Incorporating Benefit-Risk Consideration and Feature Selection into Optimal Dynamic Treatment Regimens

Optimal dynamic treatment regimen (DTR) is one of the most important strategies in precision medicine, which sequentially assigns the best treatment to patients based on their evolving health status to maximize the cumulative outcome. For many chronic diseases, treatments are often multifaceted where aggressive treatments with a higher beneficial reward are usually accompanied by an elevated risk of adverse outcomes, and ideal DTRs should both yield a higher beneficial gain while avoiding unnecessary risk. In addition, it is often that among many possible tailoring variables, only a small subset is essential for treatment, and identifying these variables is particularly important for developing sparse DTRs, which are useful in practice. To address these challenges, in the first project we propose a new machine learning-based method to learn the optimal DTRs that maximize patients' cumulative reward but at each stage, the acute short-term risk induced by the treatments is controlled lower than a pre-specified threshold. We show that this multistage-constrained problem can be decomposed into a series of single-stage single-constrained problems, which can be efficiently solved using a backward algorithm. We provide theoretical guarantees for the method and demonstrate the performance via simulation studies and an application to a clinical trial for T2D patients (DURABLE study). In the second project, we develop a general approach to estimate the optimal DTRs that maximize patients' cumulative reward but lead to a cumulative risk no higher than a pre-specified threshold. This procedure converts the problem into solving unconstrained DTRs problems, which can be accommodated to existing DTRs methods. Furthermore, we propose an estimation procedure (MRL) to solve the decision rules across all stages simultaneously. The method is justified via theoretical guarantees, simulation studies, and an application to the DURABLE study. In the third project, we develop a new machine learning-based method by extending and adding an L1-penalty to the MRL framework to implement variable selection while learning optimal DTRs across all stages contingently. A DC algorithm is developed to solve the L1-MRL problem efficiently and the performance is demonstrated via simulation studies and application to an observational electronic health record (EHR) data of T2D patients.Doctor of Philosoph

Use of Serial Smartphone-Based Assessments to Characterize Diverse Neuropsychiatric Symptom Trajectories in a Large Trauma Survivor Cohort

The authors sought to characterize adverse posttraumatic neuropsychiatric sequelae (APNS) symptom trajectories across ten symptom domains (pain, depression, sleep, nightmares, avoidance, re-experiencing, anxiety, hyperarousal, somatic, and mental/fatigue symptoms) in a large, diverse, understudied sample of motor vehicle collision (MVC) survivors. More than two thousand MVC survivors were enrolled in the emergency department (ED) and completed a rotating battery of brief smartphone-based surveys over a 2-month period. Measurement models developed from survey item responses were used in latent growth curve/mixture modeling to characterize homogeneous symptom trajectories. Associations between individual trajectories and pre-trauma and peritraumatic characteristics and traditional outcomes were compared, along with associations within and between trajectories. APNS across all ten symptom domains were common in the first two months after trauma. Many risk factors and associations with high symptom burden trajectories were shared across domains. Both across and within traditional diagnostic boundaries, APNS trajectory intercepts, and slopes were substantially correlated. Across all domains, symptom severity in the immediate aftermath of trauma (trajectory intercepts) had the greatest influence on the outcome. An interactive data visualization tool was developed to allow readers to explore relationships of interest between individual characteristics, symptom trajectories, and traditional outcomes ( http://itr.med.unc.edu/aurora/parcoord/ ). Individuals presenting to the ED after MVC commonly experience a broad constellation of adverse posttraumatic symptoms. Many risk factors for diverse APNS are shared. Individuals diagnosed with a single traditional outcome should be screened for others. The utility of multidimensional categorizations that characterize individuals across traditional diagnostic domains should be explored

Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens

Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy but may also increase risk. However, few methods for estimating DTRs can take into account both cumulative benefit and risk. In this work, we propose a general statistical learning framework to learn optimal DTRs that maximize the reward outcome while controlling the cumulative adverse risk to be below a pre-specified threshold. We convert this constrained optimization problem into an unconstrained optimization using a Lagrange function. We then solve the latter using either backward learning algorithms or simultaneously over all stages based on constructing a novel multistage ramp loss. Theoretically, we establish Fisher consistency of the proposed method and further obtain non-asymptotic convergence rates for both reward and risk outcomes under the estimated DTRs. The finite sample performance of the proposed method is demonstrated via simulation studies and through an application to a two-stage clinical trial for T2D patients.</p

Removal of low concentration ammonia nitrogen enhanced by hydraulic cavitation combined with ozonation

