Multiple Hospital Research Articles

An Unusual Case of Pulmonary Mucormycosis

ABSTRACT We report the case of a female who presented with nonresolving pneumonia and was found to have uncontrolled diabetes mellitus. She was given multiple antibiotics and antivirals from multiple hospitals. No organism was isolated from sputum/bronchoalveolar lavage/pleural fluid. Finally, she underwent Video assisted thoracoscopic surgery (VATS) and drainage of lung abscess and was diagnosed to have pulmonary mucormycosis.

Light-chain amyloidosis with dysphagia as the main symptom: a case report

BackgroundImmunoglobulin light-chain amyloidosis is a relatively rare condition with a worldwide incidence of 5.1–12.8 cases per million person-years (Baker, 2022). It is characterized by a clonal population of immunoglobulin-secreting cells that produce a monoclonal light chain of κ or λ type as either an intact molecule or a fragment.Case presentationA 69-year-old East Asian (Chinese) male patient who presented with progressive dysphagia visited multiple hospitals repeatedly for more than 2 years and was finally diagnosed with immunoglobulin light-chain amyloidosis.ConclusionsOtolaryngologists should consider immunoglobulin light-chain amyloidosis when encountering suspicious clinical manifestations and intervene early to avoid misdiagnosis.

Antipsychotic prescription patterns among schizophrenia patients in Guangdong Province, China’s 686 program: A retrospective study

Schizophrenia is a severe mental disorder. However, there is limited data on the prescribing patterns of patients under China’s Central Government Support for the Local Management and Treatment of Severe Mental Illnesses Program, known as the “686 program.” This study aimed to investigate the use of antipsychotic medications and associated factors among discharged schizophrenia patients in Guangdong Province, within the 686 Program. This study encompassed schizophrenia patients who were discharged from the Affiliated Brain Hospital, Guangzhou Medical University and enrolled in the 686 Program between January 2019 and December 2019. A total of 1645 hospitalized schizophrenia patients were included in the analysis. Clinical and sociodemographic data were acquired from medical records upon discharge. A total of 15 unique antipsychotic medications were utilized, comprising 4 first-generation (FGAs) and 11 second-generation (SGAs) options. FGAs were prescribed at a rate of 8.3%, while SGAs dominated at 98.8%. Risperidone (40.8%), olanzapine (30.2%), clozapine (24.6%), and amisulpride (15.4%) emerged as the top 4 prescribed medications. Additionally, mood stabilizers were used by 20.4%, antidepressants by 14.8%, sedative-hypnotics by 33.6%, anticholinergics by 26.9%, and other internal medicine drugs by 46.4%. Notably, 60.5% received antipsychotic monotherapy (AMT), while 39.5% underwent antipsychotic polypharmacy (APP). Predictors of polypharmacy included multiple hospital admissions, longer hospital stays, and undergoing modified Electroconvulsive Therapy (mECT) during hospitalization. In Guangdong Province, China’s 686 Program, hospitalized patients commonly receive multiple antipsychotic medications simultaneously. Due to the varying outcomes in current studies on the benefits and risks of polypharmacy, it’s vital to educate psychiatrists about the importance of AMT to reduce APP. Additionally, randomized, controlled trials are essential to identify the safest and most effective antipsychotic combinations, as well as to understand which patient profiles may benefit from these combinations.

Integrating federated learning for improved counterfactual explanations in clinical decision support systems for sepsis therapy

In recent years, we have witnessed both artificial intelligence obtaining remarkable results in clinical decision support systems (CDSSs) and explainable artificial intelligence (XAI) improving the interpretability of these models. In turn, this fosters the adoption by medical personnel and improves trustworthiness of CDSSs. Among others, counterfactual explanations prove to be one such XAI technique particularly suitable for the healthcare domain due to its ease of interpretation, even for less technically proficient staff. However, the generation of high-quality counterfactuals relies on generative models for guidance. Unfortunately, training such models requires a huge amount of data that is beyond the means of ordinary hospitals. In this paper, we therefore propose to use federated learning to allow multiple hospitals to jointly train such generative models while maintaining full data privacy. We demonstrate the superiority of our approach compared to locally generated counterfactuals. Moreover, we prove that generative models for counterfactual generation that are trained using federated learning in a suitable environment perform only marginally worse compared to centrally trained ones while offering the benefit of data privacy preservation. Finally, we integrate our method into a prototypical CDSS for treatment recommendation for sepsis patients, thus providing a proof of concept for real-world application as well as insights and sanity checks from clinical application.

