Effects of Intra-operative Cardiopulmonary Variability On Post-operative Pulmonary Complications in Major Non-cardiac Surgery: A Retrospective Cohort Study

Sylvia Ranjeva, Alexander Nagebretsky, Gabriel Odozynski, Ana Fernandez-Bustamante, Gyorgy Frendl, R. Alok Gupta, Juraj Sprung, Bala Subramaniam, Ricardo Martinez Ruiz, Karsten Bartels, Jadelis Giquel, Jae Woo Lee, Timothy Houle, Marcos Francisco Vidal Melo

Research output: Contribution to journalArticlepeer-review


Intraoperative cardiopulmonary variables are well-known predictors of postoperative pulmonary complications (PPC), traditionally quantified by median values over the duration of surgery. However, it is unknown whether cardiopulmonary instability, or wider intra-operative variability of the same metrics, is distinctly associated with PPC risk and severity. We leveraged a retrospective cohort of adults (n = 1202) undergoing major non-cardiothoracic surgery. We used multivariable logistic regression to evaluate the association of two outcomes (1)moderate-or-severe PPC and (2)any PPC with two sets of exposure variables- (a)variability of cardiopulmonary metrics (inter-quartile range, IQR) and (b)median intraoperative cardiopulmonary metrics. We compared predictive ability (receiver operating curve analysis, ROC) and parsimony (information criteria) of three models evaluating different aspects of the intra-operative cardiopulmonary metrics: Median-based: Median cardiopulmonary metrics alone, Variability-based: IQR of cardiopulmonary metrics alone, and Combined: Medians and IQR. Models controlled for peri-operative/surgical factors, demographics, and comorbidities. PPC occurred in 400(33%) of patients, and 91(8%) experienced moderate-or-severe PPC. Variability in multiple intra-operative cardiopulmonary metrics was independently associated with risk of moderate-or-severe, but not any, PPC. For moderate-or-severe PPC, the best-fit predictive model was the Variability-based model by both information criteria and ROC analysis (area under the curve, AUCVariability-based = 0.74 vs AUCMedian-based = 0.65, p = 0.0015; AUCVariability-based = 0.74 vs AUCCombined = 0.68, p = 0.012). For any PPC, the Median-based model yielded the best fit by information criteria. Predictive accuracy was marginally but not significantly higher for the Combined model (AUCCombined = 0.661) than for the Median-based (AUCMedian-based = 0.657, p = 0.60) or Variability-based (AUCVariability-based = 0.649, p = 0.29) models. Variability of cardiopulmonary metrics, distinct from median intra-operative values, is an important predictor of moderate-or-severe PPC.

Original languageEnglish (US)
Article number31
JournalJournal of Medical Systems
Issue number1
StatePublished - Dec 2024


  • Intraoperative hemodynamic variability
  • Intraoperative respiratory variability
  • Lung protective ventilation
  • Postoperative pulmonary complications

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management


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