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Jin C, Zhao H, Yang J. Reply to Letter to Editor Regarding "Auditory Effects of Acoustic Noise From 3-T Brain MRI in Neonates With Hearing Protection". J Magn Reson Imaging 2024. [PMID: 39042364 DOI: 10.1002/jmri.29515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2024] [Accepted: 06/17/2024] [Indexed: 07/24/2024] Open
Affiliation(s)
- Chao Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an, Shaanxi, China
| | - Huifang Zhao
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an, Shaanxi, China
| | - Jian Yang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Shaanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi'an, Shaanxi, China
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Koppens HJ, Onland W, Visser DH, Denswil NP, van Kaam AH, Lutterman CA. Heart Rate Characteristics Monitoring for Late-Onset Sepsis in Preterm Infants: A Systematic Review. Neonatology 2023; 120:548-557. [PMID: 37379804 PMCID: PMC10614451 DOI: 10.1159/000531118] [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: 02/16/2023] [Accepted: 05/03/2023] [Indexed: 06/30/2023]
Abstract
BACKGROUND Early diagnosis of late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) by monitoring heart rate characteristics (HRC) of preterm infants might reduce the risk of death and morbidities. We aimed to systematically assess the effects of HRC monitoring on death, LOS, and NEC. METHODS A systematic search was performed in MEDLINE, Embase, Cochrane Library, and Web of Science. RESULTS Fifteen papers were included in this review. Three of these papers reported results from the only identified randomized controlled trial (RCT). This RCT showed that HRC monitoring resulted in a small but significant reduction in mortality (absolute risk reduction 2.1% [95% confidence interval 0.01-4.14]) without any differences in neurodevelopmental impairment. The risk of bias was rated high due to performance and detection bias and failure to correct for multiple testing. Most diagnostic cohort studies showed high discriminating accuracy in predicting LOS but lacked sufficient quality and generalizability. No studies for the detection of NEC were identified. CONCLUSION Supported by multiple observational cohort studies, the RCT identified in this systematic review showed that HRC monitoring as an early warning system for LOS might reduce the risk of death in preterm infants. However, methodological weaknesses and limited generalizability do not justify implementation of HRC in clinical care. A large international RCT is warranted.
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Affiliation(s)
- Hugo J. Koppens
- Department of Neonatology, Emma Children’s Hospital, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development, Amsterdam, The Netherlands
| | - Wes Onland
- Department of Neonatology, Emma Children’s Hospital, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development, Amsterdam, The Netherlands
| | - Douwe H. Visser
- Department of Neonatology, Emma Children’s Hospital, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development, Amsterdam, The Netherlands
| | - Nerissa P. Denswil
- Amsterdam UMC Location University of Amsterdam, Medical Library, Amsterdam, The Netherlands
| | - Anton H. van Kaam
- Department of Neonatology, Emma Children’s Hospital, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Reproduction and Development, Amsterdam, The Netherlands
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Sullivan BA, Kausch SL, Fairchild KD. Artificial and human intelligence for early identification of neonatal sepsis. Pediatr Res 2023; 93:350-356. [PMID: 36127407 DOI: 10.1038/s41390-022-02274-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 07/29/2022] [Accepted: 08/05/2022] [Indexed: 11/09/2022]
Abstract
Artificial intelligence may have a role in the early detection of sepsis in neonates. Machine learning can identify patterns that predict high or increasing risk for clinical deterioration from a sepsis-like illness. In developing this potential addition to NICU care, careful consideration should be given to the data and methods used to develop, validate, and evaluate prediction models. When an AI system alerts clinicians to a change in a patient's condition that warrants a bedside evaluation, human intelligence and experience come into play to determine an appropriate course of action: evaluate and treat or wait and watch closely. With intelligently developed, validated, and implemented AI sepsis systems, both clinicians and patients stand to benefit. IMPACT: This narrative review highlights the application of AI in neonatal sepsis prediction. It describes issues in clinical prediction model development specific to this population. This article reviews the methods, considerations, and literature on neonatal sepsis model development and validation. Challenges of AI technology and potential barriers to using sepsis AI systems in the NICU are discussed.
