Patient perspectives on liver transplant evaluation: A qualitative study.

OBJECTIVE: Liver transplant (LT) evaluation is a complex process for patients involving multi-step and parallel medical, surgical, and psychosocial assessments of a patient's appropriateness for transplant. Patients may experience difficulties in navigating the evaluation process, potentially leading to disengagement and resulting in further health decline or death prior to completing evaluation. We aimed to identify and characterize patients' perceptions of undergoing LT evaluation. METHODS: We performed fourteen 30-45 min, semi-structured interviews between 3/2021-5/2021 with patients at a large LT center. Using the constant comparison method, we individually noted themes within and across interviews and codes. RESULTS: Our analysis generated 5 thematic dimensions related to patient engagement (i.e., patient involvement/activation): (1) psychological impact of evaluation on patients' lives; (2) information received during evaluation; (3) prior medical experience of the patient; 4) communication between patients and transplant providers; and (5) support system of the patients. Among these dimensions, we identified 8 themes. CONCLUSION: LT patient engagement is a multi-dimensional component of LT evaluation that incorporates the psychological impact, information received, prior medical experience, communication, and support systems of patients. PRACTICAL IMPLICATIONS: This work can inform targeted interventions for increasing patient engagement during the LT evaluation process.

Pragmatic strategies to address health disparities along the continuum of care in chronic liver disease.

Racial, ethnic, and socioeconomic disparities exist in the prevalence and natural history of chronic liver disease, access to care, and clinical outcomes. Solutions to improve health equity range widely, from digital health tools to policy changes. The current review outlines the disparities along the chronic liver disease health care continuum from screening and diagnosis to the management of cirrhosis and considerations of pre-liver and post-liver transplantation. Using a health equity research and implementation science framework, we offer pragmatic strategies to address barriers to implementing high-quality equitable care for patients with chronic liver disease.

Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.

BACKGROUND: The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions. METHODS: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data. RESULTS: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS. CONCLUSIONS: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.

Critical interactions between race and the highly granular area deprivation index in liver transplant evaluation.

Neighborhood socioeconomic deprivation may have important implications on disparities in liver transplant (LT) evaluation. In this retrospective cohort study, we constructed a novel dataset by linking individual patient-level data with the highly granular Area Deprivation Index (ADI), which is advantageous over other neighborhood measures due to: specificity of Census Block-Group (versus Census Tract, Zip code), scoring, and robust variables. Our cohort included 1377 adults referred to our center for LT evaluation 8/1/2016-12/31/2019. Using modified Poisson regression, we tested for effect measure modification of the association between neighborhood socioeconomic status (nSES) and LT evaluation outcomes (listing, initiating evaluation, and death) by race and ethnicity. Compared to patients with high nSES, those with low nSES were at higher risk of not being listed (aRR = 1.14; 95%CI 1.05-1.22; p < .001), of not initiating evaluation post-referral (aRR = 1.20; 95%CI 1.01-1.42; p = .03) and of dying without initiating evaluation (aRR = 1.55; 95%CI 1.09-2.2; p = .01). While White patients with low nSES had similar rates of listing compared to White patients with high nSES (aRR = 1.06; 95%CI .96-1.17; p = .25), Underrepresented patients from neighborhoods with low nSES incurred 31% higher risk of not being listed compared to Underrepresented patients from neighborhoods with high nSES (aRR = 1.31; 95%CI 1.12-1.5; p < .001). Interventions addressing neighborhood deprivation may not only benefit patients with low nSES but may address racial and ethnic inequities.

Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.

Demand has outstripped healthcare supply during the coronavirus disease 2019 (COVID-19) pandemic. Emergency departments (EDs) are tasked with distinguishing patients who require hospital resources from those who may be safely discharged to the community. The novelty and high variability of COVID-19 have made these determinations challenging. In this study, we developed, implemented and evaluated an electronic health record (EHR) embedded clinical decision support (CDS) system that leverages machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 h and inpatient care needs within 72 h into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. ML models were derived in a retrospective cohort of 21,452 ED patients who visited one of five ED study sites and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation; model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. Incidence of critical care needs within 24 h and inpatient care needs within 72 h were 10.7% and 22.5%, respectively and were similar across study periods. ML model performance was excellent under all conditions, with AUC ranging from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after CDS implementation.

Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions.

Racial and ethnic disparities persist in access to the liver transplantation (LT) waiting list; however, there is limited knowledge about underlying system-level factors that may be responsible for these disparities. Given the complex nature of LT candidate evaluation, a human factors and systems engineering approach may provide insights. We recruited participants from the LT teams (coordinators, advanced practice providers, physicians, social workers, dieticians, pharmacists, leadership) at two major LT centers. From December 2020 to July 2021, we performed ethnographic observations (participant-patient appointments, committee meetings) and semistructured interviews (N = 54 interviews, 49 observation hours). Based on findings from this multicenter, multimethod qualitative study combined with the Systems Engineering Initiative for Patient Safety 2.0 (a human factors and systems engineering model for health care), we created a conceptual framework describing how transplant work system characteristics and other external factors may improve equity in the LT evaluation process. Participant perceptions about listing disparities described external factors (e.g., structural racism, ambiguous national guidelines, national quality metrics) that permeate the LT evaluation process. Mechanisms identified included minimal transplant team diversity, implicit bias, and interpersonal racism. A lack of resources was a common theme, such as social workers, transportation assistance, non-English-language materials, and time (e.g., more time for education for patients with health literacy concerns). Because of the minimal data collection or center feedback about disparities, participants felt uncomfortable with and unadaptable to unwanted outcomes, which perpetuate disparities. We proposed transplant center-level solutions (i.e., including but not limited to training of staff on health equity) to modifiable barriers in the clinical work system that could help patient navigation, reduce disparities, and improve access to care. Our findings call for an urgent need for transplant centers, national societies, and policy makers to focus efforts on improving equity (tailored, patient-centered resources) using the science of human factors and systems engineering.

Abnormal liver chemistries as a predictor of COVID-19 severity and clinical outcomes in hospitalized patients.

BACKGROUND: Abnormal liver chemistries are common findings in patients with Coronavirus Disease 2019 (COVID-19). However, the association of these abnormalities with the severity of COVID-19 and clinical outcomes is poorly understood. AIM: We aimed to assess the prevalence of elevated liver chemistries in hospitalized patients with COVID-19 and compare the serum liver chemistries to predict the severity and in-hospital mortality. METHODS: This retrospective, observational study included 3380 patients with COVID-19 who were hospitalized in the Johns Hopkins Health System (Baltimore, MD, United States). Demographic data, clinical characteristics, laboratory findings, treatment measures, and outcome data were collected. Cox regression modeling was used to explore variables associated with abnormal liver chemistries on admission with disease severity and prognosis. RESULTS: A total of 2698 (70.4%) had abnormal alanine aminotransferase (ALT) at the time of admission. Other more prevalent abnormal liver chemistries were aspartate aminotransferase (AST) (44.4%), alkaline phosphatase (ALP) (16.1%), and total bilirubin (T-Bil) (5.9%). Factors associated with liver injury were older age, Asian ethnicity, other race, being overweight, and obesity. Higher ALT, AST, T-Bil, and ALP levels were more commonly associated with disease severity. Multivariable adjusted Cox regression analysis revealed that abnormal AST and T-Bil were associated with the highest mortality risk than other liver injury indicators during hospitalization. Abnormal AST, T-Bil, and ALP were associated with a need for vasopressor drugs, whereas higher levels of AST, T-Bil, and a decreased albumin levels were associated with mechanical ventilation. CONCLUSION: Abnormal liver chemistries are common at the time of hospital admission in COVID-19 patients and can be closely related to the patient's severity and prognosis. Elevated liver chemistries, specifically ALT, AST, ALP, and T-Bil levels, can be used to stratify risk and predict the need for advanced therapies in these patients.

