Descriptive analysis of clinical and laboratory findings in relation to changes in SARS-CoV-2 viral dynamics and cyclic threshold: a retrospective, single center observational study in patients treated with Hydroxychloroquine/Azithromycin combination therapy

Maha M. Alamri; Edward B. Devol; Anwar B. Al-Otaibi; Faisal A. Albaiz; Mayyadah H. Alabdely; Sahar I. Althawadi; Maysoon S. Mutabagani; Fatimah S. Alhamlan; Fahad A. Alrabiah; Reem S. Almaghrabi

doi:10.12688/f1000research.28290.1

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Research Article

Descriptive analysis of clinical and laboratory findings in relation to changes in SARS-CoV-2 viral dynamics and cyclic threshold: a retrospective, single center observational study in patients treated with Hydroxychloroquine/Azithromycin combination therapy

[version 1; peer review: 1 not approved]

Maha M. Alamri ¹, Edward B. Devol², Anwar B. Al-Otaibi², [...] Faisal A. Albaiz¹, Mayyadah H. Alabdely¹, Sahar I. Althawadi³, Maysoon S. Mutabagani³, Fatimah S. Alhamlan⁴, Fahad A. Alrabiah¹, Reem S. Almaghrabi ¹

Maha M. Alamri ¹, Edward B. Devol², [...] Anwar B. Al-Otaibi², Faisal A. Albaiz¹, Mayyadah H. Alabdely¹, Sahar I. Althawadi³, Maysoon S. Mutabagani³, Fatimah S. Alhamlan⁴, Fahad A. Alrabiah¹, Reem S. Almaghrabi ¹

PUBLISHED 11 Aug 2022

Author details Author details

¹ Department of Medicine, King Faisal Specialist Hopital and Research Center, Riyadh, 11211, Saudi Arabia
² Epidemiology & Scientific Computing Department, King Faisal Specialist Hopital and Research Center, Riyadh, 11211, Saudi Arabia
³ Department of Pathology and Laboratory Medicine, King Faisal Specialist Hopital and Research Center, Riyadh, 11211, Saudi Arabia
⁴ Department of Infection and Immunity, King Faisal Specialist Hospital, Riyadh, 11211, Saudi Arabia

Maha M. Alamri
Roles: Conceptualization, Data Curation, Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing

Edward B. Devol
Roles: Conceptualization, Formal Analysis, Methodology, Software, Supervision, Writing – Review & Editing

Anwar B. Al-Otaibi
Roles: Formal Analysis, Methodology, Writing – Original Draft Preparation

Faisal A. Albaiz
Roles: Data Curation, Investigation

Mayyadah H. Alabdely
Roles: Data Curation, Investigation

Sahar I. Althawadi
Roles: Resources, Writing – Review & Editing

Maysoon S. Mutabagani
Roles: Investigation, Resources, Supervision, Writing – Review & Editing

Fatimah S. Alhamlan
Roles: Data Curation, Writing – Review & Editing

Fahad A. Alrabiah
Roles: Writing – Review & Editing

Reem S. Almaghrabi
Roles: Conceptualization, Data Curation, Investigation, Methodology, Resources, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

This article is included in the Coronavirus collection.

Abstract

Background: Cases of undiagnosed pneumonia have emerged, and sequencing of respiratory samples indicated the presence of SARS-CoV-2 causing COVID-19. Patients with higher viral load and lower Ct values tend to have progressive disease and severe lung injury. The objective of this study is to describe the clinical manifestation and disease outcomes of COVID-19 patients in relation to their Ct values.
Methods: A retrospective, single center observational study was performed, including patients admitted to King Faisal Specialist Hospital and Research Centre (KFSH&RC) Riyadh between March 1st-29th, 2020 and have a confirmed diagnosis of COVID-19. The Ct value was identified to determine the viral load. All patients were treated according to KFSH&RC guidelines. Patients were divided into HCQ/AZI and non HCQ/AZI treated groups.
Results: There were many days where Ct values were not available. An attempt at imputing information for missing Ct values was made using logic. The logic leads to an ordinal Ct score 1, 2, 3, or 4. As a result, complete Ct score profiles were available. There was no evidence of statistically significant difference between the two groups in regard to clinical severity, duration to negative test or changes in Ct values.
Conclusion: There is little knowledge known to the time profile of Ct values and their relation to disease course of COVID-19. This study provides insight on how Ct values might be used to determine treatment efficacy. As it might be difficult to obtain Ct values at all times, this study provides an imputation method that may be used with conservative statistical assumptions for analyses of Ct profiles.

Keywords

COVID-19, Dynamics, SARS-CoV-2, Coronavirus, PCR.

Corresponding authors: Maha M. Alamri, Reem S. Almaghrabi

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2022 Alamri MM 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 work is properly cited.

How to cite: Alamri MM, Devol EB, Al-Otaibi AB et al. Descriptive analysis of clinical and laboratory findings in relation to changes in SARS-CoV-2 viral dynamics and cyclic threshold: a retrospective, single center observational study in patients treated with Hydroxychloroquine/Azithromycin combination therapy [version 1; peer review: 1 not approved]. F1000Research 2022, 11:925 (https://doi.org/10.12688/f1000research.28290.1) First published: 11 Aug 2022, 11:925 (https://doi.org/10.12688/f1000research.28290.1) Latest published: 11 Aug 2022, 11:925 (https://doi.org/10.12688/f1000research.28290.1)

Introduction

In December 2019, multiple cases of undiagnosed pneumonia emerged in China. Sequencing on respiratory samples indicated the presence of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) causing Coronavirus Disease 2019 (COVID-19).^1–3

The main method of diagnosing COVID-19 is by the isolation of SARS-CoV-2 through Reverse Transcriptase Polymerase Chain Reaction (RT-PCR or PCR) nucleic amplification testing of respiratory samples.⁴ In a study done by Yu et al. the viral load predicted by the Cycle threshold (Ct) value was higher in sputum samples than nasopharyngeal and throat swabs. Patients who had an increasing viral load and lower CT values had a progressive disease and more severe lung injury.^5,6 Furthermore patients with higher initial viral loads had worse outcomes and more prolonged viral shedding.⁷ The viral dynamics studied in 18 different patients resembled that of patients with influenza rather than SARS-CoV.⁸

Given the importance for early identification of patients with severe disease and that a low Ct value is proposed to be inversely proportional to disease severity,⁷ we aim to describe the different clinical manifestations and disease severities in relation to the initial and subsequent Ct values in patients who were admitted to King Faisal Specialist Hospital and Research Centre-Riyadh (KFSH&RC).

