Perioperative cardiovascular outcomes among older adults undergoing in-hospital noncardiac surgery

Prevention of perioperative stroke in patients undergoing non-cardiac surgery

About 300 million adults undergo non-cardiac surgery annually. Although, in this setting, the incidence of perioperative stroke is low, the absolute number of patients experiencing a stroke is substantial. Furthermore, most patients with this complication will die or end up with severe disability. Covert brain infarctions are more frequent than overt strokes and are associated with postoperative delirium, cognitive decline, and cerebrovascular events at 1 year after surgery. Evidence shows that traditional stroke risk factors including older age, hypertension, and atrial fibrillation are also associated with perioperative stroke; previous stroke is the strongest risk factor for perioperative stroke. Increasing evidence also suggests the pathogenic role of perioperative events, such as hypotension, new atrial fibrillation, paradoxical embolism, and bleeding. Clinicians involved in perioperative care should be aware of this evidence on prevention strategies to improve patient outcomes after non-cardiac surgery.

Data Science and Geriatric Anesthesia Research: Opportunity and Challenges

With an increase in geriatric population undergoing surgical procedures, research focused on enhancing their perioperative outcomes is of paramount importance. Currently, most of the evidence-based medicine protocols are driven by studies concentrating on adults encompassing all adult age groups. Given the alterations in physiology with aging, geriatric patients respond differently to anesthetics and, therefore, require specific research initiatives to further expound on the same. Large databases and the development of sophisticated analytic tools can provide meaningful insights into this. Here, we discuss a few research opportunities and challenges that data scientists face when focusing on geriatric perioperative research.

External Validation of the Revised Cardiac Risk Index and the Geriatric-Sensitive Perioperative Cardiac Risk Index in Oldest Old Patients Following Surgery Under Spinal Anaesthesia; a Retrospective Cross-Sectional Cohort Study

Background

The Revised Cardiac Risk Index (RCRI) and the Geriatric Sensitive Cardiac Risk Index (GSCRI) estimate the risk of postoperative major adverse cardiac events (MACE) regardless of the type of anesthesia and without specifying the oldest old patients. Since spinal anesthesia (SA) is a preferred technique in geriatrics, we aimed to test the external validity of these indices in patients ≥ 80 years old who underwent surgery under SA and tried to identify other potential risk factors for postoperative MACE.

Methods

The performance of both indices to estimate postoperative in-hospital MACE risk was tested through discrimination, calibration, and clinical utility. We also investigated the correlation between both indices and postoperative ICU admission and length of hospital stay (LOS).

Results

The MACE incidence was 7.5%. Both indices had limited discriminative (AUC for RCRI and GSCRI were 0.69 and 0.68, respectively) and predictive abilities. The regression analysis showed that patients with atrial fibrillation (AF) were 3.77 and those with trauma surgery were 2.03 times more likely to exhibit MACE, and the odds of MACE increased by 9% for each additional year above 80. Introducing these factors into both indices (multivariable models) increased the discriminative ability (AUC reached 0.798 and 0.777 for RCRI and GSCRI, respectively). Bootstrap analysis showed that the predictive ability of the multivariate GSCRI but not the multivariate RCRI improved. Decision curve analysis (DCA) showed that multivariate GSCRI had superior clinical utility when compared with multivariate RCRI. Both indices correlated poorly with postoperative ICU admission and LOS.

Conclusion

Both indices had limited predictive and discriminative ability to estimate postoperative in-hospital MACE risk and correlated poorly with postoperative ICU admission and LOS, following surgery under SA in the oldest-old patients. Updated versions by introducing age, AF, and trauma surgery improved the GSCRI performance but not the RCRI.

Comparison of Prognostic Value of 10 Biochemical Indices at Admission for Prediction Postoperative Myocardial Injury and Hospital Mortality in Patients with Osteoporotic Hip Fracture

Aim: To evaluate the prognostic impact at admission of 10 biochemical indices for prediction postoperative myocardial injury (PMI) and/or hospital death in hip fracture (HF) patients. Methods: In 1273 consecutive patients with HF (mean age 82.9 ± 8.7 years, 73.5% women), clinical and laboratory parameters were collected prospectively, and outcomes were recorded. Multiple logistic regression and receiver-operating characteristic analyses (the area under the curve, AUC) were preformed, the number needed to predict (NNP) outcome was calculated. Results: Age ≥ 80 years and IHD were the most prominent clinical factors associated with both PMI (with cardiac troponin I rise) and in-hospital death. PMI occurred in 555 (43.6%) patients and contributed to 80.3% (49/61) of all deaths (mortality rate 8.8% vs. 1.9% in non-PMI patients). The most accurate biochemical predictive markers were parathyroid hormone > 6.8 pmol/L, urea > 7.5 mmol/L, 25(OH)vitamin D < 25 nmol/L, albumin < 33 g/L, and ratios gamma-glutamyl transferase (GGT) to alanine aminotransferase > 2.5, urea/albumin ≥ 2.0 and GGT/albumin ≥ 7.0; the AUC for developing PMI ranged between 0.782 and 0.742 (NNP: 1.84−2.13), the AUC for fatal outcome ranged from 0.803 to 0.722, (NNP: 3.77−9.52). Conclusions: In HF patients, easily accessible biochemical indices at admission substantially improve prediction of hospital outcomes, especially in the aged >80 years with IHD.

Assessment and Correction of the Cardiac Complications Risk in Non-cardiac Operations – What's New?

Cardiovascular complications after non-cardiac surgery are the leading cause of 30-day mortality. The need for surgical interventions is approximately 5,000 procedures per 100,000 population, according to experts, the risks of non-cardiac surgical interventions are markedly higher in the elderly. It should be borne in mind that the aging of the population and the increased possibilities of medicine inevitably lead to an increase in surgical interventions in older people. Recent years have been characterized by the appearance of national and international guidelines with various algorithms for assessing and correcting cardiac risk, as well as publications on the validation of these algorithms. The purpose of this review was to provide new information about the assessment and correction of the risk of cardiac complications in non-cardiac operations. Despite the proposed new risk assessment scales, the RCRI scale remains the most commonly used, although for certain categories of patients (with oncopathology, in older age groups) the possibility of using specific questionnaires has been shown. In assessing the functional state, it is proposed to use not only a subjective assessment, but also the DASI questionnaire, 6-minute walking test and cardiopulmonary exercise test). At the next stage, it is proposed to evaluate biomarkers, primarily BNP or NT-proBNP, with a normal level – surgery, with an increased level – either an additional examination by a cardiologist or perioperative troponin screening. Currently, the prevailing opinion is that there is no need to examine patients to detect hidden lesions of the coronary arteries (non-invasive tests, coronary angiography), since this leads to excessive examination of patients, delaying the implementation of non-cardiac surgery. The extent to which this approach has an advantage over the previously used one remains to be studied.

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.