Conducted with the participation of the D'Or Institute for Research and Education (IDOR), investigated how to measure efficiency in the use of resources for patients with severe community-acquired pneumonia (CAP), an illness contracted outside hospital settings and most common among older adults. To address this problem, researchers tested the Standardized Length of Stay Ratio (SLOSR), a tool developed with machine learning techniques, a branch of Artificial Intelligence. The aim was to determine whether SLOSR could predict, in a patient risk–adjusted way, the appropriate length of ICU stay.
The study was retrospective and multicenter, analyzing 16,985 adult CAP admissions in 220 ICUs across 57 Brazilian hospitals during 2023. Variables such as age, comorbidities, need for mechanical ventilation, and disease severity were taken into account. Median length of stay was four days, and approximately 28% of patients required ventilatory support. The model showed strong explanatory power with low prediction errors, reinforcing SLOSR's potential as a reliable indicator of resource efficiency across ICUs.
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