Featured Publications
Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept
Ugwuowo U, Yamamoto Y, Arora T, Saran I, Partridge C, Biswas A, Martin M, Moledina DG, Greenberg JH, Simonov M, Mansour SG, Vela R, Testani JM, Rao V, Rentfro K, Obeid W, Parikh CR, Wilson FP. Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept. American Journal Of Kidney Diseases 2020, 76: 806-814.e1. PMID: 32505812, PMCID: PMC8667815, DOI: 10.1053/j.ajkd.2020.05.003.Peer-Reviewed Original ResearchConceptsAKI alertsHospitalized adultsKidney injuryUrban tertiary care hospitalAcute kidney injurySerum creatinine levelsObservational cohort studyTertiary care hospitalSerum creatinine concentrationBeats/minElectronic health recordsAKI diagnosisCohort studyCreatinine levelsInpatient mortalitySystolic bloodFractional excretionCenter studyBlood biomarkersUnivariable associationsUrine microscopyCreatinine concentrationClinical careElevated riskUrea nitrogen
2020
Novel Risk Factors for Progression of Diabetic and Nondiabetic CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study
Anderson AH, Xie D, Wang X, Baudier RL, Orlandi P, Appel LJ, Dember LM, He J, Kusek JW, Lash JP, Navaneethan SD, Ojo A, Rahman M, Roy J, Scialla JJ, Sondheimer JH, Steigerwalt SP, Wilson FP, Wolf M, Feldman HI, Investigators C, Go A, Townsend R. Novel Risk Factors for Progression of Diabetic and Nondiabetic CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study. American Journal Of Kidney Diseases 2020, 77: 56-73.e1. PMID: 32866540, PMCID: PMC7752839, DOI: 10.1053/j.ajkd.2020.07.011.Peer-Reviewed Original ResearchConceptsChronic Renal Insufficiency Cohort (CRIC) StudyIndependent risk factorCKD progressionNovel risk factorsRisk factorsCohort studyComposite outcomeN-terminal pro-B-type natriuretic peptidePro-B-type natriuretic peptideChronic kidney disease progressionGlomerular filtration rate (eGFR) slopeHigh-sensitivity troponin TCox proportional hazards modelStudy designProgression of diabeticsUrinary neutrophil gelatinaseUrinary NGAL levelsUS clinical centersKidney disease progressionLow serum bicarbonateProspective cohort studyKidney replacement therapyHigh-risk subgroupsProportional hazards modelNumerous risk factorsDevelopment and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation
Haimovich AD, Ravindra NG, Stoytchev S, Young HP, Wilson FP, van Dijk D, Schulz WL, Taylor RA. Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation. Annals Of Emergency Medicine 2020, 76: 442-453. PMID: 33012378, PMCID: PMC7373004, DOI: 10.1016/j.annemergmed.2020.07.022.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedBetacoronavirusClinical Laboratory TechniquesCoronavirus InfectionsCOVID-19COVID-19 TestingEmergency Service, HospitalFemaleHumansMaleMiddle AgedOxygen Inhalation TherapyPandemicsPneumonia, ViralRespiratory InsufficiencyRetrospective StudiesRisk AssessmentSARS-CoV-2Severity of Illness IndexYoung AdultConceptsCOVID-19 Severity IndexQuick COVID-19 severity indexQuick Sequential Organ Failure AssessmentSequential Organ Failure AssessmentOrgan Failure AssessmentHours of admissionRespiratory failureSeverity IndexScoring systemSevere acute respiratory syndrome coronavirus 2Acute respiratory syndrome coronavirus 2Respiratory syndrome coronavirus 2Bedside scoring systemOxygen requirementPneumonia severity scoresHours of hospitalizationElixhauser Comorbidity IndexEmergency department patientsSeverity Index scoreCOVID-19 patientsSyndrome coronavirus 2Coronavirus disease 2019Failure AssessmentSimple scoring systemIndependent test cohort
2019
Clinical Implications of the New York Heart Association Classification
