Abstract
BACKGROUND: Frailty is associated with increased 30-day mortality and non-home discharge following perioperative cardiac arrest. We estimated the predictive accuracy of frailty when added to baseline risk prediction models. METHODS: In this retrospective cohort study using 2015-2020 NSQIP data for 3048 patients aged 50+ undergoing non-cardiac surgery and resuscitation on post-operative day 0 (i.e., intraoperatively or postoperatively on the day of surgery), baseline models including age, sex, ASA physical status, preoperative sepsis or septic shock, and emergent surgery were compared to models that added frailty indices, either RAI or mFI-5, to predict 30-day mortality and non-home discharge. Predictive accuracy was characterized by area under the receiver operating characteristic curve (AUC-ROC), integrated calibration index (ICI), and continuous net reclassification index (NRI). RESULTS: 1786 patients (58.6%) died in the study cohort within 30 days, and 38.6% of eligible patients experienced non-home discharge. The baseline model showed good discrimination (AUC-ROC 0.77 for 30-day mortality and 0.74 for non-home discharge). AUC-ROC and ICI did not significantly change after adding frailty for 30-day mortality or non-home discharge. Adding RAI significantly improved NRI for 30-day mortality and non-home discharge; however, the magnitude was small and difficult to interpret, given other results including false positive and negative rates showing no difference in predictive accuracy. CONCLUSIONS: Incorporating frailty did not significantly improve predictive accuracy of models for 30-day mortality and non-home discharge following perioperative resuscitation. Thus, demonstrated associations between frailty and outcomes of perioperative resuscitation may not translate into improved predictive accuracy. When engaging patients in shared decision-making regarding do-not-resuscitate orders perioperatively, providers should acknowledge uncertainty in anticipating resuscitation outcomes.