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1. Development and Validation of a Mortality Risk Score for Repaired Tetralogy of Fallot

Author

Joshua Mayourian, Lynn A. Sleeper, Ji Hae Lee, Minmin Lu, Alon Geva, Barbara Mulder, Sonya V. Babu‐Narayan, Rachel M. Wald, Tehila Sompolinsky, Anne Marie Valente, and Tal Geva

Subjects
cardiovascular magnetic resonance, congenital heart disease, death, outcomes, risk score, tetralogy of Fallot, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background Robust risk assessment is crucial for the growing repaired tetralogy of Fallot population at risk of major adverse clinical outcomes; however, current tools are hindered by lack of validation. This study aims to develop and validate a risk prediction model for death in the repaired tetralogy of Fallot population. Methods and Results Patients with repaired tetralogy of Fallot enrolled in the INDICATOR (International Multicenter Tetralogy of Fallot Registry) cohort with clinical, arrhythmia, cardiac magnetic resonance, and outcome data were included. Patients from London, Amsterdam, and Boston sites were placed in the development cohort; patients from the Toronto site were used for external validation. Multivariable Cox regression was used to evaluate factors associated with time from cardiac magnetic resonance until the primary outcome: all‐cause death. Of 1552 eligible patients (n=1221 in development, n=331 in validation; median age at cardiac magnetic resonance 23.4 [interquartile range, 15.6–35.6] years; median follow up 9.5 years), 102 (6.6%) experienced the primary outcome. The multivariable Cox model performed similarly during development (concordance index, 0.83 [95% CI, 0.78–0.88]) and external validation (concordance index, 0.80 [95% CI, 0.71–0.90]) and identified older age at cardiac magnetic resonance, obesity, type of tetralogy of Fallot repair, higher right ventricular end‐systolic volume index, and lower biventricular global function index as independent predictors of death. A risk‐scoring algorithm dividing patients into low‐risk (score ≤4) versus high‐risk (score >4) groups was validated to effectively discriminate risk of death (15‐year survival of 95% versus 74%, respectively; P

Published
2024
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2. Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study

Author

Andrew J McMurry, Amy R Zipursky, Alon Geva, Karen L Olson, James R Jones, Vladimir Ignatov, Timothy A Miller, and Kenneth D Mandl

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundReal-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. ObjectiveThis study sought to validate and test an artificial intelligence (AI)–based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. MethodsSubjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children’s hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. ResultsThere were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. ConclusionsThis study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Published
2024
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3. Distinct clinical phenotypes in paediatric cancer patients with sepsis are associated with different outcomes—an international multicentre retrospective studyResearch in context

Author

Roelie M. Wösten-van Asperen, Hannah M. la Roi-Teeuw, Rombout BE. van Amstel, Lieuwe DJ. Bos, Wim JE. Tissing, Iolanda Jordan, Christian Dohna-Schwake, Gabriella Bottari, John Pappachan, Roman Crazzolara, Rosanna I. Comoretto, Agniezka Mizia-Malarz, Andrea Moscatelli, María Sánchez-Martín, Jef Willems, Colin M. Rogerson, Tellen D. Bennett, Yuan Luo, Mihir R. Atreya, E.Vincent S. Faustino, Alon Geva, Scott L. Weiss, Luregn J. Schlapbach, L Nelson Sanchez-Pinto, Marina Caballero, Adriana Margarit, Roi Campos, Paula Möller, Carmela Serpe, Angela Amigoni, Maria Damps, Alessia Montaguti, Giacomo Tardini, Juliane Bubeck-Wardenburg, Reid Farris Farris, Mark Hall, Grace Chong, Sareen Shah, Robinder Khemani, and Emily Stroup

Subjects
Paediatric intensive care, Oncology, Sepsis, Phenotype, Latent class analysis, Medicine (General), R5-920
Abstract

