Child Opportunity Index and Pediatric Intensive Care Outcomes: A Multicenter Retrospective Study in the United States.

OBJECTIVES: To evaluate for associations between a child's neighborhood, as categorized by Child Opportunity Index (COI 2.0), and 1) PICU mortality, 2) severity of illness at PICU admission, and 3) PICU length of stay (LOS). DESIGN: Retrospective cohort study. SETTING: Fifteen PICUs in the United States. PATIENTS: Children younger than 18 years admitted from 2019 to 2020, excluding those after cardiac procedures. Nationally-normed COI category (very low, low, moderate, high, very high) was determined for each admission by census tract, and clinical features were obtained from the Virtual Pediatric Systems LLC (Los Angeles, CA) data from each site. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 33,901 index PICU admissions during the time period, median patient age was 4.9 years and PICU mortality was 2.1%. There was a higher percentage of admissions from the very low COI category (27.3%) than other COI categories (17.2-19.5%, p < 0.0001). Patient admissions from the high and very high COI categories had a lower median Pediatric Index of Mortality 3 risk of mortality (0.70) than those from the very low, low, and moderate COI groups (0.71) ( p < 0.001). PICU mortality was lowest in the very high (1.7%) and high (1.9%) COI groups and highest in the moderate group (2.5%), followed by very low (2.3%) and low (2.2%) ( p = 0.001 across categories). Median PICU LOS was between 1.37 and 1.50 days in all COI categories. Multivariable regression revealed adjusted odds of PICU mortality of 1.30 (95% CI, 0.94-1.79; p = 0.11) for children from a very low versus very high COI neighborhood, with an odds ratio [OR] of 0.996 (95% CI, 0.993-1.00; p = 0.05) for mortality for COI as an ordinal value from 0 to 100. Children without insurance coverage had an OR for mortality of 3.58 (95% CI, 2.46-5.20; p < 0.0001) as compared with those with commercial insurance. CONCLUSIONS: Children admitted to a cohort of U.S. PICUs were often from very low COI neighborhoods. Children from very high COI neighborhoods had the lowest risk of mortality and observed mortality; however, odds of mortality were not statistically different by COI category in a multivariable model. Children without insurance coverage had significantly higher odds of PICU mortality regardless of neighborhood.

The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research.

OBJECTIVE: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. DESIGN: Scoping review and expert opinion. SETTING: We queried CINAHL Plus with Full Text (EBSCO), Cochrane Library (Wiley), Embase (Elsevier), Ovid Medline, and PubMed for articles published between 2000 and 2022 related to machine learning concepts and pediatric critical illness. Articles were excluded if the majority of patients were adults or neonates, if unsupervised machine learning was the primary methodology, or if information related to the development, validation, and/or implementation of the model was not reported. Article selection and data extraction were performed using dual review in the Covidence tool, with discrepancies resolved by consensus. SUBJECTS: Articles reporting on the development, validation, or implementation of supervised machine learning models in the field of pediatric critical care medicine. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 5075 identified studies, 141 articles were included. Studies were primarily (57%) performed at a single site. The majority took place in the United States (70%). Most were retrospective observational cohort studies. More than three-quarters of the articles were published between 2018 and 2022. The most common algorithms included logistic regression and random forest. Predicted events were most commonly death, transfer to ICU, and sepsis. Only 14% of articles reported external validation, and only a single model was implemented at publication. Reporting of validation methods, performance assessments, and implementation varied widely. Follow-up with authors suggests that implementation remains uncommon after model publication. CONCLUSIONS: Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.

Clinical and Analytic Accuracy of Simultaneously Acquired Hemoglobin Measurements: A Multi-Institution Cohort Study to Minimize Redundant Laboratory Usage.

