Evidence map›Paper›PMID 32072368›Full record

ArticleJournal of general internal medicine2020

Predicting Mortality and Adverse Outcomes: Comparing the Frailty Index to General Prognostic Indices.

Sandra M Shi, Ellen P McCarthy, Susan L Mitchell, Dae Hyun Kim

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed.

  1. Trial
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  3. The Utility of Measures of Physical Behavior, Function, and Fitness as Predictors of Mortality.Mayo Clinic proceedings. Innovations, quality & outcomes · 2026
    Article
  4. Article
  5. Article
  6. Frailty and hospital outcomes among patients with neurological disorders.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sandra M ShiHinda and Arthur Marcus Institute for Aging, Hebrew Senior Life, Boston, MA, USA. sandrashi@hsl.harvard.edu.ORCID 0000-0001-6801-5602
Ellen P McCarthyHinda and Arthur Marcus Institute for Aging, Hebrew Senior Life, Boston, MA, USA.
Susan L MitchellHinda and Arthur Marcus Institute for Aging, Hebrew Senior Life, Boston, MA, USA.
Dae Hyun KimHinda and Arthur Marcus Institute for Aging, Hebrew Senior Life, Boston, MA, USA.

Funding

Translational Researchin Aging Trainnig ProgramT32AG023480 · NIA · HEBREW REHABILITATION CENTER FOR AGED · PI LIPSITZ, LEWIS · 2004 to 2024
$7.6M
Midcareer Mentoring Award for Patient-Oriented Research in AgingK24AG033640 · NIA · HEBREW REHABILITATION CENTER FOR AGED · PI MITCHELL, SUSAN L · 2009 to 2018
$1.8M
NIA NIH HHS K24 AG033640NIA NIH HHS K24AG033640NIA NIH HHS T32 AG023480
6 · The paper itself

Abstract

backgroundMortality prediction models are useful to guide clinical decision-making based on prognosis. The frailty index, which allows prognostication and personalized care planning, has not been directly compared with validated prognostic models.

objectiveTo compare the discrimination of mortality, disability, falls, and hospitalization between a frailty index and validated prognostic indices.

designSecondary Analysis of the National Health and Aging Trends Study.

participantsSeven thousand thirty-three Medicare beneficiaries 65 years or older. MEASUREMENTS: We measured a deficit-accumulation frailty index, Schonberg index, and Lee index at the 2011 baseline assessment. Primary outcome was mortality at 5 years. Secondary outcomes were decline in activities of daily living (ADL), decline in instrumental activities of daily living (IADL), fall, and hospitalization at 1 year. We used C-statistics to compare discrimination between indices, adjusting for age and sex.

resultsThe study population included 4146 (44.8%) with age ≥ 75 years, with a median frailty index of 0.15 (interquartile range 0.09-0.25). A total of 1385 participants died (14.7%) and 2386 (35.2%) were lost to follow-up. Frailty, Schonberg, and Lee indices predicted mortality similarly: C-statistics (95% confidence interval) were 0.78 (0.77-0.80) for frailty index; 0.79 (0.78-0.81) for Schonberg index; and 0.78 (0.77-0.80) for Lee index. The frailty index had higher C-statistics for decline in ADL function (frailty index, 0.80 [0.78-0.83]; Schonberg, 0.74 [0.72-0.76]; Lee, 0.74 [0.71-0.77]) and falls (frailty index, 0.66 [0.65-0.68]; Schonberg, 0.61 [0.58-0.63]; Lee, 0.61 [0.59-0.63]). C-statistics were similar for decline in IADL function (frailty index, 0.61 [0.59-0.63]; Schonberg, 0.60 [0.59-0.62]; Lee, 0.60 [0.58-0.62]) and hospitalizations (frailty index, 0.68 [0.66-0.70]; Schonberg, 0.68 [0.66-0.69]; Lee, 0.65 [0.63-0.67]).

conclusionsA deficit-accumulation frailty index performs as well as prognostic indices for mortality prediction, and better predicts ADL disability and falls in community-dwelling older adults. Frailty assessment offers a unifying approach to risk stratification for key health outcomes relevant to older adults.

Indexed as

FrailtyActivities of Daily LivingAgedFrail ElderlyGeriatric AssessmentHumansIndependent LivingMedicarePrognosisUnited Statesdisabilityfrailtymortalitypredictionprognostication

Identifiers

PMID32072368
PMCPMC7210351

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.