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

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.

Eight Good Reasons for Careful Monitoring and Evaluation of the Vaccine Campaign against COVID-19: Lessons Learned through the Lombardy Experience for Dealing with Next Challenges

“…The strengths of our approach have been discussed above in the Section 2. In summary, the main advantages of our approach include the platform design (i.e., the populationbased structure), the stored data (i.e., information collected at individual level and the availability of a large set of demographic and clinical data), the target population (i.e., its large size), and the ability to promptly respond to important public health concerns, such as the timely identification of high-risk individuals [10] or the assessment of the safety of using of ACE inhibitors and angiotensin receptor blockers [40].…”

“…Based on ethical reasons, rationing approaches gave priority to high-risk groups. We aimed to develop and validate a novel prognostic score (which we called the COVID-19 vulnerability score or CVS) to predict critical/severe clinical manifestations of SARS-CoV-2 infection [10]. Methods.…”

Factors associated with severe or fatal clinical manifestations of SARS‐CoV‐2 infection after receiving the third dose of vaccine

“…The latter comprised categories of the number of contacts with the RHS, and the presence/absence of 59 diseases/conditions (candidate predictors) traced through hospital admissions and drug prescriptions. Details on the use of healthcare‐utilisation databases for tracing the conditions that each beneficiary of the RHS suffers from are reported elsewhere [ 8 , 9 , 10 ]. The list of candidate predictors included practically all nosological categories and was prepared taking into consideration morbidity and mortality predictors reported by selected systematic reviews and meta‐analyses [ 11 , 12 , 13 , 14 ].…”

“…Details on the use of healthcare‐utilisation databases for tracing the conditions that each beneficiary of the RHS suffers from are reported elsewhere [ 8 , 9 , 10 ]. The list of candidate predictors included practically all nosological categories and was prepared taking into consideration morbidity and mortality predictors reported by selected systematic reviews and meta‐analyses [ 11 , 12 , 13 , 14 ]. The list of candidate predictors and the corresponding codes are reported in the Supplementary Table .…”

“New Normal” Radiology

“…As “more evidence is needed about the effectiveness of testing outside of hospital settings and in mild or asymptomatic cases,” 9 the dentist should continue to assume that each patient who is asymptomatic for COVID-19 might be infectious, as it is possible “that the oral cavity actively participates in SARS-CoV-2 transmission.” 10 Enhanced history-taking and donning and doffing personal protective equipment in the appropriate manner, effective room ventilation, and cleansing and sterilisation (or disinfection) of equipment 1 protect not only dentists and their patients but also particularly those with underlying conditions that place them at a heightened risk for a severe or fatal outcome if COVID-19 is contracted. 11 …”