HemorrhoidPROCTO-CLUSTERNCT07427927
Data-driven Clustering in Hemorrhoid Surgery: Retrospective Monocentric Study for the Identification of Clinical Phenotypes
An observational study of Hemorrhoid and Hemorrhoid Prolapse, run by IRCCS Policlinico S. Donato at 1 site in Italy.
Trial summary
This retrospective, single-center observational study will use routinely collected perioperative data from adults undergoing surgery for symptomatic hemorrhoidal disease to identify data-driven clinical phenotypes. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Hemorrhoid, Hemorrhoid Prolapse
- What is being tested?
- Any surgical procedure for hemorrhoidal disease
- Type of treatment
- Surgery or procedure
- How many people
- 100 participants
- Where
- 1 siteItaly
- Study dates
- 1 Dec 2024to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients undergoing surgery for symptomatic hemorrhoidal disease
Who may be able to join
- Age ≥ 18 years
- Clinical and/or intraoperative diagnosis of symptomatic hemorrhoidal disease
- Availability of complete perioperative data: demographic, clinical, surgical, and postoperative variables Exclusion criteria
- Incomplete or missing clinical data
- Presence of anorectal neoplastic conditions (e.g., anal or rectal carcinoma)
- Anorectal surgery within the previous 6 months (to avoid confounding effects on symptoms and anatomy)
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
This retrospective, single-center observational study will use routinely collected perioperative data from adults undergoing surgery for symptomatic hemorrhoidal disease to identify data-driven clinical phenotypes. Unsupervised machine learning will be applied to characterize clusters of patients based on demographic, clinical, anatomical, and surgical variables. The study will explore whether the resulting phenotypes differ in operative complexity and postoperative course, and will generate hypotheses to inform future predictive models and personalized surgical planning.
Read the detailed description
Hemorrhoidal disease presents with heterogeneous symptom patterns, anatomical findings, and operative strategies that are not fully captured by traditional degree-based classifications. This study aims to identify latent, clinically interpretable phenotypes among surgical patients using a fully unsupervised machine learning pipeline applied to routinely collected perioperative data from a high-volume tertiary referral center.
This is a retrospective, observational analysis of de-identified institutional records. The analytic dataset will include routinely documented variables spanning baseline demographics/anthropometrics, symptom profile and relevant clinical history, operative technique and intraoperative descriptors, and routinely captured postoperative follow-up information. Data will be extracted using a predefined data dictionary and standardized preprocessing rules to support reproducibility and reduce variability in variable definitions.
The primary analytic approach will be unsupervised clustering. Variables will be cleaned and standardized prior to modeling. Dimensionality reduction will be performed using t-distributed stochastic neighbor embedding (t-SNE), initialized with principal component analysis to improve stability. Cluster discovery will then be conducted using k-means clustering on the reduced feature space. A range of cluster solutions will be explored, and the final solution will be selected using internal validity metrics (e.g., silhouette-based measures) together with assessment of clinical interpretability. Model robustness will be evaluated through repeated runs across multiple random seeds and key parameter settings to assess stability of cluster assignments.
After cluster assignment, clusters will be characterized using descriptive and comparative statistics to identify variables that most differentiate phenotypes. Post-hoc feature relevance/importance approaches will be used to explore which demographic, clinical, and surgical factors most strongly contribute to cluster formation, with emphasis on effect sizes and clinically meaningful patterns rather than hypothesis-testing alone. Findings will be used to generate hypotheses regarding phenotypes that may be associated with greater operative complexity and different postoperative trajectories, supporting future work on predictive modeling and individualized surgical decision support.
All analyses will be conducted within a controlled institutional environment using validated statistical and data-mining software, with documented parameter settings and version tracking to enable reproducibility. Only de-identified data will be used for analysis, and results will be reported in aggregate to protect patient privacy.
- Conditions
- Hemorrhoid, Hemorrhoid Prolapse
- Being tested
- Any surgical procedure for hemorrhoidal disease
- Keywords
- Hemorrhoidal Disease, Unsupervised Machine Learning
- Medical subject headings
- Hemorrhoids
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Any surgical procedure for hemorrhoidal diseaseProcedure or surgery
standard hemorrhoidectomy, advanced hemorrhoidectomy, prolapsectomy, Doppler-guided procedures, or combined techniques
| Study group | Role | What they receive |
|---|---|---|
| patients who underwent surgery for symptomatic hemorrhoidal disease | Consecutive patients who underwent surgery for symptomatic hemorrhoidal disease at IRCCS Policlinico San Donato between December 2024 and June 2025. Consecutive enrollment was chosen to minimize selection bias and to represent the full spectrum of disease severity in the surgical setting.