Comprehensive analysis of multiple sclerosis hospitalization trends in Spain (1998–2022)

ObjectiveTo analyse the trends in MS hospitalization rates in Spain (1998–2022) considering age, period, and birth cohort (A-P-C) effects. MethodsThis retrospective study used data on MS hospitalizations from the Spanish National Health System Minimum Basic Data Set (CMBD). Age-Standardized Hospitalization Rates (ASHRs) and Joinpoint analysis were used to assess trends. A-P-C analysis was performed to estimate age-specific rates, period and cohort effects. ResultsFrom 1998 to 2022, MS hospital admissions in Spain declined annually by -2.1 %, with women showing a slightly greater decrease (-2.3 %) than men (-1.7 %). Joinpoint analysis revealed non-parallel trends, with three inflection points indicating distinct periods of stabilization and decline. ASHR showed an overall decrease, with -2.0 % for men and -2.2 % for women annually. Risk of hospitalization peaked in the 25–29-year age group for men and 30–34-year age group for women, declining with age. Women consistently had higher risk ratios across age groups. Cohort analysis showed periods of stabilization and decline in MS hospitalization risk, aligning with joinpoint analysis findings. Risk increased for cohorts born in the early 20th century, peaking around 1938, followed by a progressive decline in later cohorts. ConclusionsDespite an aging population, MS hospitalization rates in Spain decreased. The risk of hospitalization for MS is affected by a person's age, the time period they were born in, the historical context of healthcare received, and potentially their sex.

Data linkage of routinely collected qualitative health data: Using qualitative Patient Reported Experience Measures to understand the impact of digital hospitals on patients

ObjectiveData linkage and secondary use of routinely collected quantitative health data is ubiquitous. In recent years, the volume and breadth of routinely collected qualitative health data has grown. These data sources provide rich narrative information that could enable a broader understanding of healthcare challenges at scale, however linkage and secondary use of this qualitative health information is not common. This study explores the potential of linkage and secondary use of qualitative patient reported experience measure (PREM) data to understand the impact of digital hospitals on patients. ApproachIn Queensland, Australia, PREM surveys are distributed to most public hospital patient. Patient responses will be linked to the hospital and ward attended; qualitative comments will be extracted from two large hospitals: a digital hospital and non-digital hospital. Comments will be compared between the hospitals, focusing on factors which relate to experience of a digital hospital, either directly (e.g. computerised workstations), or indirectly (e.g. communication). Results & ConclusionsCurrently under ethics review – one challenge identified is that deidentification of free-text qualitative data at this scale is not feasible. Ethics committees may not have experience with this type of study and may require education on using qualitative health data at scale. ImplicationsLinkage and secondary use of routinely collected qualitative PREMs data has the potential to reduce participant burden in research, by utilising existing data sources to answer research questions. It can also broaden the scope of research by utilising data collected across multiple hospitals or jurisdictions, without significantly increasing the resources required.

Improving Electronic Health Record (EHR) data linkages for identifying Aged Care Residents through publicly available data

