Preoperative Frailty and Cognitive Dysfunction Assessment

A proposal for virtual, telephone-based preoperative cognitive assessment in older adults undergoing elective surgery

OBJECTIVE

To assess the feasibility of administering the MoCA 5-minute test/Telephone (T-MoCA), an abbreviated version of the Montreal Cognitive Assessment to older adults perioperatively DESIGN: A feasibility study including patients aged ≥ 70 years scheduled for surgery from December 2020 to March 2021 SETTING: Preoperative virtual clinic PATIENTS: Patients ≥70 years undergoing major elective surgery INTERVENTION: A study investigator called eligible patients prior to surgery, obtained consent, and completed the preoperative cognitive assessment. Follow-up assessment was completed 1-month postoperatively, and participating clinicians were surveyed at the completion of the study.

MEASUREMENTS

An attention test, T-MoCA, Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), and Generalized Anxiety Disorder 2-item (GAD-2) MAIN RESULTS: Overall, 37/40 (92.5%) patients completed the pre- and post-operative assessments. The cohort was 50% female, white (97.5%), with a median age of 76 years (interquartile range (IQR) 73-79), and education level was higher than high school in 82.5% of patients. Preoperatively, the median number of medications was 8 (IQR 7-11), 27/40 (67.5%) had medications with anticholinergic effects, and 6/40 (15%) had benzodiazepines. Median completion time of the phone assessment was 10 min (IQR 8.25-12) and 4 min (IQR 3-5) for the T-MoCA with a median T-MoCA score of 13 (IQR 12-14). Most patients (37/40) completed the post-operative assessment, and 6/37 (16.2%) reported they had experienced a change in memory or attention post-operatively. Clinician's survey reported ease and feasibility in performing T-MoCA as a preoperative cognitive evaluation.

CONCLUSION

Preoperative cognitive assessment of older adults using T-MoCA over the phone is easy to perform by clinicians and had a high completion rate by patients. This test is feasible for virtual assessments. Further research is needed to better define validity and correlation with postoperative outcomes.

Cardiac Comorbidity Risk Score: Zero-Burden Machine Learning to Improve Prediction of Postoperative Major Adverse Cardiac Events in Hip and Knee Arthroplasty

Background In this retrospective, observational study we introduce the Cardiac Comorbidity Risk Score, predicting perioperative major adverse cardiac events (MACE) after elective hip and knee arthroplasty. MACE is a rare but important driver of mortality, and existing tools, eg, the Revised Cardiac Risk Index demonstrate only modest accuracy. We demonstrate an artificial intelligence-based approach to identify patients at high risk of MACE within 4 weeks (primary outcome) of arthroplasty, that imposes zero additional burden of cost/resources. Methods and Results Cardiac Comorbidity Risk Score calculation uses novel machine learning to estimate MACE risk from patient electronic health records, without requiring blood work or access to any demographic data beyond that of sex and age, and accounts for variable/missing/incomplete information across patient records. Validated on a deidentified cohort (age >45 years, n=445 391), performance was evaluated using the area under the receiver operator characteristics curve (AUROC), sensitivity/specificity, positive predictive value, and positive/negative likelihood ratios. In our cohort (age 63.5±10.5 years, 58.2% women, 34.2%/65.8% hip/knee procedures), 0.19% (882) experienced the primary outcome. Cardiac Comorbidity Risk Score achieved area under the receiver operator characteristics curve=80.0±0.4% (95% CI) for women and 80.1±0.5% (95% CI) for males, with 36.4% and 35.1% sensitivities, respectively, at 95% specificity, significantly outperforming Revised Cardiac Risk Index across all studied age-, sex-, risk-, and comorbidity-based subgroups. Conclusions Cardiac Comorbidity Risk Score, a novel artificial intelligence-based screening tool using known and unknown comorbidity patterns, outperforms state-of-the-art in predicting MACE within 4 weeks postarthroplasty, and can identify patients at high risk that do not demonstrate traditional risk factors.