Acute kidney injury detection using refined and physiological-feature augmented urine output

Abstract Acute kidney injury (AKI) is common in the intensive care unit, where it is associated with increased mortality. AKI is often defined using creatinine and urine output criteria. The creatinine-based definition is more reliable but less expedient, whereas the urine output based definition is...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Sahar Alkhairy, Leo A. Celi, Mengling Feng, Andrew J. Zimolzak
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
R
Q
Acceso en línea:https://doaj.org/article/64be432d85804c829f5bf15ebaabf404
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:64be432d85804c829f5bf15ebaabf404
record_format dspace
spelling oai:doaj.org-article:64be432d85804c829f5bf15ebaabf4042021-12-02T18:51:14ZAcute kidney injury detection using refined and physiological-feature augmented urine output10.1038/s41598-021-97735-02045-2322https://doaj.org/article/64be432d85804c829f5bf15ebaabf4042021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97735-0https://doaj.org/toc/2045-2322Abstract Acute kidney injury (AKI) is common in the intensive care unit, where it is associated with increased mortality. AKI is often defined using creatinine and urine output criteria. The creatinine-based definition is more reliable but less expedient, whereas the urine output based definition is rapid but less reliable. Our goal is to examine the urine output criterion and augment it with physiological features for better agreement with creatinine-based definitions of AKI. The objectives are threefold: (1) to characterize the baseline agreement of urine output and creatinine definitions of AKI; (2) to refine the urine output criteria to identify the thresholds that best agree with the creatinine-based definition; and (3) to build generalized estimating equation (GEE) and generalized linear mixed-effects (GLME) models with static and time-varying features to improve the accuracy of a near-real-time marker for AKI. We performed a retrospective observational study using data from two independent critical care databases, MIMIC-III and eICU, for critically ill patients who developed AKI in intensive care units. We found that the conventional urine output criterion (6 hr, 0.5 ml/kg/h) has specificity and sensitivity of 0.49 and 0.54 for MIMIC-III database; and specificity and sensitivity of 0.38 and 0.56 for eICU. Secondly, urine output thresholds of 12 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.58 and 0.48 for MIMIC-III; and urine output thresholds of 10 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.49 and 0.48 for eICU. Thirdly, the GEE model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.66 and 0.61 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.64 for eICU. The GLME model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.71 and 0.55 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.60 for eICU. The GEE model has greater performance than the GLME model, however, the GLME model is more reflective of the variables as fixed effects or random effects. The significant improvement in performance, relative to current definitions, when augmenting with patient features, suggest the need of incorporating these features when detecting disease onset and modeling at window-level rather than patient-level.Sahar AlkhairyLeo A. CeliMengling FengAndrew J. ZimolzakNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Sahar Alkhairy
Leo A. Celi
Mengling Feng
Andrew J. Zimolzak
Acute kidney injury detection using refined and physiological-feature augmented urine output
description Abstract Acute kidney injury (AKI) is common in the intensive care unit, where it is associated with increased mortality. AKI is often defined using creatinine and urine output criteria. The creatinine-based definition is more reliable but less expedient, whereas the urine output based definition is rapid but less reliable. Our goal is to examine the urine output criterion and augment it with physiological features for better agreement with creatinine-based definitions of AKI. The objectives are threefold: (1) to characterize the baseline agreement of urine output and creatinine definitions of AKI; (2) to refine the urine output criteria to identify the thresholds that best agree with the creatinine-based definition; and (3) to build generalized estimating equation (GEE) and generalized linear mixed-effects (GLME) models with static and time-varying features to improve the accuracy of a near-real-time marker for AKI. We performed a retrospective observational study using data from two independent critical care databases, MIMIC-III and eICU, for critically ill patients who developed AKI in intensive care units. We found that the conventional urine output criterion (6 hr, 0.5 ml/kg/h) has specificity and sensitivity of 0.49 and 0.54 for MIMIC-III database; and specificity and sensitivity of 0.38 and 0.56 for eICU. Secondly, urine output thresholds of 12 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.58 and 0.48 for MIMIC-III; and urine output thresholds of 10 hours and 0.6 ml/kg/h have specificity and sensitivity of 0.49 and 0.48 for eICU. Thirdly, the GEE model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.66 and 0.61 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.64 for eICU. The GLME model of four hours duration augmented with static and time-varying features can achieve a specificity and sensitivity of 0.71 and 0.55 for MIMIC-III; and specificity and sensitivity of 0.66 and 0.60 for eICU. The GEE model has greater performance than the GLME model, however, the GLME model is more reflective of the variables as fixed effects or random effects. The significant improvement in performance, relative to current definitions, when augmenting with patient features, suggest the need of incorporating these features when detecting disease onset and modeling at window-level rather than patient-level.
format article
author Sahar Alkhairy
Leo A. Celi
Mengling Feng
Andrew J. Zimolzak
author_facet Sahar Alkhairy
Leo A. Celi
Mengling Feng
Andrew J. Zimolzak
author_sort Sahar Alkhairy
title Acute kidney injury detection using refined and physiological-feature augmented urine output
title_short Acute kidney injury detection using refined and physiological-feature augmented urine output
title_full Acute kidney injury detection using refined and physiological-feature augmented urine output
title_fullStr Acute kidney injury detection using refined and physiological-feature augmented urine output
title_full_unstemmed Acute kidney injury detection using refined and physiological-feature augmented urine output
title_sort acute kidney injury detection using refined and physiological-feature augmented urine output
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/64be432d85804c829f5bf15ebaabf404
work_keys_str_mv AT saharalkhairy acutekidneyinjurydetectionusingrefinedandphysiologicalfeatureaugmentedurineoutput
AT leoaceli acutekidneyinjurydetectionusingrefinedandphysiologicalfeatureaugmentedurineoutput
AT menglingfeng acutekidneyinjurydetectionusingrefinedandphysiologicalfeatureaugmentedurineoutput
AT andrewjzimolzak acutekidneyinjurydetectionusingrefinedandphysiologicalfeatureaugmentedurineoutput
_version_ 1718377450196434944