Cost-Effectiveness of Hepatocellular Carcinoma Surveillance Strategies in Patients With Compensated Liver Cirrhosis in the United Kingdom.

OBJECTIVES: This study aimed to evaluate the cost-effectiveness (CE) of 4 hepatocellular carcinoma (HCC) surveillance strategies in the United Kingdom, the GAAD algorithm, which combines Gender (biological sex) and Age with Elecsys® biomarker assays, alpha-fetoprotein (AFP) and protein induced by vitamin K absence-II (previously Des-γ-carboxy prothrombin); ultrasound (US); US + AFP and GAAD + US. METHODS: A de novo microsimulation state-transition Markov model was developed in Microsoft Excel® from the perspective of the United Kingdom National Health Service to calculate life-years, quality-adjusted life-years (QALYs), costs, incremental CE ratios, and net monetary benefits. Parameters were sourced from peer-reviewed published literature, national guidelines, and public cost databases. Sensitivity and scenario analyses were performed to evaluate the impact of parameter and structural uncertainty on the results. RESULTS: In a simulated cohort of 100 000 patients, discounted costs and QALYs per patient were £8663 and 6·066 for US, £9095 and 6·076 for US + AFP, £8719 and 6·078 for GAAD alone, and £9114 and 6·086 for GAAD + US. At a CE threshold of £20 000/QALY, GAAD was the most cost-effective strategy; however, although most costly, GAAD + US was the most clinically effective. Sensitivity and scenario analyses indicated that HCC incidence along with costs associated with diagnostic performance influence CE. CONCLUSION: Considering the cost of US and low incidence of HCC in the United Kingdom, this study suggests that GAAD alone or in combination with US are cost-effective surveillance strategies compared with US and US + AFP. Although GAAD + US showed the highest QALY increase, GAAD alone is considered preferable regarding CE; however, better performance estimates for GAAD + US are needed to confirm.

The cost-effectiveness of NT-proBNP for assessment of suspected acute heart failure in the emergency department.

AIMS: When relying on clinical assessment alone, an estimated 22% of acute heart failure (AHF) patients are missed, so clinical guidelines recommend the use of N-terminal pro-B-type natriuretic peptide (NT-proBNP) for AHF diagnosis. Since publication of these guidelines, there has been poor uptake of NT-proBNP testing in part due to concerns over excessive false positive referrals resulting from the low specificity of a single 'rule-out' threshold of <300 pg/mL. Low specificity can be mitigated by the addition of age-specific 'rule-in' NT-proBNP thresholds. METHODS AND RESULTS: A theoretical hybrid decision tree/semi-Markov model was developed, combining global trial and audit data to evaluate the cost-effectiveness of NT-proBNP testing using age-specific rule-in/rule-out (RI/RO) thresholds, compared with NT-proBNP RO only and with clinical decision alone (CDA). Cost-effectiveness was measured as the incremental cost per quality-adjusted life year (QALY) gained and incremental net health benefit. In the base case, using UK-specific inputs, NT-proBNP RI/RO was associated with both greater QALYs and lower costs than CDA. At a willingness-to-pay threshold of £20 000/QALY, NT-proBNP RO was also cost-effective compared with CDA [incremental cost-effectiveness ratio (ICER) of £8322/QALY], but not cost-effective vs. RI/RO (ICER of £64 518/QALY). Overall, NT-proBNP RI/RO was the most cost-effective strategy. Sensitivity and scenario analyses were undertaken; the conclusions were not impacted by plausible variations in parameters, and similar conclusions were obtained for the Netherlands and Spain. CONCLUSIONS: An NT-proBNP strategy that combines an RO threshold with age-specific RI thresholds provides a cost-effective alternative to the currently recommended NT-proBNP RO only strategy, achieving greater diagnostic specificity with minimal reduction in sensitivity and thus reducing unnecessary echocardiograms and hospital admissions.

Predicting and forecasting the impact of local outbreaks of COVID-19: use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity.

BACKGROUND: The world is experiencing local/regional hotspots and spikes in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19 disease. We aimed to formulate an applicable epidemiological model to accurately predict and forecast the impact of local outbreaks of COVID-19 to guide the local healthcare demand and capacity, policy-making and public health decisions. METHODS: The model utilized the aggregated daily COVID-19 situation reports (including counts of daily admissions, discharges and bed occupancy) from the local National Health Service (NHS) hospitals and COVID-19-related weekly deaths in hospitals and other settings in Sussex (population 1.7 million), Southeast England. These data sets corresponded to the first wave of COVID-19 infections from 24 March to 15 June 2020. A novel epidemiological predictive and forecasting model was then derived based on the local/regional surveillance data. Through a rigorous inverse parameter inference approach, the model parameters were estimated by fitting the model to the data in an optimal sense and then subsequent validation. RESULTS: The inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national data sets by Biggerstaff M, Cowling BJ, Cucunubá ZM et al. (Early insights from statistical and mathematical modeling of key epidemiologic parameters of COVID-19, Emerging infectious diseases. 2020;26(11)). We validate the predictive power of our model by using a subset of the available data and comparing the model predictions for the next 10, 20 and 30 days. The model exhibits a high accuracy in the prediction, even when using only as few as 20 data points for the fitting. CONCLUSIONS: We have demonstrated that by using local/regional data, our predictive and forecasting model can be utilized to guide the local healthcare demand and capacity, policy-making and public health decisions to mitigate the impact of COVID-19 on the local population. Understanding how future COVID-19 spikes/waves could possibly affect the regional populations empowers us to ensure the timely commissioning and organization of services. The flexibility of timings in the model, in combination with other early-warning systems, produces a time frame for these services to prepare and isolate capacity for likely and potential demand within regional hospitals. The model also allows local authorities to plan potential mortuary capacity and understand the burden on crematoria and burial services. The model algorithms have been integrated into a web-based multi-institutional toolkit, which can be used by NHS hospitals, local authorities and public health departments in other regions of the UK and elsewhere. The parameters, which are locally informed, form the basis of predicting and forecasting exercises accounting for different scenarios and impacts of COVID-19 transmission.