Logistic Regression Coefficients Research Articles

Development and validation of a prediction model for outcomes after transaxillary first rib resection for neurogenic thoracic outlet syndrome following strict Society for Vascular Surgery diagnostic criteria

ObjectiveNeurogenic thoracic outlet syndrome (NTOS) is the most common form of thoracic outlet syndrome. However, NTOS has remained difficult to diagnose and treat successfully. The purpose of the present study was to generate a predictive clinical calculator for postoperative outcomes after first rib resection (FRR) for NTOS. MethodsWe performed a retrospective review of patients who had undergone FRR for NTOS at a single tertiary care institution between 2016 and 2020. A multivariate stepwise logistic regression analysis was performed to assess the association of the percentage of improvement after FRR with the patient baseline characteristics, pertinent clinical characteristics, and diagnostic criteria set by the Society for Vascular Surgery. The primary outcome was subjective patient improvement after FRR. A prediction risk calculator was developed using backward stepwise multivariate logistic regression coefficients. Bootstrapping was used for internal validation. ResultsA total of 208 patients (22.2% male; mean age, 35.8 ± 12.8 years; median follow-up, 44.9 months) had undergone 243 FRRs. Of the 208 patients, 94.7% had had symptoms localized to the supraclavicular area, and 97.6% had had symptoms in the hand. All the patients had had positive symptoms reproduced by the elevated arm stress test and upper limb tension test. Another reasonably likely diagnosis was absent for all the patients. Of the 196 patients who had received a lidocaine injection, 180 (93.3%) had experienced improvement of NTOS symptoms. Of the 95 patients who had received a Botox injection, 82 (74.6%) had experienced improvement of NTOS symptoms. Receiver operating characteristic curve analysis was used to assess the model. The area under the curve for the backward stepwise multivariate logistic regression model was 0.8. The multivariate logistic regression analyses revealed that the significant predictors of worsened clinical outcomes included hand weakness (adjusted odds ratio [aOR], 4.28; 95% confidence interval [CI], 1.04-17.74), increasing age (aOR, 0.93; 95% CI, 0.88-0.99), workers’ compensation or litigation case (aOR, 0.09; 95% CI, 0.01-0.82), and symptoms in the dominant hand (aOR, 0.20; 95% CI, 0.05-0.88). ConclusionsUsing retrospective data from a single-institution database, we have developed a prediction calculator with moderate to high predictive ability, as demonstrated by an area under the curve of 0.8. The tool (available at: https://jhhntosriskcalculator.shinyapps.io/NTOS_calc/) is an important adjunct to clinical decision-making that can offer patients and providers realistic and personalized expectations of the postoperative outcome after FRR for NTOS. The findings from the present study have reinforced the diagnostic criteria set by the Society for Vascular Surgery. The calculator could aid physicians in surgical planning, referrals, and counseling patients on whether to proceed with surgery.

USE OF IFMIS IN BUDGETARY CONTROLS AND FINANCIAL ACCOUNTABILITY IN COUNTY GOVERNMENTS IN WESTERN KENYA

<p>The purpose of this study was to determine the influence of the use of IFMIS in budgetary controls on the financial accountability of county Governments in Western Kenya. The study adopted a correlational research design. Primary data was collected using questionnaires. The study target population was 1110 county staff comprising Cabinet executive officers, IFMIS directors, finance staff, revenue officers, and planning and procurement staff. Simple random sampling was used to select 294 respondents. Reliability was tested through Cronbach Alpha, validity was tested through expert analysis and principal component factor analysis. SPSS was used to analyze descriptive and inferential statistics. Descriptive statistics consisted of frequencies. Inferential statistics consisted of Binary logistic regression analysis. Cox & Snell R Square was established as 0.699. Wald statistic was significant with p values of 0.19. Correlation analysis showed r = of 0.814. The binary logistic regression coefficient was β = 2.049, p-value .019 and Exp (β)= 7.76 for budgetary controls. It was recommended that the implementation of IFMIS should be strengthened and regularly reviewed to identify loopholes that still exist that reduce effectiveness. This would improve fiscal discipline by a very high percentage as shown by the odds ratio of budgetary controls which is greater than one. The government should enforce the use of IFMIS in budgetary controls. This will lead to minimal budget variance and budget deficits. Better ways that can make IFMIS adhered to in budgeting should therefore be enforced this will improve financial accountability as evidenced by the odd ratio of budgetary controls.</p><p><strong>JEL:</strong> G21; G29; G38</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0919/a.php" alt="Hit counter" /></p>

Development of the new risk score to predict occurrence of atrial fibrillation early after acute myocardial infarction

