Search Results (103)

Background and Objectives: Frailty can represent the transitional stage between successful aging and old age in need of care; it is a guide for setting goals for regaining robust old age in the individual at risk. Frailty is associated with longer intensive care unit duration, hospital stay, and higher mortality. The aim of this study was to evaluate the relationship between mortality and frailty in geriatric patients (65 years and older) admitted to the intensive care unit with a diagnosis of pneumonia. Materials and Methods: In total, 478 patients were included in the study. The demographic data, such as age, gender, body mass index (BMI), Charlson comorbidity index (CCI), Clinical Frailty Scale (CFS), acute physiology and chronic health evaluation (APACHE II) scores, sequential organ failure assessment score (SOFA), invasive/noninvasive mechanical ventilator days, length of stay in the hospital and intensive care unit, inotropic requirement, and 28-day mortality, were retrospectively scanned and recorded. Results: Advanced age, lower BMI, higher Charlson Comorbidity index (CCI), SOFA score, and CFS increased 28-day mortality. CFS was found to be associated with 28-day mortality similar to the use of inotropic agents, prolonged MV duration, and ICU length of stay (LOS). Conclusions: CFS is effective in predicting 28-day mortality in geriatric patients diagnosed with pneumonia in intensive care. It also provides insights into morbidity parameters such as requirement for inotropic agents, duration of mechanical ventilation (MV), and LOS ICU. Full article

Understanding the current water quality dynamics is necessary to ensure that ecological and sociocultural services are provided to the population and the natural environment. Water quality monitoring of lakes is usually performed with in situ measurements; however, these are costly, time consuming, laborious, and can have limited spatial coverage. Nowadays, remote sensing offers an alternative source of data to be used in water quality monitoring; by applying appropriate algorithms to satellite imagery, it is possible to retrieve water quality parameters. The use of global remote sensing water quality products increased in the last decade, and there are a multitude of products available from various databases. However, in Latin America, studies on the inter-comparison of the applicability of these products for water quality monitoring is rather scarce. Therefore, in this study, global remote sensing products estimating various water quality parameters were explored on Lake Titicaca and compared with each other and sources of data. Two products, the Copernicus Global Land Service (CGLS) and the European Space Agency Lakes Climate Change Initiative (ESA-CCI), were evaluated through a comparison with in situ measurements and with each other for analysis of the spatiotemporal variability of lake surface water temperature (LSWT), turbidity, and chlorophyll-a. The results of this study showed that the two products had limited accuracy when compared to in situ data; however, remarkable performance was observed in terms of exhibiting spatiotemporal variability of the WQ parameters. The ESA-CCI LSWT product performed better than the CGLS product in estimating LSWT, while the two products were on par with each other in terms of demonstrating the spatiotemporal patterns of the WQ parameters. Overall, these two global remote sensing water quality products can be used to monitor Lake Titicaca, currently with limited accuracy, but they can be improved with precise pixel identification, accurate optical water type definition, and better algorithms for atmospheric correction and retrieval. This highlights the need for the improvement of global WQ products to fit local conditions and make the products more useful for decision-making at the appropriate scale. Full article

Background/Objectives: Transanal irrigation (TAI) has been recognized as a safe and effective treatment for neurological bowel dysfunction, chronic constipation or fecal incontinence and has also been proposed for patients with low anterior resection syndrome (LARS). The aim of the present systematic review was to evaluate the feasibility and effectiveness of TAI in patients with significant LARS symptoms. Methods: We performed a systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and guidelines in addition to the Cochrane Handbook for Systematic Reviews of Interventions. The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42023436839). The risk of bias was assessed using a modified version of the Downs and Black checklist. The main outcome was improvement in low anterior resection syndrome after TAI assessed by change in LARS score. Results: After an initial screening of 3703 studies, 9 were included and underwent qualitative synthesis (among them, 3 were randomized clinical trials). All studies recorded an improvement in LARS score following TAI procedure and almost all studies showed an improvement in other bowel function outcomes (Memorial Sloan Kettering Cancer Center Bowel Function Instrument (MSKCC BFI, ), Cleveland Clinic Incontinence Score (CCIS), visual analog scale (VAS), Cleveland Clinic Florida Fecal Incontinence Score (CCFFIS), fecal incontinence score (FI score), Obstructed Defecation Syndrome (ODS) score) and quality of life (QoL) scores. The discontinuation rate ranged from 0% to 41%. The rate of adverse events was high (from 0 to 93%); moreover, no uniformity was found in the various protocols used among the different studies. Conclusions: The results of this review show that TAI is effective in the treatment of LARS, improving the LARS score, the other bowel function outcomes and the QoL scores. The absence of a treatment protocol validated by the scientific community is reflected in the high disparity in terms of adverse events and discontinuation of therapy, in addition to representing an intrinsic limitation to the study itself. Full article

