Expanding transplant outcomes research opportunities through the use of a common data model

Organ Donation and Transplantation Registries Across the Globe: A Review of the Current State

BACKGROUND

The current landscape of organ donation and transplantation (ODT) registries is not well established. This narrative review sought to identify and characterize the coverage, structure, and data capture of ODT registries globally.

METHODS

We conducted a literature search using Ovid Medline and web searches to identify ODT registries from 2000 to 2023. A list of ODT registries was compiled based on publications of registry design, studies, and reports. Extracted data elements included operational features of registries and the types of donor and recipient data captured.

RESULTS

We identified 129 registries encompassing patients from all continents except Antarctica. Most registries were active, received funding from government or professional societies, were national in scope, included both adult and pediatric patients, and reported patient-level data. Registries included kidney (n = 99), pancreas (n = 32), liver (n = 44), heart (n = 35), lung (n = 30), intestine (n = 15), and islet cell (n = 5) transplants. Most registries captured donor data (including living versus deceased) and recipient features (including demographics, cause of organ failure, and posttransplant outcomes) but there was underreporting of other domains (eg, donor comorbidities, deceased donor referral rates, waitlist statistics).

CONCLUSIONS

This review highlights existing ODT registries globally and serves as a call for increased visibility and transparency in data management and reporting practices. We propose that standards for ODT registries, a common data model, and technical platforms for collaboration, will enable a high-functioning global ODT system responsive to the needs of transplant candidates, recipients, and donors.

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.

UNOS Decisions Impact Data Integrity of the OPTN Data Registry

BACKGROUND

The Organ Procurement Transplant Network (OPTN)/United Network for Organ Sharing (UNOS) registry is an important national registry in the field of solid organ transplantation. Data collected are mission critical, given its role in organ allocation prioritization, program performance monitoring by both the OPTN and the Centers for Medicare & Medicaid Services, and countless observational analyses that helped to move the field forward. Despite the multifaceted importance of the OPTN/UNOS database, there are clear indications that investments in the database to ensure the quality and reliability of the data have been lacking.

METHODS

This analysis outlines 2 examples: (1) primary diagnosis for patients who are receiving a second transplant and (2) reporting peripheral vascular disease in kidney transplantation to illustrate the extensive challenges facing the veracity and integrity of the OPTN/UNOS database today.

RESULTS

Despite guidance that repeat kidney transplant patients should be coded as "retransplant/graft failure" rather than their native kidney disease, only 59% of new incident patients are coded in this manner. Peripheral vascular disease prevalence more than doubled in a 20-y span when the variable became associated with risk adjustment.

CONCLUSIONS

This article summarizes critical gaps in the OPTN/UNOS database, and we bring forward ideas and proposals for consideration as a path toward improvement.

Knowledge and attitude about organ donation and transplantation among Omani university students

Background

Despite the importance of organ donation and transplantation in improving the quality of life, still, there is a shortage of organ donations, worldwide. Lack of knowledge among the public could be the reason. In previous studies, the focus was predominantly on medical students at universities. The aim of this study was to assess university students' knowledge and attitude about organ donation and transplantation among different colleges at the university.

Method

A cross-sectional study was conducted among university students between August 2021 and February 2022 using a validated self-designed questionnaire. The questionnaire consisted of five sections. The first section was about the research information. The second section was informed consent. The third section was about sociodemographic information. The fourth section was about the knowledge of organ donation. The last section was about the attitude toward organ donation. The data were analyzed by descriptive statistics and chi-square tests.

Results

The study included 2,125 students. 68.1% were females, and 93.1% were in the age group 17-24 years old. Only 34.1% had good knowledge about organ donation, 70.2% had a low attitude, and 7.53% had adequate information about brain death. The most common reason for supporting donating organs among university students was to save a life (76.8%) and the most common reason for refusing organs, was I am still unaware. In addition, only 25.66% of the participants had a high attitude toward people with poor knowledge about organ donation. The majority of the students (84.13%) used online sources and social networks as the primary sources of information about organ donation.

Conclusion

The knowledge and attitudes of university students toward organ donation and transplantation were low. Saving a life was the most common reason for supporting organ donation, and knowledge was the biggest obstacle. Online sources and social networks were the primary sources of knowledge. The attitude was greatly influenced by knowledge. Organizing campaigns, and events, and incorporating organ donation and transplantation into university curricula will increase university students' knowledge and attitudes.

