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Giacobbe DR, Marelli C, Guastavino S, Signori A, Mora S, Rosso N, Campi C, Piana M, Murgia Y, Giacomini M, Bassetti M. Artificial intelligence and prescription of antibiotic therapy: present and future. Expert Rev Anti Infect Ther 2024:1-15. [PMID: 39155449 DOI: 10.1080/14787210.2024.2386669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 07/28/2024] [Indexed: 08/20/2024]
Abstract
INTRODUCTION In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription. AREAS COVERED In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024. EXPERT OPINION Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.
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Affiliation(s)
- Daniele Roberto Giacobbe
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Marelli
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Alessio Signori
- Section of Biostatistics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Sara Mora
- UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Nicola Rosso
- UO Information and Communication Technologies, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Cristina Campi
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Michele Piana
- Department of Mathematics (DIMA), University of Genoa, Genoa, Italy
- Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Ylenia Murgia
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Mauro Giacomini
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Matteo Bassetti
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
- UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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Brouwer L, Cunney R, Drew RJ. Predicting community acquired bloodstream infection in infants using full blood count parameters and C-reactive protein; a machine learning study. Eur J Pediatr 2024; 183:2983-2993. [PMID: 38634890 PMCID: PMC11192659 DOI: 10.1007/s00431-024-05441-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/04/2024] [Accepted: 01/17/2024] [Indexed: 04/19/2024]
Abstract
Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In this study, we aimed to develop a model that can reliably identify infants who do not have positive blood cultures (and, by extension, BSI) based on the full blood count (FBC) and C-reactive protein (CRP) values. Several models (i.e. multivariable logistic regression, linear discriminant analysis, K nearest neighbors, support vector machine, random forest model and decision tree) were trained using FBC and CRP values of 2693 infants aged 7 to 60 days with suspected BSI between 2005 and 2022 in a tertiary paediatric hospital in Dublin, Ireland. All models tested showed similar sensitivities (range 47% - 62%) and specificities (range 85%-95%). A trained decision tree and random forest model were applied to the full dataset and to a dataset containing infants with suspected BSI in 2023 and showed good segregation of a low-risk and high-risk group. Negative predictive values for these two models were high for the full dataset (> 99%) and for the 2023 dataset (> 97%), while positive predictive values were low in both dataset (4%-20%). Conclusion: We identified several models that can predict positive blood cultures in infants with suspected BSI aged 7 to 60 days. Application of these models could prevent administration of antimicrobial treatment and burdensome diagnostics in infants who do not need them. What is Known: • Bloodstream infection (BSI) in infants cause non-specific symptoms and may be difficult to diagnose. • Results of blood cultures can take up to 48 hours. What is New: • Machine learning models can contribute to clinical decision making on BSI in infants while blood culture results are not yet known.
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Affiliation(s)
- Lieke Brouwer
- Public Health Laboratory, HSE, Cherry Orchard Hospital, Dublin, Ireland.
- European Public Health Microbiology Training Programme (EUPHEM), European Centre for Disease Prevention and Control, Stockholm, Sweden.
| | - Robert Cunney
- Irish Meningitis and Sepsis Reference Laboratory, Children's Health Ireland at Temple Street, Dublin, Ireland
- Department of Clinical Microbiology, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Richard J Drew
- Irish Meningitis and Sepsis Reference Laboratory, Children's Health Ireland at Temple Street, Dublin, Ireland
- Department of Clinical Microbiology, Royal College of Surgeons in Ireland, Dublin, Ireland
- Clinical Innovation Unit, Rotunda Hospital, Dublin, Ireland
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Tang BH, Li QY, Liu HX, Zheng Y, Wu YE, van den Anker J, Hao GX, Zhao W. Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making. Paediatr Drugs 2024; 26:355-363. [PMID: 38880837 DOI: 10.1007/s40272-024-00638-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 06/18/2024]
Abstract
Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.
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Affiliation(s)
- Bo-Hao Tang
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui-Xin Liu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Department of Pediatrics, Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Departments of Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Wei Zhao
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
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Liporaci F, Carlotti D, Carlotti A. A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit. PLoS One 2024; 19:e0299884. [PMID: 38691554 PMCID: PMC11062549 DOI: 10.1371/journal.pone.0299884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 02/16/2024] [Indexed: 05/03/2024] Open
Abstract
Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient's outcome. Data on machine-learning models to predict BSI in pediatric patients are limited and neither study included time series data. We aimed to develop a machine learning model to predict an early diagnosis of BSI in patients admitted to the PICU. This was a retrospective cohort study of patients who had at least one positive blood culture result during stay at a PICU of a tertiary-care university hospital, from January 1st to December 31st 2019. Patients with positive blood culture results with growth of contaminants and those with incomplete data were excluded. Models were developed using demographic, clinical and laboratory data collected from the electronic medical record. Laboratory data (complete blood cell counts with differential and C-reactive protein) and vital signs (heart rate, respiratory rate, blood pressure, temperature, oxygen saturation) were obtained 72 hours before and on the day of blood culture collection. A total of 8816 data from 76 patients were processed by the models. The machine committee was the best-performing model, showing accuracy of 99.33%, precision of 98.89%, sensitivity of 100% and specificity of 98.46%. Hence, we developed a model using demographic, clinical and laboratory data collected on a routine basis that was able to detect BSI with excellent accuracy and precision, and high sensitivity and specificity. The inclusion of vital signs and laboratory data variation over time allowed the model to identify temporal changes that could be suggestive of the diagnosis of BSI. Our model might help the medical team in clinical-decision making by creating an alert in the electronic medical record, which may allow early antimicrobial initiation and better outcomes.
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Affiliation(s)
- Felipe Liporaci
- Department of Pediatrics, Division of Pediatric Critical Care Medicine, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil
| | - Danilo Carlotti
- Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil
| | - Ana Carlotti
- Department of Pediatrics, Division of Pediatric Critical Care Medicine, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil
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Conte L, Amodeo I, De Nunzio G, Raffaeli G, Borzani I, Persico N, Griggio A, Como G, Cascio D, Colnaghi M, Mosca F, Cavallaro G. Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation. Eur J Pediatr 2024; 183:2285-2300. [PMID: 38416256 PMCID: PMC11035462 DOI: 10.1007/s00431-024-05476-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/02/2024] [Accepted: 02/06/2024] [Indexed: 02/29/2024]
Abstract
Prenatal assessment of lung size and liver position is essential to stratify congenital diaphragmatic hernia (CDH) fetuses in risk categories, guiding counseling, and patient management. Manual segmentation on fetal MRI provides a quantitative estimation of total lung volume and liver herniation. However, it is time-consuming and operator-dependent. In this study, we utilized a publicly available deep learning (DL) segmentation system (nnU-Net) to automatically contour CDH-affected fetal lungs and liver on MRI sections. Concordance between automatic and manual segmentation was assessed by calculating the Jaccard coefficient. Pyradiomics standard features were then extracted from both manually and automatically segmented regions. The reproducibility of features between the two groups was evaluated through the Wilcoxon rank-sum test and intraclass correlation coefficients (ICCs). We finally tested the reliability of the automatic-segmentation approach by building a ML classifier system for the prediction of liver herniation based on support vector machines (SVM) and trained on shape features computed both in the manual and nnU-Net-segmented organs. We compared the area under the classifier receiver operating characteristic curve (AUC) in the two cases. Pyradiomics features calculated in the manual ROIs were partly reproducible by the same features calculated in nnU-Net segmented ROIs and, when used in the ML procedure, to predict liver herniation (both AUC around 0.85). Conclusion: Our results suggest that automatic MRI segmentation is feasible, with good reproducibility of pyradiomics features, and that a ML system for liver herniation prediction offers good reliability. Trial registration: https://clinicaltrials.gov/ct2/show/NCT04609163?term=NCT04609163&draw=2&rank=1 ; Clinical Trial Identification no. NCT04609163. What is Known: • Magnetic resonance imaging (MRI) is crucial for prenatal congenital diaphragmatic hernia (CDH) assessment. It enables the quantification of the total lung volume and the extent of liver herniation, which are essential for stratifying the severity of CDH, guiding counseling, and patient management. • The manual segmentation of MRI scans is a time-consuming process that is heavily reliant upon the skill set of the operator. What is New: • MRI lung and liver automatic segmentation using the deep learning nnU-Net system is feasible, with good Jaccard coefficient values and satisfactory reproducibility of pyradiomics features compared to manual results. • A feasible ML system for predicting liver herniation could improve prenatal assessments and CDH patient management.
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Affiliation(s)
- Luana Conte
- Department of Mathematics and Physics "E. De Giorgi", Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Local Health Authority (ASL) Lecce and Università del Salento, Lecce, Italy
| | - Ilaria Amodeo
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giorgio De Nunzio
- Department of Mathematics and Physics "E. De Giorgi", Laboratory of Biomedical Physics and Environment, Università del Salento, Lecce, Italy.
- Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Local Health Authority (ASL) Lecce and Università del Salento, Lecce, Italy.
| | - Genny Raffaeli
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
| | - Irene Borzani
- Pediatric Radiology Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Nicola Persico
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
- Department of Obstetrics and Gynecology, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Alice Griggio
- ASST Fatebenefratelli Sacco, Ospedale Macedonio Melloni, Milan, Italy
| | - Giuseppe Como
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Donato Cascio
- Department of Physics and Chemistry, Università Degli Studi Di Palermo, Palermo, Italy
| | - Mariarosa Colnaghi
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Mosca
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Clinical Sciences and Community Health, Università Degli Studi Di Milano, Milan, Italy
| | - Giacomo Cavallaro
- Neonatal Intensive Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
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Gunčar G, Kukar M, Smole T, Moškon S, Vovko T, Podnar S, Černelč P, Brvar M, Notar M, Köster M, Jelenc MT, Osterc Ž, Notar M. Differentiating viral and bacterial infections: A machine learning model based on routine blood test values. Heliyon 2024; 10:e29372. [PMID: 38644832 PMCID: PMC11033127 DOI: 10.1016/j.heliyon.2024.e29372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 04/02/2024] [Accepted: 04/07/2024] [Indexed: 04/23/2024] Open
Abstract
The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10-40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.
