Questions to the article: Predicting Unscheduled Emergency Department Return Visits Among Older Adults (Preprint)

2021 ◽  
Author(s):  
Shu-Chun Kuo ◽  
Tsair-wei Chien ◽  
Willy Chou
Keyword(s):  
Emergency Department ◽  
Decision Tree ◽  
Class Imbalance ◽  
Patient Characteristics ◽  
Small Sample ◽  
Clinical Settings ◽  
Data Set ◽  
Online Module ◽  
Study Results ◽  
Return Visits

UNSTRUCTURED The article published on 28 July 2021 is well-written and of interest, but remains several questions that are required for clarifications, such as (1) the Figure 1 is too complex to release the decision criteria for predicting unscheduled emergency department return visits (EDRVs); (2) the Table 1 with 11 rules is not succinct for readers to capture the core features of the influencing factors on the unscheduled EDRVs, and (3) the decision tree technique using Weka software did not demonstrate an online module that can be implemented in clinical settings. We suggested three ways to improve the study in methods and illustrated examples presented in previous studies using the decision tree technique. In addition, to solve the problem of class imbalance in data should be combined with an MP4 video(or a Multimedia Appendix) to make readers easily replicate similar research in the future. The patient characteristics and variables deposited in Multimedia Appendix 1 are insufficient. A small sample of data (e.g., one-tenth from the 10-fold cross-validation method to randomly partition the data set into ten subsets in the study) should be provided for readers to verify the decision tree that can yield appropriately 76.65% and 76.95% in sensitivity and specificity, respectively, as did in predicting the unscheduled EDRVs. Otherwise, the study results are doubtable.

scholarly journals Demonstrating an approach for evaluating synthetic geospatial and temporal epidemiologic data utility: Results from analyzing >1.8 million SARS-CoV-2 tests in the United States National COVID Cohort Collaborative (N3C)

2021 ◽  
Author(s):  
Jason A Thomas ◽  
Randi E Foraker ◽  
Noa Zamstein ◽  
Philip RO Payne ◽  
Adam B Wilcox ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Patient Characteristics ◽  
Original Data ◽  
The United States ◽  
Small Sample ◽  
Sample Sizes ◽  
Data Set ◽  
Data Utility ◽  
Small Sample Sizes ◽  
Zip Code

Objective: To evaluate whether synthetic data derived from a national COVID-19 data set could be used for geospatial and temporal epidemic analyses. Materials and Methods: Using an original data set (n = 1,854,968 SARS-CoV-2 tests) and its synthetic derivative, we compared key indicators of COVID-19 community spread through analysis of aggregate and zip-code level epidemic curves, patient characteristics and outcomes, distribution of tests by zip code, and indicator counts stratified by month and zip code. Similarity between the data was statistically and qualitatively evaluated. Results: In general, synthetic data closely matched original data for epidemic curves, patient characteristics, and outcomes. Synthetic data suppressed labels of zip codes with few total tests (mean = 2.9 ± 2.4; max = 16 tests; 66% reduction of unique zip codes). Epidemic curves and monthly indicator counts were similar between synthetic and original data in a random sample of the most tested (top 1%; n = 171) and for all unsuppressed zip codes (n = 5,819), respectively. In small sample sizes, synthetic data utility was notably decreased. Discussion: Analyses on the population-level and of densely-tested zip codes (which contained most of the data) were similar between original and synthetically-derived data sets. Analyses of sparsely-tested populations were less similar and had more data suppression. Conclusion: In general, synthetic data were successfully used to analyze geospatial and temporal trends. Analyses using small sample sizes or populations were limited, in part due to purposeful data label suppression - an attribute disclosure countermeasure. Users should consider data fitness for use in these cases.

Unplanned return visits to emergency in a regional hospital

2012 ◽  
Vol 36 (3) ◽  
pp. 336 ◽  
Author(s):  
Sue E. Kirby ◽  
Sarah M. Dennis ◽  
Upali W. Jayasinghe ◽  
Mark F. Harris
Keyword(s):  
Emergency Department ◽  
Patient Characteristics ◽  
Regional Hospital ◽  
Hospital Data ◽  
Service Usage ◽  
Return Visit ◽  
Visit Rate ◽  
Return Visits ◽  
Emergency Department Patients ◽  
Patient Service

