ABSTRACT
BACKGROUND: Clinical decisions are mainly driven by the ability of physicians to apply risk stratification to patients. However, this task is difficult as it requires complex integration of numerous parameters and is impacted by patient heterogeneity. We sought to evaluate the ability of transplant physicians to predict the risk of long-term allograft failure and compare them to a validated artificial intelligence (AI) prediction algorithm. METHODS: We randomly selected 400 kidney transplant recipients from a qualified dataset of 4000 patients. For each patient, 44 features routinely collected during the first-year post-transplant were compiled in an electronic health record (EHR). We enrolled 9 transplant physicians at various career stages. At 1-year post-transplant, they blindly predicted the long-term graft survival with probabilities for each patient. Their predictions were compared with those of a validated prediction system (iBox). We assessed the determinants of each physician's prediction using a random forest survival model. RESULTS: Among the 400 patients included, 84 graft failures occurred at 7 years post-evaluation. The iBox system demonstrates the best predictive performance with a discrimination of 0.79 and a median calibration error of 5.79%, while physicians tend to overestimate the risk of graft failure. Physicians' risk predictions show wide heterogeneity with a moderate intraclass correlation of 0.58. The determinants of physicians' prediction are disparate, with poor agreement regardless of their clinical experience. CONCLUSIONS: This study shows the overall limited performance and consistency of physicians to predict the risk of long-term graft failure, demonstrated by the superior performances of the iBox. This study supports the use of a companion tool to help physicians in their prognostic judgement and decision-making in clinical care.
The ability to predict the risk of a particular event is key to clinical decision-making, for example when predicting the risk of a poor outcome to help decide which patients should receive an organ transplant. Computer-based systems may help to improve risk prediction, particularly with the increasing volume and complexity of patient data available to clinicians. Here, we compare predictions of the risk of long-term kidney transplant failure made by clinicians with those made by our computer-based system (the iBox system). We observe that clinicians' overall performance in predicting individual long-term outcomes is limited compared to the iBox system, and demonstrate wide variability in clinicians' predictions, regardless of level of experience. Our findings support the use of the iBox system in the clinic to help clinicians predict outcomes and make decisions surrounding kidney transplants.
ABSTRACT
BACKGROUND: The use of belatacept is not yet approved for maintenance in kidney transplant patients. This retrospective multicenter European study aimed to assess the efficacy and safety of conversion to belatacept in a large cohort of patients in a real-life setting and to identify the predictive factors for improved kidney function after the switch. METHODS: Two hundred nineteen maintenance kidney transplant patients from 5 European kidney transplant centers were converted to belatacept at 21.2 months (0.1-337.1 months) posttransplantation, mainly because of impaired kidney function. Thirty-two patients were converted to belatacept within the first 3 months posttransplantation. The mean duration of follow-up was 21.9 ± 20.2 months. RESULTS: The actuarial rate of patients still on belatacept-based therapy was 77.6%. Mean estimated glomerular filtration rate increased from 32 ± 16.4 at baseline to 38 ± 20 mL/min per 1.73 m (P < 0.0001) at last follow-up. Conversion to belatacept before 3 months posttransplantation was the main predictive factor for a significant increase in estimated glomerular filtration rate (of 5 and 10 mL/min per 1.73 m at 3 and 12 months after the switch, respectively). Eighteen patients (8.2%) presented with an acute rejection episode after conversion; 3 developed a donor-specific antibody. Overall efficacy and safety were good, including for the 35 patients that had a donor-specific antibody at conversion. CONCLUSIONS: The conversion to belatacept was effective, especially when performed early after transplantation.
