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Andrea Gabrielli - One of the best experts on this subject based on the ideXlab platform.

  • breathing variability predicts the suggested need for corrective intervention due to the Perceived Severity of patient ventilator asynchrony during niv
    Journal of Clinical Monitoring and Computing, 2020
    Co-Authors: Carl Tams, Neil R. Euliano, Rohit Patel, Ali Ataya, Paul Stephan, Daniel A Martin, Andrea Gabrielli
    Abstract:

    Patient-ventilator asynchrony is associated with intolerance to noninvasive ventilation (NIV) and worsened outcomes. Our goal was to develop a tool to determine a patient needs for  intervention by a practitioner due to the presence of patient-ventilator asynchrony. We postulated that a clinician can determine when a patient needs corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. We hypothesized a new measure, patient breathing variability, would indicate when corrective intervention is suggested by a bedside practitioner due to the Perceived Severity of patient-ventilator asynchrony. With IRB approval data was collected on 78 NIV patients. A panel of experts reviewed retrospective data from a development set of 10 NIV patients to categorize them into one of the three categories. The three categories were; “No to mild asynchrony—no intervention needed”, “moderate asynchrony—non-emergent corrective intervention required”, and “severe asynchrony—immediate intervention required”. A stepwise regression with a F-test forward selection criterion was used to develop a positive linear logic model predicting the expert panel’s categorizations of the need for corrective intervention. The model was incorporated into a software tool for clinical implementation. The tool was implemented prospectively on 68 NIV patients simultaneous to a bedside practitioner scoring the need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. The categories from the tool and the practitioner were compared with the rate of agreement, sensitivity, specificity, and receiver operator characteristic analyses. The rate of agreement in categorizing the suggested need for clinical intervention due to the Perceived presence of patient-ventilator asynchrony between the tool and experienced bedside practitioners was 95% with a Kappa score of 0.85 (p < 0.001). Further analysis found a specificity of 84% and sensitivity of 99%. The tool appears to accurately match the suggested need for corrective intervention by a bedside practitioner. Application of the tool allows for continuous, real time, and non-invasive monitoring of patients receiving NIV, and may enable early corrective interventions to ameliorate potential patient-ventilator asynchrony.

  • Breathing variability predicts the suggested need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony during NIV
    Journal of Clinical Monitoring and Computing, 2019
    Co-Authors: Carl Tams, Paul J. Stephan, Neil R. Euliano, A. Daniel Martin, Rohit Patel, Ali Ataya, Andrea Gabrielli
    Abstract:

    Patient-ventilator asynchrony is associated with intolerance to noninvasive ventilation (NIV) and worsened outcomes. Our goal was to develop a tool to determine a patient needs for  intervention by a practitioner due to the presence of patient-ventilator asynchrony. We postulated that a clinician can determine when a patient needs corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. We hypothesized a new measure, patient breathing variability, would indicate when corrective intervention is suggested by a bedside practitioner due to the Perceived Severity of patient-ventilator asynchrony. With IRB approval data was collected on 78 NIV patients. A panel of experts reviewed retrospective data from a development set of 10 NIV patients to categorize them into one of the three categories. The three categories were; “No to mild asynchrony—no intervention needed”, “moderate asynchrony—non-emergent corrective intervention required”, and “severe asynchrony—immediate intervention required”. A stepwise regression with a F-test forward selection criterion was used to develop a positive linear logic model predicting the expert panel’s categorizations of the need for corrective intervention. The model was incorporated into a software tool for clinical implementation. The tool was implemented prospectively on 68 NIV patients simultaneous to a bedside practitioner scoring the need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. The categories from the tool and the practitioner were compared with the rate of agreement, sensitivity, specificity, and receiver operator characteristic analyses. The rate of agreement in categorizing the suggested need for clinical intervention due to the Perceived presence of patient-ventilator asynchrony between the tool and experienced bedside practitioners was 95% with a Kappa score of 0.85 (p 

Carl Tams - One of the best experts on this subject based on the ideXlab platform.

