Citations

Abstract Rationale Per the 2021 Sepsis Campaign, it is recommended that hospitals use a performance improvement program for sepsis screening, utilizing machine learning to improve specificity. Around 20% of sepsis at the Mayo Clinic, Rochester site is hospital acquired. As inpatient sepsis is usually detected by extreme physiologic derangement or clinical concern, we seek to leverage risk factors beyond traditional SIRS and sepsis criteria to create a machine and deep learning based risk stratification model to improve the precision of sepsis monitoring. Methods We designed a retrospective observational case control study, including patients diagnosed with sepsis as an inpatient at the Mayo Clinic, Rochester hospital and compared them to patients without a sepsis diagnosis over the last 7 years. Sepsis diagnosis was labeled by a combination of sepsis ICD codes and initial lactate elevation. We excluded transfers from outside hospitals, direct admissions for sepsis and patients diagnosed with sepsis in the emergency department. Data was extracted using Mayo Data Explorer, and data preparation and validation were performed using JMP Statistical Software and Python libraries. Feature selection was critical as we examined all data from previous contacts with healthcare, environmental factors, and dynamic trends of inpatient objective data; amongst 5,000 options of lab tests and vital measurements, we selected near 100 to reduce model complexity. We used XG Boost to train our model with a baseline cohort of 100 sepsis patients and 1000 patients without sepsis. Results We created a model that categorically classified patients into three sepsis risk level profiles (low-risk, medium-risk, high-risk) with performance metrics of a specificity of 97%, sensitivity of 65% and area under the receiver operating characteristic curve of 95%. Amongst laboratory data, lactate and creatinine had the highest feature importance (figure below). Our model also identified variables with the least importance to further refine our architecture. Conclusions Our model is one of the first sepsis risk stratification models whose algorithm is optimized for non-ICU inpatient wards, while the majority of similar studies were conducted on ICU and emergency department patients due to limited data availability. The model’s high specificity will allow for earlier sepsis recognition. By including our full cohort of 7,500 patients with sepsis and 35,000 patients without sepsis, we hope to improve class imbalance and increase sensitivity. By integrating transformer architecture, we seek to eliminate variables of low significance to create a model with more practical use, while maintaining high specificity. This abstract is funded by: None

Abstract Background We previously identified circulating and MRI biomarkers associated with the surgical management of Crohn’s disease (CD). Here we tested associations between these biomarkers and ileal resection inflammation and collagen content. Methods Fifty CD patients undergoing ileal resection were prospectively enrolled at 4 centers. Circulating CD64, extracellular matrix protein 1 (ECM1), GM-CSF autoantibodies (GM-CSF Ab), and fecal calprotectin were measured by ELISA. Ileal 3-dimensional magnetization transfer ratio (3D MTR), modified Look-Locker inversion recovery (MOLLI) T1 relaxation, diffusion-weighted intravoxel incoherent motion (IVIM), and the simplified magnetic resonance index of activity (sMaRIA) were measured by MRI. Ileal resection specimen acute inflammation was graded, and collagen content was measured quantitatively using second harmonic imaging microscopy. Associations between biomarkers and ileal collagen content were tested. Results Median (interquartile range [IQR]) age was 19.5 (16-33) years. We observed an inverse relationship between ileal acute inflammation and collagen content (r = −0.39 [95% confidence interval {CI}: −0.61, −0.10], P = .008). Most patients (33 [66%]) received biologics, with no variation in collagen content with treatment exposures. In the univariate analysis, CD64, GM-CSF Ab, fecal calprotectin, and sMaRIA were positively associated with acute inflammation and negatively associated with collagen content (P < .1). The multivariable model for ileal collagen content (R2 = 0.31 [95% CI: 0.11, 0.52]) included log CD64 (β = −.27; P = .19), log ECM1 (β = .47; P = .06), log GM-CSF Ab (β = −.15; P = .01), IVIM f (β = .29, P = .10), and IVIM D* (β = 1.69, P = .13). Conclusions Clinically available and exploratory circulating and MRI biomarkers are associated with the degree of inflammation versus fibrosis in CD ileal resections. With further validation, these biomarkers may be used to guide medical and surgical decision-making for refractory CD.

