Search Results (10,721)

Background/Objectives: To compare perioperative, pathological, functional, and oncological outcomes of robot-assisted radical nephrectomy with inferior vena cava tumor thrombectomy (RARN-TT) versus open (ORN-TT) and laparoscopic (LRN-TT) approaches. Methods: PubMed, Scopus, and Web of Science were searched for studies of adults with renal cell carcinoma and Mayo/Neves level I–IV inferior vena cava tumor thrombus undergoing RARN-TT versus ORN-TT and/or LRN-TT. Risk ratios and mean differences with 95% confidence intervals were calculated using random effects models with restricted maximum-likelihood estimation. Results: Eight retrospective studies including 1781 patients were included: 221 underwent robotic surgery, 1411 open surgery, and 149 laparoscopic surgery. Compared with ORN-TT, RARN-TT was associated with lower estimated blood loss (mean difference −900.5 mL, 95% confidence interval −1234.0 to −566.9; p = 0.001), lower transfusion probability (risk ratio 0.395, 95% confidence interval 0.159–0.979; p = 0.046), and shorter hospital stay (mean difference −3.79 days, 95% confidence interval −4.83 to −2.76; p < 0.001). No significant differences were observed in operative time, intensive care unit stay, postoperative complications, perioperative mortality, pathological outcomes, or overall survival. Evidence for cancer-specific and progression-free survival was limited. Comparisons with LRN-TT were exploratory. Conclusions: RARN-TT may reduce blood loss, transfusion requirements, and hospital stay compared with ORN-TT, without evidence of worse perioperative, pathological, or survival outcomes. However, all studies were retrospective and affected by selection bias and heterogeneity. RARN-TT may be considered for selected patients at experienced centers, particularly for lower-level thrombi. Prospective multicenter studies stratified by thrombus level are needed. Full article

Background: Falls are the most common hospital-acquired adverse event, leading to extended hospitalization, loss of independence, disability, and premature death. Routine fall risk assessments are time-consuming, even with limited factors. An AI-derived fall prediction model can provide more comprehensive and comparably accurate risk predictions quickly and as often as needed. Objective: To develop and validate a fall prediction model for fall risk in adult inpatients. Methods: Patient records from 2016 were extracted from the adult inpatient database, including information from the Electronic Inpatient Medication Records, SAP, and Hospital Incident Reporting System. The sample consisted of 1506 cases (1:5 faller to non-faller). The fall prediction model was trained using the following four variables: demographics, diagnosis, medications, and surgery. Data sources included the hospital’s data repository, integrating admission/discharge, pharmacy, laboratory, and incident reports. Results: The support vector machine model performed best among all tested models, achieving an AUC of 0.803, recall of 0.816, and precision of 0.440. In the validation cohort (978 patients: 163 fallers and 815 non-fallers), the fall prediction model demonstrated moderate-to-good discrimination (AUC 0.79), with accuracy of 0.67, sensitivity of 0.46, and specificity of 0.86. Compared with the nursing four-item fall risk assessment, which showed lower discrimination (AUC 0.65, accuracy 0.65, sensitivity 0.58, specificity 0.72), the fall prediction model had better specificity and overall discrimination, though the nursing tool was more sensitive in identifying fallers. Conclusions: The fall prediction model using demographics, diagnoses, medication, and surgery data predicts falls risk effectively. It enables timely, accurate risk assessments and supports preventive interventions, saving nurses’ time for direct patient care. Full article

This paper presents a deep learning framework for automated Parkinson’s disease (PD) detection from T1-weighted MRI scans using the NTUA dataset. The proposed pipeline combines advanced preprocessing, robustness evaluation, and explainable AI techniques to improve both diagnostic performance and clinical interpretability. Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to enhance anatomical details, while SMOTE was applied to the deep feature vectors extracted from the training MRI images to address class imbalance. Several state-of-the-art convolutional neural networks were evaluated through six patient-wise train–test splits to assess robustness and generalization across subjects. In addition, a targeted experiment using only axial MRI slices from 50 subjects was conducted to reduce irrelevant anatomical information and emphasize brain regions potentially associated with PD. Among the evaluated models, performance varied substantially across subject-wise splits, highlighting a strong dependency on patient selection. While peak configurations reached high individual metrics, the aggregate subject-wise analysis demonstrated a more conservative baseline. To improve transparency, Grad-CAM visualizations were generated, showing that the models primarily focused on central brain structures relevant to Parkinsonian neurodegeneration, with minimal attention extending to non-diagnostic regions. The results demonstrate that combining adaptive preprocessing, patient-wise evaluation, and explainable deep learning holds promise for MRI-based Parkinson’s disease detection under a preliminary, dataset-specific evaluation, though substantial performance variability remains across different subject selections, rather than demonstrating immediate clinical readiness. Full article

