The real-world impact of the BioFire FilmArray blood culture identification 2 panel on antimicrobial stewardship among patients with bloodstream infections in intensive care units with a high burden of drug-resistant pathogens.

BACKGROUND: The increasing prevalence of drug-resistant pathogens leads to delays in adequate antimicrobial treatment in intensive care units (ICU). The real-world influence of the BioFire FilmArray Blood Culture Identification 2 (BCID2) panel on pathogen identification, diagnostic concordance with conventional culture methods, and antimicrobial stewardship in the ICU remains unexplored. METHODS: This retrospective observational study, conducted from July 2021 to August 2023, involved adult ICU patients with positive blood cultures who underwent BCID2 testing. The concordance between BCID2 and conventional culture results was examined, and its impact on antimicrobial stewardship was assessed through a comprehensive retrospective review of patient records by intensivists. RESULTS: A total of 129 blood specimens from 113 patients were analysed. Among these patients, a high proportion of drug-resistant strains were noted, including carbapenem-resistant Klebsiella pneumoniae (CRKP) (57.1%), carbapenem-resistant Acinetobacter calcoaceticus-baumannii complex (100%), methicillin-resistant Staphylococcus aureus (MRSA) (70%), and vancomycin-resistant Enterococcus faecium (VRE) (100%). The time from blood culture collection to obtaining BCID2 results was significantly shorter than conventional culture (46.2 h vs. 86.9 h, p < 0.001). BCID2 demonstrated 100% concordance in genotype-phenotype correlation in antimicrobial resistance (AMR) for CRKP, carbapenem-resistant Escherichia coli, MRSA, and VRE. A total of 40.5% of patients received inadequate empirical antimicrobial treatment. The antimicrobial regimen was adjusted or confirmed in 55.4% of patients following the BCID2 results. CONCLUSIONS: In the context of a high burden of drug-resistant pathogens, BCID2 demonstrated rapid pathogen and AMR detection, with a noticeable impact on antimicrobial stewardship in BSI in the ICU.

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion.

The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion system implemented at the CMUH Respiratory Intensive Care Unit (RICU) to address the high incidence of medical errors in ICUs, which are among the top three causes of mortality in healthcare facilities. ICU patients are particularly vulnerable to medical errors due to the complexity of their conditions and the critical nature of their care. We introduce a four-layer AIoT architecture designed to manage and deliver both real-time and non-real-time medical data within the CMUH-RICU. Our system demonstrates the capability to handle 22 TB of medical data annually with an average delay of 1.72 ms and a bandwidth of 65.66 Mbps. Additionally, we ensure the uninterrupted operation of the CMUH-RICU with a three-node streaming cluster (called Kafka), provided a failed node is repaired within 9 h, assuming a one-year node lifespan. A case study is presented where the AI application of acute respiratory distress syndrome (ARDS), leveraging our AIoT data fusion approach, significantly improved the medical diagnosis rate from 52.2% to 93.3% and reduced mortality from 56.5% to 39.5%. The results underscore the potential of AIoT in enhancing patient outcomes and operational efficiency in the ICU setting.

Application of a multiplex molecular pneumonia panel and real-world impact on antimicrobial stewardship among patients with hospital-acquired and ventilator-associated pneumonia in intensive care units.

BACKGROUND: The optimal timing for applying the BioFire FilmArray Pneumonia Panel (FAPP) in intensive care unit (ICU) patients with hospital-acquired pneumonia (HAP) or ventilator-associated pneumonia (VAP) remains undefined, and there are limited data on its impact on antimicrobial stewardship. METHODS: This retrospective study was conducted at a referral hospital in Taiwan from November 2019 to October 2022. Adult ICU patients with HAP/VAP who underwent FAPP testing were enrolled. Patient data, FAPP results, conventional microbiological testing results, and the real-world impact of FAPP results on antimicrobial therapy adjustments were assessed. Logistic regression was used to determine the predictive factors for bacterial detection by FAPP. RESULTS: Among 592 respiratory specimens, including 564 (95.3%) endotracheal aspirate specimens, 19 (3.2%) expectorated sputum specimens and 9 (1.5%) bronchoalveolar lavage specimens, from 467 patients with HAP/VAP, FAPP testing yielded 368 (62.2%) positive results. Independent predictors for positive bacterial detection by FAPP included prolonged hospital stay (odds ratio [OR], 3.14), recent admissions (OR, 1.59), elevated C-reactive protein levels (OR, 1.85), Acute Physiology and Chronic Health Evaluation II scores (OR, 1.58), and septic shock (OR, 1.79). Approximately 50% of antimicrobial therapy for infections caused by Gram-negative bacteria and 58.4% for Gram-positive bacteria were adjusted or confirmed after obtaining FAPP results. CONCLUSIONS: This study identified several factors predicting bacterial detection by FAPP in critically ill patients with HAP/VAP. More than 50% real-world clinical practices were adjusted or confirmed based on the FAPP results. Clinical algorithms for the use of FAPP and antimicrobial stewardship guidelines may further enhance its benefits.

