PLoS ONE plos plosone PLOS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0273134 PONE-D-22-21582 Research Article Medicine and health sciencesMedical conditionsInfectious diseasesViral diseasesCOVID 19 Biology and life sciencesOrganismsVirusesRNA virusesCoronavirusesSARS coronavirusSARS CoV 2 Biology and life sciencesMicrobiologyMedical microbiologyMicrobial pathogensViral pathogensCoronavirusesSARS coronavirusSARS CoV 2 Medicine and health sciencesPathology and laboratory medicinePathogensMicrobial pathogensViral pathogensCoronavirusesSARS coronavirusSARS CoV 2 Biology and life sciencesOrganismsVirusesViral pathogensCoronavirusesSARS coronavirusSARS CoV 2 Biology and life sciencesPsychologyBehaviorHabitsSmoking habits Social sciencesPsychologyBehaviorHabitsSmoking habits Medicine and health sciencesMedical conditionsInfectious diseasesRespiratory infections Medicine and health sciencesMedical conditionsRespiratory disordersRespiratory infections Medicine and health sciencesPulmonologyRespiratory disordersRespiratory infections Physical sciencesChemistryChemical elementsOxygen Biology and life sciencesImmunologyVaccination and immunizationAntiviral therapy Medicine and health sciencesImmunologyVaccination and immunizationAntiviral therapy Medicine and health sciencesPublic and occupational healthPreventive medicineVaccination and immunizationAntiviral therapy Biology and life sciencesImmunologyVaccination and immunization Medicine and health sciencesImmunologyVaccination and immunization Medicine and health sciencesPublic and occupational healthPreventive medicineVaccination and immunization Biology and life sciencesPhysiologyRespiratory physiology Severity predictors of COVID-19 in SARS-CoV-2 variant, delta and omicron period; single center study Severity Predictors of COVID-19 in SARS-CoV-2 Variant https://orcid.org/0000-0002-7069-9334 Ogawa Fumihiro Conceptualization Data curation Formal analysis Investigation Project administration Writing – original draft Writing – review & editing 1 * Oi Yasufumi Investigation 1 Honzawa Hiroshi Investigation 1 Misawa Naho Investigation 1 Takeda Tomoaki Investigation 1 Kikuchi Yushi Investigation 1 Fukui Ryosuke Investigation 1 Tanaka Katsushi Investigation 2 Kano Daiki Investigation 2 https://orcid.org/0000-0002-7853-5721 Kato Hideaki Investigation 2 Abe Takeru Data curation Formal analysis Visualization 1 Takeuchi Ichiro Supervision 1 Department of Emergency Medicine, Yokohama City University, School of Medicine, Yokohama, Kanagawa, Japan Infection Prevention and Control Department, Yokohama City University Hospital, Yokohama, Kanagawa, Japan Liu Benjamin M. Editor Children’s National Hospital, George Washington University, UNITED STATES

The authors received no specific competing interests for this work.

 * E-mail: fumihiro@yokohama-cu.ac.jp 25 10 2022 2022 17 10 e0273134 2 8 2022 9 10 2022 2022 Ogawa et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background

The outcomes of coronavirus disease 2019 (COVID-19) treatment have improved due to vaccination and the establishment of better treatment regimens. However, the emergence of variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, and the corresponding changes in the characteristics of the disease present new challenges in patient management. This study aimed to analyze predictors of COVID-19 severity caused by the delta and omicron variants of SARS-CoV-2.

 Methods

We retrospectively analyzed the data of patients who were admitted for COVID-19 at Yokohama City University Hospital from August 2021 to March 2022.

 Results

A total of 141 patients were included in this study. Of these, 91 had moderate COVID-19, whereas 50 had severe COVID-19. There were significant differences in sex, vaccination status, dyspnea, sore throat symptoms, and body mass index (BMI) (p <0.0001, p <0.001, p <0.001, p = 0.02, p< 0.0001, respectively) between the moderate and severe COVID-19 groups. Regarding comorbidities, smoking habit and renal dysfunction were significantly different between the two groups (p = 0.007 and p = 0.01, respectively). Regarding laboratory data, only LDH level on the first day of hospitalization was significantly different between the two groups (p<0.001). Multiple logistic regression analysis revealed that time from the onset of COVID-19 to hospitalization, BMI, smoking habit, and LDH level were significantly different between the two groups (p<0.03, p = 0.039, p = 0.008, p<0.001, respectively). The cut-off value for the time from onset of COVID-19 to hospitalization was four days (sensitivity, 0.73; specificity, 0.70).

 Conclusions

Time from the onset of COVID-19 to hospitalization is the most important factor in the prevention of the aggravation of COVID-19 caused by the delta and omicron SARS-CoV-2 variants. Appropriate medical management within four days after the onset of COVID-19 is essential for preventing the progression of COVID-19, especially in patients with smoking habits.

 The authors received no specific funding for this work. Data Availability The data used in this paper were acquired from https://doi.org/10.6084/m9.figshare.20652678.v2. Outbreaks COVID-19 Introduction

The coronavirus disease 2019 (COVID-19) pandemic has caused a significant increase in hospitalizations for pneumonia with multiorgan disease. COVID-19 is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccines and treatment methods against SARS-CoV-2 are increasingly being developed worldwide. Although the number of patients with COVID-19 is still high, these measures have led to the reduction of disease severity and improvement of prognosis after the onset of symptoms. However, despite tremendous efforts by scientists, researchers, and health practitioners to combat the COVID-19 pandemic, the emergence of new variants of SARS-CoV-2 present new challenges in patient management. As the development of vaccines and treatments progresses and patient outcomes improve, SARS-CoV-2 repeatedly mutates and tries to survive these improved therapies. The World Health Organization and Centers for Disease Control and Prevention have identified the alpha, beta, gamma, delta, epsilon, eta, lota, kappa, mu, zeta, and omicron variants of SARS-CoV-2 [1, 2]. The recently identified delta and omicron variants are both highly infectious and are different from the previous strains in terms of infectivity, severity, and clinical symptoms. In addition, it is unknown what an optimal timing for hospitalization is to prevent exacerbation. Thus, retrospective analysis of the characteristics and aggravation markers of patients newly infected with the delta and omicron variants of SARS-CoV-2 is essential and will lay the groundwork for the management of the emergence of new mutant strains of SARS-CoV-2. Therefore, this study aimed to analyze and describe the characteristics, clinical features, and outcomes of COVID-19 caused by the delta and omicron variants of SARS-CoV-2 according to disease severity. We also identified an optimal cut-off point between onset and hospitalization for lower severity.

 Patients and methods

This was a retrospective study conducted using the data of patients with COVID-19 (except for outpatients with mild COVID-19) who underwent standard treatment, including intensive care, at Yokohama City University Hospital from June 2021 to March 2022. The clinical and biological features of the patients were analyzed. During the observation period, June 2021 to December 2021 and January 2022 to March 2022 were marked by a surge in the number of infections caused by the delta and omicron variants of SARS-CoV-2, respectively. Thus, we defined June 2021 to December 2021 as the ‘delta period’ and January 2022 to March 2022 as the ‘omicron period.’ If a patient with mild COVID-19 had a severe risk factor, such as old age, chronic kidney disease that requires hemodialysis, or severe immunosuppression, we judged the patient’s condition and COVID-19 severity and decided on hospitalization. The characteristics, risk factors, morbidity, and mortality outcomes of the patients were analyzed as well. Information on comorbidities were obtained for each patient, and outcome data were obtained during follow-up in our hospital.

