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Research Article
Pasquale De Nardo
Division of Infectious Diseases, Department of Diagnostics and Public Health, University of Verona
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: A major limitation of current predictive prognostic models in patients with COVID-19 is the heterogeneity of population in terms of disease stage and duration. This study aims at identifying a panel of clinical and laboratory parameters that at day-5 of symptoms onset could predict disease progression in hospitalized patients with COVID-19.
Methods: Prospective cohort study on hospitalized adult patients with COVID-19. Patient-level epidemiological, clinical, and laboratory data were collected at fixed time-points: day 5, 10, and 15 from symptoms onset. COVID-19 progression was defined as in-hospital death and/or ICU and/or respiratory failure (PaO2/FiO2 ratio<200) within day-11 of symptoms onset. Multivariate regression was performed to identify predictors of COVID-19 progression. Discrimination power was assessed by computing area under the receiver operating characteristic (AUC) values.
Results: A total of 235 patients with COVID-19 were prospectively included in a 3-month period. The majority of patients were male (148, 63%) and the mean age was 71 (SD 15.9). One hundred and ninety patients (81%) suffered from at least one underlying illness, most frequently cardiovascular disease (47%), neurological/psychiatric disorders (35%), and diabetes (21%). Among them 88 (37%) experienced COVID-19 progression. A model assessed at day-5 of symptoms onset including male sex, age >65 years, dyspnea, cardiovascular disease, and at least three abnormal laboratory parameters among CRP (> 80 U/L), ALT (> 40 U/L), NLR (> 4.5), LDH (> 250 U/L), and CK (> 80 U/L) showed an AUC of 0.73 (95%CI: 0.66 - 0.81) for predicting disease progression by day-11.
Conclusion: An easy-to-use panel of laboratory/clinical parameters computed at day-5 of symptoms onset predicts, with fair discrimination ability, COVID-19 progression. Assessment of these features at day-5 of symptoms onset could facilitate assessment of clinicians’ decision making. The model can also play a role as a tool to increase homogeneity of population in clinical trials on COVID-19 treatment in hospitalized patients.
Keywords
COVID-19, disease progression, outcome, public health
Figure 1
Figure 2
No competing interests reported.
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CURRENT STATUS: Under Review
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Posted 18 May, 2021
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Editorial decision: Major revision
On 18 Jul, 2021
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Received 06 Jun, 2021
Reviewers agreed
On 27 May, 2021
Reviewers invited
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On 17 May, 2021
Editor invited
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On 17 May, 2021
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Subject Areas
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A new preprint design is coming!
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This preprint is under consideration at BMC Infectious Diseases. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Pasquale De Nardo
Division of Infectious Diseases, Department of Diagnostics and Public Health, University of Verona
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 18 May, 2021
No community comments so far
Editorial decision: Major revision
On 18 Jul, 2021
Reviews received
Received 30 Jun, 2021
Reviewers agreed
On 10 Jun, 2021
Reviews received
Received 06 Jun, 2021
Reviewers agreed
On 27 May, 2021
Reviewers invited
Invitations sent on 27 May, 2021
Editor assigned
On 17 May, 2021
Editor invited
On 17 May, 2021
Submission checks complete
On 17 May, 2021
First submitted
On 28 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Infectious Diseases
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: A major limitation of current predictive prognostic models in patients with COVID-19 is the heterogeneity of population in terms of disease stage and duration. This study aims at identifying a panel of clinical and laboratory parameters that at day-5 of symptoms onset could predict disease progression in hospitalized patients with COVID-19.
Methods: Prospective cohort study on hospitalized adult patients with COVID-19. Patient-level epidemiological, clinical, and laboratory data were collected at fixed time-points: day 5, 10, and 15 from symptoms onset. COVID-19 progression was defined as in-hospital death and/or ICU and/or respiratory failure (PaO2/FiO2 ratio<200) within day-11 of symptoms onset. Multivariate regression was performed to identify predictors of COVID-19 progression. Discrimination power was assessed by computing area under the receiver operating characteristic (AUC) values.
Results: A total of 235 patients with COVID-19 were prospectively included in a 3-month period. The majority of patients were male (148, 63%) and the mean age was 71 (SD 15.9). One hundred and ninety patients (81%) suffered from at least one underlying illness, most frequently cardiovascular disease (47%), neurological/psychiatric disorders (35%), and diabetes (21%). Among them 88 (37%) experienced COVID-19 progression. A model assessed at day-5 of symptoms onset including male sex, age >65 years, dyspnea, cardiovascular disease, and at least three abnormal laboratory parameters among CRP (> 80 U/L), ALT (> 40 U/L), NLR (> 4.5), LDH (> 250 U/L), and CK (> 80 U/L) showed an AUC of 0.73 (95%CI: 0.66 - 0.81) for predicting disease progression by day-11.
Conclusion: An easy-to-use panel of laboratory/clinical parameters computed at day-5 of symptoms onset predicts, with fair discrimination ability, COVID-19 progression. Assessment of these features at day-5 of symptoms onset could facilitate assessment of clinicians’ decision making. The model can also play a role as a tool to increase homogeneity of population in clinical trials on COVID-19 treatment in hospitalized patients.
Figure 1
Figure 2
No competing interests reported.
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