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Affiliation(s)
- Gregor Gunčar
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
- Faculty of Chemistry and Chemical Technology, University of Ljubljana, Slovenia
| | - Matjaž Kukar
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
- Faculty of Computer and Information Science, University of Ljubljana, Slovenia
| | - Tim Smole
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Sašo Moškon
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Tomaž Vovko
- Department of Infectious Diseases, University Medical Centre Ljubljana, Slovenia
| | - Simon Podnar
- Division of Neurology, University Medical Centre Ljubljana, Slovenia
| | - Peter Černelč
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Miran Brvar
- Centre for Clinical Toxicology and Pharmacology, University Medical Centre Ljubljana, Slovenia
| | - Mateja Notar
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Manca Köster
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | | | - Žiga Osterc
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
| | - Marko Notar
- Smart Blood Analytics Swiss SA, CH-8008, Zürich, Switzerland
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Krack AT, Eckerle M, Mahajan P, Ramilo O, VanBuren JM, Banks RK, Casper TC, Schnadower D, Kuppermann N. Leukopenia, neutropenia, and procalcitonin levels in young febrile infants with invasive bacterial infections. Acad Emerg Med 2024. [PMID: 38661246 DOI: 10.1111/acem.14921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND AND OBJECTIVE Serum procalcitonin (PCT) is a highly accurate biomarker for stratifying the risk of invasive bacterial infections (IBIs) in febrile infants ≤60 days old. However, PCT is unavailable in some settings. We explored the association of leukopenia and neutropenia with IBIs in non-critically ill febrile infants ≤60 days old, with and without PCT. METHODS We conducted a secondary analysis of a prospective observational cohort consisting of 7407 non-critically ill infants ≤60 days old with temperatures ≥38°C. We focused on the risk of IBIs in patients with leukopenia (white blood cell [WBC] count <5000 cells/μL) or neutropenia (absolute neutrophil count [ANC] <1000 cells/μL), categorized to extremes of lower values, and the impact of PCT on these associations. Multiple logistic regression was used to identify independent predictors of IBIs. RESULTS Final analysis included 6865 infants with complete data; 45% (3098) had PCT data available. Of the 6865, a total of 111 (1.6%) had bacteremia without bacterial meningitis, 18 (0.3%) had bacterial meningitis without bacteremia, and 19 (0.3%) had both bacteremia and bacterial meningitis. IBI was present in four of 20 (20%) infants with WBC counts ≤2500 cells/μL and four of 311 (1.3%) with ANC <1000 cells/μL. In multivariable logistic regression analysis not including PCT, a WBC count <2500 cells/μL was significantly associated with IBI (OR 13.48, 95% CI 2.92-45.35). However, no patients with leukopenia or neutropenia and PCT ≤0.5 ng/mL had IBIs. CONCLUSIONS Leukopenia ≤2500 cells/μL in febrile infants ≤60 days old is associated with IBIs. However, in the presence of normal PCT levels, no patients with leukopenia had IBIs. While this suggests leukopenia ≤2500 cells/μL is a risk factor for IBIs in non-critically ill young febrile infants only when PCT is unavailable or elevated, the overall low frequency of leukopenia in this cohort warrants caution in interpretation, with future validation required.
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Affiliation(s)
- Andrew T Krack
- Department of Pediatrics, Children's Hospital Colorado, Aurora, Colorado, USA
| | - Michelle Eckerle
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | | | | | | | | | | | - David Schnadower
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Nathan Kuppermann
- University of California Davis, School of Medicine, Sacramento, California, USA
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Ballard DW, Huang J, Sharp AL, Mark DG, Nguyen THP, Young BR, Vinson DR, Van Winkle P, Kene MV, Rauchwerger AS, Zhang JY, Park SJ, Reed ME, Greenhow TL. An all-inclusive model for predicting invasive bacterial infection in febrile infants age 7-60 days. Pediatr Res 2024:10.1038/s41390-024-03141-3. [PMID: 38575694 DOI: 10.1038/s41390-024-03141-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 04/06/2024]
Abstract
BACKGROUND Invasive bacterial infections (IBIs) in febrile infants are rare but potentially devastating. We aimed to derive and validate a predictive model for IBI among febrile infants age 7-60 days. METHODS Data were abstracted retrospectively from electronic records of 37 emergency departments (EDs) for infants with a measured temperature >=100.4 F who underwent an ED evaluation with blood and urine cultures. Models to predict IBI were developed and validated respectively using a random 80/20 dataset split, including 10-fold cross-validation. We used precision recall curves as the classification metric. RESULTS Of 4411 eligible infants with a mean age of 37 days, 29% had characteristics that would likely have excluded them from existing risk stratification protocols. There were 196 patients with IBI (4.4%), including 43 (1.0%) with bacterial meningitis. Analytic approaches varied in performance characteristics (precision recall range 0.04-0.29, area under the curve range 0.5-0.84), with the XGBoost model demonstrating the best performance (0.29, 0.84). The five most important variables were serum white blood count, maximum temperature, absolute neutrophil count, absolute band count, and age in days. CONCLUSION A machine learning model (XGBoost) demonstrated the best performance in predicting a rare outcome among febrile infants, including those excluded from existing algorithms. IMPACT Several models for the risk stratification of febrile infants have been developed. There is a need for a preferred comprehensive model free from limitations and algorithm exclusions that accurately predicts IBIs. This is the first study to derive an all-inclusive predictive model for febrile infants aged 7-60 days in a community ED sample with IBI as a primary outcome. This machine learning model demonstrates potential for clinical utility in predicting IBI.
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Affiliation(s)
- Dustin W Ballard
- The Permanente Medical Group, Oakland, CA, USA.
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
| | - Jie Huang
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Adam L Sharp
- Kaiser Permanente Bernard J. Tyson School of Medicine, Health Systems Science Department, Pasadena, CA, USA
| | - Dustin G Mark
- The Permanente Medical Group, Oakland, CA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Tran H P Nguyen
- Department of Hospital Pediatrics, Kaiser Permanente Northern California, Roseville, CA, USA
| | - Beverly R Young
- Department of Hospital Pediatrics, Kaiser Permanente Northern California, Roseville, CA, USA
| | - David R Vinson
- The Permanente Medical Group, Oakland, CA, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Patrick Van Winkle
- Department of Pediatrics, Kaiser Permanente Southern California, Anaheim, CA, USA
| | | | - Adina S Rauchwerger
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Jennifer Y Zhang
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Stacy J Park
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - Mary E Reed
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Tara L Greenhow
- The Permanente Medical Group, Oakland, CA, USA
- Division of Infectious Diseases, Department of Pediatrics, Kaiser Permanente Northern California, San Francisco, CA, USA
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Kallonen A, Juutinen M, Värri A, Carrault G, Pladys P, Beuchée A. Early detection of late-onset neonatal sepsis from noninvasive biosignals using deep learning: A multicenter prospective development and validation study. Int J Med Inform 2024; 184:105366. [PMID: 38330522 DOI: 10.1016/j.ijmedinf.2024.105366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/15/2024] [Accepted: 01/31/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND Neonatal sepsis is responsible for significant morbidity and mortality worldwide. Its accurate and timely diagnosis is hindered by vague symptoms and the urgent necessity for early antibiotic intervention. The gold standard for diagnosing the condition is the identification of a pathogenic organism from normally sterile sites via laboratory testing. However, this method is resource-intensive and cannot be conducted continuously. OBJECTIVE This study aimed to predict the onset of late-onset sepsis (LOS) with good diagnostic value as early as possible using non-invasive biosignal measurements from neonatal intensive care unit (NICU) monitors. METHODS In this prospective multicenter study, we developed a multimodal machine learning algorithm based on a convolutional neural network (CNN) structure that uses the power spectral density (PSD) of recorded biosignals to predict the onset of LOS. This approach aimed to discern LOS-related pathogenic spectral signatures without labor-intensive manual artifact removal. RESULTS The model achieved an area under the receiver operating characteristic score of 0.810 (95 % CI 0.698-0.922) on the validation dataset. With an optimal operating point, LOS detection had 83 % sensitivity and 73 % specificity. The median early detection was 44 h before clinical suspicion. The results highlighted the additive importance of electrocardiogram and respiratory impedance (RESP) signals in improving predictive accuracy. According to a more detailed analysis, the predictive power arose from the morphology of the electrocardiogram's R-wave and sudden changes in the RESP signal. CONCLUSION Raw biosignals from NICU monitors, in conjunction with PSD transformation, as input to the CNN, can provide state-of-the-art prediction performance for LOS without the need for artifact removal. To the knowledge of the authors, this is the first study to highlight the independent and additive predictive potential of electrocardiogram R-wave morphology and concurrent, sudden changes in the RESP waveform in predicting the onset of LOS using non-invasive biosignals.
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Affiliation(s)
- Antti Kallonen
- Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland.
| | - Milla Juutinen
- Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland.
| | - Alpo Värri
- Faculty of Medicine and Health Technology, Tampere University, FI-33014, Tampere, Finland.
| | - Guy Carrault
- Inserm, LTSI - UMR 1099, University of Rennes, F-35000, Rennes, France.
| | - Patrick Pladys
- Inserm, LTSI - UMR 1099, University of Rennes, F-35000, Rennes, France; Pediatric Department, CHU Rennes, F-35000, Rennes, France.
| | - Alain Beuchée
- Inserm, LTSI - UMR 1099, University of Rennes, F-35000, Rennes, France; Pediatric Department, CHU Rennes, F-35000, Rennes, France.