Background/Objectives: Cardiac surgery involves unique complexities that differ from those of general ICU populations. Traditional scoring systems often underperform due to the significant class imbalance between survival and mortality. This study utilized the MIMIC-IV database, integrating machine learning (ML) and feature engineering to develop an in-hospital mortality prediction model specifically for open-heart surgery patients. Methods: We included 6941 cases (mortality: 76, 1.095%). Sixty-eight variables from the first ICU day were extracted. Following data preprocessing and imputation, four ML models—logistic regression, random forest (RF), XGBoost, and multilayer perceptron (MLP)—were constructed using stratified 10-fold cross-validation. SMOTE was applied to address class imbalance. A streamlined 17-variable model was developed and compared against the Sequential Organ Failure Assessment (SOFA) and the Oxford Acute Severity of Illness Score (OASIS). Results: Among the 68-variable models, RF achieved the highest area under the receiver operating characteristic curve (AUROC) of 0.915 (95% CI, 0.855–0.966). For the 17-variable models, MLP performed best (AUROC: 0.920; 95% CI, 0.865–0.964), significantly outperforming SOFA (0.688) and OASIS (0.690). Regarding the area under the precision-recall curve (AUCPR), the 17-variable MLP also yielded the highest score (0.203; 95% CI, 0.060–0.389) compared with SOFA (0.161) and OASIS (0.046). SHapley Additive exPlanations (SHAP) analysis identified bicarbonate levels, mechanical ventilation, and mean pulmonary arterial pressure as the top predictors, consistent with clinical expectations. Conclusions: The streamlined MLP model significantly outperforms traditional scoring systems and may serve as a useful tool for early postoperative risk stratification after open-heart surgery. Full article

Urinary tract infections (UTIs) caused by Klebsiella pneumoniae are an increasing challenge in pediatric practice due to the combined effects of biofilm formation, multidrug resistance (MDR), and limited therapeutic options for children. Biofilm development promotes bacterial persistence by impairing antibiotic penetration, enabling metabolic adaptation, promoting persister-cell formation, facilitating horizontal gene transfer (HGT), and inducing stress-induced mutagenesis, thereby reducing the effectiveness of conventional antimicrobial therapy. These mechanisms are further compounded by pediatric-specific challenges, including age-dependent pharmacokinetic variability, congenital urinary tract abnormalities, device-associated infections, and the limited availability of validated diagnostic tools for biofilm-associated infections. This narrative review integrates current knowledge of the molecular mechanisms underlying biofilm-mediated antimicrobial resistance with the unique diagnostic, pharmacological, and therapeutic challenges encountered in pediatric patients with K. pneumoniae UTIs. Emerging therapeutic strategies, such as optimized antibiotic combination therapy, bacteriophages, biofilm matrix-degrading enzymes, quorum-sensing inhibitors (QSIs), antimicrobial peptides (AMPs), and microbiome-directed approaches are critically evaluated with particular emphasis on their potential applicability in children. Although several anti-biofilm strategies have demonstrated encouraging results in experimental models, robust pediatric clinical evidence remains scarce. Current international guidelines continue to rely primarily on planktonic antimicrobial susceptibility testing without addressing biofilm-specific therapeutic considerations. In the absence of validated biofilm diagnostics, catheter stewardship and dosing optimization remain the most defensible clinical interventions available today. Broader translation of anti-biofilm strategies into pediatric practice will require dedicated pharmacokinetic studies, standardized biofilm diagnostics, and prospective clinical trials. Full article

