Daily Anesthesiology Research Analysis
Three standout studies span mechanism-to-bedside advances in anesthesiology and perioperative medicine: structural elucidation of ryanodine receptor–dantrolene/azumolene binding that guides next-generation malignant hyperthermia inhibitors; a compact, externally validated machine-learning model that outperforms cardiovascular scores for perioperative stroke prediction; and a 94,006-patient multicenter cohort showing phenotypic-age acceleration strongly predicts postoperative acute kidney injury
Summary
Three standout studies span mechanism-to-bedside advances in anesthesiology and perioperative medicine: structural elucidation of ryanodine receptor–dantrolene/azumolene binding that guides next-generation malignant hyperthermia inhibitors; a compact, externally validated machine-learning model that outperforms cardiovascular scores for perioperative stroke prediction; and a 94,006-patient multicenter cohort showing phenotypic-age acceleration strongly predicts postoperative acute kidney injury and longer hospital stay.
Research Themes
- Structure-guided pharmacology for malignant hyperthermia (RyR inhibitors)
- AI/ML-enhanced perioperative risk prediction
- Gerontology biomarkers (phenotypic aging) in perioperative outcomes
Selected Articles
1. Crystal structures of Ryanodine Receptor reveal dantrolene and azumolene interactions guiding inhibitor development.
High-resolution structures of the RyR Repeat12 domain reveal cooperative binding of dantrolene/azumolene with nucleotides, identify key tryptophan contacts, and show a clamshell-like domain closure. ITC and structural comparisons support allosteric effects on RyR gating, and structure-based screening yielded a new binder at the same pocket, guiding next-generation RyR inhibitor development.
Impact: This mechanistic study unlocks the structural basis of dantrolene/azumolene binding and cooperativity with nucleotides, directly informing rational design of safer, more effective RyR inhibitors for malignant hyperthermia and related disorders.
Clinical Implications: Structure-guided optimization may yield RyR inhibitors with improved safety and pharmacokinetics over dantrolene, potentially transforming prevention and treatment of malignant hyperthermia and other RyR-driven crises in anesthesia.
Key Findings
- Resolved high-resolution crystal structures of RyR Repeat12 bound to dantrolene/azumolene and nucleotides, showing cooperative binding in a pseudosymmetric cleft.
- Identified key interactions (Trp880, Trp994) and a clamshell-like closure upon ligand binding.
- ITC demonstrated nucleotide-enhanced affinity and lower affinity for RyR2 due to nearby substitutions.
- Structure-based screening discovered a potent compound binding the same site with a distinct mode.
Methodological Strengths
- Domain-focused crystallography combined with ITC for quantitative binding thermodynamics
- Integration with cryo-EM comparisons and structure-based ligand screening
Limitations
- Structures are domain-level and may not fully capture full-channel conformational dynamics.
- Functional validation in whole-channel electrophysiology and in vivo models remains to be established.
Future Directions: Perform full-channel functional assays and medicinal chemistry optimization guided by the R12 pocket, assessing efficacy and safety in malignant hyperthermia models.
The ryanodine receptor (RyR) is a critical drug target, yet dantrolene (DAN) remains the only FDA-approved inhibitor, limited by hepatotoxicity and unsuitable for chronic use. To guide improved inhibitor development, we determine high-resolution crystal structures of the RyR Repeat12 (R12) domain bound to DAN, its analog azumolene (AZU), and adenine nucleotides (AMP-PCP or ADP). DAN/AZU and nucleotides bind cooperatively to a pseudosymmetric cleft, with key interactions involving Trp880 and Trp994. Binding induces a clamshell-like closure of the R12 domain. Isothermal titration calorimetry (ITC) reveals higher affinity in the presence of nucleotides and lower affinity for RyR2 due to nearby substitutions. Structural comparison with cryo-EM data suggests that DAN/AZU binding allosterically influences RyR gating and functional regulation. Structure-based screening identifies a potent compound targeting the same site but with a distinct binding mode. Our findings highlight the power of domain-focused crystallography in guiding RyR inhibitor discovery and overcoming cryo-EM resolution limitations.
2. Compact machine learning model for perioperative stroke prediction prior to surgery: A retrospective cohort study.
Using 36,502 development cases and 404 external cases, a CatBoost-based model using preoperative features achieved AUC 0.867 externally and outperformed cardiovascular risk scores. A compact 10-feature model preserved strong discrimination, supporting practical perioperative stroke risk stratification.
Impact: Provides a usable, accurate, externally validated tool for preoperative stroke prediction, exceeding conventional scores and enabling targeted perioperative mitigation strategies.
Clinical Implications: Integrate the compact model into preoperative assessment to flag high-risk patients for antithrombotic review, hemodynamic targets, neuromonitoring, and postoperative surveillance within 30 days.
