Data-driven Clustering in Hemorrhoid Surgery: Retrospective Monocentric Study for the Identification of Clinical Phenotypes
Conditions: Hemorrhoid, Hemorrhoid Prolapse
Sponsor: IRCCS Policlinico S. Donato

PROCTO-CLUSTER v.1.2 (20/01/2026)  
REGISTRATION NUMBER CET 18-2026 Comitato Etico Territoriale  Lombardia 1 
 
 
PROCTO-CLUSTER v.1.2 (20/01/2026) 
 
Title: 
Data-driven clustering in hemorrhoid surgery: retrospective monocentric study for the 
identification of clinical phenotypes 
 
Principal Investigator: 
 
Dr. Angelo Stuto 
 
Co-Investigators: 
 
Dr. Carlotta La Raja 
Dr. Francesca Lecchi 
Dr. Giorgio La Greca 
Dr. Paola Cellerino 
 
Sponsor: 
 
IRCCS Policlinico San Donato 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Index  
 
1.0 Introduction​
3 
1.1 Study Aim​
6 
2.0 Materials and Methods​
7 
2.1 Study Design​
7 
2.2 Study Population​
7 
2.3 Data sources and data collection​
8 
2.4 Statistical and Machine Learning Analysis​
10 
2.5 Study Limitations related to external validity​
11 
3.0 Ethical considerations​
11 
4.0 Data Protection​
12 
5.0 Data Management and Record Keeping​
13 
6.0 Bias Assessment related to internal validity​
14 
7.0 Publication Policy​
15 
8.0 References​
15 
9.0 Contacts​
17 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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1.0 Introduction 
 
Hemorrhoidal disease (HD) is one of the most common benign anorectal conditions 
worldwide, with lifetime prevalence estimates reaching up to one-third of the adult 
population.1,2 It represents a substantial proportion of proctologic consultations and 
elective 
anorectal 
procedures, 
spanning 
from 
outpatient 
management 
of 
symptomatic internal hemorrhoids to complex surgery in patients with advanced 
prolapse or recurrent disease. Despite its benign nature, HD imposes a significant 
burden on healthcare systems, generating high volumes of outpatient visits, 
diagnostic procedures, day-surgery activity, and postoperative follow-up.2,3 
 
Clinically, HD is a heterogeneous condition rather than a single, uniform entity.1 
Patients may present with very different symptom constellations, including bleeding, 
prolapse, pain, itching, soiling, and discomfort, often in association with constipation, 
pelvic floor dysfunction, or other anorectal disorders.4 Anatomically, variations in the 
distribution and size of hemorrhoidal cushions, degree and circumferential extent of 
prolapse, associated skin tags, and coexisting anal pathology (e.g., fissures, fistulas) 
further contribute to this heterogeneity. Additionally, local inflammatory patterns, 
vascular congestion, and tissue fragility may vary significantly between patients, 
potentially influencing both the natural course of the disease and the response to 
surgical treatment.5 
 
The impact of HD on quality of life is frequently underestimated.6 Persistent bleeding 
and prolapse can lead to embarrassment, activity limitation, and social withdrawal, 
while pain and discomfort may interfere with work productivity, mobility, and sleep. In 
severe or recurrent cases, patients may experience chronic symptoms for years, with 
cumulative effects on physical, psychological, and sexual health.6 Standard clinical 
scores and grading systems only partially capture this multidimensional burden, and 
patient-reported outcomes are not routinely integrated into decision-making in 
everyday practice. 
 
From a health-service perspective, HD generates substantial resource utilization.7 
The same “degree” of HD can be treated with different techniques, such as 
conventional hemorrhoidectomy, stapled procedures, Doppler-guided ligation, or 
combined approaches, mainly based on surgeon preference and local expertise 
rather than on standardized risk stratification.4–7 This variability in indications and 
techniques can translate into heterogeneous postoperative trajectories, including 
differences in pain, complications, and recurrence, with direct implications for costs, 
length of stay, unplanned readmissions, and time away from work. The widespread 
reliance on traditional degree-based classifications implicitly assumes that these 
systems adequately reflect the true clinical complexity of HD and could guide optimal 
therapeutic choices.3 However, growing clinical experience suggests that patients 
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with the same nominal “grade” may differ substantially in symptom burden, 
comorbidity profile, anatomical features, and surgical risk, indicating that current 
grading systems are too coarse to support genuinely personalized care. 
 
