Current best evidence for clinical care (more info)
INTRODUCTION: Risk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking.
METHODS: Multivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290).
RESULTS: 983 patients (median age 70, IQR 53-83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS (SpO2, Obesity, Age, Respiratory rate, Stroke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0-1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%).
CONCLUSION: The SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.
| Discipline / Specialty Area | Score |
|---|---|
| Hospital Doctor/Hospitalists | |
| Internal Medicine | |
| Intensivist/Critical Care | |
| Emergency Medicine | |
| Respirology/Pulmonology | |
I think this paper can fill an important gap in COVID management. As rapid testing begins to accelerate, primary care health outposts will be struggling with the question of which patients need to be seen in hospital. The SOARS has good performance characteristics - high sensitivity mostly and, with the exception of remote stroke, has good face validity. How SOARS compares with clinical assessment and how it performs from an inter-rater reliability perspective remains to be determined. These rules are proliferating in the literature and how SOARS compares to existing tools would be of interest, as well as the question of how the criteria might change now that early treatment with dexamethasone has come into play. The rule may also help identify patients who would be optimally managed as outpatients but equipped with home sat monitors.
SOARS score provides a refreshing approach to COVID-19 prognostics by identifying a low-risk subset because the majority of instruments focus on stratifying high-risk subsets. Before widespread uptake of SOARS (or other COVID-19 prognostic instruments), head-to-head comparisons that include acceptability assessment and impact analyses are required. The head-to-head comparison is important because SOARS seems over-simplified, although that may prove pragmatic for widespread uptake of the instrument compared with alternatives that use an array of biomarkers. In this manuscript, I would have appreciated reporting of likelihood ratios with 95% CIs in addition to other measures of accuracy. A score of zero has LR+ 1.08 and LR- 0.10, which accurately identifies lower-risk subsets as long as the CIs are not excessively wide.
Useful prediction tool based on clinical information when first assessing patients with COVID. Appears to perform reasonably well in a validation as well as a derivation cohort.
This is a terrific article. SOARS is a new and simple score that can help us better triage our patients, especially if there is a resurgence of COVID-19 peaks.
The score is simple and shows some promise in the ROC. Further evaluation in a larger population is awaited.
Strengths are external validation and use of easily obtained predictors. In my setting, the death rate even in those with a score of 0-1 seems too high for me to be happy sending these people home. I would like something with a higher negative predictive value.