OBJECTIVES: Frequent diagnostic blood sampling contributes to anemia among critically ill children. Reducing duplicative hemoglobin testing while maintaining clinical accuracy can improve patient care efficacy. The objective of this study was to determine the analytical and clinical accuracy of simultaneously acquired hemoglobin measurements with different methods. DESIGN: Retrospective cohort study. SETTING: Two U.S. children's hospitals. PATIENTS: Children (< 18 yr old) admitted to the PICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We identified hemoglobin results from complete blood count (CBC) panels paired with blood gas (BG) panels and point-of-care (POC) devices. We estimated analytic accuracy by comparing hemoglobin distributions, correlation coefficients, and Bland-Altman bias. We measured clinical accuracy with error grid analysis and defined mismatch zones as low, medium, or high risk-based on deviance from unity and risk of therapeutic error. We calculated pairwise agreement to a binary decision to transfuse based on a hemoglobin value. Our cohort includes 49,004 ICU admissions from 29,926 patients, resulting in 85,757 CBC-BG hemoglobin pairs. BG hemoglobin was significantly higher (mean bias, 0.43-0.58 g/dL) than CBC hemoglobin with similar Pearson correlation ( R2 ) (0.90-0.91). POC hemoglobin was also significantly higher, but of lower magnitude (mean bias, 0.14 g/dL). Error grid analysis revealed only 78 (< 0.1%) CBC-BG hemoglobin pairs in the high-risk zone. For CBC-BG hemoglobin pairs, at a BG hemoglobin cutoff of greater than 8.0 g/dL, the "number needed to miss" a CBC hemoglobin less than 7 g/dL was 275 and 474 at each institution, respectively. CONCLUSIONS: In this pragmatic two-institution cohort of greater than 29,000 patients, we show similar clinical and analytic accuracy of CBC and BG hemoglobin. Although BG hemoglobin values are higher than CBC hemoglobin values, the small magnitude is unlikely to be clinically significant. Application of these findings may reduce duplicative testing and decrease anemia among critically ill children.

Clinical Decision Support in the PICU: Implications for Design and Evaluation.

OBJECTIVES: To assess the current landscape of clinical decision support (CDS) tools in PICUs in order to identify priority areas of focus in this field. DESIGN: International, quantitative, cross-sectional survey. SETTING: Role-specific, web-based survey administered in November and December 2020. SUBJECTS: Medical directors, bedside nurses, attending physicians, and residents/advanced practice providers at Pediatric Acute Lung Injury and Sepsis Network-affiliated PICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The survey was completed by 109 respondents from 45 institutions, primarily attending physicians from university-affiliated PICUs in the United States. The most commonly used CDS tools were people-based resources (93% used always or most of the time) and laboratory result highlighting (86%), with order sets, order-based alerts, and other electronic CDS tools also used frequently. The most important goal providers endorsed for CDS tools were a proven impact on patient safety and an evidence base for their use. Negative perceptions of CDS included concerns about diminished critical thinking and the burden of intrusive processes on providers. Routine assessment of existing CDS was rare, with infrequent reported use of observation to assess CDS impact on workflows or measures of individual alert burden. CONCLUSIONS: Although providers share some consensus over CDS utility, we identified specific priority areas of research focus. Consensus across practitioners exists around the importance of evidence-based CDS tools having a proven impact on patient safety. Despite broad presence of CDS tools in PICUs, practitioners continue to view them as intrusive and with concern for diminished critical thinking. Deimplementing ineffective CDS may mitigate this burden, though postimplementation evaluation of CDS is rare.

Joining Datasets Without Identifiers: Probabilistic Linkage of Virtual Pediatric Systems and PEDSnet.

