Skip to main content
PMC home page
PLOS ONE logoLink to PLOS ONE
. 2020 Dec 3;15(12):e0243268. doi: 10.1371/journal.pone.0243268

U-shaped-aggressiveness of SARS-CoV-2: Period between initial symptoms and clinical progression to COVID-19 suspicion. A population-based cohort study

Dan Morgenstern-Kaplan 1,#, Bruno Buitano-Tang 1,#, Mercedes Martínez-Gil 1,‡, Andrea Zaldívar-Pérez Pavón 1,‡, Juan O Talavera 1,2,*
Editor: Chiara Lazzeri3
PMCID: PMC7714139  PMID: 33270769

Abstract

Background

Early identification of different COVID-19 clinical presentations may depict distinct pathophysiological mechanisms and guide management strategies.

Objective

To determine the aggressiveness of SARS-CoV-2 using symptom progression in COVID-19 patients.

Design

Historic cohort study of Mexican patients. Data from January-April 2020 were provided by the Health Ministry.

Setting

Population-based. Patients registered in the Epidemiologic Surveillance System in Mexico.

Participants

Subjects who sought medical attention for clinical suspicion of COVID-19. All patients were subjected to RT-PCR testing for SARS-CoV-2.

Measurements

We measured the Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS) and compared it to the primary outcomes (mortality and pneumonia).

Results

65,500 patients were included. Reported fatalities and pneumonia were 2176 (3.32%), and 11568 (17.66%), respectively. According to the PISYCS, patients were distributed as follows: 14.89% in <24 hours, 43.25% between 1–3 days, 31.87% between 4–7 days and 9.97% >7 days. The distribution for mortality and pneumonia was 5.2% and 22.5% in <24 hours, 2.5% and 14% between 1–3 days, 3.6% and 19.5% between 4–7 days, 4.1% and 20.6% >7 days, respectively (p<0.001). Adjusted-risk of mortality was (OR [95% CI], p-value): <24 hours = 1.75 [1.55–1.98], p<0.001; 1–3 days = 1 (reference value); 4–7 days = 1.53 [1.37–1.70], p<0.001; >7 days = 1.67 [1.44–1.94], p<0.001. For pneumonia: <24 hours = 1.49 [1.39–1.58], p<0.001; 1–3 days = 1; 4–7 days = 1.48 [1.41–1.56], p<0.001; >7 days = 1.57 [1.46–1.69], p<0.001.

Limitations

Using a database fed by large numbers of people carries the risk of data inaccuracy. However, this imprecision is expected to be random and data are consistent with previous studies.

Conclusion

The PISYCS shows a U-shaped SARS-CoV-2 aggressiveness pattern. Further studies are needed to corroborate the time-related pathophysiology behind these findings.

Introduction

Coronaviruses are single-stranded RNA organisms capable of infecting humans and other animal species [1, 2]. The most recently discovered coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is cause of the clinical entity denominated Coronavirus Disease 2019 (COVID-19). This virus initially spread in the Wuhan province in China and later to the rest of the world, causing a pandemic [3]. Reported worldwide cases are continuously growing and currently (as of July 3rd, 2020) there are over 10 million infected people confirmed and over 500,000 fatalities. Global reports reveal case-fatality rate of 4.8% and more than half of the cases are in the Americas region. In Mexico, over 230,000 cases have been reported, with over 28,000 fatalities and a case-fatality rate of 12.3%, which by far surpasses the global estimate [4].

Every human in the world is susceptible to infection, for as the mean age of infected patients is 47 years, 87% of patients lie between 30 and 79 years old. COVID-19 behaves more aggressively in older patients and in patients undergoing chronic medical conditions such as obesity, diabetes [5, 6], hypertension and other cardiovascular diseases, increasing the risk of mortality in these populations [7, 8]. Approximately 80% of cases are asymptomatic with a mild disease course, while the other 20% can be accompanied of severe complications such as pneumonia, Acute Respiratory Distress Syndrome (ARDS) and other secondary infections. Among these severe cases, 80% correspond to people over 60 years. Many of these cases can be attributed to a severe clinical entity known as “cytokine storm”, which causes a rise in serum levels of many pro-inflammatory mediators and provokes massive tissue damage in several vital organs [7, 9].

In patients who developed severe symptoms, dyspnea was reported between 8–12 days after onset of symptoms, and some patients deteriorate into severe disease during the first week after onset of symptoms. This accelerated worsening has been hypothesized to be caused by the cytokine storm and to thrombotic events that may be caused by infection with SARS-CoV-2 [10].

Hospitalized patients have been thoroughly described and analyzed, with an average time between onset of symptoms to intubation of 14.5 days, and a time from intubation to death ranging from 4–5 days [7, 9, 11]. A longer period between onset of symptoms and first contact seeking medical attention has been associated with a poorer outcome in these patients. However no in-depth studies have been conducted [12].

Until now, studies have been focused on patient-centered risk factors, while SARS-CoV-2 aggressiveness has been established as provoking 20% of severe and critic patients [13], however, there are still many unanswered questions concerning the clinical aggressiveness behavior of SARS-CoV-2. This study focuses on progression of symptoms as a marker of such aggressiveness, using the Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS) to determine the risk of severe disease and mortality.