Background/Objectives: Immune exclusion in head and neck squamous cell carcinoma (HNSCC) limits immunotherapy efficacy, yet the molecular determinants of stromal-immune organization remain incompletely characterized. The desmosomal cadherin DSG2 is highly expressed in squamous epithelium; its role in shaping the tumor microenvironment (TME) is unknown. Methods: We integrated bulk RNA-seq from 836 HNSCC patients (TCGA-HNSC n = 566, GSE65858 n = 270), single-cell RNA-seq (GSE139324, n = 26 patients, 133,308 cells), spatial transcriptomics (GSE208253, n = 12), proteomics (CPTAC-HNSCC, n = 108), and external validation cohorts (GSE41613, n = 97). CellChat ligand-receptor analysis, mediation analysis, Mendelian randomization (MR), LASSO-penalized Cox regression, HPV-stratified sensitivity analysis, and transcription factor (TF) correlation analysis were employed. Results: DSG2 exhibited epithelial-specific expression and showed consistent positive correlation with CXCL8 (IL-8; TCGA ρ = 0.228, p = 4.4 × 10⁻⁸) and myCAF activation across independent cohorts. Single-cell analysis revealed that 99.5% of CXCL8-producing cells have zero DSG2 expression, establishing the bulk correlation as compositional rather than cell-intrinsic. CellChat identified CXCL8-CXCR2 as the strongest tumor-stroma interaction in DSG2-high regions (probability = 0.821, 1.80-fold enrichment). Mediation analysis demonstrated 43.6% (95% CI [34.3–53.6%]) of DSG2’s tissue-level association with myCAF activation is mediated through CXCL8 (compositional mediation). Multi-instrument MR (IVW: Beta = −0.028, p = 0.028; I² = 0.0%) corroborated the compositional model. Protein-level validation in CPTAC-HNSCC confirmed DSG2-CD8A inverse correlation (Spearman ρ = −0.35, p = 2.2 × 10⁻⁴). Pan-squamous meta-analysis confirmed negative DSG2-cytolytic activity correlations (pooled ρ = −0.213, 95% CI [−0.296, −0.128], I² = 58.6%, 4 cohorts). DSG2 correlated with TIDE score (ρ = 0.176) and TGF-β exclusion subscore (ρ = 0.428). DepMap analysis identified CXCR2 inhibitor collateral sensitivity (ρ = −0.408, p < 0.0001). An eight-gene co-expression module was validated in two independent cohorts (GSE41613: HR = 3.09, p = 0.003; GSE65858: HR = 1.57, p = 0.032). Conclusions: DSG2 marks a stromal-immune organizational state characterized by CXCL8-CXCR2 paracrine signaling, myCAF activation, and immune exclusion, conserved across squamous malignancies. DSG2-high/PD-L1-high tumors (30.4% prevalence) exhibit the worst predicted ICI response and represent a candidate population for biomarker-selected CXCR2 inhibitor trials in combination with anti-PD-1 therapy. Full article

Background/Objectives: Blunt thoracic aortic injury (BTAI) is a rare, highly lethal trauma typically occurring at the aortic isthmus. Advanced BTAI is primarily treated with thoracic endovascular aortic repair (TEVAR). Because emergent surgical debranching is rarely feasible, management depends heavily on patient anatomy, especially regarding the left subclavian artery (LSA). Patient-specific computational fluid dynamics (CFD) models offer critical insights into periprocedural planning and outcome prediction. Methods: This study investigates hemodynamic changes in a patient-specific BTAI case following intentional LSA coverage by a stent graft. Three-dimensional patient-specific models were coupled with RCR-Windkessel boundary conditions for both pre- and post-procedural imaging data to simulate blood flow in each scenario. Results: Post-intervention, flow distribution improved significantly; relative perfusion to the brachiocephalic trunk and left common carotid artery increased by 3.51% and 4.02%, respectively, alongside an elevated overall pressure throughout the entire computational domain. However, regions with high oscillatory, low magnitude shear (HOLMES), specifically wall areas with values < 0.3 Pa, expanded post-stenting. This warrants careful monitoring during follow-ups, given the associated risk of thrombus formation. Furthermore, time-averaged swirling strength (TASS) variation along the aorta decreased (standard deviation dropped from 1.8610 to 1.3835), indicating stabilized flow within the stented region, while normalized swirling strength increased distally. Conclusions: This study establishes an effective, non-invasive framework for assessing pre- and post-TEVAR hemodynamics. It demonstrates that LSA coverage induces uniformly elevated pressure and alters wall shear stress and helicity indices, highlighting the need for future research into pharmacological management to optimize long-term outcomes. Full article

