scholarly journals P112: Predicting patient admission from the emergency department using triage administrative data

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S116
Author(s):  
D.W. Savage ◽  
B. Weaver ◽  
D. Wood
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Acute Care ◽  
Administrative Data ◽  
Statistical Models ◽  
Regression Models ◽  
Patient Flow ◽  
Area Under The Curve ◽  
Patient Admission ◽  
Logistic Regression Models

Introduction: Emergency department (ED) over-crowding and increased wait times are a growing problem. Many interventions have been proposed to decrease patient length of stay and increase patient flow. Early disposition planning is one method to accomplish this goal. In this study we developed statistical models to predict patient admission based on ED administrative data. The objective of this study was to predict patient admission early in the visit with goal of preparation of the acute care bed and other resources. Methods: Retrospective administrative ED data from the Thunder Bay Regional Health Sciences Centre was obtained for the period May 2014 to April 2015. Data were divided into training and testing groups with 80% of data used to train the statistical models. Logistic regression models were developed using administrative variables (i.e., age, sex, mode of arrival, and triage level). Model accuracy was evaluated using sensitivity, specificity, and area under the curve measures. To predict hourly bed requirements, the probability of admission was summed to calculate a pooled bed requirement estimate. The estimated hourly bed requirement was then compared to the historical hourly demand. Results: The logistic regression models had a sensitivity of 23%, specificity of 97%, and an area under the curve of 0.78. Although, admission prediction for a particular individual was satisfactory, the hourly pooled probabilities showed better results. The predicted hourly bed requirements were close to historical demand for beds when compared. Conclusion: I have shown that the number of acute care beds required on an hourly basis can be predicted using triage administrative data. Early admission bed planning would allow resources to be managed more effectively. In addition, during periods of hospital over capacity, managers would be able to prioritize transfers and discharges based on early estimates of ED demand for beds.

Predicting Patient Admission From the Emergency Department Using Administrative and Diagnostic Data

2020 ◽  
Vol 2 (2) ◽  
pp. 1-11
Author(s):  
David W. Savage ◽  
Douglas G. Woolford ◽  
Mackenzie Simpson ◽  
David Wood ◽  
Robert Ohle
Keyword(s):  
Emergency Department ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Patient Flow ◽  
Patient Admission ◽  
Patient Acuity ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Ed Overcrowding ◽  
One Year

Emergency department (ED) overcrowding is a growing problem in Canada. Many interventions have been proposed to increase patient flow. The objective of this study was to predict patient admission early in the visit with the goal of reducing waiting time in ED for admitted patients. ED data for a one-year period from Thunder Bay, Canada was obtained. Initial logistic regression models were developed using age, sex, mode of arrival, and patient acuity as explanatory variables and admission yes or no as the outcome. A second stage prediction was made using the diagnostic tests ordered to further refine the predictive models. Predictive accuracy of the logistic regression model was adequate. The AUC was approximately 81%. By summing the probabilities of patients in the ED, the hourly prediction improved. This study has shown that the number of hospital beds required on an hourly basis can be predicted using triage administrative data.

scholarly journals A-140 Single-Item Screening for Anxiety and Depression using the Neurobehavioral Symptom Inventory

2020 ◽  
Vol 35 (6) ◽  
pp. 933-933
Author(s):  
Rolin S ◽  
Kitchen Andren K ◽  
Mullen C ◽  
Kurniadi N ◽  
Davis J
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Anxiety And Depression ◽  
Average Score ◽  
Characteristic Analysis ◽  
Logistic Regression Models ◽  
Symptom Inventory

