Real-time Infection Prediction in Inpatient Postoperative Care

住院术后护理中的实时感染预测

• 批准号: 10187074
• 负责人: Laura A Graham
• 金额: --
• 依托单位: VETERANS ADMIN PALO ALTO HEALTH CARE SYS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10187074
• 关键词: Accounting; Address; Affect; Antibiotics; Caring; Clinical; Collection; Communities; Data; Data Set; Data Store; Databases; Development; Diagnosis; Discharge Plannings; Environment; Feasibility Studies; Foundations; Funding; Goals; Health Care Costs; Healthcare; Healthcare Systems; Hospitals; Infection; Inpatients; Intensive Care Units; Interview; Investigator-Initiated Research; Location; Methods; Modeling; Morbidity - disease rate; Needs Assessment; Operating Rooms; Operative Surgical Procedures; Pain; Patients; Perception; Persons; Postoperative Care; Postoperative Period; Prevention strategy; Process; Protocols documentation; Provider; Qualitative Methods; Research; Research Design; Resources; Risk; Source; Structure; Surgical Specialties; Surgical complication; Techniques; Temperature; Testing; Thinking; Time; Update; Veterans; care costs; clinical care; cohort; cost; data warehouse; design; follow-up; formative assessment; healthcare-associated infections; high risk; hospital readmission; improved; infection risk; mortality; predictive modeling; predictive tools; pressure; prophylactic; prototype; research study; risk prediction; risk prediction model; support tools; tool; trend; wound; wound treatment

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.

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