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• W2912689309 abstract "IntroductionAfter dialysis-requiring acute kidney injury (AKI-D), recovery of sufficient kidney function to discontinue dialysis is an important clinical and patient-oriented outcome. Predicting the probability of recovery in individual patients is a common dilemma.MethodsThis cohort study examined all adult members of Kaiser Permanente Northern California who experienced AKI-D between January 2009 and September 2015 and had predicted inpatient mortality of <20%. Candidate predictors included demographic characteristics, comorbidities, laboratory values, and medication use. We used logistic regression and classification and regression tree (CART) approaches to develop and cross-validate prediction models for recovery.ResultsAmong 2214 patients with AKI-D, mean age was 67.1 years, 40.8% were women, and 54.0% were white; 40.9% of patients recovered. Patients who recovered were younger, had higher baseline estimated glomerular filtration rates (eGFR) and preadmission hemoglobin levels, and were less likely to have prior heart failure or chronic liver disease. Stepwise logistic regression applied to bootstrapped samples identified baseline eGFR, preadmission hemoglobin level, chronic liver disease, and age as the predictors most commonly associated with coming off dialysis within 90 days. Our final logistic regression model including these predictors had a correlation coefficient between observed and predicted probabilities of 0.97, with a c-index of 0.64. An alternate CART approach did not outperform the logistic regression model (c-index 0.61).ConclusionWe developed and cross-validated a parsimonious prediction model for recovery after AKI-D with excellent calibration using routinely available clinical data. However, the model’s modest discrimination limits its clinical utility. Further research is needed to develop better prediction tools. After dialysis-requiring acute kidney injury (AKI-D), recovery of sufficient kidney function to discontinue dialysis is an important clinical and patient-oriented outcome. Predicting the probability of recovery in individual patients is a common dilemma. This cohort study examined all adult members of Kaiser Permanente Northern California who experienced AKI-D between January 2009 and September 2015 and had predicted inpatient mortality of <20%. Candidate predictors included demographic characteristics, comorbidities, laboratory values, and medication use. We used logistic regression and classification and regression tree (CART) approaches to develop and cross-validate prediction models for recovery. Among 2214 patients with AKI-D, mean age was 67.1 years, 40.8% were women, and 54.0% were white; 40.9% of patients recovered. Patients who recovered were younger, had higher baseline estimated glomerular filtration rates (eGFR) and preadmission hemoglobin levels, and were less likely to have prior heart failure or chronic liver disease. Stepwise logistic regression applied to bootstrapped samples identified baseline eGFR, preadmission hemoglobin level, chronic liver disease, and age as the predictors most commonly associated with coming off dialysis within 90 days. Our final logistic regression model including these predictors had a correlation coefficient between observed and predicted probabilities of 0.97, with a c-index of 0.64. An alternate CART approach did not outperform the logistic regression model (c-index 0.61). We developed and cross-validated a parsimonious prediction model for recovery after AKI-D with excellent calibration using routinely available clinical data. However, the model’s modest discrimination limits its clinical utility. Further research is needed to develop better prediction tools." @default.
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• W2912689309 date "2019-04-01" @default.
• W2912689309 modified "2023-10-16" @default.
• W2912689309 title "Predicting Renal Recovery After Dialysis-Requiring Acute Kidney Injury" @default.
• W2912689309 cites W101881177 @default.
• W2912689309 cites W1950965405 @default.
• W2912689309 cites W1966490691 @default.
• W2912689309 cites W1969130769 @default.
• W2912689309 cites W1974451585 @default.
• W2912689309 cites W1980969694 @default.
• W2912689309 cites W1986087095 @default.
• W2912689309 cites W1987174498 @default.
• W2912689309 cites W1988799854 @default.
• W2912689309 cites W1989028027 @default.
• W2912689309 cites W1999577714 @default.
• W2912689309 cites W2014476441 @default.
• W2912689309 cites W2018047225 @default.
• W2912689309 cites W2021876446 @default.
• W2912689309 cites W2022095549 @default.
• W2912689309 cites W2023077190 @default.
• W2912689309 cites W2025335083 @default.
• W2912689309 cites W2051640837 @default.
• W2912689309 cites W2052455047 @default.
• W2912689309 cites W2054004588 @default.
• W2912689309 cites W2056699935 @default.
• W2912689309 cites W2060470795 @default.
• W2912689309 cites W2062541896 @default.
• W2912689309 cites W2074247530 @default.
• W2912689309 cites W2092856255 @default.
• W2912689309 cites W2096254099 @default.
• W2912689309 cites W2101752015 @default.
• W2912689309 cites W2106344857 @default.
• W2912689309 cites W2107307692 @default.
• W2912689309 cites W2110554025 @default.
• W2912689309 cites W2111704803 @default.
• W2912689309 cites W2116864272 @default.
• W2912689309 cites W2116900376 @default.
• W2912689309 cites W2117807413 @default.
• W2912689309 cites W2117958746 @default.
• W2912689309 cites W2125890875 @default.
• W2912689309 cites W2133147121 @default.
• W2912689309 cites W2135163018 @default.
• W2912689309 cites W2139316042 @default.
• W2912689309 cites W2139937737 @default.
• W2912689309 cites W2141559993 @default.
• W2912689309 cites W2143589807 @default.
• W2912689309 cites W2144891869 @default.
• W2912689309 cites W2145339507 @default.
• W2912689309 cites W2145632027 @default.
• W2912689309 cites W2149687213 @default.
• W2912689309 cites W2150079983 @default.
• W2912689309 cites W2153742435 @default.
• W2912689309 cites W2155939210 @default.
• W2912689309 cites W2155965977 @default.
• W2912689309 cites W2156058032 @default.
• W2912689309 cites W2163629007 @default.
• W2912689309 cites W2165991624 @default.
• W2912689309 cites W2170505719 @default.
• W2912689309 cites W2171953637 @default.
• W2912689309 cites W2230658344 @default.
• W2912689309 cites W2287281503 @default.
• W2912689309 cites W2324406228 @default.
• W2912689309 cites W2337762517 @default.
• W2912689309 cites W2345809966 @default.
• W2912689309 cites W2359578587 @default.
• W2912689309 cites W2380810418 @default.
• W2912689309 cites W2395969030 @default.
• W2912689309 cites W2409331912 @default.
• W2912689309 cites W2466474156 @default.
• W2912689309 cites W2494350657 @default.
• W2912689309 cites W2591419591 @default.
• W2912689309 cites W2607031541 @default.
• W2912689309 cites W2782805029 @default.
• W2912689309 cites W2795024789 @default.
• W2912689309 cites W2984566620 @default.
• W2912689309 cites W3089472946 @default.
• W2912689309 cites W4249245515 @default.
• W2912689309 doi "https://doi.org/10.1016/j.ekir.2019.01.015" @default.
• W2912689309 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6451155" @default.
• W2912689309 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30993232" @default.
• W2912689309 hasPublicationYear "2019" @default.
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