Predictors of Outcomes and a Weighted Mortality Score for Moderate to Severe Subdural Hematoma.
Sima VazquezAarti K JainBridget NolanEris SpirollariKevin ClareAnish ThomasSauson SoldozySyed AliVishad SukulJon RosenbergStephan MayerRakesh KhatriBrian T JankowitzJustin SingerChirag GandhiFawaz Al-MuftiPublished in: Life (Basel, Switzerland) (2024)
As the incidence of subdural hematoma is increasing, it is important to understand symptomatology and clinical variables associated with treatment outcomes and mortality in this population; patients with subdural hematoma were selected from the National Inpatient Sample (NIS) Database between 2016 and 2020 using International Classification of Disease 10th Edition (ICD10) codes. Moderate-to-severe subdural hematoma patients were identified using the Glasgow Coma Scale (GCS). Multivariate regression was first used to identify predictors of in-hospital mortality and then beta coefficients were used to create a weighted mortality score. Of 29,915 patients admitted with moderate-to-severe subdural hematomas, 12,135 (40.6%) died within the same hospital admission. In a multivariate model of relevant demographic and clinical covariates, age greater than 70, diabetes mellitus, mechanical ventilation, hydrocephalus, and herniation were independent predictors of mortality ( p < 0.001 for all). Age greater than 70, diabetes mellitus, mechanical ventilation, hydrocephalus, and herniation were assigned a "1" in a weighted mortality score. The ROC curve for our model showed an area under the curve of 0.64. Age greater than 70, diabetes mellitus, mechanical ventilation, hydrocephalus, and herniation were predictive of mortality. We created the first clinically relevant weighted mortality score that can be used to stratify risk, guide prognosis, and inform family discussions.
Keyphrases
- peritoneal dialysis
- end stage renal disease
- mechanical ventilation
- cardiovascular events
- acute respiratory distress syndrome
- risk factors
- intensive care unit
- magnetic resonance
- high intensity
- subarachnoid hemorrhage
- contrast enhanced
- machine learning
- type diabetes
- magnetic resonance imaging
- early onset
- computed tomography
- coronary artery disease
- emergency department
- cerebrospinal fluid
- chronic kidney disease
- respiratory failure
- cardiovascular disease
- newly diagnosed
- weight loss
- mental health
- deep learning
- skeletal muscle