2025
Proteins associated with rehospitalization, mortality and diuretic resistance in acutely decompensated heart failure
Hubbell K, Rao V, Scherzer R, Shlipak M, Ivery‐Miranda J, Bansal N, Cox Z, Testani J, Estrella M. Proteins associated with rehospitalization, mortality and diuretic resistance in acutely decompensated heart failure. ESC Heart Failure 2025 PMID: 40504107, DOI: 10.1002/ehf2.15342.Peer-Reviewed Original ResearchAcute decompensated heart failureDecompensated heart failureDiuretic responseHeart failureHF rehospitalizationRisk of HF rehospitalizationBiomarker modelAssociated with poor outcomesAssociated with decreased riskAssociated with increased riskAssociated with cardiometabolic diseasesAssociated with mortalityAssociated with rehospitalizationDiuretic resistanceMedian ageResistance cohortClinical factorsPoor outcomeSociodemographic/lifestyle factorsPrognostic toolCombined biomarkersCardiometabolic diseasesPatientsAmbulatory settingMortality riskFeasibility of machine learning–based modeling and prediction to assess osteosarcoma outcomes
Zhao Q, Hu W, Xia Y, Dai S, Wu X, Chen J, Yuan X, Zhong T, Xi X, Wang Q. Feasibility of machine learning–based modeling and prediction to assess osteosarcoma outcomes. Scientific Reports 2025, 15: 17386. PMID: 40389469, PMCID: PMC12089500, DOI: 10.1038/s41598-025-00179-z.Peer-Reviewed Original ResearchConceptsGene signatureLow-risk score groupIncreased metastasis riskLow-risk patientsSensitive to immunotherapyUnivariate Cox regression analysisReduced immune infiltrationCox regression analysisPersonalized therapeutic strategiesAggressive bone malignancyOsteosarcoma outcomesPersonalized treatment strategiesHigh-risk scoreOverall survivalHigher mortality rateProlonged survivalMetastasis riskMulticenter cohortImmune infiltrationPoor prognosisRisk stratificationC-indexDrug sensitivityClinical managementPrognostic toolELECTROCARDIOGRAPHIC HEART AGE GAP AS A PROGNOSTIC TOOL IN HEART FAILURE PATIENTS: A DEEP LEARNING APPROACH
Ramirez-Lopera V, Camargos A, Khera R. ELECTROCARDIOGRAPHIC HEART AGE GAP AS A PROGNOSTIC TOOL IN HEART FAILURE PATIENTS: A DEEP LEARNING APPROACH. Journal Of The American College Of Cardiology 2025, 85: 1526. DOI: 10.1016/s0735-1097(25)02010-8.Peer-Reviewed Original ResearchPrognostic tool
2024
OMICS Sciences for Aging Studies
Gómez-Verjan J, Rincón-Heredia R, Poot-Hernández A, Martínez-Magaña J, Montalvo-Ortiz J, Estrella-Parra E, Castillo-Vázquez S, Gutiérrez-Robledo L, Rivero-Segura N. OMICS Sciences for Aging Studies. 2024, 227-237. DOI: 10.1007/978-3-031-76469-1_16.Peer-Reviewed Original ResearchTargeted therapies for myelodysplastic syndromes/neoplasms (MDS): current landscape and future directions
Bidikian A, Bewersdorf J, Shallis R, Getz T, Stempel J, Kewan T, Stahl M, Zeidan A. Targeted therapies for myelodysplastic syndromes/neoplasms (MDS): current landscape and future directions. Expert Review Of Anticancer Therapy 2024, 24: 1131-1146. PMID: 39367718, DOI: 10.1080/14737140.2024.2414071.Peer-Reviewed Original ResearchErythropoiesis-stimulating agentsTargeted therapyLR-MDSHR-MDSHypoxia-inducible factorAllogeneic hematopoietic stem cell transplantationLandscape of targeted therapiesHematopoietic stem cell transplantationHeterogeneous group of hematologic malignanciesGroup of hematologic malignanciesMolecular prognostic toolsDuration of responseStem cell transplantationTrial designClinical trial designHypomethylating agentsCell transplantationHematologic malignanciesImprove patient outcomesRNA splicing machineryImmune evasionPrognostic toolTGF-betaTherapyEffective treatmentA practical magnetic-resonance imaging score for outcome prediction in comatose cardiac arrest survivors
