An analysis of demographic, socio-economic and clinical variables for mental illness and mental illness relapse in south-east nigeria.

Mental health has come into the front burner of development challenges amid declining socio-economic outcomes, especially in developing countries. Insecurity, conflicts and extremely threatening events, which trigger mental disorders, are common in South-East Nigeria, with many military operations, unknown gunmen attacks and kidnapping leading to the cold-blood assassination and kidnap of dear ones. The study aimed to analyze the demographic, socio-economic and clinical variables for mental illness in South-East Nigeria. Consecutive sampling technique was used to obtain a three-year records of the cases of 380 index and 180 post-index points data of mental illness cases that met the inclusion criteria in a purposively selected public mental health hospital. Data were analyzed using both descriptive and inferential statistics, including frequency count, percentages, chi-square and multinomial logistic regression. Results showed that over a half (55.0%) of the patients were males, and 65.27% had post-primary education. The mean age of patients was 39.87 years. Slightly above half of the cases (55.26%) were single and 56.84% were unemployed. Schizophrenia (68.42%) topped the list of the cases. The rate of relapse was 52.1%, with males being in the majority (61.62%). About 61.62% of those who relapsed were unemployed, 9.60% were into business/trading, and 5.05% were professionals. Those with 5 years and above illness duration had a higher percentage of relapse (52.02%) as well as those with poor drug compliance (66.16%). The mean relapse age was 34.23 years. Educational status, employment status/social class, marital status, and age were socio-economic/clinical factors that associated strongly with relapse at p-value of 0.10. Patients with ≥ 5 and ≤ 3 years duration of illness were 1.17 times on the average more likely to have relapse than those with three or less than 3 years. Onset age for illness predicted 2.479 times more likelihood of relapse. Being employed and having more family support, as against being unskilled/unemployed and not having family support, reduce the likelihood of relapse by 1.110 times on the average. The study recommended policy formulation and implementation for protection against mental illness and mental healthcare provision and access; for tackling unemployment head-on; for improvement of the access to effective treatment of mental illness; and mass education, especially among unemployed, uneducated, singles, and under-35 years of age, to help reduce the high rate of relapse of mental illness in South-East Nigeria.

Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review.

Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.

CatLet score and clinical CatLet score as predictors of long-term outcomes in patients with acute myocardial infarction presenting later than 12 hours from symptom onset.

BACKGROUND: Our recently developed Coronary Artery Tree description and Lesion EvaluaTion (CatLet) angiographic scoring system is unique in its description of the variability in the coronary anatomy, the degree of stenosis of a diseased coronary artery, and its subtended myocardial territory, and can be utilized to predict clinical outcomes for patients with acute myocardial infarction (AMI) presenting ≤12 h after symptom onset. The current study aimed to assess whether the Clinical CatLet score (CCS), as compared with CatLet score (CS), better predicted clinical outcomes for AMI patients presenting >12 h after symptom onset. METHODS: CS was calculated in 1018 consecutive AMI patients enrolled in a retrospective registry. CCS was calculated by multiplying CS by the ACEF I score (age, creatinine, and left ventricular ejection fraction). Primary endpoint was major adverse cardiac events (MACEs) at 4-year-follow-up, a composite of cardiac death, myocardial infarction, and ischemia-driven revascularization. RESULTS: Over a 4-year follow-up period, both scores were independent predictors of clinical outcomes after adjustment for a broad spectrum of risk factors. Areas-under-the-curve (AUCs) for CS and CCS were 0.72(0.68-0.75) and 0.75(0.71-0.78) for MACEs; 0.68(0.63-0.73) and 0.78(0.74-0.83) for all-cause death; 0.73(0.68-0.79) and 0.83(0.79-0.88) for cardiac death; and 0.69(0.64-0.73) and 0.75(0.7-0.79) for myocardial infarction; and 0.66(0.61-0.7) and 0.63(0.58-0.68) for revascularization, respectively. CCS performed better than CS in terms of the above-mentioned outcome predictions, as confirmed by the net reclassification and integrated discrimination indices. CONCLUSIONS: CCS was better than CS to be able to risk-stratify long-term outcomes in AMI patients presenting >12 h after symptom onset. These findings have indicated that both anatomic and clinical variables should be considered in decision-making on management of patients with AMI presenting later.

Develop a radiomics-based machine learning model to predict the stone-free rate post-percutaneous nephrolithotomy.