Methods

Study design and data source

A historic cohort study of Mexican patients that were classified as a suspect case of COVID-19 and sought medical attention in either public or private health services in Mexico, was analyzed. Data was provided by the General Directorate of Epidemiology of the Mexican Health Ministry, which is deidentified, publicly available online, and registers all patients in the Epidemiologic Surveillance System of the 32 federal states in Mexico. This analysis was done in all cases registered in this dataset up until April 25th, 2020, with a total of 65,500 patients [14]. The Institutional Review Board of Anahuac University (Mexico City, Mexico) approved this study (Protocol approval #202044).

Variable definition

Our dataset includes demographic characteristics such as age, gender, location, health sector, underlying medical conditions (obesity, diabetes, COPD, asthma, immunosuppression, hypertension, cardiovascular, chronic kidney diseases, and other comorbid diseases), pregnancy status, tobacco use, and indigenous language speaker. Furthermore, it references the dates of the onset of symptoms and the date of medical attention, including hospital admission, as well as the presence of pneumonia. Regarding in-hospital decisions, data include results of RT-PCR testing for SARS-CoV-2 (reported as positive, negative or pending), admission to the Intensive Care Unit (ICU) and requirement of mechanical ventilation. Finally, the date of death of all deceased patients is reported.

Data analysis: Coding and substitution of variables

The state where the patient sought attention was recoded according to the socio-economic level of that particular state into low, middle and high level, based on the Gross Domestic Product (GDP) of that state, as reported by the National Institute of Statistics and Geography (INEGI) [15]. The health sector variable was recoded in three categories: private, with social security and without social security.

COVID-19 clinical suspicion was defined by the government health ministry´s official guidelines, as presenting with two of these symptoms: 1) cough, 2) fever or 3) headache, plus one or more of the following: 1) breathing difficulty, 2) sore or burning throat, 3) runny nose, 4) red eyes, 5) pain in muscles or joints, or 6) being part of these high-risk groups: pregnancy, <5 or ≥60 years old, or having a chronic disease such as hypertension, diabetes mellitus, cancer or HIV.

These indications were broadcast on television, radio, newspaper and internet platforms since the beginning of the pandemic until July 1, 2020. Due to nationwide government issued stay-at-home orders, we assumed that medical attention was sought only when these criteria were met.

Upon medical evaluation, patients were asked to report the date of onset of initial symptoms (any combination of clinical features that appeared before meeting the previously mentioned criteria). Therefore, the Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS) was established as the number of days between appearance of initial symptoms and the date in which the patients sought medical attention.

PISYCS

Initially, PISYCS was categorized in days (<1, 1, 2, 3, etc.), but to improve comprehension and data management, adjacent days whose frequency of death remained in similar proportions, were grouped into 4 categories (<24 hours, 1–3 days, 4–7 days and >7 days). The primary outcomes were mortality and pneumonia. The presence of pneumonia was used as an indicator of severe disease as reported in previous studies [7, 9].

Missing data were substituted using the mode for the following categorical variables: Health Sector (338 patients, 0.5%), indigenous language speaker (1241, 2%), tobacco use (242, 0.4%), pregnancy status (166, 0.3%), diabetes (255, 0.4%), COPD (245, 0.4%), asthma (251, 0.4%), immunosuppression (259, 0.4%), hypertension (241, 0.4%), cardiovascular disease (252, 0.4%), obesity (220, 0.3%), chronic kidney disease (245, 0.4%), other comorbid condition (331, 0.5%), admission to the ICU (12, <0.01%) and mechanical ventilation (12, <0.01%).

Statistical analysis

Demographic features and comorbid conditions were initially compared between the four categories of PISYCS; age was analyzed with a one-way ANOVA and the rest of the variables with Chi squared test analysis. Afterwards, we performed a bivariate analysis comparing the four categories of PISYCS with the medical decisions made (result of PCR testing for SARS-CoV-2, hospital admission, ICU, mechanical ventilation) and outcomes (mortality and pneumonia) using the Chi squared test.

Finally, the four categories of PISYCS against primary outcomes -mortality and pneumonia-, were compared using a multivariable logistic regression model. The model was adjusted in five steps for the following variables: age, gender, indigenous language speaker, state’s socioeconomic status, pregnancy, tobacco use, obesity, hypertension, diabetes, asthma, COPD, cardiovascular disease, chronic kidney disease, immunosuppression, other comorbid conditions. This model was repeated in four groups of patients within the sample, depending on their RT-PCR testing result for SARS-CoV-2:

  1. All patients: Every patient in the dataset regardless of their test result

  2. Positives: Only patients with a positive test result

  3. Negatives: Only patients with a negative test result

  4. Pending: Only patients with pending results of the test

The PISYCS used as reference in the regression models, was 1–3 days based in the lower rate of mortality, observed in the results of the bivariate analysis. Each logistic regression model is presented with the Odds Ratio (OR) and its respective 95% Confidence Interval (CI95%). Statistical significance was set at p<0.05 and performed with SPSS version 25.0 (IBM). The full model for the group of all patients can be found in S1 and S2 Tables.