Caraballo C, Desai NR, Mulder H, Alhanti B, Wilson FP, Fiuzat M, Felker GM, Piña IL, O'Connor CM, Lindenfeld J, Januzzi JL, Cohen LS, Ahmad T. Clinical Implications of the New York Heart Association Classification. Journal Of The American Heart Association 2019, 8: e014240. PMID: 31771438, PMCID: PMC6912957, DOI: 10.1161/jaha.119.014240.Peer-Reviewed Original ResearchConceptsNew York Heart Association classificationHF clinical trialsNYHA class IIVentricular ejection fractionGUIDE-ITHeart failureNYHA classWalk distanceEjection fractionRisk stratificationHF-ACTIONAssociation classificationClinical trialsMinute ventilation-carbon dioxide production relationshipKansas City Cardiomyopathy Questionnaire scoreClass IIMulticenter National InstituteNYHA class IIINT-proBNP levelsKaplan-Meier curvesLog-rank testClinical trial eligibilityWilcoxon rank sum testMaximal oxygen uptakeRank sum testDevelopment and Validation of a Model for Predicting the Risk of Acute Kidney Injury Associated With Contrast Volume Levels During Percutaneous Coronary Intervention
Huang C, Li SX, Mahajan S, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Development and Validation of a Model for Predicting the Risk of Acute Kidney Injury Associated With Contrast Volume Levels During Percutaneous Coronary Intervention. JAMA Network Open 2019, 2: e1916021. PMID: 31755952, PMCID: PMC6902830, DOI: 10.1001/jamanetworkopen.2019.16021.Peer-Reviewed Original ResearchConceptsCreatinine level increaseAcute kidney injuryPercutaneous coronary interventionContrast volumeAKI riskKidney injuryCoronary interventionBaseline riskCardiology National Cardiovascular Data Registry's CathPCI RegistryNational Cardiovascular Data Registry CathPCI RegistryRisk of AKIAcute Kidney Injury AssociatedDifferent baseline risksPCI safetyCathPCI RegistryInjury AssociatedMean ageDerivation setPreprocedural riskMAIN OUTCOMEAmerican CollegePrognostic studiesUS hospitalsCalibration slopeValidation setA simple real-time model for predicting acute kidney injury in hospitalized patients in the US: A descriptive modeling study
Simonov M, Ugwuowo U, Moreira E, Yamamoto Y, Biswas A, Martin M, Testani J, Wilson FP. A simple real-time model for predicting acute kidney injury in hospitalized patients in the US: A descriptive modeling study. PLOS Medicine 2019, 16: e1002861. PMID: 31306408, PMCID: PMC6629054, DOI: 10.1371/journal.pmed.1002861.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedAged, 80 and overConnecticutDecision Support TechniquesElectronic Health RecordsFemaleHospital MortalityHumansInpatientsMaleMiddle AgedPatient AdmissionPredictive Value of TestsPrognosisRenal DialysisRetrospective StudiesRisk AssessmentRisk FactorsSeverity of Illness IndexTime FactorsConceptsAcute kidney injuryImminent acute kidney injuryElectronic health recordsKidney injuryHospital 1Prediction of AKIRenal replacement therapyOptimal treatment strategyLaboratory dataReceiver operator characteristic curveInternal validation setAKI occurrenceAKI severityHospitalized adultsMedical comorbiditiesOverall cohortAdverse eventsHospitalized patientsSurgical wardsSignificant morbidityReplacement therapyExternal validation data setsHospital 2Hospital 3Study hospitalQuality Improvement Goals for Acute Kidney Injury
Kashani K, Rosner MH, Haase M, Lewington AJP, O'Donoghue DJ, Wilson FP, Nadim MK, Silver SA, Zarbock A, Ostermann M, Mehta RL, Kane-Gill SL, Ding X, Pickkers P, Bihorac A, Siew ED, Barreto EF, Macedo E, Kellum JA, Palevsky PM, Tolwani AJ, Ronco C, Juncos LA, Rewa OG, Bagshaw SM, Mottes TA, Koyner JL, Liu KD, Forni LG, Heung M, Wu VC. Quality Improvement Goals for Acute Kidney Injury. Clinical Journal Of The American Society Of Nephrology 2019, 14: 941-953. PMID: 31101671, PMCID: PMC6556737, DOI: 10.2215/cjn.01250119.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsFuture quality improvement projectsManagement of AKIRisk of AKIAcute kidney injuryLong-term outcomesAcute care settingCost of careHealth care providersQuality improvement projectSignificant clinical consequencesQuality of careHealth care costsQuality Improvement ProgramQuality care deliveryHigh-quality careKidney injuryEmergency departmentInpatient careCare settingsNurse practitionersClinical consequencesHospital settingCare providersHigh incidencePatientsThe Association of Angiogenesis Markers With Acute Kidney Injury and Mortality After Cardiac Surgery
