Derivation and Validation of a Geriatric‐Sensitive Perioperative Cardiac Risk Index

Abstract

BackgroundSurgical patients aged 65 and over face a higher risk of cardiac complications from noncardiac surgery. The Revised Cardiac Risk Index (RCRI) and the Gupta Myocardial Infarction or Cardiac Arrest (MICA) calculator are widely used to predict this risk, but they are not specifically designed to predict MICA in geriatric patients. Our hypothesis is that a new geriatric‐sensitive index, derived from geriatric data, will capture this population's unique response to risk factors.Methods and ResultsThe model was developed using the NSQIP (National Surgical Quality Improvement Program) 2013 geriatric cohort (N=584,931) (210,914 age ≥65) and validated on the NSQIP 2012 geriatric cohort (N= 485,426) (172,905 age ≥65). Least Angle Shrinkage and Selection Operator regression was used for initial variable selection. The Geriatric‐Sensitive Cardiac Risk Index (GSCRI) was then evaluated in the 2012 data set. The area under the curve (AUC) was compared among the GSCRI, RCRI, and Gupta MICA in the 2012 data set. The GSCRI had an AUC of 0.76 in the validation cohort among geriatric patients. When the Gupta MICA was tested on geriatric patients in the validation cohort, a significant deterioration (≈17%) was noted, as well as a significant underestimation of the risk. The GSCRI AUC of 0.76 in the geriatric subset was significantly greater (P<0.001) than those in the RCRI (AUC=0.63) or Gupta MICA (AUC=0.70) models, outperforming the RCRI and Gupta MICA models in geriatric patients by 13% and 6%, respectively, with a ΔAUC and P‐value of 0.13 (P<0.001), and 0.06 (P<0.001).ConclusionsThe GSCRI is a significantly better predictor of cardiac risk in geriatric patients undergoing noncardiac surgery.

Examining Risk: A Systematic Review of Perioperative Cardiac Risk Prediction Indices

“…From this collection, 10 articles were selected for analysis. 1,2,[15][16][17][18][19][20][21][22] Citation chaining, the process of finding studies that reference each other as described by Ellis,23 identified one additional article for inclusion ( Figure 1). 24 The 11 identified risk indices together include 2,910,297 patients, representing 17,925 cardiac events or 0.0062 events/patient.…”

“…The area under the receiver operating characteristic curve (AUROC), a measure of the discrimination of the risk indices, 27 was very good (0.83e0.90) for the three large NSQIP studies (Davenport et al, 15 Gupta et al 2 , and Bilimoria et al 24 ), and good (0.70e0.79) for the remaining studies ( Figure 2B). Only four studies (Davenport et al, 15 Gupta et al, 2 Bilmoria et al, 24 and Alrezk et al 19 ) reported the calibration of their models, which was good in each case (Supplemental Table 2).…”

“…Five studies (Davenport et al, 15 Gupta et al, 2 Bilimoria et al, 24 Kheterpal et al, 17 and Alrezk et al 19 ) were conducted using National Surgical Quality Improvement Program (NSQIP) data, which was collected by trained nurses at sites across the United States using established protocols. 25 The study from Andersson et al 22 was conducted using data from Denmark's government registry of health careerelated variables.…”

“…2,15,19 Three studies (Kumar et al, 20 Weber et al, 21 and Kopec et al 18 ) specifically enrolled patients at higher cardiac risk. One study (Alrezk et al 19 ) created a submodel focusing on older patients (65 y). Although this author published the full model from the total population, only the geriatric substudy is available in usable form.…”

Cardiovascular death and progression to end-stage renal disease after major surgery in elderly patients

“…The Revised Cardiac Risk Index 34 estimates postoperative cardiac events in the general population; a more recent cardiovascular risk score for elderly surgical patients had moderate accuracy 35 . The Revised Cardiac Risk Index 34 estimates postoperative cardiac events in the general population; a more recent cardiovascular risk score for elderly surgical patients had moderate accuracy 35 .…”