  • breathing variability predicts the suggested need for corrective intervention due to the Perceived Severity of patient ventilator asynchrony during niv
    Journal of Clinical Monitoring and Computing, 2020
    Co-Authors: Carl Tams, Neil R. Euliano, Rohit Patel, Ali Ataya, Paul Stephan, Daniel A Martin, Andrea Gabrielli
    Abstract:

    Patient-ventilator asynchrony is associated with intolerance to noninvasive ventilation (NIV) and worsened outcomes. Our goal was to develop a tool to determine a patient needs for  intervention by a practitioner due to the presence of patient-ventilator asynchrony. We postulated that a clinician can determine when a patient needs corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. We hypothesized a new measure, patient breathing variability, would indicate when corrective intervention is suggested by a bedside practitioner due to the Perceived Severity of patient-ventilator asynchrony. With IRB approval data was collected on 78 NIV patients. A panel of experts reviewed retrospective data from a development set of 10 NIV patients to categorize them into one of the three categories. The three categories were; “No to mild asynchrony—no intervention needed”, “moderate asynchrony—non-emergent corrective intervention required”, and “severe asynchrony—immediate intervention required”. A stepwise regression with a F-test forward selection criterion was used to develop a positive linear logic model predicting the expert panel’s categorizations of the need for corrective intervention. The model was incorporated into a software tool for clinical implementation. The tool was implemented prospectively on 68 NIV patients simultaneous to a bedside practitioner scoring the need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. The categories from the tool and the practitioner were compared with the rate of agreement, sensitivity, specificity, and receiver operator characteristic analyses. The rate of agreement in categorizing the suggested need for clinical intervention due to the Perceived presence of patient-ventilator asynchrony between the tool and experienced bedside practitioners was 95% with a Kappa score of 0.85 (p < 0.001). Further analysis found a specificity of 84% and sensitivity of 99%. The tool appears to accurately match the suggested need for corrective intervention by a bedside practitioner. Application of the tool allows for continuous, real time, and non-invasive monitoring of patients receiving NIV, and may enable early corrective interventions to ameliorate potential patient-ventilator asynchrony.

  • Breathing variability predicts the suggested need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony during NIV
    Journal of Clinical Monitoring and Computing, 2019
    Co-Authors: Carl Tams, Paul J. Stephan, Neil R. Euliano, A. Daniel Martin, Rohit Patel, Ali Ataya, Andrea Gabrielli
    Abstract:

    Patient-ventilator asynchrony is associated with intolerance to noninvasive ventilation (NIV) and worsened outcomes. Our goal was to develop a tool to determine a patient needs for  intervention by a practitioner due to the presence of patient-ventilator asynchrony. We postulated that a clinician can determine when a patient needs corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. We hypothesized a new measure, patient breathing variability, would indicate when corrective intervention is suggested by a bedside practitioner due to the Perceived Severity of patient-ventilator asynchrony. With IRB approval data was collected on 78 NIV patients. A panel of experts reviewed retrospective data from a development set of 10 NIV patients to categorize them into one of the three categories. The three categories were; “No to mild asynchrony—no intervention needed”, “moderate asynchrony—non-emergent corrective intervention required”, and “severe asynchrony—immediate intervention required”. A stepwise regression with a F-test forward selection criterion was used to develop a positive linear logic model predicting the expert panel’s categorizations of the need for corrective intervention. The model was incorporated into a software tool for clinical implementation. The tool was implemented prospectively on 68 NIV patients simultaneous to a bedside practitioner scoring the need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. The categories from the tool and the practitioner were compared with the rate of agreement, sensitivity, specificity, and receiver operator characteristic analyses. The rate of agreement in categorizing the suggested need for clinical intervention due to the Perceived presence of patient-ventilator asynchrony between the tool and experienced bedside practitioners was 95% with a Kappa score of 0.85 (p 

Tanya L. Eadie - One of the best experts on this subject based on the ideXlab platform.