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Affiliation(s)
- Brynne A Sullivan
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA.
| | - Sherry L Kausch
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Karen D Fairchild
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
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Monitoring of heart rate characteristics to detect neonatal sepsis. Pediatr Res 2022; 92:1070-1074. [PMID: 34916625 DOI: 10.1038/s41390-021-01913-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/02/2021] [Accepted: 12/04/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Monitoring of heart rate characteristics (HRC) index may improve outcomes of late-onset neonatal sepsis (LOS) through early detection. We aimed at describing the association between LOS and elevated HRC index. METHODS This single-center retrospective case-control study included neonates who presented with blood culture-proven hospital-acquired LOS. Controls were matched to cases (ratio 1:2) based on gestational age, postnatal age, and birthweight. We compared the highest HRC indexes in the 48 h preceding blood culture sampling in LOS cases to the highest HRC indexes at the same postnatal days in controls. RESULTS In 59 LOS cases and 123 controls, an HRC index > 2 was associated with LOS (OR 7.1, 95% CI 2.6-19.0). Sensitivity and specificity of an HRC index > 2 to predict LOS were 53% (32/59) and 79% (98/123). Sensitivity increased from 25% in infants born > 32 weeks to 76% in infants born < 28 weeks. Specificity decreased from 97% in infants > 32 weeks to 63% in those born < 28 weeks. CONCLUSIONS An increase of HRC index > 2 has a significant association with the diagnosis of LOS, supporting the use of HRC monitoring to assist early detection of LOS. Clinicians using HRC monitoring should be aware of its diagnostic accuracy and limitations in different gestational age groups. IMPACT There is a paucity of data regarding the predictive value of heart rate characteristics (HRC) monitoring for early diagnosis of late-onset neonatal sepsis (LOS) in daily clinical practice. Monitoring of heart rate characteristics provides valuable information to assist the early diagnosis of LOS across all gestational age groups. However, the strong influence of gestational age on positive and negative predictive values adds complexity to the interpretation of HRC indexes.
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Randall Moorman J. The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU. NPJ Digit Med 2022; 5:41. [PMID: 35361861 PMCID: PMC8971442 DOI: 10.1038/s41746-022-00584-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/23/2022] [Indexed: 11/17/2022] Open
Abstract
In 2011, a multicenter group spearheaded at the University of Virginia demonstrated reduced mortality from real-time continuous cardiorespiratory monitoring in the neonatal ICU using what we now call Artificial Intelligence, Big Data, and Machine Learning. The large, randomized heart rate characteristics trial made real, for the first time that we know of, the promise that early detection of illness would allow earlier and more effective intervention and improved patient outcomes. Currently, though, we hear as much of failures as we do of successes in the rapidly growing field of predictive analytics monitoring that has followed. This Perspective aims to describe the principles of how we developed heart rate characteristics monitoring for neonatal sepsis and then applied them throughout adult ICU and hospital medicine. It primarily reflects the work since the 1990s of the University of Virginia group: the theme is that sudden and catastrophic deteriorations can be preceded by subclinical but measurable physiological changes apparent in the continuous cardiorespiratory monitoring and electronic health record.
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Affiliation(s)
- J Randall Moorman
- Cardiovascular Division, Department of Internal Medicine, Center for Advanced Medical Analytics, University of Virginia, Charlottesville, VA, USA.
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Fetal heart rate variability is a biomarker of rapid but not progressive exacerbation of inflammation in preterm fetal sheep. Sci Rep 2022; 12:1771. [PMID: 35110628 PMCID: PMC8810879 DOI: 10.1038/s41598-022-05799-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 01/11/2022] [Indexed: 12/14/2022] Open
Abstract
Perinatal infection/inflammation can trigger preterm birth and contribute to neurodevelopmental disability. There are currently no sensitive, specific methods to identify perinatal infection. We investigated the utility of time, frequency and non-linear measures of fetal heart rate (FHR) variability (FHRV) to identify either progressive or more rapid inflammation. Chronically instrumented preterm fetal sheep were randomly assigned to one of three different 5d continuous i.v. infusions: 1) control (saline infusions; n = 10), 2) progressive lipopolysaccharide (LPS; 200 ng/kg over 24 h, doubled every 24 h for 5d, n = 8), or 3) acute-on-chronic LPS (100 ng/kg over 24 h then 250 ng/kg/24 h for 4d plus 1 μg boluses at 48, 72, and 96 h, n = 9). Both LPS protocols triggered transient increases in multiple measures of FHRV at the onset of infusions. No FHRV or physiological changes occurred from 12 h after starting progressive LPS infusions. LPS boluses during the acute-on-chronic protocol triggered transient hypotension, tachycardia and an initial increase in multiple time and frequency domain measures of FHRV, with an asymmetric FHR pattern of predominant decelerations. Following resolution of hypotension after the second and third LPS boluses, all frequencies of FHRV became suppressed. These data suggest that FHRV may be a useful biomarker of rapid but not progressive preterm infection/inflammation.