IntroductionAccurate identification of Aged Care residents accessing hospital services is challenging due to multiple hospital Electronic Health Record (EHR) systems and entry points resulting in inconsistent addresses and coding. Improving accuracy and completeness of addresses is crucial for cohort identification and data linkage. ObjectiveDevelop methods to validate cross-sectoral data linkages to improve identification of aged care residents within EHR systems. ApproachAmong individuals aged ≥65 years (or ≥50 identifying as Aboriginal or Torres Strait Islander) between January 2013 to December 2023 within our Data Platform, we first used data items mandated by the Department of Health to identify aged care residency. Data quality was enhanced using a residential address index derived from Australian Geocoded National Address File (G-NAF). We evaluated sensitivity, specificity, and Positive Predictive Values (PPVs) of recorded addresses against the Australian Institute of Health and Welfare (AIHW) aged care services list. ResultsAmong 409,677 addresses extracted, 55.4% (raw) had sufficient data for validation. This increased to 89.9% (enhanced) with G-NAF. Among 79,028 admitted and 88,876 emergency patients, 22.7% and 18.4% respectively were aged care residents. We compared performance of raw vs enhanced comparisons were: sensitivity (admission: 34.1% vs 73.8%), (emergency: 42.6% vs 66.2%); specificity (admission: 96.2% vs 90.9%), emergency (96.8% vs 95.2%); Positive Predictive Value (admission: 61.3% vs 58.8%), (emergency: 64.2% vs 65.3%). Conclusions and ImplicationsOur approach significantly increased sensitivity without sacrificing specificity, enhancing Aged Care resident identification in EHR data. It also improved linkage with external datasets and Statistical Area Level 1 (SA1) mapping.

Identifying hospitalization episodes of care among people with and without HIV in British Columbia, Canada

BackgroundHospitalizations are a resource-intensive form of healthcare use, particularly for persons with chronic conditions such as those with HIV. Interhospital transfers typically appear as separate records in Canadian databases; misclassifying transfers as independent hospitalizations can bias key metrics such as readmission rates. We examined approaches of combining sequential, related records into hospitalization episodes of care (HEoCs) among persons with and without HIV (PWH; PWoH) in British Columbia (BC), Canada. MethodsBC hospitalization records (1992 to 2020) were sourced from the Comparative Outcomes and Service Utilization Trends (COAST) study, a data linkage that includes samples of PWH and PWoH. We constructed 8 HEoC definitions that varied by the: a) time gap between records, and b) transfer indication. Comparisons were informed by the proportion of multi-record HEoCs (mHEoCs; episodes with multiple hospitalization records) generated, and feasibility given data quality. ResultsWe analyzed 98,553 hospitalization records from 13,498 PWH, and 1,874,507 hospitalization records from 385,011 PWoH. Across the definitions, the proportion of mHEoCs varied from 2.46% to 5.27% for PWH and 2.73% to 4.18% for PWoH. Definitions requiring no transfer indication yielded the highest proportion of mHEoCs, whereas those requiring two-way agreement of hospital identifiers yielded the lowest proportion of mHEoCs. Patterns were comparable among PWH and PWoH. A pragmatic approach to defining HEoCs can be a reasonable option for general purposes – requiring at least one populated hospital identifier field, and ≤ 1 day gap between hospitalizations. ConclusionsVarious approaches can be employed to combine sequential, related hospitalization records into HEoCs to help provide less biased estimates of hospitalization-related metrics.

Sustained Performance of Cardiac Arrest Prevention in Pediatric Cardiac Intensive Care Units.

The Pediatric Cardiac Critical Care Consortium (PC4) cardiac arrest prevention (CAP) quality improvement (QI) project facilitated a decreased in-hospital cardiac arrest (IHCA) incidence rate across multiple hospitals. The sustainability of this outcome has not been determined. To examine the IHCA incidence rate at participating hospitals after the QI project ended and discern which factors best aligned with sustained improvement. This observational cohort study compared IHCA data from the CAP era (July 1, 2018, to December 31, 2019) with data from the 2-year follow-up era (March 1, 2020, to February 28, 2022). Data were obtained from pediatric cardiac intensive care units (CICUs) from 17 PC4 CAP-participating hospitals. The CAP practice bundle was designed to facilitate local practice integration, with the intention to implement, adapt, and continue CAP processes beyond the CAP era. A web-based survey was administered 2 years after the end of the project to estimate CAP-specific QI work. Risk-adjusted IHCA incidence rates across all admissions were compared between study eras. The survey generated a novel hospital-specific QI sustainability score, which is generally reflective of the sum of local CAP work performed. There were no clinically important differences in demographic and admission characteristics between the 13 082 CAP era admissions and 16 284 follow-up admissions (total mean [SD] age, 5.1 [8.4] years; 56.1% male). Risk-adjusted IHCA incidences were not different between the CAP vs follow-up eras (2.8% vs 2.8%; odds ratio, 1.03; 95% CI, 0.89-1.19), suggesting sustained prevention improvement. There was also no difference between eras in risk-adjusted IHCA incidence within medical, surgical, or high-risk subgroups. A lower hospital QI sustainability score was correlated with higher odds for IHCA in the follow-up vs CAP era (correlation coefficient, -0.58; P = .02). Five hospitals had increases of 1% or greater in risk-adjusted IHCA rates in the follow-up era; these hospitals had significantly lower QI sustainability scores and were less likely to have adopted sustainability elements during the CAP era or report persistent engagement for CAP-related QI processes during follow-up. In this cohort study of all CICU admissions across 17 hospitals, IHCA prevention was feasible and sustainable; the established reduction in risk-adjusted IHCA rate was maintained for at least 2 years after the end of the CAP project. Both implementation strategies and continued engagement in CAP processes during the follow-up era were associated with sustained improvement.