Key Findings
- CatBoost-based preoperative model achieved external AUC 0.867 (95% CI 0.830–0.896).
- Outperformed cardiovascular risk scores on external validation.
- A compact 10-feature model maintained strong discrimination, improving usability.
Methodological Strengths
- Large development cohort with external validation
- Comparison against established cardiovascular scores and parsimony via compact feature set
Limitations
- Retrospective design with potential residual confounding.
- Single-country datasets; generalizability to other health systems requires further validation.
Future Directions: Prospective, multicenter impact studies to assess clinical utility, calibration drift monitoring, and integration into electronic health records with decision support.
Perioperative stroke significantly impacts postoperative outcomes. Current risk stratification methods for perioperative stroke prediction lack accuracy and practicality. We aimed to develop a machine learning (ML) model that improves both accuracy and usability for predicting perioperative strokes. Data from 36,502 patients at Seoul National University Hospital (SNUH) were utilized to develop and internally validate. An external validation was conducted using data from 404 patients at Boramae Medical Center (BMC). Perioperative stroke was defined as a brain infarction of ischemic etiology, occurring within 30 days post-surgery. We developed ML-based prediction models comprising preoperative features and compared them with cardiovascular scores. Additionally, we developed a compact model utilizing the 10 most significant features of the best-performing model. The CatBoost-based prediction model showed the best discriminatory power for high-risk patients and outperformed cardiovascular scores in the external validation set (area under the receiver operating characteristics curve [AUC], 0.867 [95% CI: 0.830-0.896]; revised cardiac index score, 0.528 [95% CI: 0.497-0.575; p < 0.000]; CHA
3. Accelerated biological aging and postoperative acute kidney injury in surgical patients: a retrospective multicentre cohort analysis of 94,006 cases.
In 94,006 surgical patients, phenotypic age acceleration independently predicted AKI within 7 days, higher-stage AKI, and prolonged length of stay, with a monotonic dose-response. Biological aging markers add prognostic value beyond chronological age for perioperative renal risk.
Impact: Introduces a scalable biomarker of biological aging to perioperative risk stratification at population scale, identifying patients at risk for AKI and prolonged hospitalization.
Clinical Implications: Incorporate PhenoAge/PhenoAgeAccel into preoperative assessment to trigger renal-protective strategies (hemodynamics, nephrotoxin stewardship, goal-directed fluids) and tailored postoperative monitoring.
Key Findings
- Phenotypic age acceleration (PhenoAgeAccel) independently associated with postoperative AKI (aHR 1.50, 95% CI 1.42–1.60).
- Stronger association for higher-stage AKI (AKI 2+, aHR 2.27, 95% CI 2.06–2.50).
- Associated with prolonged length of stay; dose-response analyses showed a monotonic positive relationship.
Methodological Strengths
- Very large multicenter cohort with adjusted hazard models and dose-response analyses
- Use of validated PhenoAge metric to quantify biological aging
Limitations
- Retrospective design limits causal inference and may have residual confounding.
- Generalizability beyond three centers and different lab platforms needs evaluation.
Future Directions: Prospective validation and interventional trials targeting high PhenoAgeAccel patients to test renal-protective bundles and assess outcome improvement.
BACKGROUND: While aging is closely associated with increased risk of acute kidney injury (AKI), chronological age often fails to capture the heterogeneity in biological decline among individuals. In contrast, biological age emerges as a more accurate indicator of the aging process. However, the association between accelerated biological aging and postoperative AKI remains unexplored. Therefore, this study aimed to assess the association between accelerated biological aging and postoperative AKI. METHODS: We conducted a retrospective cohort study from 2015 to 2023 at three academic medical centers in China, including inpatients who underwent surgery under general anesthesia. Biological age was measured using Phenotypic Age (PhenoAge) approach. Biological aging was calculated by Phenotypic Age acceleration (PhenoAgeAccel). The primary endpoint was AKI within 7 days after surgery. Secondary endpoints included AKI stage 2 or 3 (AKI 2 +) and length of stay (LOS). RESULTS: Among 94,006 patients (median age 62 (51-71) years, 44.0% female), 37.7% were biologically older. The incidence of AKI was 1.62 per 100 person-days. After adjustment, accelerated biological aging was significantly associated with increased risk of AKI (adjusted hazard ratio (aHR) 1.50, 95% confidence interval (CI) 1.42-1.60), AKI 2 + (aHR 2.27, 95% CI 2.06-2.50), and prolonged LOS (adjusted coefficient 1.30, 95% CI 1.06-1.55). Dose-response relationship analyses revealed a monotonic non-linear positive association between PhenoAgeAccel and the risk of AKI. DISCUSSION: Accelerated biological aging may serve as an independent risk factor for postoperative AKI, AKI 2 +, and prolonged LOS, highlighting its potential as a target for preoperative risk stratification and intervention.