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence (AI) approaches have shown promising potential for soil moisture retrieval at the local scale but have rarely demonstrated substantial products for spatially continuous grids. This paper presents the retrieval of daily soil moisture (SM) over a 500 m grid for croplands in South Korea using random forest (RF) and automated machine learning (AutoML) models, leveraging satellite images and meteorological data. In a blind test conducted for the years 2013–2019, the AutoML-based SM model demonstrated optimal performance, achieving a root mean square error of 2.713% and a correlation coefficient of 0.940. Furthermore, the performance of the AutoML model remained consistent across all the years and months, as well as under extreme weather conditions, indicating its reliability and stability. Comparing the soil moisture data derived from our AutoML model with the reanalysis data from sources such as the European Space Agency Climate Change Initiative (ESA CCI), GLDAS, the Local Data Assimilation and Prediction System (LDAPS), and ERA5 for the South Korea region reveals that our AutoML model provides a much better representation. These experiments confirm the feasibility of AutoML-based SM retrieval, particularly for local agrometeorological applications in regions with heterogeneous land surfaces like South Korea. Full article

Background: Frailty associated with aging increases the risk of falls, disability, and death. The aim of this study is to explore gender-related disparities in the survival outcomes of pathological femoral fractures in older frail patients, while analyzing potential specific prognostic factors. Methods: This study is a retrospective observational analysis conducted at a single medical center. It enrolled all patients aged 65 and above who were admitted to our emergency department between 2016 and 2020 with a diagnosis of pathological femur fracture requiring surgical intervention. The primary study endpoint was evaluating gender-related differences in survival outcomes. The secondary endpoint involves investigating gender-specific prognostic factors through the analysis of clinical and laboratory parameters. Results: The average Charlson Comorbidity Index (CCI) was slightly lower in men, but the difference was not statistically significant (p = 0.53). The Clinical Frailty Scale (CFS) showed similar results, with men and women 5.23 (SD 1.46), also not significant (p = 0.83). An evaluation comparing patients aged 75 years or younger to those older than 75 years found significant differences in health metrics. The average CCI was higher in the over 75 group compared to the under 75 group, with a p-value of 0.001. Similarly, the CFS average was also greater in the over 75 group than in the under 75 group, with a p-value of 0.0001. Complications were more frequent in patients over 75 and those with lower educational qualifications. The evaluation analyzed cardiac patients compared to a control group, revealing that the average age of cardiac patients was 75.22 years, while the control group was younger at 73.98 years (p = 0.5119). The CCI for cardiac patients averaged 6.53, significantly higher than 4.43 for non-cardiac patients (p = 0.0003). Conclusion: Frailty assessment is therefore essential in patients with pathological fracture of the proximal femur and is an important predictor of both gender differences and hospital complications. Enhancing gender analysis in this field is crucial to gather more robust evidence and deeper comprehension of potential sex- and gender-based disparities. Full article

This study assesses the conservation effectiveness of 21 protected areas (PAs) in Northern Morocco, comprising 3 parks and 18 Sites of Ecological and Biological Interest (SBEIs), against five major landscape pressures (LSPs): deforestation, infrastructure extension, agricultural expansion, fires, and population growth. We propose a novel quantitative methodology using global remote sensing data and exploratory spatial data analysis (ESDA). Data were sourced from Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), and World Population datasets. The combined impact of the five LSPs was measured using a cumulative effect index (CEI), calculated with the Shannon–Wiener formula at a 1 km² scale. The CEI was analyzed alongside the distance to the PAs’ network using Moran’s index, identifying four spatial association types: high–high (HH), high–low (HL), low–low (LL), low–high (LH), and non-significant (NS) cells. This analysis defined four zones: inner zone (IZ), potential spillover effect zone (PSEZ), statistically non-significant zone (SNSZ), and non-potential effect zone (NPEZ). Conservation effectiveness was quantified using the conservation ratio (CR), which compared the prevalence of LL versus HL units within IZs and PSEZs. Four disturbance levels (very high, high, medium, and low) were assigned to CR values (0–25%, 25–50%, 50–75%, 75–100%), resulting in sixteen potential conservation effectiveness typologies. Initial findings indicated similar deforestation patterns between protected and unprotected zones, with wildfires causing over half of forest losses within PAs. Conservation effectiveness results categorized the 21 PAs into nine typologies, from high conservation to very high disturbance levels. A significant positive correlation (71%) between CRs in both zones underscored the uniform impact of LSPs, regardless of protection status. However, protected natural area zones in the parks category showed minimal disruption, attributed to their advanced protection status. Finally, we developed a methodological framework for potential application in other regions based on this case study. Full article