High mortality of patients with severe pneumonia caused by respiratory syncytial virus, August 2021-June 2023, Taiwan.

Among the 14 patients with respiratory syncytial virus pneumonia, the majority (n = 8, 57.1 %) were older than 65 years and had health care-associated pneumonia (57.1 %). Over 70 % (n = 10) of them exhibited bacterial co-infection, with a high proportion (64.3 %) requiring mechanical ventilation. The hospital mortality rate was 50 %.

Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study.

BACKGROUND: Although lung protective strategy and adjunctive intervention are associated with improved survival in patients with acute respiratory distress syndrome (ARDS), the implementation of effective therapies remains low. This study aimed to evaluate whether the use of business intelligence (BI) for real-time data visualization is associated with an improvement in lung protective strategy and adjunctive therapy. METHODS: A retrospective observational cohort study was conducted on patients with ARDS admitted between September 2020 and June 2021 at two intensive care units (ICUs) of a tertiary referral hospital in Taiwan. BI was imported for data visualization and integration to assist in clinical decision in one of the ICUs. The primary outcomes were the implementation of low tidal volume ventilation (defined as tidal volume/predicted body weight ≤ 8 mL/kg) within 24 h from ARDS onset. The secondary outcomes included ICU and hospital mortality rates. RESULTS: Among the 1201 patients admitted to the ICUs during the study period, 148 (12.3%) fulfilled the ARDS criteria, with 86 patients in the BI-assisted group and 62 patients in the standard-of-care (SOC) group. Disease severity was similar between the two groups. The application of low tidal volume ventilation strategy was significantly improved in the BI-assisted group compared with that in the SOC group (79.1% vs. 61.3%, p = 0.018). Despite their ARDS and disease severity, the BI-assisted group tended to achieve low tidal volume ventilation. The ICU and hospital mortality were lower in the BI-assisted group. CONCLUSIONS: The use of real-time visualization system for data-driven decision support was associated with significantly improved compliance to low tidal volume ventilation strategy, which enhanced the outcomes of patients with ARDS in the ICU.

Etiologies of delayed diagnosis and six-month outcome of patients with newly diagnosed advanced lung cancer with respiratory failure at initial presentation.

BACKGROUND: This study aimed to evaluate the characteristics of patients with newly diagnosed advanced lung cancer who initially presented with respiratory failure. METHODS: This was a retrospective study which analyzed patients in the intensive care unit (ICU) with newly diagnosed advanced lung cancer who were placed on mechanical ventilation (MV). We defined newly diagnosed lung cancer as pathological or molecular results for treatment decisions not yet determined when the patient was admitted to ICU. RESULTS: During the 14-year inclusion period, 845 lung cancer patients requiring MV were screened. A total of 56 newly diagnosed extensive lung cancer patients were analyzed. Cancer-related to central airway obstruction (n = 29, 51.8%) was the leading cause of respiratory failure. The significant etiologies of delay in the diagnosis of lung cancer were diagnostic error, mistaking cancer for tuberculosis, and missed hilar lesions. The six-month survival rate was only 7.1% (n = 4). The sequential organ failure assessment (SOFA) score was significantly associated with mortality (HR = 1.142, 95% CI = 1.012-1.288, P = 0.031). The six-month survival rate in patients receiving suitable targeted therapy and accepting chemotherapy and best supportive care was 40% (2/5), 0% (0/7), and 4.5% (2/44), respectively. CONCLUSIONS: Patients with newly diagnosed advanced lung cancer with acute life-threatening respiratory failure have poor outcomes. Cancer-related to central airway obstruction is a leading cause of respiratory failure. Diagnostic errors such as tuberculosis and missed hilar lesions are the two main etiologies of a delay in diagnosis. The SOFA score is correlated with mortality. Targeted therapy can raise the six-month survival rates in patients with oncogenic mutation adenocarcinoma, who survive after presentation in a critical condition.