OBJECTIVES: To 1) probabilistically link two important pediatric data sources, Virtual Pediatric Systems and PEDSnet, 2) evaluate linkage accuracy overall and in patients with severe sepsis or septic shock, and 3) identify variables important to linkage accuracy. DESIGN: Retrospective linkage of prospectively collected datasets from Virtual Pediatrics Systems, Inc (Los Angeles, CA) and the PEDSnet consortium. SETTING: Single-center academic PICU. PATIENTS: All PICU encounters between January 1, 2012, and December 31, 2017, that were deterministically matched between the two datasets. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We abstracted records from Virtual Pediatric Systems and PEDSnet corresponding to PICU encounters and probabilistically linked using 44 features shared by the two datasets. We generated a gold standard deterministic linkage using protected health information elements, which were then removed from datasets. We then calculated candidate pair log-likelihood ratios for all pairs of subjects and selected optimal pairs in a two-stage algorithm. A total of 22,051 gold standard PICU encounter pairs were identified over the study period. The optimal linkage model demonstrated excellent discrimination (area under the receiver operating characteristic curve > 0.99); 19,801 cases (89.9%) were matched with 13 false positives. The addition of two protected health information dates (admission month, birth day-of-year) increased to 20,189 (91.6%) the cases matched, with three false positives. Restricting to patients with Virtual Pediatric Systems diagnosis of severe sepsis or septic shock (n = 1,340 [6.1%]) matched 1,250 cases (93.2%) with zero false positives. Increased number of laboratory values present in the first 12 hours of admission significantly increased log-likelihood ratios, suggesting stronger candidate pair matching. CONCLUSIONS: We demonstrated the use of probabilistic linkage to accurately join two complementary pediatric critical care datasets at a single academic PICU in the absence of protected health information. Combining datasets with curated diagnoses and granular measurements can validate patient acuity metrics and facilitate multicenter machine learning algorithms. We anticipate these methods will generalize to other common PICU diagnoses.

Value of Procalcitonin Measurement for Early Evidence of Severe Bacterial Infections in the Pediatric Intensive Care Unit.

OBJECTIVES: To determine whether peak blood procalcitonin (PCT) measured within 48 hours of pediatric intensive care unit (PICU) admission can differentiate severe bacterial infections from sterile inflammation and viral infection and identify potential subgroups of PICU patients for whom PCT may not have clinical utility. STUDY DESIGN: This was a retrospective, observational study of 646 critically ill children who had PCT measured within 48 hours of admission to an urban, academic PICU. Patients were stratified into 6 categories by infection status. We compared test characteristics for peak PCT, C-reactive protein (CRP), white blood cell count (WBC), absolute neutrophil count (ANC), and % immature neutrophils. The area under the receiver operating characteristic curve was determined for each biomarker to discriminate bacterial infection. RESULTS: The area under the receiver operating characteristic curve was similar for PCT (0.73, 95% CI 0.69, 0.77) and CRP (0.75, 95% CI 0.71, 0.79; P = .36), but both outperformed WBC, ANC, and % immature neutrophils (P < .01 for all pairwise comparisons). The combination of PCT and CRP was no better than either PCT or CRP alone. Diagnostic patterns prone to false-positive and false-negative PCT values were identified. CONCLUSIONS: Peak blood PCT measured close to PICU admission was not superior to CRP in differentiating severe bacterial infection from viral illness and sterile inflammation; both PCT and CRP outperformed WBC, ANC, and % immature neutrophils. PCT appeared especially prone to inaccuracies in detecting localized bacterial central nervous system infections or bacterial coinfection in acute viral illness causing respiratory failure.

Life course health and healthcare utilization among adults with spina bifida.

AIM: To evaluate the prevalence of organ system disorders and describe healthcare utilization among adults with spina bifida at a regional clinic. METHOD: This study was a structured chart review using the Rochester Health Status Survey-IV. 65 males, 57 females aged 16 to 59 years were seen at the Spina Bifida Center of Central New York between January 2007 and December 2008 (annual hospitalization rate was 15 out of 100). RESULTS: Hospitalizations and acute outpatient visits were associated with having shunted hydrocephalus, whereas visits to the emergency department were associated with having a decubitus ulcer. Logistic regression models revealed that older adults made proportionately fewer visits to primary care providers than younger adults (odds ratio 0.919; p=0.02). Yet for every 1-year increase in age, the odds of being hospitalized increased by 5% (odds ratio 1.051; p=0.03). INTERPRETATION: Adults with spina bifida have multiple organ-system disorders. They have greater difficulty accessing services, and utilize emergency and inpatient healthcare at higher rates than the general population. In the future, adults with spina bifida will require access to more medical care and preventive services if they are to have optimal health, well-being, and functioning.