This study was approved by the Ethics Committee of the Yokohama City University School of Medicine. Written informed consent was obtained from each patient or their family members before treatment.

Classification of coronavirus disease severity

Disease severity was categorized into the mild-moderate stage (moderate group) and the severe-critical stage (severe group) based on previously published guidelines on the diagnosis and treatment of COVID-19 [3]. Mild cases were defined as patients with no symptoms, no need for oxygen, and no sign of pneumonia on computed tomography (CT) scans. Moderate cases were defined as patients with mild respiratory symptoms, radiological evidence of pneumonia, and oxygen saturation (SpO2) >93% and <96%. Severe cases were defined as patients with SpO2 ≤92% and requiring oxygen support. Critical cases were defined as patients that required heart–lung machine or extracorporeal membrane oxygenation (ECMO) support for acute respiratory distress syndrome (ARDS). Severe and critical cases were combined into the same group in this study. We did not consider CT score [4, 5] in the classification of COVID-19 severity.

 Sample collection

The blood samples of the patients were collected at admission depending on the clinical conditions of the patients with severe COVID-19 who needed oxygen and those in critical conditions who needed intubation management. Table 1 shows the severity classification and treatment strategies for the patients with critical COVID-19.

10.1371/journal.pone.0273134.t001 Severity classification criteria and therapeutic strategy for critical COVID-19.
● Criteria for mild, severe or critical COVID-19
1) Moderate or severe: Oxygen demand or with risk factors: age, chronic renal failure, severe obesity, etc.2) Critical: SpO2 ≤92% at 10L/min. oxygen via a reservoir mask
3) Critical: Shortness of breath with respiratory rate of >30/min.
4) Critical: Severe dyspnea due to COVID-19 pneumonia● Therapeutic strategy for moderate or severe COVID-191) Moderate: Neutralizing antibody2) Severe: antiviral therapy: Remdesivir 5–10 days3) Severe: Systemic steroid therapy: Dexamethasone 5–10 daysAntibiotics: depend on patients’ comorbidities for CAP
● Therapeutic strategy for critical COVID-19
1)Mechanical ventilator mode pressure control
(primary setting) PEEP 10–15 mmH2O
Driving Pressure 20–25 mmH2O
Respiratory Rate 12-16/min.
(optional therapies) Self-prone position
Pone position
ECMO
2) Antiviral therapy Remdesivir 5 or 10 days
3) Systemic steroid therapy Dexamethasone 10 days
4) Anticoagulant therapy UFH with therapeutic dose according to APTT (1.5–2 times as normal)
5) Protection for DVT Intermittent air compression and elastic stocking
6) Antibiotics for CAP or secondary bacterial or fungus infection
7) Rehabilitation early intervention by NS, PT and OT
8) Nutrition early intervention via tube feeding or TPN
9) Supportive therapy sedation, catecholamine support etc. via central venous catheter

CAP: community associated pneumonia; PEEP: positive end-expiratory pressure; PS: pressure support; ECMO: Extracorporeal membrane Oxygenation; UFH: unfractionated heparin; APTT: activated partial thromboplastin time; NS: nurse; PT: physical therapist; OT: occupational therapist; TPN: total parenteral nutrition.

On admission to our hospital, COVID-19 was diagnosed using a positive reverse transcriptase–polymerase chain reaction (RT-PCR) assay for SARS-CoV-2. The RT-PCR assay was performed using respiratory tract and laryngeal swab samples, which were sent to a designated diagnostic laboratory. Standard procedures for sample collection were used to ensure that all the samples were treated rapidly and equally.

 Data collection

Patients were followed up until hospital discharge or death. The patient information collected included demographic characteristics, pre-existing comorbidities on the date of hospitalization, and laboratory test results. The laboratory tests included measurement of several hemostatic biomarkers, such as white blood cell count (WBC) and D-dimer, C-reactive protein (CRP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), blood urea nitrogen (BUN), creatinine (Cre), total bilirubin (T.bil), and interleukin-6 (IL-6) levels, which have been reported to be severity markers of COVID-19. These biomarker levels measured from the day of admission to the day of discharge depending on the patient’s condition.

We recorded the clinical interventions administered during the observation period, including the use of antibiotics, antiviral agents, systemic corticosteroids, vasoactive medications, venous thromboembolism prophylaxis, antiplatelet or anticoagulation treatment, renal replacement therapy, high-flow oxygen therapy, and mechanical ventilation (invasive and noninvasive).

 Statistical analysis

Patients were divided into two groups for comparison: the moderate group and the severe group. For each group, medians (interquartile ranges) and frequencies (%) were reported for continuous and categorical variables, respectively. We used the Mann-Whitney U test for analysis of continuous variables and Fisher’s exact test for the evaluation of categorical variables. Patient characteristics, time from the onset of COVID-19 or acquisition of a positive PCR result to hospitalization, results of standard blood tests, and physical condition were analyzed. In addition, repeated measures analyses of variance were used to evaluate group and time differences, as well as their interactions, for WBC and D-dimer, CRP, AST, ALT, LDH, BUN, Cre, T.bil, and IL-6 levels. We utilized a multivariable logistic regression model to identify the relationship between COVID-19 severity and patient characteristics, including primary symptoms, comorbidities, and laboratory data. In addition, to determine an optimal cut-off value of the interval between onset and admission for different periods of COVID-19 variants, the receiver operating curve, ROC, analysis was performed. The area under the curve, AUC, sensitivity, specificity, and their 95% confidence interval were calculated. Two-sided P values < 0.05 were considered statistically significant. All statistics analyses were conducted using JMP Pro Version 15 (SAS Institute Inc., Cary, NC, USA) and IBM SPSS Statistics for Windows, Version 25.0 (Armonk, NY: IBM Corp).

 Results

We analyzed 141 patients with COVID-19 (median age, 58.0 [IQR, 56–62] years) who required hospitalization and were admitted to our hospital during the study period. Baseline demographic characteristics, sex, age, time from onset of COVID-19 to hospitalization, primary symptoms, physical characteristics, smoking habits, vaccination status, and comorbidities of the patients are reported in Table 2. Characteristics of patients admitted during the delta and omicron periods are summarized in S1 Table. The data showed significant differences in patients’ age, time from onset to hospitalization, dyspnea as a primary symptom, weight, body mass index (BMI), cardiac disease, renal dysfunction, continuous hemodialysis, and hypertension as comorbidities between the two periods (p<0.001, p<0.001, p = 0.03, p = 0.027, p = 0.024, p<0.001, p = 0.0003, p = 0.01, and p = 0.003, respectively). The major difference between the delta and omicron periods was believed to be attributable the effect of vaccination on the public.