The most widely used framework for categorizing hemorrhoidal disease remains the 
Goligher classification, a system introduced more than half a century ago and based 
exclusively on the extent of prolapse during defecation.8,9 Although pragmatic and 
easy to apply, the Goligher scale is inherently reductionist: it captures only a single 
anatomical dimension of a multifactorial condition and offers no insight into symptom 
severity, comorbidity burden, inflammatory status, or patient-specific risk factors. As 
a consequence, it provides a narrow and often misleading representation of the 
clinical 
complexity encountered in contemporary proctologic practice.8,9 A 
fundamental limitation of the Goligher system is its inability to incorporate the 
symptomatic heterogeneity that characterizes hemorrhoidal disease. Patients with 
the same nominal degree may experience vastly different levels of bleeding, pain, 
prolapse-related discomfort, pruritus, or soiling. These symptoms, in turn, do not 
correlate consistently with the degree of prolapse, making the scale poorly reflective 
of patient-reported burden and insufficient for guiding therapeutic decisions. 
Similarly, the classification does not account for comorbidities, such as obesity, 
chronic constipation, anticoagulation, pelvic floor dysfunction, or inflammatory bowel 
conditions, that may modify both the natural history of the disease and the risk profile 
associated with surgical interventions. 
 
Anatomical and morphological variations, including circumferential involvement, 
presence of external components, associated fissures, and tissue fragility, are 
likewise omitted from the Goligher framework, despite their known implications for 
technical planning and postoperative outcomes.9 The underlying inflammatory 
phenotype, characterized by degrees of vascular congestion, edema, or mucosal 
fragility, also remains unmeasured, even though it may partly explain differences in 
postoperative bleeding, pain trajectories, and recurrence risk. Another critical 
omission is the variability in postoperative recovery patterns: pain, time to return to 
normal activity, and complication rates show substantial interpatient variation that is 
not predicted by prolapse grade alone.9 Increasing evidence underscores the weak 
correlation between the Goligher degree and clinically meaningful outcomes.10 
Studies have demonstrated that the degree of prolapse does not reliably predict the 
severity of symptoms, responsiveness to office-based procedures, recurrence after 
surgery, or the intensity of postoperative pain.9,10 As a result, patients with “similar 
degrees” often behave differently across the entire continuum of care, from symptom 
presentation to surgical indication, operative complexity, and recovery course, 
highlighting a persistent mismatch between the current classification and real-world 
patient trajectories. 
 
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The limitations of degree-based classifications highlight a broader methodological 
gap in the conceptualization and management of HD.8–10 In current practice, 
treatment decisions are frequently guided by a coarse anatomical descriptor (i.e., 
prolapse extent) rather than by a nuanced understanding of the underlying clinical 
phenotypes.11 This stands in contrast with recent developments in other surgical and 
medical fields, such as colorectal surgery, oncology, bariatric and cardiovascular 
care, where phenotype-driven approaches have been increasingly embraced to 
improve risk stratification, tailor interventions, and personalize postoperative 
management.12 Patients may cluster along distinct trajectories, for example: 
bleeding-predominant, prolapse-dominant, pain-dominant, mixed symptomatic, or 
comorbidity-driven 
presentations, 
each potentially associated with different 
responses to treatment and different postoperative courses. However, these latent 
clinical subgroups remain largely unexplored because current frameworks do not 
systematically capture the multidimensionality of the disease.1,4–6,13 
 
A more precise characterization of HD is clinically relevant for several reasons. First, 
better phenotyping could improve surgical indication: identifying which patients 
benefit from conventional hemorrhoidectomy versus minimally invasive approaches 
would reduce overtreatment in some cases and undertreatment in others. Second, 
phenotype-aware planning could help anticipate perioperative complexity, for 
example, higher bleeding risk in inflammatory or vascular-congestive profiles, or 
higher postoperative pain in patients with external components, fibrotic changes, or 
associated anal fissures. Third, postoperative trajectories vary widely, and 
understanding phenotype-dependent recovery patterns (e.g., pain duration, risk of 
edema, risk of thrombosis, recurrence probability) has direct implications for patient 
counseling, work leave planning, and resource allocation. Critically, the assumption 
that anatomical degree alone determines clinical behavior has obscured the 
possibility that hemorrhoidal disease contains biologically and clinically meaningful 
subtypes. Patients classified as “Grade III” may follow completely different clinical 
paths depending on factors that remain invisible to the current classification, such as 
bowel habits, pelvic floor coordination, chronic straining, comorbidity burden, or local 
inflammatory state. The absence of a standardized, multidimensional phenotyping 
framework prevents proctologists from anticipating this heterogeneity and limits 
opportunities to move toward precision surgery. In this context, identifying 
reproducible clinical phenotypes is not a purely exploratory exercise but a necessary 
step toward modernizing proctologic care.  
 