Inclusion criteria Age ≥ 18 years Clinical and/or intraoperative diagnosis of symptomatic hemorrhoidal disease Availability of complete perioperative data: demographic, clinical, surgical, and postoperative variables Exclusion criteria Incomplete or missing clinical data Presence of anorectal neoplastic conditions (e.g., anal or rectal carcinoma) Anorectal surgery within the previous 6 months (to avoid confounding effects on symptoms and anatomy) Receives: Procedure: Any surgical procedure for hemorrhoidal disease |
What is being measured
The primary outcome is the main question the study is designed to answer.
Internal validity of the unsupervised clustering solution (silhouette coefficient)
Silhouette coefficient of the final k-means clustering solution derived from t-SNE-reduced perioperative data. The silhouette coefficient will be used as the primary internal validity metric to quantify cluster cohesion and separation for the selected number of clusters.
Measured: From completion of dataset extraction/cleaning through completion of clustering analysis (retrospective analysis of surgeries performed December 2024 to June 2025)
Secondary outcomes (6)
Cluster stability and reproducibility across model runs
Stability of cluster assignments across multiple random seeds and t-SNE parameter settings (including perplexity), summarized by reproducibility/consistency of membership and stability of internal validity metrics across runs.
Measured: From completion of dataset extraction/cleaning through completion of clustering robustness analyses (retrospective analysis of surgeries performed December 2024 to June 2025)
Operative duration (proxy of operative complexity)
Operative duration (minutes) recorded in the operative report/perioperative database; compared across identified phenotypes.
Measured: Intraoperative (day of surgery)
Postoperative pain intensity
Pain intensity as documented in routine postoperative records/follow-up notes (e.g., numeric rating scale when available or clinician-documented pain status), analyzed as pain trajectory/pattern across early and intermediate follow-up and compared across clusters.
Measured: From surgery to 6 month postoperatively
Postoperative complications (Clavien-Dindo classification)
Any postoperative complication recorded in routine follow-up, graded according to the Clavien-Dindo system; complication rates and severity compared across clusters.
Measured: From surgery to 1 month postoperatively (early complications) and up to 6 months postoperatively (late complications)
Time to return to routine activities
Time to return to routine activities/work when documented in follow-up notes; compared across phenotypes.
Measured: From surgery to 1 month postoperatively
Recurrence
Recurrence patterns or persistence/return of hemorrhoid-related symptoms (e.g., bleeding/prolapse/other symptoms) as documented in routine follow-up and need for re-evaluation or additional intervention; compared across clusters.
Measured: 1 month and 6 months postoperatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 Feb 2026
- Study start1 Dec 2024
- Primary completion31 Dec 2025
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for HemorrhoidStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- IRCCS Policlinico S. Donato
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07427927
- Sponsor's protocol ID
- CET 18-2026
- Acronym
- PROCTO-CLUSTER
- Status last verified
- Aug 2026
- First submitted
- 5 Feb 2026
- Last updated
- 27 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Bernabe-Diaz JA, Franco M, Vivo JM, Fernandez-Breis JT. Optimizing clustering-based analytical methods with trimmed and sparse clustering. Comput Biol Med. 2025 Aug;194:110436. doi: 10.1016/j.compbiomed.2025.110436. Epub 2025 Jun 16.
PubMed 40527163Xiao C, Hong S, Huang W. Optimizing graph layout by t-SNE perplexity estimation. Int J Data Sci Anal. 2023;15(2):159-171. doi:10.1007/s41060-022-00348-7
Goldenholz DM, Sun H, Ganglberger W, Westover MB. Sample Size Analysis for Machine Learning Clinical Validation Studies. Biomedicines. 2023 Feb 23;11(3):685. doi: 10.3390/biomedicines11030685.
PubMed 36979665De Marco S, Tiso D. Lifestyle and Risk Factors in Hemorrhoidal Disease. Front Surg. 2021 Aug 18;8:729166. doi: 10.3389/fsurg.2021.729166. eCollection 2021.
