Evidence map›Paper›PMID 27119583›Full record

ArticleJournal of the American Geriatrics Society2016

The Natural History of Changes in Preferences for Life-Sustaining Treatments and Implications for Inpatient Mortality in Younger and Older Hospitalized Adults.

Yan S Kim, Gabriel J Escobar, Scott D Halpern, John D Greene, Patricia Kipnis, Vincent Liu

Open access · greenAbstract readMulticenter Study
In one paragraph

Article in Journal of the American Geriatrics Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
4.5field-weighted citation impact, top 5% of its field
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

22 citing papers in PubMed, 40 citations in OpenAlex.

  1. Serum zinc level independently predicts need for inpatient intubation among patients hospitalized with COVID-19: A prospective observational study.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2026
    Observational
  2. Article
  3. Article
  4. Observational
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. The Timing of Early Antibiotics and Hospital Mortality in Sepsis.American journal of respiratory and critical care medicine · 2017
    Article
  20. Article
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

6 authors at 2 institutions in 1 country.

Yan S KimDivision of Research and Systems Research Initiative, Kaiser Permanente Northern California, Oakland, California.
Gabriel J EscobarDivision of Research and Systems Research Initiative, Kaiser Permanente Northern California, Oakland, California.
Scott D HalpernDivision of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
John D GreeneDivision of Research and Systems Research Initiative, Kaiser Permanente Northern California, Oakland, California.
Patricia KipnisDivision of Research and Systems Research Initiative, Kaiser Permanente Northern California, Oakland, California.
Vincent LiuDivision of Research and Systems Research Initiative, Kaiser Permanente Northern California, Oakland, California.
Kaiser Permanente · USCommunity Initiatives · US

Funding

Data-driven Precision medicine in sepsisK23GM112018 · NIGMS · KAISER FOUNDATION RESEARCH INSTITUTE · PI LIU, VINCENT · 2014 to 2017
$768k
NIGMS NIH HHS K23 GM112018
6 · The paper itself

Abstract

objectivesTo compare changes in preferences for life-sustaining treatments (LSTs) and subsequent mortality of younger and older inpatients.

designRetrospective cohort study.

settingKaiser Permanente Northern California (KPNC).

participantsIndividuals hospitalized at 21 KPNC hospitals between 2008 and 2012 (N = 227,525). MEASUREMENTS: Participants were divided according to age (<65, 65-84, ≥85). The effect of age on adding new and reversing prior LST limitations was evaluated. Survival to inpatient discharge was compared according to age group after adding new LST limitations.

resultsAt admission, 18,254 (54.2%) of those aged 85 and older, 18,349 (20.8%) of those aged 65 to 84, and 3,258 (3.1%) of those younger than 65 had requested that the use of LST be limited. Of the 187,664 participants who initially did not request limitations on the use of LST, 15,932 (8.5%) had new LST limitations added; of the 39,861 admitted with LST limitations, 3,017 (7.6%) had these reversed. New limitations were more likely to be seen in older participants (aged 65-84, odds ratio (OR) = 2.27, 95% confidence interval (CI) = 2.16-2.39; aged ≥85, OR = 6.43, 95% CI = 6.05-6.84), and reversals of prior limitations were less likely to be seen in older individuals (aged 65-84, OR = 0.73, 95% CI = 0.65-0.83; aged ≥85, OR = 0.46, 95% CI = 0.41-0.53) than in those younger than 65. Survival rates to inpatient discharge were 71.7% of subjects aged 85 and older who added new limitations, 57.2% of those aged 65 to 84, and 43.4% of those younger than 65 (P < .001).

conclusionChanges in preferences for LSTs were common in hospitalized individuals. Age was an important determinant of likelihood of adding new or reversing prior LST limitations. Of subjects who added LST limitations, those who were older were more likely than those who were younger to survive to hospital discharge.

Indexed as

HospitalizationHospital MortalityInpatientsPatient PreferenceAgedAged, 80 and overCaliforniaFemaleHumansLife Support CareMaleMiddle AgedRetrospective StudiesSurvival Ratehospitalizationlife support careresuscitation order

Identifiers

PMID27119583
PMCPMC4882256
OpenAlexW2344373880

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.