Causes and outcomes of non-chemotherapy induced neutropenic fever in hospitalized adults: An observational study.

Neutropenic fever in adults undergoing chemotherapy for cancer treatment is a medical emergency and has been the focus of numerous studies. However, there is a paucity of data about non-chemotherapy induced neutropenic fever (non-CINF). We retrospectively reviewed 383 adults with neutropenic fever hospitalized at one academic medical center between October 2015 and September 2020 to characterize the frequency, causes, and outcomes of non-CINF. Twenty-six percent of cases of neutropenic fever were non-chemotherapy induced. Among these, the major causes of neutropenia were hematologic malignancy, infection, and rheumatologic disease, and the major causes of fever were infections. Patients with non-CINF had a higher 30-day mortality than those with chemotherapy induced neutropenic fever (25% vs 13%, P = .01). Non-CINF constituted > 25% of neutropenic fever events in hospitalized adults and was associated with a high mortality rate.

A Predictive Algorithm for Discriminating Myeloid Malignancies and Leukemoid Reactions.

BACKGROUND: Adults presenting with a neutrophil-predominant leukocytosis (white cell count >50,000/µL) often necessitate urgent medical management. These patients are diagnosed with either acute presentations of chronic myeloid malignancies or leukemoid reactions, yet accurate models to distinguish between these entities do not exist. We used demographic and lab data to build a machine learning model capable of discriminating between these diagnoses. METHODS: The medical record at a tertiary care medical center was queried to identify adults with instances of white counts greater than 50,000/µL and >50% neutrophils from 2000 to 2021. For each patient, a full set of demographic and lab values were extracted at the time of their first presentation with a white count >50,000/µL. We generated a series of models in which the parameters most predictive of myeloid malignancies were identified, and a supervised machine learning approach was applied to the dataset. RESULTS: Our best model-using a support vector machine algorithm-produced a sensitivity of 96% and a specificity of 95.9% (area under the curve = 0.982) for identifying myeloid malignancies. We also identified a clinically meaningful and significant disparity in outcomes based on diagnosis-a 6-fold increase in 12-month mortality in those diagnosed with leukemoid reactions. CONCLUSIONS: These findings need to be validated but fill an unmet need for timely and accurate diagnosis in the setting of profound, neutrophil-predominant leukocytosis and support the use of predictive models as a means to improve patient outcomes.

Dietzia cinnamea: An increasingly recognized human pathogen.

The authors present the case of a bloodstream infection and sepsis due to Dietzia cinnamea in a severely malnourished patient with small bowel obstruction and pelvic abscess. The organism was identified with matrix-assisted laser desorption ionization-time-of-flight mass spectrometry. The authors discuss the characteristics, diagnosis, treatment, and emerging scope of clinical infection caused by Dietzia species.