A systematic review of the complications of high-risk third molar removal and coronectomy: development of a decision tree model and preliminary health economic analysis to assist in treatment planning

Abstract Background Many countries have acquired antiviral stockpiles for pandemic influenza mitigation and a significant part of the stockpile may be focussed towards community-based treatment. Methods We developed a spreadsheet-based, decision tree model to assess outcomes averted and cost-effectiveness of antiviral treatment for outpatient use from the perspective of the healthcare payer in the UK. We defined five pandemic scenarios—one based on the 2009 A(H1N1) pandemic and four hypothetical scenarios varying in measures of transmissibility and severity. Results Community-based antiviral treatment was estimated to avert 14–23% of hospitalizations in an overall population of 62.28 million. Higher proportions of averted outcomes were seen in patients with high-risk conditions, when compared to non-high-risk patients. We found that antiviral treatment was cost-saving across pandemic scenarios for high-risk population groups, and cost-saving for the overall population in higher severity influenza pandemics. Antiviral effectiveness had the greatest influence on both the number of hospitalizations averted and on cost-effectiveness. Conclusions This analysis shows that across pandemic scenarios, antiviral treatment can be cost-saving for population groups at high risk of influenza-related complications.

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Decision-tree model for health economic comparison of two long-acting somatostatin receptor ligand devices in France, Germany, and the UK

Medical Devices Evidence and Research ◽

10.2147/mder.s30913 ◽

2012 ◽

pp. 39 ◽

Cited By ~ 2

Author(s):

Hannah Kurth ◽

Marty ◽

Roze

Keyword(s):

Health Economic ◽

Decision Tree ◽

Somatostatin Receptor ◽

Decision Tree Model ◽

Tree Model ◽

Receptor Ligand ◽

Long Acting ◽

The Uk ◽

Somatostatin Receptor Ligand ◽

Economic Comparison

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Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer: a systematic review and economic analysis

Health Technology Assessment ◽

10.3310/hta23300 ◽

2019 ◽

Vol 23 (30) ◽

pp. 1-328 ◽

Cited By ~ 6

Author(s):

Sue Harnan ◽

Paul Tappenden ◽

Katy Cooper ◽

John Stevens ◽

Alice Bessey ◽

...

Keyword(s):

