OS10.3.A Predicting delirium after craniotomy in neuro-oncology

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii13-ii13
Author(s):  
P R Kappen ◽  
H J Kappen ◽  
C Dirven ◽  
M Klimek ◽  
R Osse ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Linear Models ◽  
Antipsychotic Treatment ◽  
Modifiable Risk Factors ◽  
Good Prediction ◽  
Prophylactic Measures ◽  
Oncologic Patients ◽  
Opioid Administration ◽  
Erasmus Medical

Abstract BACKGROUND Post-operative delirium (POD) is a frequent and severe complication after neurosurgical operations. Good prediction of POD after craniotomy in neuro-oncologic patients is important to install prophylactic measures, increase recognition and apply early treatment. Hence, we compared logistic regression with machine learning to build an accurate predictive model in a large dataset. MATERIAL AND METHODS POD was defined in case of a Delirium Observation Scale (DOS) ≥ 3 or start of antipsychotic treatment for delirium within 10 days after surgery. Adult patients undergoing a craniotomy for a neuro-oncologic disease in the Erasmus Medical Centre in Rotterdam were retrospectively included. The cohort was split into a training (75%), after three-fold cross validation, and test set (25%). Logistic regression and Lasso Elastic-Net Regularized Generalized Linear Models (GLMNet) were trained based on 19 pre- and intra-operative features and risk factors were identified based on the superior model. RESULTS We included 1025 neuro-oncologic craniotomies between June 2017 and September 2020. Overall incidence of POD was 18.6% (95%CI 17.4–19.8). Compared to logistic regression, Lasso GLMNet performed superior (AUC 0.73 vs. 0.76) based on the optimal tuning parameters (α=1, λ=0.014). Several non-modifiable risk factors such as age (OR1.01), prior delirium (OR1.04), memory problems (OR1.12), surgery duration (OR1.01) and modifiable risk factors, such as low potassium (OR0.97) levels and opioid administration (OR1.03), were identified. CONCLUSION POD is a frequent complication after craniotomy in neuro-oncologic patients. Lasso GLMNet was useful in predicting POD in this cohort. Validation in a prospective cohort of this model should be applied to further evaluate its value in diminishing POD.

Development of an Algorithm for Stroke Prediction: A National Health Insurance Database Study in Korea

2018 ◽  
Vol 79 (3-4) ◽  
pp. 214-220 ◽  
Author(s):  
Seung Nam Min ◽  
Se Jin Park ◽  
Dong Joon Kim ◽  
Murali Subramaniyam ◽  
Kyung-Sun Lee
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Health Insurance ◽  
National Health Insurance ◽  
National Health ◽  
Stroke Prevention ◽  
Normal Subjects ◽  
Control Group ◽  
Modifiable Risk Factors ◽  
Stroke Patients

Background: Stroke is the second leading cause of death worldwide and remains an important health burden both for the individuals and for the national healthcare systems. Potentially modifiable risk factors for stroke include hypertension, cardiac disease, diabetes, and dysregulation of glucose metabolism, atrial fibrillation, and lifestyle factors. Objects: We aimed to derive a model equation for developing a stroke pre-diagnosis algorithm with the potentially modifiable risk factors. Methods: We used logistic regression for model derivation, together with data from the database of the Korea National Health Insurance Service (NHIS). We reviewed the NHIS records of 500,000 enrollees. For the regression analysis, data regarding 367 stroke patients were selected. The control group consisted of 500 patients followed up for 2 consecutive years and with no history of stroke. Results: We developed a logistic regression model based on information regarding several well-known modifiable risk factors. The developed model could correctly discriminate between normal subjects and stroke patients in 65% of cases. Conclusion: The model developed in the present study can be applied in the clinical setting to estimate the probability of stroke in a year and thus improve the stroke prevention strategies in high-risk patients. The approach used to develop the stroke prevention algorithm can be applied for developing similar models for the pre-diagnosis of other diseases.

scholarly journals Self-Reported Modifiable Risk Factors of Cardiovascular Disease among Older Adults in Malaysia: A Cross-Sectional Study of Prevalence and Clustering

