2024
TimelinePTC: Development of a unified interface for pathways to care collection, visualization, and collaboration in first episode psychosis
Mathis W, Ferrara M, Cahill J, Karmani S, Tayfur S, Srihari V. TimelinePTC: Development of a unified interface for pathways to care collection, visualization, and collaboration in first episode psychosis. PLOS ONE 2024, 19: e0302116. PMID: 39028697, PMCID: PMC11259254, DOI: 10.1371/journal.pone.0302116.Peer-Reviewed Original ResearchMeSH KeywordsCooperative BehaviorData CollectionHumansInternetPsychotic DisordersUser-Computer InterfaceConceptsPathways to CareDuration of untreated psychosisManual transcription errorsDevelopment of targeted interventionsReduce duration of untreated psychosisHealthcare accessibility researchReal-time data entryImprove patient outcomesEpisode psychosisCare pathwaysPatient journeyHealthcare contextPatient outcomesPaper-basedConversion of collected dataFEP treatmentData collection processNew HavenData collection methodsData entryTranscription errorsUntreated psychosisOpen-source codebaseData collectionWeb-based toolThe impact of early detection (ED) campaigns on care presentations: Beyond DUP reduction
Hazan H, Ferrara M, Riley S, Li F, Zhou B, Kline E, Gibbs-Dean T, Karmani S, Tayfur S, Tek C, Keshavan M, Srihari V. The impact of early detection (ED) campaigns on care presentations: Beyond DUP reduction. Schizophrenia Research 2024, 264: 457-461. PMID: 38266513, PMCID: PMC10923115, DOI: 10.1016/j.schres.2024.01.022.Peer-Reviewed Original ResearchMeSH KeywordsEarly DiagnosisHospitalizationHumansPsychotic DisordersSchizophrenic PsychologyTime FactorsConceptsCoordinated specialty careDuration of untreated psychosisImpact of early detectionEarly detectionEarly detection campaignsSpecialty careAdjustment scoresCampaign yearDetection campaignsMediation analysisEffects of EDRegression modelsCareUntreated psychosisPositive symptomsGAFPatientsSalutary effects
2023
Sampling from different populations: Sociodemographic, clinical, and functional differences between samples of first episode psychosis individuals and clinical high-risk individuals who progressed to psychosis
Hagler M, Ferrara M, Yoviene Sykes L, Li F, Addington J, Bearden C, Cadenhead K, Cannon T, Cornblatt B, Perkins D, Mathalon D, Seidman L, Tsuang M, Walker E, Powers A, Allen A, Srihari V, Woods S. Sampling from different populations: Sociodemographic, clinical, and functional differences between samples of first episode psychosis individuals and clinical high-risk individuals who progressed to psychosis. Schizophrenia Research 2023, 255: 239-245. PMID: 37028205, PMCID: PMC10207144, DOI: 10.1016/j.schres.2023.03.047.Peer-Reviewed Original ResearchMeSH KeywordsHumansLongitudinal StudiesNorth AmericaProdromal SymptomsProtective FactorsPsychotic DisordersConceptsFirst-episode psychosis servicesClinical high riskClinical high-risk individualsEarly detectionFirst-episode psychosis individualsRecent psychiatric hospitalizationCourse of illnessHigh-risk individualsAttenuated positive symptomsCHR researchGeographic catchmentSyndromal psychosisPsychosis individualsPsychiatric hospitalizationEarly intervention effortsHigh riskPsychosis servicesPositive symptomsGlobal functioningClinical resourcesProtective factorsDifferent populationsFE participantsGeneralizability of findingsFES program
2022
Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment
Ferrara M, Franchini G, Funaro M, Cutroni M, Valier B, Toffanin T, Palagini L, Zerbinati L, Folesani F, Murri M, Caruso R, Grassi L. Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment. Current Psychiatry Reports 2022, 24: 925-936. PMID: 36399236, PMCID: PMC9780131, DOI: 10.1007/s11920-022-01399-0.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceDiagnosis, DifferentialFemaleHumansMachine LearningMalePsychotic DisordersConceptsMachine learning techniquesBinary classification caseSetting of hyperparametersSupport vector machineArtificial intelligenceLearning techniquesMachine learningML applicationsElectronic health recordsDigital contentML toolsSupervised approachClassification casesVector machineRandom forestGradient boostingML approachHealth recordsIllness detectionOverall performancePromising resultsSources of dataIdentification toolToolPreprocessing
2020
Analysis of Early Intervention Services on Adult Judicial Outcomes
Pollard JM, Ferrara M, Lin IH, Kucukgoncu S, Wasser T, Li F, Srihari VH. Analysis of Early Intervention Services on Adult Judicial Outcomes. JAMA Psychiatry 2020, 77: 871-872. PMID: 32320010, PMCID: PMC7177643, DOI: 10.1001/jamapsychiatry.2020.0448.Peer-Reviewed Original Research
