Daily Anesthesiology Research Analysis
Analyzed 118 papers and selected 3 impactful papers.
Summary
Three impactful anesthesiology-related studies stood out: a BJA systematic review shows minimally invasive pulse wave analysis has unacceptable agreement with thermodilution for cardiac output; a Critical Care Medicine study demonstrates successful real-time deployment and temporal validation of an EMR-integrated ICU prediction system; and a multicenter RCT in PLoS One finds routine nasopharyngeal airway use reduces airway interventions/desaturation during GI endoscopy under MAC.
Research Themes
- Accuracy and reliability of perioperative hemodynamic monitoring
- Operational AI decision support in critical care
- Airway management optimization during procedural sedation
Selected Articles
1. Agreement of minimally invasive pulse wave analysis with pulmonary artery and transpulmonary thermodilution cardiac output measurements in perioperative and intensive care medicine: a systematic review and meta-analysis.
Across 92 studies (3111 patients), pulse wave analysis-derived cardiac output/cardiac index showed pooled percentage errors of 44.0% and 49.1% vs thermodilution references, exceeding the commonly accepted 30% threshold. Agreement varied by device and patient population, challenging routine use of uncalibrated pulse wave analysis for absolute cardiac output decision-making.
Impact: This high-quality synthesis directly informs perioperative and ICU hemodynamic monitoring by demonstrating clinically unacceptable agreement of pulse wave analysis with reference standards.
Clinical Implications: Avoid relying on uncalibrated pulse wave analysis for absolute cardiac output decisions in high-stakes settings; prefer thermodilution-validated methods or use pulse wave analysis for trending with caution and device-specific awareness.
Key Findings
- Pooled percentage error for cardiac output was 44.0% (95% CI 38.2–49.8%).
- Pooled percentage error for cardiac index was 49.1% (95% CI 42.2–56.0%).
- Agreement varied by device and patient population in subgroup analyses.
- Mean bias for cardiac output was approximately −0.1 L·min⁻¹ with wide limits of agreement.
Methodological Strengths
- Comprehensive systematic review with random-effects meta-analysis and subgroup analyses by population and device.
- Prospective PROSPERO registration and large aggregated sample (3111 patients across 92 studies).
Limitations
- High between-study heterogeneity and device-specific variability.
- Lack of patient-centered outcome linkage; focuses on agreement rather than impact on clinical outcomes.
Future Directions: Standardize calibration protocols, evaluate device-specific performance thresholds, and link monitoring strategies to patient-centered outcomes in randomized or pragmatic trials.
BACKGROUND: Cardiac output monitoring is recommended for high-risk surgical patients and critically ill patients with circulatory shock. We performed a systematic review and meta-analysis of clinical studies published since 2010 that compared minimally invasive pulse wave analysis-derived cardiac output or cardiac index measurements with reference measurements by pulmonary artery thermodilution or transpulmonary thermodilution in adult surgical or critically ill patients. METHODS: In a random-effects meta-analysis, we calculated pooled estimates of the percentage error, mean difference and standard deviation, and 95% limits of agreement separately for studies reporting cardiac output or cardiac index. Subgroup analyses were performed by patient population and test device. RESULTS: We included 92 studies divided into 113 data sets with a total of 3111 patients. For 71 data sets reporting cardiac output, the pooled percentage error (95% confidence interval [95% CI]) was 44.0% (38.2%-49.8%) with a mean difference (standard deviation) of -0.1 (1.3) L min
2. Operational Integration and Temporal Validation of a Continuously Deployed ICU Prediction Model.
A continuously deployed EMR-integrated ICU prediction system (BEST-AI) generated hourly risk estimates with strong discrimination (AUC 0.856–0.960) and generally good calibration in forward-in-time validation. Workflow integration with automated updates and embedded visualizations was feasible, though intubation prediction lagged due to low event rates.
Impact: Demonstrates real-world, sustained deployment and temporal validity of a multi-task ICU prediction model, moving beyond retrospective benchmarking to operational utility.
Clinical Implications: Supports integrating real-time risk stratification into ICU workflows to inform situational awareness and decision support; however, clinical impact requires multicenter trials and interventional evaluations.
Key Findings
- Hourly EMR-integrated predictions achieved AUC 0.856–0.960 across six ICU outcomes.
- Calibration was generally good; hospital mortality slightly overestimated at high predicted risk.
- Intubation prediction showed lower discrimination/calibration due to low events and timing heterogeneity.
- 24-hour landmark analysis confirmed robustness beyond repeated-measure evaluations.
Methodological Strengths
- Forward-in-time temporal validation with continuous operational deployment in the EMR.
- Multiple prediction tasks with discrimination, calibration, and sensitivity analyses including landmarking.
Limitations
- Single-center design limits transportability; no mandated clinical interventions to establish outcome impact.
- Lower performance for intubation prediction due to low event counts and timing heterogeneity.
Future Directions: Prospective multicenter deployment with impact evaluation, model updating/transfer learning, and interventional trials to quantify outcome changes attributable to AI-informed care.