Burnout experience among healthcare workers post third COVID-19 wave in India; findings of a cross-sectional study

Background The pandemic exacerbated burnout experienced by healthcare personnel, whose mental health had long been a public health concern before COVID-19. This study used the Copenhagen burnout inventory (CBI) tool to assess burnout and identify predictors among Indian healthcare workers managing COVID-19. Methods A cross-sectional study was conducted from June to December 2022, after the third pandemic wave. A web-based, fillable Google form was used to recruit COVID-19 management professionals from multiple Jaipur district hospitals. Healthcare professionals provided socio-demographic, work-related, and CBI scores. Multiple linear regression was used to control for model covariant independent variables. Results We evaluated the responses of a total of 578 participants with a mean age of 36.59 ± 9.1 years. Based on the CBI cut-off score of 50, 68.1% reported burnout. A total of 67.5%, 56.4%, and 48.6% of healthcare workers reported work-related, personal, and patient-related burnout, respectively. High burnout scores were significantly associated with the nursing profession (β = 7.89, 95% CI; 3.66, 12.11, p < 0.0001). The p-value indicates the probability of observing the data if the null hypothesis is true, and the confidence interval shows the range within which we can be 95% confident that the true effect lies. An independent relationship exists between male gender and higher personal-related burnout scores (β = 4.45, 95% CI 1.9–6.9). Conclusion This study identified key indicators that need further emphasis and the need for organizational and individual-level burnout monitoring in healthcare delivery sectors. Health workers continue to experience burnout due to a combination of personal, professional, and patient-related factors. This underscores the need for targeted organizational and individual interventions. The findings also suggest that the CBI tool could identify healthcare worker burnout risk groups.

Delta Coverage: The Analytics Journey to Implement a Novel Nurse Deployment Program

This paper introduces the Delta Coverage (DC) analytics program, an innovative solution to address the nursing shortage crisis. The program designs a new flexible nursing role supported by an accompanying analytics model for dynamic nurse staffing. Using advanced data analytics, the DC program dynamically allocates flexible nurses across multiple hospitals in response to geographical and temporal nurse demand variability. The DC analytics suite integrates a nurse-demand forecast using a deep generative model and a stochastic optimization for optimal on-call and deployment decisions. A pilot study demonstrated that the DC program reduced understaffing incidents by 17% compared to traditional travel nurse solutions, with projected annual savings of $2.5 million for the partner hospital and over $1.5 billion nationally. These results highlights the potential of the DC analytics program to transform nurse staffing practices on a broader scale.

Bladder Compliance Dynamics of Pelvic Organ Prolapse in Women Undergoing Robotic-assisted Sacrocolpopexy

