Factors associated with performing tuberculosis screening of HIV-positive patients in Ghana: LASSO-based predictor selection in a large public health data set

Identifying Adolescents at Highest Risk of ART Non-adherence, Using the World Health Organization-Endorsed HEADSS and HEADSS+ Checklists

Brief tools are necessary to identify adolescents at greatest risk for ART non-adherence. From the WHO's HEADSS/HEADSS+ adolescent wellbeing checklists, we identify constructs strongly associated with non-adherence (validated with viral load). We conducted interviews and collected clinical records from a 3-year cohort of 1046 adolescents living with HIV from 52 South African government facilities. We used least absolute shrinkage and selection operator variable selection approach with a generalized linear mixed model. HEADSS constructs most predictive were: violence exposure (aOR 1.97, CI 1.61; 2.42, p < 0.001), depression (aOR 1.71, CI 1.42; 2.07, p < 0.001) and being sexually active (aOR 1.80, CI 1.41; 2.28, p < 0.001). Risk of non-adherence rose from 20.4% with none, to 55.6% with all three. HEADSS+ constructs were: medication side effects (aOR 2.27, CI 1.82; 2.81, p < 0.001), low social support (aOR 1.97, CI 1.60; 2.43, p < 0.001) and non-disclosure to parents (aOR 2.53, CI 1.91; 3.53, p < 0.001). Risk of non-adherence rose from 21.6% with none, to 71.8% with all three. Screening within established checklists can improve identification of adolescents needing increased support. Adolescent HIV services need to include side-effect management, violence prevention, mental health and sexual and reproductive health.

A Predictive Model for the Risk of Recurrence of Cervical Spondylotic Radiculopathy After Surgery

INTRODUCTION

This study aimed to analyze the risk factors affecting the recurrence of cervical spondylotic radiculopathy after surgery, construct a nomogram predictive model, and validate the model's predictive performance using a calibration plot.

METHODS

In this study, 304 cervical spondylotic radiculopathy patients who underwent computed tomography (CT)-guided radiofrequency ablation (RFA) of cervical intervertebral discs or low-temperature plasma RFA for cervical radiculopathy were enrolled at the Pain Department of Jiaxing College Affiliated Hospital from January 2019 to March 2022. The patients were randomly divided into training (n = 213) and testing (n = 91) groups in a 7:3 ratio. Lasso regression analysis was used to screen for independent predictors of recurrence 1 year after surgery. A nomogram predictive model was established based on the selected factors using multiple logistic regression analysis.

RESULTS

One year after surgery, 250 of the 304 cervical spondylotic radiculopathy patients did not have recurrences, while 54 had recurrences. Lasso regression combined with multiple logistic regression analysis revealed that duration, numbness, and the Numeric Rating Scale (NRS) were significant predictors of recurrence 1 year after surgery (P < 0.05). A nomogram predictive model was established using these variables. The area under the curve (AUC) of the nomogram predictive model for predicting recurrence in the training group was 0.918 [95% confidence interval (CI) 0.866-0.970], and the AUC in the testing group was 0.892 (95% CI 0.806-0.978). The Hosmer-Lemeshow goodness-of-fit test exhibited a good model fit (P > 0.05). Decision curve analysis (DCA) indicated that the nomogram predictive model had a higher net benefit for predicting the risk of postoperative recurrence in cervical radiculopathy patients when the threshold probability was between 0 and 0.603.

CONCLUSION

This study successfully developed and validated a high-precision nomogram prediction model (predictive variables include duration, numbness, and NRS) for predicting the risk of postoperative recurrence in cervical radiculopathy patients. The model can help improve the early identification of high-risk patients and screening for postoperative recurrence.