Radiomics and machine learning have been extensively utilized in the realm of urinary stones, particularly in forecasting stone treatment outcomes. The objective of this study was to integrate clinical variables and radiomic features to develop a machine learning model for predicting the stone-free rate (SFR) following percutaneous nephrolithotomy (PCNL). A total of 212 eligible patients who underwent PCNL surgery at the Second Affiliated Hospital of Nanchang University were included in a retrospective analysis. Preoperative clinical variables and non-contrast-enhanced CT images of all patients were collected, and radiomic features were extracted after delineating the stone ROI. Univariate analysis was conducted to identify clinical variables strongly correlated with the stone-free rate after PCNL, and the least absolute shrinkage and selection operator algorithm (lasso regression) was utilized to screen radiomic features. Four supervised machine learning algorithms, including Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Decision Tree (GBDT), were employed. The clinical variables with strong correlation and screened radiomic features were integrated into the four machine learning algorithms to construct a prediction model, and the receiver operating curve was plotted. The area under the receiver operating curve (AUC), the accuracy rate, the specificity, etc., were used to evaluate the predictive performance of the four models. After analyzing postoperative statistics, the stone-free rate following the procedure was found to be 70.3% (n = 149). Among the various clinical variables examined, factors, such as stone number, stone diameter, stone CT value, stone location, and history of stone surgery, were identified as statistically significant in relation to the stone-free rate after PCNL. A total of 121 radiomic features were extracted, and through lasso regression, 7 features most closely associated with the stone-free rate post-PCNL were identified. The predictive accuracy of different models (Logistic Regression, RF, XGBoost, and GBDT) for determining the stone-free rate after PCNL was evaluated, yielding accuracies of 78.1%, 76.6%, 75.0%, and 73.4%, respectively. The corresponding area under the curve AUC (95%CI) were 0.85 (0.83-0.89), 0.81 (0.76-0.85), 0.82 (0.78-0.85), and 0.77 (0.73-0.81), positioning these models among the top performers in logistic regression prediction. In terms of predictive importance scores, the key factors identified by the logistic regression model were number of stone, zone percentage, stone diameter, and surface area. Similarly, the RF model highlighted number of stone, stone CT value, stone diameter, and surface area as the top predictors. Among the four machine learning models, the logistic regression model demonstrated the highest accuracy and discrimination ability in predicting the stone-free rate following PCNL. In comparison to XGBoost and GBDT, RF also exhibited superior accuracy and a certain level of discrimination ability. However, based on the performance of all four models, logistic regression is more likely to aid in clinical decision-making by assisting clinicians in diagnosing PCNL in patients. This enables us to effectively predict the presence of residual stones post-surgery and ultimately select patients who are suitable candidates for PCNL.

Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients.

Background: Vascular calcification (VC) commonly occurs and seriously increases the risk of cardiovascular events and mortality in patients with hemodialysis. For optimizing individual management, we will develop a diagnostic multivariable prediction model for evaluating the probability of VC. Methods: The study was conducted in four steps. First, identification of miRNAs regulating osteogenic differentiation of vascular smooth muscle cells (VSMCs) in calcified condition. Second, observing the role of miR-129-3p on VC in vitro and the association between circulating miR-129-3p and VC in hemodialysis patients. Third, collecting all indicators related to VC as candidate variables, screening predictors from the candidate variables by Lasso regression, developing the prediction model by logistic regression and showing it as a nomogram in training cohort. Last, verifying predictive performance of the model in validation cohort. Results: In cell experiments, miR-129-3p was found to attenuate vascular calcification, and in human, serum miR-129-3p exhibited a negative correlation with vascular calcification, suggesting that miR-129-3p could be one of the candidate predictor variables. Regression analysis demonstrated that miR-129-3p, age, dialysis duration and smoking were valid factors to establish the prediction model and nomogram for VC. The area under receiver operating characteristic curve of the model was 0.8698. The calibration curve showed that predicted probability of the model was in good agreement with actual probability and decision curve analysis indicated better net benefit of the model. Furthermore, internal validation through bootstrap process and external validation by another independent cohort confirmed the stability of the model. Conclusion: We build a diagnostic prediction model and present it as an intuitive tool based on miR-129-3p and clinical indicators to evaluate the probability of VC in hemodialysis patients, facilitating risk stratification and effective decision, which may be of great importance for reducing the risk of serious cardiovascular events.

