul
Izvornik
Journal of Critical Care (0883-9441) 75
(2023);
154276, 7
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
Acute kidney injuryEarly detectionGlomerular filtration rateKinetic eGFR
Sažetak
Introduction Accurate and actionable diagnosis of Acute Kidney Injury (AKI) ahead of time is important to prevent or mitigate renal insufficiency. The purpose of this study was to evaluate the performance of Kinetic estimated Glomerular Filtration Rate (KeGFR) in timely predicting AKI in critically ill septic patients. Methods We conducted a retrospective analysis on septic ICU patients who developed AKI in AmsterdamUMCdb, the first freely available European ICU database. The reference standard for AKI was the Kidney Disease: Improving Global Outcomes (KDIGO) classification based on serum creatinine and urine output (UO). Prediction of AKI was based on stages defined by KeGFR and UO. Classifications were compared by length of ICU stay (LOS), need for renal replacement therapy and 28-day mortality. Predictive performance and time between prediction and diagnosis were calculated. Results Of 2492 patients in the cohort, 1560 (62.0%) were diagnosed with AKI by KDIGO and 1706 (68.5%) by KeGFR criteria. Disease stages had agreement of kappa = 0.77, with KeGFR sensitivity 93.2%, specificity 73.0% and accuracy 85.7%. Median time to recognition of AKI Stage 1 was 13.2 h faster for KeGFR, and 7.5 h and 5.0 h for Stages 2 and 3. Outcomes revealed a slight difference in LOS and 28-day mortality for Stage 1. Conclusions Predictive performance of KeGFR combined with UO criteria for diagnosing AKI is excellent. Compared to KDIGO, deterioration of renal function was identified earlier, most prominently for lower stages of AKI. This may shift the actionable window for preventing and mitigating renal insufficiency.
Izvorni jezik
Engleski
Znanstvena područja
Kliničke medicinske znanosti
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
- MEDLINE