Results

The study population included 65,500 patients. Among them, the average age was 41±17 years, 50.2%, were women, 55.8% belonged to a high socioeconomic level, 27.7% to a medium and 16.5% to a low one, 4.6% of patients were treated on a private health institution, 37.7% in a facility for patients with social security and 57.7% attended to a public hospital for patients without social security. Of all the patients, 41% had at least one comorbidity, hypertension being the most frequent in 17%, followed by obesity in 15.6% and diabetes 12.8%. In addition, 9.9% reported tobacco use and 2.3% of women were pregnant. Mortality was observed in 2176 patients (3.32%), and Pneumonia in 11568 patients (17.66%).

According to PISYCS patients were distributed as follows: 14.89% in <24 hours, 43.25% between 1–3 days, 31.87% between 4–7 days and 9.97% after 7 days, with no significant difference by gender. We compared PISYCS against demographic features and comorbidities. A PISYCS of <24 hours was more frequent in older patients (25.7% in patients > 80 years old vs. 15% in <30 years old) reversing in the period of 1–3 days (41.5% vs 48.4%, respectively), and returning to the initial behavior in subsequent periods. This same pattern was observed when comparing PISYCS with the presence of all comorbidities, except for asthma and obesity. Demographic Characteristics of all patients are summarized in Table 1.

Table 1. Patient demographic characteristics according to period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS).

Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS) Global P Value
<24 Hrs (n = 9759) 1–3 Days (n = 28331) 4–7 Days (n = 20877) >7 Days (n = 6533)
N % N % N % N %
Gender Female 4686 14.3%* 14371 43.7% 10562 32.1% 3260 9.9% <0.001
Male 5073 15.6% 13960 42.8% 10315 31.6% 3273 10.0%
Age < 30 2469 15.0%* 7965 48.4% 4732 28.7%** 1302 7.9%*** <0.001
30–49 3745 12.8% 12813 43.6% 9785 33.3% 3016 10.3%
50–59 1430 14.7% 3875 39.7% 3321 34.1% 1123 11.5%
60–69 1095 19.8% 2006 36.2% 1785 32.2% 656 11.8%
70–79 615 21.9% 1018 36.3% 863 30.7% 311 11.1%
> 80 405 25.7% 654 41.5% 391 24.8% 125 7.9%
Mean (SD) 43(20)* 40 (18) 42 (17)** 44 (16)*** <0.001
State’s Socioeconomic Status High 5532 15.1%* 15745 43.1% 11352 31.1%** 3921 10.7%*** <0.001
Medium 2878 15.8% 8027 44.2% 5868 32.3% 1402 7.7%
Low 1349 12.5% 4559 42.3% 3657 33.9% 1210 11.2%
Health Sector Private 468 15.6%* 1228 40.9% 885 29.5%** 418 13.9%*** <0.001
With SS 4584 18.6% 10515 42.6% 7274 29.5% 2321 9.4%
Without SS 4707 12.5% 16588 43.9% 12718 33.6% 3794 10.0%
Indigenous Language Speaker Yes 98 14.4% 277 40.8% 242 35.6% 62 9.1% 0.20
No 9661 14.9% 28054 43.4% 20635 31.8% 6471 10.0%
Pregnancy Yes 139 18.3% 383 50.5% 190 25.1%** 46 6.1%*** <0.001
No 9620 14.9% 27948 43.2% 20687 32.0% 6487 10.0%
Tobacco Use Yes 946 14.5% 2786 42.8% 2122 32.6% 661 10.1% 0.49
No 8813 14.9% 25545 43.3% 18755 31.8% 5872 10.0%
Diabetes Yes 1514 18.1%* 3307 39.5% 2684 32.1%** 867 10.4%*** <0.001
No 8245 14.4% 25024 43.8% 18193 31.8% 5666 9.9%
COPD Yes 401 22.6%* 685 38.6% 535 30.2% 153 8.6% <0.001
No 9358 14.7% 27646 43.4% 20342 31.9% 6380 10.0%
Asthma Yes 394 12.4%* 1422 44.6% 1034 32.5% 336 10.5% 0.001
No 9365 15.0% 26909 43.2% 19843 31.8% 6197 9.9%
Immunosuppression Yes 455 26.1%* 697 40.0% 429 24.6%** 163 9.3% <0.001
No 9304 14.6% 27634 43.3% 20448 32.1% 6370 10.0%
Hypertension Yes 1932 17.4%* 4415 39.7% 3593 32.3%** 1194 10.7%*** <0.001
No 7827 14.4% 23916 44.0% 17284 31.8% 5339 9.8%
Cardiovascular Disease Yes 470 22.0%* 820 38.3% 627 29.3% 224 10.5%*** <0.001
No 9289 14.7% 27511 43.4% 20250 32.0% 6309 10.0%
Obesity Yes 1381 13.5% 4093 40.0% 3633 35.5%** 1119 10.9%*** <0.001
No 8378 15.2% 24238 43.9% 17244 31.2% 5414 9.8%
Chronic Kidney Disease Yes 435 28.1%* 612 39.5% 374 24.1%** 129 8.3% <0.001
No 9324 14.6% 27719 43.3% 20503 32.1% 6404 10.0%
Other Comorbid Condition Yes 662 19.0%* 1440 41.3% 1059 30.4% 323 9.3% <0.001
No 9097 14.7% 26891 43.4% 19818 32.0% 6210 10.0%
Open in a new tab