Although mean/static compliance of bladder filling can be readily assayed via cystometry, a protocol measuring compliance dynamics at a specific stage of bladder filling has not been established in human patients. For patients with pelvic organ prolapse (POP), the objective benefits of robotic-assisted sacrocolpopexy (RSCP) surgical intervention for restoring bladder functions, primarily urine storage, have yet to be established. Also, bladder compliance is a viscoelastic parameter crucially defines the storage function. Therefore, we aimed to investigate the impact of RSCP on bladder compliance of POP patients using a pressure-volume analysis (PVA), which graphically illustrate bladder compliance. A retrospective pre- and post-operative study. Multiple hospitals in Taiwan. 27 female POP patients (stage ≥ II). RSCP for POP repair. We retrospectively reviewed the pre- and post-operative PVAs for women with POP, who underwent RSCP. The mean compliance of the entire (Cm), the early half (C1/2), and the late half (C2/2) of bladder filling were analyzed as primary outcomes. Changes in intra-vesical volume (ΔVive) and detrusor pressure (ΔPdet) of bladder filling, ΔPdet in the early (ΔPdet1/2) and late (ΔPdet2/2) filling, and post-voiding residual volume (Vres) were analyzed as secondary outcomes. Compared with the pre-operative control, RSCP increased Cm (p=0.010, N=27) and C2/2 (p<0.001, N=27) but negligibly affected C1/2 (p=0.457, N=27). Mechanistically, RSCP decreased ΔPdet (p=0.001, N=27) without significantly affecting ΔVive (p=0.863, N=27). Furthermore, RSCP decreased the ΔPdet2/2 (p<0.001, N=27) but not ΔPdet1/2 (p=0.295, N=27). This is the first report of applying PVA in assaying dynamics of bladder compliance in patients with POP. Our results suggest that RSCP improved bladder storage in women with POP since it increased bladder compliance, particularly in the late filling possibly by restoring the anatomical location and geometric conformation for bladder expansion. This study was registered in ClinicalTrials.gov (https://clinicaltrials.gov/study/NCT05682989); registration number: NCT05682989 submitted on 12/28, 2022.

Federated unsupervised random forest for privacy-preserving patient stratification.

In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. Meanwhile, clinical datasets are often small and must be aggregated from multiple hospitals. Online data sharing, however, is seen as a significant challenge due to privacy concerns, potentially impeding big data's role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing. We introduce a novel multi-omics clustering approach utilizing unsupervised random forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark datasets as well as on cancer data from The Cancer Genome Atlas. Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing. The proposed methods are available as an R-package (https://github.com/pievos101/uRF).

Observational Study of the Clinical Impact of E-scooter Injuries at a Major Trauma Centre.

Background Due to the increased use of e-scooters in the UK, associated injuries have increased, and its cost has increased as well. Worldwide data on injuries related to e-scooter use are relatively limited, owing to the short duration since their first introduction to the public. Aim There has been an increase in using e-scooters as a method of transport recently. It was noticed that the severity and frequency of its injuries are increasing as well. This study focuses on the frequency of e-scooter-associated injuries, especially open fractures, and to evaluate its burden on the health system. Methods Data on e-scooter injuries were extracted from our trauma database from November 2017 to July 2022. Patients' notes and images were reviewed. Outcome measures were the type of injuries: site of bony fractures, closed vs. open fracture (using Anderson and Gustilo classification), number of operations, complications, and length of hospital stay. Results The number of patients enrolled was 104. The mean age was 34.3 years, and 78.8% (n = 82) were male. The main mechanism of injury was riding (91%, n = 95) vs. hitting by an e-scooter (9%, n = 9). Injured patients were more likely not to wear helmets (82% of total injured patients). Patients with bony injuries were 65.4% (n = 68), with 22.1% (n = 15) of them being open fractures. The most common bony injuries were lower limb-only fractures at 45.6% (n = 31), then upper limb-only injuries at 39.7% (n = 27). Combined upper and lower limb cases were 8.8% (n = 6), and pelvic injuries were 5.9% (n = 4). Head and neck injuries composed 23.1% (n = 24) of the reported injuries, including intracranial haemorrhages (9.6%, n = 10), extensive traumatic brain injury (3.8%, n = 4), haematoma/lacerations (3.8%, n = 4), cervical spine fractures (1.9%, n = 2), and skull fractures (1.9%, n = 2). The mean duration of hospital stay was 8.6 days, and 9.6% (n = 10) of patients needed intensive therapy unit(ITU) admission. The number of patients presented as trauma calls was 55.8% (n = 58). Patients who needed surgical intervention either under orthopaedics or other specialties were 52.9% (n = 55) and 21.2% (n = 22) of total patients had complications either due to surgical intervention or a long hospital stay. Conclusion E-scooter riding can lead to serious injuries that can end with limb- or life-threatening conditions. The most common demographic characteristics were adults in their early 30s. There should be more emphasis on wearing protective gear like wrist and elbow guards in addition to helmets. Future prospective studies with larger cohorts across multiple regions and hospitals are necessary to truly characterize the nature and cost of e-scooter injuries.

