The Evaluation of Diagnostic Values of Clinical Symptoms for COVID-19 Hospitalized Patients in Northern Iran: The Syndromic Surveillance System Data
| Author | Hossein Hatami | en |
| Author | Mohammad Reza Saeidi Kiasari | en |
| Author | Maysam Rezapour | en |
| Orcid | Maysam Rezapour [0000-0002-6039-3286] | en |
| Issued Date | 2022-02-28 | en |
| Abstract | Background: A novel coronavirus led to a rapidly spreading outbreak of COVID-19, which caused morbidity and mortality worldwide. Appropriate case definitions can help diagnose COVID-19. Objectives: This study aimed to evaluate the COVID-19 clinical symptoms and their potential patterns using latent class analysis (LCA) for identifying confirmed COVID-19 cases among hospitalized patients in northern Iran according to the syndromic surveillance system data. Methods: This cross-sectional study was conducted on patients with COVID-19 admitted to hospitals in Mazandaran Province, Iran. Respiratory specimens were collected by nasopharyngeal swabs from the patients and tested for COVID-¬19 using reverse transcription polymerase chain reaction (RT-PCR). Latent class analysis was used to identify patterns of the symptoms. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) of each symptom pattern were compared and plotted. Also, multiple logistic regression was used to determine the odds ratio for each symptom pattern for predicting COVID-19 infection by adjusting for gender and age groups. Results: Among 13,724 hospitalized patients tested for COVID-19 and included in the analyses, 4,836 (35, 2%) had RT-PCR confirmed COVID-19. The symptoms of fever, chills, cough, shortness of breath, fatigue, myalgia, sore throat, diarrhea, nausea or vomiting, headache, and arthralgia were significantly more common in patients positive for COVID-19 than in other patients and were used in LCA. Latent class analysis suggested six classes (patterns) of clinical symptoms. The AUC of symptom patterns was poor, being 0.43 for class 5, comprising patients without any symptoms, and 0.53 for class 3, comprising patients with fever, chills, and cough. Also, multiple logistic regression showed that class 1, comprising patients with fever, chills, cough, shortness of breath, sore throat, and arthralgia, had an odds ratio of 2.87 (1.39, 3.43) relative to class 5 (patients without any symptoms) for positive COVID-19. Conclusions: This study showed that the clinical symptoms might help to diagnose patients with COVID-19. However, the defined clinical symptoms suggested in the surveillance system of COVID-19 in Iran during this time were not appropriate for identifying COVID-19 cases. | en |
| DOI | https://doi.org/10.5812/archcid-117465 | en |
| Keyword | COVID-19 | en |
| Keyword | Latent Class Analysis | en |
| Keyword | Clinical Symptoms | en |
| Keyword | Diagnosis | en |
| Keyword | Surveillance | en |
| Publisher | Brieflands | en |
| Title | The Evaluation of Diagnostic Values of Clinical Symptoms for COVID-19 Hospitalized Patients in Northern Iran: The Syndromic Surveillance System Data | en |
| Type | Research Article | en |
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