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  1. Article ; Online: Paediatric perioperative mortality estimation-Are we barking up the wrong tree?

    Lonsdale, Hannah / Matava, Clyde T

    Acta anaesthesiologica Scandinavica

    2022  Volume 67, Issue 2, Page(s) 124–125

    MeSH term(s) Child ; Humans ; Perioperative Period ; Child Mortality
    Language English
    Publishing date 2022-11-09
    Publishing country England
    Document type Editorial ; Comment
    ZDB-ID 80002-8
    ISSN 1399-6576 ; 0001-5172
    ISSN (online) 1399-6576
    ISSN 0001-5172
    DOI 10.1111/aas.14164
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  2. Article ; Online: Taking a byte out of APRICOT to predict which children are at low risk for critical perioperative events.

    Simpao, Allan F / Tsui, Fu-Chiang Rich / Matava, Clyde T

    Paediatric anaesthesia

    2023  Volume 33, Issue 9, Page(s) 687–688

    MeSH term(s) Child ; Humans ; Prunus armeniaca ; Risk ; Anesthesia
    Language English
    Publishing date 2023-06-13
    Publishing country France
    Document type Editorial ; Comment
    ZDB-ID 1086049-6
    ISSN 1460-9592 ; 1155-5645
    ISSN (online) 1460-9592
    ISSN 1155-5645
    DOI 10.1111/pan.14707
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  3. Article ; Online: Aerosol Retention Barriers.

    Matava, Clyde T / Gálvez, Jorge A

    Anesthesiology

    2021  Volume 134, Issue 1, Page(s) 9–10

    MeSH term(s) Aerosols ; Lung
    Chemical Substances Aerosols
    Language English
    Publishing date 2021-01-04
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 269-0
    ISSN 1528-1175 ; 0003-3022
    ISSN (online) 1528-1175
    ISSN 0003-3022
    DOI 10.1097/ALN.0000000000003620
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  4. Article ; Online: An open-source toolkit to assist authors and collaborators during manuscript preparation: AuthorAndCollaborator toolkit.

    Matava, Clyde T / Williams, R J / Simpao, Allan F

    Canadian journal of anaesthesia = Journal canadien d'anesthesie

    2022  Volume 69, Issue 5, Page(s) 680–681

    MeSH term(s) Humans ; Software
    Language English
    Publishing date 2022-02-24
    Publishing country United States
    Document type Letter
    ZDB-ID 91002-8
    ISSN 1496-8975 ; 0832-610X
    ISSN (online) 1496-8975
    ISSN 0832-610X
    DOI 10.1007/s12630-022-02211-6
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  5. Article ; Online: Walk a Tightrope or Burn a Bridge?: Sedation versus General Anesthesia for Intubation of a Pediatric Difficult Airway.

    Simpao, Allan F / Matava, Clyde T / Davidson, Andrew

    Anesthesiology

    2022  Volume 137, Issue 4, Page(s) 384–386

    MeSH term(s) Anesthesia, General ; Burns ; Child ; Humans ; Intubation, Intratracheal
    Language English
    Publishing date 2022-09-05
    Publishing country United States
    Document type Editorial ; Comment
    ZDB-ID 269-0
    ISSN 1528-1175 ; 0003-3022
    ISSN (online) 1528-1175
    ISSN 0003-3022
    DOI 10.1097/ALN.0000000000004343
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  6. Article ; Online: Comparing the Effectiveness of a Clinical Decision Support Tool in Reducing Pediatric Opioid Dose Calculation Errors: PediPain App vs. Traditional Calculators - A Simulation-Based Randomised Controlled Study.

    Matava, Clyde T / Bordini, Martina / Jasudavisius, Amanda / Santos, Carmina / Caldeira-Kulbakas, Monica

    Journal of medical systems

    2024  Volume 48, Issue 1, Page(s) 43

    Abstract: Wrong dose calculation medication errors are widespread in pediatric patients mainly due to weight-based dosing. PediPain app is a clinical decision support tool that provides weight- and age- based dosages for various analgesics. We hypothesized that ... ...

