#3836 DETERMINATION OF PREDICTORS OF OUTCOMES FOLLOWING VASCULAR ACCESS CONVERSION: WHICH PATIENTS BENEFIT MOST?
Authors: Hofmann Amun , Lama Suman , Zhang Hanjie , Assadian Afshin , Sor Murat , Kotanko Peter , Raimann Jochen 
Published: June 2023

Background and Aims

Conversion from central venous catheter (CVC) to arteriovenous fistula or graft (non-CVC) associates with favorable outcomes in hemodialysis (HD) patients. However, the “fistula first” paradigm has been debated and counter-proposed as “right access for the right patient at the right time and for the right reason”, indicating a shift towards precision medicine in vascular access care. It is unclearly how to classify patients into those suitable for conversion from CVC to non-CVC and which subgroups benefit most. Many factors (such as demographic, clinical and laboratory features) have been proposed to predict non-CVC failure after conversion and adverse outcomes. Due to confounding and potential biases it is difficult to shed light on which patients would benefit most from conversion. We investigated which factors are likely to be most helpful in the prediction of consequent benefit from conversion.

Method

We studied 54 potential predictors of incident HD patients undergoing conversion from CVC to non-CVC, between Jan. 2016 and Dec. 2019. Predictors included demographic and clinical variables such as comorbidities, drug history, and lab parameters. First, feature importance was assessed to separate weak from strong predictors using the Boruta algorithm. Second, important and tentative features were utilized in a subsequent machine learning workflow. Our main outcome was the predictive performance of different machine learning classification algorithms to predict re-conversion to central venous catheter and mortality within 1 year after conversion. Performance was quantified as accuracy, sensitivity, and Area under the receiver operating characteristics curve (AUC ROC). We compared insights from Machine learning algorithms to multivariate logistic regression models with selected inputs based on published literature (sex, age, BMI, diabetes, inflammation, blood pressure).

Results

After exclusion of patients with missing data, 38,151 out of 73,031 incident US HD patients were studied. Of these, 25,470 (66.8%) experienced no major adverse outcome within 1 year after access conversion, 3,683 (9.7%) underwent re-conversion, 7,779 (20.4%) did not survive the observational period, and 1,219 (3.2%) required re-conversion and did not survive the follow-up period. Post HD systolic blood pressure, history of a previous non-CVC that failed, and anthropometric characteristics (height, weight) had most weight in the prediction of re-conversion to CVC, according to the Boruta algorithm. Ethnicity and age were found to be the most important predictors of mortality.

Classification based on the absence/presence of any major adverse outcomes resulted in a predictive accuracy between 0.70 and 0.54, depending on the respective algorithm. Sensitivity and ROCAUC had maxima of 0.58 and 0.69, respectively. However information gain by including all additional values had no remarkable effect on the predictive qualities (Table 1).

Logistic regression for re-conversion in survivors only resulted in an accuracy of 0.78 and sensitivity of 0.29. Limiting predictors to 6 published predictors of our studied outcomes in the context resulted in an accuracy of 0.77 and sensitivity of 0.22 (Figure 1a and b).

Conclusion

Prediction of re-conversion and mortality within 1 year after catheter-to-arteriovenous access conversion is accurately feasible based on demographic and clinical features but discrimination of those benefiting most comes with low predictive accuracy. It remains reasonable to assume that not all patients will truly benefit from conversion, the inability to identify those that do not based on retrospective medical records data, again emphasizes “Fistula first” (if surgically feasible) and the need for investigation into molecular and genetic risk determinants.