Missed opportunity for tuberculosis screening among patients presenting at two health facilities in Manafwa district, Uganda

Missed opportunities for Tuberculosis (TB) screening are key drivers of continued tuberculosis transmission. To determine the proportion of and factors associated with missing TB screening amongst patients who attended Bubulo and Butiru health facilities in the Manafwa district to inform future TB prevention and control efforts in Uganda. This was a facility-based, cross-sectional study with quantitative methods of data collection. 125 patients (≥18 years) with at least one symptom suggestive of TB were systematically selected and interviewed at the exit. Data analysis was done by Stata version 15, using a cluster-based logistic regression model. Of the 125 patients enrolled at both sites, 39% (n=49) were aged between 30 and 49 years; 75.2% (n=94) were females; 44% (n=55) were married while 66.4% (n=83) had a primary level of education. Of the patients enrolled in the study, 68% (n=85) had a missed opportunity for TB screening. Having a; post-primary education level (Adjusted Odds Ratio [AOR]=5.9; 95% Confidence Interval [95% CI]=1.3, 27.1) and attending Bubulo HCIV (AOR=0.01; 95% CI: 0.01, 0.2) were significantly associated with having a missed opportunity for TB screening. Our findings show that slightly more than two-thirds of the patients who presented to the study health facilities with symptoms suggestive of TB missed the opportunity to be screened for TB. Study findings suggest a need for interventions to increase TB screening, particularly among better-educated TB patients.

Comparing and linking machine learning and semi-mechanistic models for the predictability of endemic measles dynamics

Measles is one the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. However, systematic investigation into the comparative performance of traditional mechanistic models and machine learning approaches in forecasting the transmission dynamics of this pathogen are still rare. Here, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreak trajectories and seasonality for both regular and less regular measles outbreaks in England and Wales (E&W) and the United States. First, our results indicate that the proposed LASSO model can efficiently use data from multiple major cities and achieve similar short-to-medium term forecasting performance to semi-mechanistic models for E&W epidemics. Second, interestingly, the LASSO model also captures annual to biennial bifurcation of measles epidemics in E&W caused by susceptible response to the late 1940s baby boom. LASSO may also outperform TSIR for predicting less-regular dynamics such as those observed in major cities in US between 1932-45. Although both approaches capture short-term forecasts, accuracy suffers for both methods as we attempt longer-term predictions in highly irregular, post-vaccination outbreaks in E&W. Finally, we illustrate that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. Our results characterize the limits of predictability of infectious disease dynamics for strongly immunizing pathogens with both mechanistic and machine learning models, and identify connections between these two approaches.

A Machine Learning Based Framework to Identify and Classify Non-alcoholic Fatty Liver Disease in a Large-Scale Population

Non-alcoholic fatty liver disease (NAFLD) is a common serious health problem worldwide, which lacks efficient medical treatment. We aimed to develop and validate the machine learning (ML) models which could be used to the accurate screening of large number of people. This paper included 304,145 adults who have joined in the national physical examination and used their questionnaire and physical measurement parameters as model's candidate covariates. Absolute shrinkage and selection operator (LASSO) was used to feature selection from candidate covariates, then four ML algorithms were used to build the screening model for NAFLD, used a classifier with the best performance to output the importance score of the covariate in NAFLD. Among the four ML algorithms, XGBoost owned the best performance (accuracy = 0.880, precision = 0.801, recall = 0.894, F-1 = 0.882, and AUC = 0.951), and the importance ranking of covariates is accordingly BMI, age, waist circumference, gender, type 2 diabetes, gallbladder disease, smoking, hypertension, dietary status, physical activity, oil-loving and salt-loving. ML classifiers could help medical agencies achieve the early identification and classification of NAFLD, which is particularly useful for areas with poor economy, and the covariates' importance degree will be helpful to the prevention and treatment of NAFLD.

Nomogram for Prediction of Diabetic Retinopathy Among Type 2 Diabetes Population in Xinjiang, China

PURPOSE

To establish an accurate risk prediction model of diabetic retinopathy (DR) using cost effective and easily available patients' characteristics and clinical biomarkers.

PATIENTS AND METHODS

Totally 18,904 cases diagnosed type 2 diabetes mellitus (T2DM) were collected, among which 13,980 cases were selected after quality screening. The least absolute shrinkage and selection operator (LASSO) regression models were used for univariate analysis and factors selection, and the multi-factor logistic regression analysis was used to establish the prediction model. Discrimination, calibration, and clinical usefulness of the prediction model were assessed using AUC/ Harrell's C statistic, calibration plot, and decision curve analysis. Both the development group and validation group were assessed.

