Evaluation of Outcomes of Peritoneal Dialysis Patients in the Post-COVID-19 Period: A National Multicenter Case-Control Study from Turkey

Öztürk, Savaş; Gürsu, Meltem; Arıcı, Mustafa; Şahi̇n, İdris; Eren, Necmi; Yılmaz, Mürvet; Koyuncu, Sümeyra; Şirali, Semahat Karahisar; Ural, Zeynep; Dursun, Belda; Yüksel, Enver; Uzun, Sami; Sipahi, Savaş; Ahbap, Elbis; Yazıcı, Halil; Altunören, Orçun; Tunca, Onur; Ayar, Yavuz; Oğuz, Ebru Gök; Yılmaz, Zülfükar; Kahvecioğlu, Serdar; Aşıcıoğlu, Ebru; Oruç, Ayşegül; Ataman, Rezzan; Aydın, Zeki; Huddam, Bülent; Dölarslan, Mürşide Esra; Azak, Alper; Bakırdöğen, Serkan; Yalçın, Ahmet; Karadağ, Serhat; Ulu, Memnune Sena; Güngör, Özkan; Arı, Elif; Odabaş, Ali Rıza; Seyahi, Nurhan; Yıldız, Alaattin; Ateş, Kenan

doi:10.1159/000526234

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Consistent with our research findings, patients with PD with high iron levels have a four-fold higher risk of all-cause cardiovascular death [22]. The effect of the COVID-19 pandemic on patients with PD is still being debated, and certain authors argue that COVID-19 has no effect on the survival of patients with PD [23].…”

Section: Discussionsupporting

confidence: 87%

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

Yang,

Wan,

Feng

et al. 2024

BMC Med Inform Decis Mak

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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.

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Section: Discussionsupporting

confidence: 87%

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

Yang,

Wan,

Feng

et al. 2024

BMC Med Inform Decis Mak

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“…Data from an observational study in Turkey reported that the mortality rate was high (46.2%) in PD-hospitalized patients with COVID-19 [127]. In addition, according to the European Renal Association database, COVID-19 PD patients had a higher mortality risk than HD patients, even after accounting for patient characteristics and disease severity [15].…”

Section: Covid-19 In Dialysis Patientsmentioning

confidence: 99%

Kidney Issues Associated with COVID-19 Disease

Dousdampanis,

Mouzaki,

Trigka

et al. 2023

Encyclopedia

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Infection with SARS-CoV-2 and the resulting COVID-19 can cause both lung and kidney damage. SARS-CoV-2 can directly infect renal cells expressing ACE2 receptors, resulting in kidney damage, and acute kidney injury (AKI) has been reported in COVID-19 hospitalized patients. The pathophysiology of COVID-19-associated AKI is multifactorial. Local and systemic inflammation, immune system dysregulation, blood coagulation disorders, and activation of the renin-angiotensin-aldosterone system (RAAS) are factors that contribute to the development of AKI in COVID 19 disease. COVID-19 patients with kidney involvement have a poor prognosis, and patients with chronic kidney disease (CKD) infected with SARS-CoV-2 have an increased mortality risk. CKD patients with COVID-19 may develop end-stage renal disease (ESRD) requiring dialysis. In particular, patients infected with SARS-CoV-2 and requiring dialysis, as well as patients who have undergone kidney transplantation, have an increased risk of mortality and require special consideration. Nephrologists and infectious disease specialists face several clinical dilemmas in the prophylaxis and treatment of CKD patients with COVID-19. This entry presents recent data showing the effects of COVID-19 on the kidneys and CKD patients and the challenges in the management of CKD patients with COVID-19, and discusses treatment strategies for these patients.

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“…A Turkish study of 113 PD patients in 2021 reported that COVID-19 had no significant association with PD characteristics and baseline laboratory data. 9 Another study showed a difference in vaccination rate, sex predominance and primary kidney disease between the COVID-19 and non-COVID-19 groups in 127 Tunisian PD patients from 2019 to 2021. 10 A cohort study involving 1,487 PD patients between August 2021 and July 2022 in Thailand showed that the predictors of COVID-19-related death in PD patients were similar to those in haemodialysis patients, including vaccine, corticosteroid therapy, delta variant and need for respiratory support.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 and outcomes in Chinese peritoneal dialysis patients

Liao,

Zhou,

et al. 2024

Perit Dial Int

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Background: Reports on COVID-19 in peritoneal dialysis (PD) patients are scarce in China. This study aimed to describe the characteristics and outcomes of PD patients with COVID-19 after China abandoned the ‘zero-COVID’ policy. Methods: This single-centre retrospective study included patients receiving PD who underwent testing for COVID-19 infections between 7 December 2022 and 7 January 2023. Outcomes of interest included factors associated with positive COVID-19 testing result and clinical outcomes including COVID-19-related hospitalisation and severe COVID-19, which were analysed using logistic regression analyses. Results: A total of 349 PD patients (male 53.6%, age 49 ± 13 years old) were included, and 235 patients (67.3%) were infected. There were no significant differences between COVID-19 and non-COVID-19 patients other than higher proportion of vaccinated patients and slow transporters in the patients who tested positive for COVID-19 (44.7% vs. 28.1%, p = 0.003; 8.7% vs. 1.8%, p = 0.03, respectively). Multivariate analysis showed COVID-19 was associated with vaccination (odds ratio (OR): 1.71, 95% confidence interval (CI): 1.02–2.86) and slow transport type (compared with average transport type, OR: 4.52, 95% CI: 1.01–20.21). Among the patients with infection, 38 (16.2%) patients were hospitalised, 18 (7.7%) patients had severe disease and 9 (3.8%) patients died. In multivariate logistic analysis, both age (OR: 1.04, 95% CI: 1.01–1.07; OR: 1.06, 95% CI: 1.02–1.11) and hyponatremia (OR: 5.44, 95% CI: 1.63–18.13; OR: 6.50, 95% CI: 1.77–23.85) were independent risk factors for COVID-19-related hospitalisation and severe disease. Conclusions: Although vaccinated patients were more likely to have tested positive for COVID-19 infection, they appeared to have less severe infection and less need for hospitalisation. Patients who were older with a history of hyponatremia were more likely to experience adverse outcomes from COVID-19.

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Evaluation of Outcomes of Peritoneal Dialysis Patients in the Post-COVID-19 Period: A National Multicenter Case-Control Study from Turkey

Cited by 4 publications

References 22 publications

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

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

Kidney Issues Associated with COVID-19 Disease

COVID-19 and outcomes in Chinese peritoneal dialysis patients

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