Development of a predictive model algorithm to identify patients with hypophosphatasia, using Optimum Patient Care Record Database in United Kingdom

Study type

• Study topic:
  Disease /health condition
• Study type:
  Non-interventional study
• Scope of the study:
  Disease epidemiology
• Data collection methods:
  Secondary use of data

Non-interventional study

• Non-interventional study design:
  Other
• Non-interventional study design, other:
  Retrospective observational case-control study

Study drug and medical condition

• Medical condition to be studied:
  Hypophosphatasia

Population studied

• Short description of the study population:
  Newly diagnosed hypophosphatasia (HPP) patients identified from the Optimum Patient Care Research Database (OPCRD) for the study period of 1st January 2000 to 31st March 2021 in UK.; Inclusion criteria:; 1. All eligible HPP patients newly identified during the study observation period between 1st January 2000 and 31st March 2021 will be included in the study.; ; Exclusion criteria:; 1. Patient aged less <2 years old at first HPP diagnosis will be excluded.
• Age groups:
  Children (2 to < 12 years)

  Adolescents (12 to < 18 years)

  Adults (18 to < 46 years)

  Adults (46 to < 65 years)

  Adults (65 to < 75 years)

  Adults (75 to < 85 years)

  Adults (85 years and over)
• Special population of interest:
  Other
• Special population of interest, other:
  Patients with hypophosphatasia
• Estimated number of subjects:
  4600

Study design details

• Main study objective:
  To develop and validate a scoring algorithm to aid in the diagnosis of HPP by primary care physicians, incorporating symptoms and risk factors recorded in patients’ primary care electronic health records prior to HPP diagnosis.
• Data analysis plan:
  The data analysis will be performed using Python version 3.10.0 and will include the following stages: - Machine learning - Re-sampling - Machine learning algorithms - Model validation - Model Application - Descriptive analyses - Sensitivity analyses -