Concussion Assessment Across Several Clinical Batteries: Identifying the Components That Best Discriminate Injured Adolescents From Controls

ObjectiveTo identify which sub-components of 4 clinical assessments optimize concussion diagnosis.BackgroundMultiple assessments are part of the clinical toolbox for diagnosing concussions in youth, including the Post-Concussion Symptom Inventory (PCSI), the visio-vestibular exam (VVE), the King-Devick (KD) assessment, and the Sport Concussion Assessment Tool (SCAT-5). Most of these assessments have sub-components that likely overlap in aspects of brain function they assess. Discerning the combination of sub-components that best discriminate concussed adolescents (cases) from uninjured controls would streamline concussion assessment.Design/MethodsParticipants, 12–18 years, were prospectively enrolled from August 1, 2017 to April 29, 2020 Controls (n = 189, 53% female) were recruited from a suburban high school with PCSI, VVE, KD and SCAT-5 assessments associated with their sport seasons. Cases (n = 213, 52% female) were recruited from a specialty care concussion program, with the same assessments performed ≤28 days from injury. We implemented a forward-selection sparse principal component (PC) regression procedure to group sub-components into interpretable PCs and identify the PCs best able to discriminate cases from controls while accounting for age, sex, and concussion history.ResultsThe AUC of the baseline model with age, sex, and concussion history was 62%. The PC that combined all 5 sub-components of PCSI and SCAT-5 symptom count and symptom severity provided the largest AUC increase (+10.6%) relative to baseline. Other PC factors representing (1) KD completion time, (2) Errors in BESS tandem and double-leg stances, and (C) horizontal/vertical saccades and vestibular-ocular reflex also improved model AUC relative to baseline by 5.6%, 4.7%, and 4.5%, respectively. In contrast, the SCAT5 immediate recall test and right/left monocular accommodation did little to uniquely contribute to discrimination (<1% gain in AUC). Overall, the best model included 5 PCs (AUC = 77%).ConclusionsThese data show overlapping features of clinical batteries, with symptoms providing the strongest discrimination, but unique features obtained from neurocognitive, vision, and vestibular testing.

Acetylcarnitine Is Associated With Cardiovascular Disease Risk in Type 2 Diabetes Mellitus

ObjectiveThis study aimed to identify the association between specific short-chain acylcarnitines and cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM).MethodWe retrieved 1,032 consecutive patients with T2DM who meet the inclusion and exclusion criteria from the same tertiary care center and extracted clinical information from electronic medical records from May 2015 to August 2016. A total of 356 T2DM patients with CVD and 676 T2DM patients without CVD were recruited. Venous blood samples were collected by finger puncture after 8 h fasting and stored as dried blood spots. Restricted cubic spline (RCS) analysis nested in binary logistic regression was used to identify possible cutoff points and obtain the odds ratios (ORs) and 95% confidence intervals (CIs) of short-chain acylcarnitines for CVD risk in T2DM. The Ryan–Holm step-down Bonferroni procedure was performed to adjust p-values. Stepwise forward selection was performed to estimate the effects of acylcarnitines on CVD risk.ResultThe levels of C2, C4, and C6 were elevated and C5-OH was decreased in T2DM patients with CVD. Notably, only elevated C2 was still associated with increased CVD inT2DM after adjusting for potential confounders in the multivariable model (OR = 1.558, 95%CI = 1.124–2.159, p = 0.008). Furthermore, the association was independent of previous adjusted demographic and clinical factors after stepwise forward selection (OR = 1.562, 95%CI = 1.132–2.154, p = 0.007).ConclusionsElevated C2 was associated with increased CVD risk in T2DM.

Prediksi Jumlah Kunjungan Wisatawan Asing Menggunakan Algoritma Linier Regresi Multivariet

Pariwisata merupakan salah satu komponen yang turut berpartisipasi dalam Anggaran Pendapatan Daerah (APBD).[1] Meningkat atau menurunnya jumlah kunjungan wisatawan asing tentunya memberikan efek tertentu bagi pariwisata Gorontalo, peningkatan jumlah pengunjug tentunya berimbas positif terhadap pemerintahan Gorontalo khususnya dibidang kepriwisataan sebaliknya penurunan jumlah kunjungan tentunya berimbas negative yang ditimbulkan bagi kepariwisataan. Berfluktuatifnya jumlah kunjungan wisatawan asing telah menjadi permasalahan tersendiri bagi dinas pariwisata atau pemerintahan dikarenakan ditmukannya suatu kesulitan untuk memperkirakan berapa jumlah kunjungan dimasa yang akan datang. Melihat fenomena ketidak stabilannya (fluktuatifnya) jumlah kunjungan wisatawan asing telah menjadi bahan utama peneliti untuk melakukan analisa terhadap pola kunjungan wiasatawan asing untuk dilakukan prediksi atau perkiraan jumlah kunjungan dengan menggunakan Algoritma Linier Regresi Berganda ( Multivariet ) berbasis Forward selection, sehingga pemerintah atau dinas pariwisata dapat melakukan antisipasi berdasarkan hasil prediksi yang telah diperoleh. Berdasarkan hasil pengujian metode diperoleh nilai Root Mean Square Error (RMSE) yaitu, Regresi Multivariet 2660,89, Regresi Multivariet Forward Selection 556,49, Regresi Multivariet Backward Selection 2377,44. Sehingga dapat disimpulkan bahwa metode yang paling baik digunakan untuk melakukan prediksi yaitu Regresi Multifariet Forward Selection.

