Twenty Years of Hepatitis C in the Treviso District (Local Health Unit 2): Treatments, Clinical Management and Cost Analysis

Chronic hepatitis C virus (HCV) infection is a global health problem, and about 10-30% of patients develop cirrhosis or hepatocellular carcinoma several years after being infected. In past decades, treatment of HCV infection was based on peginterferon and ribavirin, which lead to a sustained virologic response (SVR) in only 50-60% of patients. Since 2014, direct acting antiviral (DAA) agents have been available. Patients administered DAA agents usually reach SVR in 12 weeks. The aim of this study was to estimate the cost analysis of these innovative drugs while also taking into account the total health expenditure for managing HCV infection. The pharmaceutical and hospitalisation databases of the Local Health Unit (ULSS2) of Treviso were retrospectively analysed between 1997 and 2016 for each HCV patient. During this twenty-year period, people affected by HCV totalled 2,949; 277 of these patients were treated with DAA and, of these, only 2% did not reach SVR. The HCV genotype 1b was the most common, accounting for 58% of the total patients. The treatment for HCV genotype 3 was associated with higher costs. The expenses for the new treatments were found to be significantly higher compared to those for the old ones (i.e., peginterferon and ribavirin). The average costs for a cycle of therapy were €8,000 and €24,000 for interferon and DAA therapy, respectively. Total health care costs associated with HCV (excluding DAA treatments) for an individual HCV infection patient were estimated to be €32,000. Our results confirm the high efficacy of DAA therapy. Furthermore, these agents improve the clinical conditions and reduce both the treatment cost and health care in patients with HCV infection

Serum estradiol levels associated with specific gene expression patterns in normal breast tissue and in breast carcinomas

Abstract Background High serum levels of estradiol are associated with increased risk of postmenopausal breast cancer. Little is known about the gene expression in normal breast tissue in relation to levels of circulating serum estradiol. Methods We compared whole genome expression data of breast tissue samples with serum hormone levels using data from 79 healthy women and 64 breast cancer patients. Significance analysis of microarrays (SAM) was used to identify differentially expressed genes and multivariate linear regression was used to identify independent associations. Results Six genes (SCGB3A1, RSPO1, TLN2, SLITRK4, DCLK1, PTGS1) were found differentially expressed according to serum estradiol levels (FDR = 0). Three of these independently predicted estradiol levels in a multivariate model, as SCGB3A1 (HIN1) and TLN2 were up-regulated and PTGS1 (COX1) was down-regulated in breast samples from women with high serum estradiol. Serum estradiol, but none of the differentially expressed genes were significantly associated with mammographic density, another strong breast cancer risk factor. In breast carcinomas, expression of GREB1 and AREG was associated with serum estradiol in all cancers and in the subgroup of estrogen receptor positive cases. Conclusions We have identified genes associated with serum estradiol levels in normal breast tissue and in breast carcinomas. SCGB3A1 is a suggested tumor suppressor gene that inhibits cell growth and invasion and is methylated and down-regulated in many epithelial cancers. Our findings indicate this gene as an important inhibitor of breast cell proliferation in healthy women with high estradiol levels. In the breast, this gene is expressed in luminal cells only and is methylated in non-BRCA-related breast cancers. The possibility of a carcinogenic contribution of silencing of this gene for luminal, but not basal-like cancers should be further explored. PTGS1 induces prostaglandin E2 (PGE2) production which in turn stimulates aromatase expression and hence increases the local production of estradiol. This is the first report studying such associations in normal breast tissue in humans

Gonadal function in male patients after treatment for malignant lymphomas, with emphasis on chemotherapy