Predictors of Successful First-Line Helicobacter pylori Eradication with Fluoroquinolones in Pakistan: A Prospective Exploration of Demographic and Clinical Factors.

Growing antibiotic resistance complicates H. pylori eradication, posing a public health challenge. Inconclusive research on sociodemographic and clinical factors emphasizes the necessity for further investigations. Hence, this study aims to evaluate the correlation between demographic and clinical factors and the success rates of H. pylori eradication. A group of 162 H. pylori-positive patients were allocated randomly to receive either a ten-day moxifloxacin-based triple therapy or a levofloxacin-based sequential therapy. Eradication success was determined through the stool antigen test. Logistic regression analysis was utilized to figure out potential factors that contribute to H. pylori eradication success. Significantly higher H. pylori eradication rates were observed in the middle age group (COR: 3.671, p = 0.007), among females (p = 0.035), those with BMI ≥ 25 (COR: 2.011, p = 0.045), and non-smokers (COR: 2.718, p = 0.018). In multivariate analysis, age and smoking emerged as significant predictors (p < 0.05). Patients with comorbidities, excluding diabetes and hypertension (COR: 4.432, p = 0.019), dyspepsia (COR: 0.178, p < 0.001), and moxifloxacin triple therapy (COR: 0.194, p = 0.000), exhibited higher chances of eradication (p < 0.05). Further research is vital for tailored approaches to enhance eradication success.

Early-onset neonatal sepsis: Effectiveness of classification based on ante- and intrapartum risk factors and clinical monitoring.

INTRODUCTION: In 2017, the French public health authority HAS published new guidelines for the management of newborns at risk of early bacterial neonatal infection. These guidelines were based on ante- and intrapartum risk factors and clinical monitoring. In January 2021, we implemented a new protocol based on these guidelines in our tertiary maternity unit. OBJECTIVES: To assess the impact of the protocol implemented on neonates' antibiotic prescriptions. METHOD: An "old protocol" group comprising newborns hospitalized between July 1, 2020 and December 31, 2020, was compared to a "new protocol" group formed between January 14, 2021 and July 13, 2021. Data were collected on infectious risk factors, antibiotic prescriptions, and emergency room visits within 2 weeks for an infection or suspected infection. RESULTS: The "old protocol" population comprised 1565 children and the "new protocol" population 1513. Antibiotic therapy was prescribed for 29 newborns (1.85 %) in the old protocol group versus 15 (0.99 %) in the new one (p = 0.05). The median duration was 5 days and 2 days respectively (p = 0.08). With the new protocol, newborns in category B were about 20 times more likely (p = 0.01), and those in category C about 54 times more likely (p = 0.005) to have an infection than those classified in categories N or A. CONCLUSION: This study demonstrates that clinical monitoring criteria enable reduced use and duration of antibiotic therapy and are reliable.

Clinical Variables and Peripheral Biomarkers Associated with Substance-Induced Psychotic Disorder: Differences Related to Alcohol, Cannabis, and Psychostimulant Abuse.

Background: The present retrospective observational study aims to identify differences in clinical features and peripheral biomarkers among patients affected by substance-induced psychotic disorder (SIPD) according to the primary substance of abuse. Methods: A sample of 218 patients was divided into three groups according to the type of consumed substance: alcohol, cannabis, and psychostimulants. The three groups were compared using one-way analyses of variance (ANOVAs) for continuous variables and χ2 tests for qualitative variables. After excluding the alcohol-induced psychotic disorder group, the same analyses were repeated. The statistically significant variables from these subsequent analyses were included in a binary logistic regression model to confirm their reliability as predictors of cannabis- or psychostimulant-induced psychotic disorder. Results: Psychotic cannabis abusers were younger (p < 0.01), with illness onset at an earlier age (p < 0.01). Alcohol consumers presented a longer duration of illness (p < 0.01), more frequent previous hospitalizations (p = 0.04) and medical comorbidities (p < 0.01), and higher mean Modified Sad Persons Scale scores (p < 0.01). Finally, psychostimulant abusers had a higher frequency of lifetime history of poly-substance use disorders (p < 0.01). A binary logistic regression analysis revealed that higher mean Brief Psychiatric Rating Scale scores (p < 0.01) and higher sodium (p = 0.012) and hemoglobin (p = 0.040) plasma levels were predictors of cannabis misuse in SIPD patients. Conclusions: Different clinical factors and biochemical parameters con be associated with SIPD according to the main substance of abuse, thus requiring specific management by clinicians.