SS = Social Security. COPD = Chronic Obstructive Pulmonary Disease. SD = Standard Deviation. Statistical Significance p<0.05

* Significant Difference between periods of <24 hrs. and 1–3 days

** Significant Difference between periods of 4–7 days and 1–3 days.

*** Significant Difference between periods of >7 days and 1–3 days.

Table 2 shows the initial medical decisions according to their PISYCS. More people were hospitalized in the first 24 hours (43.2%), with a drop towards the period of 1–3 days (19.7%), and a slight increase in subsequent days. A similar phenomenon is observed in terms of admission to the ICU, with admission being 2.8% when the period is <24 hours, falling to 1.8% in 1–3 days, and gradually increasing to 2.7% in 4–7 days and 3.2% in >7 days. The proportion of patients under mechanical ventilation steadily increased over time, starting from 1.6% in the period of <24 hours, up to 2.9% in the period of >7 days.

Table 2. Medical decisions according period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS).

Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS)PISYCS Global P Value
<24 Hrs (n = 9759) 1–3 Days (n = 28331) 4–7 Days (n = 20877) >7 Days (n = 6533)
N % N % N % N %
Type of care Ambulatory Care 5547 56.8%* 22742 80.3% 15674 75.1%** 4837 74.0%*** <0.001
Hospital Admission 4212 43.2% 5589 19.7% 5203 24.9% 1696 26.0%
Admission to the ICU 271 2.8%* 503 1.8% 563 2.7%** 208 3.2%*** <0.001
Mechanical Ventilation 155 1.6% 479 1.7% 525 2.5%** 189 2.9%*** <0.001
Result of RT-PCR Test+ Not Positive to SARS-CoV 2 7036 81.4%* 19801 80.4% 12672 69.8%** 3910 66.5%*** <0.001
Positive to SARS-CoV-2 1601 18.6% 4818 19.6% 5462 30.2% 1961 33.5%
Open in a new tab

ICU = Intensive Care Unit. PCR = Polymerase Chain Reaction. SD = Standard Deviation.

Statistical Significance p<0.05

*Significant Difference between periods of <24 hrs and 1–3 days.

** Significant Difference between periods of 4–7 days and 1–3 days.

*** Significant Difference between periods of >7 days and 1–3 days. + Undefined were not included.

Table 3 and Fig 1 show the risks for mortality and pneumonia related to PISYCS. A “U-shaped distribution” was observed according to PISYCS (<24 hrs., followed by 1–3 days, 4–7, and >7). The proportion of patients with Mortality was 5.2%, 2.5%, 3.6%, and 4.1% (p<0.001), and for Pneumonia 22.5%, 14%, 19.5% and 20.6% (p<0.001). The adjusted-risk of mortality for all patients evaluated for clinical suspicion of COVID-19 according to PISYCS, was for <24 hours OR of 1.75 (95% CI, 1.55 to 1.98, p = <0.001), 1–3 days OR = 1 (reference value), 4–7 days, OR 1.53 (1.37–1.70, p = <0.001), and >7 days, OR 1.67 (1.44–1.94, p = <0.001), while for Pneumonia it was for <24 hours OR of 1.49 (95% CI, 1.39 to 1.58, p = <0.001), 1–3 days OR = 1 (reference value), 4–7 days, OR 1.48 (1.41–1.56, p = <0.001), and >7 days, OR 1.57 (1.46–1.69, p = <0.001).

Table 3. Mortality and pneumonia among patients with COVID-19 according to the period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS).

Variable Mortality Pneumonia
All Patients (N = 65,500)
PISYCS (N) %* OR (95% CI) p-value %* OR (95% CI) p-value
< 24 Hours (9759) 5.2% 1.75 (1.55–1.98) <0.001 22.5% 1.49 (1.39–1.58) <0.001
1–3 Days (28331) 2.5% 1 1 14% 1 1
4–7 Days (20877) 3.6% 1.53 (1.37–1.70) <0.001 19.5 1.48 (1.41–1.56) <0.001
> 7 Days (6533) 4.1% 1.67 (1.44–1.94) <0.001 20.6% 1.57 (1.46–1.69) <0.001
“p value” <0.001 <0.001
SUBGROUP ANALYSIS
Positive Test (N = 13,842)        
PISYCS OR (95% CI) p-value OR (95% CI) p-value
< 24 Hours 2.11 (1.75–2.55) <0.001 1.66 (1.45–1.90) <0.001
1–3 Days 1 1 1 1
4–7 Days 1.42 (1.22–1.65) <0.001 1.69 (1.53–1.85) <0.001
> 7 Days 1.22 (1.00–1.49) 0.053 1.83 (1.62–2.07) <0.001
Negative Test (N = 43,419)        
PISYCS OR (95% CI) p-value OR (95% CI) p-value
< 24 Hours 1.55 (1.29–1.86) <0.001 1.58 (1.46–1.70) <0.001
1–3 Days 1 1 1 1
4–7 Days 1.00 (0.83–1.21) 0.967 1.10 (1.01–1.16) 0.038
> 7 Days 1.39 (1.10–1.81) 0.014 1.11 (1.00–1.24) 0.061
Test Result Pending (N = 8,239)        
PISYCS OR (95% CI) p-value OR (95% CI) p-value
< 24 Hours 1.41 (0.68–2.90) 0.357 0.80 (0.64–0.99) 0.043
1–3 Days 1 1 1 1
4–7 Days 1.22 (0.70–2.13) 0.484 2.03 (1.77–2.34) <0.001
> 7 Days 1.71 (0.78–3.74) 0.182 1.96 (1.57–2.44) <0.001
Open in a new tab

This Global Multiple Logistic Regression Model is adjusted for all demographic characteristics and comorbid conditions present in the patients. Adjustments for the group of all patients can be found in S1 and S2 Tables.

