Abstract
Objective
Understanding the clinical factors associated with the severity of coronavirus disease 2019 (COVID-19) is very important for the effective use of limited medical resources, including the appropriate evaluation of the need for hospitalization and discharge.
Methods
Patients hospitalized with a diagnosis of COVID-19 from March 2021 to October 2022 were included in the study. Patients admitted to our facility were classified into four waves: 4th (April to June 2021), 5th (July to October 2021), 6th (January to June 2022), and 7th waves (July to October 2022). We analyzed the severity, patients’ background characteristics, presence of pneumonia on chest computed tomography (CT), and blood test results in each wave. Patients were further classified into respiratory failure and nonrespiratory failure groups and statistically compared.
Results
Of the 565 patients diagnosed with COVID-19, 546 were included in this study. The percentage of patients classified as mild was approximately 10% in the 4th and 5th waves, but the rate increased after the 6th wave, with rates of 55.7% and 54.8% in each wave. Although more than 80% of patients in the 4th and 5th waves showed pneumonia on chest CT, the percentage decreased to approximately 40% after the 6th wave. Further comparisons between the respiratory failure group (n=75) and the nonrespiratory failure group (n=471) revealed significant differences in the age, sex, vaccination history, and biomarker values between the two groups.
Conclusion
In this study, elderly men were found to be more likely to develop severe disease than others, and biomarkers of COVID-19, such as C-reactive protein and lactate dehydrogenase, were useful for predicting severity. This study also suggested that vaccination may have contributed to a reduced disease severity.
Keywords: COVID-19, respiratory failure, vaccine
Introduction
At the end of 2019, a case of coronavirus disease 2019 (COVID-19) was first confirmed in Wuhan, Hubei Province, China. In Japan, the first case was confirmed on January 16, 2020, followed by an outbreak on the Diamond Princess docked in the port of Yokohama in early February. The outbreak quickly spread throughout the world, and as of June 2022, more than 500 million people had been infected worldwide, resulting in more than 6 million deaths. In Japan, 7,833,878 people were infected, and 29,540 deaths were confirmed as of the end of April 2022 (1).
In Japan, in addition to basic infection control measures, preventative measures such as “three-density prevention” have been adopted since the early stages of the pandemic, which has kept the COVID-19 pandemic relatively in check compared to Western countries. In addition, vaccination and new therapeutic methods have been aggressively introduced into the medical field, which have reduced the scale of the pandemic and direct health risks. However, the number of people infected with COVID-19 was still high at the end of 2022 due to the repeated appearance of mutant variants, and there are also limitations on restricting the behavior of people in normal socioeconomic activities, which has hampered disease control.
Most COVID-19 patients have cold-like symptoms, such as a fever, cough, and sore throat, and most recover within a week after infection and avoid a fatal outcome, successfully returning to society within a few weeks with no adverse effects. However, some patients may develop acute respiratory distress syndrome (ARDS), which can lead to severe respiratory failure, so patients with factors associated with severe COVID-19 infection require an early diagnosis and aggressive therapeutic intervention, including antiviral and neutralizing antibody drugs and high-concentration oxygen inhalation.
Understanding COVID-19 severity-related factors means identifying patients who require aggressive therapeutic intervention. As the number of COVID-19 patients continues to increase, it is necessary to make effective use of limited medical resources. In a previous report, Tamura developed an original model using the, “Kanagawa admission priority assessment score (KAPAS),” to determine the need for hospitalization for COVID-19 patients. The KAPAS comprises clinical factors, such as the age, body mass index (BMI), vital signs, chest imaging findings, and underlying conditions. They showed that the KAPAS was significantly correlated with oxygen requirement, so this score may be useful for determining which patients are most likely to require hospitalization (2).
In the present study, we attempted to clarify the clinical characteristics of COVID-19 patients themselves in 2021-2022 by analyzing the patients' background characteristics, chest imaging findings, and laboratory data, as well as investigating the clinical factors related to severity and identifying patient groups that might be susceptible to severe disease.
Materials and Methods
This retrospective cohort study was conducted at a municipal hospital in Kagawa Prefecture in Shikoku, Japan. Our facility has 20 hospital beds set aside for COVID-19 patients and maintains a high bed occupancy rate, mainly for patients with mild to moderate disease. This retrospective study was approved by the institutional review board of Takamatsu Municipal Hospital (No. 2022-01, approved on May 2, 2022).
