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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Adams, James
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article
Prediction of Time to Hemodynamic Stabilization of Unstable Injured Patient Encounters Using Electronic Medical Record Data
Abstract
<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>This study sought to predict time to patient hemodynamic stabilization during trauma resuscitations of hypotensive patient encounters using electronic medical records (EMR) data.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>This observational cohort study leveraged EMR data from a nine-hospital academic system composed of Level I, Level II and non-trauma centers. Injured, hemodynamically unstable (initial systolic blood pressure < 90 mmHg) emergency encounters from 2015-2020 were identified. Stabilization was defined as documented subsequent systolic blood pressure <jats:underline>></jats:underline> 90 mmHg. We predicted time to stabilization testing random forests, gradient boosting and ensembles using patient, injury, treatment, EPIC Trauma Narrator and hospital features from the first four hours of care.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Of 177,127 encounters, 1347 (0.8%) arrived hemodynamically unstable; 168 (12.5%) presented to Level I trauma centers, 853 (63.3%) to Level II, and 326 (24.2%) to non-trauma centers. Of those, 747 (55.5%) were stabilized with a median of 50 minutes (IQR 21-101 min). Stabilization was documented in 94.6% of unstable patient encounters at Level I, 57.6% at Level II and 29.8% at non-trauma centers (p < 0.001). Time to stabilization was predicted with a C-index of 0.80. The most predictive features were EPIC Trauma Narrator measures; documented patient arrival, provider exam, and disposition decision. In-hospital mortality was highest at Level I, 3.0% vs. 1.2% at Level II, and 0.3% at non-trauma centers (p < 0.001). Importantly, non-trauma centers had the highest re-triage rate to another acute care hospital (12.0%) compared to Level II centers (4.0%, p < 0.001).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Time to stabilization of unstable injured patients can be predicted with EMR data.</jats:p></jats:sec>