Evidence map›Paper›PMID 32388726›Full record

ReviewCurrent reviews in musculoskeletal medicine2020

Predicting Patient-Centered Outcomes from Spine Surgery Using Risk Assessment Tools: a Systematic Review.

Hannah J White, Jensyn Bradley, Nicholas Hadgis, Emily Wittke, Brett Piland, Brandi Tuttle, Melissa Erickson, Maggie E Horn

Abstract readReview
In one paragraph

Review in Current reviews in musculoskeletal medicine, 2020. 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. Review
  2. Article
  3. Review
  4. Article
  5. Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine.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 · 2022
    Article
  6. Article
  7. 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

8 authors.

Hannah J WhiteDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA. hjw15@duke.edu.ORCID https://orcid.org/0000-0002-8935-490X
Jensyn BradleyDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Nicholas HadgisDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Emily WittkeDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Brett PilandDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Brandi TuttleMedical Center Library & Archives, Duke University, Durham, NC, USA.
Melissa EricksonDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.
Maggie E HornDepartment of Orthopaedic Surgery, Duke University, Durham, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewThe purpose of this systematic review is to evaluate the current literature in patients undergoing spine surgery in the cervical, thoracic, and lumbar spine to determine the available risk assessment tools to predict the patient-centered outcomes of pain, disability, physical function, quality of life, psychological disposition, and return to work after surgery. RECENT

findingsRisk assessment tools can assist surgeons and other healthcare providers in identifying the benefit-risk ratio of surgical candidates. These tools gather demographic, medical history, and other pertinent patient-reported measures to calculate a probability utilizing regression or machine learning statistical foundations. Currently, much is still unknown about the use of these tools to predict quality of life, disability, and other factors following spine surgery. A systematic review was conducted using PRISMA guidelines that identified risk assessment tools that utilized patient-reported outcome measures as part of the calculation. From 8128 identified studies, 13 articles met inclusion criteria and were accepted into this review. The range of c-index values reported in the studies was between 0.63 and 0.84, indicating fair to excellent model performance. Post-surgical patient-reported outcomes were identified in the following categories (n = total number of predictive models): return to work (n = 3), pain (n = 9), physical functioning and disability (n = 5), quality of life (QOL) (n = 6), and psychosocial disposition (n = 2). Our review has synthesized the available evidence on risk assessment tools for predicting patient-centered outcomes in patients undergoing spine surgery and described their findings and clinical utility.

Indexed as

Patient-reported outcomeRisk assessmentRisk calculatorsSpine surgery

Identifiers

PMID32388726
PMCPMC7251002

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.