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Sunflower oil is an indispensable product of functional purpose, which at its reasonable consumption increases immunity, cleanses the body of toxins, improves the brain and heart, and as a result, having a complex effect on the whole body provides an undeniable benefit to human health. Modern development of sunflower oil purification technologies involves improvement of existing and development of new methods and technical means, environmentally safe, accompanied by minimum energy consumption and characterised by increased indicators of technological efficiency, providing the possibility of separation of unnecessary impurities and preservation of valuable components extracted from the oil for their further use. Such a promising method is the purification of sunflower oil in the electric field. The paper considers the possibility of cleaning sunflower oil by an electrostatic precipitator from suspended particles by means of an electric field acting on them. The planned experiment is described and the process of removal of unnecessary impurities is analysed. The justification of the factors and parameters of the experimental study is considered separately. The dependences of the electrical characteristics of oil on the parameters of the purification process are established. Analytical expressions for empirical dependences of energy parameters of the purification process, mode and parameters of ESP operation are determined. The adequacy of theoretical and experimental results has been checked. Having analysed the obtained results, the optimum parameters of effective cleaning and ESP operation were determined, which on the basis of the obtained regression equations should be the following: voltage at ESP electrodes U = 4.4 kV, interelectrode distance b = 0.01 m, oil temperature at the inlet θ = 42°С. The calculated energy consumption at these parameters is equal to 12 W-h/kg.

The article is aimed at creating a correct worldview about labor protection among engineering and technical workers of the agro-industrial complex engaged in the repair and maintenance of energy facilities. In our case, modern tractors and combines act as energy means. The main method is the analysis and conditions of operations for the repair and maintenance of tractors and combines, on the basis of which there is a need to properly organize the supervision of labor safety. As a result, the directions of work of the occupational safety specialist and the organization of his activities for the supervision of occupational safety have been developed and formulated, and the duties of engineering and technical workers in relation to this direction are also given. This article will be useful for masters and postgraduates studying the organization of maintenance and repair of tractors and combines, as well as engineering and technical workers of agro-industrial enterprises.

The article analyzes the factors affecting the soil, which directly affect the yield. Modern domestic agricultural machinery mainly has technical and technological solutions that make it possible to significantly advance in the direction of ecological balance of the impact of crop production on soils. These include non-fallow, combined techniques of basic and pre-sowing tillage, as well as the use of a new complex of heavy agrophilic running systems of heavy mobile power facilities.

The article provides the analysis of the criteria for the sustainable functioning of the automobile operating company in emergency situations (ES), indicating the factors that most influence this process, and the main characteristics of the company, on which the degree of its stability depends. The assessment of the stability of the automobile operating company (AOC) objects is carried out by the two most common damaging factors of emergency situations in peacetime: shock wave and heat of detonation. Tables provide characteristics of the constituent elements of such a automobile operating company and the damage rate of the elements of automobile operating companies, which characterize the damage rate and the corresponding pressure resulting from the explosion. The graph of the dependence of the destruction probability of the main production factors on the stability indicators of the object elements is presented. The characteristics of the damage rate of buildings are also presented, which will make it possible to coordinate properly the work on the stability of the object. The sequence of assessing the stability of the automobile operating company is determined. Recommendations for ensuring the protection of enterprise equipment are given. Examples of protective screens for protecting equipment and the basics of the methodology for their calculations are proposed.Basing on the results of the analysis, the constructive solution for the protective structure for AOC enterprises is proposed, designed to reduce the impact of secondary damaging factors of emergency situations on the surrounding area.

