Evidence map›Paper›PMID 39538084›Full record

ArticleGeroScience2025

Analysis of six consecutive waves of ICU-admitted COVID-19 patients: key findings and insights from a Portuguese population.

Cristiana P Von Rekowski, Iola Pinto, Tiago A H Fonseca, Rúben Araújo, Cecília R C Calado, Luís Bento

Abstract read
In one paragraph

Article in GeroScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Observational
  3. Observational
  4. Article
  5. 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.

Cristiana P Von RekowskiNMS - NOVA Medical School, FCM - Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056, Lisbon, Portugal. crisvr66@hotmail.com.ORCID 0009-0009-6843-1935
Iola PintoDepartment of Mathematics, ISEL - Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007, Lisbon, Portugal.ORCID 0000-0002-2945-1441
Tiago A H FonsecaNMS - NOVA Medical School, FCM - Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056, Lisbon, Portugal.ORCID 0000-0003-0741-2211
Rúben AraújoNMS - NOVA Medical School, FCM - Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056, Lisbon, Portugal.ORCID 0000-0002-9369-6486
Cecília R C CaladoISEL - Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007, Lisbon, Portugal.ORCID 0000-0002-5264-9755
Luís BentoIntensive Care Department, ULSSJ - Unidade Local de Saúde São José, Rua José António Serrano, 1150-199, Lisbon, Portugal.ORCID 0000-0002-0260-003X

Funding

Fundação para a Ciência e a Tecnologia DSAIPA/DS/0117/2020Fundação para a Ciência e a Tecnologia UIDB/00297/2020Fundação para a Ciência e a Tecnologia UIDP/00297/2020
6 · The paper itself

Abstract

Identifying high-risk patients, particularly in intensive care units (ICUs), enhances treatment and reduces severe outcomes. Since the pandemic, numerous studies have examined COVID-19 patient profiles and factors linked to increased mortality. Despite six pandemic waves, to the best of our knowledge, there is no extensive comparative analysis of patients' characteristics across these waves in Portugal. Thus, we aimed to analyze the demographic and clinical features of 1041 COVID-19 patients admitted to an ICU and their relationship with the different SARS-Cov-2 variants in Portugal. Additionally, we conducted an in-depth examination of factors contributing to early and late mortality by analyzing clinical data and laboratory results from the first 72 h of ICU admission. Our findings revealed a notable decline in ICU admissions due to COVID-19, with the highest mortality rates observed during the second and third waves. Furthermore, immunization could have significantly contributed to the reduction in the median age of ICU-admitted patients and the severity of their conditions. The factors contributing to early and late mortality differed. Age, wave number, D-dimers, and procalcitonin were independently associated with the risk of early death. As a measure of discriminative power for the derived multivariable model, an AUC of 0.825 (p < 0.001; 95% CI, 0.719-0.931) was obtained. For late mortality, a model incorporating age, wave number, hematologic cancer, C-reactive protein, lactate dehydrogenase, and platelet counts resulted in an AUC of 0.795 (p < 0.001; 95% CI, 0.759-0.831). These findings underscore the importance of conducting comprehensive analyses across pandemic waves to better understand the dynamics of COVID-19.

Indexed as

COVID-19Intensive Care UnitsAdultAgedAged, 80 and overAge FactorsFemaleFibrin Fibrinogen Degradation ProductsHospital MortalityHumansMaleMiddle AgedPandemicsPortugalRisk FactorsSARS-CoV-2Fibrin Fibrinogen Degradation Productsfibrin fragment DCOVID-19 vaccinationCOVID-19 wavesEarly mortalityICULate mortality

Identifiers

PMID39538084
PMCPMC11979077

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.