RESULTS

Candidate variables were selected by Lasso regression and multivariate logistic regression analysis. Finally, the candidate predictive variables were included diabetic peripheral neuropathy (DPN), age, neutrophilic granulocyte (NE), high-density lipoprotein (HDL), hemoglobin A1c (HbA1C), duration of T2DM, and glycosylated serum protein (GSP) were used to establish a nomogram model for predicting the risk of DR. In the development group, the area under the receiver operating characteristic curve (AUC) was 0.882 (95% CI, 0.875-0.888). In the validation group, the AUC was 0.870 (95% CI, 0.856-0.881). Meanwhile, the optimism-corrected Harrell's C statistic were 0.878 and 0.867 in the development group and the validation group, respectively. Decision curve analysis demonstrated that the nomogram was clinically useful.

CONCLUSION

We constructed and verified nomograms that could accurately predict the risk of DR in T2DM patients, which could be used to predict the personalized risk of DR patients in Xinjiang, China.

MRI-Based Radiomics for Differentiating Orbital Cavernous Hemangioma and Orbital Schwannoma

Aim: The purpose of this work was to develop and evaluate magnetic resonance imaging (MRI)-based radiomics for differentiation of orbital cavernous hemangioma (OCH) and orbital schwannoma (OSC).

Methods: Fifty-eight patients (40 OCH and 18 OSC, confirmed pathohistologically) screened out from 216 consecutive patients who presented between 2015 and 2020 were divided into a training group (28 OCH and 12 OSC) and a validation group (12 OCH and 6 OSC). Radiomics features were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). T-tests, the least absolute shrinkage and selection operator (LASSO), and principal components analysis (PCA) were used to select features for use in the classification models. A logistic regression (LR) model, support vector machine (SVM) model, decision tree (DT) model, and random forest (RF) model were constructed to differentiate OCH from OSC. The models were evaluated according to their accuracy and the area under the receiver operator characteristic (ROC) curve (AUC).

Results: Six features from T1WI, five features from T2WI, and eight features from combined T1WI and T2WI were finally selected for building the classification models. The models using T2WI features showed superior performance on the validation data than those using T1WI features, especially the LR model and SVM model, which showed accuracy of 93% (85–100%) and 92%, respectively, The SVM model showed high accuracy of 93% (91–96%) on the combined feature group with an AUC of 98% (97–99%). The DT and RF models did not perform as well as the SVM model.

Conclusion: Radiomics analysis using an SVM model achieved an accuracy of 93% for distinguishing OCH and OSC, which may be helpful for clinical diagnosis.

E-Cigarette Use in Young Adult Never Cigarette Smokers with Disabilities: Results from the Behavioral Risk Factor Surveillance System Survey

Young adult never cigarette smokers with disabilities may be at particular risk for adopting e-cigarettes, but little attention has been paid to these people. This study examines the associations between different types of disability and e-cigarette use in this population. Young adult never-smokers from the 2016-2017 Behavioral Risk Factor Surveillance System (BRFSS) survey who were either never or current e-cigarette users (n = 79,177) were selected for the analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select confounders for multivariable logistic regression models. Multivariable logistic regression models were used to determine the associations between current e-cigarette use and different types of disability after incorporating BRFSS survey design and adjusting for confounders. Young adult never-smokers who reported any disability had increased odds (OR 1.44, 95% CI 1.18-1.76) of e-cigarette use compared to those who reported no disability. Young adult never-smokers who reported self-care, cognitive, vision, and independent living disabilities had higher odds of e-cigarette use compared to those who reported no disability. There was no statistically significant difference in the odds of e-cigarette use for those reporting hearing and mobility disabilities compared to those who reported no disability. This study highlights the need for increased public education and cessation programs for this population.