The article is intended for engineering and technological workers involved in the exploitation of road transport, for specialists of automobile enterprises who are responsible for organizing road safety. Prevention of diseases and increase of efficiency of drivers, development of such functions as attention, speed of reaction, perception of space and other professionally important psycho-physiological qualities, can be achieved only through purposeful and dosed application of specially designed complex sports and health-improving classes. The article presents the simplified methodology for determining the physical condition of the personnel of the car enterprise in order to design exercises and complexes based on it to achieve the goal. It also offers forms of physical education, which contributes to the optimal method of their organization and conduct. In addition, the frequency of classes is of great importance, which directly affects the speed of recovery of drivers. The purpose of this article was to determine the content, organizational and methodological features of complex physical exercises for drivers of vehicles. In the course of the research, the comprehensive program was designed to prevent fatigue, prevent occupational diseases and improve the efficiency of drivers. The following forms of physical exercises were included in the complex program: introductory gymnastics, physical culture minute, physical culture pause, self-massage, corrective exercises for the eyes, physical culture and health classes.

For many decades, any organization involved in the operation and maintenance of vehicles cannot imagine its activities without compressor units. Compressors are used in all sectors of the national economy, and are valued for their reliability, high efficiency, long service life. The scope of the equipment is very wide, without it the work of any large industrial enterprise is indispensable. Compressors are used in mechanical engineering, metallurgy, oil and gas industry, car services, construction and other industries. Each compressor unit is equipped with an emergency protection system that provides: automatic shutdown of the compressor; sound and light alarm. According to the requirements of the Rules, all compressor units are equipped with instrumentation: pressure gauges for measuring pressure, thermometers or other sensors for measuring temperature. The compressor is placed in a separate room, which should not be connected with the premises where explosive and chemically hazardous industries are located. In the premises of compressor units, it is not allowed to place equipment and equipment that are technologically and structurally not related to compressors.

Greening the agro-industrial complex became a problem in the last two decades of the 20th century, although some problems in this relatively new direction were raised in the 60s of the 20th century. In this article, the problem of the influence of technical means on the ecology of the agricultural engineering sphere is considered from the point of view of the impact on the human body of substances generated as a result of the technical work of the agro-industrial complex. The problem of greening the agro-industrial complex in modern conditions is of great importance in connection with the observed intensification of the development of agricultural production, which has a direct negative impact on the environment, which is the key to the health of people involved in the labor process. When studying human-machine systems in crop production, the place of ecology in the “external environment” block is determined. The dependences of the conceptual model of transformation of ecosystems under the influence of pollution are presented, which allows us to determine the vector of research in this direction.

Abstract Background Neurocognitive impairment and psychiatric symptoms have been associated with deficits in psychosocial and occupational functioning in patients with schizophrenia. This post-hoc analysis evaluates the relationships among cognition, psychopathology, and psychosocial functioning in patients with schizophrenia at baseline and following sustained treatment with antipsychotic drugs. Methods Data were obtained from a clinical trial assessing the cognitive effects of selected antipsychotic drugs in patients with schizophrenia. Patients were randomly assigned to 24 weeks of treatment with olanzapine (n = 159), risperidone (n = 158), or haloperidol (n = 97). Psychosocial functioning was assessed with the Heinrichs-Carpenter Quality of Life Scale [QLS], cognition with a standard battery of neurocognitive tests; and psychiatric symptoms with the Positive and Negative Syndrome Scale [PANSS]. A path-analytic approach was used to evaluate the effects of changes in cognitive functioning on subdomains of quality of life, and to determine whether such effects were direct or mediated via changes in psychiatric symptoms. Results At baseline, processing speed affected functioning mainly indirectly via negative symptoms. Positive symptoms also affected functioning at baseline although independent of cognition. At 24 weeks, changes in processing speed affected changes in functioning both directly and indirectly via PANSS negative subscale scores. Positive symptoms no longer contributed to the path-analytic models. Although a consistent relationship was observed between processing speed and the 3 functional domains, variation existed as to whether the paths were direct and/or indirect. Working memory and verbal memory did not significantly contribute to any of the path-analytic models studied. Conclusion Processing speed demonstrated direct and indirect effects via negative symptoms on three domains of functioning as measured by the QLS at baseline and following 24 weeks of antipsychotic treatment.