    Abstract Wrong dose calculation medication errors are widespread in pediatric patients mainly due to weight-based dosing. PediPain app is a clinical decision support tool that provides weight- and age- based dosages for various analgesics. We hypothesized that the use of a clinical decision support tool, the PediPain app versus pocket calculators for calculating pain medication dosages in children reduces the incidence of wrong dosage calculations and shortens the time taken for calculations. The study was a randomised controlled trial comparing the PediPain app vs. pocket calculator for performing eight weight-based calculations for opioids and other analgesics. Participants were healthcare providers routinely administering opioids and other analgesics in their practice. The primary outcome was the incidence of wrong dose calculations. Secondary outcomes were the incidence of wrong dose calculations in simple versus complex calculations; time taken to complete calculations; the occurrence of tenfold; hundredfold errors; and wrong-key presses. A total of 140 residents, fellows and nurses were recruited between June 2018 and November 2019; 70 participants were randomized to control group (pocket calculator) and 70 to the intervention group (PediPain App). After randomization two participants assigned to PediPain group completed the simulation in the control group by mistake. Analysis was by intention-to-treat (PediPain app = 68 participants, pocket calculator = 72 participants). The overall incidence of wrong dose calculation was 178/576 (30.9%) for the control and 23/544 (4.23%) for PediPain App, P < 0·001. The risk difference was - 32.8% [-38.7%, -26.9%] for complex and - 20.5% [-26.3%, -14.8%] for simple calculations. Calculations took longer within control group (median of 69 Sects. [50, 96]) compared to PediPain app group, (median 48 Sects. [38, 63]), P < 0.001. There were no differences in other secondary outcomes. A weight-based clinical decision support tool, the PediPain app reduced the incidence of wrong doses calculation. Clinical decision support tools calculating medications may be valuable instruments for reducing medication errors, especially in the pediatric population.
    MeSH term(s) Humans ; Child ; Analgesics, Opioid/therapeutic use ; Decision Support Systems, Clinical ; Mobile Applications ; Research Design ; Computer Simulation
    Chemical Substances Analgesics, Opioid
    Language English
    Publishing date 2024-04-17
    Publishing country United States
    Document type Randomized Controlled Trial ; Journal Article
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-024-02060-4
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  7. Article ; Online: Refocusing on work-based hazards for the anaesthesiologist in a post-pandemic era.

    Nelson, Olivia / Greenwood, Eric / Simpao, Allan F / Matava, Clyde T

    BJA open

    2023  Volume 8, Page(s) 100234

    Abstract: The coronavirus pandemic has raised public awareness of one of the many hazards that healthcare workers face daily: exposure to harmful pathogens. The anaesthesia workplace encompasses the operating room, interventional radiology suite, and other sites ... ...

    Abstract The coronavirus pandemic has raised public awareness of one of the many hazards that healthcare workers face daily: exposure to harmful pathogens. The anaesthesia workplace encompasses the operating room, interventional radiology suite, and other sites that contain many other potential occupational and environmental hazards. This review article highlights the work-based hazards that anaesthesiologists and other clinicians may encounter in the anaesthesia workplace: ergonomic design, physical, chemical, fire, biological, or psychological hazards. As the anaesthesia work environment enters a post-COVID-19 pandemic phase, anaesthesiologists will do well to review and consider these hazards. The current review includes proposed solutions to some hazards and identifies opportunities for future research.
    Language English
    Publishing date 2023-10-25
    Publishing country England
    Document type Journal Article ; Review
    ISSN 2772-6096
    ISSN (online) 2772-6096
    DOI 10.1016/j.bjao.2023.100234
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  8. Article ; Online: Registries in pediatric anesthesiology: A brief history and a new way forward.

    Nelson, Olivia / Wang, Jue T / Matava, Clyde T / Stricker, Paul A

    Paediatric anaesthesia

    2023  Volume 34, Issue 1, Page(s) 7–12

    Abstract: Clinical registries are multicenter prospective observational datasets that have been used to examine current perioperative practices in pediatric anesthesia. These datasets have proven useful in quantifying the incidence of rare adverse outcomes. Data ... ...