A phenotype-based approach has the potential to: (i) improve the granularity of 
preoperative assessment; (ii) guide technique selection on the basis of patient rather 
than surgeon factors; (iii) enhance prognostication about postoperative pain, 
bleeding, or recurrence; (iv) provide a foundation for developing predictive models 
for surgical outcomes; (v) and, in the long term, support the formulation of 
evidence-based, personalized care pathways. The current gap between the 
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complexity of real-world hemorrhoidal disease and the simplicity of existing 
classification systems, therefore, represents a significant unmet need. This provides 
the rationale for adopting data-driven, unsupervised methods as a means of 
capturing the multidimensional nature of HD and revealing clinically meaningful 
phenotypes that are currently undetected by traditional scoring systems. 
 
 
1.1 Study Aim 
 
The overarching aim of this study is to identify and characterize distinct clinical 
phenotypes among patients undergoing surgical treatment for hemorrhoidal disease 
by applying unsupervised machine learning techniques to routinely collected 
perioperative data. The study aims to identify latent patient subgroups that are not 
captured by traditional degree-based classifications and may be relevant for surgical 
planning and predicting postoperative outcomes.  
 
Primary objective: 
To assess the feasibility and clinical interpretability of a fully unsupervised clustering 
pipeline, based on t-distributed stochastic neighbor embedding (t-SNE) for 
dimensionality reduction followed by k-means clustering, to derive reproducible, 
data-driven phenotypes of hemorrhoidal disease from real-world perioperative 
datasets. 
 
Secondary objectives: 
 
●​ To describe the clinical, anatomical, and perioperative characteristics that 
differentiate 
the 
identified 
phenotypes, 
including 
symptom 
profiles, 
comorbidity burden, bowel habits, anatomical variations, and surgical 
technique selection. 
 
●​ To evaluate whether the emergent clusters correspond to different levels of 
perioperative complexity, as reflected by operative duration, intraoperative 
events, and technical modifications performed during surgery. 
 
●​ To compare postoperative outcomes across clusters, including pain intensity 
trajectories, 
early 
and 
late 
complications 
(classified 
according 
to 
Clavien–Dindo), time to return to routine activities, and recurrence patterns at 
short-term follow-up. 
 
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●​ To assess the contribution of demographic, clinical, and surgical variables to 
cluster formation, exploring their relative importance in defining latent 
phenotypes through post-hoc feature relevance analyses. 
 
●​ To examine the internal validity and stability of the clustering solution, 
including silhouette coefficients, cluster separation metrics, and reproducibility 
across multiple seeds and perplexity settings. 
 
●​ To generate hypothesis-driven insights for future predictive modeling, 
identifying candidate variables or phenotypic profiles that may inform 
individualized 
surgical planning, risk stratification, and postoperative 
counseling. 
 
 
2.0 Materials and Methods 
 
2.1 Study Design 
This study is designed as a retrospective, observational, monocentric analysis 
conducted at the IRCCS Policlinico San Donato, a high-volume tertiary referral 
center for proctologic surgery. The study utilizes routinely collected perioperative 
data from all eligible patients who underwent surgical treatment for hemorrhoidal 
disease within the predefined study period. No intervention, modification of clinical 
practice, or additional patient contact was required. 
 
Data extraction was conducted according to a predefined protocol to ensure 
consistency, completeness, and adherence to Good Clinical Practice and institutional 
data protection standards. The retrospective nature of the study allows for the 
analysis of real-world surgical variability and postoperative outcomes, providing an 
ideal framework for evaluating the feasibility and interpretability of unsupervised 
machine learning methodologies applied to heterogeneous clinical datasets. 
 
