ArticleKidney medicine2022
Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model.
Article in Kidney medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Seasonal Variations in the Risk of Outpatient Acute Kidney Injury in Patients with Chronic Kidney Disease.Diagnostics (Basel, Switzerland) · 2026Article
- What is Outpatient AKI? Insights from the SPRINT AKI Cohort.Clinical journal of the American Society of Nephrology : CJASN · 2026Article
- A novel age-norm-capped baseline serum creatinine approach for acute kidney injury diagnosis in critically Ill children: a retrospective cohort study.Frontiers in pediatrics · 2026Article
- Outpatient acute kidney disease detection by national and institutional health data.Communications medicine · 2025Article
- A clinical decision support system promotes the appropriate use of drugs in hospitalized patients with kidney impairment.Journal of pharmaceutical health care and sciences · 2025Review
- Cigarette Smoking and Risk of Hospitalization With Acute Kidney Injury: The Atherosclerosis Risk in Communities (ARIC) Study.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2024Observational
- Online Risk Calculator for Acute Kidney Injury in the Outpatient Setting.Kidney medicine · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
RATIONALE &
objectiveRisk 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
designPredictive model development and external validation using observational electronic health record data. SETTING &
participantsPatients 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.
resultsOutpatient 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.
conclusionsThe outpatient AKI risk prediction model performed well in both the development and validation cohorts in both continuous and binary forms.
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