Translation and validation of the Indonesian version of the chronic otitis media questionnaire-12

Chronic suppurative otitis media (CSOM) is a pathological disorder affecting the ear, characterized by a persistent and long-lasting middle ear infection accompanied by the absence of an intact tympanic membrane. Multiple measures can be employed to evaluate the quality of life among patients with CSOM. One such instrument is the chronic otitis media questionnaire-12 (COMQ-12), which has demonstrated satisfactory levels of validity and reliability. The utilization of the COMQ-12 questionnaire has the potential to improve the quality of life experienced by CSOM patients. This study aimed to evaluate the validity and reliability of the Indonesian adaptation instrument of the COMQ-12 in patients with CSOM in the Otorhinolaryngology department of multiple hospitals in Yogyakarta. The study was conducted from February to July 2023. The study comprised 48 participants, consisting of 16 males and 32 females. The adapted Indonesian version of the COMQ-12 instrument demonstrates validity and reliability in assessing patients' quality of life with CSOM, as evidenced by a statistically significant r-value of 0.2353. The internal consistency analysis reveals that Cronbach's α coefficient is 0.868. The Indonesian adaptation of the COMQ-12 demonstrates validity and reliability as an assessment tool for measuring the health-related quality of life in patients diagnosed with CSOM.

Soulbound Tokens: Enabler for Privacy-Aware and Decentralized Authentication Mechanism in Medical Data Storage

Context: The digitalization of the healthcare sector faces significant challenges due to the diverse representation of data and their distribution across various hospitals. Moreover, security is a key concern as healthcare-related data are subject to the legal obligations of GDPR and similar data protection legislations. Standardization efforts like HL7 have been implemented to enhance data interoperability. However, authentication still remains a critical issue, even significant challenges. Aim: This research aims to improve and strengthen the authentication process by introducing a novel architecture for decentralized authentication. Additionally, it proposes a new approach to decentralized data management, which is crucial for handling sensitive medical data efficiently. Methodology: The proposed architecture adopts a user-centric approach, utilizing Self-Sovereign Identity (SSI). It introduced a new non-fungible token (NFT) type called Soulbound token (SBT) in the medical context, which will facilitate user authentication across different hospitals, effectively creating a federation of interconnected institutions. Results: The implementation of the proposed architecture demonstrated a significant reduction in authentication time across multiple hospitals. The use of SBT ensured secure and seamless user authentication, enhancing overall system interoperability and data security. The decentralized approach also mitigated the risks associated with centralized authentication servers. Conclusion: The study successfully presents a novel decentralized authentication architecture for the healthcare domain, leveraging SSI and SBTs. This approach not only accelerates the authentication process but also enhances data security and interoperability among hospitals. Future research should explore the scalability of this architecture and its application in other sectors requiring stringent data security measures.

Transurethral Resection of the Prostate (TURP) and concomitant inguinal hernioplasty: a single-center experience