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Galdo B, Pazos C, Pardo J, Solar A, Llamas D, Fernández-Blanco E, Pazos A. Artificial intelligence in paediatrics: Current events and challenges. An Pediatr (Barc) 2024; 100:195-201. [PMID: 38461129 DOI: 10.1016/j.anpede.2024.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 02/05/2024] [Indexed: 03/11/2024] Open
Abstract
This article examines the use of artificial intelligence (AI) in the field of paediatric care within the framework of the 7P medicine model (Predictive, Preventive, Personalized, Precise, Participatory, Peripheral and Polyprofessional). It highlights various applications of AI in the diagnosis, treatment and management of paediatric diseases as well as the role of AI in prevention and in the efficient management of health care resources and the resulting impact on the sustainability of public health systems. Successful cases of the application of AI in the paediatric care setting are presented, placing emphasis on the need to move towards a 7P health care model. Artificial intelligence is revolutionizing society at large and has a great potential for significantly improving paediatric care.
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Affiliation(s)
- Brais Galdo
- Universidad de A Coruña, A Coruña, Spain; INIBIC, A Coruña, Spain; RNASA-IMEDIR, A Coruña, Spain; Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain; Avances en Telemedicina e Informática Sanitaria, A Coruña, Spain
| | - Carla Pazos
- New Vision University, Faculty of Medicine, Tiflis, Georgia
| | - Jerónimo Pardo
- Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain
| | - Alfonso Solar
- Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain
| | - Daniel Llamas
- INIBIC, A Coruña, Spain; Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain; Avances en Telemedicina e Informática Sanitaria, A Coruña, Spain
| | - Enrique Fernández-Blanco
- Universidad de A Coruña, A Coruña, Spain; INIBIC, A Coruña, Spain; RNASA-IMEDIR, A Coruña, Spain; CITIC, A Coruña, Spain
| | - Alejandro Pazos
- Medical University of Byalistok, Byalistok, Podlaquia, Poland.
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Beam K, Sharma P, Levy P, Beam AL. Artificial intelligence in the neonatal intensive care unit: the time is now. J Perinatol 2024; 44:131-135. [PMID: 37443271 DOI: 10.1038/s41372-023-01719-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
Artificial intelligence (AI) has the potential to revolutionize the neonatal intensive care unit (NICU) care by leveraging the large-scale, high-dimensional data that are generated by NICU patients. There is an emerging recognition that the confluence of technological progress, commercialization pathways, and rich data sets provides a unique opportunity for AI to make a lasting impact on the NICU. In this perspective article, we discuss four broad categories of AI applications in the NICU: imaging interpretation, prediction modeling of electronic health record data, integration of real-time monitoring data, and documentation and billing. By enhancing decision-making, streamlining processes, and improving patient outcomes, AI holds the potential to transform the quality of care for vulnerable newborns, making the excitement surrounding AI advancements well-founded and the potential for significant positive change stronger than ever before.
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Affiliation(s)
- Kristyn Beam
- Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Puneet Sharma
- Division of Newborn Medicine, Department of Pediatrics Boston Children's Hospital, Boston, MA, USA
| | - Phil Levy
- Division of Newborn Medicine, Department of Pediatrics Boston Children's Hospital, Boston, MA, USA
| | - Andrew L Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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12
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Liu J, Chen J, Dong Y, Lou Y, Tian Y, Sun H, Jin Y, Li J, Qiu Y. Clinical Timing-Sequence Warning Models for Serious Bacterial Infections in Adults Based on Machine Learning: Retrospective Study. J Med Internet Res 2023; 25:e45515. [PMID: 38109177 PMCID: PMC10758945 DOI: 10.2196/45515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/22/2023] [Accepted: 11/22/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Serious bacterial infections (SBIs) are linked to unplanned hospital admissions and a high mortality rate. The early identification of SBIs is crucial in clinical practice. OBJECTIVE This study aims to establish and validate clinically applicable models designed to identify SBIs in patients with infective fever. METHODS Clinical data from 945 patients with infective fever, encompassing demographic and laboratory indicators, were retrospectively collected from a 2200-bed teaching hospital between January 2013 and December 2020. The data were randomly divided into training and test sets at a ratio of 7:3. Various machine learning (ML) algorithms, including Boruta, Lasso (least absolute shrinkage and selection operator), and recursive feature elimination, were utilized for feature filtering. The selected features were subsequently used to construct models predicting SBIs using logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) with 5-fold cross-validation. Performance metrics, including the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), accuracy, sensitivity, and other relevant parameters, were used to assess model performance. Considering both model performance and clinical needs, 2 clinical timing-sequence warning models were ultimately confirmed using LR analysis. The corresponding predictive nomograms were then plotted for clinical use. Moreover, a physician, blinded to the study, collected additional data from the same center involving 164 patients during 2021. The nomograms developed in the study were then applied in clinical practice to further validate their clinical utility. RESULTS In total, 69.9% (661/945) of the patients developed SBIs. Age, hemoglobin, neutrophil-to-lymphocyte ratio, fibrinogen, and C-reactive protein levels were identified as important features by at least two ML algorithms. Considering the collection sequence of these indicators and clinical demands, 2 timing-sequence models predicting the SBI risk were constructed accordingly: the early admission model (model 1) and the model within 24 hours of admission (model 2). LR demonstrated better stability than RF and XGBoost in both models and performed the best in model 2, with an AUC, accuracy, and sensitivity of 0.780 (95% CI 0.720-841), 0.754 (95% CI 0.698-804), and 0.776 (95% CI 0.711-832), respectively. XGBoost had an advantage over LR in AUC (0.708, 95% CI 0.641-775 vs 0.686, 95% CI 0.617-754), while RF achieved better accuracy (0.729, 95% CI 0.673-780) and sensitivity (0.790, 95% CI 0.728-844) than the other 2 approaches in model 1. Two SBI-risk prediction nomograms were developed for clinical use based on LR, and they exhibited good performance with an accuracy of 0.707 and 0.750 and a sensitivity of 0.729 and 0.927 in clinical application. CONCLUSIONS The clinical timing-sequence warning models demonstrated efficacy in predicting SBIs in patients suspected of having infective fever and in clinical application, suggesting good potential in clinical decision-making. Nevertheless, additional prospective and multicenter studies are necessary to further confirm their clinical utility.
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Affiliation(s)
- Jian Liu
- Department of Intensive Care Unit, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jia Chen
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China
| | - Yongquan Dong
- Department of Respiratory Disease, Yinzhou Second Hospital, Ningbo, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation,Department of Clinical Pharmacy, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yu Tian
- Engineering Research Center of Electronic Medical Record and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Huiyao Sun
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China
| | - Yuqing Jin
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China
| | - Jingsong Li
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Engineering Research Center of Electronic Medical Record and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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Chong SL, Niu C, Ong GYK, Piragasam R, Khoo ZX, Koh ZX, Guo D, Lee JH, Ong MEH, Liu N. Febrile infants risk score at triage (FIRST) for the early identification of serious bacterial infections. Sci Rep 2023; 13:15845. [PMID: 37740004 PMCID: PMC10516995 DOI: 10.1038/s41598-023-42854-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/15/2023] [Indexed: 09/24/2023] Open
Abstract
We aimed to derive the Febrile Infants Risk Score at Triage (FIRST) to quantify risk for serious bacterial infections (SBIs), defined as bacteremia, meningitis and urinary tract infections. We performed a prospective observational study on febrile infants < 3 months old at a tertiary hospital in Singapore between 2018 and 2021. We utilized machine learning and logistic regression to derive 2 models: FIRST, based on patient demographics, vital signs and history, and FIRST + , adding laboratory results to the same variables. SBIs were diagnosed in 224/1002 (22.4%) infants. Among 994 children with complete data, age (adjusted odds ratio [aOR] 1.01 95%CI 1.01-1.02, p < 0.001), high temperature (aOR 2.22 95%CI 1.69-2.91, p < 0.001), male sex (aOR 2.62 95%CI 1.86-3.70, p < 0.001) and fever of ≥ 2 days (aOR 1.79 95%CI 1.18-2.74, p = 0.007) were independently associated with SBIs. For FIRST + , abnormal urine leukocyte esterase (aOR 16.46 95%CI 10.00-27.11, p < 0.001) and procalcitonin (aOR 1.05 95%CI 1.01-1.09, p = 0.009) were further identified. A FIRST + threshold of ≥ 15% predicted risk had a sensitivity of 81.8% (95%CI 70.5-91.0%) and specificity of 65.6% (95%CI 57.8-72.7%). In the testing dataset, FIRST + had an area under receiver operating characteristic curve of 0.87 (95%CI 0.81-0.94). These scores can potentially guide triage and prioritization of febrile infants.
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Affiliation(s)
- Shu-Ling Chong
- Department of Emergency Medicine, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore, 229899, Singapore.
