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Medication Error: A Leading Cause of Anesthesia-related Morbidity and Mortality

2007· letter· en· W1975896248 on OpenAlexaffabout
Rachel Meyer, Beverley A. Orser, Robert Byrick

Bibliographic record

VenueAnesthesiology · 2007
Typeletter
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAnesthesiaIntensive care medicine

Abstract

fetched live from OpenAlex

We read with great interest the analysis of anesthesia-related deaths registered by the Danish Patient Insurance Association.1Hove et al. 1are to be congratulated for reporting these important results. The authors categorized 24 fatal cases by their underlying causes: airway management, ventilation management, placement of a central venous catheter, medication errors, transfusion error, infusion pump problems, and regional blockade. We noted that 8 of the 24 anesthesia-related deaths described were most likely attributable to a drug error: 4 overdoses (benzodiazepines, methohexital, thiopental, nitroglycerine), 3 infusion pump errors, and 1 patient likely received a large intrathecal dose of mepivacaine. Therefore, the frequency of medication-related incidents exceeded the 4 deaths that resulted from loss of the airway and 4 from complications related to central venous line insertion. It seems that the single most common cause of anesthetic-related death was a drug error.These findings are consistent with an analysis from the Canadian Medical Protective Association of closed medicolegal claims against anesthesiologists.2The Canadian Medical Protective Association provides malpractice insurance for most physicians in Canada. From 1998 to 2002, there were 232 closed legal actions against anesthesiologists. Medication error was the most common cause involving 52% of the claims. It is noteworthy that the American Society of Anesthesiologists Closed Claims Project reports the proportion of drug errors as 4%.2This number has been consistent throughout the 1980s and 1990s. Reasons for the discrepancy in the relative frequency of medication errors reported in the American Society of Anesthesiologists Closed Claims Database from those in Demark and Canada requires further exploration but may be attributed, in part, to differences in the categorization of root causes.The impact of drug error in anesthetic practice is not new and will not surprise experienced anesthesiologists. A survey by the Canadian Anesthesiologists' Society found that 85% of participants had experienced at least one drug error or “near miss.”3Most of these errors were of minor consequence; however, 1.8% resulted in major morbidity (cardiac arrest, stroke, permanent injury) or death. The misidentification of a syringe was the most common cause. In 1984, Cooper et al. 4published a classic analysis of critical incidents in anesthesia management. Breathing circuit disconnect was the most common identified factor; however, reanalyzing their data set indicates that medication-related events far exceeded airway and ventilation problems. Of a total of 507 incidents, 169 were attributed to errors or problems in drug administration. Equally important, when incidents with “substantive negative outcomes” were further analyzed (defined as mortality, cardiac arrest, cancelled operative procedure, or extended recovery room, intensive care unit, or hospital stay) approximately 25% of them resulted from a drug error.Together, these studies suggest that the impact of medication error has been underestimated by the lack of a common taxonomy for anesthesia-related adverse events. More importantly, the data beseech us to acknowledge the problem and develop innovative strategies to reduce the likelihood of a drug error in anesthetic practice.*University of Toronto, Toronto, Ontario, Canada. rachel.meyer@utoronto.ca

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.137
GPT teacher head0.430
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2007
Admission routes2
Has abstractyes

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