2.2 Study Population 
The study population consists of all consecutive adult patients who underwent 
surgical treatment for hemorrhoidal disease at the IRCCS Policlinico San Donato 
between December 2024 and June 2025. Consecutive-case sampling minimizes 
selection bias, enhances representativeness of routine clinical practice, and ensures 
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that the derived phenotypes reflect the full spectrum of hemorrhoidal disease 
severity and clinical presentations seen in surgical settings. 
 
Inclusion criteria: 
Patients were eligible for inclusion if they met all of the following criteria: 
●​ Age ≥ 18 years 
●​ Clinical and/or surgical diagnosis of hemorrhoidal disease, documented by the 
treating surgeon 
●​ Complete perioperative records available, including demographic, clinical, 
surgical, and postoperative data necessary for machine learning analyses 
 
Exclusion criteria: 
Patients were excluded if any of the following conditions were present: 
●​ Incomplete or missing clinical data preventing reliable integration into the 
analytic pipeline 
●​ Neoplastic anorectal conditions (e.g., anal carcinoma, rectal cancer), given 
their distinct pathophysiology, management, and postoperative course 
●​ History of anal surgery within the previous 6 months, due to potential 
confounding effects on anatomy, symptomatology, and recovery patterns. 
 
These criteria were selected to ensure a homogeneous dataset suitable for 
unsupervised clustering and to avoid biases related to incomplete information or 
comorbid anorectal conditions with different disease trajectories. 
 
2.3 Data sources and data collection  
 
As this is a retrospective observational study, all data were obtained exclusively from 
existing institutional clinical records. Specifically, information was sourced from the 
electronic health record (EHR) system and the internal perioperative database used 
routinely at the IRCCS Policlinico San Donato for documentation of preoperative 
assessments, operative reports, postoperative monitoring, and scheduled follow-up 
visits. These data were originally collected for clinical care and administrative 
purposes, not for research. Prior to extraction for analysis, all records underwent a 
standardized anonymization process to remove direct identifiers (e.g., name, date of 
birth, contact information) and to re-code indirect identifiers in compliance with 
institutional GDPR procedures. No new data were generated, and no additional 
patient contact occurred. Only de-identified variables relevant to the study aims were 
included in the analytic dataset, ensuring full adherence to the methodological 
requirements of retrospective research and to the applicable data-protection 
framework. 
 
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Data for this study were extracted from an anonymized institutional database 
prospectively maintained at the IRCCS Policlinico San Donato. The database 
captures all routinely collected perioperative and follow-up variables for patients 
undergoing proctologic surgery and is curated through standardized data-entry 
procedures implemented by the clinical team. For the purposes of this retrospective 
analysis, all data were accessed only after irreversible anonymization, with removal 
of direct and indirect identifiers in accordance with institutional data-protection 
policies. 
The analytic dataset encompassed four major domains: 
 
1. Demographic and anthropometric variables 
These included age, sex, and body mass index (BMI). These data were 
systematically recorded during the preoperative assessment and served as essential 
baseline descriptors that potentially influenced clinical presentation, surgical 
indications, and postoperative trajectories. 
2. Clinical history and symptom profile 
A detailed set of preoperative clinical variables was collected, including: 
●​ comorbidities 
(e.g., 
cardiovascular 
disease, 
metabolic 
disorders, 
gastrointestinal conditions), 
●​ current medications (e.g., anticoagulants, steroids, analgesics), 
●​ history of previous surgeries (abdominal, pelvic, or anorectal), 
●​ presenting symptoms (bleeding, prolapse, pain, pruritus, soiling), 
●​ duration 
and 
pattern 
of 
symptoms, 
including 
chronicity, 
episodic 
exacerbations, and bowel habit characteristics. 
 
 
3. Surgical characteristics 
All intraoperative data were derived from standardized operative reports and 
included: 
●​ type of procedure performed (standard hemorrhoidectomy, advanced 
hemorrhoidectomy, prolapsectomy, Doppler-guided procedures, or combined 
techniques), 
●​ presence of external components or associated anorectal pathology 
addressed during surgery, 
●​ operative duration and intraoperative events where available.​
 
 
 
4. Postoperative outcomes and follow-up 
Postoperative variables included: 
●​ complications classified according to the Clavien–Dindo grading system, 
●​ symptom persistence or resolution at early (1 month) and intermediate (6 
months) follow-up, 
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●​ indicators of pain, bleeding, edema, or recurrence when documented, 
●​ need for re-evaluation, medical therapy, or additional interventions.​
These outcomes were extracted from follow-up visits performed as part of 
routine postoperative care. 
 
