Evidence map›Paper›PMID 24076052›Full record

ArticleWorld neurosurgery

Identifying better surgical candidates among recursive partitioning analysis class 2 patients who underwent surgery for intracranial metastases.

Kaisorn L Chaichana, Shami Acharya, Mariana Flores, Olindi Wijesekera, Daniele Rigamonti, Jon D Weingart, Alessandro Olivi, Chetan Bettegowda, Gary L Gallia, Henry Brem and 2 more

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Article in World neurosurgery. 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
–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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Kaisorn L ChaichanaDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA. Electronic address: Kaisorn@jhmi.edu.
Shami AcharyaDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Mariana FloresDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Olindi WijesekeraDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Daniele RigamontiDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Jon D WeingartDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Alessandro OliviDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Chetan BettegowdaDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Gary L GalliaDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Henry BremDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Michael LimDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA.
Alfredo Quinones-HinojosaDepartment of Neurosurgery, Johns Hopkins University, Neuro-Oncology Outcomes Laboratory, Baltimore, Maryland, USA. Electronic address: aquinon2@jhmi.edu.

Funding

Translational Research Central ServicesP30CA006973 · NCI · JOHNS HOPKINS UNIVERSITY · PI Rosalie Wollett · 1985 to 2026
$208.6M
NCI NIH HHS P30 CA006973
6 · The paper itself

Abstract

objectiveThe management of patients with brain metastases is typically dependent on their prognosis. Recursive partitioning analysis (RPA) is the most commonly used method for prognosticating survival, but has limitations for patients in the intermediate class. The aims of this study were to ascertain preoperative risk factors associated with survival, develop a preoperative prognostic grading system, and evaluate the utility of this system in predicting survival for RPA class 2 patients.

methodsAdult patient who underwent intracranial metastatic tumor surgery at an academic tertiary care institution from 1997 to 2011 were retrospectively reviewed. Multivariate proportional hazards regression analysis was used to identify preoperative factors associated with survival. The identified associations were then used to develop a grading system. Survival as a function of time was plotted using the Kaplan-Meier method, and survival rates were compared using log-rank analyses.

resultsA total of 421 (59%) of 708 patients were RPA class 2. The preoperative factors found to be associated with poorer survival were: male gender (P < 0.0001), motor deficit (P = 0.0007), cognitive deficit (P = 0.0004), nonsolitary metastases (P = 0.002), and tumor size >2 cm (P = 0.003). Patients having 0-1, 2, and 3-5 of these variables were assigned a preoperative grade of A, B, and C, respectively. Patients with a preoperative grade of A, B, and C had a median survival of 17.0, 10.3, and 7.3 months, respectively. These grades had distinct survival times (P < 0.05).

conclusionsThe present study devised a preoperative grading system that may provide prognostic information for RPA class 2 patients, which may also guide medical and surgical therapies before any intervention is pursued.

Indexed as

Patient SelectionAdultAgedAged, 80 and overBiopsy, NeedleBrain EdemaBrain NeoplasmsFemaleHumansImage Processing, Computer-AssistedKaplan-Meier EstimateKarnofsky Performance StatusMagnetic Resonance ImagingMaleMiddle AgedNeoplasm MetastasisBreastGastrointestinalLungMelanomaMetastatic brain tumorPrognosisRecurrenceRenalRPASurgerySurvival

Identifiers

PMID24076052
PMCPMC3995859

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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.