Health-related quality of life, anxiety, depression, beliefs of medication, and self-efficacy in individuals with severe asthma - a population-based study.

OBJECTIVE: Individuals with severe asthma often report poor Health-related quality of life (HRQoL) and more research is essential to increase understanding of how they may be helped to improve HRQoL. The main aim of the current paper is to evaluate HRQoL, and possible factors influencing HRQoL, in individuals with severe asthma. The aim is also to explore associations among anxiety, depression, beliefs of medication, self-efficacy, and HRQoL among individuals with severe and other asthma as well as those with no asthma. METHODS: Participants with severe asthma (n = 59), other asthma (n = 526), and no asthma (n = 902) were recruited from West Sweden Asthma Study, a population-based study, which includes both questionnaire surveys and clinical examinations. RESULTS: Individuals with severe asthma had worse physical HRQoL (measured with SF-8) than those with other and no asthma (median 48.4, 51.9, and 54.3, respectively). They also had worse mental HRQoL (median 46.7) and reported higher anxiety and depression scores (measured using HADS, median 5.0 and 3.5, respectively) compared to no asthma (median 4.0 and 2.0, respectively). HRQoL was particularly affected among women with severe asthma. Individuals with severe asthma believed that their asthma medication was more necessary than those with other asthma, but they reported more concern for the medication. Asthma control and packyears predicted physical HRQoL and anxiety predicted mental HRQoL among individuals with severe asthma. CONCLUSIONS: Efforts to improve asthma control and to reduce anxiety may improve HRQoL in individuals with severe asthma. Especially, women with severe asthma seem to need support to improve their HRQoL. Reducing concerns with asthma medication is most likely essential as high concerns may lead to poor adherence, which in turn may negatively affect asthma control and HRQoL.

Proteomic prediction of incident heart failure and its main subtypes.

AIM: To examine the ability of serum proteins in predicting future heart failure (HF) events, including HF with reduced or preserved ejection fraction (HFrEF or HFpEF), in relation to event time, and with or without considering established HF-associated clinical variables. METHODS AND RESULTS: In the prospective population-based Age, Gene/Environment Susceptibility Reykjavik Study (AGES-RS), 440 individuals developed HF after their first visit with a median follow-up of 5.45 years. Among them, 167 were diagnosed with HFrEF and 188 with HFpEF. A least absolute shrinkage and selection operator regression model with non-parametric bootstrap were used to select predictors from an analysis of 4782 serum proteins, and several pre-established clinical parameters linked to HF. A subset of 8-10 distinct or overlapping serum proteins predicted different future HF outcomes, and C-statistics were used to assess discrimination, revealing proteins combined with a C-index of 0.80 for all incident HF, 0.78 and 0.80 for incident HFpEF or HFrEF, respectively. In the AGES-RS, protein panels alone encompassed the risk contained in the clinical information and improved the performance characteristics of prediction models based on N-terminal pro-B-type natriuretic peptide and clinical risk factors. Finally, the protein predictors performed particularly well close to the time of an HF event, an outcome that was replicated in the Cardiovascular Health Study. CONCLUSION: A small number of circulating proteins accurately predicted future HF in the AGES-RS cohort of older adults, and they alone encompass the risk information found in a collection of clinical data. Incident HF events were predicted up to 8 years, with predictor performance significantly improving for events occurring less than 1 year ahead, a finding replicated in an external cohort study.

Selective adaptation of SARS-CoV-2 Omicron under booster vaccine pressure: a multicentre observational study.

