Cat Scratch Disease: 9 Years of Experience at a Pediatric Center.

Background: A more complete understanding of the epidemiology, risk factors, and clinical features of cat scratch disease (CSD) in children could help guide patient care. Methods: We conducted a retrospective analysis of children presenting to a tertiary pediatric hospital system in Atlanta, Georgia between January 1, 2010 and December 31, 2018 who had serology, polymerase chain reaction, and/or cytopathological results consistent with a Bartonella henselae infection. We also retrospectively reviewed veterinary diagnostic results performed at the University of Georgia from 2018 to 2020 to ascertain the burden of bartonellosis in companion animals within the state. Results: We identified 304 children with CSD over 9 years with the largest proportion of diagnoses made during August (41 of 304, 13.5%) and September (47 of 304, 15.5%). The median age of child cases was 8.1 years (interquartile range [IQR], 5.4-12.1); 156 (51.3%) were female; 242 of 262 (92.4%) reported feline exposure; and 55 of 250 (22%) reported canine exposure of those with exposure histories documented in the medical record. Although lymphadenopathy was present on physical examination in the majority of cases (78.8%), atypical presentations lacking lymphadenopathy were also common (63 of 304, 20.7%). Among children with radiographic imaging, 20 of 55 (36.4%) had splenomegaly and 21 of 55 (38.1%) had splenic and/or hepatic microabscesses. Among veterinary data, Bartonella seroprevalence was 12 of 146 (8.2%), all among canines, with a geographic distribution that spanned the state of Georgia. Conclusions: Distinguishing clinical features of CSD included subacute regional lymphadenopathy in school-aged children in the late summer, almost all of whom had cat exposure. Atypical clinical manifestations of CSD were also commonly identified.

Utility of Fluorescence In Situ Hybridization Panel for Myelodysplastic Syndrome in Evaluation of Cytopenia in a Pediatric Hospital: A 5-Year Retrospective Review and Utilization Management.

BACKGROUND: MDS FISH was routinely ordered together with chromosome analysis for patients with cytopenia in our hospital. The utility of MDS FISH in the pediatric population is unknown. OBJECTIVE: To analyze the utility of fluorescence in situ hybridization panel for myelodysplastic syndrome (MDS FISH) in the management of patients with cytopenia. METHODS: We performed a retrospective review over a 5-year period, from 2009 to 2014 to determine whether chromosome analysis (CA) plus MDS FISH added useful information compared to chromosome analysis alone. Both CA and MDS FISH were performed on 253 bone marrow biopsies from 182 patients. RESULTS: CA was highly correlated with MDS FISH (P < .0001) and detected all of the abnormalities seen by MDS FISH in 93.7% of the cases. CA is less expensive and detects additional chromosomal abnormalities not tested in the myelodysplastic syndrome panel. We propose MDS FISH should be ordered when CA fails to give adequate results.

Medical alert management: a real-time adaptive decision support tool to reduce alert fatigue.

With the adoption of electronic medical records (EMRs), drug safety alerts are increasingly recognized as valuable tools for reducing adverse drug events and improving patient safety. However, even with proper tuning of the EMR alert parameters, the volume of unfiltered alerts can be overwhelming to users. In this paper, we design an adaptive decision support tool in which past cognitive overriding decisions of users are learned, adapted and used for filtering actions to be performed on current alerts. The filters are designed and learned based on a moving time window, number of alerts, overriding rates, and monthly overriding fluctuations. Using alerts from two separate years to derive filters and test performance, predictive accuracy rates of 91.3%-100% are achieved. The moving time window works better than a static training approach. It allows continuous learning and capturing of the most recent decision characteristics and seasonal variations in drug usage. The decision support system facilitates filtering of non-essential alerts and adaptively learns critical alerts and highlights them prominently to catch providers' attention. The tool can be plugged into an existing EMR system as an add-on, allowing real-time decision support to users without interfering with existing EMR functionalities. By automatically filtering the alerts, the decision support tool mitigates alert fatigue and allows users to focus resources on potentially vital alerts, thus reducing the occurrence of adverse drug events.

Improving Patient Safety through Medical Alert Management: An Automated Decision Tool to Reduce Alert Fatigue.

Drug safety alerts, a feature of electronic medical records (EMRs), are increasingly recognized as valuable tools for reducing adverse drug events and improving patient safety. However, there has also been increased understanding that alert fatigue, a state in which users become overwhelmed and unresponsive to alerts in general, is a threat to patient safety. In this paper, we seek to mitigate alert fatigue by filtering superfluous alerts. We design a method of predicting alert overrides based on past alert override rate, range in override rate, and sample size. Using a dataset from a large pediatric network, we retroactively test and validate our method. For the test implementation, alerts are filtered with 91-96% accuracy, depending on the parameter values selected. By filtering these alerts, we reduce alert fatigue and allow users to refocus resources to potentially vital alerts, reducing the occurrence of adverse drug events.