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  1. Article ; Online: Use of telemedicine for the identification and treatment of sulfamethoxazole-induced methaemoglobinemia.

    Kohl, Benjamin A / Domski, Ann / Pavan, Kimberly / Fortino, Margaret

    Journal of telemedicine and telecare

    2012  Volume 18, Issue 6, Page(s) 362–364

    Abstract: Telemedicine can be used in intensive care units (ICUs) with linked electronic medical records to enable remote clinicians to assess patients and focus on those who are deviating from their expected course. We report the case of a woman admitted to our ... ...

    Abstract Telemedicine can be used in intensive care units (ICUs) with linked electronic medical records to enable remote clinicians to assess patients and focus on those who are deviating from their expected course. We report the case of a woman admitted to our ICU with apparent hypoxaemia, whose pulse oximetry readings were not believed by the treating team. The intensivist at the telemedicine centre was consulted and instituted treatment on the assumption that methaemoglobinemia was present. Without rapid therapy, ongoing tissue ischaemia and shock was inevitable. Within 60 min of methylene blue administration, the patient's oxygen saturation improved dramatically. The methaemoglobin level was eventually reported as 9.9% (normal value <1%). This case report demonstrates how, with the aid of a tele-intensivist, a rare diagnosis was made rapidly and successful therapy was provided.
    MeSH term(s) Anti-Infective Agents/adverse effects ; Female ; Humans ; Intensive Care Units/organization & administration ; Methemoglobinemia/chemically induced ; Methemoglobinemia/diagnosis ; Methemoglobinemia/drug therapy ; Methylene Blue/therapeutic use ; Middle Aged ; Sulfamethoxazole/adverse effects ; Telemedicine
    Chemical Substances Anti-Infective Agents ; Sulfamethoxazole (JE42381TNV) ; Methylene Blue (T42P99266K)
    Language English
    Publishing date 2012-09
    Publishing country England
    Document type Case Reports ; Journal Article
    ZDB-ID 1340281-x
    ISSN 1758-1109 ; 1357-633X
    ISSN (online) 1758-1109
    ISSN 1357-633X
    DOI 10.1258/jtt.2012.120307
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4525: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

    Giannini, Heather M / Ginestra, Jennifer C / Chivers, Corey / Draugelis, Michael / Hanish, Asaf / Schweickert, William D / Fuchs, Barry D / Meadows, Laurie / Lynch, Michael / Donnelly, Patrick J / Pavan, Kimberly / Fishman, Neil O / Hanson, C William / Umscheid, Craig A

    Critical care medicine

    2019  Volume 47, Issue 11, Page(s) 1485–1492

    Abstract: Objectives: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.: Design: Retrospective cohort for algorithm derivation and validation, pre- ... ...

    Abstract Objectives: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.
    Design: Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation.
    Setting: Tertiary teaching hospital system in Philadelphia, PA.
    Patients: All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n = 162,212); algorithm validation October to December 2015 (n = 10,448); silent versus alert comparison January 2016 to February 2017 (silent n = 22,280; alert n = 32,184).
    Interventions: A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction.
    Measurement and main result: Patients identified for training the algorithm were required to have International Classification of Diseases, 9th Edition codes for severe sepsis or septic shock and a positive blood culture during their hospital encounter with either a lactate greater than 2.2 mmol/L or a systolic blood pressure less than 90 mm Hg. The algorithm demonstrated a sensitivity of 26% and specificity of 98%, with a positive predictive value of 29% and positive likelihood ratio of 13. The alert resulted in a small statistically significant increase in lactate testing and IV fluid administration. There was no significant difference in mortality, discharge disposition, or transfer to ICU, although there was a reduction in time-to-ICU transfer.
    Conclusions: Our machine learning algorithm can predict, with low sensitivity but high specificity, the impending occurrence of severe sepsis and septic shock. Algorithm-generated predictive alerts modestly impacted clinical measures. Next steps include describing clinical perception of this tool and optimizing algorithm design and delivery.
    MeSH term(s) Algorithms ; Cohort Studies ; Decision Support Systems, Clinical ; Diagnosis, Computer-Assisted ; Electronic Health Records ; Hospitals, Teaching ; Humans ; Machine Learning ; Retrospective Studies ; Sensitivity and Specificity ; Sepsis/diagnosis ; Shock, Septic/diagnosis ; Text Messaging
    Language English
    Publishing date 2019-08-05
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 197890-1
    ISSN 1530-0293 ; 0090-3493
    ISSN (online) 1530-0293
    ISSN 0090-3493
    DOI 10.1097/CCM.0000000000003891
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1261: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

    Ginestra, Jennifer C / Giannini, Heather M / Schweickert, William D / Meadows, Laurie / Lynch, Michael J / Pavan, Kimberly / Chivers, Corey J / Draugelis, Michael / Donnelly, Patrick J / Fuchs, Barry D / Umscheid, Craig A

    Critical care medicine

    2019  Volume 47, Issue 11, Page(s) 1477–1484

    Abstract: Objective: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).: Design: Prospective observational study.: Setting: Tertiary teaching hospital in ... ...

    Abstract Objective: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).
    Design: Prospective observational study.
    Setting: Tertiary teaching hospital in Philadelphia, PA.
    Patients: Non-ICU admissions November-December 2016.
    Interventions: During a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert's helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert.
    Measurements and main results: For the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first survey. Of these, 43 nurses (24% response rate) and 44 providers (41% response rate) completed the second survey. Few (24% nurses, 13% providers) identified new clinical findings after responding to the alert. Perceptions of the presence of sepsis at the time of alert were discrepant between nurses (13%) and providers (40%). The majority of clinicians reported no change in perception of the patient's risk for sepsis (55% nurses, 62% providers). A third of nurses (30%) but few providers (9%) reported the alert changed management. Almost half of nurses (42%) but less than a fifth of providers (16%) found the alert helpful at 6 hours.
    Conclusions: In general, clinical perceptions of Early Warning System 2.0 were poor. Nurses and providers differed in their perceptions of sepsis and alert benefits. These findings highlight the challenges of achieving acceptance of predictive and machine learning-based sepsis alerts.
    MeSH term(s) Algorithms ; Attitude of Health Personnel ; Decision Support Systems, Clinical ; Diagnosis, Computer-Assisted ; Electronic Health Records ; Hospitals, Teaching ; Humans ; Machine Learning ; Medical Staff, Hospital ; Nursing Staff, Hospital ; Practice Patterns, Nurses'/statistics & numerical data ; Practice Patterns, Physicians'/statistics & numerical data ; Prospective Studies ; Sepsis/diagnosis ; Shock, Septic/diagnosis ; Text Messaging
    Language English
    Publishing date 2019-05-27
    Publishing country United States
    Document type Journal Article ; Observational Study ; Research Support, N.I.H., Extramural
    ZDB-ID 197890-1
    ISSN 1530-0293 ; 0090-3493
    ISSN (online) 1530-0293
    ISSN 0090-3493
    DOI 10.1097/CCM.0000000000003803
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1261: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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