Summary: Background: Identifying phenotypes in sepsis patients may enable precision medicine approaches. However, the generalisability of these phenotypes to specific patient populations is unclear. Given that paediatric cancer patients with sepsis have different host response and pathogen profiles and higher mortality rates when compared to non-cancer patients, we determined whether unique, reproducible, and clinically-relevant sepsis phenotypes exist in this specific patient population. Methods: We studied patients with underlying malignancies admitted with sepsis to one of 25 paediatric intensive care units (PICUs) participating in two large, multi-centre, observational cohorts from the European SCOTER study (n = 383 patients; study period between January 1, 2018 and January 1, 2020) and the U.S. Novel Data-Driven Sepsis Phenotypes in Children study (n = 1898 patients; study period between January 1, 2012 and January 1, 2018). We independently used latent class analysis (LCA) in both cohorts to identify phenotypes using demographic, clinical, and laboratory data from the first 24 h of PICU admission. We then tested the association of the phenotypes with clinical outcomes in both cohorts. Findings: LCA identified two distinct phenotypes that were comparable across both cohorts. Phenotype 1 was characterised by lower serum bicarbonate and albumin, markedly increased lactate and hepatic, renal, and coagulation abnormalities when compared to phenotype 2. Patients with phenotype 1 had a higher 90-day mortality (European cohort 29.2% versus 13.4%, U.S. cohort 27.3% versus 11.4%, p

Published
2023
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4. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortiumResearch in context