  • Does the accuracy of case history affect interpretation of videolaryngostroboscopic exams
    Laryngoscope, 2019
    Co-Authors: Cara Sauder, Martin T. Nevdahl, Mara Kapsner-smith, Albert L. Merati, Tanya L. Eadie
    Abstract:

    OBJECTIVE: To determine the effect of initial diagnostic hypotheses on clinicians' 1) detection and Perceived Severity of abnormalities, and 2) clinical impressions and treatment recommendations for individuals with and without voice disorders following interpretation of videolaryngostroboscopy (VLS). METHODS: Thirty-two experienced speech-language pathologists and otolaryngologists specializing in voice disorders read case histories prior to interpreting exams. Case histories suggested specific accurate or inaccurate laryngeal diagnoses, or a control scenario that suggested a normal larynx. The effects of the accuracy of case histories on Perceived Severity of associated visual-perceptual parameters, clinical impressions, and treatment recommendations were examined. RESULTS: Significant increases in Perceived Severity of posterior laryngeal appearance (P < 0.05) and mucosal wave (P < 0.02) were observed when these abnormalities were suggested by case histories. Overall agreement with clinical impressions improved from 49% to 72% when the case history was consistent with the examination. Case histories (accurate and inaccurate) indicating voice symptoms predicted recommendations for treatment above and beyond that of VLS presentation alone, P < 0.001. CONCLUSION: Case histories suggesting specific abnormalities significantly affected Severity ratings for two of three associated visual-perceptual parameters selected as primary outcome measures. Accurate case histories suggesting specific abnormalities increased the probability of detection and Perceived Severity. Inaccurate case histories led to false-positive findings and failures to detect abnormalities or to interpret them as less severe. Case histories affected visual-perceptual judgments and contributed to decisions about clinical impressions and treatment. LEVEL OF EVIDENCE: 2b Laryngoscope, 130:718-725, 2020.

  • Does the accuracy of case history affect interpretation of videolaryngostroboscopic exams
    The Laryngoscope, 2019
    Co-Authors: Cara Sauder, Martin T. Nevdahl, Mara Kapsner-smith, Albert L. Merati, Tanya L. Eadie
    Abstract:

    OBJECTIVE To determine the effect of initial diagnostic hypotheses on clinicians' 1) detection and Perceived Severity of abnormalities, and 2) clinical impressions and treatment recommendations for individuals with and without voice disorders following interpretation of videolaryngostroboscopy (VLS). METHODS Thirty-two experienced speech-language pathologists and otolaryngologists specializing in voice disorders read case histories prior to interpreting exams. Case histories suggested specific accurate or inaccurate laryngeal diagnoses, or a control scenario that suggested a normal larynx. The effects of the accuracy of case histories on Perceived Severity of associated visual-perceptual parameters, clinical impressions, and treatment recommendations were examined. RESULTS Significant increases in Perceived Severity of posterior laryngeal appearance (P 

Keith V. Bletzer - One of the best experts on this subject based on the ideXlab platform.

  • Perceived Severity: Do They Experience Illness Severity As We Conceive It?
    Human Organization, 1993
    Co-Authors: Keith V. Bletzer
    Abstract:

    This essay examines the concept of Perceived Severity of illness and argues that careful translation and appropriate operationalization of constructs is important to the validity and reliability of its study. Current methods of measuring illness Severity cross-culturally are found lacking in their neglect of local interpretations of this dimension of illness. The implications of an analysis of data from an illness recall survey conducted among the Ngawbere of western Panama are discussed and alternative considerations are proposed for the study of Perceived Severity of illness cross-culturally.

Ali Ataya - One of the best experts on this subject based on the ideXlab platform.

