Acta Informatica Medica最新文献

Background: Aortic Lower Urinary Tract Symptoms (LUTS) refer to a range of urinary symptoms affecting both men and women. These symptoms include increased frequency of urination, urgency, weak urine flow, incomplete emptying of the bladder, and nocturia.

Objective: LUTS encompass various urinary issues, with emerging evidence suggesting a potential connection to COVID-19. This study aimed to evaluate the occurrence and long-term effects of COVID-19 on LUTS and IPSS across different infection severities.

Methods: The prospective observational study enrolled 85 COVID-19 patients. IPSS was used to assess LUTS before, during, and 3 and 6 months after infection. Data collection included demographics and clinical characteristics, with follow-up assessments of all participants, highlighting significant differences between severity groups through statistical analysis.

Results: The highest IPSS values occurred during the acute phase of infection in all groups. Patients with severe or critical COVID-19 exhibited the highest IPSS, while asymptomatic or mild cases had the lowest scores. IPSS scores showed a significant association across all phases in patients with a history of urological disorders compared to those without. Age was positively correlated with IPSS before and during infection. Long-term bladder dysfunction associated with COVID-19 may cause persistent LUTS in patients with mild cases.

Conclusion: A significant association exists between COVID-19 and LUTS, indicating that LUTS should be considered in COVID-19 cases. Monitoring LUTS is recommended throughout COVID-19 and during recovery. However, pre-infection LUTS were assessed retrospectively through recall of IPSS scores, which may be subject to recall bias and could affect the accuracy of pre- and post-infection comparisons. This study is among the first to provide a 6-month longitudinal follow-up with predictors such as age and urological history. Limitations include the lack of control for confounding factors and potential recall bias, though the within-subject repeated design enhances the strength of the results.

Background: Treatment of Hori's nevus can help reduce psychological burden, restore normal skin, improve aesthetics, and quality of life for patients. However, studies related to the morphological characteristics and effectiveness of Hori's nevus treatment are still limited in Vietnam.

Objective: The objective of this study is to investigate some factors related to the degree of pigmentation of Hori's nevus and evaluate the results of Hori's nevus treatment with Pico Nd:YAG laser and Q-switched Nd:YAG laser.

Methods: A descriptive study of a series of cases was conducted on patients diagnosed with Hori's nevus who visited Can Tho Dermatology Hospital and FOB International Dermatology and Aesthetic Research Institute, Can Tho City, Vietnam, from March 2021 to June 2023.

Results: The study results showed that 53.7% of patients with Hori's nevus had grade III hyperpigmentation; The predominant color was blue-brown (46.3%); 100% of patients had lesions on both cheeks. Older age (p=0.008), frequent sun exposure (p=0.001), non-using sunscreen (p=0.031), and not wearing masks (p<0.001) were statistically significantly associated with greater pigmentation of Hori's nevus. In the Pico Nd:YAG laser treatment group, the lesion color score decreased from 26.62±1.9 before treatment to 21.9±2.6 after 7-10 treatments (p<0.001). In the Q-switched Nd:YAG laser treatment group, the lesion color score decreased from 27.29±1.3 before treatment to 22.13±2.3 after 7-10 treatments (p<0.001).

Conclusion: The study showed that Hori's nevus is common in people over 30 years old and often exposed to sunlight. Two laser methods have been effectively applied in treatment. Measures such as protecting the skin from the sun and using sunscreen and masks can prevent or reduce the progression of Hori's nevus. These two methods can be applied in Vietnam depending on the conditions of the medical facility.

Background: Barlow's disease is characterized by excess myxomatous degenerative tissue, leaflets prolapse and/or billowing, chordal-elongation, and annular dilation. Various mitral valve repair techniques are in use. Resection techniques were conventionally performed. Non-resection techniques have been popularized; however, their efficacy in Barlow's disease is yet unclear.

Objective: This study aimed to evaluate the early outcomes of minimally invasive mitral valve repair with non-resection approach compared to resection approach in patients with Barlow's disease.

Methods: We reviewed our experience in minimally invasive mitral valve repair non-resection techniques to treat this complex mitral pathology. Between 2020 and 2024 a group of 61 patients was identified as Barlow's disease undergoing minimally invasive mitral valve repair. The diagnosis of Barlow disease was based upon preoperative echocardiography and confirmed by the surgeon's assessment during operation. Data were collected at the Medical University Center (UMC) in Ho Chi Minh City.

Results: A total of 61 patients met the inclusion criteria. Successful valve repair was achieved in 57 patients (93.4%). Among the 57 patients who underwent repair, 26 patients (45.6%) received mitral valve repair using the non-resection approach, while 31 patients (54.4%) were treated with the resection approach. The mean cardiopulmonary bypass time and aortic cross-clamp time were 146.1 ± 23.8 minutes and 84.6 ± 15.9 minutes, respectively. During the 30-day postoperative follow-up, no mortality or SAM (systolic anterior motion) complications were observed, with one case of moderate mitral regurgitation post-surgery. Additionally, no reoperations were required during the follow-up period.