Objective. The aim of this study was to determine the patient characteristics associated with unplanned return visits, using routinely collected hospital data, to assist in developing strategies to reduce their occurrence. Methods. Emergency department data from a regional hospital were analysed using univariate and multivariate methods to determine the influence of clinical, service usage and demographic patient characteristics on unplanned return visits. Results. Around 80% of the 16 000 patients attending emergency presented on only one occasion in a year. Five per cent of patients presented with an unplanned return visit. Older patients, those with minor and low urgency conditions and with non-psychotic mental health conditions, those presenting during winter and after hours were significantly more likely to present as unplanned return visits. Conclusion. Although patient characteristics associated with unplanned return visits have been identified, the reasons underpinning the unplanned return visit rate, such as patient service preference and attitudes, need to be more fully investigated. What is known about the topic? Patients who present as unplanned return visits are older and have a range of chronic and acute conditions. Some unplanned return visits occur because of limited access to other non-hospital service. What does this paper add? This paper adds to the field by providing information from a regional hospital in NSW Australia on the patient characteristics associated with unplanned return visits. It provides a basis for differentiating between other groups of frequent emergency department patients. However, the reasons behind the unplanned return visit rate need to be more fully investigated. What are the implications for practitioners? The implications of the findings of this study for policy makers, administrators and clinicians are that access to alternative services for the conditions associated with unplanned return visits need to be further investigated in the context of the role for emergency department services.

scholarly journals Prediction of massive bleeding in pancreatic surgery based on preoperative patient characteristics using a decision tree

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259682
Author(s):  
Taiichi Wakiya ◽  
Keinosuke Ishido ◽  
Norihisa Kimura ◽  
Hayato Nagase ◽  
Shunsuke Kubota ◽  
...  
Keyword(s):  
Decision Tree ◽  
Distal Pancreatectomy ◽  
Patient Characteristics ◽  
Massive Bleeding ◽  
Training Data ◽  
Ductal Adenocarcinoma ◽  
Correct Prediction ◽  
Binary Logistic Regression Analysis ◽  
Curative Surgery ◽  
Data Set

Massive intraoperative blood loss (IBL) negatively influence outcomes after surgery for pancreatic ductal adenocarcinoma (PDAC). However, few data or predictive models are available for the identification of patients with a high risk for massive IBL. This study aimed to build a model for massive IBL prediction using a decision tree algorithm, which is one machine learning method. One hundred and seventy-five patients undergoing curative surgery for resectable PDAC at our facility between January 2007 and October 2020 were allocated to training (n = 128) and testing (n = 47) sets. Using the preoperatively available data of the patients (34 variables), we built a decision tree classification algorithm. Of the 175 patients, massive IBL occurred in 88 patients (50.3%). Binary logistic regression analysis indicated that alanine aminotransferase and distal pancreatectomy were significant predictors of massive IBL occurrence with an overall correct prediction rate of 70.3%. Decision tree analysis automatically selected 14 predictive variables. The best predictor was the surgical procedure. Though massive IBL was not common, the outcome of patients with distal pancreatectomy was secondarily split by glutamyl transpeptidase. Among patients who underwent PD (n = 83), diabetes mellitus (DM) was selected as the variable in the second split. Of the 21 patients with DM, massive IBL occurred in 85.7%. Decision tree sensitivity was 98.5% in the training data set and 100% in the testing data set. Our findings suggested that a decision tree can provide a new potential approach to predict massive IBL in surgery for resectable PDAC.

Selection of children with ultra-severe traumatic brain injury for neurosurgical intervention

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decision Tree ◽  
Prediction Model ◽  
Severe Traumatic Brain Injury ◽  
Pupillary Response ◽  
Data Set ◽  
Robust Model ◽  
Gcs Score ◽  
Combined Data

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

The Impact of Green Labels on U.S. Hotel Net Operating Income: Operating Statements Analyses

2019 ◽  
Vol 11 (1) ◽  
pp. 156-173
Author(s):  
Spenser Robinson ◽  
A.J. Singh
Keyword(s):  
Environmental Design ◽  
Green Buildings ◽  
Small Sample ◽  
Sample Survey ◽  
Data Set ◽  
Operating Income ◽  
Energy Star ◽  
The Impact ◽  
Industry Professionals

