Citations

Abstract Background The opioid crisis and the increasing prevalence of multi-drug resistant organism (MDRO) infections pose a threat to public health. Opioid Use Disorder (OUD) patient are prone to MDRO colonization and infections, thus potentially leading to adverse outcomes during periods of critical illness. Understanding the prevalence and predictors of MDRO infections within this vulnerable population is critical to improving patient outcomes by optimizing interventions and treatment plans. Methods We conducted a retrospective, multicenter, cohort study to evaluate the prevalence of MDRO isolation among adults with OUD admitted to an intensive care unit (ICU) with an infection across four Mayo Clinic regions. Patients were classified to the MDRO cohort if an MDRO was isolated from standard cultures or rapid diagnostic testing within 48 hours of admission. We utilized descriptive statistics, univariate and multivariate least absolute shrinkage and selection operator (LASSO) regression analysis including random effect to account for site variability, to analyze demographic data, clinical characteristics, and outcomes. Results 790 critically ill adult OUD patients with infection-related admission were included in the study cohort. The prevalence of MDRO isolation was 22.5% (n=178), with MRSA being the predominant isolated MDRO. In LASSO regression analyses, we identified homelessness (OR: 1.79, p = 0.022), no Medications for OUD (MOUD) treatment (OR: 1.56, p=0.023), and positive Hepatitis C Virus (HCV) status (OR: 2.19, p = 0.012) as significant predictors for isolation of an MDRO. Intravenous (IV) antibiotic use in the preceding 90 days was also positively associated (OR: 1.04 per 24 hours, p = 0.007), alongside mental health disorders (OR: 2.03, p < 0.001) and diabetes (OR: 1.54, p = 0.023). Conclusion The high prevalence of MDRO isolation in critically ill OUD patients with infection is associated with complex social and clinical factors. Disclosures All Authors: No reported disclosures

ABSTRACT Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55–76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56–87), hospital length of stay (LOS) of 7 (IQR, 4–12) days, and ICU LOS of 2 (IQR, 1–4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2–15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6–13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0–1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3–8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.

Abstract Objectives This study aimed to evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision support system (DSS). Methods We conducted an anonymous web-based survey among critical care providers at Mayo Clinic Rochester between November 25, 2023, and January 15, 2024, to evaluate their experience with rare and atypical infection diagnostic processes and AI-based DSSs. We also assessed the perceived usefulness of AI-based DSSs, their potential impact on improving diagnostic practices for rare and atypical infections, and the perceived risks and benefits of their use. Results A total of 47/143 providers completed the survey. Thirty-eight out of 47 agreed that there was a delay in diagnosing rare and atypical infections. Among those who agreed, limited assessment of specific patient factors and failure to consider them were the most frequently cited important contributing factors (33/38). Thirty-eight out of 47 reported familiarity with the AI-based DSS applications available to critical care providers. Less than half (18/38) thought AI-based DSSs often provided valuable insights into patient care, but almost three-quarters (34/47) thought AI-based DDSs often provided valuable insight when specifically asked about their ability to improve the diagnosis of rare and atypical infections. All respondents rated reliability as important in enhancing the perceived utility of AI-based DSSs (47/47) and almost all rated interpretability and integration into the workflow as important (45/47). The primary concern about implementing an AI-based DSS in this context was alert fatigue (44/47). Conclusion Most critical care providers perceive that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors. Reliability, interpretability, workflow integration, and alert fatigue emerged as key factors impacting the usability of AI-based DSS. These findings will inform the development and implementation of an AI-based diagnostic algorithm to aid in identifying rare and atypical infections.