Chan W, Nguyen C, Kim N, Tripodis Y, Gilmore E, Greer D, Beekman R. A practical magnetic-resonance imaging score for outcome prediction in comatose cardiac arrest survivors. Resuscitation 2024, 202: 110370. PMID: 39178939, DOI: 10.1016/j.resuscitation.2024.110370.Peer-Reviewed Original ResearchHypoxic-ischemic brain injuryDeep gray nucleiArea under the receiver operating curveMagnetic resonance imagingMagnetic Resonance Imaging ScorePoor neurological outcomeCerebral Performance CategoryIntra-class correlationNeurological outcomeMRI scoreCardiac arrestComatose CA survivorsCA survivorsComatose cardiac arrest survivorsPredicting neurologic outcomeCardiac arrest survivorsInter-rater reliabilityReceiver operating curveAcademic medical centerMedian timePoor outcomePrognostic toolGray nucleiArrest survivorsScore groupDeep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan
Chen Y, Rivier C, Mora S, 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, 10: 225-235. 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 modelsHemorrhageThe Evolving Landscape of Biomarkers for Immune Checkpoint Blockade in Genitourinary Cancers
Mustafa S, Jansen C, Jani Y, Evans S, Zhuang T, Brown J, Nazha B, Master V, Bilen M. The Evolving Landscape of Biomarkers for Immune Checkpoint Blockade in Genitourinary Cancers. Biomarker Insights 2024, 19: 11772719241254179. PMID: 38827239, PMCID: PMC11143877, DOI: 10.1177/11772719241254179.Peer-Reviewed Original ResearchImmune-related adverse eventsImmune checkpoint inhibitorsResponse to immunotherapyAdverse eventsTreatment of genitourinary malignanciesImmune checkpoint blockadeSelection of therapySignificant side effectsCheckpoint blockadeCheckpoint inhibitorsGU malignanciesGenitourinary malignanciesPatient tumorsTreatment landscapeReview of biomarkersGenitourinary cancersGU tumorsPreclinical studiesPrognostic toolSide effectsTherapyBiomarker developmentTumorPatientsImmunotherapy
2023
When to use which molecular prognostic scoring system in the management of patients with MDS?
Kewan T, Bewersdorf J, Gurnari C, Xie Z, Stahl M, Zeidan A. When to use which molecular prognostic scoring system in the management of patients with MDS? Best Practice & Research Clinical Haematology 2023, 36: 101517. PMID: 38092484, DOI: 10.1016/j.beha.2023.101517.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsInternational Prognostic Scoring SystemPrognostic scoring systemAcute myeloid leukemiaScoring systemRisk stratificationRecurrent molecular alterationsHigh-risk patientsAppropriate risk stratificationManagement of patientsRecurrent genetic mutationsIntensive therapyMyeloid leukemiaTreatment strategiesPrognostic toolDisease pathogenesisMolecular alterationsHematopoietic cancersClinical decisionHeterogeneous groupGenetic mutationsNext-generation sequencingPrognostic systemPatientsVariable propensitySubsequent revisionAssessment of eligibility criteria in advanced urothelial cancer (aUC) trials based on ASCO-FCR recommendations.
Castro D, Feng M, Prajapati S, Chan E, Lee K, Sehgal I, Patel J, O'Dell A, Zengin Z, Li X, Chehrazi-Raffle A, Dizman N, Tripathi A, Rock A, Liu S, Mercier B, Meza L, Philip E, Dorff T, Pal S. Assessment of eligibility criteria in advanced urothelial cancer (aUC) trials based on ASCO-FCR recommendations. Journal Of Clinical Oncology 2023, 41: 453-453. DOI: 10.1200/jco.2023.41.6_suppl.453.Peer-Reviewed Original ResearchHIV positivityHCV positivityEligibility criteriaCancer trialsExclusion criteriaConcurrent malignancyBrain metastasesCombination therapyExact testClass of therapyTrial eligibility criteriaReal-world populationFisher's exact testRestrictive eligibility criteriaSpecific study populationMultiple cancer typesChemotherapy trialsReal-world practiceInvestigational treatmentStudy populationRadiation therapyClinical OncologyPrognostic toolTherapySignificant associationEvaluation of eligibility criteria in contemporary renal cell carcinoma based on ASCO-FCR recommendations.