The graph in Fig. 1 shows the number of COVID-19 patients accepted at our facility. Since March 2020, we have been treating COVID-19 patients in the infection ward, and by the end of October 2022, a total of 565 COVID-19 patients had been admitted. This study covers inpatients seen from March 2021, when the number of COVID-19 patients in Kagawa Prefecture began to increase, to October 2022, when the 7th wave of COVID-19 was ending.
Figure 1.
The number of patients accepted at our facility from March 2021 to October 2022. The highest number of patients admitted to our hospital per month was in August 2021 during the 5th wave, with 62 COVID-19 patients. No patients were admitted to the infection ward in our facility in November or December 2021.
Patients were classified according to the following four periods: 4th (April to June 2021), 5th (July to October 2021), 6th (January to June 2022), and 7th waves (July to September 2022), which correspond to major pandemic periods in Japan. At our facility, all patients admitted to the hospital with COVID-19 are assessed for severity by evaluating their oxygenation and performing blood tests and chest computed tomography (CT) at the time of admission. In accordance with the “Guideline for the Clinical Practice of Novel Coronavirus Infections, Version 8.0" formulated by the Ministry of Health, Labour and Welfare, patients were divided into four groups based on disease severity: mild, moderate I, moderate II, and severe. Patients' background characteristics (age, sex, underlying disease, etc.), symptoms, presence of pneumonia on chest CT, and blood test findings were analyzed in each period.
Patients were then further classified into two groups based on the presence of respiratory failure (Fig. 2). Patients who had a decrease in SpO2 ＜90% during hospitalization were classified into the respiratory failure group, while all others were classified into the nonrespiratory failure group. Both groups were analyzed with respect to the age, presence of obesity, presence of underlying or preexisting diseases, pneumonia, vaccination history, and COVID-19 severity markers [C-reactive protein (CRP), lactate dehydrogenase (LD), D-dimer, ferritin, interleukin (IL)-6, interferon (IFN)-λ3], and these parameters were statistically compared between the groups. Severity biomarkers were basically measured at the time of admission, but some markers, such as the CRP and LD, were measured frequently during hospitalization. The highest value among multiple measurements was then adopted. The diagnosis of COVID-19 was based on a positive result by the nicking endonuclease amplification reaction (NEAR) or polymerase chain reaction (PCR) method, and patients under 18 years old and pregnant women were excluded (Fig. 2).
Figure 2.
Flowchart of the patients included in the study. Patients were divided into a respiratory failure group and a nonrespiratory failure group. Pregnant women and children under 18 years old were excluded.
When comparing the clinical characteristics of patients and severity biomarkers between the respiratory failure and nonrespiratory failure groups, a multivariate logistic regression model was applied. Clinical and laboratory variables collected at admission or during hospitalization with a p value ＜0.05 in a univariate analysis were included in the model as factors that might be related to respiratory failure. To examine the parameters for predicting severity associated with COVID-19, the adjusted odds ratio (AOR) with a 95% confidence interval (CI) was estimated, and p values ＜0.05 were considered statistically significant.
Results
A total of 565 patients who were hospitalized with COVID-19 were included in the study. Excluding 19 patients, 546 patients (321 men and 225 women) were included in the analysis. These 546 patients were classified by wave with respect to the age, sex ratio, inpatient days, and severity (Table 1). The highest number of patients admitted to our hospital per month was during the 5th wave in the summer of 2021, with 62 COVID-19 patients admitted per month. During the 4th and 5th waves in 2021, despite the relatively young age of the admitted patients, the proportion of patients classified as mild was very small, accounting for approximately 10% of the total.
Table 1.
Clinical Characteristics of the Patients Classified According to the Severity in Each Wave.