Breast Cancer ◽

Systematic Review ◽

Health Economic ◽

Cost Effectiveness ◽

Economic Analysis ◽

Health Economic Analysis ◽

Node Negative ◽

Post Test ◽

Patient Subgroups ◽

Risk Of Relapse

BackgroundBreast cancer and its treatment can have an impact on health-related quality of life and survival. Tumour profiling tests aim to identify whether or not women need chemotherapy owing to their risk of relapse.ObjectivesTo conduct a systematic review of the effectiveness and cost-effectiveness of the tumour profiling tests oncotypeDX®(Genomic Health, Inc., Redwood City, CA, USA), MammaPrint®(Agendia, Inc., Amsterdam, the Netherlands), Prosigna®(NanoString Technologies, Inc., Seattle, WA, USA), EndoPredict®(Myriad Genetics Ltd, London, UK) and immunohistochemistry 4 (IHC4). To develop a health economic model to assess the cost-effectiveness of these tests compared with clinical tools to guide the use of adjuvant chemotherapy in early-stage breast cancer from the perspective of the NHS and Personal Social Services.DesignA systematic review and health economic analysis were conducted.Review methodsThe systematic review was partially an update of a 2013 review. Nine databases were searched in February 2017. The review included studies assessing clinical effectiveness in people with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative, stage I or II cancer with zero to three positive lymph nodes. The economic analysis included a review of existing analyses and the development of a de novo model.ResultsA total of 153 studies were identified. Only one completed randomised controlled trial (RCT) using a tumour profiling test in clinical practice was identified: Microarray In Node-negative Disease may Avoid ChemoTherapy (MINDACT) for MammaPrint. Other studies suggest that all the tests can provide information on the risk of relapse; however, results were more varied in lymph node-positive (LN+) patients than in lymph node-negative (LN0) patients. There is limited and varying evidence that oncotypeDX and MammaPrint can predict benefit from chemotherapy. The net change in the percentage of patients with a chemotherapy recommendation or decision pre/post test ranged from an increase of 1% to a decrease of 23% among UK studies and a decrease of 0% to 64% across European studies. The health economic analysis suggests that the incremental cost-effectiveness ratios for the tests versus current practice are broadly favourable for the following scenarios: (1) oncotypeDX, for the LN0 subgroup with a Nottingham Prognostic Index (NPI) of > 3.4 and the one to three positive lymph nodes (LN1–3) subgroup (if a predictive benefit is assumed); (2) IHC4 plus clinical factors (IHC4+C), for all patient subgroups; (3) Prosigna, for the LN0 subgroup with a NPI of > 3.4 and the LN1–3 subgroup; (4) EndoPredict Clinical, for the LN1–3 subgroup only; and (5) MammaPrint, for no subgroups.LimitationsThere was only one completed RCT using a tumour profiling test in clinical practice. Except for oncotypeDX in the LN0 group with a NPI score of > 3.4 (clinical intermediate risk), evidence surrounding pre- and post-test chemotherapy probabilities is subject to considerable uncertainty. There is uncertainty regarding whether or not oncotypeDX and MammaPrint are predictive of chemotherapy benefit. The MammaPrint analysis uses a different data source to the other four tests. The Translational substudy of the Arimidex, Tamoxifen, Alone or in Combination (TransATAC) study (used in the economic modelling) has a number of limitations.ConclusionsThe review suggests that all the tests can provide prognostic information on the risk of relapse; results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence that oncotypeDX and MammaPrint are predictive of chemotherapy benefit. Health economic analyses indicate that some tests may have a favourable cost-effectiveness profile for certain patient subgroups; all estimates are subject to uncertainty. More evidence is needed on the prediction of chemotherapy benefit, long-term impacts and changes in UK pre-/post-chemotherapy decisions.Study registrationThis study is registered as PROSPERO CRD42017059561.FundingThe National Institute for Health Research Health Technology Assessment programme.

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POS0320 POOR PROGNOSIS PREDICTION IN ANTI-MDA5 POSITIVE DERMATOMYOSITIS ASSOCIATED WITH INTERSTITIAL LUNG DISEASE: THE CROSS-CAR DECISION TREE MODEL

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3514 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 387.2-387

Author(s):

L. Wang ◽

C. Lv ◽

F. Yuan ◽

J. Li ◽

M. Wu ◽

...

Keyword(s):