Abstract Background/Introduction New onset of atrial fibrillation (AF) is associated with adverse short- and long-term outcomes after acute myocardial infarction (AMI), and its prediction is relevant for the risk stratification in patients with AMI. Although several risk scores were developed for AF in the general population, there is no established risk score for AF occurrence after AMI. Purpose To develop a risk score to predict occurrence of AF early after AMI. Methods We enrolled consecutive 751 patients with AMI who admitted to our hospital between April 2006 and September 2012 for the present study. New occurrence of AF was defined as AF detected during hospital stay in a patient showing normal regular sinus rhythm at admission. Parameters relevant to the occurrence of AF was selected from the clinical characteristics, physical status and blood test data at admission, and peak CK/CK-MB, by stepwise logistic regression analysis. We constructed a risk score model to predict the new occurrence of AF, using selected parameters and their logistic regression coefficients. C-statistics was determined by constructing a receiver operating characteristic curve to evaluate the accuracy of the risk score for prediction of AF occurrence. Results We excluded 48 patients (6.4%) who had AF at admission, and 208 patients without sufficient data at admission, and thus, the study group consisted of 459 patients (age; 65±13 years, male gender; 79.6%). New AF occurrence was observed in 72 patients (14.5%). The following 7 parameters was selected as parameters related with AF (as p<0.1); Inferior/posterior AMI, use of β blockers, use of diuretics, single vessel disease, absence of reperfusion therapy, systolic blood pressure (sBP) at admission, and smoking. One point was given to sBP>128mmHg, 2 points to absence of reperfusion, and one point to other parameters. Sum of these points was calculated as the AF risk score (Table 1). AF occurred in 27.1% of patients with ≥5 points whereas it was observed 5.2% of those with <5 points. C-statistics of the risk score was 0.75 (95% CI 0.68–0.83). Conclusion We developed a novel risk score to estimate the risk of AF occurrence early after AMI, which can be a useful tool for the risk stratification after AMI. Funding Acknowledgement Type of funding sources: None.

Is Use of IFMIS in Financial Reporting the Solution to Financial Accountability? Answers from County Governments in Western Kenya

Purpose: The implementation of Integrated Financial Management Information System (IFMIS) is aimed at increasing the effectiveness and efficiency of state financial management and facilitate the adoption of modern public expenditure practices in keeping with international standards and benchmarks. Despite IFMIS being implemented, many county governments’ still face accountability challenges. The purpose of this study was to evaluate the influence of financial reporting on financial accountability of county Governments in western Kenya.
 Methodology: The study was guided by Agency theory, Accountability theory and Technological Acceptance model. The study adopted correlational research design. Primary data was collected using questionnaires. The study target population was 1110 county staffs comprising of Cabinet executive officers, IFMIS directors, finance staff, revenue officers, planning and procurement staffs. Simple random sampling was used to select 294 respondents. Reliability was tested through Cronbach Alpha, validity was tested through expert analysis and principal component factor analysis. SPSS was used to analyze descriptive and inferential statistics. Descriptive statistics consisted of frequencies. Inferential statistics consisted of Binary logistic regression analysis.
 Findings: Cox & Snell R Square was established as 0.699. Wald statistic was significant with p values of 0.00, and 0.022 for financial reporting. Correlation analysis for financial reporting and internal controls was r = 0.944. The binary logistic regression coefficient was β of 6.17, p value .000 and Exp (β) = 479.88 for financial reporting. Data was presented using tables.
 Recommendation: It was recommended that implementation of IFMIS should be strengthened and regularly reviewed to identify loop holes that still exist that reduce effectiveness. This would improve fiscal discipline by a very high percentage as shown by the odds ratio of budgetary controls, financial reporting and internal controls which are all greater than one. There will be improved adherence to statutory regulations, unsupported expenditures would reduce and Misappropriations and budget variances will be minimal.

Independent value of serum β-human chorionic gonadotropin in predicting early pregnancy loss risks in IVF/ICSI cycles.

