Evidence map›Paper›PMID 35378587›Full record

ArticleNeurospine2022

Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors.

Michael C Jin, Allen L Ho, Austin Y Feng, Zachary A Medress, Arjun V Pendharkar, Paymon Rezaii, John K Ratliff, Atman M Desai

Abstract read
In one paragraph

Article in Neurospine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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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

6 citing papers in PubMed.

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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

8 authors.

Michael C JinDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Allen L HoDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Austin Y FengDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Zachary A MedressDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Arjun V PendharkarDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Paymon RezaiiDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
John K RatliffDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
Atman M DesaiDepartment of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIntradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors.

methodsIBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset.

resultsA total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n = 5,023, 99.3%) and tumors were most commonly found in the thoracic region (n = 1,941, 38.4%), followed by the lumbar (n = 1,781, 35.2%) and cervical (n = 1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%).

conclusionUsing a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions.

Indexed as

Intradural spine tumorMachine learningPredictive modeling

Identifiers

PMID35378587
PMCPMC8987552

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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.