Changes in platelet levels and prognosis in patients with acute liver failure and late-onset hepatic failure.
Manabu HayashiMasashi FujitaKazumichi AbeAtsushi TakahashiHiromasa OhiraPublished in: Medicine (2022)
The therapeutic strategies for acute liver failure (ALF) and late-onset hepatic failure (LOHF) still have room for improvement. Recent studies have reported an association between platelets and the pathophysiology of ALF. In this study, we investigated changes in platelet levels and clinical findings in ALF and LOHF patients. We retrospectively investigated the clinical data of 62 patients with ALF and LOHF. We analyzed the association between changes in platelet levels for 7 days after admission and the prognosis in patients with ALF and LOHF. The factors associated with changes in platelet levels were also analyzed. The platelet levels on days 1, 3, and 7 were significantly lower in the patients who died or underwent liver transplantation than in the spontaneous survivors. Administration of recombinant thrombomodulin was associated with spontaneous survival. The platelet levels in patients who met the King's College Hospital Criteria or the Japanese scoring system (JSS) for ALF ≥ 4 were significantly decreased 7 days after admission. The area under the receiver operating characteristic curve (AUROC) of a JSS score of 3 for predicting low platelet levels on day 7 was 0.903. Decreased platelet levels were associated with poor prognosis in patients with ALF and LOHF. The patients with low platelet levels and JSS scores on admission showed a high AUROC for predicting low platelet levels on day 7. Decreased platelet levels after admission may be a simple prognostic marker in ALF and LOHF patients.
Keyphrases
- liver failure
- late onset
- poor prognosis
- emergency department
- healthcare
- hepatitis b virus
- newly diagnosed
- long non coding rna
- machine learning
- prognostic factors
- electronic health record
- extracorporeal membrane oxygenation
- tyrosine kinase
- artificial intelligence
- data analysis
- respiratory failure
- deep learning
- acute care
- mechanical ventilation