Evidence map›Paper›PMID 31642024›Full record

ArticleJournal of neuro-oncology2019

Analysis of intra-operative variables as predictors of 30-day readmission in patients undergoing glioma surgery at a single center.

Iahn Cajigas, Anil K Mahavadi, Ashish H Shah, Veronica Borowy, Nathalie Abitbol, Michael E Ivan, Ricardo J Komotar, Richard H Epstein

Abstract read
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In one paragraph

Article in Journal of neuro-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Development of a Clinical Decision Support System to Predict Unplanned Cancer Readmissions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022
    Article
  5. Article
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  7. Review
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

8 authors.

Iahn CajigasDepartment of Neurological Surgery, University of Miami Miller School of Medicine, Miami, 33136, USA. icajigas@med.miami.edu.ORCID http://orcid.org/0000-0002-5808-453X
Anil K MahavadiDepartment of Neurological Surgery, University of Miami Miller School of Medicine, Miami, 33136, USA.
Ashish H ShahDepartment of Neurological Surgery, University of Miami Miller School of Medicine, Miami, 33136, USA.
Veronica BorowyDepartment of Neurological Surgery, University of Miami Miller School of Medicine, Miami, 33136, USA.
Nathalie AbitbolDepartment of Anesthesiology, Perioperative Medicine and Pain Management, University of Miami Miller School of Medicine, Miami, FL, USA.
Michael E IvanDepartment of Neurological Surgery, University of Miami Miller School of Medicine, Miami, 33136, USA.
Ricardo J KomotarDepartment of Neurological Surgery, University of Miami Miller School of Medicine, Miami, 33136, USA.
Richard H EpsteinDepartment of Anesthesiology, Perioperative Medicine and Pain Management, University of Miami Miller School of Medicine, Miami, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeReducing the time from surgery to adjuvant chemoradiation, by decreasing unnecessary readmissions, is paramount for patients undergoing glioma surgery. The effects of intraoperative risk factors on 30-day readmission rates for such patients is currently unclear. We utilized a predictive model-driven approach to assess the impact of intraoperative factors on 30-day readmission rates for the cranial glioma patient.

methodsRetrospectively, the intraoperative records of 290 patients who underwent glioma surgery at a single institution by a single surgeon were assessed. Data on operative variables including anesthesia specific factors were analyzed via univariate and stepwise regression analysis for impact on 30-day readmission rates. A predictive model was built to assess the capability of these results to predict readmission and validated using leave-one-out cross-validation.

resultsIn multivariate analysis, end case hypothermia (OR 0.28, 95% CI [0.09, 0.84]), hypertensive time > 15 min (OR 2.85, 95% CI [1.21, 6.75]), and pre-operative Karnofsky performance status (KPS) (OR 0.63, 95% CI [0.41, 0.98] were identified as being significantly associated with 30-day readmission rates (chi-squared statistic vs. constant model 25.2, p < 0.001). Cross validation of the model resulted in an overall accuracy of 89.7%, a specificity of 99.6%, and area under the receiver operator curve (AUC) of 0.763.

conclusionIntraoperative risk factors may help risk-stratify patients with a high degree of accuracy and improve postoperative patient follow-up. Attention should be paid to duration of hypertension and end-case final temperature as these represent potentially modifiable factors that appear to be highly associated with 30-day readmission rates. Prospective validation of our model is needed to assess its potential for implementation as a screening tool to identify patients undergoing glioma surgery who are at a higher risk of post-operative readmission within 30 days.

Indexed as

Patient ReadmissionAdultAgedFemaleGliomaHumansIntraoperative PeriodMaleMiddle AgedNeurosurgical ProceduresRetrospective StudiesRisk AssessmentRisk FactorsSupratentorial NeoplasmsCranialGliomaHospital readmissionNeurosurgeryPredictive modeling

Identifiers

PMID31642024

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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.