Pediatric Antifungal Prescribing Patterns Identify Significant Opportunities to Rationalize Antifungal Use in Children.

OBJECTIVE: The need for pediatric antifungal stewardship programs has been driven by an increasing consumption of antifungals for prophylactic and empirical use. Drivers and rational of antifungal prescribing need to be identified to optimize prescription behaviors. METHODS: A prospective modified weekly Point Prevalence Survey capturing antifungal prescriptions for children (> 90 days to < 18 years of age) in 12 centers in England during 26 consecutive weeks was performed. Demographic, diagnostic and treatment information was collected for each patient. Data were entered into an online REDCap database. RESULTS: One thousand two hundred fifty-eight prescriptions were included for 656 pediatric patients, 44.9% were girls, with a median age of 6.4 years (interquartile range, 2.5-11.3). Most common underlying condition was malignancy (55.5%). Four hundred nineteen (63.9%) received antifungals for prophylaxis, and 237 (36.1%) for treatment. Among patients receiving antifungal prophylaxis, 40.2% did not belong to a high-risk group. In those receiving antifungal treatment, 45.9%, 29.4%, 5.1% and 19.6% had a diagnosis of suspected, possible, probable of proven invasive fungal disease (IFD), respectively. Proven IFD was diagnosed in 78 patients, 84.6% (n = 66) suffered from invasive candidiasis and 15.4% (n = 12) from an invasive mold infection. Liposomal amphotericin B was the most commonly prescribed antifungal for both prophylaxis (36.6%) and empiric and preemptive treatment (47.9%). Throughout the duration of the study, 72 (11.0%) patients received combination antifungal therapy. CONCLUSIONS: Antifungal use in pediatric patients is dominated by liposomal amphotericin B and often without evidence for the presence of IFD. A significant proportion of prophylactic and empiric antifungal use was seen in pediatric patients not at high-risk for IFD.

Validation of a classification system for treatment-related mortality in children with cancer.

BACKGROUND: Death not directly due to cancer has been termed 'treatment-related mortality' (TRM). Appreciating the differences between TRM and disease-related death is critical in directing strategies to improve supportive care, interventions delivered or disease progression. Recently, a global collaboration developed and validated a consensus-based classification tool and attribution system. OBJECTIVES: To evaluate the reliability of the newly developed consensus-based definition of TRM and explore the use of the cause-of-death attribution system outside the centre it was initially validated (Toronto, Canada). In the initial study, reviewers listed multiple causes of death. In this study, reviewers identified a primary cause for simplicity. SETTING: The paediatric haematology and oncology department at Leeds Teaching Hospital in Leeds, UK. PARTICIPANTS: Two consultants and two clinical research associates (CRAs). METHODS: Thirty medical records of the most recent deaths in children with cancer, 2 and 4 weeks prior to death, were anonymised and presented to the participants. Reviewers independently classified deaths as 'treatment related mortality' or 'not treatment related' according to the algorithm developed. When TRM occurred, reviewers applied the cause-of-death attribution system to identify the primary cause of death. Inter-relater reliability was assessed using the kappa statistic (k). MAIN OUTCOME: Inter-relater reliability between CRA and consultants. RESULTS: Reliability of the classification was deemed 'very good' between CRA and consultants (k=0.86, 95% CI 0.72 to 0.97). Ten deaths were classified as TRM, of which infection was the most frequent cause identified. Reviewers disagreed on the primary cause of death (eg, respiratory vs infection) when applying the cause-of-death attribution system in six cases and probable and possible causes in four cases. The study identified how the algorithm may not detect TRM in patients receiving non-curative therapy. CONCLUSIONS: The classification and cause of death attribution system could be implemented in different healthcare settings. Adaptation of the classification tool in patients receiving non-curative interventions and the cause of death attribution system should be considered.