BACKGROUND/AIMS: Spontaneous bacterial peritonitis (SBP) contributes to poorer short-term mortality in cirrhotic patients with ascites. However, it is unknown how long the effect of the first SBP event persists in these patients. METHODS: The National Health Insurance Database, derived from the Taiwan National Health Insurance Program, was used to identify and enroll 7,892 cirrhotic patients with ascites who were hospitalized between January 1 and December 31, 2007. All patients were free from episodes of SBP from 1996 to 2006. RESULTS: The study included 1,176 patients with SBP. The overall 30-day, 90-day, 1-year, and 3-year mortality rates in this group were 21.8%, 38.9%, 57.5%, and 73.4%, respectively. The overall 30-day, 90-day, 1-year, and 3-year mortality rates in the non-SBP group were 15.7%, 32.5%, 53.3%, and 72.5%, respectively. After adjusting for gender, age, and other medical comorbidities, the adjusted hazard ratios of SBP for 30-day, 30- to 90-day, 90-day to 1-year, and 1- to 3-year mortality were 1.49 (95% confidence interval [CI], 1.30 to 1.71), 1.19 (95% CI, 1.02 to 1.38), 1.04 (95% CI, 0.90 to 1.20), and 0.90 (95% CI, 0.77 to 1.05), respectively, compared with the non-SBP group. CONCLUSIONS: The effect of SBP on the mortality of cirrhotic patients with ascites disappeared in those surviving more than 90 days after the first SBP event.
Ascites* ; Comorbidity ; Fibrosis ; Humans ; Mortality* ; National Health Programs ; Peritonitis* ; Taiwan*

INTRODUCTIONNecrotising fasciitis (NF) is often found in patients with diabetes mellitus, chronic renal failure, alcoholism, malignancy or liver cirrhosis. However, it remains unknown whether liver cirrhosis is an independent risk factor for the occurrence of NF. This study aimed to determine whether liver cirrhosis is an independent risk factor for the occurrence of NF, and to identify the relationship between severity of liver cirrhosis and occurrence of NF.

METHODSThe National Health Insurance Research Database, maintained by Taiwan's National Health Insurance programme, was retrospectively analysed, and the hospitalisation data of 40,802 cirrhotic patients and 40,865 randomly selected, age‑ and gender‑matched non‑cirrhotic control patients was collected. The medical records of all patients were individually followed for a three‑year period from the patients' first hospitalisation in 2004.

RESULTSDuring the three‑year follow‑up period, there were 299 (0.7%) cirrhotic patients with NF and 160 (0.4%) non‑cirrhotic patients with NF. Cox regression analysis showed that liver cirrhosis was a risk factor for the occurrence of NF during the study period (hazard ratio 1.982; p < 0.001). Among cirrhotic patients, those with complicated liver cirrhosis had a higher risk for the occurrence of NF than patients with non‑complicated liver cirrhosis (hazard ratio 1.320; p = 0.028).

CONCLUSIONCirrhotic patients had a higher risk for the occurrence of NF than non‑cirrhotic patients, and the risk for NF was especially high among patients with complicated liver cirrhosis.

Adult ; Age Factors ; Aged ; Alcoholism ; complications ; Comorbidity ; Fasciitis, Necrotizing ; complications ; physiopathology ; Female ; Follow-Up Studies ; Hospitalization ; Humans ; Incidence ; Liver Cirrhosis ; complications ; physiopathology ; Male ; Middle Aged ; Proportional Hazards Models ; Retrospective Studies ; Risk Factors ; Sex Factors ; Taiwan ; Treatment Outcome

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.