Evidence map›Paper›PMID 31506075›Full record

ArticleBMC musculoskeletal disorders2019

Readmissions, revisions, and mortality after treatment for proximal humeral fractures in three large states.

Dominique I Dabija, Hongshu Guan, Andrew Neviaser, Nitin B Jain

Open access · goldAbstract read
In one paragraph

Article in BMC musculoskeletal disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.7field-weighted citation impact, top 28% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Analysis of predictors of mortality after surgical and non-surgical management in proximal humerus fractures.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2021
    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

4 authors at 3 institutions in 1 country.

Dominique I DabijaVanderbilt University School of Medicine, Vanderbilt University, Nashville, TN, USA.
Hongshu GuanDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Andrew NeviaserDepartment of Orthopaedics, Ohio State University, Columbus, OH, USA.
Nitin B JainDepartment of Physical Medicine and Rehabilitation, Vanderbilt University Medical Center, 2201 Children's Way, Suite 1318, Nashville, TN, 37212, USA. nitin.jain@vanderbilt.edu.ORCID http://orcid.org/0000-0002-4362-8522
Brigham and Women's Hospital · USVanderbilt University · USVanderbilt University Medical Center · US

Funding

Rotator Cuff Tears: Optimizing Diagnosis and Treatment StrategiesK23AR059199 · NIAMS · VANDERBILT UNIVERSITY · PI JAIN, NITIN B · 2010 to 2014
$650k
Operative versus Non-Operative Treatments for Atraumatic Rotator Cuff Tears: A Mutlicenter Randomized Controlled TrialU34AR069201 · NIAMS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI JAIN, NITIN B · 2016 to 2017
$395k
NIAMS NIH HHS 1K23AR059199NIAMS NIH HHS 1U34AR069201
6 · The paper itself

Abstract

backgroundProximal humeral fractures can be treated non-operatively or operatively with open reduction and internal fixation (ORIF) and arthroplasty. Our objective was to assess practice patterns for operative and non-operative treatment of proximal humeral fractures. We also report on complications, readmissions, in-hospital mortality, and need for surgery after initial treatment of proximal humeral fractures in California, Florida, and New York.

methodsThe State Inpatient Databases and State Emergency Department Databases from the Healthcare Cost and Utilization Project, sponsored by the Agency for Healthcare Research and Quality, were used for the states of California (2005-2011), Florida (2005-2014), and New York (2008-2014). Data on patients with proximal humeral fractures was extracted. Patients underwent non-operative or operative (ORIF or arthroplasty) treatment at baseline and were followed for at least 4 years from the index presentation. If the patient needed subsequent surgery, time to event was calculated in days, and Kaplan-Meier survival curves were plotted.

resultsAt the index visit, 90.3% of patients with proximal humeral fractures had non-operative treatment, 6.7% had ORIF, and 3.0% had arthroplasty. 7.6% of patients initially treated non-operatively, 6.6% initially treated with ORIF, and 7.2% initially treated with arthroplasty needed surgery during follow-up. Device complications were the primary reason for readmission in 5.3% of ORIF patients and 6.7% of arthroplasty patients (p < 0.0001). All-cause in-hospital mortality was 9.8% for patients managed non-operatively, 8.8% for ORIF, and 10.0% for arthroplasty (p = 0.003).

conclusionsA majority of patients with proximal humeral fractures underwent non-operative treatment. There was a relatively high all-cause in-hospital mortality irrespective of treatment. Given the recent debate on operative versus non-operative treatment for proximal humeral fractures, our study provides valuable information on the need for revision surgery after initial treatment. The differences in rates of revision surgery between patients treated non-operatively, with ORIF, and with arthroplasty were small in magnitude. At nine years of follow-up, ORIF had the lowest probability of needing follow-up surgery, and arthroplasty had the highest.

Indexed as

AdultAgedAged, 80 and overArthroplastyCaliforniaDatabases, FactualFemaleFloridaFollow-Up StudiesFracture FixationHospital MortalityHumansHumerusKaplan-Meier EstimateLongitudinal StudiesMaleArthroplastyOpen reduction and internal fixationProximal humeral fractures

Identifiers

PMID31506075
PMCPMC6737688
OpenAlexW2973014849

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.