Probabilistic Linkage Creates a Novel Database to Study Bronchiolitis Care in the PICU.

OBJECTIVES: Lack of a comprehensive database containing diagnosis, patient and clinical characteristics, diagnostics, treatments, and outcomes limits needed comparative effectiveness research (CER) to improve care in the PICU. Combined, the Pediatric Hospital Information System (PHIS) and Virtual Pediatric Systems (VPS) databases contain the needed data for CER, but limits on the use of patient identifiers have thus far prevented linkage of these databases with traditional linkage methods. Focusing on the subgroup of patients with bronchiolitis, we aim to show that probabilistic linkage methods accurately link data from PHIS and VPS without the need for patient identifiers to create the database needed for CER. METHODS: We used probabilistic linkage to link PHIS and VPS records for patients admitted to a tertiary children's hospital between July 1, 2017 to June 30, 2019. We calculated the percentage of matched records, rate of false-positive matches, and compared demographics between matched and unmatched subjects with bronchiolitis. RESULTS: We linked 839 of 920 (91%) records with 4 (0.5%) false-positive matches. We found no differences in age (P = .76), presence of comorbidities (P = .16), admission illness severity (P = .44), intubation rate (P = .41), or PICU stay length (P = .36) between linked and unlinked subjects. CONCLUSIONS: Probabilistic linkage creates an accurate and representative combined VPS-PHIS database of patients with bronchiolitis. Our methods are scalable to join data from the 38 hospitals that jointly contribute to PHIS and VPS, creating a national database of diagnostics, treatment, outcome, and patient and clinical data to enable CER for bronchiolitis and other conditions cared for in the PICU.

Guidance for reporting analyses of metadata on electronic health record use.

INTRODUCTION: Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies. RECOMMENDATIONS: In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata-or measures of EHR use derived from them-to foster clarity, standardization, and reproducibility in this emerging and critical area of research.

Measuring Training Disruptions Using an Informatics Based Tool.

OBJECTIVE: Training disruptions, such as planned curricular adjustments or unplanned global pandemics, impact residency training in ways that are difficult to quantify. Informatics-based medical education tools can help measure these impacts. We tested the ability of a software platform driven by electronic health record data to quantify anticipated changes in trainee clinical experiences during the COVID-19 pandemic. METHODS: We previously developed and validated the Trainee Individualized Learning System (TRAILS) to identify pediatric resident clinical experiences (i.e. shifts, resident provider-patient interactions (rPPIs), and diagnoses). We used TRAILS to perform a year-over-year analysis comparing pediatrics residents at a large academic children's hospital during March 15-June 15 in 2018 (Control #1), 2019 (Control #2), and 2020 (Exposure). RESULTS: Residents in the exposure cohort had fewer shifts than those in both control cohorts (P < .05). rPPIs decreased an average of 43% across all PGY levels, with interns experiencing a 78% decrease in Continuity Clinic. Patient continuity decreased from 23% to 11%. rPPIs with common clinic and emergency department diagnoses decreased substantially during the exposure period. CONCLUSIONS: Informatics tools like TRAILS may help program directors understand the impact of training disruptions on resident clinical experiences and target interventions to learners' needs and development.

The association of auditory function measures with low-level methylmercury from oceanic fish consumption and mercury vapor from amalgam: The Seychelles Child Development Study Nutrition 1 Cohort.

