Objective Activity Parameters Track Patient-specific Physical Recovery Trajectories After Surgery and Link With Individual Preoperative Immune States

Dexamethasone vs. placebo modulation of the perioperative blood immune proteome in patients undergoing total knee arthroplasty

BACKGROUND

Pre- and post-operative immune status has gained interest in recent years, as it has been shown to be related to postoperative complications and recovery. The change in immune status has also been known to constitute a large part of the surgical stress response, and it has been speculated that immunomodulatory treatment by glucocorticoids may impact it. Profiling of the impact of specific surgeries and medications on immune status are therefore needed.

METHODS

We characterized the postoperative blood immune proteome in 83 patients receiving either placebo (n = 20) or IV 24 mg dexamethasone (n = 60) preoperative before total knee arthroplasty (TKA). The primary outcome was the effect of dexamethasone on total knee arthroplasty surgical stress by comparing postoperative immune proteome in the dexamethasone group and the placebo group. Secondary outcomes were the surgical stress by total knee arthroplasty by comparing pre- to postoperative immune proteome in the placebo group, and the combined effect of surgical stress and dexamethasone by comparing pre- to postoperative immune proteome in the dexamethasone group. Characterization was performed with the Olink Explorer Inflammation panel on blood samples from the biobank for future research collected during the randomized, clinical DEX-2-TKA Trial. Protein change was reported as log2-fold-change and p-values were corrected a.m. Benjamini-Hochberg.

RESULTS

The surgical stress (placebo) was characterized by a 4.7 log2-fold-change of IL6 (adjusted p-value < 0.01) and up-regulation of central immune signaling pathways and bone marrow mobilization. The combined effect of surgery and dexamethasone showed a less pro-inflammatory profile: IL6 2.5 log2-fold-change (adjusted p-value < 0.01), with decreased signaling for osteoclast activity and innate, immune cell reaction. The effect of dexamethasone showed upregulation of CSF3 (1.55 log2-fold-change, adjusted p-value < 0.01) and an inhibitory effect on both innate and adaptive immune response, immune cell reactivity and formation of extracellular matrix.

CONCLUSIONS

Preoperative dexamethasone indicated anti-inflammatory properties on both innate and adaptive immune response, while surgery was pro-inflammatory. the combination of total knee arthroplasty and dexamethasone inhibited pathways for osteoclast-activity, indicating possible implications on aseptic prosthesis loosening. Dexamethasone showed strong modulation of the surgical stress response following total knee arthroplasty and future studies must explore the clinical associations of these findings.

TRIAL REGISTRATION

NCT03506789.

Prioritising mental health in the perioperative period: understanding postoperative patterns in anxiety and depression through ecological momentary assessment

A recent study in the British Journal of Anaesthesia examining trajectories of anxiety and depressive symptoms after diverse surgical procedures sheds light on an often overlooked, yet important, factor in postoperative recovery-mental health. The authors applied ecological momentary assessment to collect high-resolution data to identify and characterise a subgroup of vulnerable patients who experience worsening of psychological symptoms after surgery. The study prompts not only consideration of psychological factors, but also how best to leverage ecological momentary assessment to understand the perioperative experience.

Postpartum sleep quality and physical activity profiles following elective cesarean delivery: a longitudinal prospective cohort pilot study utilizing a wearable actigraphy device

INTRODUCTION

While sleep and activity levels are impacted by childbirth, these changes before and after cesarean delivery are under explored. Few studies have characterized sleep and physical activity before and after cesarean delivery using objective measures. The aim of this study was to characterize sleep and activity before and after cesarean delivery using wrist-worn Actigraphy. Secondary aims were to explore associations between physical activity and sleep following scheduled cesarean delivery.

METHODS

Following IRB approval, ASA 2 and 3 patients aged 18-50 years, term gestation, singleton pregnancy, undergoing scheduled cesarean delivery under neuraxial anesthesia were invited to participate. Consented patients continuously wore an Actigraph GT9X device on their non-dominant wrist from 7 days prior to scheduled cesarean delivery until 28 days post-delivery. Sleep metrics included quality, duration, disruption and efficiency. Physical activity metrics included average daily moderate to vigorous physical activity bouts, metabolic equivalents (METs) and caloric expenditure. Granular data regarding sleep and activity were recorded and analyzed based on established algorithms and trend analysis using methodology previously described.

