A Retrospective Observational Study to Describe Anaemia Management, Burden of Disease and Outcomes in Chinese Patients on Peritoneal Dialysis Using a Clinical Database
Condition: Anemia
MeSH terms: Anemia
Study type: Observational

Summary: 
      The overall objective is to describe the epidemiology, health care resource utilisation
      (HCRU) and selected clinical outcomes in patients initiating PD by anaemia status in
      real-world setting in China.
    

Detailed description: 
      Real-world evidence for chronic kidney disease (CKD)-associated anemia in China is scarce.
      This study aims to provide supporting novel evidence for the real-world management and
      treatment of patients on PD with CKD-associated anemia. This is an observational cohort study
      using secondary data. The data source is the PD Telemedicine-based Management Platform
      (PDTAP) database, which is a clinical database that prospectively collects data in patients
      receiving PD and evaluates PD management and clinical practice in China. The PDTAP database
      has enrolled around 7,000 patients receiving PD from 27 hospitals in 14 provinces located in
      all seven geographical regions (northwest, northeast, north, central, southwest, southeast
      and south) in China.

      The overall objective is to describe the epidemiology, patient characteristics, treatment
      patterns relevant to anemia and clinical outcomes in patients on PD in real-world practice in
      China. Specifically, the incidence and prevalence of anemia will be estimated. The patient
      characteristics, medication use, select laboratory values and clinical outcomes, and
      healthcare resource utilization (HCRU) will be described in PD patients with and without
      anemia. The primary outcomes are 1) the incidence and prevalence of anemia, including the
      factors that predict the incidence of anemia; 2) the number and percentage of patients who
      experienced all-cause hospitalizations; 3) adverse clinical outcomes, such as mortality MACE
      and modified MACE+. The secondary outcomes are 1) hemoglobin levels or anemia, iron
      variables, medications on anemia management and clinical outcomes in patients with or without
      inflammation; 2) ESA responsiveness; 3) Other clinical outcomes, such as transfer to
      hemodialysis and peritonitis.

      Associations between anemia, anemia-relevant factors and above clinical outcomes can be
      analyzed in series of paper with specific aims. For example, 1) the relationship between
      hemoglobin levels or anemia and clinical outcomes (such as mortality, MACE, modified MACE+,
      hospitalization, transfer to hemodialysis and peritonitis); 2) prognostic factors that
      predict the incidence of anemia during the follow up; 3) hemoglobin levels or anemia, iron
      variables, medications on anemia management and clinical outcomes in patients with or without
      inflammation; 4) the relationship between hemoglobin levels or anemia, erythropoietin
      responsiveness and clinical outcomes; 5) prognostic factors that predict the
      hyporesponsiveness to erythropoietin; 6) determine cut-off values of the iron index (such as
      serum iron, ferritin, TIBC and TSAT) to predicting worse clinical outcomes. Each study could
      select hemoglobin relevant variables including clinical characteristics and treatment
      patterns at baseline and/or during the follow up, and explore their associations with
      specific clinical outcomes.

      Confounding, interaction or mediation effects could be evaluated. Subgroups, such as with or
      without diabetes, with or without CVD, with or without inflammation status et al could be
      analyzed as needed. Cox, competing risk models, Poisson or NegBin regression models may be
      used as needed.
    

Observational model: Cohort
Time perspective: Retrospective
Sampling method: Probability Sample
Study population: 
        This study is primarily descriptive and all patients meeting the inclusion criteria will be
        included. We expect to analysis data on approximately 2000 to 2500 peritoneal dialysis
        patients.