Performance of AAP Clinical Practice Guideline for Febrile Infants at One Pediatric Hospital.

BACKGROUND: In the absence of procalcitonin, the American Academy of Pediatrics' clinical practice guideline (CPG) for evaluating and managing febrile infants recommends using previously untested combinations of inflammatory marker thresholds. Thus, CPG performance in detecting invasive bacterial infections (IBIs; bacteremia, bacterial meningitis) is poorly understood. OBJECTIVE: To evaluate CPG performance without procalcitonin in detecting IBIs in well-appearing febrile infants 8 to 60 days old. METHODS: For this cross-sectional, single-site study, we manually abstracted data for febrile infants using electronic health records from 2011 to 2018. We used CPG inclusion/exclusion criteria to identify eligible infants and stratified IBI risk with CPG inflammatory marker thresholds for temperature, absolute neutrophil count, and C-reactive protein. Because the CPG permits a wide array of interpretations, we performed 3 sensitivity analyses, modifying age and inflammatory marker thresholds. For each approach, we calculated area-under-the-receiver operating characteristic curve, sensitivity, and specificity in detecting IBIs. RESULTS: For this study, 507 infants met the inclusion criteria. For the main analysis, we observed an area-under-the-receiver operating characteristic curve of 0.673 (95% confidence interval 0.652-0.694), sensitivity of 100% (66.4%-100%), and specificity of 34.5% (30.4%-38.9%). For the sensitivity analyses, sensitivities were all 100% and specificities ranged from 9% to 38%. CONCLUSION: Findings suggest that the CPG is highly sensitive, minimizing missed IBIs, but specificity may be lower than previously reported. Future studies should prospectively investigate CPG performance in larger, multisite samples.

Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.

BACKGROUND: Febrile infants are at risk for invasive bacterial infections (IBIs) (i.e., bacteremia and bacterial meningitis), which, when undiagnosed, may have devastating consequences. Current IBI predictive models rely on serum biomarkers, which may not provide timely results and may be difficult to obtain in low-resource settings. OBJECTIVE: The aim of this study was to derive a clinical-based IBI predictive model for febrile infants. DESIGNS, SETTING, AND PARTICIPANTS: This is a cross-sectional study of infants brought to two pediatric emergency departments from January 2011 to December 2018. Inclusion criteria were age 0-90 days, temperature ≥38°C, and documented gestational age, fever duration, and illness duration. MAIN OUTCOME AND MEASURES: To detect IBIs, we used regression and ensemble machine learning models and evidence-based predictors (i.e., sex, age, chronic medical condition, gestational age, appearance, maximum temperature, fever duration, illness duration, cough status, and urinary tract inflammation). We up-weighted infants with IBIs 8-fold and used 10-fold cross-validation to avoid overfitting. We calculated the area under the receiver operating characteristic curve (AUC), prioritizing a high sensitivity to identify the optimal cut-point to estimate sensitivity and specificity. RESULTS: Of 2311 febrile infants, 39 had an IBI (1.7%); the median age was 54 days (interquartile range: 35-71). The AUC was 0.819 (95% confidence interval: 0.762, 0.868). The predictive model achieved a sensitivity of 0.974 (0.800, 1.00) and a specificity of 0.530 (0.484, 0.575). Findings suggest that a clinical-based model can detect IBIs in febrile infants, performing similarly to serum biomarker-based models. This model may improve health equity by enabling clinicians to estimate IBI risk in any setting. Future studies should prospectively validate findings across multiple sites and investigate performance by age.

Derivation of a natural language processing algorithm to identify febrile infants.

BACKGROUND: Diagnostic codes can retrospectively identify samples of febrile infants, but sensitivity is low, resulting in many febrile infants eluding detection. To ensure study samples are representative, an improved approach is needed. OBJECTIVE: To derive and internally validate a natural language processing algorithm to identify febrile infants and compare its performance to diagnostic codes. METHODS: This cross-sectional study consisted of infants aged 0-90 days brought to one pediatric emergency department from January 2016 to December 2017. We aimed to identify infants with fever, defined as a documented temperature ≥38°C. We used 2017 clinical notes to develop two rule-based algorithms to identify infants with fever and tested them on data from 2016. Using manual abstraction as the gold standard, we compared performance of the two rule-based algorithms (Models 1, 2) to four previously published diagnostic code groups (Models 5-8) using area under the receiver-operating characteristics curve (AUC), sensitivity, and specificity. RESULTS: For the test set (n = 1190 infants), 184 infants were febrile (15.5%). The AUCs (0.92-0.95) and sensitivities (86%-92%) of Models 1 and 2 were significantly greater than Models 5-8 (0.67-0.74; 20%-74%) with similar specificities (93%-99%). In contrast to Models 5-8, samples from Models 1 and 2 demonstrated similar characteristics to the gold standard, including fever prevalence, median age, and rates of bacterial infections, hospitalizations, and severe outcomes. CONCLUSIONS: Findings suggest rule-based algorithms can accurately identify febrile infants with greater sensitivity while preserving specificity compared to diagnostic codes. If externally validated, rule-based algorithms may be important tools to create representative study samples, thereby improving generalizability of findings.