Author

Francesca Sperotto, Alba Gutiérrez-Sacristán, Simran Makwana, Xiudi Li, Valerie N. Rofeberg, Tianxi Cai, Florence T. Bourgeois, Gilbert S. Omenn, David A. Hanauer, Carlos Sáez, Clara-Lea Bonzel, Emily Bucholz, Audrey Dionne, Matthew D. Elias, Noelia García-Barrio, Tomás González González, Richard W. Issitt, Kate F. Kernan, Jessica Laird-Gion, Sarah E. Maidlow, Kenneth D. Mandl, Taha Mohseni Ahooyi, Cinta Moraleda, Michele Morris, Karyn L. Moshal, Miguel Pedrera-Jiménez, Mohsin A. Shah, Andrew M. South, Anastasia Spiridou, Deanne M. Taylor, Guillaume Verdy, Shyam Visweswaran, Xuan Wang, Zongqi Xia, Joany M. Zachariasse, Jane W. Newburger, Paul Avillach, James R. Aaron, Atif Adam, Giuseppe Agapito, Adem Albayrak, Giuseppe Albi, Mario Alessiani, Anna Alloni, Danilo F. Amendola, François Angoulvant, Li LLJ. Anthony, Bruce J. Aronow, Fatima Ashraf, Andrew Atz, Vidul Ayakulangara Panickan, Paula S. Azevedo, Rafael Badenes, James Balshi, Ashley Batugo, Brendin R. Beaulieu-Jones, Brett K. Beaulieu-Jones, Douglas S. Bell, Antonio Bellasi, Riccardo Bellazzi, Vincent Benoit, Michele Beraghi, José Luis Bernal-Sobrino, Mélodie Bernaux, Romain Bey, Surbhi Bhatnagar, Alvar Blanco-Martínez, Martin Boeker, John Booth, Silvano Bosari, Robert L. Bradford, Gabriel A. Brat, Stéphane Bréant, Nicholas W. Brown, Raffaele Bruno, William A. Bryant, Mauro Bucalo, Anita Burgun, Mario Cannataro, Aldo Carmona, Anna Maria Cattelan, Charlotte Caucheteux, Julien Champ, Jin Chen, Krista Y. Chen, Luca Chiovato, Lorenzo Chiudinelli, Kelly Cho, James J. Cimino, Tiago K. Colicchio, Sylvie Cormont, Sébastien Cossin, Jean B. Craig, Juan Luis Cruz-Bermúdez, Jaime Cruz-Rojo, Arianna Dagliati, Mohamad Daniar, Christel Daniel, Priyam Das, Batsal Devkota, Rui Duan, Julien Dubiel, Scott L. DuVall, Loic Esteve, Hossein Estiri, Shirley Fan, Robert W. Follett, Thomas Ganslandt, Lana X. Garmire, Nils Gehlenborg, Emily J. Getzen, Alon Geva, Rachel SJ. Goh, Tobias Gradinger, Alexandre Gramfort, Romain Griffier, Nicolas Griffon, Olivier Grisel, Pietro H. Guzzi, Larry Han, Christian Haverkamp, Derek Y. Hazard, Bing He, Darren W. Henderson, Martin Hilka, Yuk-Lam Ho, John H. Holmes, Jacqueline P. Honerlaw, Chuan Hong, Kenneth M. Huling, Meghan R. Hutch, Anne Sophie Jannot, Vianney Jouhet, Mundeep K. Kainth, Kernan F. Kate, Ramakanth Kavuluru, Mark S. Keller, Chris J. Kennedy, Daniel A. Key, Katie Kirchoff, Jeffrey G. Klann, Isaac S. Kohane, Ian D. Krantz, Detlef Kraska, Ashok K. Krishnamurthy, Sehi L'Yi, Judith Leblanc, Guillaume Lemaitre, Leslie Lenert, Damien Leprovost, Molei Liu, Ne Hooi Will Loh, Qi Long, Sara Lozano-Zahonero, Yuan Luo, Kristine E. Lynch, Sadiqa Mahmood, Adeline Makoudjou, Alberto Malovini, Chengsheng Mao, Anupama Maram, Monika Maripuri, Patricia Martel, Marcelo R. Martins, Jayson S. Marwaha, Aaron J. Masino, Maria Mazzitelli, Diego R. Mazzotti, Arthur Mensch, Marianna Milano, Marcos F. Minicucci, Bertrand Moal, Jason H. Moore, Jeffrey S. Morris, Sajad Mousavi, Danielle L. Mowery, Douglas A. Murad, Shawn N. Murphy, Thomas P. Naughton, Carlos Tadeu Breda Neto, Antoine Neuraz, Jane Newburger, Kee Yuan Ngiam, Wanjiku FM. Njoroge, James B. Norman, Jihad Obeid, Marina P. Okoshi, Karen L. Olson, Nina Orlova, Brian D. Ostasiewski, Nathan P. Palmer, Nicolas Paris, Lav P. Patel, Ashley C. Pfaff, Emily R. Pfaff, Danielle Pillion, Sara Pizzimenti, Tanu Priya, Hans U. Prokosch, Robson A. Prudente, Andrea Prunotto, Víctor Quirós-González, Rachel B. Ramoni, Maryna Raskin, Siegbert Rieg, Gustavo Roig-Domínguez, Pablo Rojo, Nekane Romero-Garcia, Paula Rubio-Mayo, Paolo Sacchi, Elisa Salamanca, Malarkodi Jebathilagam Samayamuthu, L. Nelson Sanchez-Pinto, Arnaud Sandrin, Nandhini Santhanam, Janaina C.C. Santos, Fernando J. Sanz Vidorreta, Maria Savino, Emily R. Schriver, Petra Schubert, Juergen Schuettler, Luigia Scudeller, Neil J. Sebire, Pablo Serrano-Balazote, Patricia Serre, Arnaud Serret-Larmande, Zahra Shakeri Hossein Abad, Domenick Silvio, Piotr Sliz, Jiyeon Son, Charles Sonday, Zachary H. Strasser, Amelia LM. Tan, Bryce W.Q. Tan, Byorn W.L. Tan, Suzana E. Tanni, Ana I. Terriza-Torres, Valentina Tibollo, Patric Tippmann, Emma MS. Toh, Carlo Torti, Enrico M. Trecarichi, Andrew K. Vallejos, Gael Varoquaux, Margaret E. Vella, Jill-Jênn Vie, Michele Vitacca, Kavishwar B. Wagholikar, Lemuel R. Waitman, Demian Wassermann, Griffin M. Weber, Martin Wolkewitz, Scott Wong, Xin Xiong, Ye Ye, Nadir Yehya, William Yuan, Janet J. Zahner, Alberto Zambelli, Harrison G. Zhang, Daniela Zöller, Valentina Zuccaro, and Chiara Zucco

Subjects
Multisystem inflammatory syndrome, Paediatric inflammatory multisystem syndrome, COVID-19, SARS-CoV-2, Variants, Pediatrics, Medicine (General), R5-920
Abstract

