2025
Practical Guide on the Use of Induction Immunosuppression in Heart Transplantation.
Nikolova A, Bellumkonda L, Bhardwaj A, Fida N, Holzhauser L, Umapathi P, De Marco T, Contreras J. Practical Guide on the Use of Induction Immunosuppression in Heart Transplantation. Circulation Heart Failure 2025, e012382. PMID: 40371476, DOI: 10.1161/circheartfailure.124.012382.Peer-Reviewed Original ResearchInduction immunosuppressionHeart transplantationImpact of induction immunosuppressionHepatitis C positivityRenal protective strategiesRisk stratification modelAt-risk patientsInduction therapyInduction regimensCirculatory death donorsMalignant complicationsMechanical circulatory devicesPerioperative periodImmunomodulatory effectsRecipient riskImmunosuppressionDeath donorsStratification modelTransplantationTherapyPatientsCirculatory devicesAt-riskInductionPreoperative Multivariable Model for Risk Stratification of Hypoxemia During One-Lung Ventilation
Zorrilla-Vaca A, Grant M, Mendez-Pino L, Rehman M, Sarin P, Nasra S, Varelmann D. Preoperative Multivariable Model for Risk Stratification of Hypoxemia During One-Lung Ventilation. Anesthesia & Analgesia 2025, 140: 1029-1036. PMID: 39773746, DOI: 10.1213/ane.0000000000007306.Peer-Reviewed Original ResearchOne-lung ventilationArea under the receiver operating curveRisk of hypoxemiaIntraoperative hypoxemiaRisk stratificationClinical variablesRisk of intraoperative hypoxemiaEpisodes of oxygen desaturationBody mass index >Preoperative clinical variablesStratification modelLung perfusion scanElective lung surgeryLateral decubitus positionPositive predictive valueRetrospective cohort studyCongestive heart failureHighest Youden indexPreoperative multivariable modelLogistic regressionRisk stratification modelRight-sided surgeryMultivariate logistic regressionIncidence of hypoxemiaDouble lumen tubePrediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis
Nishibe T, Iwasa T, Matsuda S, Kano M, Akiyama S, Fukuda S, Koizumi J, Nishibe M, Dardik A. Prediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis. Journal Of Surgical Research 2025, 306: 197-202. PMID: 39793306, DOI: 10.1016/j.jss.2024.11.049.Peer-Reviewed Original ResearchConceptsAneurysm sac shrinkageEndovascular aortic repairAbdominal aortic aneurysmSac shrinkageType II endoleakAortic repairII endoleakUnivariate analysisElective endovascular aortic repairTokyo Medical University HospitalMedical University HospitalDecision tree analysisPulse wave velocityAortic aneurysmUniversity HospitalCurrent smokingAneurysmPatientsStratification modelEndoleakLow likelihoodVariables of ageSmokingRepair
2023
POSTER: MDS-266 A Comprehensive Assessment of the Molecular International Prognostic Scoring System (IPSS-M) and Other Molecularly Integrated Risk Stratification Models in Chronic Myelomonocytic Leukemia (CMML)
Aguirre L, Al Ali N, Sallman D, Kuykendall A, Chan O, Sweet K, Lancet J, Padron E, Komrokji R. POSTER: MDS-266 A Comprehensive Assessment of the Molecular International Prognostic Scoring System (IPSS-M) and Other Molecularly Integrated Risk Stratification Models in Chronic Myelomonocytic Leukemia (CMML). Clinical Lymphoma Myeloma & Leukemia 2023, 23: s181. DOI: 10.1016/s2152-2650(23)00585-2.Peer-Reviewed Original ResearchMDS-266 A Comprehensive Assessment of the Molecular International Prognostic Scoring System (IPSS-M) and Other Molecularly Integrated Risk Stratification Models in Chronic Myelomonocytic Leukemia (CMML)