10.1371/journal.pone.0273134.t002 Patients’ characteristics.
All Cases (n = 141)
Sex (Male, %)   99 (70.2%)
Age (y.o., Median ±SD, range)   58±16.1 (18–93)
Period from onset to hospitalization (days, Median ±SD, range)   5±4.2 (0–22)
Period from onset to PCR positive (days, Median ±SD, range)   1±2.3 (0–13)
Symptom (cases, %)    
  fever 126 (89.4)
  dyspnea 79 (56.0)
  cough 54 (38.3)
  fatigue 48 (34.0)
  sore throat 10 7.1)
  consciousness disorder 1 (0.7)
  headache 4 (2.8)
Height (cm, median ±SD, range)   165±10.1 (123–189)
Weight (kg, median ±SD, range)   65±17.1 (34–121)
BMI (median ±SD, range)   24±5.0 (14–46)
smoking habit (cases, %)   77 (54.6)
vaccination (yes, %)   55 (39.0)
comorbidities (cases, %)    
  respiratory disease 20 (14.2)
  cardiovascular disease 29 (20.6)
  renal disease 40 (28.4)
  continuous hemodialysis 30 (21.3)
  diabetes 38 (27.0)
  hypertension 61 (43.3)
  hyperlipidemia 25 (17.7)
  collagen diseases 5 (3.5)
  with malignant tumor 9 (6.4)
  pregnancy 2 (1.4)
  immunosuppression drugs 7 (5.0)

y.o.: year-old, SD: Standard Deviation, PCR: Polymerase Chain Reaction

BMI: Body Mass Index

 Classification of coronavirus disease severity

Of the 141 patients analyzed in this study, 91 had moderate COVID-19 and did not need intensive care, whereas 50 had severe COVID-19 and needed mechanical ventilation. The characteristics, primary symptoms, comorbidities, and primary laboratory data of the patients classified according to COVID-19 severity are shown in Table 3.

10.1371/journal.pone.0273134.t003 Clinical data in severity classification (n = 141).
Moderate group (n = 91) Severe group (n = 50) p value
Sex (Male, %) 55 (60.4) 44 (88.0) ***<0.001
Age (y.o. Median ±SD, range) 59±17.5 (18–93) 57±13.3 (30–93) 0.312
Period from onset to hospitalization (days, Median ±SD, range) 3±3.6 (0–13) 8±4.2(0–22) ***<0.001
Period from onset to PCR positive (days, Median ±SD, range) 1±2.3 (0–13) 2±2.3 (0–10) 0.062
Symptom (cases, %)      
  fever 79 (86.8) 47 (94.0) 0.192
  dyspnea 39 (42.9) 40 (80.0) ***<0.001
  cough 36 (39.6) 18 (36.0) 0.681
  fatigue 36 (39.6) 12 (24.0) 0.062
  sore throat 10 (11.0) 0 (0) 0.151
  consciousness disorder 1 (1.1) 0 (0) 0.462
  headache 4 (4.4) 0 (0) 0.132
Height (cm, median ±SD, range) 162.5±10.8 (123–189) 168.5±8.0 (148–181) **0.005
Weight (kg, median ±SD, range) 59.9±16.0 (34.1–121) 74.1±16.5 (43.5–110) ***<0.001
BMI (median ±SD, range) 22.8±10.8 (13.8–45.5) 26.3±8.0 (16.6–38) ***<0.001
smoking habit (cases, %) 42 (46.2) 35 (70.0) **0.007
vaccination (cases, %) 44 (48.4) 11 (22.0) **0.002
comorbidities (cases, %)      
  respiratory disease 14 (15.4) 6 (12.0) 0.584
  cardiovascular disease 23 (25.3) 6 (12.0) 0.062
  renal disease 32 (35.2) 8 (16.0) *0.016
  continuous hemodialysis 24 (26.4) 6 (12.0) *0.046
  diabetes 26 (28.6) 12 (24.0) 0.562
  hypertension 38 (41.8) 23 (46.0) 0.631
  hyperlipidemia 12 (13.2) 13 (26.0) 0.062
  collagen diseases 4 (4.4) 1 (2.0) 0.461
  with malignant tumor 8 (8.8) 1 (2.0) 0.112
  pregnancy 1 (1.1) 1 (2.0) 0.672
  immunosuppression drugs 5 (.5) 2 (4.0) 0.723
Laboratory Data      
  IL-6 (pg/ml) 52.3±121.7 (0.2–646) 31.3±75.3 (2.1–455) 0.162
  WBC (103/μl) 6.1±3.3 (1.5–18.7) 6.3±6.4 (2.7–45.5) 0.871
  CRP (mg/dl) 5.7±6.8 (0.2–34.5) 6.4±7.5 (0.1–37.8) 0.221
  D-dimer (μg/ml) 1.2±13.2 (0.5–122) 1.1±9.6 (0.5–67.3) 0.852
  AST (IU/l) 34±40.2 (5–256) 50.5±46.9 (21–252) ***<0.001
  ALT (IU/l) 21±30.0 (3–165) 50.5±41.3 (3–162) ***<0.001
  LDH (IU/l) 300±136.7 (143–832) 499.5±192.1 (224–1130) ***<0.001
  BUN (mg/dl) 18±20.5 (1–91) 19.5±19.2 (6–80) 0.516
  Cre (mg/dl) 1.1±3.6 (0.4–12.7) 0.89±3.9 (0.4–14.3) 0.062
  T.bil (mg/dl) 0.5±0.3 (0.2–1.7 0.6±0.3 (0.3–1.8) 0.162
Period of whole hospitalization (days, Median ±SD, range) 9±5.4 (2–36) 16±25.6 (2–159) ***<0.001
Outcome (Death, cases, %) 1 (1.1) 4 (8.0) *0.032

y.o: year-old, SD: Standard Deviation, BMI: Body Mass Index, IL-6: Interleukin-6, WBC: White Blood Cell, CRP: C-reactive Protein, AST: Aspartate Aminotransferase, ALT: Alanine Aminotransferase, LDH: Lactate Dehydrogenase, Cre: Creatinine, T.bil: Total Bilirubin, Statistically significant difference

*p<0.05

**p<0.01

***p< 0.001.

There was no significant difference in age between the moderate group (59±17.5 years; range, 18–93 years) and the severe group (57±13.3 years; range, 30–93 years) (p = 0.312). Male patients were more likely to progress to severe COVID-19 than were female patients (p<0.001). Time from the onset of COVID-19 symptoms to the day of hospitalization was significantly longer in the severe group than in the moderate group (moderate group: 3±3.6 days; range, 0–13 days vs. severe group: 8±4.2 days; range, 0–22 days) (p<0.001). However, there was no significant difference between the two groups in terms of the time from the acquisition of a positive PCR test result to hospitalization (moderate group: 1±2.3 days; range, 0–13 days vs. severe group: 2±2.3 days, range, 0–10 days) (p = 0.062). Regarding primary symptoms, there was a significant difference in the frequency of dyspnea between the two groups (p<0.001). Regarding physical features, patients in the severe group generally had a greater body weight (59.9±16.0 kg vs. 74.1±16.5 kg) and higher BMI (22.8±10.8 vs. 26.3±8.0) than those in the moderate group (p<0.001 and p<0.001, respectively). Smoking habit was significantly different between the two groups (p = 0.007). No significant frequency of any comorbidity was observed in the severe group. However, renal disease was significantly more frequent in the moderate group than in the severe group (p = 0.016).

Regarding laboratory data on the day of admission, there were significant differences in AST (34.0±40.2 IU/l vs. 50.5±46.9 IU/l), ALT (21.0±30.0 IU/l vs. 50.5±41.3 IU/l), and LDH (300.0±136.7 IU/l vs. 499.5±192.1 IU/l) levels between the severe and moderate groups (p<0.001, p<0.001, and p<0.001, respectively). However, differences in IL-6 (52.3±121.7 pg/ml vs. 31.3±75.3 pg/ml), CRP (5.7±6.8 mg/dl vs. 6.4±7.5 mg/dl), and D-dimer (1.2±13.2 μg/ml vs. 1.1±9.6 μg/ml) levels between the two groups were not statistically significant (p = 0.162, p = 0.211, and p = 0.852, respectively). S1 Fig showed box plot for the relationship between severity of COVID-19 and clinical data represented by continuous variables.

