Evidence map›Paper›PMID 40917488›Full record

ArticleJournal of clinical & translational endocrinology2025

The CuPeR model: A dynamic online tool for predicting Cushing's disease persistence and recurrence after pituitary surgery.

Guive Sharifi, Elham Paraandavaji, Nader Akbari Dilmaghani, Tohid Emami Meybodi, Ibrahim Mohammadzadeh, Neginalsadat Sadeghi, Amirali Vaghari, Behnaz Niroomand, Seyed Mohammad Tavangar, Mohammad Reza Mohajeri Tehrani and 4 more

Abstract read
In one paragraph

Article in Journal of clinical & translational endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Guive SharifiDepartment of Neurosurgery, Loghman Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Elham ParaandavajiClinical Research Development Center, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Nader Akbari DilmaghaniSkull Base Research Center, Loghman Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Tohid Emami MeybodiNeurosurgery Department, Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ibrahim MohammadzadehSkull Base Research Center, Loghman Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Neginalsadat SadeghiFarhikhtegan Medical Convergence Sciences Research Center, Farhikhtegan Hospital Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
Amirali VaghariDepartment of Neurosurgery, Loghman Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Behnaz NiroomandSkull Base Research Center, Loghman Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Seyed Mohammad TavangarDepartment of Pathology, Dr. Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Mohammad Reza Mohajeri TehraniEndocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Zahra DavoudiDepartment of Endocrinology, Skull Base Research Center of Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Marjan MirsalehiENT and Head and Neck Research Center and Department, The Five Senses Health Institute, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Seyed Ali MousavinejadSkull Base Research Center, Loghman Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Farzad Taghizadeh-HesaryENT and Head and Neck Research Center and Department, The Five Senses Health Institute, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Predicting postoperative persistence and recurrence of Cushing's disease (CD) remains a clinical challenge, with no universally reliable models available. This study introduces the CuPeR model, an online dynamic nomogram developed to address these gaps by predicting postoperative outcomes in patients with CD undergoing pituitary surgery. Methods: A retrospective cohort of 211 patients treated for CD between 2010 and 2024 was analyzed. Key patient and tumor characteristics, imaging findings, and treatment details were evaluated. Multivariate logistic regression identified independent predictors of postoperative persistence or recurrence of CD (PoRP-CD), which were then incorporated into the CuPeR model using stepwise selection based on Akaike Information Criterion. Internal validation was performed using a testing dataset, and a user-friendly online nomogram was developed to facilitate immediate, patient-specific risk estimation in clinical practice. Results: The final predictive model identified four key factors: symptom duration, MRI Hardy's grade, tumor site, and prior pituitary surgery. Longer symptom duration and a history of prior surgery significantly increased the risk of recurrence, while bilateral tumor location reduced this risk. The model demonstrated an area under the receiver operating characteristic curve (AUC-ROC) of 0.70, with 83% accuracy, specificity of 96%, and sensitivity of 33%. Conclusions: The CuPeR model may offer a practical tool for predicting PoRP-CD, enhancing preoperative decision-making by providing personalized risk assessments.

Indexed as

Cushing’s diseaseDisease PersistenceNomogramRecurrenceTranssphenoidal surgery

Identifiers

PMID40917488
PMCPMC12410461

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.