Assessment of tissue allograft safety monitoring with administrative healthcare databases: a pilot project using Medicare data. Cell Tissue Bank. 2014;15:75-84. [PMID: 23824508 DOI: 10.1007/s10561-013-9376-y]

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.

New blood-borne virus infections among organ transplant recipients: An Australian data-linked cohort study examining donor transmissions and other HIV, hepatitis C and hepatitis B notifications, 2000-2015

BACKGROUND

Blood-borne viral infections can complicate organ transplantation. Systematic monitoring to distinguish donor-transmitted infections from other new infections post transplant is challenging. Administrative health data can be informative. We aimed to quantify post-transplant viral infections, specifically those transmitted by donors and those reactivating or arising new in recipients.

METHODS

We linked transplant registries with administrative health data for all solid organ donor-recipient pairs in New South Wales, Australia, 2000-2015. All new recipient notifications of hepatitis B (HBV), C (HCV), or human immunodeficiency virus (HIV) after transplant were identified. Proven/probable donor transmissions within 12 months of transplant were classified using an international algorithm.

RESULTS

Of 2120 organ donors, there were 72 with a viral infection (9/72 active, 63/72 past). These 72 donors donated to 173 recipients, of whom 24/173 already had the same infection as their donor, and 149/173 did not, so were at risk of donor transmission. Among those at risk, 3/149 recipients had proven/probable viral transmissions (1 HCV, 2 HBV); none were unrecognized by donation services. There were no deaths from transmissions. There were no donor transmissions from donors without known blood-borne viruses. An additional 68 recipients had new virus notifications, of whom 2/68 died, due to HBV infection.

CONCLUSION

This work confirms the safety of organ donation in an Australian cohort, with no unrecognized viral transmissions and most donors with viral infections not transmitting the virus. This may support targeted increases in donation from donors with viral infections. However, other new virus notifications post transplant were substantial and are preventable. Data linkage can enhance current biovigilance systems.

Hepatitis B transmission by cell and tissue allografts: How safe is safe enough?

More than 2 million human tissue transplants (bone, tendon, cartilage, skin, cornea, amniotic membrane, stem cells, heart valve, blood vessel, etc.), are performed worldwide every year. Cells and tissues are shared between countries which have different regulations and laboratory equipment and represent a risk of hepatitis B virus (HBV) transmission that has become a global safety concern. While the risk of transfusion-transmitted HBV infection from blood donations has been estimated, the rate of HBV transmission from donors to recipients of allografts is unknown and varies between different tissues. There are various important ways of reducing the transmission risk, but donor screening and donor testing are still the main factors for preventing HBV transmission. HBV detection is included in the routine screening tests for cell and tissue donors. The standard test for preventing transplant-transmitted hepatitis B is the hepatitis B surface antigen. The implementation of methods involving nucleic acid amplification and the new generation of reactives to detect viral antibodies or antigens with an immunoassay, has increased the sensitivity and the specificity of the screening tests. The objective of our research was to review the literature and critically analyse the different steps for avoiding HBV transmission in cell and tissue donors, focusing on the screening tests performed.