Lal, Amos

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.

Background and Objectives: Ventilator-associated pneumonia (VAP) poses a significant threat to the clinical outcomes and hospital stays of mechanically ventilated patients, particularly those recovering from cardiac arrest. Given the already elevated mortality rates in cardiac arrest cases, the addition of VAP further diminishes the chances of survival. Consequently, a paramount focus on VAP prevention becomes imperative. This review endeavors to comprehensively delve into the nuances of VAP, specifically in patients requiring mechanical ventilation in post-cardiac arrest care. The overarching objectives encompass (I) exploring the etiology, risk factors, and pathophysiology of VAP, (II) delving into available diagnostic modalities, and (III) providing insights into the management options and recent treatment guidelines. Methods: A literature search was conducted using PubMed, MEDLINE, and Google Scholar databases for articles about VAP and Cardiac arrest. We used the MeSH terms “VAP”, “Cardiac arrest”, “postcardiac arrest syndrome”, and “postcardiac arrest syndrome”. The clinical presentation, diagnostic, and management strategies of VAP were summarized, and all authors reviewed the selection and decided which studies to include. Key Content and Findings: The incidence and mortality rates of VAP exhibit significant variability, yet a recurring pattern emerges, marked by prolonged hospitalization and exacerbated clinical outcomes. This pattern is attributed to the elevated incidence of drug-resistant infections and the delayed initiation of antimicrobial treatment. This review focuses on VAP, aiming to offer valuable insights into the efficient identification and management of this fatal complication in post-cardiac arrest patients. Conclusion: The prognosis for survival after cardiac arrest is already challenging, and the outlook becomes even more daunting when complicated by VAP. The timely diagnosis of VAP and initiation of antibiotics pose considerable challenges, primarily due to the invasive nature of obtaining high-quality samples and the time required for speciation and identification of antimicrobial sensitivity. The controversy surrounding prophylactic antibiotics persists, but promising new strategies have been proposed; however, they are still awaiting well-designed clinical trials.

Purpose: This study evaluated the predictive value of SCAI shock staging for mortality in patients with sepsis and septic shock admitted to the medical ICU. Materials and Methods: This is a single-center historical cohort study. We analyzed data for adults (≥ 18-year-old) admitted to the medical ICU at Mayo Clinic St. Mary’s campus with sepsis between June 1, 2018, and December 31, 2021. Sepsis was identified using the Sepsis-III criteria. Patients were stratified based on SCAI shock staging. Our primary outcome was all-cause 30-day mortality. Results: We identified 3,079 eligible adult patients with sepsis or septic shock. The distribution of SCAI shock stages A through E was 9%, 12%, 25%, 49%, and 5%, respectively. The overall 30-day mortality was 24%. There was progression in all outcomes including ICU, hospital and 30-day mortality across SCAI shock stages. However, only SCAI shock stages D and E, had statistically significant adjusted HRs of 1.6 and 3, respectively. When compared to SOFA score, SCAI shock staging performed similarly in predicting ICU mortality with no statistically significant difference in AUCs, p-value of 0.07. Conclusions: Our results support the use of SCAI shock staging in critically ill medical patients with sepsis and septic shock for risk stratification. We propose that the SCAI shock staging may be used as a universal system for grading the severity of shock in critically ill patients regardless of etiology.

ObjectiveAvacopan, an activated complement factor 5 receptor antagonist, has been approved as adjunct therapy for severe active antineutrophil cytoplasmic antibody–associated vasculitis (AAV). Current evidence supports the management of AAV presenting with diffuse alveolar hemorrhage (DAH) by administering glucocorticoids combined with either rituximab or cyclophosphamide in addition to supportive care. The role of avacopan in patients with DAH as a primary severe disease manifestation of AAV has not been well established. Furthermore, concerns remain regarding timely access to avacopan, the best glucocorticoid tapering regimen, and long‐term efficacy and safety of the drug. We sought to identify clinical features and outcomes of patients presenting with DAH secondary to AAV who received avacopan in addition to glucocorticoids and rituximab or cyclophosphamide.MethodsWe performed a retrospective cohort study of all consecutive patients presenting with DAH as part of active severe granulomatosis with polyangiitis or microscopic polyangiitis. Demographic and clinical characteristics were collected at presentation and follow‐up.ResultsFifteen patients met inclusion criteria and were observed for a median time of 17 weeks (interquartile range [IQR] 6–37 weeks) after initiation of avacopan. Patients were predominantly female and White, had never smoked, and were a median age of 66 years (IQR 52–72 years) at diagnosis. The majority had newly diagnosed severe AAV with renal involvement. Three patients progressed to respiratory failure. The timing of avacopan introduction and patterns of glucocorticoid tapers varied widely in this cohort. Two serious adverse events related to infection were observed, including one opportunistic infection leading to the patient's death, although neither was directly attributed to avacopan administration.ConclusionWe describe the clinical course of patients who presented with the severe AAV disease manifestation of DAH and received avacopan as adjunct therapy. Most patients achieved remission during follow‐up, and adverse events, including infection, were observed.

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical assessments, but its reliability depends on the operator. Machine learning (ML) models offer an alternative by automatically classifying lung sounds. ML models require substantial data, and public databases aim to address this limitation. This systematic review compares characteristics, diagnostic accuracy, concerns, and data sources of existing models in the literature. Papers published from five major databases between 1990 and 2022 were assessed. Quality assessment was accomplished with a modified QUADAS-2 tool. The review encompassed 62 studies utilizing ML models and public-access databases for lung sound classification. Artificial neural networks (ANN) and support vector machines (SVM) were frequently employed in the ML classifiers. The accuracy ranged from 49.43% to 100% for discriminating abnormal sound types and 69.40% to 99.62% for disease class classification. Seventeen public databases were identified, with the ICBHI 2017 database being the most used (66%). The majority of studies exhibited a high risk of bias and concerns related to patient selection and reference standards. Summarizing, ML models can effectively classify abnormal lung sounds using publicly available data sources. Nevertheless, inconsistent reporting and methodologies pose limitations to advancing the field, and therefore, public databases should adhere to standardized recording and labeling procedures.