{"doi":"10.1111/jgs.16436","title":"Changes in Predictive Performance of a Frailty Index with Availability of Clinical Domains","abstract":"OBJECTIVES: Determine the effects of missing data in frailty identification and risk prediction. DESIGN: Analysis of the National Health in Aging Trends Study. SETTING: Community. PARTICIPANTS: About 6206 older adults. MEASUREMENTS: A 41-variable frailty index (FI) was constructed with the following domains: comorbidities, activities of daily living (ADLs), instrumental activities of daily living, self-reported physical limitations, physical performance, and neuropsychiatric tests. We evaluated discrimination after removing single and multiple domains, comparing C-statistics for predicting 5-year risk of mortality and 1-year risks of disability and falls. RESULTS: The full FI yielded a mean of .18 and C-statistics of .72 (95% confidence interval, .70-.74) for mortality, .80 (.77-.82) for disability, and .66 (.64-.68) for falls. Removal of any single domain shifted the FI distribution, resulting in a mean FI ranging from .13 (removing comorbidities) to .20 (removing ADLs) and frailty prevalence (FI ≥ .25) from 16.0% to 28.7%. Among robust participants models missing ADLs misclassified most often, (19% as pre-frail). Among pre-frail and frail participants missing comorbidities misclassified most often(69.2% from pre-frail to robust, 24% from frail to pre-frail, and 4.9% from frail to robust). Removal of any single domain minimally changed C-statistics: mortality, .71-.73; disability, .79-.80; and falls, .64-.66. Removing neuropsychiatric testing and physical performance yielded comparable C-statistics of .70, .78, and .66 for mortality, ADLs, and falls, respectively. However, removal of three or four domains based on likely availability decreased C-statistics for mortality (.69, .66),disability (.75, .70), and falls (.64, .63), respectively. CONCLUSION: While FI discrimination is robust to missing information in any single domain, risk prediction is affected by absence of multiple domains. This work informs the application of FI as a clinical and research tool. J Am Geriatr Soc 68:1771-1777, 2020.","journal":"Journal of the American Geriatrics Society","year":2020,"id":66583,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":50,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7484,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":270249,"name":"Ellen P. McCarthy","orcid":"0000-0003-1705-6249","position":1,"is_corresponding":false},{"id":312977,"name":"Susan L. Mitchell","orcid":null,"position":2,"is_corresponding":false},{"id":270250,"name":"Dae Hyun Kim","orcid":"0000-0001-7290-6838","position":3,"is_corresponding":false},{"id":270247,"name":"Sandra Shi","orcid":"0000-0001-6801-5602","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-18T21:14:50.590588Z","pmid":"32274807","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}