Citations

Backround: Atrial fibrillation (AF) is a common complication following veno-venous extracorporeal membrane oxygenation (VV-ECMO), leading to further complications from anticoagulation or stroke. The Artificial Intelligence AF Prediction Dashboard developed at Mayo Clinic generates two predictive outputs: (1) a continuous probability score and (2) a binary threshold-based signal to predict the likelihood of AF at an individual level after 12-lead electrocardiogram (ECG) is obtained. Using both continuous risk (%) and binary (yes/no) likelihood, we present a novel method to understand AF risk after VV-ECMO cannulation based on pre-ECMO 12-lead ECG. Methods: We reviewed a total of 147 patients on ECMO between 2018 and 2024, from which 85 VV-ECMO patients with complete data were included. AI Dashboard outputs were quantified as binary (1/0) or percent likelihood for AF. R was utilized for statistical analysis. Results: 85 patients met our inclusion criteria from which AF after cannulation occurred in 31 (36%). 17 patients had a history of AF prior to VV-ECMO initiation. The mean age of our cohort was 49 years with a mean BMI of 29.3. Baseline AI Dashboard continuous assessment identified 39 patients (46%) compared to the binary assessment identifying 31 patients (36%). Logistic regression in the binary assessment demonstrated an odds ratio of 1.08 (95% CI 1.04 – 1.12) with a p value of <0.001. The binary AF Prediction yielded a 58% sensitivity, 76% specificity, 58% PPV, and 76% NPV, with an accuracy of 67%. The continuous AF Probability score achieved 72% balanced accuracy with sensitivity 74%, specificity 70%, PPV 59% and NPV 83%, indicating fair overall performance and suggesting good utility for ruling out AF in low-risk patients. Although both outputs provided moderate predictive accuracy, the continuous score reported before ECMO cannulation demonstrated stronger overall performance with fewer false negatives and higher rule-out value. Conclusions: We demonstrate the novel use of an AI predictive model in the VV-ECMO population for the prediction of AF risk with both continuous and binary risk cutoffs from baseline ECG. This data highlights the need for further development in ECMO-specific AI models to help mitigate risk after the onset of atrial arrhythmias. As AI-driven tools become increasingly integrated into care, their performance must be continuously validated and recalibrated for specific populations to ensure their safe and effective use.

AbstractNational performance metrics ultimately enhance patient decision‐making and promote meaningful improvements in health care delivery, which makes having valid and reliable measures essential. This study examined US News and World Report metrics from 2019 to 2012 and used electronic health record data, combined with detailed chart review across 3 in‐system hospitals, to assess the provision of care compared to the attribution of patients assigned to the ear, nose, and throat (ENT) mortality group. Of the initial 47 ENT‐attributed deaths, 23 of those were verified, dimensioning the mortality rate from 1.7% to just 0.8%. These results underscore the necessity of rethinking measures and mortality attribution methodologies to be more accurate. Current methods use Medicare Severity Diagnosis Related Group billing coding to map the attribution. We suggest transitioning away from specialty ranking approaches and towards a procedure and condition “rating” approach to ensure that these ranking types capture data about the provision of care within a given encounter.

Background The COVID-19 pandemic introduced unique challenges to healthcare systems, particularly in relation to patient safety and adverse events during hospitalization. There is limited understanding of COVID-19’s association with some patient safety indicators (PSIs). Objectives This study aimed to investigate the impact of COVID-19 infection on the rate of PSI-3 events and its implications on quality metrics. We compared PSI-3 event rates between COVID-19–infected and uninfected patients and examined the clinical characteristics of COVID-19 patients experiencing PSI-3 events. Methods This is a retrospective study at Mayo Clinic hospitals between January 2020 and February 2022, analyzing patients meeting PSI-3 denominator eligibility criteria. PSI-3 events were identified using AHRQ WinQI software. Patients were categorized based on COVID-19 status. Patient demographics, characteristics, and PSI-3 rates were compared. A case series analysis described clinical details of COVID-19 patients with PSI-3 events. Results Of 126,781 encounters meeting PSI-3 criteria, 8674 (6.8%) had acute COVID-19 infection. COVID-19–infected patients were older, more likely to be male, non-white, and had private insurance. PSI-3 rates were significantly higher in COVID-19 patients (0.21% versus 0.06%, P < 0.0001), even after risk adjustment (adjusted risk ratio, 3.24, P < 0.0001). The case series of 17 COVID-19 patients with PSI-3 events showed distinctive clinical characteristics, including higher medical device–related pressure injuries, and greater predisposition for head, face, and neck region. Conclusions Acute COVID-19 infection correlates with higher PSI-3 event rates. Current quality indicators may require adaptation to address the pandemic’s complexities and impact on patient safety. Further research is needed to comprehensively understand the intricate relationship between COVID-19 and patient outcomes.

Aim This study aims to evaluate the impact of frailty on the outcomes of older patients with pulmonary embolism (PE). Methods Using the National Inpatient Sample database, we identified 288 070 patients aged 65 or older who were admitted with a primary diagnosis of PE from 2017 to 2019. Frailty was assessed using the Hospital Frailty Risk Score (HFRS), and patients were categorized into low‐, intermediate‐, and high‐frailty‐risk groups. Multivariate logistic regression was used to calculate adjusted odds ratios for all outcomes. Results These patients were categorized into low‐risk (57.6%, 161 420), medium‐risk (39.9%, 111 805), and high‐risk (2.5%, 7075) groups. High‐risk patients, predominantly females with multiple comorbidities, exhibited significantly higher mortality rates and adverse outcomes. The HFRS showed a good discriminating ability in predicting mortality (area under the receiver operating characteristic curve = 0.7796). Frailty was associated with increased use of advanced therapeutic interventions and critical care resources such as thrombolysis, catheter‐directed therapies, inferior vena cava filter placement, mechanical ventilation, vasopressor use, and intensive care unit admission. Conclusion Frailty markedly affects outcomes in older PE patients. The HFRS offers a valuable prognostic tool in this population, suggesting that integrating frailty assessments into clinical practice could enhance care strategies and improve patient outcomes. Our findings underscore the need for further research to refine frailty‐based care paradigms. Geriatr Gerontol Int 2024; 24: 924–929 .

Abstract Background Impella is an advanced ventricular assist device frequently used as a bridge to heart transplantation. The association of Impella with increased rates of gout flares has not been studied. Our primary aim is to determine the rates of gout flares in patients on Impella support. Methodology A retrospective study was conducted between January 2017 and September 2022 involving all patients who underwent heart transplantation. The cohort was divided into groups based on Impella support for statistical analysis. In patients receiving Impella support, outcome measures were compared based on the development of gout flares. Results 213 patients were analyzed, with 19.71% supported by Impella. Impella and non-Impella groups had similar age, race, and BMI, but more males were in the Impella group. Gout and chronic kidney disease were more prevalent in Impella-supported patients, while coronary artery disease was less common. The incidence of gout flare was significantly higher in Impella patients (31% vs. 5.3%). The Impella group had an adjusted odds ratio for in-hospital gout flare of 8.069 (95% CI = 2.13–35.08, P = 0.003). Impella-supported patients with gout flares had longer intensive care unit and hospital stays but no difference in mortality or other outcomes. Conclusion Our study is the first to identify the potential association between Impella support and increased rates of gout flares in hospitalized patients. Future studies are required to confirm this association and further elucidate the biological pathways. It is imperative to consider introducing appropriate measures to prevent and promptly manage gout flares in Impella-supported patients.