Evidence map›Paper›PMID 39115577›Full record

ArticleObesity surgery2024

The Role of Preoperative Abdominal Ultrasound in the Preparation of Patients Undergoing Primary Metabolic and Bariatric Surgery: A Machine Learning Algorithm on 4418 Patients' Records.

Mohamed Hany, Mohamed El Shafei, Mohamed Ibrahim, Ann Samy Shafiq Agayby, Anwar Ashraf Abouelnasr, Moustafa R Aboelsoud, Ehab Elmongui, Bart Torensma

Abstract read
In one paragraph

Article in Obesity surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Mohamed HanyDepartment of Surgery, Medical Research Institute, Alexandria University, 165 Horreya Avenue, Hadara, 21561, Alexandria, Egypt. mohamed.ashour@alexu.edu.eg.ORCID 0000-0001-6650-8112
Mohamed El ShafeiDepartment of Radiology, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Mohamed IbrahimDepartment of Surgery, Medical Research Institute, Alexandria University, 165 Horreya Avenue, Hadara, 21561, Alexandria, Egypt.
Ann Samy Shafiq AgaybyDepartment of Surgery, Medical Research Institute, Alexandria University, 165 Horreya Avenue, Hadara, 21561, Alexandria, Egypt.
Anwar Ashraf AbouelnasrDepartment of Surgery, Medical Research Institute, Alexandria University, 165 Horreya Avenue, Hadara, 21561, Alexandria, Egypt.
Moustafa R AboelsoudDepartment of Surgery, Medical Research Institute, Alexandria University, 165 Horreya Avenue, Hadara, 21561, Alexandria, Egypt.
Ehab ElmonguiBiomedical Informatics and Medical Statistics, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Bart TorensmaLeiden University Medical Center (LUMC), Leiden, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe utility of preoperative abdominal ultrasonography (US) in evaluating patients with obesity before metabolic bariatric surgery (MBS) remains ambiguously defined.

methodRetrospective analysis whereby patients were classified into four groups based on ultrasound results. Group 1 had normal findings. Group 2 had non-significant findings that did not affect the planned procedure. Group 3 required additional or follow-up surgeries without changing the surgical plan. Group 4, impacting the procedure, needed further investigations and was subdivided into 4A, delaying surgery for more assessments, and 4B, altering or canceling the procedure due to critical findings. Machine learning techniques were utilized to identify variables.

resultsFour thousand four hundred eighteen patients' records were analyzed. Group 1 was 45.7%. Group 2, 35.7%; Group 3, 17.0%; Group 4, 1.5%, Group 4A, 0.8%; and Group 4B, 0.7%, where surgeries were either canceled (0.3%) or postponed (0.4%). The hyperparameter tuning process identified a Decision Tree classifier with a maximum tree depth of 7 as the most effective model. The model demonstrated high effectiveness in identifying patients who would benefit from preoperative ultrasound before MBS, with training and testing accuracies of 0.983 and 0.985. It also showed high precision (0.954), recall (0.962), F1 score (0.958), and an AUC of 0.976.

conclusionOur study found that preoperative ultrasound demonstrated clinical utility for a subset of patients undergoing metabolic bariatric surgery. Specifically, 15.9% of the cohort benefited from the identification of chronic calculous cholecystitis, leading to concomitant cholecystectomy. Additionally, surgery was postponed in 1.4% of the cases due to other findings. While these findings indicate a potential benefit in certain cases, further research, including a cost-benefit analysis, is necessary to fully evaluate routine preoperative ultrasound's overall utility and economic impact in this patient population.

Indexed as

Bariatric SurgeryMachine LearningPreoperative CareUltrasonographyAbdomenAdultAlgorithmsFemaleHumansMaleMiddle AgedObesity, MorbidRetrospective StudiesArea under the curveComputed tomographyMagnetic resonance imagingMetabolic bariatric surgeryPrecisionPreoperative evaluationTesting accuracyTraining accuracyUltrasound

Identifiers

PMID39115577
PMCPMC11349839

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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.