Clinical value of predictive models based on liver stiffness measurement in predicting liver reserve function of compensated chronic liver disease.
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BACKGROUND: Assessment of liver reserve function (LRF) is essential for predicting the prognosis of patients with chronic liver disease (CLD) and determines the extent of liver resection in patients with hepatocellular carcinoma. AIM: To establish noninvasive models for LRF assessment based on liver stiffness measurement (LSM) and to evaluate their clinical performance. METHODS: A total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort. The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort (132 patients). RESULTS: Our study defined indocyanine green retention rate at 15 min (ICGR15) ≥ 10% as mildly impaired LRF and ICGR15 ≥ 20% as severely impaired LRF. We constructed predictive models of LRF, named the mLPaM and sLPaM, which involved only LSM, prothrombin time international normalized ratio to albumin ratio (PTAR), age and model for end-stage liver disease (MELD). The area under the curve of the mLPaM model (0.855, 0.872, respectively) and sLPaM model (0.869, 0.876, respectively) were higher than that of the methods for MELD, albumin-bilirubin grade and PTAR in the two cohorts, and their sensitivity and negative predictive value were the highest among these methods in the training cohort. In addition, the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort. CONCLUSION: The new models had a good predictive performance for LRF and could replace the indocyanine green (ICG) clearance test, especially in patients who are unable to undergo ICG testing.
