Postoperative infectious complications in elderly patients after elective surgery in China: results of a 7-day cohort study from the International Surgical Outcomes Study

α5GABAA receptor: A potential therapeutic target for perioperative neurocognitive disorders, a review of preclinical studies

Perioperative neurocognitive disorders (PND) are a common complication in elderly patients following surgery, which not only prolongs the recovery period but also affects their future quality of life and imposes a significant burden on their family and society. Multiple factors, including aging, vulnerability, anesthetic drugs, cerebral oxygen desaturation, and severe pain, have been associated with PND. Unfortunately, no effective drug is currently available to prevent PND. α5 γ-aminobutyric acid subtype A (α5GABAA) receptors have been implicated in cognitive function modulation. Positive or negative allosteric modulators of α5GABAA receptors have been found to improve cognitive impairment under different conditions. Therefore, targeting α5GABAA receptors may represent a promising treatment strategy for PND. This review focuses on preclinical studies of α5GABAA receptors and the risk factors associated with PND, primarily including aging, anesthetics, and neuroinflammation. Specifically, positive allosteric modulators of α5GABAA receptors have improved cognitive function in aged experimental animals. In contrast, negative allosteric modulators of α5GABAA receptors have been found to facilitate cognitive recovery in aged or adult experimental animals undergoing anesthesia and surgery but not in aged experimental animals under anesthesia alone. The reasons for the discordant findings have yet to be elucidated. In preclinical studies, different strategies of drug administration, as well as various behavioral tests, may influence the stability of the results. These issues need to be carefully considered in future studies.

Symptom Trajectories Informing Patient Care After Lung Cancer Surgery: A Longitudinal Patient-Reported Outcome Study

BACKGROUND

Application of patient-reported outcomes (PROs) in surgical oncology has been limited because of patient heterogeneity. We analyzed symptom trajectories and their associations with recovery outcomes after lung cancer surgery, aiming to profile the heterogeneity of patients' experiences and to identify patients needing extensive care.

METHODS

Symptoms were assessed with the MDASI-LC before surgery, daily after surgery in hospital and weekly within 1 month after discharge. Patients were clustered based on symptoms from post-operative day 1 (POD1) to POD5, using the latent-class-trajectory-model. Functional recovery was compared across the trajectories. Logistic regression was used to explore risk factors for trajectories of more severe symptoms.

RESULTS

Based on the five most severe post-surgery symptoms (pain, fatigue, coughing, shortness of breath, and disturbed sleep), we identified three distinct symptom trajectories among 424 patients [mild, N = 225 (53.07%); severe-to-mild, N = 86 (20.28%); severe, N = 104 (24.53%)]. At discharge, more 'severe' patients (73.96%) did not achieve a functional recovery compared with those in mild (32.54%, P < 0.0001) or severe-to-mild (56.96%, P = 0.0274) groups. Factors of significant symptom increase on POD1 were younger-than-55 (OR = 1.94 [95% CI 1.30-2.93], P = 0.001), undergoing open or multi-port video-assisted thoracoscopic surgery (OR = 1.59 [95% CI 1.05-2.41], P = 0.03), and using two chest tubes (OR = 1.72 [95% CI 1.12-2.65], P = 0.01). For patients experiencing dramatic symptom increase on POD1, older age (OR = 2.51 [95% CI 1.40-4.59], P = 0.002) was associated with 'severe' trajectory.

CONCLUSIONS

This study demonstrated that PRO measures were capable of profiling heterogeneous symptom trajectories after lung cancer surgery. Those in-hospital trajectories were able to differentiate patients' responses to treatments and signal the needs for extensive post-discharge care.

Development and Validation of Predictive Model—HASBLAD Score—For Major Adverse Cardiovascular Events During Perioperative Period of Non-cardiac Surgery: A Single Center Experience in China

Background

Major adverse cardiovascular events (MACEs) represent a significant reason of morbidity and mortality in non-cardiac surgery during perioperative period. The prevention of perioperative MACEs has always been one of the hotspots in the research field. Current existing models have not been validated in Chinese population, and have become increasingly unable to adapt to current clinical needs.

Objectives

To establish and validate several simple bedside tools for predicting MACEs during perioperative period of non-cardiac surgery in Chinese hospitalized patients.

Design

We used a nested case-control study to establish our prediction models. A nomogram along with a risk score were developed using logistic regression analysis. An internal cohort was used to evaluate the performance of discrimination and calibration of these predictive models including the revised cardiac risk index (RCRI) score recommended by current guidelines.

Setting

Peking University Third Hospital between January 2010 and December 2020.

Patients

Two hundred and fifty three patients with MACEs and 1,012 patients without were included in the training set from January 2010 to December 2019 while 38,897 patients were included in the validation set from January 2020 and December 2020, of whom 112 patients had MACEs.

Main Outcome Measures

The MACEs included the composite outcomes of cardiac death, non-fatal myocardial infarction, non-fatal congestive cardiac failure or hemodynamically significant ventricular arrhythmia, and Takotsubo cardiomyopathy.

Results

Seven predictors, including Hemoglobin, CARDIAC diseases, Aspartate aminotransferase (AST), high Blood pressure, Leukocyte count, general Anesthesia, and Diabetes mellitus (HASBLAD), were selected in the final model. The nomogram and HASBLAD score all achieved satisfactory prediction performance in the training set (C statistic, 0.781 vs. 0.768) and the validation set (C statistic, 0.865 vs. 0.843). Good calibration was observed for the probability of MACEs in the training set and the validation set. The two predictive models both had excellent discrimination that performed better than RCRI in the validation set (C statistic, 0.660, P < 0.05 vs. nomogram and HASBLAD score).

Conclusion

The nomogram and HASBLAD score could be useful bedside tools for predicting perioperative MACEs of non-cardiac surgery in Chinese hospitalized patients.