RESUMEN
OBJECTIVE: Decision for intervention in acute subdural hematoma patients is based on a combination of clinical and radiographic factors. Age has been suggested as a factor to be strongly considered when interpreting midline shift (MLS) and hematoma volume data for assessing critical clinical severity during operative intervention decisions for acute subdural hematoma patients. The objective of this study was to demonstrate the use of an automated volumetric analysis tool to measure hematoma volume and MLS and quantify their relationship with age. METHODS: A total of 1789 acute subdural hematoma patients were analyzed using qER-Quant software (Qure.ai, Mumbai, India) for MLS and hematoma volume measurements. Univariable and multivariable regressions analyzed association between MLS, hematoma volume, age, and MLS:hematoma volume ratio. RESULTS: In comparison to young patients (≤ 70 years), old patients (>70 years) had significantly higher average hematoma volume (old: 62.2 mL vs. young 46.8 mL, P < 0.0001), lower average MLS (old: 6.6 mm vs. young: 7.4 mm, P = 0.025), and lower average MLS:hematoma volume ratio (old: 0.11 mm/mL vs. young 0.15 mm/mL, P < 0.0001). Young patients had an average of 1.5 mm greater MLS for a given hematoma volume in comparison to old patients. With increasing age, the ratio between MLS and hematoma volume significantly decreases (P = 0.0002). CONCLUSIONS: Commercially available, automated, artificial intelligence (AI)-based tools may be used for obtaining quantitative radiographic measurement data in patients with acute subdural hematoma. Our quantitative results are consistent with the qualitative relationship previously established between age, hematoma volume, and MLS, which supports the validity of using AI-based tools for acute subdural hematoma volume estimation.