BACKGROUND: Some authors have reported that low-level exposure to methylmercury (MeHg) adversely impacts measures of auditory function. These reports, however, are not consistent in their findings. Consequently, we examined auditory function in a population exposed to low-level methylmercury (MeHg) exposure from fish consumption and to mercury vapor (Hg0) from dental amalgams. We analyzed their associations with the participants hearing acuity, absolute and interwave ABR latencies, and otoacoustic emissions (distortion product/DPOAE and click evoked/CEOAE). DESIGN: We administered an audiometry test battery to 246 participants from the Seychelles Child Development Study (SCDS) Nutrition Cohort 1 (NC1) at 9 years of age. The test battery included standard pure-tone audiometry, tympanometry, Auditory Brainstem Responses (ABR) and Distortion Product and Click Evoked Otoacoustic Emissions (DPOAE and CEOAE) testing. We measured prenatal MeHg exposure in maternal hair and postnatal MeHg in children's hair. We approximated prenatal Hg0 exposure using maternal amalgam surface area and postnatal Hg0 using children amalgam surface area. Complete exposure records and audiometric data were available on 210 participants and in them we analyzed the association of MeHg and Hg0 exposures with auditory outcomes using covariate-adjusted linear regression models adjusted for sex and tympanometric pressure. RESULTS: Hg exposures were similar for both sexes. Seven of the 210 evaluable participants examined had either a mild (5) or moderate (2) hearing loss. Four had a mild monaural hearing loss and 3 had either a mild (1) or moderate (2) bilateral hearing loss. No participant had greater than a moderate hearing loss in either ear. Hg exposures were higher in participants with either a mild or moderate hearing loss, but these differences were not statistically significant. Among the 210 with complete data, neither prenatal nor postnatal MeHg nor Hg0 exposure was statistically significantly associated with any of the ABR endpoints (p > 0.05 for all 72 associations). Neither prenatal nor postnatal Hg0 exposure was associated with any of the OAE endpoints (p > 0.05). MeHg exposure was statistically associated with 6 of the 56 DPOAE endpoints (p-values between 0.0001 and 0.023), but none of the 40 CEOAE endpoints. Two of the associations occurred with prenatal MeHg exposures and 1 of those would suggest a beneficial effect. Four of the other associations occurred with postnatal MeHg exposures with only 2 found in left ears of both males and females and the other 2 in the left and right ear of females at only one frequency. CONCLUSION: Overall, these data do not present a clear and consistent pattern to suggest that the auditory system is negatively affected by low-level methylmercury exposure due to dietary consumption of oceanic fish or mercury vapor exposure from dental amalgams.

Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts.

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

Rescuing Auditory Temporal Processing with a Novel Augmented Acoustic Environment in an Animal Model of Congenital Hearing Loss.

Congenital sensorineural hearing loss (SNHL) affects thousands of infants each year and results in significant delays in speech and language development. Previous studies have shown that early exposure to a simple augmented acoustic environment (AAE) can limit the effects of progressive SNHL on hearing sensitivity. However, SNHL is also accompanied by hearing loss that is not assessed on standard audiological examinations, such as reduced temporal processing acuity. To assess whether sound therapy may improve these deficits, a mouse model of congenital SNHL was exposed to simple or temporally complex AAE. The DBA/2J mouse strain develops rapid, base to apex, progressive SNHL beginning at birth and is functionally deaf by six months of age. Hearing sensitivity and auditory brainstem function was measured using otoacoustic emissions, auditory brainstem response (ABR) and extracellular recording from the inferior colliculus (IC) in mice following exposure to 30 d of continuous AAE. Peripheral function and sound sensitivity in auditory midbrain neurons improved following exposure to both types of AAE. However, exposure to a novel, temporally complex AAE more strongly improved a measure of temporal processing acuity, neural gap-in-noise detection in the auditory midbrain. These experiments suggest that targeted sound therapy may be harnessed to improve hearing outcomes for children suffering from congenital SNHL.

Alert burden in pediatric hospitals: a cross-sectional analysis of six academic pediatric health systems using novel metrics.

BACKGROUND: Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden. OBJECTIVE: (1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics. MATERIALS AND METHODS: We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric. RESULTS: Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden. CONCLUSION: Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.

Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs.

OBJECTIVE: Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider-patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs). METHODS: We prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow. RESULTS: We surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19-278.56; ED: OR = 19.06, 95% CI: 9.53-41.65' inpatient: OR = 2.95, 95% CI: 2.23-3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%). CONCLUSION: Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.

Association Between Mobile Telephone Interruptions and Medication Administration Errors in a Pediatric Intensive Care Unit.