Influencing Indicators and Spatial Variation of Diabetes Mellitus Prevalence in Shandong, China: A Framework for Using Data-Driven and Spatial Methods

To control and prevent the risk of diabetes, diabetes studies have identified the need to better understand and evaluate the associations between influencing indicators and the prevalence of diabetes. One constraint has been that influencing indicators have been selected mainly based on subjective judgment and tested using traditional statistical modeling methods. We proposed a framework new to diabetes studies using data-driven and spatial methods to identify the most significant influential determinants of diabetes automatically and estimated their relationships. We used data from diabetes mellitus patients' health insurance records in Shandong province, China, and collected influencing indicators of diabetes prevalence at the county level in the sociodemographic, economic, education, and geographical environment domains. We specified a framework to identify automatically the most influential determinants of diabetes, and then established the relationship between these selected influencing indicators and diabetes prevalence. Our autocorrelation results showed that the diabetes prevalence in 12 Shandong cities was significantly clustered (Moran's I = 0.328, p < 0.01). In total, 17 significant influencing indicators were selected by executing binary linear regressions and lasso regressions. The spatial error regressions in different subgroups were subject to different diabetes indicators. Some positive indicators existed significantly like per capita fruit production and other indicators correlated with diabetes prevalence negatively like the proportion of green space. Diabetes prevalence was mainly subjected to the joint effects of influencing indicators. This framework can help public health officials to inform the implementation of improved treatment and policies to attenuate diabetes diseases.

Distinguishing brain inflammation from grade II glioma in population without contrast enhancement: a radiomics analysis based on conventional MRI

PURPOSE

In populations without contrast enhancement, the imaging features of atypical brain parenchyma inflammations can mimic those of grade II gliomas. The aim of this study was to assess the value of the conventional MR-based radiomics signature in differentiating brain inflammation from grade II glioma.

METHODS

Fifty-seven patients (39 patients with grade II glioma and 18 patients with inflammation) were divided into primary (n = 44) and validation cohorts (n = 13). Radiomics features were extracted from T₁-weighted images (T₁WI) and T₂-weighted images (T₂WI). Two-sample t-test and least absolute shrinkage and selection operator (LASSO) regression were adopted to select features and build radiomics signature models for discriminating inflammation from glioma. The predictive performance of the models was evaluated via area under the receiver operating characteristic curve (AUC) and compared with the radiologists' assessments.

RESULTS

Based on the primary cohort, we developed T₁WI, T₂WI and combination (T₁WI + T₂WI) models for differentiating inflammation from glioma with 4, 8, and 5 radiomics features, respectively. Among these models, T₂WI and combination models achieved better diagnostic efficacy, with AUC of 0.980, 0.988 in primary cohort and that of 0.950, 0.925 in validation cohort, respectively. The AUCs of radiologist 1's and 2's assessments were 0.661 and 0.722, respectively.

CONCLUSION

The signature based on radiomics features helps to differentiate inflammation from grade II glioma and improved performance compared with experienced radiologists, which could potentially be useful in clinical practice.

A simple nomogram score for screening patients with type 2 diabetes to detect those with hypertension: A cross-sectional study based on a large community survey in China

OBJECTIVES

Compared with unaffected individuals, patients with type 2 diabetes (T2DM) have higher risk of hypertension, and diabetes combined with hypertension can lead to server cardiovascular disease. Therefore, the purpose of this study was to establish a simple nomogram model to identify the determinants of hypertension in patients with T2DM and to quickly calculate the probability of hypertension in individuals with T2DM.

MATERIALS AND METHODS

A total of 643,439 subjects participating in the national physical examination has been recruited in this cross-sectional study. After excluding unqualified subjects, 30,507 adults with T2DM were included in the final analysis. 21,355 and 9,152 subjects were randomly assigned to the model developing group and validation group, respectively, with a ratio of 7:3. The potential risk factors used in this study to assess hypertension in patients with T2DM included questionnaire investigation and physical measurement variables. We used the least absolute shrinkage and selection operator models to optimize feature selection, and the multivariable logistic regression analysis was for predicting model. Discrimination and calibration were assessed using the receiver operating curve (ROC) and calibration curve.

RESULTS

The results showed that the major determinants of hypertension in patients with T2DM were age, gender, drinking, exercise, smoking, obesity and atherosclerotic vascular disease. The area under ROC curve of developing group and validation group are both 0.814, indicating that the prediction model owns high disease recognition ability. The p values of the two calibration curves are 0.625 and 0.445, suggesting that the nomogram gives good calibration.

CONCLUSION

The individualized nomogram model can facilitate improved screening and early identification of patients with hypertension in T2DM. This procedure will be useful in developing regions with high epidemiological risk and poor socioeconomic status just like Urumqi, in Northern China.

