Heroin Epidemic and Acute Kidney Injury: An Under-Recognized but Important Consequence of Opioid Overdose
Bibliographic record
Abstract
Heroin abuse and overdose are increasing at an alarming rate in the United States. Due to its relatively low cost and ease of availability, over a million people are abusing this agent. What is most disturbing is the fact that heroin-associated deaths have tripled over the past 5 years. While heroin has a major negative impact on the cardiopulmonary system, acute kidney injury (AKI) following heroin overdose is emerging as a major problem. AKI increases mortality and is a major cause of the development of chronic kidney disease and its antecedent long-term mortality. Timely diagnosis of AKI and its treatment reduces mortality. In this article, we present two cases (a 25-year-old man and a 22-year-old woman) of heroin-induced AKI. Both presented with altered mental status, respiratory depression and low blood pressure. AKI was diagnosed by the treating internist in a timely fashion and optimally treated in the 25-year-old man. In the 22-year-old woman who presented to the emergency department, AKI could not be recognized. She was discharged home after the management of overdose with resolution of pulmonary and neurological issues. Six days later, she returned to the emergency department with shortness of breath, volume overload, and severe acute tubular necrosis, required hemodialysis and left the hospital with serum creatinine of 2.5 mg/dL (estimated glomerular filtration rate (eGFR) = 46 mL/min). At a 7-month follow-up, this patient continues to have eGFR of 45 mL/min (stage III chronic kidney disease). This article presents the mechanism of heroin-induced AKI as well as its management strategy and calls for heightened awareness for early diagnosis and prompt treatment. J Med Cases. 2017;8(10):305-310 doi: https://doi.org/10.14740/jmc2898w
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".