Identifying relative cut-off scores with neural networks for interpretation of the Minnesota Living with Heart Failure questionnaire

Hassan Behlouli, Deborah E Feldman, Anique Ducharme, Marc Frenette, Nadia Giannetti, François Grondin, Caroline Michel, Richard Sheppard, Louise Pilote
Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2009, 2009: 6242-6

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.

Full Text Links

Find Full Text Links for this Article


You are not logged in. Sign Up or Log In to join the discussion.

Related Papers

Remove bar
Read by QxMD icon Read

Save your favorite articles in one place with a free QxMD account.


Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"