Perioperative myocardial ischemia and isolated systolic hypertension in non-cardiac surgery

One Molecule, Many Faces: Repositioning Cardiovascular Agents for Advanced Wound Healing

Chronic wound treatments pose a challenge for healthcare worldwide, particularly for the people in developed countries. Chronic wounds significantly impair quality of life, especially among the elderly. Current research is devoted to novel approaches to wound care by repositioning cardiovascular agents for topical wound treatment. The emerging field of medicinal products' repurposing, which involves redirecting existing pharmaceuticals to new therapeutic uses, is a promising strategy. Recent studies suggest that medicinal products such as sartans, beta-blockers, and statins have unexplored potential, exhibiting multifaceted pharmacological properties that extend beyond their primary indications. The purpose of this review is to analyze the current state of knowledge on the repositioning of cardiovascular agents' use and their molecular mechanisms in the context of wound healing.

Risk factors for acute postoperative hypertension in non-cardiac major surgery: a case control study

PURPOSE

Acute postoperative hypertension (APH) is a common complication during the anesthesia recovery period that can lead to adverse outcomes, including cardiovascular and cerebrovascular accidents. Identification of risk factors for APH will allow for preoperative optimization and appropriate perioperative management. This study aimed to identify risk factors for APH.

PATIENTS AND METHODS

In this retrospective single-center study, 1,178 cases were included. Data was entered by two investigators, and consistency analysis was performed by another. Patients were divided into APH and non-APH groups. A predictive model was built by multivariate stepwise logistic regression. The predictive ability of the logistic regression model was tested by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). Hosmer and Lemeshow goodness-of-fit (GOF) test was performed to reflect the goodness of fit of the model. Calibration curve was created to represent the relationship between predicted risk and observed frequency. Sensitivity analysis was performed to evaluate the robustness of the results.

RESULTS

Multivariate logistic regression analysis showed that age over 65 years (OR = 3.07, 95% CI: 2.14 ~ 4.42, P < 0.001), female patients (OR = 1.37, 95% CI: 1.02 ~ 1.84, P = 0.034), presence of intraoperative hypertension (OR = 2.15, 95% CI: 1.57 ~ 2.95, P < 0.001), and use of propofol in PACU (OR = 2.14, 95% CI: 1.49 ~ 3.06, P < 0.001) were risk factors for APH. Intraoperative use of dexmedetomidine (OR = 0.66, 95% CI: 0.49 ~ 0.89, P = 0.007) was a protective factor. Higher baseline SBP (OR = 0.90, 95% CI: 0.89 ~ 0.92, P < 0.001) also showed some correlation with APH.

CONCLUSIONS

The risk of acute postoperative hypertension increased with age over 65 years, female patients, intraoperative hypertension and restlessness during anesthesia recovery. Intraoperative use of dexmedetomidine was a protective factor for APH.

The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery

BACKGROUND

The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this.

OBJECTIVES

Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery.

SEARCH METHODS

We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period.

SELECTION CRITERIA

We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews.

MAIN RESULTS

We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS₂, CHA₂DS₂-VASc, R₂CHADS₂, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively.

AUTHORS' CONCLUSIONS

Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.

Postanesthesia Care for the Elderly Patient

PURPOSE

As the general population lives longer, the perioperative physician is more likely to encounter disease states that increase in incidence in an aging population. This review focuses on anesthetic considerations for rational drug choices during the perioperative care of elderly patients. The primary aim of the review was to identify intraoperative and postanesthetic considerations for diseases associated with advancing age; it includes highlights of the commonly impaired major organs (eg, cardiovascular, pulmonary, neurologic, renal, hepatic systems). We also outline an approach to frequent issues that arise in the immediate postsurgical period while caring for these patients.

METHODS

A systematic review was performed on aspects of the perioperative and postoperative periods that relate to the elderly. A list of pertinent key words was derived from the authors, and a PubMed database search was performed.

FINDINGS

The anesthesiologist must account for changes in various organ systems that affect perioperative care, including the cardiovascular, pulmonary, renal, hepatic, and central nervous systems. The pharmacokinetic principles frequently differ and are often unpredictable because of anatomic changes and decreased renal and hepatic function. The most important pharmacodynamic consideration is that elderly patients tend to exhibit an exaggerated hypoactivity after anesthesia.

IMPLICATIONS

Before surgery, it is essential to identify those patients at risk for delirium and other commonly encountered postanesthesia scenarios. Failure to manage these conditions appropriately can lead to an escalation of care and prolonged hospitalization.

Obesity & hypertension are determinants of poor hemodynamic control during total joint arthroplasty: a retrospective review

Background

Proper blood pressure control during surgical procedures such as total joint arthroplasty (TJA) is considered critical to good outcome. There is poor understanding of the pre-operative risk factors for poor intra-operative hemodynamic control. The purpose of this study is to identify risk factors for poor hemodynamic control during TJA.

Methods

We performed a retrospective cohort analysis of 118 patients receiving TJA in the Dominican Republic. We collected patient demographic and comorbidity data. We developed an a priori definition for poor hemodynamic control: 1) Mean arterial pressure (MAP) <65% of preoperative MAP or 2) MAP >135% of preoperative MAP. We performed bivariate and multivariate analyses to identify risk factors for poor hemodynamic control during TJA.

Results

Hypertension was relatively common in our study population (76 of 118 patients). Average preoperative mean arterial pressure was 109.0 (corresponding to an average SBP of 149 and DBP of 89). Forty-nine (41.5%) patients had intraoperative blood pressure readings consistent with poor hemodynamic control. Based on multi-variable analysis preoperative hypertension of any type (RR 2.9; 95% CI 1.3-6.3) and an increase in BMI (RR 1.2 per 5 unit increase; 95% CI 1.0-1.5) were significant risk factors for poor hemodynamic control.

Conclusions

Preoperative hypertension and being overweight/obese increase the likelihood of poor blood pressure control during TJA. Hypertensive and/or obese patients warrant further attention and medical optimization prior to TJA. More work is required to elucidate the relationship between these risk factors and overall outcome.