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Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model
RATIONALE & OBJECTIVE: Risk factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk factors for identifying high-risk patients for AKI occurring and managed in the outpatient setting are unknown and may differ. STUDY DESIGN: Predictive model development and extern...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767133/ https://www.ncbi.nlm.nih.gov/pubmed/35072041 http://dx.doi.org/10.1016/j.xkme.2021.08.011 |
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author | Murphy, Daniel Reule, Scott Vock, David Drawz, Paul |
author_facet | Murphy, Daniel Reule, Scott Vock, David Drawz, Paul |
author_sort | Murphy, Daniel |
collection | PubMed |
description | RATIONALE & OBJECTIVE: Risk factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk factors for identifying high-risk patients for AKI occurring and managed in the outpatient setting are unknown and may differ. STUDY DESIGN: Predictive model development and external validation using observational electronic health record data. SETTING & PARTICIPANTS: Patients aged 18-90 years with recurrent primary care encounters, known baseline serum creatinine, and creatinine measured during an 18-month outcome period without established advanced kidney disease. NEW PREDICTORS & ESTABLISHED PREDICTORS: Established predictors for inpatient AKI were considered. Potential new predictors were hospitalization history, smoking, serum potassium levels, and prior outpatient AKI. OUTCOMES: A ≥50% increase in the creatinine level above a moving baseline of the recent measurement(s) without a hospital admission within 7 days defined outpatient AKI. ANALYTICAL APPROACH: Logistic regression with bootstrap sampling for backward stepwise covariate elimination was used. The model was then transformed into 2 binary tests: one identifying high-risk patients for research and another identifying patients for additional clinical monitoring or intervention. RESULTS: Outpatient AKI was observed in 4,611 (3.0%) and 115,744 (2.4%) patients in the development and validation cohorts, respectively. The model, with 18 variables and 3 interaction terms, produced C statistics of 0.717 (95% CI, 0.710-0.725) and 0.722 (95% CI, 0.720-0.723) in the development and validation cohorts, respectively. The research test, identifying the 5.2% most at-risk patients in the validation cohort, had a sensitivity of 0.210 (95% CI, 0.208-0.213) and specificity of 0.952 (95% CI, 0.951-0.952). The clinical test, identifying the 20% most at-risk patients, had a sensitivity of 0.494 (95% CI, 0.491-0.497) and specificity of 0.806 (95% CI, 0.806-0.807). LIMITATIONS: Only surviving patients with measured creatinine levels during a baseline period and outcome period were included. CONCLUSIONS: The outpatient AKI risk prediction model performed well in both the development and validation cohorts in both continuous and binary forms. |
format | Online Article Text |
id | pubmed-8767133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-87671332022-01-21 Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model Murphy, Daniel Reule, Scott Vock, David Drawz, Paul Kidney Med Original Research RATIONALE & OBJECTIVE: Risk factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk factors for identifying high-risk patients for AKI occurring and managed in the outpatient setting are unknown and may differ. STUDY DESIGN: Predictive model development and external validation using observational electronic health record data. SETTING & PARTICIPANTS: Patients aged 18-90 years with recurrent primary care encounters, known baseline serum creatinine, and creatinine measured during an 18-month outcome period without established advanced kidney disease. NEW PREDICTORS & ESTABLISHED PREDICTORS: Established predictors for inpatient AKI were considered. Potential new predictors were hospitalization history, smoking, serum potassium levels, and prior outpatient AKI. OUTCOMES: A ≥50% increase in the creatinine level above a moving baseline of the recent measurement(s) without a hospital admission within 7 days defined outpatient AKI. ANALYTICAL APPROACH: Logistic regression with bootstrap sampling for backward stepwise covariate elimination was used. The model was then transformed into 2 binary tests: one identifying high-risk patients for research and another identifying patients for additional clinical monitoring or intervention. RESULTS: Outpatient AKI was observed in 4,611 (3.0%) and 115,744 (2.4%) patients in the development and validation cohorts, respectively. The model, with 18 variables and 3 interaction terms, produced C statistics of 0.717 (95% CI, 0.710-0.725) and 0.722 (95% CI, 0.720-0.723) in the development and validation cohorts, respectively. The research test, identifying the 5.2% most at-risk patients in the validation cohort, had a sensitivity of 0.210 (95% CI, 0.208-0.213) and specificity of 0.952 (95% CI, 0.951-0.952). The clinical test, identifying the 20% most at-risk patients, had a sensitivity of 0.494 (95% CI, 0.491-0.497) and specificity of 0.806 (95% CI, 0.806-0.807). LIMITATIONS: Only surviving patients with measured creatinine levels during a baseline period and outcome period were included. CONCLUSIONS: The outpatient AKI risk prediction model performed well in both the development and validation cohorts in both continuous and binary forms. Elsevier 2021-10-16 /pmc/articles/PMC8767133/ /pubmed/35072041 http://dx.doi.org/10.1016/j.xkme.2021.08.011 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Murphy, Daniel Reule, Scott Vock, David Drawz, Paul Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model |
title | Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model |
title_full | Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model |
title_fullStr | Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model |
title_full_unstemmed | Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model |
title_short | Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model |
title_sort | acute kidney injury in the outpatient setting: developing and validating a risk prediction model |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8767133/ https://www.ncbi.nlm.nih.gov/pubmed/35072041 http://dx.doi.org/10.1016/j.xkme.2021.08.011 |
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