tetano
Editor, Senior Moderator
PLoS One
. 2021 Mar 3;16(3):e0247995.
doi: 10.1371/journal.pone.0247995. eCollection 2021.
Assessing the likelihood of contracting COVID-19 disease based on a predictive tree model: A retrospective cohort study
Francesc X Marin-Gomez[SUP] 1 2 [/SUP], Mireia F?bregas-Escurriola[SUP] 3 [/SUP], Francesc L?pez Segu?[SUP] 4 [/SUP], Eduardo Hermosilla P?rez[SUP] 5 [/SUP], M?ncia Ben?tez Camps[SUP] 3 6 [/SUP], Jacobo Mendioroz Pe?a[SUP] 1 7 [/SUP], Anna Ruiz Comellas[SUP] 1 8 [/SUP], Josep Vidal-Alaball[SUP] 1 2 [/SUP]
Affiliations
Abstract
Background: Primary care is the major point of access in most health systems in developed countries and therefore for the detection of coronavirus disease 2019 (COVID-19) cases. The quality of its IT systems, together with access to the results of mass screening with Polymerase chain reaction (PCR) tests, makes it possible to analyse the impact of various concurrent factors on the likelihood of contracting the disease.
Methods and findings: Through data mining techniques with the sociodemographic and clinical variables recorded in patient's medical histories, a decision tree-based logistic regression model has been proposed which analyses the significance of demographic and clinical variables in the probability of having a positive PCR in a sample of 7,314 individuals treated in the Primary Care service of the public health system of Catalonia. The statistical approach to decision tree modelling allows 66.2% of diagnoses of infection by COVID-19 to be classified with a sensitivity of 64.3% and a specificity of 62.5%, with prior contact with a positive case being the primary predictor variable.
Conclusions: The use of a classification tree model may be useful in screening for COVID-19 infection. Contact detection is the most reliable variable for detecting Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. The model would support that, beyond a symptomatic diagnosis, the best way to detect cases would be to engage in contact tracing.
. 2021 Mar 3;16(3):e0247995.
doi: 10.1371/journal.pone.0247995. eCollection 2021.
Assessing the likelihood of contracting COVID-19 disease based on a predictive tree model: A retrospective cohort study
Francesc X Marin-Gomez[SUP] 1 2 [/SUP], Mireia F?bregas-Escurriola[SUP] 3 [/SUP], Francesc L?pez Segu?[SUP] 4 [/SUP], Eduardo Hermosilla P?rez[SUP] 5 [/SUP], M?ncia Ben?tez Camps[SUP] 3 6 [/SUP], Jacobo Mendioroz Pe?a[SUP] 1 7 [/SUP], Anna Ruiz Comellas[SUP] 1 8 [/SUP], Josep Vidal-Alaball[SUP] 1 2 [/SUP]
Affiliations
- PMID: 33657164
- DOI: 10.1371/journal.pone.0247995
Abstract
Background: Primary care is the major point of access in most health systems in developed countries and therefore for the detection of coronavirus disease 2019 (COVID-19) cases. The quality of its IT systems, together with access to the results of mass screening with Polymerase chain reaction (PCR) tests, makes it possible to analyse the impact of various concurrent factors on the likelihood of contracting the disease.
Methods and findings: Through data mining techniques with the sociodemographic and clinical variables recorded in patient's medical histories, a decision tree-based logistic regression model has been proposed which analyses the significance of demographic and clinical variables in the probability of having a positive PCR in a sample of 7,314 individuals treated in the Primary Care service of the public health system of Catalonia. The statistical approach to decision tree modelling allows 66.2% of diagnoses of infection by COVID-19 to be classified with a sensitivity of 64.3% and a specificity of 62.5%, with prior contact with a positive case being the primary predictor variable.
Conclusions: The use of a classification tree model may be useful in screening for COVID-19 infection. Contact detection is the most reliable variable for detecting Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. The model would support that, beyond a symptomatic diagnosis, the best way to detect cases would be to engage in contact tracing.