Table 4 shows therapeutic strategies implemented for the treatment of the cases of moderate and severe COVID-19. There was a significant difference between the moderate and severe groups in terms of oxygen demand and intensive care beyond ventilator management. In addition, there were differences in treatment between the two groups in terms of the characteristics of each treatment due to differences in treatment between moderate and severe cases of COVID-19 as indicated by therapeutic strategy in Table 1.

10.1371/journal.pone.0273134.t004 Therapeutic strategy between moderate group and severe group.
Moderate Group (n = 91) Severe Group (n = 50) p value
Oxygen supply (cases, %)   64 (70.3) 50 (100) ***<0.001
Period of oxygenation (days, Median ±SD, range) 4.0±6.8 (0–43) 15±26.2 (4–164) ***<0.001
Mechanical ventilation (cases, %)   0 (0) 30 (60) ***<0.001
Period of ventilation (days, Median ±SD, range) 0(0) 7.5±24.6 (3–133) ***<0.001
Tracheostomy (cases, %)   0 (0) 7 (14) ***<0.001
ECMO (cases, %)   0 (0) 5 (10) ***<0.001
NHF (cases, %)   3 (2.1) 23 (46) ***<0.001
Period of NHF (days, Median ±SD, range) 9±3.5 (5–12) 6±2.3 (1–10) 0.112
Prone position (cases, %)   3 (2.1) 21 (42) ***<0.001
Period of prone position (days, Median ±SD, range) 4±1.5 (2–5) 4±2.8 (2–12) 0.681
Therapy      
  steroid 69 (48.9) 50 (100) ***<0.001
  remdesivir 69 (48.9) 50 (100) ***<0.001
  tocilizumab 0 (0) 3 (6) *0.021
  baricitinib 3 (2.1) 4 (8) 0.222
  casirivimab 2 (1.4) 0 0.291
  molnupiravir 16 (11.3) 0 **0.002
  heparin 61 (43.) 50 (100) ***<0.001

SD: Standard Deviation, ECMO: Extracorporeal Membrane Oxygenation, NHF: Nasal High Flow, Statistically significant difference

*p<0.05

**p<0.01

***p< 0.001

The above-mentioned results regarding the characteristics and clinical features of the moderate and severe groups, as well as the clinical characteristics of the patients admitted during the delta and omicron periods stratified according to disease severity, are outlined in S2 Table. The greatest difference between the delta and omicron periods was the difference in the spread of vaccination. Thus, there were some significant differences in vaccination-associated factors between the two periods.

 Multivariable logistic regression analysis

Multivariable logistic regression analysis was performed to identify the relationship between COVID-19 severity and patient characteristics, including primary symptoms, comorbidities, and laboratory data. The results showed that time from the onset of COVID-19 to hospitalization, BMI, smoking habits, and LDH level were significantly associated with the COVID-19 severity (OR = 1.16, p = 0.026; OR = 1.10, p = 0.039; OR = 3.70, p = 0.008; and OR = 1.01, p< 0.001, respectively) (Table 5). The ROC curve (Fig 1) showed that the time from the onset of COVID-19 to hospitalization is an important factor associated with the COVID-19 severity (AUC, 0.77 [95%CI, 0.69–0.84]; sensitivity, 0.73 [95%CI, 0.59–0.84]; specificity, 0.70 [0.59–0.79]). The ROC also showed that the cut-off value for the time from the onset of COVID-19 to hospitalization was four days.

10.1371/journal.pone.0273134.g001 Receiver Operatorating Characteristic of the period from onset to hospitalization related to severity of COVID-19 with area under the curve 0.77, sensitivity 0.73, specificity 0.70. From the ROC, the cut-off value of the period from onset of COVID-19 to hospitalization was 4 days. 10.1371/journal.pone.0273134.t005 Multivariable logistic regression (n = 141).
  OR 95% CI p-value
The period between from onset to hospitalization 1.16 (1.02–1.32) 0.026
BMI 1.10 (1.01–1.21) 0.039
Smoking habit 3.70 (1.41–9.68) 0.008
LDH 1.01 (1.00–1.01) < 0.001

OR: odds ratio; CI: confidence intervals, BMI: Body Mass Index, LDH: Lactate Dehydrogenase

 Discussion

In this single-center retrospective study, we analyzed the characteristics, clinical features, and outcomes of COVID-19 caused by the delta and omicron variants of SARS-CoV-2 according to disease severity. The authors of previous reports [6, 7] have suggested that the hyperactivation of the inflammatory cascade, leading to a cytokine storm, is a critical biological response in patients with severe COVID-19. In addition, D-dimer, LDH, and CRP levels are reported to be potential biomarkers for COVID-19 severity [6, 7]. Significantly elevated levels of inflammatory cytokines TNF-α, IL-1, IL-6, and IL-10 have been documented in cases of severe COVID-19 compared to cases of non-severe disease [8–12]. In the present study, we did not focus on the levels of inflammatory cytokines for the classification of COVID-19 severity. The present results showed that LDH, which is reported to be a predictive biomarker for the aggravation of COVID-19 caused by the original variant of SARS-CoV-2, is involved in the aggravation of COVID-19 caused by the delta and omicron variants. Abnormalities in the levels of markers of cellular injury, particularly elevated LDH level, have been linked to greater disease severity [13, 14]. Data from recent studies suggest that LDH may be related to respiratory function and could be an important predictor of respiratory failure in patients with COVID-19 [15]. On the contrary, the present results indicated that IL-6, which has received considerable attention as a biomarker of the progression of COVID-19, is not a predictive factor for the aggravation of COVID-19. It has been reported [16–18] that SARS-CoV-2 infection increases the level of the inflammatory cytokine IL-6, which is believed to cause multiple organ damage. Therefore, administration of an IL-6 inhibitor has been established as a therapeutic strategy for COVID-19. However, as the IL-6 levels of the patients in the present study were not significantly high, even in the severe group, it could be concluded that IL-6 inhibitors may not be effective against COVID-19. The possible reasons for the lack of significant increase in IL-6 levels in the severe group are as follows: 1) the delta and omicron variants of SARS-CoV-2 could be less toxic than the original variant due to mutations, 2) SARS-CoV-2 may have mutated to induce inflammatory cytokines other than IL-6, and 3) IL-6 may not completely respond to SARS-CoV-2 infection due to the effect of vaccination. However, since vaccination was not yet widespread during the delta period, its effect may not have been extensive. In addition, since IL-6 level was measured on the first day of hospitalization in this study, it is unlikely that it was affected by the therapeutic drug administered. Thus, although we considered that various factors may be responsible for the lack of significant increase in IL-6 levels, the influence of the mutation of SARS-CoV-2 may be large. Furthermore, the lack of a significant increase in CRP and D-dimer levels was also considered to be due to the mutation of the virus.

