Clinical, Hematological, Biochemical and Radiological Characteristics for Patients With Splenic Infarction: Case Series With Literature Review
Bibliographic record
Abstract
Background: Splenic infarction is a frequently missed diagnosis in acute clinical conditions and is often under-diagnosed due to the lack of high-quality evidence on pathophysiology of splenic infarction. Due to the scarcity of such evidence, no consensus guidelines regarding the diagnostic approach and management of patients with splenic infarction exist. Most of published articles on splenic infarction are case reports and there was no systematic review on splenic infarction. Methods: We conducted a retrospective analysis of all radiologically confirmed cases of splenic infarction patients with any history of admission at National Center for Global Health and Medicine Kohnodai Hospital, from 2014 to 2020. Further, to understand the pathophysiology that causes splenic infarction, we searched the literatures on splenic infarction. Results: We found 18 patients with splenic infarction. The average age was 78 years, and about half of patients had abdominal pain; however, the other half did not have abdominal pain. One-third of patients with splenic infarction died. Leukocytosis with neutrophilia, a decrease of lymphocytes, anemia, hypoalbuminemia, and liver dysfunction were observed. Fibrinogen was decreased and D-dimer was remarkably elevated. Lactate dehydrogenase (LDH) and C-reactive protein (CRP) were remarkably increased. Six patients (33.3%) had cancer, four patients (22.2%) had atrial fibrillation, and four patients (22.2%) had infection. We found 466 case reports on splenic infarction published from 1975 to 2021. Recently, the number of case reports on splenic infarction due to infection, especially, coronavirus disease 2019 (COVID-19), has been remarkably increasing. Furthermore, we found that leukocytosis, a decrease of lymphocytes, elongated activated partial thromboplastin time, decrease of fibrinogen, liver dysfunction, elevation of LDH and blood urea nitrogen can be the prognosis predicting factors for patients with splenic infarction. Conclusion: Our study elucidated clinical, hematological, biochemical and radiological characteristics for patients with splenic infarction. We newly found significant differences in blood cell counts, coagulation markers, transaminases, LDH and blood urea nitrogen between patients who died and those who survived, suggesting that these parameters can be the prognosis predicting factors for splenic infarction. Further, our systematic review on case reports about splenic infarction showed the etiology of splenic infarction and the trend of the causative diseases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".