A novel evidence-based predictor tool for hospitalization and length of stay: insights from COVID-19 patients in New York city.

Predictive models for key outcomes of coronavirus disease 2019 (COVID-19) can optimize resource utilization and patient outcome. We aimed to design and internally validate a web-based calculator predictive of hospitalization and length of stay (LOS) in a large cohort of COVID-19-positive patients presenting to the Emergency Department (ED) in a New York City health system. The study cohort consisted of consecutive adult (> 18 years) patients presenting to the ED of Mount Sinai Health System hospitals between March 2020 and April 2020, diagnosed with COVID-19. Logistic regression was utilized to construct predictive models for hospitalization and prolonged (> 3 days) LOS. Discrimination was evaluated using area under the receiver operating curve (AUC). Internal validation with bootstrapping was performed, and a web-based calculator was implemented. From 5859 patients, 65% were hospitalized. Independent predictors of hospitalization and extended LOS included older age, chronic kidney disease, elevated maximum temperature, and low minimum oxygen saturation (p < 0.001). Additional predictors of hospitalization included male sex, chronic obstructive pulmonary disease, hypertension, and diabetes. AUCs of 0.881 and 0.770 were achieved for hospitalization and LOS, respectively. Elevated levels of CRP, creatinine, and ferritin were key determinants of hospitalization and LOS (p < 0.05). A calculator was made available under the following URL: https://covid19-outcome-prediction.shinyapps.io/COVID19_Hospitalization_Calculator/ . This study yielded internally validated models that predict hospitalization risk in COVID-19-positive patients, which can be used to optimize resource allocation. Predictors of hospitalization and extended LOS included older age, CKD, fever, oxygen desaturation, elevated C-reactive protein, creatinine, and ferritin.

Giant cellulitis-like Sweet syndrome: An underrecognized clinical variant mimicking skin and soft tissue infection.

A new clinical variant of Sweet syndrome, called giant cellulitis-like Sweet syndrome, can masquerade as cellulitis because the patients present with an acute onset of large erythematous plaques, fever, and leukocytosis with neutrophil predominance. This case describes a 90-year-old female with a history of invasive ductal carcinoma of the breast who presented with 3 days of erythema of the right chest and right leg. Physical examination was notable for well-demarcated, blanching erythematous rashes involving the right chest and right lower extremity. Laboratory data was notable for neutrophilic leukocytosis. A clinical diagnosis of cellulitis was made initially, and intravenous cefazolin was initiated. The rash had only partially improved with antibiotics. Skin biopsy revealed a dense neutrophilic infiltrate, which was consistent with Sweet syndrome. Based on the widespread plaques, this case was considered a "giant cellulitis-like" variant of Sweet syndrome. Clinicians should have a high index of suspicion for Sweet syndrome when assessing a patient with fever, neutrophilia and erythematous skin plaques atypical of cellulitis because this condition does not respond to antimicrobial therapy and requires systemic glucocorticoid therapy.

An insight into Clostridium difficile infection: a single-center retrospective case-control study.

BACKGROUND: Clostridium difficile infection (CDI) has become a worldwide health problem in view of its significant incidence and medical and economic impact on the health system. Prior studies have been undergone about risk factors and disease characteristics. We wanted to study the characteristics, prognostic factors associated with CDI at our institute, as well as a new prognostic factor. METHODS: Our study aimed at describing the risk factors, patient characteristics, and outcomes associated with healthcare facility-acquired CDI (HCFA-CDI) and community-acquired CDI (CA-CDI). We intended to identify the factors associated with worse outcomes. We evaluated the characteristics associated with CDI over 3 years. We also evaluated a simple neutrophil-lymphocyte ratio (NLR) and its predictive value for worse outcomes. RESULTS: Six hundred patients were enrolled (333 in a control group; 171 in the HCFA-CDI group and 96 in the CA-CDI group). NLR > 5 predicted increased mortality and intensive care unit transfer in all CDI if done as early as day 2 after CDI diagnosis. In HCFA-CDI, NLR > 5 predicted a higher ICU transfer if done as early as day 1 post-diagnosis and with increased mortality as early as day 2 post-diagnosis. In CA-CDI, NLR > 5 predicted a higher mortality and ICU transfer if done at least 4 days after diagnosis. Moreover, every 10-unit increase in NLR was associated with a significant increase in mortality and ICU transfer in patients with CDI. CONCLUSION: A timely use of NLR can be used as a mean to predict worse outcomes, namely ICU transfer and mortality, in patients with CDI.

Hepatitis C Infection Patterns at a Tertiary Care Center in New York: A Cross-Sectional Study.

Introduction In the United States, 2.7 to 3.9 million patients are infected with the hepatitis C virus (HCV) with 3,500 new cases reported yearly. According to the Centers for Disease Control and Prevention, HCV was the underlying or contributing cause of death of 19,659 patients in 2014. These facts underscore the need for a better understanding of the scope of this disease. Our epidemiologic study aimed at analyzing the pattern of occurrence of HCV infection at Staten Island University Hospital (SIUH) by evaluating the characteristics of newly infected patients with hepatitis C in 2014. The identified features served to better distinguish the targets for preventive health care in our particular population. Methodology A cross-sectional study of all newly diagnosed patients with HCV infections in the year 2014 presenting to SIUH was conducted using International Classification of Disease-9 codes (ICD-9) for hepatitis C. We included all patients with a positive HCV antibody confirmed by polymerase chain reaction testing. Patients were divided into groups according to age to simulate the age groups in the 2013 - 2014 Hepatitis B and C Annual Report of the New York City (NYC) Department of Health and Mental Hygiene published in 2016 (abbreviated to 2014 NYCDOH Report, hereafter). Gender and HCV genotypes were also collected. We compared disease frequency between age groups, gender, and genotype with the results of the 2014 NYCDOH Report. Results A total of 378 newly diagnosed HCV cases were identified; 60.05% were men, and 39.95% were women. The rate of infection with genotype 1a was the highest (36. 5%) followed by 1b (25.9%). In women, genotype 1b was predominant (13.76%) versus genotype 1a as the most common in men. The mean age was 54 years for men and 57 years for women. Most cases fell into the 60 to 69-year age group (32.28%), followed by the 50 to 59-year age group (31.48%). More so, all patients 80 years and older were exclusively women. Conclusions We found most new HCV infections at SIUH were diagnosed in patients aged 60 to 69 years, and the 2014 NYC DOH Report indicates most new HCV infections occur in patients aged 40 to 59 years. Also, all HCV infections detected in patients older than 80 years of age were found in women. These findings provide a better understanding of the patient demographics for appropriate HCV screening policies. Increased awareness and strict adherence to screening policies in baby boomers and high-risk populations are paramount in order to diagnose HCV infection early, offer therapy, and prevent HCV-related mortality and morbidity.