BackgroundEarly pregnancy loss (EPL) is the most prevalent complication, particularly in couples undergoing assisted reproductive technology treatment. The present study aimed to determine whether the serum β-human chorionic gonadotropin (β-hCG) level after 14 days of embryo transfer, either alone or in conjunction with other parameters in IVF/ICSI cycles, could be used to predict subsequent EPL.MethodsThis was a retrospective cohort study of all couples who received clinical pregnancy and underwent fresh IVF/ICSI cycles at a single large reproductive medical center between January 2013 and June 2020. The research involved a total of 6600 cycles. For risk variables, we conducted the least absolute shrinkage and selection operator (LASSO) analysis, and for risk scoring, we used logistic regression coefficients. To analyze relevant risk factors for EPL, univariate and multivariate logistic regression analyses were employed. Areas under the curve (AUC) were determined and compared between β-hCG and other factors using receiver operating characteristic (ROC) curves.Results β-hCG level was considerably lower in women who had EPL than in those who were ongoing pregnancy (564.03 ± 838.16 vs 1139.04 ± 1048.72 IU/L, p< 0.001). Univariable and multivariable logistic regression revealed that β-hCG levels were significantly correlated with the probability of EPL, independent of other risk factors. More importantly, the β-hCG level could independently predict the occurrence of EPL and was comparable to the model that combined other risk factors. The optimal serum β-hCG cut-off value for predicting EPL was 542.45 IU/L.ConclusionsOur results suggest that the serum β-hCG level has a strong independent predictive value for EPL occurrence in fresh IVF/ICSI cycles.

The Relationship of Behavioral, Social and Diabetes Factors with LVEF Measured Using Machine Learning Techniques

Purpose: Using a data and machine learning approach, from classical to complex, we aim to approximate the relationship between factors such as behavioral, social or comorbidity and the ejection fraction for hospitalized patients. To measure how much the independent variables influence the left ventricular ejection fraction (LVEF), classification models will be made and the influences of the independent variables will be interpreted. Through the data obtained, it is desired to improve the management of patients with heart failure (treatment, monitoring in primary medicine) in order to reduce morbidity and mortality. Patients and Methods: In this study, we enrolled 201 patients hospitalized with decompensated chronic heart failure. The models used are extreme gradient boosting (XGB) and logistic regression (LR). To have a deeper analysis of the independent variables, their influences will be analyzed in two ways. The first is a modern technique, Shapley values, from game theory, adapted in the context of Machine Learning for XGB; and the second, the classical approach, is by analysis of Logistic Regression coefficients. Results: The importance of several factors related to behavior, social and diabetes are measured. Smoking, low education and obesity are the most harmful factors, while diabetes controlled by diet or medication does not significantly affect LVEF, indeed, there is a tendency to increase the LVEF. Conclusions: Using machine learning techniques, we can better understand to what extent certain factors affect LVEF in this sample. Following further studies on larger groups and from different regions, prevention could be better understood and applied.

Characteristics associated with the perception of high-impact disease (PsAID ≥4) in patients with recent-onset psoriatic arthritis. Machine learning-based model

ObjectivesTo evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset psoriatic arthritis. MethodsWe performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset was generated using data for each patient at the 3 visits (baseline, first year, and second year of follow-up) matched with the PsAID values at each of the 3 visits. PsAID was categorized into two groups (<4 and ≥4). We trained a logistic regression model and random forest–type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. A k-fold cross-validation with k = 5 was performed. ResultsThe sample comprised 158 patients. Of the patients who attended the clinic, 45.8% scored PsAID ≥4 at baseline; 27.1%, at the first follow-up visit, and in 23.0%, at the second follow-up visit. The variables associated with PsAID ≥4 were, in decreasing order of importance: HAQ, pain, educational level, and physical activity. Higher HAQ (logistic regression coefficient 10.394; IC95% 7.777,13.011), higher pain (5.668; 4.016, 7.320), lower educational level (-2.064; -3.515, -0.613) and high level of physical activity (1.221; 0.158, 2.283) were associated with a higher frequency of PsAID ≥4. The mean values of the measures of validity of the algorithms were all ≥85%. ConclusionsDespite the higher weight given to pain when scoring PsAID, we observed a greater influence of physical function on disease impact.

Analysis of Key Clinical Variables and Radiological Manifestations Associated with the Treatment Response of Patients with Brain Metastases to Stereotactic Radiosurgery.

