Multiple Treatment Variables Research Articles

AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke.

Automated machine learning or autoML has been widely deployed in various industries. However, their adoption in healthcare, especially in clinical settings is constrained due to a lack of clear understanding and explainability. The aim of this study is to utilize autoML for the prediction of functional outcomes in patients who underwent mechanical thrombectomy and compare it with traditional ML models with a focus on the explainability of the trained models. A total of 156 patients of acute ischemic stroke with Large Vessel Occlusion (LVO) who underwent mechanical thrombectomy within 24 h of stroke onset were included in the study. A total of 34 treatment variables including clinical, demographic, imaging, and procedure-related data were extracted. Various conventional machine learning models such as decision tree classifier, logistic regression, random forest, kNN, and SVM as well as various autoML models such as AutoGluon, MLJAR, Auto-Sklearn, TPOT, and H2O were used to predict the modified Rankin score (mRS) at the time of patient discharge and 3 months follow-up. The sensitivity, specificity, accuracy, and AUC for traditional ML and autoML models were compared. The autoML models outperformed the traditional ML models. For the prediction of mRS at discharge, the highest testing accuracy obtained by traditional ML models for the decision tree classifier was 74.11%, whereas for autoML which was obtained through AutoGluon, it showed an accuracy of 88.23%. Similarly, for mRS at 3 months, the highest testing accuracy of traditional ML was that of the SVM classifier at 76.5%, whereas that of autoML was 85.18% obtained through MLJAR. The 24-h ASPECTS score was the most important predictor for mRS at discharge whereas for prediction of mRS at 3 months, the most important factor was mRS at discharge. Automated machine learning models based on multiple treatment variables can predict the functional outcome in patients more accurately than traditional ML models. The ease of clinical coding and deployment can assist clinicians in the critical decision-making process. We have developed a demo application which can be accessed at https://mrs-score-calculator.onrender.com/.

Evaluation of Practice Patterns and Outcomes after Implementing SMART for Pancreatic Cancer

<h3>Purpose/Objective(s)</h3> Stereotactic MRI-guided adaptive radiation therapy (SMART) enables safe dose escalation for locally advanced, borderline resectable, and medically inoperable pancreatic cancer and has shown favorable toxicity and survival outcomes. In late 2018, our institution commissioned SMART for these patients, making it available to all patient referrals. We wanted to review changes in our patient population and differences in clinical outcomes for patients before and after the implementation of SMART. <h3>Materials/Methods</h3> In this IRB approved analysis, we retrospectively reviewed 167 consecutive patients from 2015-2021 with locally advanced, borderline resectable, or medically unresectable pancreatic cancer who were treated with radiation therapy. Chemoradiation (chemoRT) was defined as any 28-30 fraction radiation regimen that included concurrent chemotherapy. SMART was defined as 50 Gy over 5 consecutive daily fractions without concurrent chemotherapy. Baseline patient characteristics were compared between groups. Overall survival (OS) was evaluated by Kaplan-Meier (KM) and log-rank test. Univariate (UVA) and multivariate analysis (MVA) were also performed on multiple treatment variables. <h3>Results</h3> Of the patients included, 58 received chemoRT (2015-2018) and 109 received SMART (2018-2021). Median follow up from time of diagnosis for the chemoRT and SMART cohorts were 53.7 months and 29.2 months respectively. Cohorts did not have significant differences in age, gender, race, T or N staging, rates of surgery or surgical margin status. Patients receiving SMART had overall worse performance (p = 0.011) including a lower percentage of PS 0 patients (22.9% vs 44.8%) and a higher percentage of PS 2+ patients (34% vs 15.5%). Similarly, the SMART patients did numerically more often have locally advanced (50% vs 43%) and medically inoperable (26% vs 21%) disease (p = 0.294). The SMART cohort did have longer neoadjuvant chemotherapy with mean of 3.5 months vs 2.3 months in the chemoRT cohort (p = 0.002). There was no OS difference between each group when measured from diagnosis (p=0.79) or from first day of radiation (p=0.2). Median survival in the chemoRT and SMART groups was 18.7 vs 17.4 months from diagnosis. When including only PS 0-1 patients, the median survival in the chemoRT and SMART groups was 18.8 vs 22.3 months (p=0.37). There was also no difference in locoregional control, distant control, or progression free survival using KM. On MVA positive prognostic factors for OS from diagnosis included ECOG <2 (HR 0.54, p=0.015), increasing months of neoadjuvant chemo (HR 0.88, p=0.004) and pancreatectomy (HR 0.14, p <0.001). <h3>Conclusion</h3> Despite the fact that the patient cohort receiving radiation therapy per the SMART approach had poorer performance status compared with chemoRT, OS was not significantly different. The multidisciplinary team was highly supportive of SMART with increased patients being treated.

Synthetic Control Estimation Beyond Case Studies: Does the Minimum Wage Reduce Employment?

