Evidence map›Paper›PMID 37957439›Full record

ArticleCanadian journal of anaesthesia = Journal canadien d'anesthesie2023

Development and validation of an instrument to predict prolonged length of stay in the postanesthesia care unit following ambulatory surgery.

Samuel Rupp, Elena Ahrens, Maira I Rudolph, Omid Azimaraghi, Maximilian S Schaefer, Philipp Fassbender, Carina P Himes, Preeti Anand, Parsa Mirhaji, Richard Smith and 3 more

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Article in Canadian journal of anaesthesia = Journal canadien d'anesthesie, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 3 countries.

Samuel Rupp *Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Elena Ahrens *Department of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Maira I Rudolph *Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Omid AzimaraghiDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Maximilian S SchaeferDepartment of Anesthesia, Critical Care and Pain Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Philipp FassbenderDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Carina P HimesDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Preeti AnandDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Parsa MirhajiCenter for Health Data Innovations, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Richard SmithDepartment of Otorhinolaryngology - Head & Neck Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Jeffrey FredaSurgical Services, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Matthias EikermannDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA. meikermann@montefiore.org.ORCID 0000-0002-7893-0596
Karuna WongtangmanDepartment of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA.
Albert Einstein College of Medicine · USBeth Israel Deaconess Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe sought to develop and validate an Anticipated Surveillance Requirement Prediction Instrument (ASRI) for prediction of prolonged postanesthesia care unit length of stay (PACU-LOS, more than four hours) after ambulatory surgery.

methodsWe analyzed hospital registry data from patients who received anesthesia care in ambulatory surgery centres (ASCs) of university-affiliated hospital networks in New York, USA (development and internal validation cohort [n = 183,711]) and Massachusetts, USA (validation cohort [n = 148,105]). We used stepwise backwards elimination to create ASRI.

resultsThe model showed discriminatory ability in the development, internal, and external validation cohorts with areas under the receiver operating characteristic curve of 0.82 (95% confidence interval [CI], 0.82 to 0.83), 0.82 (95% CI, 0.81 to 0.83), and 0.80 (95% CI, 0.79 to 0.80), respectively. In cases started in the afternoon, ASRI scores ≥ 43 had a total predicted risk for PACU stay past 8 p.m. of 32% (95% CI, 31.1 to 33.3) vs 8% (95% CI, 7.9 to 8.5) compared with low score values (P-for-interaction < 0.001), which translated to a higher direct PACU cost of care of USD 207 (95% CI, 194 to 2,019; model estimate, 1.68; 95% CI, 1.64 to 1.73; P < 0.001) The effects of using the ASRI score on PACU use efficiency were greater in a free-standing ASC with no limitations on PACU bed availability.

conclusionWe developed and validated a preoperative prediction tool for prolonged PACU-LOS after ambulatory surgery that can be used to guide scheduling in ambulatory surgery to optimize PACU use during normal work hours, particularly in settings without limitation of PACU bed availability.

Indexed as

Ambulatory Surgical ProceduresAnesthesiaAnesthesia Recovery PeriodHumansLength of StayROC Curveambulatory surgerypostoperative length of stayprediction model

Identifiers

PMID37957439
OpenAlexW4388623484

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.