Predicting Kidney Transplant Recipient Cohorts' 30-Day Rehospitalization Using Clinical Notes and Electronic Health Care Record Data

Introduction

Rehospitalization after kidney transplant is costly to patients and health care systems and is associated with poor outcomes. Few prediction model studies have examined whether inclusion of clinical notes data from the electronic medical record (EMR) enhances prediction of rehospitalization.

Methods

In a retrospective, observational study of first-time, adult kidney transplant recipients at a large, urban hospital in southeastern United States (2005-2015), we examined 30-day rehospitalization (30DR) using structured EMR and unstructured (i.e., clinical notes) data. We used natural language processing (NLP) methods on 8 types of clinical notes and included terms in predictive models using unsupervised machine learning approaches. Both the area under the receiver operating curve and precision-recall curve (ROC and PRC, respectively) were used to determine and compare model accuracy, and 5-fold cross-validation tested model performance.

Results

Among 2060 kidney transplant recipients, 30.7% were readmitted within 30 days. Predictive models using clinical notes did not meaningfully improve performance over previous models using structured data alone (ROC 0.6821; 95% confidence interval [CI]: 0.6644, 0.6998). Predictive models built using solely clinical notes performed worse than models using both clinical notes and structured data. The data that contributed to the top performing models were not identical but both included structured data and progress notes (ROC 0.6902; 95% CI: 0.6699, 0.7105).

Conclusions

Including new features from clinical notes in risk prediction models did not substantially increase predictive accuracy for 30DR for kidney transplant recipients. Future research should consider pooling data from multiple institutions to increase sample size and avoid overfitting models.

A Population Health Approach to Transplant Access: Challenging the Status Quo

Transplant referral and evaluation are critical steps to waitlisting yet remain an elusive part of the transplant process. Despite calls for more data collection on pre-waitlisting steps, there are currently no national surveillance data to aid in understanding the causes and potential solutions for the extreme variation in access to transplantation. As population health scientists, epidemiologists, clinicians, and ethicists we submit that the transplant community has an obligation to better understand disparities in transplant access as a first necessary step to effectively mitigating these inequities. Our position is grounded in a population health approach, consistent with several new overarching national policy and quality initiatives. The purpose of this Perspective is to (1) provide an overview of how a population health approach should inform current multisystem policies impacting kidney transplantation and demonstrate how these efforts could be enhanced with national data collection on pre-waitlisting steps; (2) demonstrate the feasibility and concrete next steps for pre-waitlisting data collection; and (3) identify potential opportunities to use these data to implement effective population-level interventions, policies, and quality measures to improve equity in access to kidney transplantation.

Co-establishing an infrastructure for routine data collection to address disparities in infant mortality: planning and implementation

Background

Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations.

Methods

Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings.

Results

By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis.

Conclusion

While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07393-1.

Association of low center performance evaluations and pediatric heart transplant center behavior in the United States

BACKGROUND

To date, no study has evaluated the effects of low center performance evaluations (CPE) on pediatric heart transplant center behavior. We sought to assess the impact of low CPE flags on pediatric heart transplant center listing and transplant volumes and center recipient and donor characteristics.

METHODS

We included centers performing at least 10 pediatric (age <18 years) transplants during the Scientific Registry of Transplant Recipients reporting period January 2009-June 2011 and evaluated consecutive biannual program specific reports until the last reporting period January 2016-June 2018. We evaluated changes in center behavior at following time points: a year before flagging, a year and two years after the flag; and at last reporting period.

RESULTS

During our study period, 24 pediatric centers were non-flagged and 6 were flagged. Compared to non-flagged centers, there was a decline in candidate listings in flagged centers at the last reporting period (mean increase of 5.5 ± 12.4 listings vs"?> mean decrease of 14.0 ± 14.9 listings; p = .003). Similarly, the number of transplants declined in flagged centers (mean increase of 2.6 ± 9.6 transplants vs"?> mean decrease of 10.0 ± 12.8 transplants; p = .012). Flagged centers had declines in listings for patients with restrictive cardiomyopathy, re-transplant, renal dysfunction, those on mechanical ventilation and extracorporeal membrane oxygenation. There was no significant change in donor characteristics between flagged and non-flagged centers.

