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  1. Article ; Online: Individualized Treatment Effects of Oxygen Targets in Mechanically Ventilated Critically Ill Adults.

    Buell, Kevin G / Spicer, Alexandra B / Casey, Jonathan D / Seitz, Kevin P / Qian, Edward T / Graham Linck, Emma J / Self, Wesley H / Rice, Todd W / Sinha, Pratik / Young, Paul J / Semler, Matthew W / Churpek, Matthew M

    JAMA

    2024  Volume 331, Issue 14, Page(s) 1195–1204

    Abstract: Importance: Among critically ill adults, randomized trials have not found oxygenation targets to affect outcomes overall. Whether the effects of oxygenation targets differ based on an individual's characteristics is unknown.: Objective: To determine ... ...

    Abstract Importance: Among critically ill adults, randomized trials have not found oxygenation targets to affect outcomes overall. Whether the effects of oxygenation targets differ based on an individual's characteristics is unknown.
    Objective: To determine whether an individual's characteristics modify the effect of lower vs higher peripheral oxygenation-saturation (Spo2) targets on mortality.
    Design, setting, and participants: A machine learning model to predict the effect of treatment with a lower vs higher Spo2 target on mortality for individual patients was derived in the Pragmatic Investigation of Optimal Oxygen Targets (PILOT) trial and externally validated in the Intensive Care Unit Randomized Trial Comparing Two Approaches to Oxygen Therapy (ICU-ROX) trial. Critically ill adults received invasive mechanical ventilation in an intensive care unit (ICU) in the United States between July 2018 and August 2021 for PILOT (n = 1682) and in 21 ICUs in Australia and New Zealand between September 2015 and May 2018 for ICU-ROX (n = 965).
    Exposures: Randomization to a lower vs higher Spo2 target group.
    Main outcome and measure: 28-Day mortality.
    Results: In the ICU-ROX validation cohort, the predicted effect of treatment with a lower vs higher Spo2 target for individual patients ranged from a 27.2% absolute reduction to a 34.4% absolute increase in 28-day mortality. For example, patients predicted to benefit from a lower Spo2 target had a higher prevalence of acute brain injury, whereas patients predicted to benefit from a higher Spo2 target had a higher prevalence of sepsis and abnormally elevated vital signs. Patients predicted to benefit from a lower Spo2 target experienced lower mortality when randomized to the lower Spo2 group, whereas patients predicted to benefit from a higher Spo2 target experienced lower mortality when randomized to the higher Spo2 group (likelihood ratio test for effect modification P = .02). The use of a Spo2 target predicted to be best for each patient, instead of the randomized Spo2 target, would have reduced the absolute overall mortality by 6.4% (95% CI, 1.9%-10.9%).
    Conclusion and relevance: Oxygenation targets that are individualized using machine learning analyses of randomized trials may reduce mortality for critically ill adults. A prospective trial evaluating the use of individualized oxygenation targets is needed.
    MeSH term(s) Adult ; Humans ; Oxygen/therapeutic use ; Critical Illness/therapy ; Respiration, Artificial ; Prospective Studies ; Oxygen Inhalation Therapy ; Intensive Care Units
    Chemical Substances Oxygen (S88TT14065)
    Language English
    Publishing date 2024-03-18
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 2958-0
    ISSN 1538-3598 ; 0254-9077 ; 0002-9955 ; 0098-7484
    ISSN (online) 1538-3598
    ISSN 0254-9077 ; 0002-9955 ; 0098-7484
    DOI 10.1001/jama.2024.2933
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Machine Learning-Driven Analysis of Individualized Treatment Effects Comparing Buprenorphine and Naltrexone in Opioid Use Disorder Relapse Prevention.

    Afshar, Majid / Graham Linck, Emma J / Spicer, Alexandra B / Rotrosen, John / Salisbury-Afshar, Elizabeth M / Sinha, Pratik / Semler, Matthew W / Churpek, Matthew M

    Journal of addiction medicine

    2024  

    Abstract: Objective: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient ... ...

    Abstract Objective: A trial comparing extended-release naltrexone and sublingual buprenorphine-naloxone demonstrated higher relapse rates in individuals randomized to extended-release naltrexone. The effectiveness of treatment might vary based on patient characteristics. We hypothesized that causal machine learning would identify individualized treatment effects for each medication.
    Methods: This is a secondary analysis of a multicenter randomized trial that compared the effectiveness of extended-release naltrexone versus buprenorphine-naloxone for preventing relapse of opioid misuse. Three machine learning models were derived using all trial participants with 50% randomly selected for training (n = 285) and the remaining 50% for validation. Individualized treatment effect was measured by the Qini value and c-for-benefit, with the absence of relapse denoting treatment success. Patients were grouped into quartiles by predicted individualized treatment effect to examine differences in characteristics and the observed treatment effects.
    Results: The best-performing model had a Qini value of 4.45 (95% confidence interval, 1.02-7.83) and a c-for-benefit of 0.63 (95% confidence interval, 0.53-0.68). The quartile most likely to benefit from buprenorphine-naloxone had a 35% absolute benefit from this treatment, and at study entry, they had a high median opioid withdrawal score (P < 0.001), used cocaine on more days over the prior 30 days than other quartiles (P < 0.001), and had highest proportions with alcohol and cocaine use disorder (P ≤ 0.02). Quartile 4 individuals were predicted to be most likely to benefit from extended-release naltrexone, with the greatest proportion having heroin drug preference (P = 0.02) and all experiencing homelessness (P < 0.001).
    Conclusions: Causal machine learning identified differing individualized treatment effects between medications based on characteristics associated with preventing relapse.
    Language English
    Publishing date 2024-05-22
    Publishing country Netherlands
    Document type Journal Article
    ISSN 1935-3227
    ISSN (online) 1935-3227
    DOI 10.1097/ADM.0000000000001313
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: metPropagate: network-guided propagation of metabolomic information for prioritization of metabolic disease genes.

