Should patients with abnormal liver function tests in primary care be tested for chronic viral hepatitis: cost minimisation analysis based on a comprehensively tested cohort

Background Liver function tests (LFTs) are ordered in large numbers in primary care, and the Birmingham and Lambeth Liver Evaluation Testing Strategies (BALLETS) study was set up to assess their usefulness in patients with no pre-existing or self-evident liver disease. All patients were tested for chronic viral hepatitis thereby providing an opportunity to compare various strategies for detection of this serious treatable disease. Methods This study uses data from the BALLETS cohort to compare various testing strategies for viral hepatitis in patients who had received an abnormal LFT result. The aim was to inform a strategy for identification of patients with chronic viral hepatitis. We used a cost-minimisation analysis to define a base case and then calculated the incremental cost per case detected to inform a strategy that could guide testing for chronic viral hepatitis. Results Of the 1,236 study patients with an abnormal LFT, 13 had chronic viral hepatitis (nine hepatitis B and four hepatitis C). The strategy advocated by the current guidelines (repeating the LFT with a view to testing for specific disease if it remained abnormal) was less efficient (more expensive per case detected) than a simple policy of testing all patients for viral hepatitis without repeating LFTs. A more selective strategy of viral testing all patients for viral hepatitis if they were born in countries where viral hepatitis was prevalent provided high efficiency with little loss of sensitivity. A notably high alanine aminotransferase (ALT) level (greater than twice the upper limit of normal) on the initial ALT test had high predictive value, but was insensitive, missing half the cases of viral infection. Conclusions Based on this analysis and on widely accepted clinical principles, a "fast and frugal" heuristic was produced to guide general practitioners with respect to diagnosing cases of viral hepatitis in asymptomatic patients with abnormal LFTs. It recommends testing all patients where a clear clinical indication of infection is present (e.g. evidence of intravenous drug use), followed by testing all patients who originated from countries where viral hepatitis is prevalent, and finally testing those who have a notably raised ALT level (more than twice the upper limit of normal). Patients not picked up by this efficient algorithm had a risk of chronic viral hepatitis that is lower than the general population

Differences in pregnancy outcomes in donor egg frozen embryo transfer (FET) cycles following preimplantation genetic screening (PGS): a single center retrospective study

PURPOSE: This study aims to test the hypothesis, in a single-center retrospective analysis, that live birth rates are significantly different when utilizing preimplantation genetic screening (PGS) compared to not utilizing PGS in frozen–thawed embryo transfers in our patients that use eggs from young, anonymous donors. The question therefore arises of whether PGS is an appropriate intervention for donor egg cycles. METHODS: Live birth rates per cycle and live birth rates per embryo transferred after 398 frozen embryo transfer (FET) cycles were examined from patients who elected to have PGS compared to those who did not. Blastocysts derived from donor eggs underwent trophectoderm biopsy and were tested for aneuploidy using array comparative genomic hybridization (aCGH) or next-generation sequencing (NGS), then vitrified for future use (test) or were vitrified untested (control). Embryos were subsequently warmed and transferred into a recipient or gestational carrier uterus. Data was analyzed separately for single embryo transfer (SET), double embryo transfer (DET), and for own recipient uterus and gestational carrier (GC) uterus recipients. RESULTS: Rates of implantation of embryos leading to a live birth were significantly higher in the PGS groups transferring two embryos (DET) compared to the no PGS group (GC, 72 vs. 56 %; own uterus, 60 vs. 36 %). The live birth implantation rate in the own uterus group for SET was higher in the PGS group compared to the control (58 vs. 36 %), and this almost reached significance but the live birth implantation rate for the SET GC group remained the same for both tested and untested embryos. Live births per cycle were nominally higher in the PGS GC DET and own uterus SET and DET groups compared to the non-PGS embryo transfers. These differences almost reached significance. The live birth rate per cycle in the SET GC group was almost identical. CONCLUSIONS: Significant differences were noted only for DET; however, benefits need to be balanced against risks associated with multiple pregnancies. Results observed for SET need to be confirmed on larger series and with randomized cohorts

The Use of Health State Utility Values In Decision Models

Methodological issues of how to use health state utility values (HSUVs) in decision models arise frequently, including the most appropriate evidence to use as the baseline (e.g. the baseline HSUVs associated with avoiding a particular health condition or event), how to capture changes due to adverse events and how to appropriately capture uncertainty in progressive conditions where the expected change in quality of life is likely to be monotonically decreasing over time. As preference-based measures provide different values when collected from the same patient, it is important to ensure that all HSUVs used within a single model are obtained from the same instrument where ever possible. When people enter the model without the condition of interest (e.g. primary prevention of cardiovascular disease, screening or vaccination programmes), appropriate age- and gender-adjusted HSUVs from people without the particular condition should be used as the baseline. General population norms may be used as a proxy if the exact condition-specific evidence is not available. Individual discrete health states should be used for serious adverse reactions to treatment and the corresponding HSUVs sourced as normal. Care should be taken to avoid double counting when capturing the effects for both less severe adverse reactions (e.g. itchy skin rash or dry cough) and more severe adverse events (e.g. fatigue in oncology). Transparency in reporting standards for both the justification of the evidence used and any ‘adjustments’ is important to increase readers’ confidence that the evidence used is the most appropriate available

