PNH, Thrombosis
Conditions
Keywords
PNH, Thrombosis, anemia, aplastic anemia, myelodysplastic syndrome (MDS)
Brief summary
The present study is a non-interventional retrospective chart review study assessing the prevalence of PNH-clones in patients with PNH risk-factors aged ≥14 years and treated at our hospital. The objective of this study is to develop a PNH screening tool on the hospital Electronic Health Record (EHR) system. An algorithm defining PNH risk groups is developed.
Detailed description
Paroxysmal Nocturnal Hemoglobinuria (PNH) is a life-threatening hematological disorder, but with an effective therapy. Prevalence is estimated between 1-5 per million people, often manifested by cardiovascular, gastrointestinal, neurological or haematological symptoms. Referral is therefore typically to several specialists, resulting in PNH underdiagnosis. This chart review study consists primarily of developing an algorithm to identify a high-risk cohort of potential PNH patients who need treatment from all registered patients, with maximum ability to find relevant cases. Secondly, this cohort will be manually reviewed by clinicians for final screening. The challenge hence is maximizing the ability to find all relevant PNH patients yet limiting the number to ensure manual review is possible.
Interventions
OTHERNo intervention
No interventions
Sponsors
Alexion Pharmaceuticals, Inc.
AZ Delta
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
14 Years to No maximum
Inclusion criteria
* Patient ≥14 years of age * At least 1 record encoded in the General Hospital Delta (AZ Delta) patient database (HiX) between 20 April 2018 and 1 March 2022 * A history or presence of at least one PNH risk factors identified following retrospective screening of electronic patient records
Exclusion criteria
* None
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| PNH risk factors translation into query codes that can be interpreted by a computer system for analysis | 2022 | Three main PNH risk sub-cohorts were constructed, representing patients exhibiting PNH risk factors such as hemolytic anemia (group 1), bone marrow dysfunction (group 2) and thrombosis (group 3). To build these sub-cohorts, queries were created using a combination of structured and unstructured electronic health record (EHR) data, including lab results, diagnoses, medication, questionnaire data, text from medical and radiology reports, notes, and Internation Classification Codes (ICD-10). These sub-cohorts were validated by two hematologists who reviewed randomly selected patients, resulting in several iterations and query optimizations. |
| Number of patients identified with high risk for PNH clone and per type of screening criteria by developing a computational screening algorithm | 2022 | A computational algorithm was employed for a retrospective EHR analysis, to identify high-risk cohorts of potential PNH patients who need treatment from all registered patients, with maximum ability to find relevant cases. Three main PNH risk sub-cohorts were constructed, representing patients exhibiting PNH risk factors such as hemolytic anemia (group 1), bone marrow dysfunction (group 2) and thrombosis (group 3). These sub-cohorts were validated by two hematologists who reviewed randomly selected patients, resulting in several iterations and query optimizations. Sub-cohorts were subsequently merged and refined into high risk cohorts that undergo further analysis and manual review. Two hematologists independently reviewed and rated medical records to achieve a manual risk stratification of the high risk cohorts. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The number of patients at high risk for PNH, categorized by risk factor, across each medical department | 2023 | To increase awareness of PNH risk factors by medical departments that need to consider PNH lab testing |
Countries
Belgium
Outcome results
None listed