Current best evidence for clinical care (more info)
OBJECTIVE: To develop and validate a prediction model of mortality in patients with COVID-19 attending hospital emergency rooms.
DESIGN: Multivariable prognostic prediction model.
SETTING: 127 Spanish hospitals.
PARTICIPANTS: Derivation (DC) and external validation (VC) cohorts were obtained from multicentre and single-centre databases, including 4035 and 2126 patients with confirmed COVID-19, respectively.
INTERVENTIONS: Prognostic variables were identified using multivariable logistic regression.
MAIN OUTCOME MEASURES: 30-day mortality.
RESULTS: Patients' characteristics in the DC and VC were median age 70 and 61 years, male sex 61.0% and 47.9%, median time from onset of symptoms to admission 5 and 8 days, and 30-day mortality 26.6% and 15.5%, respectively. Age, low age-adjusted saturation of oxygen, neutrophil-to-lymphocyte ratio, estimated glomerular filtration rate by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, dyspnoea and sex were the strongest predictors of mortality. Calibration and discrimination were satisfactory with an area under the receiver operating characteristic curve with a 95% CI for prediction of 30-day mortality of 0.822 (0.806-0.837) in the DC and 0.845 (0.819-0.870) in the VC. A simplified score system ranging from 0 to 30 to predict 30-day mortality was also developed. The risk was considered to be low with 0-2 points (0%-2.1%), moderate with 3-5 (4.7%-6.3%), high with 6-8 (10.6%-19.5%) and very high with 9-30 (27.7%-100%).
CONCLUSIONS: A simple prediction score, based on readily available clinical and laboratory data, provides a useful tool to predict 30-day mortality probability with a high degree of accuracy among hospitalised patients with COVID-19.
| Discipline / Specialty Area | Score |
|---|---|
| Emergency Medicine | |
| Respirology/Pulmonology | |
| Hospital Doctor/Hospitalists | |
| Internal Medicine | |
| Infectious Disease | |
I suspect this will be one of a number of mortality risk predictors coming out on COVID hospitalizations. This one certainly seems robustly derived and validated and has potential to guide ICU-directed admissions. A comparison with clinical gestalt would be interesting. The components of the score are not entirely surprising but some are unique.
During the peak of COVID-19 (hopefully never to return again), EDs filled with boarding SARS-CoV-2 patients and no ICU or hospital beds available anywhere had to allocate scarce hospital resources based on uncertain and constantly shifting prognostic models. As noted by these authors, dozens of models have since been developed across Asia, Europe, and North America, but most have significant potential flaws. The COVID-19 SEIMC Score offers more rigorous TRIPOD-adherent methods analyzing a large derivation and separate validation population. However, the external validity outside of Spanish citizens is untested and the ethnic distribution of the populations is unreported (presumably predominantly Spanish), but important with ongoing cries for diversity, equity, and inclusion. On a pragmatic note, some elements of the score are not available at every hospital (no differential reported, GFR not reported).
Very interesting and thorough study.
Nothing surprising in terms of variables. Looks like very good calibration, but would be good to validate in an independent sample.
A useful prediction model for 30-day mortality in hospitalised patients with COVID-19.
Developed a score predicting 30-day mortality based on data from a Spanish cohort.