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Persad E, Jost K, Honoré A, Forsberg D, Coste K, Olsson H, Rautiainen S, Herlenius E. Neonatal sepsis prediction through clinical decision support algorithms: A systematic review. Acta Paediatr 2021; 110:3201-3226. [PMID: 34432903 DOI: 10.1111/apa.16083] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/14/2021] [Accepted: 08/24/2021] [Indexed: 12/12/2022]
Abstract
AIM To systematically summarise the current evidence of employing clinical decision support algorithms (CDSAs) using non-invasive parameters for sepsis prediction in neonates. METHODS A comprehensive search in PubMed, CENTRAL and EMBASE was conducted. Screening, data extraction and risk of bias were performed by two authors. The certainty of the evidence was assessed using GRADE. PROSPERO ID CRD42020205143. RESULTS After abstract and full-text screening, 36 studies comprising 18,096 infants were included. Most CDSAs evaluated heart rate (HR)-based parameters. Two publications derived from one randomised-controlled trial assessing HR characteristics reported significant reduction in 30-day septicaemia-related mortality. Thirty-four non-randomised studies found promising yet inconclusive results. CONCLUSION Heart rate-based parameters are reliable components of CDSAs for sepsis prediction, particularly in combination with additional vital signs and demographics. However, inconclusive evidence and limited standardisation restricts clinical implementation of CDSAs outside of a controlled research environment. Further experimentation and comparison of parameter combinations and testing of new CDSAs are warranted.
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Affiliation(s)
- Emma Persad
- Department of Women's & Children’s Health Karolinska Institutet Stockholm Sweden
- Astrid Lindgren Children’s HospitalKarolinska University Hospital Stockholm Sweden
- Karl Landsteiner University of Health Sciences Krems Austria
- Department of Evidence‐based Medicine and Evaluation Danube University Krems Krems Austria
| | - Kerstin Jost
- Department of Women's & Children’s Health Karolinska Institutet Stockholm Sweden
- Astrid Lindgren Children’s HospitalKarolinska University Hospital Stockholm Sweden
| | - Antoine Honoré
- Department of Women's & Children’s Health Karolinska Institutet Stockholm Sweden
- Astrid Lindgren Children’s HospitalKarolinska University Hospital Stockholm Sweden
- Division of Information Science and Engineering KTH Royal Institute of Technology Stockholm Sweden
| | - David Forsberg
- Department of Women's & Children’s Health Karolinska Institutet Stockholm Sweden
- Astrid Lindgren Children’s HospitalKarolinska University Hospital Stockholm Sweden
| | - Karen Coste
- Department of Women's & Children’s Health Karolinska Institutet Stockholm Sweden
- CNRS INSERM GReD Université Clermont Auvergne Clermont‐Ferrand France
| | - Hanna Olsson
- Department of Women's & Children’s Health Karolinska Institutet Stockholm Sweden
| | - Susanne Rautiainen
- Department of Women's & Children’s Health Karolinska Institutet Stockholm Sweden
- Astrid Lindgren Children’s HospitalKarolinska University Hospital Stockholm Sweden
- Department of Global Public Health Karolinska Institutet Stockholm Sweden
| | - Eric Herlenius
- Department of Women's & Children’s Health Karolinska Institutet Stockholm Sweden
- Astrid Lindgren Children’s HospitalKarolinska University Hospital Stockholm Sweden
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Cerebral oxygen saturation-a useful bedside vital sign for neonatal encephalopathy. J Perinatol 2021; 41:2577-2579. [PMID: 33547404 DOI: 10.1038/s41372-021-00916-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 11/24/2020] [Accepted: 01/14/2021] [Indexed: 11/08/2022]
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Van Laere D, Meeus M, Beirnaert C, Sonck V, Laukens K, Mahieu L, Mulder A. Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit. Clin Perinatol 2020; 47:435-448. [PMID: 32713443 DOI: 10.1016/j.clp.2020.05.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Hemodynamic support in neonatal intensive care is directed at maintaining cardiovascular wellbeing. At present, monitoring of vital signs plays an essential role in augmenting care in a reactive manner. By applying machine learning techniques, a model can be trained to learn patterns in time series data, allowing the detection of adverse outcomes before they become clinically apparent. In this review we provide an overview of the different machine learning techniques that have been used to develop models in hemodynamic care for newborn infants. We focus on their potential benefits, research pitfalls, and challenges related to their implementation in clinical care.