Abstract Background This study focuses on exploring the relationship between changes in appetite or eating behaviors and subsequent weight change for adult patients with schizophrenia or bipolar disorder treated with olanzapine and adjunctive potential weight mitigating pharmacotherapy. The aim is not to compare different weight mitigating agents, but to evaluate patients' characteristics and changes in their eating behaviors during treatment. Identification of patient subgroups with different degrees of susceptibility to the effect of weight mitigating agents during olanzapine treatment may aid clinicians in treatment decisions. Methods Data were obtained from 3 randomized, double-blind, placebo-controlled, 16-week clinical trials. Included were 158 patients with schizophrenia or bipolar disorder and a body mass index (BMI) ≥ 25 kg/m2 who had received olanzapine treatment in combination with nizatidine (n = 68), sibutramine (n = 42), or amantadine (n = 48). Individual patients were analyzed for categorical weight loss ≥ 2 kg and weight gain ≥ 1 kg. Variables that were evaluated as potential predictors of weight outcomes included baseline patient characteristics, factors of the Eating Inventory, individual items of the Eating Behavior Assessment, and the Visual Analog Scale. Results Predictors/correlates of weight loss ≥ 2 kg included: high baseline BMI, low baseline interest in food, and a decrease from baseline to endpoint in appetite, hunger, or cravings for carbohydrates. Reduced cognitive restraint, increase in hunger, and increased overeating were associated with a higher probability of weight gain ≥ 1 kg. Conclusion The association between weight gain and lack of cognitive restraint in the presence of increased appetite suggests potential benefit of psychoeducational counseling in conjunction with adjunctive pharmacotherapeutic agents in limiting weight gain during antipsychotic drug therapy. Trial Registration This analysis was not a clinical trial and did not involve any medical intervention.

Abstract Background To make well informed treatment decisions for their patients, clinicians need credible information about potential risk for substantial weight gain. We therefore conducted a post-hoc analysis of clinical trial data, examining early weight gain as a predictor of later substantial weight gain. Methods Data from 669 (Study 1) and 102 (Study 2) olanzapine-treated patients diagnosed with schizophrenia, schizophreniform, or schizoaffective disorder were analyzed to identify and validate weight gain cut-offs at Weeks 1–4 that were predictive of substantial weight gain (defined as an increase of ≥ 5, 7, 10 kg or 7% of baseline weight) after approximately 30 weeks of treatment. Baseline characteristics alone, baseline characteristics plus weight change from baseline to Weeks 1, 2, 3 or 4, and weight change from baseline to Weeks 1, 2, 3, or 4 alone were evaluated as predictors of substantial weight gain. Similar analyses were performed to determine BMI increase cut-offs at Weeks 1–4 of treatment that were predictive of substantial increase in BMI (1, 2 or 3 kg/m2 increase from baseline). Results At Weeks 1 and 2, predictions based on early weight gain plus baseline characteristics were more robust than those based on early weight gain alone. However, by Weeks 3 and 4, there was little difference between the operating characteristics associated with these two sets of predictors. The positive predictive values ranged from 30.1% to 73.5%, while the negative predictive values ranged from 58.1% to 89.0%. Predictions based on early BMI increase plus baseline characteristics were not uniformly more robust at any time compared to those based on early BMI increase alone. The positive predictive values ranged from 38.3% to 83.5%, while negative predictive values ranged from 42.1% to 84.7%. For analyses of both early weight gain and early BMI increase, results for the validation dataset were similar to those observed in the primary dataset. Conclusion Results from these analyses can be used by clinicians to evaluate risk of substantial weight gain or BMI increase for individual patients. For instance, negative predictive values based on data from these studies suggest approximately 88% of patients who gain less than 2 kg by Week 3 will gain less than 10 kg after 26–34 weeks of olanzapine treatment. Analysis of changes in BMI suggests that approximately 84% of patients who gain less than .64 kg/m2 in BMI by Week 3 will gain less than 3 kg/m2 in BMI after 26–34 weeks of olanzapine treatment. Further research in larger patient populations for longer periods is necessary to confirm these results.

