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.

A multiscale continuum model of the vertebrate outer retina: The temporal dynamics of background-induced flicker enhancement.

The retina is a part of the central nervous system that is accessible, well documented, and studied by researchers spanning the clinical, experimental, and theoretical sciences. Here, we mathematically model the subcircuits of the outer plexiform layer of the retina on two spatial scales: that of an individual synapse and that of the scale of the receptive field (hundreds to thousands of synapses). To this end we formulate a continuum spine model (a partial differential equation system) that incorporates the horizontal cell syncytium and its numerous processes (spines) within cone pedicles. With this multiscale modeling approach, detailed biophysical mechanisms at the synaptic level are retained while scaling up to the receptive field level. As an example of its utility, the model is applied to study background-induced flicker enhancement in which the onset of a dim background enhances the center flicker response of horizontal cells. Simulation results, in comparison with flicker enhancement data for square, slit, and disk test regions, suggest that feedback mechanisms that are voltage-axis modulators of cone calcium channels (for example, ephaptic and/or pH feedback) are robust in capturing the temporal dynamics of background-induced flicker enhancement. The value and potential of this continuum spine approach is that it provides a framework for mathematically modeling the input-output properties of the entire receptive field of the outer retina while implementing the latest models for transmission mechanisms at the synaptic level.

Drift-diffusion simulation of the ephaptic effect in the triad synapse of the retina.

Experimental evidence suggests the existence of a negative feedback pathway between horizontal cells and cone photoreceptors in the outer plexiform layer of the retina that modulates the flow of calcium ions into the synaptic terminals of cones. However, the underlying mechanism for this feedback is controversial and there are currently three competing hypotheses: the ephaptic hypothesis, the pH hypothesis, and the GABA hypothesis. The goal of this investigation is to demonstrate the ephaptic hypothesis by means of detailed numerical simulations. The drift-diffusion (Poisson-Nernst-Planck) model with membrane boundary current equations is applied to a realistic two-dimensional cross-section of the triad synapse in the goldfish retina to verify the existence of strictly electrical feedback, as predicted by the ephaptic hypothesis. The effect on electrical feedback from the behavior of the bipolar cell membrane potential is also explored. The computed steady-state cone calcium transmembrane current-voltage curves for several cases are presented and compared with experimental data on goldfish. The results provide convincing evidence that an ephaptic mechanism can produce the feedback effect seen in experiments. The model and numerical methods presented here can be applied to any neuronal circuit where dendritic spines are invaginated in presynaptic terminals or boutons.

Comparing Outcomes for Emergent Cranial Neurosurgical Procedures Performed "During Hours" and "After Hours".

OBJECTIVE: Surgery performed at night and on weekends is thought to be associated with increased complications. However, the impact of time of day on outcomes has not been studied within cranial neurosurgery. We aim to determine if there are differences in outcomes for cranial neurosurgery performed after hours (AH) compared with during hours (DH). METHODS: We performed a single-center retrospective study of cranial neurosurgery patients who underwent emergent surgery from January 2015 through December 2019. Surgery was considered DH if the incision occurred between 8 am and 5 pm Monday through Friday. We assessed outcome measures for differences between operations performed DH or AH. RESULTS: Three-hundred and ninety-three patients (114 DH, 279 AH) underwent surgery. There was a lower rate of return to the operating room within 30 days for AH (8.6%) compared with DH (14.0%), P = 0.03, on multivariate analysis. There were no significant differences in length of operation, estimated blood loss, improvement in Glasgow Coma Scale, intensive care unit and total hospital length of stay, 30-day readmission, 30-day mortality, and in-hospital mortality for cases performed DH compared with AH. Further subgroup analyses were performed for patients who underwent immediate surgery for subdural hematomas, with no differences noted in outcomes on multivariate analysis. CONCLUSIONS: This study suggests that operating AH does not appear to negatively impact outcomes when compared with operating DH, in cases of cranial neurosurgical emergencies. Further study assessing the impact on elective neurosurgical cases is required.

Youth's energy intake during a laboratory-based loss-of-control eating paradigm: Associations with reported current dieting.