Summary: Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras. Methods: We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites. Findings: Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES −1.18 years [95% CI −2.05, −0.32]), had fewer respiratory symptoms (RD −0.15 [95% CI −0.33, −0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD −0.35 [95% CI −0.64, −0.07]), lower lymphocyte count (ES −0.16 × 109/uL [95% CI −0.30, −0.01]), lower C-reactive protein (ES −28.5 mg/L [95% CI −46.3, −10.7]), and lower troponin (ES −0.14 ng/mL [95% CI −0.26, −0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES −1.6 years [95% CI −2.5, −0.8]), had less frequent SIRS (RD −0.18 [95% CI −0.30, −0.05]), lower lymphocyte count (ES −0.39 × 109/uL [95% CI −0.52, −0.25]), lower troponin (ES −0.16 ng/mL [95% CI −0.30, −0.01]) and less frequently received anticoagulation therapy (RD −0.19 [95% CI −0.37, −0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (−1.3 days [95% CI −2.3, −0.4]). Interpretation: Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time. Funding: None.

Published
2023
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5. Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort studyResearch in Context

Author

Arianna Dagliati, Zachary H. Strasser, Zahra Shakeri Hossein Abad, Jeffrey G. Klann, Kavishwar B. Wagholikar, Rebecca Mesa, Shyam Visweswaran, Michele Morris, Yuan Luo, Darren W. Henderson, Malarkodi Jebathilagam Samayamuthu, Bryce W.Q. Tan, Guillame Verdy, Gilbert S. Omenn, Zongqi Xia, Riccardo Bellazzi, Shawn N. Murphy, John H. Holmes, Hossein Estiri, James R. Aaron, Giuseppe Agapito, Adem Albayrak, Giuseppe Albi, Mario Alessiani, Anna Alloni, Danilo F. Amendola, François Angoulvant, Li L.L.J. Anthony, Bruce J. Aronow, Fatima Ashraf, Andrew Atz, Paul Avillach, Paula S. Azevedo, James Balshi, Brett K. Beaulieu-Jones, Douglas S. Bell, Antonio Bellasi, Vincent Benoit, Michele Beraghi, José Luis Bernal-Sobrino, Mélodie Bernaux, Romain Bey, Surbhi Bhatnagar, Alvar Blanco-Martínez, Clara-Lea Bonzel, John Booth, Silvano Bosari, Florence T. Bourgeois, Robert L. Bradford, Gabriel A. Brat, Stéphane Bréant, Nicholas W. Brown, Raffaele Bruno, William A. Bryant, Mauro Bucalo, Emily Bucholz, Anita Burgun, Tianxi Cai, Mario Cannataro, Aldo Carmona, Charlotte Caucheteux, Julien Champ, Jin Chen, Krista Y. Chen, Luca Chiovato, Lorenzo Chiudinelli, Kelly Cho, James J. Cimino, Tiago K. Colicchio, Sylvie Cormont, Sébastien Cossin, Jean B. Craig, Juan Luis Cruz-Bermúdez, Jaime Cruz-Rojo, Mohamad Daniar, Christel Daniel, Priyam Das, Batsal Devkota, Audrey Dionne, Rui Duan, Julien Dubiel, Scott L. DuVall, Loic Esteve, Shirley