The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review.

BACKGROUND: Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear. METHODS: We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines. RESULTS: From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%-100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%-100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms. CONCLUSIONS: Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported.

Optimising the use of SARC-F for the identification of muscle weakness by considering alternative cut-points: findings from the Newcastle SarcScreen project.

PURPOSE: We assessed the impact of applying different SARC-F cut-points for the identification of muscle weakness in an older clinical population. METHODS: We included 159 men and 311 women aged 56-98 years who had completed the SARC-F questionnaire and had their maximum grip strength measured at an Older People's Medicine Day Unit. We applied cut-points of ≥ 4, 3 and 2 to SARC-F and tested agreement with muscle weakness (grip strength < 27kg men, < 16kg women) in analyses stratified by sex and obesity status. RESULTS: Prevalence of muscle weakness was 86.8% and 82.6% in men and women, respectively. Sensitivity of the SARC-F increased at lower cut-points (e.g. 81% for ≥ 4 vs 97% for ≥ 2 in women). There was typically greater sensitivity among women than men and among those classified as obese vs non-obese. CONCLUSIONS: These findings suggest that different cut-points may be required to optimise the utility of SARC-F for identifying muscle weakness in different patient sub-groups.