Abstract Background Despite advances in medical therapy, many children and adults with ileal Crohn’s disease (CD) progress to fibrostenosis requiring surgery. We aimed to identify MRI and circulating biomarkers associated with the need for surgical management. Methods This prospective, multicenter study included pediatric and adult CD cases undergoing ileal resection and CD controls receiving medical therapy. Noncontrast research MRI examinations measured bowel wall 3-dimensional magnetization transfer ratio normalized to skeletal muscle (normalized 3D MTR), modified Look-Locker inversion recovery (MOLLI) T1 relaxation, intravoxel incoherent motion (IVIM) diffusion-weighted imaging metrics, and the simplified magnetic resonance index of activity (sMaRIA). Circulating biomarkers were measured on the same day as the research MRI and included CD64, extracellular matrix protein 1 (ECM1), and granulocyte-macrophage colony-stimulating factor (GM-CSF) autoantibodies (Ab). Associations between MRI and circulating biomarkers and need for ileal resection were tested using univariate and multivariable LASSO regression. Results Our study sample included 50 patients with CD undergoing ileal resection and 83 patients with CD receiving medical therapy; mean participant age was 23.9 ± 13.1 years. Disease duration and treatment exposures did not vary between the groups. Univariate biomarker associations with ileal resection included log GM-CSF Ab (odds ratio [OR], 2.87; P = .0009), normalized 3D MTR (OR, 1.05; P = .002), log MOLLI T1 (OR, 0.01; P = .02), log IVIM perfusion fraction (f; OR, 0.38; P = .04), and IVIM apparent diffusion coefficient (ADC; OR, 0.3; P = .001). The multivariable model for surgery based upon corrected Akaike information criterion included age (OR, 1.03; P = .29), BMI (OR, 0.91; P = .09), log GM-CSF Ab (OR, 3.37; P = .01), normalized 3D MTR (OR, 1.07; P = .007), sMaRIA (OR, 1.14; P = .61), luminal narrowing (OR, 10.19; P = .003), log C-reactive protein (normalized; OR, 2.75; P = .10), and hematocrit (OR, 0.90; P = .13). Conclusion After accounting for clinical and MRI measures of severity, normalized 3D MTR and GM-CSF Ab are associated with the need for surgery in ileal CD.

PurposeChemical shift‐encoded MRI (CSE‐MRI) is well‐established to quantify proton density fat fraction (PDFF) as a quantitative biomarker of hepatic steatosis. However, temperature is known to bias PDFF estimation in phantom studies. In this study, strategies were developed and evaluated to correct for the effects of temperature on PDFF estimation through simulations, temperature‐controlled experiments, and a multi‐center, multi‐vendor phantom study.Theory and MethodsA technical solution that assumes and automatically estimates a uniform, global temperature throughout the phantom is proposed. Computer simulations modeled the effect of temperature on PDFF estimation using magnitude‐, complex‐, and hybrid‐based CSE‐MRI methods. Phantom experiments were performed to assess the temperature correction on PDFF estimation at controlled phantom temperatures. To assess the temperature correction method on a larger scale, the proposed method was applied to data acquired as part of a nine‐site multi‐vendor phantom study and compared to temperature‐corrected PDFF estimation using an a priori guess for ambient room temperature.ResultsSimulations and temperature‐controlled experiments show that as temperature deviates further from the assumed temperature, PDFF bias increases. Using the proposed correction method and a reasonable a priori guess for ambient temperature, PDFF bias and variability were reduced using magnitude‐based CSE‐MRI, across MRI systems, field strengths, protocols, and varying phantom temperature. Complex and hybrid methods showed little PDFF bias and variability both before and after correction.ConclusionCorrection for temperature reduces temperature‐related PDFF bias and variability in phantoms across MRI vendors, sites, field strengths, and protocols for magnitude‐based CSE‐MRI, even without a priori information about the temperature.

ABSTRACT The last decade has seen a sharp increase in the number of scientific publications describing physiological and pathological functions of extracellular vesicles (EVs), a collective term covering various subtypes of cell‐released, membranous structures, called exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, and many other names. However, specific issues arise when working with these entities, whose size and amount often make them difficult to obtain as relatively pure preparations, and to characterize properly. The International Society for Extracellular Vesicles (ISEV) proposed Minimal Information for Studies of Extracellular Vesicles (“MISEV”) guidelines for the field in 2014. We now update these “MISEV2014” guidelines based on evolution of the collective knowledge in the last four years. An important point to consider is that ascribing a specific function to EVs in general, or to subtypes of EVs, requires reporting of specific information beyond mere description of function in a crude, potentially contaminated, and heterogeneous preparation. For example, claims that exosomes are endowed with exquisite and specific activities remain difficult to support experimentally, given our still limited knowledge of their specific molecular machineries of biogenesis and release, as compared with other biophysically similar EVs. The MISEV2018 guidelines include tables and outlines of suggested protocols and steps to follow to document specific EV‐associated functional activities. Finally, a checklist is provided with summaries of key points.