Evidence map›Paper›PMID 42678644›Full record

ArticleJournal of robotic surgery2026

Interhospital transfer and outcomes after robotic emergency general surgery: a national analysis.

Ioannis Karikis, Yasmin Arda, Nikolaos Pachos, Matthew A Bartek, Lydia R Maurer, Joshua S Ng-Kamstra, John O Hwabejire, Michael P DeWane, Haytham M Kaafarani, Ali Salim and 2 more

Abstract read
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In one paragraph

Article in Journal of robotic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ioannis KarikisDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA. ikarikis@mgh.harvard.edu.
Yasmin ArdaDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Nikolaos PachosDepartment of Surgery, NYU Langone Health, New York, NY, USA.
Matthew A BartekDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Lydia R MaurerDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Joshua S Ng-KamstraDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
John O HwabejireDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Michael P DeWaneDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Haytham M KaafaraniDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Ali SalimDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
George C VelmahosDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
Charudutt N ParanjapeDivision of Trauma, Emergency Surgery & Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outcomes of patients transferred to receiving centers who subsequently undergo robotic EGS remain uncharacterized at a national level. We aimed to quantify the association between transfer and outcomes among adults undergoing robotic EGS. We performed a retrospective cohort study of the Nationwide Readmissions Database (2016-2019) including adult nonelective admissions undergoing robotic EGS. Interhospital transfer versus direct admission was the exposure. Survey-weighted logistic regression estimated adjusted odds ratios (aOR) for clinical outcomes; generalized linear models with gamma family and log link estimated adjusted mean ratios (aMR) for length of stay (LOS) and cost. Average marginal effects provided adjusted risks/means and absolute differences. Among 26,869 unweighted robotic EGS admissions, representing an estimated 46,517 admissions nationally, 246 unweighted admissions were interhospital transfers, representing an estimated 444 transfers (1.0%) nationally. Transfers were older, more comorbid, and more severely ill and were treated predominantly at large, teaching hospitals. After adjustment, transfer was associated with a higher risk of postprocedural complications (8.0% vs. 3.5%; aRR 2.26, 95% CI 1.25-3.27), non-home discharge (31.2% vs. 18.9%; aRR 1.65, 95% CI 1.38-1.92), longer LOS (11.49 vs. 5.53 days; AMR 2.08, 95% CI 1.78-2.42), and higher cost ($43,340 vs. $21,821; AMR 1.99, 95% CI 1.68-2.35). The association with postprocedural complications was attenuated after additional adjustment for APR-DRG Severity of Illness, whereas associations with non-home discharge, LOS, and cost persisted. Among patients undergoing robotic EGS, interhospital transfer is independently associated with higher complication burden and greater resource use. Transferred patients represent a small but distinctly high-risk subgroup whose worse outcomes may reflect drivers that extend beyond the choice of surgical approach.

Indexed as

General SurgeryPatient TransferPostoperative ComplicationsRobotic Surgical ProceduresAdultAgedFemaleHumansLength of StayMaleMiddle AgedPatient ReadmissionRetrospective StudiesTreatment OutcomeUnited StatesEmergency general surgeryHealthcare costsInterhospital transferRobotic surgeryTransfer penalty

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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.