It has been reported that cardiovascular disease, chronic renal disease, chronic lung diseases, diabetes mellitus, hypertension, immunosuppression, obesity, malignant tumor, and sickle cell disease are prognostic factors of COVID-19 severity [13, 19–22]. We investigated these comorbidities in the present study and found that except for obesity and renal disease, there were no significant differences between the comorbidities of the patients in the moderate and severe COVID-19 groups. However, significantly more patients with moderate COVID-19 had chronic renal failure and continuous hemodialysis than those with severe COVID-19. The reason for this finding is that it was difficult for patients to receive medical treatment in the outpatient dialysis clinic because chronic renal failure is one of the risk factors for aggravation of COVID-19. Regarding obesity, BMI >30, which is a proxy for obesity, was considered a strong predictor in a previous report [22]. There was a significant difference in BMI between the moderate and severe groups in the present study. During the delta period, patients with a high BMI developed more severe COVID-19. In addition, it was extremely difficult to manage their systemic conditions using mechanical ventilation, prone position therapy, and ECMO. However, BMI was considered to be slightly associated with COVID-19 severity during the omicron period. It has been suggested that the effect of the COVID-19 vaccine may contribute to the prevention of aggravation in patients with a high BMI.

In this study, we analyzed diabetes mellitus and glycated hemoglobin level, which have been reported to be linked to inflammation, hypercoagulation, and high mortality (27.7%) [23], and found no significant differences between the moderate and severe groups. Therefore, abnormal glucose tolerance was not a predictor of aggravation in this study.

Regarding the time from the onset of COVID-19 to hospitalization, most patients felt some symptoms of COVID-19, such as fever, sore throat, and fatigue, and rested while waiting for recovery of their physical condition. In such a scenario, the respiratory status of the patient gradually worsens and the disease severity progresses. This is because after SARS-CoV-2 infection is established, the patient’s respiratory condition gradually deteriorates and the patient is placed in a condition in which breathing is not difficult, even in a hypoxic state. This condition is called ‘happy hypoxia’ and is considered to be a precursor to the deterioration of respiratory function. This respiratory disorder caused by excessive spontaneous breathing without ventilator management is called patient self-inflated lung injury (P-SILI). This concept was previously described [24, 25] as a possible interplay between ventilation, surfactant dysfunction, and atelectasis during spontaneous ventilation, which leads to ventilation-induced lung injury. In clinical settings, intravenous injection of small doses of endotoxin in humans has a strong effect on respiratory drive, independent of fever or symptoms [26]. In addition, some patients without any pre-existing lung injury develop lung injury associated with hyperventilation [27]. Thus, in some patients, lung injury due to increased tidal volumes and ventilation may occur during spontaneous breathing, initiated by a high respiratory drive, which, in turn, leads to the development of lesions that appear similar to the ventilator-induced lung injury (VILI) observed in mechanically ventilated subjects. In these patients, the large spontaneous tidal volumes may be viewed as the cause of injury. Hence, any therapy that minimizes the generation of these large tidal volumes should be viewed as a prophylactic therapy against the progression of lung injury. Considering the above-mentioned concept, if a spontaneously breathing patient has a high respiratory drive that leads to increased minute ventilation with high tidal volumes, the goal of therapy must be to minimize P-SILI. If the patient engages in spontaneous breathing based on self-judgement, some parameters, such as changes in spontaneous breathing styles, tachypnea, changes in respiratory patterns, and oxygen demand, should be strictly managed, and any changes should be addressed immediately. If the changes are not managed immediately, the condition of the patient may become more severe. It is important to ascertain whether a spontaneously breathing patient has a high respiratory drive and has adopted a ventilatory pattern that will lead to subsequent lung injury. This concept is important for the management of respiratory conditions under mechanical ventilation for patients with severe COVID-19. The specific time from the onset of COVID-19 to aggravation and how long the deterioration of respiratory status should be observed have not been reported in any previous study. In the present study, the ROC showed that the optimal time from the onset of COVID-19 to hospitalization is four days. In other words, it is highly likely that COVID-19 will progress to severe if a patient’s respiratory status is altered within four days after the onset of COVID-19, even if the patient is receiving home care; thus, immediate intervention is required. Proper intubation and a lung-protective ventilatory strategy guided by the severity of lung injury, including elevations in dead space, may be the easiest and most efficient way to achieve this goal. It is possible that by applying the same principles, mechanical ventilation can be administered “prophylactically or early” to protect the lung from P-SILI. As such, under defined conditions, mechanical ventilation, far from being just supportive or even damaging, becomes a true preventive measure against the progression of lung injury and perhaps ARDS. ECMO is another method that can be used to prevent P-SILI. When ventilator management for patients with severe COVID-19 reaches its limits, introduction of ECMO as a lung protection strategy may be expected for the prevention of or recovery from ARDS. However, regarding the introduction of ECMO, systemic management is necessary for weighing the possibility of the occurrence of serious complications caused by ECMO.

This study had several limitations. First, this was a single-center retrospective study performed in one of the leading hospitals in Japan, which has generally accepted and treated a large number of severely ill COVID-19 patients since the outbreak of disease. Unlike some countries, few hospitals in Japan collectively treat patients with COVID-19. Patients with COVID-19 are dispersed to nearby hospitals for treatment; thus, a single hospital does not treat many cases. Second, the moderate COVID-19 group in this study included elderly patients who did not need hospitalization for COVID-19 but could not stay alone at home due to lack of assistance from a caregiver. In addition, the moderate COVID-19 group included patients with mild COVID-19 who had several comorbidities and risk factors or patients who were hospitalized for the treatment of other illnesses but incidentally tested positive for COVID-19 without showing any symptoms. Thus, there may be some bias in our clinical data. Third, we did not establish exclusion criteria for this study and excluded patients undergoing treatment for other diseases to ensure that only the severity predictors of COVID-19 are considered. However, considering the spread of COVID-19 worldwide, the high infectivity of SARS-CoV-2, and the severity of patient symptoms, we analyzed the severity predictors without setting any exclusion criteria. Finally, this study focused on the identification of predictors of the aggravation of COVID-19 using information available in routine practice, results of measurable specimen testing, and data on patient management based on standard of care. Combining these with the analysis of results of more advanced examinations and diagnostic imaging tests should further narrow down the severity predictors. However, although this may be possible for progressive research institutes, such as advanced medical institutions and universities, we believed that it was more important to distinguish COVID-19 severity in general medical institutions considering the spread of COVID-19 worldwide. The predictors identified this study, including the time from the onset of COVID-19 to hospitalization, LDH level at hospitalization, BMI, and smoking habits, are easy to understand and can be measured at any facility.

 Conclusion

This study showed that the time from the onset of COVID-19 to hospitalization is the most important factor in the prevention of the aggravation of COVID-19 caused by the delta and omicron variants of SARS-CoV-2. To prevent the aggravation of COVID-19, it is necessary to initiate appropriate medical management within four days after the onset of COVID-19, especially in patients with smoking habits, high BMI, and elevated LDH levels. These findings may facilitate preparations for the next wave of COVID-19 caused by other possible variants of SARS-CoV-2.

 Supporting information Patients’ characteristics of different periods (Delta Period, Omicron Period) (n = 141).

(DOCX)

 Patients’ clinical features by severity classification of different periods (Delta Period & Omicron Period) (n = 141).

(DOCX)

 Relationship to severity and clinical and laboratory data represented by continuous variables.

(TIF)

We would like to thank our colleagues in Department of Emergency Medicine and clinical nurses in the intensive care unit of Yokohama City University Hospital for their kind assistance. And, we would like to thank Editage (www.editage.com) for English language editing.

