Evidence map›Paper›PMID 38848350›Full record

SynthesisPloS one2024

Risk factors affecting spinal fusion: A meta-analysis of 39 cohort studies.

Shudong Yang, Beijun Zhou, Jiaxuan Mo, Ruidi He, Kunbo Mei, Zhi Zeng, Gaigai Yang, Yuwei Chen, Mingjiang Luo, Siliang Tang and 1 more

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
–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, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Multimodal model predicts screw loosening at user-specified postoperative time points after single-level lumbar interbody fusion.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 · 2026
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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

11 authors.

Shudong YangDepartment of Orthopedic Trauma, Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Beijun ZhouHengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Jiaxuan MoHengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Ruidi HeHengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Kunbo MeiHengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Zhi ZengHengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Gaigai YangHengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Yuwei ChenHengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Mingjiang LuoDepartment of Spine Surgery, Lishui Hospital of Wenzhou Medical University, Lishui People's Hospital, Lishui, Zhejiang, China.ORCID 0000-0002-0693-8695
Siliang TangDepartment of Spine Surgery, Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang City, Hunan Province, China.
Zhihong XiaoDepartment of Spine Surgery, Lishui Hospital of Wenzhou Medical University, Lishui People's Hospital, Lishui, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe performed a meta-analysis to identify risk factors affecting spinal fusion.

methodsWe systematically searched PubMed, Embase, and the Cochrane Library from inception to January 6, 2023, for articles that report risk factors affecting spinal fusion. The pooled odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using fixed-effects models for each factor for which the interstudy heterogeneity I2 was < 50%, while random-effects models were used when the interstudy heterogeneity I2 was ≥ 50%. Using sample size, Egger's P value, and heterogeneity across studies as criteria, we categorized the quality of evidence from observational studies as high-quality (Class I), moderate-quality (Class II or III), or low-quality (Class IV). Furthermore, the trim-and-fill procedure and leave-one-out protocol were conducted to investigate potential sources of heterogeneity and verify result stability.

resultsOf the 1,257 citations screened, 39 unique cohort studies comprising 7,145 patients were included in the data synthesis. High-quality (Class I) evidence showed that patients with a smoking habit (OR, 1.57; 95% CI, 1.11 to 2.21) and without the use of bone morphogenetic protein-2 (BMP-2) (OR, 4.42; 95% CI, 3.33 to 5.86) were at higher risk for fusion failure. Moderate-quality (Class II or III) evidence showed that fusion failure was significantly associated with vitamin D deficiency (OR, 2.46; 95% CI, 1.24 to 4.90), diabetes (OR, 3.42; 95% CI, 1.59 to 7.36), allograft (OR, 1.82; 95% CI, 1.11 to 2.96), conventional pedicle screw (CPS) fixation (OR, 4.77; 95% CI, 2.23 to 10.20) and posterolateral fusion (OR, 3.63; 95% CI, 1.25 to 10.49).

conclusionsConspicuous risk factors affecting spinal fusion include three patient-related risk factors (smoking, vitamin D deficiency, and diabetes) and four surgery-related risk factors (without the use of BMP-2, allograft, CPS fixation, and posterolateral fusion). These findings may help clinicians strengthen awareness for early intervention in patients at high risk of developing fusion failure.

Indexed as

Spinal FusionBone Morphogenetic Protein 2Cohort StudiesHumansRisk FactorsSmokingBone Morphogenetic Protein 2

Identifiers

PMID38848350
PMCPMC11161075

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.