Abstract Background Patients with acute mania respond differentially to treatment and, in many cases, fail to obtain or sustain symptom remission. The objective of this exploratory analysis was to characterize response in bipolar disorder by identifying groups of patients with similar manic symptom response profiles. Methods Patients (n = 222) were selected from a randomized, double-blind study of treatment with olanzapine or divalproex in bipolar I disorder, manic or mixed episode, with or without psychotic features. Hierarchical clustering based on Ward's distance was used to identify groups of patients based on Young-Mania Rating Scale (YMRS) total scores at each of 5 assessments over 7 weeks. Logistic regression was used to identify baseline predictors for clusters of interest. Results Four distinct clusters of patients were identified: Cluster 1 (n = 64): patients did not maintain a response (YMRS total scores ≤ 12); Cluster 2 (n = 92): patients responded rapidly (within less than a week) and response was maintained; Cluster 3 (n = 36): patients responded rapidly but relapsed soon afterwards (YMRS ≥ 15); Cluster 4 (n = 30): patients responded slowly (≥ 2 weeks) and response was maintained. Predictive models using baseline variables found YMRS Item 10 (Appearance), and psychosis to be significant predictors for Clusters 1 and 4 vs. Clusters 2 and 3, but none of the baseline characteristics allowed discriminating between Clusters 1 vs. 4. Experiencing a mixed episode at baseline predicted membership in Clusters 2 and 3 vs. Clusters 1 and 4. Treatment with divalproex, larger number of previous manic episodes, lack of disruptive-aggressive behavior, and more prominent depressive symptoms at baseline were predictors for Cluster 3 vs. 2. Conclusion Distinct treatment response profiles can be predicted by clinical features at baseline. The presence of these features as potential risk factors for relapse in patients who have responded to treatment should be considered prior to discharge. Trial registration The clinical trial cited in this report has not been registered because it was conducted and completed prior to the inception of clinical trial registries.

Abstract Background The true dose effect in flexible-dose clinical trials may be obscured and even reversed because dose and outcome are related. Methods To evaluate dose effect in response on primary efficacy scales from 2 randomized, double-blind, flexible-dose trials of patients with bipolar mania who received olanzapine (N = 234, 5–20 mg/day), or patients with schizophrenia who received olanzapine (N = 172, 10–20 mg/day), we used marginal structural models, inverse probability of treatment weighting (MSM, IPTW) methodology. Dose profiles for mean changes from baseline were evaluated using weighted MSM with a repeated measures model. To adjust for selection bias due to non-random dose assignment and dropouts, patient-specific time-dependent weights were determined as products of (i) stable weights based on inverse probability of receiving the sequence of dose assignments that was actually received by a patient up to given time multiplied by (ii) stable weights based on inverse probability of patient remaining on treatment by that time. Results were compared with those by unweighted analyses. Results While the observed difference in efficacy scores for dose groups for the unweighted analysis strongly favored lower doses, the weighted analyses showed no strong dose effects and, in some cases, reversed the apparent "negative dose effect." Conclusion While naïve comparison of groups by last or modal dose in a flexible-dose trial may result in severely biased efficacy analyses, the MSM with IPTW estimators approach may be a valuable method of removing these biases and evaluating potential dose effect, which may prove useful for planning confirmatory trials.