*Bivariate analysis between PISYCS vs. Mortality or Pneumonia.

Fig 1. U-shaped distribution of the odds ratio for the primary outcomes (mortality/pneumonia) vs. PISYCS.

Fig 1

Open in a new tab

Association according to the Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS) and the primary outcomes of the study (Mortality and Pneumonia), including all patients. A U-shaped distribution is observed, with higher OR for PISYCS <24 hours and ≥4 days.

Discussion

In this study, we found an association concerning the Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS) with the risk of severe disease and mortality in patients with suspected COVID-19. A “U shaped” distribution was observed, with a high risk of death and pneumonia when PISYCS is <24 hours (OR 1.75, and 1.49, respectively), with a decrease of this risk in 1–3 days (OR 1), and with an additional rise in subsequent periods of 4–7 days (OR 1.53, and 1.48) and >7 days (OR 1.67 and 1.57).

The increased risk of mortality and pneumonia observed in patients with PISYCS <24 hours, may be associated with the presence of a cytokine storm, which has been previously described as an early factor for severity [16]. This phenomenon is due to the uncontrolled release of pro-inflammatory mediators that lead to apoptosis of epithelial and endothelial lung cells, causing vascular extravasation, alveolar edema and hypoxia [17]. This inflammatory response in conjunction with the production of reactive oxygen species triggers an acute respiratory distress syndrome (ARDS) leading to pulmonary fibrosis and death [18]. This could support the pharmacodynamic basis for the use of corticosteroids as adjuvant therapy in patients with COVID-19, which has been reported in other studies [19]. Chronic use of inhaled corticosteroids may be the reason why asthmatic patients manifest less severe symptoms [20], which was consistent with our results (See S1 and S2 Tables).

The increased risk of mortality and pneumonia in patients with PISYCS ≥4 days, could be explained by the thrombotic events that have been reported in patients with COVID-19. These events are caused by the excessive inflammation produced by the virus and platelet activation with accompanying endothelial damage [21, 22]. This occurs once the virus has colonized the respiratory system, impairing microvascular permeability, helping it spread even further. Hemostatic disorders are established by the presence of thrombocytopenia, and an increase in the D-dimer and fibrinogen, for which the use of antithrombotic therapies has been suggested [21, 23].

Additionally, an increased risk of lung superinfections must be considered. So far, bacterial and fungal pneumonias have been the most common etiologies. A study conducted in Wuhan, China reported a rate of lung superinfection from 5–27% in adults with COVID-19 [24]. Historically, superinfections have been associated with increased mortality in other viral respiratory infections, such as influenza [25].

Our findings may have further clinical implications if the pathophysiological processes were to be confirmed. The PISYCS could be useful as a prognostic marker and a decision-making tool for clinicians. Identifying individuals at higher risk of developing early-onset complications (with a PISYCS <24 hours) could justify a more aggressive treatment plan and monitorization strategies, focused on preventing complications of cytokine storm and ARDS. Additionally, patients with a higher risk of late-onset complications (with a PISYCS ≥4 days) could be identified and treated accordingly, justifying the use of thromboprophylaxis, preventing superinfections.

The PISYCS could also prove useful as a research categorization parameter for clinical studies exploring timing and efficacy of therapeutics. Immunomodulatory agents (such as IL-6 antagonists) and corticosteroids may only prove beneficial for patients with a PISYCS of < 24 hours and may further increase the risk of late-onset complications (superinfections) if used in a later PISYCS category [24].

Using a database fed by large numbers of people carries its risk, such as data inaccuracy. However, this imprecision is expected to be random and data are consistent with results of previous studies. Furthermore, we set April 25th, 2020 as our cut-off date with the aim of including patients treated at an early stage of the pandemic in Mexico, at a time when hospitals were not yet working at overcapacity. This increases the probability of good quality of healthcare, decreases confounding factors for the outcomes evaluated because all required medical decisions could be made and were not limited by medical resources available at the time (i.e. number of ventilators or ICU beds).

Plenty of studies have described the incubation period and hospital stay of affected patients [7, 26, 27]. However, nobody has considered the progression of symptoms in patients with COVID-19 (PISYCS), as a guide for explaining the time-specific pathophysiology associated with the U-Shaped SARS-CoV-2 aggressiveness. Further studies are needed to corroborate the time-related pathophysiology behind these findings. Eventually, this could help identify specific therapies aimed towards the temporal progression of the disease.

Supporting information

S1 Table. Stepwise analyses for binary logistic regression with mortality as outcome for all patients in the study.