PubMed 34485376Robinson PN, Mungall CJ, Haendel M. Capturing phenotypes for precision medicine. Cold Spring Harb Mol Case Stud. 2015 Oct;1(1):a000372. doi: 10.1101/mcs.a000372.
PubMed 27148566Brillantino A, Renzi A, Talento P, Brusciano L, Marano L, Grillo M, Maglio MN, Foroni F, Palumbo A, Sotelo MLS, Vicenzo L, Lanza M, Frezza G, Antropoli M, Gambardella C, Monaco L, Ferrante I, Izzo D, Giordano A, Pinto M, Fantini C, Gasparrini M, Schiano Di Visconte M, Milazzo F, Ferreri G, Braini A, Cocozza U, Pezzatini M, Gianfreda V, Di Leo A, Landolfi V, Favetta U, Agradi S, Marino G, Varriale M, Mongardini M, Pagano CEFA, Contul RB, Gallese N, Ucchino G, D'Ambra M, Rizzato R, Sarzo G, Masci B, Da Pozzo F, Ascanelli S, Liguori P, Pezzolla A, Iacobellis F, Boriani E, Cudazzo E, Babic F, Geremia C, Bussotti A, Cicconi M, Sarno AD, Mongardini FM, Brescia A, Lenisa L, Mistrangelo M, Zuin M, Mozzon M, Chiriatti AP, Bottino V, Ferronetti A, Rispoli C, Carbone L, Calabro G, Tirro A, de Vito D, Ioia G, Lamanna GL, Asciore L, Greco E, Bianchi P, D'Oriano G, Stazi A, Antonacci N, Renzo RMD, Poto GE, Ferulano GP, Longo A, Docimo L. The Italian Unitary Society of Colon-Proctology (Societa Italiana Unitaria di Colonproctologia) guidelines for the management of acute and chronic hemorrhoidal disease. Ann Coloproctol. 2024 Aug;40(4):287-320. doi: 10.3393/ac.2023.00871.0124. Epub 2024 Aug 30.
PubMed 39228195De Gregorio MA, Guirola JA, Serrano-Casorran C, Urbano J, Gutierrez C, Gregorio A, Sierre S, Ciampi-Dopazo JJ, Bernal R, Gil I, De Blas I, Sanchez-Ballestin M, Millera A. Catheter-directed hemorrhoidal embolization for rectal bleeding due to hemorrhoids (Goligher grade I-III): prospective outcomes from a Spanish emborrhoid registry. Eur Radiol. 2023 Dec;33(12):8754-8763. doi: 10.1007/s00330-023-09923-3. Epub 2023 Jul 17.
PubMed 37458757van Oostendorp JY, Grossi U, Hoxhaj I, Kimman ML, Kuiper SZ, Breukink SO, Han-Geurts IJM, Gallo G. Limitations of the Goligher classification in randomized trials for hemorrhoidal disease: a qualitative systematic review of selection criteria. Tech Coloproctol. 2025 Jun 10;29(1):133. doi: 10.1007/s10151-025-03170-y.
PubMed 40493094Dekker L, Han-Geurts IJM, Grossi U, Gallo G, Veldkamp R. Is the Goligher classification a valid tool in clinical practice and research for hemorrhoidal disease? Tech Coloproctol. 2022 May;26(5):387-392. doi: 10.1007/s10151-022-02591-3. Epub 2022 Feb 9.
PubMed 35141793Bozovic B, Radoicic M, Jankovic S, Andelkovic J, Kostic M. Pharmacoeconomic Aspects of Treating Hemorrhoidal Disease-Cost of Illness Study Based on Data from Balkan Country with Recent History of Social and Economic Transition. Iran J Public Health. 2021 Jun;50(6):1288-1290. doi: 10.18502/ijph.v50i6.6433. No abstract available.
PubMed 34540753Rorvik HD, Davidsen M, Gierloff MC, Brandstrup B, Olaison G. Quality of life in patients with hemorrhoidal disease. Surg Open Sci. 2023 Feb 24;12:22-28. doi: 10.1016/j.sopen.2023.02.004. eCollection 2023 Mar.
PubMed 36876020Pata F, Gallo G, Pellino G, Vigorita V, Podda M, Di Saverio S, D'Ambrosio G, Sammarco G. Evolution of Surgical Management of Hemorrhoidal Disease: An Historical Overview. Front Surg. 2021 Aug 30;8:727059. doi: 10.3389/fsurg.2021.727059. eCollection 2021.
