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Record W2134792552

Ketorolac Prescribing Practices in an Acute Care Hospital and the Incidence of Acute Renal Failure

2014· article· en· W2134792552 on OpenAlexvenueno aff
Joseph Chan, Anil Bajnath, Beth Fromkin, Debbie Haine, Rute Paixao, Dianne Sandy, Umair Rhandhawa, Fei Wang, Mauro Braun

Bibliographic record

VenueWorld Journal of Nephrology and Urology · 2014
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKetorolacAcute kidney injuryHyperkalemiaIncidence (geometry)Renal functionCreatinineInternal medicineAnesthesiaAnalgesic
DOInot available

Abstract

fetched live from OpenAlex

Background: Ketorolac has been documented to cause acute kidney injury (AKI) but current data suggest that it is safe for those who have low risk for renal dysfunction. In our facility, there have been cases of AKI in those treated with K etorolac but the incidence is not known. This study describes the prescribing habits of K etorolac in our facility and determines the incidence of AKI while on this therapy. Methods: Electronic medical records of patients who received K etorolac were reviewed during the last 3 months of 2012. AKI was defined as an increase of serum creatinine of 0.3 mg/dL or greater and a decrease in estimated glomerular filtration rate (eGFR) to less than 60 mL/min ute . Results: A total of 633 patient charts were reviewed and 341 patients met the inclusion criteria. The mean age was 45.7 years. Sixty-five percent of the patients were females and 35% were males. The most common diagnosis for prescribing Ketorolac was osteoarthrosis. Thirty milligram IV every 6 hours is the conventional prescribed dose. Of the patients 6.4% developed AKI during treatment with Ketorolac, 68% of those with AKI were 65 or older, 68% had hypertension, 41% were diabetic, 40% were concomitantly receiving either an angiotensin converting enzyme-inhibitor (ACE-I) or an angiotensin receptor blocker ( ARB), 40% were also being given diuretics, 72% received Ketorolac during the time of AKI and 3.8% of all patients who received Ketorolac developed hyperkalemia while on treatment. Conclusions: AKI occurs more commonly than previously anticipated in Ketorolac treated patients even at average doses and short durations. Hypertension and diabetes are the two most common comorbidities in patients who developed AKI. Those who are greater than 65 years old may be at higher risk. Concomitant use of drugs that affect renal function, such as ACE-I, ARBs and diuretics, may increase the risk of AKI. Ketorolac prescribing in the acute care hospital should consider individual comorbidities, and use of other drugs that can increase kidney failure risk. Awareness of current renal function through diligent review of daily labs may help prevent administration of Ketorolac in those with impaired renal function. Medication alerts that notifying the ordering physician of the eGFR may help prevent inadvertent prescription in those with AKI or chronic kidney disease . World J Nephrol Urol. 2014;3(3):113-117 doi: http://dx.doi.org/10.14740/wjnu169w

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2014
Admission routes1
Has abstractyes

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