Uncontrolled donation after circulatory death improves access to kidney transplantation: A decision analysis

Influence of donor capnometry on renal graft evolution in uncontrolled donation after circulatory death

AIM

To analyse the association between donor capnometry data and the short-term evolution of kidney grafts in cases of uncontrolled donation after circulatory death (uDCD).

METHOD

We used an ambispective observational study design, conducted in the Community of Madrid between January and December 2019, inclusive. Patients who suffered out-of-hospital cardiac arrest (CA) with no response to advanced cardiopulmonary resuscitation (CPR) were selected as potential donors. Donor capnometry levels were measured at the start, midpoint and transfer to hospital then compared with indicators of renal graft evolution.

RESULTS

The initial selection included 34 possible donors, of which 12 (35.2%) were viable donors from whom 22 (32.3%)kidneys were recovered. There was a correlation between the highest capnometry values and less need for post-transplant dialysis (≥24 mmHg, p< 0.017), fewer dialysis sessions and fewer days to recover correct renal function (Rho -0.47, p< 0.044). There was a significant inverse correlation between the capnometry values at transfer and 1-month post-transplant creatinine levels (Rho -0.62, p< 0.033). There were no significant differences between the capnometry values at transfer and primary nonfunction (PNF) or warm ischaemia time. One-year patient survival was 100% for patient receiving organ donation, while graft survival was 95%.

CONCLUSIONS

Capnometry levels at transfer are a useful predictor of the short-term function and viability of kidney transplants from uncontrolled donations after circulatory death.

A Systematic Review of Kidney Transplantation Decision Modelling Studies

BACKGROUND

Genome-based precision medicine strategies promise to minimize premature graft loss after renal transplantation, through precision approaches to immune compatibility matching between kidney donors and recipients. The potential adoption of this technology calls for important changes to clinical management processes and allocation policy. Such potential policy change decisions may be supported by decision models from health economics, comparative effectiveness research and operations management.

OBJECTIVE

We used a systematic approach to identify and extract information about models published in the kidney transplantation literature and provide an overview of the status of our collective model-based knowledge about the kidney transplant process.

METHODS

Database searches were conducted in MEDLINE, Embase, Web of Science and other sources, for reviews and primary studies. We reviewed all English-language papers that presented a model that could be a tool to support decision making in kidney transplantation. Data were extracted on the clinical context and modelling methods used.

RESULTS

A total of 144 studies were included, most of which focused on a single component of the transplantation process, such as immunosuppressive therapy or donor-recipient matching and organ allocation policies. Pre- and post-transplant processes have rarely been modelled together.

CONCLUSION

A whole-disease modelling approach is preferred to inform precision medicine policy, given its potential upstream implementation in the treatment pathway. This requires consideration of pre- and post-transplant natural history, risk factors for allograft dysfunction and failure, and other post-transplant outcomes. Our call is for greater collaboration across disciplines and whole-disease modelling approaches to more accurately simulate complex policy decisions about the integration of precision medicine tools in kidney transplantation.