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time–frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios. Full article

Background and Objectives: Sepsis represents a global health challenge and requires advanced diagnostic and prognostic approaches due to its elevated rate of morbidity and fatality. Our study aimed to assess the value of a novel set of six biomarkers combined with severity scores in predicting 28 day mortality among patients presenting with sepsis in the Emergency Department (ED). Materials and Methods: This single-center, observational, prospective cohort included sixty-seven consecutive patients with septic shock and sepsis enrolled from November 2020 to December 2022, categorized into survival and non-survival groups based on outcomes. The following were assessed: procalcitonin (PCT), soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1), the soluble form of the urokinase plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and azurocidin 1 (AZU1), alongside clinical scores such as the Quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation II (APACHE II), the Simplified Acute Physiology Score II and III (SAPS II/III), the National Early Warning Score (NEWS), Mortality in Emergency Department Sepsis (MEDS), the Charlson Comorbidity Index (CCI), and the Glasgow Coma Scale (GCS). The ability of each biomarker and clinical score and their combinations to predict 28 day mortality were evaluated. Results: The overall mortality was 49.25%. Mechanical ventilation was associated with a higher mortality rate. The levels of IL-6 were significantly higher in the non-survival group and had higher AUC values compared to the other biomarkers. The GCS, SOFA, APACHEII, and SAPS II/III showed superior predictive ability. Combining IL-6 with suPAR, AZU1, and clinical scores SOFA, APACHE II, and SAPS II enhanced prediction accuracy compared with individual biomarkers. Conclusion: In our study, IL-6 and SAPS II/III were the most accurate predictors of 28 day mortality for sepsis patients in the ED. Full article

Introduction and objectives: To evaluate the oncological and functional outcomes of transperineal laser ablation (TPLA) as the focal therapy for localized prostate cancer (PCa) after a 12-month follow-up. Materials and methods: Patients with low- and intermediate-risk localized PCa were prospectively treated with focal TPLA between July 2021 and December 2022. The inclusion criteria were the following: clinical stage < T2b; PSA < 20 ng/mL; International Society of Urological Pathology (ISUP) grade ≤ 2; MRI-fusion biopsy-confirmed lesion classified as PI-RADS v2.1 ≥ 3. Intra-, peri-, and post-operative data were collected. Variables including age, PSA, prostate volume (PVol), Charlson’s Comorbidity Index (CCI), International Prostate Symptom Score (IPSS) with QoL score, International Index of Erectile Function (IIEF-5), International Consultation on Incontinence Questionnaire—Short Form (ICIQ-SF), and Male Sexual Health Questionnaire—Ejaculatory Dysfunction Short Form (MSHQ-EjD) were collected at baseline and at 3, 6 and 12 months after TPLA. Post-operative mpMRI was performed at 3 and 12 months. Finally, all patients underwent prostatic re-biopsy under fusion guidance at 12 months. The success of this technique was defined as no recurrence in the target treated lesion at the 12-month follow up. Results: Twenty-four patients underwent focal TPLA. Baseline features were age [median 67 years (IQR 12)], PSA [5.7 ng/mL (3.9)], PVol [49 mL (27)], CCI [0 (0)], IPSS [11 (9)], IPSS-QoL [2 (2)], IIEF-5 [21 (6)], ICIQ-SF [0 (7)], MSHQ-EjD ejaculation domain [14 (4)] and bother score [0 (2)]. Median operative time was 34 min (IQR 12). Median visual analogue scale (VAS) 6 h after TPLA was 0 (IQR 1). The post-operative course was regular for all patients, who were discharged on the second post-operative day and underwent catheter removal on the seventh post-operative day. No patient had incontinence at catheter removal. A significant reduction in PSA (p = 0.01) and an improvement in IPSS (p = 0.009), IPSS-QoL (p = 0.02) and ICIQ-SF scores (p = 0.04) compared to baseline were observed at the 3-month follow-up. Erectile and ejaculatory functions did not show any significant variation during the follow-up. No intra- and peri-operative complications were recorded. Three Clavien–Dindo post-operative complications were recorded (12%): grade 1 (two cases of urinary retention) and grade 2 (one case of urinary tract infection). At the 12-month follow-up, eight patients showed mpMRI images referable to suspicious recurrent disease (PIRADS v2.1 ≥ 3). After re-biopsy, 7/24 patients’ (29%) results were histologically confirmed as PCa, 3 of which were recurrences in the treated lesion (12.5%). The success rate was 87.5%. Conclusions: The focal TPLA oncological and functional results seemed to be encouraging. TPLA is a safe, painless, and effective technique with a good preservation of continence and sexual outcomes. Recurrence rate at 12 months was about 12.5%. Full article

