Inclusion of non-medical interventions in model-based economic evaluations for tuberculosis: A scoping review.

BACKGROUND: The economic evaluation of health interventions is important in priority setting. Several guidance documents exist to support the conduct of economic evaluations, however, there is limited guidance for the evaluation of non-medical interventions. For tuberculosis (TB), where equity-deserving groups are disproportionately impacted, assessing interventions aimed at addressing social risk factors is necessary to effectively reduce TB burden. OBJECTIVE: This scoping review seeks to assess the existing literature on model-based economic evaluations of TB interventions to gauge the extent to which non-medical interventions have been evaluated in low-TB-incidence jurisdictions. As a secondary objective, this review aims to characterize key features of existing economic evaluations of medical and non-medical interventions. METHODS: A literature search was conducted in the grey literature and MEDLINE, Embase, EconLit, and PsychINFO databases to September 6, 2022 following the Arksey and O'Malley framework. Eligible articles were those that used decision-analytic modeling for economic evaluation of TB interventions in low-TB-incidence jurisdictions. RESULTS: This review identified 127 studies that met the inclusion criteria; 11 focused on prevention, 73 on detection, and 43 on treatment of TB. Only three studies (2%) evaluated non-medical interventions, including smoking reduction strategies, improving housing conditions, and providing food vouchers. All three non-medical intervention evaluations incorporated TB transmission and robust uncertainty analysis into the evaluation. The remainder of the studies evaluated direct medical interventions, eight of which were focused on specific implementation components (e.g., video observed therapy) which shared similar methodological challenges as the non-medical interventions. The majority of remaining evaluated medical interventions were focused on comparing various screening programs (e.g., immigrant screening program) and treatment regimens. CONCLUSIONS: This scoping review identified a gap in literature in the evaluation of non-medical TB interventions. However, the identified articles provided useful examples of how economic modeling can be used to explore non-traditional interventions using existing economic evaluation methods.

Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review.

Corneal and ocular surface diseases (OSDs) carry significant psychosocial and economic burden worldwide. We set out to review the literature on the application of artificial intelligence (AI) and bioinformatics for analysis of biofluid biomarkers in corneal and OSDs and evaluate their utility in clinical decision making. MEDLINE, EMBASE, Cochrane and Web of Science were systematically queried for articles using AI or bioinformatics methodology in corneal and OSDs and examining biofluids from inception to August 2021. In total, 10,264 articles were screened, and 23 articles consisting of 1058 individuals were included. Using various AI/bioinformatics tools, changes in certain tear film cytokines that are proinflammatory such as increased expression of apolipoprotein, haptoglobin, annexin 1, S100A8, S100A9, Glutathione S-transferase, and decreased expression of supportive tear film components such as lipocalin-1, prolactin inducible protein, lysozyme C, lactotransferrin, cystatin S, and mammaglobin-b, proline rich protein, were found to be correlated with pathogenesis and/or treatment outcomes of dry eye, keratoconus, meibomian gland dysfunction, and Sjögren's. Overall, most AI/bioinformatics tools were used to classify biofluids into diseases subgroups, distinguish between OSD, identify risk factors, or make predictions about treatment response, and/or prognosis. To conclude, AI models such as artificial neural networks, hierarchical clustering, random forest, etc., in conjunction with proteomic or metabolomic profiling using bioinformatics tools such as Gene Ontology or Kyoto Encylopedia of Genes and Genomes pathway analysis, were found to inform biomarker discovery, distinguish between OSDs, help define subgroups with OSDs and make predictions about treatment response in a clinical setting.

Prognostic Features of Preoperative OCT in Retinal Detachments: A Systematic Review and Meta-analysis.

TOPIC: To evaluate the prognostic association between preoperative features seen on OCT imaging and postoperative visual acuity (VA) outcomes in rhegmatogenous retinal detachments (RRDs). CLINICAL RELEVANCE: Currently, there is limited literature on the prognostic value of preoperative RRD OCT features. METHODS: A literature search was conducted on Ovid MEDLINE, Ovid Embase, and Cochrane CENTRAL from inception to September 15, 2022. A meta-analysis was performed using a random-effects model. Quality of studies and evidence were assessed using the Joanna Briggs Institute tools and the Grading of Recommendations, Assessment, Development and Evaluation framework, respectively. RESULTS: A total of 1671 eyes of 1670 patients from 29 observational studies were included. Of these, 89% of eyes had a macula-off RRD at presentation. The mean average duration of detachment was 15 ± 10 days. Most eyes (62%) underwent pars plana vitrectomy. Six preoperative OCT features were analyzed: height of retinal detachment (HRD) at the fovea, central macular thickness (CMT), disruption of the ellipsoid zone (EZ) and/or external limiting membrane (ELM), intraretinal cystic cavities (ICCs), outer retinal corrugations (ORCs), and macular detachment. A greater HRD was weakly associated with postoperative VA (Pearson correlation r = 0.35; 95% confidence interval [CI], 0.20-0.48; P < 0.01), and there was no change in this association throughout the postoperative follow-up period. The CMT was not associated with postoperative VA. Eyes with disruption of the EZ and/or ELM had a postoperative VA worse by 0.35 logarithm of the minimum angle of resolution (logMAR) (95% CI, 0.15-0.54; P < 0.01) or 3 Snellen lines. Eyes with ICCs had a postoperative VA worse by 0.14 logMAR (95% CI, 0.01-0.26; P < 0.01) or 2 Snellen lines. Eyes with ORCs did not have a significantly different postoperative VA than eyes without ORCs. Eyes with macular detachment had a postoperative VA worse by 0.15 logMAR (95% CI, -0.31 to 0.00; P = 0.02) or 2 Snellen lines. Overall, the quality of studies ranged from moderate to good (73%-100%). All associations had a low quality of evidence, with CMT being of very low quality. CONCLUSION: Despite the low quality of evidence, a greater HRD, disruption of the EZ and/or ELM, presence of ICCs, and macular detachment were associated with a poor postoperative VA. We propose a standardized nomenclature for consistency and accuracy in reporting preoperative RRD OCT features for future studies. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Emerging applications of bioinformatics and artificial intelligence in the analysis of biofluid markers involved in retinal occlusive diseases: a systematic review.