PubMed 34527700Roberts K. What Are Hemorrhoids? JAMA. 2025 Nov 6. doi: 10.1001/jama.2025.17253. Online ahead of print.
PubMed 41196608Wang L, Ni J, Hou C, Wu D, Sun L, Jiang Q, Cai Z, Fan W. Time to change? Present and prospects of hemorrhoidal classification. Front Med (Lausanne). 2023 Oct 11;10:1252468. doi: 10.3389/fmed.2023.1252468. eCollection 2023.
PubMed 37901411Al-Masoudi RO, Shosho R, Alquhra D, Alzahrani M, Hemdi M, Alshareef L. Prevalence of Hemorrhoids and the Associated Risk Factors Among the General Adult Population in Makkah, Saudi Arabia. Cureus. 2024 Jan 3;16(1):e51612. doi: 10.7759/cureus.51612. eCollection 2024 Jan.
PubMed 38318578
Study at a glance
From the registry record, last updated by the sponsor on 27 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Data-driven Clustering in Hemorrhoid Surgery: Retrospective Monocentric Study for the Identification of Clinical Phenotypes
- ClinicalTrials.gov ID
- NCT07427927
- Acronym / sponsor ID
- PROCTO-CLUSTER / CET 18-2026
- Conditions
- Hemorrhoid, Hemorrhoid Prolapse
- Keywords
- Hemorrhoidal Disease, Unsupervised Machine Learning
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- IRCCS Policlinico S. DonatoUniversity, hospital or other
- Study start
- 1 Dec 2024 (actual)
- Primary completion
- 31 Dec 2025 (actual)
- Study completion
- Dec 2026 (estimated)
- First posted
- 23 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This retrospective, single-center observational study will use routinely collected perioperative data from adults undergoing surgery for symptomatic hemorrhoidal disease to identify data-driven clinical phenotypes. Unsupervised machine learning will be applied to characterize clusters of patients based on demographic, clinical, anatomical, and surgical variables. The study will explore whether the resulting phenotypes differ in operative complexity and postoperative course, and will generate hypotheses to inform future predictive models and personalized surgical planning.
Detailed description
Hemorrhoidal disease presents with heterogeneous symptom patterns, anatomical findings, and operative strategies that are not fully captured by traditional degree-based classifications. This study aims to identify latent, clinically interpretable phenotypes among surgical patients using a fully unsupervised machine learning pipeline applied to routinely collected perioperative data from a high-volume tertiary referral center.
This is a retrospective, observational analysis of de-identified institutional records. The analytic dataset will include routinely documented variables spanning baseline demographics/anthropometrics, symptom profile and relevant clinical history, operative technique and intraoperative descriptors, and routinely captured postoperative follow-up information. Data will be extracted using a predefined data dictionary and standardized preprocessing rules to support reproducibility and reduce variability in variable definitions.
The primary analytic approach will be unsupervised clustering. Variables will be cleaned and standardized prior to modeling. Dimensionality reduction will be performed using t-distributed stochastic neighbor embedding (t-SNE), initialized with principal component analysis to improve stability. Cluster discovery will then be conducted using k-means clustering on the reduced feature space. A range of cluster solutions will be explored, and the final solution will be selected using internal validity metrics (e.g., silhouette-based measures) together with assessment of clinical interpretability. Model robustness will be evaluated through repeated runs across multiple random seeds and key parameter settings to assess stability of cluster assignments.
After cluster assignment, clusters will be characterized using descriptive and comparative statistics to identify variables that most differentiate phenotypes. Post-hoc feature relevance/importance approaches will be used to explore which demographic, clinical, and surgical factors most strongly contribute to cluster formation, with emphasis on effect sizes and clinically meaningful patterns rather than hypothesis-testing alone. Findings will be used to generate hypotheses regarding phenotypes that may be associated with greater operative complexity and different postoperative trajectories, supporting future work on predictive modeling and individualized surgical decision support.
