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Stability analysis of clustering of Norris' visual analogue scale: Applying the consensus clustering approach.

ABSTRACT:

Abstract

Visual analogue scales are widely used to measure subjective responses. Norris' 16 visual analogue scales (N_VAS) measure subjective feelings of alertness and mood. Up to now, different scientists have clustered items of N_VAS into different ways and Bond and Lader's way has been the most frequently used in clinical research. However, there are concerns about the stability of this clustering over different subject samples and different drug classes. The aim of this study was to test whether Bond and Lader's clustering was stable in terms of subject samples and drug effects. Alternative clustering of N_VAS was tested.Data from studies with 3 types of drugs: cannabinoid receptor agonist (delta-9-tetrahydrocannabinol [THC]), muscarinic antagonist (scopolamine), and benzodiazepines (midazolam and lorazepam), collected between 2005 and 2012, were used for this analysis. Exploratory factor analysis (EFA) was used to test the clustering algorithm of Bond and Lader. Consensus clustering was performed to test the stability of clustering results over samples and over different drug types. Stability analysis was performed using a three-cluster assumption, and then on other alternative assumptions.Heat maps of the consensus matrix (CM) and density plots showed instability of the three-cluster hypothesis and suggested instability over the 3 drug classes. Two- and four-cluster hypothesis were also tested. Heat maps of the CM and density plots suggested that the two-cluster assumption was superior.In summary, the two-cluster assumption leads to a provably stable outcome over samples and the 3 drug types based on the data used.

SUBMITTER: Guan Z

PROVIDER: S-EPMC8084085 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster's key features. We assessed the association of the clusters with mortality. In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.

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Stability analysis of clustering of Norris' visual analogue scale: Applying the consensus clustering approach.

Abstract

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