Differential Diagnosis of COVID – 19 from Pneumonias Caused by Other Etiologies in a Fever Clinic in Beijing

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Differential Diagnosis of COVID – 19 from Pneumonias Caused by Other Etiologies in a Fever Clinic in Beijing

In the early days of the COVID – 19 pandemic, fever clinics played a crucial role in identifying and managing suspected cases. However, differentiating COVID – 19 from other types of pneumonia was a significant challenge. This article presents a study conducted at Peking University Third Hospital to explore the differences between COVID – 19 and non – COVID – 19 pneumonias.

Introduction

The world was hit hard by the COVID – 19 pandemic. Fever clinics were on the frontline, tasked with screening patients with fever. But not all fevers and pneumonias were due to COVID – 19. In Beijing, like many other places, there were cases of pneumonia caused by other factors such as influenza. Physicians needed reliable information to distinguish COVID – 19 from these other etiologies.

Study Design and Methods

Study Design

This was a retrospective, single – centered study. Patients visiting the fever clinic at Peking University Third Hospital from January 21 to February 15, 2020, were recruited. The study followed the Declaration of Helsinki and was approved by the hospital’s Ethics Committee. Pediatric patients were excluded.

Data Collection

  • Epidemiological Data: Collected information on whether the patient came from Wuhan or other Hubei cities in the past two weeks, had close contact with people from those areas or confirmed COVID – 19 cases, and if there was a short – term onset of pneumonia in family members.
  • Clinical and Laboratory Data: Recorded clinical symptoms on admission, blood routine test results (including white blood cell (WBC), neutrophil, lymphocyte counts, etc.), and computed tomography (CT) findings. CT features were reviewed by experts in Pulmonary and Critical Care Medicine and Radiology.

Statistical Analysis

Continuous variables were expressed as mean ± standard deviation or median (interquartile range). Categorical variables were expressed as numbers (%). Appropriate statistical tests (Student’s t – test, Mann – Whitney U test, Chi – square test, Fisher’s exact test) were used to compare differences between groups, with P < 0.05 considered statistically significant. SPSS software version 22.0 was used for analysis.

Results

Patient Selection

A total of 156 cases were referred for panel discussion. After discussion, 110 were considered possible or probable COVID – 19 cases. Among them, 19 had positive SARS – CoV – 2 RT – PCR tests (COVID – 19 group). For the remaining 91 cases with negative viral detection, 2 were excluded due to unclear exclusion of COVID – 19, 22 due to lack of CT scan or no pneumonia signs on CT, and finally 67 were included as the non – COVID – 19 group.

Demographics and Baseline Characteristics of COVID – 19 Group

  • Age: The median age was 44.0 (33.0 – 69.0) years.
  • Gender: Men and women were equally affected.
  • Epidemiological History: 17 patients (89.5%) had a clear epidemiological history. 6 had close contact with infected family members. For the 2 with unclear links, one was a social worker whose wife was later confirmed with COVID – 19, and the other had a dinner companion who was later diagnosed.
  • Incubation Period: Elicited from 11 patients, ranging from 2 to 10 days with a median of 6.0 days.
  • Disease Severity: Most (89.5%) were mild – moderate. 2 were severe (SpO2 ≤ 93%), and no critical cases.

Clinical Symptoms

  • Fever: Most COVID – 19 patients (89.5%) had fever with a mean body temperature of 37.8°C.
  • Other Symptoms: Cough (36.8%), expectoration (31.6%), fatigue (52.6%), headache or dizziness (36.8%) were common. Compared to non – COVID – 19 patients, COVID – 19 patients had a higher proportion of identified epidemiological history (89.5% vs. 32.8%, P < 0.001). Clinical symptoms were similar except for cough (COVID – 19: 36.8% vs. non – COVID – 19: 79.1%, P < 0.001).

Blood Routine Test

  • Lymphocyte Count: Decreased in 42.1% (8/19) of COVID – 19 patients.
  • WBC and Neutrophil Counts: More than 80% of COVID – 19 patients had normal WBC and neutrophil counts. Non – COVID – 19 patients had higher total WBC ([7.21 ± 2.94] × 10⁹/L vs. [5.30 ± 1.48] × 10⁹/L, t = – 3.861, P < 0.001) and neutrophil counts (4.45 [3.24, 7.11] × 10⁹/L vs. 3.10 [2.28, 4.51] × 10⁹/L, Z = – 2.758, P = 0.006). More non – COVID – 19 patients had increased WBC (20.9% vs. 0.0%, P = 0.047) and neutrophil counts (31.3% vs. 5.3%, P = 0.049). Lymphocyte counts and the proportion of patients with decreased lymphocytes were not statistically different between the two groups.

CT Findings

  • Lobe Involvement: In non – COVID – 19 patients, infiltrates mainly involved 1 – 2 lung lobes. In COVID – 19 patients, lesions involving 4 – 5 lobes were more common (50.0% vs. 16.4%, P = 0.009).
  • Lesion Distribution: In COVID – 19 patients, lung lesions were mostly peripheral or sub – pleural (77.8%), and in severe cases diffusely distributed (22.2%). In non – COVID – 19 patients, 34.3% showed airway – dominant lesions, and only 4.5% had diffuse distribution.
  • Lesion Patterns: The patterns (ground – glass opacity [GGO], consolidation or mixed GGO and consolidation) showed no significant difference (P = 0.165). Centrilobular nodules were observed in non – COVID – 19 patients but not in COVID – 19 patients.

Discussion

Epidemiological History

Epidemiological history was extremely important for identifying COVID – 19. In the early phase of the study, many cases were imported from Wuhan or Hubei. Later, family cluster cases were found. This highlights the need for detailed epidemiological inquiries.

Blood Routine Test

Leukocytosis and neutrophilia were more common in non – COVID – 19 pneumonia. Although lymphocyte decrease was common in COVID – 19, the difference with non – COVID – 19 was not significant (likely because the non – COVID – 19 pneumonias were mostly viral or atypical). Leukocytosis and neutrophilia may help exclude COVID – 19, especially in those without clear epidemiological history. Persistent lymphopenia is a predictor of severe COVID – 19.

CT Findings

COVID – 19 had unique CT characteristics. Multi – lobe involvement was more common. Lesion distribution (peripheral/sub – pleural in mild – moderate, diffuse in severe) was different from non – COVID – 19. These features were consistent with previous studies.

Conclusion

Epidemiological evidence was crucial for prompt COVID – 19 case finding. Lower blood WBC and neutrophil counts may help differentiate it from other pneumonias. This study added to the knowledge of COVID – 19 for fever clinic practice. However, being a single – center study in the early epidemic, it had limitations (small sample size, unknown pathogens in non – COVID – 19 pneumonias), which need to be addressed in larger studies.

doi.org/10.1097/CM9.0000000000001121

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