- Paediatrics Academic Clinical Programme, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
- Emergency Medicine Academic Clinical Programme, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore.
| | - Chenglin Niu
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Gene Yong-Kwang Ong
- Department of Emergency Medicine, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore, 229899, Singapore
- Paediatrics Academic Clinical Programme, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Emergency Medicine Academic Clinical Programme, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - Rupini Piragasam
- KK Research Centre, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore, 229899, Singapore
| | - Zi Xean Khoo
- Paediatrics Academic Clinical Programme, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Department of Paediatrics, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore, 229899, Singapore
| | - Zhi Xiong Koh
- Department of Emergency Medicine, Singapore General Hospital, 1 Hospital Crescent, Outram Road, Singapore, 169608, Singapore
| | - Dagang Guo
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Department of Emergency Medicine, Singapore General Hospital, 1 Hospital Crescent, Outram Road, Singapore, 169608, Singapore
| | - Jan Hau Lee
- Paediatrics Academic Clinical Programme, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Children's Intensive Care Unit, KK Women's and Children's Hospital, 100 Bukit Timah Road, Singapore, 229899, Singapore
| | - Marcus Eng Hock Ong
- Emergency Medicine Academic Clinical Programme, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Department of Emergency Medicine, Singapore General Hospital, 1 Hospital Crescent, Outram Road, Singapore, 169608, Singapore
- Health Services Research Centre, Singapore Health Services, 8 College Road, Singapore, 169857, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
- Department of Emergency Medicine, Singapore General Hospital, 1 Hospital Crescent, Outram Road, Singapore, 169608, Singapore
- Health Services Research Centre, Singapore Health Services, 8 College Road, Singapore, 169857, Singapore
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Ramgopal S, Kapes J, Alpern ER, Carroll MS, Heffernan M, Simon NJE, Florin TA, Macy ML. Perceptions of Artificial Intelligence-Assisted Care for Children With a Respiratory Complaint. Hosp Pediatr 2023; 13:802-810. [PMID: 37593809 DOI: 10.1542/hpeds.2022-007066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
OBJECTIVES To evaluate caregiver opinions on the use of artificial intelligence (AI)-assisted medical decision-making for children with a respiratory complaint in the emergency department (ED). METHODS We surveyed a sample of caregivers of children presenting to a pediatric ED with a respiratory complaint. We assessed caregiver opinions with respect to AI, defined as "specialized computer programs" that "help make decisions about the best way to care for children." We performed multivariable logistic regression to identify factors associated with discomfort with AI-assisted decision-making. RESULTS Of 279 caregivers who were approached, 254 (91.0%) participated. Most indicated they would want to know if AI was being used for their child's health care (93.5%) and were extremely or somewhat comfortable with the use of AI in deciding the need for blood (87.9%) and viral testing (87.6%), interpreting chest radiography (84.6%), and determining need for hospitalization (78.9%). In multivariable analysis, caregiver age of 30 to 37 years (adjusted odds ratio [aOR] 3.67, 95% confidence interval [CI] 1.43-9.38; relative to 18-29 years) and a diagnosis of bronchospasm (aOR 5.77, 95% CI 1.24-30.28 relative to asthma) were associated with greater discomfort with AI. Caregivers with children being admitted to the hospital (aOR 0.23, 95% CI 0.09-0.50) had less discomfort with AI. CONCLUSIONS Caregivers were receptive toward the use of AI-assisted decision-making. Some subgroups (caregivers aged 30-37 years with children discharged from the ED) demonstrated greater discomfort with AI. Engaging with these subgroups should be considered when developing AI applications for acute care.
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Affiliation(s)
- Sriram Ramgopal
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Jack Kapes
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Elizabeth R Alpern
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Michael S Carroll
- Data Analytics and Reporting
- Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Marie Heffernan
- Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Norma-Jean E Simon
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Todd A Florin
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Michelle L Macy
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
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15
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O'Sullivan C, Tsai DHT, Wu ICY, Boselli E, Hughes C, Padmanabhan D, Hsia Y. Machine learning applications on neonatal sepsis treatment: a scoping review. BMC Infect Dis 2023; 23:441. [PMID: 37386442 DOI: 10.1186/s12879-023-08409-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023] Open
Abstract
INTRODUCTION Neonatal sepsis is a major cause of health loss and mortality worldwide. Without proper treatment, neonatal sepsis can quickly develop into multisystem organ failure. However, the signs of neonatal sepsis are non-specific, and treatment is labour-intensive and expensive. Moreover, antimicrobial resistance is a significant threat globally, and it has been reported that over 70% of neonatal bloodstream infections are resistant to first-line antibiotic treatment. Machine learning is a potential tool to aid clinicians in diagnosing infections and in determining the most appropriate empiric antibiotic treatment, as has been demonstrated for adult populations. This review aimed to present the application of machine learning on neonatal sepsis treatment. METHODS PubMed, Embase, and Scopus were searched for studies published in English focusing on neonatal sepsis, antibiotics, and machine learning. RESULTS There were 18 studies included in this scoping review. Three studies focused on using machine learning in antibiotic treatment for bloodstream infections, one focused on predicting in-hospital mortality associated with neonatal sepsis, and the remaining studies focused on developing machine learning prediction models to diagnose possible sepsis cases. Gestational age, C-reactive protein levels, and white blood cell count were important predictors to diagnose neonatal sepsis. Age, weight, and days from hospital admission to blood sample taken were important to predict antibiotic-resistant infections. The best-performing machine learning models were random forest and neural networks. CONCLUSION Despite the threat antimicrobial resistance poses, there was a lack of studies focusing on the use of machine learning for aiding empirical antibiotic treatment for neonatal sepsis.
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Affiliation(s)
| | - Daniel Hsiang-Te Tsai
- Centre for Neonatal and Paediatric Infection, St. George's, University of London, London, UK
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Ian Chang-Yen Wu
- Centre for Neonatal and Paediatric Infection, St. George's, University of London, London, UK
- School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan
- Department of Pharmacy, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Emanuela Boselli
- Department of Pediatrics, V. Buzzi Children's Hospital, University of Milan, Milan, Italy
| | - Carmel Hughes
- School of Pharmacy, Queen's University Belfast, Belfast, UK
| | - Deepak Padmanabhan
- School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
| | - Yingfen Hsia
- School of Pharmacy, Queen's University Belfast, Belfast, UK
- Centre for Neonatal and Paediatric Infection, St. George's, University of London, London, UK
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Yang Y, Wang YM, Lin CHR, Cheng CY, Tsai CM, Huang YH, Chen TY, Chiu IM. Explainable deep learning model to predict invasive bacterial infection in febrile young infants: A retrospective study. Int J Med Inform 2023; 172:105007. [PMID: 36731394 DOI: 10.1016/j.ijmedinf.2023.105007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/09/2022] [Accepted: 01/24/2023] [Indexed: 01/29/2023]
Abstract
BACKGROUND Machine learning models have demonstrated superior performance in predicting invasive bacterial infection (IBI) in febrile infants compared to commonly used risk stratification criteria in recent studies. However, the black-box nature of these models can make them difficult to apply in clinical practice. In this study, we developed and validated an explainable deep learning model that can predict IBI in febrile infants ≤ 60 days of age visiting the emergency department. METHODS We conducted a retrospective study of febrile infants aged ≤ 60 days who presented to the pediatric emergency department of a medical center in Taiwan between January 1, 2011 and December 31, 2019. Patients with uncertain test results and complex chronic health conditions were excluded. IBI was defined as the growth of a pathogen in the blood or cerebrospinal fluid. We used a deep neural network to develop a predictive model for IBI and compared its performance to the IBI score and step-by-step approach. The SHapley Additive Explanations (SHAP) technique was used to explain the model's predictions at different levels. RESULTS Our study included 1847 patients, 53 (2.7%) of whom had IBI. The deep learning model performed similarly to the IBI score and step-by-step approach in terms of sensitivity and negative predictive value, but provided better specificity (54%), positive predictive value (5%), and area under the receiver-operating characteristic curve (0.87). SHapley Additive exPlanations identified five influential predictive variables (absolute neutrophil count, body temperature, heart rate, age, and C-reactive protein). CONCLUSION We have developed an explainable deep learning model that can predict IBI in febrile infants aged 0-60 days. The model not only performs better than previous scoring systems, but also provides insight into how it arrives at its predictions through individual features and cases.
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Affiliation(s)
- Ying Yang
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Yi-Min Wang
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Chun-Hung Richard Lin
- Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Chi-Yung Cheng
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Chi-Ming Tsai
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Ying-Hsien Huang
- Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Tien-Yu Chen
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - I-Min Chiu
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan; Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
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Filler G, Gipson DS, Iyamuremye D, Díaz González de Ferris ME. Artificial Intelligence in Pediatric Nephrology-A Call for Action. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:17-24. [PMID: 36723276 DOI: 10.1053/j.akdh.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/24/2022] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical care, hemodialysis prescriptions, and follow-up of transplant recipients. This article provides an overview of medical artificial intelligence applications relevant to pediatric nephrology. We describe the core concepts of artificial intelligence and machine learning and cover the basics of neural networks and deep learning. We also discuss some examples for clinical applications of artificial intelligence in pediatric nephrology, including neonatal kidney function, early recognition of acute kidney injury, renally cleared drug dosing, intrapatient variability, urinary tract infection workup in infancy, and longitudinal disease progression. Furthermore, we consider the future of artificial intelligence in clinical pediatric nephrology and its potential impact on medical practice and address the ethical issues artificial intelligence raises in terms of clinical decision-making, health care provider-patient relationship, patient privacy, and data collection. This article also represents a call for action involving those of us striving to provide optimal services for children, adolescents, and young adults with chronic conditions.
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Affiliation(s)
- Guido Filler
- Division of Pediatric Nephrology, Departments of Paediatrics, Western University, London, Ontario, Canada; Departments of Medicine, Western University, London, Ontario, Canada; Department of Pathology and Laboratory Medicine, Western University, London, Ontario, Canada.
| | - Debbie S Gipson
- Department of Pediatrics, University of Michigan, Ann Arbor, Michigan
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Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res 2023; 93:334-341. [PMID: 35906317 PMCID: PMC9668209 DOI: 10.1038/s41390-022-02226-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/29/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022]
Abstract
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.
