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To characterize the prevalence, associations, management, and outcomes of supraventricular tachycardia (SVT) in neonates with congenital diaphragmatic hernia (CDH).Retrospective chart and cardiology code review within a cohort of patients with CDH was used to define a subpopulation with atrial arrhythmia. SVT mechanisms were confirmed by electrocardiogram analysis. Cox proportional hazard regression identified risk factors for SVT and association with clinical outcomes.Medical Surgical ICU in a single, tertiary center, Boston Children's Hospital.Eligible patients included neonates presenting with classic Bochdalek posterolateral CDH between 2005 and 2017, excluding newborns with Morgagni hernia or late diagnoses of CDH (28 d).None.SVT arose in 25 of 232 neonates with CDH, (11%); 14 of 25 infants (56%) had recurrent SVT; atrioventricular node-dependent tachycardia was the most frequent mechanism (32%). The majority (71%) of SVT episodes received intervention. Nine patients (36%) received preventative antiarrhythmic medications. SVT was associated with lower Apgar score at 1 min, structural heart disease, larger defect size, extracorporeal membrane oxygenation (ECMO) support, and prostaglandin therapy for ductal patency as well as hospital stay greater than or equal to 8 weeks and use of supplemental oxygen at discharge.SVT can occur in neonates with CDH and frequently requires treatment. Odds of occurrence are increased with greater CDH disease severity, ECMO, and prostaglandin use. In unadjusted logistic regression analysis, SVT was associated with adverse hospital outcomes, underscoring the importance of recognition and management in this vulnerable population.

ObjectiveTo identify a cohort of COVID-19 cases, including when evidence of virus positivity was only mentioned in the clinical text, not in structured laboratory data in the electronic health record (EHR).Materials and MethodsStatistical classifiers were trained on feature representations derived from unstructured text in patient electronic health records (EHRs). We used a proxy dataset of patientswithCOVID-19 polymerase chain reaction (PCR) tests for training. We selected a model based on performance on our proxy dataset and applied it to instances without COVID-19 PCR tests. A physician reviewed a sample of these instances to validate the classifier.ResultsOn the test split of the proxy dataset, our best classifier obtained 0.56 F1, 0.6 precision, and 0.52 recall scores for SARS-CoV2 positive cases. In an expert validation, the classifier correctly identified 90.8% (79/87) as COVID-19 positive and 97.8% (91/93) as not SARS-CoV2 positive. The classifier identified an additional 960 positive cases that did not have SARS-CoV2 lab tests in hospital, and only 177 of those cases had the ICD-10 code for COVID-19.DiscussionProxy dataset performance may be worse because these instances sometimes include discussion of pending lab tests. The most predictive features are meaningful and interpretable. The type of external test that was performed is rarely mentioned.ConclusionCOVID-19 cases that had testing done outside of the hospital can be reliably detected from the text in EHRs. Training on a proxy dataset was a suitable method for developing a highly performant classifier without labor intensive labeling efforts.