Methods

Study design and patients

A retrospective, single center observational study was undertaken and included all patients who were admitted to KFSH&RC-Riyadh in one month in 2020 and had a confirmed diagnosis of COVID-19 based on a positive PCR taken from a respiratory sample. The study was approved by the policies and guidelines for clinical research at KFSH&RC and the KFSH&RC-Riyadh Institutional Review Board (IRB) (RAC # 2201054). As this is a retrospective study, consent to participate from patients was waived by the IRB.

All but two patients were followed for a total duration of 14 days after the initial diagnosis. Repeat PCR results were sought during data abstraction for days 3, 7, 10 and 14. The Ct value was identified on the obtained samples. All patients were treated according to KFSH&RC treatment guidelines (as detailed below).

Epidemiological, demographic, clinical, laboratory, treatment administered, and outcome of the disease data were obtained from the patient’s electronic medical records. All data were entered in a password-protected database developed using the Research Electronic Data Capture (REDCap) version 9.4 software. The Ct values on admission and follow-up samples were obtained from the microbiology laboratory. As hydroxychloroquine (HCQ) and azithromycin (AZI) were one of the commonly used regimens for treatment, we divided our cohort into HCQ/AZI and non-HCQ/AZI treated groups.

Laboratory procedures

Sample: Combined nasopharyngeal and throat swabs were collected from patients.

COVID-19 RNA extraction method: RNA was extracted utilizing Qiagen QIAamp® DSP Viral RNA Mini Kit or QIAamp® Viral RNA Mini Kit EZ1 following the manufacturer recommendations, utilizing 200 μL of sample and eluted with 60 μL of buffer.

RNA amplification and detection: RealStar ® SARS-CoV-2 RT-PCR Kit RUO Altona was used for RNA detection and differentiation of lineage B-betacoronavirus (β-CoV) and SARS-CoV-2 specific RNA. Reagent system included an internal control and positive control for both targets, B-βCoV and SARS-CoV-2 following manufacturer recommendations. Tests were done on Rotor-Gene ® Q. Tests were reported as positive and negative accordingly. Each test was run into cycles and up to 45 cycles, and the Ct value was recorded according to the cycle for which the test became positive.

Management and treatment regimens

All patients admitted were treated according to hospital guidelines that had been revised and edited according to emerging evidence. Up to the time of writing, the hospital had issued six versions of COVID-19 treatment and management guidelines, the initial three versions were released at the time of data collection and analysis. The first guideline was issued on March 5^th, 2020 followed by the second and third versions that were released on March 19^th and 26^th, 2020 respectively.

At first (and as per the Ministry of Health guidelines for Saudi Arabia), all patients who were eligible to be treated in KFSH& RC-Riyadh and who had a positive COVID-19 PCR were admitted to the hospital regardless of the presence or absence of symptoms.

All admitted patients had baseline lab tests requested including Complete Blood Count with Differential (CBCD), urea and electrolytes, creatinine, C-Reactive Protein (CRP), Liver Function Test (LFT), blood glucose, and ferritin level. Electrocardiograms (ECGs) were also conducted to look at the corrected QT interval prior to starting medications.

Multiplex PCR using the QIAstat-Dx Respiratory SARS-CoV-2 Panel is intended for the detection and differentiation of nucleic acid from SARS-CoV-2 and multiple other respiratory viruses and atypical bacteria. In addition, if Middle East Respiratory Syndrome Coronavirus (MERS-CoV) is needed, a BIOFIRE® RP2.1plus is used which detects SARS-CoV-2, MERS-CoV and other common respiratory pathogens and atypical bacteria.

In the latest revision, further investigations were added as the available literature suggested their correlation with disease severity and outcome. These included erythrocyte sedimentation rate (ESR), D-Dimer, Creatinine Kinase (CK), troponin, Lactate Dehydrogenase (LD) and haptoglobin. All patients had a baseline chest X-Ray, and those with an abnormal finding or those at high risk underwent Computed Tomography (CT) scan of the chest.

In the earliest guidelines issued, patients were divided according to the presence or absence of symptoms, and radiological evidence of pneumonia. Severity was indicated by the need for Intensive Care Unit (ICU) admission. Following the latest guideline, patients were categorized into four categories (asymptomatic, mild, moderate and severe infection) (Table 1).

Table 1. Definition of disease severity

Stage A	Asymptomatic	Patients with no signs or symptoms of infection
Stage B	Mild infection	Patients with upper respiratory tract infection symptoms and other mild symptoms (including fever and gastrointestinal symptoms) without evidence of radiological pneumonia
Stage C	Moderate infection	Patients with hypoxia and oxygen saturation less than 93% at rest or presence of pneumonia radiologically
Stage D	Severe infection	Patients presenting with any of the following: 1. Respiratory rate of 30 breaths/min., 2. Arterial oxygen partial pressure to fractional inspiratory oxygen ratio (PaO2/FiO2) less than 300, 3. More than 50% lung involvement on imaging within 24-48 hours, 4. Critical respiratory failure requiring mechanical ventilation, septic shock or multi-organ dysfunction.

