{
    "abstract": "Background Bladder pain syndrome/interstitial cystitis (BPS/IC) is a chronic condition characterized by pelvic pain and urinary symptoms. Despite its significant impact on patients' quality of life, the heterogeneity of BPS/IC symptoms and the presence of comorbidities such as vulvodynia may not be adequately captured by validated questionnaires. Identifying vulvodynia in BPS/IC patients is crucial for providing appropriate treatment options. This study aimed to identify subtypes of BPS/IC patients using unsupervised machine learning and to investigate the prevalence of vulvodynia in each subtype. Methods We conducted a prospective cross-sectional study of 123 BPS/IC patients and 64 age-matched controls. Hierarchical clustering was performed using data from validated questionnaires, including the Numerical Rating Scale-11, Interstitial Cystitis Symptom Index (ICSI), Interstitial Cystitis Problem Index (ICPI), Pelvic Pain and Urgency/Frequency scores, Overactive Bladder Questionnaire Short Form (OABq SF), Overactive Bladder Symptom Score (OABSS), and Pelvic Floor Distress Inventory-20. The optimal number of clusters was determined using the elbow method, and the characteristics of each cluster were analyzed. All participants underwent a vulvodynia swab test to assess vulvodynia symptoms. Results Unsupervised machine learning revealed three distinct clusters of BPS/IC patients. Clusters 0 and 2 differed significantly, with Cluster 2 characterized by significantly higher vulvodynia scores compared to other clusters (P < 0.001). In contrast, Cluster 2 had lower bladder pain scores (ICSI and ICPI) and overactive bladder symptom scores (OABq SF and OABSS) compared to other clusters. Clusters 0 and 1 were characterized by a predominance of bladder pain and urinary frequency symptoms, with Cluster 0 exhibiting more severe symptoms. Conclusions Our study identified distinct subtypes of BPS/IC patients using unsupervised machine learning, with Cluster 2 representing a vulvodynia-predominant subtype. This finding, along with the potential of targeted therapies such as non-ablative erbium YAG laser for vulvodynia, underscores the importance of assessing extravesical symptoms, particularly vulvodynia, for the diagnosis and treatment of BPS/IC. A tailored approach, including laser therapy for vulvodynia-predominant patients, may be necessary for optimal management of BPS/IC. The vulvodynia swab test plays a crucial role in assessing vulvodynia symptoms, underlining the limitations of validated questionnaires in capturing the full spectrum of BPS/IC symptoms. A comprehensive evaluation of patients, including the vulvodynia swab test, is essential for accurate subtyping and management of BPS/IC. Further research with larger sample sizes and investigation of the relationship between identified subtypes and other clinical data is warranted to advance our understanding and management of BPS/IC.",
    "authors": [
        {
            "affiliation": "Dentistry, Kanagawa Dental University, Yokosuka, JPN.",
            "firstname": "Nobuo",
            "initials": "N",
            "lastname": "Okui"
        }
    ],
    "conclusions": null,
    "copyrights": "Copyright \u00a9 2024, Okui et al.",
    "doi": "10.7759/cureus.62585",
    "journal": "Cureus",
    "keywords": [
        "bladder pain syndrome",
        "interstitial cystitis",
        "interstitial cystitis problem index",
        "interstitial cystitis symptom index",
        "numerical rating scale-11",
        "overactive bladder questionnaire short form",
        "overactive bladder symptom score",
        "pelvic pain and urgency/frequency scores",
        "vulvodynia",
        "vulvodynia swab test"
    ],
    "methods": null,
    "publication_date": "2024-07-19",
    "pubmed_id": "39027744\n25623737\n32944561\n36112181\n18276064\n34023828\n34888917\n34199379\n27790294\n26813805\n37449291\n37284382\n9146003\n7495124\n16418145\n19228224\n37195425\n33866884\n28085891\n35036991\n26291799\n33124452\n36116559\n36505169\n27564065\n27218442\n15548433\n21107589",
    "results": null,
    "title": "Unsupervised Machine Learning Reveals a Vulvodynia-Predominant Subtype in Bladder Pain Syndrome/Interstitial Cystitis.",
    "xml": "<Element 'PubmedArticle' at 0x788504957470>"
}