BACKGROUND: High rates of vaccination and natural infection drive immunity and redirect selective viral adaptation. Updated boosters are installed to cope with drifted viruses, yet data on adaptive evolution under increasing immune pressure in a real-world situation are lacking. METHODS: Cross-sectional study to characterise SARS-CoV-2 mutational dynamics and selective adaptation over >1 year in relation to vaccine status, viral phylogenetics, and associated clinical and demographic variables. FINDINGS: The study of >5400 SARS-CoV-2 infections between July 2021 and August 2022 in metropolitan New York portrayed the evolutionary transition from Delta to Omicron BA.1-BA.5 variants. Booster vaccinations were implemented during the Delta wave, yet booster breakthrough infections and SARS-CoV-2 re-infections were almost exclusive to Omicron. In adjusted logistic regression analyses, BA.1, BA.2, and BA.5 had a significant growth advantage over co-occurring lineages in the boosted population, unlike BA.2.12.1 or BA.4. Selection pressure by booster shots translated into diffuse adaptive evolution in Delta spike, contrasting with strong, receptor-binding motif-focused adaptive evolution in BA.2-BA.5 spike (Fisher Exact tests; non-synonymous/synonymous mutation rates per site). Convergent evolution has become common in Omicron, engaging spike positions crucial for immune escape, receptor binding, or cleavage. INTERPRETATION: Booster shots are required to cope with gaps in immunity. Their discriminative immune pressure contributes to their effectiveness but also requires monitoring of selective viral adaptation processes. Omicron BA.2 and BA.5 had a selective advantage under booster vaccination pressure, contributing to the evolution of BA.2 and BA.5 sublineages and recombinant forms that predominate in 2023. FUNDING: The study was supported by NYU institutional funds and partly by the Cancer Center Support Grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center.

Are There Any Differences in Clinical and Biochemical Variables between Bipolar Patients with or without Lifetime Psychotic Symptoms?

INTRODUCTION: Psychotic symptoms occur in more than half of patients affected by Bipolar Disorder (BD) and are associated with an unfavorable course of the disorder. The objective of this study is to identify the differences in the clinical and biochemical parameters between bipolar patients with or without psychotic symptoms. METHODS: A total of 665 inpatients were recruited. Demographic, clinical, and biochemical data related to the first day of hospitalization were obtained via a screening of the clinical charts and intranet hospital applications. The two groups identified via the lifetime presence of psychotic symptoms were compared using t tests for quantitative variables and χ2 tests for qualitative ones; binary logistic regression models were subsequently performed. RESULTS: Patients with psychotic BD (compared to non-psychotic ones) showed a longer duration of hospitalization (p < 0.001), higher Young Mania Rating Scale scores (p < 0.001), lower Global Assessment of Functioning scores (p = 0.002), a less frequent history of lifetime suicide attempts (p = 0.019), less achievement of remission during the current hospitalization (p = 0.028), and a higher Neutrophile to Lymphocyte Ratio (NLR) (p = 0.006), but lower total cholesterol (p = 0.018) and triglycerides (p = 0.013). CONCLUSIONS: Patients with psychotic BD have a different clinical and biochemical profile compared to their counterparts, characterized by more clinical severity, fewer metabolic alterations, and a higher grade of inflammation. Further multi-center studies have to confirm the results of this present study.

The Clinical Variables Predicting the Acquisition of Independent Ambulation in the Acute Phase of Stroke: A Retrospective Study.

BACKGROUND: Predictive factors associated with independent ambulation post-stroke are less commonly reported for patients during the acute phase of stroke. This study aimed to identify the clinical variables predicting ambulation independence in the acute phase of stroke and test the superiority of their prediction accuracy. METHODS: Sixty-nine patients, hospitalized in the acute phase for an initial unilateral, supratentorial stroke lesion, were divided into independent (n = 24) and dependent ambulation (n = 45) groups, with functional ambulation category scores of 4-5 and ≤ 3, respectively. They were evaluated upon admission using the modified Rankin scale (mRS), Stroke Impairment Assessment Set (SIAS) concerning the motor function of the lower extremities, Ability for Basic Movement Scale modified version 2 (ABMS2), and Functional Independence Measure (FIM). RESULTS: The scores of the four measures were significantly different between the groups. A univariate logistic regression analysis identified these variables as prognostic factors for independent ambulation. A receiver operating characteristic curve analysis identified the cutoff values (area under the curve) for the mRS, SIAS, FIM, and ABMS2 as 3 (0.74), 12 (0.73), 55 (0.85), and 23 (0.84), respectively. CONCLUSION: In summary, the FIM and ABMS2 may be more accurate in predicting ambulation independence in patients with stroke during the acute phase.

Relationships between Clinical and Non-Clinical Variables concerning Traumatic Dental Injuries in Deciduous Teeth Attended in a Children's Hospital.