  • breathing variability predicts the suggested need for corrective intervention due to the Perceived Severity of patient ventilator asynchrony during niv
    Journal of Clinical Monitoring and Computing, 2020
    Co-Authors: Carl Tams, Neil R. Euliano, Rohit Patel, Ali Ataya, Paul Stephan, Daniel A Martin, Andrea Gabrielli
    Abstract:

    Patient-ventilator asynchrony is associated with intolerance to noninvasive ventilation (NIV) and worsened outcomes. Our goal was to develop a tool to determine a patient needs for  intervention by a practitioner due to the presence of patient-ventilator asynchrony. We postulated that a clinician can determine when a patient needs corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. We hypothesized a new measure, patient breathing variability, would indicate when corrective intervention is suggested by a bedside practitioner due to the Perceived Severity of patient-ventilator asynchrony. With IRB approval data was collected on 78 NIV patients. A panel of experts reviewed retrospective data from a development set of 10 NIV patients to categorize them into one of the three categories. The three categories were; “No to mild asynchrony—no intervention needed”, “moderate asynchrony—non-emergent corrective intervention required”, and “severe asynchrony—immediate intervention required”. A stepwise regression with a F-test forward selection criterion was used to develop a positive linear logic model predicting the expert panel’s categorizations of the need for corrective intervention. The model was incorporated into a software tool for clinical implementation. The tool was implemented prospectively on 68 NIV patients simultaneous to a bedside practitioner scoring the need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. The categories from the tool and the practitioner were compared with the rate of agreement, sensitivity, specificity, and receiver operator characteristic analyses. The rate of agreement in categorizing the suggested need for clinical intervention due to the Perceived presence of patient-ventilator asynchrony between the tool and experienced bedside practitioners was 95% with a Kappa score of 0.85 (p < 0.001). Further analysis found a specificity of 84% and sensitivity of 99%. The tool appears to accurately match the suggested need for corrective intervention by a bedside practitioner. Application of the tool allows for continuous, real time, and non-invasive monitoring of patients receiving NIV, and may enable early corrective interventions to ameliorate potential patient-ventilator asynchrony.

  • Breathing variability predicts the suggested need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony during NIV
    Journal of Clinical Monitoring and Computing, 2019
    Co-Authors: Carl Tams, Paul J. Stephan, Neil R. Euliano, A. Daniel Martin, Rohit Patel, Ali Ataya, Andrea Gabrielli
    Abstract:

    Patient-ventilator asynchrony is associated with intolerance to noninvasive ventilation (NIV) and worsened outcomes. Our goal was to develop a tool to determine a patient needs for  intervention by a practitioner due to the presence of patient-ventilator asynchrony. We postulated that a clinician can determine when a patient needs corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. We hypothesized a new measure, patient breathing variability, would indicate when corrective intervention is suggested by a bedside practitioner due to the Perceived Severity of patient-ventilator asynchrony. With IRB approval data was collected on 78 NIV patients. A panel of experts reviewed retrospective data from a development set of 10 NIV patients to categorize them into one of the three categories. The three categories were; “No to mild asynchrony—no intervention needed”, “moderate asynchrony—non-emergent corrective intervention required”, and “severe asynchrony—immediate intervention required”. A stepwise regression with a F-test forward selection criterion was used to develop a positive linear logic model predicting the expert panel’s categorizations of the need for corrective intervention. The model was incorporated into a software tool for clinical implementation. The tool was implemented prospectively on 68 NIV patients simultaneous to a bedside practitioner scoring the need for corrective intervention due to the Perceived Severity of patient-ventilator asynchrony. The categories from the tool and the practitioner were compared with the rate of agreement, sensitivity, specificity, and receiver operator characteristic analyses. The rate of agreement in categorizing the suggested need for clinical intervention due to the Perceived presence of patient-ventilator asynchrony between the tool and experienced bedside practitioners was 95% with a Kappa score of 0.85 (p