Fan, Robert W. Follett, Thomas Ganslandt, Noelia García- Barrio, Lana X. Garmire, Nils Gehlenborg, Emily J. Getzen, Alon Geva, Tobias Gradinger, Alexandre Gramfort, Romain Griffier, Nicolas Griffon, Olivier Grisel, Alba Gutiérrez-Sacristán, Larry Han, David A. Hanauer, Christian Haverkamp, Derek Y. Hazard, Bing He, Martin Hilka, Yuk-Lam Ho, Chuan Hong, Kenneth M. Huling, Meghan R. Hutch, Richard W. Issitt, Anne Sophie Jannot, Vianney Jouhet, Ramakanth Kavuluru, Mark S. Keller, Chris J. Kennedy, Daniel A. Key, Katie Kirchoff, Isaac S. Kohane, Ian D. Krantz, Detlef Kraska, Ashok K. Krishnamurthy, Sehi L'Yi, Trang T. Le, Judith Leblanc, Guillaume Lemaitre, Leslie Lenert, Damien Leprovost, Molei Liu, Ne Hooi Will Loh, Qi Long, Sara Lozano-Zahonero, Kristine E. Lynch, Sadiqa Mahmood, Sarah E. Maidlow, Adeline Makoudjou, Alberto Malovini, Kenneth D. Mandl, Chengsheng Mao, Anupama Maram, Patricia Martel, Marcelo R. Martins, Jayson S. Marwaha, Aaron J. Masino, Maria Mazzitelli, Arthur Mensch, Marianna Milano, Marcos F. Minicucci, Bertrand Moal, Taha Mohseni Ahooyi, Jason H. Moore, Cinta Moraleda, Jeffrey S. Morris, Karyn L. Moshal, Sajad Mousavi, Danielle L. Mowery, Douglas A. Murad, Thomas P. Naughton, Carlos Tadeu Breda Neto, Antoine Neuraz, Jane Newburger, Kee Yuan Ngiam, Wanjiku F.M. Njoroge, James B. Norman, Jihad Obeid, Marina P. Okoshi, Karen L. Olson, Nina Orlova, Brian D. Ostasiewski, Nathan P. Palmer, Nicolas Paris, Lav P. Patel, Miguel Pedrera-Jiménez, Emily R. Pfaff, Ashley C. Pfaff, Danielle Pillion, Sara Pizzimenti, Hans U. Prokosch, Robson A. Prudente, Andrea Prunotto, Víctor Quirós-González, Rachel B. Ramoni, Maryna Raskin, Siegbert Rieg, Gustavo Roig-Domínguez, Pablo Rojo, Paula Rubio-Mayo, Paolo Sacchi, Carlos Sáez, Elisa Salamanca, L. Nelson Sanchez-Pinto, Arnaud Sandrin, Nandhini Santhanam, Janaina C.C. Santos, Fernando J. Sanz Vidorreta, Maria Savino, Emily R. Schriver, Petra Schubert, Juergen Schuettler, Luigia Scudeller, Neil J. Sebire, Pablo Serrano-Balazote, Patricia Serre, Arnaud Serret-Larmande, Mohsin Shah, Domenick Silvio, Piotr Sliz, Jiyeon Son, Charles Sonday, Andrew M. South, Anastasia Spiridou, Amelia L.M. Tan, Byorn W.L. Tan, Suzana E. Tanni, Deanne M. Taylor, Ana I. Terriza-Torres, Valentina Tibollo, Patric Tippmann, Emma M.S. Toh, Carlo Torti, Enrico M. Trecarichi, Yi-Ju Tseng, Andrew K. Vallejos, Gael Varoquaux, Margaret E. Vella, Guillaume Verdy, Jill-Jênn Vie, Michele Vitacca, Lemuel R. Waitman, Xuan Wang, Demian Wassermann, Griffin M. Weber, Martin Wolkewitz, Scott Wong, Xin Xiong, Ye Ye, Nadir Yehya, William Yuan, Alberto Zambelli, Harrison G. Zhang, Daniela Zo¨ller, Valentina Zuccaro, and Chiara Zucco