BackgroundUntangling the heterogeneity of sepsis in children and identifying clinically relevant phenotypes could lead to the development of enrichment strategies and targeted therapies. In this study, our aim was to analyze the organ dysfunction-based trajectories of children with sepsis-associated multiple organ dysfunction syndrome (MODS) to identify and characterize reproducible and clinically relevant sepsis phenotypes using a data-driven approach.MethodsWe collected data from patients admitted with suspected infections to 13 pediatric intensive care units (PICUs) in the U.S. between 2012-2018. We used subgraph-augmented nonnegative matrix factorization to identify candidate trajectory-driven phenotypes based on the type, severity, and progression of organ dysfunction in the first 72 hours of PICU admission. We analyzed the candidate phenotypes to determine reproducibility as well as prognostic, therapeutic, and biological relevance.ResultsOverall, 38,732 children had suspected infection, of which 15,246 (39.4%) had sepsis-associated MODS. Amongst patients with sepsis-associated MODS, 1,537 (10.1%) died in the hospital. We identified an organ dysfunction trajectory-based phenotype (which we termedpersistent hypoxemia and shock) that was highly reproducible, had features of systemic inflammation and coagulopathy, and was independently associated with higher mortality. In a propensity score matched analysis, patients with thepersistent hypoxemia and shockphenotype appeared to have a higher likelihood to benefit from adjuvant therapy with hydrocortisone and albumin than other patients. When compared to other high-risk clinical syndromes, thepersistent hypoxemia and shockphenotype only overlapped with 50 to 60% of patients with septic shock, those with moderate-to-severe pediatric acute respiratory distress syndrome, or those in the top tertile of organ dysfunction burden, suggesting that it represents a distinct clinical phenotype of sepsis-associated MODS with a disproportionately high risk of mortality.ConclusionsWe derived and validated thepersistent hypoxemia and shockphenotype, a trajectory-based organ dysfunction phenotype which is highly reproducible, clinically relevant, and associated with heterogeneity of treatment effect to common adjuvant therapies. Further validation is warranted. Future studies are needed to validate this phenotype, assess whether it can be predicted earlier in the course, study possible biological mechanisms underlying it, and investigate candidate therapeutic targets.

While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.Advanced age (HR 2.77, 95%CI 2.53-3.04, p 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.Authors are supported by various funders, with full details stated in the acknowledgement section.

OBJECTIVES Design, implement, and evaluate a rounding checklist with deeply embedded, dynamic electronic health record integration. DESIGN Before-after quality-improvement study. SETTING Quaternary PICU in an academic, free-standing children's hospital. PATIENTS All patients in the PICU during daily morning rounds. INTERVENTIONS Implementation of an updated dynamic checklist (eSIMPLER) providing clinical decision support prompts with display of relevant data automatically pulled from the electronic health record. MEASUREMENTS AND MAIN RESULTS The prior daily rounding checklist, eSIMPLE, was implemented for 49,709 patient-days (7,779 patients) between October 30, 2011, and October 7, 2018. eSIMPLER was implemented for 5,306 patient-days (971 patients) over 6 months. Checklist completion rates were similar (eSIMPLE: 95% [95% CI, 88-98%] vs eSIMPLER: 98% [95% CI, 92-100%] of patient-days; p = 0.40). eSIMPLER required less time per patient (28 ± 1 vs 47 ± 24 s; p < 0.001). Users reported improved satisfaction with eSIMPLER (p = 0.009). Several checklist-driven process measures-discordance between electronic health record orders for stress ulcer prophylaxis and user-recorded indication for stress ulcer prophylaxis, rate of venous thromboembolism prophylaxis prescribing, and recognition of reduced renal function-improved during the eSIMPLER phase. CONCLUSIONS eSIMPLER, a dynamic, electronic health record-informed checklist, required less time to complete and improved certain care processes compared with a prior, static checklist with limited electronic health record data. By focusing on the "Five Rights" of clinical decision support, we created a well-accepted clinical decision support tool that was integrated efficiently into daily rounds. Generalizability of eSIMPLER's effectiveness and its impact on patient outcomes need to be examined.

OBJECTIVE: Examine the association of implementing a revised analgesia-sedation protocol with midazolam usage in the pediatric intensive care unit (PICU). DESIGN: A single center non-randomized before-after study. SETTING: Pediatric intensive care unit at a quaternary pediatric hospital (Boston Children’s Hospital, Boston, MA). PATIENTS: Children admitted to the PICU who were mechanically ventilated for greater than 24 hours. The pre-implementation cohort included 190 eligible patients admitted between July 29, 2017 – February 28, 2018, and the post-implementation cohort included 144 patients admitted between July 29, 2019 – February 28, 2020. INTERVENTIONS: Implementation of a revised analgesia-sedation protocol. MEASUREMENTS AND MAIN RESULTS: Our primary outcome, total dose of intravenous midazolam administered in mechanically ventilated patients up to day 14 of ventilation, decreased by 72% (95% confidence interval (CI) [61%, 80%], p-value