This study aimed to describe Traumatic Dental Injuries (TDI) in a child population, with a discussion focused on the impact of non-clinical variables on TDI. A cross-sectional, descriptive, and relational study about TDI in deciduous dentition in a children's hospital was performed. A total of 166 patients were included, of which 51.8% were male and 48.2% were female. Subluxation was the most observed injury (37.5%), and high-severity lesions predominated (60.2%). Regarding non-clinical variables, 89.2% of the patients attended urgent care centers within 24 h, and 43.4% within the first 3 h. Pointed objects were the leading cause of TDI (47%). Most TDIs were concentrated between the ages of 2 and 4 (53.5%). Concerning the place of TDI occurrence, the school (41.6%) was associated with faster urgent dental care attendance, and the home (37.3%) was associated with TDI occurrence in children under 2 years of age. Previous TDI experience (24.1% of patients) did not generate differences in the time interval between the TDI and arrival at the hospital, compared with children without a TDI history. While the behavior of clinical variables agrees with the literature reviewed, several non-clinical variables show wide differences. There is a need to identify the non-clinical variables that can significantly interact with phenomena specific to the study population (social, demographic, and cultural). The study of these variables can be useful in applying health policies. In the group studied, the non-clinical data reveals the need to educate parents or guardians on the importance of timely care in TDI, the long-term consequences of traumatism affecting deciduous dentition, and the implication of the maturation of the child's motor skills in TDI.

Clinical correlates of pain in adults with hard-to-heal leg ulcers: a cross-sectional study.

OBJECTIVE: Pain is a complex symptom associated with hard-to-heal (chronic) leg ulcers that is often poorly managed. The objective of this study was to gain greater understanding by investigating relationships between physical and psychosocial factors, and pain severity in adults with hard-to-heal leg ulcers. METHOD: A secondary analysis of data collected for a longitudinal, observational study of adults with hard-to-heal leg ulcers was undertaken. Data were collected over a 24-week period, including variables relating to sociodemographics, clinical variables, medical status, health, ulcer and vascular histories, and psychosocial measures. Multiple linear regression modelling was used to determine the independent influences of these variables on pain severity, as measured with a Numerical Rating Scale (NRS). RESULTS: Of 142 participants who were recruited, 109 met the inclusion criteria for this study, of whom: 43.1% had venous ulcers; 41.3% had mixed ulcers; 7.3% had arterial ulcers; and 8.3% had ulcers from some other cause. The final model explained 37% (adjusted r2=0.370) of the variation in the pain NRS scores. Controlling for analgesic use, salbutamol use (p=0.005), clinical signs of infection (p=0.027) and ulcer severity (p=0.001) were significantly associated with increased pain, while the presence of diabetes (p=0.007) was significantly associated with a decrease in pain. CONCLUSION: Pain is a highly complex and pervasive symptom associated with hard-to-heal leg ulcers. Novel variables were identified as being associated with pain in this population. The model also included wound type as a variable; however, despite being significantly correlated to pain at the bivariate level of analysis, in the final model, the variable did not reach significance. Of the variables included in the model, salbutamol use was the second most significant. This is a unique finding that, to the authors' knowledge, has not been previously reported or studied. Further research is required to better understand these findings and pain in general.

DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels.

The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, highlighting the need for accurate and timely risk prediction models that can prioritize patient care and allocate resources effectively. This study presents DeepCOVID-Fuse, a deep learning fusion model that predicts risk levels in patients with confirmed COVID-19 by combining chest radiographs (CXRs) and clinical variables. The study collected initial CXRs, clinical variables, and outcomes (i.e., mortality, intubation, hospital length of stay, Intensive care units (ICU) admission) from February to April 2020, with risk levels determined by the outcomes. The fusion model was trained on 1657 patients (Age: 58.30 ± 17.74; Female: 807) and validated on 428 patients (56.41 ± 17.03; 190) from the local healthcare system and tested on 439 patients (56.51 ± 17.78; 205) from a different holdout hospital. The performance of well-trained fusion models on full or partial modalities was compared using DeLong and McNemar tests. Results show that DeepCOVID-Fuse significantly (p < 0.05) outperformed models trained only on CXRs or clinical variables, with an accuracy of 0.658 and an area under the receiver operating characteristic curve (AUC) of 0.842. The fusion model achieves good outcome predictions even when only one of the modalities is used in testing, demonstrating its ability to learn better feature representations across different modalities during training.

Nomogram for Early Prediction of Parkinson's Disease Based on microRNA Profiles and Clinical Variables.

