2022
Machine learning models for prediction of adverse events after percutaneous coronary intervention
Niimi N, Shiraishi Y, Sawano M, Ikemura N, Inohara T, Ueda I, Fukuda K, Kohsaka S. Machine learning models for prediction of adverse events after percutaneous coronary intervention. Scientific Reports 2022, 12: 6262. PMID: 35428765, PMCID: PMC9012739, DOI: 10.1038/s41598-022-10346-1.Peer-Reviewed Original ResearchConceptsPercutaneous coronary interventionAdverse eventsRisk scoreHospital mortalityCoronary interventionConsecutive coronary artery disease patientsCoronary artery disease patientsAdverse periprocedural eventsLow-risk patientsMajor adverse eventsGood discriminationMulticenter registryPeriprocedural eventsDisease patientsClinical decisionLogistic regressionSpecific interventionsRisk predictionOverall risk predictionPatientsScoresInterventionMortalityAdequate discriminationOutcomes
2020
Association of decreasing hemoglobin levels with the incidence of acute kidney injury after percutaneous coronary intervention: a prospective multi-center study
Kuno T, Numasawa Y, Mikami T, Niimi N, Sawano M, Kodaira M, Suzuki M, Ueno K, Ueda I, Fukuda K, Kohsaka S. Association of decreasing hemoglobin levels with the incidence of acute kidney injury after percutaneous coronary intervention: a prospective multi-center study. Heart And Vessels 2020, 36: 330-336. PMID: 33034713, DOI: 10.1007/s00380-020-01706-w.Peer-Reviewed Original ResearchConceptsAcute kidney injuryPercutaneous coronary interventionRisk of AKIHemoglobin dropAKI incidenceKidney injuryCoronary interventionHemoglobin levelsProspective multi-center studySerum creatinine levelsMulti-center studyRelative decreaseConsecutive patientsCreatinine levelsIndependent predictorsRisk factorsHemoglobin changeHigh riskAbsolute decreaseLogistic regressionPatientsIncidenceHemoglobinInjuryDl