The clinical effectiveness of the STUMBL score for the management of ED patients with blunt chest trauma compared to clinical evaluation alone.
Elena CallistoGiorgio CostantinoAndrew John TabnerDean KerslakeMatthew James ReedPublished in: Internal and emergency medicine (2022)
The STUMBL (STUdy of the Management of BLunt chest wall trauma) score is a new prognostic score to assist ED (Emergency Department) decision making in the management of blunt chest trauma. This is a retrospective cohort chart review study conducted in a UK University Hospital ED seeing 120,000 patients a year, comparing its performance characteristics to ED clinician judgement. All blunt chest trauma patients that presented to our ED over a 6-month period were included. Patients were excluded if age < 18, if they had immediate life-threatening injury, required critical care admission for other injuries or in case of missing identification data. Primary endpoint was complication defined as any of lower respiratory tract infection, pulmonary consolidation, empyema, pneumothorax, haemothorax, splenic or hepatic injury and 30-day mortality. Clinician judgement (clinician decision to admit) and STUMBL score were compared using the receiver-operating curve (ROC) and sensitivity analysis. Three hundred and sixty-nine patients were included. ED clinicians admitted 95 of 369 patients. ED clinician decision to admit had a sensitivity of 83.9% and specificity of 86.0% for predicting complications. STUMBL score ≥ 11 had a sensitivity of 79.0% and specificity of 77.9% for the same and would have led to 117 of 369 patients being admitted. Area under the curve (AUC) of STUMBL score and ED clinician decision to admit was 0.84 (95% CI 0.78-0.90) and 0.85 (95% CI 0.79-0.91), respectively. Our findings show that a STUMBL score ≥ 11 performs no better than ED clinician judgement and leads to more patients being admitted to hospital.
Keyphrases
- emergency department
- end stage renal disease
- newly diagnosed
- ejection fraction
- trauma patients
- chronic kidney disease
- prognostic factors
- randomized controlled trial
- type diabetes
- healthcare
- systematic review
- coronary artery disease
- risk factors
- deep learning
- artificial intelligence
- cardiovascular events
- electronic health record
- acute care