Search Results (71)

Background: Nutritional disorders may affect short-term recovery after major orthopaedic surgery, but evidence is lacking. This study assessed whether and how different nutritional disorders diagnosed at admission could influence early recovery after hip replacement. Methods: A prospective analytical study was designed to include 60 patients scheduled for elective primary hip replacement and assess their nutritional status to diagnose 5 malnutrition phenotypes: undernutrition, sarcopenia, obesity, sarcopenic obesity, and sarcopenic undernutrition. Outcome measures were 24 h change in neutrophils, 72 h change in haemoglobin, and 10-day gait speed regain. Results: Haemoglobin reached the nadir at day 2–3 and partially recovered by day 10 in all patients, with sarcopenia and undernutrition being the strongest predictors of the postoperative drop (−2.37 g∙dL⁻¹ and −0.80 g∙dL⁻¹, p < 0.05). Neutrophils peaked immediately after surgery and returned to baseline levels at discharge, with sarcopenic undernutrition displaying a blunted response after surgery (−16.20%, p < 0.01). Undernutrition was found to be the most influential preoperative variable on gait speed recovery, but with a marginal effect. None of the patients covered the reference energy and protein needs through diet in the 10 postoperative days. Conclusions: In this cohort, nutritional disorders with reduced body function and reserves (sarcopenia and undernutrition) grounded a greater vulnerability to surgery in terms of early stress response and short-term recovery. This calls for both advanced planning of nutritional prehabilitation strategies for these conditions and adequate postoperative nutritional support. Full article

Background/Objectives: Patients diagnosed with decompensated congestive heart failure (HF) often have elevated CA-125 levels, attributed to systemic congestion. However, a subgroup of patients presents with normal CA-125 levels. The primary objective of this study was to characterize the clinical, analytical, and echocardiographic profiles of patients admitted for decompensated congestive HF according to their CA-125 levels. The secondary objective was to analyze mortality after discharge. Methods: We conducted a retrospective study of patients hospitalized for a decompensated congestive HF episode. Recruitment was consecutive over more than 4 years (December 2019–June 2024), with 3151 patients recruited. Scheduled admissions, transfers from other hospitals, pulmonary congestion patterns, mixed patterns, and low output were the exclusion criteria. The final number of patients included was 166, all with an isolated systemic congestion pattern: CA-125 ≤ 50 U/mL: 38, and CA-125 > 50 U/mL: 128. Results: The comparative analysis between the groups showed that patients with CA-125 ≤ 50 U/mL were more often women (p < 0.05). They also had lower bilirubin and GOT/AST levels (p < 0.05). The percentage of patients with a preserved left ventricular ejection fraction (≥50%) was higher in the CA-125 ≤ 50 U/mL group (p < 0.05). The right ventricular (RV) size and inferior vena cava (IVC) were enlarged in both groups but with no significant differences (p < 0.05). However, the degree of RV dysfunction was greater in the CA-125 > 50 U/mL group, while the proportion of patients with inspiratory collapse of the IVC was higher in the CA-125 ≤ 50 U/mL group (p < 0.05). Survival curves differed from the first month and throughout the follow-up, with higher mortality in the CA-125 > 50 U/mL group. Thus, the probability of being alive at the end of the follow-up was over 50% in the CA-125 ≤ 50 U/mL group, while in the CA-125 > 50 U/mL group, it was around 25% (p < 0.05). Conclusions: The proportion of patients with decompensated congestive HF and systemic congestion who present with a low CA-125 level is close to 25%. These patients are mostly women with a preserved ejection fraction and inspiratory collapse of the IVC of >50%. Moreover, they have a higher survival rate, so a low CA-125 could help identify a subgroup of patients with a better prognosis. Full article