Mansour SG, Zhang WR, Moledina D, Coca SG, Jia Y, Thiessen-Philbrook H, McArthur E, Inoue K, Koyner JL, Shlipak MG, Wilson FP, Garg AX, Ishibe S, Parikh CR, Consortium T. The Association of Angiogenesis Markers With Acute Kidney Injury and Mortality After Cardiac Surgery. American Journal Of Kidney Diseases 2019, 74: 36-46. PMID: 30955944, PMCID: PMC6591032, DOI: 10.1053/j.ajkd.2019.01.028.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedBiomarkersCardiac Surgical ProceduresCreatinineEndpoint DeterminationFemaleHumansKidneyMaleMiddle AgedNeovascularization, PhysiologicOutcome Assessment, Health CarePostoperative ComplicationsProspective StudiesReceptors, Vascular Endothelial Growth FactorRisk AssessmentUnited StatesVascular Endothelial Growth Factor AConceptsAcute kidney injuryCardiac surgeryAKI durationKidney injuryProangiogenic markersAngiogenesis markersOutcomes of AKILong-term outcomesPlasma VEGF concentrationsTRIBE-AKI cohortAntiangiogenic markersCause mortalityPreoperative concentrationsHospital dischargeProcess of angiogenesisMarker levelsVEGFR1 levelsPGF concentrationsHigher oddsMortality riskHigh riskLower oddsVEGF concentrationsAngiogenic markersSurgery
2018
Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine 2018, 15: e1002703. PMID: 30481186, PMCID: PMC6258473, DOI: 10.1371/journal.pmed.1002703.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedClinical Decision-MakingData MiningDecision Support TechniquesFemaleHumansMachine LearningMaleMiddle AgedPercutaneous Coronary InterventionProtective FactorsRegistriesReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionNational Cardiovascular Data RegistryRisk prediction modelAKI eventsAKI riskCoronary interventionAKI modelMean ageCardiology-National Cardiovascular Data RegistryAcute kidney injury riskAKI risk predictionRetrospective cohort studyIdentification of patientsCandidate variablesAvailable candidate variablesCohort studyPCI proceduresPoint of careBrier scoreAmerican CollegeData registryPatientsCalibration slopeInjury riskSame cohort
2017
Approaches to Predicting Outcomes in Patients with Acute Kidney Injury
Saly D, Yang A, Triebwasser C, Oh J, Sun Q, Testani J, Parikh CR, Bia J, Biswas A, Stetson C, Chaisanguanthum K, Wilson FP. Approaches to Predicting Outcomes in Patients with Acute Kidney Injury. PLOS ONE 2017, 12: e0169305. PMID: 28122032, PMCID: PMC5266278, DOI: 10.1371/journal.pone.0169305.Peer-Reviewed Original ResearchConceptsAcute kidney injuryLength of stayKidney injuryReceiver operator characteristic curveOutcomes of interestOperator characteristic curveValidation cohortClinical eventsAccurate prognosticationOutcome eventsPredicting OutcomePrognostic modelDeath predictionLab valuesCharacteristic curveGood discrimination abilityPatientsStayInjuryDialysisModel discriminationOutcomesDaysMedicationsMorbidityAssociation of Alternative Approaches to Normalizing Peritoneal Dialysis Clearance with Mortality and Technique Failure: A Retrospective Analysis Using the United States Renal Data System-Dialysis Morbidity and Mortality Study, Wave 2
