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Introduction: Carbapenem-resistant Enterobacterales (CRE) are becoming increasingly prevalent and have been associated with increased mortality. Due to the paucity of data from the region, we evaluated the risk factors and outcomes of infections caused by CRE at a tertiary care center in Lebanon. Methodology: The study had three arms in a case-case-control design: patients with CRE infections, patients with infections due to ceftriaxone-resistant carbapenem-susceptible Enterobacterales (CSE), and uninfected controls (UC). Logistic regression was performed to identify risk factors uniquely associated with CRE. A CRE infection score was also created to assess the likelihood of having a CRE infection. Results: We included 337 patients (112 CRE, 75 CSE, 150 UC). Predictors unique to CRE infection included recent surgery (Odds Ratio (OR) 25.7; 95% confidence interval (CI95 5.7-115.2), carbapenem use within 30 days (OR 19.1; CI95 3.3-109.6), and malignancy (OR 4.2; CI95 1.6-10.5). The mean CRE score was 4.2 ± 2.2 in the CRE group and 2.4 ± 2.4 in the CSE group (p < 0.001). Infection-related mortality was higher among CRE patients (63.6% vs. 20.0%; p = 0.015), and CRE was independently associated with all-cause in-hospital mortality. Conclusions: We developed a scoring system that would allow risk stratification and would guide empiric antibiotic therapy. CRE infections were associated with a worse outcome compared to CSE infections.

To identify risk factors for surgical site infections (SSIs) following abdominal hysterectomy in patients cared for in a large urban public hospital system. Retrospective case control study. Multicenter safety net hospital system. Women undergoing hysterectomy from 2015-2023. Propensity score matching, using Centers for Medicare and Medicaid Services (CMS) risk variables, created control groups. Receiver operating characteristics curves were created using current and augmented risk adjustment variables. There were 6142 hysterectomy surgeries reported during the 9-year time period, with 160 (2.61%) with reportable SSIs. Compared to a matched control group, patients with SSIs were more likely to be of Black race, to have longer duration of surgery, to have open surgery (vs. laparoscopic), and to have received a clindamycin ± gentamicin for surgical prophylaxis. The addition of duration of surgery, endoscopic surgery, and wound class to current CMS risk variables significantly improved the prediction for SSI when all SSIs were included, but did not when patients with superficial SSIs were excluded from analysis. Predicting SSIs following hysterectomy is complex and current CMS risk assessments are overly simplistic. Until more robust and comprehensive risk assessment criteria are developed, use of SSIs following hysterectomy as a quality measure for reimbursement should be reconsidered.

As of 10 April 2020, New York State had 180,458 cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 9,385 reported deaths. Patients with cancer comprised 8.4% of deceased individuals 1 . Population-based studies from China and Italy suggested a higher coronavirus disease 2019 (COVID-19) death rate in patients with cancer 2 , 3 , although there is a knowledge gap as to which aspects of cancer and its treatment confer risk of severe COVID-19 4 . This information is critical to balance the competing safety considerations of reducing SARS-CoV-2 exposure and cancer treatment continuation. From 10 March to 7 April 2020, 423 cases of symptomatic COVID-19 were diagnosed at Memorial Sloan Kettering Cancer Center (from a total of 2,035 patients with cancer tested). Of these, 40% were hospitalized for COVID-19, 20% developed severe respiratory illness (including 9% who required mechanical ventilation) and 12% died within 30 d. Age older than 65 years and treatment with immune checkpoint inhibitors (ICIs) were predictors for hospitalization and severe disease, whereas receipt of chemotherapy and major surgery were not. Overall, COVID-19 in patients with cancer is marked by substantial rates of hospitalization and severe outcomes. The association observed between ICI and COVID-19 outcomes in our study will need further interrogation in tumor-specific cohorts. Analysis of a large, single-center cohort of patients with cancer who were infected with COVID-19 uncovers factors associated with disease severity and interactions with anti-cancer therapies

Background The objective of this study was to determine whether patients infected with extended-spectrum beta-lactamase (ESBL)-producing organisms are colonized at multiple body sites. Methods This was a prospective cohort study at a tertiary care center in Beirut, Lebanon. Hospitalized patients with infections caused by ESBL-producing organisms were included. Cultures were obtained from the primary site of infection as well as from other sites (skin, nasopharynx, urine, rectum). Molecular analysis was performed on isolates to determine clonal relatedness. Results One hundred patients were included in the study. Only 22 patients had positive cultures from sites other than the primary site of infection. The most common ESBL gene was CTX-M-15 followed by TEM-1. In 11 of 22 patients, isolates collected from the same patient were 100% genetically related, while in the remaining patients, genomic relatedness ranged from 42.9% to 97.1%. Conclusions Colonization at sites other than the primary site of infection was not common among our patient population infected with ESBL-producing organisms. The dynamics of transmission of these bacterial strains should be studied in further prospective studies to determine the value of routine active surveillance and the need for expanded precautions in infected and colonized patients.

New York State had 180,458 cases of SARS-CoV-2 and 9385 reported deaths as of April 10th, 2020. Patients with cancer comprised 8.4% of deceased individuals1. Population-based studies from China and Italy suggested a higher COVID-19 death rate in patients with cancer2,3, although there is a knowledge gap as to which aspects of cancer and its treatment confer risk of severe COVID-19 disease4. This information is critical to balance the competing safety considerations of reducing SARS-CoV-2 exposure and cancer treatment continuation. From March 10th to April 7th, 2020, 423 cases of symptomatic COVID-19 illness were diagnosed at Memorial Sloan Kettering Cancer Center (from a total of 2035 cancer patients tested). Of these, 40% were hospitalized for COVID-19 illness, 20% developed severe respiratory illness, (including 9% who required mechanical ventilation), and 12% died within 30 days. Age >65 years and treatment with immune checkpoint inhibitors (ICI) were predictors for hospitalization and severe disease, while receipt of chemotherapy and major surgery were not. Overall, COVID-19 illness in cancer patients is marked by substantial rates of hospitalization and severe outcomes. The association observed between ICI and COVID-19 outcomes in our study will need further interrogation in tumor-specific cohorts.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10−3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10−3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.