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Early prediction of acquiring acute kidney injury for older inpatients using most effective laboratory test results.

ABSTRACT: BACKGROUND:Acute Kidney Injury (AKI) is common among inpatients. Severe AKI increases all-cause mortality especially in critically ill patients. Older patients are more at risk of AKI because of the declined renal function, increased comorbidities, aggressive medical treatments, and nephrotoxic drugs. Early prediction of AKI for older inpatients is therefore crucial. METHODS:We use 80 different laboratory tests from the electronic health records and two types of representations for each laboratory test, that is, we consider 160 (laboratory test, type) pairs one by one to do the prediction. By proposing new similarity measures and employing the classification technique of the K nearest neighbors, we are able to identify the most effective (laboratory test, type) pairs for the prediction. Furthermore, in order to know how early and accurately can AKI be predicted to make our method clinically useful, we evaluate the prediction performance of up to 5?days prior to the AKI event. RESULTS:We compare our method with two existing works and it shows our method outperforms the others. In addition, we implemented an existing method using our dataset, which also shows our method has a better performance. The most effective (laboratory test, type) pairs found for different prediction times are slightly different. However, Blood Urea Nitrogen (BUN) is found the most effective (laboratory test, type) pair for most prediction times. CONCLUSION:Our study is first to consider the last value and the trend of the sequence for each laboratory test. In addition, we define the exclusion criteria to identify the inpatients who develop AKI during hospitalization and we set the length of the data collection window to ensure the laboratory data we collect is close to the AKI time. Furthermore, we individually select the most effective (laboratory test, type) pairs to do the prediction for different days of early prediction. In the future, we will extend this approach and develop a system for early prediction of major diseases to help better disease management for inpatients.

SUBMITTER: Chen YS

PROVIDER: S-EPMC7032003 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Early prediction of acquiring acute kidney injury for older inpatients using most effective laboratory test results.

Chen Yi-Shian YS Chou Che-Yi CY Chen Arbee L P ALP

BMC medical informatics and decision making 20200220 1

<h4>Background</h4>Acute Kidney Injury (AKI) is common among inpatients. Severe AKI increases all-cause mortality especially in critically ill patients. Older patients are more at risk of AKI because of the declined renal function, increased comorbidities, aggressive medical treatments, and nephrotoxic drugs. Early prediction of AKI for older inpatients is therefore crucial.<h4>Methods</h4>We use 80 different laboratory tests from the electronic health records and two types of representations fo ...[more]

PMID: 32079533

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Project description:ImportanceEffective treatment of acute kidney injury (AKI) is predicated on timely diagnosis; however, the lag in the increase in serum creatinine levels after kidney injury may delay therapy initiation.ObjectiveTo determine the derivation and validation of predictive models for AKI after cardiac surgery.Design, setting, and participantsMultivariable prediction models were derived based on a retrospective observational cohort of adult patients undergoing cardiac surgery between January 2000 and December 2019 from a US academic medical center (n = 58 526) and subsequently validated on an external cohort from 3 US community hospitals (n = 4734). The date of final follow-up was January 15, 2020.ExposuresPerioperative change in serum creatinine and postoperative blood urea nitrogen, serum sodium, potassium, bicarbonate, and albumin from the first metabolic panel after cardiac surgery.Main outcomes and measuresArea under the receiver-operating characteristic curve (AUC) and calibration measures for moderate to severe AKI, per Kidney Disease: Improving Global Outcomes (KDIGO), and AKI requiring dialysis prediction models within 72 hours and 14 days following surgery.ResultsIn a derivation cohort of 58 526 patients (median [IQR] age, 66 [56-74] years; 39 173 [67%] men; 51 503 [91%] White participants), the rates of moderate to severe AKI and AKIrequiring dialysis were 2674 (4.6%) and 868 (1.48%) within 72 hours and 3156 (5.4%) and 1018 (1.74%) within 14 days after surgery. The median (IQR) interval to first metabolic panel from conclusion of the surgical procedure was 10 (7-12) hours. In the derivation cohort, the metabolic panel-based models had excellent predictive discrimination for moderate to severe AKI within 72 hours (AUC, 0.876 [95% CI, 0.869-0.883]) and 14 days (AUC, 0.854 [95% CI, 0.850-0.861]) after the surgical procedure and for AKI requiring dialysis within 72 hours (AUC, 0.916 [95% CI, 0.907-0.926]) and 14 days (AUC, 0.900 [95% CI, 0.889-0.909]) after the surgical procedure. In the validation cohort of 4734 patients (median [IQR] age, 67 (60-74) years; 3361 [71%] men; 3977 [87%] White participants), the models for moderate to severe AKI after the surgical procedure showed AUCs of 0.860 (95% CI, 0.838-0.882) within 72 hours and 0.842 (95% CI, 0.820-0.865) within 14 days and the models for AKI requiring dialysis and 14 days had an AUC of 0.879 (95% CI, 0.840-0.918) within 72 hours and 0.873 (95% CI, 0.836-0.910) within 14 days after the surgical procedure. Calibration assessed by Spiegelhalter z test showed P >.05 indicating adequate calibration for both validation and derivation models.Conclusions and relevanceAmong patients undergoing cardiac surgery, a prediction model based on perioperative basic metabolic panel laboratory values demonstrated good predictive accuracy for moderate to severe acute kidney injury within 72 hours and 14 days after the surgical procedure. Further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.

Dataset Information

Early prediction of acquiring acute kidney injury for older inpatients using most effective laboratory test results.

Publications

Early prediction of acquiring acute kidney injury for older inpatients using most effective laboratory test results.

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