Connective Tissue Disorders in Patients With Thrombotic Thrombocytopenic Purpura: A Retrospective Analysis Using a National Database
Bibliographic record
Abstract
BACKGROUND: Prior reports have shown acquired thrombotic thrombocytopenic purpura (TTP) co-existing with connective tissue disorders (CTD). However, these are mainly limited to case reports and case-series reports, and the patient characteristics and clinical outcomes in these patients are not well known. METHODS: We used National Inpatient Sample and Nationwide Inpatient Sample (NIS) database for the years 2009 to 2016 to identify all adult patients with TTP and searched for either the presence or absence of any co-existing CTD. These two cohorts of TTP patients were then compared using statistical methods for baseline patient characteristics and clinical outcomes. The primary outcome of interest was the all-cause in-hospital mortality and the secondary outcomes were in-hospital length of stay, in-hospital total charge and in-hospital complications. RESULTS: Of the 14,400 cases of TTP diagnosed between 2009 and 2016, nearly 9% (n = 1,247) had one or more underlying CTD. Patients with TTP were more likely to be young, black, female, with more than one comorbidity and with private insurance if they had an underlying CTD than when they did not have any underlying CTD. There was no difference in regards to the size, location or type of the hospital, or the time taken to initiate plasmapheresis. Patients being managed for TTP had a longer mean length of hospital stay and a greater mean total inpatient stay charge if they had underlying CTD. There was however no difference in the risks of inpatient mortality, acute coronary syndrome, cardiac arrest, acute stroke, need for mechanical ventilation or hemodialysis. CONCLUSION: TTP and CTD frequently co-existed and contributed to a longer hospital stay and a greater hospital charge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".