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Eur Radiol. 2020;30: 5446–5454. doi: 10.1007/s00330-020-06879-6 32367418 Ayanian S, Reyes J, Lynn L, Teufel K. The association between biomarkers and clinical outcomes in novel coronavirus pneumonia in a US cohort. Biomark Med. 2020;14: 1091–1097. doi: 10.2217/bmm-2020-0309 32677844 Nakajima K, Abe T, Saji R, Ogawa F, Taniguchi H, Yamaguchi K, et al. Serum cholinesterase associated with COVID-19 pneumonia severity and mortality. J Infect. 2021;82: 282–327. doi: 10.1016/j.jinf.2020.08.021 32822684 Chen L, Liu HG, Liu W, Liu J, Liu K, Shang J, et al. [Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia]. Zhonghua Jie He He Hu Xi Za Zhi. 2020;43: E005. doi: 10.3760/cma.j.issn.1001-0939.2020.0005 32026671 Levi M, Thachil J, Iba T, Levy JH. Coagulation abnormalities and thrombosis in patients with COVID-19. Lancet Haematol. 2020;7: e438–e440. doi: 10.1016/S2352-3026(20)30145-9 32407672 Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020;46: 846–848. doi: 10.1007/s00134-020-05991-x 32125452 Wang W, Liu X, Wu S, Chen S, Li Y, Nong L, et al. Definition and risks of cytokine release syndrome in 11 critically ill COVID-19 patients with pneumonia: Analysis of disease characteristics. J Infect Dis. 2020;222:1444–1451. doi: 10.1093/infdis/jiaa387 32601708 Sugiyama M, Kinoshita N, Ide S, Nomoto H, Nakamoto T, Saito S, et al. Serum CCL17 level becomes a predictive marker to distinguish between mild/moderate and severe/critical disease in patients with COVID-19. Gene. 2021;766: 145145. doi: 10.1016/j.gene.2020.145145 32941953 Cecconi M, Piovani D, Brunetta E, Aghemo A, Greco M, Ciccarelli M, et al. Early predictors of clinical deterioration in a cohort of 239 patients hospitalized for COVID-19 infection in Lombardy, Italy. J Clin Med. 2020;9: 1548 Li Q, Cao Y, Chen L, Wu D, Yu J, Wang H, et al. Hematological features of persons with COVID-19. Leukemia. 2020;34: 2163–2172. doi: 10.1038/s41375-020-0910-1 32528042 Poggiali E, Zaino D, Immovilli P, Rovero L, Losi G, Dacrema A, et al. Lactate dehydrogenase and C-reactive protein as predictors of respiratory failure in COVID-19 patients. Clin Chim Acta. 2020; 509:135–138. doi: 10.1016/j.cca.2020.06.012 32531257 Liu B, Li M, Zhou Z, Guan X, Xiang Y. Can we use interleukin-6 (IL-6) blockade for coronavirus disease 2019 (COVID-19)-induced cytokine release syndrome (CRS)? J Autoimmun. 2020;111: 102452. doi: 10.1016/j.jaut.2020.102452 32291137 Tharmarajah E, Buazon A, Patel V, Hannah JR, Adas M, Allen VB, et al. IL-6 inhibition in the treatment of COVID-19: A meta-analysis and meta-regression. J Infect. 2021;82: 178–185. doi: 10.1016/j.jinf.2021.03.008 33745918 Tasoudis PT, Arvaniti CK, Adamou AT, Belios I, Stone JH, Horick N, et al. Interleukin-6 inhibitors reduce mortality in coronavirus disease-2019: An individual patient data meta-analysis from randomized controlled trials. Eur J Intern Med. 2022; 101: 41–48. doi: 10.1016/j.ejim.2022.04.004 35422374 Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet. 2020;395: 1054–1062. doi: 10.1016/S0140-6736(20)30566-3 32171076 Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395: 497–506. doi: 10.1016/S0140-6736(20)30183-5 31986264 Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. BMJ. 2020;368: m1091. doi: 10.1136/bmj.m1091 32217556 Centers for Disease Control and Prevention. People with certain medical conditions. 2022 May 2 [cited 2022 Jun 21]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html?%20CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fcoronavirus%%202F2019-ncov%2Fneed-extra-precautions%2Fgroups-at-higher-risk.%20html. Wang Z, Du Z, Zhu F. Glycosylated hemoglobin is associated with systemic inflammation, hypercoagulability, and prognosis of COVID-19 patients. Diabetes Res Clin Pract. 2020;164: 108214. doi: 10.1016/j.diabres.2020.108214 32416121 Yoshida T, Fujino Y, Amato MB, Kavanagh BP. Fifty years of research in ARDS. Spontaneous breathing during mechanical ventilation. Risks, mechanisms, and management. Am J Respir Crit Care Med. 2017;195: 985–992. doi: 10.1164/rccm.201604-0748CP 27786562 Albert RK. The role of ventilation-induced surfactant dysfunction and atelectasis in causing acute respiratory distress syndrome. Am J Respir Crit Care Med. 2012;185: 702–708. doi: 10.1164/rccm.201109-1667PP 22227381 Preas HL, Jubran A, Vandivier RW, Reda D, Godin PJ, Banks SM, et al. Effect of endotoxin on ventilation and breath variability: role of cyclooxygenase pathway. Am J Respir Crit Care Med. 2001;164: 620–626. doi: 10.1164/ajrccm.164.4.2003031 11520726 Brun-Buisson CJ, Bonnet F, Bergeret S, Lemaire F, Rapin M. Recurrent high-permeability pulmonary edema associated with diabetic ketoacidosis. Crit Care Med. 1985;13: 55–56 doi: 10.1097/00003246-198501000-00015 3917389 10.1371/journal.pone.0273134.r001 Decision Letter 0 Liu Benjamin M. Academic Editor 2022 Benjamin M. Liu This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 0

22 Aug 2022

PONE-D-22-21582Severity Predictors of COVID-19 in SARS-CoV-2 Variant, Delta and Omicron Period; Single Center StudyPLOS ONE

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Reviewer #1: This article overcame the limitations of the clinical real world, finally collected 141 patients to explore the predictors of COVID-19 severity caused by SARS-CoV-2 variants. Research focused on the specific time from the onset of COVID-19 to aggravation, creatively followed with interest of 4 days of attention from onset to hospitalization of the patients, and suggested appropriate medical management within this time, which might provide reference for clinicians and future diagnosis and treatment.

In this paper, the characteristics and data of 141 enrolled patients were analyzed statistically, which provided support for the above conclusions. However, there are still some problems, which might be troubled the support.

1.Table 5

We can see that the four indicators have statistical significance. We know that you must have done the ROC curve analysis for the other three indicators, and in order to highlight the key points. The problem is that it seems that only four positive indicators are selected and the findings are highlighted. After all, the OR value of Smoking is the largest among the four indicators, and the p value of LDH is the largest among the four indicators. Why the period is the final target ? And it is shown as a unique figure.

Looking at the above question from another view, it is suggested in Table 3 that Gender, Dyspnea, Height and other indicators showed significant differences.

In addition, Figure 2 of reference 12 provides a good data display of the ROC curve analysis.

2.Table 5

From the results in Table 5, which is the most important argument supporting the conclusion, there seems to be no separate statistics for Delta and Omicron, but regression analysis for disease severity. It is not difficult to understand the painstaking efforts of clinical observation to divide the virus variants into two time periods, however, it raises a new question that are all 89 enrolled patients diagnosed with delta typing in the delta time period, and all 52 omicron?

This prompts us to pay attention to the contents in supplementary Table 1. It seems that only part of contents in supplementary Table 2 is involved in supplementary Table 1. The data of clinical observation, treatment and laboratory diagnosis are missing or selectively deleted. This part of data can be used as a reference for readers to understand the comparison between Delta and Omicron period. It is suggested to supplement this part of data. And the supplementary Tables 1 and 2 might be displayed together. After all, supplementary Table 2 is a stratification reanalysis of the data in supplementary Table 1, according to disease severity.