Background/Objectives: Surgery is the treatment of choice for symptomatic disc herniation after unsuccessful conservative management. This prospective study compared the impact on clinical and hospital outcomes of intradiscal ozone treatment vs. surgery (microdiscectomy/discectomy) in our clinical practice. Methods: Intradiscal ozone treatment was offered to 70 patients with scheduled surgery because of lumbar disc herniation. Initial treatment was surgery in 38 patients and ozone infiltration in 32 patients: lumbar and sciatic pain (Visual Analog Scale), Roland-Morris Disability Questionnaire score, days of hospital admission, and direct hospital costs were recorded during 24 months of follow-up. Results: At 12 and 24 months, lumbar pain, sciatic pain, and Roland-Morris score decreased significantly within both groups (p < 0.001). At 24 months, compared to the initial surgery, the initial intradiscal ozone treatment showed similar clinical outcomes with significantly lower requirements of surgery (100% versus 47%, p < 0.001) and lower hospital stay [median 2.5 (2–3) versus 0.5 (0–2) days, p < 0.001]. Direct hospital costs were significantly lower with initial ozone treatment at 12 months (p = 0.040). Conclusions: In our real-world prospective study, after 24 months of follow-up, initial intradiscal ozone treatment avoided surgery in more than half of patients and provided similar clinical outcomes with lower hospitalization requirements. In patients with lumbar disc herniation requiring surgery (microdiscectomy/discectomy), initial intradiscal ozone treatment could offer benefits for patients and healthcare service providers (NCT00566007). Full article

Background: The Patient Outcome Assessment and Decision Support (PADS) model is a real-time framework designed to predict both mortality and the likelihood of discharge within 48 h in critically ill patients. By combining these predictions, PADS enables clinically meaningful stratification of patient trajectories, supporting bedside decision-making and the planning of critical care resources such as nursing allocation and surgical scheduling. Methods: PADS integrates routinely collected clinical data: SOFA variables, age, gender, admission type, and comorbidities. It consists of two Long Short-Term Memory (LSTM) neural networks—one predicting the probability of death and the other the probability of discharge within 48 h. The combination places each patient into one of four states: alive/discharged within 48 h, alive/not discharged, dead within 48 h, or dead later. The model was trained using MIMIC-IV data, emphasizing ease of implementation in units with electronic health records. Out of the 76,540 stays present in MIMIC-IV (53,150 patients), 32,875 (25,555 patients) were used after excluding those with short stays (<48 h) or life support treatment limitations. The code is open, well-documented, and designed for reproducibility and external validation. Results: The model achieved strong performance: AUCROC of 0.94 (±0.03) for mortality and 0.89 (±0.07) for discharge on training data, and 0.87 (±0.02) and 0.88 (±0.03), respectively, on the test set. As a comparison, benchmark models obtain worse accuracy (−13.4% for APS III, −19% for OASIS, and −7.4% for SAPS II). Predictions are visualized in an intuitive format to support clinical interpretation. Conclusions: PADS offers a transparent, reproducible, and practical tool that supports both individual patient care and the strategic organization of intensive care resources by anticipating short-term outcomes. Full article

The aim of this study is to examine the impact of poor air quality and adverse meteorological conditions on health risks in the Greater Athens Area (GAA), Greece, during the period from 2018 to 2022. Specifically, the aim is to assess the Relative Risk (RR) of hospital admissions (HAs) for cardiovascular diseases (CVDs) and respiratory diseases (RDs), due to air pollution in combination with thermal discomfort, as well as to identify the time lag effect on admissions. For this purpose, data from six (6) different hospitals within the GAA were collected and used. Statistical analysis of hourly measurements of key pollutants (NO₂, O₃, PM_2.5, and PM₁₀) obtained from the Directorate of Climate Change and Air Quality (DCCAQ), which falls under the auspices of the Ministry of Environment and Energy (MEE), and meteorological parameters (T, RH, and wind velocity), is performed to calculate the daily air quality and human thermal comfort–discomfort levels, respectively. These conditions were examined using appropriate indexes for both air quality and human thermal comfort–discomfort, as independent variables in a Negative Binomial regression model developed in R, with daily HAs (not including scheduled cases or pre-existing health conditions) as the response variable. Moreover, a spatiotemporal analysis of air quality and meteorological parameters is conducted to identify associated variations in health risks. This analysis highlights key risk patterns linked to environmental conditions and the relevant measures to both manage and mitigate the risk. Findings indicate that extreme environmental conditions significantly elevate health risks, with cumulative RR over a one-week period peaking at 1.540 (95% CI: 1.158–2.050) during the warm season, while prolonged increases in the RR are also observed during the cold season, reaching 1.214 (95% CI: 0.937–1.572) under extreme cold exposures. Full article