RESULTS

Among the 38 recruited patients, analyzable actigraphy data were available in 21 patients. Trend analysis from day -7 (pre-delivery) to 28 (post-delivery) demonstrated that most variables did not differ significantly, indicating that at month 1, most activity and sleeping variables returned to third trimester levels. Some metrics of sleep improved in the first week postpartum compared to third trimester, however, total sleep time worsened and did not recover by day 28 compared to the third trimester durations. Physical activity levels dropped significantly immediately after delivery, then improved from day 0 to 28 post-surgery.

CONCLUSIONS

Most sleep and physical activity metrics return to third trimester levels by 1 month postpartum. Several sleep metrics such as sleep efficiency and awakening after sleep were better in the first postpartum week than in the third trimester of pregnancy, but total sleep continues to be significantly impacted at day 28 postpartum. Physical activity returns to third trimester levels by one month postpartum. Future studies are needed to identify risk factors for worse physical recovery and sleep following cesarean delivery and to compare metrics following different peripartum complications.

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Artificial intelligence (AI) applications have great potential to enhance perioperative care. This paper explores promising areas for AI in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.

The activity advantage: Objective measurement of preoperative activity is associated with postoperative recovery and outcomes in patients undergoing surgery with gynecologic oncologists

OBJECTIVE

To examine the association between objectively-measured preoperative physical activity with postoperative outcomes and recovery milestones in patients undergoing gynecologic oncology surgeries.

METHODS

Prospective cohort study of patients undergoing surgery with gynecologic oncologists who wore wearable actigraphy rings before and after surgery from 03/2021-11/2023. Exposures encompassed preoperative activity intensity (moderate- and vigorous-intensity metabolic equivalent of task-minutes [MAVI MET-mins] over seven days) and level (average daily steps over seven days). Intensity was categorized as <500, 500-1000, and >1000 MAVI MET-mins; level categorized as <8000 and ≥8000 steps/day. Primary outcome was 30-day complications. Secondary outcomes included reaching postoperative goal (≥70% of recommended preoperative intensity and level thresholds) and return to baseline (≥70% of individual preoperative intensity and level).

RESULTS

Among 96 enrolled, 87 met inclusion criteria, which constituted 39% (n = 34) with <500 MET-mins and 56.3% (n = 49) with <8000 steps preoperatively. Those with <500 MET-mins and <8000 steps had higher ECOG scores (p = 0.042 & 0.037) and BMI (p = 0.049 & 0.002) vs those with higher activity; all other perioperative characteristics were similar between groups. Overall, 29.9% experienced a 30-day complication, 29.9% reached postoperative goal, and 64.4% returned to baseline. On multivariable models, higher activity was associated with lower odds of complications: 500-1000 MET-mins (OR = 0.26,95%CI = 0.07-0.92) and >1000 MET-mins (OR = 0.25,95%CI = 0.07-0.94) vs <500 MET-mins; ≥8000 steps (OR = 0.25,95%CI = 0.08-0.73) vs <8000 steps. Higher preoperative activity was associated fewer days to reach postoperative goal.

CONCLUSION

Patients with high preoperative activity are associated with fewer postoperative complications and faster attainment of recovery milestones. Physical activity may be considered a modifiable risk factor for adverse postoperative outcomes.

Physical activity among pregnant inpatients and outpatients and associations with anxiety

OBJECTIVE

Physical activity is linked to lower anxiety, but little is known about the association during pregnancy. This is especially important for antepartum inpatients, who are known to have increased anxiety yet may not be able to achieve target levels of physical activity during hospitalization. We compared physical activity metrics between pregnant inpatients and outpatients and explored correlations with anxiety.

MATERIALS AND METHODS

This was a prospective cohort between 2021 and 2022 of pregnant people aged 18-55 years carrying singleton gestations ≥ 16 weeks. Three exposure groups were matched for gestational age: 1) outpatients from general obstetric clinics; 2) outpatients from high-risk Maternal-Fetal Medicine obstetric clinics; and 3) antepartum inpatients. Participants wore Actigraph GT9X Link accelerometer watches for up to 7 days to measure physical activity. The primary outcome was mean daily step count. Secondary outcomes were metabolic equivalent tasks (METs), hourly kilocalories (kcals), moderate to vigorous physical activity (MVPA) bursts, and anxiety (State-Trait Anxiety Inventory [STAI]). Step counts were compared using multivariable generalized estimating equations adjusting for maternal age, body-mass index, and insurance type as a socioeconomic construct, accounting for within-group clustering by gestational age. Spearman correlations were used to correlate anxiety scores with step counts.