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Affiliation(s)
- David Van Laere
- Department of Neonatal Intensive Care, University Hospital Antwerp, Wilrijkstraat 10, Edegem BE-2650, Belgium; Laboratory of Pediatrics, Department of Life Sciences, University of Antwerp, Prinsstraat 13, Antwerpen 2000, Belgium.
| | - Marisse Meeus
- Department of Neonatal Intensive Care, University Hospital Antwerp, Wilrijkstraat 10, Edegem BE-2650, Belgium; Laboratory of Pediatrics, Department of Life Sciences, University of Antwerp, Prinsstraat 13, Antwerpen 2000, Belgium
| | - Charlie Beirnaert
- Adrem Data Lab, Department of Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, Antwerpen 2020, Belgium
| | - Victor Sonck
- ML6, Esplanade Oscar Van De Voorde 1, Ghent 9000, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Mathematics and Computer Science, University of Antwerp, Middelheimlaan 1, Antwerpen 2020, Belgium
| | - Ludo Mahieu
- Department of Neonatal Intensive Care, University Hospital Antwerp, Wilrijkstraat 10, Edegem BE-2650, Belgium; Laboratory of Pediatrics, Department of Life Sciences, University of Antwerp, Prinsstraat 13, Antwerpen 2000, Belgium
| | - Antonius Mulder
- Department of Neonatal Intensive Care, University Hospital Antwerp, Wilrijkstraat 10, Edegem BE-2650, Belgium; Laboratory of Pediatrics, Department of Life Sciences, University of Antwerp, Prinsstraat 13, Antwerpen 2000, Belgium
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Chakraborty M, Watkins WJ, Tansey K, King WE, Banerjee S. Predicting extubation outcomes using the Heart Rate Characteristics index in preterm infants: a cohort study. Eur Respir J 2020; 56:13993003.01755-2019. [PMID: 32444402 DOI: 10.1183/13993003.01755-2019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 05/15/2020] [Indexed: 11/05/2022]
Abstract
A strategy of early extubation to noninvasive respiratory support in preterm infants could be boosted by the availability of a decision support tool for clinicians. Using the Heart Rate Characteristics index (HRCi) with clinical parameters, we derived and validated predictive models for extubation readiness and success.Peri-extubation demographic, clinical and HRCi data for up to 96 h were collected from mechanically ventilated infants in the control arm of a randomised trial involving eight neonatal centres, where clinicians were blinded to the HRCi scores. The data were used to produce a multivariable regression model for the probability of subsequent re-intubation. Additionally, a survival model was produced to estimate the probability of re-intubation in the period after extubation.Of the 577 eligible infants, data from 397 infants (69%) were used to derive the pre-extubation model and 180 infants (31%) for validation. The model was also fitted and validated using all combinations of training (five centres) and test (three centres) centres. The estimated probability for the validation episodes showed discrimination with high statistical significance, with an area under the curve of 0.72 (95% CI 0.71-0.74; p<0.001). Data from all infants were used to derive models of the predictive instantaneous hazard of re-intubation adjusted for clinical parameters.Predictive models of extubation readiness and success in real-time can be derived using physiological and clinical variables. The models from our analyses can be accessed using an online tool available at www.heroscore.com/extubation, and have the potential to inform and supplement the confidence of the clinician considering extubation in preterm infants.