Aguirre L, Al Ali N, Sallman D, Kuykendall A, Chan O, Sweet K, Lancet J, Padron E, Komrokji R. MDS-266 A Comprehensive Assessment of the Molecular International Prognostic Scoring System (IPSS-M) and Other Molecularly Integrated Risk Stratification Models in Chronic Myelomonocytic Leukemia (CMML). Clinical Lymphoma Myeloma & Leukemia 2023, 23: s359-s360. DOI: 10.1016/s2152-2650(23)01176-x.Peer-Reviewed Original ResearchA framework for assessing interactions for risk stratification models: the example of ovarian cancer
Phung M, Lee A, McLean K, Anton-Culver H, Bandera E, Carney M, Chang-Claude J, Cramer D, Doherty J, Fortner R, Goodman M, Harris H, Jensen A, Modugno F, Moysich K, Pharoah P, Qin B, Terry K, Titus L, Webb P, Wu A, Zeinomar N, Ziogas A, Berchuck A, Cho K, Hanley G, Meza R, Mukherjee B, Pike M, Pearce C, Trabert B. A framework for assessing interactions for risk stratification models: the example of ovarian cancer. Journal Of The National Cancer Institute 2023, 115: 1420-1426. PMID: 37436712, PMCID: PMC10637032, DOI: 10.1093/jnci/djad137.Peer-Reviewed Original ResearchConceptsFamily history of ovarian cancerOvarian Cancer Association ConsortiumHistory of ovarian cancerFirst-degree family historyMenopausal statusRisk stratification modelCase-control studyRisk prediction modelOvarian cancerDisease riskAccurate risk stratification modelsStratification modelRisk/protective factorsDepot medroxyprogesterone acetate useProtective factorsFactor analysisRiskComprehensive analysis of interactionsCancerAcetate useUnequivocal riskStatusBreastfeedingAnalysis of interactionsPairwise interactionsUpdates in Risk Stratification in Myelodysplastic Syndromes
Aguirre L, Sallman D, Stone R, Komrokji R. Updates in Risk Stratification in Myelodysplastic Syndromes. The Cancer Journal 2023, 29: 138-142. PMID: 37195769, DOI: 10.1097/ppo.0000000000000654.Peer-Reviewed Original ResearchConceptsInternational Prognostic Scoring SystemPrognostic scoring systemMyelodysplastic syndromeRisk stratificationMolecular International Prognostic Scoring SystemScoring systemRisk stratification modelClinical trial enrollmentDisease-specific phenotypesTreatment paradigmPrognostic toolEstimate prognosisTrial enrollmentClonal dynamicsTherapeutic relevanceStratification modelSyndromeTreatment susceptibilityCytogenetic dataTreatmentDNA sequencing techniquesRiskMolecular dataSequencing techniquesPrognosisDeep learning‐based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia
Zhang X, Gleber‐Netto F, Wang S, Martins‐Chaves R, Gomez R, Vigneswaran N, Sarkar A, William W, Papadimitrakopoulou V, Williams M, Bell D, Palsgrove D, Bishop J, Heymach J, Gillenwater A, Myers J, Ferrarotto R, Lippman S, Pickering C, Xiao G. Deep learning‐based pathology image analysis predicts cancer progression risk in patients with oral leukoplakia. Cancer Medicine 2023, 12: 7508-7518. PMID: 36721313, PMCID: PMC10067069, DOI: 10.1002/cam4.5478.Peer-Reviewed Original ResearchConceptsLow-risk groupOral leukoplakiaOL patientsProgression riskOral mucosaHigh-risk patientsOral cancer developmentRisk stratification modelCancer progression riskLarge interobserver variabilityEarly diagnosisHigh riskDysplasia gradingAbnormal morphological featuresOral epitheliumOC developmentEarly interventionLow-risk onesPatientsStratification modelCancer developmentCancer progressionInterobserver variabilityLeukoplakiaRisk
2022
Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy
