Real-time Infection Prediction in Inpatient Postoperative Care

术后感染是最常见的手术并发症，每天影响40多名退伍军人 每例感染的护理总成本估计增加25，000美元。超过一半的术后SSI 在患者出院后确诊。目前的决策支持工具， 术后感染的预测准确性较差。这使得识别患者变得特别困难 出院后有发生SSI的风险，这使出院计划复杂化。准确 在出院时识别感染风险高的患者有助于改善出院计划， 适当的出院后随访时间，以及更好的目标资源昂贵的出院后监测。 生命体征，如体温或疼痛，一直被证明可以预测感染。他们通常 在住院期间收集，但仍然是感染风险预测的未开发信息来源 模型我们假设我们可以提高现有感染风险预测模型的准确性 包括实时的生命体征数据。在规划我们的大型研究时，我们确定了几个可行性 在我们开始开发该工具并在临床中测试它之前应该解决的问题 环境因此，我们正在寻求HSR&D试点资金，以最终确定我们计划的HSR&D的协议 研究者发起的研究。 我们的研究目的如下： 1.进行发展形成性评价，评估可行性、可接受性、潜在有用性 以及用于预测出院时感染风险的决策支持工具的初步设计。 2.检查VA公司数据仓库中收集的住院患者生命体征数据的完整性。 我们使用以人为中心的设计思维框架来规划我们的研究。对于目标1，我们将使用定性 方法分析数据，从20个结构化观察在两个VA设施和15个半结构化访谈 至少有六个退伍军人事务部的设施这些定性分析将验证我们提出的工具可以被纳入 在VA的临床护理中，临床提供者将接受该工具，并且它将被认为是有用的。我们 也将开始为该工具的用户界面和VA临床护理中的部署开发初始原型。 目标2解决了关于使用可能不完整的生命体征数据构建模型的可行性问题。的 VA企业数据仓库（CDW）是访问在以下地点收集的住院患者生命体征的标准位置： 整个VA。不幸的是，我们发现并非所有本地生命体征数据都能进入CDW， 生命体征数据仅在VISN专用数据库中捕获。这使我们对以下问题的影响感到关切： 当使用国家CDW数据来完善具有生命体征数据的现有模型时，数据缺失。对于目标2，我们将使用 定量方法分析VISN 21中六家VA机构收治的所有患者的现有数据库。到 确认开发我们的模型和工具原型的可行性，我们计划确定所有生命体征是如何 在VA中捕获，检查生命体征数据的完整性，并探索影响和潜在偏倚 使用不完整的CDW生命体征数据。目标2的调查结果将确定开发该工具的战略 在VA Palo Alto医疗保健系统，并将该工具扩展到其他设施和VISN。 用生命体征数据更新当前感染风险预测评分有可能大大增加 术后感染风险预测的准确性。更准确地预测当时的高危患者 将改善出院计划，并帮助提供者识别高风险患者， 术后早期随访。它还可以帮助更好地分配其他资源密集型和昂贵的 预防性策略，如预防性抗生素或负压伤口治疗。这将导致 成本、发病率和死亡率的大幅降低。此外，这项研究将告知VA研究界 关于VA内存储的生命体征数据的可用性和使用。

Postoperative infections are the most common surgical complication, affecting upwards of 40 Veterans each day and adding an estimated $25,000 to the total cost of care per infection. More than half of all postoperative SSIs are diagnosed after the patient has been discharged from the hospital. Current decision support tools for postoperative infections suffer from poor predictive accuracy. This makes it particularly hard to identify patients at risk for developing an SSI after hospital discharge which complicates discharge planning. Accurately identifying patients with a high risk of infection at discharge could help to improve discharge planning, identify adequate timing for post-discharge follow up, and better target resource-expensive post-discharge surveillance. Vital signs, such as temperature or pain, have consistently been shown to predict infections. They are routinely collected during inpatient stays but have remained an untapped source of information for infection risk prediction models. We hypothesize that we can improve the accuracy of existing infection risk prediction models by including this real-time vital sign data. While planning our larger study, we have identified several feasibility concerns that should be addressed before we embark on developing the tool and testing it in a clinical environment. Thus, we are seeking HSR&D Pilot funding to finalize the protocol of our planned HSR&D Investigator-Initiated Research study. Our study aims are as follows: 1. Perform a developmental formative evaluation assessing the feasibility, acceptability, potential usefulness and initial design of a decision support tool for predicting infection risk at discharge. 2. Examine the completeness of inpatient vital sign data collected in the VA Corporate Data Warehouse. We have planned our study using a person-centric design thinking framework. For aim 1, we will use qualitative methods to analyze data from 20 structured observations at two VA facilities and 15 semi-structured interviews across at least six VA facilities. These qualitative analyses will verify that our proposed tool can be incorporated into clinical care at the VA, that clinical providers will accept the tool, and that it will be perceived as useful. We will also begin to develop an initial prototype for the tool’s user interface and deployment within VA clinical care. Aim 2 addresses a feasibility concern about building our model with potentially incomplete vital sign data. The VA Corporate Data Warehouse (CDW) is the standard location for accessing inpatient vital signs collected across the entire VA. Unfortunately, we have discovered that not all local vital sign data make it into the CDW and some vital sign data are only captured in VISN-specific databases. This leads us to concerns about the impact of missing data when using national CDW data to refine existing models with vital sign data. For aim 2, we will use quantitative methods to analyze an existing database of all patients admitted to six VA facilities in VISN 21. To confirm the feasibility of developing our model and tool prototype, we plan to determine how all vital signs are captured in the VA, examine the completeness of the vital sign data, and explore the impact and potential biases of using incomplete CDW vital sign data. The findings of aim 2 will determine strategies for developing the tool at VA Palo Alto Health Care System and expanding the tool across other facilities and VISNs. Updating current infection risk prediction scores with vital sign data has the potential to greatly increase the accuracy of risk prediction for postoperative infection. A more accurate prediction of high-risk patients at the time of discharge will improve discharge planning and help providers identify high-risk patients that would benefit from earlier postoperative follow-up. It could also help better allocate other resource-intensive and expensive preventative strategies such as prophylactic antibiotics or negative pressure wound therapy. This would result in substantial reductions in costs, morbidity, and mortality. Also, this study will inform the VA research community on the availability and use of vital sign data stored within the VA.