Background and Objective: Lung transplantation is the only life-extending therapy for end-stage pulmonary disease patients, but its risks necessitate an understanding of outcome predictors, with the frailty index and nutritional status being key assessment tools. This study aims to evaluate the relationship between preoperative frailty and nutritional indexes and the postoperative mortality rate in patients receiving lung transplants, and to determine which measure is a more potent predictor of outcomes. Materials and Methods: This study reviewed 185 adults who received lung transplants at a single medical center between January 2013 and May 2023. We primarily focused on postoperative 7-year overall survival. Other outcomes measured were short-term mortalities, acute rejection, kidney complications, infections, and re-transplantation. We compared the predictive abilities of preoperative nutritional and frailty indicators for survival using receiver operating characteristic curve analysis and identified factors affecting survival through regression analyses. Results: There were no significant differences in preoperative nutritional indicators between survivors and non-survivors. However, preoperative frailty indicators did differ significantly between these groups. Multivariate analysis revealed that the American Society of Anesthesiologists Class V, clinical frailty scale, and Charlson Comorbidity Index (CCI) were key predictors of 7-year overall survival. Of these, the CCI had the strongest predictive ability with an area under the curve of 0.755, followed by the modified frailty index at 0.731. Conclusions: Our study indicates that for critically ill patients undergoing lung transplantation, frailty indexes derived from preoperative patient history and functional autonomy are more effective in forecasting postoperative outcomes, including survival, than indexes related to preoperative nutritional status. Full article

Coastal areas are among the most productive areas in the world, ecologically as well as economically. Sea Surface Temperature (SST) has evolved as the major essential climate variable (ECV) and ocean variable (EOV) to monitor land–ocean interactions and oceanic warming trends. SST monitoring can be achieved by means of remote sensing. The current relatively coarse spatial resolution of established SST products limits their potential in small-scale, coastal zones. This study presents the first analysis of the TIMELINE 1 km SST product from AVHRR in four key European regions: The Northern and Baltic Sea, the Adriatic Sea, the Aegean Sea, and the Balearic Sea. The analysis of monthly anomaly trends showed high positive SST trends in all study areas, exceeding the global average SST warming. Seasonal variations reveal peak warming during the spring, early summer, and early autumn, suggesting a potential seasonal shift. The spatial analysis of the monthly anomaly trends revealed significantly higher trends at near-coast areas, which were especially distinct in the Mediterranean study areas. The clearest pattern was visible in the Adriatic Sea in March and May, where the SST trends at the coast were twice as high as that observed at a 40 km distance to the coast. To validate our findings, we compared the TIMELINE monthly anomaly time series with monthly anomalies derived from the Level 4 CCI SST anomaly product. The comparison showed an overall good accordance with correlation coefficients of R > 0.82 for the Mediterranean study areas and R = 0.77 for the North and Baltic Seas. This study highlights the potential of AVHRR Local Area Coverage (LAC) data with 1 km spatial resolution for mapping long-term SST trends in areas with high spatial SST variability, such as coastal regions. Full article