Refinement and Validation of a Clinical-Based Approach to Evaluate Young Febrile Infants.

BACKGROUND AND OBJECTIVE: For febrile infants, predictive models to detect bacterial infections are available, but clinical adoption remains limited by implementation barriers. There is a need for predictive models using widely available predictors. Thus, we previously derived 2 novel predictive models (machine learning and regression) by using demographic and clinical factors, plus urine studies. The objective of this study is to refine and externally validate the predictive models. METHODS: This is a cross-sectional study of infants initially evaluated at one pediatric emergency department from January 2011 to December 2018. Inclusion criteria were age 0 to 90 days, temperature ≥38°C, documented gestational age, and insurance type. To reduce potential biases, we derived models again by using derivation data without insurance status and tested the ability of the refined models to detect bacterial infections (ie, urinary tract infection, bacteremia, and meningitis) in the separate validation sample, calculating areas-under-the-receiver operating characteristic curve, sensitivities, and specificities. RESULTS: Of 1419 febrile infants (median age 53 days, interquartile range = 32-69), 99 (7%) had a bacterial infection. Areas-under-the-receiver operating characteristic curve of machine learning and regression models were 0.92 (95% confidence interval [CI] 0.89-0.94) and 0.90 (0.86-0.93) compared with 0.95 (0.91-0.98) and 0.96 (0.94-0.98) in the derivation study. Sensitivities and specificities of machine learning and regression models were 98.0% (94.7%-100%) and 54.2% (51.5%-56.9%) and 96.0% (91.5%-99.1%) and 50.0% (47.4%-52.7%). CONCLUSIONS: Compared with the derivation study, the machine learning and regression models performed similarly. Findings suggest a clinical-based model can estimate bacterial infection risk. Future studies should prospectively test the models and investigate strategies to optimize clinical adoption.

An Interprofessional, Multimodal, Family-Centered Quality Improvement Project for Sleep Safety of Hospitalized Infants.

The American Academy of Pediatrics published expanded guidelines for infant safe sleep in 2011, expanding the definition from "back to sleep" to "safe to sleep," more fully describing risk factors and guidelines. In 2016, the guidelines were revised to promote "providers modeling safe sleep behavior" to the highest level of recommendation. Previous studies have addressed the difficulty in creating clear, consistent communication between health care providers and families during an infant's inpatient stay. This institutional update describes an interprofessional and family-centered quality improvement project to improve sleep safety for hospitalized infants through a multimodal approach. Five family-centered interventions were designed: a designated safe sleep web page, a clear bedside guide to safe sleep, additional training for nursing staff in motivational interviewing, a Kamishibai card audit system, and electronic health record smart phrases. These coordinated interventions reflect advantages of an interprofessional and family-centered approach: building rapport and achieving improvements to infant sleep safety.

Using Clinical History Factors to Identify Bacterial Infections in Young Febrile Infants.

OBJECTIVE: To develop a novel predictive model using primarily clinical history factors and compare performance to the widely used Rochester Low Risk (RLR) model. STUDY DESIGN: In this cross-sectional study, we identified infants brought to one pediatric emergency department from January 2014 to December 2016. We included infants age 0-90 days, with temperature ≥38°C, and documented gestational age and illness duration. The primary outcome was bacterial infection. We used 10 predictors to develop regression and ensemble machine learning models, which we trained and tested using 10-fold cross-validation. We compared areas under the curve (AUCs), sensitivities, and specificities of the RLR, regression, and ensemble models. RESULTS: Of 877 infants, 67 had a bacterial infection (7.6%). The AUCs of the RLR, regression, and ensemble models were 0.776 (95% CI 0.746, 0.807), 0.945 (0.913, 0.977), and 0.956 (0.935, 0.975), respectively. Using a bacterial infection risk threshold of .01, the sensitivity and specificity of the regression model was 94.6% (87.4%, 100%) and 74.5% (62.4%, 85.4%), compared with 95.5% (87.5%, 99.1%) and 59.6% (56.2%, 63.0%) using the RLR model. CONCLUSIONS: Compared with the RLR model, sensitivities of the novel predictive models were similar whereas AUCs and specificities were significantly greater. If externally validated, these models, by producing an individualized bacterial infection risk estimate, may offer a targeted approach to young febrile infants that is noninvasive and inexpensive.