Panel data are often used in empirical work to account for additive fixed time and unit effects. More recently, the synthetic control estimator relaxes the assumption of additive fixed effects for case studies, using pre-treatment outcomes to create a weighted average of other units which best approximate the treated unit. The synthetic control estimator is currently limited to case studies in which the treatment variable can be represented by a single indicator variable. Applying this estimator more generally, such as applications with multiple treatment variables or a continuous treatment variable, is problematic. This paper generalizes the case study synthetic control estimator to permit estimation of the effect of multiple treatment variables, which can be discrete or continuous. The estimator jointly estimates the impact of the treatment variables and creates a synthetic control for each unit. Additive fixed effect models are a special case of this estimator. Because the number of units in panel data and synthetic control applications is often small, I discuss an inference procedure for fixed N. The estimation technique generates correlations across clusters so the inference procedure will also account for this dependence. Simulations show that the estimator works well even when additive fixed effect models do not. I estimate the impact of the minimum wage on the employment rate of teenagers. I estimate an elasticity of -0.44, substantially larger than estimates generated using additive fixed effect models, and reject the null hypothesis that there is no effect.

Treat George Jetson: Video Chat for Better Patient Care

In Brief Caring for people with diabetes is frought with challenges, including a rapidly growing population, shrinking reimbursements for care, multiple treatment variables, an onslaught of numeric data, and the need for effective lifestyle intervention. Use of video chat technology for conducting appointments allows for provision of a wide range of clinical services while improving efficiency, accessibility, and communication between patients and providers.

Optimizing a control plan using a structural equation model with an application to statistical process analysis

In the case where non-experimental data are available from an industrial process and a directed graph for how various factors affect a response variable is known based on a substantive understanding of the process, we consider a problem in which a control plan involving multiple treatment variables is conducted in order to bring a response variable close to a target value with variation reduction. Using statistical causal analysis with linear (recursive and non-recursive) structural equation models, we configure an optimal control plan involving multiple treatment variables through causal parameters. Based on the formulation, we clarify the causal mechanism for how the variance of a response variable changes when the control plan is conducted. The results enable us to evaluate the effect of a control plan on the variance of a response variable from non-experimental data and provide a new application of linear structural equation models to engineering science.

Neurobiological Correlates of Acupuncture: November 17–18, 2005

The "Neurobiological Correlates of Acupuncture" Conference was convened November 17-18, 2005 in Bethesda, Maryland. The conference was sponsored by the National Center for Complementary and Alternative Medicine (NCCAM) of the National Institutes of Health (NIH), U.S. Department of Health and Human Services (DHHS). Its goals were to encourage exchange of ideas regarding the direction of neuroimaging in acupuncture research as well as to discuss some of the challenges in this field. The use of neuroimaging, a relatively recent advance in the study of acupuncture, holds the promise of localizing and characterizing brain activity associated with acupuncture interventions in real time and in a minimally invasive way. Among the main challenges to research into the biological mechanisms of acupuncture are the multiple treatment variables and the difficulties of selecting appropriate experimental controls. Despite these challenges, numerous findings from acupuncture neuroimaging experiments were presented and discussed at the conference on topics related to possible signaling networks, sham acupuncture controls, acupoint specificity, acupuncture analgesia, acupuncture-associated brain response, and the potential for using neuroimaging in conjunction with translational and clinical acupuncture research. Future directions in acupuncture neuroimaging research, as recommended by conference participants, should focus on (1) continuing exploration of acupuncture signaling networks; (2) establishing standards and recommendations for performing and reporting acupuncture neuroimaging results; (3) enabling data sharing in the acupuncture neuroimaging community; (4) gaining a better understanding of placebo and control groups in acupuncture neuroimaging experiments; and (5) developing biomarkers that relate to physiologically and/or clinically relevant acupuncture responses to neuroimaging results.

EDITOR'S PERSPECTIVE ON ARTICLE 3

The new information from this small retrospective study of middle cerebral artery aneurysms and intracerebral hemorrhage associated with their rupture is that the outcome is better if the clot is contained within brain parenchyma without spillage into the subarachnoid space, whether it is located only in the sylvian fissure or becomes diffuse. The other prognostic items identified, such as, clot size, neurological condition, age, and operative problems, are well described by others. The study presents a logical story and conclusion, albeit from a small and retrospective point of view. The problem presented by multiple treatment variables besides surgery remain inherent in this retrospective approach to the problem.

A brief component analysis of potential treatment packages in an outpatient clinic setting with young children.

The purpose of this study was to develop outpatient clinic assessments that allowed us to assess multiple treatment variables, produce relatively quick effects, and isolate active treatment components. Assessment began by evaluating a treatment package consisting of antecedent and consequence variables followed by a component analysis to determine the separate effects of the treatment variables. Three examples of the findings are presented.