Leveraging pandemic practice changes to optimize evidence-based pneumonia care
Leveraging pandemic practice changes to optimize evidence-based pneumonia care
批准号:
10640043
负责人:
Barbara Ellen Jones
金额:
$0.0万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-05-01 至 2026-04-30
关键词:
Acute Respiratory Distress SyndromeAdoptedAdoptionAlgorithmsAntibioticsBeliefCOVID-19COVID-19 pandemicCaringCause of DeathCessation of lifeClinicalClinical Practice GuidelineClinical assessmentsCommunicable DiseasesComplexCoupledDataData AnalysesDecision MakingDecision TreesDiagnosisDiagnostics ResearchDisease modelEtiologyFaceFailureFutureHealthcareHeterogeneityHospitalizationImmune responseInfectionInformaticsInterventionInterviewKnowledgeLeftMachine LearningMedicalMethodologyMethodsModelingNatural Language ProcessingNatural experimentObservational StudyPatient-Focused OutcomesPatientsPneumoniaPragmatic clinical trialProviderRecommendationResearchResearch MethodologyResearch PersonnelResistanceRespiratory Tract InfectionsSepsisSiteSteroidsStreamSupportive careSurveysSystemTestingTherapeutic ResearchUnited StatesVariantVeteransViralWorkbehavior changecausal modelclinical carecognitive processcommunity acquired pneumoniacompare effectivenessdesignevidence baseexperiencefuture pandemicimplementation effortsimplementation strategyindividual patientinnovationmortalitynovel strategiesnovel therapeutic interventionpandemic diseasepathogenpathogenic viruspersonalized approachpre-pandemicprogramsprovider adoptionrandomized trialresponsesecondary infectionstandard caretreatment and outcometreatment optimizationviral pandemic
中文摘要
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英文摘要
Background: The COVID-19 pandemic exposed critical failures in the management of pneumonia.
Pneumonia is the leading cause of death from infectious diseases, resulting in over 20,000 hospitalizations
and thousands of deaths across the VA system each year. For the past thirty years, the mainstays of
treatment have been antibiotics and supportive care, with little recognition of viral pathogens and the host
immune response. Our reliance on antibiotics has led not only to overuse and resistance, but also to a
stagnation in diagnostic and therapeutic research that left us ill-equipped for the viral pandemic.
Significance: The devastation of COVID19 has made it clear that our old models of disease are inadequate
for the optimal management of respiratory infection. Existing evidence surrounding empiric treatment in
pneumonia is poor, fraught with previous research that has been challenged by heterogeneity and a failure
to characterize patients with enough detail to identify beneficial treatment approaches. It is unlikely that
more of the same approach will advance care. This proposal contributes to a direction of clinical approach
toward a more complex causal model of infection that requires complex solutions.
Innovation and Impact: We will use state-of-the-art exploratory mixed methods that integrate EHR data
with survey and qualitative data to examine practice change. National analyses will allow for more inclusive
and feasible implementation solutions in diverse VA settings. This proposal breaks scientific ground in VA
informatics by leveraging variation with state-of-the-art causal inference methods. If we take the opportunity
to study new treatment approaches based on more complex clinical assessments, we will take an important
step toward developing better treatments in pneumonia and being better prepared for future pandemics.
Specific Aims: Aim 1. Describe emerging changes in the empiric use of antibiotic and steroids for
pneumonia using national practice data and qualitative interviews. Aim 2. Identify local conditions related to
emergent change in the use of empiric antibiotics and steroids using an exploratory mixed-methods design.
Aim 3. Identify and evaluate optimized, interpretable, tailored decision trees for empiric antibiotic and steroid
treatments in Veterans with pneumonia.
Methodology: Our mixed methods approach includes secondary data analyses of patient-, provider-, and
setting-level EHR data including treatment decisions and patient outcomes, combined with natural language
processing. We will apply mixed effects models to model the changes in selected treatments and outcomes
(hospitalization, deaths, secondary infection) between the pre-pandemic and later (July 2021-present)
periods, and to characterize heterogeneity in the trajectories of these variables across VA sites. To that
quantitative analysis, we will add qualitative data examining changes in VA providers’ cognitive processes
of diagnosis and management of pneumonia, including beliefs and norms surrounding treatment. We will
conduct configurational analyses and validate our analytic results with our expert advisory group for face
validity, feasibility and usefulness. We will then identify a optimized treatment regimes, in the form of
interpretable decision trees that minimize 30-day mortality, for empiric antibiotic and steroid use in Veterans
with pneumonia using machine-learning-based, causal inference algorithms, coupled with clinical expertise.
Next Steps/Implementation: Results will inform recommendations for the management of Veterans with
pneumonia that can be integrated with other evidence streams and disseminated by the national program
directors in the Advisory Group. We will produce recommendations for implementation strategies of
interventions in pneumonia care for Veterans that will be developed and tested in future work. We will also
produce recommendations for future research, including (1) pragmatic clinical trials; (2) creation of VHA-
approved living guidance for pneumonia care; and (3) decision support and other implementation strategies.
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会议论文
Understanding and Improving Decision-making in Pneumonia with Informatics
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批准号:9768342
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项目类别:
-
资助金额:$0.0万
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财政年份:2017
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负责人:Barbara Ellen Jones
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依托单位:
Understanding and Improving Decision-making in Pneumonia with Informatics
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批准号:10186488
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项目类别:
-
资助金额:$0.0万
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财政年份:2017
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负责人:Barbara Ellen Jones
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依托单位:
Understanding and Improving Decision-making in Pneumonia with Informatics
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批准号:10308553
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项目类别:
-
资助金额:$0.0万
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财政年份:2017
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负责人:Barbara Ellen Jones
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依托单位:
海外基金