Dataset Information

Laboratory test surveillance following acute kidney injury.

ABSTRACT:

Background

Patients with hospitalized acute kidney injury (AKI) are at increased risk for accelerated loss of kidney function, morbidity, and mortality. We sought to inform efforts at improving post-AKI outcomes by describing the receipt of renal-specific laboratory test surveillance among a large high-risk cohort.

Methods

We acquired clinical data from the Electronic health record (EHR) of 5 Veterans Affairs (VA) hospitals to identify patients hospitalized with AKI from January 1st, 2002 to December 31st, 2009, and followed these patients for 1 year or until death, enrollment in palliative care, or improvement in renal function to estimated GFR (eGFR) ? 60 L/min/1.73 m(2). Using demographic data, administrative codes, and laboratory test data, we evaluated the receipt and timing of outpatient testing for serum concentrations of creatinine and any as well as quantitative proteinuria recommended for CKD risk stratification. Additionally, we reported the rate of phosphorus and parathyroid hormone (PTH) monitoring recommended for chronic kidney disease (CKD) patients.

Results

A total of 10,955 patients admitted with AKI were discharged with an eGFR<60 mL/min/1.73 m2. During outpatient follow-up at 90 and 365 days, respectively, creatinine was measured on 69% and 85% of patients, quantitative proteinuria was measured on 6% and 12% of patients, PTH or phosphorus was measured on 10% and 15% of patients.

Conclusions

Measurement of creatinine was common among all patients following AKI. However, patients with AKI were infrequently monitored with assessments of quantitative proteinuria or mineral metabolism disorder, even for patients with baseline kidney disease.

SUBMITTER: Matheny ME

PROVIDER: S-EPMC4130516 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Laboratory test surveillance following acute kidney injury.

Matheny Michael E ME Peterson Josh F JF Eden Svetlana K SK Hung Adriana M AM Speroff Theodore T Abdel-Kader Khaled K Parr Sharidan K SK Ikizler T Alp TA Siew Edward D ED

PloS one 20140812 8

<h4>Background</h4>Patients with hospitalized acute kidney injury (AKI) are at increased risk for accelerated loss of kidney function, morbidity, and mortality. We sought to inform efforts at improving post-AKI outcomes by describing the receipt of renal-specific laboratory test surveillance among a large high-risk cohort.<h4>Methods</h4>We acquired clinical data from the Electronic health record (EHR) of 5 Veterans Affairs (VA) hospitals to identify patients hospitalized with AKI from January 1 ...[more]

PMID: 25117447

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Project description: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.

Project description:ObjectivesThe study objectives were to describe the incidence, risk factors, and outcomes of acute kidney injury after cardiopulmonary bypass in Jamaica.MethodWe performed a review of the medical records of adult patients (aged ≥ 18 years) with no prior dialysis requirement undergoing cardiopulmonary bypass at the University Hospital of the West Indies, Mona, between January 1, 2016, and June 30, 2019. Demographic, preoperative, intraoperative, and postoperative data were abstracted. Acute kidney injury was defined using Kidney Disease Improving Global Outcomes criteria. The primary outcomes were acute kidney injury incidence and all-cause 30-day mortality. Multivariable logistic regression and Cox proportional analyses were used to examine the association between the acute kidney injury risk factors and the primary outcome.ResultsData for 210 patients (58% men, mean age 58.1 ± 12.9 years) were analyzed. Acute kidney injury occurred in 80 patients (38.1%), 44% with Kidney Disease Improving Global Outcomes I, 33% with Kidney Disease Improving Global Outcomes II, and 24% with Kidney Disease Improving Global Outcomes III. From multivariable logistic regression models, European System for Cardiac Operative Risk Evaluation II (odds ratio, 1.19; 95% confidence interval, 1.01-1.39 per unit), bypass time (odds ratio, 1.94; 95% confidence interval, 1.40-2.67 per hour), perioperative red cell transfusion (odds ratio, 3.03; 95% confidence interval, 1.36-6.76), and postoperative neutrophil lymphocyte ratio (odds ratio, 1.65; 95% confidence interval, 1.01-2.68 per 10-unit difference) were positively associated with acute kidney injury. Acute kidney injury resulted in greater median hospital stay (18 vs 11 days, P < .001) and intensive care unit stay (5 vs 3 days, P < .001), and an 8-fold increase in 30-day mortality (hazard ratio, 8.15; 95% confidence interval, 2.76-24.06, P < .001).ConclusionsAcute kidney injury after cardiopulmonary bypass surgery occurs frequently in Jamaica and results in poor short-term outcomes. Further studies coupled with quality interventions to reduce the mortality of those with acute kidney injury are needed in the Caribbean.

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

Laboratory test surveillance following acute kidney injury.

Background

Methods

Results

Conclusions

Publications

Laboratory test surveillance following acute kidney injury.

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