Dieting is theorized as a risk factor for loss-of-control (LOC)-eating (i.e., feeling a sense of lack of control while eating). Support for this association has largely relied on retrospective self-report data, which does not always correlate with objectively assessed eating behavior in youth. We hypothesized that during a laboratory-based LOC-eating paradigm, children and adolescents who reported current (at the time of the visit) dieting would consume meals consistent with LOC-eating (greater caloric intake, and intake of carbohydrates and fats, but less intake of protein). Participants were presented with a buffet-style meal and instructed to "Let yourself go and eat as much as you want." Current dieting (i.e., any deliberate change to the amount or type of food eaten to influence shape or weight, regardless of how effective the changes are) was assessed via interview. General linear models were adjusted for fat mass (%), lean mass (kg), height, sex, protocol, race and ethnicity, pre-meal hunger and minutes since consumption of a breakfast shake. Of 337 participants (Mage 12.8 ± 2.7y; 62.3 % female; 45.7 % non- Hispanic White and 26.1 % non-Hispanic Black; MBMIz 0.78 ± 1.11), only 33 (9.8 %) reported current dieting. Current dieting was not significantly associated with total energy intake (F = 1.63, p = .20, ηp2 = 0.005), or intake from carbohydrates (F = 2.45, p = .12, ηp2 = 0.007), fat (F = 2.65, p = .10, ηp2 = 0.008), or protein (F = 0.39, p = .53, ηp2 = 0.001). Contrary to theories that dieting promotes LOC-eating, current dieting was not associated with youth's eating behavior in a laboratory setting. Experimental approaches for investigating dieting are needed to test theories that implicate dieting in pediatric LOC-eating.

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.

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.

Electrodiffusion model simulation of the potassium channel.

The drift-diffusion (Poisson-Nernst-Planck) model is applied to the potassium channel in a biological membrane plus surrounding solution baths. Two-dimensional cylindrically symmetric simulations of the K channel in KCl solutions are presented which show significant boundary layers at the ends of the channel and display the spreading of charge into the bath regions. The computed current-voltage curve shows excellent agreement with experimental measurements. In addition, the response of the K channel to time-dependent applied voltages is investigated.

Mobile Software as a Medical Device (SaMD) for the Treatment of Epilepsy: Development of Digital Therapeutics Comprising Behavioral and Music-Based Interventions for Neurological Disorders.

Digital health technologies for people with epilepsy (PWE) include internet-based resources and mobile apps for seizure management. Since non-pharmacological interventions, such as listening to specific Mozart's compositions, cognitive therapy, psychosocial and educational interventions were shown to reduce epileptic seizures, these modalities can be integrated into mobile software and delivered by mobile medical apps as digital therapeutics. Herein, we describe: (1) a survey study among PWE about preferences to use mobile software for seizure control, (2) a rationale for developing digital therapies for epilepsy, (3) creation of proof-of-concept mobile software intended for use as an adjunct digital therapeutic to reduce seizures, and (4) broader applications of digital therapeutics for the treatment of epilepsy and other chronic disorders. A questionnaire was used to survey PWE with respect to preferred features in a mobile app for seizure control. Results from the survey suggested that over 90% of responders would be interested in using a mobile app to manage their seizures, while 75% were interested in listening to specific music that can reduce seizures. To define digital therapeutic for the treatment of epilepsy, we designed and created a proof-of-concept mobile software providing digital content intended to reduce seizures. The rationale for all components of such digital therapeutic is described. The resulting web-based app delivered a combination of epilepsy self-care, behavioral interventions, medication reminders and the antiseizure music, such as the Mozart's sonata K.448. To improve long-term patient engagement, integration of mobile medical app with music and multimedia streaming via smartphones, tablets and computers is also discussed. This work aims toward development and regulatory clearance of software as medical device (SaMD) for seizure control, yielding the adjunct digital therapeutic for epilepsy, and subsequently a drug-device combination product together with specific antiseizure medications. Mobile medical apps, music, therapeutic video games and their combinations with prescription medications present new opportunities to integrate pharmacological and non-pharmacological interventions for PWE, as well as those living with other chronic disorders, including depression and pain.

Residency Surgical Training at an Independent Academic Medical Center.

