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

Bhavnani SK, Dang B, Visweswaran S, Divekar R, Tan A, Karmarkar A, Ottenbacher K - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Inst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.

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.

No MeSH data available.


Related in: MedlinePlus

A bipartite network of 4,632 patients (cases and controls) and 69 HCC comorbidities displayed using a 3D layout (A), and an exploded 2D layout (B) to show the size and spread of the nodes in each cluster. Cluster-1 (magenta nodes in the upper left corner) and Cluster-2 (yellow nodes in the lower left corner) had significantly lower and higher risk respectively of 30-day readmission compared to the risk of readmission in rest of the data.
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f1-2092233: A bipartite network of 4,632 patients (cases and controls) and 69 HCC comorbidities displayed using a 3D layout (A), and an exploded 2D layout (B) to show the size and spread of the nodes in each cluster. Cluster-1 (magenta nodes in the upper left corner) and Cluster-2 (yellow nodes in the lower left corner) had significantly lower and higher risk respectively of 30-day readmission compared to the risk of readmission in rest of the data.

Mentions: 1. Exploratory Visual Analysis was conducted using network visualization and analysis9. Networks are increasingly being used to analyze a wide range9 of complex molecular, clinical, and social phenomena such as gene and protein-protein interactions, how symptoms co-occur across toxic chemical exposure, and how infections spread across a social group. A network consists of nodes and edges; nodes represent one or more types of entities (e.g., patients or genes), and edges between the nodes represent a specific relationship between the entities. Figure 1A shows a bipartite network where edges exist only between patients (circles) and comorbidities (triangles).


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

Bhavnani SK, Dang B, Visweswaran S, Divekar R, Tan A, Karmarkar A, Ottenbacher K - AMIA Jt Summits Transl Sci Proc (2015)

A bipartite network of 4,632 patients (cases and controls) and 69 HCC comorbidities displayed using a 3D layout (A), and an exploded 2D layout (B) to show the size and spread of the nodes in each cluster. Cluster-1 (magenta nodes in the upper left corner) and Cluster-2 (yellow nodes in the lower left corner) had significantly lower and higher risk respectively of 30-day readmission compared to the risk of readmission in rest of the data.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4525217&req=5

f1-2092233: A bipartite network of 4,632 patients (cases and controls) and 69 HCC comorbidities displayed using a 3D layout (A), and an exploded 2D layout (B) to show the size and spread of the nodes in each cluster. Cluster-1 (magenta nodes in the upper left corner) and Cluster-2 (yellow nodes in the lower left corner) had significantly lower and higher risk respectively of 30-day readmission compared to the risk of readmission in rest of the data.
Mentions: 1. Exploratory Visual Analysis was conducted using network visualization and analysis9. Networks are increasingly being used to analyze a wide range9 of complex molecular, clinical, and social phenomena such as gene and protein-protein interactions, how symptoms co-occur across toxic chemical exposure, and how infections spread across a social group. A network consists of nodes and edges; nodes represent one or more types of entities (e.g., patients or genes), and edges between the nodes represent a specific relationship between the entities. Figure 1A shows a bipartite network where edges exist only between patients (circles) and comorbidities (triangles).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Inst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.

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.

No MeSH data available.


Related in: MedlinePlus