Weekly Anesthesiology Research Analysis
This week in anesthesiology saw large pragmatic trials and influential cohort studies emphasizing individualized perioperative respiratory care, advanced data‑science methods for small‑cohort biomarker discovery, and durable phenotyping of post‑ICU recovery. Key clinical signals include high‑fidelity implementation of perioperative lung expansion bundles, a machine‑learning transfer‑learning framework (COMET) that leverages EHRs to boost omics analyses, and a multicenter phenotyping study that i
Summary
This week in anesthesiology saw large pragmatic trials and influential cohort studies emphasizing individualized perioperative respiratory care, advanced data‑science methods for small‑cohort biomarker discovery, and durable phenotyping of post‑ICU recovery. Key clinical signals include high‑fidelity implementation of perioperative lung expansion bundles, a machine‑learning transfer‑learning framework (COMET) that leverages EHRs to boost omics analyses, and a multicenter phenotyping study that identifies four distinct post‑sepsis recovery trajectories with implications for tailored follow‑up. Together these studies push toward personalized ventilation targets, data‑driven perioperative risk stratification, and phenotype‑guided survivorship pathways.
Selected Articles
1. Perioperative lung expansion and pulmonary outcomes after open abdominal surgery versus usual care in the USA (PRIME-AIR): a multicentre, randomised, controlled, phase 3 trial.
PRIME-AIR is a multicentre phase‑3 RCT enrolling intermediate/high‑risk patients undergoing major open abdominal surgery that tested an individualized perioperative lung‑expansion bundle. Implementation fidelity was high (72–98% adherence) and the intervention increased intraoperative mean PEEP (reported mean ≈7.5 cmH2O) compared with usual care; full PPC outcome details are pending in the reported excerpt.
Impact: A large, NIH‑funded, rigorously conducted phase‑3 trial demonstrating high implementation fidelity of a perioperative lung bundle can directly influence ventilation protocols and guideline development for PPC mitigation in open abdominal surgery.
Clinical Implications: Centers may emulate high‑adherence protocols (bundle checklists, individualized PEEP titration) for at‑risk open abdominal surgery patients while awaiting full outcome publication; emphasize training and tools that preserved high fidelity in PRIME‑AIR.
Key Findings
- Multicentre phase‑3 RCT with mITT analysis of 751 participants (379 intervention, 372 usual care).
- High adherence to bundle components (72–98%) and higher intraoperative mean PEEP in the intervention group (~7.5 cmH2O).
- Eligibility focused on ARISCAT ≥26 and BMI <35 kg/m² for elective open abdominal surgeries ≥2 hours.
2. A machine learning approach to leveraging electronic health records for enhanced omics analysis.
COMET introduces a transfer‑learning, multimodal machine‑learning framework that pretrains on large observational EHR datasets and fuses clinical and omics data to improve predictive performance and biological discovery in small omics cohorts. Validated across two independent datasets, COMET outperformed omics‑only approaches and enabled finer patient stratification beyond binary case–control labels.
Impact: Methodological advance likely to accelerate perioperative and critical‑care biomarker discovery by enabling robust analyses from smaller omics cohorts through EHR transfer learning — a strategic enabler for precision anesthesiology research.
Clinical Implications: Not directly a clinical trial, but COMET can enable identification of perioperative biomarkers and risk signatures (e.g., for delirium, AKI, pulmonary complications) with smaller cohorts, informing targeted interventions and future prospective validation.
Key Findings
- Introduces COMET, combining EHR pretraining and adaptive early/late fusion for omics analysis.
- Improved predictive modeling and biological discovery across two independent datasets compared with omics‑only methods.
- Enables finer, non‑binary patient stratification to enhance discovery in limited cohort sizes.
3. Phenotypes of Functional Decline or Recovery in Sepsis ICU Survivors: Insights From a 1-Year Follow-Up Multicenter Cohort Analysis.
In a 21‑ICU prospective cohort of 220 sepsis survivors, four distinct discharge phenotypes were identified (no PICS, mild, moderate, severe across physical/cognitive/psychiatric domains). Mild deficits improved by 3 months, but moderate and severe phenotypes showed persistent disability, sustained reduced quality of life, low employment, and higher ongoing mortality in the severe group through 12 months.
Impact: Provides a pragmatic, longitudinal phenotyping framework that can direct individualized post‑ICU rehabilitation and resource allocation for sepsis survivors—an area of growing clinical need in perioperative and critical care pathways.
Clinical Implications: Incorporate standardized discharge assessments (physical, cognitive, psychiatric, frailty measures) to assign phenotype and prioritize multidisciplinary rehabilitation and mental‑health support for moderate/severe groups; use phenotype to inform follow‑up intensity and survivorship planning.
Key Findings
- Identified four phenotypes at discharge: no PICS (n=62), mild (physical & cognitive; n=55), moderate (all domains; n=53), severe (all domains; n=50).
- Mild phenotype generally improved by 3 months; moderate and severe phenotypes showed persistent disability at 12 months.
- Severe phenotype had persistent depressive symptoms and continuously decreasing survival; all groups had reduced QoL and low employment rates.