Early Detection of Respiratory Compromise to Prevent Harm of the Hospitalized Opioid Treated Patient

Setting: This research project will be performed at an inner-city hospital in Western New York, Buffalo General Medical Center (BGMC) is typical of an under-resourced facility providing care to a substantial proportion of the indigent, minority, immigrant and medically underserved population of a region. In 2017, 36% of BGMC patients identified themselves as Black or African American. Typical patients face economic, cultural or linguistic barriers to healthcare. By focusing on OIRC at BGMC, this study will help in informing how health disparities may impact the incidence of OIRC. At BGMC in 2017, 11,744 surgical procedures were performed and 2% of general surgical cases experienced an adverse event (code blue). Information on adverse events related to OIRC is not available.

Aim 1. After recruiting and performing informed consent pre-operatively, we will monitor post-operative patients using pulse oximetry, capnography, TCpCO2, and minute ventilation during recovery from anesthesia (in PACU), and on the general care floor for up to 72 hours. An observational study of 50 surgical patients will be performed to record electronic respiratory monitoring data as well as patient characteristics. This information will be used for validation and iterative development of prediction models using machine learning techniques. In our preliminary work, we used data that was collected by the research assistants reading the data off the device display. During the proposal proposed study, we will record the data from each electronic device directly on USB memory sticks attached to the device. In our preliminary work, we had data from the PACU stay only. During this study, we will collect data prospectively throughout the hospital stay to further inform changes in respiratory compromise as the patient transitions away from the anesthetic and paralytic agents . On the machine learning side, we will explore long-short term memory networks (LSTM), which have become the state of art machine learning models to deal with sequence and time series data (24), including applications in the healthcare domain (25), including recent work by Co-I Chandola. The justification behind using these models over the support vector machine model used in our preliminary study is that they are able to explicitly model the temporal dependencies in the data, which is expected to provide significant improvements in the predictive performance of the model (26).

Aim 2. To further understand factors related to OIRC and to assist in responding to the AHRQ reviewers' comments, we will perform a root cause analysis of all adverse events found in the patients we recruited for Aim 1, as well as all rapid response calls, naloxone deliveries, and code blue calls for 2018 at Buffalo General Medical Center (BGMC ). We will examine each case specifically for nursing assessment and monitoring procedures as well as all patient and environmental factors that may have contributed to the adverse event. The patient safety physician and quality assurance nurses from BGMC will be interviewed to perform root cause analysis of all opioid-related adverse events that have occurred over the past year at the facility. Each event will be broken down by who was involved, what they were doing, what technologies were used, where did the event take place, and what outside factors may have contributed to the event. This information will be used to group the potential causes and the progression toward the adverse event, which will allow for identification of the roles of staff workload and patient monitoring on OIRC occurrence. We have received a letter of support from the medical director of patient safety at BGMC and Kaleida Health chief nursing office for our intended projects.