RESULTS

58 participants were analyzed. Compared to outpatients, inpatients had significantly lower mean daily steps (primary outcome, adjusted beta -2185, 95 % confidence interval [CI] -3146, -1224, p < 0.01), METs (adjusted beta -0.18, 95 % CI -0.23, -0.13, p < 0.01), MVPAs (adjusted beta -38.2, 95 % CI -52.3, -24.1, p < 0.01), and kcals (adjusted beta -222.9, 95 % CI -438.0, -7.8, p = 0.04). Over the course of the week, steps progressively decreased for inpatients (p-interaction 0.01) but not for either of the outpatient groups. Among the entire cohort, lower step counts correlated with higher anxiety scores (r = 0.30, p = 0.02).

CONCLUSION

We present antenatal population norms and variance for step counts, metabolic equivalent tasks, moderate to vigorous physical activity bursts, and kcals, as well as correlations with anxiety. Antepartum inpatients had significantly lower physical activity than outpatients, and lower step counts correlated with higher anxiety levels. These results highlight the need for physical activity interventions, particularly for hospitalized pregnant people.

Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends

Background

Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field.

Methods

The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test.

Results

The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning".

Conclusion

Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

An immune signature of postoperative cognitive decline in elderly patients

Postoperative cognitive decline (POCD) is the predominant complication affecting elderly patients following major surgery, yet its prediction and prevention remain challenging. Understanding biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This longitudinal study involving 26 elderly patients undergoing orthopedic surgery aimed to characterize the impact of peripheral immune cell responses to surgical trauma on POCD. Trajectory analyses of single-cell mass cytometry data highlighted early JAK/STAT signaling exacerbation and diminished MyD88 signaling post-surgery in patients who developed POCD. Further analyses integrating single-cell and plasma proteomic data collected before surgery with clinical variables yielded a sparse predictive model that accurately identified patients who would develop POCD (AUC = 0.80). The resulting POCD immune signature included one plasma protein and ten immune cell features, offering a concise list of biomarker candidates for developing point-of-care prognostic tests to personalize perioperative management of at-risk patients. The code and the data are documented and available at https://github.com/gregbellan/POCD .

Teaser

Modeling immune cell responses and plasma proteomic data predicts postoperative cognitive decline.

Determining Differences in Perioperative Functional Mobility Patterns in Lumbar Decompression Versus Fusion Patients Using Smartphone Activity Data

BACKGROUND AND OBJECTIVES

Smartphone activity data recorded through high-fidelity accelerometry can provide accurate postoperative assessments of patient mobility. The "big data" available through smartphones allows for advanced analyses, yielding insight into patient well-being. This study compared rate of change in functional activity data between lumbar fusion (LF) and lumbar decompression (LD) patients to determine preoperative and postoperative course differences.

METHODS

Twenty-three LF and 18 LD patients were retrospectively included. Activity data (steps per day) recorded in Apple Health, encompassing over 70 000 perioperative data points, was classified into 6 temporal epochs representing distinct functional states, including acute preoperative decline, immediate postoperative recovery, and postoperative decline. The daily rate of change of each patient's step counts was calculated for each perioperative epoch.

RESULTS

Patients undergoing LF demonstrated steeper preoperative declines than LD patients based on the first derivative of step count data (P = .045). In the surgical recovery phase, LF patients had slower recoveries (P = .041), and LF patients experienced steeper postoperative secondary declines than LD patients did (P = .010). The rate of change of steps per day demonstrated varying perioperative trajectories that were not explained by differences in age, comorbidities, or levels operated.

CONCLUSION

Patients undergoing LF and LD have distinct perioperative activity profiles characterized by the rate of change in the patient daily steps. Daily steps and their rate of change is thus a valuable metric in phenotyping patients and understanding their postsurgical outcomes. Prospective studies are needed to expand upon these data and establish causal links between preoperative patient mobility, patient characteristics, and postoperative functional outcomes.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Integrated Single-cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study

OBJECTIVE

The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery.

SUMMARY BACKGROUND DATA

SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs.

METHODS

Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery.

RESULTS

A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82).

CONCLUSIONS

The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.