Independent academic medical centers have been training surgeons for more than a century; this environment is distinct from university or military programs. There are several advantages to training at a community program, including a supportive learning environment with camaraderie between residents and faculty, early and broad operative experience, and improved graduate confidence. Community programs also face challenges, such as resident recruitment and faculty engagement. With the workforce needs for general surgeons, independent training programs will continue to play an integral role.

The allelic spectrum of Charcot-Marie-Tooth disease in over 17,000 individuals with neuropathy.

We report the frequency, positive rate, and type of mutations in 14 genes (PMP22, GJB1, MPZ, MFN2, SH3TC2, GDAP1, NEFL, LITAF, GARS, HSPB1, FIG4, EGR2, PRX, and RAB7A) associated with Charcot-Marie-Tooth disease (CMT) in a cohort of 17,880 individuals referred to a commercial genetic testing laboratory. Deidentified results from sequencing assays and multiplex ligation-dependent probe amplification (MLPA) were analyzed including 100,102 Sanger sequencing, 2338 next-generation sequencing (NGS), and 21,990 MLPA assays. Genetic abnormalities were identified in 18.5% (n = 3312) of all individuals. Testing by Sanger and MLPA (n = 3216) showed that duplications (dup) (56.7%) or deletions (del) (21.9%) in the PMP22 gene accounted for the majority of positive findings followed by mutations in the GJB1 (6.7%), MPZ (5.3%), and MFN2 (4.3%) genes. GJB1 del and mutations in the remaining genes explained 5.3% of the abnormalities. Pathogenic mutations were distributed as follows: missense (70.6%), nonsense (14.3%), frameshift (8.7%), splicing (3.3%), in-frame deletions/insertions (1.8%), initiator methionine mutations (0.8%), and nonstop changes (0.5%). Mutation frequencies, positive rates, and the types of mutations were similar between tests performed by either Sanger (n = 17,377) or NGS (n = 503). Among patients with a positive genetic finding in a CMT-related gene, 94.9% were positive in one of four genes (PMP22, GJB1, MPZ, or MFN2).

Skeletal adaptation to intramedullary pressure-induced interstitial fluid flow is enhanced in mice subjected to targeted osteocyte ablation.

Interstitial fluid flow (IFF) is a potent regulatory signal in bone. During mechanical loading, IFF is generated through two distinct mechanisms that result in spatially distinct flow profiles: poroelastic interactions within the lacunar-canalicular system, and intramedullary pressurization. While the former generates IFF primarily within the lacunar-canalicular network, the latter generates significant flow at the endosteal surface as well as within the tissue. This gives rise to the intriguing possibility that loading-induced IFF may differentially activate osteocytes or surface-residing cells depending on the generating mechanism, and that sensation of IFF generated via intramedullary pressurization may be mediated by a non-osteocytic bone cell population. To begin to explore this possibility, we used the Dmp1-HBEGF inducible osteocyte ablation mouse model and a microfluidic system for modulating intramedullary pressure (ImP) to assess whether structural adaptation to ImP-driven IFF is altered by partial osteocyte depletion. Canalicular convective velocities during pressurization were estimated through the use of fluorescence recovery after photobleaching and computational modeling. Following osteocyte ablation, transgenic mice exhibited severe losses in bone structure and altered responses to hindlimb suspension in a compartment-specific manner. In pressure-loaded limbs, transgenic mice displayed similar or significantly enhanced structural adaptation to Imp-driven IFF, particularly in the trabecular compartment, despite up to ∼50% of trabecular lacunae being uninhabited following ablation. Interestingly, regression analysis revealed relative gains in bone structure in pressure-loaded limbs were correlated with reductions in bone structure in unpressurized control limbs, suggesting that adaptation to ImP-driven IFF was potentiated by increases in osteoclastic activity and/or reductions in osteoblastic activity incurred independently of pressure loading. Collectively, these studies indicate that structural adaptation to ImP-driven IFF can proceed unimpeded following a significant depletion in osteocytes, consistent with the potential existence of a non-osteocytic bone cell population that senses ImP-driven IFF independently and potentially parallel to osteocytic sensation of poroelasticity-derived IFF.