In the first guidelines, active treatment was given for patients with radiological evidence of pneumonia. Those patients were treated as community acquired pneumonia in addition to Lopinavir/Ritonavir +/- Ribavirin. Other treatment options were decided after discussion with an infectious disease (ID) physician. As the evidence evolved and as the roles of HCQ and AZI were thought to lead to favorable outcomes, hospital guidelines changed to include HCQ and AZI for all patients presenting with symptoms regardless of symptoms severity and presence or absence of pneumonia. The HCQ regimen involved a loading dose of 400 mg oral twice daily in the first day, followed by 200 mg orally every 12 hours for a duration of 6 days. AZI was given at a dose of 500 mg daily for 3 days. Lopinavir/Ritonavir were used if the above regimen was contraindicated. In the latest guidelines, Tocilizumab was added as a possible treatment for cytokine release syndrome. Its use was strictly prescribed by an ID physician and for patients who were admitted to the ICU with evidence of cytokine storm and elevated Interleukin (IL) 6. The recommended dose was 4-8 mg/kg and repeated after 12 hours if the response for the 1^st dose was poor and to a maximum of two doses.

Definitions

In this study, a confirmed case is defined as a patient with at least one positive PCR test for COVID-19. Cure is defined as a single negative COVID-19 PCR test. Persistent disease is defined as a positive PCR on day 14 following the initial positive test or sometime afterwards. A high risk patient is any patient who is positive for COVID-19 and has any of the following comorbidities: Age above 60 years, lung disease, cancer, cardiovascular or cerebrovascular disease, renal or liver disease, diabetes, hypertension, immunocompromising conditions (Human Immunodeficiency Virus (HIV), post solid organ or hematopoietic stem cell transplant, on active chemotherapy or receiving an immunomodulator or immunosuppressant therapy), or pregnancy.

Statistical analysis

Data were entered in an electronic database system – REDCap version 9.4. Over 300 data items were abstracted from the medical records for each included patient. JMP/SAS software (version 15.0) and Cytel (StatXact version 6.0 and LogXact version 6.0) were used for data analysis. Data were summarized using mean (+/− standard deviation) or median (with interquartile range) for continuous data. Categorical data were summarized using frequencies and percentages. All analysis methods are available and could be reproduced with R, a freely available software platform.

Patients were divided into two groups - a group receiving a hydroxychloroquine-based regimen and the second group receiving a regimen not including hydroxychloroquine. The Ct values were compared between the two groups and correlated with disease severity and outcome with a Spearman’s correlation coefficient. Cure, persistent disease and death were the three main outcome categories. Comparisons between the two treatment groups were evaluated with nonparametric methods (Wilcoxon Rank Sum Test) given the small numbers and inability to evaluate distributional requirements. Time-to-event analyses were also carried out with the Kaplan-Meier technique.

Results

During the study time period, 35 subjects had presented to the KFSH&RC-Riyadh and for whom sufficient follow-up information was available (21 women and 14 men). In total, 28 patients were considered at presentation mild (stage B), 5 were moderate (stage C), and 2 were asymptomatic (stage A). The average age was 49.9±21.9 years. Comorbidities were observed in 68.6% of the cases. All patients had tested positive for SARS-CoV-2 by PCR. Table 2 shows the disease severity progression through the course of 14 days following admission. Table 3 presents symptoms that developed sometime during the course of infection.

Table 2. Disease severity progression through the course of 14 days.

Clinical severity	Admission	Day 3	Day 7	Day 10	Day 14^*
No disease	0	0	0	2	7
Asymptomatic (Stage A)	2	2	7	13	14
Mild (Stage B)	28	21	14	8	6
Moderate (Stage C)	5	11	10	8	1
Severe (Stage D)	0	1	4	3	3
Dead	0	0	0	1	2

* 2 patients were followed for 10 days instead of 14 days.

Table 3. Symptom development during infection.

Symptom	N (%)
Cough	24 (68.57)
Fever	22 (62.86)
Sore throat	15 (42.86)
Rhinorrhea	12 (34.29)
Fatigue	11 (31.42)
Headache	8 (22.86)
Sputum productive	5 (14.29)
Anosmia	5 (14.29)
Diarrhea	5 (14.29)
Shortness of breath	4 (11.42)
Asymptomatic	2 (5.71)
Ear pain	2 (5.71)
Loss of taste	2 (5.71)
Myalgia	2 (5.71)
Anorexia	2 (5.71)
Seizure	2 (5.71)
Nausea	1 (2.86)
Vomiting	1 (2.86)
Abdominal pain	1 (2.86)
Severe headache (>5/10)	1 (2.86)
Loss of consciousness	1 (2.86)
Confusion	1 (2.86)
Rash	1 (2.86)

There were nine different treatment profiles administered across the 35 subjects when considering the seven different drugs used during the course of illness – HCQ, AZI, Kaletra, Ribavirin, Tocilizumab, IVIG, and other. Of interest is to differentially identify from among the 35 study subjects those who were given the combination of HCQ and AZI as their initial treatment from those who were not given this combination as their initial treatment. The schematic in Figure 1 depicts the course of treatments received by each of the 35 subjects and how it was decided into which of the two treatment groups each was assigned post-hoc. It is seen that 21 (60%) pateints were given HCQ and AZI as their initial treatment. Henceforth, this treatment group will be referred to as the HCQ/AZI group. Those subjects not in the HCQ/AZI treatment group will be denoted the $\bar{HCQ / AZI}$ group.

Figure 1. Treatment profile of all 36 patients.

Table 4 displays a comparison of baseline characteristics between the two treatment groups. There was no significant difference in age and gender between the two groups. The frequency of individuals with a past history of smoking in the $HCQ / AZI$ group was significantly less in the $\bar{HCQ / AZI}$ group. No differences were observed in the radiological findings or vital signs between the two groups. Among the baseline laboratory values, ALT was lower in the $HCQ / AZI$ group. Table 5 displays a comparison of the baseline clinical severity of the two treatment groups; there is no statistically significant difference in the two.

Table 4. Baseline laboratory and radiology findings by treatment groups.