ObjectiveTo advance use of real-world data (RWD) for pharmacovigilance, we sought to integrate a high-sensitivity natural language processing (NLP) pipeline for detecting potential adverse drug events (ADEs) with easily interpretable output for high-efficiency human review and adjudication of true ADEs.Materials and methodsThe adverse drug event presentation and tracking (ADEPT) system employs an open source NLP pipeline to identify in clinical notes mentions of medications and signs and symptoms potentially indicative of ADEs. ADEPT presents the output to human reviewers by highlighting these drug-event pairs within the context of the clinical note. To measure incidence of seizures associated with sildenafil, we applied ADEPT to 149 029 notes for 982 patients with pediatric pulmonary hypertension.ResultsOf 416 patients identified as taking sildenafil, NLP found 72 [17%, 95% confidence interval (CI) 14–21] with seizures as a potential ADE. Upon human review and adjudication, only 4 (0.96%, 95% CI 0.37–2.4) patients with seizures were determined to have true ADEs. Reviewers using ADEPT required a median of 89 s (interquartile range 57–142 s) per patient to review potential ADEs.DiscussionADEPT combines high throughput NLP to increase sensitivity of ADE detection and human review, to increase specificity by differentiating true ADEs from signs and symptoms related to comorbidities, effects of other medications, or other confounders.ConclusionADEPT is a promising tool for creating gold standard, patient-level labels for advancing NLP-based pharmacovigilance. ADEPT is a potentially time savings platform for computer-assisted pharmacovigilance based on RWD.

OBJECTIVE: Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR data renders such tasks challenging, time-consuming, and prohibitively expensive, thus leading to a scarcity of clinical annotations in EHRs. Weakly supervised learning algorithms have been successfully applied to various EHR phenotyping problems, due to their ability to leverage information from large quantities of unlabeled samples to better inform predictions based on a far smaller number of patients. However, most weakly supervised methods are subject to the challenge to choose the right cutoff value to generate an optimal classifier. Furthermore, since they only utilize the most informative features (i.e., main ICD and NLP counts) they may fail for episodic phenotypes that cannot be consistently detected via ICD and NLP data. In this paper, we propose a label-efficient, weakly semi-supervised deep learning algorithm for EHR phenotyping (WSS-DL), which overcomes the limitations above. MATERIALS AND METHODS: WSS-DL classifies patient-level disease status through a series of learning stages: 1) generating silver standard labels, 2) deriving enhanced-silver-standard labels by fitting a weakly supervised deep learning model to data with silver standard labels as outcomes and high dimensional EHR features as input, and 3) obtaining the final prediction score and classifier by fitting a supervised learning model to data with a minimal number of gold standard labels as the outcome, and the enhanced-silverstandard labels and a minimal set of most informative EHR features as the input. To assess the generalizability of WSS-DL across different phenotypes and medical institutions, we apply WSS-DL to classify a total of 17 diseases, including both acute and chronic conditions, using EHR data from three healthcare systems. Additionally, we determine the minimum quantity of training labels required by WSSDL to outperform existing supervised and semi-supervised phenotyping methods. RESULTS: The proposed method, in combining the strengths of deep learning and weakly semi-supervised learning, successfully leverages the crucial phenotyping information contained in EHR features from unlabeled samples. Indeed, the deep learning model’s ability to handle high-dimensional EHR features allows it to generate strong phenotype status predictions from silver standard labels. These predictions, in turn, provide highly effective features in the final logistic regression stage, leading to high phenotyping accuracy in notably small subsets of labeled data (e.g. n = 40 labeled samples). CONCLUSION: Our method’s high performance in EHR datasets with very small numbers of labels indicates its potential value in aiding doctors to diagnose rare diseases as well as conditions susceptible to misdiagnosis.