All data were reviewed for completeness and internal consistency prior to analysis. 
De-identified records were stored in a secure research environment, ensuring full 
compliance with Good Clinical Practice (GCP), GDPR, and institutional policies. Only 
anonymized variables were imported into the machine learning workflow. 
 
 
2.4 Statistical and Machine Learning Analysis 
All statistical analyses will be conducted on an anonymized retrospective dataset 
extracted from institutional clinical records. The final cohort will consist of 
approximately 100 patients, selected based on the availability of comprehensive 
perioperative data. Prior to analysis, data will be inspected for completeness, 
distributional characteristics, and potential inconsistencies. Continuous variables will 
be summarized using means and standard deviations or medians and interquartile 
ranges, depending on their distribution, while categorical variables will be described 
using frequencies and percentages. Missing values will be quantified for each 
variable, and cases with insufficient perioperative information will be excluded based 
on predefined criteria. No imputation procedures will be applied, given the 
retrospective nature of the dataset and the primary aim of assessing feasibility and 
identifying potential clinical phenotypes through exploratory clustering. A formal 
sample size calculation will not be performed, as the study is exploratory in nature 
and not intended to test predefined hypotheses. 
The machine learning workflow will follow a fully unsupervised analytic approach 
designed to identify latent clinical phenotypes. All eligible variables—demographic, 
clinical, anatomical, surgical, and postoperative—will be standardizedto ensure 
comparability across scales. A dimensionality reduction step will be performed using 
t-distributed stochastic neighbor embedding (t-SNE), initialized with principal 
component analysis (PCA) to enhance stability and reduce noise.14,15 t-SNE will be 
applied to project high-dimensional clinical data into a two-dimensional embedding 
while 
preserving 
the 
local 
structure 
essential 
for 
phenotype 
discovery. 
Hyperparameters (including perplexity) will be selectedthrough an iterative process 
of inspecting embedding stability and cluster separation. Following dimensionality 
reduction, k-means clustering will be applied to the t-SNE coordinates to identify 
homogeneous patient groups. Multiple candidate values for k (range 2–10) will be 
explored. 
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The optimal number of clusters will be determined through a combination of internal 
validity metrics, primarily silhouette coefficients, alongside consideration of clinical 
interpretability and cluster stability across multiple random seeds. The resulting 
clusters will be treated as emergent clinical phenotypes. After cluster assignment, 
comparative analyses will be conducted to characterize differences across 
phenotypes. Continuous variables will be compared using ANOVA or Kruskal–Wallis 
tests, and categorical variables will be analyzed using chi-square or Fisher’s exact 
tests, as appropriate. Given the exploratory aim of the study, emphasis will be placed 
on effect sizes and observed patterns rather than on inferential statistical 
significance alone. 
 
All analyses will be      performed using R, Orange Data Mining, and SPSS v.28 in a 
controlled analytic environment that ensures      reproducibility through fixed random 
seeds, version tracking, and complete documentation of all parameter settings. This 
workflow reflects the primary feasibility objective of applying unsupervised machine 
learning to real-world perioperative data to identify latent and clinically meaningful 
phenotypes in hemorrhoidal disease. 
 
2.5 Study Limitations related to external validity 
 
This study has several inherent limitations that should be taken into account when 
interpreting the findings. First, its retrospective and monocentric design may limit 
generalizability. All data were derived from a single high-volume tertiary center, and 
patterns observed in this population may not fully reflect those encountered in 
different clinical settings, healthcare systems, or patient demographics. The 
retrospective nature of the analysis also restricts control over data completeness and 
variable standardization, despite the use of predefined extraction procedures. 
 
Second, the study does not include external validation or prospective follow-up, 
which constrains the ability to confirm the reproducibility and predictive value of the 
identified phenotypes. While unsupervised clustering can reveal latent structures 
within perioperative data, validation in independent cohorts and within prospective 
designs is necessary to determine whether these phenotypes are stable, clinically 
actionable, and predictive of long-term outcomes, such as recurrence, chronic 
symptoms, or the need for reintervention. 
 
