Browsing by Author "Harwayne-Gidansky, Ilana"

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    Lessons Learned from Web and Social Media Based Educational Initiatives By Pulmonary, Critical Care & Sleep Societies
    (Elsevier, 2019) Carroll, Christopher L.; Dangayach, Neha S.; Khan, Roozehra; Carlos, W. Graham; Harwayne-Gidansky, Ilana; Grewal, Harpreet Singh; Seay, Brandon; Simpson, Steven Q.; Szakmany, Tamas; Medicine, School of Medicine
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    Neurologic Involvement in Children and Adolescents Hospitalized in the United States for COVID-19 or Multisystem Inflammatory Syndrome
    (AMA, 2021-03) LaRovere, Kerri L.; Riggs, Becky J.; Poussaint, Tina Y.; Young, Cameron C.; Newhams, Margaret M.; Maamari, Mia; Walker, Tracie C.; Singh, Aalok R.; Dapul, Heda; Hobbs, Charlotte V.; McLaughlin, Gwenn E.; Son, Mary Beth F.; Maddux, Aline B.; Clouser, Katharine N.; Rowan, Courtney M.; McGuire, John K.; Fitzgerald, Julie C.; Gertz, Shira J.; Shein, Steven L.; Munoz, Alvaro Coronado; Thomas, Neal J.; Irby, Katherine; Levy, Emily R.; Staat, Mary A.; Tenforde, Mark W.; Feldstein, Leora R.; Halasa, Natasha B.; Giuliano, John S.; Hall, Mark W.; Kong, Michele; Carroll, Christopher L.; Schuster, Jennifer E.; Doymaz, Sule; Loftis, Laura L.; Tarquinio, Keiko M.; Babbitt, Christopher J.; Nofziger, Ryan A.; Kleinman, Lawrence C.; Keenaghan, Michael A.; Cvijanovich, Natalie Z.; Spinella, Philip C.; Hume, Janet R.; Wellnitz, Kari; Mack, Elizabeth H.; Michelson, Kelly N.; Flori, Heidi R.; Patel, Manish M.; Randolph, Adrienne G.; Overcoming COVID-19 Investigators; Gaspers, Mary G; Typpo, Katri V; Sanders, Ronald C; Schwarz, Adam J; Harvey, Helen; Zinter, Matt S; Mourani, Peter M; Coates, Bria M; Bhoojhawon, Guru; Havlin, Kevin M; Montgomery, Vicki L; Sullivan, Janice E; Bradford, Tamara T; Bembea, Melania M; Lipton, Susan V; Graciano, Ana Lia; Chen, Sabrina R; Kucukak, Suden; Newburger, Jane W; Carroll, Ryan W; Fernandes, Neil D; Yager, Phoebe H; Marohn, Kimberly L; Heidemann, Sabrina M; Cullimore, Melissa L; McCulloh, Russell J; Horwitz, Steven M; Li, Simon; Walsh, Rowan F; Ratner, Adam J; Soma, Vijaya L; Gillen, Jennifer K; Zackai, Sheemon P; Ackerman, Kate G; Cholette, Jill M; Harwayne-Gidansky, Ilana; Hymes, Saul R; Overby, Philip J; Schwartz, Stephanie P; Lansell, Amanda N; Koncicki, Monica L; Carcillo, Joseph; Fink, Ericka; Kimura, Dai; Bowens, Cindy; Crandall, Hillary; Smith, Lincoln S; Cengiz, Pelin; Pediatrics, School of Medicine
    Importance Coronavirus disease 2019 (COVID-19) affects the nervous system in adult patients. The spectrum of neurologic involvement in children and adolescents is unclear. Objective To understand the range and severity of neurologic involvement among children and adolescents associated with COVID-19. Setting, Design, and Participants Case series of patients (age <21 years) hospitalized between March 15, 2020, and December 15, 2020, with positive severe acute respiratory syndrome coronavirus 2 test result (reverse transcriptase-polymerase chain reaction and/or antibody) at 61 US hospitals in the Overcoming COVID-19 public health registry, including 616 (36%) meeting criteria for multisystem inflammatory syndrome in children. Patients with neurologic involvement had acute neurologic signs, symptoms, or diseases on presentation or during hospitalization. Life-threatening involvement was adjudicated by experts based on clinical and/or neuroradiologic features. Exposures Severe acute respiratory syndrome coronavirus 2. Main Outcomes and Measures Type and severity of neurologic involvement, laboratory and imaging data, and outcomes (death or survival with new neurologic deficits) at hospital discharge. Results Of 1695 patients (909 [54%] male; median [interquartile range] age, 9.1 [2.4-15.3] years), 365 (22%) from 52 sites had documented neurologic involvement. Patients with neurologic involvement were more likely to have underlying neurologic disorders (81 of 365 [22%]) compared with those without (113 of 1330 [8%]), but a similar number were previously healthy (195 [53%] vs 723 [54%]) and met criteria for multisystem inflammatory syndrome in children (126 [35%] vs 490 [37%]). Among those with neurologic involvement, 322 (88%) had transient symptoms and survived, and 43 (12%) developed life-threatening conditions clinically adjudicated to be associated with COVID-19, including severe encephalopathy (n = 15; 5 with splenial lesions), stroke (n = 12), central nervous system infection/demyelination (n = 8), Guillain-Barré syndrome/variants (n = 4), and acute fulminant cerebral edema (n = 4). Compared with those without life-threatening conditions (n = 322), those with life-threatening neurologic conditions had higher neutrophil-to-lymphocyte ratios (median, 12.2 vs 4.4) and higher reported frequency of D-dimer greater than 3 μg/mL fibrinogen equivalent units (21 [49%] vs 72 [22%]). Of 43 patients who developed COVID-19–related life-threatening neurologic involvement, 17 survivors (40%) had new neurologic deficits at hospital discharge, and 11 patients (26%) died. Conclusions and Relevance In this study, many children and adolescents hospitalized for COVID-19 or multisystem inflammatory syndrome in children had neurologic involvement, mostly transient symptoms. A range of life-threatening and fatal neurologic conditions associated with COVID-19 infrequently occurred. Effects on long-term neurodevelopmental outcomes are unknown.