Background: Multisystem inflammatory syndrome in children (MIS-C) consensus criteria were designed for maximal sensitivity and therefore capture patients with acute COVID-19 pneumonia. Methods: We performed unsupervised clustering on data from 1,526 patients (684 labeled MIS-C by clinicians)

BACKGROUND: Noninvasive ventilation (NIV) is commonly used to support children with respiratory failure, but detailed patterns of real-world use are lacking. The aim of our study was to describe use patterns of NIV via electronic medical record (EMR) data. METHODS: We performed a retrospective electronic chart review in a tertiary care pediatric ICU in the United States. Subjects admitted to the pediatric ICU from 2014 to 2017 who were mechanically ventilated were included in the study. RESULTS: The median number of discrete device episodes, defined as a time on support without interruption, was 20 (interquartile range [IQR] 8–49) per subject. The median duration of bi-level positive airway pressure (BPAP) support prior to interruption was 6.3 h (IQR 2.4–10.4); the median duration of CPAP was 6 h (IQR 2.1–10.4). Interruptions to BPAP had a median duration of 6.3 h (IQR 2–15.5); interruptions to CPAP had a median duration of 8.6 h (IQR 2.2–16.8). Use of NIV followed a diurnal pattern, with 44% of BPAP and 42% of CPAP subjects initiating support between 7:00 pm and midnight, and 49% of BPAP and 46% of CPAP subjects stopping support between 5:00 am and 10:00 am. CONCLUSIONS: NIV was frequently interrupted, and initiation and discontinuation of NIV follows a diurnal pattern. Use of EMR data collected for routine clinical care allowed the analysis of granular details of typical use patterns. Understanding NIV use patterns may be particularly important to understanding the burden of pediatric ICU bed utilization for nocturnal NIV. To our knowledge, this is the first study to examine in detail the use of pediatric NIV and to define diurnal use and frequent interruptions to support.

Objective Multimodal automated phenotyping (MAP) is a scalable, high-throughput phenotyping method, developed using electronic health record (EHR) data from an adult population. We tested transportability of MAP to a pediatric population. Materials and Methods Without additional feature engineering or supervised training, we applied MAP to a pediatric population enrolled in a biobank and evaluated performance against physician-reviewed medical records. We also compared performance of MAP at the pediatric institution and the original adult institution where MAP was developed, including for 6 phenotypes validated at both institutions against physician-reviewed medical records. Results MAP performed equally well in the pediatric setting (average AUC 0.98) as it did at the general adult hospital system (average AUC 0.96). MAP’s performance in the pediatric sample was similar across the 6 specific phenotypes also validated against gold-standard labels in the adult biobank. Conclusions MAP is highly transportable across diverse populations and has potential for wide-scale use.

Objective The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. Materials and Methods Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability—up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. Conclusions We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

IntroductionThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) includes hundreds of hospitals internationally using a federated computational approach to COVID-19 research using the EHR.ObjectiveWe sought to develop and validate a standard definition of COVID-19 severity from readily accessible EHR data across the Consortium.MethodsWe developed an EHR-based severity algorithm and validated it on patient hospitalization data from 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also used a machine learning approach to compare selected predictors of severity to the 4CE algorithm at one site.ResultsThe 4CE severity algorithm performed with pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of single code categories for acuity were unacceptably inaccurate - varying by up to 0.65 across sites. A multivariate machine learning approach identified codes resulting in mean AUC 0.956 (95% CI: 0.952, 0.959) compared to 0.903 (95% CI: 0.886, 0.921) using expert-derived codes. Billing codes were poor proxies of ICU admission, with 49% precision and recall compared against chart review at one partner institution.DiscussionWe developed a proxy measure of severity that proved resilient to coding variability internationally by using a set of 6 code classes. In contrast, machine-learning approaches may tend to overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold standard outcomes, possibly due to pandemic conditions.ConclusionWe developed an EHR-based algorithm for COVID-19 severity and validated it at 12 international sites.

BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients9 physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the ability to store and rapidly process big data sets in recent years, these physiologic data show promise in identifying specific outcomes and/or events during patients9 ICU hospitalization. METHODS: We introduced a methodology designed to automatically extract information from continuous-in-time vital sign data collected from bedside monitors to predict if a patient will experience a prolonged stay (length of stay) on mechanical ventilation, defined as >4 d, in a pediatric ICU. RESULTS: Continuous-in-time vital signs information and clinical history data were retrospectively collected for 284 ICU subjects from their first 24 h on mechanical ventilation from a medical-surgical pediatric ICU at Boston Children9s Hospital. Multiple machine learning models were trained on multiple subsets of these subjects to predict the likelihood that each of these subjects would experience a long stay. We evaluated the predictive power of our models strictly on unseen hold-out validation sets of subjects. Our methodology achieved model performance of >83% (area under the curve) by using only vital sign information as input, and performances of 90% (area under the curve) by combining vital sign information with subjects9 static clinical data readily available in electronic health records. We implemented this approach on 300 independently trained experiments with different choices of training and hold-out validation sets to ensure the consistency and robustness of our results in our study sample. The predictive power of our approach outperformed recent efforts that used deep learning to predict a similar task. CONCLUSIONS: Our proposed workflow may prove useful in the design of scalable approaches for real-time predictive systems in ICU environments, exploiting real-time vital sign information from bedside monitors. (ClinicalTrials.gov registration NCT02184208.)

Supplemental Digital Content is available in the text., Objectives: To determine whether machine learning algorithms can better predict PICU mortality than the Pediatric Logistic Organ Dysfunction-2 score. Design: Retrospective study. Setting: Quaternary care medical-surgical PICU. Patients: All patients admitted to the PICU from 2013 to 2019. Interventions: None. Measurements and Main Results: We investigated the performance of various machine learning algorithms using the same variables used to calculate the Pediatric Logistic Organ Dysfunction-2 score to predict PICU mortality. We used 10,194 patient records from 2013 to 2017 for training and 4,043 patient records from 2018 to 2019 as a holdout validation cohort. Mortality rate was 3.0% in the training cohort and 3.4% in the validation cohort. The best performing algorithm was a random forest model (area under the receiver operating characteristic curve, 0.867 [95% CI, 0.863–0.895]; area under the precision-recall curve, 0.327 [95% CI, 0.246–0.414]; F1, 0.396 [95% CI, 0.321–0.468]) and significantly outperformed the Pediatric Logistic Organ Dysfunction-2 score (area under the receiver operating characteristic curve, 0.761 [95% CI, 0.713–0.810]; area under the precision-recall curve (0.239 [95% CI, 0.165–0.316]; F1, 0.284 [95% CI, 0.209–0.360]), although this difference was reduced after retraining the Pediatric Logistic Organ Dysfunction-2 logistic regression model at the study institution. The random forest model also showed better calibration than the Pediatric Logistic Organ Dysfunction-2 score, and calibration of the random forest model remained superior to the retrained Pediatric Logistic Organ Dysfunction-2 model. Conclusions: A machine learning model achieved better performance than a logistic regression-based score for predicting ICU mortality. Better estimation of mortality risk can improve our ability to adjust for severity of illness in future studies, although external validation is required before this method can be widely deployed.

Provider interactions other than explicit care coordination, which is challenging to measure, may influence practice and outcomes. We performed a network analysis using claims data from a commercial payor. Networks were identified based on provider pairs billing outpatient care for the same patient. We compared network variables among patients who had and did not have a 30-day readmission after hospitalization for heart failure. After adjusting for comorbidities, high median provider connectedness—normalized degree, which for each provider is the number of connections to other providers normalized to the number of providers in the region—was the network variable associated with reduced odds of readmission after heart failure hospitalization (odds ratio = 0.55; 95% confidence interval [0.35, 0.86]). We conclude that heart failure patients with high provider connectedness are less likely to require readmission. The structure and importance of provider relationships using claims data merits further study.