All analyses will be conducted within a controlled institutional environment using validated statistical and data-mining software, with documented parameter settings and version tracking to enable reproducibility. Only de-identified data will be used for analysis, and results will be reported in aggregate to protect patient privacy.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| patients who underwent surgery for symptomatic hemorrhoidal disease | – | Procedure: Any surgical procedure for hemorrhoidal diseaseConsecutive patients who underwent surgery for symptomatic hemorrhoidal disease at IRCCS Policlinico San Donato between December 2024 and June 2025. Consecutive enrollment was chosen to minimize selection bias and to represent the full spectrum of disease severity in the surgical setting. Inclusion criteria Age ≥ 18 years Clinical and/or intraoperative diagnosis of symptomatic hemorrhoidal disease Availability of complete perioperative data: demographic, clinical, surgical, and postoperative variables Exclusion criteria Incomplete or missing clinical data Presence of anorectal neoplastic conditions (e.g., anal or rectal carcinoma) Anorectal surgery within the previous 6 months (to avoid confounding effects on symptoms and anatomy) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing surgery for symptomatic hemorrhoidal disease
Key inclusion criteria (6 listed)
- Age ≥ 18 years
- Clinical and/or intraoperative diagnosis of symptomatic hemorrhoidal disease
- Availability of complete perioperative data: demographic, clinical, surgical, and postoperative variables Exclusion criteria
- Incomplete or missing clinical data
- Presence of anorectal neoplastic conditions (e.g., anal or rectal carcinoma)
Key exclusion criteria (0 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Any surgical procedure for hemorrhoidal diseasestandard hemorrhoidectomy, advanced hemorrhoidectomy, prolapsectomy, Doppler-guided procedures, or combined techniques | Procedure or surgery | patients who underwent surgery for symptomatic hemorrhoidal disease |
Primary outcome
Internal validity of the unsupervised clustering solution (silhouette coefficient)
Time frame: From completion of dataset extraction/cleaning through completion of clustering analysis (retrospective analysis of surgeries performed December 2024 to June 2025)
Silhouette coefficient of the final k-means clustering solution derived from t-SNE-reduced perioperative data. The silhouette coefficient will be used as the primary internal validity metric to quantify cluster cohesion and separation for the selected number of clusters.
6 secondary outcomes
Cluster stability and reproducibility across model runs
Time frame: From completion of dataset extraction/cleaning through completion of clustering robustness analyses (retrospective analysis of surgeries performed December 2024 to June 2025)
Operative duration (proxy of operative complexity)
Time frame: Intraoperative (day of surgery)
Postoperative pain intensity
Time frame: From surgery to 6 month postoperatively
Postoperative complications (Clavien-Dindo classification)
Time frame: From surgery to 1 month postoperatively (early complications) and up to 6 months postoperatively (late complications)
Time to return to routine activities
Time frame: From surgery to 1 month postoperatively
Recurrence
Time frame: 1 month and 6 months postoperatively
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Aug 2026.
- Background: PubMed 40527163Bernabe-Diaz JA, Franco M, Vivo JM, Fernandez-Breis JT. Optimizing clustering-based analytical methods with trimmed and sparse clustering. Comput Biol Med. 2025 Aug;194:110436. doi: 10.1016/j.compbiomed.2025.110436. Epub 2025 Jun 16.
- BackgroundXiao C, Hong S, Huang W. Optimizing graph layout by t-SNE perplexity estimation. Int J Data Sci Anal. 2023;15(2):159-171. doi:10.1007/s41060-022-00348-7
- Background: PubMed 36979665Goldenholz DM, Sun H, Ganglberger W, Westover MB. Sample Size Analysis for Machine Learning Clinical Validation Studies. Biomedicines. 2023 Feb 23;11(3):685. doi: 10.3390/biomedicines11030685.
- Background: PubMed 34485376De Marco S, Tiso D. Lifestyle and Risk Factors in Hemorrhoidal Disease. Front Surg. 2021 Aug 18;8:729166. doi: 10.3389/fsurg.2021.729166. eCollection 2021.
- Background: PubMed 27148566Robinson PN, Mungall CJ, Haendel M. Capturing phenotypes for precision medicine. Cold Spring Harb Mol Case Stud. 2015 Oct;1(1):a000372. doi: 10.1101/mcs.a000372.