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Affiliation(s)
- Sriram Ramgopal
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - L. Nelson Sanchez-Pinto
- grid.16753.360000 0001 2299 3507Division of Critical Care Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA ,grid.16753.360000 0001 2299 3507Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Christopher M. Horvat
- grid.21925.3d0000 0004 1936 9000Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
| | - Michael S. Carroll
- grid.16753.360000 0001 2299 3507Data Analytics and Reporting, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Yuan Luo
- grid.16753.360000 0001 2299 3507Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Todd A. Florin
- grid.16753.360000 0001 2299 3507Division of Emergency Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA
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Ramgopal S, Heffernan ME, Bendelow A, Davis MM, Carroll MS, Florin TA, Alpern ER, Macy ML. Parental Perceptions on Use of Artificial Intelligence in Pediatric Acute Care. Acad Pediatr 2023; 23:140-147. [PMID: 35577283 DOI: 10.1016/j.acap.2022.05.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/26/2022] [Accepted: 05/07/2022] [Indexed: 02/09/2023]
Abstract
BACKGROUND Family engagement is critical in the implementation of artificial intelligence (AI)-based clinical decision support tools, which will play an increasing role in health care in the future. We sought to understand parental perceptions of computer-assisted health care of children in the emergency department (ED). METHODS We conducted a population-weighted household panel survey of parents with minor children in their home in a large US city to evaluate perceptions of the use of computer programs for the care of children with respiratory illness. We identified demographics associated with discomfort with AI using survey-weighted logistic regression. RESULTS Surveys were completed by 1620 parents (panel response rate = 49.7%). Most respondents were comfortable with the use of computer programs to determine the need for antibiotics (77.6%) or bloodwork (76.5%), and to interpret radiographs (77.5%). In multivariable analysis, Black non-Hispanic parents reported greater discomfort with AI relative to White non-Hispanic parents (odds ratio [OR] 1.67, 95% confidence interval [CI] 1.03-2.70) as did younger parents (18-25 years) relative to parents ≥46 years (OR 2.48, 95% CI 1.31-4.67). The greatest perceived benefits of computer programs were finding something a human would miss (64.2%, 95% CI 60.9%-67.4%) and obtaining a more rapid diagnosis (59.6%; 56.2%-62.9%). Areas of greatest concern were diagnostic errors (63.0%, 95% CI 59.6%-66.4%), and recommending incorrect treatment (58.9%, 95% CI 55.5%-62.3%). CONCLUSIONS Parents were generally receptive to computer-assisted management of children with respiratory illnesses in the ED, though reservations emerged. Black non-Hispanic and younger parents were more likely to express discomfort about AI.
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Affiliation(s)
- Sriram Ramgopal
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (S Ramgopal, TA Florin, ER Alpern, and ML Macy), Chicago, Ill.
| | - Marie E Heffernan
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago (ME Heffernan, MM Davis, M Carroll, and ML Macy), Chicago, Ill; Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (ME Heffernan and MM Davis), Chicago, Ill
| | - Anne Bendelow
- Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (A Bendelow and M Carroll), Chicago, Ill
| | - Matthew M Davis
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago (ME Heffernan, MM Davis, M Carroll, and ML Macy), Chicago, Ill; Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (ME Heffernan and MM Davis), Chicago, Ill
| | - Michael S Carroll
- Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago (ME Heffernan, MM Davis, M Carroll, and ML Macy), Chicago, Ill; Data Analytics and Reporting, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (A Bendelow and M Carroll), Chicago, Ill
| | - Todd A Florin
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (S Ramgopal, TA Florin, ER Alpern, and ML Macy), Chicago, Ill
| | - Elizabeth R Alpern
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (S Ramgopal, TA Florin, ER Alpern, and ML Macy), Chicago, Ill
| | - Michelle L Macy
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine (S Ramgopal, TA Florin, ER Alpern, and ML Macy), Chicago, Ill; Mary Ann & J. Milburn Smith Child Health Outcomes, Research, and Evaluation Center, Stanley Manne Children's Research Institute, Ann & Robert H. Lurie Children's Hospital of Chicago (ME Heffernan, MM Davis, M Carroll, and ML Macy), Chicago, Ill
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The use of machine learning and artificial intelligence within pediatric critical care. Pediatr Res 2023; 93:405-412. [PMID: 36376506 PMCID: PMC9660024 DOI: 10.1038/s41390-022-02380-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 09/15/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022]
Abstract
The field of pediatric critical care has been hampered in the era of precision medicine by our inability to accurately define and subclassify disease phenotypes. This has been caused by heterogeneity across age groups that further challenges the ability to perform randomized controlled trials in pediatrics. One approach to overcome these inherent challenges include the use of machine learning algorithms that can assist in generating more meaningful interpretations from clinical data. This review summarizes machine learning and artificial intelligence techniques that are currently in use for clinical data modeling with relevance to pediatric critical care. Focus has been placed on the differences between techniques and the role of each in the clinical arena. The various forms of clinical decision support that utilize machine learning are also described. We review the applications and limitations of machine learning techniques to empower clinicians to make informed decisions at the bedside. IMPACT: Critical care units generate large amounts of under-utilized data that can be processed through artificial intelligence. This review summarizes the machine learning and artificial intelligence techniques currently being used to process clinical data. The review highlights the applications and limitations of these techniques within a clinical context to aid providers in making more informed decisions at the bedside.
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Performance Evaluation of Host Biomarker Combinations for the Diagnosis of Serious Bacterial Infection in Young Febrile Children: A Double-Blind, Multicentre, Observational Study. J Clin Med 2022; 11:jcm11216563. [DOI: 10.3390/jcm11216563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/30/2022] [Accepted: 11/03/2022] [Indexed: 11/10/2022] Open
Abstract
The diagnosis of serious bacterial infection (SBI) in young febrile children remains challenging. This prospective, multicentre, observational study aimed to identify new protein marker combinations that can differentiate a bacterial infection from a viral infection in 983 children, aged 7 days–36 months, presenting with a suspected SBI at three French paediatric emergency departments. The blood levels of seven protein markers (CRP, PCT, IL-6, NGAL, MxA, TRAIL, IP-10) were measured at enrolment. The patients received the standard of care, blinded to the biomarker results. An independent adjudication committee assigned a bacterial vs. viral infection diagnosis based on clinical data, blinded to the biomarker results. Computational modelling was applied to the blood levels of the biomarkers using independent training and validation cohorts. Model performances (area under the curve (AUC), positive and negative likelihood ratios (LR+ and LR–)) were calculated and compared to those of the routine biomarkers CRP and PCT. The targeted performance for added value over CRP or PCT was LR+ ≥ 5.67 and LR− ≤ 0.5. Out of 652 analysed patients, several marker combinations outperformed CRP and PCT, although none achieved the targeted performance criteria in the 7 days–36 months population. The models seemed to perform better in younger (7–91 day-old) patients, with the CRP/MxA/TRAIL combination performing best (AUC 0.895, LR+ 10.46, LR− 0.16). Although computational modelling using combinations of bacterial- and viral-induced host-protein markers is promising, further optimisation is necessary to improve SBI diagnosis in young febrile children.
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McCahill C, Laycock HC, Guris RJD, Chigaru L. State-of-the-art management of the acutely unwell child. Anaesthesia 2022; 77:1288-1298. [PMID: 36089884 PMCID: PMC9826095 DOI: 10.1111/anae.15816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2022] [Indexed: 01/11/2023]
Abstract
Children make up around one-fifth of all emergency department visits in the USA and UK, with an increasing trend of emergency admissions requiring intensive care. Anaesthetists play a vital role in the management of paediatric emergencies contributing to stabilisation, emergency anaesthesia, transfers and non-technical skills that optimise team performance. From neonates to adolescents, paediatric patients have diverse physiology and present with a range of congenital and acquired pathologies that often differ from the adult population. With increasing centralisation of paediatric services, staff outside these centres have less exposure to caring for children, yet are often the first responders in managing these high stakes situations. Staying abreast of the latest evidence for managing complex low frequency emergencies is a challenge. This review focuses on recent evidence and pertinent clinical updates within the field. The challenges of maintaining skills and training are explored as well as novel advancements in care.
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Affiliation(s)
- C. McCahill
- Department of AnaesthesiaGreat Ormond Street HospitalLondonUK
| | - H. C. Laycock
- Department of AnaesthesiaGreat Ormond Street HospitalLondonUK,Department of Surgery and CancerImperial CollegeLondonUK
| | - R. J. Daly Guris
- Department of Anesthesiology and Critical Care MedicineChildren's Hospital of PhiladelphiaPhiladelphiaPAUSA,Department of Anesthesiology and Critical CareUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - L. Chigaru
- Department of AnaesthesiaGreat Ormond Street HospitalLondonUK,Children's Acute Transport ServiceLondonUK
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Matsushita FY, Krebs VLJ, Carvalho WBD. Artificial intelligence and machine learning in pediatrics and neonatology healthcare. Rev Assoc Med Bras (1992) 2022; 68:745-750. [PMID: 35766685 PMCID: PMC9575899 DOI: 10.1590/1806-9282.20220177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 02/09/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Felipe Yu Matsushita
- Universidade de São Paulo, Faculty of Medicine, Department of Pediatrics - São Paulo (SP), Brazil
| | - Vera Lucia Jornada Krebs
- Universidade de São Paulo, Faculty of Medicine, Department of Pediatrics - São Paulo (SP), Brazil
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Han JH, Yoon SJ, Lee HS, Park G, Lim J, Shin JE, Eun HS, Park MS, Lee SM. Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants. Yonsei Med J 2022; 63:640-647. [PMID: 35748075 PMCID: PMC9226835 DOI: 10.3349/ymj.2022.63.7.640] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 01/05/2023] Open
Abstract
PURPOSE The aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants. MATERIALS AND METHODS Of 10425 VLBW infants registered in the Korean Neonatal Network between 2013 and 2017, 7954 infants were included. PGF was defined as a decrease in Z score >1.28 at discharge, compared to that at birth. Six metrics [area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity, specificity, and F1 score] were obtained at five time points (at birth, 7 days, 14 days, 28 days after birth, and at discharge). Machine learning models were built using four different techniques [extreme gradient boosting (XGB), random forest, support vector machine, and convolutional neural network] to compare against the conventional multiple logistic regression (MLR) model. RESULTS The XGB algorithm showed the best performance with all six metrics across the board. When compared with MLR, XGB showed a significantly higher AUROC (p=0.03) for Day 7, which was the primary performance metric. Using optimal cut-off points, for Day 7, XGB still showed better performances in terms of AUROC (0.74), accuracy (0.68), and F1 score (0.67). AUROC values seemed to increase slightly from birth to 7 days after birth with significance, almost reaching a plateau after 7 days after birth. CONCLUSION We have shown the possibility of predicting PGF through machine learning algorithms, especially XGB. Such models may help neonatologists in the early diagnosis of high-risk infants for PGF for early intervention.