Variables	HCQ/AZI (n=21)	$\bar{HCQ / AZI}$ (n=14)	p-value
Age, years, mean (min – max)	54(20-80)	38.5(21-91)	0.7873
Female, n (%)	15(71.43)	6(42.86)	0.0910
History of smoking, n (%)	3(14.29)	8(57.14)	0.0115
Radiology findings, n (%)
Clear chest imaging	16(76.19)	10(71.43)	0.752
Resolution at day 7	1(12.50)	0(0.0)	0.2319
Resolution at day 14	1(20.0)	3(37.50)	0.0916
Vital signs, mean (min – max)
Oxygen saturation	98(95-100)	97.5(88-100)	0.4404
Respiratory rate	20(20-22)	20(18-24)	0.1296
Systolic Blood Pressure	117(99-156)	112.5(100-136)	0.7107
Laboratory findings, mean (min – max)
ALT,	20(10-43)	31(5-67)	0.0294
WBC	4.82(2.79-13.2)	4.96(3.2-8.06)	0.9731
Neutrophil	2.97(0.81-7.12)	2.48(1.18-4.76)	0.7111
Lymphocyte	1.47(0.6-3.64)	1.3(0.68-2.78)	0.3998
D-dimer	0.34(0.27-1.96)	0.36(0.27-2.44)	0.9127
Ferritin	129(4.2-753)	271.5(18-815)	0.1780

Table 5. Comparison of baseline clinical severity between the two treatment groups (p = 0.1256).

	Asymptomatic (Stage A)	Mild (Stage B)	Moderate (Stage C)	Severe (Stage D)	Total
	n (%)
$\bar{HCQ / AZI}$	2 (14.29)	11 (78.57)	1 (7.14)	0 (0.00)	14
$HCQ / AZI$	0 (0.00)	17 (80.95)	4 (19.05)	0 (0.00)	21
Total	2	28	5	0	35

Table 6 shows a comparison for the number of days to reach negative RT-PCR results between the two treatment groups. From the table it can be seen that 7 of the 21 $HCQ / AZI$ -treated subjects remained positive for 14 days after admission whereas 12 were able to reach a negative status within that time period. There were 2 subjects not fully followed for 14 days post-admission for whom a conclusion could not be reached. Moreover, 9 of the 14 $\bar{HCQ / AZI}$ -treated subjects remained positive for 14 days after admission, whereas 5 were able to reach a negative status within that time period. Considered as two ordered multinomial distributions, the two columns did not show a statistically significant difference between the two treatment groups (p = 0.1571) with respect to the time to reach negativity. Figure 2 presents a time-to-negativity actuarial chart for the two treatment groups with no statistically significant difference in the two curves.

Table 6. Days to reach negative RT-PCR results.

Days to reach negative	$HCQ / AZI$	$\bar{HCQ / AZI}$
7	3	1
10	4	1
14	5	3
Never	7	9
Unclear^*	2	0
Total	21	14

* subjects were followed for less than 14 days.

Figure 2. Kaplan-Meier comparison between two treatment groups for time to reach negative PCR test (p = 0.1390).

The Ct values ranged from a minimum of 11.44 to a maximum of 38.35 across the 5 time points (days 0, 3, 7, 10, and 14), across the two treatment groups, and during the 14 days. There were many days among the 35 subjects for which Ct values were not available (i.e. a PCR test was not carried out). These missing results presented a challenge in trying to evaluate the relative Ct profiles between the two treatment groups. It was noted however that, although the Ct value was missing, a positivity/negativity indication was often present in the medical record when the Ct value was missing. Among those missing both the Ct value and an indication of positivity/negativity, a clinical severity stage value (A, B, C, D, or dead) was present for each of the 5 days. Using this information together with whatever Ct values were present, an attempt at imputing information for missing Ct values was made using the following logic. The logic lead to an ordinal Ct score 1, 2, 3, or 4 for each subject for each of the 5 days (0, 3, 7, 10, and 14), and it will be referred to as the Ct score. The methodology includes the following five steps:

1. Ct values were firstly regarded as falling within one of four categories:
- • Less than 20 as category 1, scored 1;
- • At least 20 to less than 30 as category 2, scored 2;
- • At least 30 to less than 40 as category 3, scored 3;
- • At least 40 as category 4, scored 4.

Note that, even though a Ct value of 40 or more was not seen among the 35 subjects, this category was conceptualized to accommodate a Ct value for a subject who reached a negative RT-PCR test or a status of no disease.

2. For each day when a subject’s Ct value was available, a score (as described in #1 above) was assigned to the Ct score.
3. It was noted that a Ct value was present (i.e. not missing) on day 0 for all 35 subjects. It was therefore the case that a true Ct based score was able to be assigned to each subject’s day 0.
4. For all but 8 of the subjects, at least one Ct value was available at one of the follow-up days (3, 7, 10, or 14). In those situations, a linearly interpolated (assuming the 5 days 0, 3, 7, 10, and 14 as equidistant) score was assigned. Reassuringly, for every interpolation, the value was consistent with the associated clinical severity stage.
5. In addition to the 8 subjects for whom no Ct values subsequent to day 0 were available, there were 16 subjects who had a subsequent Ct value but were missing day 14, i.e. for which interpolation was not possible based on an endpoint Ct value (two boundary endpoints being required in order to interpolate). That is, there were 24 subjects for whom it was necessary to rely on the positivity/negativity result or the clinical severity stage in order to impute a Ct score. The following was inferred:
- • For 12 subjects, a score of 4 could be assigned as on the days subsequent to their last Ct value, they were recorded as being RT-PCR negative;
- • For 3 subjects, a score of 1 could be assigned as they either died or they were at stage D;
- • For 5 subjects, a score of 4 could be assigned to day 14 as a result of a negative RT-PCR, and intermediate interpolations were then made (e.g. scores of 2 or 3 as appropriate);
- • For 2 subjects, there was no information on positivity/negativity on the days when the Ct value was missing, but the clinical severity stage strongly suggested the score (scores of 2 and 3 were imputed);
- • For 2 subjects both treated with $HCQ / AZI$ , too little information was available, and an inference could not be made, and they were not included in this analysis.