Background/Objectives: Obesity is associated with respiratory infection risk, but its relationship with post-discharge healthcare burden remains unclear. This study examined the associations of body mass index (BMI) and waist circumference (WC), evaluated separately, with hospitalization and post-discharge outcomes in analyses stratified by sex among South Korean adults with claims-defined hospitalizations involving influenza or pneumonia. Methods: This retrospective cohort study used the 2018 National Health Insurance Service (NHIS) Customized Research Database linked to health-screening data. Adults aged 20–89 years with claims-defined hospitalizations involving influenza or pneumonia were included if BMI and WC measurements were available from the most recent NHIS health-screening examination conducted within two years before the index admission. Length of stay and index hospitalization costs were summarized descriptively. Cox proportional hazards models were used to estimate hazard ratios (HRs) for all-cause and pneumonia-related readmission within two years after discharge, and two-part models were used to estimate readmission-related costs, expressed in US dollars (USD). Results: The cohort included 175,774 patients, of whom 80,257 (45.7%) were male. Category-specific associations varied according to the anthropometric measure, outcome, and sex stratum. In female patients, the highest WC category had the largest estimated all-cause readmission HR (HR 1.195, 95% confidence interval 1.157–1.235), and the highest model-based mean cost of the first all-cause readmission was also observed in this category (USD 1407.4). In male patients, several higher BMI categories had lower estimated readmission HRs than the reference category; however, these estimates should not be interpreted as protective effects because competing mortality was not accounted for using competing-risk methods. Patterns for pneumonia-related readmission and costs were less consistent. Conclusions: BMI and WC showed category-specific associations with readmission, while model-based mean readmission costs varied across anthropometric categories and sex strata. Higher WC categories were associated with higher all-cause readmission HRs in the female stratum; however, the independent or incremental prognostic value of BMI and WC was not evaluated. Full article

Objective: While advancements in radiotherapy and systemic agents have significantly improved survival rates in head and neck squamous cell carcinoma (HNSCC), managing long-term, treatment-induced toxicities remains a critical clinical challenge. This study aimed to develop a personalized, supervised machine learning-driven predictive model for multiple chronic toxicities by integrating clinical, dosimetric, and genetic data specifically evaluating the impact of the NBN gene rs1805794 (c.553G>C) polymorphism. Methods: This study enrolled 125 patients with HNSCC who received curative-intent radiotherapy and remained disease-free during follow-up with a median of 98 months. Comprehensive clinical and dosimetric data were collected, and chronic toxicities were recorded. Peripheral blood samples were analyzed for the NBN rs1805794 polymorphism using allele-specific PCR (AS-PCR). Following feature selection, four supervised machine learning classifiers were trained and evaluated to identify the optimal model for predicting multiple chronic toxicities. Results: The XGBoost algorithm emerged as the highest performing model. Baseline clinico-dosimetric predictors of multiple chronic toxicities included PTV70 volume, the addition of concurrent chemotherapy, advanced T and N stages, and continued smoking. Integrating the NBN rs1805794 genotype into the XGBoost architecture enhances its predictive capability. The final model accurately identified patients at high risk for multiple chronic toxicities, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, a sensitivity of 0.74, and a specificity of 0.79. Conclusions: Integrating clinical, dosimetric, and genetic data within a machine learning framework effectively predicts multiple chronic toxicities in HNSCC. This approach enabled early risk stratification, providing the potential for personalized therapy. Full article

Differentiating active ulcerative colitis (UC) from Crohn’s disease (CD) is one of the unmet needs addressed by biomarkers in inflammatory bowel disease (IBD). The immune landscapes of UC and CD differ, justifying the search for discriminatory markers and novel therapy targets among their mediators. Herein, 27 systemic cytokines were measured using flow cytometry-based methodology in 138 IBD patients, with an additional 21 being determined in 67 of the patients. Their discriminatory power was assessed individually and as exploratory multivariable signatures generated using logistic regression, hierarchical clustering, and principal component analysis. Eotaxin-1, macrophage inflammatory protein (MIP)-1β, and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) showed fair discriminatory potential, while multivariable models performed better. Interleukin (IL)-1β, IL-4, and MIP-1α were strongly associated with CD, whereas IL-5, granulocyte-macrophage colony-stimulating factor (GM-CSF), MIP-1β, and TRAIL were associated with UC. Active UC was characterized by mediators linked to eosinophil-, mastocyte-, and neutrophil-driven inflammation and tissue repair (eotaxin-1, IL-5, growth-regulated oncogene (GRO), MIP-1β, stem cell factor (SCF), GM-CSF, IL-1 receptor antagonist, TRAIL, stem cell growth factor (SCGF)-β, and cutaneous T cell-attracting chemokine (CTACK)), whereas active CD was associated with Th1/Th17 immunity, myeloid activation, fibrosis, angiogenesis, and neuroimmune remodeling (IL-1β, IL-12p70, IL-15, ‘regulated on activation, normal T-cell expressed and secreted’ (RANTES), MIP-1α, stromal cell-derived factor (SDF)-1α, nerve growth factor β (β-NGF), and leukemia inhibitory factor (LIF)). In conclusion, integrated circulating immune signatures identify several understudied cytokines as potential contributors to disease-specific pathways and show potential in distinguishing active UC from CD warranting further mechanistic studies and independent validation in larger cohorts. Full article