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Affiliation(s)
- Jung Ho Han
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea
| | - So Jin Yoon
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
| | - Goeun Park
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
| | - Joohee Lim
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea
| | - Jeong Eun Shin
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea
| | - Ho Seon Eun
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea
| | - Min Soo Park
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea
| | - Soon Min Lee
- Department of Pediatrics, Yonsei University College of Medicine, Seoul, Korea.
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Wang P, Cheng S, Li Y, Liu L, Liu J, Zhao Q, Luo S. Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms. Front Public Health 2022; 10:910479. [PMID: 35836985 PMCID: PMC9273930 DOI: 10.3389/fpubh.2022.910479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background Lumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms. Methods We utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed. Results In the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy. Conclusions The RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.
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Affiliation(s)
- Peng Wang
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Shuwen Cheng
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Yaxin Li
- West China Fourth Hospital/West China School of Public Health, Sichuan University, Chengdu, China
| | - Li Liu
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Jia Liu
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Qiang Zhao
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
| | - Shuang Luo
- Department of Neurosurgery, Cancer Prevention and Treatment Institute of Chengdu, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, China
- *Correspondence: Shuang Luo
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Yin Z, Chen Y, Zhong W, Shan L, Zhang Q, Gong X, Li J, Lei X, Zhou Q, Zhao Y, Chen C, Zhang Y. A Novel Algorithm With Paired Predictive Indexes to Stratify the Risk Levels of Neonates With Invasive Bacterial Infections: A Multicenter Cohort Study. Pediatr Infect Dis J 2022; 41:e149-e155. [PMID: 34955526 PMCID: PMC8919942 DOI: 10.1097/inf.0000000000003437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/15/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Our aim was to develop a predictive model comprising clinical and laboratory parameters for early identification of full-term neonates with different risks of invasive bacterial infections (IBIs). METHODS We conducted a retrospective study including 1053 neonates presenting in 9 tertiary hospitals in China from January 2010 to August 2019. An algorithm with paired predictive indexes (PPIs) for risk stratification of neonatal IBIs was developed. Predictive performance was validated using k-fold cross-validation. RESULTS Overall, 166 neonates were diagnosed with IBIs (15.8%). White blood cell count, C-reactive protein level, procalcitonin level, neutrophil percentage, age at admission, neurologic signs, and ill-appearances showed independent associations with IBIs from stepwise regression analysis and combined into 23 PPIs. Using 10-fold cross-validation, a combination of 7 PPIs with the highest predictive performance was picked out to construct an algorithm. Finally, 58.1% (612/1053) patients were classified as low-risk cases. The sensitivity and negative predictive value of the algorithm were 95.3% (95% confidence interval: 91.7-98.3) and 98.7% (95% confidence interval: 97.8-99.6), respectively. An online calculator based on this algorithm was developed for clinical use. CONCLUSIONS The new algorithm constructed for this study was a valuable tool to screen neonates with suspected infection. It stratified risk levels of IBIs and had an excellent predictive performance.
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Affiliation(s)
- Zhanghua Yin
- From the Department of Neonatology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Chen
- From the Department of Neonatology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wenhua Zhong
- Department of Neonatology, The Maternal and Child Health Hospital of Jiaxing, Jiaxing, China
| | - Liqin Shan
- Department of Neonatology, The Maternal and Child Health Hospital of Jiaxing, Jiaxing, China
| | - Qian Zhang
- Department of Neonatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaohui Gong
- Department of Neonatology, Children’s Hospital of Shanghai, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Li
- Department of Neonatology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoping Lei
- Department of Neonatology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Qin Zhou
- Department of Neonatology, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi, China
| | - Youyan Zhao
- Department of Neonatology, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Chen
- Department of Neonatology, Children’s Hospital of Fudan University, Shanghai, China
| | - Yongjun Zhang
- From the Department of Neonatology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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27
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Yaeger JP, Jones J, Ertefaie A, Caserta MT, van Wijngaarden E, Fiscella K. Refinement and Validation of a Clinical-Based Approach to Evaluate Young Febrile Infants. Hosp Pediatr 2022; 12:399-407. [PMID: 35347337 DOI: 10.1542/hpeds.2021-006214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND AND OBJECTIVE For febrile infants, predictive models to detect bacterial infections are available, but clinical adoption remains limited by implementation barriers. There is a need for predictive models using widely available predictors. Thus, we previously derived 2 novel predictive models (machine learning and regression) by using demographic and clinical factors, plus urine studies. The objective of this study is to refine and externally validate the predictive models. METHODS This is a cross-sectional study of infants initially evaluated at one pediatric emergency department from January 2011 to December 2018. Inclusion criteria were age 0 to 90 days, temperature ≥38°C, documented gestational age, and insurance type. To reduce potential biases, we derived models again by using derivation data without insurance status and tested the ability of the refined models to detect bacterial infections (ie, urinary tract infection, bacteremia, and meningitis) in the separate validation sample, calculating areas-under-the-receiver operating characteristic curve, sensitivities, and specificities. RESULTS Of 1419 febrile infants (median age 53 days, interquartile range = 32-69), 99 (7%) had a bacterial infection. Areas-under-the-receiver operating characteristic curve of machine learning and regression models were 0.92 (95% confidence interval [CI] 0.89-0.94) and 0.90 (0.86-0.93) compared with 0.95 (0.91-0.98) and 0.96 (0.94-0.98) in the derivation study. Sensitivities and specificities of machine learning and regression models were 98.0% (94.7%-100%) and 54.2% (51.5%-56.9%) and 96.0% (91.5%-99.1%) and 50.0% (47.4%-52.7%). CONCLUSIONS Compared with the derivation study, the machine learning and regression models performed similarly. Findings suggest a clinical-based model can estimate bacterial infection risk. Future studies should prospectively test the models and investigate strategies to optimize clinical adoption.
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Affiliation(s)
- Jeffrey P Yaeger
- Departments of Pediatrics, and.,Public Health Sciences, University of Rochester Medical Center, Rochester, New York
| | | | | | | | | | - Kevin Fiscella
- Family Medicine, University of Rochester School of Medicine and Dentistry, Rochester, New York
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Development and validation of machine learning-driven prediction model for serious bacterial infection among febrile children in emergency departments. PLoS One 2022; 17:e0265500. [PMID: 35333881 PMCID: PMC8956167 DOI: 10.1371/journal.pone.0265500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 03/02/2022] [Indexed: 12/03/2022] Open
Abstract
Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as ‘Refined Lab-score’ or ‘clinical prediction rule’ have been developed and used. However, these tools can predict SBI only when there are values of all factors used in the tool, and if even one of them is missing, the tools become useless. Therefore, the purpose of this study was to develop and validate a machine learning-driven model to predict SBIs among febrile children, even with missing values. This was a multicenter retrospective observational study including febrile children <6 years of age who visited Emergency departments (EDs) of 3 different tertiary hospitals from 2016 to 2018. The SBI prediction model was trained with a derivation cohort (data from two hospitals) and externally tested with a validation cohort (data from a third hospital). A total of 11,973 and 2,858 patient records were included in the derivation and validation cohorts, respectively. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) of the RF model was 0.964 (95% confidence interval [CI], 0.943–0.986), and the area under the precision-recall curve (AUPRC) was 0.753 (95% CI, 0.681–0.824). The conventional LR (CLR) model showed corresponding values of 0.902 (95% CI, 0.894–0.910) and 0.573 (95% CI, 0.560–0.586), respectively. In the validation cohort, the AUROC (95% CI) of the RF model was 0.950 (95% CI, 0.945–0.956), the AUPRC was 0.605 (95% CI, 0.593–0.616), and the CLR presented corresponding values of 0.815 (95% CI, 0.789–0.841) and 0.586 (95% CI, 0.553–0.619), respectively. We developed a machine learning-driven prediction model for SBI among febrile children, which works robustly despite missing values. And it showed superior performance compared to CLR in both internal validation and external validation.
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Stocker M, Daunhawer I, van Herk W, El Helou S, Dutta S, Schuerman FABA, van den Tooren-de Groot RK, Wieringa JW, Janota J, van der Meer-Kappelle LH, Moonen R, Sie SD, de Vries E, Donker AE, Zimmerman U, Schlapbach LJ, de Mol AC, Hoffmann-Haringsma A, Roy M, Tomaske M, Kornelisse RF, van Gijsel J, Plötz FB, Wellmann S, Achten NB, Lehnick D, van Rossum AMC, Vogt JE. Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis. Pediatr Infect Dis J 2022; 41:248-254. [PMID: 34508027 DOI: 10.1097/inf.0000000000003344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. STUDY DESIGN Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. RESULTS One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. CONCLUSIONS Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics.