CONCLUSIONS

Low CPE may have unintended negative consequences on center behavior leading to declines in listing and transplant volumes and potentially leading to decreased listing for higher risk recipients.

Development of Phenotyping Algorithms for the Identification of Organ Transplant Recipients: Cohort Study

Background

Studies involving organ transplant recipients (OTRs) are often limited to the variables collected in the national Scientific Registry of Transplant Recipients database. Electronic health records contain additional variables that can augment this data source if OTRs can be identified accurately.

Objective

The aim of this study was to develop phenotyping algorithms to identify OTRs from electronic health records.

Methods

We used Vanderbilt’s deidentified version of its electronic health record database, which contains nearly 3 million subjects, to develop algorithms to identify OTRs. We identified all 19,817 individuals with at least one International Classification of Diseases (ICD) or Current Procedural Terminology (CPT) code for organ transplantation. We performed a chart review on 1350 randomly selected individuals to determine the transplant status. We constructed machine learning models to calculate positive predictive values and sensitivity for combinations of codes by using classification and regression trees, random forest, and extreme gradient boosting algorithms.

Results

Of the 1350 reviewed patient charts, 827 were organ transplant recipients while 511 had no record of a transplant, and 12 were equivocal. Most patients with only 1 or 2 transplant codes did not have a transplant. The most common reasons for being labeled a nontransplant patient were the lack of data (229/511, 44.8%) or the patient being evaluated for an organ transplant (174/511, 34.1%). All 3 machine learning algorithms identified OTRs with overall >90% positive predictive value and >88% sensitivity.

Conclusions

Electronic health records linked to biobanks are increasingly used to conduct large-scale studies but have not been well-utilized in organ transplantation research. We present rigorously evaluated methods for phenotyping OTRs from electronic health records that will enable the use of the full spectrum of clinical data in transplant research. Using several different machine learning algorithms, we were able to identify transplant cases with high accuracy by using only ICD and CPT codes.

Knowledge and Attitudes Regarding Organ Transplantation Among Cyprus Residents

BACKGROUND

Organ transplantation was one of the greatest achievements of medical science during the 20th century. Knowledge, education, and culture all play prominent roles in transplantation because of the complexity of the process from donation to transplantation.

PURPOSE

The aim of this research was to determine and analyze the knowledge and attitudes about organ donation and transplantation among the general population in Limassol, Cyprus.

METHODS

A quantitative research approach was followed, and a questionnaire consisting of closed-ended questions was completed by adults from the general population in Limassol.

RESULTS

One thousand two hundred adults out of the 1,346 adults who were contacted responded to the survey (response rate: 89%) and were included as participants. Of the participants, 93.4% (p < .05) considered organ donation to be lifesaving, 57% expressed interest (and 39.8% expressed disinterest) in becoming organ donors, 80.6% (p < .05) expressed awareness of there being a waiting list for people in need of organ transplantation, 50.4% agreed that brain death must be confirmed before organ removal for transplantation, and 47% recalled having been informed about organ donation through the media, with 31.5% stating that they had never been informed about organ donation.

CONCLUSIONS

The participants demonstrated limited awareness regarding the organ donation system in Cyprus. Furthermore, a significant percentage stated that they lacked a source for obtaining related information. The Cypriot society should be informed and encouraged to participate in organ donation to increase the rate of organ transplantation.

Content Coverage Evaluation of the OMOP Vocabulary on the Transplant Domain Focusing on Concepts Relevant for Kidney Transplant Outcomes Analysis

BACKGROUND

Improving outcomes of transplant recipients within and across transplant centers is important with the increasing number of organ transplantations being performed. The current practice is to analyze the outcomes based on patient level data submitted to the United Network for Organ Sharing (UNOS). Augmenting the UNOS data with other sources such as the electronic health record will enrich the outcomes analysis, for which a common data model (CDM) can be a helpful tool for transforming heterogeneous source data into a uniform format.

OBJECTIVES

In this study, we evaluated the feasibility of representing concepts from the UNOS transplant registry forms with the Observational Medical Outcomes Partnership (OMOP) CDM vocabulary to understand the content coverage of OMOP vocabulary on transplant-specific concepts.

