Predicting Need for Escalation of Care or Death From Repeated Daily Clinical Observations and Laboratory Results in Patients With Severe Acute Respiratory Syndrome Coronavirus 2
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
We compared the performance of prognostic tools for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using parameters fitted either at the time of hospital admission or across all time points of an admission. This cohort study used clinical data to model the dynamic change in prognosis of SARS-CoV-2 at a single hospital center in the United Kingdom, including all patients admitted from February 1, 2020, to December 31, 2020, and then followed up for 60 days for intensive care unit (ICU) admission, death, or discharge from the hospital. We incorporated clinical observations and blood tests into 2 time-varying Cox proportional hazards models predicting daily 24- to 48-hour risk of admission to the ICU for those eligible for escalation of care or death for those ineligible for escalation. In developing the model, 491 patients were eligible for ICU escalation and 769 were ineligible for escalation. Our model had good discrimination of daily risk of ICU admission in the validation cohort (n = 1,141; C statistic: C = 0.91, 95% confidence interval: 0.89, 0.94) and our score performed better than other scores (National Early Warning Score 2, International Severe Acute Respiratory and Emerging Infection Comprehensive Clinical Characterisation Collaboration score) calculated using only parameters measured on admission, but it overestimated the risk of escalation (calibration slope = 0.7). A bespoke daily SARS-CoV-2 escalation risk prediction score can predict the need for clinical escalation better than a generic early warning score or a single estimation of risk calculated at admission.
Article activity feed
-
-
SciScore for 10.1101/2020.12.14.20248181: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. 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: We detected the following sentences addressing limitations in the study:Strengths and weaknesses: Our study included all patients who were admitted to a single hospital serving the population of a city throughout an eight month period of 2020 and so should be widely generalisable to similar …
SciScore for 10.1101/2020.12.14.20248181: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. 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: We detected the following sentences addressing limitations in the study:Strengths and weaknesses: Our study included all patients who were admitted to a single hospital serving the population of a city throughout an eight month period of 2020 and so should be widely generalisable to similar populations elsewhere. Unlike many other reports we have included all patients both clinically diagnosed and PCR confirmed meaning that our findings should be generalisable where such clinical decisions have been taken. Our sensitivity analysis shows that our model performed better when restricted to those PCR confirmed, and table S1 suggests those patients with more severe disease were more likely to have a positive test. This is likely to reflect the longer period of time in hospital these patients had during which they would be retested and will be less of an issue in more recent data with more rapid test processing. Through our use of electronic patient record systems, we had access to comprehensive sociodemographic, clinical and laboratory variables including all measurements recorded electronically through the patient’s admission. We also had available complete follow up for escalation of care, death (including out of hospital death) or discharge from hospital for 30 days from admission and importantly, therefore, have little bias due to missing data, loss to follow up or other common biases of observational cohorts. However it must be recognised that though this richness and uniformity of data is a strength, it is gained at the cost of limiting our anal...
Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04473105 Recruiting Optimising Resource Allocation Via Prediction of Outcomes fo… 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.
-