Finally, postoperative outcomes were limited to routinely collected variables at short- 
and intermediate-term follow-up, and finer-grained patient-reported outcomes were 
not available. These constraints reflect the real-world nature of the dataset but may 
limit the depth of phenotype characterization. Despite these limitations, the study 
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provides an important proof-of-concept for applying unsupervised machine learning 
to characterize clinical heterogeneity in hemorrhoidal disease. 
 
3.0 Ethical considerations  
This study was conducted in accordance with the principles outlined in the 
Declaration of Helsinki (2013 revision), Good Clinical Practice (ICH-GCP E6[R3]), 
and relevant European and national regulations governing retrospective research. 
Because the analysis relies exclusively on previously collected clinical data and 
involves no direct patient contact or modification of care, no additional procedures or 
interventions were performed. 
 
All data were extracted only after irreversible anonymization, which involved removal 
of all direct identifiers (e.g., name, date of birth, contact details) and recoding of 
indirect identifiers in compliance with the General Data Protection Regulation 
(GDPR, EU Regulation 2016/679) and Italian privacy legislation (Legislative Decree 
196/2003, as amended by Legislative Decree 101/2018). The key linking codes to 
patient identity are maintained solely within the clinical system and are not 
accessible to the research team. 
 
Given the retrospective design and full anonymization of data, informed consent was 
not required, in accordance with institutional and national guidelines for secondary 
use of health data for research purposes. The study was reviewed and 
acknowledged by the institutional governance structures responsible for data 
protection and research oversight at IRCCS Policlinico San Donato. 
 
No identifiable information will appear in any presentation, publication, or external 
dissemination of the results. All analyses were performed on de-identified datasets 
stored on secure institutional servers with restricted access, encrypted storage, and 
routine audit trails. 
 
These safeguards ensure that the project meets all ethical and regulatory 
requirements for retrospective observational research involving healthcare data. 
 
 
4.0 Data Protection 
 
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All data processing activities associated with this study will comply with the General 
Data Protection Regulation (GDPR, EU Regulation 2016/679), the Italian Privacy 
Code (Legislative Decree 196/2003, as amended by Legislative Decree 101/2018), 
and institutional policies governing the secondary use of clinical data for research. 
Because this is a retrospective observational study, all data will be accessed 
exclusively after irreversible anonymization, ensuring that neither the research team 
nor any external entity will be able to directly or indirectly re-identify individual 
patients. 
 
Personal Data will be processed in accordance with art. 6.1.e and art. 9.2.j of the 
GDPR in conjunction with art. 110bis, c.4. 
This rule allows IRCCS, as the Promoter, to carry out scientific research activities by 
further processing personal data previously collected for clinical activity. 
The information relating to the processing of personal data for the retrospective 
observational study will be made available to participants through the publication of 
the Information form on the Promoter's website. 
A Data Protection Impact Assessment (DPIA) will be prepared. The DPIA will be 
published on the Promoter's website. 
 
Anonymization procedures will include the removal of all direct identifiers (e.g., 
names, birth dates, contact information) and the transformation of indirect identifiers 
through de-identification protocols validated by the institution’s Data Protection 
Officer (DPO). A unique study identification code will be assigned to each record in 
the analytic dataset, with the correspondence to original patient identifiers retained 
solely within the clinical information system and kept inaccessible to the research 
team. 
 
The anonymized dataset will be stored on secure institutional servers at IRCCS 
Policlinico San Donato, protected by encrypted access, multi-factor authentication, 
and routine automated backups. Access rights will be restricted to authorized study 
personnel only, applying the principle of least privilege. All data handling and analytic 
operations will generate audit trails documenting access, modification, and export 
events. 
 
No data will be transferred outside the institution, and no cloud-based or third-party 
storage services will be used. Analytical workflows in R, Orange Data Mining, and 
SPSS v.28 will be executed within a controlled computing environment operating on 
institutional infrastructure. Only aggregate results, summary statistics, and 
anonymized tables will be disseminated. 
 
Given the anonymized nature of the dataset, the study will pose minimal risk to 
patient privacy. All procedures will be reviewed by the institutional DPO to ensure 
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compliance with the regulatory framework for retrospective research involving health 
data. 
 