Background: In the Democratic Republic of Congo (DRC), the 13-valent pneumococcal conjugate vaccine (PCV13) was introduced in 2011 through a three-dose schedule, targeting infants as part of the Expanded Program on Immunization (EPI), to reduce pneumococcal-related morbidity and mortality. The aim of this study was to determine the proportion of pneumonia and meningitis cases and deaths prevented in children under five following the introduction of this vaccine. Methods: This is a systematic review. We synthesized findings from studies carried out in the DRC between 2011 and 2023. We searched scientific articles, published and unpublished doctoral theses and conference proceedings. Only papers written in French or English and those reporting the results of original analytical studies were selected. We assessed the direct effect of PCV13 by calculating the proportion of infections avoided, using Odds Ratios or prevalence ratios related to infection or pneumococcal carriage. Results: Four studies were included in this review. Regarding pneumococcal carriage, when children received three PCV13 doses, the prevalence of carriage was reduced by 93.3% (95% CI: 86.3 to 96.6%), while a single dose did not significantly reduce the prevalence of carriage compared with children who had not received any dose. Concerning pneumonia prevention, three doses of PCV13 prevented 66.7% (95% CI: 37.2 to 82.2) of cases among vaccinated children. The proportion of meningitis attributable to S. pneumoniae prevented was 75.0% (95% CI: 6% to 93.3%) among children vaccinated with PCV13. S. pneumoniae serotypes 19F and 23F were the most frequent causes of invasive pneumonia in children. Serotypes 35B/35C, 15B/C, 10A and 11A/D were the most frequently identified causes of morbidity in Congolese children. In 2022, with PCV13 vaccination coverage at 79.0%, an estimated 113,359 cases of severe pneumonia and 17,255 pneumonia-related deaths were prevented in the DRC, with 3313 cases and 1544 deaths attributable to pneumococcal meningitis prevented. Conclusions: There is clear, but scattered, evidence of reduced colonization by S. pneumoniae and hospital admissions due to pneumococcal pneumonia and meningitis. The results also show that S. pneumoniae serotypes 35B/35C, 15B/C, 10A and 11A/D not included in PCV13 were the main cause of pneumococcal disease in unvaccinated or under-vaccinated children. These data support the need to continue improving vaccination coverage among children who are unvaccinated or incompletely vaccinated with PCV13 to reduce the burden of pneumococcal infections in the DRC. Full article

Background/Objectives: Whether remote monitoring reduces mortality in patients with heart failure remains controversial, and research on remote monitoring in South Korea is generally lacking. Therefore, we aim to evaluate the safety and efficacy of remote monitoring for patients in South Korea with severe structural heart diseases who have an implantable cardioverter–defibrillator or cardiac resynchronisation therapy pacemaker/defibrillator. Methods: This ReVe study is a multicentre, prospective, observational cohort study in which we will comprehensively evaluate the impact of remote monitoring on cardiovascular-related death and hospital admissions related to pre-existing cardiovascular disease (primary outcomes) and satisfaction with and cost of remote monitoring and the healthcare provider workload (secondary outcomes). Two patient groups are being recruited: (1) Patients in the historical group (n = 225) already had a cardiac implantable electronic device implanted after January 2020 and have attended outpatient device check-ups. (2) Those in the initiating group (planned n = 225) will undergo cardiac implantable electronic device implantation during this study. In-office visits are scheduled for every 3–6 months. The time/medical cost efficiency and satisfaction index will be evaluated during the 24-month follow-up period. Questionnaires regarding patient satisfaction will be administered every 6 months. Conclusions: This is the first prospective study involving patients with structural heart diseases who have implanted high-power cardiac electronic devices. It will provide insights into remote monitoring applications in South Korea and evidence for their use in such patients. It will also provide evidence of the efficacy, safety, and satisfaction with remote monitoring in this population. Full article

