ABSTRACT
SESSION TITLE: Quality Improvement SESSION TYPE: Original Investigations PRESENTED ON: 10/17/22 1:30 pm - 2:30 pm PURPOSE: Organ transplant is the ultimate necessity in managing many end-stage organ pathologies. As per the health resource and service administration, 17 people die every day while waiting for an organ transplant. In the year 2020, 169 million Americans registered as organ donors, but due to the limitations of organ donation such as cause of death and condition of the organ at the time of death, only about 3 in 1000 people die in such a way that their organs are in an optimum condition for transplantation 1. The role of nurses in organ donation is critical in both acute and critical care settings 2. Educating nurses on certain aspects of organ donation, such as approaching the families and counseling regarding moral and legal considerations, will acclererate the process of organs retrieval from the interested donors. We hypothesized that in addition to Best Practice Alert (BPA) on Electronic Medical Record (EMR), educating nurses can optimize organ donation by timely referring the organs for transplantation. METHODS: ICU-wide nursing education sessions were conducted elucidating that when a ventilated patient qualifies for Life Gift notification and a BPA does not pop up in EMR, Nurses should immediately call the Houston Methodist organ donation service, Life Gift within one hour of the following two triggers: 1) Loss of one or more brainstem reflex(es), 2) Glasgow Coma Scale (GCS) ≤ 5. Nurses were also educated to start a timely discussion with the family proposing Life Gift prior to discussing the withdrawal of life-sustaining treatments, popularly known as terminal extubation. The data for timely organ referral from the preceding six months (January 2021 to June 2021) was compared to the four months (July 2021 to October 2021) following the nursing education sessions. RESULTS: The total number of timely referrals in the pre- and post-education period were n=23/33 Vs. n=29/31. The overall timely referral of the organ for transplantation increased from 69.2% to 95%. Out of four months post-education, two months record the compliance of 100%. Our chi-square statistic was 5.969 with a p-value of 0.01456. We performed Yates continuity correction due to small sample size and to compensate for deviations from the theoretical (smooth) probability distribution. Our chi-square statistic with Yates correction was 4.506, and the p-value was 0.034 (Significant at p < 0.05). Our study was limited by the small sample size, high nursing turnover due to the COVID-19 pandemic, and logistic restrictions due to the pandemic. CONCLUSIONS: The overall referring time for organs improved after nursing education sessions, including targeted triggers. CLINICAL IMPLICATIONS: Nursing education plays a crucial role in organ donation programs. Further studies are needed to better understand the issues that nurses face and develop new strategies that can be implemented to improve the organ and tissue referrals for organ donation. DISCLOSURES: No relevant relationships by Muhammad Mohsin Abid No relevant relationships by Sana Jogezai No relevant relationships by Iqbal Ratnani No relevant relationships by Salim Surani No relevant relationships by Muhammad Hassan Virk
ABSTRACT
SESSION TITLE: Use of Machine Learning and Artificial Intelligence SESSION TYPE: Original Investigations PRESENTED ON: 10/16/22 10:30 am - 11:30 am PURPOSE: The COVID-19 pandemic has significantly impacted the US healthcare system. Between March 1, 2020, and January 2, 2021, a 22.9% increase in all-cause mortality was reported [1]. We used Artificial Intelligence (AI) for data analysis to have a prototype national average by matching various characteristics. This is a novel approach known as Digital Twinning Method (DTM). We intend to compare non-COVID mortality between 2020 and 2019 using this DTM approach. METHODS: Data was collected by a contracted vendor that provided analysis utilizing an AI framework. Mortality rates were calculated at four points of care categorized as 1) In-patient mortality, 2) 30-day on-admission, 3) 30-day on discharge, and 4) 90-day on-admission. Baseline risk predictions were generated using DTM for matching patient demographics such as age, gender, race, Medicare status, and community-dwelling status. Hence, each person was compared to a "twin” with the same risk of hospitalization, death, acute myocardial infarction, or stroke. RESULTS: Our institution had a higher actual non-COVID mortality in 2020 compared to the actual mortality in 2019 across all four points of care studied. The highest increase was noticed in the 90-day on-admission category (9.7% in 2019 vs 12.6% in 2020) followed by 30-day on-admission (5.0% in 2019, to 6.6% in 2020), 30-day on-discharge (4.2% in 2019, to 5.7% in 2020), and in-patient mortality (1.8% in 2019, to 2.6% in 2020). However, when compared to twinned patients at other hospitals, our institution had a lower non-COVID mortality rate across all categories in 2019 and 2020. We utilized the Sign Test to evaluate our repeated-paired-measures for the above four points of care categories during two different conditions, i.e., under a normal healthcare situation (2019) and in the pandemic year (2020). Our two-tailed p-value was 0.0455 with statistical significance at p < 0.05, with M1-M2 (M=measure) difference of -0.8 (in-patient mortality), -1.6 (30 day on-admission), -1.5 (30 day on-discharge), and -2.9 (90 day on-admission) for the four categories. Our z-score was +2 under the formula z = (X - pn) / √npq, signifying positive deviation from the mean. Our study was limited by the unavailable data of patients who may have had COVID but were undiagnosed. CONCLUSIONS: AI is a novel method to obtain reliable data. Based on our results, we conclude that the non-COVID mortality rate at our institution increased during the pandemic. Further studies are needed to specify the underlying causes attributable to the increased mortality. CLINICAL IMPLICATIONS: By leveraging Artificial intelligence in healthcare to analyze big datasets and perform complex analyses, it may be of clinical importance to utilize AI-generated risk prediction models to accurately identify variables that can be controlled in future pandemics to decrease mortality while increasing overall efficiency of the healthcare system. DISCLOSURES: No relevant relationships by Muhammad Mohsin Abid No relevant relationships by Sana Jogezai No relevant relationships by Iqbal Ratnani No relevant relationships by Muhammad Hassan Virk No relevant relationships by Anza Zahid