5.0 Data Management and Record Keeping 
All study-related data will be managed in accordance with institutional data 
governance policies and international standards for observational research. Data 
extraction, cleaning, and preparation will be overseen by the Principal Investigator 
and an authorized data manager with expertise in handling clinical databases. The 
data manager will be responsible for ensuring the integrity, accuracy, and 
completeness of the dataset before analysis, including verifying variable ranges, 
conducting internal consistency checks, and cross-referencing with source 
documents as needed. 
 
All documentation related to data management, including the data dictionary, coding 
schemes, preprocessing logs, clustering parameter configurations, and statistical 
analysis scripts, will be maintained in a secure institutional repository. Version control 
procedures will be applied to all scripts and derived datasets, ensuring full 
traceability of analytic decisions. Audit trails will be automatically generated to 
document access and modifications to the anonymized dataset. 
 
The anonymized working dataset, together with all associated metadata (analysis 
logs, codebooks, and script files), will be stored on encrypted servers at IRCCS 
Policlinico San Donato with restricted, role-based access. No data will be stored on 
personal devices or external cloud platforms. Backup procedures will be executed 
automatically daily, and all files will be archived under controlled conditions. 
 
All study records will be retained for a minimum of 10 years following study 
completion, in alignment with institutional requirements and international standards 
for data retention in clinical research. After the retention period, data and supporting 
files will be securely destroyed or permanently anonymized in accordance with 
institutional policies and GDPR-compliant procedures. 
 
6.0 Bias Assessment related to internal validity 
Given the retrospective nature of the study, several methodological risks and 
potential sources of bias are anticipated. First, the reliance on routinely collected 
clinical data introduces the possibility of misclassification bias, particularly in 
variables derived from operative notes or symptom descriptions. Although 
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standardized documentation procedures are routinely used in the institution, 
variability in clinical reporting cannot be entirely excluded. 
 
Second, missing data represent an inherent limitation of retrospective studies. 
Missingness 
may 
occur 
in 
demographic variables, symptom profiles, or 
postoperative assessments, potentially leading to reduced cluster stability or biased 
comparisons between phenotypes. Because no imputation will be performed, cases 
with insufficient perioperative data will be excluded, which may introduce selection 
bias. The extent and pattern of missing data will be evaluated and reported, and 
sensitivity analyses will be performed when appropriate. 
 
Third, the study design does not allow for control of confounding variables, such as 
bowel habits, lifestyle factors, or unmeasured clinical characteristics that may 
influence both surgical decisions and postoperative outcomes. Although clustering 
methods can help reveal latent structure, they cannot fully account for the 
confounding inherent in routine clinical practice. 
 
Fourth, limitations intrinsic to EHR extraction must be acknowledged. Data 
availability is restricted to what was documented during routine care, and important 
patient-reported 
outcomes 
or 
detailed 
anatomical 
descriptors 
may 
be 
underrepresented or absent. Furthermore, variation in follow-up intensity may lead to 
underdetection of some postoperative events. 
 
Finally, the monocentric nature of the study may limit external generalizability. 
Patterns identified in this cohort may not fully reflect those in different populations, 
institutions, or healthcare systems. 
 
Despite these aspects, the study is expected to generate clinically meaningful 
insights and provide a foundational feasibility assessment for the application of 
unsupervised machine learning to hemorrhoidal disease. Transparent reporting of 
biases and methodological constraints will enhance interpretability and guide future 
multicenter or prospective validation efforts. 
 
 
7.0 Publication Policy 
The findings from this study will be disseminated through submission to 
peer-reviewed scientific journals and presentation at national and international 
conferences relevant to proctology, colorectal surgery, and data-driven clinical 
research. All dissemination activities will adhere to the International Committee of 
Medical Journal Editors (ICMJE) criteria for authorship and responsible reporting. 
Only fully anonymized and aggregated data will be reported. No identifiable patient 
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information, images, or data elements that could facilitate re-identification will be 
included in any publication or presentation. Results from the clustering analysis, 
postoperative outcomes, and perioperative characteristics will be presented 
exclusively at the group level. The Principal Investigator will coordinate manuscript 
preparation, with authorship allocated based on scientific contribution and in 
accordance 
with 
institutional 
policies. 
Any 
analytic 
code, 
supplementary 
documentation, or methodological appendices submitted with manuscripts will 
include only synthetic or fully anonymized content. The final dataset and all analysis 
scripts will be archived securely on institutional servers to ensure reproducibility and 
potential follow-up analyses. Data sharing with external researchers will not occur 
unless explicitly authorized by the institutional DPO and the Ethics Committee, and 
only after ensuring complete anonymity and full regulatory compliance. 
All data and results will be property of the IRCSS Policlinico San Donato. 
8.0 References 
 