Subjects
Post-acute sequelae of SARS-CoV-2, PASC, COVID-19, SARS-CoV-2, Electronic health records, Medicine (General), R5-920
Abstract

Summary: Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.

Published
2023
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6. Heart Rate Change as a Potential Digital Biomarker of Brain Death in Critically Ill Children With Acute Catastrophic Brain Injury

Author

Kerri L. LaRovere, MD, MMSCI, Matthew Luchette, MD, Alireza Akhondi-Asl, PhD, Bradley J. DeSouza, MB, BCh, BAO, Robert C. Tasker, MD, MBBS, Nilesh M. Mehta, MD, and Alon Geva, MD, MPH

Subjects
Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

IMPORTANCE:. Bedside measurement of heart rate (HR) change (HRC) may provide an objective physiologic marker for when brain death (BD) may have occurred, and BD testing is indicated in children. OBJECTIVES:. To determine whether HRC, calculated using numeric HR measurements sampled every 5 seconds, can identify patients with BD among patients with catastrophic brain injury (CBI). DESIGN, SETTING, AND PARTICIPANTS:. Single-center, retrospective study (2008–2020) of critically ill children with acute CBI. Patients with CBI had a neurocritical care consultation, were admitted to an ICU, had acute neurologic injury on presentation or during hospitalization based on clinical and/or imaging findings, and died or survived with Glasgow Coma Scale (GCS) less than 13 at hospital discharge. Patients meeting BD criteria (BD group) were compared with those with cardiopulmonary death (CD group) or those who survived to discharge. MAIN OUTCOMES AND MEASURES:. HRC was calculated as the interquartile range of HR divided by median HR using 5-minute windows with 50% overlap for up to 5 days before death or end of recording. HRC was compared among the BD, CD, and survivor groups. RESULTS:. Of 96 patients with CBI (69% male, median age 4 years), 28 died (8 BD, 20 CD) and 20 survived (median GCS 9 at discharge). Within 24 hours before death, HRC was lower in BD compared with CD patients or survivors (0.01 vs 0.03 vs 0.04, p = 0.001). In BD patients, HRC decreased at least 1 day before death. HRC discriminated BD from CD patients and survivors with 90% sensitivity, 70% specificity, 44% positive predictive value, 96% negative predictive value (area under the receiver operating characteristic curve 0.88, 95% CI, 0.80–0.93). CONCLUSIONS AND RELEVANCE:. HRC is a novel digital biomarker that, with further validation, may be useful as a classifier for BD in the overall course of patients with CBI.

Published
2023
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8. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort studyResearch in context