Further more, as discussed in the article Line 189-192, the impact of mutations may be huge, so why not analyze and display the existing data? Line144-145, what are the factors between the two periods?

3.Table 2

The same data missing display occurred in Table 2. Compared with Table 3, it can be seen that there is a lack of baseline data of laboratory diagnosis and the length of hospitalization which is the most relevant data to the focus of the paper.

It is suggested that Table 2 and Table 3 can be combined. Although it could be understandable that Table 2 needs to be more concise, please refer to the data display mode of Table 1 in reference 14, which may provide more information. After all, the data in Table 3 is also the continuation of the stratification reanalysis of the data in Table 2.

The above three questions are the main questions of the manuscript and the places that need to be analyzed. Next questions 4-15, there are some specific details that need to pay attention, but no longer affect the logic of the full text.

4. Numerical standard of SpO2

There are three numerical value of 93% in Line 45, 94% in Line 46 and 92% in Table 1.

In particular, the first two values may be confusing to readers, because they are very close.

5.Median age of 141 patients

59.6 in Line 95, meanwhile 58 in Table 2.

6.Range of age of moderate group

18-83 in Table 3, meanwhile 19-93 in Line 112.

7.Cases of vaccination

According to supplementary Table 2, the two p values should be 0.165 and 0.595, respectively, with the latter one missing.

47 and 39 in Table 3 seem to represent the number of patients without immunization, which should be 44 and 11, respectively.

Can these data support the conclusion in Line 105-106?

8.Cases of comorbidities

23 in 50 (46%) of patients in severe group have hypertension, although no significant discussion in Line 125.

9.Table 4

Baricitinib and casirivimab have no significance, while tocilizumab and molnupiravir include the case where the admitted patient is 0. However, discussion in Line 139 says each treatment.

10.Line 167

We did not focus, however, a whole section from Line 168 to 192 was discussed.

11.Line 196

There were no significances except obesity, however, renal disease and continuous hemodialysis were in Table 3, although it explained in the following paragraph. And additional references are required in Line 201 to help readers understanding, instead of listing them in Line 195.

12.Line 212

The negative data conclusion is not shown in the chart. This is not a problem. The key point is part of the data of the full text seems to consider displaying in a scatter chart instead of a full table, as Figure 1 in reference 23.

In addition, Figure 1 in reference 15 and Figure 2 in reference 23 are also another good way to display data of correlation analysis.

13.Line 247

Previous studies of supplementary Table 1 in correction version of reference 10 compared time from onset of symptom to test of two groups, so do Table 1 in reference 14, Table 1 in reference 19, and Table 1 in reference 20.

14.Line 251-253

Is the ultimate reason, that the treatment plan of the one unfortunate patient in the moderate group in Table 3, just violates this way mentioned here?

15.P value

For the p value in the full text, especially in the table, please uniformly keep the digits after the decimal point, and it is better to mark the p value of statistical significance with an asterisk or other common symbols.

Reviewer #2: In this study, a total of 141 patients were enrolled and divided into two groups according to disease severity to analyze the characteristics, clinical features, and outcomes of COVID-19 caused by the delta and omicron variants of SARS-CoV-2. The article has unique significance. I appreciate the work, however if the pulmonary CT of cases can be supplemented, the article will be more complete and full.

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Reviewer #2: No

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 10.1371/journal.pone.0273134.r002 Author response to Decision Letter 0 Submission Version 1

8 Sep 2022

Response to Reviewer #1

Comment of Reviewer #1

1.Table 5

We can see that the four indicators have statistical significance. We know that you must have done the ROC curve analysis for the other three indicators, and in order to highlight the key points. The problem is that it seems that only four positive indicators are selected and the findings are highlighted. After all, the OR value of Smoking is the largest among the four indicators, and the p value of LDH is the largest among the four indicators. Why the period is the final target? And it is shown as a unique figure.

Looking at the above question from another view, it is suggested in Table 3 that Gender, Dyspnea, Height and other indicators showed significant differences.

In addition, Figure 2 of reference 12 provides a good data display of the ROC curve analysis.

Thank you for your comment for the result of Table 5. We agreed with you that the OR value of Smoking is the largest among the four indicators, and the p value of LDH is the largest among the four indicators. These factors are very important to predict severity of COVID-19 pneumonia compared to the period from onset to hospitalization. We focused the period from onset to hospitalization in this study because no one knows it specifically in previous report. In this study, by clarifying the specific cut-off value of the period from onset to hospitalization, we considered the possibility that it could be a predictor. Then, we added two sentences of this point in Line 16-17 and Line 23-24 in Revised Manuscript with Tracking Change.

2.Table 5

From the results in Table 5, which is the most important argument supporting the conclusion, there seems to be no separate statistics for Delta and Omicron, but regression analysis for disease severity. It is not difficult to understand the painstaking efforts of clinical observation to divide the virus variants into two time periods, however, it raises a new question that are all 89 enrolled patients diagnosed with delta typing in the delta time period, and all 52 omicron?

This prompts us to pay attention to the contents in supplementary Table 1. It seems that only part of contents in supplementary Table 2 is involved in supplementary Table 1. The data of clinical observation, treatment and laboratory diagnosis are missing or selectively deleted. This part of data can be used as a reference for readers to understand the comparison between Delta and Omicron period. It is suggested to supplement this part of data. And the supplementary Tables 1 and 2 might be displayed together. After all, supplementary Table 2 is a stratification reanalysis of the data in supplementary Table 1, according to disease severity.

Furthermore, as discussed in the article Line 189-192, the impact of mutations may be huge, so why not analyze and display the existing data? Line144-145, what are the factors between the two periods?

Thank you for your comment. We are very sympathetic to the points you focused. In fact, the current study only separates the mutant strains at the period when they were considered to be the majority of the strains, which did not mean that the strains are accurately identified in the PCR test. If we had been able to identify all of these strains with certainty, we would have been able to present the characteristics in each strain in more detail. For this reason, we presented this as a supplement table because there was no certainty of the characteristics of the delta and omicron variants in the main text

As for the supplement table2, all the data were presented and we don’t think there are any missing parts, but is there anything missing? We described all of data in all case without any selection bias.

3.Table 2

The same data missing display occurred in Table 2. Compared with Table 3, it can be seen that there is a lack of baseline data of laboratory diagnosis and the length of hospitalization which is the most relevant data to the focus of the paper.

It is suggested that Table 2 and Table 3 can be combined. Although it could be understandable that Table 2 needs to be more concise, please refer to the data display mode of Table 1 in reference 14, which may provide more information. After all, the data in Table 3 is also the continuation of the stratification reanalysis of the data in Table 2.

Thank you for your comment. We have carefully reviewed the missing data that you focused, and we have not been able to find any missing data, and we have included all of the laboratory data, length of hospital stay, and so on.

Regarding the integration of table 2 and table 3, we thought it would be easier to understand if we described the overall characteristics of all patients as a whole (table 2), and then separate moderate and severe COVID-19 cases and compared further clinical and laboratory data. So, we have separated table 2 and table 3.

4. Numerical standard of SpO2

There are three numerical value of 93% in Line 45, 94% in Line 46 and 92% in Table 1.

In particular, the first two values may be confusing to readers, because they are very close.

Thank you for your comment for confusing you about the standard value of SpO2 for severe COVID-19. We made a mistake for numerical standard of SpO2. We consolidated the standard value of SpO2 according to global standard.