(XLSX)

Click here for additional data file. (25.1KB, xlsx)
S2 Table. Stepwise analyses for binary logistic regression with pneumonia as outcome for all patients in the study.

(XLSX)

Click here for additional data file. (24KB, xlsx)

Data Availability

Data is held in a public Data Repository at Mendeley Data DOI: http://dx.doi.org/10.17632/k6cw45366d.1.

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Tyrrell DA, Bynoe ML. Cultivation of viruses from a high proportion of patients with colds. Lancet. 1966;1: 76–77. 10.1016/s0140-6736(66)92364-6 [DOI] [PubMed] [Google Scholar]
  • 2.Velavan TP, Meyer CG. The COVID-19 epidemic. Trop Med Int Heal. 2020;25: 278–280. 10.1111/tmi.13383 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Battegay M, Kuehl R, Tschudin-Sutter S, Hirsch HH, Widmer AF, Neher RA. 2019-novel Coronavirus (2019-nCoV): estimating the case fatality rate—a word of caution. Swiss Med Wkly. 2020;150: w20203 10.4414/smw.2020.20203 [DOI] [PubMed] [Google Scholar]
  • 4.World Health Organization. WHO Coronavirus Disease (COVID-19) Dashboard In: WHO; [Internet]. 2020. Available: https://covid19.who.int/ [Google Scholar]
  • 5.Maddaloni E, D’Onofrio L, Alessandri F, Mignogna C, Leto G, Coraggio L, et al. Clinical features of patients with type 2 diabetes with and without Covid-19: A case control study (CoViDiab I). Diabetes Res Clin Pract. 2020;169: 1–7. 10.1016/j.diabres.2020.108454 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Maddaloni E, D’Onofrio L, Alessandri F, Mignogna C, Leto G, Pascarella G, et al. Cardiometabolic multimorbidity is associated with a worse Covid-19 prognosis than individual cardiometabolic risk factors: A multicentre retrospective study (CoViDiab II). Cardiovasc Diabetol. 2020;19: 1–11. 10.1186/s12933-020-01140-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Wang L, He W, Yu X, Hu D, Bao M, Liu H, et al. Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up. J Infect. 2020. 10.1016/j.jinf.2020.03.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R. Features, Evaluation and Treatment Coronavirus (COVID-19)—StatPearls—NCBI Bookshelf. StatPearls. 2020. pp. 1–17. Available: https://www.ncbi.nlm.nih.gov/pubmed/32150360 [PubMed] [Google Scholar]
  • 9.Zhou P, Yang X Lou, Wang XG, Hu B, Zhang L, Zhang W, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579: 270–273. 10.1038/s41586-020-2012-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Centers For Disease Control. Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). 2020. Available: https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients.html [Google Scholar]
  • 11.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. 10.1016/S0140-6736(20)30566-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Liang W, Guan W, Li C, Li Y, Liang H, Zhao Y, et al. Clinical characteristics and outcomes of hospitalised patients with COVID-19 treated in Hubei (epicentre) and outside Hubei (non-epicentre): a nationwide analysis of China. Eur Respir J. 2020;55: 2000562 10.1183/13993003.00562-2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Azoulay E, Fartoukh M, Darmon M, Géri G, Voiriot G, Dupont T, et al. Increased mortality in patients with severe SARS-CoV-2 infection admitted within seven days of disease onset. Intensive Care Med. 2020;46: 1714–1722. 10.1007/s00134-020-06202-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Direccion General de Epidemiologia, Secretaría de Salud. Datos Abiertos—Bases Históricas. 2020. Available: https://www.gob.mx/salud/documentos/datos-abiertos-bases-historicas-direccion-general-de-epidemiologia
  • 15.INEGI. Comunicado De Prensa Núm. 384 / 19 31 De Julio De 2019 El Inegi Da a Conocer Los Resultados De La Encuesta Nacional De Ingresos Y Gastos De Los Hogares (Enigh) 2018 Comun prensa INEGI; 2019. [Google Scholar]
  • 16.Henderson LA, Canna SW, Schulert GS, Volpi S, Lee PY, Kernan KF, et al. On the alert for cytokine storm: Immunopathology in COVID-19. Arthritis Rheumatol. 2020;72: 1059–1063. 10.1002/art.41285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Pascarella G, Strumia A, Piliego C, Bruno F, Del Buono R, Costa F, et al. COVID-19 diagnosis and management: a comprehensive review. J Intern Med. 2020;288: 192–206. 10.1111/joim.13091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Sun X, Wang T, Cai D, Hu Z, Chen J, Liao H, et al. Cytokine storm intervention in the early stages of COVID-19 pneumonia. Cytokine Growth Factor Rev. 2020;53: 38–42. 10.1016/j.cytogfr.2020.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Horby P, Lim WS, Emberson J, Mafham M, Bell J, Linsell L, et al. Effect of Dexamethasone in Hospitalized Patients with COVID-19: Preliminary Report. medRxiv. 2020; 2020.06.22.20137273 10.1101/2020.06.22.20137273 32690491 [DOI] [Google Scholar]
  • 20.Liu S, Zhi Y, Ying S. COVID-19 and Asthma: Reflection During the Pandemic. Clin Rev Allergy Immunol. 2020. 10.1007/s12016-020-08797-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Bikdeli B, Madhavan M V., Jimenez D, Chuich T, Dreyfus I, Driggin E, et al. COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-Up: JACC State-of-the-Art Review. J Am Coll Cardiol. 2020;75: 2950–2973. 10.1016/j.jacc.2020.04.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Cannegieter SC, Klok FA. COVID‐19 associated coagulopathy and thromboembolic disease: Commentary on an interim expert guidance. Res Pract Thromb Haemost. 2020;4: 439–445. 10.1002/rth2.12350 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Yuki K, Fujiogi M, Koutsogiannaki S. COVID-19 pathophysiology: A review. Clin Immunol. 2020;215 10.1016/j.clim.2020.108427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Clancy CJ, Nguyen MH. Coronavirus Disease 2019, Superinfections, and Antimicrobial Development: What Can We Expect? Clin Infect Dis. 2020. 10.1093/cid/ciaa524 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Rynda-Apple A, Robinson KM, Alcorn JF. Influenza and bacterial superinfection: Illuminating the immunologic mechanisms of disease. Infect Immun. 2015;83: 3764–3770. 10.1128/IAI.00298-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of coronavirus disease 2019 (CoVID-19) from publicly reported confirmed cases: Estimation and application. Ann Intern Med. 2020;172: 577–582. 10.7326/M20-0504 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Sun Q, Qiu H, Huang M, Yang Y. Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province. Ann Intensive Care. 2020;10: 2–5. 10.1186/s13613-020-00650-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Chiara Lazzeri