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Affiliation(s)
- Martin Stocker
- From the Department of Paediatrics, Neonatal and Paediatric Intensive Care Unit, Children's Hospital Lucerne, Lucerne
| | | | - Wendy van Herk
- Department of Paediatrics, Division of Paediatric Infectious Diseases and Immunology, Erasmus MC University Medical Centre-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Salhab El Helou
- Division of Neonatology, McMaster University Children's Hospital, Hamilton Health Sciences, Hamilton, ON, Canada
| | - Sourabh Dutta
- Division of Neonatology, McMaster University Children's Hospital, Hamilton Health Sciences, Hamilton, ON, Canada
| | - Frank A B A Schuerman
- Department of Neonatal Intensive Care Unit, Isala Women and Children's Hospital, Zwolle
| | | | - Jantien W Wieringa
- Department of Paediatrics, Haaglanden Medical Centre, 's Gravenhage, The Netherlands
| | - Jan Janota
- Department of Obstetrics and Gynecology, Motol University Hospital, Second Medical Faculty, Prague, Czech Republic
| | | | - Rob Moonen
- Department of Neonatology, Zuyderland Medical Centre, Heerlen
| | - Sintha D Sie
- Department of Neonatology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam
| | - Esther de Vries
- Department of Jeroen Bosch Academy Research, Jeroen Bosch Hospital, 's-Hertogenbosch
- Department of Tranzo, Tilburg University, Tilburg
| | - Albertine E Donker
- Department of Paediatrics, Maxima Medical Centre, Veldhoven, The Netherlands
| | - Urs Zimmerman
- Department of Paediatrics, Kantonsspital Winterthur, Winterthur
| | - Luregn J Schlapbach
- Neonatal and Pediatric Intensive Care Unit, Children`s Research Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Amerik C de Mol
- Department of Neonatology, Albert Schweitzer Hospital, Dordrecht
| | | | - Madan Roy
- Department of Neonatology, St. Josephs Healthcare, Hamilton Health Sciences, Hamilton, ON, Canada
| | - Maren Tomaske
- Department of Paediatrics, Stadtspital Triemli, Zürich, Switzerland
| | - René F Kornelisse
- Department of Paediatrics, Division of Neonatology, Erasmus MC University Medical Centre-Sophia Children's Hospital, Rotterdam
| | | | - Frans B Plötz
- Department of Pediatrics, Tergooi Hospital, Blaricum, the Netherlands and Amsterdam University Medical Center, Department of Pediatrics, Amsterdam, The Netherlands
| | - Sven Wellmann
- Department of Neonatology, University Children's Hospital Regensburg (KUNO), University of Regensburg, Regensburg, Germany
| | - Niek B Achten
- Department of Pediatrics, Tergooi Hospital, Blaricum, the Netherlands and Amsterdam University Medical Center, Department of Pediatrics, Amsterdam, The Netherlands
| | - Dirk Lehnick
- Department of Health Sciences and Medicine, Head Biostatistics and Methodology, University of Lucerne, Lucerne, Switzerland
| | - Annemarie M C van Rossum
- Department of Paediatrics, Division of Paediatric Infectious Diseases and Immunology, Erasmus MC University Medical Centre-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Julia E Vogt
- From the Department of Paediatrics, Neonatal and Paediatric Intensive Care Unit, Children's Hospital Lucerne, Lucerne
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30
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Fan H, Cui Y, Xu X, Zhang D, Yang D, Huang L, Ding T, Lu G. Validation of a Classification Model Using Complete Blood Count to Predict Severe Human Adenovirus Lower Respiratory Tract Infections in Pediatric Cases. Front Pediatr 2022; 10:896606. [PMID: 35712623 PMCID: PMC9197341 DOI: 10.3389/fped.2022.896606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Human adenovirus (HAdV) lower respiratory tract infections (LRTIs) are prone to severe cases and even cause death in children. Here, we aimed to develop a classification model to predict severity in pediatric patients with HAdV LRTIs using complete blood count (CBC). METHODS The CBC parameters from pediatric patients with a diagnosis of HAdV LRTIs from 2013 to 2019 were collected during the disease's course. The data were analyzed as potential predictors for severe cases and were selected using a random forest model. RESULTS We enrolled 1,652 CBC specimens from 1,069 pediatric patients with HAdV LRTIs in the present study. Four hundred and seventy-four patients from 2017 to 2019 were used as the discovery cohort, and 470 patients from 2013 to 2016 were used as the validation cohort. The monocyte ratio (MONO%) was the most obvious difference between the mild and severe groups at onset, and could be used as a marker for the early accurate prediction of the severity [area under the subject operating characteristic curve (AUROC): 0.843]. Four risk factors [MONO%, hematocrit (HCT), red blood cell count (RBC), and platelet count (PLT)] were derived to construct a classification model of severe and mild cases using a random forest model (AUROC: 0.931 vs. 0.903). CONCLUSION Monocyte ratio can be used as an individual predictor of severe cases in the early stages of HAdV LRTIs. The four risk factors model is a simple and accurate risk assessment tool that can predict severe cases in the early stages of HAdV LRTIs.
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Affiliation(s)
- Huifeng Fan
- Department of Respiration, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Ying Cui
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xuehua Xu
- Pediatric Intensive Care Unit, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Dongwei Zhang
- Pediatric Intensive Care Unit, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Diyuan Yang
- Department of Respiration, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Li Huang
- Pediatric Intensive Care Unit, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
| | - Tao Ding
- Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China.,Key Laboratory of Tropical Disease Control, Ministry of Education, Sun Yat-sen University, Guangzhou, China.,Provincial Engineering Technology Research Center for Biological Vector Control, Guangzhou, China
| | - Gen Lu
- Department of Respiration, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
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31
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Nomura O, Ihara T, Morikawa Y, Sakakibara H, Horikoshi Y, Inoue N. Predictor of Early Administration of Antibiotics and a Volume Resuscitation for Young Infants with Septic Shock. Antibiotics (Basel) 2021; 10:1414. [PMID: 34827352 PMCID: PMC8615069 DOI: 10.3390/antibiotics10111414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 11/16/2021] [Accepted: 11/17/2021] [Indexed: 12/02/2022] Open
Abstract
(1) Background: It is critical to administer antibiotics and fluid bolus within 1 h of recognizing sepsis in pediatric patients. This study aimed to identify the predictor of the successful completion of a 1-h sepsis bundle for infants with suspected sepsis. (2) Methods: This is an observational study using a prospective registry including febrile young infants (aged < 90 days) who visited a pediatric emergency department with a core body temperature of 38.0 °C or higher and 36.0 °C or lower. Univariate and logistic regression analyses were conducted to determine the predictor (s) of successful sepsis bundle completion. (3) Results: Of the 323 registered patients, 118 patients with suspected sepsis were analyzed, and 38 patients (32.2%) received a bundle-compliant treatment. Among potential variables, such as age, sex, and vital sign parameters, the logistic regression analysis showed that heart rate (odds ratio: OR 1.02; 95% confidence interval: 1.00-1.04) is a significant predictor of the completion of a 1-h sepsis bundle. (4) Conclusions: We found that tachycardia facilitated the sepsis recognition and promoted the successful completion of a 1-h sepsis bundle for young infants with suspected septic shock and a possible indicator for improving the quality of the team-based sepsis management.
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Affiliation(s)
- Osamu Nomura
- Department of Emergency and Disaster Medicine, Hirosaki University, Hirosaki 036-8562, Japan
- Division of Pediatric Emergency Medicine, Tokyo Metropolitan Children’s Medical Center, Tokyo 183-8561, Japan;
| | - Takateru Ihara
- Division of Pediatric Emergency Medicine, Tokyo Metropolitan Children’s Medical Center, Tokyo 183-8561, Japan;
| | - Yoshihiko Morikawa
- Clinical Research Support Center, Tokyo Metropolitan Children’s Medical Center, Tokyo 183-8561, Japan;
| | - Hiroshi Sakakibara
- Division of General Pediatrics, Department of Pediatrics, Tokyo Metropolitan Children’s Medical Center, Tokyo 183-8561, Japan;
| | - Yuho Horikoshi
- Division of Infectious Diseases, Department of Pediatrics, Tokyo Metropolitan Children’s Medical Center, Tokyo 183-8561, Japan;
| | - Nobuaki Inoue
- Department of Human Resources and Development, National Center for Global Health and Medicine, Tokyo 162-8655, Japan;
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32
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Zhao Y, Chen Q, Liu T, Luo P, Zhou Y, Liu M, Xiong B, Zhou F. Development and Validation of Predictors for the Survival of Patients With COVID-19 Based on Machine Learning. Front Med (Lausanne) 2021; 8:683431. [PMID: 34631727 PMCID: PMC8493244 DOI: 10.3389/fmed.2021.683431] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 07/29/2021] [Indexed: 12/13/2022] Open
Abstract
Background: The outbreak of COVID-19 attracted the attention of the whole world. Our study aimed to explore the predictors for the survival of patients with COVID-19 by machine learning. Methods: We conducted a retrospective analysis and used the idea of machine learning to train the data of COVID-19 patients in Leishenshan Hospital through the logical regression algorithm provided by scikit-learn. Results: Of 2010 patients, 42 deaths were recorded until March 29, 2020. The mortality rate was 2.09%. There were 6,812 records after data features combination and data arrangement, 3,025 records with high-quality after deleting incomplete data by manual checking, and 5,738 records after data balancing finally by the method of Borderline-1 Smote. The results of 10 times of data training by logistic regression model showed that albumin, saturation of pulse oxygen at admission, alanine aminotransferase, and percentage of neutrophils were possibly associated with the survival of patients. The results of 10 times of data training including age, sex, and height beyond the laboratory measurements showed that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients. The rates of precision, recall, and f1-score of the two training models were all higher than 0.9 and relatively stable. Conclusions: We demonstrated that percentage of neutrophils, saturation of pulse oxygen at admission, alanine aminotransferase, sex, and albumin were possibly associated with the survival of patients with COVID-19.