A nomogram model for screening the risk of diabetes in a large-scale Chinese population: an observational study from 345,718 participants

Our study is major to establish and validate a simple type||diabetes mellitus (T2DM) screening model for identifying high-risk individuals among Chinese adults. A total of 643,439 subjects who participated in the national health examination had been enrolled in this cross-sectional study. After excluding subjects with missing data or previous medical history, 345,718 adults was included in the final analysis. We used the least absolute shrinkage and selection operator models to optimize feature selection, and used multivariable logistic regression analysis to build a predicting model. The results showed that the major risk factors of T2DM were age, gender, no drinking or drinking/time > 25 g, no exercise, smoking, waist-to-height ratio, heart rate, systolic blood pressure, fatty liver and gallbladder disease. The area under ROC was 0.811 for development group and 0.814 for validation group, and the p values of the two calibration curves were 0.053 and 0.438, the improvement of net reclassification and integrated discrimination are significant in our model. Our results give a clue that the screening models we conducted may be useful for identifying Chinses adults at high risk for diabetes. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

Use of Digital Technology to Enhance Tuberculosis Control: Scoping Review

Background

Tuberculosis (TB) is the leading cause of death from a single infectious agent, with around 1.5 million deaths reported in 2018, and is a major contributor to suffering worldwide, with an estimated 10 million new cases every year. In the context of the World Health Organization’s End TB strategy and the quest for digital innovations, there is a need to understand what is happening around the world regarding research into the use of digital technology for better TB care and control.

Objective

The purpose of this scoping review was to summarize the state of research on the use of digital technology to enhance TB care and control. This study provides an overview of publications covering this subject and answers 3 main questions: (1) to what extent has the issue been addressed in the scientific literature between January 2016 and March 2019, (2) which countries have been investing in research in this field, and (3) what digital technologies were used?

Methods

A Web-based search was conducted on PubMed and Web of Science. Studies that describe the use of digital technology with specific reference to keywords such as TB, digital health, eHealth, and mHealth were included. Data from selected studies were synthesized into 4 functions using narrative and graphical methods. Such digital health interventions were categorized based on 2 classifications, one by function and the other by targeted user.

Results

A total of 145 relevant studies were identified out of the 1005 published between January 2016 and March 2019. Overall, 72.4% (105/145) of the research focused on patient care and 20.7% (30/145) on surveillance and monitoring. Other programmatic functions 4.8% (7/145) and electronic learning 2.1% (3/145) were less frequently studied. Most digital health technologies used for patient care included primarily diagnostic 59.4% (63/106) and treatment adherence tools 40.6% (43/106). On the basis of the second type of classification, 107 studies targeted health care providers (107/145, 73.8%), 20 studies targeted clients (20/145, 13.8%), 17 dealt with data services (17/145, 11.7%), and 1 study was on the health system or resource management. The first authors’ affiliations were mainly from 3 countries: the United States (30/145 studies, 20.7%), China (20/145 studies, 13.8%), and India (17/145 studies, 11.7%). The researchers from the United States conducted their research both domestically and abroad, whereas researchers from China and India conducted all studies domestically.

Conclusions

The majority of research conducted between January 2016 and March 2019 on digital interventions for TB focused on diagnostic tools and treatment adherence technologies, such as video-observed therapy and SMS. Only a few studies addressed interventions for data services and health system or resource management.

Performance of and Factors Associated With Tuberculosis Screening and Diagnosis Among People Living With HIV: Analysis of 2012-2016 Routine HIV Data in Tanzania