High Risk ◽

Interstitial Lung Disease ◽

Decision Tree ◽

Prediction Model ◽

Decision Tree Model ◽

C Reactive Protein ◽

Tree Model ◽

Reactive Protein ◽

Risk Patients ◽

The Cross

Background:The prognosis of anti-melanoma differentiation-associated gene 5 positive dermatomyositis (anti-MDA5+ DM) – associated interstitial lung disease (ILD) is poor and heterogeneity.Objectives:The aim of this study was to evaluate prognostic factors and to develop a simple and generally applicable bedside decision tree model for predicting outcomes in patients with anti-MDA5+ DM and to guide treatment.Methods:We analyzed data for 246 anti-MDA5+ DM patients from Myositis Study Group-Jiangsu, a multicenter cohort across eighteen tertiary hospitals in Jiangsu province, from March 2019 to October 2020. The primary end point was all-cause death, and the secondary end point was occurring of rapidly progressive-ILD (rp-ILD). We used a multivariable Cox proportional hazards model to identify the independent prognostic risk factors of death and rp-ILD respectively. A decision-tree prediction model was developed by using data from 10 hospital of southern region (n=163), with validation by using contemporaneous data from northern region (n=83).Results:To assess the risk of rp-ILD, we developed a combined risk score, the CROSS score, that included the following values and scores: C-reactive protein (≤8mg/L, 0; >8mg/L, 3), anti-Ro52 antibody (negative, 0; positive, 4), Sex (Female, 0; Male, 2) and Short course of disease (More than 3 months, 0; Less than 3 months, 2). The mortality risk was identified by the CAR score, including C-reactive protein (≤8mg/L, 0; >8mg/L, 1), Alanine Transaminase (≤50units/L, 0; >50units/L, 1) and rp-ILD (non-rpILD, 0; rp-ILD, 3). We divided patients into three risk groups according to the CROSS score: low, 0 to 3; medium, 4 to 7; and high 8-11. And then Use of a simple decision tree prediction model permitted stratification into three different outcome prediction groups. High-risk patients had significantly higher mortality rates than low- and medium-risk patients in both discovery and validation cohorts (p < 0.0001).Conclusion:The CROSS-CAR decision tree model is easy to evaluate the poor prognostic risk in MDA5+ DM patients during any follow-up period. Unnecessary lung examination, such as chest CT scan and arterial blood gas analysis was avoided in low- and medium- rpILD risk patients. The special ambulance, with red cross sign tagged on car in China, may help to screen the high risk patients and to guide further treatment.Disclosure of Interests:None declared

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Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients

BMC Infectious Diseases ◽

10.1186/s12879-021-06478-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qiao Yang ◽

Jixi Li ◽

Zhijia Zhang ◽

Xiaocheng Wu ◽

Tongquan Liao ◽

...

Keyword(s):

High Risk ◽

Decision Tree ◽

Clinical Characteristics ◽

Lactic Dehydrogenase ◽

Decision Tree Model ◽

Severe Illness ◽

Tree Model ◽

Risk Of Death ◽

Male Patients ◽

Novel Coronavirus

Abstract Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.

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Estimation of Diabetes in a High-Risk Adult Chinese Population Using J48 Decision Tree Model

Diabetes Metabolic Syndrome and Obesity Targets and Therapy ◽

10.2147/dmso.s279329 ◽

2020 ◽

Vol Volume 13 ◽

pp. 4621-4630

Author(s):

Dongmei Pei ◽

Tengfei Yang ◽

Chengpu Zhang

Keyword(s):

High Risk ◽

Decision Tree ◽

Chinese Population ◽

Decision Tree Model ◽

Tree Model ◽

J48 Decision Tree

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A novel enhanced decision tree model for detecting chronic kidney disease

Network Modeling Analysis in Health Informatics and Bioinformatics ◽

10.1007/s13721-021-00302-w ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Avijit Kumar Chaudhuri ◽

Deepankar Sinha ◽

Dilip K. Banerjee ◽

Anirban Das

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Decision Tree ◽

Decision Tree Model ◽

Tree Model

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Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽

10.3390/diagnostics11061094 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1094

Author(s):

Michael Wong ◽

Nikolaos Thanatsis ◽

Federica Nardelli ◽

Tejal Amin ◽

Davor Jurkovic

Keyword(s):

Decision Tree ◽

Expectant Management ◽

Decision Tree Model ◽

Decision Tree Analysis ◽

Focal Lesions ◽

Tree Model ◽

Normal Endometrium ◽

Endometrial Polyps ◽

Tree Analysis ◽

Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

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Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

International Journal of Endocrinology ◽

10.1155/2017/3894870 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Zhong Xin ◽

Lin Hua ◽

Xu-Hong Wang ◽

Dong Zhao ◽

Cai-Guo Yu ◽

...

Keyword(s):

Decision Tree ◽

Predictive Value ◽

Early Stage ◽

Current Model ◽

External Validation ◽

Area Under The Curve ◽

Decision Tree Model ◽

Tree Model ◽

Chinese Adult ◽

Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

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