Background: Stereotactic radiosurgery (SRS) is considered a promising treatment for brain metastases (BM) with better healing efficacy, relatively faster treatment time, and lower neurotoxicity, which can achieve local control rates above 70%. Although SRS improves the local control of BM, this may not translate into improvements in survival time. Thus, screening out the key factors influencing the treatment response to SRS, instead of the survival time following SRS, might be of more significance. This may assist doctors when making adjustments to treatment strategies for patients with BM. Methods: This is a retrospective review of 696 patients with BM who were treated with SRS at Huashan Hospital, Fudan University between June 2015 and February 2020. According to the patients’ treatment response to SRS, the patients were divided into an improved group (IG) and a progressive group (PG). The clinical data and magnetic resonance imaging (MRI) performed pre- and post-treatment were collected for the two groups. Five clinical variables (gender, age, Karnofsky performance status (KPS), primary tumor type, and extracranial lesion control) and seven radiological manifestations (location, number, volume, maximum diameter, edema index (EI), diffusion weighted imaging (DWI) sequence signal, and enhanced pattern) were selected and compared. A stepwise regression analysis was performed in order to obtain the best prediction effect of a group of variables and their regression coefficients, and finally to build an SRS treatment response scoring model based on the coefficients. The performance of the model was evaluated by calculating the AUC and performing the Hosmer–Lemeshow test. Results: A total of 323 patients were enrolled in the study based on the inclusion and exclusion criteria, including 209 patients in the IG and 114 patients in the PG. In the Chi-square test and t-test analysis, the significant p values of KPS, extracranial lesion control, volume, and EI were less than 0.05. Moreover, the cut-off values for volume and EI were 1801.145 mm3 and 3.835, respectively. The scoring model that was based on multivariate logistic regression coefficients performed better, achieving AUCs of 0.755 ± 0.062 and 0.780 ± 0.061 for the internal validation and validation cohorts, with p values of 0.168 and 0.073 for the Hosmer–Lemeshow test. Conclusions: KPS, extracranial lesion control, tumor volume, and EI had a certain correlation with the treatment response to SRS. Scoring models that are based on these variables can accurately predict the treatment response of patients with BM to SRS, thereby assisting doctors to make an appropriate first treatment strategy for patients with BM to a certain degree.

Validation of 5 models predicting transfusion, bleeding, and mortality in liver transplantation: an observational cohort study.

Historically, orthotopic liver transplantation (OLT) has been associated with massive blood loss, blood transfusion and morbidity. In order to predict such outcomes five nomograms have been published relating to transfusions and morbidity associated with OLTs. These nomograms, developed on the basis of three cohorts of patients consisting of 406, 750, and 800 having undergone OLTs, aimed to predict a transfusion of ≥1 red blood cell unit (RBC), a transfusion of >2 RBC units, a blood loss of >900ml, as well as one-month and one-year survival rates. The aim of this study was to validate these five nomograms in a contemporary, independent cohort of patients. Five nomograms were previously developed based on 406, 750, and 800 OLTs. In this study we performed a temporal validation of these nomograms on contemporary patients that consisted of three cohorts of 800, 250, and 200 OLTs. Logistic regression coefficients from the historic development cohorts were applied to the three contemporary temporal validation cohorts. The most accurate nomogram was able to predict transfusion of ≥1 RBC units with an area under the curve (AUC) was 0.91. The second-best nomogram was able to predict bleeding of >900ml with an AUC of 0.70. T he AUC of the third nomogram (transfusion of >2 RBC units) was 0.70. However, is temporal validation was suboptimal, due to a low prevalence of OLTs transfused with >2 RBC units. The last 2 nomograms exhibited clearly suboptimal AUC values of 0.54 and 0.61. Two of the five nomograms predict blood transfusion and blood loss with excellent accuracy. Transfusion of ≥1 RBC unit and blood loss of >900ml can be predicted on the basis of these nomograms. However, these nomograms are not accurate to predict one-month and one-year survival rates. These results should be further cross-validated, ideally prospectively, in additional external independent cohorts.

Diarrheagenic toxins in stool correlate to drinking water from improved water sources in Ethiopia