2019
Predictive validity of conversion from the clinical high risk syndrome to frank psychosis
Yoviene Sykes LA, Ferrara M, Addington J, Bearden CE, Cadenhead KS, Cannon TD, Cornblatt BA, Perkins DO, Mathalon DH, Seidman LJ, Tsuang MT, Walker EF, McGlashan TH, Woodberry KA, Powers AR, Ponce AN, Cahill JD, Pollard JM, Srihari VH, Woods SW. Predictive validity of conversion from the clinical high risk syndrome to frank psychosis. Schizophrenia Research 2019, 216: 184-191. PMID: 31864837, PMCID: PMC7239715, DOI: 10.1016/j.schres.2019.12.002.Peer-Reviewed Original ResearchConceptsFrank psychosisFirst-episode psychosis patientsOne-yearNorth American Prodromal Longitudinal StudySeverity of illnessClinical high-risk syndromeCurrent antipsychotic medicationsHigh-risk syndromePsychosis risk syndromeClinical high riskPredictive validityFEP casesPrescription ratesAntipsychotic medicationPsychosis patientsRisk syndromePsychosis onsetHigh riskLittle investigative attentionDiagnostic stabilityCHR individualsPsychosis paradigmPsychosisLongitudinal studySyndromeParsing the impact of early detection on duration of untreated psychosis (DUP): Applying quantile regression to data from the Scandinavian TIPS study
Ferrara M, Guloksuz S, Li F, Burke S, Tek C, Friis S, Ten Velden Hegelstad W, Joa I, Johannessen JO, Melle I, Simonsen E, Srihari VH. Parsing the impact of early detection on duration of untreated psychosis (DUP): Applying quantile regression to data from the Scandinavian TIPS study. Schizophrenia Research 2019, 210: 128-134. PMID: 31204063, DOI: 10.1016/j.schres.2019.05.035.Peer-Reviewed Original ResearchEarly intervention service for first episode psychosis in Modena, Northern Italy: The first hundred cases
Ferrara M, Tedeschini E, Baccari F, Musella V, Vacca F, Mazzi F, Ferri M, Srihari V, Starace F, Group F. Early intervention service for first episode psychosis in Modena, Northern Italy: The first hundred cases. Early Intervention In Psychiatry 2019, 13: 1011-1017. PMID: 30672134, DOI: 10.1111/eip.12788.Peer-Reviewed Original ResearchConceptsEarly intervention servicesClinical improvementMedian ageIntervention servicesCommunity mental health centerCommunity outpatient servicesPsychiatric hospital unitMeaningful clinical improvementFirst-episode psychosisModel of careNon-affective psychosisNation Outcome ScalesMental health centersHigher median ageEarly detection effortsRefinement of treatmentMain referralsPharmacological consultationYounger patientsUntreated psychosisOutcome ScaleGeneral practitionersEpisode psychosisHealth centersOutpatient services
2014
The concomitant use of second-generation antipsychotics and long-term antiretroviral therapy may be associated with increased cardiovascular risk
Ferrara M, Umlauf A, Sanders C, Meyer JM, McCutchan J, Duarte N, Atkinson J, Grant I, Ellis RJ, Group T. The concomitant use of second-generation antipsychotics and long-term antiretroviral therapy may be associated with increased cardiovascular risk. Psychiatry Research 2014, 218: 201-208. PMID: 24794030, PMCID: PMC4082695, DOI: 10.1016/j.psychres.2014.04.015.Peer-Reviewed Original ResearchConceptsSecond-generation antipsychoticsBody mass indexAntiretroviral therapyCardiovascular riskMultivariable modelExact testUse of SGAsHIV Neurobehavioral Research ProgramLong-term antiretroviral therapyHigher body mass indexHigher mean triglyceridesMetabolic syndrome componentsHuman immunodeficiency virusDiabetes mellitus incidenceLogistic multivariable modelFisher's exact testMean triglyceridesMetabolic complicationsHIV diseaseSerum lipidsSyndrome componentsArterial pressureMedication combinationsMass indexImmunodeficiency virus
2011
Physical Restraints in an Italian Psychiatric Ward: Clinical Reasons and Staff Organization Problems
Di Lorenzo R, Baraldi S, Ferrara M, Mimmi S, Rigatelli M. Physical Restraints in an Italian Psychiatric Ward: Clinical Reasons and Staff Organization Problems. Perspectives In Psychiatric Care 2011, 48: 95-107. PMID: 22458723, DOI: 10.1111/j.1744-6163.2011.00308.x.Peer-Reviewed Original ResearchConceptsPsychiatric wardsHr of hospitalizationItalian psychiatric wardPhysical restraint rateAcute psychiatric wardsPhysical restraint useNursing chartsClinical reasonsRestraint usePsychotic disordersRestraint ratesVoluntary admissionPhysical restraintMechanical restraintPatientsAggressive behaviorWardsHospitalizationAltered statesAdmissionSchizophrenia