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis., Competing Interests: This work was funded by Eli Lilly and Company and all authors are employees of Eli Lilly and Company. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Alan J.M. Brnabic was involved with the conceptualization, methodology, investigation and formal analysis of the data for the work and contributed to the original draft preparation, review and editing of the manuscript. Sarah E. Curtis was involved with the conceptualization, methodology, investigation and formal analysis of the data for the work and contributed to the review and editing of the manuscript. Joseph A. Johnston was involved with the conceptualization, methodology and investigation of the data for the work, and contributed to the original draft preparation, review and editing of the manuscript. Albert contributed to the review and editing of the manuscript. Anthony J. Zagar was involved with the methodology and investigation of the data for the work and contributed to the original draft preparation of the manuscript. Ilya Lipkovich was involved with the methodology and validation of the data for the work and contributed to the original draft preparation of the manuscript. Zbigniew Kadziola was involved with the formal analysis of the data for the work and contributed to the review and editing of the manuscript. Megan H. Murray was involved with the investigation, methodology and formal analysis of the data for the work and contributed to the original draft preparation of the manuscript. Timothy Ryan was involved with the conceptualization and investigation of the data for the work and contributed to the original draft preparation, review and editing of the manuscript. All authors have participated sufficiently in the work to agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors give final approval of the manuscript to be published., (Copyright: © 2024 Brnabic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Digital health studies using electronic patient-reported outcomes (ePROs) and wearables bring new challenges, including the need for participants to consistently provide trial data., Objective: This study aims to characterize the engagement, protocol adherence, and data completeness among participants with rheumatoid arthritis enrolled in the Digital Tracking of Arthritis Longitudinally (DIGITAL) study., Methods: Participants were invited to participate in this app-based study, which included a 14-day run-in and an 84-day main study. In the run-in period, data were collected via the ArthritisPower mobile app to increase app familiarity and identify the individuals who were motivated to participate. Successful completers of the run-in period were mailed a wearable smartwatch, and automated and manual prompts were sent to participants, reminding them to complete app input or regularly wear and synchronize devices, respectively, during the main study. Study coordinators monitored participant data and contacted participants via email, SMS text messaging, and phone to resolve adherence issues per a priori rules, in which consecutive spans of missing data triggered participant contact. Adherence to data collection during the main study period was defined as providing requested data for >70% of 84 days (daily ePRO, ≥80% daily smartwatch data) or at least 9 of 12 weeks (weekly ePRO)., Results: Of the 470 participants expressing initial interest, 278 (59.1%) completed the run-in period and qualified for the main study. Over the 12-week main study period, 87.4% (243/278) of participants met the definition of adherence to protocol-specified data collection for weekly ePRO, and 57.2% (159/278) did so for daily ePRO. For smartwatch data, 81.7% (227/278) of the participants adhered to the protocol-specified data collection. In total, 52.9% (147/278) of the participants met composite adherence., Conclusions: Compared with other digital health rheumatoid arthritis studies, a short run-in period appears useful for identifying participants likely to engage in a study that collects data via a mobile app and wearables and gives participants time to acclimate to study requirements. Automated or manual prompts (ie, "It's time to sync your smartwatch") may be necessary to optimize adherence. Adherence varies by data collection type (eg, ePRO vs smartwatch data)., International Registered Report Identifier (irrid): RR2-10.2196/14665., (©William B Nowell, Jeffrey R Curtis, Hong Zhao, Fenglong Xie, Laura Stradford, David Curtis, Kelly Gavigan, Jessica Boles, Cassie Clinton, Ilya Lipkovich, Shilpa Venkatachalam, Amy Calvin, Virginia S Hayes. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 07.11.2023.)

Background: Obesity is associated with an increased risk of multiple conditions, ranging from heart disease to cancer. However, there are few predictive models for these outcomes that have been developed specifically for people with overweight/obesity., Objective: To develop predictive models for obesity-related complications in patients with overweight and obesity., Methods: Electronic health record data of adults with body mass index 25-80 kg/m 2 treated in primary care practices between 2000 and 2019 were utilized to develop and evaluate predictive models for nine long-term clinical outcomes using a) Lasso-Cox models and b) a machine-learning method random survival forests (RSF). Models were trained on a training dataset and evaluated on a test dataset over 100 replicates. Parsimonious models of <10 variables were also developed using Lasso-Cox., Results: Over a median follow-up of 5.6 years, study outcome incidence in the cohort of 433,272 patients ranged from 1.8% for knee replacement to 11.7% for atherosclerotic cardiovascular disease. Harrell C-index averaged over replicates ranged from 0.702 for liver outcomes to 0.896 for death for RSF, and from 0.694 for liver outcomes to 0.891 for death for Lasso-Cox. The Harrell C-index for parsimonious models ranged from 0.675 for liver outcomes to 0.850 for knee replacement., Conclusions: Predictive modeling can identify patients at high risk of obesity-related complications. Interpretable Cox models achieve results close to those of machine learning methods and could be helpful for population health management and clinical treatment decisions., Competing Interests: Turchin reports equity in Brio Systems, consulting for Novo Nordisk and Proteomics International, and research support from Astra‐Zeneca, Eli Lilly and Company and Novo Nordisk. Lipkovich, Shinde, Ahmad and Kan are employees and stockholders of Eli Lilly and Company. None of the other authors report any conflicts of interest., (© 2023 The Authors. Obesity Science & Practice published by World Obesity and The Obesity Society and John Wiley & Sons Ltd.)