2021 ◽  
Vol 18 (15) ◽  
pp. 7941
Author(s):  
Ying Ying Chan ◽  
Norhafizah Sahril ◽  
Muhammad Solihin Rezali ◽  
Lim Kuang Kuay ◽  
Azli Baharudin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Older Adults ◽  
Logistic Regression ◽  
Modifiable Risk Factors ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Cross Sectional ◽  
Targeted Interventions ◽  
Multivariable Logistic Regression

The co-occurrence of multiple modifiable risk factors increases the risk of cardiovascular disease (CVD) morbidity or mortality. This study examines the prevalence and clustering of self-reported modifiable CVD risk factors among older adults in Malaysia. A total of 7117 adults aged ≥50 years participated in the National Health and Morbidity Survey (NHMS) 2018: Elderly Health, a community-based cross-sectional survey. Data were collected using a standardized structured questionnaire. Multivariable logistic regression was used to determine the factors associated with the clustering of self-reported modifiable CVD risk factors. The prevalence of self-reported diabetes, hypertension, hypercholesterolemia, overweight/obesity, and current smoking was 23.3%, 42.2%, 35.6%, 58.4%, and 17.5%, respectively. Overall, the prevalence of clustering of ≥1, ≥2, and ≥3 modifiable CVD risk factors was 83.3%, 75.4%, and 62.6%, respectively. Multivariable logistic regression analysis showed that men, 60–69 age group, urban dwellers, having no formal education, unemployed/retirees/homemakers, and being physically inactive were independently associated with self-reported modifiable CVD risk factors clustering. There are also ethnic differences in self-reported modifiable CVD risk factors clustering. Our findings underscore the necessity of targeted interventions and integrated strategies for early detection and treatment of modifiable CVD risk factors among older adults, considering age, sex, ethnicity, and socioeconomic status.

scholarly journals Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Shouliang Lu ◽  
Yanhua Wang ◽  
Guangfei Liu ◽  
Lu Wang ◽  
Pengfei Wu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Treatment Strategies ◽  
Multivariate Logistic Regression Analysis ◽  
Postoperative Treatment ◽  
Good Prediction ◽  
Seer Database ◽  
Multivariate Logistic Regression

Abstract Background Osteosarcoma is most common malignant bone tumors. OS patients with metastasis have a poor prognosis. There are few tools to assess metastasis; we want to establish a nomogram to evaluate metastasis of osteosarcoma. Methods Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with osteosarcoma were retrieved for retrospective analysis. We identify risk factors through univariate logistic regression and multivariate logistic regression analysis. Based on the results of multivariate analysis, we established a nomogram to predict metastasis of patients with osteosarcoma and used the concordance index (C-index) and calibration curves to test models. Results One thousand fifteen cases were obtained from the SEER database. In the univariate and multivariate logistic regression analysis, age, primary site, grade, T stage, and surgery are risk factors. The nomogram for metastasis was constructed based on these factors. The C-index of the training and validation cohort was 0.754 and 0.716. This means that the nomogram predictions of patients with metastasis are correct, and the calibration plots also show the good prediction performance of the nomogram. Conclusion We successfully develop the nomogram which can reliably predict metastasis in different patients with osteosarcoma and it only required basic information of patients. The nomogram that we developed can help clinicians better predict the metastasis with OS and determine postoperative treatment strategies.

scholarly journals Prevalence of and risk factors associated with latent tuberculosis infection in a Latin American region

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9429
Author(s):  
Javier Andrés Bustamante-Rengifo ◽  
Luz Ángela González-Salazar ◽  
Nicole Osorio-Certuche ◽  
Yesica Bejarano-Lozano ◽  
José Rafael Tovar Cuevas ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Latin American ◽  
Linear Models ◽  
Latent Tuberculosis Infection ◽  
Latent Tuberculosis ◽  
Decisive Role ◽  
Tuberculosis Infection ◽  
Cross Sectional ◽  
Latin American Region

