Evidence map›Paper›PMID 35944857›Full record

ArticleWorld neurosurgery2022

Impact of the COVID-19 Pandemic on Neurosurgical Transfers: A Single Tertiary Center Study.

Sheantel J Reihl, Joseph H Garcia, Ramin A Morshed, Sujatha Sankaran, Anthony DiGiorgio, Dean Chou, Philip V Theodosopoulos, Manish K Aghi, Mitchel S Berger, Edward F Chang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World neurosurgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Sheantel J ReihlDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Joseph H GarciaDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Ramin A MorshedDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA. Electronic address: ramin.morshed@ucsf.edu.
Sujatha SankaranDepartments of Internal Medicine, University of California, San Francisco, San Francisco, California, USA.
Anthony DiGiorgioDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Dean ChouDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Philip V TheodosopoulosDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Manish K AghiDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Mitchel S BergerDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Edward F ChangDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.
Praveen V MummaneniDepartments of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveInterfacility transfers represent a large proportion of neurosurgical admissions to tertiary care centers each year. In this study, the authors examined the impact of the COVID-19 pandemic on the number of transfers, timing of transfers, demographic profile of transfer patients, and clinical outcomes including rates of surgical intervention.

methodsA retrospective review of neurosurgical transfer patients at a single tertiary center was performed. Patients transferred from April to November 2020 (the "COVID Era") were compared with an institutional database of transfer patients collected before the COVID-19 pandemic (the "Pre-COVID Era"). During the COVID Era, both emergent and nonemergent neurosurgical services had resumed. A comparison of demographic and clinical factors between the 2 cohorts was performed.

resultsA total of 674 patients were included in the study (331 Pre-COVID and 343 COVID-Era patients). Overall, there was no change in the average monthly number of transfers (P = 0.66) or in the catchment area of referral hospitals. However, COVID-Era patients were more likely to be uninsured (1% vs. 4%), had longer transfer times (COVID vs. Pre-COVID Era: 18 vs. 9 hours; P < 0.001), required higher rates of surgical intervention (63% vs. 50%, P = 0.001), had higher rates of spine pathology (17% vs. 10%), and less frequently were admitted to the intensive care unit (34% vs. 52%, P < 0.001). Overall, COVID-Era patients did not experience delays to surgical intervention (3.1 days vs. 3.6 days, P = 0.2). When analyzing the subgroup of COVID-Era patients, COVID infection status did not impact the time of transfer or rates of operation, although COVID-infected patients experienced a longer time to surgery after admission (14 vs. 2.9 days, P < 0.001).

conclusionThe COVID-19 pandemic did not reduce the number of monthly transfers, operation rates, or catchment area for transfer patients. Transfer rates of uninsured patients increased during the COVID Era, potentially reflecting changes in access to community neurosurgery care. Shorter time to surgery seen in COVID-Era patients possibly reflects institutional policies that improved operating room efficiency to compensate for surgical backlogs. COVID status affeted time to surgery, reflecting the preoperative care that these patients require before intervention.

Indexed as

COVID-19NeurosurgeryHumansPandemicsPatient TransferRetrospective StudiesTertiary Care CentersCatchment areaCOVID-19NeurosurgeryPandemicTransfers

Identifiers

PMID35944857
PMCPMC9356759

What OpenQuestion holds

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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.