Predicting the Absence of Serious Bacterial Infection in the PICU
预测 PICU 中不存在严重细菌感染
基本信息
- 批准号:10806039
- 负责人:
- 金额:$ 16.28万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-21 至 2028-08-31
- 项目状态:未结题
- 来源:
- 关键词:AccelerationAcute Renal Failure with Renal Papillary NecrosisAdmission activityAdolescentAdverse effectsAntibiotic ResistanceAntibiotic TherapyAntibioticsAutomobile DrivingAwardBacterial InfectionsBiologicalBiometryCalibrationChildChildhoodChildhood InjuryClinicalClinical DataClinical Decision Support SystemsCritical IllnessCritically ill childrenDataData SetDecision MakingDevelopmentEducationElectronic Health RecordEquityEthicsEvaluationExclusionFunctional disorderFundingGoalsHarm ReductionHigh PrevalenceHospitalsHourInfectionInstitutionInterviewKnowledgeLaboratoriesLeftLength of StayLifeMachine LearningMentorsMethodsMissionModelingMulticenter TrialsNational Institute of Child Health and Human DevelopmentOrganOutcomePatient-Focused OutcomesPatientsPatternPediatric HospitalsPediatric Intensive Care UnitsPerformancePredictive ValueProbabilityProviderPseudomembranous ColitisPublic HealthQualifyingResearchResearch DesignResearch PersonnelRetrospective cohortRiskSepsisSiteSystemTechnical ExpertiseTestingTimeTranscriptTrustUnited States National Institutes of HealthWorkcareer developmentclinical decision supportcohortcomputer human interactiondesigndisabilityexperienceimprovedimproved outcomeinnovationmachine learning methodmachine learning modelmachine learning predictionmodel designmortalitymultidisciplinarynovelpediatric sepsispredictive modelingpredictive toolsprospectivesevere injuryskillssupport toolstooltool developmenttreatment durationuser centered design
项目摘要
Proposal Summary
There are no validated systems for identifying children without serious bacterial infection (SBI) upon admission
to a pediatric ICU (PICU). Given the high prevalence of SBI among critically ill children (up to 46%) and risks
associated with delayed antibiotic administration, nearly 50% of children without SBI receive antibiotics while
microbiologic studies are pending. However, antibiotics can have adverse effects including acute kidney injury,
clostridium difficile colitis, and development of antibiotic resistance. The long-term goal of this research is to
validate and disseminate machine learning (ML)-based clinical decision support (CDS) tools able to improve
PICU antibiotic decision-making thereby reducing antibiotic associated harm among critically ill children. In
prior work, Dr. Martin developed ML-based predictive models, which use electronic health record (EHR) inputs
(vital sign, laboratory, and other clinical data), to accurately identify children without SBI upon PICU admission
in a single center retrospective cohort. The central hypothesis is that these models will demonstrate similar
robust performance during prospective and multicenter evaluations, and that an antibiotic decisional needs
analysis of PICU clinicians will inform the optimal design of model-based CDS tools. The central hypothesis will
be tested via three aims: 1) prospectively evaluate two SBI predictive models within a single center EHR and
determine the potential effect on antibiotic-days per child; 2) evaluate ML model generalizability by testing
them in a multicenter EHR cohort; and 3) perform a multicenter, multidisciplinary antibiotic decisional needs
analysis of PICU clinicians to facilitate user-centered design of equitable model-based CDS tools. In Aim 1, two
SBI predictive models will be prospectively evaluated in silent fashion (predictions not revealed to clinicians) at
a single center over two years. Model predictions will be compared to patient SBI outcomes to determine their
negative predictive value and potential to reduce unnecessary antibiotics. In Aim 2, the same models will be
applied to a retrospective dataset of six US children's hospital PICUs (~178,000 encounters over 8+ years) to
assess generalizability by determining each model's negative predictive value and potential to reduce
unnecessary antibiotics. In Aim 3, a rigorous qualitative content analysis of PICU clinician interviews from five
institutions will identify the values driving antibiotic decision-making and enable user-centered design of model-
based CDS tools. The research is innovative because it involves development of the first clinically validated
system for excluding SBI at PICU admission and uses a ML approach to do so. The research is significant as it
accelerates development of generalizable antibiotic decision-making tools to assist PICU clinicians in safely
minimizing unnecessary antibiotics and associated harm. The educational component of this application will
allow Dr. Martin to attain expertise in biostatistics, probability, ML bias, and study design, as well as technical
skills in programming, ML, and CDS. This will allow him to transition to independence and make him uniquely
qualified to develop, validate, and implement CDS tools able to improve the outcomes of critically ill children.
建议总结
项目成果
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