Management and outcomes in secondary diabetes among pediatric patients hospitalized with hemophagocytic lymphohistiocytosis.

OBJECTIVES: Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening disorder marked by massive cytokine release from macrophage and T-cell activation. Hallmarks include fever, splenomegaly, cytopenias, hypertriglyceridemia, hypofibrinogemia, and elevations in ferritin and soluble IL-2 receptor. Given the association of HLH with inflammation and glucocorticoid therapy, the development of hyperglycemia is not unexpected. Descriptions of the prevalence of secondary diabetes in youth diagnosed with HLH are lacking. METHODS: Retrospective review from 2010 through 2019 of hospitalized youth 0-21 years diagnosed with HLH. The primary outcome of interest was the development of secondary diabetes, defined as a serum glucose 200 mg/dL or higher necessitating insulin therapy. RESULTS: Of 28 patients with HLH, 36 % (n=10) developed secondary diabetes. The only risk factor associated with secondary diabetes was an infectious cause of HLH (60 % vs. 27.8 %, p 0.041). Intravenous regular insulin was used in 80 % of patients with a mean duration of 9.5 days (2-24 days). Most (70 %) needed insulin within 5 days of starting steroids. Stays in the ICU were longer (median 20 vs. 3 days, p 0.007) and intubation more likely (90 vs. 45 %, p 0.041) among those with secondary diabetes. Mortality was high (16-30 %) regardless of insulin use (p 0.634). CONCLUSIONS: One-third of hospitalized pediatric patients with HLH developed secondary diabetes requiring insulin therapy. Insulin is typically started within 5 days of initiating steroids, limited to IV infusions, and often is not needed by discharge. Secondary diabetes was associated with longer ICU stays and heightened risk of intubation.

Data standards in pediatric oncology: Past, present, and future.

In this commentary, we highlight the central role that data standards play in facilitating data-driven efforts to advance research in pediatric oncology. We discuss the current state of data standards for pediatric oncology and propose five steps to achieve an improved future state with benefits for clinicians, researchers, and patients.

Aligning EHR Data for Pediatric Leukemia With Standard Protocol Therapy.

PURPOSE: Children with acute lymphoblastic leukemia (ALL) are treated according to risk-based protocols defined by the Children's Oncology Group (COG). Alignment between real-world clinical practice and protocol milestones is not widely understood. Aggregate deidentified electronic health record (EHR) data offer a useful resource to evaluate real-world clinical practice. METHODS: A cohort of children with ALL was identified in the Cerner Health Facts deidentified aggregate EHR data. Manual review identified candidate procedural milestones. Automated methods were developed to classify likely standard-risk precursor B-cell ALL patients. Milestone procedures were adjusted relative to initiation of therapy and then aligned to the COG protocols for standard induction therapy. RESULTS: We identified 7,728 patients with pediatric ALL with 188,187 encounters. Records for lumbar punctures (LP) and bone marrow biopsies were frequently present in the data and were appropriate targets to evaluate guideline performance. Alluvial graph analysis of 14 health systems indicated that none of the systems have data from all three COG-recommended lumbar procedures for all patients but alignment demonstrated that most systems test at the recommended times. CONCLUSION: Source-system variation introduces inconsistency and incompleteness into aggregate EHR data. Data visualization was helpful in characterizing and interpreting the data. Health systems with patients meeting the inclusion criteria demonstrated strong alignment with the recommended milestones for LP. Large-scale aggregate EHR data are useful to evaluate alignment of recommended versus actual clinical milestones in support of treating children with ALL. This work can inform other guideline and protocol driven care.

Discrimination of ventricular tachycardia from supraventricular tachycardia by a downloaded wavelet-transform morphology algorithm: a paradigm for development of implantable cardioverter defibrillator detection algorithms.

INTRODUCTION: Present implantable cardioverter defibrillators (ICDs) have algorithms that discriminate supraventricular tachycardia (SVT) from ventricular tachycardia (VT). One type of algorithm is based on differences in morphology of ventricular electrograms during VT and SVT. Prior SVT-VT discrimination algorithms have not undergone real-time evaluation in ambulatory patients until they were incorporated permanently into ICDs. This approach may result in incomplete testing of electrogram morphology algorithms because they are influenced by posture, activity, and electrogram maturation. We downloaded software into implanted ICDs to study a novel algorithm that compares morphologies of baseline and tachycardia electrograms based on differences between corresponding coefficients of their wavelet transforms. This comparison is expressed as a match-percent score. METHODS AND RESULTS: In 23 patients, we downloaded the wavelet algorithm into implanted ICDs to assess the temporal and postural stability of baseline electrograms as measured by this algorithm and its accuracy for SVT-VT discrimination. Median follow-up was 6 months. Software was downloaded into all ICDs without altering other device functions. With few exceptions, percent template match in baseline rhythm was stable with changes in body position, rest versus walking, isometric exercise, and over time (1 and 3 months). Using the nominal match-percent threshold of 70%, sensitivity for detection of 38 VTs was 100%. Specificity for rejection of 65 SVTs was 78%. SVTs were rejected for a total of 2.7 hours. Inappropriate detections of SVT as VT were caused by electrogram truncation, myopotential interference with low-amplitude electrograms, waveform alignment error, and rate-dependent aberrancy. The first three accounted for 69% of inappropriate detections and could have been prevented by optimal programming. The optimal match-percent threshold was 60% to 70% based on a receiver-operator characteristic curve. After shocks, the median time for baseline electrogram morphology to normalize was 85 seconds. CONCLUSION: The wavelet morphology algorithm has high sensitivity for VT detection. Inappropriate detections of SVT as VT may be reduced by optimal programming. Downloadable software permits evaluation of new algorithms in implanted ICDs.