PSG (polysomnographic) Cluster Classifier
This computational tool was designed to accept polysomnography (PSG) parameters as user input and then determine the PSG cluster to which a given patient or group of patients belongs. Figures 1-3 below can be used to learn more about the PSG clusters and the association between PSG clusters and all-cause mortality (Figure 1) and incident cancer (Figure 2).
PSG parameter Value Range
Sleep Efficiency (%) (0-100)
# of Awakenings in TST (0-500)
Arousal Index (0-300)
PLMI (0-300)
% REM Sleep (0-100)
% Stage 1 Sleep (0-100)
Sleep Latency (mins) (0-500)
% of TST with SaO2 <90% (0-100)
AHI (0-300)
Mean SO2 (%) (0-100)
Output: PSG Phenotype
How has this calculator provided impact?
How was this tool used?
Comments

View the published article in the journal Chest

View Figure 1. Unadjusted Kaplan-Meier survival curves for all-cause mortality stratified by polysomnographic (PSG) clusters: Cluster 1: Severe Obstructive Sleep Apnea/Sleep Fragmentation; Cluster 2: Periodic Limb movements of Sleep; Cluster 3: Mild – representing a mildly abnormal polysomnogram; Cluster 4: Severe Desaturations; Cluster 5: Poor Sleep.

View Figure 2. Estimated cumulative incidence of incident cancer stratified by polysomnographic (PSG) clusters: Cluster 1: Severe Obstructive Sleep Apnea/Sleep Fragmentation; Cluster 2: Periodic Limb movements of Sleep; Cluster 3: Mild – representing a mildly abnormal polysomnogram; Cluster 4: Severe Desaturations; Cluster 5: Poor Sleep.

View Figure 3. K-Means Cluster Heatmap: Heat Map of the Mean Values of each variable by Cluster. The variables are standardized with a mean of 0 and standard deviation of 1 to be able to compare on the same scale. Darker colors indicate a higher value of the variables. Values above 1 are above the mean (per unit of standard deviation), and values below 1 are below the mean.

Methodology: The PSG clusters/phenotypes were created using the K-Means Cluster algorithm, where a combination of clinical expertise and select cluster validation statistics were used to derive and select the 5 phenotypes. Our cluster validation analysis evaluated construct validity (how well the clusters measure a specific construct) and predictive validity (how well the clusters predict future related outcomes).

Disclaimer: The dataset from this study is held securely in coded form at ICES. While data sharing agreements prohibit ICES from making the dataset publicly available, access may be granted to those who meet prespecified criteria for confidential access, available at www.ices.on.ca/DAS. The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are, therefore either inaccessible or may require modification.

Parts of this material are based on data and/or information compiled and provided by CIHI. However, the analyses, conclusions, opinions and statements expressed in the material are those of the author(s), and not necessarily those of CIHI.

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