Daily Anesthesiology Research Analysis
Analyzed 24 papers and selected 3 impactful papers.
Summary
Across anesthesiology-relevant research, an intraoperative dexmedetomidine infusion reduced postoperative pulmonary complications in older adults undergoing laparoscopic surgery. A machine learning model using routine ICU data accurately identified pulmonary embolism and outperformed benchmark scores, while a large Medicare cohort analysis found no overall long-term advantage of peripheral nerve blocks after hip fracture, with signals of benefit in recent years.
Research Themes
- Perioperative pulmonary protection via dexmedetomidine
- Machine learning for thromboembolism detection in critical care
- Regional anesthesia and long-term outcomes after hip fracture surgery
Selected Articles
1. Dexmedetomidine reduces pulmonary complications in older patients undergoing abdominal laparoscopic surgery: a prospective, single-blinded, randomized controlled trial.
In a single-blinded RCT of 120 older adults undergoing laparoscopic abdominal surgery, intraoperative dexmedetomidine infusion reduced 7-day postoperative pulmonary complications, particularly hypoxemia. Improvements were also observed in blood gas parameters, respiratory mechanics, and postoperative recovery.
Impact: Demonstrates clinically meaningful reduction in postoperative pulmonary complications with a readily deployable anesthetic strategy. Addresses a common and morbid perioperative outcome in older adults.
Clinical Implications: Consider intraoperative dexmedetomidine infusion as part of lung-protective anesthesia strategies in older patients undergoing laparoscopy, with attention to monitoring hemodynamics and sedation depth.
Key Findings
- Dexmedetomidine reduced 7-day postoperative pulmonary complications (30.2% vs 52.8%, P=0.018).
- Hypoxemia incidence was significantly lower with dexmedetomidine (13.2% vs 32.1%, P=0.020).
- Intraoperative dexmedetomidine improved blood gas metrics, respiratory mechanics, and postoperative recovery.
Methodological Strengths
- Prospective, randomized, single-blinded trial design with predefined endpoints.
- Trial registration specifying protocol (ChiCTR2300075746).
Limitations
- Single-center, modest sample size (n=120), limiting generalizability.
- Short follow-up window (7 days) and single-blind design; not powered for rare outcomes.
Future Directions: Multicenter, double-blind RCTs to validate effect size, define optimal dosing/timing, and assess safety and longer-term pulmonary outcomes.
BACKGROUND: Dexmedetomidine (Dex) has promising lung-protecting effects. This study evaluated the effect of Dex on postoperative pulmonary complications (PPCs) in older patients undergoing abdominal laparoscopic surgery. METHODS: In this prospective, single-blinded, randomized controlled trial, 120 patients were assigned to the Dex or control group. All patients underwent surgical procedures with their designated anesthesia protocols. Patients in the Dex group received Dex intraoperatively, whereas those in the control group received the same volume of normal saline. The primary endpoints were the incidence and severity of PPCs. The secondary outcomes included blood gas analysis, breathing mechanics indices, postoperative recovery, vital capacity, pain scores, and the incidence of adverse events. RESULTS: The incidence of PPCs within 7 days after surgery was significantly lower in the Dex group (30.2% vs. 52.8%, P = 0.018). Notably, the incidence of hypoxemia was significantly lower in the Dex group (13.2% vs. 32.1%, P = 0.020). Intraoperative Dex administration enhanced improved blood gas outcomes, respiratory mechanics, and postoperative recovery. CONCLUSIONS: Intraoperative infusion of Dex significantly reduced the incidence of PPCs within 7 days, especially hypoxemia, in older patients undergoing laparoscopic abdominal surgery. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR2300075746, Date of registration: 2023-09-14).
2. A machine learning model to identify pulmonary embolism in patients admitted to intensive care.
Using two large multicenter ICU datasets, a logistic regression-based machine learning model identified acute pulmonary embolism with AUROC ~0.83 and outperformed two benchmark risk scores. Performance generalized to an external health system, supporting potential for clinical deployment.
Impact: Demonstrates externally validated, superior diagnostic performance using routinely available ICU data, enabling scalable, low-friction decision support for PE workup.
Clinical Implications: Can support early PE suspicion and triage in ICU using existing EHR data, potentially optimizing imaging utilization and timely anticoagulation decisions pending prospective impact evaluation.
Key Findings
- Development cohort (n=164,383) PE prevalence 1.61%; external validation (n=64,923) 1.16%.
- Model AUROC 0.829 (dev) vs 0.704 and 0.667 for benchmark scores; AUPRC 0.150 vs 0.080 and 0.081.
- External validation AUROC 0.819 confirmed generalizability using first 48-hour ICU data.
Methodological Strengths
- Large multicenter development with independent external validation.
- Comparison against established benchmark scores using multiple discrimination metrics.
Limitations
- Retrospective design with potential coding/ascertainment bias for PE diagnosis.
- Lack of prospective clinical impact assessment and potential class imbalance issues.
Future Directions: Prospective, real-time evaluation of model integration into ICU workflows to assess impact on diagnostic yield, time-to-diagnosis, anticoagulation timing, and patient outcomes.
BACKGROUND: Pulmonary embolism (PE) is a leading cause of preventable death, yet statistical prediction models have shown inconsistent validity. Our primary objective was to determine if a machine learning model trained with data routinely collected in clinical care can successfully identify acute PE in critically ill patients. METHODS: Leveraging two multicenter datasets acquired nationally (development cohort) and within the Johns Hopkins Health System (external validation cohort), we trained machine learning models with features extracted from demographics, comorbidities, physiologic and laboratory data available following intensive care unit (ICU) admission. The primary endpoint was the identification of acute PE during ICU admission. Model performance was contrasted with two benchmark PE risk scores. FINDINGS: PE was diagnosed in 2647 of 164,383 (1.61%) and 754 of 64,923 admissions (1.16%) in the development and external validation datasets respectively. Using data from the first 48 h after ICU admission, the mean (95% CI) discrimination measured by area under the receiver characteristic curve (AUROC) was 0.829 (0.808-0.852), 0.704 (0.681-0.727), and 0.667 (0.653-0.681) for our logistic regression machine learning model and for the two benchmark scores, respectively; mean area under the precision recall curve was 0.150 (0.138-0.162), 0.080 (0.071-0.089), and 0.081 (0.071-0.091), respectively. Discrimination was maintained in the external validation dataset with an AUROC of 0.819 (0.802-0.836). INTERPRETATION: Findings indicate that PE can be detected accurately in ICU patients using routinely collected clinical data. The machine learning model successfully validated and outperformed existing benchmark risk scores. Such a model could become a valuable tool for assessing the likelihood of PE among critically ill patients.
3. Long-Term Outcomes Associated With Peripheral Nerve Blocks for Hip Fracture Surgery: A Retrospective Comparison of Medicare Data.
In a large Medicare propensity-matched cohort of older adults undergoing hip fracture fixation, peripheral nerve blocks were not associated with improved days alive and at home or 1-year mortality overall. However, in 2017–2018, PNB recipients had more days alive and at home within 1 year, suggesting potential benefits with contemporary techniques.
Impact: Provides high-volume, real-world evidence on long-term patient-centered outcomes (days alive and at home) after hip fracture surgery with regional anesthesia. Important negative results with a signal in contemporary practice.
Clinical Implications: While routine PNB use may not guarantee long-term gains, contemporary techniques could confer benefits; implementation should focus on up-to-date block approaches and multimodal pathways, with ongoing outcome monitoring.
Key Findings
- Primary analysis (2010–2018): no difference in days alive and at home within 120 days (68.1 vs 68.4; p=0.64).
- No difference in 365-day days alive and at home (244.5 vs 240.7; p=0.12) or 1-year mortality (21% vs 22%; p=0.22).
- 2017–2018 subgroup: more days alive and at home within 365 days with PNBs (248.6 vs 241.6; p=0.04), with a trend toward lower mortality.
Methodological Strengths
- Large national dataset with 1:1 propensity score matching (5700 matched pairs).
- Use of patient-centered outcomes (days alive and at home) and contemporary subgroup analyses.
Limitations
- Observational design with residual confounding and reliance on administrative claims.
- Heterogeneity in PNB techniques and lack of granular clinical details (e.g., block type, timing, catheter use).
Future Directions: Pragmatic randomized trials comparing contemporary PNB strategies, with protocolized ERAS pathways and linkage to long-term functional outcomes.
BACKGROUND: Peripheral nerve blocks (PNBs) are increasingly recommended as analgesia for hip fractures. Their association with outcomes beyond the immediate pharmacological effects remains unclear. This study examined the association between the use of PNBs and the number of days alive and at home after hip fracture surgery among Medicare beneficiaries. METHODS: To examine the association between PNBs and long-term outcomes in older adults undergoing surgical fixation for hip fractures, we analyzed Medicare data from 2010 to 2018. Patients who received PNBs (exposure group) (n = 5701) were compared to those who did not receive a PNB (comparator group) using 1:1 propensity score matching, creating 5700 matched pairs. The primary outcome was days alive and at home within 120 days of admission. Secondary outcomes included days alive and at home within 365 days and 1-year mortality. A subgroup analysis of propensity score matched patients from 2018 examined outcomes when techniques had improved and use had increased. RESULTS: In the primary analysis (2010-2018), no significant differences were observed between groups for days alive and at home within 120 days (68.1 vs. 68.4 days; p = 0.64), days alive and at home within 365 days (244.5 vs. 240.7 days; p = 0.12), or 1-year mortality (21% vs. 22%; p = 0.22). In 2017 and 2018, when peripheral nerve block use increased, patients who received PNBs spent more days alive and at home within 365 days than patients who did not receive peripheral nerve blocks (248.6 vs. 241.6 days; p = 0.04). CONCLUSION: PNBs showed no association with improved outcomes across the 2010-2018 study period. Analysis of 2017 and 2018 revealed more days alive and at home within 365 days and a trend toward reduced mortality among patients who received PNBs. PNBs may provide benefits beyond their immediate analgesic effects, potentially improving long-term outcomes.