METHODS

Two annotators manually mapped a total of 3,571 unique concepts extracted from the UNOS registry forms to concepts in the OMOP vocabulary. Concept mappings were evaluated by (1) examining the agreement among the initial two annotators and (2) investigating the number of UNOS concepts not mapped to a concept in the OMOP vocabulary and then classifying them. A subset of mappings was validated by clinicians.

RESULTS

There was a substantial agreement between annotators with a kappa score of 0.71. We found that 55.5% of UNOS concepts could not be represented with OMOP standard concepts. The majority of unmapped UNOS concepts were categorized into transplant, measurement, condition, and procedure concepts.

CONCLUSION

We identified categories of unmapped concepts and found that some transplant-specific concepts do not exist in the OMOP vocabulary. We suggest that adding these missing concepts to OMOP would facilitate further research in the transplant domain.

Relative Risk of Cervical Neoplasms Among Copper and Levonorgestrel-Releasing Intrauterine System Users

Copper intrauterine device users have a lower risk of high-grade cervical neoplasms compared with levonorgestrel-releasing intrauterine system users.

OBJECTIVE:

To evaluate the relative risk of cervical neoplasms among copper intrauterine device (Cu IUD) and levonorgestrel-releasing intrauterine system (LNG-IUS) users.

METHODS:

We performed a retrospective cohort analysis of 10,674 patients who received IUDs at Columbia University Medical Center. Our data were transformed to a common data model and are part of the Observational Health Data Sciences and Informatics network. The cohort patients and outcomes were identified by a combination of procedure codes, condition codes, and medication exposures in billing and claims data. We adjusted for confounding with propensity score stratification and propensity score 1:1 matching.

RESULTS:

Before propensity score adjustment, the Cu IUD cohort included 8,274 patients and the LNG-IUS cohort included 2,400 patients. The median age for both cohorts was 29 years at IUD placement. More than 95% of the LNG-IUS cohort used a device with 52 mg LNG. Before propensity score adjustment, we identified 114 cervical neoplasm outcomes. Seventy-seven (0.9%) cervical neoplasms were in the Cu IUD cohort and 37 (1.5%) were in the LNG-IUS cohort. The propensity score matching analysis identified 7,114 Cu IUD and 2,174 LNG-IUS users, with covariate balance achieved over 16,827 covariates. The diagnosis of high-grade cervical neoplasia was 0.7% in the Cu IUD cohort and 1.8% in the LNG-IUS cohort (2.4 [95% CI 1.5–4.0] cases/1,000 person-years and 5.2 [95% CI 3.7–7.1] cases/1,000 person-years, respectively). The relative risk of high-grade cervical neoplasms among Cu IUD users was 0.38 (95% CI 0.16–0.78, P<.02) compared with LNG-IUS users. By inspection, the Kaplan-Meier curves for each cohort diverged over time.

CONCLUSION:

Copper IUD users have a lower risk of high-grade cervical neoplasms compared with LNG-IUS users. The relative risk of cervical neoplasms of LNG-IUS users compared with the general population is unknown.

Patients prioritize waitlist over posttransplant outcomes when evaluating kidney transplant centers

Factors that patients value when choosing a transplant center have not been well studied. In order to guide the improvement of patient-facing materials, we conducted an anonymous electronic survey of patients that assessed the relative importance of patient experience, practical considerations, transplant center reputation, center experience, and waitlist when selecting a transplant center. A total of 409 respondents completed the survey, of whom 68% were kidney transplant recipients and 32% had chronic kidney disease or were on dialysis. Participants had mean age 56 ± 12 years and were predominantly female (61%), white (79%), and had an associate's degree or higher (68%). Participants most often prioritized waitlist when evaluating transplant centers (transplanted 26%, chronic kidney disease 40%), and waitlist was almost twice as likely as outcomes to be ranked most important (30% vs 17%). Education level and transplant status were significantly associated with factors used for center prioritization. Waitlisted respondents most commonly (48%) relied on physicians for information when selecting a center, while a minority cited transplant-specific organizations. In order to improve shared decision-making, materials outlining center-specific waitlist features should be prioritized. Novel patient-oriented metrics for measuring transplant center quality that align with patient priorities must be explored.