In the end, as a result of the above, a complete Ct score profile was available for 33 of the 35 subjects. A breakdown of these profiles separately for the two treatment groups is as shown in Tables 7 and 8. In reviewing the tables, it can be seen that there are 12 unique Ct score profiles for those treated with $\bar{HCQ / AZI}$ , and 14 unique Ct score profiles for those treatment with $HCQ / AZI$ . It was found that 5 of the profiles from Table 7 were the same as 5 of the profiles from Table 8, i.e. 18 unique profiles of Ct scores across all subjects. The 18 profiles are presented in Table 9 and ranked in order of favorability, where a more favorable outcome is one for which a higher score is reached earlier. A rank of 1 is the least favorable profile and larger ranks assigned to a profile indicate a more favorable one. A rank-based comparison was performed on these two sets of the ranks, and the results suggested no statistically significant difference in the two treatment groups (HCQ/AZI median rank: 11; $\bar{HCQ / AZI}$ median rank: 5.5; p = 0.2212 (Kruskal-Wallis)) with respect to the time to a recovering Ct value.

Table 7. CT S score value profiles for those treated with $\bar{HCQ / AZI}$

Profile	Day 0	Day 3	Day 7	Day 10	Day 14	N
A1	1	1	1	1	3	1
A2	2	2	2	2	2	2
A3	2	2	2	3	4	1
A4	1	1	2	2	2	1
A5	3	3	2	3	4	1
A6	3	3	4	4	4	1
A7	1	1	2	2	4	1
A8	1	2	2	2	2	2
A9	1	2	2	3	3	1
A10	1	1	1	2	2	1
A11	2	2	3	4	4	1
A12	1	1	1	1	1	1

Table 8. CT S score value profiles for those treated with $HCQ / AZI$

Profile	Day 0	Day 3	Day 7	Day 10	Day 14	N
B1	1	2	2	2	2	1
B2	1	2	2	2	4	3
B3	2	3	3	4	4	1
B4	2	3	4	4	4	2
B5	2	2	3	4	4	1
B6	1	2	3	4	4	1
B7	1	1	1	2	4	1
B8	1	1	1	1	1	2
B9	2	1	1	2	3	1
B10	3	3	2	3	4	1
B11	3	3	4	4	4	1
B12	2	2	3	2	2	2
B13	2	2	2	4	4	1
B14	3	1	2	2	2	1

Table 9. Ranking of CT S score profiles across the two treatment groups

Treatment table profile	Day 0	Day 3	Day 7	Day 10	Day 14	Rank
A12, B8	1	1	1	1	1	1
A10	1	1	1	2	2	2
A4	1	1	2	2	2	3
B14	3	1	2	2	2	3
A8, B1	1	2	2	2	2	4
A2	2	2	2	2	2	5
B12	2	2	3	2	2	5
A1	1	1	1	1	3	6
B9	2	1	1	2	3	7
A9	1	2	2	3	3	8
B7	1	1	1	2	4	9
A7	1	1	2	2	4	10
B2	1	2	2	2	4	11
A3	2	2	2	3	4	12
A5, B10	3	3	2	3	4	12
B13	2	2	2	4	4	13
B6	1	2	3	4	4	14
A11, B5	2	2	3	4	4	15
B3	2	3	3	4	4	16
B4	2	3	4	4	4	17
A6, B11	3	3	4	4	4	18

Table 10 describes the change in disease severity from admission to day 14. One patient was classified at admission with stage A and then his disease developed at day 14 to stage D. Around 36% of the patients presented with mild (stage B) severity and then lessened to asymptomatic (stage A). In total, 7 patients presented with mild stage then they were tested negative by day 14. Table 11 provides an overall comparison of the clinical severity on day 14 between the two treatment groups with no statistically significant difference between the two (p > 0.05).

Table 10. Clinical severity on admission by clinical severity on day 14

Clinical severity		Day 14
Clinical severity		No disease	Asymptomatic (Stage A)	Mild (Stage B)	Moderate (Stage C)	Severe (Stage D)	Dead
Admission	No disease	0	0	0	0	0	0
	Asymptomatic (Stage A)	0	1	0	0	1	0
	Mild (Stage B)	7	12	6	1	1	0
	Moderate (Stage C)	0	1	0	0	1	2
	Severe (Stage D)	0	0	0	0	0	0
	Dead	0	0	0	0	0	0

* Note that the above table involves 33 subjects (two subjects were followed for less than 14 days – one initially stage B and one initially stage C).

Table 11. Day 14 clinical severity (p > 0.05).

Treatment group	No disease	Stage A	Stage B	Stage C	Stage D	Dead
$HCQ / AZI$	5	8	4	0	1	1
$\bar{HCQ / AZI}$	2	6	2	1	2	1

Several lab tests were carried out on the study subjects during admission (Table 12). These included hemoglobin, creatinine, D-dimer, CD4, IgG, and others. From among the lab tests the following were found to significantly relate to disease severity at admission: D-dimer, troponin, LD, C4, absolute CD8 (p < 0.05).

Table 12. Lab tests at admission by clinical severity.

