Predictors of outcome in children with disorders of mitochondrial metabolism in the pediatric intensive care unit.

BACKGROUND: The aim of this study was to identify factors predicting outcome in patients with mitochondrial disease admitted to pediatric intensive care units (PICU). METHODS: Retrospective study of 2434 patients (age <21 years) admitted to a PICU from 1 January 2006 through 31 March 2016 and captured in the Virtual Pediatric Systems database with ICD9 diagnosis 277.87, disorders of mitochondrial metabolism. Factors influencing mortality and prolonged length of stay (≥14 days) were analyzed using logistic regression. RESULTS: Predictors independently affecting mortality (adjusted odds ratios and 95% confidence intervals, p < 0.05): age 1-23 months 3.4 (1.7-6.6) and mechanical ventilation 4.7 (2.6-8.6) were risk factors; post-operative 0.2 (0.1-0.6), readmission 0.5 (0.3-0.9), and neurologic reason for admittance 0.3 (0.1-0.9) were factors reducing risk. Predictors affecting prolonged length of stay: mechanical ventilation 7.4 (5.2-10.3) and infectious reason for admittance 2.0 (1.3-3.2) were risk factors, post-operative patients 0.3 (0.2-0.5) had lower risk. The utility of PRISM and PIM2 scores in this patient group was evaluated. CONCLUSIONS: The single most predictive factor for both mortality and prolonged length of stay is the presence of mechanical ventilation. Age 1-23 months is a risk factor for mortality, and infectious reason for admittance indicates risk for prolonged length of stay. IMPACT: Presence of mechanical ventilation is the factor most strongly associated with negative outcome in patients with mitochondrial disease in pediatric intensive care. Age 1-23 months is a risk factor for mortality, and infectious reason for admittance indicates risk for prolonged length of stay PRISM3 and PIM2 are not as accurate in patients with mitochondrial disease as in a mixed patient population.

Strategies for Success in Regulatory Readiness.

This article describes the formation of a Regulatory Advisory Council to address regulatory preparedness. The council used quality improvement methods to address data and findings from previous mock surveys and created 2 categories of work, an environment of care and clinical standards group, with checklists and work streams to improve organizational success with regulatory readiness.

Implementation of a Pragmatic Biomarker-Driven Algorithm to Guide Antibiotic Use in the Pediatric Intensive Care Unit: the Optimizing Antibiotic Strategies in Sepsis (OASIS) II Study.

BACKGROUND: Biomarkers can facilitate safe antibiotic discontinuation in critically ill patients without bacterial infection. METHODS: We tested the ability of a biomarker-based algorithm to reduce excess antibiotic administration in patients with systemic inflammatory response syndrome (SIRS) without bacterial infections (uninfected) in our pediatric intensive care unit (PICU). The algorithm suggested that PICU clinicians stop antibiotics if (1) C-reactive protein <4 mg/dL and procalcitonin <1 ng/mL at SIRS onset and (2) no evidence of bacterial infection by exam/testing by 48 hours. We evaluated excess broad-spectrum antibiotic use, defined as administration on days 3-9 after SIRS onset in uninfected children. Incidence rate ratios (IRRs) compared unadjusted excess length of therapy (LOT) in the 34 months before (Period 1) and 12 months after (Period 2) implementation of this algorithm, stratified by biomarker values. Segmented linear regression evaluated excess LOT among all uninfected episodes over time and between the periods. RESULTS: We identified 457 eligible SIRS episodes without bacterial infection, 333 in Period 1 and 124 in Period 2. When both biomarkers were below the algorithm's cut-points (n = 48 Period 1, n = 31 Period 2), unadjusted excess LOT was lower in Period 2 (IRR, 0.53; 95% confidence interval, 0.30-0.93). Among all 457 uninfected episodes, there were no significant differences in LOT (coefficient 0.9, P = .99) between the periods on segmented regression. CONCLUSIONS: Implementation of a biomarker-based algorithm did not decrease overall antibiotic exposure among all uninfected patients in our PICU, although exposures were reduced in the subset of SIRS episodes where biomarkers were low.

A Pragmatic Biomarker-Driven Algorithm to Guide Antibiotic Use in the Pediatric Intensive Care Unit: The Optimizing Antibiotic Strategies in Sepsis (OASIS) Study.

BACKGROUND.: Biomarkers that identify critically ill children with systemic inflammatory response syndrome (SIRS) at low risk for bacterial infection may help clinicians reduce unnecessary antibiotic use. METHODS.: We conducted a prospective cohort study of children with SIRS and suspected infection admitted to a pediatric intensive care unit from January 5, 2012 to March 7, 2014. We enrolled patients upon initiation of new antibiotics (Time 0) and measured a panel of 8 serum biomarkers daily over 72 hours. Microbiology, imaging, and clinical data were reviewed to classify bacterial infections using Centers for Disease Control and Prevention definitions. We identified cut points of biomarker combinations to maximize the negative predictive value (NPV) and specificity for bacterial infection. Excess antibiotics were calculated as days of therapy beyond day 2 after SIRS onset in patients without bacterial infection. RESULTS.: Infections were identified in 46 of 85 patients: bacterial (n = 22) and viral (24), whereas 39 patients had no infection identified. At Time 0, C-reactive protein (CRP) <5 mg/dL plus serum amyloid A <15.0 µg/mL had an NPV of 0.92 (95% confidence interval [CI], 0.79-1.0) and specificity of 0.54 (95% CI, 0.42-0.66) to identify patients without bacterial infection, whereas CRP <4 mg/dL plus procalcitonin <1.75 ng/mL had an NPV of 0.90 (95% CI, 0.79-1.0) and specificity of 0.43 (95% CI, 0.30-0.55). Patients without bacterial infection received a mean of 3.8 excess days of therapy. CONCLUSIONS.: Early measurement of select biomarkers can identify children with SIRS in whom antibiotics might be safely discontinued when there is no other objective evidence of infection at 48 hours.