2 Nov 2020

PONE-D-20-23492

U-shaped-aggressiveness of SARS-CoV-2: Period between onset of nonspecific-specific symptoms for COVID-19. A population-based cohort study

PLOS ONE

Dear Dr. Talavera,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Specific and no specific symptoms should be better described. The role of comorbidities is clinically interesting. We suggest to better describe it, elucidating, if possibile, the impact of each single comorbidities. We also suggest the Authors to describe the clinical impact of their findings. 

Please submit your revised manuscript by Dec 17 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Chiara Lazzeri

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Authors,

I consider your study original, interesting and well written. However, I have several suggestions to enhance the overall quality of the manuscript.

- Page 3, line 76; “SARS-CoV-2 aggressiveness has been stablished as provoking 20% of severe and critic patients” : please add a reference to this sentence.

- Please add in the methods text the ethic committee approval protocol number

- The distinction you propose about non-specific and specific symptoms lacks of a clear interpretation. All the proposed symptoms are specific of covid-19 disease. However, you described as “specific” the combination of conditions which allowed the medical/hospital admission. If so, the difference you proposed should be rediscussed or better explained. More than talk about “non-specific” and “specific” symptoms, It should be better to talk about “non-specific” and “specific” medical admission criteria. In this regard, the manuscript Title could should be revised too.

- When you discuss about covid-19 physiopathology, you could cite the review article of Dr. Pascarella et al. (COVID-19 diagnosis and management: a comprehensive review. J Intern Med. 2020 Aug;288(2):192-206. doi: 10.1111/joim.13091. Epub 2020 May 13. PMID: 32348588), which well resumes this mechanism.

- Among the PONSSs correlated with higher incidence of bad prognosis, it could be interest to discuss if there is a significant with any of the reported comorbidities (cardiovascular, diabetes, etc.). In this regard you could mention and include in this discussion and/or in the itroduction two recent observational studies published by Dr. Maddaloni et al: Clinical features of patients with type 2 diabetes with and without Covid-19: A case control study (CoViDiab I). Diabetes Res Clin Pract. 2020 Sep 21; PMID: 32971157; Cardiometabolic multimorbidity is associated with a worse Covid-19 prognosis than individual cardiometabolic risk factors: a multicentre retrospective study (CoViDiab II). Cardiovasc Diabetol. 2020 Oct 1; PMID: 33004045

- The results of your study show an inverse correlation between PONSS and clinical course severity. You may discuss the clinical relevance of this finding, proposing some management settings. For instance, a pharmacologic prophylaxis may be proposed from the onset of any COVID-19 symptom, even if non-specific for medical/hospital admission, especially in high risk patients, having a positive rt-PCR swab test.

Best Regards

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Dec 3;15(12):e0243268. doi: 10.1371/journal.pone.0243268.r002

Author response to Decision Letter 0

15 Nov 2020

The authors wish to thank the editors and reviewers for their time while reviewing this manuscript. We´ve addressed all comments and reviews in the manuscript, and the changes are described in the present document as well.

Academic Editor Comments:

• Specific and no specific symptoms should be better described.

o “Specific symptoms” and “non-specific symptoms” were replaced with “initial symptoms” and “COVID-19 suspicion”, respectively. These are further defined in the methods section of the article (page 5, lines 105-117)

• The role of comorbidities is clinically interesting. We suggest to better describe it, elucidating, if possible, the impact of each single comorbidities.

o Although interesting, the role of comorbidities on COVID-19 severity has been thoroughly described in previous studies and the authors believe that is beyond the scope of this particular study, which focuses mainly on clinical manifestation of the disease and possible pathophysiological phenomena that could explain early and late onset mortality.

o However, the full model analysis was adjusted for many comorbidities (diabetes, asthma, cardiovascular disease, etc.), which is described with further detail in the supplementary materials.

