Objective: To analyze the performance of adenosine deaminase in pleural fluid combined with other parameters routinely measured in clinical practice and assisted by machine learning algorithms for the diagnosis of pleural tuberculosis in a low prevalence setting, and secondly, to identify effusions that are non-tuberculous and most likely malignant. Patients and methods: We prospectively analyzed 230 consecutive patients diagnosed with lymphocytic exudative pleural effusion from March 2013 to June 2020. Diagnosis according to the composite reference standard was achieved in all cases. Pre-test probability of pleural tuberculosis was 3.8% throughout the study period. Parameters included were: levels of adenosine deaminase, pH, glucose, proteins, and lactate dehydrogenase, red and white cell counts and lymphocyte percentage in pleural fluid, as well as age. We tested six different machine learning-based classifiers to categorize the patients. Two different classifications were performed: a) tuberculous/non-tuberculous and b) tuberculous/malignant/other. Results: Out of a total of 230 patients with pleural effusion included in the study, 124 were diagnosed with malignant effusion and 44 with pleural tuberculosis, while 62 were given other diagnoses. In the tuberculous/non-tuberculous classification, and taking into account the validation predictions, the support vector machine yielded the best result: an AUC of 0.98, accuracy of 97%, sensitivity of 91%, and specificity of 98%, whilst in the tuberculous/malignant/other classification, this type of classifier yielded an overall accuracy of 80%. With this three-class classifier, the same sensitivity and specificity was achieved in the tuberculous/other classification, but it also allowed the correct classification of 90% of malignant cases. Conclusion: The level of adenosine deaminase in pleural fluid together with cell count, other routine biochemical parameters and age, combined with a machine-learning approach, is suitable for the diagnosis of pleural tuberculosis in a low prevalence scenario. Secondly, non-tuberculous effusions that are suspected to be malignant may also be identified with adequate accuracy.

Background When healthcare budgets are exogenous, cost-effectiveness thresholds (CETs) used to inform funding decisions should represent the health opportunity cost (HOC) of such funding decisions, but HOC-based CET estimates have not been available until recently. In recent years, empirical HOC-based CETs for multiple countries have been published, but the use of these CETs in the cost-effectiveness analysis (CEA) literature has not been investigated. Analysis of the use of HOC-based CETs by researchers undertaking CEAs in countries with different decision-making contexts will provide valuable insights to further understand barriers and facilitators to the acceptance and use of HOC-based CETs. Objectives We aimed to identify the CET values used to interpret the results of CEAs published in the scientific literature before and after the publication of jurisdiction-specific empirical HOC-based CETs in four countries. Methods We undertook a scoping review of CEAs published in Spain, Australia, the Netherlands and South Africa between 2016 (2014 in Spain) and 2020. CETs used before and after publication of HOC estimates were recorded. We conducted logit regressions exploring factors explaining the use of HOC values in identified studies and linear models exploring the association of the reported CET value with study characteristics and results. Results 1171 studies were included in this review (870 CEAs and 301 study protocols). HOC values were cited in 28% of CEAs in Spain and in 11% of studies conducted in Australia, but they were not referred to in CEAs undertaken in the Netherlands and South Africa. Regression analyses on Spanish and Australian studies indicate that more recent studies, studies without a conflict of interest and studies estimating an incremental cost-effectiveness ratio (ICER) below the HOC value were more likely to use the HOC as a threshold reference. In addition, we found a small but significant impact indicating that for every dollar increase in the estimated ICER, the reported CET increased by US$0.015. Based on the findings of our review, we discuss the potential factors that might explain the lack of adoption of HOC-based CETs in the empirical CEA literature. Conclusions The adoption of HOC-based CETs by identified published CEAs has been uneven across the four analysed countries, most likely due to underlying differences in their decision-making processes. Our results also reinforce a previous finding indicating that CETs might be endogenously selected to fit authors’ conclusions.

Objectives: This study assesses the cost-effectiveness of adding sickle-cell disease (SCD) to the Spanish newborn screening (NBS) program, and explores the sensitivity of the results to key model parameters. Methods: A discrete event simulation model was developed that compared NBS for SCD versus clinical detection. The model followed a simulated cohort of newborns for 10 years, and estimated the impact in costs and life expectancy of prophylactic treatments established after early detection. Probabilistic, one-way and two-way sensitivity analysis were performed. Results: NBS was found to be more costly and more effective than clinical detection of SCD. The estimated incremental cost per life year (LY) gained was 34,169.46 €/LY. This result was very sensitive to the cost of the screening test, the birth prevalence and the proportion of severe cases among the affected children. Conclusions: There is uncertainty regarding the cost-effectiveness of NBS for SCD in the Spanish context. Our base case estimate of the cost per LY gained lies near the 30,000€/LY commonly cited in Spain.