- A newly validated 4C (Coronavirus Clinical Characterisation Consortium) Mortality Score prediction tool appears to accurately identify patients with high mortality risk at hospital admission for COVID-19.
Why this matters
- 4C Mortality Score (range, 0-21 points) is based on 8 readily available variables: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, C-reactive protein.
- Score can guide clinical decision-making:
- Low risk score (0-3): manage in community.
- Intermediate (4-8): ward level monitoring.
- High (9-14): consider aggressive treatment.
- Very high (≥15): critical care, isolation, invasive ventilation, other supportive measures.
- Derivation cohort: 35,463 patients; mortality rate, 32.2%.
- Validation cohort: 22 ,361 patients; mortality rate, 30.1%.
- 4C demonstrated high model discrimination vs machine learning (areas under the curve; 95% CIs):
- Derivation cohort: 0.786 (0.781-0.790) vs 0.796 (0.786-0.807).
- Validation cohort: 0.767 (0.760-0.773) vs 0.779 (0.772-0.785).
- Low risk (0-3):
- Mortality rate: 1.2%.
- Sensitivity: 99.7%.
- Negative predictive value (NPV): 98.8%.
- Intermediate risk (4-8):
- Mortality rate: 9.9%.
- NPV: 90.1%.
- High risk (9-14):
- Mortality: 31.4%.
- NPV: 68.6%.
- Very high risk (≥15):
- Mortality: 61.5%.
- Positive predictive value: 61.5%.
- 4C discriminated better than tools developed with other COVID-19 cohorts (areas under the curve, 0.61-0.76).
- Prospective, observational cohort study.
- Funding: National Institute for Health Research.
- Inability to assess predictive performance of other tests.
- Selection bias.
- Limited generalizability.