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

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.

scholarly journals Antiviral treatment for outpatient use during an influenza pandemic: a decision tree model of outcomes averted and cost-effectiveness

2018 ◽  
Vol 41 (2) ◽  
pp. 379-390
Author(s):  
Sudhir Venkatesan ◽  
Cristina Carias ◽  
Matthew Biggerstaff ◽  
Angela P Campbell ◽  
Jonathan S Nguyen-Van-Tam ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
High Risk ◽  
Cost Saving ◽  
Decision Tree ◽  
Antiviral Treatment ◽  
Decision Tree Model ◽  
High Risk Population ◽  
Tree Model ◽  
Community Based ◽  
Population Groups

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.

scholarly journals A machine learning model to predict death outcome in severe COVID-19 patients

2021 ◽  
Author(s):  
Qiao Yang ◽  
Jixi Li ◽  
Zhijia Zhang ◽  
Xiaocheng Wu ◽  
Tongquan Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lactic Dehydrogenase ◽  
Learning Model ◽  
Severe Illness ◽  
Decision Tree Classifier ◽  
Risk Of Death ◽  
Global Pandemic ◽  
Machine Learning Model ◽  
Tree Classifier ◽  
Novel Coronavirus

Abstract BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between survivors and non-survivors. A decision tree classifier, 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 train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

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

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

scholarly journals A Machine Learning Model to Predict Death Outcome in Severe COVID-19 Patients

2020 ◽  
Author(s):  
Qiao Yang ◽  
Jixi Li ◽  
Zhijia Zhang ◽  
Xiaocheng Wu ◽  
Tongquan Liao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Area Under The Curve ◽  
Lactic Dehydrogenase ◽  
Learning Model ◽  
Severe Illness ◽  
Decision Tree Classifier ◽  
Risk Of Death ◽  
Machine Learning Model ◽  
Tree Classifier

Abstract BackgroundThe novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death.MethodsA total of 2,169 adult COVID-19 patients were enrolled from Wuhan, China between February 10th and 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 classifier to identify risk factors for death outcome.ResultsOf the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. The most common system symptoms were respiratory, systemic and digestive symptoms. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A machine learning model was developed to predict death outcome in severe patients. The decision tree classifier included three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase. The area under the curve of the receiver operating characteristic of this model was 0.96. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively.ConclusionThe machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

A novel enhanced decision tree model for detecting chronic kidney disease

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

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

Diagnostics ◽  
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.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
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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