Tuberculosis (TB) represents a health problem in Colombia, and its control is focused on the search for contacts and treatment of TB cases underscoring the role of latent tuberculosis infection (LTBI) as a reservoir of Mycobacterium tuberculosis. The burden of LTBI in Colombia is unknown. We aimed to estimate the prevalence of LTBI and identify the associated risk factors. In this cross-sectional study, we recruited participants from four health care centers in Cali, Colombia. The participants were eligible if they were aged between 14 and 70 years, and all participants answered a survey evaluating their medical history and sociodemographic and lifestyle factors. LTBI status was based on tuberculin skin test (TST) positivity using two thresholds: ≥10 mm (TST-10) and ≥15 mm (TST-15). The magnitude of the associations between independent factors and dependent outcomes (LTBI status and TST induration) were evaluated by logistic regression and generalized linear models, respectively. A total of 589 individuals were included with TST positivity rates of 25.3% (TST-10) and 13.2% (TST-15). Logistic regression showed that being between age 40 and 69 years (OR = 7.28, 95% CI [1.62–32.7]), being male (OR = 1.71, 95% CI [1.04–2.84]), being employed (OR = 1.56, 95% CI [1.02–2.38]), and having a low intake of alcohol (OR = 2.40, 95% CI [1.13–5.11]) were risk factors for TST positivity, while living in the north zone (OR = 0.32, 95% CI [0.18–0.55]), living in the suburb zone (OR = 0.28, 95% CI [0.15–0.52]) and having a secondary education (OR = 0.49 95% CI [0.29–0.83]) lowered the risk of TST positivity. The generalized linear model showed that the previous predictors, as well as a low body mass index, had an effect on TST reaction size. The LTBI prevalence found in the population was moderate, reflecting the continuous transmission of M. tuberculosis. Social factors seem to play a decisive role in the risk of LTBI. Employed males, who are over 40 years of age, are overweight, have a lower level of education and have a low intake of alcohol (50–100 mL, once/week) should be a priority group for prophylactic treatment as a strategy for TB control in this city.

scholarly journals Modifiable Risk Factor Possession Patterns of Dementia in Elderly with MCI: A 4-Year Repeated Measures Study

2020 ◽  
Vol 9 (4) ◽  
pp. 1076
Author(s):  
Osamu Katayama ◽  
Sangyoon Lee ◽  
Seongryu Bae ◽  
Keitaro Makino ◽  
Yohei Shinkai ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Repeated Measures ◽  
Missing Values ◽  
The Elderly ◽  
Community Dwelling ◽  
Low Risk ◽  
Modifiable Risk Factors

This study clarified the patterns of possessing modifiable risk factors of dementia that can be corrected by the elderly who were primarily determined to have mild cognitive impairment (MCI), and then determined the relationship between retention patterns and outcomes from MCI through a 4-year follow-up study. The participants were 789 community-dwelling elders who were ≥65 years old with MCI at baseline. After 4 years, participants were classified into reverters and nonreverters, according to their cognitive function. Repeated measures analysis was performed after imputing missing values due to dropout. Nine modifiable risk factors at baseline were classified by latent class analysis. Subsequently, we performed binomial logistic regression analysis. The reversion rate of 789 participants was 30.9%. The possession patterns of modifiable risk factors among the elderly with MCI were classified into five patterns: low risk, psychosocial, health behavior, educational, and smoking factors. According to logistic regression analysis, the low risk factors class was more likely to recover from MCI to normal cognitive than the other classes (p < 0.05). These results may provide useful information for designing interventions to prevent cognitive decline and dementia in individuals with MCI.

scholarly journals Coxiella Burnetii in Horses of Algeria: Seroprevalence and Associated Risk Factors

2020 ◽  
Vol 10 (4) ◽  
pp. 602-608
Author(s):  
Samir Ansel ◽  
Karima Benfodil ◽  
Abdellah Mohamed Cherif ◽  
Amine Abdelli ◽  
Rachid Kaidi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Coxiella Burnetii ◽  
Q Fever ◽  
Univariate Analysis ◽  
Small Ruminants ◽  
Water Source ◽  
Enzyme Linked Immunosorbent Assay ◽  
Multivariate Logistic Regression ◽  
Prophylactic Measures

