2018
Defining the learning curve in robot-assisted thoracoscopic lobectomy
Arnold BN, Thomas DC, Bhatnagar V, Blasberg JD, Wang Z, Boffa DJ, Detterbeck FC, Kim AW. Defining the learning curve in robot-assisted thoracoscopic lobectomy. Surgery 2018, 165: 450-454. PMID: 30061043, DOI: 10.1016/j.surg.2018.06.011.Peer-Reviewed Original ResearchConceptsLength of stayRobot-assisted thoracoscopic lobectomyChest tube durationThoracoscopic lobectomyPostoperative complicationsTube durationBlood lossLearning curveSignificant differencesCumulative sum analysisPhase 1Complication ratePatient demographicsPulmonary lobectomySingle institutionLobectomyRATS lobectomySafe approachStayCase 1Cases 23Operating timeComorbiditiesComplicationsFurther studies
2016
A model for predicting prolonged length of stay in patients undergoing anatomical lung resection: a National Surgical Quality Improvement Program (NSQIP) database study
DeLuzio MR, Keshava HB, Wang Z, Boffa DJ, Detterbeck FC, Kim AW. A model for predicting prolonged length of stay in patients undergoing anatomical lung resection: a National Surgical Quality Improvement Program (NSQIP) database study. Interdisciplinary CardioVascular And Thoracic Surgery 2016, 23: 208-215. PMID: 27073262, DOI: 10.1093/icvts/ivw090.Peer-Reviewed Original ResearchConceptsAnatomical lung resectionProlonged lengthLung resectionRisk factorsRisk stratificationNational Surgical Quality Improvement Program database studyAnatomical pulmonary resectionBetter risk stratificationOverall patient outcomesHigh-risk populationLength of stayExternal validation groupLogistic regression analysisPostoperative complicationsPostoperative variablesPulmonary resectionNSQIP databaseComparison of receiverPatient outcomesDatabase studyResectionPatientsStayValidation groupScoring system