MCQ-From Prediction to Prevention: Can AKI Risk Scores Change Outcomes?

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1. A 68-year-old man with septic shock secondary to pneumonia is receiving norepinephrine 0.08 µg/kg/min. His serum creatinine has increased from 1.0 to 1.1 mg/dL. Urine output is 0.35 mL/kg/h over the previous 6 hours. Urinary [TIMP-2] × [IGFBP7] is 1.2. What is the most appropriate next step?

A. Reassure the team that AKI has not yet developed because creatinine remains normal

B. Repeat creatinine in 24 hours before making management changes

C. Initiate a kidney-protective strategy including review of nephrotoxins and reassessment of hemodynamics

D. Commence renal replacement therapy immediately

E. Discontinue vasopressor support to improve renal perfusion

2. An ICU introduces an AI-based AKI prediction model. A patient with septic shock receives an alert predicting an 85% probability of Stage 2–3 AKI within 24 hours. Which statement best reflects current best practice?

A. AI predictions should supersede clinician judgment when confidence exceeds 80%

B. The alert should prompt reassessment and preventive measures but not automatically dictate therapy

C. Biomarker testing becomes unnecessary when AI predictions are available

D. High-risk predictions should trigger automatic dialysis consultation

E. The alert should be ignored unless creatinine rises

3. Which factor most limits the generalizability of a single AKI prediction model across all ICU populations?

A. Variability in serum creatinine assays

B. Differences in urine output measurement techniques

C. Distinct pathophysiological mechanisms underlying different AKI syndromes

D. Lack of computing power

E. Variability in ICU staffing levels

4. An intensivist is shown an AI-generated AKI risk score without any explanation of how the prediction was derived. Which feature would most improve clinical adoption?

A. Displaying the mathematical equations behind the neural network

B. Showing historical model training datasets

C. Presenting the major risk drivers and associated recommended actions

D. Providing a confidence interval only

E. Displaying risk as a color-coded heat map

5. Which intervention is LEAST likely to be included in a nephroprotective bundle for a septic patient identified as high risk for AKI?

A. Review of nephrotoxic medications

B. Renal dose adjustment of antimicrobials

C. Optimization of perfusion pressure

D. Delaying antimicrobial therapy until renal function stabilizes

E. Monitoring fluid balance closely

6. A hospital implements a real-time AKI alert system. Initially, clinicians respond to alerts, but after several months most notifications are ignored. Which intervention is most likely to improve effectiveness?

A. Increasing alert frequency

B. Adding more biomarker measurements

C. Linking alerts to a mandatory response pathway with clear accountability

D. Restricting alerts to consultants only

E. Lowering the alert threshold

7. A patient’s AKI risk score changes from 20% to 75% over 8 hours despite stable creatinine. Which factor most strongly supports recalculating risk dynamically?

A. Creatinine is an unreliable marker of renal function

B. AKI risk changes as clinical variables evolve over time

C. Risk prediction models require hourly updates by design

D. Dynamic models eliminate false-positive predictions

E. Dynamic models prevent alert fatigue

8. A future ICU system combines genomics, proteomics, urinary biomarkers, and continuous physiological monitoring. What is the principal advantage of such an approach?

A. Elimination of serum creatinine testing

B. Identification of patients likely to require dialysis

C. Earlier recognition of individual susceptibility and biological endotypes of AKI

D. Replacement of clinician assessment

E. Removal of the need for external validation

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MCQ-From Prediction to Prevention: Can AKI Risk Scores Change Outcomes?

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