This paper shows Leadership in Energy and Environmental Design (LEED) certified hospitality properties exhibit increased expenses and earn lower net operating income (NOI) than non-certified buildings. ENERGY STAR certified properties demonstrate lower overall expenses than non-certified buildings with statistically neutral NOI effects. Using a custom sample of all green buildings and their competitive data set as of 2013 provided by Smith Travel Research (STR), the paper documents potential reasons for this result including increased operational expenses, potential confusion with certified and registered LEED projects in the data, and qualitative input. The qualitative input comes from a small sample survey of five industry professionals. The paper provides one of the only analyses on operating efficiencies with LEED and ENERGY STAR hospitality properties.

scholarly journals Injuries and frequent use of emergency department services: a systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e040272
Author(s):  
Catherine Laferté ◽  
Andréa Dépelteau ◽  
Catherine Hudon
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Cohort Studies ◽  
Eligibility Criteria ◽  
Geriatric Population ◽  
Physical Injuries ◽  
Frequent Use ◽  
Different Types ◽  
Ed Visits ◽  
Return Visits

ObjectiveTo review all studies having examined the association between patients with physical injuries and frequent emergency department (ED) attendance or return visits.DesignSystematic review.Data sourceMedline, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PsycINFO databases were searched up to and including July 2019.Eligibility criteriaEnglish and French language publications reporting on frequent use of ED services (frequent attendance and return visits), evaluating injured patients and using regression analysis.Data extraction and synthesisTwo independent reviewers screened the search results, and assessed methodological quality using the Joanna Briggs Institute tool for prevalence studies. Results were collated and summarised using a narrative synthesis. A sensitivity analysis was performed to evaluate the repercussions of removing a study that did not meet the quality criteria.ResultsOf the 2184 studies yielded by this search, 1957 remained after the removal of duplicates. Seventy-eight studies underwent full-text screening leaving nine that met the eligibility criteria and were included in this study: five retrospective cohort studies; two prospective cohort studies; one cross-sectional study; and one case-control study. Different types of injuries were represented, including fractures, trauma and physical injuries related to falls, domestic violence or accidents. Sample sizes ranged from 200 to 1 259 809. Six studies included a geriatric population while three addressed a younger population. Of the four studies evaluating the relationship between injuries and frequent ED use, three reported an association. Additionally, of the five studies in which the dependent variable was return ED visits, three articles identified a positive association with injuries.ConclusionsPhysical injuries appear to be associated with frequent use of ED services (frequent ED attendance as well as return ED visits). Further research into factors including relevant youth-related covariates such as substance abuse and different types of traumas should be undertaken to bridge the gap in understanding this association.

Functional Decline in Emergency Department Patients with Dyspnea; a Register-Based Cohort

2021 ◽  
Author(s):  
Karoline Stentoft Rybjerg Larsen ◽  
Marianne Lisby ◽  
Hans Kirkegaard ◽  
Annemette Krintel Petersen
Keyword(s):  
Emergency Department ◽  
Electronic Patient Record ◽  
Hospital Admissions ◽  
Functional Decline ◽  
Patient Characteristics ◽  
Risk Of Death ◽  
Record Data ◽  
Emergency Department Patients ◽  
One Year ◽  
Historic Cohort

Abstract Background Functional decline is associated with frequent hospital admissions and elevated risk of death. Presumably patients acutely admitted to hospital with dyspnea have a high risk of functional decline. The aim of this study was to describe patient characteristics, hospital trajectory, and use of physiotherapy services of dyspneic patients in an emergency department. Furthermore, to compare readmission and death among patients with and without a functional decline, and to identify predictors of functional decline. Methods Historic cohort study of patients admitted to a Danish Emergency Department using prospectively collected electronic patient record data from a Business Intelligence Registry of the Central Denmark Region. The study included adult patients that due to dyspnea in 2015 were treated at the emergency department (ED). The main outcome measures were readmission, death, and functional decline. Results In total 2,048 dyspneic emergency treatments were registered. Within 30 days after discharge 20% was readmitted and 3.9% had died. Patients with functional decline had a higher rate of 30-day readmission (31.2% vs. 19.1%, p&lt;0.001) and mortality (9.3% vs. 3.6%, p=0.009) as well as mortality within one year (36.1% vs. 13.4%, p&lt;0.001). Predictors of functional decline were age ≥60 years and hospital stay ≥6 days. Conclusion Patients suffering from acute dyspnea are seen at the ED at all hours. In total one in five patients were readmitted and 3.9% died within 30 days. Patients with a functional decline at discharge seems to be particularly vulnerable.

scholarly journals Candida infective endocarditis: A retrospective study of patient characteristics and risk factors for death in 703 U.S. cases, 2015-2019

