Identifying patient subgroups and processes of care that cause outcome differences following ICU vs. ward triage among patients with acute respiratory failure and sepsis
确定急性呼吸衰竭和脓毒症患者在 ICU 与病房分诊后导致结局差异的患者亚组和护理流程
基本信息
- 批准号:10734357
- 负责人:
- 金额:$ 75.18万
- 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:2023-09-01 至 2027-06-30
- 项目状态:未结题
- 来源:
- 关键词:Accident and Emergency departmentAccountingAcuteAcute Renal Failure with Renal Papillary NecrosisAcute Respiratory Distress SyndromeAcute respiratory failureAdmission activityAffectAlgorithmsAmericanAntibioticsCOVID-19COVID-19 impactCaliforniaCaringCessation of lifeChronic Obstructive Pulmonary DiseaseClassificationClinicalCollaborationsComplexComplicationConsultationsDataData SetDeliriumDevelopmentDiagnosticElectronic Health RecordElementsGoalsGuidelinesHealth Care CostsHealth systemHeart failureHeterogeneityHospitalizationHospitalsHourIatrogenesisInfectionInpatientsIntensive Care UnitsKnowledgeLength of StayLifeLiquid substanceLocationMachine LearningMeasuresMediationMediatorMethodologyMethodsModelingOutcomePalliative CarePatient AdmissionPatient CarePatient-Focused OutcomesPatientsPatternPennsylvaniaPhenotypePopulation HeterogeneityProbabilityProcessPublic HealthRecommendationResistanceSepsisSteroidsSubgroupSyndromeTestingTriageUniversitiesUpdateUrinary tract infectionWorkacute carecohortcoronavirus diseasecurrent pandemicend of life careexperiencefuture pandemichigh riskimprovedimproved outcomeindexinginnovationinsightmeetingsmortalitymortality riskmultidisciplinarynovelpandemic diseasepatient subsetspreservationseptic patientstreatment patternunsupervised learningward
项目摘要
PROJECT SUMMARY
Decisions to admit patients with acute respiratory failure (ARF) and sepsis (the most common and lethal cause
of the acute respiratory distress syndrome) to intensive care units (ICUs) are highly variable across the US.
And, yet, these triage decisions have a substantial impact on patient outcomes. In our prior work, we used
detailed electronic health record (EHR) data from 9.2 million hospitalizations and found that decisions to admit
ARF patients to wards were associated with a 3.8% absolute increase in mortality. In contrast, choices to admit
sepsis patients to ICUs resulted in considerably longer length of stay and a 5.1% absolute increase in death.
The nationwide impact of such discretionary triage would be exponentially greater. Our findings highlight
tremendous opportunities to improve ARF and sepsis outcomes by identifying the patient subgroups and
processes of care that most strongly contribute to the benefits and harms of ICU- versus ward-based care.
This application proposes to update our ARF and sepsis cohort such that it includes all admissions from 2013
through 2022 across 29 hospitals in the Kaiser Permanente Northern California and University of Pennsylvania
health systems, and incorporate more than 100 more data fields per patient. This curation of highly granular
EHR data will enable us to identify the: (1) distinct patient subgroups and phenotypes among those meeting
the syndromic criteria of `ARF' and `sepsis;' and the (2) processes of care and (3) inpatient complications that
causally explain the observed associations of ICU vs. ward triage with patient outcomes. Our multidisciplinary
team will apply diverse expertise in instrumental variable regression, mediation analyses, machine learning,
complex EHR data, and probabilistic phenotyping to complete three aims that promote our long-term goal of
improving care, and hence outcomes, for patients with ARF and sepsis regardless of where they are treated.
Several methodological innovations will enable us to achieve these goals, and, in turn, to not only surmount
key limitations of prior studies that sought to determine which acutely ill patients benefit from ICU admission,
but identify the mechanisms underlying such triage effects. These data will also allow us to quantify the impact
of COVID-19 on ICU and ward triage patterns, care processes, and outcomes among ARF and sepsis patients,
thereby modernizing our results and enabling their applicability to pandemic eras.
Completing the aims of this study will improve public health by identifying ways in which emergency
departments, ICUs, and wards can improve outcomes for the more than 4 million Americans hospitalized each
year with ARF and/or sepsis. Such results will enable development and testing of personalized triage
algorithms, and guide optimal care for patients without always requiring ICU admission, thereby improving
patient outcomes, reducing health care costs, and preserving ICU capacity for patients who truly need it.
项目总结
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Scott D Halpern其他文献
The future of the orthopaedic clinician-scientist: part II: Identification of factors that may influence orthopaedic residents' intent to perform research.
骨科临床医生科学家的未来:第二部分:确定可能影响骨科住院医师开展研究意图的因素。
- DOI:
- 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Jaimo Ahn;Derek J. Donegan;J. Todd R Lawrence;Scott D Halpern;S. Mehta - 通讯作者:
S. Mehta
Rebuttal From Dr Halpern
- DOI:
10.1378/chest.14-1586 - 发表时间:
2014-11-01 - 期刊:
- 影响因子:
- 作者:
Scott D Halpern - 通讯作者:
Scott D Halpern
Scott D Halpern的其他文献
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{{ truncateString('Scott D Halpern', 18)}}的其他基金
Transforming residential palliative care for persons with dementia through behavioral economics and data science
通过行为经济学和数据科学改变痴呆症患者的住院姑息治疗
- 批准号:
10474380 - 财政年份:2019
- 资助金额:
$ 75.18万 - 项目类别:
Transforming residential palliative care for persons with dementia through behavioral economics and data science
通过行为经济学和数据科学改变痴呆症患者的住院姑息治疗
- 批准号:
10017845 - 财政年份:2019
- 资助金额:
$ 75.18万 - 项目类别:
Transforming residential palliative care for persons with dementia through behavioral economics and data science
通过行为经济学和数据科学改变痴呆症患者的住院姑息治疗
- 批准号:
10251982 - 财政年份:2019
- 资助金额:
$ 75.18万 - 项目类别:
Transforming residential palliative care for persons with dementia through behavioral economics and data science
通过行为经济学和数据科学改变痴呆症患者的住院姑息治疗
- 批准号:
10657602 - 财政年份:2019
- 资助金额:
$ 75.18万 - 项目类别:
Transforming residential palliative care for persons with dementia through behavioral economics and data science
通过行为经济学和数据科学改变痴呆症患者的住院姑息治疗
- 批准号:
9810433 - 财政年份:2019
- 资助金额:
$ 75.18万 - 项目类别:
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