Risk assessment and risk stratification for perioperative complications and mitigation: Where should the focus be? How are we doing?

The impact of comorbidities on outcomes of concomitant mitral valve intervention with ascending aortic surgery

INTRODUCTION

The Charlson Comorbidity Index (CCI) is widely utilized for risk stratification for non-cardiac surgical patients, yet it has not been broadly validated in patients undergoing cardiac surgery. We aim to assess its ability to predict early and late outcomes of concomitant mitral valve intervention with ascending aortic surgery.

METHODS

Patients who underwent surgery between 1997 and 2022 were reviewed. Age-adjusted CCI scores were calculated based on clinical status at a time of index operation. The primary endpoint was all causes mortality while secondary outcomes were major adverse events (MAE) that included combined perioperative mortality, dialysis, myocardial infarction, and stroke in addition to the individual outcomes and take back for bleeding and tracheostomy. Chi-square test, Logistic and Cox regression analysis, and Kaplan-Meier curves were used. Maximally selected rank statistics were used to identify best cutoff of CCI for late mortality.

RESULTS

186 patients (median age 65 [interquartile range (IQR): 54-76] and 69% males) were included with a median CCI of 4 [IQR: 3-6]. Five and ten-years overall survival were 95.9% and 67.1% vs 59.7%, and 19.9% in CCI ≤ 5 vs >5 (P < 0.001). On multivariate Cox regression analysis, higher CCI (HR 1.60 [1.17;2.18], P = 0.00), and lower EF (HR 0.89 [0.83;0.96], P = 0.002) were associated with late mortality. There was a trend to lower mortality in recent surgery years (HR 0.91 [0.83;1.01], P = 0.070)). Perioperative MAE was higher in CCI >5 (11.0% vs 2.1%, P = 0.017), and postoperative need for tracheostomy and CVA had a trend to be higher in CCI > 5 (P = 0.055). Logistic regression revealed that higher CCI, as a continuous variable, was associated with significantly higher odds of MAE, postoperative dialysis, and need for tracheostomy.

CONCLUSIONS

The CCI can be a helpful tool in predicting outcomes of patients undergoing concomitant mitral valve intervention with ascending aortic surgery.

Predictive modeling of perioperative patient deterioration: combining unanticipated ICU admissions and mortality for improved risk prediction

OBJECTIVE

This paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes.

MATERIALS AND METHODS

With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost.

RESULTS

During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction.

DISCUSSION

The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies.

CONCLUSIONS

This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.

Multi-Modal Prehabilitation in Thoracic Surgery: From Basic Concepts to Practical Modalities

Over the last two decades, the invasiveness of thoracic surgery has decreased along with technological advances and better diagnostic tools, whereas the patient's comorbidities and frailty patterns have increased, as well as the number of early cancer stages that could benefit from curative resection. Poor aerobic fitness, nutritional defects, sarcopenia and "toxic" behaviors such as sedentary behavior, smoking and alcohol consumption are modifiable risk factors for major postoperative complications. The process of enhancing patients' physiological reserve in anticipation for surgery is referred to as prehabilitation. Components of prehabilitation programs include optimization of medical treatment, prescription of structured exercise program, correction of nutritional deficits and patient's education to adopt healthier behaviors. All patients may benefit from prehabilitation, which is part of the enhanced recovery after surgery (ERAS) programs. Faster functional recovery is expected in low-risk patients, whereas better clinical outcome and shorter hospital stay have been demonstrated in higher risk and physically unfit patients.

Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.

Digital Online Patient Informed Consent for Anesthesia before Elective Surgery-Recent Practice in Europe

BACKGROUND

Digitalization in the health system is a topic that is rapidly gaining popularity, and not only because of the current pandemic. As in many areas of daily life, digitalization is becoming increasingly important in the medical field amid the exponential rise in the use of computers and smartphones. This opens up new possibilities for optimizing patient education in the context of anesthesia. The main aim of this study was to assess the implementation of remote consent in Europe.

METHODS

An online survey entitled "Digital online Patient Informed Consent for Anesthesia before Elective Surgery. Recent practice in Europe," with a total of 27 questions, was sent by the European Society of Anesthesiology and Intensive Care (ESAIC) to their members in 47 European countries. To assess the effect of the economy on digitalization and legal status with regard to anesthesia consent, data were stratified based on gross domestic product per capita (GDPPC).

RESULTS

In total, 23.1% and 37.2% of the 930 participants indicated that it was possible to obtain consent online or via telephone, respectively. This observation was more often reported in countries with high GDPPC levels than in countries with low GDPPC levels. Furthermore, 27.3% of the responses for simple anesthesia, 18.7% of the responses for complex anesthesia, and 32.2% of the responses for repeated anesthesia indicated that remote consent was in accordance with the law, and this was especially prevalent in countries with high GDPPC. Concerning the timing of consent, patients were informed at least one day before in 67.1% of cases for simple procedures and in 85.2% of cases for complex procedures.

CONCLUSION

Even European countries with high GDPPC use remote informed consent only in a minority of cases, and most of the time for repeated anesthetic procedures. This might reflect the inconsistent legal situation and inhomogeneous medical technical structures across Europe.

Hemorrhage in pheochromocytoma surgery: evaluation of preoperative risk factors

OBJECTIVE

Pheochromocytoma surgery carries a higher risk of hemorrhage. Our objective was to identify preoperative risk factors for hemorrhage during pheochromocytoma surgery.

METHODS

Patients who underwent surgery and with postoperative pathological confirmation were enrolled. A total of 251 patients from our center were included in the investigation, and 120 patients from the First Affiliated Hospital, Sun Yat-sen University were included as an external validation dataset. Family and medical history, demographics, hemodynamics, biochemical parameters, image data, anesthesia and operation records, postoperative outcomes were collected. Postoperative complications were graded by the Clavien-Dindo classification. Correlation between intraoperative hemorrhage volume and postoperative outcomes was assessed. The features associated with intraoperative hemorrhage were identified by linear regression. All features that were statistically significant in the multiple linear regression were then used to construct models and nomograms for predicting intraoperative hemorrhage. The constructed models were evaluated by Akaike Information Criterion. Finally, internal and external validations were carried out by tenfold cross-validation.

RESULTS

Intraoperative hemorrhage volume was positively correlated with the postoperative hospitalization time (R = 0.454, P < 0.001) and the Clavien-Dindo grades (R = 0.664, P < 0.001). Features associated with intraoperative hemorrhage were male gender (β = 0.533, OR = 1.722, P = 0.002), tumor diameter (β = 0.027, OR = 1.027, P < 0.001), preoperative CCB use (β = 0.318, OR = 1.308, P = 0.123) and open surgery (β = 1.175, OR = 3.234, P < 0.001). Validations showed reliable results (internal (R = 0.612, RMSE = 1.355, MAE = 1.111); external (R = 0.585, RMSE = 1.398, MAE = 0.964)).

CONCLUSION

More intraoperative hemorrhage is correlated with longer postoperative hospitalization time and more severe postoperative complications. Male gender, larger tumor, preoperative CCB use and open surgery are preoperative risk factors for hemorrhage in PCC surgery.