A Multi-Stage Stochastic Programming Model for the Multi-Echelon Multi-Period Reverse Logistics Problem

Reverse logistics planning plays a crucial role in supply chain management. Stochasticity in different parameters along with time horizon can be a challenge in solving reverse logistics problems. This paper proposes a multi-stage, multi-period reverse logistics with lot sizing decisions under uncertainties. The main uncertain factors are return and demand quantities, and return quality. Moment matching method was adopted to generate a discrete set of scenarios to represent the original continuous distribution of stochastic parameters. Fast forward selection algorithm was employed to select the most representative scenarios and facilitate computational tractability. A case study was conducted and optimal solution of the recursive problem obtained by solving extensive form. Sensitivity analysis was implemented on different elements of stochastic solution. Results sow that solution of recursive problem (RP) outperforms the solution obtained from the problem with expected values of uncertain parameters (EEV).

Predictive biomarker modeling of pediatric atopic dermatitis severity based on longitudinal serum collection

The pathogenesis of atopic dermatitis (AD) results from complex interactions between environmental factors, barrier defects, and immune dysregulation resulting in systemic inflammation. Therefore, we sought to characterize circulating inflammatory profiles in pediatric AD patients and identify potential signaling nodes which drive disease heterogeneity and progression. We analyzed a sample set of 87 infants that were at high risk for atopic disease based on atopic dermatitis diagnoses. Clinical parameters, serum, and peripheral blood mononuclear cells (PBMCs) were collected upon entry, and at one and four years later. Within patient serum, 126 unique analytes were measured using a combination of multiplex platforms and ultrasensitive immunoassays. We assessed the correlation of inflammatory analytes with AD severity (SCORAD). Key biomarkers, such as IL-13 (rmcorr=0.47) and TARC/CCL17 (rmcorr=0.37), among other inflammatory signals, significantly correlated with SCORAD across all timepoints in the study. Flow cytometry and pathway analysis of these analytes implies that CD4 T cell involvement in type 2 immune responses were enhanced at the earliest time point (year 1) relative to the end of study collection (year 5). Importantly, forward selection modeling identified 18 analytes in infant serum at study entry which could be used to predict change in SCORAD four years later. We have identified a pediatric AD biomarker signature linked to disease severity which will have predictive value in determining AD persistence in youth and provide utility in defining core systemic inflammatory signals linked to pathogenesis of atopic disease.

Psychological Distress in Caregivers for People with Dementia: A Population-Based Analysis of a National Cross-Sectional Study

Background: Taking care of patients with dementia is often stressful and exhausting. The burden placed on caregivers (CGs) for care recipients with dementia (CRDs) has been reported to cause psychological distress. Objective: The aim of this study was to evaluate the psychological distress experienced by CGs for CRDs and identify the sociodemographic factors affecting that distress. Methods: We utilized the 2013 Comprehensive Survey of the Living Conditions for CRDs and CGs. Linked data from 643 pairs of CRDs and CGs were extracted. Serious psychological distress experienced by CGs was measured by Kessler’s Psychological Distress scale (K6) with a cutoff point of 13. Factors predictive of psychological distress were evaluated using multivariable logistic regression analysis with the forward selection method. Results: Overall, the mean age of the CGs was 63.5±11.6 years, and 5.3%(34/643) experienced serious psychological distress. Male sex of CRDs, knowing how to access consulting services, spending almost all day for nursing care, and having subjective symptoms within a few days of completing the survey were associated with having serious psychological distress, while older age, participating in shopping as part of the nursing activities, and having their own house were related to freedom from serious psychological distress. Conclusion: Clinicians should be aware of the risk factors for psychological distress in CGs and consider providing support to reduce the distress imposed by modifiable factors. Further studies are warranted to examine whether such efforts would improve the mental health of CGs for CRDs.

