Vaccination and mortality from COVID-19: a comparative study between hemodialysis, peritoneal dialysis, and non-dialysis adult population in Panama

The Importance of Natural and Acquired Immunity to SARS-CoV-2 Infection in Patients on Peritoneal Dialysis

The pandemic caused by the SARS-CoV-2 virus had a great impact on the population of patients treated with peritoneal dialysis (PD). This study demonstrates the impact of infection and vaccination in 66 patients treated with PD and their outcomes during a 6-month follow-up. This is the first research that has studied the dynamics of anti-SARS-CoV-2 IgG in serum and effluent. In our research, 57.6% of PD patients were vaccinated, predominantly with Sinopharm (81.6%), which was also the most frequently administered vaccine in the Republic of Serbia at the beginning of immunization. During the monitoring period, the level of anti-SARS-CoV-2 IgG antibodies in the PD patients had an increasing trend in serum. In the group of vaccinated patients with PD, anti-SARS-CoV-2 IgG antibodies had an increasing trend in both serum and effluent, in contrast to non-vaccinated patients, where they decreased in effluent regardless of the trend of increase in serum, but statistical significance was not reached. In contrast to vaccinated (immunized) patients who did not acquire infection, the patients who only underwent the COVID-19 infection, but were not immunized, were more prone to reinfection upon the outbreak of a new viral strain, yet without severe clinical presentation and with no need for hospital treatment.

Machine learning algorithms for the prediction of adverse prognosis in patients undergoing peritoneal dialysis

BACKGROUND

An appropriate prediction model for adverse prognosis before peritoneal dialysis (PD) is lacking. Thus, we retrospectively analysed patients who underwent PD to construct a predictive model for adverse prognoses using machine learning (ML).

METHODS

A retrospective analysis was conducted on 873 patients who underwent PD from August 2007 to December 2020. A total of 824 patients who met the inclusion criteria were included in the analysis. Five commonly used ML algorithms were used for the initial model training. By using the area under the curve (AUC) and accuracy (ACC), we ranked the indicators with the highest impact and displayed them using the values of Shapley additive explanation (SHAP) version 0.41.0. The top 20 indicators were selected to build a compact model that is conducive to clinical application. All model-building steps were implemented in Python 3.8.3.

RESULTS

At the end of follow-up, 353 patients withdrew from PD (converted to haemodialysis or died), and 471 patients continued receiving PD. In the complete model, the categorical boosting classifier (CatBoost) model exhibited the strongest performance (AUC = 0.80, 95% confidence interval [CI] = 0.76-0.83; ACC: 0.78, 95% CI = 0.72-0.83) and was selected for subsequent analysis. We reconstructed a compression model by extracting 20 key features ranked by the SHAP values, and the CatBoost model still showed the strongest performance (AUC = 0.79, ACC = 0.74).

CONCLUSIONS

The CatBoost model, which was built using the intelligent analysis technology of ML, demonstrated the best predictive performance. Therefore, our developed prediction model has potential value in patient screening before PD and hierarchical management after PD.