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    Prevalence of Errors in Anaphylaxis in Kids (PEAK): A Multicenter Simulation-Based Study
    (Elsevier, 2020-04) Maa, Tensing; Scherzer, Daniel; Harwayne-Gidansky, Ilana; Capua, Tali; Kessler, David O.; Trainor, Jennifer L.; Jani, Priti; Damazo, Becky; Abulebda, Kamal; Diaz, Maria Carmen G.; Sharara-Chami, Rana; Srinivasan, Sushant; Zurca, Adrian; Deutsch, Ellen S.; Hunt, Elizabeth A.; Auerbach, Marc; Pediatrics, School of Medicine
    Background Multi-institutional, international practice variation of pediatric anaphylaxis management by health care providers has not been reported. Objective To characterize variability in epinephrine administration for pediatric anaphylaxis across institutions, including frequency and types of medication errors. Methods A prospective, observational, study using a standardized in situ simulated anaphylaxis scenario was performed across 28 health care institutions in 6 countries. The on-duty health care team was called for a child (patient simulator) in anaphylaxis. Real medications and supplies were obtained from their actual locations. Demographic data about team members, institutional protocols for anaphylaxis, timing of epinephrine delivery, medication errors, and systems safety issues discovered during the simulation were collected. Results Thirty-seven in situ simulations were performed. Anaphylaxis guidelines existed in 41% (15 of 37) of institutions. Teams used a cognitive aid for medication dosing 41% (15 of 37) of the time and 32% (12 of 37) for preparation. Epinephrine autoinjectors were not available in 54% (20 of 37) of institutions and were used in only 14% (5 of 37) of simulations. Median time to epinephrine administration was 95 seconds (interquartile range, 77-252) for epinephrine autoinjector and 263 seconds (interquartile range, 146-407.5) for manually prepared epinephrine (P = .12). At least 1 medication error occurred in 68% (25 of 37) of simulations. Nursing experience with epinephrine administration for anaphylaxis was associated with fewer preparation (P = .04) and administration (P = .01) errors. Latent safety threats were reported by 30% (11 of 37) of institutions, and more than half of these (6 of 11) involved a cognitive aid. Conclusions A multicenter, international study of simulated pediatric anaphylaxis reveals (1) variation in management between institutions in the use of protocols, cognitive aids, and medication formularies, (2) frequent errors involving epinephrine, and (3) latent safety threats related to cognitive aids among multiple sites.
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    The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research
    (Wolters Kluwer, 2024) Heneghan, Julia A.; Walker, Sarah B.; Fawcett, Andrea; Bennett, Tellen D.; Dziorny, Adam C.; Sanchez-Pinto, L. Nelson; Farris, Reid W. D.; Winter, Meredith C.; Badke, Colleen; Martin, Blake; Brown, Stephanie R.; McCrory, Michael C.; Ness-Cochinwala, Manette; Rogerson, Colin; Baloglu, Orkun; Harwayne-Gidansky, Ilana; Hudkins, Matthew R.; Kamaleswaran, Rishikesan; Gangadharan, Sandeep; Tripathi, Sandeep; Mendonca, Eneida A.; Markovitz, Barry P.; Mayampurath, Anoop; Spaeder, Michael C.; Pediatric Data Science and Analytics (PEDAL) subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) Network; Pediatrics, School of Medicine
    Objective: Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. Design: Scoping review and expert opinion. Setting: We queried CINAHL Plus with Full Text (EBSCO), Cochrane Library (Wiley), Embase (Elsevier), Ovid Medline, and PubMed for articles published between 2000 and 2022 related to machine learning concepts and pediatric critical illness. Articles were excluded if the majority of patients were adults or neonates, if unsupervised machine learning was the primary methodology, or if information related to the development, validation, and/or implementation of the model was not reported. Article selection and data extraction were performed using dual review in the Covidence tool, with discrepancies resolved by consensus. Subjects: Articles reporting on the development, validation, or implementation of supervised machine learning models in the field of pediatric critical care medicine. Interventions: None. Measurements and main results: Of 5075 identified studies, 141 articles were included. Studies were primarily (57%) performed at a single site. The majority took place in the United States (70%). Most were retrospective observational cohort studies. More than three-quarters of the articles were published between 2018 and 2022. The most common algorithms included logistic regression and random forest. Predicted events were most commonly death, transfer to ICU, and sepsis. Only 14% of articles reported external validation, and only a single model was implemented at publication. Reporting of validation methods, performance assessments, and implementation varied widely. Follow-up with authors suggests that implementation remains uncommon after model publication. Conclusions: Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.