Association of Cough Status With Bacterial Infections in Febrile Infants.

OBJECTIVES: To examine the association between cough status and bacterial infections (BIs) to more accurately stratify risk and predict BIs in febrile infants. METHODS: A retrospective cohort study was performed by identifying all infants ≤60 days old with temperature ≥38°C at an urban pediatric emergency department from 2014 to 2016. The Rochester Risk model was used to stratify risk. Cough status (with or without) was the main covariate of interest. The primary outcome was a BI, including urinary tract infection, bacteremia, or meningitis. Analyses consisted of descriptive statistics, simple and multiple regression to compare the odds of BI on the basis of cough status, as well as χ2 statistics to compare the BI rates among high-risk infants with and without cough. RESULTS: Of 508 febrile infants ≤60 days old, 46 (9.1%) had a BI, 13 of which were either bacteremia or meningitis. There were no BIs among low-risk infants with a cough. The odds of BI increased progressively, peaking at 14.6 (95% confidence interval: 4.3-49.7) for high-risk infants without a cough. The adjusted odds of BI among infants with cough was 0.47 (95% confidence interval: 0.22-0.99). CONCLUSIONS: In our findings, an inverse relationship is demonstrated between presence of cough and odds of BI, suggesting that cough status may be a useful marker of viral infections in febrile infants. Considering that detecting cough status is noninvasive, inexpensive, and immediately available, it represents an attractive value-based risk factor to enhance current BI prediction models.

Associations of Neighborhood-Level Social Determinants of Health with Bacterial Infections in Young, Febrile Infants.

OBJECTIVE: To examine the sociodemographic characteristics of one population of young, febrile infants and identify associations between neighborhood-level social determinants of health (SDHs) with bacterial infections. STUDY DESIGN: This was a retrospective cross sectional study of all infants ≤90 days old with a temperature of ≥38°C who presented in 2014 to the emergency department of an urban children's hospital in a large east coast city. The primary outcome was the presence of a bacterial infection, defined as a positive urine, blood, or cerebrospinal fluid culture that was treated clinically as a pathogen. The home address of each infant was geocoded and linked to neighborhood data based on census tract. Neighborhood-level SDHs included deprivation index, median household income, poverty, childhood poverty, social capital, and crowded housing. Associations were estimated using generalized estimating equations and negative binomial regression analysis. Models were adjusted for age, prematurity, and race/ethnicity. RESULTS: Of 232 febrile infants, the median age was 54 days, 58% were male, 49% were Hispanic, and 88% had public health insurance; 31 infants (13.4%) had a bacterial infection. In the adjusted analyses, the risk of bacterial infection among infants from neighborhoods with high rates of childhood poverty was >3 times higher (relative risk, 3.16; 95% CI, 1.04-9.6) compared with infants from neighborhoods with low rates of childhood poverty. CONCLUSIONS: Our findings suggest that SDHs may be associated with bacterial infections in young, febrile infants. If confirmed in subsequent studies, the inclusion of SDHs in predictive tools may improve accuracy in detecting bacterial infections among young, febrile infants.

The role of age and setting in adolescents' first drinking experience for predicting college problem drinking.

OBJECTIVE: The purpose of this study was to determine the reliability of longitudinally reporting age at first drink (AFD), and to test AFD and setting of first drink (SFD) as predictors of collegiate problem drinking. PARTICIPANTS: 338 first-year college students were interviewed multiple times during their first academic year, from May 2011 through August 2012. METHODS: AFD, SFD, and problem drinking were measured using the Alcohol Use Disorders Identification Test (AUDIT) during the first year of college. Bivariate analysis and parsimonious multivariate linear regression model were conducted. RESULTS: 62% of respondents were inconsistent in reporting AFD over time. Social SFD was the strongest independent predictor for higher AUDIT scores (b = 4.74, 95% confidence interval; 1.91, 7.57; p = .002). CONCLUSIONS: Findings suggest caution should be used in relying upon using AFD as a sole predictor of problem drinking. SFD may be a complementary measure to identify students at high risk of collegiate problem drinking.