BACKGROUND: Few efficient and simple models for the early prediction of Parkinson's disease (PD) exists. OBJECTIVE: To develop and validate a novel nomogram for early identification of PD by incorporating microRNA (miRNA) expression profiles and clinical indicators. METHODS: Expression levels of blood-based miRNAs and clinical variables from 1,284 individuals were downloaded from the Parkinson's Progression Marker Initiative database on June 1, 2022. Initially, the generalized estimating equation was used to screen candidate biomarkers of PD progression in the discovery phase. Then, the elastic net model was utilized for variable selection and a logistics regression model was constructed to establish a nomogram. Additionally, the receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were utilized to evaluate the performance of the nomogram. RESULTS: An accurate and externally validated nomogram was constructed for predicting prodromal and early PD. The nomogram is easy to utilize in a clinical setting since it consists of age, gender, education level, and transcriptional score (calculated by 10 miRNA profiles). Compared with the independent clinical model or 10 miRNA panel separately, the nomogram was reliable and satisfactory because the area under the ROC curve achieved 0.72 (95% confidence interval, 0.68-0.77) and obtained a superior clinical net benefit in DCA based on external datasets. Moreover, calibration curves also revealed its excellent prediction power. CONCLUSION: The constructed nomogram has potential for large-scale early screening of PD based upon its utility and precision.

Deep Survival Analysis With Clinical Variables for COVID-19.

OBJECTIVE: Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients. METHODS AND PROCEDURES: We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups. RESULTS: Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19. CONCLUSION: Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner. CLINICAL IMPACT: The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.

A CT-based radiomics approach to predict immediate response of radiofrequency ablation in colorectal cancer lung metastases.

Objectives: To objectively and accurately assess the immediate efficacy of radiofrequency ablation (RFA) on colorectal cancer (CRC) lung metastases, the novel multimodal data fusion model based on radiomics features and clinical variables was developed. Methods: This case-control single-center retrospective study included 479 lung metastases treated with RFA in 198 CRC patients. Clinical and radiological data before and intraoperative computed tomography (CT) scans were retrieved. The relative radiomics features were extracted from pre- and immediate post-RFA CT scans by maximum relevance and minimum redundancy algorithm (MRMRA). The Gaussian mixture model (GMM) was used to divide the data of the training dataset and testing dataset. In the process of modeling in the training set, radiomics model, clinical model and fusion model were built based on a random forest classifier. Finally, verification was carried out on an independent test dataset. The receiver operating characteristic curves (ROC) were drawn based on the obtained predicted scores, and the corresponding area under ROC curve (AUC), accuracy, sensitivity, and specificity were calculated and compared. Results: Among the 479 pulmonary metastases, 379 had complete response (CR) ablation and 100 had incomplete response ablation. Three hundred eighty-six lesions were selected to construct a training dataset and 93 lesions to construct a testing dataset. The multivariate logistic regression analysis revealed cancer antigen 19-9 (CA19-9, p<0.001) and the location of the metastases (p< 0.05) as independent risk factors. Significant correlations were observed between complete ablation and 9 radiomics features. The best prediction performance was achieved with the proposed multimodal data fusion model integrating radiomic features and clinical variables with the highest accuracy (82.6%), AUC value (0.921), sensitivity (80.3%), and specificity (81.4%). Conclusion: This novel multimodal data fusion model was demonstrated efficient for immediate efficacy evaluation after RFA for CRC lung metastases, which could benefit necessary complementary treatment.

Impact of traumatic life events on clinical variables of individuals with first-episode psychosis and healthy controls.

BACKGROUND: Traumatic life events (TLEs) are one of the most robust environmental risk factors for the onset of first-episode psychosis (FEP). AIMS: To explore TLEs in FEP patients and healthy controls (HC), to analyze gender differences and to examine whether TLEs were associated with sociodemographic, clinical and psychofunctional variables in all FEP sample and split by age. METHODS: Descriptive and cross-sectional study. Three hundred and thirty-five FEP and 253 HC were recruited at 16 Spanish mental health research centers. The Traumatic Experiences in Psychiatric Outpatients Questionnaire was administered. RESULTS: We found a higher number of TLEs in FEP than in HC, and the proportion of individuals with three or more TLEs was significantly higher in the FEP group. No differences were found in terms of gender and age. There was no relationship between total number of TLEs and psychotic symptomatology and functional outcomes. CONCLUSIONS: The number and cumulative TLEs should be taken into account in the detection, epidemiology and process of recovery in FEP.