A Patient Outcomes-Driven Feedback Platform for Emergency Medicine Clinicians: Human-Centered Design and Usability Evaluation of Linking Outcomes Of Patients (LOOP).

BACKGROUND: The availability of patient outcomes-based feedback is limited in episodic care environments such as the emergency department. Emergency medicine (EM) clinicians set care trajectories for a majority of hospitalized patients and provide definitive care to an even larger number of those discharged into the community. EM clinicians are often unaware of the short- and long-term health outcomes of patients and how their actions may have contributed. Despite large volumes of patients and data, outcomes-driven learning that targets individual clinician experiences is meager. Integrated electronic health record (EHR) systems provide opportunity, but they do not have readily available functionality intended for outcomes-based learning. OBJECTIVE: This study sought to unlock insights from routinely collected EHR data through the development of an individualizable patient outcomes feedback platform for EM clinicians. Here, we describe the iterative development of this platform, Linking Outcomes Of Patients (LOOP), under a human-centered design framework, including structured feedback obtained from its use. METHODS: This multimodal study consisting of human-centered design studios, surveys (24 physicians), interviews (11 physicians), and a LOOP application usability evaluation (12 EM physicians for ≥30 minutes each) was performed between August 2019 and February 2021. The study spanned 3 phases: (1) conceptual development under a human-centered design framework, (2) LOOP technical platform development, and (3) usability evaluation comparing pre- and post-LOOP feedback gathering practices in the EHR. RESULTS: An initial human-centered design studio and EM clinician surveys revealed common themes of disconnect between EM clinicians and their patients after the encounter. Fundamental postencounter outcomes of death (15/24, 63% respondents identified as useful), escalation of care (20/24, 83%), and return to ED (16/24, 67%) were determined high yield for demonstrating proof-of-concept in our LOOP application. The studio aided the design and development of LOOP, which integrated physicians throughout the design and content iteration. A final LOOP prototype enabled usability evaluation and iterative refinement prior to launch. Usability evaluation compared to status quo (ie, pre-LOOP) feedback gathering practices demonstrated a shift across all outcomes from "not easy" to "very easy" to obtain and from "not confident" to "very confident" in estimating outcomes after using LOOP. On a scale from 0 (unlikely) to 10 (most likely), the users were very likely (9.5) to recommend LOOP to a colleague. CONCLUSIONS: This study demonstrates the potential for human-centered design of a patient outcomes-driven feedback platform for individual EM providers. We have outlined a framework for working alongside clinicians with a multidisciplined team to develop and test a tool that augments their clinical experience and enables closed-loop learning.

Pretransplant Hepatitis C Virus Treatment Decreases Access to High-quality Livers.

BACKGROUND: Despite the revolutionary role of direct-acting antivirals for hepatitis C virus (HCV), the treatment timing for liver transplant candidates remains controversial. We hypothesize that deferring treatment until after liver transplantation improves access to a larger and higher-quality donor pool without a detrimental impact on post-liver transplantation outcomes. METHODS: This single-center study includes recipients that underwent deceased-donor liver transplant with HCV as the primary indication January 1, 2014, to December 31, 2018. For recipients that were untreated (n = 87) versus treated (n = 42) pre-LT, we compared post-LT mortality using Cox regression with inverse probability of treatment-weighted data. RESULTS: Among pre-LT untreated recipients, 95% were willing to accept an HCV+ donor, and 44.8% received a positive HCV antibody and nucleic acid amplification test (NAT) liver. Among pre-LT treated recipients, 5% were willing to accept an HCV+ donor, and 100% received a negative HCV antibody and NAT liver. The median calculated model for end-stage liver disease at transplant was similar between pre-LT untreated (13, IQR = 9-22) and treated recipients (11, IQR = 8-14) (P = 0.1). Pre-LT treated recipients received livers from older (47 y old versus 37, P < 0.01) and higher body mass index donors (30.2 versus 26.6; P = 0.04) and spent longer on the waiting list (319 d 180, P < 0.001). Unadjusted post-LT mortality at 1 year was higher in the pre-LT treated recipients (14.6% versus 3.5%, P = 0.02). After adjusting for recipient factors, pre-LT treated recipients trended toward a 3.9 times higher risk of mortality compared with the pre-LT untreated recipients (adjusted hazard ratio = 0.973.8615.4) (P = 0.06). CONCLUSIONS: Deferring HCV treatment improves access to higher-quality donors and may improve post-LT survival.

