Absolute vs Additive Net Reclassification Index

Accuracy of frailty instruments in predicting outcomes following perioperative cardiac arrest

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.

Developing an Internally Validated Veterans Affairs Women Cardiovascular Disease Risk Score Using Veterans Affairs National Electronic Health Records

Background

The current American College of Cardiology/American Heart Association women cardiovascular disease (CVD) risk score suboptimally estimates CVD risk for young and minority women in the military. The current study developed an internally validated CVD risk score for women military service members and veterans using the Veterans Affairs (VA) national electronic health records data.

Methods and Results

The study cohort included 69 574 White, Black, and Hispanic women service members and veterans aged 30 to 79 years in 2007 treated in the VA Health Care System between January 1, 2007 and December 31, 2017 (henceforth, VA women). Stratified by race and ethnicity, the new VA women CVD risk model estimated risk coefficients and 10‐year CVD risk using a time‐variant covariate Cox model. Harrell C‐statistics, calibration plots, and net classification index were used to assess accuracy and prognostic performance of the new VA women CVD risk model. The new internally validated VA women CVD risk score performed better in predicting VA women 10‐year atherosclerosis cardiovascular disease risk than the pooled cohort American College of Cardiology/American Heart Association risk score in both accuracy (White Harrell C‐statistics, 70% versus 61%; Black, 68% versus 63%) and prognostic performance (White net classification index, 0.31; 95% CI, 0.26–0.33; Black net classification index, 0.06; 95% CI, 0.03–0.09).

Conclusions

The proposed VA women CVD risk score improves accuracy of the existing American College of Cardiology/American Heart Association CVD risk assessment tool in predicting long‐term CVD risk for VA women, particularly in young and racial/ethnic minority women.