Evidence map›Paper›PMID 40503213›Full record

ArticleTherapeutics and clinical risk management2025

Preoperative Prognostic Nutritional Index as a Useful Tool for Predicting Postoperative Delirium in Elderly Patients with Degenerative Lumbar Diseases.

Jinlei Zhou, Jing Tang, Shanggao Xie, Haiyu Shao, Chen Xia, Yazeng Huang, Tingxiao Zhao

Abstract read
In one paragraph

Article in Therapeutics and clinical risk management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Jinlei Zhou *Center for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, People's Republic of China.
Jing Tang *Zhejiang Rehabilitation Medical Center, Department of Orthopedics, Hangzhou, Zhejiang, People's Republic of China.
Shanggao Xie *Center for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, People's Republic of China.
Haiyu ShaoCenter for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, People's Republic of China.
Chen XiaCenter for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, People's Republic of China.ORCID 0000-0001-8901-0827
Yazeng HuangCenter for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, People's Republic of China.
Tingxiao ZhaoCenter for Plastic & Reconstructive Surgery, Department of Orthopedics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, People's Republic of China.ORCID 0000-0003-0154-3595

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The high incidence of postoperative delirium (POD) among patients undergoing lumbar spine surgery presents a significant barrier to achieving enhanced postoperative recovery. Accordingly, a thorough understanding and precise identification of common risk factors for POD in this surgical context are crucial to mitigating its occurrence and facilitating improved recovery outcomes. This study is the first to assess the predictive value of the Prognostic Nutritional Index (PNI) for POD in elderly patients undergoing transforaminal lumbar interbody fusion (TLIF) for degenerative lumbar spine disease. Methods: We conducted a retrospective analysis of the outcomes of TLIF surgery in elderly patients with degenerative lumbar spine disease between 2020 and 2024. The diagnosis of POD was based on postoperative medical records during hospitalization, using the Confusion Assessment Method. The PNI was calculated based on preoperative serum albumin levels and total lymphocyte counts. Multivariate logistic regression analysis was performed to evaluate the relationship between preoperative PNI scores and the occurrence of POD. Finally, the optimal PNI cut-off point for predicting POD was determined using the receiver operating characteristic curve. Results: POD was observed in 61 of 370 patients, with PNI being significantly lower in the delirium group. The mean PNI values were 43.02±3.44 in the non-delirium group and 48.53±3.72 in the delirium group. Multivariate logistic regression analysis revealed that low PNI (OR: 0.743; 95% CI: 0.597-0.924; p = 0.008), low serum albumin levels (OR: 0.759; 95% CI: 0.584-0.987; p = 0.040), and advanced age (OR: 1.096; 95% CI: 1.030-1.166; p = 0.004) were independent predictors of POD following TLIF. The area under the curve (AUC) for PNI on the receiver operating characteristic curve was 0.864±0.025. The cutoff value for PNI, determined using the Youden index, was 45.825, with a sensitivity of 82.0% and a specificity of 74.4%. Conclusion: The preoperative use of PNI holds promise as a valuable tool for assessing the risk of POD in elderly patients undergoing TLIF.

Indexed as

controlled nutritional status indexdeliriumdystrophyelderly populationpostoperative deliriumprognostic nutritional indextransforaminal lumbar interbody fusion

Identifiers

PMID40503213
PMCPMC12153961

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.