Labs/disease severity median (min – max)	Asymptomatic (Stage A) n = 2	Mild (Stage B) n = 28	Moderate (Stage C) n = 5	p‐value
Hemoglobin	147.5(144-151)	131.5(92-160)	138(114-155)	0.4997
Hematocrit	0.439(0.437-0.44)	0.402(0.322-0.512)	0.43(0.346-0.494)	0.6691
Platelets	182.5(152-213)	187.5(112-369)	148(110-455)	0.3594
WBC	4.315(3.38-5.25)	4.835(2.79-10.51)	5.5(2.9-13.2)	0.6431
Neutrophil	1.82(1.4-2.24)	2.75(0.81-7.12)	3.12(1.36-5.28)	0.5094
Lymphocyte	1.545(1.27-1.82)	1.475(0.6-3.64)	1.21(1.09-2.09)	0.7218
Neutrophil/Lymphocyte	1.165(1.1-1.23)	1.465(0.4-6.8)	2.52(1.22-3.11)	0.499
Monocyte	0.805(0.59-1.02)	0.46(0.15-1.45)	0.39(0.25-0.83)	0.1962
PT	29.45(14.8-44.1)	14.3(12-20)	15.9(12.1-24)	0.4232
PTT	41(33.9-48.1)	36.2(1-44)	36.8(32.7-57.5)	0.5273
INR	2.25(1.1-3.4)	1.1(1-59.6)	1.2(0.9-1.8)	0.7111
Fibrinogen	3.685(3.26-4.11)	3.75(2.4-6.07)	3.62(2.88-4.33)	0.8818
D-Dimer	0.27(0.27-0.27)	0.325(0.27-1.96)	0.69(0.49-2.44)	0.0084
ESR	63(63-63)	16(2-65)	22(10-58)	0.1884
Urea	4.55(3.2-5.9)	3.75(2.6-8)	8.9(2.7-13.6)	0.7165
Creatinine	94(76-112)	63(46-108)	104(26-150)	0.1518
Na	140(140-140)	140.5(4-144)	137(129-156)	0.7014
Albumin	41(40-42)	40.5(37-48)	42(34-48)	0.9997
Bilirubin	3.75(2.5-5)	4.8(2.6-67)	8(3-13)	0.2619
Haptoglobin	1.6(1.6-1.6)	1.8(1-3.6)	1.9(1.6-2.2)	0.8475
CK	75.5(63-88)	57.5(28-251)	69(56-539)	0.2184
Troponin	7(3-11)	5.5(3-76)	34(8-115)	0.0349
LD	181(154-208)	200(111-465)	275(240-414)	0.0203
ALT	37.5(32-43)	21(5-67)	26(20-34)	0.1451
AST	33(32-34)	22(13-228)	32(21-56)	0.1568
Alkaline Phosphatase	67(65-69)	65(38-136)	73(50-161)	0.7771
Ferritin	251.5(173-330)	112(4.2-815)	421(207-494)	0.1395
Procalcitonin	0.03(0.03-0.03)	0.04(0.012-0.35)	0.11(0.04-0.56)	0.0652
Zinc	10.25(10-10.5)	10.6(9-12.3)	10.55(10.3-10.7)	0.3276
C3	NA	1.35(0.82-1.55)	1.32(1.1-1.51)	1.1263
C4	NA	0.26(0.17-0.53)	0.46(0.3-0.62)	0.0140
IgG	12.5(9.3-15.7)	11.5(7.5-15.9)	10.5(10.2-14.5)	0.9615
IgA	2.32(2.08-2.56)	2.81(1.05-4.96)	2.4(1.76-5.95)	0.7138
IgM	0.995(0.81-1.18)	1.18(0.25-4.13)	0.805(0.25-0.95)	0.1440
CRP	8.45(1.4-15.5)	3.2(0.2-70)	31(6.4-300)	0.0430
Absolute CD4	792(547-1037)	597.5(358-1445)	518(295-606)	0.2767
Absolute CD8	903(812-994)	384(134-1179)	169(34-752)	0.0432
CD4/CD8	0.7(0.7-0.7)	1.65(0.8-2.7)	1.6(0.8-2.4)	0.2731
Absolute CD19	235(64-406)	115.5(57-370)	123(56-1427)	0.8850
Absolute NK	185.5(85-286)	175(42-617)	104(77-168)	0.1119
NK %	16(16-16)	12(7-29)	10.5(10-11)	0.6384
B2 Macroglobulin	4.31(4.31-4.31)	2.72(1.63-7.61)	8.26(8.26-8.26)	0.1920

Discussion

The current study involves subjects presenting early in the COVID-19 pandemic and much knowledge about the virus and the ensuing disease has been generated since the time of these subjects’ presentations. However, there is still relatively little new knowledge related to the time profile of Ct values and their potential value in understanding the course of disease. This study aims to add some related insights and particularly how treatment efficacy (here a comparison of HCQ and AZI) may be able to be evaluated based on Ct values. Additionally, the study recognizes the challenges associated with timely RT-PCR testing and always documenting the Ct values and proposes an imputation method that may be used with conservative statistical assumptions for analyses of Ct profiles. There are several limitations of this study. It is a retrospective study that has a small sample size and was conducted in a single center.

In this study, a treatment regimen of HCQ and AZI was identified for 21 of 35 subjects testing positive for SARS-Cov2 and admitted to a single tertiary care hospital early in the COVID-19 pandemic.

This study presents the follow-up information gathered on the patients for up to 14 days after admission, specifically days 0 (admission), 3, 7 10, and 14. Of particular interest for the follow-up was the clinical staging of COVID-19 and the Ct value from the RT-PCR. Ct values were scored as 1, 2, 3, or 4 based on the value of the Ct being below 20, 20-30, 30-40, or 40 or more. The follow-up Ct value was not consistently available at each of the four follow-up timepoints following admission, and imputation of missing Ct scores (1-4) was made using an algorithm based on coincident clinical stage and interpolation. Recognizing the assumptions associated with the designed imputation scheme, a conservative, nonparametric (i.e. rank-based) method was used for comparing the two treatment groups with respect to outcomes. A validation study of this method is going to follow.

The patients treated with the HCQ and AZI were similar to those not treated with respect to all demographic, clinical, radiological, and laboratory features with the exception of a higher rate of smoking history among those treated with HCQ and AZI. The follow-up profiles of the two groups were compared with respect to improvement trends (i.e. improving clinical staging and improving Ct scores). There was no evidence of any difference in the sets of profiles for the two treatment groups.