The combination of hydraulic cavitation and ozone oxidation improves the removal efficiency of low concentration ammonia nitrogen. Focusing on the treatment of low concentration ammonia nitrogen wastewater (about 25mg/L), the study discusses the treatment effect of cavitation combined with different gases (O2, O3) on low concentration ammonia nitrogen. The treatment effect of ozone 3L/min combined with cavitation on ammonia nitrogen is 33.07%. The treatment effect of oxygen (3mg/L) combined with cavitation on ammonia nitrogen was 16.91%. At the same time, the treatment efficiency of ammonia nitrogen at different PH values was compared. When the initial concentration was adjusted from 7 to 10, the treatment efficiency increased from 33.07% to 84.55%. When PH=10, the ammonia nitrogen concentration in water finally dropped to 3.5mg/L, reaching the Chinese first-level emission standard (15mg/L). In addition, the mechanism of treating ammonia nitrogen in water by hydraulic cavitation combined with ozonation was discussed. The results show that ozone combined cavitation is very effective for the treatment of low concentration ammonia nitrogen wastewater

Investigating the causal effect of socioeconomic status on quality of care under a universal health insurance system - a marginal structural model approach

Abstract Background Social disparities in healthcare persist in the US despite the expansion of Medicaid under the Affordable Care Act. We investigated the causal impact of socioeconomic status on the quality of care in a setting with minimal confounding bias from race, insurance type, and access to care. Methods We designed a retrospective population-based study with a random 25% sample of adult Taiwan population enrolled in Taiwan’s National Health Insurance system from 2000 to 2016. Patient’s income levels were categorized into low-income group (<25th percentile) and high-income group (≥25th percentile). We used marginal structural modeling analysis to calculate the odds of hospital admissions for 11 ambulatory care sensitive conditions identified by the Agency for Healthcare Research and Quality and the odds of having an Elixhauser comorbidity index greater than zero for low-income patients. Results Among 2,844,334 patients, those in lower-income group had 1.28 greater odds (95% CI 1.24–1.33) of experiencing preventable hospitalizations, and 1.04 greater odds (95% CI 1.03–1.05) of having a comorbid condition in comparison to high-income group. Conclusions Income was shown to be a causal factor in a patient’s health and a determinant of the quality of care received even with equitable access to care under a universal health insurance system. Policies focusing on addressing income as an important upstream causal determinant of health to provide support to patients in lower socioeconomic status will be effective in improving health outcomes for this vulnerable social stratum.http://deepblue.lib.umich.edu/bitstream/2027.42/173767/1/12913_2019_Article_4793.pd

Impact of Cumulative Corticosteroid Dosage on Preventable Hospitalization among Taiwanese Patients with Ankylosing Spondylitis and Inflammatory Bowel Disease

Background: Corticosteroids are commonly prescribed for autoimmune conditions, but their impact on preventable hospitalization rates is unclear. This study sought to investigate the effect of corticosteroid use on hospitalization for ambulatory care sensitive conditions among Taiwanese patients with ankylosing spondylitis (AS) or inflammatory bowel disease (IBD). Methods: This was a retrospective cohort study using adults in the Taiwan National Health Insurance Research database receiving a new diagnosis of AS (n = 40,747) or IBD (n = 4290) between January 2002 and June 2013. Our main outcome measure was odds of preventable hospitalization for eight ambulatory care-sensitive conditions defined by the Agency for Healthcare Research and Quality. Results: In the first quarter (three months) following diagnosis, corticosteroid usage was common among patients with AS and IBD (18.5% and 30%, respectively). For every 100 mg increase in corticosteroid dose per quarter, adjusted odds of preventable hospitalization in the following quarter increased by 5.5% for patients with AS (aOR = 1.055, 95% CI 1.037&#8722;1.074) and 6.4% for those with IBD (aOR = 1.064, 95% CI 1.046&#8722;1.082). Conclusions: Relatively low doses of corticosteroids significantly increase AS and IBD patients&#8217; short-term odds of hospitalization for ambulatory care-sensitive conditions. As recommended by current clinical guidelines, physicians should use corticosteroids sparingly in these populations, and prioritize initiation/escalation of disease-modifying anti-rheumatic drugs for long-term management. If corticosteroids cannot be avoided, patients may require monitoring and/or prophylaxis for corticosteroid-associated comorbidities (e.g., diabetes) which can result in preventable hospitalizations

Utility of Wrist-Wearable Data for Assessing Pain, Sleep, and Anxiety Outcomes After Traumatic Stress Exposure