The Q fever is a worldwide zoonotic disease caused by Coxiella burnetii (an obligate intracellular bacterium). This pathogen affects humans, ruminants, equines, carnivores, rodents, and birds. A cross-sectional study was carried out from March 2017 to May 2018 to assess the seroprevalence and identify the risk factors of C. burnetii infection in horses (Equus Caballus) residing in three districts of Algerian, namely Tiaret, El-Bayadh, and Ghardaia. Serum samples collected from 182 horses were analyzed via enzyme-linked immunosorbent assay (ELISA). Association of seropositivity with potential risk factors related to animals (e.g., age, gender, breed, housing, and presence of ticks), breeding characteristics (e.g., geographical localization, contact with animals), and environmental characteristics (i.e., presence of water source) was analyzed by univariate and multivariate logistic regression. An overall seroprevalence of 9.9% (18/182) was obtained. The univariate analysis of risk factors for C. burnetii seroprevalence demonstrated higher seropositivity in horses that had contact with small ruminants (p=0.004) and dromedaries (p=0.002) as well as in those living near a water source (p=0.036) and in El-Bayadh district (p=0.005). The multivariate logistic regression analysis indicated that the risk of C. burnetii infection was significantly higher in horses that were in contact with small ruminants (RR: 15.6). Algeria is endemic for Q fever in horses and prophylactic measures must be taken to reduce /prevent its transmission to animals and humans.

scholarly journals 2425. Correlating Use of High-Risk Antimicrobials and the Incidence of Hospital-Onset Clostridium difficile Infection: Targeting Prescribing Trends for Antimicrobial Stewardship

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S838-S838
Author(s):  
Elizabeth Thottacherry ◽  
Philip L Whitfield ◽  
Taylor D Steuber ◽  
Chao Li ◽  
Adam J Sawyer ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
High Risk ◽  
Clostridium Difficile ◽  
Clostridium Difficile Infection ◽  
Univariate Analysis ◽  
Antibiotic Use ◽  
Financial Strain ◽  
Modifiable Risk Factors ◽  
Prescribing Trends

Abstract Background Hospital-onset Clostridium difficile infection (HO-CDI) has a significant morbidity and mortality risk. It also poses increasing financial strain on the healthcare system. Certain antibiotics have been associated with increased HO-CDI incidence and novel strategies are needed to determine what modifiable risk factors exist. Choices of antibiotic have changed overtime time to overcome potential side effects, leading to a possibility that changed prescribing trends could be linked to significant differences in the rate of HO-CDI. Methods This study took place at a 971-bed community hospital from January 2016 to January 2018. Monthly utilization (grams) of 11 antimicrobials considered high risk of HO-CDI was collected, along with monthly HO-CDI rate. Antimicrobials included cephalosporins, carbapenems, fluoroquinolones and clindamycin. Correlational (Pearson’s) and logistic regression analyses were completed to identify association with HO-CDI. A P-value of < 0.05 was considered statistically significant. Results 215 cases of HO-CDI were identified during the study period with 30 being classified as severe. The average HO-CDI rate was 4.3 cases/1000 patient-days. There were no significant correlations identified for any antimicrobials and HO-CDI rate (p> 0.05 for all interactions). Pearson’s correlation coefficients were not significant for any antimicrobial. The multivariable logistic regression model including all antimicrobials, indicated that only ceftazidime had a statistically significant positive effect on the HO-CDI rate. Bearing in mind that only a small number of ceftazidime was prescribed, additional univariate analysis was performed indicating that there was no significant linear association between the HO-CDI rate and ceftazidime utilization (P = 0.3527). Conclusion Our study shows that there is no significant correlation between specific antimicrobial use and HO-CDI rates, even though there has been a general increase in HO-CDI rates. Additional analysis involving control groups of antibiotic use in patients without HO-CDI as well as incidence of HO-CDI in patients without antibiotic use at all is required to further assess possible modifiable risk factors in the inpatient population. Disclosures All authors: No reported disclosures.

scholarly journals Prevalence of hypertension and its risk factors among high school children in Bangalore, India