Background: Frailty is increasingly being recognized as a risk factor for adverse outcomes in older surgical patients undergoing surgery. We investigated the association between frailty and intraoperative complications using multiple frailty assessment tools in older patients undergoing elective intermediate- to high-risk non-cardiac surgery. Methods: This retrospective cohort study included 637 older patients scheduled for elective non-cardiac surgery. Frailty was assessed using the Clinical Frailty Scale (CFS), FRAIL scale, and modified Frailty Index-11 (mFI-11). The predictive ability of frailty tools was analyzed and compared using the area under the receiver operating characteristic curve (AUC). Results: Frailty was significantly associated with higher intraoperative complication rates (FRAIL scale: p = 0.01; mFI-11: p = 0.046). Patients considered frail using the mFI-11 were more likely to have unplanned intensive care unit admissions (p < 0.001). Those classified as frail by the FRAIL scale and mFI-11 had significantly higher rates of vasopressor/inotrope use (p = 0.001 and p = 0.005, respectively) and mechanical ventilation (p = 0.033 and p = 0.007, respectively). In the univariate analysis, frailty measured using the FRAIL scale was significantly associated with intraoperative complications (odds ratio [OR], 2.41; 95% confidence interval [CI]: 1.33–4.38; p = 0.004); this association was not significant in the multivariate analysis (adjusted OR, 1.69; 95% CI: 0.83–3.43; p = 0.148; AUC = 0.550). Atrial fibrillation, hemoglobin levels, anesthesia type, and surgical subspecialty were stronger predictors of intraoperative complications. Conclusions: Frailty assessments demonstrate the limited predictive ability for intraoperative complications. Specific comorbidities, surgical techniques, and anesthesia types play more critical roles. Comprehensive preoperative evaluations integrating frailty with broader risk stratification methods are necessary to enhance patient outcomes and ensure safety. Full article

In today’s data–driven world, the explosive growth of network traffic often leads to network congestion, which seriously affects service performance and user experience. Network traffic scheduling is one of the key technologies to deal with congestion problems. Traditional traffic scheduling methods often rely on static rules or pre–defined policies, which make it difficult to cope with dynamically changing network traffic patterns. Additionally, the inability to efficiently manage tail contributors that disproportionately contribute to traffic can further exacerbate congestion issues. In this paper, we propose ESTS, an efficient and secure traffic scheduling based on private sketch, capable of identifying tail contributors to adjust routing and prevent congestion. The key idea is to develop a randomized admission ($RA$) structure, linking two count–mean–min (CMM) sketches. The first CMM sketch records cold items, while the second, following the $RA$ structure, stores hot items with high frequency. Moreover, considering that tail contributors may leak private information, we incorporate Gaussian noise uniformly into the CMM sketch and $RA$ structure. Experimental evaluations on real and synthetic datasets demonstrate that ESTS significantly improves the accuracy of feature distribution estimation and privacy preservation. Compared to baseline methods, the ESTS framework achieves a 25% reduction in average relative error and a 30% improvement in tail contributor identification accuracy. These results underline the framework’s efficiency and reliability. Full article