People Living with HIV (PLHIV) should be screened for tuberculosis (TB) at every visit to the HIV care and treatment clinic (CTC), and those with positive results on screening should undergo further diagnostic investigations. We evaluated the performance of the TB diagnosis cascade among PLHIV attending CTC between January 2012 and December 2016 in three regions of Tanzania: Dar es Salaam, Iringa, and Njombe. We used descriptive epidemiology to evaluate performance and logistic regression to determine odds ratios (OR) for factors associated with TB screening and further TB diagnosis after positive TB screening. We analyzed 169,741 PLHIV who made 2,638,876 visits to CTC between January 2012 and December 2016. We excluded 2,074 (0.80%) visits as these involved PLHIV enrolled in CTC with a prior TB disease diagnosis. Of the 2,636,802 visits, 2,524,494 (95.67%) had TB screening according to national guidelines, of which 88,028 (3.49%) had TB screening positive results. Of the 88,028 visits with a positive TB screening, 27,810 (31.59%) had no records for further TB diagnosis following positive TB screening. Of all visits with positive TB screening, 32,986 (37.50%) had a TB disease diagnosis. On multivariate logistic regression, those who visited with World Health Organization (WHO) clinical stage four (aOR = 3.61, 95% CI 3.48–3.75, P < 0.001), enrolled in health center (aOR = 1.26, 95% CI 1.24–1.29, P < 0.001), enrolled in Iringa region (aOR = 1.54, 95% CI 1.50–1.57, P < 0.001), and enrolled in 2015 (aOR = 1.20, 95% CI 1.18–1.24, P < 0.001) were more likely to have no TB screening. Visits involving those who were of the female sex (aOR = 1.14, 95% CI 1.11–1.18, P < 0.001), enrolled in Njombe region (aOR = 4.36, 95% CI 4.09–4.65, P < 0.001), and enrolled in 2016 (aOR = 2.62, 95% CI 2.49–2.77, P < 0.001) were more likely to have no further TB diagnosis after positive TB screening. The study documented high performance of TB screening for PLHIV in HIV CTCs but a low transition of presumptive TB case undergoing further investigations. Better systems are needed for ensuring presumptive TB cases are diagnosed including using more efficient diagnostic methods like Gene pert.

Factors contributing to the ceiling effect of the EQ-5D-5L: an analysis of patients with prostate cancer judged "no-problems"

Purpose

The goal of the present study was to determine factors related to a ceiling effect (CE) on the EQ-5D-5L among Japanese patients with prostate cancer (PC).

Methods

An existent cross-sectional observational study dataset was used. Patients were ≥ 20 years of age and diagnosed with PC. For CE determinants on the EQ-5D-5L, we excluded possible “full-health” patients flagged by the EQ-VAS (score = 100) and/or FACT-P (score = 156) instruments. We then divided them into binary variables: A CE group (EQ-5D-5L score = 1) and others (< 1). The associations between CE, sociodemographic and medical characteristics, and FACT-P subscale scores were examined using a multivariate LASSO selection followed by a binomial logistic regression analysis performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs).

Results

A total of 362 patients were analyzed. The LASSO selection variables, including all obtained variables, were as follows: age, palliative treatment, FACT-P physical well-being, and PC subscale score. Statistically significant variables predicting CE were palliative treatment (OR 0.23; 95% CI 0.09–0.60), physical well-being (OR 1.54; 95% CI 1.34–1.76), and PC subscale (OR 1.08; 95% CI 1.03–1.14).

Conclusions

This study revealed that palliative treatment and two FACT-P physical well-being and PC subscale scores were positively related to CE on the EQ-5D-5L. To our knowledge, this is the first study to examine predictors of CE on the EQ-5D-5L. The present results may be helpful for facilitating the consideration of “bolt-on” studies from the standpoint of PC patients.

Levels and predictors of TB-HIV Diagnostic service linkage and testing in government hospitals of Southern zone of Tigray, Northern Ethiopia

BACKGROUND

Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) are global public health problems. TB and HIV diagnostic services linkage is imperative for the fight against the two diseases.

OBJECTIVE

To assess the diagnostic service linkage and testing of TB-HIV diagnostic services and identify predictors in Public hospitals of Northern Ethiopia.

METHODS

A cross-sectional study was conducted in five hospitals of Northern Ethiopia. Study subjects' socio-demographic, household and clinical variables were assessed. Data was analyzed using SPSS. Logistic regressions were used to determine the predictors of uptake of TB and HIV testing among HIV and TB patients, respectively.