Recent technological advancements have significantly transformed the diagnosis and management of cardiovascular disease. Traditional reliance on two-dimensional (2D) and three-dimensional (3D) imaging has been enhanced by emerging 3D visualization technologies, particularly 3D printing and extended reality (XR). Three-dimensional printing enables the creation of patient-specific physical models that accurately replicate cardiovascular anatomy and pathology. These models play a crucial role in surgical planning, simulation of interventional procedures, medical education, and patient communication, offering tangible insights into complex cardiovascular structures. XR, on the other hand, provides an immersive 3D environment for exploring volumetric imaging data under interaction with the physical world. This enhances the understanding of intricate cardiovascular anatomy and pathology, supporting more informed clinical decision making and pre-surgical planning. This review summarizes the current applications of 3D printing and XR in cardiovascular surgery planning and intervention, emphasizing their potential to address challenges in planning complex procedures. Limitations and future directions for research and clinical integration are also discussed. Full article

Objectives: Eligibility for Lutetium-177 labeled prostate-specific membrane antigen ([¹⁷⁷Lu]Lu-PSMA) radioligand therapy depends on PSMA PET/CT interpretation and may vary across observers and centres. We aimed to identify imaging and clinical correlates of VISION-like PSMA PET-defined eligibility and to compare exploratory clinical, PET-based, and combined models. Materials and Methods: Seventy-six consecutive patients with de novo metastatic prostate cancer undergoing PSMA PET/CT were retrospectively assessed for [¹⁷⁷Lu]Lu-PSMA eligibility. Candidacy was determined by consensus of two nuclear medicine physicians using VISION-like criteria. Three stepwise logistic regression models used clinical variables, PSMA PET/CT variables, or both, and were internally validated with bootstrap out-of-bag predictions. Results: Forty-three patients (56.6%) met VISION-like criteria for RLT. Internally validated area under the curve (AUC) values were 0.731, 0.817, and 0.829 for the clinical, PSMA PET/CT, and combined models. PET-defined candidacy was associated with higher PSMA-positive lesion count, greater PET-derived tumour burden, and higher mean standardised uptake value (SUVmean). Clinically, CHAARTED low-volume disease and prior docetaxel exposure were linked to lower probability, whereas disease state at imaging was not significant. In the combined multivariable model, SUVmean was independently associated with higher candidacy (odds ratio [OR]: 1.22, 95% confidence interval [CI]: 1.04–1.42; p = 0.015), whereas prior docetaxel showed the opposite association (OR: 0.10, 95% CI: 0.0129–0.776; p = 0.028). Conclusions: PSMA-positive lesion count and SUVmean were the most reproducible determinants of VISION-like eligibility. Prior docetaxel exposure was associated with lower candidacy in the combined model, although the exposed subgroup was small and the confidence interval wide. Whether systemic therapy modifies PSMA expression, and with it access to subsequent PSMA-targeted lines, requires paired imaging before and after treatment in the same patients. Full article

Introduction: The demand for structured training in orthopaedic surgery, particularly in total hip arthroplasty (THA), is increasing. Cadaver labs provide valuable experience for surgical access and anatomical exposure; however, the availability of specimens with predefined pathological features, such as dysplastic hip anatomy, is limited and difficult to standardize. This study presents a preliminary assessment of an integrated curriculum combining cadaver-based practice and patient-specific phantom simulation for task-oriented THA training. Methods: The curriculum included theoretical lessons, cadaver lab practice, and phantom-based training. The phantom models were developed from CT data of a patient with severe hip dysplasia and designed to reproduce selected anatomical and procedural challenges. Eight novice trainees and three expert tutors completed a multidomain 5-point Likert questionnaire assessing realism, face and content validity, perceived usefulness, and the complementary role of cadaver- and phantom-based sessions. Results: Participants reported high satisfaction and perceived the integrated pathway as useful. Cadaver training was rated higher for surgical access realism, whereas phantom simulation was considered valuable for rehearsing pathology-specific tasks involving altered femoral and acetabular anatomy. Conclusions: Integrating patient-specific phantoms into cadaver-based THA training appears feasible and was perceived as educationally valuable, adding a reproducible, pathology-specific simulation component and supporting future assessment strategies with objective performance metrics. Full article