Conclusion: Minimally invasive Barlow mitral valve repair has favorable 30-day postoperative outcomes. The non-resection approach is feasible and as safe as the resection approach.

Background: Nevus spilus (NS) is a congenital melanocytic nevus with specific clinical characteristics. The condition generally appears in the first year of life and shows no gender preference while affecting individuals with lighter skin tones. It has a predilection for lower limbs and trunk areas the NS lesions consist of tiny heavily pigmented papules and macules accumulated on a well-bordered, lighter brown macular backdrop, comparable to a café-au-lait macule, frequently along Blaschko lines. The type of nevus has been reported as a blue nevus, centrofacial lentiginosis, nevus sebaceous, and bilateral nevus of Ito.

Objective: To highlight laser options for nevus spilus.

Methods: A review of various laser-based treatment modalities was conducted, focusing on their application in NS. The lasers included Q-switched ruby (QSRL), alexandrite (QSAL), Nd:YAG 532 and 1064 nm, carbon dioxide (CO2), intense pulsed light (IPL), and pulsed dye laser (PDL).

Results and discussion: The laser modalities have shown varying degrees of success in improving the appearance of NS lesions. The QSRL, QSAL and QSNYL lasers are commonly used because they are specific for melanin. Ablative lasers like CO2 and broad-spectrum devices like IPL and PDL have also been employed, each offering different advantages depending on lesion characteristics and treatment goals.

Conclusion: Regular monitoring for suspicious malignant degeneration should be done and if any signs of melanoma are present, histological examination is warranted. While no treatment is usually necessary, surgery has been proposed to eliminate the risk of melanoma. Aside from surgical management, various lasers have been tried to treat NS with varied success. QSRL, QSAL, QSNYL 532, and 1064 nm, CO2, IPL and PDL have all been demonstrated to improve NS.

Background: Hypoalbuminemia is a poor clinical indicator of mortality. However, data on the correlation between hypoalbuminemia and inflammatory markers in COVID-19 infections are scarce.

Objective: The study aims to investigate the effects of hypoalbuminemia on inflammatory markers, serum creatinine, hemoglobin levels, and clinical outcomes in critically ill COVID-19 patients.

Methods: A retrospective review of all adult patients admitted to the intensive care unit (ICU) in King Fahd University Hospital with a moderate to severe COVID-19 infection from January 2020 to August 2022 was performed. The value of serum albumin levels was obtained on the day of admission while, the serum ferritin, lactate dehydrogenase (LDH), C-reactive protein (CRP), creatinine, and hemoglobin levels on the first and third days of ICU admission. Demographic data, oxygen support modality, ICU length of stay (ICU-LOS), renal replacement therapy (RRT), and deaths were collected.

Results: Of 420 patients, 255 were included, 131 (51.4%) of whom had hypoalbuminemia. The numbers of patients with hypoalbuminemia who had increases in ferritin, CRP, and LDH between the first and third days of admission were 65 (49.6%), 9 (6.9%), and 10 (7.6%), respectively. In contrast, to the cohort with normal albumin levels, increases in ferritin, CRP, and LDH were noted in 57 (46%), 7 (5.6%), and 14 (11.3%) patients, respectively. The decrease in hemoglobin levels was similar between both groups (p=0.1). No significant association was found between hypoalbuminemia and increases in serum creatinine, the requirement of RRT, the need for endotracheal intubation, and mortality.

Conclusion: This study did not show a significant correlation between hypoalbuminemia and worsening inflammatory markers or clinical outcomes in critically ill COVID-19 patients.

Background: Triage is a critical component of Emergency department care. Erroneous patient classification and mis-triaging are common in present triage systems worldwide. Therefore, several institutes worldwide have developed artificial intelligence-based algorithms that use machine learning approaches to sort and triage patients effectively.

Objective: This study aims were to propose a machine learning model to predict the triage level for emergency medicine department patients and compare its performance to the standard nursing triage system.

Methods: This retrospective pilot study collected the dataset of emergency department records from King Fahad Hospital of the University in khobar, between January 1, 2020, and December 31, 2022. A sample of 998 randomly selected patients was included in this cohort. The machine learning model was trained using 10-fold cross-validation. Two experiments were conducted, including five triage levels, and the second combing triage levels 2, 3, 4, and 5.

Results: The machine learning model achieved an accuracy of 84% in experiment 1 and 64% in experiment 2. The mis-triage rates of the machine learning model were significantly lower than those of the standard nursing triage system.

Conclusion: The machine learning model achieved higher accuracy and lower mis-triage rates than the standard nursing triage system. Thus, the proposed machine learning model can be a helpful tool for emergency department triage, enabling more efficient and accurate patient management.

Background: Diabetes mellitus (DM) is highly prevalent and often remains undiagnosed until complications appear, especially in low- and middle-income countries. Simple tools that use routinely collected clinical and demographic variables may support earlier identification of individuals at increased risk.

Objective: This study aimed to build a supervised achine-learning model to classify individuals as diabetic or non-diabetic using a large publicly available dataset, and to identify which variables contributed most to the model decisions.

