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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
Published on: June 10, 2025
Identifying relative cut-off scores with neural networks for interpretation of the Minnesota Living with Heart
Hassan Behlouli1, Deborah E Feldman, Anique Ducharme
1Divisions of Internal Medicine and Clinical Epidemiology, McGill University Health Centre.
Background:
Quality of life (QoL) is an important end point in heart failure (HF) studies. The Minnesota Living with Heart Failure questionnaire (MLHFQ) is the instrument most widely used to evaluate QoL in Heart Failure (HF) patients. It is a questionnaire containing 21 questions with scores ranging from 0 to 105. A best cut-off value for MLHFQ scores to identify those patients with good, moderate or poor QoL has not been determined.
Objective:
To determine a cut-off score for the MLHFQ based on the neural network (NN) approach. These cut-off scores will help discriminate between HF patients having good, moderate or poor QoL.
Methods:
This research was carried out in the context of a longitudinal cohort study of new patients attending specialized HF clinics in six participating centers in Quebec, Canada. Patients completed a questionnaire that included the MLHFQ. In addition to this scale, self-perceived health status and clinical information related to the severity of HF were obtained including: the New York Heart Association (NYHA) functional class, 6 minute walk test and survival status. We analyzed the database using NN and conventional statistical tools. The NN is a statistical program that recognizes clusters of MLHFQ and relates similar QoL measures to one another. Among the 531 eligible patients, 447 patients with complete questionnaires were used to build randomly two sets for training (learning set) and for testing (validation set) the NN.
Results:
Participants had a mean age of 65 years and 24% were women. The median MLHFQ score was 45 (inter-quartile range: 27 to 64). NN identified 3 distinct clusters of MLHFQ that represent the full spectrum of possible scores on the MLHFQ. We estimated that a score of < 24 on the MLHFQ represents a good QoL, a score between 24 and 45 represents a moderate QoL, and a score > 45 represents a poor QoL. Validation with the different severity measures confirmed these categories. These cut-offs allowed us to reach a good total accuracy (91%). These cutoffs were strongly correlated with survival status (p = 0.004), self-perceived health status (p = 0.0032), NYHA functional class (p<0.0001) and standardized 6 minutes walk test (p = 0.05)
Conclusion:
The identification of three levels of MLHFQ should be useful in clinical decision making.
