TY - JOUR
T1 - Approaches to predicting outcomes in patients with acute kidney injury
AU - Saly, Danielle
AU - Yang, Alina
AU - Triebwasser, Corey
AU - Oh, Janice
AU - Sun, Qisi
AU - Testani, Jeffrey
AU - Parikh, Chirag R.
AU - Bia, Joshua
AU - Biswas, Aditya
AU - Stetson, Chess
AU - Chaisanguanthum, Kris
AU - Wilson, F. Perry
N1 - Funding Information:
This study was supported by K23 DK097201 to FPW, by K24 DK090203 to CRP, and by K23HL114868, L30HL115790 to JT. Helynx, inc employs CS and KC. The funder provided support in the form of salaries for these authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
PY - 2017/1
Y1 - 2017/1
N2 - Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into training and validation cohorts. Outcomes of interest were death within 7 days, dialysis within 7 days, and length of stay. Data elements eligible for model-building included lab values, medications and dosages, procedures, and demographics. We assessed model discrimination using the area under the receiver operator characteristic curve and r-squared values. 2241 individuals were available for analysis. Both modeling techniques created viable models with very good discrimination ability, with AUCs exceeding 0.85 for dialysis and 0.8 for death prediction. Model performance was similar across model building strategies, though the strategy employing more advanced variable selection was more parsimonious. Very good to excellent prediction of outcome events is feasible in patients with AKI. More advanced techniques may lead to more parsimonious models, which may facilitate adoption in other settings.
AB - Despite recognition that Acute Kidney Injury (AKI) leads to substantial increases in morbidity, mortality, and length of stay, accurate prognostication of these clinical events remains difficult. It remains unclear which approaches to variable selection and model building are most robust. We used data from a randomized trial of AKI alerting to develop time-updated prognostic models using stepwise regression compared to more advanced variable selection techniques. We randomly split data into training and validation cohorts. Outcomes of interest were death within 7 days, dialysis within 7 days, and length of stay. Data elements eligible for model-building included lab values, medications and dosages, procedures, and demographics. We assessed model discrimination using the area under the receiver operator characteristic curve and r-squared values. 2241 individuals were available for analysis. Both modeling techniques created viable models with very good discrimination ability, with AUCs exceeding 0.85 for dialysis and 0.8 for death prediction. Model performance was similar across model building strategies, though the strategy employing more advanced variable selection was more parsimonious. Very good to excellent prediction of outcome events is feasible in patients with AKI. More advanced techniques may lead to more parsimonious models, which may facilitate adoption in other settings.
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U2 - 10.1371/journal.pone.0169305
DO - 10.1371/journal.pone.0169305
M3 - Article
C2 - 28122032
AN - SCOPUS:85010953626
SN - 1932-6203
VL - 12
JO - PloS one
JF - PloS one
IS - 1
M1 - e0169305
ER -