Evidence map›Paper›PMID 34794995›Full record

Observational studyBMJ open2021

Stratification of the risk of developing severe or lethal Covid-19 using a new score from a large Italian population: a population-based cohort study.

Giovanni Corrao, Federico Rea, Flavia Carle, Salvatore Scondotto, Alessandra Allotta, Vito Lepore, Antonio D'Ettorre, Cinzia Tanzarella, Patrizia Vittori, Sabrina Abena and 10 more

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. "New Normal" Radiology.International dental journal · 2022
    Review
  6. Article
  7. 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

20 authors at 9 institutions in 1 country.

Giovanni CorraoDepartment of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy giovanni.corrao@unimib.it.ORCID 0000-0002-1034-8444
Federico ReaDepartment of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy.ORCID 0000-0001-7988-5101
Flavia CarleNational Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
Salvatore ScondottoNational Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
Alessandra AllottaDepartment of Health Services and Epidemiological Observatory, Regional Health Authority of Sicily, Palermo, Italy.
Vito LeporeRegional Health Agency of Puglia, Bari, Italy.ORCID 0000-0002-5466-780X
Antonio D'EttorreRegional Health Agency of Puglia, Bari, Italy.
Cinzia TanzarellaRegional Health Agency of Puglia, Bari, Italy.
Patrizia VittoriRegional Health Authority, Aosta, Italy.
Sabrina AbenaRegional Health Authority, Aosta, Italy.
Marica IommiCenter of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy.
Liana SpazzafumoNational Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
Michele ErcolanoniRegional Welfare Service, Milan, Italy.
Roberto BlacoRegional Welfare Service, Milan, Italy.
Simona CarboneDepartment of Health Planning, Italian Health Ministry, Rome, Italy.
Cristina GiordaniDepartment of Health Planning, Italian Health Ministry, Rome, Italy.
Dario ManfellottoDepartment of Internal Medicine, Hospital Fatebenefratelli, Rome, Italy.
Massimo GalliInstitute of Tropical and Infectious Diseases, University of Milan L Sacco Hospital, Milan, Italy.
Giuseppe ManciaUniversity of Milano-Bicocca, Milan, Italy.
'Monitoring and Assessing care Pathways (MAP)' working group of the Italian Ministry of Health
Agenzia Regionale Sanitaria della Puglia · ITMarche Polytechnic University · ITMinistero della Salute · ITRegione Siciliana · ITUniversity of Milano-Bicocca · ITAgenzia Regionale per la Protezione Ambientale del Piemonte · ITFatebenefratelli Hospital · ITFondazione Lanza · ITUniversity of Milan · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop a population-based risk stratification model (COVID-19 Vulnerability Score) for predicting severe/fatal clinical manifestations of SARS-CoV-2 infection, using the multiple source information provided by the healthcare utilisation databases of the Italian National Health Service.

designRetrospective observational cohort study.

settingPopulation-based study using the healthcare utilisation database from five Italian regions.

participantsBeneficiaries of the National Health Service, aged 18-79 years, who had the residentship in the five participating regions. Residents in a nursing home were not included. The model was built from the 7 655 502 residents of Lombardy region.

main outcome measureThe score included gender, age and 29 conditions/diseases selected from a list of 61 conditions which independently predicted the primary outcome, that is, severe (intensive care unit admission) or fatal manifestation of COVID-19 experienced during the first epidemic wave (until June 2020). The score performance was validated by applying the model to several validation sets, that is, Lombardy population (second epidemic wave), and the other four Italian regions (entire 2020) for a total of about 15.4 million individuals and 7031 outcomes. Predictive performance was assessed by discrimination (areas under the receiver operating characteristic curve) and calibration (plot of observed vs predicted outcomes).

resultsWe observed a clear positive trend towards increasing outcome incidence as the score increased. The areas under the receiver operating characteristic curve of the COVID-19 Vulnerability Score ranged from 0.85 to 0.88, which compared favourably with the areas of generic scores such as the Charlson Comorbidity Score (0.60). A remarkable performance of the score on the calibration of observed and predicted outcome probability was also observed.

conclusionsA score based on data used for public health management accurately predicted the occurrence of severe/fatal manifestations of COVID-19. Use of this score may help health decision-makers to more accurately identify high-risk citizens who need early preventive or treatment interventions.

Indexed as

COVID-19AdultCohort StudiesHumansItalyRetrospective StudiesSARS-CoV-2State MedicineCOVID-19health policypublic health

Identifiers

PMID34794995
PMCPMC8602929
OpenAlexW3213370807

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.