Demographic manifestations associated with sleep disorders in the acute stage of COVID-19

Of the 53 patients enrolled in our cohort, most reported a medium education level (56.6%, 30/53) followed by a high education level (35.83%, 19/53), and a low education level was the least common response (7.55%, 4/53). Regarding marital status, 43 patients (81.13%, 43/53) were married, 7 (7/53, 13.21%) were unmarried, and 3 (3/53, 5.66%) were widowed.

The mean PSQI score of the 53 patients enrolled was 7.51 ± 5.80. In total, 25 (47.2%) patients were included in the sleep disorder group, and 28 were included in the normal group based on a PSQI total score ≥ 7. The mean PSQI scores of the sleep disorder group and the normal group were 12.64 ± 4.05 and 2.93 ± 2.04, respectively. When comparing the demographic data of the normal group and sleep disorder group, several demographic factors associated with sleep disorders in COVID-19 were revealed.

Overall, the average age of patients in the sleep disorder group (PSQI total score ≥ 7) was older than that in the normal group (59.44 ± 14.69 years vs. 49.64 ± 18.80 years) (t test, p = 0.041), and age > 50 years was more common in the sleep disorder group compared with the normal group (84% (21/25) vs. 50% (14/28) (chi-square test, p = 0.009). In addition, regarding the components of the PSQI, older age was further indicated to be related to sleep quality and sleep latency, sleep efficiency, sleep disturbances, and daytime dysfunction in our cohort. In detail, the average age of patients with decreased sleep quality (p < 0.001), prolonged sleep latency (p = 0.043), decreased sleep efficiency (p = 0.037), more sleep disturbances (p = 0.007), and daytime dysfunction (p = 0.009) was greater than that noted for those without. However, no significant differences in the distribution of gender, education level, and marital status were noted between the sleep disorder and normal. Further analysis also failed to establish any correlation between all the components of the PSQI and gender, education level, or marital status in our cohort (Tables 1 and 2, and Additional file 1: Tables S3).

Table 1 Comparison of demographic and clinical symptoms between the normal group (PSQI total score < 7) and the sleep disorder group (PSQI total score ≥ 7)Table 2 Age was associated with various abnormal PSQI item scores in the acute stage of COVID-19Manifestations of sleep disorders during the acute stage of COVID-19

In our cohort, 47.2% (25/53) of patients presented sleep disorders (PSQI total score ≥ 7). For the scores of various components of the PSQI, the most common presentation of sleep disorders was sleep disturbances (88.7%, 47/53) followed by decreased sleep quality (71.7%, 38/53), prolonged sleep latency (62.3%, 33/53), shorter sleep duration (66.0%, 35/53), and daytime dysfunction (58.5%, 31/53). The least common presentations included decreased sleep efficiency (45.3%, 24/53) and sleep medication usage (18.9%, 10/53).

Respiratory symptoms, hypoxemia, and carbon dioxide retention correlated with sleep disorders in the acute phase of COVID-19

Among subjects in our cohort, 60.4% (32/53) complained of respiratory symptoms, including chest distress or dyspnea; 52.8% (28/53) complained of chest distress, and 34.0% (18/53) presented dyspnea. All patients underwent chest CT scanning, and arterial blood gas analysis was performed in 51.0% (27/53) of the subjects. When comparing the two groups, we obtained the correlation between sleep disorders and respiratory dysfunction biomarkers.

Carbon dioxide retention (PaCO2 > 45 mmHg) was more commonly present in the sleep disorder group (PSQI total score ≥ 7) (5/15, 33%) than in the normal group (0/12, 0%) (Fisher’s exact test, p = 0.041). No significant differences in the percentage of patients with other respiratory dysfunction biomarkers, including chest distress, dyspnea, hypoxemia (PaO2 < 80 mmHg), the number of lobes involved in chest CT, pH value, AB, SB, tCO2and SaO2were noted between the sleep disorder group and the normal group (Tables 1 and 3, Additional file 1: Tables S1 and S2).

Table 3 Anemia and carbon dioxide retention were correlated with sleep disorders (PSQI total score ≥ 7) in the acute stage of COVID-19

Through the analysis of the association between respiratory dysfunction biomarkers and seven components of the PSQI, we found that carbon dioxide retention was more likely to be present in patients with longer sleep latency (5/15, 33%) than in those without (0/12, 0%) (Fisher’s exact test, p = 0.047). Dyspnea was associated with impaired sleep quality and sleep latency. Thus, the percentages of patients with dyspnea among patients with decreased sleep quality (42.1% (16/38) vs. 13.3% (2/15), p = 0.046) and prolonged sleep latency (45.4% (15/33) vs. 15% (3/20), p = 0.023) were greater than that noted among those without. The number of lobes involved in chest CT was greater in subjects with prolonged sleep latency (4.19 ± 1.98) compared with those without prolonged sleep latency (2.74 ± 2.33) (p = 0.022). However, we did not find an association between chest distress, hypoxemia, pH value, AB, SB, tCO2, or SaO2, and any of the components of the PSQI (Table 4, Additional file 1: Tables S3, and S4).

Table 4 Respiratory dysfunction-related factors (dyspnea, anemia, greater number of lobes involved in chest CT and carbon dioxide retention (PaCO2 > 45 mmHg)) were associated with abnormal PSQI scores in the acute stage of COVID-19Anemia is associated with sleep disorders in the acute phase of COVID-19

Furthermore, we also analyzed the association between anemia and sleep disorders in our COVID-19 cohort and found that anemia was more common in the sleep disorder group (PSQI total score ≥ 7) (8/25, 32%) compared with the normal group (2/28, 8%) (chi-square test, p = 0.021). Moreover, anemia was more likely to be present in patients with decreased sleep efficiency (8/24, 33%) than in those without (2/29, 7%) (chi-square test, p = 0.036). However, we did not find any correlation between anemia and any of the other six components of the PSQI except sleep efficiency (Tables 3 and 4).

Factors not associated with sleep disorders during the acute phase of COVID-19

In addition, we further analyzed whether immune and inflammatory factors, including fever, leukocyte count, lymphocyte count, hsCRP, and PCT, were associated with the incidence of sleep disorders in our COVID-19 cohort. However, we did not find significant differences between the sleep disorder group and the normal group for the factors above, including the percentage of patients with fever, leukocyte count, lymphocyte count, hsCRP level, and PCT level. Further analysis also did not indicate any association between these factors and the seven components of the PSQI, except for the association between PCT level and sleep efficiency (p = 0.047) and leukocyte count and sleep efficiency (p = 0.030). However, the limited patient number in our cohort prevented us from establishing any correlations (Additional file 1: Tables S1 and S4).

Besides, no significant differences in the proportion of patients with increased D-dimer, FIB, CK, or LDH levels were noted between the sleep disorder group and the normal group or between patients with or without any of the components of the PSQI (Additional file 1: Tables S1 and S4). Furthermore, no association was found between increased D-dimer or FIB and any of the components of the PSQI (Additional file 1: Tables S1 and S4).

Finally, no significant differences in the percentage of patients with digestive symptoms, neurological symptoms, elevated alanine transaminase (ALT) levels, and aspartate aminotransferase levels (AST) were noted between the sleep disorder group and the normal group or between patients with or without any of the components of the PSQI (Additional file 1: Tables S1 and S4).