Browsing by Author "Tejwani, Rohit"

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    Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity.
    (BMC medical informatics and decision making, 2022-03) Sabharwal, Paul; Hurst, Jillian H; Tejwani, Rohit; Hobbs, Kevin T; Routh, Jonathan C; Goldstein, Benjamin A

    Background

    Clinical decision support (CDS) tools built using adult data do not typically perform well for children. We explored how best to leverage adult data to improve the performance of such tools. This study assesses whether it is better to build CDS tools for children using data from children alone or to use combined data from both adults and children.

    Methods

    Retrospective cohort using data from 2017 to 2020. Participants include all individuals (adults and children) receiving an elective surgery at a large academic medical center that provides adult and pediatric services. We predicted need for mechanical ventilation or admission to the intensive care unit (ICU). Predictor variables included demographic, clinical, and service utilization factors known prior to surgery. We compared predictive models built using machine learning to regression-based methods that used a pediatric or combined adult-pediatric cohort. We compared model performance based on Area Under the Receiver Operator Characteristic.

    Results

    While we found that adults and children have different risk factors, machine learning methods are able to appropriately model the underlying heterogeneity of each population and produce equally accurate predictive models whether using data only from pediatric patients or combined data from both children and adults. Results from regression-based methods were improved by the use of pediatric-specific data.

    Conclusions

    CDS tools for children can successfully use combined data from adults and children if the model accounts for underlying heterogeneity, as in machine learning models.
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    Correction to: Combining adult with pediatric patient data to develop a clinical decision support tool intended for children: leveraging machine learning to model heterogeneity.
    (BMC medical informatics and decision making, 2022-05) Sabharwal, Paul; Hurst, Jillian H; Tejwani, Rohit; Hobbs, Kevin T; Routh, Jonathan C; Goldstein, Benjamin A
    Following publication of the original article [1], it was reported that part of the ‘Outcome Variable Definition’ and the entirety of the ‘Descriptive statistics’ subsection was missing. These two subsections are given below with the previously missing text highlighted in bold. The original article [1] has been updated. Outcome Variable Definition In the initial development of the CDS tool, we were tasked with predicting four outcomes related to hospital resource utilization: overall length of stay, admission to the intensive care unit (ICU), requirement for mechanical ventilation, and discharge to a skilled nursing facility. Because children are rarely discharged to a skilled nursing facility and evaluating continuous outcomes poses unique challenges, we focused on the two binary outcomes: admission to the ICU and requirement for mechanical ventilation. Statistical Analysis Descriptive statistics We compared the pediatric and adult patient populations. We report standardized mean differences (SMDs) where an SMD > 0.10 indicates that the two groups are out of balance.
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    The Importance of Early Diagnosis and Management of Pediatric Neurogenic Bladder Dysfunction.
    (Research and reports in urology, 2021-01) Hobbs, K Tyler; Krischak, Madison; Tejwani, Rohit; Purves, J Todd; Wiener, John S; Routh, Jonathan C
    Neurogenic bladder dysfunction is a major source of urologic morbidity in children, especially in those with spina bifida (SB). Complications from progression of bladder dysfunction can include urinary tract infections (UTIs), urinary incontinence, upper tract deterioration, and renal dysfunction or failure. In these children, there has been a recent trend toward proactive rather than expectant management of neurogenic bladder. However, there is a lack of consensus on how to best achieve the three main goals of neurogenic bladder management: 1) preserving kidney function, 2) achieving continence (if desired by the family/individual), and 3) achieving social and functional urologic independence (if appropriate). Hence, our objective was to perform a narrative literature review to evaluate the approaches to diagnosis and management of pediatric neurogenic bladder dysfunction, with special focus on children with SB. The approach strategies vary across a spectrum, with a proactive strategy on one end of the spectrum and an expectant strategy at the other end. The proactive management strategy is characterized by early and frequent labs, imaging, and urodynamic (UDS) evaluation, with early initiation of clean intermittent catheterization (CIC) and proceeding with pharmacotherapy, or surgery if indicated. The expectant management strategy prioritizes surveillance labs and imaging prior to proceeding with invasive assessments and interventions such as UDS or pharmacotherapy. Both treatment strategies are currently utilized and data have historically been inconclusive in demonstrating efficacy of one regimen over the other. We performed a narrative literature evaluating proactive and expectant treatment strategies as they relate to diagnostics and management of Spina Bifida. From the available literature and our practice, a proactive strategy favors greater benefit in preventative management and may decrease risk of renal dysfunction compared with expectant management.