Symptoms that predict positive COVID-19 testing and hospitalization: an analysis of 9,000 patients

  1. Mehrsa Jalalizadeh
  2. Patricia A. F. Leme
  3. Keini Buosi
  4. Franciele A. V. Dionato
  5. Cristiane F. Giacomelli
  6. Luciana S.B. Dal Col
  7. Karen L. Ferrari
  8. Ana Carolina Pagliarone
  9. Lucas M. Gon
  10. Douglas F.O. Cezar
  11. Akbar A. Esfahani
  12. Leonardo O. Reis
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Abstract

Purpose

To develop a reliable tool that predicts which patients are most likely to be COVID-19 positive and which ones have an increased risk of hospitalization.

Methods

From February 2020 to April 2021, trained nurses recorded age, gender, and symptoms in an outpatient COVID-19 testing center. All positive patients were followed up by phone for 14 days or until symptom-free. We calculated the symptoms odds ratio for positive results and hospitalization and proposed a “random forest” machine-learning model to predict positive testing.

Results

A total of 8,998 patients over 16 years old underwent COVID-19 RT-PCR, with 1,914 (21.3%) positives. Fifty patients needed hospitalization (2.6% of positives), and three died (0.15%). Most common symptoms were: cough, headache, sore throat, coryza, fever, myalgia (57%, 51%, 44%, 36%, 35%, 27%, respectively). Cough, fever, and myalgia predicted positive COVID-19 test, while others behaved as protective factors. The best predictors of positivity were fever plus anosmia/ageusia (OR=6.31), and cough plus anosmia/ageusia (OR=5.82), both p<0.0001. Our random forest model had an ROC-AUC of 0.72 (specificity=0.70, sensitivity=0.61, PPV=0.38, NPV=0.86). Having steady fever during the first days of infection and persistent dyspnea increased the risk of hospitalization (OR=6.66, p<0.0001 and OR=3.13, p=0.003, respectively), while anosmia-ageusia (OR=0.36, p=0.009) and coryza (OR=0.31, p=0.014) were protective.

Conclusion

Present study and algorithm may help identify patients at higher risk of having SARS-COV-2 (online calculator <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://wdchealth.covid-map.com/shiny/calculator/">http://wdchealth.covid-map.com/shiny/calculator/</ext-link> ), and also disease severity and hospitalization based on symptoms presence, pattern, and duration, which can help physicians and health care providers.

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