Objectives: To explore the associations among activation, physical activity, hemoglobin A1c (HbA1c), and healthy days in older adults with type 2 diabetes (T2D) who participated in wellness programs., Study Design: Observational, longitudinal cohort study utilizing survey, claims, and wellness program data., Methods: From January to May 2018, individuals enrolled in a commercial or Medicare Advantage and prescription drug plan with T2D (aged 55-89 years) and SilverSneakers or step count data were eligible. Three waves of surveys were mailed (n = 5000) to collect information on activation (Consumer Health Activation Index; Influence, Motivation, and Patient Activation for Diabetes) and health-related quality of life (Healthy Days). Generalized linear models and predictive models evaluated the associations of unhealthy days and HbA1c with physical activity and activation factors. Additional models tested the relationship between physical activity and future acute care visits, accounting for potential confounders via inverse probability of treatment weighting., Results: Respondents to all 3 waves (n = 1147) had higher comorbidity indices but lower HbA1c than individuals with T2D without physical activity data (P < .0001). Individuals with moderate and high activation levels had 67.4% to 74.0% and 71.6% to 85.6% fewer unhealthy days, respectively, than those with lower activation (P < .01). Individuals with high (> 8000/day) step counts at baseline were predicted to have 2.04 fewer unhealthy days/month at follow-up (P < .05) and 0.19% (P < .02) lower HbA1c units, respectively, compared with those with less than 4000 steps per day. High SilverSneakers activity (> 2 activities per week) reduced subsequent acute care visits by 49%., Conclusions: Increasing patient activation levels encourages physical activity, which can help improve glycemic control and health-related quality of life, especially among older adults.

Background: The aim of this study was to determine how clusters or subgroups of insulin-treated people with diabetes, based upon healthcare resource utilization, select social demographic and clinical characteristics, and diabetes management parameters, are related to health outcomes including acute care visits and hospital admissions., Methods: This was a non-experimental, retrospective cluster analysis. We utilized Aetna administrative claims data to identify insulin-using people with diabetes with service dates from 01 January 2015 to 30 June 2018. The study included adults over the age of 18 years who had a diagnosis of type 1 (T1DM) or type 2 diabetes mellitus (T2DM) on insulin therapy and had Aetna medical and pharmacy coverage for at least 18 months (6 months prior and 12 months after their index date, defined as either their first insulin prescription fill date or their earliest date allowing for 6 months' prior coverage). We used K-means clustering methods to identify relevant subgroups of people with diabetes based on 13 primary outcome variables., Results: A total of 100,650 insulin-using people with diabetes were identified in the Aetna administrative claims database and met study criteria, including 11,826 (11.7%) with T1DM and 88,824 (88.3%) with T2DM. Of these 79,053 (78.5%) people were existing insulin users. Seven distinct clusters were identified with different characteristics and potential risks of diabetes complications. Overall, clusters were significantly associated with differences in healthcare utilization (emergency room visits, inpatient admissions, and total inpatient days) after multivariable adjustment., Conclusions: This analysis of healthcare claims data using clustering methodologies identified meaningful subgroups of patients with diabetes using insulin. The subgroups differed in comorbidity burden, healthcare utilization, and demographic factors which could be used to identify higher risk patients and/or guide the management and treatment of diabetes.

