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Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

Quantitative tools for addressing hospital readmissions.

Ronald J Lagoe¹, Diane S Nanno, Mary E Luziani

¹Hospital Executive Council, PO Box 35089, Syracuse, NY 13235, USA. hospexcl@cnymail.com

BMC Research Notes

|November 6, 2012

Summary

This summary is machine-generated.

Hospitals can reduce patient readmissions using quantitative tools. This study highlights definitions, risk estimation, and patient tracking for improved outcomes and cost containment.

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Last Updated: May 17, 2026

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

Area of Science:

Health Services Research
Healthcare Management

Background:

Growing focus on healthcare cost containment drives efforts to reduce hospital readmissions.
Financial penalties from payors incentivize providers to minimize excess readmissions.
Development of resources like Potentially Preventable Readmissions software aids this initiative.

Purpose of the Study:

To describe quantitative tools for reducing hospital readmissions.
To outline methods for patient identification, risk assessment, and tracking.
To support hospitals in improving patient outcomes and managing readmission rates.

Main Methods:

Utilized Potentially Preventable Readmissions software.
Developed two distinct definitions for identifying and managing readmissions.
Employed analytical approaches for estimating individual patient readmission risk based on age, discharge status, and illness severity.
Created patient-specific spreadsheets for tracking target populations and evaluating intervention impact.

Main Results:

Successfully developed quantitative tools for readmission management.
Defined readmission criteria to facilitate patient identification and care.
Established risk estimation models incorporating patient demographics and clinical factors.
Implemented tracking systems for monitoring patient cohorts and intervention effectiveness.

Conclusions:

Quantitative tools, including definitions, risk estimation, and patient tracking, can enhance hospital patient outcomes.
The study provides a framework for hospitals to proactively manage and reduce readmissions.
Effective use of these tools can lead to improved quality of care and cost savings.