Background/Objectives: Urinary incontinence after radical prostatectomy remains a significant concern affecting postoperative quality of life. This study aimed to investigate whether the early postoperative International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF) score is associated with mid-term urinary continence outcomes, using a patient-reported outcome approach. Methods: We retrospectively analyzed patients who underwent robot-assisted radical prostatectomy between May 2020 and July 2023 at the International University of Health and Welfare Narita Hospital and completed follow-up at one and six months postoperatively. Urinary incontinence severity was assessed using the ICIQ-SF (range 0–21, with higher scores indicating more severe symptoms). The primary outcome was incontinence at 6 months, defined as any pad use. Multivariable logistic regression and exploratory receiver operating characteristic (ROC) analyses were performed. Results: A total of 74 patients with complete follow-up data at both time points were included in the analysis. The ICIQ-SF score at one month was significantly associated with urinary incontinence at six months in both univariable and multivariable analyses. In the adjusted model, a higher one-month ICIQ-SF score independently predicted urinary incontinence at six months (odds ratio 1.15; 95% confidence interval 1.04–1.27; p = 0.005). Age, body mass index, and nerve-sparing status were not significantly associated with incontinence at six months. Exploratory ROC analysis demonstrated acceptable discriminative ability (area under the curve (AUC) 0.74, 95% confidence interval (CI) 0.61–0.87). A cut-off value of 13 yielded a sensitivity of 76.9% and a specificity of 59.1%. Conclusions: The ICIQ-SF score assessed one month after radical prostatectomy significantly predicts persistent urinary incontinence at six months. Early postoperative ICIQ-SF assessment may help identify patients at higher risk of persistent urinary incontinence. Further prospective studies are warranted to determine whether risk-stratified postoperative management based on early ICIQ-SF assessment improves clinical outcomes. Full article

Background/Objectives: Pelvic floor dysfunctions (PFDs) are highly prevalent chronic conditions associated with substantial functional and psychosocial burden. Although multidisciplinary pelvic floor rehabilitation is recommended as first-line management, its effectiveness depends not only on intervention efficacy but also on care delivery, communication, patient engagement, and long-term adherence. This study applied the Delphi-Dialogue Patients–Professionals (DDPP) framework to quantify patient–professional alignment and translate identified patient–professional perceptual differences into candidate service-improvement strategies. Methods: A two-phase observational study incorporating parallel stakeholder surveys and a modified Real-Time Delphi feasibility-assessment process was conducted under the auspices of the Spanish Society of Physical Medicine and Rehabilitation (SERMEF). In Phase 1, parallel online questionnaires were completed by 225 patients with PFD and 164 rehabilitation professionals. Shared items were rated on a six-point Likert scale, and patient–professional divergence was quantified using an operational composite index integrating standardized differences in mean ratings and high-agreement responses. In Phase 2, the implementation feasibility of 10 improvement recommendations was evaluated by a Delphi panel comprising 35 PM&R physicians and two representatives of the Spanish Incontinence Association (ASIA). Results: Patient–professional agreement was domain-specific. The greatest divergences concerned digital competence, timing of patient education, bladder and bowel diary use, and adherence to prescribed rehabilitation programmes. Professionals attributed greater value to bladder and bowel diaries and selected group-based interventions, whereas patients reported higher digital competence, more timely information delivery, and better adherence. Comparatively similar response patterns were observed regarding hybrid rehabilitation models, educational resources, app-supported follow-up, and overall satisfaction with rehabilitation care. The Delphi process identified structured home exercise programmes, adherence-monitoring strategies, and improved visibility of rehabilitation pathways as the recommendations with the highest perceived implementation feasibility. Conclusions: The findings suggest that important challenges in pelvic floor rehabilitation may arise from implementation-related factors in addition to the effectiveness of available interventions. The DDPP framework provides a structured, stakeholder-informed methodology for translating patient–professional divergence into candidate organizational recommendations with comparatively higher perceived implementation feasibility. Full article