Machine learning to predict vasopressin responsiveness in patients with septic shock

Aileen Scheibner, Kevin D. Betthauser, Alice F. Bewley, Paul Juang, Bryan Lizza, Scott Micek, Patrick G. Lyons

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Study Objectives: The objective of this study was to develop and externally validate a model to predict adjunctive vasopressin response in patients with septic shock being treated with norepinephrine for bedside use in the intensive care unit. Design: This was a retrospective analysis of two adult tertiary intensive care unit septic shock populations. Setting: Barnes-Jewish Hospital (BJH) from 2010 to 2017 and Beth Israel Deaconess Medical Center (BIDMC) from 2001 to 2012. Patients: Two septic shock populations (548 BJH patients and 464 BIDMC patients) that received vasopressin as second-line vasopressor. Intervention: Patients who were vasopressin responsive were compared with those who were nonresponsive. Vasopressin response was defined as survival with at least a 20% decrease in maximum daily norepinephrine requirements by one calendar day after vasopressin initiation, without a third-line vasopressor. Measurements: Two supervised machine learning models (gradient-boosting machine [XGBoost] and elastic net penalized logistic regression [EN]) were trained in 1000 bootstrap replications of the BJH data and externally validated in the BIDMC data to predict vasopressin responsiveness. Main Results: Vasopressin responsiveness was similar among each cohort (BJH 45% and BIDMC 39%). Mortality was lower for vasopressin responders compared with nonresponders in the BJH (51% vs. 73%) and BIDMC (45% vs. 83%) cohorts, respectively. Both models demonstrated modest discrimination in the training (XGBoost area under receiver operator curve [AUROC] 0.61 [95% confidence interval (CI) 0.61–0.61], EN 0.59 [95% CI 0.58–0.59]) and external validation (XGBoost 0.68 [95% CI 0.63–0.73], EN 0.64 [95% CI 0.59–0.69]) datasets. Conclusion: Vasopressin nonresponsiveness is common and associated with increased mortality. The models' modest performances highlight the complexity of septic shock and indicate that more research will be required before clinical decision support tools can aid in anticipating patient-specific responsiveness to vasopressin.

Original languageEnglish
Pages (from-to)460-471
Number of pages12
Issue number6
StatePublished - Jun 2022


  • artificial intelligence
  • pharmacology
  • predictive modeling
  • septic shock
  • vasopressors


Dive into the research topics of 'Machine learning to predict vasopressin responsiveness in patients with septic shock'. Together they form a unique fingerprint.

Cite this