Evidence map›Paper›PMID 38320835›Full record

ArticleBMJ open2024

Analysis and clinical determinants of post-COVID-19 syndrome in the Lombardy region: evidence from a longitudinal cohort study.

Fabio Borgonovo, Pietro Giorgio Lovaglio, Chiara Mariani, Paolo Berta, Maria Vittoria Cossu, Giuliano Rizzardini, Giorgio Vittadini, Amedeo Ferdinando Capetti

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.7field-weighted citation impact, top 31% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 2 institutions in 1 country.

Fabio BorgonovoInfectious Diseases, Luigi Sacco University Hospital, Milano, Italy borgonovo.fabio@asst-fbf-sacco.it.ORCID 0000-0001-5796-671X
Pietro Giorgio LovaglioInteruniversity Research Centre on Public Services (CRISP), Department of Statistics and Quantitative Methods, University Bicocca-Milan, Milan, Italy.
Chiara MarianiInfectious Diseases, Luigi Sacco University Hospital, Milano, Italy.
Paolo BertaInteruniversity Research Centre on Public Services (CRISP), Department of Statistics and Quantitative Methods, University Bicocca-Milan, Milan, Italy.
Maria Vittoria CossuInfectious Diseases, Luigi Sacco University Hospital, Milano, Italy.
Giuliano RizzardiniInfectious Diseases, Luigi Sacco University Hospital, Milano, Italy.
Giorgio VittadiniInteruniversity Research Centre on Public Services (CRISP), Department of Statistics and Quantitative Methods, University Bicocca-Milan, Milan, Italy.
Amedeo Ferdinando CapettiInfectious Diseases, Luigi Sacco University Hospital, Milano, Italy.
Luigi Sacco Hospital · ITUniversity of Milano-Bicocca · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo define macro symptoms of long COVID and to identify predictive factors, with the aim of preventing the development of the long COVID syndrome.

designA single-centre longitudinal prospective cohort study conducted from May 2020 to October 2022.

settingThe study was conducted at Luigi Sacco University Hospital in Milan (Italy). In May 2020, we activated the ARCOVID (Ambulatorio Rivalutazione COVID) outpatient service for the follow-up of long COVID.

participantsHospitalised and non-hospitalised patients previously affected by COVID-19 were either referred by specialists or general practitioners or self-referred.

interventionDuring the first visit, a set of questions investigated the presence and the duration of 11 symptoms (palpitations, amnesia, headache, anxiety/panic, insomnia, loss of smell, loss of taste, dyspnoea, asthenia, myalgia and telogen effluvium). The follow-up has continued until the present time, by sending email questionnaires every 3 months to monitor symptoms and health-related quality of life. PRIMARY AND SECONDARY OUTCOME MEASURES: Measurement of synthetic scores (aggregation of symptoms based on occurrence and duration) that may reveal the presence of long COVID in different clinical macro symptoms. To this end, a mixed supervised and empirical strategy was adopted. Moreover, we aimed to identify predictive factors for post-COVID-19 macro symptoms.

resultsIn the first and second waves of COVID-19, 575 and 793 patients (respectively) were enrolled. Three different post-COVID-19 macro symptoms (neurological, sensorial and physical) were identified. We found significant associations between post-COVID-19 symptoms and (1) the patients' comorbidities, and (2) the medications used during the COVID-19 acute phase. ACE inhibitors (OR=2.039, 95% CI: 1.095 to 3.892), inhaled steroids (OR=4.08, 95% CI: 1.17 to 19.19) and COVID therapies were associated with increased incidence of the neurological macro symptoms. Age (OR=1.02, 95% CI: 1.01 to 1.04), COVID-19 severity (OR=0.42, 95% CI: 0.21 to 0.82), number of comorbidities (OR=1.22, 95% CI: 1.01 to 1.5), metabolic (OR=2.52, 95% CI: 1.25 to 5.27), pulmonary (OR=1.87, 95% CI: 1.10 to 3.32) and autoimmune diseases (OR=4.57, 95% CI: 1.57 to 19.41) increased the risk of the physical macro symptoms.

conclusionsBeing male was the unique protective factor in both waves. Other factors reflected different medical behaviours and the impact of comorbidities. Evidence of the effect of therapies adds valuable information that may drive future medical choices.

Indexed as

COVID-19Cohort StudiesFemaleHumansLongitudinal StudiesMalePost-Acute COVID-19 SyndromeProspective StudiesQuality of LifeCOVID-19general medicine (see internal medicine)infectious diseasespost-acute COVID-19 syndrome

Identifiers

PMID38320835
PMCPMC10860093
OpenAlexW4391560432

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.