OBJECTIVES: To operationalize and temporally validate an electronic medical record (EMR)-integrated machine learning system (Big data-driven Evaluation of Survival and Treatment in Acute Illness [BEST-AI]) that generates hourly predictions for multiple ICU outcomes, with emphasis on discrimination, calibration, and workflow integration. DESIGN: Single-center hybrid study with stepwise clinical deployment and forward-in-time temporal validation. SETTING: Thirty-bed tertiary mixed medical-surgical ICU in Japan. PATIENTS: All ICU admissions from August 2017 to March 2025. Exclusions: age younger than 16 years or ICU stay less than 4 hours. Development cohort (n = 11,176; from August 2017 to July 2024) and temporal validation cohort (n = 1,127; from August 2024 to March 2025). INTERVENTIONS: EMR-integrated deployment of BEST-AI providing hourly probabilistic predictions to clinicians within the EMR; no protocolized clinical interventions were mandated. MEASUREMENTS AND MAIN RESULTS: Six prediction tasks (in-hospital mortality, ICU mortality, ICU discharge ≤ 72 hr, intubation ≤ 72 hr, extubation ≤ 72 hr, tracheostomy at ICU discharge) were evaluated. In temporal validation, the area under the receiver operating characteristic curves ranged from 0.856 to 0.960, and the area under the precision-recall curves from 0.302 to 0.786. Decile-based calibration showed overall good agreement; hospital mortality was slightly overestimated at higher predicted probabilities, whereas ICU mortality remained well aligned. The intubation task had comparatively lower discrimination and greater deviation from perfect calibration, consistent with low event counts and heterogeneous timing. A 24-hour landmark sensitivity analysis (one prediction per patient at 24 hr after ICU admission) preserved discrimination and calibration relative to the main analysis, supporting robustness beyond repeated-measures evaluation. The system was successfully maintained with automated hourly updates and EMR-embedded patient- and unit-level visualizations, without prescriptive alerts. CONCLUSIONS: A continuously deployed, EMR-integrated ICU prediction system achieved strong temporal discrimination and generally good calibration. Embedding real-time predictions into routine workflow was feasible, and the system was maintained with automated hourly updates. Prospective multicenter studies are warranted to assess transportability and clinical impact.
3. The routine use of nasopharyngeal airway in the setting of monitored anesthesia care during gastrointestinal endoscopy: A multi-center single blinded randomized controlled trial.
In 329 patients undergoing GI endoscopy under MAC, routine nasopharyngeal airway use halved the composite risk of at least one airway intervention/desaturation (18.5% vs 40.1%; P<0.001). Benefits were observed across OSA risk strata, with higher provider satisfaction and only mild, self-limited epistaxis (3.1%).
Impact: This multicenter RCT delivers practice-ready evidence that a simple airway adjunct improves respiratory safety during MAC for GI endoscopy.
Clinical Implications: Consider routine NPA placement during MAC for GI endoscopy to lower airway interventions/desaturation, especially in patients with suspected OSA; counsel about minor epistaxis risk.
Key Findings
- Primary composite endpoint reduced with NPA (18.5% vs 40.1%; P<0.001).
- Effect present in low OSA risk (OR 0.23, 95% CI 0.11–0.49) and intermediate/high OSA risk (OR 0.45, 95% CI 0.21–0.95).
- Higher healthcare provider satisfaction in NPA group (p<0.001).
- Mild, self-limited epistaxis in 3.1% of NPA patients.
Methodological Strengths
- Multicenter, randomized, single-blind design with prespecified composite primary endpoint.
- Preplanned subgroup analysis by OSA risk demonstrating consistent benefit.
Limitations
- Single-blind design; generalizability may vary across procedural settings and sedation regimens.
- Composite endpoint mixes interventions and desaturation; individual component effects may differ.
Future Directions: Assess cost-effectiveness, patient comfort, and head-to-head comparisons with alternative airway adjuncts; evaluate standardized protocols across diverse endoscopy and sedation settings.
BACKGROUND AND AIMS: Monitored anesthesia care (MAC) is commonly used for gastrointestinal (GI) endoscopy, allowing patients to breathe spontaneously while sedated. Despite its benefits, MAC is associated with respiratory adverse events, such as desaturation or the need for interventions. Limited studies have examined the effectiveness of nasopharyngeal airways (NPA) in reducing airway maneuvers and improving respiratory stability during MAC. The study evaluates the efficacy and safety of NPA in reducing the occurrence of at least one airway intervention/desaturation in patients undergoing GI endoscopy under MAC. METHODS: This multi-center, single-blinded randomized controlled trial involved patients undergoing GI endoscopy under MAC. A total of 329 patients were randomly assigned to either the NPA or control group. Primary outcomes included a composite primary outcome: occurrence of at least one airway intervention/desaturation. Secondary outcomes included adverse events, satisfaction scores of patients and healthcare providers, and the joint effects of NPA use and OSA risk on the occurrence of at least one airway intervention or desaturation. RESULTS: The NPA group showed significantly lower incidence of occurrence of at least one airway intervention/desaturation compared to controls (18.5% vs. 40.1%, P < 0.001). NPA significantly reduced the occurrence of at least one airway intervention/desaturation in both low and intermediate/high risk OSA groups. The reduction was stronger in low OSA risk group (OR=0.23, 95%CI [0.11-0.49]) versus intermediate/high OSA risk group (OR=0.45, 95%CI [0.21-0.95]. Health care provider satisfaction was significantly higher in the NPA group (p < 0.001). Mild, self-resolving epistaxis occurred in 3.1% of NPA patients. CONCLUSIONS: NPA use reduces airway interventions and enhances satisfaction among anesthesiologists and gastroenterologists during GI endoscopy under MAC. Routine utilization of NPAs in this context may be considered.