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  1. Article ; Online: Atypical symptoms in emergency department patients with urosepsis challenge current urinary tract infection management guidelines.

    Biebelberg, Brett / Kehoe, Iain E / Zheng, Hui / O'Connell, Abigail / Filbin, Michael R / Heldt, Thomas / Reisner, Andrew T

    Academic emergency medicine : official journal of the Society for Academic Emergency Medicine

    2024  

    Language English
    Publishing date 2024-04-25
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1329813-6
    ISSN 1553-2712 ; 1069-6563
    ISSN (online) 1553-2712
    ISSN 1069-6563
    DOI 10.1111/acem.14914
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4479: 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: Diagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection.

    Prasad, Varesh / Aydemir, Baturay / Kehoe, Iain E / Kotturesh, Chaya / O'Connell, Abigail / Biebelberg, Brett / Wang, Yang / Lynch, James C / Pepino, Jeremy A / Filbin, Michael R / Heldt, Thomas / Reisner, Andrew T

    PLOS digital health

    2023  Volume 2, Issue 11, Page(s) e0000365

    Abstract: Many early warning algorithms are downstream of clinical evaluation and diagnostic testing, which means that they may not be useful when clinicians fail to suspect illness and fail to order appropriate tests. Depending on how such algorithms handle ... ...

    Abstract Many early warning algorithms are downstream of clinical evaluation and diagnostic testing, which means that they may not be useful when clinicians fail to suspect illness and fail to order appropriate tests. Depending on how such algorithms handle missing data, they could even indicate "low risk" simply because the testing data were never ordered. We considered predictive methodologies to identify sepsis at triage, before diagnostic tests are ordered, in a busy Emergency Department (ED). One algorithm used "bland clinical data" (data available at triage for nearly every patient). The second algorithm added three yes/no questions to be answered after the triage interview. Retrospectively, we studied adult patients from a single ED between 2014-16, separated into training (70%) and testing (30%) cohorts, and a final validation cohort of patients from four EDs between 2016-2018. Sepsis was defined per the Rhee criteria. Investigational predictors were demographics and triage vital signs (downloaded from the hospital EMR); past medical history; and the auxiliary queries (answered by chart reviewers who were blinded to all data except the triage note and initial HPI). We developed L2-regularized logistic regression models using a greedy forward feature selection. There were 1164, 499, and 784 patients in the training, testing, and validation cohorts, respectively. The bland clinical data model yielded ROC AUC's 0.78 (0.76-0.81) and 0.77 (0.73-0.81), for training and testing, respectively, and ranged from 0.74-0.79 in four hospital validation. The second model which included auxiliary queries yielded 0.84 (0.82-0.87) and 0.83 (0.79-0.86), and ranged from 0.78-0.83 in four hospital validation. The first algorithm did not require clinician input but yielded middling performance. The second showed a trend towards superior performance, though required additional user effort. These methods are alternatives to predictive algorithms downstream of clinical evaluation and diagnostic testing. For hospital early warning algorithms, consideration should be given to bias and usability of various methods.
    Language English
    Publishing date 2023-11-01
    Publishing country United States
    Document type Journal Article
    ISSN 2767-3170
    ISSN (online) 2767-3170
    DOI 10.1371/journal.pdig.0000365
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: The influence of body mass index on clinical short-term outcomes in robotic colorectal surgery.

    Lagares-Garcia, Jorge / O'Connell, Abigail / Firilas, Anthony / Robinson, Christopher Chad / Dumas, Bonnie P / Hagen, Monika E

    The international journal of medical robotics + computer assisted surgery : MRCAS

    2016  Volume 12, Issue 4, Page(s) 680–685

    Abstract: Background: Robotic surgery has been developed to address the technical limitations of laparoscopic surgery and might result in similar outcomes for patients with low and high body mass index (BMI).: Methods: Demographic, peri-operative data and ... ...

    Abstract Background: Robotic surgery has been developed to address the technical limitations of laparoscopic surgery and might result in similar outcomes for patients with low and high body mass index (BMI).
    Methods: Demographic, peri-operative data and surrogate oncologic markers for colorectal cancer of patients that underwent robotic colorectal procedures were collected in a prospective database and analyzed.
    Results: 103 consecutive patients (36 normal-weight, 33 overweight, 34 obese) underwent robotic colorectal surgery from 11/2011 to 05/2012. While operating room (OR) time was longer for the obese patients (123.4 vs 137.9 and 154.7 min), results for estimated blood loss (104.2 vs 153 and 155.9 mL), conversions (2.8 vs 6.1 and 5.9%), complications (19.4 vs 21.2 and 32.4%), re-admissions (11.1 vs 112.1 and 20.6) and mortality (0% for all) were comparable. BMI did not affect the surrogate markers in patients with malignancies.
    Conclusions: Data demonstrates that patient BMI does not have a significant impact on short-term clinical outcomes during robotic colorectal surgery. Copyright © 2015 John Wiley & Sons, Ltd.
    MeSH term(s) Aged ; Body Mass Index ; Colon/surgery ; Colorectal Neoplasms/complications ; Colorectal Neoplasms/surgery ; Colorectal Surgery/methods ; Female ; Humans ; Laparoscopes ; Laparoscopy/methods ; Male ; Middle Aged ; Obesity/complications ; Obesity/surgery ; Operative Time ; Overweight/complications ; Overweight/surgery ; Perioperative Period ; Postoperative Complications ; Prospective Studies ; Rectum/surgery ; Robotic Surgical Procedures/methods ; Treatment Outcome
    Language English
    Publishing date 2016-12
    Publishing country England
    Document type Journal Article
    ZDB-ID 2151860-9
    ISSN 1478-596X ; 1478-5951
    ISSN (online) 1478-596X
    ISSN 1478-5951
    DOI 10.1002/rcs.1695
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6382: Show issues Location:
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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
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