Escherichia coli toxins are the most common types found in association with disease in E. coli bacteria. They are found in nearly all pathogenic E. coli bacteria either released from the bacteria or E. coli cell or both transmitted via the orofecal route. The present study evaluated the relationship between E. coli toxins detected in ‘improved’ drinking water sources and toxins found in stool samples from individuals with diarrhea that consumed the water. Improved water is defined as the water source, by the nature of their construction or through active intervention, are protected from external contamination, particularly with fecal matter. A correlation between toxins in the water with toxins in the stools suggests that contaminated drinking water is a potential infection source. Stool samples were collected from people with diarrhea (248 stool samples) and their drinking water at their home (248 water samples). Only diarrhea patients that reported use of improved drinking water sources as per the definition of the WHO/UNICEF Joint Monitoring Programme were included in the study. E. coli isolates were analysed for 5 major toxins (enteroaggregative E. coli heat-stable enterotoxin 1 (EAST1), heat-stable enterotoxin a (Sta); shiga-like toxin 1 (Stx1), shiga-like toxin 2 (Stx2), and heat-liable enterotoxin (LT)) using a multiplex polymerase chain reaction (mPCR). Descriptive statistics, Chi-square (χ2) test, Fisher's exact, logistic regression, and bivariate Pearson correlation coefficients were used to examine the association and correlation of toxins in the drinking water with those in the stool samples. Of the 248 households, 24% had positive results in both water and stool samples, 63% [Confidence Interval (CI): 55–67%] had positive results in water samples only and 46% [CI: 37- 49%] were found to be positive for toxins in stool samples only. EAST1 was the most frequently detected from the 5 toxins evaluated, with 33% in the water and 38% in the stool. ESAT1, Stx1, and LT toxins were the most commonly found contaminants in water from improved water sources. The prevalence of toxins in household water samples was higher than that obtained from yard connections (51%) and piped systems connected to public stands (42%), compared to the other types of water sources. Both a positive correlation (P < 0.05; r = 0.412) and statistically significant association (P=0.0001) was found between the toxins in the water and toxins in the stool. EAST1 in the stool was significantly correlated with overall toxins in water (r = 0.378) and increased the probability of the occurrence of EAST1 in water (odds ratio (OR)=4.96, 95% CI, 2.81–8.74; P=0.000). E. coli toxins have been found to be effective biomarkers for detecting possible drinking water contamination in households with diarrhea. Long-term planning for the protection and/or treatment of stored household drinking water to ensure toxins free water at the point of use are required.

Dedicated staff for patient education improves bowel preparation quality and reduces the cecal intubation time of colonoscopy: A single institution retrospective study.

Adequate bowel preparation is an essential part of a high-quality colonoscopy. Recent studies showed that the small-volume bowel cleansing agent Bowklean performs better in terms of tolerability and acceptability. However, its split-dose regimen is sometimes confusing to the patient. To promote Bowklean in Fu Jen Catholic University Hospital, dedicated staff for patient education on bowel preparation were provided by Universal Integrated Corporation (Taiwan), but not in every period because of the clinic room availability and manpower capacity. This provided us an opportunity to compare the quality of colonoscopy between those with and without the dedicated patient education. This study aimed to compare various quality indices between the two groups. We set bowel preparation quality as the primary endpoint, assessed by modified Aronchick scale, and other quality indices including procedure time and adenoma detection rate as the secondary endpoints. We performed a single institution retrospective study. All patients who received colonoscopy from an outpatient setting with Bowklean as the bowel cleansing agent from October 2020 to November 2020 were reviewed. Primary and secondary endpoints were then compared between the conventional group and the dedicated staff group, with StataSE 14 by Wilcoxon rank sum test or logistic regression. Four hundred ten patients were recruited, including 217 patients with dedicated patient education and 193 without. The proportion of bowel preparation quality “Excellent + Good + Fair” was significantly higher in dedicated staff group than conventional group (97.7% vs 93.3%, P = .03; logistic regression coefficient = 1.12). The cecal intubation time was significantly shorter in the dedicated staff group (3.68 ± 2.02 minutes vs 4.52 ± 3.25 minutes, P < .01). After excluding those with polypectomy or biopsy, the total procedure time tended to be shorter in the dedicated staff group (10.2 ± 3.35 minutes vs 9.40 ± 2.43 minutes, P = .06). There was no significant difference regarding adenoma detection rate between the two groups. Our study shows that patient education by dedicated staff can improve bowel preparation quality and has the potential to decrease procedure time. Further large-scale prospective trials are still needed to evaluate if it can also achieve a better adenoma detection rate.

Attitudes of Indian Undergraduate Occupational Therapy Students Toward Mental Health and Psychiatry

Background: In India, the number of occupational therapy practitioners currently working in mental health settings is lower than most other specialties. The attitudes of students influence their interest in any field and their desire for working in it. Objectives: The objective of this study was to examine the attitudes of Indian undergraduate occupational therapy students toward mental health and psychiatry, and to explore the differences in attitudes across different years of study, gender, resulting from personal experiences, and educational coursework, and to examine if the attitudes influence future career choices. Study Design: This was a cross-sectional survey. Methods: Permission was obtained from heads of institutions of 14 occupational therapy colleges and the survey link was sent. In order to obtain a representative sample, the link was also shared on social groups. The purpose of the study, voluntary participation, and confidentiality were explained on the landing page. The subsequent sections consisted of the demographic questionnaire, the Attitudes toward Psychiatry-30 (ATP-30) Questionnaire, and the Career Preference Questionnaire. A total of 402 responses were obtained within the stipulated period. Statistical analysis was performed, and the odds ratio using logistic regression and Pearson's correlation coefficient were determined. Results: Gender and contact with persons with mental illness were significant predictors of positive ATP. Females are 5.387 times more likely to have positive ATP (odds ratio [OR] = 5.387, 95% confidence interval [CI]: 2.101–13.814, P = 0.000). Furthermore, those in contact with persons with mental illness are 2.883 times more likely to have positive ATP (OR = 2.883, 95% CI: 1.076–7.724, P = 0.035). Moderately high positive correlations existed between ATP and career preference in psychiatry. Conclusions: Overall, positive attitudes were found. Significant correlations were seen across the domains of the ATP-30 and career preference in psychiatry. Female students are five times more likely to have positive attitudes than male students. Those in contact with persons with mental illness were almost thrice more likely to have positive attitudes than those without any contact.