| Wave | 4th | 5th | 6th | 7th | Total |
|---|---|---|---|---|---|
| All patients | 91 | 106 | 231 | 137 | 565 |
| Exclusion | 1 | 5 | 7 | 6 | 19 |
| Including patients | 90 | 101 | 224 | 131 | 546 |
| Male:Female (male ratio) | 51:39 (56.7%) | 66:35 (65.3%) | 127:97 (56.4%) | 74:57 (56.5%) | 318:229 (58.6%) |
| Age | 55 | 49.5 | 74 | 76 | 66 |
| Length of stay | 15 | 11 | 11 | 14 | 12 |
| Severity | |||||
| Mild (n, %) | 7 (7.8%) | 12 (11.9%) | 123 (55.7%) | 69 (54.8%) | 211 (38.6%) |
| Moderate I (n, %) | 70 (77.8%) | 63 (60.4%) | 53 (23.7%) | 35 (26.7%) | 221 (40.5%) |
| Moderate II (n, %) | 6 (6.7%) | 18 (17.8%) | 31 (14.0%) | 16 (12.7%) | 71 (13.0%) |
| Severe (n, %) | 6 (6.7%) | 7 (6.9%) | 13 (5.9%) | 5 (4.0%) | 31 (5.7%) |
| Death (n, %) | 1 (1.1%) | 1 (1.0%) | 4 (1.8%) | 6 (4.6%) | 12 (2.2%) |
Age and Length of stay represent the median.
Toward the end of 2021, COVID-19 decreased its pace gradually, and there was a period of approximately two months when no patients were admitted to the infection ward in our facility. In early 2022, however, the number of COVID-19 patients increased again at an unprecedented rate as the 6th wave began in Japan. This period coincided with the replacement of the Delta variant with the Omicron variant as most prevalent, and in January 2022, 52 COVID-19 patients, the maximum number per month in our facility during the 6th wave, were admitted.
Although the number of patients has been increasing rapidly since the 6th wave, Table 1 shows that the proportion of patients classified as mild has been increasing, and the proportion of patients who develop respiratory failure during hospitalization has been decreasing. While the number of patients classified as mild has clearly increased since the beginning of 2022, Table 1 also shows that a certain percentage of patients still fell into the severe category from the 4th to 7th waves. Although the proportion of patients in the 6th and 7th waves who were classified as having severe disease tended to decrease, the number and proportion of patients who died during hospitalization increased. During the 4th and 5th waves, approximately 90% of hospitalized patients had images of bilateral pneumonia consistent with COVID-19 and were classified as moderate I or more severe cases. However, after the 6th wave, the percentage of patients with images of pneumonia decreased to approximately 40% among hospitalized patients, and the percentage developing respiratory failure during hospitalization also decreased (Table 2).
Table 2.
The Percentage of Patients with Pneumonia, Respiratory Failure and Oxygen Support.
| Wave | 4th | 5th | 6th | 7th | Total |
|---|---|---|---|---|---|
| Pneumonia (n, %) | 82 (91.1%) | 86 (86.1%) | 92 (40.9%) | 50 (38.2%) | 310 (56.7%) |
| Respiratory failure (n, %) | 12 (13.3%) | 18 (17.8%) | 29 (13.1%) | 16 (12.7%) | 75 (13.7%) |
| Oxygenation (n, %) | 17 (18.9%) | 31 (30.7%) | 50 (22.2%) | 34 (26.0%) | 132 (24.1%) |
| Use of ventilation | HFNC 6 (6.7%) | HFNC 7 (6.9%) | HFNC 9 (4.0%) | HFNC 7 (5.3%) | HFNC 29 (5.3%) |
| (HFNC, NPPV) (n, %) | NPPV 3 (3.5%) | NPPV 2 (2.0%) | NPPV 3 (1.3%) | NPPV 4 (3.1%) | NPPV 12 (2.2%) |
| Both 3 (3.3%) | Both 2 (2.0%) | Both 2 (1.0%) | Both 3 (2.3%) | Both 32 (1.8%) |
We also summarized how patients' symptoms changed during each wave (Table 3). From the 4th to 7th waves, the two most common symptoms of COVID-19 patients were a fever and cough, which were present in approximately 90% of patients admitted to our facility. Around the 4th and 5th waves, many patients complained of dyspnea, and in the 5th wave, approximately one-third of patients admitted to the hospital complained of breathlessness. However, as the proportion of patients with pneumonia on chest CT decreased, the proportion of patients complaining of dyspnea also tended to decrease, falling to 12.2% of all hospitalized patients by the 7th wave. In contrast, the proportion of patients complaining of a sore throat increased in the 7th wave.
Table 3.
