Laboratory Testing Implications of Risk-Stratification and Management for Improving Clinical Outcomes of COVID-19 Patients
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Abstract
The high mortality rate of COVID-19 patients is mainly caused by the progression from mild to critical illness. To identify the key laboratory indicators and stratify high-risk COVID-19 patients with progression to severe/critical illness, we compared 474 moderate patients and 74 severe/critical patients. The laboratory indicators, including lactate dehydrogenase (LDH), monocytes percentage, etc. were significantly higher in the severe/critical patients (P <0.001) and showed a noticeable change at about a week before the diagnosis. Based on these indicators, we constructed a risk-stratification model, which can accurately grade the severity of patients with COVID-19 (accuracy = 0.96, 95% CI: 0.94 - 0.989, sensitivity = 0.98, specificity = 0.84). Also, compared with non-COVID-19 viral pneumonia, we found that COVID-19 had weaker dysfunction to the heart, liver, and kidney. The prognostic model based on laboratory indicators could help to diagnose, monitor, and predict severity at an early stage to those patients with COVID-19.
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SciScore for 10.1101/2020.06.12.20129098: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Prediction accuracy was determined from a randomly 25% sample of the training data as well as the independent test set. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical …
SciScore for 10.1101/2020.06.12.20129098: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization Prediction accuracy was determined from a randomly 25% sample of the training data as well as the independent test set. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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