Chen Y, Li S, Ge W, Jing J, Chen HY, Doherty D, Herman A, Kaleem S, Ding K, Osman G, Swisher CB, Smith C, Maciel CB, Alkhachroum A, Lee JW, Dhakar MB, Gilmore EJ, Sivaraju A, Hirsch LJ, Omay SB, Blumenfeld H, Sheth KN, Struck AF, Edlow BL, Westover MB, Kim JA. Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy. Journal Of Neurology Neurosurgery & Psychiatry 2022, 94: 245-249. PMID: 36241423, PMCID: PMC9931627, DOI: 10.1136/jnnp-2022-329542.Peer-Reviewed Original ResearchConceptsPost-traumatic epilepsyTraumatic brain injuryCT abnormalitiesElectroencephalography featuresAdmission Glasgow Coma Scale scoreGlasgow Coma Scale scoreRetrospective case-control studyMultivariable logistic regression analysisRisk stratification modelCase-control studyLogistic regression analysisTBI admissionsSevere complicationsFuture trialsBrain injuryCT reportsSeizure diagnosisPredictive valueScale scorePatientsLogistic regressionStratification modelQuantitative electroencephalogramTBI mechanismsRegression analysis
2020
Evaluation of a Risk Stratification Model Using Preoperative and Intraoperative Data for Major Morbidity or Mortality After Cardiac Surgical Treatment
Durant TJS, Jean RA, Huang C, Coppi A, Schulz WL, Geirsson A, Krumholz HM. Evaluation of a Risk Stratification Model Using Preoperative and Intraoperative Data for Major Morbidity or Mortality After Cardiac Surgical Treatment. JAMA Network Open 2020, 3: e2028361. PMID: 33284333, PMCID: PMC11841993, DOI: 10.1001/jamanetworkopen.2020.28361.Peer-Reviewed Original Research
2017
Predicting death after acute myocardial infarction
Castro-Dominguez Y, Dharmarajan K, McNamara RL. Predicting death after acute myocardial infarction. Trends In Cardiovascular Medicine 2017, 28: 102-109. PMID: 28826668, DOI: 10.1016/j.tcm.2017.07.011.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionMyocardial infarctionRisk factorsClinical presentation characteristicsRisk stratification modelImportant risk factorPatients important informationPatient riskClinical informationStratification modelPresentation characteristicsMortalityInfarctionRiskHospitalizationPrognosisClinicians
2015
Physicians' perceptions of the Thrombolysis in Myocardial Infarction (TIMI) risk score in older adults with acute myocardial infarction
Feder SL, Schulman-Green D, Geda M, Williams K, Dodson JA, Nanna MG, Allore HG, Murphy TE, Tinetti ME, Gill TM, Chaudhry SI. Physicians' perceptions of the Thrombolysis in Myocardial Infarction (TIMI) risk score in older adults with acute myocardial infarction. Heart & Lung 2015, 44: 376-381. PMID: 26164651, PMCID: PMC4567390, DOI: 10.1016/j.hrtlng.2015.05.005.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionMyocardial Infarction (TIMI) risk scoreRisk scoreOlder adultsMyocardial infarctionMedian sample ageTIMI risk scoreRisk stratification modelSemi-structured telephone interviewsRisk factorsNew risk modelAMI treatmentPhysicians' perceptionsMortality riskClinical experienceClinical practiceStratification modelTelephone interviewsAdultsThrombolysisInfarctionConstant comparative methodPhysiciansScoresQualitative study
2005
Risk stratification models fail to predict hospital costs of cardiac surgery patients
Hekmat K, Raabe A, Kroener A, Fischer U, Suedkamp M, Geissler H, Schwinger R, Kampe S, Mehlhorn U. Risk stratification models fail to predict hospital costs of cardiac surgery patients. Clinical Research In Cardiology 2005, 94: 748-753. PMID: 16258777, DOI: 10.1007/s00392-005-0300-8.Peer-Reviewed Original ResearchConceptsRisk stratification modelLength of stayTotal hospital costsCardiac surgical patientsHospital costsStratification modelSurgical patientsICU LOSCardiac surgery patientsConsecutive adult patientsPreoperative diagnostic testsICU staySurgery patientsAdult patientsCardiac surgeryRisk stratificationSpearman correlation coefficientProspective studyMean ageResultsA totalMethodsBetween OctoberMortality ratePatientsLinear regression analysisRoom costs
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