1.​ Ashburn JH. Hemorrhoidal Disease: A Review. JAMA. 2025;334(17):1541. 
doi:10.1001/jama.2025.13083 
2.​ Al-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;16(1):e51612. 
doi:10.7759/cureus.51612 
3.​ Wang L, Ni J, Hou C, et al. Time to change? Present and prospects of 
hemorrhoidal 
classification. 
Front 
Med. 
2023;10:1252468. 
doi:10.3389/fmed.2023.1252468 
4.​ Roberts K. What Are Hemorrhoids? JAMA. Published online November 6, 2025. 
doi:10.1001/jama.2025.17253 
5.​ Pata F, Gallo G, Pellino G, et al. Evolution of Surgical Management of 
Hemorrhoidal Disease: An Historical Overview. Front Surg. 2021;8:727059. 
doi:10.3389/fsurg.2021.727059 
6.​ Rørvik HD, Davidsen M, Gierløff MC, Brandstrup B, Olaison G. Quality of life in 
patients with hemorrhoidal disease. Surgery Open Science. 2023;12:22-28. 
doi:10.1016/j.sopen.2023.02.004 
7.​ Božović B, Radoičić M, Janković S, Anđelković J, Kostić 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;50(6):1288-1290. doi:10.18502/ijph.v50i6.6433 
8.​ Dekker L, Han-Geurts IJM, Grossi U, Gallo G, Veldkamp R. Is the Goligher 
classification a valid tool in clinical practice and research for hemorrhoidal 
16 
 

PROCTO-CLUSTER v.1.2 (20/01/2026)  
REGISTRATION NUMBER CET 18-2026 Comitato Etico Territoriale  Lombardia 1 
 
disease? 
Tech 
Coloproctol. 
2022;26(5):387-392. 
doi:10.1007/s10151-022-02591-3 
9.​ Van Oostendorp JY, Grossi U, Hoxhaj I, et al. Limitations of the Goligher 
classification in randomized trials for hemorrhoidal disease: a qualitative 
systematic review of selection criteria. Tech Coloproctol. 2025;29(1):133. 
doi:10.1007/s10151-025-03170-y 
10.​De Gregorio MA, Guirola JA, Serrano-Casorran C, et al. Catheter-directed 
hemorrhoidal embolization for rectal bleeding due to hemorrhoids (Goligher 
grade I–III): prospective outcomes from a Spanish emborrhoid registry. Eur 
Radiol. 2023;33(12):8754-8763. doi:10.1007/s00330-023-09923-3 
11.​Brillantino A, Renzi A, Talento P, et al. The Italian Unitary Society of 
Colon-Proctology (Società Italiana Unitaria di Colonproctologia) guidelines for the 
management of acute and chronic hemorrhoidal disease. Ann Coloproctol. 
2024;40(4):287-320. doi:10.3393/ac.2023.00871.0124 
12.​Robinson PN, Mungall CJ, Haendel M. Capturing phenotypes for precision 
medicine. 
Cold 
Spring 
Harb 
Mol 
Case 
Stud. 
2015;1(1):a000372. 
doi:10.1101/mcs.a000372 
13.​De Marco S, Tiso D. Lifestyle and Risk Factors in Hemorrhoidal Disease. Front 
Surg. 2021;8:729166. doi:10.3389/fsurg.2021.729166 
14.​Xiao 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 
15.​Bernabé-Díaz JA, Franco M, Vivo JM, Fernández-Breis JT. Optimizing 
clustering-based analytical methods with trimmed and sparse clustering. 
Computers 
in 
Biology 
and 
Medicine. 
2025;194:110436. 
doi:10.1016/j.compbiomed.2025.110436 
 
9.0 Contacts 
 
Principal Investigator: 
Dr. Angelo Stuto – angelo.stuto@grupposandonato.it 
Co-Investigators: 
Dr. Carlotta La Raja – carlotta.laraja@grupposandonato.it 
 
 
17