Evidence map›Paper›PMID 27170268›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2017

Predictors of discharge to an inpatient rehabilitation facility after a single-level posterior spinal fusion procedure.

Steven Niedermeier, Ryle Przybylowicz, Sohrab S Virk, Kari Stammen, Daniel S Eiferman, Safdar N Khan

Abstract read
PubMed Publisher
In one paragraph

Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2017. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Development of a machine learning algorithm predicting discharge placement after surgery for spondylolisthesis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2019
    Article
  3. Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2019
    Article
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

6 authors.

Steven NiedermeierDivision of Spine, Department of Orthopaedics, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.
Ryle PrzybylowiczDivision of Spine, Department of Orthopaedics, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.
Sohrab S VirkDivision of Spine, Department of Orthopaedics, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.
Kari StammenDivision of Spine, Department of Orthopaedics, Wexner Medical Center, The Ohio State University, Columbus, OH, USA.
Daniel S EifermanDivision of Trauma, Critical Care, and Burn, Department of Surgery, Wexner Medical Center, The Ohio State University, 395 West 12th Avenue, Columbus, OH, 43210, USA.
Safdar N KhanDivision of Spine, Department of Orthopaedics, Wexner Medical Center, The Ohio State University, Columbus, OH, USA. Safdar.Khan@osumc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo determine perioperative characteristics of patients undergoing single-level spinal fusion surgery that could help predict discharge to an inpatient rehabilitation facility (IRF).

methodsDemographic, peri- and postoperative characteristics were reviewed for 107 patients who underwent single-level spinal fusion surgery at a high-volume level I trauma center between January 2011 and December 2013. The relationships between discharge to IRF and gender, age, body mass index (BMI), Charlson Comorbidity Index (CCI), insurance provider, length of stay (LOS), intra- and postoperative outcomes and readmission rates in patients undergoing single-level spinal fusion surgery were analyzed using unpaired and paired t testing.

results21.5 % (n = 23) of patients were discharged to an IRF. By using unpaired and paired t tests, it was determined that age, BMI, CCI, LOS and insurance provider were all correlated with a higher probability of being discharged to an IRF. Additionally, a logistic regression model demonstrated a correlation between lower CCI and discharge to an IRF.

conclusionsStatistically significant differences were seen regarding age, BMI, CCI, LOS and insurance provider when determining the necessity of a patient being discharged to an IRF. These characteristics can be used to begin the process of setting up discharge disposition preoperatively rather than postoperatively. There were no perioperative characteristics that were statistically significant in determining discharge disposition; therefore, physicians can utilize these preoperative demographics in deciding and organizing discharge before the day of surgery, which can diminish LOS and lead to substantial health system savings.

Indexed as

AdultAgedBody Mass IndexComorbidityFemaleHumansLength of StayMaleMiddle AgedPatient DischargeRehabilitation CentersSpinal DiseasesSpinal FusionDischargeDispositionInpatient extended-care facilitySpinal fusion

Identifiers

PMID27170268

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.