Leveraging EHR Information to Measure Pressure Ulcer Risk in Veterans with SCI

利用 EHR 信息测量 SCI 退伍军人的压疮风险

• 批准号: 8750788
• 负责人: Stephen L Luther
• 金额: --
• 依托单位: JAMES A. HALEY VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8750788
• 关键词: Acute; Admission activity; Algorithms; Caring; Chi-Square Tests; Classification; Clinical; Code; Cohort Studies; Comorbidity; Data; Databases; Decubitus ulcer; Development; Disease; Electronic Health Record; Frequencies; Goals; Health Care Costs; Health behavior; Healthcare; Hospitalization; Hospitals; Hour; ICD-9-CM; Impairment; Individual; Informatics; Injury; Inpatients; Literature; Living Costs; Logistic Regressions; Machine Learning; Measures; Methods; Modeling; Natural Language Processing; Patients; Peer Review; Population; Prevention strategy; Probability; Provider; Psychosocial Factor; Quality of life; Research; Research Infrastructure; Risk; Risk Assessment; Risk Factors; Spinal Cord; Statistical Models; Structure; Surveys; System; Text; Veterans; base; clinically relevant; cohort; cost; demographics; health record; high risk; improved; patient home care; predictive modeling; prevent; programs; tool

DESCRIPTION (provided by applicant): Pressure ulcers (PrU) are among the most significant complications in Veterans with spinal cord impairment (SCI) in terms of quality of life and cost of care. The currently used risk assessment tool, the Braden Scale, suffers from a number of limitations. (1) The Braden scale is not SCI-specific and has severe ceiling effects in the SCI population. VA External Peer Review Program (EPRP) surveys, conducted between 2008 and 2010 at 22 VA SCI/D Centers, found on average 95.3% of all patients received a Braden score within 24 hours of an impatient admission with 91.3% of those measured identified as being at high risk. (2) Some risk variables for which there is research evidence or strong clinical support are not well represented in existing assessment tool. (3) The current risk assessment tool was primarily developed for use in the inpatient setting. However, after the acute post-injury period, most individuals with SCI acquire PrUs outside the hospital. Based on the EPRP survey between 2008 and 2010, less than 2% of Veterans admitted to VHA SCI/D Centers in 2010 developed hospital acquired PrUs. The Aims for this study are: 1) Develop natural language processing (NLP) programs to identify the occurrence of PrUs; 2) Develop predictive models of occurrence of PrUs based on available structured data for early impact on PrU risk assessment; 3) Develop NLP programs to reliably extract information about potential predictors from text in clinical notes; 4) Combine risk information obtained through structured and text- extracted NLP data, and develop robust risk assessment predictive of PrUs. Project Methods: This is a retrospective cohort study of Veterans with SCI. The inception of the cohort includes all Veterans with SCI cared for in the VHA in FY 2009 that had no record of a pressure ulcer in the previous 12 months. Potential risk factors (e.g. demographics, diseases status, co-morbidities, health behaviors, psychosocial factors, home care) identified in the literature will be reviewed by an expert panel for logical consistency, completeness and clinical relevance. Review of the EHR will be conducted to determine if the identified risk factors are found in structured (coded in database/table) or in narrative data (text in clinical notes). All structured and narrative data for the targeted cohort or FY 2009-2013 (anticipated most recent data available) will be obtained through the VA Informatics and Computing Infrastructure (VINCI). In Aim 1 we will use 2X2 (Chi-square test) frequency tables to compare the rates of PrU occurrence based on NLP with those based on structured (ICD-9-CM) data. In Aim 2 predictive models of PrU occurrence based on available structured data alone will be developed and compared with the predictions based on the Braden Scale. In Aim 3 NLP systems will be developed to extract risk factors from the EHR text. In Aim 4 predictions based on the new risk models, combining structured data with NLP data will be compared with predictions based on structured data alone and the Braden Scale. Prediction models will be developed with multivariable logistic regression models.

描述（由申请人提供）： 就生活质量和护理费用而言，压疮 (PrU) 是脊髓损伤 (SCI) 退伍军人最重要的并发症之一。目前使用的风险评估工具布雷登量表存在许多局限性。 (1) Braden 量表不是 SCI 专用的，并且在 SCI 人群中具有严重的天花板效应。 2008 年至 2010 年间在 22 个 VA SCI/D 中心进行的 VA 外部同行评审计划 (EPRP) 调查发现，平均 95.3% 的患者在不耐烦入院后 24 小时内获得了 Braden 评分，其中 91.3% 的患者被确定为处于高风险状态。 (2)一些有研究证据或强有力的临床支持的风险变量在现有的评估工具中没有得到很好的体现。 (3) 目前的风险评估工具主要是为住院患者使用而开发的。然而，在急性损伤后一段时间后，大多数 SCI 患者会在医院外获得 PrU。根据 EPRP 2008 年至 2010 年间的调查，2010 年入住 VHA SCI/D 中心的退伍军人中只有不到 2% 患有医院获得性 PrU。本研究的目的是：1）开发自然语言处理（NLP）程序来识别 PrU 的发生； 2) 根据可用的结构化数据开发 PrU 发生的预测模型，以便对 PrU 风险评估产生早期影响； 3) 开发 NLP 程序，从临床记录的文本中可靠地提取有关潜在预测因子的信息； 4) 结合通过结构化和文本提取的 NLP 数据获得的风险信息，并开发预测 PrU 的稳健风险评估。项目方法：这是一项针对患有 SCI 的退伍军人的回顾性队列研究。该队列的初始成员包括 2009 财年在 VHA 接受护理的所有患有 SCI 的退伍军人，这些退伍军人在过去 12 个月内没有压疮记录。专家小组将审查文献中确定的潜在风险因素（例如人口统计、疾病状况、合并症、健康行为、社会心理因素、家庭护理）的逻辑一致性、完整性和临床相关性。将进行 EHR 审查，以确定是否在结构化数据（数据库/表格中编码）或叙述性数据（临床记录中的文本）中发现已识别的风险因素。目标群体或 2009-2013 财年的所有结构化和叙述性数据（预期的最新可用数据）将通过 VA 信息学和计算基础设施 (VINCI) 获得。在目标 1 中，我们将使用 2X2（卡方检验）频率表来比较基于 NLP 的 PrU 发生率与基于结构化 (ICD-9-CM) 数据的 PrU 发生率。在目标 2 中，将开发仅基于可用结构化数据的 PrU 发生预测模型，并将其与基于 Braden 量表的预测进行比较。在目标 3 中，将开发 NLP 系统以从 EHR 文本中提取风险因素。在基于新风险模型的 Aim 4 预测中，将结构化数据与 NLP 数据相结合的预测将与仅基于结构化数据和布雷登量表的预测进行比较。预测模型将使用多变量逻辑回归模型来开发。