Prajapati S, Feng M, Castro D, Lee K, Chan E, Sehgal I, Patel J, O'Dell A, Zengin Z, Li X, Chehrazi-Raffle A, Dizman N, Tripathi A, Rock A, Liu S, Mercier B, Meza L, Philip E, Dorff T, Pal S. Evaluation of eligibility criteria in contemporary renal cell carcinoma based on ASCO-FCR recommendations. Journal Of Clinical Oncology 2023, 41: 612-612. DOI: 10.1200/jco.2023.41.6_suppl.612.Peer-Reviewed Original ResearchRestrictive eligibility criteriaEligibility criteriaHCV positivityHIV positivityInclusion criteriaExclusion criteriaExact testClass of therapyReal-world populationRenal cell carcinomaFisher's exact testChi-square testConcurrent malignancyBrain metastasesAdult patientsCell carcinomaCombination therapyPatient populationCancer trialsRCC trialsConsensus statementRadiation therapyClinical OncologyPrognostic toolReal-world setting
2022
A tumor volume and performance status model to predict outcome prior to treatment in diffuse large B-cell lymphoma
Thieblemont C, Chartier L, Dührsen U, Vitolo U, Barrington S, Zaucha J, Vercellino L, da Silva M, Patrocinio-Carvalho I, Decazes P, Viailly P, Tilly H, Berriolo-Riedinger A, Casasnovas O, Hüttmann A, Ilyas H, Mikhaeel N, Dunn J, Cottereau A, Schmitz C, Kostakoglu L, Paulson J, Nielsen T, Meignan M. A tumor volume and performance status model to predict outcome prior to treatment in diffuse large B-cell lymphoma. Blood Advances 2022, 6: 5995-6004. PMID: 36044385, PMCID: PMC9691911, DOI: 10.1182/bloodadvances.2021006923.Peer-Reviewed Original ResearchConceptsLarge B-cell lymphomaAggressive large B-cell lymphomaB-cell lymphomaRisk factorsTumor volumePerformance statusDiffuse large B-cell lymphomaInternational Prognostic IndexMetabolic tumor volumeProgression-free survivalReal-world seriesReal-world clinicsREMARC trialOverall survivalRefractory diseaseECOG-PSPrognostic indexIntermediate riskInitial treatmentTreatment initiationRisk stratificationC-indexOlder patientsPrognostic toolClinical trialsThe SWIFT Model for Lichen Sclerosus Among Premenarchal Girls
Wang M, Wininger M, Vash-Margita A. The SWIFT Model for Lichen Sclerosus Among Premenarchal Girls. Journal Of Lower Genital Tract Disease 2022, 26: 46-52. PMID: 34928252, DOI: 10.1097/lgt.0000000000000634.Peer-Reviewed Original ResearchConceptsPremenarchal girlsLichen sclerosusLog oddsPilot studyAccurate risk stratificationRetrospective chart reviewAdolescent gynecology clinicUseful prognostic toolMajor academic centersPremenarchal patientsChart reviewUrinary incontinenceGynecology clinicRisk stratificationUnique patientsPatient populationSingle institutionVulvovaginal complaintsTimely diagnosisPrognostic toolAcademic centersClitoral hoodClinical diagnosisPatientsDiagnosis
2021
Prognostic Value of Electrocardiographic QRS Diminution in Patients Hospitalized With COVID-19 or Influenza
Lampert J, Miller M, Halperin J, Oates C, Giustino G, Nelson K, Feinman J, Kocovic N, Pulaski M, Musikantow D, Turagam M, Sofi A, Choudry S, Langan M, Koruth J, Whang W, Miller M, Dukkipati S, Bassily-Marcus A, Kohli-Seth R, Goldman M, Reddy V. Prognostic Value of Electrocardiographic QRS Diminution in Patients Hospitalized With COVID-19 or Influenza. The American Journal Of Cardiology 2021, 159: 129-137. PMID: 34579830, PMCID: PMC8349698, DOI: 10.1016/j.amjcard.2021.07.048.Peer-Reviewed Original ResearchConceptsCourse of COVID-19 infectionQRS amplitudeBaseline clinical variablesFollow-up electrocardiogramsPredictors of deathC-reactive proteinAssociated with mortalityInfluenza infectionVasopressor requirementsPeak troponinConsecutive adultsPrognostic valueMedian timeClinical decompensationD-dimerPrognostic utilityClinical reassessmentCOVID-19 infectionPrognostic toolClinical variablesInfluenzaPrecordial leadsPatientsSurface electrocardiogramQRS complex amplitude
2020
Machine Learning Prognostic Models for Gastrointestinal Bleeding Using Electronic Health Record Data.