    Abstract Clinical registries are multicenter prospective observational datasets that have been used to examine current perioperative practices in pediatric anesthesia. These datasets have proven useful in quantifying the incidence of rare adverse outcomes. Data from registries can highlight associations between severe patient safety events and patient and procedure-related risk factors. Registries are an effective tool to delineate practices and outcomes in niche patient populations. They have been used to quantify uncommon complications of medications and procedures. Registries can be used to generate knowledge and to support quality improvement. Multicenter engagement can promote best clinical practices and foster professional networks. Registries are limited by their observational nature, which entails a lack of randomization as well as selection and treatment bias. The maintenance of registries over time can be challenging due to difficulties in modifying the included variables, collaborator fatigue, and continued outlay of resources to maintain the database and onboard new sites. These latter issues can lead to decreased data quality. In this article, we discuss key insights from several pediatric anesthesia registries and propose a new type of registry that addresses some shortcomings of the current paradigm.
    MeSH term(s) Child ; Humans ; Anesthesiology ; Anesthesia/adverse effects ; Risk Factors ; Registries ; Quality Improvement
    Language English
    Publishing date 2023-10-04
    Publishing country France
    Document type Observational Study ; Multicenter Study ; Journal Article
    ZDB-ID 1086049-6
    ISSN 1460-9592 ; 1155-5645
    ISSN (online) 1460-9592
    ISSN 1155-5645
    DOI 10.1111/pan.14775
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  9. Article ; Online: Standardized Anesthesia InductioN Tool (SAINT) - The development and international adoption of an integrated electronic tool for documenting the induction of anesthesia in children.

    Matava, Clyde T / Gentry, Heidi / Simpao, Allan F / Weintraub, Ari

    Paediatric anaesthesia

    2023  Volume 33, Issue 5, Page(s) 347–354

    Abstract: Background: The induction of anesthesia in children poses a challenge for the anesthesiologist, the parent and child. Anxiety and negative behaviours and strategies that effectively mitigate should be documented accurately and be available for future ... ...

    Abstract Background: The induction of anesthesia in children poses a challenge for the anesthesiologist, the parent and child. Anxiety and negative behaviours and strategies that effectively mitigate should be documented accurately and be available for future patient encounters. To address the need for a structured and standardized electronic documentation tool.
    Aims: Our aim was to develop a comprehensive electronic tool to capture and report behaviours during induction of anesthesia.
    Methods: We performed a literature search on existing validated tools for documenting behaviours during anesthesia induction. We used the nominal group technique to achieve agreement on the components to include. We used Agile software development techniques to design and review the integrated electronic tool. Twelve international hospitals informed the development of the tool.
    Results: We developed an electronic tool, the Standardized Anesthesia InductioN Tool (SAINT). SAINT incorporates validated scales for documenting key stages of the anesthesia induction journey (separation from caregivers, mask acceptance, induction behaviour, parental presence, the use of adjuncts and their effectiveness). In addition, the standardised data elements used in SAINT allow for local reporting, quality metrics and can assist in data across multi-centre trials. To date the tool has been adopted by 133 institutions across four countries and is freely available.
    Conclusion: We show that collaborative development and rapid adoption of the comprehensive induction tool SAINT has led to its rapid adoption in the routine practice of pediatric anesthesiology across several countries. Further studies on how the SAINT is being used for quality improvement or research are warranted.
    MeSH term(s) Anesthesia, General ; Anxiety ; Humans ; Child ; Pediatrics ; Patient Satisfaction ; Anesthesiologists
    Language English
    Publishing date 2023-01-09
    Publishing country France
    Document type Journal Article
    ZDB-ID 1086049-6
    ISSN 1460-9592 ; 1155-5645
    ISSN (online) 1460-9592
    ISSN 1155-5645
    DOI 10.1111/pan.14625
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  10. Article ; Online: Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects.

    Lonsdale, Hannah / Gray, Geoffrey M / Ahumada, Luis M / Matava, Clyde T

    Anesthesia and analgesia

    2023  Volume 137, Issue 4, Page(s) 830–840

    Abstract: Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some ... ...

    Abstract Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty. Early research for machine vision in anesthesia has focused on automated recognition of anatomical structures during ultrasound-guided regional anesthesia or line insertion; recognition of the glottic opening and vocal cords during video laryngoscopy; prediction of the difficult airway using facial images; and clinical alerts for endobronchial intubation detected on chest radiograph. Current machine vision applications measuring the distance between endotracheal tube tip and carina have demonstrated noninferior performance compared to board-certified physicians. The performance and potential uses of machine vision for anesthesia will only grow with the advancement of underlying machine vision algorithm technical performance developed outside of medicine, such as convolutional neural networks and transfer learning. This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.
    MeSH term(s) Humans ; Artificial Intelligence ; Anesthesiology ; Anesthesia, Conduction ; Anesthesiologists ; Algorithms
    Language English
    Publishing date 2023-09-05
    Publishing country United States
    Document type Review ; Journal Article
    ZDB-ID 80032-6
    ISSN 1526-7598 ; 0003-2999
    ISSN (online) 1526-7598
    ISSN 0003-2999
    DOI 10.1213/ANE.0000000000006679
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