Evidence map›Paper›PMID 33025547›Full record

ArticlePituitary2021

Machine learning in predicting early remission in patients after surgical treatment of acromegaly: a multicenter study.

Nidan Qiao, Ming Shen, Wenqiang He, Min He, Zhaoyun Zhang, Hongying Ye, Yiming Li, Xuefei Shou, Shiqi Li, Changzhen Jiang and 2 more

Abstract read
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In one paragraph

Article in Pituitary, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 11% of its field
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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

  1. Pooled it
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  18. Machine Learning in Pituitary Surgery.Acta neurochirurgica. Supplement · 2022
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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

12 authors at 4 institutions in 2 countries.

Nidan QiaoDepartment of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China.ORCID http://orcid.org/0000-0001-5478-3555
Ming ShenDepartment of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China.
Wenqiang HeDepartment of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China.
Min HeDepartment of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China.
Zhaoyun ZhangDepartment of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China.
Hongying YeDepartment of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China.
Yiming LiDepartment of Endocrinology, Shanghai Medical School, Huashan Hospital, Fudan University, Shanghai, China.
Xuefei ShouDepartment of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China.
Shiqi LiDepartment of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China.
Changzhen JiangDepartment of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fujian Medical University, 20 Chazhong Road, Fujian, China. 893416880@qq.com.
Yongfei WangDepartment of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China. eamns@hotmail.com.
Yao ZhaoDepartment of Neurosurgery, Shanghai Medical School, Huashan Hospital, Fudan University, 12 Wulumuqi Zhong Road, Shanghai, China. zhaoyaohs@qq.com.
Fudan University · CNFujian Medical University · CNHarvard University · USShanghai Medical College of Fudan University · CN

Funding

Shanghai Committee of Science and Technology 17YF1426700
6 · The paper itself

Abstract

purposeAccurate prediction of postoperative remission is beneficial for effective patient-physician communication in acromegalic patients. This study aims to train and validate machine learning prediction models for early endocrine remission of acromegalic patients.

methodsThe training cohort included 833 patients with growth hormone (GH) secreting pituitary adenoma from 2010 to 2018. We trained a partial model (only using pre-operative variables) and a full model (using all variables) to predict off-medication endocrine remission at six-month follow-up after surgery using multiple algorithms. The models were validated in 99 prospectively collected patients from a second campus and 52 patients from a third institution.

resultsC-statistic and the accuracy of the best partial model was 0.803 (95% CI 0.757-0.849) and 72.5% (95% CI 67.6-77.5%), respectively. C-statistic and the accuracy of the best full model was 0.888 (95% CI 0.861-0.914) and 80.3% (95% CI 77.5-83.1%), respectively. The c-statistics (and accuracy) of using only Knosp grade, total resection, or postoperative day 1 GH level as the single predictor were lower than our partial model or full model (p < 0.001). C-statistics remained similar in the prospective cohort (partial model 0.798, and full model 0.903) and in the external cohort (partial model 0.771, and full model 0.871). A web-based application integrated with the trained models was published at  https://deepvep.shinyapps.io/Acropred/ .

conclusionWe developed and validated interpretable and applicable machine learning models to predict early endocrine remission after surgical resection of a GH-secreting pituitary adenoma. Predication accuracy of the trained models were better than those using single variables.

Indexed as

Machine LearningAcromegalyAlgorithmsGrowth HormoneHumansMulticenter Studies as TopicNerve NetGrowth Hormone2010 consensusAcromegalyNeural networkPrediction

Identifiers

PMID33025547
OpenAlexW3091812914

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.