What is the correlation between Apache (Acute Physiology and Chronic Health Evaluation) score and ICU (Intensive Care Unit) mortality?

APACHE Score and Its Correlation with ICU Mortality

The APACHE II score demonstrates a strong positive correlation with ICU mortality, with higher scores significantly associated with increased mortality risk. 1, 2

Understanding the APACHE II Score

• APACHE II (Acute Physiology and Chronic Health Evaluation II) is one of the most effective tools for predicting mortality in ICU patients, with superior discriminative power (pooled AUC of 0.81) compared to other scoring systems 3
• The score incorporates 12 physiological measurements, age, chronic health evaluation, and admission type to provide a comprehensive assessment of disease severity 1, 3
• Unlike other scoring systems such as SOFA, APACHE II includes age and comorbidities, which are important factors in predicting outcomes 3

Correlation with ICU Mortality

• An APACHE II score of 8 or higher is considered the optimal cut-off point for predicting mortality, with a sensitivity of 83.3% and specificity of 91% 2
• The score demonstrates a 55.6% positive predictive value after 48 hours of ICU admission 1, 2
• Recent research shows a clear mortality gradient based on APACHE II score ranges 4:
  • Scores 31-40: Extremely high mortality (approaching 100% in some studies)
  • Scores 21-30: Very high mortality (84.6% mortality observed in some cohorts)
  • Scores 11-20: Moderate mortality (approximately 28.45%)
  • Scores 3-10: Low mortality (approximately 10%) 5, 4

Clinical Applications

• APACHE II serves as an early warning indicator of death and can prompt clinicians to upgrade treatment protocols 4
• The score can be calculated within the first 24 hours of ICU admission and provides risk estimates for hospital mortality that are within 3% of actually observed outcomes 6
• Regular recalculation of the APACHE II score can provide valuable information about patient progress and response to treatment 3

Regional and Contextual Variations

• The original US APACHE II model showed variable ability to accurately predict risk of death when applied to UK patients, leading to a 'local' UK recalibration of the model 1
• Different care patterns before ICU admission may explain why mortality prediction models like APACHE require adjustment in different regions or countries 1
• Pre-ICU care quality can significantly impact physiological measurements used in the APACHE II calculation, potentially creating a "lead time bias" 1

Comparison with Other Scoring Systems

• APACHE II has demonstrated higher accuracy for predicting severe acute conditions compared to other scoring systems like Ranson, BISAP, and CTSI 1, 2
• In a direct comparison, APACHE II outperformed SOFA in discriminative power for mortality prediction (AUC 0.81 vs. 0.75) 3
• A study comparing APACHE II with SAPS II found that APACHE II was a better predictor of hospital outcome in patients with respiratory disease requiring intensive care (area under ROC curve 80.88% vs. 73.52%) 7

Limitations and Considerations

• The APACHE II calculation is cumbersome and not all required parameters are routinely collected 1, 2
• The relationship between APACHE II score and length of ICU stay is less clear, with some studies showing an inverse correlation as higher-scoring patients may die earlier 5
• Pre-ICU care quality can significantly impact the physiological measurements used in the APACHE II calculation 1

Clinical Decision-Making Algorithm

1. Calculate APACHE II score within first 24 hours of ICU admission 3, 6
2. Interpret mortality risk based on score range:
   • Scores 31-40: Extremely high mortality risk (>80%) 5, 4
   • Scores 21-30: High mortality risk (50-80%) 4
   • Scores 11-20: Moderate mortality risk (20-50%) 5, 4
   • Scores 3-10: Low mortality risk (<20%) 5, 4
3. Consider recalculating score daily to track patient progress 3
4. Adjust treatment intensity and resource allocation based on score trends 4
5. Consider regional variations and potential need for local calibration 1