Aim: To predict optimal treatments maximizing overall survival (OS) and time to treatment discontinuation (TTD) for patients with metastatic breast cancer (MBC) using machine learning methods on electronic health records. Patients/methods: Adult females with HR+/HER2- MBC on first- or second-line systemic therapy were eligible. Random survival forest (RSF) models were used to predict optimal regimen classes for individual patients and each line of therapy based on baseline characteristics. Results: RSF models suggested greater use of CDK4 & 6 inhibitor-based therapies may maximize OS and TTD. RSF-predicted optimal treatments demonstrated longer OS and TTD compared with nonoptimal treatments across line of therapy (hazard ratios = 0.44∼0.79). Conclusion: RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.

Background: Tralokinumab is an anti-interleukin (IL)-13 monoclonal antibody investigated for the treatment of severe, uncontrolled asthma in two Phase III clinical trials, STRATOS 1 and 2. The STRATOS 1 biomarker analysis plan was developed to identify biomarker(s) indicative of IL-13 activation likely to predict tralokinumab efficacy and define a population in which there was an enhanced treatment effect; this defined population was then tested in STRATOS 2., Methods: The biomarkers considered were blood eosinophil counts, fractional exhaled nitric oxide (FeNO), serum dipeptidyl peptidase-4, serum periostin and total serum immunoglobulin E. Tralokinumab efficacy was measured as the reduction in annualised asthma exacerbation rate (AAER) compared with placebo (primary endpoint measure of STRATOS 1 and 2). The biomarker analysis plan included negative binomial and generalised additive models, and the Subgroup Identification based on Differential Effect Search (SIDES) algorithm, supported by robustness and sensitivity checks. Effects on the key secondary endpoints of STRATOS 1 and 2, which included changes from baseline in standard measures of asthma outcomes, were also investigated. Prior to the STRATOS 1 read-out, numerous simulations of the methodology were performed with hypothetical data., Results: FeNO and periostin were identified as the only biomarkers potentially predictive of treatment effect, with cut-offs chosen by the SIDES algorithm of > 32.3 ppb and > 27.4 ng/ml, respectively. The FeNO > 32.3 ppb subgroup was associated with greater AAER reductions and improvements in key secondary endpoints compared with the periostin > 27.4 ng/ml subgroup. Upon further evaluation of AAER reductions at different FeNO cut-offs, ≥37 ppb was chosen as the best cut-off for predicting tralokinumab efficacy., Discussion: A rigorous statistical approach incorporating multiple methods was used to investigate the predictive properties of five potential biomarkers and to identify a participant subgroup that demonstrated an enhanced tralokinumab treatment effect. Using STRATOS 1 data, our analyses identified FeNO at a cut-off of ≥37 ppb as the best assessed biomarker for predicting enhanced treatment effect to be tested in STRATOS 2. Our findings were inconclusive, which reflects the complexity of subgroup identification in the severe asthma population., Trial Registration: STRATOS 1 and 2 are registered on ClinicalTrials.gov ( NCT02161757 registered on June 12, 2014, and NCT02194699 registered on July 18, 2014).

Introduction: Solanezumab is an anti-amyloid monoclonal antibody in clinical testing for treatment of Alzheimer's disease (AD). Its mechanism suggests the possibility of slowing the progression of AD., Methods: A possible disease-modifying effect of solanezumab was assessed using a new statistical method including noninferiority testing. Performance differences were compared during the placebo-controlled period with performance differences after the placebo patients crossed over to solanezumab in the delayed-start period., Results: Noninferiority of the 14-item Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog 14 ) and Alzheimer's Disease Cooperative Study Activities of Daily Living inventory instrumental items (ADCS-iADL) differences was met through 132 weeks, indicating that treatment differences observed in the placebo-controlled period remained, within a predefined margin, after the placebo group initiated solanezumab. Solanezumab was well tolerated, and no new safety concerns were identified., Discussion: The results of this secondary analysis show that the mild subgroup of solanezumab-treated patients who initiated treatment early, at the start of the placebo-controlled period, retained an advantage at most time points in the delayed-start period.

