Recent decision rules for the management of febrile infants support the identification of infants at higher risk of serious bacterial infections (SBIs) without the performance of routine lumbar ...puncture. We derive and validate a model to identify febrile infants ≤60 days of age at low risk for SBIs using supervised machine learning approaches.
We conducted a secondary analysis of a multicenter prospective study performed between December 2008 and May 2013 of febrile infants. Our outcome was SBI, (culture-positive urinary tract infection, bacteremia, and/or bacterial meningitis). We developed and validated 4 supervised learning models: logistic regression, random forest, support vector machine, and a single-hidden layer neural network.
A total of 1470 patients were included (1014 >28 days old). One hundred thirty-eight (9.3%) had SBIs (122 urinary tract infections, 20 bacteremia, and 8 meningitis; 11 with concurrent SBIs). Using 4 features (urinalysis, white blood cell count, absolute neutrophil count, and procalcitonin), we demonstrated with the random forest model the highest specificity (74.9, 95% confidence interval: 71.5%-78.2%) with a sensitivity of 98.6% (95% confidence interval: 92.2%-100.0%) in the validation cohort. One patient with bacteremia was misclassified. Among 1240 patients who received a lumbar puncture, this model could have prevented 849 (68.5%) such procedures.
We derived and internally validated a supervised learning model for the risk-stratification of febrile infants. Although computationally complex, lacking parameter cutoffs, and in need of external validation, this strategy may allow for reductions in unnecessary procedures, hospitalizations, and antibiotics while maintaining excellent sensitivity.
There is an increasing interest in clinical prediction tools that can achieve high prediction accuracy and provide explanations of the factors leading to increased risk of adverse outcomes. However, ...approaches to explaining complex machine learning (ML) models are rarely informed by end-user needs and user evaluations of model interpretability are lacking in the healthcare domain. We used extended revisions of previously-published theoretical frameworks to propose a framework for the design of user-centered displays of explanations. This new framework served as the basis for qualitative inquiries and design review sessions with critical care nurses and physicians that informed the design of a user-centered explanation display for an ML-based prediction tool.
We used our framework to propose explanation displays for predictions from a pediatric intensive care unit (PICU) in-hospital mortality risk model. Proposed displays were based on a model-agnostic, instance-level explanation approach based on feature influence, as determined by Shapley values. Focus group sessions solicited critical care provider feedback on the proposed displays, which were then revised accordingly.
The proposed displays were perceived as useful tools in assessing model predictions. However, specific explanation goals and information needs varied by clinical role and level of predictive modeling knowledge. Providers preferred explanation displays that required less information processing effort and could support the information needs of a variety of users. Providing supporting information to assist in interpretation was seen as critical for fostering provider understanding and acceptance of the predictions and explanations. The user-centered explanation display for the PICU in-hospital mortality risk model incorporated elements from the initial displays along with enhancements suggested by providers.
We proposed a framework for the design of user-centered displays of explanations for ML models. We used the proposed framework to motivate the design of a user-centered display of an explanation for predictions from a PICU in-hospital mortality risk model. Positive feedback from focus group participants provides preliminary support for the use of model-agnostic, instance-level explanations of feature influence as an approach to understand ML model predictions in healthcare and advances the discussion on how to effectively communicate ML model information to healthcare providers.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Horvat and Pelletier discuss the cluster randomized clinical trial by Haskell et al which aimed to improve bronchiolitis management by deimplementing potentially unnecessary care, including the use ...of albuterol. Among the trial's findings was a significant increase in the proportion of children who did not receive albuterol.
Severe sepsis in immunocompromised children is associated with increased mortality. This paper describes the epidemiology landscape, clinical acuity, and outcomes for severe sepsis in pediatric ...intestinal (ITx) and multi‐visceral (MVTx) transplant recipients requiring admission to the pediatric intensive care unit (PICU). Severe sepsis episodes were retrospectively reviewed in 51 ITx and MVTx patients receiving organs between 2009 and 2015. Twenty‐nine (56.8%) patients had at least one sepsis episode (total of 63 episodes) through December 2016. Bacterial etiologies accounted for 66.7% of all episodes (n = 42), occurring a median of 122.5 days following transplant (IQR 59–211.8 days). Multidrug‐resistant organisms (MDROs) accounted for 73.8% of bacterial infections; extended spectrum beta‐lactamase producers, vancomycin‐resistant enterococcus, and highly‐resistant Pseudomonas aeruginosa were the most commonly identified. Increased mechanical ventilation and vasoactive requirements were noted in MDRO episodes (OR 3.03, 95% CI 1.09–8.46 and OR 3.07, 95% CI 1.09–8.61, respectively; p < .05) compared to non‐MDRO episodes. PICU length of stay was significantly increased for MDRO episodes (7 vs. 3 days, p = .02). Graft loss was 24.1% (n = 7) and mortality was 24.1% (n = 7) in patients who experienced severe sepsis. Further attention is needed for MDRO risk mitigation and modification of sepsis treatment guidelines to ensure MDRO coverage for this population.
In pediatric intestinal and multi‐visceral transplant recipients, MDROs account for a large majority of severe sepsis episodes related to bacterial infections and are associated with an increased need for mechanical ventilation, use of vasoactive medications, and length of intensive care unit stay.
To identify trends in pediatric emergency department (ED) utilization following the COVID-19 pandemic.