Methods: We analysed a cleaned subset of 89,540 records from a Kaggle diabetes dataset. A multilayer perceptron artificial neural network (ANN) was trained and tested on separate subsets. Model performance was evaluated by overall accuracy and misclassification rates, and post-hoc variable importance scores were used to summarise the contribution of each predictor.

Results: The ANN achieved an overall prediction accuracy of 96.8% in both the training and testing samples. Most records were correctly classified, although the error pattern suggested that non-diabetic cases were recognised more easily than diabetic cases. Blood glucose, HbA1c and body mass index (BMI) showed the highest importance values, whereas demographic and lifestyle variables contributed less to the classification.

Conclusion: In this dataset, an ANN based on simple clinical and demographic variables was able to distinguish between diabetic and non-diabetic records with high internal accuracy and a plausible pattern of variable importance. The model could form the basis for a practical screening aid, but it requires external validation and further work on handling class imbalance and explainability before use in routine care.

Background: In everyday oncology practice, routine blood tests and a small panel of tumour markers are almost always requested when a patient is being worked up for possible malignancy. With the rise of machine-learning methods, it is tempting to treat these familiar measurements as raw material for automated cancer prediction. In reality, it is not clear how far such data can help once a patient has already entered a specialist pathway, and careless modelling may easily introduce target leakage.

Objective: We set out to explore, in a straightforward way, how cancer diagnosis (cancer vs no cancer) relates to a set of basic demographic, haematological, biochemical, and clinical variables in a cancer-enriched cohort, and to reflect on what this means for neural-network based prediction.

Methods: We undertook a secondary, analytical cross-sectional study using 1,000 cases from the Cancer Risk Stratification Using Lab Parameters dataset. Age, sex, smoking status, family history, complete blood count indices, blood glucose, CA-125, PSA, CEA, risk level, stage, treatment outcome and survival were extracted. Cancer status was coded as cancer vs no cancer. Neural network analysis was conducted.

Results: Just over four out of five patients in the cohort had a malignant diagnosis. Apart from haemoglobin, most routine laboratory and biochemical values did not differ significantly between cancer and non-cancer cases, and the raw means generally lay within conventional reference ranges in both groups. Haemoglobin was modestly but significantly lower in patients with cancer. Cancer status was, by definition, strongly linked to stage and showed a weaker relationship with survival.

Conclusion: In this referred, cancer-enriched population, familiar clinical risk factors and single routine laboratory parameters offered little additional discrimination beyond the simple presence or absence of a cancer stage. Haemoglobin behaved as a non-specific indicator of overall illness rather than a diagnostic test.

Background: The primary therapeutic approach for promptly increasing haemoglobin concentration is red blood cell transfusion. However, the risk associated with RBC transfusion and the overall accessibility exhibit significant variation.

Objective: This meta-analysis evaluates the comparison and outcome of restrictive and liberal transfusion strategies in intensive care unit.

Methods: A comprehensive search was carried out on various databases, including CENTRAL, PubMed, MEDLINE, and ScienceDirect for randomized control clinical trials evaluating the comparison of clinical outcomes of restrictive and liberal transfusion strategies in critically ill patients. The primary outcomes measure was mortality, which included ICU mortality and hospital mortality. In addition, data were pooled using random-effect models and heterogeneity was evaluated through I² statistics.

Results: Out of 15 eligible RCTs obtained, involving 12,439 patients. The result demonstrated no significant difference of restrictive red-cell transfusion strategy over liberal red-cell transfusion strategy in reducing mortality outcomes with a pooled effect size (relative risk [RR] 0.92; 95% confidence interval [CI] 0.78 - 1.08; I2 = 0%, indicating minimal to no difference. Similarly, analyses of secondary outcomes identified no significant differences in several clinical results.

Conclusion: This present meta-analysis provides evidence that restrictive red-cell transfusion strategy doesn't significantly difference in overall mortality and several secondary clinical results of critically ill patients in ICU compared to liberal red-cell transfusion strategy. Otherwise, restrictive transfusion strategy lowered RBC transfusion requirements, and was expected to be more cost-effective.

Background: An application of a novel method of a quantifier elimination for the SEIS model was presented in this paper. The appearance of the AIDS disease was crucial for developing numerous new epidemic models. We decided to analyse one of these complex models by QE method.

Objective: A main aim was to investigate the existence of the Hopf bifurcation for the SEIS model. We have also analysed one complex epidemic model appropriate for AIDS disease by QE method. We applied the SEIR model in order to analyse the early phase of COVID-19 in BiH and different regions in Italy.

Methods: The implementation of a new method for quantifier elimination for the theory of real closed fields (a method was implemented in Mathematica).

Results: The main result was that the system which describes the SEIS model does not have a Hopf bifurcation for any parameter values for the epidemiological relevant cases.

Conclusion: We applied an original implementation of QE method successfully in order to investigate the SEIS model. Considering the application of QE method to a model appropriate for AIDS disease, we were interested in change of the qualitative behaviour of a parametrized system of differential equations.