Evaluation of Early vs Standard Liver Transplant for Alcohol-Associated Liver Disease.

Importance: Traditionally, liver transplant (LT) for alcohol-associated liver disease (ALD) requires 6 months of abstinence. Although early LT before 6 months of abstinence has been associated with decreased mortality for decompensated ALD, this practice remains controversial and concentrated at a few centers. Objective: To define patient, allograft, and relapse-free survival in early LT for ALD, and to investigate the association between these survival outcomes and early vs standard LT. Design, Setting, and Participants: This cohort study analyzed all patients with ALD who underwent their first LT at a single academic referral center between October 1, 2012, and November 13, 2020. Patients with known pretransplant hepatocellular carcinoma, hepatitis B or C, or an alternative cause of liver failure were excluded. Follow-up period was defined as the time from LT to the most recent encounter with a transplant center or death. Exposures: The exposure of interest was early LT, which was defined as less than 180 days of pre-LT abstinence. Standard LT was defined as 180 days or more of pre-LT abstinence. Patients were separated into early LT and standard LT by time from abstinence to LT. Main Outcomes and Measures: The outcomes were patient, allograft, relapse-free, and hazardous relapse-free survival for patients who underwent early LT or standard LT. These groups were compared by log-rank testing of Kaplan-Meier estimates. Hazardous relapse was defined as binge, at-risk, or frequent drinking. Abstinence was reassessed at the most recent follow-up visit for all patients. Results: Of the 163 patients with ALD included in this study, 88 (54%) underwent early LT and 75 (46%) underwent standard LT. This cohort had a mean (SD) age at transplant of 52 (10) years and was predominantly composed of 108 male patients (66%). Recipients of early LT vs standard LT were younger (median [interquartile range (IQR)] age, 49.7 [39.0-54.2] years vs 54.6 [48.7-60.0] years; P < .001) and had a higher median (IQR) Model for End-stage Liver Disease score at listing (35.0 [29.0-39.0] vs 20.0 [13.0-26.0]; P < .001). Both recipients of early LT and standard LT had similar 1-year patient survival (94.1% [95% CI, 86.3%-97.5%] vs 95.9% [95% CI, 87.8%-98.7%]; P = .60), allograft survival (92.7% [95% CI, 84.4%-96.7%] vs 90.5% [95% CI, 81.0%-95.3%]; P = .42), relapse-free survival (80.4% [95% CI, 69.1%-88.0%] vs 83.5% [95% CI, 72.2%-90.6%]; P = .41), and hazardous relapse-free survival (85.8% [95% CI, 75.1%-92.2%] vs 89.6% [95% CI, 79.5%-94.9%]; P = .41). Conclusions and Relevance: Adherence to the 6-month rule was not associated with superior patient survival, allograft survival, or relapse-free survival among selected patients. This finding suggests that patients with ALD should not be categorically excluded from LT solely on the basis of 6 months of abstinence, but rather alternative selection criteria should be identified that are based on need and posttransplant outcomes.

A patient-centered framework for health systems engineering in gastroenterology: improving inpatient colonoscopy bowel preparation.

