{"doi":"10.1093/gerona/glaa237","title":"Association Between the Multidimensional Prognostic Index and Mortality During 15 Years of Follow-up in the InCHIANTI Study","abstract":"BACKGROUND: Multidimensional Prognostic Index (MPI) is recognized as a prognostic tool in hospitalized patients, but data on the value of MPI in community-dwelling older persons are limited. Using data from a representative cohort of community-dwelling persons, we tested the hypothesis that MPI explains mortality during 15 years of follow-up. METHODS: A standardized comprehensive geriatric assessment was used to calculate the MPI and to categorize participants in low-, moderate-, and high-risk classes. The results were reported as hazard ratios (HRs) and the accuracy was evaluated with the area under the curve (AUC), with 95% confidence intervals (CIs) and the C-index. We also reported the median survival time by standard age groups. RESULTS: All 1453 participants (mean age 68.9 years, women = 55.8%) enrolled in the InCHIANTI study at baseline were included. Compared to low-risk group, participants in moderate (HR = 2.10; 95% CI: 1.73-2.55) and high-risk MPI group (HR = 4.94; 95% CI: 3.91-6.24) had significantly higher mortality risk. The C-index of the model containing age, sex, and MPI was 82.1, indicating a very good accuracy of this model in explaining mortality. Additionally, the time-dependent AUC indicated that the accuracy of the model incorporating MPI to age and sex was excellent (>85.0) during the whole follow-up period. Compared to participants in the low-risk MPI group across different age groups, those in moderate- and high-risk groups survived 2.9-7.0 years less and 4.3-8.9 years less, respectively. CONCLUSIONS: In community-dwelling individuals, higher MPI values are associated with higher risk of all-cause mortality with a dose-response effect.","journal":"The Journals of Gerontology Series A","year":2020,"id":105718,"datarank":0.8352271934787153,"base_score":3.1780538303479458,"endowment":3.1780538303479458,"self_citation_contribution":0.47670807455219194,"citation_network_contribution":0.3585191189265234,"self_endowment_contribution":0.47670807455219194,"citer_contribution":0.3585191189265234,"corpus_percentile":75.90314844898275,"corpus_rank":3116,"citation_count":23,"citer_count":11,"citers_with_citation_signal":8,"citers_with_endowment":8,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5124,"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":687,"name":"Nicola Veronese","orcid":"0000-0002-9328-289X","position":1,"is_corresponding":false},{"id":511396,"name":"Giacomo Siri","orcid":"0000-0001-8006-0668","position":2,"is_corresponding":false},{"id":21731,"name":"Stefania Bandinelli","orcid":"0000-0002-6491-0850","position":3,"is_corresponding":false},{"id":21720,"name":"Toshiko Tanaka","orcid":"0000-0002-4161-3829","position":4,"is_corresponding":false},{"id":511397,"name":"Alberto Cella","orcid":"0000-0001-6234-6320","position":5,"is_corresponding":false},{"id":21956,"name":"Luigi Ferrucci","orcid":"0000-0002-6273-1613","position":6,"is_corresponding":false},{"id":511395,"name":"Alberto Pilotto","orcid":"0000-0002-8615-1955","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-18T23:12:21.275494Z","pmid":"32941606","pmcid":"PMC8361337","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":[]}