Objectives: While left ventricular (LV) venting reduces LV distension in cardiogenic shock patients on venoarterial extracorporeal membrane oxygenation (ECMO), it may also amplify risk of acute brain injury (ABI). We investigated the hypothesis that LV venting is associated with increased risk of ABI. We also compared ABI risk of the two most common LV venting strategies, percutaneous microaxial flow pump (mAFP) and intra-aortic balloon pump (IABP). Design: Retrospective observational cohort study. Setting: The Extracorporeal Life Support Organization registry. Patients: Adult patients on peripheral venoarterial ECMO for cardiogenic shock (2013–2024). Interventions: None. Measurements and Main Results: ABI was defined as hypoxic-ischemic brain injury, ischemic stroke, or intracranial hemorrhage. Secondary outcome was hospital mortality. We compared no LV venting with: 1) LV venting, 2) mAFP, and 3) IABP using multivariable logistic regression. To compare ABI risk of mAFP vs. IABP, propensity-score matching was performed. Of 13,276 patients (median age = 58.2, 69.9% male), 1,456 (11.0%) received LV venting (65.5% mAFP and 29.9% IABP), and 525 (4.0%) had ABI. After multivariable regression, LV-vented patients had increased odds of ABI (adjusted odds ratio [aOR], 1.67; 95% CI, 1.22–2.26; p = 0.001) but no difference in mortality (aOR, 1.07; 95% CI, 0.90–1.27; p = 0.45) compared with non-LV-vented patients. In the propensity-matched cohort of IABP ( n = 231) vs. mAFP ( n = 231) patients, there was no significant difference in odds of ABI (aOR, 1.35; 95% CI, 0.69–2.71; p = 0.39) or mortality (aOR, 0.88; 95% CI, 0.58–1.31; p = 0.52). Conclusions: LV venting was associated with increased odds of ABI but not mortality in patients receiving peripheral venoarterial ECMO for cardiogenic shock. There was no difference in odds of ABI or mortality for IABP vs. mAFP patients.

Objective: We aimed to characterize the impact of body mass index (BMI) on stroke in patients receiving extracorporeal cardiopulmonary resuscitation (ECPR). Methods: We queried the Extracorporeal Life Support Organization registry for patients receiving ECPR (2020–2024). Patients were categorized into five BMI groups: underweight (<18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25–29.9 kg/m²), class 1 obesity (30–34.9 kg/m²), and class 2 obesity or above (≥35 kg/m²). A generalized additive model (GAM) analysis was used to identify the BMI range with the greatest stroke risk. Multivariable regression was used to compare odds of stroke between standard BMI groups and normal weight. Propensity score matching was used to compare stroke and mortality between normal weight and the BMI group with the highest predicted stroke risk. Results: Of 6390 patients (median age = 57.5, 68.6% male), 470 (7.4%) had a stroke during ECMO support (4.5% ischemic; 3.4% hemorrhagic). A total of 9.6% (n = 131) of class 1 obesity patients experienced stroke compared with 6.6% (n = 111) of normal weight, 6.9% (n = 79) of class 2 obesity or above, 6.9% (n = 143) of overweight, and 5.4% (n = 6) of underweight patients (p = 0.01). The GAM analysis showed a highest predicted stroke risk for class 1 obesity patients (n = 1366), which was confirmed by multivariable regression (adjusted odds ratio (aOR) = 1.63, 95%CI = 1.01–2.62, p = 0.045). After propensity matching (n = 357 each), class 1 obesity was associated with ischemic (aOR = 2.01, 95%CI = 1.02–4.08, p = 0.047) but not hemorrhagic stroke. Odds of hospital mortality were higher in both class 1 and 2 obesity patients compared with normal weight. Conclusions: Class 1 obesity was associated with increased odds of ischemic but not hemorrhagic stroke compared with normal weight patients.

Acute brain injury (ABI) is prevalent among patients undergoing venoarterial extracorporeal membrane oxygenation (VA-ECMO) and significantly impact recovery. Early prediction of ABI could enable timely interventions to prevent adverse outcomes, but existing predictive methods remain suboptimal. This study aimed to enhance ABI prediction using machine learning (ML) models and high-temporal-resolution granular data. We retrospectively analyzed 355 VA-ECMO patients treated at Johns Hopkins Hospital (JHH) from 2016 to 2024, collecting over 3 million data points from the JHH Research Electronic Data Capture (REDCap) database, with an average of 80,000 data points per patient. Acute brain injury was defined as ischemic stroke, intracranial hemorrhage, hypoxic-ischemic brain injury, or seizure. Four ML models were used: Random Forest, Categorical Boosting, Adaptive Boosting, and Extreme Gradient Boosting. Among 355 patients (median age 59 years, 56.9% male), 13.5% developed ABI. The models achieved an optimal area under the receiver operating characteristic curve (AUROC) of 0.79, accuracy of 87%, sensitivity of 53%, specificity of 99%, and precision-recall (PR)-AUC of 0.47. Key predictors included high minimum values of systolic blood pressure and variability in on-ECMO pulse pressure. High-resolution granular data enhanced ML performance for ABI prediction. Future efforts should focus on integrating continuous data platforms to enable real-time monitoring and personalized care, optimizing patient outcomes.

Despite advancements in lung transplantation (LT), infection remains a major cause of morbidity and mortality following LT. We examined trends in hospitalizations for infection in the first year after LT. We identified adult LT recipients in the United States (March 1, 2018-March 9, 2023) using the Organ Procurement and Transplantation Network database. We categorized transplants into 3 eras to account for the Composite Allocation Score allocation policy change and coronavirus disease 2019: March 2018 to March 2020, March 2020 to March 2022, and March 2022 to March 9, 2023. One-year post-LT survival was compared using Kaplan-Meier survival analysis and Cox proportional hazards regression. Hospitalizations for infection were compared using multivariable logistic regression, adjusted for era and donor and recipient characteristics. Of 12,388 LT recipients (median age = 62, male = 61.2%), hospitalization for infection in the first-year post transplant was 5.2% for patients transplanted from March 2018 to March 2020 (N = 5,031), 7.6% from March 2020 to March 2022 (N = 4,659), and 13.2% post-March 2022 (N = 3,640) (p < 0.001). Compared to March 2018 to March 2020, patients transplanted from March 2020 to March 2022 (adjusted aoods ratio [aOR] = 1.50, 95% confidence interval [CI] = 1.26-1.79) and post-March 2022 (aOR = 2.89, 95% CI = 2.29-3.65) were more likely to be hospitalized for an infection. After adjustment, we found no significant difference in risk of death following LT for recipients transplanted between March 2020 and March 2022 (aHR = 1.09, 95% CI = 0.96-1.23, p = 0.175) compared to March 2018 and March 2020. Post-March 2022 risk of death was elevated (aHR = 1.21, 95% CI = 1.04, 1.40, p = 0.014). Odds of hospitalization for infection in the first year after LT performed between March 2020 and March 2022 and post-March 2022 were 1.50 and 2.89 times as high, respectively, as LT performed between March 2018 and March 2020. IRB00352819