Gonadal function was assessed in male lymphoma survivors based on serum hormone levels (LH, FSH, testosterone, SHBG), and was related to treatment, age and observation time. Male patients ⩽50 years at diagnosis treated for Hodgkin's (HL) and/or non-Hodgkin's lymphoma (NHL) at the Norwegian Radium Hospital from 1 January 1980 to 31 December 2002 were included. Five treatment groups were defined: 1: radiotherapy only and/or low gonadotoxic chemotherapy (both HL and NHL)(‘No/low'), 2: medium gonadotoxicity chemotherapy for NHL (‘med-NHL'), 3: medium gonadotoxicity chemotherapy for HL (‘med-HL'), 4: highly gonadotoxic chemotherapy for NHL (‘high-NHL'), 5: highly gonadotoxic chemotherapy for HL (‘high-HL'). Gonadal hormone levels were categorised into three groups: 1: All gonadal hormones within normal range (normal), 2: Isolated elevated FSH, with LH, SHBG and testosterone within normal range (exocrine hypogonadism), 3: Testosterone below and/or LH above normal range (endocrine hypogonadism). One hundred and forty-four (49%) of the patients had normal gonadal hormones, 60 (20%) displayed exocrine hypogonadism and almost one-third (n=90, 30%) had endocrine hypogonadism. Compared to those treated with no/low gonadotoxic chemotherapy patients from all other treatment groups had significantly elevated risk for exocrine hypogonadism. Patients from the other treatment groups, except those in the med-NHL group, also had significantly elevated risk for endocrine hypogonadism compared with the group treated with no/low gonadotoxic chemotherapy. Men aged above 50 years at survey were about five times more likely to have endocrine hypogonadism compared with those less than 40 years. Because of the adverse health effects following long-lasting endocrine hypogonadism, gonadal hormones should be assessed regularly in male lymphoma survivors, especially after treatment with alkylating agents and high-dose chemotherapy with autologous stem cell support and in male patients who are 50 years and older

Effect of e-learning program on risk assessment and pressure ulcer classification — A randomized study

Background Pressure ulcers (PUs) are a problem in health care. Staff competency is paramount to PU prevention. Education is essential to increase skills in pressure ulcer classification and risk assessment. Currently, no pressure ulcer learning programs are available in Norwegian. Objectives: Develop and test an e-learning program for assessment of pressure ulcer risk and pressure ulcer classification. Methods Design, participants and setting: Forty-four nurses working in acute care hospital wards or nursing homes participated and were assigned randomly into two groups: an e-learning program group (intervention) and a traditional classroom lecture group (control). Data was collected immediately before and after training, and again after three months. The study was conducted at one nursing home and two hospitals between May and December 2012. Analysis: Accuracy of risk assessment (five patient cases) and pressure ulcer classification (40 photos [normal skin, pressure ulcer categories I–IV] split in two sets) were measured by comparing nurse evaluations in each of the two groups to a pre-established standard based on ratings by experts in pressure ulcer classification and risk assessment. Inter-rater reliability was measured by exact percent agreement and multi-rater Fleiss kappa. A Mann–Whitney U test was used for continuous sum score variables. Results An e-learning program did not improve Braden subscale scoring. For pressure ulcer classification, however, the intervention group scored significantly higher than the control group on several of the categories in post-test immediately after training. However, after three months there were no significant differences in classification skills between the groups. Conclusion An e-learning program appears to have a greater effect on the accuracy of pressure ulcer classification than classroom teaching in the short term. For proficiency in Braden scoring, no significant effect of educational methods on learning results was detected

The prevalence, prevention and multilevel variance of pressure ulcers in Norwegian hospitals: A cross-sectional study

Background Pressure ulcers are preventable adverse events. Organizational differences may influence the quality of prevention across wards and hospitals. Objective To investigate the prevalence of pressure ulcers, patient-related risk factors, the use of preventive measures and how much of the pressure ulcer variance is at patient, ward and hospital level. Design A cross-sectional study. Setting Six of the 11 invited hospitals in South-Eastern Norway agreed to participate. Participants Inpatients ≥18 years at 88 somatic hospital wards (N = 1209). Patients in paediatric and maternity wards and day surgery patients were excluded. Methods The methodology for pressure ulcer prevalence studies developed by the European Pressure Ulcer Advisory Panel was used, including demographic data, the Braden scale, skin assessment, the location and severity of pressure ulcers and preventive measures. Multilevel analysis was used to investigate variance across hierarchical levels. Results The prevalence was 18.2% for pressure ulcer category I–IV, 7.2% when category I was excluded. Among patients at risk of pressure ulcers, 44.3% had pressure redistributing support surfaces in bed and only 22.3% received planned repositioning in bed. Multilevel analysis showed that although the dominant part of the variance in the occurrence of pressure ulcers was at patient level there was also a significant amount of variance at ward level. There was, however, no significant variance at hospital level. Conclusions Pressure ulcer prevalence in this Norwegian sample is similar to comparable European studies. At-risk patients were less likely to receive preventive measures than patients in earlier studies. There was significant variance in the occurrence of pressure ulcers at ward level but not at hospital level, indicating that although interventions for improvement are basically patient related, improvement of procedures and organization at ward level may also be important

Machine Learning-Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma

Purpose: Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS).Patients and methods: This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort).Results: In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis.Conclusion: The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI