Omercikoglu, Elcin

Objectives: Pulmonary function tests (PFTs) are essential for diagnosis, management, and follow-up of a wide range of respiratory conditions as they provide quantitative insights into various aspects of lung function. Precision in ordering and scheduling PFTs, like any test, is crucial as errors can lead to misdiagnoses, delayed treatments, unnecessary retesting, inflated healthcare costs, and potentially compromised patient care. Hence, the aim of this quality improvement initiative was to reduce the PFT ordering error rate to below 5% by redesigning the ordering workflow and implementing a standardized order set tailored to specific clinical scenarios. Methods: The quality improvement initiative was implemented from August 2023 to March 2024. Data was collected from the electronic medical record (EMR) using predefined operational definitions to identify incorrect PFT orders and scheduling errors. A redesigned orderset was created to align with specific clinical presentations, including symptoms, suspected diagnosis, confirmed diagnosis, and presurgical evaluation. Scheduling was also restructured to match the expected testing time for each category. Education sessions were conducted with ordering providers, and ongoing feedback loops were established. Results: Among 575 preintervention PFT orders, 130 (22.6%) contained errors. Following implementation, review of 607 postintervention orders showed a reduction in error rates to 4.1%, representing an ∼80% improvement ( P <0.01). There was no increase in unnecessary testing, supporting the effectiveness of the intervention without unintended negative consequences. Conclusions: Implementation of a knowledge-based CDSS with standardized order sets significantly reduced PFT ordering errors and variability, improving guideline adherence and care quality. Continued refinement and integration with evolving technologies are essential for broader applicability.

Introduction Post-intubation hypotension (PIH) is a frequent complication of emergency endotracheal intubation and is associated with increased mortality. Early identification of high-risk patients remains challenging, and existing prediction tools demonstrate moderate accuracy. Objective We aim to identify predictors of PIH and develop machine learning models for pre-intubation risk stratification. Methods We conducted a multicentre retrospective study across 3 Mayo Clinic hospitals, including 600 adults undergoing urgent intubation in the intensive care unit. PIH was defined by mean arterial pressure<65 mm Hg, systolic blood pressure (SBP)<80 mm Hg or ≥40% decline or vasopressor initiation/escalation within 30 min. Three model frameworks were developed: Shock Indices (SIs), Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA), incorporating demographic, haemodynamic, laboratory and sedation variables. Various machine learning algorithms were applied to analyse the three models. Results PIH occurred in 46% of patients included in the study. In the SIs model, stepwise logistic regression achieved an area under the curve (AUC) of 0.76. The APACHE II framework showed the strongest performance, with random forest achieving an ROC AUC of 0.82 and the ensemble model demonstrating the highest overall discrimination (Receiver operator Curve [ROC] AUC 0.83) with favourable accuracy and F1 metrics. The SOFA framework showed moderate performance, with an ensemble ROC AUC of 0.73. Key predictors included pre-intubation vasopressor use, SBP, platelet count and haematological parameters. Conclusions Machine learning models using routinely available pre-intubation data showed potential to predict PIH in this retrospective proof-of-concept study. These findings support further development of anticipatory artificial intelligence-driven risk stratification in critical care, but the models should not be considered ready for bedside implementation until they undergo prospective validation in larger, independent and more diverse cohorts.

Abstract Background Ventilated patients in the intensive care unit are often complex and require multiple care team members including bedside nurses, respiratory therapists, advanced practice providers, and physicians. A Quality Improvement initiative was created to address any barriers in communication. Spontaneous breathing trials (SBT) are often used as a surrogate for multidisciplinary communication. Both Respiratory therapist (RT) and provider led spontaneous breathing trial protocols are often performed but not necessarily documented or communicated appropriately. Our primary aim was to increase spontaneous breathing trial documentation from a baseline of 12% to 80% with a counterbalance of not increasing the 24-hour reintubation rate. Methods Using the DMAIC framework, we defined the gap in quality by getting baseline patient data for 3 months including SBT completion rate, SBT documentation rate, time to first SBT, total time on ventilator, 24-hour reintubation rate, and total ICU days. All care team members including providers, RTs, and nurses completed a 10-questions Likert 5-scale survey regarding their views of multidisciplinary communication and SBT completion. We identified the lack of documentation of SBTs and implemented a standardized respiratory therapy shift summary note which included SBT details and major ventilator changes. We then collected the same data over 3 months after implementation. Results Post intervention analysis revealed documentation of daily spontaneous breathing trials improved from 12% to 79%. Pre and post intervention means for patient focused outcomes were statistically unchanged for time to first SBT 26 hours vs 32 hours (p = 0.16), Total ventilator time 5.6 vs 5.4 (p = 0.83), total ICU days 1.9 days vs 2.7 days (p = 0.11), and reintubation rate 14% vs 15%.Survey results showed that there was a statistically significant improvement in overall satisfaction regarding multidisciplinary communication, satisfaction regarding ventilator management discussion on rounds, and if documentation amount was appropriate. Also, there was a significant improvement in the percentage of care team members that were aware of daily SBTs occurring. Conclusion The implementation of RT shift summary greatly improved staff member satisfaction, multidisciplinary discussion during rounds, and awareness of SBT occurrence among providers, RTs, and nurses. While patient centered outcomes were statistically unchanged over this very brief period of observation, having better staff satisfaction and communication may eventually lead to improved outcomes over time and can lead to further analysis to avoid mishaps that can arise from miscommunication. This abstract is funded by: None