• We also suggest the Authors to describe the clinical impact of their findings.

o Further explanation on the clinical impact of the use of PISCS was added to the discussion (page 12 lines 232-241). We believe that the PISCS could be useful as a prognostic marker to guide therapy once the pathophysiological processes have been better elucidated.

Reviewer comments:

Reviewer #1

• I consider your study original, interesting and well written. However, I have several suggestions to enhance the overall quality of the manuscript

o The authors wish to thank reviewer 1 for the comments and reviews, the following changes have been added.

• Page 3, line 76; “SARS-CoV-2 aggressiveness has been stablished as provoking 20% of severe and critic patients”: please add a reference to this sentence.

o Thank you for the remark, a reference has been added to this sentence (citation #13 Azoulay et. al).

• Please add in the methods text the ethic committee approval protocol number

o The ethics committee approval number is 202044, this has been added to the manuscript (page 4, line 89)

• The distinction you propose about non-specific and specific symptoms lacks of a clear interpretation. All the proposed symptoms are specific of covid-19 disease. However, you described as “specific” the combination of conditions which allowed the medical/hospital admission. If so, the difference you proposed should be rediscussed or better explained. More than talk about “non-specific” and “specific” symptoms, It should be better to talk about “non-specific” and “specific” medical admission criteria. In this regard, the manuscript Title could should be revised too.

o Thank you for the remark. “Specific symptoms” and “non-specific symptoms” were replaced with “initial symptoms” and “COVID-19 clinical suspicion”, respectively. These are further defined in the methods section of the article (page 5, lines 105-117)

o The PONSS was further changed to address the new definition, elucidating the difference between the initial symptoms and the clinical suspicion of COVID-19.

• When you discuss about covid-19 physiopathology, you could cite the review article of Dr. Pascarella et al. (COVID-19 diagnosis and management: a comprehensive review. J Intern Med. 2020 Aug;288(2):192-206. doi: 10.1111/joim.13091. Epub 2020 May 13. PMID: 32348588), which well resumes this mechanism.

o Thank you for the suggestion, this is a very comprehensive review and was added as a citation (page 11 line 216 citation #17)

• Among the PONSSs correlated with higher incidence of bad prognosis, it could be interest to discuss if there is a significant with any of the reported comorbidities (cardiovascular, diabetes, etc.). In this regard you could mention and include in this discussion and/or in the itroduction two recent observational studies published by Dr. Maddaloni et al: Clinical features of patients with type 2 diabetes with and without Covid-19: A case control study (CoViDiab I). Diabetes Res Clin Pract. 2020 Sep 21; PMID: 32971157; Cardiometabolic multimorbidity is associated with a worse Covid-19 prognosis than individual cardiometabolic risk factors: a multicentre retrospective study (CoViDiab II). Cardiovasc Diabetol. 2020 Oct 1; PMID: 33004045

o The interaction between the PISCS (Previously known as PONSS) with comorbidities such as diabetes is beyond the scope of this article, furthermore, the model was adjusted for these comorbidities and can be found in the supplementary material section.

o Both papers about the interaction of COVID-19 with diabetes are of clinical importance and were cited in the introduction (citation #5-6)

• The results of your study show an inverse correlation between PONSS and clinical course severity. You may discuss the clinical relevance of this finding, proposing some management settings. For instance, a pharmacologic prophylaxis may be proposed from the onset of any COVID-19 symptom, even if non-specific for medical/hospital admission, especially in high-risk patients, having a positive rt-PCR swab test.

o We recognize that further details about the clinical impact of these findings were necessary, therefore they were added to the discussion section, in which we propose the use of PISCS as a prognostic marker to guide COVID-19 therapy once the pathophysiology of disease is corroborated. (page 12 lines 232-241)

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file. (17.7KB, docx)

Decision Letter 1

Chiara Lazzeri

19 Nov 2020

U-shaped-aggressiveness of SARS-CoV-2: Period between initial symptoms and clinical progression to COVID-19 suspicion. A population-based cohort study

PONE-D-20-23492R1

Dear Dr. Talavera,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Chiara Lazzeri

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Chiara Lazzeri

24 Nov 2020

PONE-D-20-23492R1

U-shaped-aggressiveness of SARS-CoV-2: Period between initial symptoms and clinical progression to COVID-19 suspicion. A population-based cohort study

Dear Dr. Talavera:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Chiara Lazzeri

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Stepwise analyses for binary logistic regression with mortality as outcome for all patients in the study.

    (XLSX)

    Click here for additional data file. (25.1KB, xlsx)
    S2 Table. Stepwise analyses for binary logistic regression with pneumonia as outcome for all patients in the study.

    (XLSX)

    Click here for additional data file. (24KB, xlsx)
    Attachment

    Submitted filename: Response to reviewers.docx

    Click here for additional data file. (17.7KB, docx)

    Data Availability Statement

    Data is held in a public Data Repository at Mendeley Data DOI: http://dx.doi.org/10.17632/k6cw45366d.1.

    Articles from PLoS ONE are provided here courtesy of PLOS

    RESOURCES