Validation of Sickle Cell Disease Severity Score in a Cohort of Hemoglobin SC Disease Patients
Bibliographic record
Abstract
Abstract Introduction: Hemoglobin SC (HbSC) disease is a variant of sickle cell disease (SCD), with a distinct clinical profile from the more common homozygous sickle cell anemia (HbSS) (Nagel RL, et al. 2003). As part of an ongoing study seeking to correlate genome-wide-association data with a clinical phenotypic profile of HbSC; the study attempts to validate the "Sickle Cell Disease Severity Score (SSS) calculator" (Sebastiani et al. 2007) in a longitudinal cohort of HbSC disease patients. Methods: The entire cohort of adult (≥18 years of age) HbSC disease patients enrolled within the ongoing cross-sectional phenotype-genotype correlation study were assessed and scored according to the SSS calculator. Utilizing the Sickle Cell Disease Severity Scores generated from patient data, the study assessed the ability for the calculator to predict patient mortality and morbidity within the cohort. All associated morbidities were defined in accordance to the Cooperative Study of Sickle Cell Disease (CSSSD). Results: A total of 111 adult HbSC disease patients were enrolled. Of the 23 patients with intermediate or high SSS, only 1 died within the study period. A high SSS did not correlate with the presence of SCD clinical outcomes (retinopathy, chronic renal failure, leg ulcers, hearing disorders, cholecystectomy, splenomegaly, splenectomy status, splenic sequestration crisis, osteonecrosis, acute chest syndrome, priapism, painful vaso-occlusive crisis, proteinuria and serum ferritin), when univariate analyses were conducted. In contrast, the study identified association between SSS and cerebral vascular accident (CVA; composite of overt stroke, hemorrhage or silent cerebral infarct) (OR 1.935 for every 0.25 increase in SSS, P = 0.016), as well as tricuspid regurgitant jet velocity (TRJV) > 2.5 m/s (OR 1.944 for every 0.25 increase in SSS, P = 0.022). Elevated TRJV was further analysed after correcting for patient-related factors (weight, hydroxyurea use, regular therapeutic phlebotomy, hemoglobin, hematocrit, red blood cell count, and platelet count) and remained correlative. In addition, SSS was associated with creatinine clearance by a quadratic function (R2 = 16.4%, P = 0.008); which may be indicative of the natural history of declining renal function in HbSC disease patients. SSS was not independently predictive of either the presence or number of SCD morbidities (proteinuria, retinopathy, splenic complications, leg ulcers, cholecystectomy, and hearing disorder) in the cohort after adjusting for patient-related factors. Conclusion: Despite having been derived from a SCD population that was 26% HbSC, the study was unable to validate the SSS within the cohort of HbSC patients. This may reflect the differences in patient population and/or therapeutic intervention between this cohort and the CSSCD cohort used in the construction of the SSS calculator. While SSS was found to correlate with 3 discrete markers of disease morbidity (TRJV, CVA, creatinine clearance), it appears that a new scoring system is required to accurately predict clinical mortality and morbidity in contemporary cohorts of adult HbSC disease patients. Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".