Shung D, Laine L. Machine Learning Prognostic Models for Gastrointestinal Bleeding Using Electronic Health Record Data. The American Journal Of Gastroenterology 2020, 115: 1199-1200. PMID: 32530828, PMCID: PMC7415736, DOI: 10.14309/ajg.0000000000000720.Commentaries, Editorials and LettersConceptsRisk assessment toolGastrointestinal bleedingIntensive care unit patientsClinical risk assessment toolCare unit patientsElectronic health record dataHealth record dataLevel of careAssessment toolElectronic health recordsAPACHE IVaHospital mortalityHospital courseUnit patientsPrognostic toolClinical practicePrognostic modelHealth recordsRecord dataBleedingExternal validationPatientsLack of generalizabilityMortalityCarePitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer
Kos Z, Roblin E, Kim RS, Michiels S, Gallas BD, Chen W, van de Vijver KK, Goel S, Adams S, Demaria S, Viale G, Nielsen TO, Badve SS, Symmans WF, Sotiriou C, Rimm DL, Hewitt S, Denkert C, Loibl S, Luen SJ, Bartlett JMS, Savas P, Pruneri G, Dillon DA, Cheang MCU, Tutt A, Hall JA, Kok M, Horlings HM, Madabhushi A, van der Laak J, Ciompi F, Laenkholm AV, Bellolio E, Gruosso T, Fox SB, Araya JC, Floris G, Hudeček J, Voorwerk L, Beck AH, Kerner J, Larsimont D, Declercq S, Van den Eynden G, Pusztai L, Ehinger A, Yang W, AbdulJabbar K, Yuan Y, Singh R, Hiley C, Bakir MA, Lazar AJ, Naber S, Wienert S, Castillo M, Curigliano G, Dieci MV, André F, Swanton C, Reis-Filho J, Sparano J, Balslev E, Chen IC, Stovgaard EIS, Pogue-Geile K, Blenman KRM, Penault-Llorca F, Schnitt S, Lakhani SR, Vincent-Salomon A, Rojo F, Braybrooke JP, Hanna MG, Soler-Monsó MT, Bethmann D, Castaneda CA, Willard-Gallo K, Sharma A, Lien HC, Fineberg S, Thagaard J, Comerma L, Gonzalez-Ericsson P, Brogi E, Loi S, Saltz J, Klaushen F, Cooper L, Amgad M, Moore DA, Salgado R. Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer. Npj Breast Cancer 2020, 6: 17. PMID: 32411819, PMCID: PMC7217863, DOI: 10.1038/s41523-020-0156-0.Peer-Reviewed Original ResearchStromal tumor-infiltrating lymphocytesEarly TNBCBreast cancerHER2-positive breast cancerTumor-infiltrating lymphocytesLymphocyte distributionStromal tumorsInflammatory cellsPredictive biomarkersTreatment selectionPrognostic toolClinical practiceOutcome estimatesLymphocytesReproducible assessmentTNBCTumorsCancerScoring guidelinesMultiple areasTumor boundariesRisk estimationImpact of discrepanciesRing studiesAssessmentCui bono? Finding the value of allogeneic stem cell transplantation for lower-risk myelodysplastic syndromes
Shallis RM, Podoltsev NA, Gowda L, Zeidan AM, Gore SD. Cui bono? Finding the value of allogeneic stem cell transplantation for lower-risk myelodysplastic syndromes. Expert Review Of Hematology 2020, 13: 447-460. PMID: 32182435, DOI: 10.1080/17474086.2020.1744433.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsAllogeneic stem cell transplantationStem cell transplantationAcute myeloid leukemiaMyelodysplastic syndromeMDS patientsRisk stratificationCell transplantationPrognostic toolLower-risk myelodysplastic syndromesHigh-risk myelodysplastic syndromeLow-risk MDS patientsSevere bone marrow failureLow-risk diseaseLow-risk patientsOnly curative optionPrognosis of patientsBone marrow failureAggressive therapyCurative optionPrognostic impactEtiologic roleDisease progressionMyeloid leukemiaAlloSCTPatients
