Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: A population-based cohort study from Italy

  1. Valentina Orlando
  2. Federico Rea
  3. Laura Savaré
  4. Ilaria Guarino
  5. Sara Mucherino
  6. Alessandro Perrella
  7. Ugo Trama
  8. Enrico Coscioni
  9. Enrica Menditto
  10. Giovanni Corrao

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Abstract

The novel coronavirus (SARS-CoV-2) pandemic spread rapidly worldwide increasing exponentially in Italy. To date, there is lack of studies describing clinical characteristics of the people at high risk of infection. Hence, we aimed (i) to identify clinical predictors of SARS-CoV-2 infection risk, (ii) to develop and validate a score predicting SARS-CoV-2 infection risk, and (iii) to compare it with unspecific scores.

Methods

Retrospective case-control study using administrative health-related database was carried out in Southern Italy (Campania region) among beneficiaries of Regional Health Service aged over than 30 years. For each person with SARS-CoV-2 confirmed infection (case), up to five controls were randomly matched for gender, age and municipality of residence. Odds ratios and 90% confidence intervals for associations between candidate predictors and risk of infection were estimated by means of conditional logistic regression. SARS-CoV-2 Infection Score (SIS) was developed by generating a total aggregate score obtained from assignment of a weight at each selected covariate using coefficients estimated from the model. Finally, the score was categorized by assigning increasing values from 1 to 4. Discriminant power was used to compare SIS performance with that of other comorbidity scores.

Results

Subjects suffering from diabetes, anaemias, Parkinson’s disease, mental disorders, cardiovascular and inflammatory bowel and kidney diseases showed increased risk of SARS-CoV-2 infection. Similar estimates were recorded for men and women and younger and older than 65 years. Fifteen conditions significantly contributed to the SIS. As SIS value increases, risk progressively increases, being odds of SARS-CoV-2 infection among people with the highest SIS value (SIS = 4) 1.74 times higher than those unaffected by any SIS contributing conditions (SIS = 1).

Conclusion

Conditions and diseases making people more vulnerable to SARS-CoV-2 infection were identified by the current study. Our results support decision-makers in identifying high-risk people and adopting of preventive measures to minimize the spread of further epidemic waves.

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  1. Version published to 10.1371/journal.pone.0237202
  2. ScreenIT

    SciScore for 10.1101/2020.07.23.217331: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The latter is a validated pharmaceutical-based comorbidity index derived from dispensation data using Anatomical Therapeutic Chemical (ATC) classification codes [20,21].
    ATC
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The reasons are likely linked with the several limitations of our approach that, in general, generates estimates biased towards the null. First, exposure misclassification regards our inability to careful capturing conditions and diseases through algorithms based on healthcare utilization databases [52]. Second, it is well known that outcome misclassification can bias epidemiologic results. For Covid-19, suboptimal test sensitivity, despite excellent specificity, results in an overestimation of cases in the early stages of an outbreak, and substantial underestimation of cases as prevalence increases [53]. It should be noticed, however, that both, exposure and outcome misclassification likely drew estimates towards the null (i.e., underestimate the strength of the association between their presence and the outcome risk) so generating uncertainty for the weighting approach of score developing. Third, the lack of specific data regarding the clinical outcome for the stratification of Covid-19 positive patients in terms of home isolation, hospitalization and admission in intensive care. Fourth, the lack of information on biologic markers potentially able to predict infection, and severity of its clinical manifestations, is another limitation of our study, as for example, according to the current literature, some laboratory hallmarks have been shown to predict infection, particularly in more severe cases [54]. Finally, our choice of accepting a 0.10 first type error, and of consequ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.07.23.217331v1 on bioRxiv