In-Vehicle Alcohol Detection Using Low-Cost Sensors and Genetic Algorithms to Aid in the Drinking and Driving Detection

Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-related accidents playing a significant role, particularly in child death. Aiming to aid in the prevention of this type of accidents, a novel non-invasive method capable of detecting the presence of alcohol inside a motor vehicle is presented. The proposed methodology uses a series of low-cost alcohol MQ3 sensors located inside the vehicle, whose signals are stored, standardized, time-adjusted, and transformed into 5 s window samples. Statistical features are extracted from each sample and a feature selection strategy is carried out using a genetic algorithm, and a forward selection and backwards elimination methodology. The four features derived from this process were used to construct an SVM classification model that detects presence of alcohol. The experiments yielded 7200 samples, 80% of which were used to train the model. The rest were used to evaluate the performance of the model, which obtained an area under the ROC curve of 0.98 and a sensitivity of 0.979. These results suggest that the proposed methodology can be used to detect the presence of alcohol and enforce prevention actions.

Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms

Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machine and deep learning methods is necessary for early detection in order to provide the appropriate treatment as early as possible to patients. Thus, the objective of this work is to determine the most relevant parameters with respect to the different classifiers used for keratoconus classification based on the keratoconus dataset of Harvard Dataverse. A total of 446 parameters are analyzed out of 3162 observations by 11 different feature selection algorithms. Obtained results showed that sequential forward selection (SFS) method provided a subset of 10 most relevant variables, thus, generating the highest classification performance by the application of random forest (RF) classifier, with an accuracy of 98% and 95% considering 2 and 4 keratoconus classes, respectively. Found classification accuracy applying RF classifier on the selected variables using SFS method achieves the accuracy obtained using all features of the original dataset.

Development and internal validation of an algorithm to predict intraoperative risk of inadvertent hypothermia based on preoperative data

Intraoperative hypothermia increases perioperative morbidity and identifying patients at risk preoperatively is challenging. The aim of this study was to develop and internally validate prediction models for intraoperative hypothermia occurring despite active warming and to implement the algorithm in an online risk estimation tool. The final dataset included 36,371 surgery cases between September 2013 and May 2019 at the Vienna General Hospital. The primary outcome was minimum temperature measured during surgery. Preoperative data, initial vital signs measured before induction of anesthesia, and known comorbidities recorded in the preanesthetic clinic (PAC) were available, and the final predictors were selected by forward selection and backward elimination. Three models with different levels of information were developed and their predictive performance for minimum temperature below 36 °C and 35.5 °C was assessed using discrimination and calibration. Moderate hypothermia (below 35.5 °C) was observed in 18.2% of cases. The algorithm to predict inadvertent intraoperative hypothermia performed well with concordance statistics of 0.71 (36 °C) and 0.70 (35.5 °C) for the model including data from the preanesthetic clinic. All models were well-calibrated for 36 °C and 35.5 °C. Finally, a web-based implementation of the algorithm was programmed to facilitate the calculation of the probabilistic prediction of a patient’s core temperature to fall below 35.5 °C during surgery. The results indicate that inadvertent intraoperative hypothermia still occurs frequently despite active warming. Additional thermoregulatory measures may be needed to increase the rate of perioperative normothermia. The developed prediction models can support clinical decision-makers in identifying the patients at risk for intraoperative hypothermia and help optimize allocation of additional thermoregulatory interventions.

Prediction of sustained biologic and targeted synthetic DMARD free remission in rheumatoid arthritis patients

Objectives To develop a prediction model of sustained remission following biologic or targeted synthetic disease modifying antirheumatic drug (b/tsDMARD) stop in rheumatoid arthritis (RA). Methods We conducted an explorative cohort study among b/tsDMARD RA treatment episodes courses stopped due to remission in the Swiss Clinical Quality Management registry (SCQM) [2008–2019]. The outcome was sustained b/tsDMARD free remission of ≥ 12 months. We applied logistic regression model selection algorithms using stepwise, forward, backward selection, and penalized regression to identify patient characteristics predictive of sustained b/tsDMARD free remission. We compared c-statistics corrected for optimism between models. The three models with highest c-statistics were validated in new SCQM data until 2020 (validation dataset). Results We identified 302 eligible episodes of which 177 episodes (59%) achieved sustained b/tsDMARD free remission. Two backward and one forward selection model with eight, four, and seven variables, respectively, obtained highest c-statistics corrected for optimism of c = 0·72, c = 0·70, and c = 0·69, respectively. In the validation dataset (47 eligible episodes), the models performed with c = 0·99, c = 0·80, and c = 0·74, respectively, and excellent calibration. The best model included the following 8 variables (measured at b/tsDMARD stop): RA duration, b/tsDMARD duration, other pain/anti-inflammatory drug use, quality of life (EuroQol), DAS28-erythrocyte sedimentation rate score, health assessment questionnaire (HAQ) score, education, and interactions of RA duration and other pain/anti-inflammatory drug use, and of b/tsDMARD duration and HAQ score. Conclusion Our results suggest that models with up to eight unique variables may predict sustained b/tsDMARD free remission with good efficiency. External validation is warranted.