Dataset Information

Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model.

ABSTRACT:

Rationale & objective

Risk 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 design

Predictive model development and external validation using observational electronic health record data.

Setting & participants

Patients 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.

Results

Outpatient 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.

Conclusions

The outpatient AKI risk prediction model performed well in both the development and validation cohorts in both continuous and binary forms.

SUBMITTER: Murphy D

PROVIDER: S-EPMC8767133 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceThe Simple Postoperative AKI Risk (SPARK) index is a prediction model for postoperative acute kidney injury (PO-AKI) in patients undergoing noncardiac surgery. External validation has not been performed.ObjectiveTo externally validate the SPARK index.Design, setting, and participantsThis single-center retrospective cohort study included adults who underwent noncardiac surgery under general anesthesia from 2007 to 2011. Those with obstetric or urological surgery, estimated glomerular filtration rate (eGFR) of less than 15 mL/min/1.73 m2, preoperative dialysis, or an expected surgical duration of less than 1 hour were excluded. The study was conducted at Nara Medical University Hospital. Data analysis was conducted from January to July 2021.ExposuresRisk factors for AKI included in SPARK index.Main outcomes and measuresPO-AKI, defined as an increase in serum creatinine of at least 0.3 mg/dL within 48 hours or 150% compared with preoperative baseline value or urine output of less than 0.5 mL/kg/h for at least 6 hours within 1 week after surgery, and critical AKI, defined as either AKI stage 2 or greater and/or any AKI connected to postoperative death or requiring kidney replacement therapy before discharge. The discrimination and calibration of the SPARK index were examined with area under the receiver operating characteristic curves (AUC) and calibration plots, respectively.ResultsAmong 5135 participants (2410 [46.9%] men), 303 (5.9%) developed PO-AKI, and 137 (2.7%) developed critical AKI. Compared with the SPARK cohort, participants in our cohort were older (median [IQR] age, 56 [44-66] years vs 63 [50-73] years), had lower baseline eGFR (median [IQR], 82.1 [71.4-95.1] mL/min/1.73 m2 vs 78.2 [65.6-92.2] mL/min/1.73 m2), and had a higher prevalence of comorbidities (eg, diabetes: 3956 of 51 041 [7.8%] vs 802 [15.6%]). The incidence of PO-AKI and critical AKI increased as the scores on the SPARK index increased. For example, 10 of 593 participants (1.7%) in SPARK class A, indicating lowest risk, experienced PO-AKI, while 53 of 332 (16.0%) in SPARK class D, indicating highest risk, experienced PO-AKI. However, AUCs for PO-AKI and critical AKI were 0.67 (95% CI, 0.63-0.70) and 0.62 (95% CI, 0.57-0.67), respectively, and the calibration was poor (PO-AKI: y = 0.24x + 3.28; R2 = 0.86; critical AKI: y = 0.20x + 2.08; R2 = 0.51). Older age, diabetes, expected surgical duration, emergency surgery, renin-angiotensin-aldosterone system blockade use, and hyponatremia were not associated with PO-AKI in our cohort, resulting in overestimation of the predicted probability of AKI in our cohort.Conclusions and relevanceIn this study, the incidence of PO-AKI increased as the scores on the SPARK index increased. However, the predicted probability might not be accurate in cohorts with older patients with more comorbidities.