Clinical Prediction Rule Validation for Ambulation Outcome After Traumatic Spinal Cord Injury in a Spanish Population

Predicting the ability to walk after traumatic spinal cord injury is of utmost importance in the clinical setting. Nevertheless, only a small fraction of predictive models are evaluated on their performance by other authors using external data. The Dutch Clinical Prediction Rule for long-term walking ability was developed and validated using neurological assessments performed within 15 days postinjury. However, in reality, this assessment is most often performed between 11 and 55 days. When considering a longer time from injury to neurological assessments, the Dutch Clinical Prediction Rule has only been externally validated for patients after non-traumatic spinal cord injury. We aimed to validate the Dutch Clinical Prediction Rule with neurological assessment performed within 3-90 days after traumatic spinal cord injury, using (a) the Dutch Clinical Prediction Rule logistic regression coefficients (Equation 1); (b) the Dutch Clinical Prediction Rule weighted coefficients (Equation 2); and (c) the reestimated (using a Spanish population) weighted coefficients (Equation 3). We conducted a retrospective (STROBE-compliant) study involving 298 adults with traumatic spinal cord injury admitted to a hospital between 2010 and 2019 in Spain. The Spinal Cord Independence Measure item-12 was used for walking assessment. Using Equation 1, the model yielded 86.2% overall classification accuracy, 94.5% sensitivity, and 83.4% specificity (area under the curve [AUC] = 0.939, 95% confidence interval [CI]: 0.915-0.965; p < .001).Using Equation 2 yielded 86.2% overall classification accuracy, 93.2% sensitivity, and 83.9% specificity (AUC = 0.9392, 95% CI: 0.914-0.964; p < .001).Using Equation 3 yielded 86.9% overall classification accuracy, 68.9% sensitivity, and 92.8% specificity (AUC = 0.939, 95% CI: 0.914-0.964; p < .001). This study validates the Dutch Clinical Prediction Rule in a Spanish traumatic spinal cord injury population with assessments performed up to 90 days postinjury with similar performance, using the original coefficients and including a reestimation of the coefficients.

Mental Health Status Among Iranian Medical University Students: A Cross-sectional Study

Background: Mental health status of university students is a critical issue due to its essential role in students’ academic performance. Therefore, this study aimed to evaluate the mental health status of first-year students at Hamadan University of Medical Sciences in 2020. Methods: All first-year students of Hamadan University of Medical Sciences entered this cross-sectional study through census sampling. Two Questionnaires, the Goldberg’s General Health Questionnaire-12 (GHQ-12) as a screening instrument, and a researcher-made questionnaire were used to collect demographic information. After data collection, the chi-square test and logistic regression coefficient were applied to analyze the data at a significance level of 0.05. Results: The results showed that out of 875 students, 170 cases (19.4%) suffered from mental disorders, including depressed mood (93.5%) and anxiety attack (22%) as the most and least common disorders, respectively. Fitting the logistic regression model revealed that the highest educational degree (P&lt;0.001, medical residency), admission quotas other than the free quota (P=0.03), high (P=0.04) and low (P=0.01) financial status, and fathers’ unemployment (P=0.04) increased the chance of mental health disorders. Conclusion: Due to the high rate and negative consequences of mental health problems in university students, it is recommended that university counseling centers provide services for students with a focus on coping with more frequent mental disorders. Finally, it is suggested they have yearly follow-up surveys to recognize the effects of the university environment and different conducted programs on the students’ mental status.

Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature Importance Logistic Regression Coefficient

The evolution of computer network technology is now experiencing substantial changes, particularly with the introduction of a new paradigm, Software Defined Networking (SDN). The SDN architecture has been applied in a variety of networks, including the Internet of Things (IoT), which is known as SD-IoT. IoT is made up of billions of networking devices that are interconnected and linked to the Internet. Since the SD-IoT was considered as a complex entity, several types of attack on vulnerabilities vary greatly and can be exploited by careless individuals. Low-Rate Distributed Denial of Service (LRDDoS) is one of the availability-based attack that may affect the SD-IoT integration paradigm. Therefore, it is necessary to have an Intrusion Detection System (IDS) to overcome the security hole caused by LRDDoS. The main objective of this research was the establishment of an IDS application for resolving LRDDoS attack using the SVM algorithm combined with the Feature Importance method, namely the Logistic Regression Coefficient. The implemented approach was developed to reduce the complexity or resource’s consumption during the classification process as well as increasing the accuracy. It could be concluded that the Linear kernel SVM algorithm acquired the highest results on the test schemes at 100% accuracy, but the training time required for this model was longer, about 23.6 seconds compared to the Radial Basis Function model which only takes about 1.5 seconds.

Primary Healthcare Quality in Conflict and Fragility: a subnational analysis of disparities using Population Health surveys

BackgroundRecent global reports highlighted the importance of addressing the quality of care in all settings including fragile and conflict-affected situations (FCS), as a central strategy for the attainment of sustainable development goals and universal health coverage. Increased mortality burden in FCS reflects the inability to provide routine services of good quality. There is also paucity of research documenting the impact of conflict on the quality of care within fragile states including disparities in service delivery. This study addresses this measurement gap by examining disparities in the quality of primary healthcare services in four conflict-affected fragile states using proxy indicators.MethodsA secondary analysis of publicly available data sources was performed in four conflict-affected fragile states: Cameroon, the Democratic Republic of Congo, Mali, and Nigeria. Two main databases were utilized: the Demographic Health Survey and the Uppsala Conflict Data Program for information on components of care and conflict events, respectively. Three equity measures were computed for each country: absolute difference, concentration index, and coefficients of mixed-effects logistic regression. Each computed measure was then compared according to the intensity of organized violence events at the neighborhood level.ResultsOverall, the four studied countries had poor quality of PHC services, with considerable subnational variation in the quality index. Poor quality of PHC services was not only limited to neighborhoods where medium or high intensity conflict was recorded but was also likely to be observed in neighborhoods with no or low intensity conflict. Both economic and educational disparities were observed in individual quality components in both categories of conflict intensity.ConclusionEach of the four conflict-affected countries had an overall poor quality of PHC services with both economic and educational disparities in the individual components of the quality index, regardless of conflict intensity. Multi-sectoral efforts are needed to improve the quality of care and disparities in these settings, without a limited focus on sub-national areas where medium or high intensity conflict is recorded.

Objective Infrared Eye Tracking Aids in the Identification of Concussion-Related Vision Disorders in Adolescen Ts with Persistent Post-Concussive Symptoms

Background:The visio-vestibular examination (VVE) of smooth pursuit, saccades, vestibulo-oculomotor reflex, near point of convergence (NPC), accommodative amplitude (AA), and complex tandem gait, has enhanced the physical examination of concussed individuals. The EyeBOX is a FDA-approved device for the diagnosis of concussion.Hypothesis:The goal of this investigation was to determine the diagnostic accuracy for concussion-related vision disorders of infrared eye-tracking via the EyeBOX.Methods:A cross-sectional study of 112 adolescents ages 11-18 years of age experiencing persistent post-concussion symptoms 4-12 weeks after concussion was conducted. All subjects had a clinical visio-vestibular examination (VVE) with a sports medicine physician, and a formal, comprehensive vision examination conducted by a pediatric developmental optometrist. A binocular eye tracking assessment was performed using the EyeBOX. Binocular pupil gaze positions are captured to ensure quality data acquisition and binocular movements in the X and Y axes are recorded at 500 Hz. Dynamic time series eye tracking data is acquired with 89 different metrics from eye movement in different planes recorded for each eye at 500 Hz captured over 220 seconds. From the time series data, a summary score, between 0 and 20, the BOX score, is generated.Logistic regression was used to estimate the association between each clinical VVE parameter and concussion-related vision diagnosis. Univariate and multivariate analyses were performed to determine the predictive ability of all VVE parameters to predict a concussion-related vision diagnosis. The area under the receiver-operator (ROC) curve was estimated by treating the linear predictor from the logistic regression as a continuous diagnostic parameter. A p-value is reported for the Wald test of whether a logistic regression coefficient differs from zero. Results:The clinical VVE variables had an AUC = 0.64 for tandem gait errors (p=0.06), 0.64 for monocular AA (p=0.01 for each), and 0.66 for NPC (p<0.01). A multivariable model including all nine VVE variables had predictive ability to identify concussion-related vision disorders in adolescents with persistent post-concussion symptoms with an AUC = 0.73. The BOX score alone had an AUC = 0.67 (p<0.01).Conclusions:The addition of the BOX score, a composite measure of objectively acquired eye tracking data, to the multivariable model with all nine dichotomous VVE variables added significant additional diagnostic ability (p<0.01), yielding an AUC = 0.81. The combination of a clinical visio-vestibular examination and objective infrared eye tracking has relevant diagnostic value, facilitating identification and treatment of concussion-related vision disorders.