Boyle SM, Li Y, Wilson FP, Glickman JD, Feldman HI. Association of Alternative Approaches to Normalizing Peritoneal Dialysis Clearance with Mortality and Technique Failure: A Retrospective Analysis Using the United States Renal Data System-Dialysis Morbidity and Mortality Study, Wave 2. Advances In Peritoneal Dialysis 2017, 37: 85-93. PMID: 27680757, PMCID: PMC5448711, DOI: 10.3747/pdi.2015.00227.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedBlood Urea NitrogenCause of DeathCohort StudiesCreatinineDialysis SolutionsFemaleHumansKaplan-Meier EstimateKidney Failure, ChronicKidney Function TestsMaleMiddle AgedPeritoneal Dialysis, Continuous AmbulatoryProportional Hazards ModelsRegistriesRenal DialysisRetrospective StudiesRisk AssessmentSurvival AnalysisUnited StatesUreaConceptsKt/body surface areaKt/VBody surface areaTechnique failureHarrell's C-statisticPeritoneal urea clearanceC-statisticHazard ratioUrea clearanceUrine volumeUnited States Renal Data System Dialysis MorbidityBody mass index strataMortality Study Wave 2Peritoneal Kt/VUnited States Renal Data SystemIncident peritoneal dialysis patientsIdeal weightSignificant differencesCox proportional hazards modelPeritoneal dialysis clearanceMedian patient ageRetrospective cohort studyPeritoneal dialysis patientsGlomerular filtration rateOutcomes of mortality
2016
Urine biomarkers of tubular injury do not improve on the clinical model predicting chronic kidney disease progression
Hsu CY, Xie D, Waikar SS, Bonventre JV, Zhang X, Sabbisetti V, Mifflin TE, Coresh J, Diamantidis CJ, He J, Lora CM, Miller ER, Nelson RG, Ojo AO, Rahman M, Schelling JR, Wilson FP, Kimmel PL, Feldman HI, Vasan RS, Liu KD, Investigators C, Appel L, Feldman H, Go A, He J, Kusek J, Lash J, Ojo A, Rahman M, Townsend R, Consortium C. Urine biomarkers of tubular injury do not improve on the clinical model predicting chronic kidney disease progression. Kidney International 2016, 91: 196-203. PMID: 28029431, PMCID: PMC5362331, DOI: 10.1016/j.kint.2016.09.003.Peer-Reviewed Original ResearchMeSH KeywordsAcetylglucosaminidaseAgedAlbuminuriaBiomarkersCreatinineDisease ProgressionFatty Acid-Binding ProteinsFemaleFollow-Up StudiesGlomerular Filtration RateHepatitis A Virus Cellular Receptor 1HumansKidney Failure, ChronicKidney TubulesLipocalin-2MaleMiddle AgedProportional Hazards ModelsProspective StudiesRenal Insufficiency, ChronicRisk AssessmentRisk FactorsConceptsGlomerular filtration rateUrinary albumin/creatinine ratioAlbumin/creatinine ratioKidney disease progressionTubular injury biomarkersCKD progressionInjury biomarkersFiltration rateClinical modelSerum creatinineCreatinine ratioDisease progressionProspective Chronic Renal Insufficiency Cohort StudyChronic Renal Insufficiency Cohort (CRIC) StudyIncident end-stage renal diseaseUnadjusted Cox proportional hazards modelUrinary kidney injury molecule-1Renal tubular injury biomarkersChronic kidney disease progressionKidney injury molecule-1End-stage renal diseaseNeutrophil gelatinase-associated lipocalinCox proportional hazards modelBase clinical modelInjury molecule-1
2014
Dialysis versus Nondialysis in Patients with AKI: A Propensity-Matched Cohort Study
Wilson FP, Yang W, Machado CA, Mariani LH, Borovskiy Y, Berns JS, Feldman HI. Dialysis versus Nondialysis in Patients with AKI: A Propensity-Matched Cohort Study. Clinical Journal Of The American Society Of Nephrology 2014, 9: 673-681. PMID: 24651073, PMCID: PMC3974360, DOI: 10.2215/cjn.07630713.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAdultAgedBiomarkersCreatinineFemaleHospitalizationHumansKaplan-Meier EstimateLogistic ModelsMaleMiddle AgedMultivariate AnalysisOdds RatioPatient SelectionPennsylvaniaPropensity ScoreProportional Hazards ModelsRenal DialysisRisk AssessmentRisk FactorsSeverity of Illness IndexTime FactorsTreatment OutcomeConceptsInitiation of dialysisSerum creatinine concentrationCreatinine concentrationDialysis initiationDialyzed patientsSevere AKICohort studyPropensity-matched cohort studyPropensity scoreElevated creatinine levelOverall hazard ratioGreater survival benefitProportional hazards analysisAcute care hospitalsTime-varying propensity scoresPennsylvania Health SystemCause mortalityCreatinine levelsHazard ratioSurvival benefitCare hospitalDL increaseNondialyzed patientsPatient factorsLaboratory variables