BACKGROUND: Inpatient colonoscopy bowel preparation (ICBP) is frequently inadequate and can lead to adverse events, delayed or repeated procedures, and negative patient outcomes. Guidelines to overcome the complex factors in this setting are not well established. Our aims were to use health systems engineering principles to comprehensively evaluate the ICBP process, create an ICBP protocol, increase adequate ICBP, and decrease length of stay. Our goal was to provide adaptable tools for other institutions and procedural specialties. METHODS: Patients admitted to our tertiary care academic hospital that underwent inpatient colonoscopy between July 3, 2017 to June 8, 2018 were included. Our multi-disciplinary team created a protocol employing health systems engineering techniques (i.e., process mapping, cause-effect diagrams, and plan-do-study-act cycles). We collected demographic and colonoscopy data. Our outcome measures were adequate preparation and length of stay. We compared pre-intervention (120 ICBP) vs. post-intervention (129 ICBP) outcomes using generalized linear regression models. Our new ICBP protocol included: split-dose 6-L polyethylene glycol-electrolyte solution, a gastroenterology electronic note template, and an education plan for patients, nurses, and physicians. RESULTS: The percent of adequate ICBPs significantly increased with the intervention from 61% pre-intervention to 74% post-intervention (adjusted odds ratio of 1.87, p value = 0.023). The median length of stay decreased by approximately 25%, from 4 days pre-intervention to 3 days post-intervention (p value = 0.11). CONCLUSIONS: By addressing issues at patient, provider, and system levels with health systems engineering principles, we addressed patient safety and quality of care provided by improving rates of adequate ICBP.

Transplanting Organs from Donors with HIV or Hepatitis C: The Viral Frontier.

A wide gap between the increasing demand for organs and the limited supply leads to immeasurable loss of life each year. The organ shortage could be attenuated by donors with human immunodeficiency virus (HIV) or hepatitis C virus (HCV). The transplantation of organs from HIV+ deceased donors into HIV+ individuals (HIV D+ /R+) was initiated in South Africa in 2010; however, this practice was forbidden in the USA until the HIV Organ Policy Equity (HOPE) Act in 2013. HIV D+/R+ transplantation is now practiced in the USA as part of ongoing research studies, helping to reduce waiting times for all patients on the waitlist. The introduction of direct acting antivirals for HCV has revolutionized the utilization of donors with HCV for HCV-uninfected (HCV-) recipients. This is particularly relevant as the HCV donor pool has increased substantially in the context of the rise in deaths related to drug overdose from injection drug use. This article serves to review the current literature on using organs from donors with HIV or HCV.

Machine learning and artificial intelligence: applications in healthcare epidemiology.

Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.

Liver transplantation in the United States during the COVID-19 pandemic: National and center-level responses.

COVID-19 has profoundly affected the American health care system; its effect on the liver transplant (LT) waitlist based on COVID-19 incidence has not been characterized. Using SRTR data, we compared observed LT waitlist registrations, waitlist mortality, deceased donor LTs (DDLT), and living donor LTs (LDLT) 3/15/2020-8/31/2020 to expected values based on historical trends 1/2016-1/2020, stratified by statewide COVID-19 incidence. Overall, from 3/15 to 4/30, new listings were 11% fewer than expected (IRR = 0.84 0.890.93 ), LDLTs were 49% fewer (IRR = 0.37 0.510.72 ), and DDLTs were 9% fewer (IRR = 0.85 0.910.97 ). In May, new listings were 21% fewer (IRR = 0.74 0.790.84 ), LDLTs were 42% fewer (IRR = 0.39 0.580.85 ) and DDLTs were 13% more (IRR = 1.07 1.151.23 ). Centers in states with the highest incidence 3/15-4/30 had 59% more waitlist deaths (IRR = 1.09 1.592.32 ) and 34% fewer DDLTs (IRR = 0.50 0.660.86 ). By August, waitlist outcomes were occurring at expected rates, except for DDLT (13% more across all incidences). While the early COVID-affected states endured major transplant practice changes, later in the pandemic the newly COVID-affected areas were not impacted to the same extent. These results speak to the adaptability of the transplant community in addressing the pandemic and applying new knowledge to patient care.