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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.
Corrao, Giovanni; Rea, Federico; Carle, Flavia; Scondotto, Salvatore; Allotta, Alessandra; Lepore, Vito; D'Ettorre, Antonio; Tanzarella, Cinzia; Vittori, Patrizia; Abena, Sabrina; Iommi, Marica; Spazzafumo, Liana; Ercolanoni, Michele; Blaco, Roberto; Carbone, Simona; Giordani, Cristina; Manfellotto, Dario; Galli, Massimo; Mancia, Giuseppe.
  • Corrao G; Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy giovanni.corrao@unimib.it.
  • Rea F; National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
  • Carle F; Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy.
  • Scondotto S; National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
  • Allotta A; National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
  • Lepore V; Center of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy.
  • D'Ettorre A; National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
  • Tanzarella C; Department of Health Services and Epidemiological Observatory, Regional Health Authority of Sicily, Palermo, Italy.
  • Vittori P; Department of Health Services and Epidemiological Observatory, Regional Health Authority of Sicily, Palermo, Italy.
  • Abena S; Regional Health Agency of Puglia, Bari, Italy.
  • Iommi M; Regional Health Agency of Puglia, Bari, Italy.
  • Spazzafumo L; Regional Health Agency of Puglia, Bari, Italy.
  • Ercolanoni M; Regional Health Authority, Aosta, Italy.
  • Blaco R; Regional Health Authority, Aosta, Italy.
  • Carbone S; Center of Epidemiology and Biostatistics, Polytechnic University of Marche, Ancona, Italy.
  • Giordani C; National Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
  • Manfellotto D; Regional Health Agency of Marche, Ancona, Italy.
  • Galli M; Regional Welfare Service, Milan, Italy.
  • Mancia G; Regional Welfare Service, Milan, Italy.
BMJ Open ; 11(11): e053281, 2021 11 18.
Article in English | MEDLINE | ID: covidwho-1526504
ABSTRACT

OBJECTIVES:

To 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.

DESIGN:

Retrospective observational cohort study.

SETTING:

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

PARTICIPANTS:

Beneficiaries 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

MEASURE:

The 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).

RESULTS:

We 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.

CONCLUSIONS:

A 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study Limits: Adult / Humans Country/Region as subject: Europa Language: English Journal: BMJ Open Year: 2021 Document Type: Article Affiliation country: Bmjopen-2021-053281

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study Limits: Adult / Humans Country/Region as subject: Europa Language: English Journal: BMJ Open Year: 2021 Document Type: Article Affiliation country: Bmjopen-2021-053281