Author

Byorn W.L. Tan, Bryce W.Q. Tan, Amelia L.M. Tan, Emily R. Schriver, Alba Gutiérrez-Sacristán, Priyam Das, William Yuan, Meghan R. Hutch, Noelia García Barrio, Miguel Pedrera Jimenez, Noor Abu-el-rub, Michele Morris, Bertrand Moal, Guillaume Verdy, Kelly Cho, Yuk-Lam Ho, Lav P. Patel, Arianna Dagliati, Antoine Neuraz, Jeffrey G. Klann, Andrew M. South, Shyam Visweswaran, David A. Hanauer, Sarah E. Maidlow, Mei Liu, Danielle L. Mowery, Ashley Batugo, Adeline Makoudjou, Patric Tippmann, Daniela Zöller, Gabriel A. Brat, Yuan Luo, Paul Avillach, Riccardo Bellazzi, Luca Chiovato, Alberto Malovini, Valentina Tibollo, Malarkodi Jebathilagam Samayamuthu, Pablo Serrano Balazote, Zongqi Xia, Ne Hooi Will Loh, Lorenzo Chiudinelli, Clara-Lea Bonzel, Chuan Hong, Harrison G. Zhang, Griffin M. Weber, Isaac S. Kohane, Tianxi Cai, Gilbert S. Omenn, John H. Holmes, Kee Yuan Ngiam, James R. Aaron, Giuseppe Agapito, Adem Albayrak, Giuseppe Albi, Mario Alessiani, Anna Alloni, Danilo F. Amendola, François Angoulvant, Li L.L.J. Anthony, Bruce J. Aronow, Fatima Ashraf, Andrew Atz, Vidul Ayakulangara Panickan, Paula S. Azevedo, James Balshi, Brett K. Beaulieu-Jones, Brendin R. Beaulieu-Jones, Douglas S. Bell, Antonio Bellasi, Vincent Benoit, Michele Beraghi, José Luis Bernal-Sobrino, Mélodie Bernaux, Romain Bey, Surbhi Bhatnagar, Alvar Blanco-Martínez, Martin Boeker, John Booth, Silvano Bosari, Florence T. Bourgeois, Robert L. Bradford, Stéphane Bréant, Nicholas W. Brown, Raffaele Bruno, William A. Bryant, Mauro Bucalo, Emily Bucholz, Anita Burgun, Mario Cannataro, Aldo Carmona, Anna Maria Cattelan, Charlotte Caucheteux, Julien Champ, Jin Chen, Krista Y. Chen, James J. Cimino, Tiago K. Colicchio, Sylvie Cormont, Sébastien Cossin, Jean B. Craig, Juan Luis Cruz-Bermúdez, Jaime Cruz-Rojo, Mohamad Daniar, Christel Daniel, Batsal Devkota, Audrey Dionne, Rui Duan, Julien Dubiel, Scott L. DuVall, Loic Esteve, Hossein Estiri, Shirley Fan, Robert W. Follett, Thomas Ganslandt, Noelia García-Barrio, Lana X. Garmire, Nils Gehlenborg, Emily J. Getzen, Alon Geva, Tomás González González, Tobias Gradinger, Alexandre Gramfort, Romain Griffier, Nicolas Griffon, Olivier Grisel, Pietro H. Guzzi, Larry Han, Christian Haverkamp, Derek Y. Hazard, Bing He, Darren W. Henderson, Martin Hilka, Jacqueline P. Honerlaw, Kenneth M. Huling, Richard W. Issitt, Anne Sophie Jannot, Vianney Jouhet, Ramakanth Kavuluru, Mark S. Keller, Chris J. Kennedy, Kate F. Kernan, Daniel A. Key, Katie Kirchoff, Ian D. Krantz, Detlef Kraska, Ashok K. Krishnamurthy, Sehi L'Yi, Trang T. Le, Judith Leblanc, Guillaume Lemaitre, Leslie Lenert, Damien Leprovost, Molei Liu, Qi Long, Sara Lozano-Zahonero, Kristine E. Lynch, Sadiqa Mahmood, Simran Makwana, Kenneth D. Mandl, Chengsheng Mao, Anupama Maram, Monika Maripuri, Patricia Martel, Marcelo R. Martins, Jayson S. Marwaha, Aaron J. Masino, Maria Mazzitelli, Diego R. Mazzotti, Arthur Mensch, Marianna Milano, Marcos F. Minicucci, Taha Mohseni Ahooyi, Jason H. Moore, Cinta Moraleda, Jeffrey S. Morris, Karyn L. Moshal, Sajad Mousavi, Douglas A. Murad, Shawn N. Murphy, Thomas P. Naughton, Carlos Tadeu Breda Neto, Jane Newburger, Wanjiku F.M. Njoroge, James B. Norman, Jihad Obeid, Marina P. Okoshi, Karen L. Olson, Nina Orlova, Brian D. Ostasiewski, Nathan P. Palmer, Nicolas Paris, Miguel Pedrera-Jiménez, Ashley C. Pfaff, Emily R. Pfaff, Danielle Pillion, Sara Pizzimenti, Tanu Priya, Hans U. Prokosch, Robson A. Prudente, Andrea Prunotto, Víctor Quirós-González, Rachel B. Ramoni, Maryna Raskin, Siegbert Rieg, Gustavo Roig-Domínguez, Pablo Rojo, Paula Rubio-Mayo, Paolo Sacchi, Carlos Sáez, Elisa Salamanca, L. Nelson Sanchez-Pinto, Arnaud Sandrin, Nandhini Santhanam, Janaina C.C. Santos, Fernando J. Sanz Vidorreta, Maria Savino, Petra Schubert, Juergen Schuettler, Luigia Scudeller, Neil J. Sebire, Pablo Serrano-Balazote, Patricia Serre, Arnaud Serret-Larmande, Mohsin Shah, Zahra Shakeri Hossein Abad, Domenick Silvio, Piotr Sliz, Jiyeon Son, Charles Sonday, Francesca Sperotto, Anastasia Spiridou, Zachary H. Strasser, Suzana E. Tanni, Deanne M. Taylor, Ana I. Terriza-Torres, Emma M.S. Toh, Carlo Torti, Enrico M. Trecarichi, Andrew K. Vallejos, Gael Varoquaux, Margaret E. Vella, Jill-Jênn Vie, Michele Vitacca, Kavishwar B. Wagholikar, Lemuel R. Waitman, Xuan Wang, Demian Wassermann, Martin Wolkewitz, Scott Wong, Xin Xiong, Ye Ye, Nadir Yehya, Joany M. Zachariasse, Janet J. Zahner, Alberto Zambelli, Valentina Zuccaro, and Chiara Zucco

Subjects
COVID-19, Acute kidney injury, SARS-CoV-2, Chronic kidney disease, Electronic health records, Medicine (General), R5-920
Abstract

Summary: Background: 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. Methods: 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. Findings: Advanced age (HR 2.77, 95%CI 2.53–3.04, p

Published
2023
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9. Implementation Science in Pediatric Critical Care – Sedation and Analgesia Practices as a Case Study