2020 ◽  
Vol 7 (3) ◽  
pp. 938
Author(s):  
Srikanth Srirama ◽  
Mangala Subramanian
Keyword(s):  
Risk Factors ◽  
High School ◽  
Logistic Regression ◽  
High School Students ◽  
Dietary Habits ◽  
Salt Intake ◽  
Modifiable Risk Factors ◽  
Age Group ◽  
School Students ◽  
High Salt Intake

Background: Hypertension is a prominent health disorder that leads to 12.8% of deaths worldwide. Although predominantly considered a disorder of the adults, the roots of hypertension start from childhood. In the past children usually suffered from secondary hypertension. However, now with increased incidence of obesity, reduced physical activity, unhealthy dietary habits, use of tobacco and alcohol among adolescents there is now an increased prevalence of primary hypertension in this age group. The present study was conducted with the objective of assessing the prevalence of hypertension and its modifiable risk factors in high school children.Methods: The study was conducted among high school students aged 13 to 16 years in urban Bangalore. A self-administered questionnaire was used to assess the lifestyle. Age, sex, height, weight and resting blood pressure were recorded. Odds ratio, Chi square test and logistic regression were used in the analysis.Results: There were 550 students who participated in the study, 300 (54.54%) were males and 250 (45.45%) were females. The prevalence of prehypertension was 21.6% and hypertension was 8.9%.  Logistic regression revealed that overweight, obesity, high salt intake, tobacco use, and stress were significantly associated with elevated blood pressure.Conclusions: There is a rise in the prevalence of hypertension among high school students. Changes in lifestyle seem to influence the development of hypertension in this age group. Behaviour change communication should be used to reduce the modifiable risk factors and promote healthy lifestyle among adolescents.

scholarly journals Data Analysis and Computational Methods for Assessing Knowledge of Obesity Risk Factors among Saudi Citizens

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Alanazi Talal Abdulrahman ◽  
Dalia Kamal Alnagar
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Saudi Arabia ◽  
Decision Tree ◽  
Linear Models ◽  
World Health ◽  
Cross Sectional ◽  
Level Of Education ◽  
Awareness Campaigns ◽  
Health Organization

Introduction. According to the World Health Organization (2020), obesity is a growing problem worldwide. In fact, obesity is characterized as an epidemic. Objective. The aim of this paper is to use a logistic regression model as one of the generalized linear models and decision tree as one of the machine learning in order to assess the knowledge of the risk factors for obesity among citizens in Saudi Arabia. Methods and Materials. A cross-sectional questionnaire was given to the general population in KSA, using Google forms, to collect data. A total of 1369 people responded. Results. The findings showed that there is widespread knowledge of risk factors for obesity among citizens in Saudi Arabia. Participants’ knowledge of risk factors was very high (95.5%). In addition, a significant association was found between demographics (gender, age, and level of education) and knowledge of risk factors for obesity, in assessing variables for knowledge of the risk factors for obesity in relation to the demographics of gender and level of education. In addition, from decision tree results, we found that level of education and marital status were the most important variables to affect knowledge of risk factors for obesity among respondents. The accuracy of correctly classified cases was 95.5%, the same in logistic regression and decision tree. Conclusion. The majority of participants saw regular exercise and diet as an essential way to reduce obesity; however, awareness campaigns should be maintained in order to avoid complacency and combat the disease.

scholarly journals Statistical Analysis of Cardiovascular Risk Factors and Links with Mental Health of Adolescents

2016 ◽  
Vol 55 (1) ◽  
pp. 52-60
Author(s):  
Ignė Dapkutė ◽  
Tomas Rekašius ◽  
Kazys Simanauskas
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Logistic Regression ◽  
Cardiovascular Risk ◽  
Linear Models ◽  
Well Being ◽  
Excessive Drinking ◽  
Sports Activity ◽  
Psychological Difficulties ◽  
Log Linear

The paper analyses whether arterial hypertension, overweight, excessive drinking, smoking andinsufficient sports activity have an impact on the behaviour and emotional well-being of young people aged17–18. A questionnaire of Achenbach is used for investigating teenagers’ psychological difficulties. Categoricalvariables are analysed using logistic regression and log-linear models.

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