Citations

Introduction Superficial parasternal intercostal plane blocks have grown in clinical use for cardiac surgery as a targeted fascial plane block for median sternotomy. Despite this, there remains no consensus on technique for optimal parasternal spread of injectate. The primary objective of this cadaveric study was to evaluate parasternal spread of injectate of single versus two-level superficial parasternal intercostal plane injections with the needle tip positioned on the anterior surface of the costal cartilage. Methods We performed 10 single-level injections at T4 and 10 two-level injections at T3 and T5 on alternating sides in 10 fresh frozen cadavers. All injections were ultrasound guided with a parasagittal ultrasound probe orientation and an in-plane needle orientation. Anatomic dissections were performed immediately following injections. Results Parasternal spread of injectate was greater with two-level injections at T3 and T5 costal cartilages than with single-level injections at the T4 costal cartilage, with two-level injections consistently spreading to the T2–T5 intercostal spaces. Median (Q1, Q3) intercostal space spread for single-level injections was 2.0 (2.0–2.5). Median (Q1, Q3) intercostal space spread for two-level injections was 4.0 (4.0–4.375). Conclusions Two-level injections with the needle tip located on the anterior surface of the costal cartilage resulted in improved parasternal spread compared with single-level injections.

Abstract Background There is a lack of comprehensive, large cohort studies investigating the predictors for severity and hospitalization in human granulocytic anaplasmosis (HGA). Methods We conducted a retrospective cohort study including all cases of HGA identified by positive blood Anaplasma polymerase chain reaction (PCR) at the Mayo Clinic Laboratory, Rochester between 2011 and 2021. Multivariable logistic regression was performed to evaluate risk factors associated with hospitalization. Results A total of 465 cases were identified. Of those, 67% (n = 312) were managed in the outpatient setting. Hospitalized patients (n = 153, 33%) were more likely to be older (median age of 71 vs 61; P ≤ .001) and immunocompromised (17% vs 7%; P ≤ .001). The triad of leukopenia, thrombocytopenia, and transaminitis was observed in 24% of the cases, but was associated with risk of hospitalization (odds ratio [OR] 1.78, 95% confidence interval [CI]: 1.05–3.03; P ≤ .033). The presence of a coinfection did not impact mortality or hospitalization. Conclusions The risk factors for hospitalization in patients with HGA include altered mental status, higher absolute neutrophil count (ANC), advanced age, comorbidities, and immunosuppression. The classic hematological and biochemical profile may be absent in the majority of cases. Co-testing may be of higher benefit in select cases.

Abstract Studies have identified genetic and epidemiologic factors associated with mammographic density (MD) phenotypes. However, MD-associated genetic variants only account for a small proportion of the total estimated heritability. Interrogating interactions between genetic and epidemiologic factors could potentially identify additional MD-associated loci, expand our understanding of the genetic basis of MD phenotypes, and clarify how epidemiologic factors modulate relationships between genetic variants and MD. We conducted six separate genome-wide, gene–environment (GxE) interaction analyses, applying 2 degrees of freedom (df) and 1df interaction tests, for each of three MD phenotypes (percent density, dense area (DA), and nondense area (NDA)). The six epidemiologic factors considered were height, ever parous, parity, ever menopausal hormone therapy, ever breastfeeding, and months of breastfeeding. We included European ancestry participants from multiple studies within the Markers of Density consortium and the Breast Cancer Association Consortium (n = 4895-16 218 depending on specific analyses). We identified 11 loci with genome-wide significant (P < 5 × 10−8) interaction tests including two novel common genetic signals interacting with parity (8p21.2) and ever breastfeeding (19p13.2) for NDA. Our results suggest that epidemiologic risk factors might influence relationships between common genetic variants and MD phenotypes at particular genomic loci.

Background: Severe alcohol-associated hepatitis (sAH) is a well-characterized disease with high short-term mortality. However, there is limited research on those with a “less severe condition” (moderate AH). This study aims to characterize in-depth patients with moderate AH (mAH), including the performance of mortality scoring systems, key prognostic factors, and survival over time. Methods: A multicenter retrospective cohort study (2009–2019) included patients with mAH (MELD score ≤20 at admission). Cox regression and receiver operating characteristic curves with AUC were used for analysis. Results: We included 1845 patients with AH (20 centers, 8 countries) between 2009 and 2019. mAH was defined as a MELD score ≤20 at admission. Twenty-four percent met the criteria for an mAH episode. Patients with mAH tend to be older and have a higher proportion of females, with a median MELD of 17 (15–19), Maddrey discriminant function (mDF) of 33 (22–40), the trajectory of serum bilirubin of 0.83 (0.60–1.21), and neutrophil-to-lymphocyte ratio (NLR) of 5 (2.96–8.60). The primary causes of death in mAH included multiple organ failure (34.1%) and infections (16.6%). The cumulative survival rates at 30, 90, and 180 days were 94.3%, 90.4%, and 88.2%, respectively. In multivariable analysis, age was the only significant predictor of 30-day mortality (HR 1.49, 95% CI: 1.27–1.76, p <0.001). Mortality prediction models showed poor performance, with AUC for MELD (0.671), mDF (0.726), trajectory of serum bilirubin (0.733), and NLR (0.697). Conclusions: Patients with moderate AH exhibited a mortality of 11.8% at 6 months, primarily driven by multiple organ failure and infections. These patients also exhibit a different clinical profile compared to those with sAH. Tailored models and therapeutic strategies are needed to improve long-term outcomes in mAH.

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850–0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16–13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.