2017
Development of Imminent Mortality Predictor for Advanced Cancer (IMPAC), a Tool to Predict Short-Term Mortality in Hospitalized Patients With Advanced Cancer
Adelson K, Lee DKK, Velji S, Ma J, Lipka SK, Rimar J, Longley P, Vega T, Perez-Irizarry J, Pinker E, Lilenbaum R. Development of Imminent Mortality Predictor for Advanced Cancer (IMPAC), a Tool to Predict Short-Term Mortality in Hospitalized Patients With Advanced Cancer. JCO Oncology Practice 2017, 14: jop.2017.023200. PMID: 29206553, DOI: 10.1200/jop.2017.023200.Peer-Reviewed Original ResearchConceptsShort-term mortalityAdvanced cancerChance of deathLife carePrognostic toolNovel prognostic toolObjective prognostic toolSmilow Cancer HospitalDay of admissionElectronic health record dataMedian survival timeSubjective clinical assessmentHealth record dataPositive predictive valueMedian survivalHospitalized patientsMortality predictorsCancer HospitalPrognostication toolsClinical assessmentSurvival timePatientsPredictive valueCancerMortality
2016
Conditional probability of survival in gallbladder carcinoma as a prognostic tool for long term survivors.
Rajeev R, Berger N, Hammad A, Miura J, Johnston F, Gamblin T, Turaga K. Conditional probability of survival in gallbladder carcinoma as a prognostic tool for long term survivors. Journal Of Clinical Oncology 2016, 34: 455-455. DOI: 10.1200/jco.2016.34.4_suppl.455.Peer-Reviewed Original ResearchGallbladder carcinomaLong-term survivorsProbability of survivalConditional probability of survivalRadical resectionTerm survivorsPatients of gallbladder carcinomaStage IV diseaseKaplan-Meier methodStage III andGallbladder carcinoma patientsCumulative incidence methodFollow-up guidelinesPrognosis to patientsNodal harvestOS ratesCurative surgeryIV diseasePrognostic informationSurgical interventionPrognostic toolInitial prognosisConditional survivalAdvanced stageIII and
2015
Comparison of risk stratification tools in predicting outcomes of patients with higher-risk myelodysplastic syndromes treated with azanucleosides
Zeidan AM, Sekeres MA, Garcia-Manero G, Steensma DP, Zell K, Barnard J, Ali NA, Zimmerman C, Roboz G, DeZern A, Nazha A, Jabbour E, Kantarjian H, Gore SD, Maciejewski JP, List A, Komrokji R. Comparison of risk stratification tools in predicting outcomes of patients with higher-risk myelodysplastic syndromes treated with azanucleosides. Leukemia 2015, 30: 649-657. PMID: 26464171, PMCID: PMC4775363, DOI: 10.1038/leu.2015.283.Peer-Reviewed Original ResearchConceptsInternational Prognostic Scoring SystemPrognostic scoring systemMD Anderson Prognostic Scoring SystemMyelodysplastic syndromePrognostic toolScoring systemDifferent prognostic scoring systemsHigh-risk myelodysplastic syndromeRelative prognostic performanceOutcomes of patientsFirst-line therapyRisk stratification toolHigh-risk groupWorld Health OrganizationHR-MDSMedian OSObjective responseOverall survivalStandard therapyPrognostic utilityStratification toolPatient cohortPrognostic performancePatientsHealth Organization
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