Roles of Clinician, Patient, and Community Characteristics in the Management of Pediatric Upper Respiratory Tract Infections.

PURPOSE: Prior studies have evaluated factors predictive of inappropriate antibiotic prescription for upper respiratory tract infections (URIs). Community factors, however, have not been examined. The aim of this study was to evaluate the roles of patient, clinician, and community factors in predicting appropriate management of URIs in children. METHODS: We used a novel database exchange, linking electronic health record data with community statistics, to identify all patients aged 3 months to 18 years in whom URI was diagnosed in the period from 2007 to 2012. We followed the Healthcare Effectiveness Data and Information Set (HEDIS) quality measurement titled "Appropriate treatment for children with upper respiratory infection" to determine the rate of appropriate management of URIs. We then stratified data across individual and community characteristics and used multiple logistic regression modeling to identify variables that independently predicted antibiotic prescription. RESULTS: Of 20,581 patients, the overall rate for appropriate management for URI was 93.5%. Family medicine clinicians (AOR = 1.5; 95% CI 1.31, 1.71; reference = pediatric clinicians), urgent care clinicians (AOR = 2.23; 95% CI 1.93, 2.57; reference = pediatric clinicians), patients aged 12 to 18 years (AOR = 1.44; 95% CI 1.25, 1.67; reference = age 3 months to 4 years), and patients of white race/ ethnicity (AOR = 1.83; 95% CI 1.41, 2.37; reference = black non-Hispanic) were independently predictive of antibiotic prescription. No community factors were independently predictive of antibiotic prescription. CONCLUSIONS: Results correlate with prior studies in which non-pediatric clinicians and white race/ethnicity were predictive of antibiotic prescription, while association with older patient age has not been previously reported. Findings illustrate the promise of linking electronic health records with community data to evaluate health care disparities.

A survey of certification for cardiopulmonary resuscitation in high school athletic coaches.

BACKGROUND: Cardiopulmonary resuscitation (CPR) can increase survival in instances of sudden cardiac arrest. Nationally, high school coaches are the first responders to sudden cardiac arrest in up to one-third of high school athlete collapses, but little is known about the status of their CPR certification. The primary goal of this study was to assess the proportion of Wisconsin high school coaches that are certified in CPR. METHODS: A prospective web-based survey was developed and distributed to high school athletic directors in Wisconsin. RESULTS: Seventy-eight percent of respondents reported that coaches are the primary responders to a collapse. The majority of high schools do not require CPR certification and only 50% of coaches are currently CPR certified. Athletic directors with greater than 12 years of experience were the most likely to have an emergency action plan in place (P= 0.004). CONCLUSION: In Wisconsin, the proportion of coaches who act as the primary responder to a collapse is greater than previously reported. Although the majority of coaches in Wisconsin serve as the primary responder to an episode of sudden cardiac arrest, only about 50% are CPR certified. Due to the severe consequences of sudden cardiac arrest, CPR certification among coaches should be required.

Pediatric global health education: correlation of website information and curriculum.

OBJECTIVE: Web sites describing residency programs are initial sources of information for applicants. The correlation of global health content on pediatric residency program Web sites with reported curricula is unknown. To determine the accuracy of global health education, information on program Web sites was compared with queried program content responses. STUDY DESIGN: The Fellowship and Residency Electronic Interactive Database was used to assess pediatric residency programs' Web sites for global health education, applying American Academy of Pediatrics consensus guidelines. The authors developed a questionnaire using these consensus guidelines and contacted each program to assess Web site findings, and χ(2) tests were used to compare data from these 2 sources. RESULTS: Of 194 programs, 177 had operational Web sites, of which 98 participated in the questionnaire (55%). Ninety-three of 177 programs (53%) reported global health education on Web sites, whereas 80 of 98 programs (82%) reported global health education through direct questioning (P < .001). Results include provision of resident salaries during global health elective (Web site 5% vs questionnaire 98%, P < .001), mandatory training before global health elective (8% vs 20%, P = .02), presence of global health elective curriculum (24% vs 75%, P < .001), postexperience debriefing (16% vs 29%, P = .05), and bidirectional resident exchange (2% vs 13%, P = .01). CONCLUSIONS: Results indicate continued expansion of pediatric global health education, but significant differences exist between information on Web sites and data obtained through direct questioning. Accurate representation of global health opportunities would allow for more informed decision-making among prospective applicants. Findings also suggest substantial variability in global health curricula that needs to be addressed through improved planning and cooperation among training programs.