Background/Objectives: The increasing medical and nursing care complexity in hospitalized children represents a significant challenge for healthcare systems. However, the link between these two dimensions remains partially explored. This study aims to decipher the relationship between Diagnosis-Related Group (DRG) weight and nursing care complexity in hospitalized children and to identify the determinants of medical complexity. Methods: This retrospective study, conducted in an Italian university hospital, included children aged 2 to 11 years admitted to the hospital in 2022 with a minimum hospital stay of 2 days. Data were gathered from the Neonatal Pediatric Professional Assessment Instrument and the Hospital Discharge Register. DRG weight was used as an indicator of medical complexity, while the number of nursing diagnoses (NDs) documented in the first 24 h from hospital admission and the nursing actions (NAs) recorded during the patient’s hospital stay were used to measure nursing care complexity. Correlation analyses were conducted to explore the associations between DRG weight, NDs, and NAs. Stepwise regression was run to identify the key determinants of medical complexity across sociodemographic, clinical, organizational, and nursing variables. Results: Among 914 patients (mean age of 6.11 ± 2.90 years), the median DRG weight was 0.6982 (IQR: 0.5522). Patients had an average of 3.89 ± 2.83 NDs and a median of 17 NAs (IQR: 8). Significant correlations were found between NDs and NAs (r_s = 0.507; p < 0.001), as well as between DRG weight and the frequency of NDs (r_s = 0.232; p < 0.001) and NAs (r_s = 0.184; p < 0.001). Stepwise regression indicated that the number of NAs, surgical DRG, scheduled admissions, and ND frequency were significant determinants of DRG weight (R² = 0.311; adjusted R² = 0.308; p < 0.001). Conclusions: In children, DRG weight is also influenced by nursing care complexity, alongside clinical and organizational factors. An integrated approach is essential to enhance pediatric care and patient outcomes. Full article

Background and Objectives: The COVID-19 pandemic posed significant challenges to healthcare systems worldwide, and mortality rates were driven by a complex interaction of patient-specific factors, one of the most important being those related to the scheduling of invasive mechanical ventilation. This study examined the sociodemographic, clinical, and ventilatory factors associated with mortality in COVID-19 patients admitted to the ICU of a hospital in Colombia. Methods: A retrospective cohort study was conducted, involving 116 patients over the age of 18 who were admitted to the ICU with a confirmed diagnosis of COVID-19 between March 2020 and May 2021. Data were collected from the patients’ medical records. Statistical analysis was performed using SPSS version 24^®. Odds ratios (OR) and 95% confidence intervals were calculated to identify factors associated with COVID-19 mortality, followed by adjustment through binary logistic regression. Results: It was found that 65.5% of the patients were male, with a mean age of 64 ± 14 years, and the overall mortality rate was 49%. Factors significantly associated with higher mortality included male sex (OR: 6.9, 95% CI: 1.5–31.7), low oxygen saturation on admission (OR: 7.6, 95% CI: 1.1–55), and PEEP settings at 96 h (OR: 8, 95% CI: 1.4–45). Mortality was not influenced by socioeconomic status or health system affiliation. Conclusions: This study identified male sex, age over 65 years, PEEP greater than 10 cmH₂O at 96 h of mechanical ventilation, and low oxygen saturation as significant factors associated with higher mortality in COVID-19 patients, while no significant associations were found with socioeconomic status or health system affiliation. These findings highlight the importance of focusing on clinical management and ventilatory strategies in reducing mortality, particularly for high-risk groups, rather than relying on socioeconomic factors as predictors of outcomes. Full article

As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications. Full article

Purpose: This study investigates the impact of varying degrees of immunosuppression on the clinical outcomes of immunocompromised individuals, particularly those with autoimmune diseases or post-solid organ transplant statuses, in the context of COVID-19. By focusing on these highly vulnerable populations, the study underscores the significant health inequalities faced by immunocompromised patients, who experience disproportionately worse outcomes in comparison to the general population. Methods: A retrospective cohort analysis of the K-COV-N dataset was conducted, comparing the effects of immunosuppression in autoimmune and transplant groups with matched control groups. Propensity score matching was employed to minimize inequalities in baseline characteristics, ensuring a more equitable comparison between immunocompromised and non-immunocompromised individuals. Outcomes included COVID-19-related in-hospital mortality, 28-day mortality, ICU admissions, and the need for respiratory support among 323,890 adults in the Republic of Korea. Patients with cancer or other immunosuppressive conditions, such as HIV, were excluded. Subgroup analyses assessed the influence of specific immunosuppressive medications and vaccination extent. Results: Significantly elevated in-hospital mortality was found for patients with autoimmune diseases (adjusted Odds Ratio [aOR] 2.749) and transplant recipients (aOR 7.567), with similar patterns in other outcomes. High-dose steroid use and a greater number of immunosuppressant medications markedly increased the risk of poor outcomes. Vaccination emerged as a protective factor, with a single dose substantially improving outcomes for autoimmune patients and at least two doses necessary for transplant recipients. Conclusions: Immunocompromised patients, particularly those with autoimmune diseases and transplant recipients, are highly vulnerable to severe COVID-19 outcomes. High-dose steroid use and multiple immunosuppressants further increase risks. Vaccination significantly improves outcomes, with at least one dose benefiting autoimmune patients and two doses necessary for transplant recipients. Personalized vaccination schedules based on immunosuppression levels are essential to mitigate healthcare inequalities and improve outcomes, particularly in underserved populations, informing both clinical and public health strategies. Full article

