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BackgroundDiagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making. ObjectiveThis study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. MethodsThis study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts. ResultsThis federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors. ConclusionsThe use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety. International Registered Report Identifier (IRRID)DERR1-10.2196/24642

Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.

Epithelial and stromal/mesenchymal limbal stem cells contribute to corneal homeostasis and cell renewal. Extracellular vesicles (EVs), including exosomes (Exos), can be paracrine mediators of intercellular communication. Previously, we described cargos and regulatory roles of limbal stromal cell (LSC)-derived Exos in non-diabetic (N) and diabetic (DM) limbal epithelial cells (LECs). Presently, we quantify the miRNA and proteome profiles of human LEC-derived Exos and their regulatory roles in N- and DM-LSC. We revealed some miRNA and protein differences in DM vs. N-LEC-derived Exos cargos, including proteins involved in Exo biogenesis and packaging that may affect Exo production and ultimately cellular crosstalk and corneal function. Treatment by N-Exos, but not by DM-Exos, enhanced wound healing in cultured N-LSCs and increased proliferation rates in N and DM LSCs vs. corresponding untreated (control) cells. N-Exos-treated LSCs reduced the keratocyte markers ALDH3A1 and lumican and increased the MSC markers CD73, CD90, and CD105 vs. control LSCs. These being opposite to the changes quantified in wounded LSCs. Overall, N-LEC Exos have a more pronounced effect on LSC wound healing, proliferation, and stem cell marker expression than DM-LEC Exos. This suggests that regulatory miRNA and protein cargo differences in DM- vs. N-LEC-derived Exos could contribute to the disease state.

Pancreatic cancer (PC) is one of the deadliest cancers. Developing biomarkers for chemotherapeutic response prediction is crucial for improving the dismal prognosis of advanced-PC patients (pts). To evaluate the potential of plasma metabolites as predictors of the response to chemotherapy for PC patients, we analyzed plasma metabolites using high-performance liquid chromatography-mass spectrometry from 31 cachectic, advanced-PC subjects enrolled into the PANCAX-1 (NCT02400398) prospective trial to receive a jejunal tube peptide-based diet for 12 weeks and who were planned for palliative chemotherapy. Overall, there were statistically significant differences in the levels of intermediates of multiple metabolic pathways in pts with a partial response (PR)/stable disease (SD) vs. progressive disease (PD) to chemotherapy. When stratified by the chemotherapy regimen, PD after 5-fluorouracil-based chemotherapy (e.g., FOLFIRINOX) was associated with decreased levels of amino acids (AAs). For gemcitabine-based chemotherapy (e.g., gemcitabine/nab-paclitaxel), PD was associated with increased levels of intermediates of glycolysis, the TCA cycle, nucleoside synthesis, and bile acid metabolism. These results demonstrate the feasibility of plasma metabolomics in a prospective cohort of advanced-PC patients for assessing the effect of enteral feeding as their primary source of nutrition. Metabolic signatures unique to FOLFIRINOX or gemcitabine/nab-paclitaxel may be predictive of a patient's response and warrant further study.

Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc-ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc-ILD using an unbiased, high-throughput approach. We classified SSc-ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc-ILD via logistic regression. The proteins associated with at a p value of

Endotracheal aspirate cultures (EACs) help diagnose lower respiratory tract infections in mechanically ventilated patients but are limited by contamination with normal microbiota and variation in laboratory reporting. Increased use of EACs is associated with increased antimicrobial prescribing, but the impact of microbiology reporting on prescribing practices is unclear. This study was a retrospective analysis of EACs from mechanically ventilated patients at Children's Hospital Colorado (CHCO) admitted between 1 January 2019 and 31 December 2019. Chart review was performed to collect all culture and Gram stain components, as well as antibiotic use directed to organisms in culture. Reporting concordance was determined for each organism using American Society for Microbiology guidelines. Days of therapy were calculated for overreported and guideline-concordant organisms. A multivariable model was used to assess the relationship between organism reporting and total days of therapy. Overall, 448 patients with 827 EACs were included in this study. Among patients with tracheostomy, 25 (8%) organisms reported from EACs were overreported and contributed 48 days of excess therapy, while 227 (29%) organisms from the EACs of endotracheally intubated patients were overreported, contributing 472 excess days of therapy. After adjustment, organism overreporting was associated with a >2-fold-higher rate of antimicrobial therapy than guideline-concordant reporting (incident rate ratio [IRR], 2.83; 95% confidence interval [CI], 1.23, 6.53; P < 0.05). Overreported organisms from respiratory cultures contribute to excess antimicrobial therapy exposure in mechanically ventilated patients. Microbiology laboratories have an opportunity to mitigate antimicrobial overuse through standardized reporting practices.