Fires are one of the main sources of disturbance in fire-sensitive ecosystems such as the Amazon. Any attempt to characterize their impacts and establish actions aimed at combating these events presupposes the correct identification of the affected areas. However, accurate mapping of burned areas in humid tropical forest regions remains a challenging task. In this paper, we evaluate the performance of four operational BA products (MCD64A1, Fire_cci, GABAM and MapBiomas Fogo) on a regional scale in the southwestern Amazon and propose a new approach to BA mapping using fraction images extracted from data cubes of the Brazilian orbital sensors CBERS-4/WFI and CBERS-4A/WFI. The methodology for detecting burned areas consisted of applying the Linear Spectral Mixture Model to the images from the CBERS-4/WFI and CBERS-4A/WFI data cubes to generate shadow fraction images, which were then segmented and classified using the ISOSEG non-supervised algorithm. Regression and similarity analyses based on regular grid cells were carried out to compare the BA mappings. The results showed large discrepancies between the mappings in terms of total area burned, land use and land cover affected (forest and non-forest) and spatial location of the burned area. The global products MCD64A1, GABAM and Fire_cci tended to underestimate the area burned in the region, with Fire_cci underestimating BA by 88%, while the regional product MapBiomas Fogo was the closest to the reference, underestimating by only 7%. The burned area estimated by the method proposed in this work (337.5 km²) was 12% higher than the reference and showed a small difference in relation to the MapBiomas Fogo product (18% more BA). These differences can be explained by the different datasets and methods used to detect burned areas. The adoption of global products in regional studies can be critical in underestimating the total area burned in sensitive regions. Our study highlights the need to develop approaches aimed at improving the accuracy of current global products, and the development of regional burned area products may be more suitable for this purpose. Our proposed approach based on WFI data cubes has shown high potential for generating more accurate regional burned area maps, which can refine BA estimates in the Amazon. Full article

Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C₀ + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields. Full article

The increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may be more decisive. Artificial intelligence and machine learning techniques (ML) offer a promising avenue for more effective AF evolution prediction. Five ML models were developed to obtain predictors of MACE in AF patients. Two-thirds of the data were used for training, employing diverse approaches and optimizing to minimize prediction errors, while the remaining third was reserved for testing and validation. AdaBoost emerged as the top-performing model (accuracy: 0.9999; recall: 1; F1 score: 0.9997). Noteworthy features influencing predictions included the Charlson Comorbidity Index (CCI), diabetes mellitus, cancer, the Wells scale, and CHA₂DS₂-VASc, with specific associations identified. Elevated MACE risk was observed, with a CCI score exceeding 2.67 ± 1.31 (p < 0.001), CHA2DS2-VASc score of 4.62 ± 1.02 (p < 0.001), and an intermediate-risk Wells scale classification. Overall, the AdaBoost ML offers an alternative predictive approach to facilitate the early identification of MACE risk in the assessment of patients with AF. Full article

Radical Cystectomy (RC) and Urinary Diversion (UD) is a complex surgery associated with a significant impact on health-related quality of life (HRQoL). However, HRQoL assessment is too often overlooked, with survival and complications being the most commonly investigated outcomes. This study aimed to identify the most impaired HRQoL features in patients receiving RC, compared to a healthy population (HP) control, as well as patients’ recovery after surgery, differentiating between patients receiving ORC and RARC. Patients with Bca, who were candidates for RC with curative intent, were enrolled in the “BCa cohort”. HRQoL outcomes were collected with an EORTC QLQ-C30 questionnaire. These were collected at baseline, and then at 6-, 12- and 24 mo after surgery in the BCa cohorts, and at baseline in the HP cohort. A 1:1 propensity score matched (PSM)-analysis, adjusted for age, Charlson Comorbidity Index (CCI) and smoking history, was performed. Between January 2018 and February 2023, a total of 418 patients were enrolled in the study, 116 and 302 in the BCa and HP cohorts, respectively. After applying the 1:1 propensity scored match (PSM) analysis, two homogeneous cohorts were selected, including 85 patients in each group. Baseline HRQoL assessment showed a significant impairment in terms of emotional and cognitive functioning, appetite loss and financial difficulties for the BCa cohort. Among secondary outcomes, we investigated patients’ recovery after RC and UD, comparing HRQoL outcome questionnaires between the HP and BCa cohorts at 6-, 12- and 24 mo after surgery, and a subgroup analysis was performed differentiating between patients receiving ORC and RARC with totally intracorporeal UD. Interestingly, ORC compared to RARC provided a major impact on HRQoL recovery across the early, mid and long term. In particular, the ORC cohort experienced a major impairment in terms of symptoms scales items such as fatigue, nausea and vomiting, pain and appetite loss. Consequently, comparing ORC and RARC vs. HP reported a major HRQoL impairment in the ORC cohort, possibly defining a benefit of RARC in early, mid- and long-term recovery. To conclude, this study confirmed the undeniable impact of RC on HRQoL. Interestingly, we highlighted the benefit of RARC in early, mid- and long-term recovery, expressed as less impairment of symptoms scales. Full article