In this paper we describe an evaluation of the potential of classical information extraction methods to extract drug-related attributes, including adverse drug events, and compare to more recently developed neural methods. We use the 2018 N2C2 shared task data as our gold standard data set for training. We train support vector machine classifiers to detect drug and drug attribute spans, and pair these detected entities as training instances for an SVM relation classifier, with both systems using standard features. We compare to baseline neural methods that use standard contextualized embedding representations for entity and relation extraction. The SVM-based system and a neural system obtain comparable results, with the SVM system doing better on concepts and the neural system performing better on relation extraction tasks. The neural system obtains surprisingly strong results compared to the system based on years of research in developing features for information extraction.

Despite widespread adoption of in-house call for ICU attendings, there is a paucity of research on optimal scheduling of intensivists to provide continuous on-site coverage. Overnight call duties have traditionally been added onto 7 days of continuous daytime clinical service. We designed an alternative ICU staffing model to increase continuity of attending physician care for patients while also decreasing interruptions to attendings' nonclinical weeks.Computer-based simulation of a 1-year schedule.A simulated ICU divided into two daytime teams each covered by a different attending and both covered by one overnight on-call attending.Simulated patients were randomly admitted on different service days to assess continuity of care.A "shared service schedule" was compared to a standard "7 days on schedule." For the 7 days on schedule, an attending covered a team for 7 consecutive days and off-service attendings cross-covered each night. For the shared schedule, four attendings shared the majority of daytime and nighttime service for two teams over 2 weeks, with recovery periods built into the scheduled service time.Continuity of care as measured by the Continuity of Attending Physician Index increased by 9% with the shared schedule. Annually, the shared service schedule was predicted to increase free weekends by 3.4 full weekends and 1.3 weekends with either Saturday or Sunday off. Full weeks without clinical obligations increased by 4 weeks. Mean time between clinical obligations increased by 5.8 days.A shared service schedule is predicted to improve continuity of care while increasing free weekends and continuity of uninterrupted nonclinical weeks for attendings. Computer-based simulation allows assessment of benefits and tradeoffs of the alternative schedule without disturbing existing clinical systems.

Rationale: Pediatric pulmonary hypertension (PH) is a heterogeneous condition with varying natural history and therapeutic response. Precise classification of PH subtypes is, therefore, crucial for individualizing care. However, gaps remain in our understanding of the spectrum of PH in children. Objective: We seek to study the manifestations of PH in children and to assess the feasibility of applying a network-based approach to discern disease subtypes from comorbidity data recorded in longitudinal data sets. Methods and Results: A retrospective cohort study comprising 6 943 263 children (, National Institutes of Health (U.S.) (Award U01HL121518)

The purpose of the study was to examine the effect of soccer dribbling on performance indices and physiological responses during repeated sprints (6 × 30-m) test, in straight and slalom run, of young (13.1 ± 0.4 y) soccer players. In the straight run repeated sprints test: Ideal (fastest) sprint time and total sprint time were significantly faster without dribbling (37.24 ± 2.56 versus 40.68 ± 2.84 and 38.50 ± 2.47 versus 41.78 ± 2.81 s, p