Guidance for developing and implementing antimicrobial stewardship programs for children is lacking. This review article describes unique considerations for planning antimicrobial management of children that may impact stewardship strategies. A variety of methods and training tools are described along with metrics specific to measuring antibiotic use and outcomes in children. Handshake stewardship is specifically explained and is considered a best practice. Information on stewardship in unique settings, including the neonatal intensive care unit and outpatient settings, are included.

Using a mixed-methods approach, we assessed the effect of the coronavirus disease 2019 (COVID-19) pandemic on antimicrobial stewardship programs (ASPs) in Colorado hospitals. ASP leaders reported decreased time and resources, reduced rigor of stewardship interventions, inability to complete new initiatives, and interpersonal challenges. Stewardship activities may be threatened during times of acute resource pressure.

RationalePhosphorylation of sarcomeric proteins has been implicated in heart failure with preserved ejection fraction (HFpEF); such changes may contribute to diastolic dysfunction by altering contractility, cardiac stiffness, Ca2+-sensitivity, and mechanosensing. Treatment with cardiosphere-derived cells (CDCs) restores normal diastolic function, attenuates fibrosis and inflammation, and improves survival in a rat HFpEF model.ObjectivePhosphorylation changes that underlie HFpEF and those reversed by CDC therapy, with a focus on the sarcomeric subproteome were analyzed.Methods and resultsDahl salt-sensitive rats fed a high-salt diet, with echocardiographically verified diastolic dysfunction, were randomly assigned to either intracoronary CDCs or placebo. Dahl salt-sensitive rats receiving low salt diet served as controls. Protein and phosphorylated Ser, Thr, and Tyr residues from left ventricular tissue were quantified by mass spectrometry. HFpEF hearts exhibited extensive hyperphosphorylation with 98% of the 529 significantly changed phospho-sites increased compared with control. Of those, 39% were located within the sarcomeric subproteome, with a large group of proteins located or associated with the Z-disk. CDC treatment partially reverted the hyperphosphorylation, with 85% of the significantly altered 76 residues hypophosphorylated. Bioinformatic upstream analysis of the differentially phosphorylated protein residues revealed PKC as the dominant putative regulatory kinase. PKC isoform analysis indicated increases in PKC α, β, and δ concentration, whereas CDC treatment led to a reversion of PKCβ. Use of PKC isoform specific inhibition and overexpression of various PKC isoforms strongly suggests that PKCβ is the dominant kinase involved in hyperphosphorylation in HFpEF and is altered with CDC treatment.ConclusionsIncreased protein phosphorylation at the Z-disk is associated with diastolic dysfunction, with PKC isoforms driving most quantified phosphorylation changes. Because CDCs reverse the key abnormalities in HFpEF and selectively reverse PKCβ upregulation, PKCβ merits being classified as a potential therapeutic target in HFpEF, a disease notoriously refractory to medical intervention.

Kawasaki disease (KD) is the leading cause of acquired heart disease among children. Murine and human data suggest that the NLRP3-IL-1β pathway is the main driver of KD pathophysiology. NLRP3 can be activated during defective autophagy/mitophagy. We used the Lactobacillus casei cell wall extract (LCWE) murine model of KD vasculitis to examine the role of autophagy/mitophagy on cardiovascular lesion development. LCWE-injected mice had impaired autophagy/mitophagy and increased levels of ROS in cardiovascular lesions, together with increased systemic 8-OHdG release. Enhanced autophagic flux significantly reduced cardiovascular lesions in LCWE-injected mice, whereas autophagy blockade increased inflammation. Vascular smooth muscle cell-specific deletion of Atg16l1 and global Parkin-/- significantly increased disease formation, supporting the importance of autophagy/mitophagy in this model. Ogg1-/- mice had significantly increased lesions with increased NLRP3 activity, whereas treatment with MitoQ reduced vascular tissue inflammation, ROS production, and systemic 8-OHdG release. Treatment with MN58b or Metformin (increasing AMPK and reducing ROS) resulted in decreased cardiovascular lesions. Our results demonstrate that impaired autophagy/mitophagy and ROS-dependent damage exacerbate the development of murine KD vasculitis. This pathway can be efficiently targeted to reduce disease severity. These findings enhance our understanding of KD pathogenesis and identify potentially novel therapeutic avenues for KD treatment.