A phenotypic risk score for predicting mortality in sickle cell disease

Br J Haematol. 2021 Mar;192(5):932-941. doi: 10.1111/bjh.17342. Epub 2021 Jan 28.


Risk assessment for patients with sickle cell disease (SCD) remains challenging as it depends on an individual physician's experience and ability to integrate a variety of test results. We aimed to provide a new risk score that combines clinical, laboratory, and imaging data. In a prospective cohort of 600 adult patients with SCD, we assessed the relationship of 70 baseline covariates to all-cause mortality. Random survival forest and regularised Cox regression machine learning (ML) methods were used to select top predictors. Multivariable models and a risk score were developed and internally validated. Over a median follow-up of 4·3 years, 131 deaths were recorded. Multivariable models were developed using nine independent predictors of mortality: tricuspid regurgitant velocity, estimated right atrial pressure, mitral E velocity, left ventricular septal thickness, body mass index, blood urea nitrogen, alkaline phosphatase, heart rate and age. Our prognostic risk score had superior performance with a bias-corrected C-statistic of 0·763. Our model stratified patients into four groups with significantly different 4-year mortality rates (3%, 11%, 35% and 75% respectively). Using readily available variables from patients with SCD, we applied ML techniques to develop and validate a mortality risk scoring method that reflects the summation of cardiopulmonary, renal and liver end-organ damage. Trial Registration: ClinicalTrials.gov Identifier: NCT#00011648.

Trial registration: ClinicalTrials.gov NCT00011648.

Keywords: machine learning; risk assessment; sickle cell anaemia.

Publication types

  • Observational Study
  • Research Support, N.I.H., Intramural

MeSH terms

  • Adolescent
  • Adult
  • Age Factors
  • Aged
  • Aged, 80 and over
  • Alkaline Phosphatase / blood
  • Anemia, Sickle Cell / blood
  • Anemia, Sickle Cell / mortality*
  • Blood Urea Nitrogen
  • Body Mass Index
  • Case-Control Studies
  • Cluster Analysis
  • Female
  • Follow-Up Studies
  • Heart Rate
  • Heart Valves / physiopathology
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Models, Biological
  • Phenotype*
  • Prognosis
  • Proportional Hazards Models
  • Prospective Studies
  • Risk Assessment*
  • Young Adult


  • Alkaline Phosphatase

Associated data

  • ClinicalTrials.gov/NCT00011648