Importance: Adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure are common and have higher incidence among socioeconomically disadvantaged populations. Pain, depression, avoidance of trauma reminders, reexperiencing trauma, anxiety, hyperarousal, sleep disruption, and nightmares have been reported. Wrist-wearable devices with accelerometers capable of assessing 24-hour rest-activity characteristics are prevalent and may have utility in measuring these outcomes. Objective: To evaluate whether wrist-wearable devices can provide useful biomarkers for recovery after traumatic stress exposure. Design, setting, and participants: Data were analyzed from a diverse cohort of individuals seen in the emergency department after experiencing a traumatic stress exposure, as part of the Advancing Understanding of Recovery After Trauma (AURORA) study. Participants recruited from 27 emergency departments wore wrist-wearable devices for 8 weeks, beginning in the emergency department, and completed serial assessments of neuropsychiatric symptoms. A total of 19 019 patients were screened. Of these, 3040 patients met study criteria, provided informed consent, and completed baseline assessments. A total of 2021 provided data from wrist-wearable devices, completed the 8-week assessment, and were included in this analysis. The data were randomly divided into 2 equal parts (n = 1010) for biomarker identification and validation. Data were collected from September 2017 to January 2020, and data were analyzed from May 2020 to November 2022. Exposures: Participants were recruited for the study after experiencing a traumatic stress exposure (most commonly motor vehicle collision). Main outcomes and measures: Rest-activity characteristics were derived and validated from wrist-wearable devices associated with specific self-reported symptom domains at a point in time and changes in symptom severity over time. Results: Of 2021 included patients, 1257 (62.2%) were female, and the mean (SD) age was 35.8 (13.0) years. Eight wrist-wearable device biomarkers for symptoms of adverse posttraumatic neuropsychiatric sequelae exceeded significance thresholds in the derivation cohort. One of these, reduced 24-hour activity variance, was associated with greater pain severity (r = -0.14; 95% CI, -0.20 to -0.07). Changes in 6 rest-activity measures were associated with changes in pain over time, and changes in the number of transitions between sleep and wake over time were associated with changes in pain, sleep, and anxiety. Simple cutoffs for these biomarkers identified individuals with good recovery for pain (positive predictive value [PPV], 0.85; 95% CI, 0.82-0.88), sleep (PPV, 0.63; 95% CI, 0.59-0.67, and anxiety (PPV, 0.76; 95% CI, 0.72-0.80) with high predictive value. Conclusions and relevance: These findings suggest that wrist-wearable device biomarkers may have utility as screening tools for pain, sleep, and anxiety symptom outcomes after trauma exposure in high-risk populations

Utility of Wrist-Wearable Data for Assessing Pain, Sleep, and Anxiety Outcomes After Traumatic Stress Exposure

IMPORTANCE: Adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure are common and have higher incidence among socioeconomically disadvantaged populations. Pain, depression, avoidance of trauma reminders, reexperiencing trauma, anxiety, hyperarousal, sleep disruption, and nightmares have been reported. Wrist-wearable devices with accelerometers capable of assessing 24-hour rest-activity characteristics are prevalent and may have utility in measuring these outcomes. OBJECTIVE: To evaluate whether wrist-wearable devices can provide useful biomarkers for recovery after traumatic stress exposure. DESIGN, SETTING, AND PARTICIPANTS: Data were analyzed from a diverse cohort of individuals seen in the emergency department after experiencing a traumatic stress exposure, as part of the Advancing Understanding of Recovery After Trauma (AURORA) study. Participants recruited from 27 emergency departments wore wrist-wearable devices for 8 weeks, beginning in the emergency department, and completed serial assessments of neuropsychiatric symptoms. A total of 19 019 patients were screened. Of these, 3040 patients met study criteria, provided informed consent, and completed baseline assessments. A total of 2021 provided data from wrist-wearable devices, completed the 8-week assessment, and were included in this analysis. The data were randomly divided into 2 equal parts (n = 1010) for biomarker identification and validation. Data were collected from September 2017 to January 2020, and data were analyzed from May 2020 to November 2022. EXPOSURES: Participants were recruited for the study after experiencing a traumatic stress exposure (most commonly motor vehicle collision). MAIN OUTCOMES AND MEASURES: Rest-activity characteristics were derived and validated from wrist-wearable devices associated with specific self-reported symptom domains at a point in time and changes in symptom severity over time. RESULTS: Of 2021 included patients, 1257 (62.2%) were female, and the mean (SD) age was 35.8 (13.0) years. Eight wrist-wearable device biomarkers for symptoms of adverse posttraumatic neuropsychiatric sequelae exceeded significance thresholds in the derivation cohort. One of these, reduced 24-hour activity variance, was associated with greater pain severity (r = -0.14; 95% CI, -0.20 to -0.07). Changes in 6 rest-activity measures were associated with changes in pain over time, and changes in the number of transitions between sleep and wake over time were associated with changes in pain, sleep, and anxiety. Simple cutoffs for these biomarkers identified individuals with good recovery for pain (positive predictive value [PPV], 0.85; 95% CI, 0.82-0.88), sleep (PPV, 0.63; 95% CI, 0.59-0.67, and anxiety (PPV, 0.76; 95% CI, 0.72-0.80) with high predictive value. CONCLUSIONS AND RELEVANCE: These findings suggest that wrist-wearable device biomarkers may have utility as screening tools for pain, sleep, and anxiety symptom outcomes after trauma exposure in high-risk populations