We performed a cross-sectional study from 37 geographically diverse US children's hospitals. We ...included ED encounters between January 1, 2010 and December 31, 2020, transformed into time-series data. We constructed ensemble forecasting models of the most common presenting diagnoses and the most common diagnoses leading to admission, using data from 2010 through 2019. We then compared the most common presenting diagnoses and the most common diagnoses leading to admission in 2020 to the forecasts.
29,787,815 encounters were included, of which 1,913,085 (6.4%) occurred during 2020. ED encounters during 2020 were lower compared to prior years, with a 65.1% decrease in April relative to 2010–2019. In forecasting models, encounters for depression and diabetic ketoacidosis remained within the 95% confidence interval CI; fever, bronchiolitis, hyperbilirubinemia, skin/subcutaneous infections and seizures occurred within the 80–95% CI during the portions of 2020, and all other diagnoses (abdominal pain, otitis media, asthma, pneumonia, trauma, upper respiratory tract infections, and urinary tract infections) occurred below the predicted 95% CI.
Pediatric ED utilization has remained low following the COVID-19 pandemic, and below forecasted utilization for most diagnoses. Nearly all conditions demonstrated substantial declines below forecasted rates from the prior decade and which persisted through the end of the year. Some declines in non-communicable diseases may represent unmet healthcare needs among children. Further study is warranted to understand the impact of policies aimed at curbing pandemic disease on children.
Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based ...recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.
There are no therapies shown to improve outcome after severe traumatic brain injury (TBI) in humans, a leading cause of morbidity and mortality. We sought to verify brain exposure of the systemically ...administered antioxidant N-acetylcysteine (NAC) and the synergistic adjuvant probenecid, and identify adverse effects of this drug combination after severe TBI in children.
IRB-approved, randomized, double-blind, placebo controlled Phase I study in children 2 to 18 years-of-age admitted to a Pediatric Intensive Care Unit after severe TBI (Glasgow Coma Scale GCS score ≤8) requiring an externalized ventricular drain for measurement of intracranial pressure (ICP). Patients were recruited from November 2011-August 2013. Fourteen patients (n = 7/group) were randomly assigned after obtaining informed consent to receive probenecid (25 mg/kg load, then 10 mg/kg/dose q6h×11 doses) and NAC (140 mg/kg load, then 70 mg/kg/dose q4h×17 doses), or placebos via naso/orogastric tube. Serum and CSF samples were drawn pre-bolus and 1-96 h after randomization and drug concentrations were measured via UPLC-MS/MS. Glasgow Outcome Scale (GOS) score was assessed at 3 months.
There were no adverse events attributable to drug treatment. One patient in the placebo group was withdrawn due to adverse effects. In the treatment group, NAC concentrations ranged from 16,977.3±2,212.3 to 16,786.1±3,285.3 in serum and from 269.3±113.0 to 467.9±262.7 ng/mL in CSF, at 24 to 72 h post-bolus, respectively; and probenecid concentrations ranged from 75.4.3±10.0 to 52.9±25.8 in serum and 5.4±1.0 to 4.6±2.1 μg/mL in CSF, at 24 to 72 h post-bolus, respectively (mean±SEM). Temperature, mean arterial pressure, ICP, use of ICP-directed therapies, surveillance serum brain injury biomarkers, and GOS at 3 months were not different between groups.
Treatment resulted in detectable concentrations of NAC and probenecid in CSF and was not associated with undesirable effects after TBI in children.
ClinicalTrials.gov NCT01322009.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In early 2020, the United States declared a public health emergency in response to coronavirus disease 2019 (COVID-19) and implemented a variety of social distancing measures. The association between ...the COVID-19 pandemic and the number of pediatric admissions is unclear.
To determine the changes in patterns of pediatric admissions in 2020 compared with the prior decade.
This cross-sectional study included 49 US hospitals contributing to the Pediatric Health Information Systems database. Inpatient admissions were transformed into time-series data, and ensemble forecasting models were generated to analyze admissions across a range of diagnoses in 2020 compared with previous years. The setting was inpatient admissions. All patients discharged between January 1, 2010, and June 30, 2020, from an inpatient hospital encounter were included.
Number of hospital admissions by primary diagnosis for each encounter.
Of 5 424 688 inpatient encounters among 3 372 839 patients (median interquartile range age, 5.1 0.7-13.3 years; 2 823 748 52.1% boys; 3 171 224 58.5% White individuals) at 49 hospitals, 213 571 (3.9%) were between January 1, 2020, and June 30, 2020. There was a decrease in the number of admissions beginning in March 2020 compared with the period from 2010 to 2019. At the nadir, admissions in April 2020 were reduced 45.4% compared with prior years (23 798 in April 2020 compared with a median interquartile range of 43 550 42 110-43 946 in April 2010-2019). Inflation-adjusted hospital charges decreased 27.7% in the second quarter of 2020 compared with prior years ($4 327 580 511 in 2020 compared with a median interquartile range of $5 983 142 102 $5 762 690 022-$6 324 978 456 in 2010-2019). Seasonal patterns were evident between 2010 and 2019 for a variety of common pediatric conditions, including asthma, atrial septal defects, bronchiolitis, diabetic ketoacidosis, Kawasaki syndrome, mental health admissions, and trauma. Ensemble models were able to discern seasonal patterns in admission diagnoses and accurately predicted admission rates from July 2019 until December 2019 but not from January 2020 to June 2020. All diagnoses except for birth decreased below the model 95% CIs between January 2020 and June 2020.
In this cross-sectional study, pediatric admissions to US hospitals decreased in 2020 across an array of pediatric conditions. Although some conditions may have decreased in incidence, others may represent unmet needs in pediatric care during the COVID-19 pandemic.
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