PURPOSE: To review the literature on the application of bioinformatics and artificial intelligence (AI) for analysis of biofluid biomarkers in retinal vein occlusion (RVO) and their potential utility in clinical decision-making. METHODS: We systematically searched MEDLINE, Embase, Cochrane, and Web of Science databases for articles reporting on AI or bioinformatics in RVO involving biofluids from inception to August 2021. Simple AI was categorized as logistics regressions of any type. Risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Tools. RESULTS: Among 10,264 studies screened, 14 eligible articles, encompassing 578 RVO patients, met the inclusion criteria. The use and reporting of AI and bioinformatics was heterogenous. Four articles performed proteomic analyses, two of which integrated AI tools such as discriminant analysis, probabilistic clustering, and string pathway analysis. A metabolomic study used AI tools for clustering, classification, and predictive modeling such as orthogonal partial least squares discriminant analysis. However, most studies used simple AI (n = 9). Vitreous humor sample levels of interleukin-6 (IL-6), vascular endothelial growth factor (VEGF), and aqueous humor levels of intercellular adhesion molecule-1 and IL-8 were implicated in the pathogenesis of branch RVO with macular edema. IL-6 and VEGF may predict visual acuity after intravitreal injections or vitrectomy, respectively. Metabolomics and Kyoto Encyclopedia of Genes and Genomes enrichment analysis identified the metabolic signature of central RVO to be related to lower aqueous humor concentration of carbohydrates and amino acids. Risk of bias was low or moderate for included studies. CONCLUSION: Bioinformatics has applications for analysis of proteomics and metabolomics present in biofluids in RVO with AI for clinical decision-making and advancing the future of RVO precision medicine. However, multiple limitations such as simple AI use, small sample volume, inconsistent feasibility of office-based sampling, lack of longitudinal follow-up, lack of sampling before and after RVO, and lack of healthy controls must be addressed in future studies.

Cost-Effectiveness of Preoperative Topical Antibiotic Prophylaxis for Endophthalmitis Following Cataract Surgery.

PURPOSE: To determine the cost-effectiveness of preoperative topical antibiotic prophylaxis for the prevention of endophthalmitis following cataract surgery. DESIGN: Cost-effectiveness analysis using a decision-analytic microsimulation model. METHODS: Preoperative topical antibiotic prophylaxis vs no-prophylaxis costs and effects were projected over a life-time horizon for a simulated cohort of 500 000 adult patients (≥18 years old) requiring cataract surgery in theoretical surgical centers in the United States. Efficacy and cost (2021 US dollars) values were obtained from the literature and discounted at 3% per year. RESULTS: Based on inputted parameters, the mean incidence of endophthalmitis following cataract surgery for preoperative topical antibiotic prophylaxis vs no-prophylaxis was 0.034% (95% CI 0%-0.2%) and 0.042% (95% CI 0%-0.3%), respectively-an absolute risk reduction of 0.008%. The mean life-time costs for cataract surgery with prophylaxis and no-prophylaxis were $2486.67 (95% CI $2193.61-$2802.44) and $2409.03 (95% CI $2129.94-$2706.69), respectively. The quality-adjusted life-years (QALYs) associated with prophylaxis and no-prophylaxis were 10.33495 (95% CI 7.81629-12.38158) and 10.33498 (95% CI 7.81284-12.38316), respectively. Assuming a cost-effectiveness criterion of ≤$50 000 per QALY gained, the threshold analyses indicated that prophylaxis would be cost-effective if the incidence of endophthalmitis after cataract surgery was greater than 5.5% or if the price of the preoperative topical antibiotic prophylaxis was less than $0.75. CONCLUSIONS: General use of preoperative topical antibiotic prophylaxis is not cost-effective compared with no-prophylaxis for the prevention of endophthalmitis following cataract surgery. Preoperative topical antibiotic prophylaxis, however, would be cost-effective at a higher incidence of endophthalmitis and/or a substantially lower price for prophylaxis.

The Application of Artificial Intelligence in the Analysis of Biomarkers for Diagnosis and Management of Uveitis and Uveal Melanoma: A Systematic Review.

Purpose: This study aims to identify the available literature describing the utilization of artificial intelligence (AI) as a clinical tool in uveal diseases. Methods: A comprehensive literature search was conducted in 5 electronic databases, finding studies relating to AI and uveal diseases. Results: After screening 10,258 studies,18 studies met the inclusion criteria. Uveal melanoma (44%) and uveitis (56%) were the two uveal diseases examined. Ten studies (56%) used complex AI, while 13 studies (72%) used regression methods. Lactate dehydrogenase (LDH), found in 50% of studies concerning uveal melanoma, was the only biomarker that overlapped in multiple studies. However, 94% of studies highlighted that the biomarkers of interest were significant. Conclusion: This study highlights the value of using complex and simple AI tools as a clinical tool in uveal diseases. Particularly, complex AI methods can be used to weigh the merit of significant biomarkers, such as LDH, in order to create staging tools and predict treatment outcomes.