No abstract

Ninety-five (62%) patients died during the follow-up. In 79 (85%) individuals, the cause of death was liver related. The median survival time was 13 months. Independent predictors of survival were Child score [hazard ratio (HR), 1.2; 95% confidence interval (CI), 1.08-1.37; P = 0.001], CD4+ cell count at decompensation lower than 100 cells/microl (HR, 2.48; 95% CI, 1.52-4.06; P < 0.001) and hepatic encephalopathy as the first hepatic decompensation (HR, 2.45; 95% CI, 1.41-4.27; P = 0.001). HAART was prescribed to 101 (66%) patients. The cumulative probability of survival in patients under HAART was 60% at 1 year and 40% at 3 years, versus 38 and 18%, respectively, in patients not treated with HAART (P < 0.0001). The HR (95% CI) of death in patients on HAART was 0.5 (0.3-0.9), (P = 0.03). CONCLUSIONS The survival of HIV/HCV-co-infected patients with ESLD is extremely poor. Immunosuppression and markers of severe liver disease predict liver-related mortality in these patients. HAART seems to be associated with a reduced liver-related mortality.

Hyponatremia is an alteration in patients with advanced liver disease. Although survival is significantly reduced in patients with spontaneous development of hyponatremia, a reduced sodium concentration cannot be considered as a independent predictor of the risk for death.

Background: Liver biopsy is an invasive technique with associated major complications. There is no information on the validity of five non-invasive indexes based on routinely available parameters, estimated and validated in hepatitis C virus (HCV) monoinfected patients, in human immunodeficiency virus (HIV)/ HCV coinfected patients. Aim: To validate these predictive models of liver fibrosis in HIV/HCV coinfected patients. Patients: A total of 357 (90%) of 398 patients from five hospitals were investigated, who underwent liver biopsy and who had complete data to validate all of the models considered. Methods: The predictive accuracy of the indexes was tested by measuring areas under the receiver operating characteristic curves. Diagnostic accuracy was calculated by estimating sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values. Results: The models performed better when liver biopsies >15 mm were used as reference. In this setting, the Forns and Wai indexes, models aimed at discriminating significant fibrosis, showed PPV of 94% and 87%, respectively. Using these models, 27-34% of patients could benefit from exclusion of liver biopsy. If both models were applied sequentially, 41% of liver biopsies could be spared. The indexes aimed at predicting cirrhosis achieved NPV of up to 100%. However, they showed very low PPV. Conclusions: The diagnostic accuracy of these models was lower in HIV/HCV coinfected patients than in the validation studies performed in HCV monoinfected patients. However, simple fibrosis tests may render liver biopsy unnecessary in deciding anti-HCV treatment in over one third of patients with HIV infection and chronic hepatitis C.

Pneumococcal disease was studied prospectively to determine the risk factors associated with resistance to penicillin and other antibiotics. One hundred twelve clinically significant pneumococcal isolates were recovered from 95 patients. Approximately one-half (49.47%) of the cases were due to penicillin-resistant strains. Multivariate analysis showed that previous use of beta-lactam antibiotics (odds ratio [OR], 2.81; 95% confidence interval [CI], 0.95-8.27), alcoholism (OR, 5.22; 95% CI, 1.43-19.01), and noninvasive disease (OR, 4.53; 95% CI, 1.54-13.34) were associated with penicillin resistance, whereas intravenous drug use (OR, 0.14; 95% CI, 0.03-0.74) was not. Statistical analyses of the variables associated with resistance to multiple antibiotics detected age of younger than 5 years (OR, 16.79; 95% CI, 1.60-176.34) or of 65 years or older (OR, 4.33; 95% CI, 1.42-13.21) and previous use of beta-lactam antibiotics by patients with noninvasive disease (OR, 7.92; 95% CI, 1.84-34.06) as parameters associated with increased risk. We conclude that multivariate analysis provides clues for empirical therapy for pneumococcal infection.