OBJECTIVE:Methicillin-resistant Staphylococcus aureus (MRSA) colonization in NICUs increases the risk of nosocomial infection. Network analysis provides tools to examine the interactions among patients and staff members that put patients at risk of colonization.METHODS:Data from MRSA surveillance cultures were combined with patient room locations, nursing assignments, and sibship information to create patient- and unit-based networks. Multivariate models were constructed to quantify the risk of incident MRSA colonization as a function of exposure to MRSA-colonized infants in these networks.RESULTS:A MRSA-negative infant in the NICU simultaneously with a MRSA-positive infant had higher odds of becoming colonized when the colonized infant was a sibling, compared with an unrelated patient (odds ratio: 8.8 [95% confidence interval [CI]: 5.3–14.8]). Although knowing that a patient was MRSA-positive and was placed on contact precautions reduced the overall odds of another patient becoming colonized by 35% (95% CI: 20%–47%), having a nurse in common with that patient still increased the odds of colonization by 43% (95% CI: 14%–80%). Normalized group degree centrality, a unitwide network measure of connectedness between colonized and uncolonized patients, was a significant predictor of incident MRSA cases (odds ratio: 18.1 [95% CI: 3.6–90.0]).CONCLUSIONS:Despite current infection-control strategies, patients remain at significant risk of MRSA colonization from MRSA-positive siblings and from other patients with whom they share nursing care. Strategies that minimize the frequency of staff members caring for both colonized and uncolonized infants may be beneficial in reducing the spread of MRSA colonization.

To use meta-analytic techniques to examine the effect of dexamethasone on the risk of postoperative bleeding following tonsillectomy.PubMed and Embase databases accessed on April 23, 2009, and April 28, 2009.Using principles of meta-analysis, inclusion and exclusion criteria were developed to identify all randomized controlled trials of patients undergoing tonsillectomy in which perioperative intravenous dexamethasone was administered in at least 1 treatment arm and bleeding complications were reported. Electronic databases were searched to identify candidate articles. Two authors independently abstracted data from each article. Discrepancies were resolved by consensus. A fixed-effects model was used to pool relative risks among studies using the Mantel-Haenszel method. Studies were assessed for publication bias using a funnel plot of studies' effect size vs standard error of the effect size as well as Begg test and Egger test. A P value.05 was considered significant.The primary search identified 85 potential studies. Fourteen met inclusion criteria and were selected for meta-analysis. No significant heterogeneity was found among studies (I(2)0.1%; 95% confidence interval [CI], 0%-55%; P = .68). The pooled relative risk (RR) of postoperative bleeding did not differ significantly between patients receiving dexamethasone and controls (RR, 1.02; 95% CI, 0.65-1.61; P = .92). When studies were stratified by age, primary vs secondary hemorrhage, and follow-up duration, no further significant differences in bleeding rate were identified. No evidence of publication bias was found using Begg (P = .70) or Egger (P = .73) tests.The results of this meta-analysis indicate that perioperative dexamethasone does not confer an increased risk of postoperative bleeding in patients undergoing tonsillectomy.

ObjectivesThis study aimed to identify independent predictors of reoperation after successful resection of discrete subaortic stenosis (DSS).BackgroundRecurrence of DSS has been reported to range from 0% to 55% of patients. Factors associated with recurrence have not been adequately defined.MethodsPatients were included if they had a diagnosis of DSS, normal segmental cardiac anatomy, previous resection of DSS, and at least 36 months’ follow-up. Demographic, surgical, and echocardiographic data were analyzed. Primary outcome was repeat resection of DSS in patients after successful primary resection.ResultsOf 111 subjects who had successful surgical resection of DSS, 16 patients (14%) required reoperation. Median follow-up time was 8.2 years. Form of DSS and gender did not differ significantly between those with reoperation and those without. In multivariate analysis, independent predictors of reoperation that would be available before first surgery were

A baby girl presented with symptomatic sickle cell disease exacerbated by mild hypoxemia, despite a newborn-screening diagnosis of sickle cell trait. DNA sequencing of the beta globin gene revealed that her maternal beta globin allele was normal. Her paternal allele had not only the expected sickle-trait mutation, betaGlu6Val, but also a second, charge-neutral mutation, betaLeu68Phe. Analysis of the patient's hemoglobin revealed that the double-mutant protein, which we called "hemoglobin Jamaica Plain," had severely reduced oxygen affinity. Structural modeling suggested destabilization of the oxy conformation as a molecular mechanism for sickling in a heterozygote at an ambient partial pressure of oxygen.