Evidence map›Paper›PMID 37393891›Full record

ArticlePediatric neurosurgery2023

Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning.

Shane Shahrestani, Nathan Shlobin, Julian L Gendreau, Nolan J Brown, Alexander Himstead, Neal A Patel, Noah Pierzchajlo, Sachiv Chakravarti, Darrin Jason Lee, Peter A Chiarelli and 2 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Pediatric neurosurgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 33% 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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
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 6 institutions in 1 country.

Shane ShahrestaniDepartment of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Nathan ShlobinDepartment of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Julian L GendreauDepartment of Biomedical Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
Nolan J BrownSchool of Medicine, University of California, Irvine, California, USA.
Alexander HimsteadDepartment of Neurological Surgery, University of California, Irvine, California, USA.
Neal A PatelSchool of Medicine, Mercer University, Macon, Georgia, USA.
Noah PierzchajloSchool of Medicine, Mercer University, Savannah, Georgia, USA, npierzchajlo@gmail.com.
Sachiv ChakravartiDepartment of Neurosurgery, Johns Hopkins University, Baltimore, Maryland, USA.
Darrin Jason LeeDepartment of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Peter A ChiarelliDepartment of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Carli L BullisDepartment of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Jason ChuDepartment of Neurosurgery, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
University of Southern California · USJohns Hopkins University · USUniversity of California, Irvine · USMercer University · USMercer University Health Sciences Center · USNorthwestern University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionHydrocephalus is a common pediatric neurosurgical pathology, typically treated with a ventricular shunt, yet approximately 30% of patients experience shunt failure within the first year after surgery. As a result, the objective of the present study was to validate a predictive model of pediatric shunt complications with data retrieved from the Healthcare Cost and Utilization Project (HCUP) National Readmissions Database (NRD).

methodsThe HCUP NRD was queried from 2016 to 2017 for pediatric patients undergoing shunt placement using ICD-10 codes. Comorbidities present upon initial admission resulting in shunt placement, Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining criteria, and Major Diagnostic Category (MDC) at admission classifications were obtained. The database was divided into training (n = 19,948), validation (n = 6,650), and testing (n = 6,650) datasets. Multivariable analysis was performed to identify significant predictors of shunt complications which were used to develop logistic regression models. Post hoc receiver operating characteristic (ROC) curves were created.

resultsA total of 33,248 pediatric patients aged 6.9 ± 5.7 years were included. Number of diagnoses during primary admission (OR: 1.05, 95% CI: 1.04-1.07) and initial neurological admission diagnoses (OR: 3.83, 95% CI: 3.33-4.42) positively correlated with shunt complications. Female sex (OR: 0.87, 95% CI: 0.76-0.99) and elective admissions (OR: 0.62, 95% CI: 0.53-0.72) negatively correlated with shunt complications. ROC curve for the regression model utilizing all significant predictors of readmission demonstrated area under the curve of 0.733, suggesting these factors are possible predictors of shunt complications in pediatric hydrocephalus.

conclusionEfficacious and safe treatment of pediatric hydrocephalus is of paramount importance. Our machine learning algorithm delineated possible variables predictive of shunt complications with good predictive value.

Indexed as

HydrocephalusVentriculoperitoneal ShuntChildComorbidityFemaleHumansNeurosurgical ProceduresRetrospective StudiesDiagnosesHydrocephalusMachine learning surgical outcomeNeurosurgeryPediatric patientsRisk factorShunt

Identifiers

PMID37393891
PMCPMC10614444
OpenAlexW4382918287

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.