Evidence map›Paper›PMID 36764980›Full record

ArticleAnnals of intensive care2023

Critically ill patient mortality by age: long-term follow-up (CIMbA-LT).

João Gonçalves-Pereira, André Oliveira, Tatiana Vieira, Ana Rita Rodrigues, Maria João Pinto, Sara Pipa, Ana Martinho, Sofia Ribeiro, José-Artur Paiva

Abstract read
In one paragraph

Article in Annals of intensive care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Light therapy in medicine: where do we stand?Annals of translational medicine · 2026
    Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Redefining success: Incorporating long-term survival outcomes into routine benchmarking.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Observational
  20. Review
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

9 authors.

João Gonçalves-PereiraIntensive Care Unit, Hospital Vila Franca de Xira, Estrada Carlos Lima Costa, N2, 2600-009, Vila Franca de Xira, Portugal. joaogpster@gmail.com.ORCID http://orcid.org/0000-0002-7538-3777
André OliveiraIntensive Care Unit, Hospital Vila Franca de Xira, Estrada Carlos Lima Costa, N2, 2600-009, Vila Franca de Xira, Portugal.
Tatiana VieiraIntensive Care Department, Centro Hospitalar Universitário de S. João, Porto, Portugal.
Ana Rita RodriguesIntensive Care Department, Centro Hospitalar Universitário de Lisboa Norte, Lisbon, Portugal.
Maria João PintoIntensive Care Department, Centro Hospitalar Trás-os-Montes e Alto Douro, Vila Real, Portugal.
Sara PipaIntensive Care Department, Centro Hospitalar Vila Nova de Gaia e Espinho, Vila Nova de Gaia, Portugal.
Ana MartinhoIntensive Care Department, Centro Hospitalar Universitário de Coimbra, Coimbra, Portugal.
Sofia RibeiroIntensive Care Department, Centro Hospitalar Universitário do Algarve, Faro, Portugal.
José-Artur PaivaGrupo de Investigação e Desenvolvimento em Infeção e Sépsis (GISID), Porto, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe past years have witnessed dramatic changes in the population admitted to the intensive care unit (ICU). Older and sicker patients are now commonly treated in this setting due to the newly available sophisticated life support. However, the short- and long-term benefit of this strategy is scarcely studied.

methodsThe Critically Ill patients' mortality by age: Long-Term follow-up (CIMbA-LT) was a multicentric, nationwide, retrospective, observational study addressing short- and long-term prognosis of patients admitted to Portuguese multipurpose ICUs, during 4 years, according to their age and disease severity. Patients were followed for two years after ICU admission. The standardized hospital mortality ratio (SMR) was calculated according to the Simplified Acute Physiology Score (SAPS) II and the follow-up risk, for patients discharged alive from the hospital, according to official demographic national data for age and gender. Survival curves were plotted according to age group.

resultsWe included 37.118 patients, including 15.8% over 80 years old. The mean SAPS II score was 42.8 ± 19.4. The ICU all-cause mortality was 16.1% and 76% of all patients survive until hospital discharge. The SAPS II score overestimated hospital mortality [SMR at hospital discharge 0.7; 95% confidence interval (CI) 0.63-0.76] but accurately predicted one-year all-cause mortality [1-year SMR 1.01; (95% CI 0.98-1.08)]. Survival curves showed a peak in mortality, during the first 30 days, followed by a much slower survival decline thereafter. Older patients had higher short- and long-term mortality and their hospital SMR was also slightly higher (0.76 vs. 0.69). Patients discharged alive from the hospital had a 1-year relative mortality risk of 6.3; [95% CI 5.8-6.7]. This increased risk was higher for younger patients [21.1; (95% CI 15.1-39.6) vs. 2.4; (95% CI 2.2-2.7) for older patients].

conclusionsCritically ill patients' mortality peaked in the first 30 days after ICU admission. Older critically ill patients had higher all-cause mortality, including a higher hospital SMR. A long-term increased relative mortality risk was noted in patients discharged alive from the hospital, but this was more noticeable in younger patients.

Indexed as

AgeFollow-upLong termMortalitySAPS-IIStandard mortality ratioSurvival

Identifiers

PMID36764980
PMCPMC9918627

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.