Background/Objectives: Routine postoperative ICU admission following brain tumor surgery may not benefit selected patients. The objective of this study was to develop a risk prediction instrument for early (within 24 h) postoperative adverse events using machine learning techniques. Methods: Retrospective cohort of 1000 consecutive adult patients undergoing elective brain tumor resection. Nine events/interventions (CPR, reintubation, return to OR, mechanical ventilation, vasopressors, impaired consciousness, intracranial hypertension, swallowing disorders, and death) were chosen as target variables. Potential prognostic features (n = 27) from five categories were chosen and a gradient boosting algorithm (XGBoost) was trained and cross-validated in a 5 × 5 fashion. Prognostic performance, potential clinical impact, and relative feature importance were analyzed. Results: Adverse events requiring ICU intervention occurred in 9.2% of cases. Other events not requiring ICU treatment were more frequent (35% of cases). The boosted decision trees yielded a cross-validated ROC-AUC of 0.81 ± 0.02 (mean ± CI95) when using pre- and post-op data. Using only pre-op data (scheduling decisions), ROC-AUC was 0.76 ± 0.02. PR-AUC was 0.38 ± 0.04 and 0.27 ± 0.03 for pre- and post-op data, respectively, compared to a baseline value (random classifier) of 0.092. Targeting a NPV of at least 95% would require ICU admission in just 15% (pre- and post-op data) or 30% (only pre-op data) of cases when using the prediction algorithm. Conclusions: Adoption of a risk prediction instrument based on boosted trees can support decision-makers to optimize ICU resource utilization while maintaining adequate patient safety. This may lead to a relevant reduction in ICU admissions for surveillance purposes. Full article

Background: The long-term effect of the 2019 coronavirus (COVID-19) pandemic is not fully known. Severe cases of COVID-19 have resulted in disability that can be assessed in a biopsychosocial manner with the International Classification of Functioning, Disability and Health with the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) questionnaire. This study aimed to evaluate the long-term effects on disability of COVID-19 three years after post-acute rehabilitation using WHODAS 2.0. Methods: This single-center cohort study included patients with severe COVID-19 who underwent immediate post-discharge post-acute rehabilitation intervention. Three years later, patients were assessed via telephone using the WHODAS 2.0 questionnaire. Results: Of the 69 patients identified in the hospital database, 27 responded. A total of 16 patients refused to respond due to emotional distress. The mean age was 63.4 ± 8.6 years, 81.5% were independent in the community, 55.3% had been previously admitted to the ICU, and the median rehabilitation hospitalization duration was 18 (11.5,24) days. Comorbidities included type 2 diabetes mellitus (DM) (55.5%), grade 2 high blood pressure (62.9%), pressure ulcers (37%), peripheral neurologic deficits (62.9%), and central neurological deficits (14.8%). ICU admission was significantly correlated with advanced rehabilitation needs (measured by the level of the rehabilitation (p < 0.01) and longer hospitalizations (measured by total days in the hospital (p < 0.001). The overall disability score was 35.09%, significantly influenced by DM and central neurological deficits. Conclusions: Central neurological deficits and DM are associated with higher disability scores. Tailored rehabilitation programs, ongoing medical assessment, integrated care models, and patient education are essential for improving long-term outcomes after COVID-19 disease. Full article