Data availability

Underlying data

Dryad: SARS-CoV-2 Viral dynamics, https://doi.org/10.5061/dryad.15dv41nwm.⁹

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

References

1. Su S, Wong G, Shi W, et al.: Epidemiology, Genetic Recombination, and Pathogenesis of Coronaviruses. Trends Microbiol 2016; 24: 490–502. PubMed Abstract | Publisher Full Text | Free Full Text
2. Lu R, Zhao X, Li J, et al.: Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 2020; 395: 565–74. PubMed Abstract | Publisher Full Text | Free Full Text
3. Huang C, Wang Y, Li X, et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: 497–506. PubMed Abstract | Publisher Full Text | Free Full Text
4. Liu R, Han H, Liu F, et al.: Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clin Chim Acta 2020; 505: 172–5. PubMed Abstract | Publisher Full Text | Free Full Text
5. Yu F, Yan L, Wang N, et al.: Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients. Clin Infect Dis 2020. PubMed Abstract | Publisher Full Text | Free Full Text
6. Liu Y, Yang Y, Zhang C, et al.: Clinical and biochemical indexes from 2019- nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci 2020; 63: 364–74. PubMed Abstract | Publisher Full Text | Free Full Text
7. Liu Y, Yan L, Wan L, et al.: Viral dynamics in mild and severe cases of COVID-19Dysregulation of immune response in patients with COVID-19 in Wuhan, China Chuan. Lancet Infect Dis 2020; 2019: 2019–20. PubMed Abstract | Publisher Full Text | Free Full Text
8. Zou L, Ruan F, Huang M, et al.: SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients. N Engl J Med 2020; 382: 1177–9. PubMed Abstract | Publisher Full Text | Free Full Text
9. Alamri M: SARS-CoV-2 Viral dynamics. Dryad, [Dataset]. 2022. Publisher Full Text

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Author details Author details

¹ Department of Medicine, King Faisal Specialist Hopital and Research Center, Riyadh, 11211, Saudi Arabia
² Epidemiology & Scientific Computing Department, King Faisal Specialist Hopital and Research Center, Riyadh, 11211, Saudi Arabia
³ Department of Pathology and Laboratory Medicine, King Faisal Specialist Hopital and Research Center, Riyadh, 11211, Saudi Arabia
⁴ Department of Infection and Immunity, King Faisal Specialist Hospital, Riyadh, 11211, Saudi Arabia

Maha M. Alamri
Roles: Conceptualization, Data Curation, Formal Analysis, Writing – Original Draft Preparation, Writing – Review & Editing

Edward B. Devol
Roles: Conceptualization, Formal Analysis, Methodology, Software, Supervision, Writing – Review & Editing

Anwar B. Al-Otaibi
Roles: Formal Analysis, Methodology, Writing – Original Draft Preparation

Faisal A. Albaiz
Roles: Data Curation, Investigation

Mayyadah H. Alabdely
Roles: Data Curation, Investigation

Sahar I. Althawadi
Roles: Resources, Writing – Review & Editing

Maysoon S. Mutabagani
Roles: Investigation, Resources, Supervision, Writing – Review & Editing

Fatimah S. Alhamlan
Roles: Data Curation, Writing – Review & Editing

Fahad A. Alrabiah
Roles: Writing – Review & Editing

Reem S. Almaghrabi
Roles: Conceptualization, Data Curation, Investigation, Methodology, Resources, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

The author(s) declared that no grants were involved in supporting this work.

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Alamri MM, Devol EB, Al-Otaibi AB et al. Descriptive analysis of clinical and laboratory findings in relation to changes in SARS-CoV-2 viral dynamics and cyclic threshold: a retrospective, single center observational study in patients treated with Hydroxychloroquine/Azithromycin combination therapy [version 1; peer review: 1 not approved] F1000Research 2022, 11:925 (https://doi.org/10.12688/f1000research.28290.1)

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Reviewer Report 03 Jul 2023

Reza Zolfaghari Emameh, NIGEB, Tehran, Tehran, Iran

Not Approved

https://doi.org/10.5256/f1000research.31288.r179429

The manuscript entitled “Descriptive analysis of clinical and laboratory findings in relation to changes in SARS-CoV-2 viral dynamics and cyclic threshold: a retrospective, single center observational study in patients treated with Hydroxychloroquine/Azithromycin combination therapy “ was reviewed carefully.

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The manuscript entitled “Descriptive analysis of clinical and laboratory findings in relation to changes in SARS-CoV-2 viral dynamics and cyclic threshold: a retrospective, single center observational study in patients treated with Hydroxychloroquine/Azithromycin combination therapy “ was reviewed carefully.

The study was performed in March 2020 and three years have been passed since then. The subjects such as HCQ/AZI treatment protocols, have been published many times. In addition, hydroxychloroquine has been banned by the World Health Organization (WHO) in treatment of COVID-19. Finally, there is no novelty in this manuscript.

As the result, the manuscript is not acceptable.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: SARS-CoV-2

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reza Zolfaghari Emameh, NIGEB, Tehran, Tehran, Iran

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Not Approved

The manuscript entitled “Descriptive analysis of clinical and laboratory findings in relation to changes in SARS-CoV-2 viral dynamics and cyclic threshold: a retrospective, single center observational study in patients treated with Hydroxychloroquine/Azithromycin combination therapy “ was reviewed carefully.

The study was performed in March 2020 and three years have been passed since then. The subjects such as HCQ/AZI treatment protocols, have been published many times. In addition, hydroxychloroquine has been banned by the World Health Organization (WHO) in treatment of COVID-19. Finally, there is no novelty in this manuscript.

As the result, the manuscript is not acceptable.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

No
Are sufficient details of methods and analysis provided to allow replication by others?