2020 ◽  
Author(s):  
Jonathan P Huggins ◽  
Samuel Hohmann ◽  
Michael Z David
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Liver Failure ◽  
Patient Characteristics ◽  
Medication Administration ◽  
Small Sample ◽  
Risk Of Death ◽  
Opiate Abuse ◽  
Small Sample Sizes ◽  
Valve Pathology

Abstract Background Candida endocarditis is a rare, sometimes fatal complication of candidemia. Past investigations of this condition are limited by small sample sizes. We used the Vizient clinical database to report on characteristics of patients with Candida endocarditis and to examine risk factors for in-hospital mortality. Methods This was a multicenter, retrospective cohort study of 703 inpatients admitted to 179 United States hospitals between October 2015 and April 2019. We reviewed demographic, diagnostic, medication administration, and procedural data from each patient’s initial encounter. Univariate and multivariate logistic regression analyses were used to identify predictors of in-hospital mortality. Results Of 703 patients, 114 (16.2%) died during the index encounter. One hundred and fifty-eight (22.5%) underwent an intervention on a cardiac valve. On multivariate analysis, acute and subacute liver failure was the strongest predictor of death (OR 9.2, 95% CI 4.8 –17.7). Female sex (OR 1.9, 95% CI 1.2 – 3.0), transfer from an outside medical facility (OR 1.8, 95% CI 1.1 – 2.8), aortic valve pathology (OR 2.7, 95% CI 1.5 – 4.9), hemodialysis (OR 2.1, 95% CI 1.1 – 4.0), cerebrovascular disease (OR 2.2, 95% CI 1.2 – 3.8), neutropenia (OR 2.5, 95% CI 1.3 – 4.8), and alcohol abuse (OR 2.9, 95% CI 1.3 – 6.7) were also associated with death on adjusted analysis, whereas opiate abuse was associated with a lower odds of death (OR 0.5, 95% CI 0.2 – 0.9). Conclusions We found that the inpatient mortality rate was 16.2% among patients with Candida endocarditis. Acute and subacute liver failure was associated with a high risk of death while opiate abuse was associated with a lower risk of death.

scholarly journals Parkinson’s Patients’ Tolerance for Risk and Willingness to Wait for Potential Benefits of Novel Neurostimulation Devices: A Patient-Centered Threshold Technique Study

2021 ◽  
Vol 6 (1) ◽  
pp. 238146832097840
Author(s):  
Brett Hauber ◽  
Brennan Mange ◽  
Mo Zhou ◽  
Shomesh Chaudhuri ◽  
Heather L. Benz ◽  
...  
Keyword(s):  
Trial Design ◽  
Patient Characteristics ◽  
Risk Tolerance ◽  
Level Of Evidence ◽  
Treatment Benefit ◽  
Study Results ◽  
Potential Benefits ◽  
Risk Thresholds ◽  
Time Motor ◽  
Threshold Technique

Background. Parkinson’s disease (PD) is neurodegenerative, causing motor, cognitive, psychological, somatic, and autonomic symptoms. Understanding PD patients’ preferences for novel neurostimulation devices may help ensure that devices are delivered in a timely manner with the appropriate level of evidence. Our objective was to elicit preferences and willingness-to-wait for novel neurostimulation devices among PD patients to inform a model of optimal trial design. Methods. We developed and administered a survey to PD patients to quantify the maximum levels of risks that patients would accept to achieve potential benefits of a neurostimulation device. Threshold technique was used to quantify patients’ risk thresholds for new or worsening depression or anxiety, brain bleed, or death in exchange for improvements in “on-time,” motor symptoms, pain, cognition, and pill burden. The survey elicited patients’ willingness to wait to receive treatment benefit. Patients were recruited through Fox Insight, an online PD observational study. Results. A total of 2740 patients were included and a majority were White (94.6%) and had a 4-year college degree (69.8%). Risk thresholds increased as benefits increased. Threshold for depression or anxiety was substantially higher than threshold for brain bleed or death. Patient age, ambulation, and prior neurostimulation experience influenced risk tolerance. Patients were willing to wait an average of 4 to 13 years for devices that provide different levels of benefit. Conclusions. PD patients are willing to accept substantial risks to improve symptoms. Preferences are heterogeneous and depend on treatment benefit and patient characteristics. The results of this study may be useful in informing review of device applications and other regulatory decisions and will be input into a model of optimal trial design for neurostimulation devices.

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