Importance: Incoming text messages and calls on nurses' mobile telephones may interrupt medication administration, but whether such interruptions are associated with errors has not been established. Objective: To assess whether a temporal association exists between mobile telephone interruptions and subsequent errors by pediatric intensive care unit (PICU) nurses during medication administration. Design, Setting, and Participants: A retrospective cohort study was performed using telecommunications and electronic health record data from a PICU in a children's hospital. Data were collected from August 1, 2016, through September 30, 2017. Participants included 257 nurses and the 3308 patients to whom they administered medications. Exposures: Primary exposures were incoming telephone calls and text messages received on the institutional mobile telephone assigned to the nurse in the 10 minutes leading up to a medication administration attempt. Secondary exposures were the nurse's PICU experience, work shift (day vs night), nurse to patient ratio, and level of patient care required. Main Outcomes and Measures: Primary outcome, errors during medication administration, was a composite of reported medication administration errors and bar code medication administration error alerts generated when nurses attempted to give medications without active orders for the patient whose bar code they scanned. Results: Participants included 257 nurses, of whom 168 (65.4%) had 6 months or more of PICU experience; and 3308 patients, of whom 1839 (55.6%) were male, 1539 (46.5%) were white, and 2880 (87.1%) were non-Hispanic. The overall rate of errors during 238 540 medication administration attempts was 3.1% (95% CI, 3.0%-3.3%) when nurses were uninterrupted by incoming telephone calls and 3.7% (95% CI, 3.4%-4.0%) when they were interrupted by such calls. During day shift, the odds ratios (ORs) for error when interrupted by calls (compared with uninterrupted) were 1.02 (95% CI, 0.92-1.13; P = .73) among nurses with 6 months or more of PICU experience and 1.22 (95% CI, 1.00-1.47; P = .046) among nurses with less than 6 months of experience. During night shift, the ORs for error when interrupted by calls were 1.35 (95% CI, 1.16-1.57; P < .001) among nurses with 6 months or more of PICU experience and 1.53 (95% CI, 1.16-2.03; P = .003) among nurses with less than 6 months of experience. Nurses administering medications to 1 or more patients receiving mechanical ventilation and arterial catheterization while caring for at least 1 other patient had an increased risk of error (OR, 1.21; 95% CI, 1.03-1.42; P = .02). Incoming text messages were not associated with error (OR, 0.97; 95% CI, 0.92-1.02; P = .22). Conclusions and Relevance: This study's findings suggest that incoming telephone call interruptions may be temporally associated with medication administration errors among PICU nurses. Risk of error varied by shift, experience, nurse to patient ratio, and level of patient care required.

Association of Audiometric Measures with plasma long chain polyunsaturated fatty acids in a high-fish eating population: The Seychelles Child Development Study.

