Comparison of the Efficacy of Simple, Partial Manual and Automated Exchange Transfusions in the Management of Sickle Cell Disease in Pediatric Patients
Bibliographic record
Abstract
Abstract Background: Chronic transfusion therapy in sickle cell disease is used to prevent complications of sickle cell disease by reducing hemoglobin S levels, most commonly used for primary or secondary stroke prophylaxis, amongst other indications. Transfusions can be completed as simple, partial manual exchange or automated exchange.Comparative evidence on the long term efficacy of simple, partial manual or automated exchange in the management of children with sickle cell disease is lacking. Methods: A retrospective study of patients aged less than 18 years with a diagnosis of sickle cell disease on a chronic transfusion program (simple top-up transfusion, partial manual exchange or automated exchange transfusion), followed at the Hospital for Sick Children, Toronto, Ontario from January 2003- July 2020.We excluded patients who received transfusions for acute complications. Data collected included: demographics, indication for transfusion, type of transfusion (simple, partial manual exchange or automated exchange), access for transfusion (peripheral intravenous (PIV), central venous line (CVL)), pre-transfusion hemoglobin and hemoglobin S values. Analysis: Exploratory data analysis was conducted where descriptive statistics were used to summarize data for both continuous and categorical variables. Continuous variables were summarized using measures of central tendency and dispersion where mean and standard deviations for normally distributed data and medians and interquartile ranges were used where the data was skewed. Chi-squared tests were employed when demonstrating relationships between two categorical variables. All statistical analyses were two-sided tests with 0.05 as the critical level of significance. Ethics: This study was approved by The Hospital of Sick Children Research Ethics Board(REB). Results: Sixty-one participants were observed between January 2003 and July 2020. Majority 38 out of (62.3%) of the participants were male. The most common indication for transfusion was primary stroke prevention (following abnormal transcranial doppler (TCD) 36 %) followed by vasculopathy 11 (%, stroke 9 % , abnormal TCD & silent infarct 8 %, and splenic sequestration 2 %) There were 744 total transfusions. 491/744 (66%) transfusions were simple transfusions, 168/744 (22.6%) were PMEs while 85/744 (11.4%) were apheresis transfusions. Average pre-transfusion hemoglobin S (HbS) was similar between the two types of access (p=0.416) and also across the three types of transfusion (p=0.158). The type of access did not appear to have an effect on the changes in HbS per transfusion(p=0.561.) The trends of pre-transfusion HbS %were similar over time between participants whose access was PIV and those whose access was CVL/ PORT.(Figure 1 below). Achievement of target HbS was similar between peripheral intravenous and central venous line access (p=0.337) and across the three types of transfusion (p=0.086).See Table 1 below. The type of transfusion had an effect on the reduction in HBS with simple transfusion having the highest percentage change of HbSS(-3.69%) followed by Apheresis (-1.32%) and PME (-0.75%), p=0.018., that is per every transfusion. The reason for this is that is the values being compared are quite different. The automated exchange values are on established patients (pre transfusion hemoglobin S had already been lowered), while the simple transfusion values are on new to transfusion patients (pre transfusion S high), the patients on partial manual exchange started off with lower HbS levels, so consequently their change was less. Conclusion: All three types of transfusion had equal efficiency in reducing HbS over time. Apheresis showed a quicker reduction in the hemoglobin S level in the initial transfusions. Simple transfusions and PME are as efficient as apheresis in achieving target HbS levels to prevent complications associated with SCD. Figure 1 Figure 1. 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".