Author

Youyang Yang, Alon Geva, Kate Madden, and Nilesh M. Mehta

Subjects
sedation, analgesia, pediatric critical care, implementation science, barriers, Pediatrics, RJ1-570
Abstract

Sedation and analgesia (SA) management is essential practice in the pediatric intensive care unit (PICU). Over the past decade, there has been significant interest in optimal SA management strategy, due to reports of the adverse effects of SA medications and their relationship to ICU delirium. We reviewed 13 studies examining SA practices in the PICU over the past decade for the purposes of reporting the study design, outcomes of interest, SA protocols used, strategies for implementation, and the patient-centered outcomes. We highlighted the paucity of evidence-base for these practices and also described the existing gaps in the intersection of implementation science (IS) and SA protocols in the PICU. Future studies would benefit from a focus on effective implementation strategies to introduce and sustain evidence-based SA protocols, as well as novel quasi-experimental study designs that will help determine their impact on relevant clinical outcomes, such as the occurrence of ICU delirium. Adoption of the available evidence-based practices into routine care in the PICU remains challenging. Using SA practice as an example, we illustrated the need for a structured approach to the implementation science in pediatric critical care. Key components of the successful adoption of evidence-based best practice include the assessment of the local context, both resources and barriers, followed by a context-specific strategy for implementation and a focus on sustainability and integration of the practice into the permanent workflow.

Published
2022
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10. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents

Author

Alon Geva, Manish M. Patel, Margaret M. Newhams, Cameron C. Young, Mary Beth F. Son, Michele Kong, Aline B. Maddux, Mark W. Hall, Becky J. Riggs, Aalok R. Singh, John S. Giuliano, Charlotte V. Hobbs, Laura L. Loftis, Gwenn E. McLaughlin, Stephanie P. Schwartz, Jennifer E. Schuster, Christopher J. Babbitt, Natasha B. Halasa, Shira J. Gertz, Sule Doymaz, Janet R. Hume, Tamara T. Bradford, Katherine Irby, Christopher L. Carroll, John K. McGuire, Keiko M. Tarquinio, Courtney M. Rowan, Elizabeth H. Mack, Natalie Z. Cvijanovich, Julie C. Fitzgerald, Philip C. Spinella, Mary A. Staat, Katharine N. Clouser, Vijaya L. Soma, Heda Dapul, Mia Maamari, Cindy Bowens, Kevin M. Havlin, Peter M. Mourani, Sabrina M. Heidemann, Steven M. Horwitz, Leora R. Feldstein, Mark W. Tenforde, Jane W. Newburger, Kenneth D. Mandl, and Adrienne G. Randolph

Subjects
COVID-19, Multisystem inflammatory syndrome, Pediatrics, Critical care medicine, Clustering, Medicine (General), R5-920
Abstract

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)

Published
2021
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11. A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation

Author

Remi D. Prince, BS, Alireza Akhondi-Asl, PhD, Nilesh M. Mehta, MD, and Alon Geva, MD, MPH

Subjects
Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

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.

Published
2021
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12. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.

Author

Jeffrey G. Klann, Hossein Estiri, Griffin M. Weber, Bertrand Moal, Paul Avillach, Chuan Hong, Amelia L. M. Tan, Brett K. Beaulieu-Jones, Victor M. Castro, Thomas Maulhardt, Alon Geva, Alberto Malovini, Andrew M. South, Shyam Visweswaran, Michele Morris, Malarkodi J. Samayamuthu, Gilbert S. Omenn, Kee Yuan Ngiam, Kenneth D. Mandl, Martin Boeker, Karen L. Olson, Danielle L. Mowery, Robert W. Follett, David A. Hanauer, Riccardo Bellazzi, Jason H. Moore, Ne-Hooi Will Loh, Douglas S. Bell, Kavishwar B. Wagholikar, Luca Chiovato, Valentina Tibollo, Siegbert Rieg, Anthony L. L. J. Li, Vianney Jouhet, Emily Schriver, Zongqi Xia, Meghan Hutch, Yuan Luo 0001, Isaac S. Kohane, Gabriel A. Brat, and Shawn N. Murphy

Published
2021
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