BackgroundBenign prostatic hyperplasia (BPH) is a prevalent condition in aging males, leading to bladder outlet obstruction (BOO) and associated urinary symptoms. With increasing life expectancy, the incidence of BPH and its co-morbidities, like inguinal hernia, has risen. This study explores the efficacy of combining transurethral resection of the prostate (TURP) and inguinal hernioplasty in a single surgical session to address both conditions, potentially reducing the need for multiple hospitalizations and surgical interventions.MethodsThis retrospective study at Chi Mei Medical Center included patients from 2014 to 2023 who underwent concurrent TURP and inguinal hernioplasty. A total of 85 patients met the criteria defined for this study. Preoperative, intraoperative, and postoperative characteristics were meticulously documented. Outcomes evaluated included the duration of the surgery, incidence of intraoperative and postoperative complications, duration of Foley catheterization, length of hospital stay, and treatment efficacy. Additionally, we conducted a comparative assessment of the surgical outcomes between two distinct techniques for inguinal hernia repair: open hernioplasty and laparoscopic hernioplasty (LH).ResultsIn 85 patients who met the criteria, the mean age was 71.1 ± 7.8 years. The study reported no significant intraoperative complications, and postoperative care was focused on monitoring for blood loss, infection, and managing pain. The average postoperative hospital stay was 2.9 ± 1.0 days and the mean duration of catheterization was 51.6 ± 16.7 h, with a minimal complication rate observed during the one-year follow-up. A significant reduction in both operative duration and catheterization interval was observed in patients undergoing LH as opposed to those receiving open hernioplasty.ConclusionConcurrent TURP and inguinal hernioplasty effectively manage BOO due to BPH and inguinal hernias with minimal complications, suggesting a viable approach to reducing hospital stays and surgical interventions. Laparoscopic techniques, in particular, offer benefits in operative efficiency and recovery time, making combined surgery a feasible option for selected patients.

Risk Factors Associated with Bell's Palsy in Faisalabad: A Retrospective Study

Background: Bell's palsy is a common neurological disorder characterized by sudden facial paralysis, leading to significant distress and reduced quality of life. Despite extensive research, the exact etiology remains unclear, with various factors such as age, gender, and comorbid conditions like hypertension and diabetes implicated.Objective: This study aimed to identify the risk factors associated with Bell's palsy in a specific population, focusing on demographic and clinical variables.Methods: A retrospective study was conducted on 98 patients diagnosed with Bell's palsy in Faisalabad, Pakistan. Data were collected from multiple healthcare settings, including government and private hospitals, polyclinics, and independent medical practitioners. Variables such as age, gender, BMI, comorbidities, and history of exposure to cold were analyzed. Statistical analysis was performed using SPSS version 25.Results: Of the 98 patients, 56.12% were female, and 43.88% were male. The right side of the face was affected in 67.3% of patients, while 32.7% had left-side involvement. Significant associations were found between Bell's palsy and hypertension (p &lt; 0.05), diabetes mellitus (p &lt; 0.05), and exposure to cold (p &lt; 0.05).Conclusion: Bell's palsy was more prevalent in females and younger individuals. Hypertension, diabetes, and cold exposure were identified as significant risk factors. These findings contribute to understanding the potential causes and risk factors for Bell's palsy, informing clinical management and prevention strategies.

Good syndrome combined with multiple microbial pulmonary infections: case report and review of the literature.

Good syndrome (GS), a rare acquired immunodeficiency disorder characterized by thymoma and hypogammaglobulinemia, predisposes individuals to recurrent infections. This study reports a case of a 37-year-old male GS with multiple pulmonary infections and reviews relevant literature. The patient, with a history of thymoma resection, experienced multiple hospitalizations due to lung infections and neutropenia. The alveolar lavage fluid was detected by macro-genomic sequencing (NGS) to detect multiple pathogens, and targeted anti-infective and immunity-enhancing treatments led to improved symptoms and normal neutrophil counts. A literature review of 98 case reports from 2000 to 2023 was conducted, summarizing the associated diseases and pathogens in GS patients. Regular immunoglobulin monitoring in thymoma patients is essential for early GS diagnosis. When empirical antimicrobial therapy fails, mNGS for pathogen detection and targeted therapy are crucial, and regular IVIG injections can reduce infection rates in GS patients.

Secure Extraction of Personal Information from EHR by Federated Machine Learning.

Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges within the context of French EHRs. By utilizing a multilingual BERT model in an FL simulation involving 20 hospitals, each represented by a unique medical department or pole, we compared the performance of two setups: individual models, where each hospital uses only its own training and validation data without engaging in the FL process, and federated models, where multiple hospitals collaborate to train a global FL model. Our findings demonstrate that FL models not only preserve data confidentiality but also outperform the individual models. In fact, the Global FL model achieved an F1 score of 75,7%, slightly comparable to that of the Centralized approach at 78,5%. This research underscores the potential of FL in extracting PIIs from EHRs, encouraging its broader adoption in health data analysis.