5.Median age of 141 patients

59.6 in Line 95, meanwhile 58 in Table 2.

Thank you for your comment. I made a mistake of median age of 141 patients. 58 was correct value of median age. We corrected it.

6.Range of age of moderate group

18-83 in Table 3, meanwhile 19-93 in Line 112.

Thank you for your comment. I made a mistake of range of age of moderate group in manuscript. The value in Table 3 was correct. So, we corrected it.

7.Cases of vaccination

According to supplementary Table 2, the two p values should be 0.165 and 0.595, respectively, with the latter one missing.

47 and 39 in Table 3 seem to represent the number of patients without immunization, which should be 44 and 11, respectively.

Can these data support the conclusion in Line 105-106?

Thank you for your comment. You are right. I made a mistake of the value of vaccination. I corrected this value. The data support the conclusion in Line 109-110.

8.Cases of comorbidities

23 in 50 (46%) of patients in severe group have hypertension, although no significant discussion in Line 125.

Thank you for your comment. We already described “No significant frequency of any comorbidity was observed in the severe group” in Line 141 of Revised Manuscript with Tracking Change.

9.Table 4

Baricitinib and casirivimab have no significance, while tocilizumab and molnupiravir include the case where the admitted patient is 0. However, discussion in Line 139 says each treatment.

Thank you for your comment for the therapeutic strategy. You are correct. Casirivimab and molnupiravir were only used for moderate cases of COVID-19 and not for severe cases, so there were significant differences There was a significant difference from therapeutic strategy in Table 1 because remdisivir and steroid were absolutely used for severe cases of COVID-19. We replaced these sentences to correct sentences in Line 156-158 in Revised Manuscript with Tracking Change. “There were differences in treatment between the two groups in terms of the characteristics of each treatment due to differences in treatment between moderate and severe cases of COVID-19 as indicated by therapeutic strategy in table 1.”

10.Line 167

We did not focus, however, a whole section from Line 168 to 192 was discussed.

Thank you for your comment. The purpose of this sentence was that many of the biochemical factors that have been reported as existing severity predictors were in the early stages of COVID-19, and most of them have been reported as severity predictors after the start of vaccination and standard therapy for COVID-19. Since it had not been done, I decided to discuss this here because I paid attention to whether these severity predictors changed after vaccination and treatment methods were standardized. Originally, LDH, CRP, D-dimer, and IL-6 were listed as severity predictors of COVID-19, but in our study, only LDH was listed as a significant severity predictor. We thought that this could be done because it was transformed due to vaccinations and standard treatment for COVID-19.

11.Line 196

There were no significances except obesity, however, renal disease and continuous hemodialysis were in Table 3, although it explained in the following paragraph. And additional references are required in Line 201 to help readers understanding, instead of listing them in Line 195.

Thank you for your comment. As you said, we thought this paragraph was confusing to the readers, so we changed the sentence in Line 220 and 223. As written in the main text, COVID-19 patients with renal failure and hemodialysis had high risk of severity, so they cannot be managed on an outpatient clinic. Due to this reason, mild COVID-19 patients with chronic renal disease and hemodialysis tended to hospitalize more often.

12.Line 212

The negative data conclusion is not shown in the chart. This is not a problem. The key point is part of the data of the full text seems to consider displaying in a scatter chart instead of a full table, as Figure 1 in reference 23.

In addition, Figure 1 in reference 15 and Figure 2 in reference 23 are also another good way to display data of correlation analysis.

Thank you for your thoughtful comment. Accordingly, we added figures including negative data posted a box plot for the relationship between severity of COVID-19 and clinical data represented by continuous variables. We put them in the supplementary material (Supple Fig.1), so that we would be able to avoid redundancy of information in the main manuscript (Line 150-152).

13.Line 247

Previous studies of supplementary Table 1 in correction version of reference 10 compared time from onset of symptom to test of two groups, so do Table 1 in reference 14, Table 1 in reference 19, and Table 1 in reference 20.

Thank you for your comment. We knew the data for correlation with period from illness onset to hospital admission in these reports. These data obtained from the conventional strain data of SARS-CoV-2. In clinical practice, the incubation period, symptom onset period, and period of transition to severe COVID-19 in variants of SARS-CoV-2 seemed to be shortened compared to conventional strains, so we focused on this point again. As mentioned in the discussion, as the factors for the establishment of COVID-19 pneumonia are being gradually elucidated, it was important to consider that the concept of P-SILI can occur during the waiting period for medical treatment. This is the reason of this sentence.

14.Line 251-253

Is the ultimate reason, that the treatment plan of the one unfortunate patient in the moderate group in Table 3, just violates this way mentioned here?

Thank you for your comment. The only patient who died of moderate disease was an elderly patient with renal failure and hemodialysis who unfortunately died of natural causes because he did not wish to undergo further invasive treatment, in spite of severe progression.

15.P value

For the p value in the full text, especially in the table, please uniformly keep the digits after the decimal point, and it is better to mark the p value of statistical significance with an asterisk or other common symbols.

Thank you for your comment. We agreed with you and amended the consistent decimal for p-values with asterisk for significant differences in the revised manuscript.

Response to Reviewer #2

Comment of Reviewer #2: In this study, a total of 141 patients were enrolled and divided into two groups according to disease severity to analyze the characteristics, clinical features, and outcomes of COVID-19 caused by the delta and omicron variants of SARS-CoV-2. The article has unique significance. I appreciate the work, however if the pulmonary CT of cases can be supplemented, the article will be more complete and full.

Thank you for your comment. We appreciate your interest in our work. We agreed with your comment that the pulmonary CT of these cases are more interesting to evaluate severity of COVID-19. Other researcher evaluates the CT score of these cases from chest CT scan for another article now, so we can’t use the data for this article. We apologize you.

Submitted filename: Response to Reviewer.docx

 10.1371/journal.pone.0273134.r003 Decision Letter 1 Liu Benjamin M. Academic Editor 2022 Benjamin M. Liu This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 1

10 Oct 2022

Severity Predictors of COVID-19 in SARS-CoV-2 Variant, Delta and Omicron Period; Single Center Study

PONE-D-22-21582R1

Dear Dr. Ogawa,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: The revise manuscript explores the predictors of COVID-19 severity caused by SARS-CoV-2 variants in 141 enrolled patients, and focused on the specific time from the onset of COVID-19 to hospitalization, which might provide reference for clinicians and future diagnosis and treatment. The authors can be able to review opinions one by one and make modifications or explanations. I appreciate the research in this article and recommend.

But I must also point out that, compared with Table 3, the data in Table 2 only includes data from sex to comorbidities in list column, missing from laboratory data to outcome. Similar situations occur in Supplementary Table 1, compared with Supplementary Table 2. However, the method of presentation of the data does not affect the current logic of the paper. I also perceive the author's explanation.

Reviewer #2: (No Response)

Reviewer #3: Although this paper is a retrospective, single-center study, the data provided are relatively detailed and the statistical methods are appropriate, which can support the conclusions drawn by the study. I personally think this paper can be published.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Aimei Liu

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 10.1371/journal.pone.0273134.r004 Acceptance letter Liu Benjamin M. Academic Editor 2022 Benjamin M. Liu This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

14 Oct 2022

PONE-D-22-21582R1

Severity Predictors of COVID-19 in SARS-CoV-2 Variant, Delta and Omicron Period; Single Center Study

Dear Dr. Ogawa:

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