Development and validation of a dynamic 48-hour in-hospital mortality risk stratification for COVID-19 in a UK teaching hospital: a retrospective cohort study
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Abstract
To develop a disease stratification model for COVID-19 that updates according to changes in a patient’s condition while in hospital to facilitate patient management and resource allocation.
Design
In this retrospective cohort study, we adopted a landmarking approach to dynamic prediction of all-cause in-hospital mortality over the next 48 hours. We accounted for informative predictor missingness and selected predictors using penalised regression.
Setting
All data used in this study were obtained from a single UK teaching hospital.
Participants
We developed the model using 473 consecutive patients with COVID-19 presenting to a UK hospital between 1 March 2020 and 12 September 2020; and temporally validated using data on 1119 patients presenting between 13 September 2020 and 17 March 2021.
Primary and secondary outcome measures
The primary outcome is all-cause in-hospital mortality within 48 hours of the prediction time. We accounted for the competing risks of discharge from hospital alive and transfer to a tertiary intensive care unit for extracorporeal membrane oxygenation.
Results
Our final model includes age, Clinical Frailty Scale score, heart rate, respiratory rate, oxygen saturation/fractional inspired oxygen ratio, white cell count, presence of acidosis (pH <7.35) and interleukin-6. Internal validation achieved an area under the receiver operating characteristic (AUROC) of 0.90 (95% CI 0.87 to 0.93) and temporal validation gave an AUROC of 0.86 (95% CI 0.83 to 0.88).
Conclusions
Our model incorporates both static risk factors (eg, age) and evolving clinical and laboratory data, to provide a dynamic risk prediction model that adapts to both sudden and gradual changes in an individual patient’s clinical condition. On successful external validation, the model has the potential to be a powerful clinical risk assessment tool.
Trial registration
The study is registered as ‘researchregistry5464’ on the Research Registry ( www.researchregistry.com ).
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SciScore for 10.1101/2021.02.15.21251150: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Ethics: The study was approved by a UK Health Research Authority ethics committee (20/WM/0125).
Consent: Patient consent was waived because the de-identified data presented here were collected during routine clinical practice; there was no requirement for informed consent.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 …SciScore for 10.1101/2021.02.15.21251150: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Ethics: The study was approved by a UK Health Research Authority ethics committee (20/WM/0125).
Consent: Patient consent was waived because the de-identified data presented here were collected during routine clinical practice; there was no requirement for informed consent.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:There are several limitations to our study. We have not incorporated imaging data that have been used as a proxy for disease severity in some clinical trials, although we have included an extensive set of relevant clinical information. We also chose to include only laboratory results up to 48 hours and vital signs up to 24 hours before the landmark time. Exploiting older data in addition might improve the predictive ability of our model, but would also be likely to increase the model’s complexity considerably, and therefore decrease its real-world utility. Our data were gathered from a single centre, and therefore the generalisability of our findings to other centres and populations are uncertain. Further, our model was generated from a relatively modest sample size due to the relatively low prevalence of COVID-19 patients in the catchment population of the hospital, particularly during the early months of the pandemic. One advantage of using this single dataset from a large, tertiary hospital was that the hospital never became overwhelmed with patients, and therefore it is considered that patients received care according to what was felt to be clinically appropriate rather than according to what resources were available. It is also important to note that as the pandemic has evolved in the UK, there have been changes in both the clinical care of patients (notably with the routine inclusion of steroid therapy for patients requiring oxygen) and in the strains of the virus circu...
Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title ISRCTN11188345 NA NA ISRCTN67000769 NA NA ISRCTN50189673 NA NA 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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