One method for demonstrating disease modification is a delayed-start design, consisting of a placebo-controlled period followed by a delayed-start period wherein all patients receive active treatment. To address methodological issues in previous delayed-start approaches, we propose a new method that is robust across conditions of drug effect, discontinuation rates, and missing data mechanisms. We propose a modeling approach and test procedure to test the hypothesis of noninferiority, comparing the treatment difference at the end of the delayed-start period with that at the end of the placebo-controlled period. We conducted simulations to identify the optimal noninferiority testing procedure to ensure the method was robust across scenarios and assumptions, and to evaluate the appropriate modeling approach for analyzing the delayed-start period. We then applied this methodology to Phase 3 solanezumab clinical trial data for mild Alzheimer's disease patients. Simulation results showed a testing procedure using a proportional noninferiority margin was robust for detecting disease-modifying effects; conditions of high and moderate discontinuations; and with various missing data mechanisms. Using all data from all randomized patients in a single model over both the placebo-controlled and delayed-start study periods demonstrated good statistical performance. In analysis of solanezumab data using this methodology, the noninferiority criterion was met, indicating the treatment difference at the end of the placebo-controlled studies was preserved at the end of the delayed-start period within a pre-defined margin. The proposed noninferiority method for delayed-start analysis controls Type I error rate well and addresses many challenges posed by previous approaches. Delayed-start studies employing the proposed analysis approach could be used to provide evidence of a disease-modifying effect. This method has been communicated with FDA and has been successfully applied to actual clinical trial data accrued from the Phase 3 clinical trials of solanezumab.

Caregivers are regularly faced with decisions between competing treatments. Large observational health care databases provide a golden opportunity for research on heterogeneity in patient response to guide caregiver decisions, due to their sample size, diverse populations, and real-world setting. Local control is a promising tool for using observational data to detect patient subgroups with differential response on one treatment relative to another. While standard data mining approaches find subgroups with optimal responses for a particular population, detecting subgroups that reveal treatment differences while also adjusting for confounding in observational data is challenging. Local control utilizes unsupervised clustering to form non-parametric patient-level counterfactual treatment differences and displays them as an observed distribution of effect-size estimates. Classification and regression trees (CART) then find the factors that drive the greatest outcome differentiation between treatments. In this manuscript, we demonstrate the use of this two-step strategy using local control plus CART to identify depression patients most (least) likely to benefit from treatment with duloxetine relative to extended-release venlafaxine. Prior medication costs and age were found to be factors most associated with differential outcome, with prior medication costs remaining as an important factor after sensitivity analyses using a second dataset., (Copyright © 2013 John Wiley & Sons, Ltd.)

We examined the potential risks and benefits of switching from olanzapine to quetiapine in mentally stable, obese, or overweight patients with schizophrenia or schizoaffective disorder. Patients receiving olanzapine were randomized to continuing olanzapine treatment (N =68; 7.5-20 mg/day) or switching to quetiapine (N =65; 300-800 mg/day). Time to relapse was the primary study objective; secondary objectives included changes in weight, metabolic parameters, and psychiatric symptoms, and discontinuation rates. No significant difference in time to relapse was observed (p =0.293), but significantly more patients remained on treatment in the olanzapine group compared with the quetiapine group (70.6% vs 43.1%; p =0.002). Olanzapine-treated patients had significantly lower rates of study discontinuation for lack of efficacy and psychiatric adverse events (AEs) compared to quetiapine (2.94% vs 15.38%, p =0.015). Significantly more patients in the olanzapine group experienced an increase in BMI ≥1 kg/m(2). Olanzapine-treated patients experienced significantly greater increases in weight from Weeks 2 through 13. Switching patients with stable disease from olanzapine to quetiapine did not significantly shorten time to relapse, but produced more frequent study discontinuations due to lack of efficacy or psychiatric AEs with moderate but variable improvement in weight and no significant between-group differences in mean changes in metabolic laboratory parameters.