The Changes of Clinical Symptoms in Each Wave.
| Wave | 4th | 5th | 6th | 7th | Total |
|---|---|---|---|---|---|
| Fever (n,%) | 82 (91.1%) | 101 (100%) | 202 (90.0%) | 123 (93.9%) | 508 (92.9%) |
| Cough (n,%) | 76 (84.4%) | 95 (94.1%) | 204 (90.7%) | 123 (93.9%) | 498 (91.0%) |
| Sore throat (n,%) | 23 (25.6%) | 38 (37.6%) | 77 (34.2%) | 59 (45.0%) | 197 (36.0%) |
| Dyspnea (n,%) | 23 (25.6%) | 34 (33.7%) | 46 (20.4%) | 16 (12.2%) | 119 (21.8%) |
| Taste/smell disorder (n,%) | 33 (36.7%) | 38 (37.6%) | 21 (9.3%) | 8 (6.1%) | 29 (5.3%) |
We further classified patients into a respiratory failure group (n=75) and a nonrespiratory failure group (n=471) and performed statistical analyses between the two groups for the patient characteristics, such as the age, sex, underlying diseases, history of emphysema, vaccination history, and levels of COVID-19 severity biomarkers (Table 4). The average age in the nonrespiratory failure group was 62.5 years old, while that in the respiratory failure group was 71.9 years old, which was significantly older (p＜0.001). The ratio of men was higher than that of women in all waves, and 77.3% of patients in the respiratory failure group were men (p＜0.001). The percentage of patients with pneumonia on CT (p＜0.001) and a history of emphysema (p=0.047) was also higher in the respiratory failure group than in the nonrespiratory failure group. However, a statistical comparison of the two groups with respect to the history of smoking, diabetes, hypertension, and dyslipidemia showed no significant differences, although the respiratory failure group had a higher proportion of smokers and a greater number of patients with diabetes, which can be risk factors for COVID-19, than the nonrespiratory failure group. The proportion of obese patients also did not differ significantly between the two groups. However, a comparison of the patients who had received at least two doses of a vaccine between the two groups showed that there were significantly more patients who had received a vaccination in the nonrespiratory failure group than in the respiratory failure group (p=0.025). In addition, a comparison of the two groups with respect to COVID-19 severity biomarkers (CRP, LD, ferritin, D-dimer, IL-6 and IFN-λ3) revealed higher values in the respiratory failure group than in the nonrespiratory failure group (Table 4).
Table 4.
Risk Factors and Laboratory Data of COVID-19 Patients Associated with Severity between Respiratory Failure Group and Non-respiratory Failure Group.
| Group | Respiratory failure (n=75) | Non respiratory failure (n=471) | p value | |
|---|---|---|---|---|
| Age, average | 71.9 | 62.5 | <0.001 | |
| Male to female ratio (%) | 58 (77.3%):17 (22.7%) | 263 (55.8%):208 (44.2%) | <0.001 | |
| Pneumonia (n, %) | 67 (89.3%) | 243 (51.6%) | <0.001 | |
| Obesity (n, %) | 18 (24.0%) | 137 (29.1%) | 0.41 | |
| Smoking history (n, %) | 21 (28.0%) | 95 (20.2%) | 0.13 | |
| Diabetes (n, %) | 21 (28.0%) | 95 (20.2%) | 0.13 | |
| Hypertension (n, %) | 25 (33.3%) | 162 (34.4%) | 0.896 | |
| Dyslipidemia (n, %) | 5 (6.7%) | 57 (12.1%) | 0.238 | |
| Emphysema (n, %) | 16 (21.3%) | 59 (12.5%) | 0.047 | |
| Vaccination ≥2 (n, %) | 27 (36.0%) | 237 (50.3%) | 0.025 | |
| (numbers of vaccine doses) | (2:14, 3:12, 4:1) | (2:131, 3:91, 4:15) | ||
| Laboratory data, average | ||||
| CRP (mg/dL) | 12.5 | 4.3 | <0.001 | |
| CRP (n, %) | ≥10 mg/dL | 47 (62.7%) | 55 (11.7%) | <0.001 |
| LD (IU/mL) | 391.5 | 232.2 | <0.001 | |
| LD (n, %) | ≥300 IU/mL | 43 (58.1%) | 62 (13.2%) | <0.001 |
| Ferritin (μg/L) | 1,057.0 | 443.1 | <0.001 | |
| D-dimer (μg/mL) | 7.8 | 2.7 | 0.005 | |
| IL-6 (pg/mL) | 167.6 | 30.0 | <0.001 | |
| IFN-λ3 (pg/mL) | 18.8 | 6.7 | <0.001 |
Obesity is defined as BMI (Body mass index) ≥25kg/m2.
