PloS one · 2026
Predicting elevated transcranial doppler velocity among patients with sickle cell anemia in Uganda: A cross-sectional study.
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Nayiga Doreen, Mukunya David, Odoch Simon, Oguttu Faith, Munabi Ian, Musaba Milton W, Kimbugwe Charles, Makoko Brian Tonny, Babuya Jonathan, Mugabi Joshua, Nyalihama Alain, Chebet Martin, Rynn Lisa, Kizito Samuel, Ssentumbwe Vincent, Kiguli Sarah, Olupot Peter Olupot
Аннотация
BACKGROUND: Sickle cell anemia (SCA) is an autosomal recessive blood disorder resulting from a specific point mutation in the β-globin gene. Over half a million children are born with sickle cell anemia annually. Transcranial Doppler (TCD) velocity is an accurate predictor of the risk of stroke among children with sickle cell anemia. Unfortunately, TCD screening is not routinely done in developing countries due to limited resources. There is a need to develop a model that predicts elevated TCD velocity, utilizing routinely collected data to guide management of children with sickle cell anemia.
METHODS: We conducted a cross-sectional study from 1st July 2024-30th August 2024 among children with SCA attending the Sickle Cell Clinic. We developed a risk-prediction model for elevated TCD (≥ 170 cm/s) using sociodemographic, hematological, and clinical factors. We used the least absolute shrinkage and selection operator (LASSO) penalized regression to select the best subset of predictors of increased TCD velocity. Model performance was assessed by determining the discrimination using the area under the curve and calibration by drawing a calibration plot.
RESULTS: We enrolled 385 children; the mean age was 10.3 (SD 3.8) years. The prevalence of elevated TCD, defined as ≥170 cm/s was 8.3% (95% CI: 5.8, 11.5; n = 32/385). Using a lambda of 0.008, the final model had 12 predictors. The predictors included neuropathy, red blood cell count, heart rate, age, adherence to hydroxyurea, headache, hematocrit, serum lactate dehydrogenase, gender, malnutrition, blood transfusion, and neutrophils. The model predicted elevated TCD with an AUC of 84.7% (95%CI: 74.7, 90.8).
CONCLUSION: We developed and validated a model to predict elevated TCD among children living with SCA in Uganda. Further exploration is needed to assess whether this model predicts stroke.
METHODS: We conducted a cross-sectional study from 1st July 2024-30th August 2024 among children with SCA attending the Sickle Cell Clinic. We developed a risk-prediction model for elevated TCD (≥ 170 cm/s) using sociodemographic, hematological, and clinical factors. We used the least absolute shrinkage and selection operator (LASSO) penalized regression to select the best subset of predictors of increased TCD velocity. Model performance was assessed by determining the discrimination using the area under the curve and calibration by drawing a calibration plot.
RESULTS: We enrolled 385 children; the mean age was 10.3 (SD 3.8) years. The prevalence of elevated TCD, defined as ≥170 cm/s was 8.3% (95% CI: 5.8, 11.5; n = 32/385). Using a lambda of 0.008, the final model had 12 predictors. The predictors included neuropathy, red blood cell count, heart rate, age, adherence to hydroxyurea, headache, hematocrit, serum lactate dehydrogenase, gender, malnutrition, blood transfusion, and neutrophils. The model predicted elevated TCD with an AUC of 84.7% (95%CI: 74.7, 90.8).
CONCLUSION: We developed and validated a model to predict elevated TCD among children living with SCA in Uganda. Further exploration is needed to assess whether this model predicts stroke.