- Background: PubMed 39228195Brillantino A, Renzi A, Talento P, Brusciano L, Marano L, Grillo M, Maglio MN, Foroni F, Palumbo A, Sotelo MLS, Vicenzo L, Lanza M, Frezza G, Antropoli M, Gambardella C, Monaco L, Ferrante I, Izzo D, Giordano A, Pinto M, Fantini C, Gasparrini M, Schiano Di Visconte M, Milazzo F, Ferreri G, Braini A, Cocozza U, Pezzatini M, Gianfreda V, Di Leo A, Landolfi V, Favetta U, Agradi S, Marino G, Varriale M, Mongardini M, Pagano CEFA, Contul RB, Gallese N, Ucchino G, D'Ambra M, Rizzato R, Sarzo G, Masci B, Da Pozzo F, Ascanelli S, Liguori P, Pezzolla A, Iacobellis F, Boriani E, Cudazzo E, Babic F, Geremia C, Bussotti A, Cicconi M, Sarno AD, Mongardini FM, Brescia A, Lenisa L, Mistrangelo M, Zuin M, Mozzon M, Chiriatti AP, Bottino V, Ferronetti A, Rispoli C, Carbone L, Calabro G, Tirro A, de Vito D, Ioia G, Lamanna GL, Asciore L, Greco E, Bianchi P, D'Oriano G, Stazi A, Antonacci N, Renzo RMD, Poto GE, Ferulano GP, Longo A, Docimo L. The Italian Unitary Society of Colon-Proctology (Societa Italiana Unitaria di Colonproctologia) guidelines for the management of acute and chronic hemorrhoidal disease. Ann Coloproctol. 2024 Aug;40(4):287-320. doi: 10.3393/ac.2023.00871.0124. Epub 2024 Aug 30.
- Background: PubMed 37458757De Gregorio MA, Guirola JA, Serrano-Casorran C, Urbano J, Gutierrez C, Gregorio A, Sierre S, Ciampi-Dopazo JJ, Bernal R, Gil I, De Blas I, Sanchez-Ballestin M, Millera A. Catheter-directed hemorrhoidal embolization for rectal bleeding due to hemorrhoids (Goligher grade I-III): prospective outcomes from a Spanish emborrhoid registry. Eur Radiol. 2023 Dec;33(12):8754-8763. doi: 10.1007/s00330-023-09923-3. Epub 2023 Jul 17.
- Background: PubMed 40493094van Oostendorp JY, Grossi U, Hoxhaj I, Kimman ML, Kuiper SZ, Breukink SO, Han-Geurts IJM, Gallo G. Limitations of the Goligher classification in randomized trials for hemorrhoidal disease: a qualitative systematic review of selection criteria. Tech Coloproctol. 2025 Jun 10;29(1):133. doi: 10.1007/s10151-025-03170-y.
- Background: PubMed 35141793Dekker L, Han-Geurts IJM, Grossi U, Gallo G, Veldkamp R. Is the Goligher classification a valid tool in clinical practice and research for hemorrhoidal disease? Tech Coloproctol. 2022 May;26(5):387-392. doi: 10.1007/s10151-022-02591-3. Epub 2022 Feb 9.
- Background: PubMed 34540753Bozovic B, Radoicic M, Jankovic S, Andelkovic J, Kostic M. Pharmacoeconomic Aspects of Treating Hemorrhoidal Disease-Cost of Illness Study Based on Data from Balkan Country with Recent History of Social and Economic Transition. Iran J Public Health. 2021 Jun;50(6):1288-1290. doi: 10.18502/ijph.v50i6.6433. No abstract available.
- Background: PubMed 36876020Rorvik HD, Davidsen M, Gierloff MC, Brandstrup B, Olaison G. Quality of life in patients with hemorrhoidal disease. Surg Open Sci. 2023 Feb 24;12:22-28. doi: 10.1016/j.sopen.2023.02.004. eCollection 2023 Mar.
- Background: PubMed 34527700Pata F, Gallo G, Pellino G, Vigorita V, Podda M, Di Saverio S, D'Ambrosio G, Sammarco G. Evolution of Surgical Management of Hemorrhoidal Disease: An Historical Overview. Front Surg. 2021 Aug 30;8:727059. doi: 10.3389/fsurg.2021.727059. eCollection 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hemorrhoid and Hemorrhoid Prolapse, run by IRCCS Policlinico S. Donato at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 0 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 27 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Internal validity of the unsupervised clustering solution (silhouette coefficient) (time frame: From completion of dataset extraction/cleaning through completion of clustering analysis (retrospective analysis of surgeries performed December 2024 to June 2025)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07427927, which also lists 16 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Aug 2026.