A multivariate logistic regression analysis for characteristics of patients and biomarkers was used to identify the risk factors related to the severity of COVID-19. The model included the following variables: age, male sex, pneumonia, emphysema, vaccination, and each biomarker. The other variables were excluded because they had p values ＞0.05. According to this model, for characteristics of patients, age ≥65 years old (p＜0.001; AOR 3.60; 95% CI 1.95 to 6.65), male sex (p=0.004; AOR 2.54; 95% CI 1.35 to 4.77), and pneumonia on CT (p＜0.001; AOR 8.53; 95% CI 3.88 to 18.70) were extracted as risk factors for developing severe disease (Table 5). It was also implied that having received ≥2 vaccinations might reduce the possibility of developing severe disease (p=0.027; AOR 0.50; 95% CI 0.27 to 0.93). This model was applied to biomarkers of COVID-19, and the following variables remained in the model: CRP ≥10 mg/dL (p＜0.001; AOR 9.44; 95% CI 4.23 to 21.00) and LD ≥300 IU/mL (p＜0.001; AOR 5.92; 95% CI 2.51 to 14.00) (Table 6). While IL-6 was statistically significant in the model (p=0.004; AOR 1.01; 95% CI 1.00 to 1.01), the impact was deemed less useful in clinical practice, considering its AOR.
Table 5.
Multivariate Logistic Regression for Severity in Characteristics of the Patients
| Adjusted odds ratio | 95%CI | p value | ||
|---|---|---|---|---|
| Lower limit | Upper limit | |||
| Age ≥65 | 3.60 | 1.95 | 6.65 | <0.001 |
| Sex (male) | 2.54 | 1.35 | 4.77 | 0.004 |
| Pneumonia | 8.53 | 3.88 | 18.70 | <0.001 |
| Emphysema | 1.30 | 0.68 | 2.82 | 0.372 |
| Vaccination ≥2 | 0.50 | 0.27 | 0.93 | 0.027 |
Table 6.
Multivariate Logistic Regression for Severity in Biomarkers of COVID-19
| Adjusted odds ratio | 95%CI | p value | ||
|---|---|---|---|---|
| Lower limit | Upper limit | |||
| CRP ≥10 mg/dL | 9.44 | 4.23 | 21.00 | <0.001 |
| LD ≥300 IU/mL | 5.92 | 2.51 | 14.00 | <0.001 |
| Ferritin (μg/L) | 1.00 | 1.00 | 1.00 | 0.805 |
| D-dimer (μg/mL) | 1.01 | 1.00 | 1.03 | 0.146 |
| IL-6 (pg/mL) | 1.01 | 1.00 | 1.01 | 0.004 |
| IFN-λ3 (pg/mL) | 1.02 | 0.99 | 1.05 | 0.197 |
Discussion
Three years have passed since the global outbreak of COVID-19 began. At the beginning of the pandemic, many people said, “the virus will spontaneously disappear soon,” but this theory proved false, and today, the majority public opinion is, “We have to live with COVID-19," based on the understanding that this virus will never disappear. As of early 2023, the number of COVID-19 patients has been increasing, not decreasing, so it is important to understand the trend in COVID-19 infection itself as well as to identify which patients are at greatest risk of developing severe disease and to which patients limited medical resources should be allocated.
In our analysis, only a small number of patients were classified as mild in the 4th and 5th waves (7.8% and 11.9%, respectively). In particular, in the summer of 2021 (August-October), when the Delta variant swept through Japan in the 5th wave, the proportion of patients who experienced respiratory failure increased, even though the age range of hospitalized patients was younger (median age: 49.5 years old) than in other waves. During the 5th wave, 17.8% of hospitalized patients experienced respiratory failure, and approximately 1 in 3 patients (30.7%) required oxygen inhalation during their stay. Subsequently, the number of COVID-19 patients in Japan tended to decrease toward the end of 2021 due to the end of the 5th wave.
