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ObjectivesPediatric cardiac ICUs should be adept at treating both critical medical and surgical conditions for patients with cardiac disease. There are no case-mix adjusted quality metrics specific to medical cardiac ICU admissions. We aimed to measure case-mix adjusted cardiac ICU medical mortality rates and assess variation across cardiac ICUs in the Pediatric Cardiac Critical Care Consortium.DesignObservational analysis.SettingPediatric Cardiac Critical Care Consortium clinical registry.PatientsAll cardiac ICU admissions that did not include cardiac surgery.InterventionsNone.Measurements and main resultsThe primary endpoint was cardiac ICU mortality. Based on multivariable logistic regression accounting for clustering, we created a case-mix adjusted model using variables present at cardiac ICU admission. Bootstrap resampling (1,000 samples) was used for model validation. We calculated a standardized mortality ratio for each cardiac ICU based on observed-to-expected mortality from the fitted model. A cardiac ICU was considered a statistically significant outlier if the 95% CI around the standardized mortality ratio did not cross 1. Of 11,042 consecutive medical admissions from 25 cardiac ICUs (August 2014 to May 2017), the observed mortality rate was 4.3% (n = 479). Final model covariates included age, underweight, prior surgery, time of and reason for cardiac ICU admission, high-risk medical diagnosis or comorbidity, mechanical ventilation or extracorporeal membrane oxygenation at admission, and pupillary reflex. The C-statistic for the validated model was 0.87, and it was well calibrated. Expected mortality ranged from 2.6% to 8.3%, reflecting important case-mix variation. Standardized mortality ratios ranged from 0.5 to 1.7 across cardiac ICUs. Three cardiac ICUs were outliers; two had lower-than-expected (standardized mortality ratio 1) mortality.ConclusionsWe measured case-mix adjusted mortality for cardiac ICU patients with critical medical conditions, and provide the first report of variation in this quality metric within this patient population across Pediatric Cardiac Critical Care Consortium cardiac ICUs. This metric will be used by Pediatric Cardiac Critical Care Consortium cardiac ICUs to assess and improve outcomes by identifying high-performing (low-mortality) centers and engaging in collaborative learning.

BackgroundResistance to intravenous immunoglobulin (IVIG) occurs in 10-20% of patients with Kawasaki disease (KD). The risk of resistance is about two-fold higher in patients with elevated gamma glutamyl transferase (GGT) levels. We sought to understand the biological mechanisms underlying IVIG resistance in patients with elevated GGT levels.MethodWe explored the association between elevated GGT levels and IVIG-resistance with a cohort of 686 KD patients (Cohort I). Gene expression data from 130 children with acute KD (Cohort II) were analyzed using the R square statistic and false discovery analysis to identify genes that were differentially represented in patients with elevated GGT levels with regard to IVIG responsiveness. Two additional KD cohorts (Cohort III and IV) were used to test the hypothesis that sialylation and GGT may be involved in IVIG resistance through neutrophil apoptosis.ResultsThirty-six genes were identified that significantly explained the variations of both GGT levels and IVIG responsiveness in KD patients. After Bonferroni correction, significant associations with IVIG resistance persisted for 12 out of 36 genes among patients with elevated GGT levels and none among patients with normal GGT levels. With the discovery of ST6GALNAC3, a sialyltransferase, as the most differentially expressed gene, we hypothesized that sialylation and GGT are involved in IVIG resistance through neutrophil apoptosis. We then confirmed that in Cohort III and IV there was significantly less reduction in neutrophil count in IVIG non-responders.ConclusionsGene expression analyses combining molecular and clinical datasets support the hypotheses that: (1) neutrophil apoptosis induced by IVIG may be a mechanism of action of IVIG in KD; (2) changes in sialylation and GGT level in KD patients may contribute synergistically to IVIG resistance through blocking IVIG-induced neutrophil apoptosis. These findings have implications for understanding the mechanism of action in IVIG resistance, and possibly for development of novel therapeutics.

BackgroundAn easily accessible real-time Web-based utility to assess patient risks of future emergency department (ED) visits can help the health care provider guide the allocation of resources to better manage higher-risk patient populations and thereby reduce unnecessary use of EDs. ObjectiveOur main objective was to develop a Health Information Exchange-based, next 6-month ED risk surveillance system in the state of Maine. MethodsData on electronic medical record (EMR) encounters integrated by HealthInfoNet (HIN), Maine’s Health Information Exchange, were used to develop the Web-based surveillance system for a population ED future 6-month risk prediction. To model, a retrospective cohort of 829,641 patients with comprehensive clinical histories from January 1 to December 31, 2012 was used for training and then tested with a prospective cohort of 875,979 patients from July 1, 2012, to June 30, 2013. ResultsThe multivariate statistical analysis identified 101 variables predictive of future defined 6-month risk of ED visit: 4 age groups, history of 8 different encounter types, history of 17 primary and 8 secondary diagnoses, 8 specific chronic diseases, 28 laboratory test results, history of 3 radiographic tests, and history of 25 outpatient prescription medications. The c-statistics for the retrospective and prospective cohorts were 0.739 and 0.732 respectively. Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. Cluster analysis in both the retrospective and prospective analyses revealed discrete subpopulations of high-risk patients, grouped around multiple “anchoring” demographics and chronic conditions. With the Web-based population risk-monitoring enterprise dashboards, the effectiveness of the active case finding algorithm has been validated by clinicians and caregivers in Maine. ConclusionsThe active case finding model and associated real-time Web-based app were designed to track the evolving nature of total population risk, in a longitudinal manner, for ED visits across all payers, all diseases, and all age groups. Therefore, providers can implement targeted care management strategies to the patient subgroups with similar patterns of clinical histories, driving the delivery of more efficient and effective health care interventions. To the best of our knowledge, this prospectively validated EMR-based, Web-based tool is the first one to allow real-time total population risk assessment for statewide ED visits.

PARTICIPATION IN RESEARCH during residency training is recognized as an important component of physician education. (1-9) Although many residents express interest in pursuing research during residency, rates of participation tend [...], Resident physicians from a pediatric academic training program developed a hospital-wide research project in an effort to enhance their residency research experience. In this model, residents themselves assumed primary responsibility for each stage of a large prospective clinical research study. The project, which was integrated successfully into the residency program, enabled a large group of residents, with mentorship from a dedicated faculty member, to benefit from a structured clinical research experience while providing the flexibility necessary to meet the demands of a busy residency curriculum. Careful topic selection with a well-defined end point, faculty involvement, resident collegiality, and institutional support were factors identified by study leaders as central to the success of this model. Key Words: research, resident education/training, residency/internship