{
    "abstract": "Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist with the early recognition of sepsis may improve outcomes, but relatively few studies have examined their impact on real-world patient outcomes. Our objective was to assess the impact of a deep-learning model (COMPOSER) for the early prediction of sepsis on patient outcomes. We completed a before-and-after quasi-experimental study at two distinct Emergency Departments (EDs) within the UC San Diego Health System. We included 6217 adult septic patients from 1/1/2021 through 4/30/2023. The exposure tested was a nurse-facing Best Practice Advisory (BPA) triggered by COMPOSER. In-hospital mortality, sepsis bundle compliance, 72-h change in sequential organ failure assessment (SOFA) score following sepsis onset, ICU-free days, and the number of ICU encounters were evaluated in the pre-intervention period (705 days) and the post-intervention period (145 days). The causal impact analysis was performed using a Bayesian structural time-series approach with confounder adjustments to assess the significance of the exposure at the 95% confidence level. The deployment of COMPOSER was significantly associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality (95% CI, 0.3%-3.5%), a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance (95% CI, 2.4%-8.0%), and a 4% (95% CI, 1.1%-7.1%) reduction in 72-h SOFA change after sepsis onset in causal inference analysis. This study suggests that the deployment of COMPOSER for early prediction of sepsis was associated with a significant reduction in mortality and a significant increase in sepsis bundle compliance.",
    "authors": [
        {
            "affiliation": "Department of Medicine, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Aaron",
            "initials": "A",
            "lastname": "Boussina"
        },
        {
            "affiliation": "Department of Medicine, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Supreeth P",
            "initials": "SP",
            "lastname": "Shashikumar"
        },
        {
            "affiliation": "Department of Medicine, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Atul",
            "initials": "A",
            "lastname": "Malhotra"
        },
        {
            "affiliation": "Department of Medicine, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Robert L",
            "initials": "RL",
            "lastname": "Owens"
        },
        {
            "affiliation": "Department of Medicine, University of California San Diego, San Diego, CA, USA.\nDepartment of Quality, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Robert",
            "initials": "R",
            "lastname": "El-Kareh"
        },
        {
            "affiliation": "Department of Medicine, University of California San Diego, San Diego, CA, USA.\nDepartment of Quality, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Christopher A",
            "initials": "CA",
            "lastname": "Longhurst"
        },
        {
            "affiliation": "Department of Quality, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Kimberly",
            "initials": "K",
            "lastname": "Quintero"
        },
        {
            "affiliation": "Department of Emergency Medicine, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Allison",
            "initials": "A",
            "lastname": "Donahue"
        },
        {
            "affiliation": "Department of Emergency Medicine, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Theodore C",
            "initials": "TC",
            "lastname": "Chan"
        },
        {
            "affiliation": "Department of Medicine, University of California San Diego, San Diego, CA, USA.\nDepartment of Emergency Medicine, University of California San Diego, San Diego, CA, USA.",
            "firstname": "Shamim",
            "initials": "S",
            "lastname": "Nemati"
        },
        {
            "affiliation": "Department of Medicine, University of California San Diego, San Diego, CA, USA. gwardi@health.ucsd.edu.\nDepartment of Emergency Medicine, University of California San Diego, San Diego, CA, USA. gwardi@health.ucsd.edu.",
            "firstname": "Gabriel",
            "initials": "G",
            "lastname": "Wardi"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 2024. The Author(s).",
    "doi": "10.1038/s41746-023-00986-6\n10.1001/jama.2016.0287\n10.1016/S0140-6736(19)32989-7\n10.1007/s00134-017-4683-6\n10.1097/01.CCM.0000217961.75225.E9\n10.1097/CCM.0000000000000330\n10.1164/rccm.201609-1848OC\n10.1016/j.chest.2019.02.008\n10.1097/CCM.0000000000004145\n10.1088/1361-6579/aa9772\n10.1038/s41467-020-17431-x\n10.1097/CCM.0000000000002936\n10.1126/scitranslmed.aab3719\n10.1038/s41746-021-00504-6\n10.1038/s41591-022-01894-0\n10.1097/CCM.0000000000003891\n10.1136/bmjresp-2017-000234\n10.1136/bmjoq-2017-000158\n10.1001/jamainternmed.2022.7182\n10.1097/CCM.0000000000005894\n10.1007/s00268-022-06728-1\n10.1016/j.ajem.2015.10.005\n10.4338/ACI-2010-09-RA-0054\n10.1002/jhm.2347\n10.2196/15182\n10.1002/emp2.12297\n10.1038/s41467-021-20910-4\n10.1016/j.annemergmed.2020.11.007\n10.1097/CCM.0000000000005175\n10.1038/s41598-022-12497-7\n10.1055/s-0041-1730027\n10.1016/S0140-6736(07)61602-X\n10.1001/jama.2017.13836\n10.1016/j.jbi.2020.103611\n10.1186/s13012-018-0842-6\n10.1016/j.jeconbus.2020.105968\n10.1080/1351847X.2020.1869992\n10.1097/01.CCM.0000253405.17038.43\n10.1016/j.jhealeco.2019.102279",
    "journal": "NPJ digital medicine",
    "keywords": [],
    "methods": null,
    "publication_date": "2024-01-24",
    "pubmed_id": "38263386\n26903338\n31954465\n28101605\n16625125\n24717459\n28345952\n30779916\n21324711\n31939789\n29091053\n32737308\n29286945\n26246167\n34504260\n35864252\n31389839\n29435343\n29450295\n34152373\n37010858\n37098790\n36599913\n36109367\n26573784\n23616849\n25758641\n32673244\n33392549\n33936391\n33514699\n38083775\n33455840\n34259450\n35590018\n34041734\n18064739\n28903154\n38083765\n33157313\n30611302\n33318718\n17167351\n32062054",
    "results": null,
    "title": "Impact of a deep learning sepsis prediction model on quality of care and survival.",
    "xml": "<Element 'PubmedArticle' at 0x7885029ca110>"
}