At our hospital, no patients were admitted to the infection ward for approximately two months in November and December 2021, and COVID-19 seemed to be under control for a moment, until the 6th wave of the Omicron variant began in January 2022. It was characterized by the rapid spread of infection, especially among young people, during year-end and New Year's parties and coming-of-age ceremonies at the end of the year and the beginning of the New Year, with the largest-ever increase in the rate of infection. We noted an increase in the proportion of patients who were classified as mild after the 6th wave in the present study. The Omicron variant, which has become the main variant since the 6th wave, grows mainly in the bronchi rather than lungs and is characterized by lower toxicity than the conventional variant (3). Epidemiological studies in Western countries with early pandemics reported lower hospitalization and severity rates with the Omicron variant than with the Delta variant. An analysis in the United Kingdom estimated that the risk of hospitalization or death from the Omicron variant was approximately one-third that from the Delta variant after adjusting for the age, sex, and vaccination history (4). The mutation to the Omicron variant caused an increase in the number of patients classified as mild. A similar trend was seen not only at our institution but also in other countries around the world.
However, as shown in Table 1, the presence of a certain percentage of patients classified as severe and deaths in each pandemic period seems paradoxical. It is natural to assume that, if the number of patients classified as mild is increasing, the number of patients classified as severe and deaths will consequently decrease; however, at our institution, the number of deaths actually increased in the 6th and 7th waves. In this regard, we should pay attention to whether severe patients are severely ill due to COVID-19 itself or due to comorbidities. Although 6-8% of patients in each wave were considered “severe," it was difficult to distinguish strictly whether COVID-19 itself or comorbidities (e.g. heart failure, aspiration pneumonia, chronic lung disease, etc.) were triggering their severe illness, especially in elderly patients or those with underlying diseases. It has often been pointed out that COVID-19 infection can trigger severe illness, especially in elderly patients and those with underlying diseases. Therefore, it should be noted that even when COVID-19 itself is “mild," with no appearance of pneumonia characteristic of COVID-19, there are a certain number of patients whose general condition will deteriorate upon infection and consequently fall into the “severe" category. In other words, it should be kept in mind that “severe" is a very broad term and that the number of patients whose respiratory condition deteriorated purely due to COVID-19 itself was likely small, especially in the 6th and 7th waves.
We also examined the clinical factors associated with severe disease, which has been a controversial issue in countries around the world since the beginning of the pandemic. Among these factors, age was reported to be a particularly important factor in the progression to respiratory failure (5). At our institution, the respiratory failure group was significantly older than the nonrespiratory failure group, suggesting that older age itself is a factor that leads to more severe COVID-19. Although obesity, chronic heart disease, and chronic kidney disease have been reported to be risk factors for progression to severe disease (6), there were no significantly different factors, including preexisting conditions, such as diabetes and hypertension, between respiratory failure group and nonrespiratory failure group at our institution. In addition, a large cohort study of 10,131 patients in the US reported that men were at a higher risk than women for mechanical ventilation and death (7). In this study, there was also a larger proportion of men in the respiratory failure group than in the nonrespiratory failure group (77.3% vs. 55.8%).
Serum biomarkers that predict the severity of disease have also been investigated, including IL-6 and IFN-λ3, which reflect cytokine storms caused by COVID-19, and D-dimer, which reflects hypercoagulability caused by vascular endothelial cell damage, in addition to inflammatory markers, such as CRP, LD, and ferritin. Initially, inflammation markers, such as CRP and LD, while simple and quick to test, lacked disease specificity and were considered insufficient for diagnostic use. Therefore, IL-6 and IFN-λ3, which reflect the cytokine storm caused by an excessive immune response due to infection, came to be covered by insurance in Japan. At our facility, we routinely measure biomarkers of hospitalized patients, and when comparing respiratory failure and nonrespiratory failure groups, the respiratory failure group was found to have significantly higher levels of CRP and LD than the nonrespiratory failure group. The measurement of serum biomarkers thus seems useful as an adjunctive diagnostic tool in determining whether or not hospitalization and monitoring the patients' clinical condition after a diagnosis of COVID-19 is appropriate. Even if a patient is classified as having mild disease on admission, elderly patients and patients with elevated serum biomarkers should be carefully monitored as being at a high risk of developing severe disease.
The present study also examined the efficacy of the COVID-19 vaccine. mRNA vaccination has been shown to induce high neutralizing antibody titers against the Wuhan variant following the second vaccination, and the US CDC reported that 2 doses of the vaccine reduced the hospitalization rate for those ≥65 years old by ＞90% (8). The National Institute of Infectious Diseases in Japan also reported that the vaccine maintained 87% efficacy in preventing the onset of disease, but acquired humoral immunity naturally attenuated after vaccination. Indeed, serum antibody titers after vaccination with Pfizer's vaccine were reported to have decreased to approximately 1/10 by 6 months after the second vaccinations (9), although cellular immunity persisted even 6 months after vaccination, and the effect of preventing severe disease was reported to persist in some cases (10).
