Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease

Schilling, Chris; Mortimer, Duncan; Dalziel, Kim; Heeley, Emma; Chalmers, John; Clarke, Philip

doi:10.1007/s40273-015-0342-3

Cited by 22 publications

(13 citation statements)

References 37 publications

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“…A previous study indicated that, in combination with Cox proportional hazards regression, the survival tree method may aid prognostic analysis (28). To the best of our knowledge, none of the published prognostic classification models have involved EGFR mutation-positive NSCLC patients with brain metastases.…”

Section: Discussionmentioning

confidence: 99%

Prognostic factors analysis in EGFR mutation-positive non-small cell lung cancer with brain metastases treated with whole brain-radiotherapy and EGFR-tyrosine kinase inhibitors

Wei

Lin

et al. 2016

Oncology Letters

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Abstract. The survival time of non-small cell lung cancer (NSCLC) patients with brain metastases has been previously reported to be 6.5-10.0 months, even with systematic treatment. Patients that possess a certain epidermal growth factor receptor (EGFR) mutation alongside NSCLC with brain metastases also have a short survival rate, and a reliable prognostic model for these patients demonstrates a strong correlation between the outcome and treatment recommendations. The Cox proportional hazards regression and classification tree models were used to explore the prognostic factors in EGFR mutation-positive NSCLC patients with brain metastases following whole-brain radiation therapy (WBRT) and EGFR-tyrosine kinase inhibitor (EGFR-TKI) treatment. A total of 66 EGFR mutation-positive NSCLC patients with brain metastases were retrospectively reviewed. Univariate and multivariate analyses by Cox proportional hazards regression were then performed. The classification tree model was applied in order to identify prognostic groups of the patients. In the survival analysis, age, carcinoembryonic antigen (CEA) and status of the primary tumor were prognostic factors for progression free survival (P=0.006, 0.014 and 0.005, respectively) and overall survival (P= 0.009, 0.013 and 0.009, respectively). The classification tree model was subsequently applied, which revealed 3 patient groups with significantly different survival times: Group I, age <65 years and CEA ≤10 µg/ml; Group II, age <65 years and CEA >10 µg/ml or age ≥65 years and CEA ≤10 µg/ml; and Group III, age ≥65 years and CEA >10 µg/ml. The major prognostic predictors for EGFR mutation-positive NSCLC patients with brain metastases following WBRT and EGFR-TKI were age and CEA. In addition, primary tumor control may be important for predicting survival. IntroductionPatients with non-small cell lung cancer (NSCLC) possess a high risk of developing brain metastases. The incidence rate of brain metastases for patients with lung cancer is 23-65% (1). Once intracerebral metastases develop, the prognosis is poor. The median survival time for patients with untreated brain metastases is only ~1 month (2,3). The principle therapeutic modality for brain metastases is whole-brain radiation therapy (WBRT), and the median survival time following this treatment may increase to 4-6 months. Although therapies, including surgery, WBRT, stereotactic radiotherapy and systematic chemotherapy, are rapidly improving, the prognosis of patients with brain metastases from lung cancer remains poor, and the median survival time for patients remains at ~6.5-10 months (4-7).In 2004, the genes encoding the read code box of epidermal growth factor receptor (EGFR) were sequenced (8). In addition, the gene mutation status has been elucidated and is now widely used in clinical practice. As a result, the identification of the EGFR mutation and the introduction of treatment with EGFR-tyrosine kinase inhibitors (EGFR-TKI) has improved clinical outcomes (9). EGFR-TKI regimens have a good efficacy against brain...

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Section: Discussionmentioning

confidence: 99%

Prognostic factors analysis in EGFR mutation-positive non-small cell lung cancer with brain metastases treated with whole brain-radiotherapy and EGFR-tyrosine kinase inhibitors

Wei

Lin

et al. 2016

Oncology Letters

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“…Regression tree analysis, a form of supervised sub-grouping commonly used in machine learning, recursively partitioned the practices into mutually exclusive groupings based on the frequency of strategies. This resulted in a set of practice groups with minimal intra-group variation and maximal inter-group variation of completed interventions [33][34][35]. Regression tree algorithms select features and determine cutoffs for splitting groups into sub-groups that would result in the best partition in terms of the variance, resulting in two sub-groups that have the greatest degree of difference with regard to the target outcome, and add this split to the tree.…”