Abstract Rationale Propofol is one of the first-line sedative-hypnotic agents for critically ill adults requiring mechanical ventilation. Although propofol can elevate triglyceride levels, and the latter is a risk factor for pancreatitis, the association between propofol and acute pancreatitis is unclear. Objectives We sought to determine the clinical impact and potential associations between propofol infusion, hypertriglyceridemia, and acute pancreatitis. Methods This is an observational multicenter study of adults (⩾18 yr old) who were admitted to an intensive care unit, who required mechanical ventilation and received continuous propofol infusion for at least 24 hours. The primary outcomes were the frequency of hypertriglyceridemia (&gt;400 mg/dl) and acute pancreatitis. Further analyses were done to determine the clinical impact of elevated triglyceride levels (i.e., sedation changes) and risk factors for pancreatitis development. Results Of 11,828 patients included, 33.2% (n = 3,922) had triglyceride levels measured, of whom 21.7% (n = 851) had hypertriglyceridemia at 4.5 days (SD = 6.8) after propofol initiation. Of those still requiring sedation, 70.4% (n = 576/818) received alternative sedatives after developing hypertriglyceridemia. Pancreatitis occurred in 1.2% of patients (n = 47/3,922) and was more frequent in those with hypertriglyceridemia (3.2%, 27/851; vs. 0.7%, 20/3,071; P &lt; 0.001). After adjustment for potential confounding variables, each 100 mg/dl increase in triglyceride levels was associated with an 11% increase in risk of pancreatitis. Propofol dose was not associated with pancreatitis development. Conclusions Acute pancreatitis is uncommon in patients receiving propofol infusion, and it occurs over a wide range of triglyceride levels, indicating a multifactorial pathophysiology. Hypertriglyceridemia frequently prompts the use of alternative sedatives. Further study is needed to determine how to best monitor and treat hypertriglyceridemia in critically ill patients receiving propofol infusion.

To identify medical phrases utilized by the critical care team that may have an unintended impact on the critically ill patient, we administered an anonymous survey to multi-professional critical care team members. We elicited examples of imprecise language that may have a negative emotional impact on the critically ill. Of the 1600 providers surveyed, 265 offered 1379 examples (912 unique) which were clustered into 5 categories. Medical jargon (eg, “riding the vent”) was most prevalent (n = 549). There were 217 negative suggestions (eg, “you will feel a stick and a burn”). Hyperboles (eg, “black cloud”) were common (n = 198) while homonyms (ie “he fibbed”) accounted for 150 examples. Phrases such as “code brown in there” were categorized as metonyms (n = 144). 121 metaphors/similes (eg, “rearranging deck chairs on the Titanic”) were provided. Phrases that have the potential to negatively impact critically ill patient perceptions are commonplace in critical care practice. Whether these everyday communication habits lead to an unintended nocebo effect on mental health outcomes of the critically ill deserves further study.

Abstract Beta-lactam antibiotics are widely used in the intensive care unit due to their favorable effectiveness and safety profiles. Beta-lactams given to patients with sepsis must be delivered as soon as possible after infection recognition (early), treat the suspected organism (appropriate), and be administered at a dose that eradicates the infection (adequate). Early and appropriate antibiotic delivery occurs in &gt;90% of patients, but less than half of patients with sepsis achieve adequate antibiotic exposure. This project aimed to address this quality gap and improve beta-lactam adequacy using the Define, Measure, Analyze, Improve, and Control Lean Six Sigma quality improvement framework. A multidisciplinary steering committee was formed, which completed a stakeholder analysis to define the gap in practice. An Ishikawa cause and effect (Fishbone) diagram was used to identify the root causes and an impact/effort grid facilitated prioritization of interventions. An intervention that included bundled education with the use of therapeutic drug monitoring (TDM; i.e. drug-level testing) was projected to have the highest impact relative to the amount of effort and selected to address beta-lactam inadequacy in the critically ill. The education and TDM intervention were deployed through a Plan, Do, Study, Act cycle. In the 3 months after “go-live,” 54 episodes of beta-lactam TDM occurred in 41 unique intensive care unit patients. The primary quality metric of beta-lactam adequacy was achieved in 94% of individuals after the intervention. Ninety-four percent of clinicians gauged the education provided as sufficient. The primary counterbalance of antimicrobial days of therapy, a core antimicrobial stewardship metric, was unchanged over time (favorable result; P = .73). Application of the Define, Measure, Analyze, Improve, and Control Lean Six Sigma quality improvement framework effectively improved beta-lactam adequacy in critically ill patients. The approach taken in this quality improvement project is widely generalizable to other drugs, drug classes, or settings to increase the adequacy of drug exposure.