In the present study, patients in the respiratory failure and nonrespiratory failure groups who had received at least two doses of vaccine were compared, and the number of patients in the nonrespiratory failure group who had been vaccinated was significantly higher, suggesting that vaccination may have contributed to the prevention of severity in COVID-19 patients with respiratory failure. However, the Omicron variant, which has been prevalent since early 2022, has a higher immune evasion ability than conventional variants, and it has been pointed out that two doses of vaccination may not be sufficient (11). Vaccines against the Omicron variant are rapidly spreading both in Japan and overseas, but it is not possible to conclude from our study to what extent the efficacy of vaccination differs depending on the mutant strain.
While many new therapeutic agents have been introduced since the beginning of the pandemic, the RNA synthesis inhibitor remdesivir has been mainly used at our facility. In a previously reported case, the remdesivir administration group showed a shorter time to clinical improvement than the control group among patients with moderate or less severe disease, while no notable improvement in the survival was shown in cases that were already severely ill (12). Severe acute respiratory syndrome coronavirus 2 grows most actively in the early stages of infection, followed by an excessive inflammatory response due to the host's immune response, resulting in severe disease. For this reason, antiviral drugs are most effective in the early stages of infection, when viral proliferation is active (13). Therefore, at our facility, remdesivir was aggressively administered to patients who had a higher risk of developing severe disease, even if they were diagnosed with mild disease on the day of admission. There were some cases in which steroids were used concurrently for patients with extensive ground-glass opacities on chest CT, but these agents became less frequently used after the 6th wave as the number of patients presenting with pneumonia decreased. Favipiravir, baricitinib, and antibody cocktail therapy was also administered, but there were few opportunities for their use after the 6th wave as well. Given these points, the introduction of new therapeutic agents has certainly expanded our treatment options, but the opportunity to use these drugs in clinical practice has been limited. It is therefore difficult to say, at least at our facility, whether or not the introduction of new therapeutic agents has contributed to an increase in the number of COVID-19 patients with mild disease since the 6th wave.
Several limitations associated with the present study warrant mention. First, we have not been able to track mutant variants strictly on a patient-by-patient basis. It is well known that the Alpha variant mutated to the Delta variant in the summer of 2021 and again, in early 2022, to the Omicron variant, which is considered less toxic than conventional Delta variants. Our patients seem to have followed this trend, but sufficient data on mutant variants for each patient have not been accumulated. Therefore, the reason for the increase in patients with mild disease after the 6th wave - whether due to the increase in Omicron strains, the spread of vaccination, or both - cannot be strictly determined. In the present study, we compared the respiratory failure and nonrespiratory failure groups in overall patients from the 4th through 7th waves, but analyses limited to individual waves, e.g. only among patients in the 6th wave, were not conducted. If such analyses had been conducted, the efficacy of the vaccine could have been evaluated in just patients with the Omicron strain. This seems to be an issue that should be addressed in the future. Second, in addition to measurements at admission, severity markers were sometimes retested during hospitalization, but we have not been able to track these changes. Some reports have indicated that changes in severity markers are useful in predicting severity. For instance, it has been reported that peak values of IFN-λ3, a marker of critical illness, during the clinical course of COVID-19 patients are reached a few days before the transition to respiratory failure (14). However, our report is limited in that it only compared peak values during the hospitalization period and did not consider the changes over time.
Despite the above limitations, our study also has several strengths. First, we were able to analyze a large number of variables in a single medical institution among patients with various backgrounds, ranging from young to old, and with widely varying severities of illness. Another strength is that we were able to compare not only clinical characteristics between severe and nonsevere patients but also the changes in severity and patient background during each pandemic period.
In conclusion, this study revealed that after the 6th wave, the proportion of patients who presented with pneumonia on chest CT decreased, and the number of patients classified as having mild disease increased, with vaccination potentially contributing to this trend. Elderly men were more likely to have a deteriorated respiratory condition and had a higher risk of developing severe disease than others. This study also suggested that biomarkers of COVID-19, such as CRP and LD, were useful for predicting severity.
The authors state that they have no Conflict of Interest (COI).
Acknowledgement
The authors are grateful for the dedicated work of the staff in the infection ward in our hospital. We are convinced that our practice would not be possible without their support.
References
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