Informatics approaches to assessing patient frailty in surgical care
Informatics approaches to assessing patient frailty in surgical care
批准号:
10652340
负责人:
Elizabeth Marjorie Cespedes Feliciano
金额:
$58.85万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-09-01 至 2025-05-31
关键词:
AbdomenAcuteAddressAgeAgingAlgorithmsAmericanBiologicalCaringChronic DiseaseChronologyClinicalCodeComplexComputer softwareConsumptionCounselingDataData SetDecision MakingDiagnosticElderlyElectronic Health RecordElectronicsExhibitsFutureGoalsHospital CostsHospital MortalityHospitalsInfiltrationInformaticsInterventionLaboratoriesLength of StayManualsMeasuresMethodologyModelingMorbidity - disease rateMuscleMuscular AtrophyNational Institute on AgingOperative Surgical ProceduresPathway interactionsPatient-Focused OutcomesPatientsPerformancePerioperativePerioperative CarePhysical FunctionPhysiologicalPopulationPopulation HeterogeneityPostoperative PeriodPredictive ValueProcessResearchRiskRisk FactorsScanningSeverity of illnessSkeletal MuscleStandardizationSurgical complicationTechnologyTimeValidationX-Ray Computed Tomographyadverse outcomeclinical carecomputer sciencecostdigitalelectronic datafrailtyfunctional statushigh riskhospital readmissionimprovedinnovationinpatient surgerymortalitymuscle formolder patientpatient responseperformance testspostoperative recoverypredictive modelingprofiles in patientsprogramsprospectiverisk stratificationsarcopeniasecondary analysisshared decision makingstressorsurgery outcomesurgical risktool
中文摘要
摘要
手术并发症很常见,费用很高,而且是致命的。老年患者有很高的手术不良风险。
结果,特别是当它们表现出脆弱的时候。虚弱是一种生理储备减少和身体机能丧失的状态
适应压力的能力。在过去的十年里,尽管脆弱越来越被认为是一种
手术结果不佳的重要风险因素,将标准化的脆弱性指标整合到临床护理中
没有实现。一个关键障碍是现有的脆弱性评估不是标准化的、客观的或广泛的
这限制了它们在外科手术决策中的常规应用。以改进为长期目标
对于老年人的外科护理,我们将评估两个可以自动从
已经作为常规临床护理的一部分收集的电子或数字数据。这些电子脆弱性指标
包括电子健康记录(EHR)数据细粒度患者档案(基于索赔数据的风险评分
或基于生理和实验室价值),第二,根据手术前计算评估的肌肉损失
体层摄影术(CT)扫描(低骨骼肌块,称为骨质疏松症和脂肪渗入肌肉
表明身体功能下降,称为肌骨瘤病)。在目标1中,我们将计算这两个电子脆弱性
在超过41,000名腹部手术患者的不同人群中的指标;描述重叠的特征
被两个e-脆弱指标指定为虚弱的患者之间的关系;并评估他们与30天的相关性
再入院和其他不良手术结果(30天和1年死亡率、并发症、非家庭
出院和停留时间>;7天)。在目标2中,我们将比较电子脆弱性指标的性能
与标准风险分层工具相比,预测30天再入院和其他不良手术结果
(急性和慢性病严重程度指标)目前已嵌入EHR中,使用交叉验证和
超过14,000例最近腹部手术的独立验证数据集。在目标3中,我们将研究
E-脆弱性指标是否修改了患者从实现术后目标中获得的好处-包括
早期和持续的动员-在手术后最大的增强恢复计划(ERAS)中
这个国家。我们将研究e-脆弱性指标作为预测发病率和发病率的生物年龄的显著指标。
死亡率。总而言之,电子脆弱性指标在识别外科领域的高危患者方面显示出巨大的希望,
但它们需要集成到临床工作流中,才能具有可扩展性和可持续性。这项提议将
计算从电子健康记录数据自动派生的标准化电子脆弱性指标,并提供新信息
关于这些电子脆弱性指标对于改善老年人外科护理的潜在价值。本研究
还将为未来将电子脆弱性指标集成到临床护理中的前瞻性干预奠定基础
改善考虑手术的患者的风险分层和咨询,加强围手术期护理
虚弱的外科病人。
英文摘要
ABSTRACT
Surgical complications are common, costly, and deadly. Older patients are at high risk of adverse surgical
outcomes, especially when they exhibit frailty. Frailty is a state of decreased physiologic reserve and loss of
capacity to adapt to stressors. Over the past decade, while frailty has been increasingly recognized as an
important risk factor for poor surgical outcomes, integration of a standardized frailty metric into clinical care has
not been achieved. A key barrier is that existing frailty assessments are not standardized, objective, or widely
available, limiting their routine application in surgical decision-making. With the long-term goal of improving
surgical care for older adults, we will evaluate two “e-frailty” metrics that can be automatically derived from
electronic or digital data that are already collected as part of routine clinical care. These e-frailty metrics
include, first, granular patient profiles of electronic health record (EHR) data (risk scores based on claims data
or on physiologic and laboratory values), and second, muscle loss assessed from pre-surgical computed
tomography (CT) scans (low skeletal muscle mass, known as sarcopenia, and fatty infiltration into muscle
indicative of reduced physical function, known as myosteatosis). In Aim 1, we will calculate these two e-frailty
metrics among a diverse population of over 41,000 abdominal surgical patients; characterize the overlap
between patients designated as frail by the two e-frailty metrics; and evaluate their associations with 30-day
readmission and other adverse surgical outcomes (30-day and 1-year mortality, complications, non-home
discharge, and length of stay >7 days). In Aim 2, we will compare the performance of e-frailty metrics for
predicting 30-day readmission and other adverse surgical outcomes to that of standard risk stratification tools
(acute and chronic illness severity metrics) already embedded in EHRs today using cross-validation and an
independent validation dataset of over 14,000 more recent abdominal surgeries. In Aim 3, we will examine
whether e-frailty metrics modify the benefits that patients derive from achieving postoperative targets -including
early and sustained mobilization- in one of the largest Enhanced Recovery After Surgery (ERAS) programs in
the nation. We will examine e-frailty metrics as salient indicators of biologic age for predicting morbidity and
mortality. In sum, e-frailty metrics show great promise for identifying high-risk patients in the surgical domain,
but they need to be integrated within clinical workflows to be scalable and sustainable. This proposal will
compute standardized e-frailty metrics automatically derived from EHR data and provide new information
regarding the potential value of these e-frailty metrics for improving surgical care for older adults. This study
will also lay the groundwork for future prospective interventions integrating e-frailty metrics into clinical care to
improve risk stratification and counseling of patients considering surgery and enhance perioperative care for
frail surgical patients.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
Quantifying Frailty Requires a Conceptual Model Before a Statistical Model-Reply.
量化衰弱需要在统计模型回复之前建立概念模型。
DOI:
10.1001/jamasurg.2022.3113
发表时间:
2022
期刊:
JAMA surgery
影响因子:
16.9
作者:
[Le,SidneyT, Liu,VincentX, CespedesFeliciano,ElizabethM]
通讯作者:
CespedesFeliciano,ElizabethM
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