Evidence map›Paper›PMID 26306228›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015

How Comorbidities Co-Occur in Readmitted Hip Fracture Patients: From Bipartite Networks to Insights for Post-Discharge Planning.

Suresh K Bhavnani, Bryant Dang, Shyam Visweswaran, Rohit Divekar, Alai Tan, Amol Karmarkar, Kenneth Ottenbacher

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. 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
–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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Comparing Comorbidity Indices to Predict Post-Acute Rehabilitation Outcomes in Older Adults.American journal of physical medicine & rehabilitation · 2016
    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

7 authors.

Suresh K BhavnaniInst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.
Bryant DangInst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.
Shyam VisweswaranDepartment of Biomedical Informatics, Univ. of Pittsburgh, Pittsburgh, PA.
Rohit DivekarDivision of Allergic Diseases, Mayo Clinic, Rochester, MN.
Alai TanInst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.
Amol KarmarkarDept. of Rehabilitation Sciences, UTMB, Galveston, TX.
Kenneth OttenbacherDept. of Rehabilitation Sciences, UTMB, Galveston, TX.

Funding

UTMB Clinical and Translational Science AwardUL1TR000071 · NCATS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI BRASIER, ALLAN R. · 2012 to 2014
$14.4M
Center for Rehabilitation Research Using Large DatasetsR24HD065702 · NICHD · UNIVERSITY OF TEXAS MED BR GALVESTON · PI OTTENBACHER, KENNETH J. · 2010 to 2014
$4.0M
Hospital Readmission and Inpatient Medical RehabilitationR01HD069443 · NICHD · UNIVERSITY OF TEXAS MED BR GALVESTON · PI OTTENBACHER, KENNETH J. · 2012 to 2015
$927k
NCATS NIH HHS UL1 TR000071NICHD NIH HHS R01 HD069443NICHD NIH HHS R24 HD065702
6 · The paper itself

Abstract

Although a majority of 30-day readmissions of hip-fracture (HFx) patients in the elderly are caused by non-surgical complications, little is known about which specific combinations of comorbidities are associated with increased risk of readmission. We therefore used bipartite network analysis to explore the complex associations between 70 comorbidities (defined by hierarchal condition categories as critical in this population) and (a) cases consisting of all 2,316 HFx patients without hospital complications in the 2010 Medicare claims database who were re-admitted within 30 days of discharge, and (b) controls consisting of an equal number of matched HFx patients who were not readmitted for at least 90 days since discharge. A network-wide analysis revealed nine patient/comorbidity co-clusters, of which two had a significantly different proportion of cases compared to the rest of the data. A cluster-specific analysis of the most significant co-cluster revealed that a pair of comorbidities (Renal Failure and Diabetes with no Complications) within the co-cluster had significantly higher risk of 30-day readmission, whereas another pair of comorbidities (Renal Failure and Diabetes with Renal or Peripheral Circulatory Manifestations), despite having a relatively more serious comorbidity, did not confer a higher risk. This counter-intuitive result suggests that HFx patients with more serious comorbidities may have better follow-up that reduces the risk of 30-day readmission, whereas those with specific relatively less-serious comorbidities may have less stringent follow-up resulting in unanticipated incidents that precipitate readmission. These analyses reveal the strengths and limitations of bipartite networks for identifying hypotheses for complex phenomena related to readmissions, with the goal of improving follow-up care for patients with specific combinations of comorbidities.

Identifiers

PMID26306228
PMCPMC4525217

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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.