Quality of life utility values for hereditary haemochromatosis in Australia

Background: Hereditary hemochromatosis (HH) is a common autosomal recessive disorder amongst persons of northern European heritage. If untreated, iron accumulates in parenchymal tissues causing morbidity and mortality. As diagnosis often follows irreversible organ damage, screening programs have been suggested to increase early diagnosis. A lack of economic evidence has been cited as a barrier to establishing such a program. Previous analyses used poorly estimated utility values. This study sought to measure utilities directly from people with HH in Australia. Methods: Volunteers with HH were recruited to complete a web-based survey. Utility was assessed using the Assessment of Quality of Life 4D (AQOL-4D) instrument. Severity of HH was graded into four categories. Multivariable regression analysis was performed to identify parameters associated with HSUV. Results: Between November 2013 and November 2014, 221 people completed the survey. Increasing severity of HH was negatively associated with utility. Mean (standard deviation) utilities were 0.76 (0.21), 0.81 (0.18), 0.60 (0.27), and 0.50 (0.27) for categories 1-4 HH respectively. Lower mean utility was found for symptomatic participants (categories 3 and 4) compared with asymptomatic participants (0.583 v. 0.796). Self-reported HH-related symptoms were negatively associated with HSUV (r = -0.685). Conclusions: Symptomatic stages of HH and presence of multiple self-reported symptoms were associated with decreasing utility. Previous economic analyses have used higher utilities which likely resulted in underestimates of the cost effectiveness of HH interventions. The utilities reported in this paper are the most robust available, and will contribute to improving the validity of future economic models for HH

Is Meta-Analysis for Utility Values Appropriate Given the Potential Impact Different Elicitation Methods Have on Values?

A growing number of published articles report estimates from meta-analysis or meta-regression on health state utility values (HSUVs), with a view to providing input into decision-analytic models. Pooling HSUVs is problematic because of the fact that different valuation methods and different preference-based measures (PBMs) can generate different values on exactly the same clinical health state. Existing meta-analyses of HSUVs are characterised by high levels of heterogeneity, and meta-regressions have identified significant (and substantial) impacts arising from the elicitation method used. The use of meta-regression with few utility values and inclusion criteria that extend beyond the required utility value has not helped. There is the potential to explore greater use of mapping between different PBMs and valuation methods prior to data synthesis, which could support greater use of pooling values. Researchers wishing to populate decision-analytic models have a responsibility to incorporate all high-quality evidence available. In relation to HSUVs, greater understanding of the differences between different methods and greater consistency of methodology is required before this can be achieved

Health state utilities associated with attributes of treatments for hepatitis C

BACKGROUND: Cost-utility analyses are frequently conducted to compare treatments for hepatitis C, which are often associated with complex regimens and serious adverse events. Thus, the purpose of this study was to estimate the utility associated with treatment administration and adverse events of hepatitis C treatments. DESIGN: Health states were drafted based on literature review and clinician interviews. General population participants in the UK valued the health states in time trade-off (TTO) interviews with 10- and 1-year time horizons. The 14 health states described hepatitis C with variations in treatment regimen and adverse events. RESULTS: A total of 182 participants completed interviews (50 % female; mean age = 39.3 years). Utilities for health states describing treatment regimens without injections ranged from 0.80 (1 tablet) to 0.79 (7 tablets). Utilities for health states describing oral plus injectable regimens were 0.77 (7 tablets), 0.75 (12 tablets), and 0.71 (18 tablets). Addition of a weekly injection had a disutility of −0.02. A requirement to take medication with fatty food had a disutility of −0.04. Adverse events were associated with substantial disutilities: mild anemia, −0.12; severe anemia, −0.32; flu-like symptoms, −0.21; mild rash, −0.13; severe rash, −0.48; depression, −0.47. One-year TTO scores were similar to these 10-year values. CONCLUSIONS: Adverse events and greater treatment regimen complexity were associated with lower utility scores, suggesting a perceived decrease in quality of life beyond the impact of hepatitis C. The resulting utilities may be used in models estimating and comparing the value of treatments for hepatitis C. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10198-014-0649-6) contains supplementary material, which is available to authorized users

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE: To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES: PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION: Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS: 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity