SA3 Utility of Artificial Intelligence in Systematic Literature Reviews for Health Technology Assessment Submissions

Cichewicz, A; Burnett, H; Huelin, R; Kadambi, A

doi:10.1016/j.jval.2022.04.1669

Cited by 1 publication

(1 citation statement)

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“…It works by training itself multiple times using the previously screened references in a 10-fold k-fold crossvalidation method [61] and allows for double-checking exclusion. This tool's false exclusion rate [62,63] is comparable with human performance [64-66] and has thus been suggested as a second screener alternative [67][68][69][70][71]. The remaining studies' full texts were independently screened by two authors (NB and VT) using the pre-established eligibility criteria.…”

Section: Review Processmentioning

confidence: 99%

Agreement between heart rate variability - derived vs. ventilatory and lactate thresholds A systematic review with meta-analyses

Tanner,

Millet,

Bourdillon

2024

Preprint

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Background This systematic review with meta-analyses aims to assess the overall validity of the first and second heart rate variability - derived threshold (HRVT1 and HRVT2, respectively) by computing global effect sizes for agreement and correlation between HRVTs and reference – lactate and ventilatory (LT-VTs) – thresholds. Furthermore, this review aims to assess the impact of subjects’ characteristics, HRV methods, and study protocols on the agreement and correlation between LT-VTs and HRVTs. Methods Systematic computerised searches for studies determining HRVTs during incremental exercise in humans were conducted between March and August 2023 using electronic databases (Cochrane Library, EBSCO, Embase.com, Google Scholar, Ovid, ProQuest, PubMed, Scopus, SportDiscus, Virtual Health Library and Web of science). The agreements and correlations meta-analyses were conducted using a random-effect model. Causes of heterogeneity were explored by subgroup analysis and meta-regression with subjects’ characteristics, incremental exercise protocols and HRV methods variables and compared using statistical tests for interaction. The methodological quality was assessed using QUADAS-2 and STARDHRV tools. The risk of bias was assessed by funnel plots, fail-safe N test, Egger's test of the intercept and the Begg and Mazumdar rank correlation test. Results Fifty included studies (1’160 subjects) assessed 314 agreements (95 for HRVT1, 219 for HRVT2) and 246 correlations (82 for HRVT1, 164 for HRVT2) between LT-VTs and HRVTs. The standardized mean differences were trivial between HRVT1 and LT1-VT1 (SMD = 0.08, 95% CI -0.04–0.19, n = 22) and between HRVT2 and LT2-VT2 (SMD = -0.06, 95% CI -0.15–0.03, n = 42). The correlations were very strong between HRVT1 and LT1-VT1 (r = 0.85, 95% CI 0.75–0.91, n = 22), and between HRVT2 and LT2-VT2 (r = 0.85, 95% CI 0.80–0.89, n = 41). Moderator analyses showed that HRVT1 better agreed with LT1 and HRVT2 with VT2. Moreover, subjects’ characteristics, type of ergometer, or initial and incremental workload had no impact on HRVTs determination. Simple visual HRVT determinations were reliable, as well as both frequency and non-linear HRV indices. Finally, short increment yielded better HRVT2 determination. Conclusion HRVTs showed trivial differences and very strong correlations with LT-VTs and might thus serve as surrogate thresholds and, consequently for the determination of the intensity zones. However, heterogeneity across study results and differences in agreement when comparing separately LTs and VTs to HRVTs were observed, underscoring the need for further research. These results emphasize the usefulness of HRVTs as promising, accessible, and cost-effective means for exercise and clinical prescription purposes

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Section: Review Processmentioning

confidence: 99%