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Affiliation(s)
- Mallinath Chakraborty
- Regional Neonatal Intensive Care Unit, University Hospital of Wales, Cardiff, UK.,Centre for Medical Education, School of Medicine, Cardiff University, Cardiff, UK.,These authors contributed equally to this work
| | - William John Watkins
- Dept of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK.,These authors contributed equally to this work
| | - Katherine Tansey
- Dept of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - William E King
- Medical Predictive Science Corporation, Charlottesville, VA, USA
| | - Sujoy Banerjee
- Neonatal Intensive Care Unit, Singleton Hospital, Swansea, UK
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Towards development of alert thresholds for clinical deterioration using continuous predictive analytics monitoring. J Clin Monit Comput 2019; 34:797-804. [PMID: 31327101 DOI: 10.1007/s10877-019-00361-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 07/16/2019] [Indexed: 10/26/2022]
Abstract
Patients who deteriorate while on the acute care ward and are emergently transferred to the Intensive Care Unit (ICU) experience high rates of mortality. To date, risk scores for clinical deterioration applied to the acute care wards rely on static or intermittent inputs of vital sign and assessment parameters. We propose the use of continuous predictive analytics monitoring, or data that relies on real-time physiologic monitoring data captured from ECG, documented vital signs, laboratory results, and other clinical assessments to predict clinical deterioration. A necessary step in translation to practice is understanding how an alert threshold would perform if applied to a continuous predictive analytic that was trained to detect clinical deterioration. The purpose of this study was to evaluate the positive predictive value of 'risk spikes', or large abrupt increases in the output of a statistical model of risk predicting clinical deterioration. We studied 8111 consecutive patient admissions to a cardiovascular medicine and surgery ward with continuous ECG data. We first trained a multivariable logistic regression model for emergent ICU transfer in a test set and tested the characteristics of the model in a validation set of 4059 patient admissions. Then, in a nested analysis we identified large, abrupt spikes in risk (increase by three units over the prior 6 h; a unit is the fold-increase in risk of ICU transfer in the next 24 h) and reviewed hospital records of 91 patients for clinical events such as emergent ICU transfer. We compared results to 59 control patients at times when they were matched for baseline risk including the National Warning Score (NEWS). There was a 3.4-fold higher event rate for patients with risk spikes (positive predictive value 24% compared to 7%, p = 0.006). If we were to use risk spikes as an alert, they would fire about once per day on a 73-bed acute care ward. Risk spikes that were primarily driven by respiratory changes (ECG-derived respiration (EDR) or charted respiratory rate) had highest PPV (30-35%) while risk spikes driven by heart rate had the lowest (7%). Alert thresholds derived from continuous predictive analytics monitoring are able to be operationalized as a degree of change from the person's own baseline rather than arbitrary threshold cut-points, which can likely better account for the individual's own inherent acuity levels. Point of care clinicians in the acute care ward settings need tailored alert strategies that promote a balance in recognition of clinical deterioration and assessment of the utility of the alert approach.
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Amess P, Rabe H, Wertheim D. Visual assessment of heart rate variability patterns associated with neonatal infection in preterm infants. Early Hum Dev 2019; 134:31-33. [PMID: 31154051 DOI: 10.1016/j.earlhumdev.2019.05.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 05/14/2019] [Accepted: 05/21/2019] [Indexed: 11/19/2022]
Abstract
Early identification of neonatal sepsis may help reduce morbidity. From Heart Rate Variability (HRV) visually assessed in preterm infants, eight of nine recordings in babies with positive blood cultures had low HRV and six infants without positive cultures had normal HRV. Straightforward HRV display could help identify infection in infants.
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Affiliation(s)
- Phil Amess
- Royal Sussex County Hospital, Brighton, UK
| | - Heike Rabe
- Royal Sussex County Hospital, Brighton, UK; Brighton & Sussex Medical School, Brighton, UK
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Dear Mommy and Daddy, I Wish to Go Home at the Right Date, Not Too Early But Not Too Late…. Pediatr Crit Care Med 2018; 19:1175-1176. [PMID: 30520844 DOI: 10.1097/pcc.0000000000001755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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