No
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

SARS-CoV-2

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Respond to this report

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[1] 1. Su S, Wong G, Shi W, et al.: Epidemiology, Genetic Recombination, and Pathogenesis of Coronaviruses. Trends Microbiol 2016; 24: 490–502. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Lu R, Zhao X, Li J, et al.: Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 2020; 395: 565–74. PubMed Abstract | Publisher Full Text | Free Full Text

[3] 3. Huang C, Wang Y, Li X, et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: 497–506. PubMed Abstract | Publisher Full Text | Free Full Text

[4] 4. Liu R, Han H, Liu F, et al.: Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clin Chim Acta 2020; 505: 172–5. PubMed Abstract | Publisher Full Text | Free Full Text

[5] 5. Yu F, Yan L, Wang N, et al.: Quantitative Detection and Viral Load Analysis of SARS-CoV-2 in Infected Patients. Clin Infect Dis 2020. PubMed Abstract | Publisher Full Text | Free Full Text

[6] 6. Liu Y, Yang Y, Zhang C, et al.: Clinical and biochemical indexes from 2019- nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci 2020; 63: 364–74. PubMed Abstract | Publisher Full Text | Free Full Text

[7] 7. Liu Y, Yan L, Wan L, et al.: Viral dynamics in mild and severe cases of COVID-19Dysregulation of immune response in patients with COVID-19 in Wuhan, China Chuan. Lancet Infect Dis 2020; 2019: 2019–20. PubMed Abstract | Publisher Full Text | Free Full Text

[8] 8. Zou L, Ruan F, Huang M, et al.: SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients. N Engl J Med 2020; 382: 1177–9. PubMed Abstract | Publisher Full Text | Free Full Text

[9] 9. Alamri M: SARS-CoV-2 Viral dynamics. Dryad, [Dataset]. 2022. Publisher Full Text

Profile	Day 0	Day 3	Day 7	Day 10	Day 14	N
A1	1	1	1	1	3	1
A2	2	2	2	2	2	2
A3	2	2	2	3	4	1
A4	1	1	2	2	2	1
A5	3	3	2	3	4	1
A6	3	3	4	4	4	1
A7	1	1	2	2	4	1
A8	1	2	2	2	2	2
A9	1	2	2	3	3	1
A10	1	1	1	2	2	1
A11	2	2	3	4	4	1
A12	1	1	1	1	1	1

Profile	Day 0	Day 3	Day 7	Day 10	Day 14	N
B1	1	2	2	2	2	1
B2	1	2	2	2	4	3
B3	2	3	3	4	4	1
B4	2	3	4	4	4	2
B5	2	2	3	4	4	1
B6	1	2	3	4	4	1
B7	1	1	1	2	4	1
B8	1	1	1	1	1	2
B9	2	1	1	2	3	1
B10	3	3	2	3	4	1
B11	3	3	4	4	4	1
B12	2	2	3	2	2	2
B13	2	2	2	4	4	1
B14	3	1	2	2	2	1

Profile	Day 0	Day 3	Day 7	Day 10	Day 14	N
A1	1	1	1	1	3	1
A2	2	2	2	2	2	2
A3	2	2	2	3	4	1
A4	1	1	2	2	2	1
A5	3	3	2	3	4	1
A6	3	3	4	4	4	1
A7	1	1	2	2	4	1
A8	1	2	2	2	2	2
A9	1	2	2	3	3	1
A10	1	1	1	2	2	1
A11	2	2	3	4	4	1
A12	1	1	1	1	1	1

Profile	Day 0	Day 3	Day 7	Day 10	Day 14	N
B1	1	2	2	2	2	1
B2	1	2	2	2	4	3
B3	2	3	3	4	4	1
B4	2	3	4	4	4	2
B5	2	2	3	4	4	1
B6	1	2	3	4	4	1
B7	1	1	1	2	4	1
B8	1	1	1	1	1	2
B9	2	1	1	2	3	1
B10	3	3	2	3	4	1
B11	3	3	4	4	4	1
B12	2	2	3	2	2	2
B13	2	2	2	4	4	1
B14	3	1	2	2	2	1

Descriptive analysis of clinical and laboratory findings in relation to changes in SARS-CoV-2 viral dynamics and cyclic threshold: a retrospective, single center observational study in patients treated with Hydroxychloroquine/Azithromycin combination therapy

Abstract

Keywords

Introduction

Methods

Study design and patients

Laboratory procedures

Management and treatment regimens

Table 1. Definition of disease severity

Definitions

Statistical analysis

Results

Table 2. Disease severity progression through the course of 14 days.

Table 3. Symptom development during infection.

Figure 1. Treatment profile of all 36 patients.

Table 4. Baseline laboratory and radiology findings by treatment groups.

Table 5. Comparison of baseline clinical severity between the two treatment groups (p = 0.1256).

Table 6. Days to reach negative RT-PCR results.

Figure 2. Kaplan-Meier comparison between two treatment groups for time to reach negative PCR test (p = 0.1390).

Table 7. CT S score value profiles for those treated with HCQ/AZI¯

Table 8. CT S score value profiles for those treated with HCQ/AZI

Table 9. Ranking of CT S score profiles across the two treatment groups

Table 10. Clinical severity on admission by clinical severity on day 14

Table 11. Day 14 clinical severity (p > 0.05).

Table 12. Lab tests at admission by clinical severity.

Discussion

Data availability

Underlying data

Competing interests

Grant information

References

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Table 7. CT S score value profiles for those treated with $\bar{HCQ / AZI}$

Table 8. CT S score value profiles for those treated with $HCQ / AZI$

Profile	Day 0	Day 3	Day 7	Day 10	Day 14	N
A1	1	1	1	1	3	1
A2	2	2	2	2	2	2
A3	2	2	2	3	4	1
A4	1	1	2	2	2	1
A5	3	3	2	3	4	1
A6	3	3	4	4	4	1
A7	1	1	2	2	4	1
A8	1	2	2	2	2	2
A9	1	2	2	3	3	1
A10	1	1	1	2	2	1
A11	2	2	3	4	4	1
A12	1	1	1	1	1	1

Profile	Day 0	Day 3	Day 7	Day 10	Day 14	N
B1	1	2	2	2	2	1
B2	1	2	2	2	4	3
B3	2	3	3	4	4	1
B4	2	3	4	4	4	2
B5	2	2	3	4	4	1
B6	1	2	3	4	4	1
B7	1	1	1	2	4	1
B8	1	1	1	1	1	2
B9	2	1	1	2	3	1
B10	3	3	2	3	4	1
B11	3	3	4	4	4	1
B12	2	2	3	2	2	2
B13	2	2	2	4	4	1
B14	3	1	2	2	2	1