    Graham Linck, Emma J / Richmond, Phillip A / Tarailo-Graovac, Maja / Engelke, Udo / Kluijtmans, Leo A J / Coene, Karlien L M / Wevers, Ron A / Wasserman, Wyeth / van Karnebeek, Clara D M / Mostafavi, Sara

    NPJ genomic medicine

    2020  Volume 5, Page(s) 25

    Abstract: Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis is imperative. Whole-exome sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is typically used to ... ...

    Abstract Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis is imperative. Whole-exome sequencing (WES) variant prioritization coupled with phenotype-guided clinical and bioinformatics expertise is typically used to identify disease-causing variants; however, it can be challenging to identify the causal candidate gene when a large number of rare and potentially pathogenic variants are detected. Here, we present a network-based approach, metPropagate, that uses untargeted metabolomics (UM) data from a single patient and a group of controls to prioritize candidate genes in patients with suspected IEMs. We validate metPropagate on 107 patients with IEMs diagnosed in Miller et al. (2015) and 11 patients with both CNS and metabolic abnormalities. The metPropagate method ranks candidate genes by label propagation, a graph-smoothing algorithm that considers each gene's metabolic perturbation in addition to the network of interactions between neighbors. metPropagate was able to prioritize at least one causative gene in the top 20
    Language English
    Publishing date 2020-07-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2813848-X
    ISSN 2056-7944 ; 2056-7944
    ISSN (online) 2056-7944
    ISSN 2056-7944
    DOI 10.1038/s41525-020-0132-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Individualized Treatment Effects of Bougie versus Stylet for Tracheal Intubation in Critical Illness.

    Seitz, Kevin P / Spicer, Alexandra B / Casey, Jonathan D / Buell, Kevin G / Qian, Edward T / Graham Linck, Emma J / Driver, Brian E / Self, Wesley H / Ginde, Adit A / Trent, Stacy A / Gandotra, Sheetal / Smith, Lane M / Page, David B / Vonderhaar, Derek J / West, Jason R / Joffe, Aaron M / Doerschug, Kevin C / Hughes, Christopher G / Whitson, Micah R /
    Prekker, Matthew E / Rice, Todd W / Sinha, Pratik / Semler, Matthew W / Churpek, Matthew M

    American journal of respiratory and critical care medicine

    2023  Volume 207, Issue 12, Page(s) 1602–1611

    Abstract: Rationale: ...

    Abstract Rationale:
    MeSH term(s) Adult ; Humans ; Critical Illness/therapy ; Intubation, Intratracheal/adverse effects ; Calibration ; Laryngoscopy
    Language English
    Publishing date 2023-03-06
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1180953-x
    ISSN 1535-4970 ; 0003-0805 ; 1073-449X
    ISSN (online) 1535-4970
    ISSN 0003-0805 ; 1073-449X
    DOI 10.1164/rccm.202209-1799OC
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Use of face coverings by the public during the COVID-19 pandemic: an observational study

    Arp, Nicholas L / Nguyen, Tung H / Graham Linck, Emma J / Feeney, Austin K / Schrope, Jonathan H / Ruedinger, Katrina L / Gao, Anqi / Miranda-Katz, Margot / Kates, Ashley E / Safdar, Nasia

    medRxiv

    Abstract: Public health agencies have recommended that the public wear face coverings as a major non-pharmaceutical intervention to mitigate COVID-19 transmission. However, there is concern whether or not the public has adopted this recommendation. An ... ...

    Abstract Public health agencies have recommended that the public wear face coverings as a major non-pharmaceutical intervention to mitigate COVID-19 transmission. However, there is concern whether or not the public has adopted this recommendation. An observational study of 3,271 members of the public was conducted during the COVID-19 pandemic and examined the use of face coverings at grocery stores across 20 Wisconsin counties between May 16th and June 1st, 2020. Of the total individuals observed, we found that only 41.2% used face coverings. Individuals who appeared to be adults (aOR = 1.65; 95% CI = 1.20-2.27) or older adults (aOR = 3.67; 95% CI = 2.59-5.19) were more likely to be wearing face coverings than younger individuals. Additionally, individuals with female gender expression (aOR = 1.43; 95% CI = 1.24-1.66) and individuals shopping at a more expensive grocery store (aOR = 1.95; 95% CI = 1.69-2.25) were more likely to be wearing face coverings. We did not find an association between county level prevalence of COVID-19 cases and face covering use. To our knowledge, this is the first study using direct observations to examine face covering behavior by the public and our findings have implications for public health agencies.
    Keywords covid19
    Language English
    Publishing date 2020-06-12
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.06.09.20126946
    Database COVID19

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