RESULT

The level of HIV testing among TB patients was 94.4% and of TB screening among HIV patients was 90.5%. Factors that independently predict HIV testing among TB patients were Residence AOR=0.187(95% CI 0.05-0.76), being 9 grade and above AOR=13.17 (95%CI 2.67-65.03) and drinking alcohol AOR=0.03(95% CI 0.002-0.475). Likewise, being grade 9 and above AOR=6.92 (95% CI 1.75-27.4) and having chronic cough AOR=0.23 (95% CI 0.06-0.92) were predictor variables for having TB screening among HIV patients.

CONCLUSION

The levels of TB-HIV linkages and testing are high. Moreover, educational status is a strong predictor of TB screening among HIV patients and HIV testing among TB cases. The regional health bureau has to continue supporting its TB and HIV case teams in every health facility.

Correlates of viral suppression among HIV-infected men who have sex with men and transgender women in Lima, Peru

In Peru, HIV is concentrated among men who have sex with men (MSM) and transgender women (TGW). Between June 2015 and August 2016, 591 HIV-positive MSM and TGW were recruited at five clinical care sites in Lima, Peru. We found that 82.4% of the participants had achieved viral suppression (VS; VL < 200) and 73.6% had achieved maximal viral suppression (MVS; VL < 50). Multivariable modeling indicated that patients reporting transportation as a barrier to HIV care were less likely to achieve VS (aOR = 0.47; 95% CI = 0.30-0.75) and MVS (aOR = 0.56; 95% CI = 0.37-0.84). Alcohol use disorders were negatively associated with MVS (aOR = 0.62; 95% CI = 0.30-0.75) and age was positively associated with achieving MVS (aOR = 1.29; 95% CI = 1.04-1.59). These findings underscore the need for more accessible HIV care with integrated behavioral health services in Lima, Peru.

Factors related to and economic implications of inhospital death in German lung cancer patients - results of a Nationwide health insurance claims data based study

Background

When patients die in a hospital their quality of life is lower than when they die at home or in a hospice. Despite efforts to improve palliative care supply structures, still about 60% of lung cancer patients die in a hospital. Studies have examined factors related to inhospital death in lung cancer patients, yet none used data of a representative German population, additionally including economic aspects. This study aimed to identify factors related to inhospital death in German lung cancer patients and analysed resulting costs.

Methods

We analysed a dataset of health insurance claims of 17,478 lung cancer patients (incident 2009) with 3 year individual follow-up. We grouped patients into inhospital death and death elsewhere. Studied factors were indicators of healthcare utilization, palliative care, comorbidities and disease spread. We used logistic regression models with LASSO selection method to identify relevant factors. We compared all-cause healthcare expenditures for the last 30 days of life between both groups using generalized linear models with gamma distribution.

Results

Twelve thousand four hundred fifty-seven patients died in the observation period, thereof 6965 (55.9%) in a hospital. The key factors for increased likelihood of inhospital death were receipt of inpatient palliative care (OR = 1.85), chemotherapeutic treatments in the last 30 days of life (OR = 1.61) and comorbid Congestive Heart Failure (OR = 1.21), and Renal Disease (OR = 1.19). In contrast, higher care level (OR = 0.16), nursing home residency (OR = 0.25) and receipt of outpatient palliative care (OR = 0.25) were associated with a reduced likelihood. All OR were significant (p-values< 0.05). Expenditures in the last 30 days of life were significantly higher for patients with inhospital death (€ 6852 vs. € 33,254, p-value< 0.0001).

Conclusion

Findings suggest that factors associated with inhospital death often relate to previous contact with hospitals like prior hospitalizations, and treatment of the tumour or comorbidities. Additionally, factors associated with dying elsewhere relate to access to care settings which are more focused on palliation than hospitals. From these results, we can derive that implementing tools like palliative care into tumour-directed therapy might help patients make self-determined decisions about their place of death. This can possibly be achieved at reduced economic burden for SHIs.

Electronic supplementary material

The online version of this article (10.1186/s12913-018-3599-3) contains supplementary material, which is available to authorized users.

MR-based radiomics signature in differentiating ocular adnexal lymphoma from idiopathic orbital inflammation

OBJECTIVES

To assess the value of the MR-based radiomics signature in differentiating ocular adnexal lymphoma (OAL) and idiopathic orbital inflammation (IOI).