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Affiliation(s)
- Yongfeng Zhao
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China.,Department of Hematology, The First Affiliated Hospital of Yangtze University, Jingzhou, China
| | - Qianjun Chen
- National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China.,The State Key Laboratory of Biocatalysis and Enzyme Engineering of China, College of Life Sciences, Hubei University, Wuhan, China
| | - Tao Liu
- Department of Urology, Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Ping Luo
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yi Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Minghui Liu
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bei Xiong
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, China
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Machine Learning Algorithms to Predict Mortality of Neonates on Mechanical Intubation for Respiratory Failure. Biomedicines 2021; 9:biomedicines9101377. [PMID: 34680497 PMCID: PMC8533201 DOI: 10.3390/biomedicines9101377] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/30/2021] [Accepted: 09/30/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Early identification of critically ill neonates with poor outcomes can optimize therapeutic strategies. We aimed to examine whether machine learning (ML) methods can improve mortality prediction for neonatal intensive care unit (NICU) patients on intubation for respiratory failure. METHODS A total of 1734 neonates with respiratory failure were randomly divided into training (70%, n = 1214) and test (30%, n = 520) sets. The primary outcome was the probability of NICU mortality. The areas under the receiver operating characteristic curves (AUCs) of several ML algorithms were compared with those of the conventional neonatal illness severity scoring systems including the NTISS and SNAPPE-II. RESULTS For NICU mortality, the random forest (RF) model showed the highest AUC (0.939 (0.921-0.958)) for the prediction of neonates with respiratory failure, and the bagged classification and regression tree model demonstrated the next best results (0.915 (0.891-0.939)). The AUCs of both models were significantly better than the traditional NTISS (0.836 (0.800-0.871)) and SNAPPE-II scores (0.805 (0.766-0.843)). The superior performances were confirmed by higher accuracy and F1 score and better calibration, and the superior and net benefit was confirmed by decision curve analysis. In addition, Shapley additive explanation (SHAP) values were utilized to explain the RF prediction model. CONCLUSIONS Machine learning algorithms increase the accuracy and predictive ability for mortality of neonates with respiratory failure compared with conventional neonatal illness severity scores. The RF model is suitable for clinical use in the NICU, and clinicians can gain insights and have better communication with families in advance.
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Abstract
PURPOSE OF REVIEW Neonatal bloodstream infections (BSI) are a major contributor to morbidity and mortality within neonatal intensive care units. BSI, including central line-associated BSI, have decreased over the past 15 years but remain common in extremely preterm infants. The purpose of this review is to highlight recent advances in the causes, diagnosis, management, and prevention of neonatal BSI. RECENT FINDINGS Continued quality improvement efforts and bundles have reduced BSI incidence, and novel approaches are highlighted. An update of emerging pathogens as well as traditional pathogens with novel antimicrobial resistance, which are an increasingly common cause of neonatal BSI, is included. Finally, current and future investigations into serum or noninvasive biomarkers for neonatal BSI are reviewed. SUMMARY Neonatal BSIs continue to decrease due to enhanced infection control and prevention techniques. However, many challenges remain, including emerging bacterial and fungal resistance and the continued need for novel diagnostics that hasten time to pathogen identification and effective treatment. This review of the past 18 months highlights the rapid changes in this area. Ongoing efforts to reduce the morbidity and mortality caused by neonatal BSI must remain a priority.
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Affiliation(s)
| | - Joseph B Cantey
- Department of Pediatrics, Division of Neonatology
- Department of Pediatrics, Division of Pediatric Infectious Diseases, University of Texas Health San Antonio, San Antonio, Texas, USA
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Chiu IM, Cheng CY, Zeng WH, Huang YH, Lin CHR. Using Machine Learning to Predict Invasive Bacterial Infections in Young Febrile Infants Visiting the Emergency Department. J Clin Med 2021; 10:jcm10091875. [PMID: 33925973 PMCID: PMC8123681 DOI: 10.3390/jcm10091875] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 04/19/2021] [Accepted: 04/23/2021] [Indexed: 12/03/2022] Open
Abstract
Background: The aim of this study was to develop and evaluate a machine learning (ML) model to predict invasive bacterial infections (IBIs) in young febrile infants visiting the emergency department (ED). Methods: This retrospective study was conducted in the EDs of three medical centers across Taiwan from 2011 to 2018. We included patients age in 0–60 days who were visiting the ED with clinical symptoms of fever. We developed three different ML algorithms, including logistic regression (LR), supportive vector machine (SVM), and extreme gradient boosting (XGboost), comparing their performance at predicting IBIs to a previous validated score system (IBI score). Results: During the study period, 4211 patients were included, where 126 (3.1%) had IBI. A total of eight, five, and seven features were used in the LR, SVM, and XGboost through the feature selection process, respectively. The ML models can achieve a better AUROC value when predicting IBIs in young infants compared with the IBI score (LR: 0.85 vs. SVM: 0.84 vs. XGBoost: 0.85 vs. IBI score: 0.70, p-value < 0.001). Using a cost sensitive learning algorithm, all ML models showed better specificity in predicting IBIs at a 90% sensitivity level compared to an IBI score > 2 (LR: 0.59 vs. SVM: 0.60 vs. XGBoost: 0.57 vs. IBI score >2: 0.43, p-value < 0.001). Conclusions: All ML models developed in this study outperformed the traditional scoring system in stratifying low-risk febrile infants after the standardized sensitivity level.
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Affiliation(s)
- I-Min Chiu
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan; (I.-M.C.); (C.-Y.C.)
- Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan;
| | - Chi-Yung Cheng
- Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan; (I.-M.C.); (C.-Y.C.)
- Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan;
| | - Wun-Huei Zeng
- Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan;
| | - Ying-Hsien Huang
- Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan;
| | - Chun-Hung Richard Lin
- Department of Computer Science and Engineering, National Sun Yat-Sen University, Kaohsiung 804, Taiwan;
- Correspondence:
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El-Bouri R, Taylor T, Youssef A, Zhu T, Clifton DA. Machine learning in patient flow: a review. PROGRESS IN BIOMEDICAL ENGINEERING (BRISTOL, ENGLAND) 2021; 3:022002. [PMID: 34738074 PMCID: PMC8559147 DOI: 10.1088/2516-1091/abddc5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/18/2021] [Accepted: 01/20/2021] [Indexed: 12/13/2022]
Abstract
This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services. We decompose the patient flow problem into four subcategories: prediction of demand on a healthcare institution, prediction of the demand and resource required to transfer patients from the emergency department to the hospital, prediction of potential resource required for the treatment and movement of inpatients and prediction of length-of-stay and discharge timing. We argue that there are benefits to both approaches of considering the healthcare institution as a whole as well as the patient by patient case and that ideally a combination of these would be best for improving patient flow through hospitals. We also argue that it is essential for there to be a shared dataset that will allow researchers to benchmark their algorithms on and thereby allow future researchers to build on that which has already been done. We conclude that machine learning for the improvement of patient flow is still a young field with very few papers tailor-making machine learning methods for the problem being considered. Future works should consider the need to transfer algorithms trained on a dataset to multiple hospitals and allowing for dynamic algorithms which will allow real-time decision-making to help clinical staff on the shop floor.
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Affiliation(s)
- Rasheed El-Bouri
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Thomas Taylor
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Alexey Youssef
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Tingting Zhu
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - David A Clifton
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
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Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study. Diagnostics (Basel) 2021; 11:diagnostics11040602. [PMID: 33800653 PMCID: PMC8065596 DOI: 10.3390/diagnostics11040602] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/23/2021] [Accepted: 03/26/2021] [Indexed: 11/17/2022] Open
Abstract
Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0-14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.
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38
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Tsai SJ, Ramgopal S. External Validation of an Invasive Bacterial Infection Score for Young Febrile Infants. Hosp Pediatr 2021; 11:239-244. [PMID: 33602794 DOI: 10.1542/hpeds.2020-003178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
OBJECTIVES The Aronson rule is a point-based clinical decision rule for the identification of febrile infants ≤60 days of age at low risk of invasive bacterial infection (IBI) in the emergency department. This rule uses variables of temperature, age, urinalysis, and absolute neutrophil count. We sought to externally validate this decision rule. METHODS We conducted a secondary analysis of a multicenter prospective cohort of febrile infants ≤60 days old presenting to the emergency department between December 2008 and May 2013. Infants were excluded if they had clinical sepsis or chronic conditions or were missing any laboratory components of the Aronson score. Our outcome was IBI (bacteremia and/or bacterial meningitis). We assessed the accuracy of the Aronson rule by reporting metrics of diagnostic accuracy with 95% confidence intervals (CIs) at different point thresholds. RESULTS Of 4130 included patients (780 <21 days of age; 2362 boys), 87 (2.1%) had an IBI, including 65 with isolated bacteremia and 22 with meningitis. Using an Aronson cutoff score of 2 resulted in a sensitivity of 93.1% (95% CI 85.6%-97.4%), specificity of 26.6% (95% CI 25.3%-28.0%), and negative predictive value of 99.4% (95% CI 98.8%-99.8%). Six patients with IBI (3 with bacterial meningitis) were misclassified as low risk when using a threshold of 2. CONCLUSIONS The Aronson rule demonstrates metrics of diagnostic accuracy that are comparable to the derivation study. Our findings suggest that the rule may be generalizable for the risk stratification of well-appearing febrile infants.
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Affiliation(s)
- Stacy J Tsai
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and
| | - Sriram Ramgopal
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois
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39
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Chamberlain JM, Chamberlain DB, Zorc JJ. Machine Learning and Clinical Prediction Rules: A Perfect Match? Pediatrics 2020; 146:peds.2020-012203. [PMID: 32855348 DOI: 10.1542/peds.2020-012203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/19/2020] [Indexed: 11/24/2022] Open
Affiliation(s)
| | | | - Joseph J Zorc
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
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