Model for End Stage Liver Disease Na Calculator
Calculate MELD-Na score for liver transplant prioritization including sodium. Enter values for instant results with step-by-step formulas.
Reviewed by Rahul Singh, Health & Wellness Specialist
Formula
MELD = 10 x (0.957 x ln(Cr) + 0.378 x ln(Bili) + 1.120 x ln(INR) + 0.643)
MELD-Na = MELD + 1.32 x (137 - Na) - 0.033 x MELD x (137 - Na). Sodium is bounded between 125-137 mEq/L. Lab values below 1.0 are set to 1.0. Creatinine is capped at 4.0 mg/dL (set to 4.0 if on dialysis). Final score bounded between 6 and 40.
Worked Examples
Example 1: Compensated Cirrhosis
Problem:A patient with hepatitis C cirrhosis has bilirubin 1.8 mg/dL, creatinine 0.9 mg/dL, INR 1.3, sodium 139 mEq/L. Not on dialysis.
Solution:Creatinine set to 1.0 (minimum)\nMELD = 10 x (0.957 x ln(1.0) + 0.378 x ln(1.8) + 1.120 x ln(1.3) + 0.643)\nMELD = 10 x (0 + 0.222 + 0.294 + 0.643) = 11.6 = 12\nSodium = 139 > 137, bounded to 137\nMELD-Na = 12 + 1.32(137-137) - 0.033(12)(137-137) = 12
Result:MELD: 12 | MELD-Na: 12 | 3-Month Mortality: ~6% | Moderate priority
Example 2: Decompensated Cirrhosis with Hyponatremia
Problem:A patient with alcoholic cirrhosis and ascites has bilirubin 4.5 mg/dL, creatinine 1.8 mg/dL, INR 2.1, sodium 126 mEq/L. Not on dialysis.
Solution:MELD = 10 x (0.957 x ln(1.8) + 0.378 x ln(4.5) + 1.120 x ln(2.1) + 0.643)\nMELD = 10 x (0.563 + 0.569 + 0.832 + 0.643) = 26.1 = 26\nSodium bounded: max(125, min(137, 126)) = 126\nMELD-Na = 26 + 1.32(137-126) - 0.033(26)(137-126)\n= 26 + 14.52 - 9.44 = 31.1 = 31
Result:MELD: 26 | MELD-Na: 31 | 3-Month Mortality: ~52.6% | Highest priority
Frequently Asked Questions
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Key metrics include accuracy (correct predictions / total predictions), precision (true positives / predicted positives), recall (true positives / actual positives), and F1 score (harmonic mean of precision and recall). For regression tasks, use RMSE, MAE, and R-squared. Choose metrics based on your problem type and cost of errors.
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Consider task complexity, latency requirements, cost budget, and accuracy needs. Smaller models (7B parameters) work for simple classification and extraction. Medium models (70B) handle most general tasks. Large models (400B+) excel at complex reasoning and generation. Start with the smallest adequate model and scale up only if needed.
References
Reviewed by Rahul Singh, Health & Wellness Specialist · Editorial policy