Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.
Chen Y, Rivier C, Mora S, Torres Lopez V, Payabvash S, Sheth K, Harloff A, Falcone G, Rosand J, Mayerhofer E, Anderson C. Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan. European Stroke Journal 2024, 23969873241260154. PMID: 38880882, PMCID: PMC11569453, DOI: 10.1177/23969873241260154.Peer-Reviewed Original ResearchIntracerebral hemorrhage scoreNon-contrast CT scanIntracerebral hemorrhageCT scanFUNC scoreIntracerebral hemorrhage patientsNon-contrast CTFunctional impairmentSevere disabilityDependent living statusLong-term functional impairmentC-indexPrognostic toolFunctional outcomesTreatment decisionsAcute settingClinical implementationRehabilitation strategiesDependent livingPatientsPredicting functional impairmentLong-term care needsPlanning of patient careDeep learning modelsHemorrhage0486 Performing Polysomnography in the Acute Post-stroke Setting
Masroor K, Schwarz J, Radulescu R, Tsang S, Sheth K, Redeker N, Yaggi H, Geer J. 0486 Performing Polysomnography in the Acute Post-stroke Setting. Sleep 2024, 47: a209-a209. DOI: 10.1093/sleep/zsae067.0486.Peer-Reviewed Original ResearchObstructive sleep apneaSleep apneaRates of obstructive sleep apneaSymptoms of obstructive sleep apneaSevere obstructive sleep apneaObstructive sleep apnea diagnosisAcute settingSleep-disordered breathingPercentage of patientsProportion of patientsYale-New Haven HospitalAHI cutoffOSA prevalenceOSA diagnosisPreventing recurrent strokeImprove stroke recoveryPolysomnography testNew Haven HospitalPolysomnographyScreening patientsTherapeutic windowApneaPatientsRisk factorsMedical records