METHODS

One hundred fifty-seven patients with pathology-proven OAL (84 patients) and IOI (73 patients) were divided into primary and validation cohorts. Eight hundred six radiomics features were extracted from morphological MR images. The least absolute shrinkage and selection operator (LASSO) procedure and linear combination were used to select features and build radiomics signature for discriminating OAL from IOI. Discriminating performance was assessed by the area under the receiver-operating characteristic curve (AUC). The predictive results were compared with the assessment of radiologists by chi-square test.

RESULTS

Five radiomics features were included in the radiomics signature, which differentiated OAL from IOI with an AUC of 0.74 and 0.73 in the primary and validation cohorts respectively. There was a significant difference between the classification results of the radiomics signature and those of a radiology resident (p < 0.05), although there was no significant difference between the results of the radiomics signature and those of a more experienced radiologist (p > 0.05).

CONCLUSIONS

Radiomics features have the potential to differentiate OAL from IOI.

KEY POINTS

• Clinical and imaging findings of OAL and IOI often overlap, which makes diagnosis difficult. • Radiomics features can potentially differentiate OAL from IOI non invasively. • The radiomics signature discriminates OAL from IOI at the same level as an experienced radiologist.

Cytokine signatures in chronic fatigue syndrome patients: a Case Control Study and the effect of anakinra treatment

BACKGROUND

Cytokine disturbances have been suggested to be associated with the Chronic Fatigue Syndrome/Myalgic encephalomyelitis (CFS/ME) for decades.

METHODS

Fifty female CFS patients were included in a study on the effect of the interleukin-1-receptor antagonist anakinra or placebo during 4 weeks. EDTA plasma was collected from patients before and directly after treatment. At baseline, plasma samples were collected at the same time from 48 healthy, age-matched female neighborhood controls. A panel of 92 inflammatory markers was determined in parallel in 1 μL samples using a 'proximity extension assay' (PEA) based immunoassay. Since Transforming growth factor beta (TGF-β) and interleukin-1 receptor antagonist (IL-1Ra) were not included in this platform, these cytokines were measured with ELISA.

RESULTS

In CFS/ME patients, the 'normalized protein expression' value of IL-12p40 and CSF-1 was significantly higher (p value 0.0042 and 0.049, respectively). Furthermore, using LASSO regression, a combination of 47 markers yielded a prediction model with a corrected AUC of 0.73. After correction for multiple testing, anakinra had no effect on circulating cytokines. TGF-β did not differ between patients and controls.

CONCLUSIONS

In conclusion, this study demonstrated increased IL-12p40 and CSF-1 concentrations in CFS/ME patients in addition to a set of predictive biomarkers. There was no effect of anakinra on circulating cytokines other than IL-1Ra.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02108210 , Registered April 2014.

CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma

PURPOSE

To evaluate the value of CT-based radiomics signature for differentiating Borrmann type IV gastric cancer (GC) from primary gastric lymphoma (PGL).

MATERIALS AND METHODS

40 patients with Borrmann type IV GC and 30 patients with PGL were retrospectively recruited. 485 radiomics features were extracted and selected from the portal venous CT images to build a radiomics signature. Subjective CT findings, including gastric wall peristalsis, perigastric fat infiltration, lymphadenopathy below the renal hila and enhancement pattern, were assessed to construct a subjective findings model. The radiomics signature, subjective CT findings, age and gender were integrated into a combined model by multivariate analysis. The diagnostic performance of these three models was assessed with receiver operating characteristics curves (ROC) and were compared using DeLong test.

RESULTS

The subjective findings model, the radiomics signature and the combined model showed a diagnostic accuracy of 81.43% (AUC [area under the curve], 0.806; 95% CI [confidence interval]: 0.696-0.917; sensitivity, 63.33%; specificity, 95.00%), 84.29% (AUC, 0.886 [95% CI: 0.809-0.963]; sensitivity, 86.67%; specificity, 82.50%), 87.14% (AUC, 0.903 [95%CI: 0.831-0.975]; sensitivity, 70.00%; specificity, 100%), respectively. There were no significant differences in AUC among these three models (P=0.051-0.422).

CONCLUSION

Radiomics analysis has the potential to accurately differentiate Borrmann type IV GC from PGL.