LRDDoS Attack Detection on SD-IoT Using Random Forest with Logistic Regression Coefficient

Software Defined Internet of Things (SD-IoT) is currently developed extensively. The architecture of the Software Defined Network (SDN) allows Internet of Things (IoT) networks to separate control and data delivery areas into different abstraction layers. However, Low-Rate Distributed Denial of Service (LRDDoS) attacks are a major problem in SD-IoT networks, because they can overwhelm centralized control systems or controllers. Therefore, a system is needed that can identify and detect these attacks comprehensively. In this paper, the authors built an LRDDoS detection system using the Random Forest (RF) algorithm as the classification method. The dataset used during the experiment was considered as a new dataset schema that had 21 features. The dataset was selected using feature importance - logistic regression with the aim of increasing the classification accuracy results as well as reducing the computational burden of the controller during the attack prediction process. The results of the RF classification with the LRDDoS packet delivery speed of 200 packets per second (pps) had the highest accuracy of 98.7%. The greater the delivery rates of the attack pattern, the accuracy results increased.&#x0D;

Spatial distribution patterns and potential exposure risks of urban floods in Chinese megacities

Serious urban floods are projected to occur more frequently in cities in the future due to continued global climate changes and accelerated urbanization. However, research dedicated to investigating the distribution patterns and exposure risks of urban floods among multiple cities is missing. The urban flood records of 8 Chinese megacities were used to explore the spatial distribution patterns and influencing factors of urban flooding and the potential exposure risks to population, housing value and traffic conditions. The results indicated that among the 8 megacities, Shanghai had the most flood events (264 points), and Wuhan had the highest heavy flood ratio (74.3%). The flood points in all cities were significantly clustered, but the mean distance between flood points in each city was quite different (443–1470 m). The main factors affecting urban flooding in these Chinese megacities were Patch Density of underlying surface, Green Space Ratio and Road Density based on the standardized coefficients of logistic regression. The most vulnerable cities in terms of the total flood exposure risk to the population, housing value, and traffic conditions were Tianjin, Shanghai and Shanghai, respectively. However, the flood points in Tianjin, Beijing and Beijing were the most vulnerable in terms of population, housing value and traffic condition, respectively. According to the exposure risk index results, the exposure risk of each flood point in Beijing was the highest (ERIpoint = 0.57), while the exposure risk of the whole city in Shanghai was the highest (ERIcity = 0.71). The comprehensive urban exposure risk index we proposed based on the potential exposure risks to population, housing value and traffic conditions makes it possible to evaluate socioeconomic vulnerability among different cities and may provide guidance for stormwater management in megacities.

Клиническая оценка эффективности алгоритма прогнозирования повышения уровня внутриглазного давления после факоэмульсификации у пациентов с первичной открытоугольной глаукомой

Purpose. To evaluate the clinical effectiveness of the algorithm for predicting the risk of increased intraocular pressure (IOP) after phacoemulsification (PE) in patients with primary pen-angle glaucoma (POAG). Material and methods. 103 patients (103 eyes) with POAG who underwent PE were studied. The level of IOP according to Maklakov before the operation was 18.7±1.9 mm Hg. To identify the risk of an increase in IOP after PE, a prediction algorithm was used, with the help of which significant predictors were selected, logistic regression coefficients were determined, and the cut-off point for making prediction decisions was determined using ROC analysis. A favorable prognosis (without an increase in IOP) was given to 81 patients, an unfavorable prognosis (an increase in IOP to intolerant values) was given to 22 patients. Results. In the course of dynamic observation for 24 months, 20 cases of an increase in the level of IOP to intolerant values were identified. In 2 patients with a poor prognosis, there was no increase in IOP during the entire follow-up period. The specificity of the forecast was 90 % (20 out of 22). Conclusion. The developed predictive algorithm is reasonable and effective, its specificity is 90 %. When predicting the risk of an increase in IOP in the postoperative period of PE, patients with POAG should be carefully monitored postoperatively. This will provide an individual strategy for managing the patient to preserve visual functions. Keywords: primary open-angle glaucoma, phacoemulsification, intraocular pressure, prediction algorithm.