Therapeutic Plasmapheresis in Primary Presentation of Diabetes Mellitus With Diabetic Ketoacidosis, Hypertriglyceridemia and Acute Pancreatitis
Bibliographic record
Abstract
Severe hypertriglyceridemia (SHTG)-induced acute pancreatitis has been well described. Currently accepted therapeutic options are limited. We report a case of acute pancreatitis associated with hypertriglyceridemia and diabetic ketoacidosis that was safely and effectively managed with plasmapheresis. A 38-year-old female with history of gestational diabetes presented with acute onset of nausea and abdominal pain. She denied alcohol use and was on oral contraceptive pills. On physical exam, she was afebrile, normotensive, and tachycardic with mild abdominal distension and diffuse tenderness. Diagnostic tests revealed a serum glucose of 414 mg/dL, triglycerides > 816 mg/dL, amylase of 106 U/L, lipase of 272 U/L, anion gap of 23, BUN of 13 mg/dL, creatinine of 0.3 mg/dL, sodium of 120 mmol/L, HCO 3 of 14 mmol/L, and positive urine ketones. Computed tomography (CT) findings were consistent with severe acute pancreatitis. The patient was managed conservatively on day 1. Due to persistence of symptoms, plasma exchange using NxStage plasmafilter was performed on day 2 and day 3, which resulted in significant reduction of the triglyceride level and resolution of abdominal pain. Patient was discharged home with gemfibrozil and glyburide as maintenance therapy. The exact mechanism of hypertriglyceridemia-induced pancreatitis is not clear. It has been postulated that hyperviscosity of blood due to lipid particles causes ischemia in the pancreas, releasing inflammatory mediators and leading to pancreatic necrosis and inflammation. The advantage of plasmapheresis over conservative management is the removal of lipid particles in a relatively short period of time and clearance of triglyceridemia-associated pro-inflammatory agents. World J Nephrol Urol. 2014;3(4):162-166 doi: http://dx.doi.org/10.14740/wjnu189w
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".