Section: Discussionmentioning

confidence: 99%

Does coaching matter? Examining the impact of specific practice facilitation strategies on implementation of quality improvement interventions in the Healthy Hearts in the Heartland study

Walunas

Bannon

et al. 2021

Implementation Sci

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Background Practice facilitation is a multicomponent implementation strategy used to improve the capacity for practices to address care quality and implementation gaps. We sought to assess whether practice facilitators use of coaching strategies aimed at improving self-sufficiency were associated with improved implementation of quality improvement (QI) interventions in the Healthy Hearts in the Heartland Study. Methods We mapped 27 practice facilitation activities to a framework that classifies practice facilitation strategies by the degree to which the practice develops its own process expertise (Doing Tasks, Project Management, Consulting, Teaching, and Coaching) and then used regression tree analysis to group practices by facilitation strategies experienced. Kruskal-Wallis tests were used to assess whether practice groups identified by regression tree analysis were associated with successful implementation of QI interventions and practice and study context variables. Results There was no association between number of strategies performed by practice facilitators and number of QI interventions implemented. Regression tree analysis identified 4 distinct practice groups based on the number of Project Management and Coaching strategies performed. The median number of interventions increased across the groups. Practices receiving > 4 project management and > 6 coaching activities implemented a median of 17 of 35 interventions. Groups did not differ significantly by practice size, association with a healthcare network, or practice type. Statistically significant differences in practice location, number and duration of facilitator visits, and early study termination emerged among the groups, compared to the overall practice population. Conclusions Practices that engage in more coaching-based strategies with practice facilitators are more likely to implement more QI interventions, and practice receptivity to these strategies was not dependent on basic practice demographics.

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“…The test results in the test sample indicated that the model in predicting the risk of non-fatal drowning in students was acceptable, with an area under the ROC curve (AUC) of 0.680. Generally speaking, the AUC is around 0.7 in the population-based prediction models of a certain event [29, 30], and the AUC is usually larger than 0.85 in prediction models of clinical trials [31, 32].…”

Section: Discussionmentioning

confidence: 99%

An exploratory model for the non-fatal drowning risks in children in Guangdong, China

Zhu

ZhiShan

et al. 2019

BMC Public Health

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Background Drowning is a leading cause of accidental death in children under 14 years of age in Guangdong, China. We developed a statistical model to classify the risk of drowning among children based on the risk factors. Methods A multiple-stage cluster random sampling was employed to select the students in Grades 3 to 9 in two townships in Qingyuan, Guangdong. Questionnaire was a self-reported measure consisting of general information, knowledge, attitudes and activities. A univariate logistic regression model was used to preliminarily select the independent variables at a P value of 0.1 for multivariable model. Three-quarters of the participants were randomly selected as a training sample to establish the model, and the remaining were treated as a testing sample to validate the model. Results A total of 8390 children were included in this study, about 12.18% (1013) experienced drowning during the past one year. In the univariate logistic regression model, introvert personality, unclear distributions of water areas on the way to school, and bad relationships with their classmates and families were positively associated with drowning. However, females, older age and lower swimming skills were negatively associated with drowning. After employing the prediction model with these factors to estimate drowning risk of the students in the testing samples, the results of Hosmer-Lemeshow tests showed non-significant differences between the predictive results and actual risk (χ2 = 5.97, P = 0.65). Conclusions Male, younger children, higher swimming skills, bad relationship with their classmates and families, introvert personality and unclear distributions of water areas on the way to school were important risk factors of non-fatal drowning among children. The prediction model based on these variables has an acceptable predictive ability.

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Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease

Cited by 22 publications

References 37 publications

Prognostic factors analysis in EGFR mutation-positive non-small cell lung cancer with brain metastases treated with whole brain-radiotherapy and EGFR-tyrosine kinase inhibitors

Prognostic factors analysis in EGFR mutation-positive non-small cell lung cancer with brain metastases treated with whole brain-radiotherapy and EGFR-tyrosine kinase inhibitors

Does coaching matter? Examining the impact of specific practice facilitation strategies on implementation of quality improvement interventions in the Healthy Hearts in the Heartland study

An exploratory model for the non-fatal drowning risks in children in Guangdong, China

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