OBJECTIVES: To determine if auditory function is associated with current long chain polyunsaturated fatty acids (LCPUFA) concentrations in a cohort of young adults who consume oceanic fish with naturally acquired methylmercury (MeHg). We measured participants plasma LCPUFA concentrations (total n-3, total n-6 and the n-6:n-3 ratio) and looked for an association with Auditory Brain Response (ABR) latencies and Otoacoustic Emissions (OAE) amplitudes. DESIGN: Auditory function of 534 participants from the Seychelles Child Development Study (SCDS) main cohort was examined at 19 years of age. Tests included standard pure-tone audiometry, tympanometry, ABR and both Click-Evoked OAE (CEOAE) and Distortion-Product OAE (DPOAE). Associations of LCPUFA status, measured at the time of examination, and auditory outcomes were examined using covariate-adjusted linear regression models. All models were adjusted for sex, prenatal and current MeHg exposure and hearing status. RESULTS: LCPUFA concentrations were similar for both sexes and when comparing participants with normal hearing (90.4 %) to those who had a sensorineural hearing loss in one or both ears (9.6 %). When looking at a subset of only hearing impaired participants, LCPUFA concentrations were similar in those participants who had a mild sensorineural hearing loss as compared with participants that had a moderate sensorineural hearing loss. LCPUFA concentrations were not correlated with current hair MeHg. LCPUFA concentrations were statistically significantly associated with only 6 of 174 ABR and OAE endpoints examined. Four of the 6 significant associations were present in only one sex. In female participants as n-6 concentrations increased, the ABR wave I absolute latency increased for a 60 dBnHL 19 click/sec stimulus. For male participants the interwave I-III latencies for a 60 dBnHL 69 clicks/sec stimulus increased as the n-6:n-3 LCPUFA ratio increased and the interwave I-V interval decreased for a 60 dBnHL 39 clicks/sec stimulus as the n-6 concentration increased. For both sexes interwave latencies were prolonged for the III-V interwave interval for an 80 dBnHL 39 clicks/sec as n-3 LCPUFA concentration increased. As the n-3 LCPUFA concentrations increased, the amplitude of the 6000 Hz DPOAE in the right ear increased for both sexes. As the n-6:n-3 ratio increased, the amplitude of the 1500 Hz DPOAE in the left ear decreased for females. The amplitude of the CEOAE was not associated with n-3, n-6 LCPUFA concentrations or the n-6:n-3 ratio. CONCLUSION: There was no evidence to suggest LCPUFA status was associated with hearing acuity, ABR latencies or OAE amplitudes, even though our participants tended to have higher LCPUFA concentrations as compared to individuals consuming a more western diet. No association was observed between LCPUFA status and a participants hearing status (normal hearing or hearing loss). Although we found a few associations between current plasma LCPUFA status and ABR and OAE auditory endpoints examined, no clear pattern exists. Some of these associations would be considered detrimental resulting in prolonged ABR latencies or smaller OAE amplitudes, while others would be considered beneficial resulting in shortened ABR latencies or larger OAE amplitudes.

Pediatric trainees systematically under-report duty hour violations compared to electronic health record defined shifts.

Duty hour monitoring is required in accredited training programs, however trainee self-reporting is onerous and vulnerable to bias. The objectives of this study were to use an automated, validated algorithm to measure duty hour violations of pediatric trainees over a full academic year and compare to self-reported violations. Duty hour violations calculated from electronic health record (EHR) logs varied significantly by trainee role and rotation. Block-by-block differences show 36.8% (222/603) of resident-blocks with more EHR-defined violations (EDV) compared to self-reported violations (SRV), demonstrating systematic under-reporting of duty hour violations. Automated duty hour tracking could provide real-time, objective assessment of the trainee work environment, allowing program directors and accrediting organizations to design and test interventions focused on improving educational quality.

Automatic Detection of Front-Line Clinician Hospital Shifts: A Novel Use of Electronic Health Record Timestamp Data.

OBJECTIVE: Excess physician work hours contribute to burnout and medical errors. Self-report of work hours is burdensome and often inaccurate. We aimed to validate a method that automatically determines provider shift duration based on electronic health record (EHR) timestamps across multiple inpatient settings within a single institution. METHODS: We developed an algorithm to calculate shift start and end times for inpatient providers based on EHR timestamps. We validated the algorithm based on overlap between calculated shifts and scheduled shifts. We then demonstrated a use case by calculating shifts for pediatric residents on inpatient rotations from July 1, 2015 through June 30, 2016, comparing hours worked and number of shifts by rotation and role. RESULTS: We collected 6.3 × 107 EHR timestamps for 144 residents on 771 inpatient rotations, yielding 14,678 EHR-calculated shifts. Validation on a subset of shifts demonstrated 100% shift match and 87.9 ± 0.3% overlap (mean ± standard error [SE]) with scheduled shifts. Senior residents functioning as front-line clinicians worked more hours per 4-week block (mean ± SE: 273.5 ± 1.7) than senior residents in supervisory roles (253 ± 2.3) and junior residents (241 ± 2.5). Junior residents worked more shifts per block (21 ± 0.1) than senior residents (18 ± 0.1). CONCLUSION: Automatic calculation of inpatient provider work hours is feasible using EHR timestamps. An algorithm to assess provider work hours demonstrated criterion validity via comparison with scheduled shifts. Differences between junior and senior residents in calculated mean hours worked and number of shifts per 4-week block were also consistent with differences in scheduled shifts and duty-hour restrictions.