Abstract Objective Previous research in a Veterans Affairs sample proposed using single items on the Neurobehavioral Symptom Inventory (NSI) to screen for anxiety (item 19) and depression (item 20). This study examined the approach in an outpatient physical medicine and rehabilitation sample. Method Participants (N = 84) underwent outpatient neuropsychological evaluation using the NSI, BDI-II, GAD-7, MMPI-2-RF, and Memory Complaints Inventory (MCI) among other measures. Anxiety and depression were psychometrically determined via cutoffs on the GAD-7 (>4) and MMPI-2-RF ANX (>64 T), and BDI-II (>13) and MMPI-2-RF RC2 (>64 T), respectively. Analyses included receiver operating characteristic analysis (ROC) and logistic regression. Logistic regression models used dichotomous anxiety and depression as outcomes and relevant NSI items and MCI average score as predictors. Results ROC analysis using NSI items to classify cases showed area under the curve (AUC) values of .77 for anxiety and .85 for depression. The logistic regression model predicting anxiety correctly classified 80% of cases with AUC of .86. The logistic regression model predicting depression correctly classified 79% of cases with AUC of .88. Conclusion Findings support the utility of NSI anxiety and depression items as screening measures in a rehabilitation population. Consideration of symptom validity via the MCI improved classification accuracy of the regression models. The approach may be useful in other clinical settings for quick assessment of psychological issues warranting further evaluation.

A Prospective Validation of the IOTA Logistic Regression Models (LR1 and LR2) in Comparison to Subjective Pattern Recognition for the Diagnosis of Ovarian Cancer

2013 ◽  
Vol 23 (9) ◽  
pp. 1583-1589 ◽  
Author(s):  
Natalie Nunes ◽  
Gareth Ambler ◽  
Wee-Liak Hoo ◽  
Joel Naftalin ◽  
Xulin Foo ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Models ◽  
Area Under The Curve ◽  
Positive Likelihood Ratio ◽  
Tertiary Hospital ◽  
Ovarian Tumour ◽  
Logistic Regression Models ◽  
Operative Findings

ObjectivesThis study aimed to assess the accuracy of the International Ovarian Tumour Analysis (IOTA) logistic regression models (LR1 and LR2) and that of subjective pattern recognition (PR) for the diagnosis of ovarian cancer.Methods and MaterialsThis was a prospective single-center study in a general gynecology unit of a tertiary hospital during 33 months. There were 292 consecutive women who underwent surgery after an ultrasound diagnosis of an adnexal tumor. All examinations were by a single level 2 ultrasound operator, according to the IOTA guidelines. The malignancy likelihood was calculated using the IOTA LR1 and LR2. The women were then examined separately by an expert operator using subjective PR. These were compared to operative findings and histology. The sensitivity, specificity, area under the curve (AUC), and accuracy of the 3 methods were calculated and compared.ResultsThe AUCs for LR1 and LR2 were 0.94 [95% confidence interval (CI), 0.92–0.97] and 0.93 (95% CI, 0.90–0.96), respectively. Subjective PR gave a positive likelihood ratio (LR+ve) of 13.9 (95% CI, 7.84–24.6) and a LR−ve of 0.049 (95% CI, 0.022–0.107). The corresponding LR+ve and LR−ve for LR1 were 3.33 (95% CI, 2.85–3.55) and 0.03 (95% CI, 0.01–0.10), and for LR2 were 3.58 (95% CI, 2.77–4.63) and 0.052 (95% CI, 0.022–0.123). The accuracy of PR was 0.942 (95% CI, 0.908–0.966), which was significantly higher when compared with 0.829 (95% CI, 0.781–0.870) for LR1 and 0.836 (95% CI, 0.788–0.872) for LR2 (P < 0.001).ConclusionsThe AUC of the IOTA LR1 and LR2 were similar in nonexpert’s hands when compared to the original and validation IOTA studies. The PR method was the more accurate test to diagnose ovarian cancer than either of the IOTA models.

scholarly journals 616. Predicting Misdiagnoses of Infectious Disease in Emergency Department Visits

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S411-S411
Author(s):  
Alec B Chapman ◽  
Kelly Peterson ◽  
Wathsala Widanagamaachchi ◽  
Makoto M Jones
Keyword(s):  
Infectious Disease ◽  
Emergency Department ◽  
Logistic Regression ◽  
Regression Models ◽  
Emergency Department Visits ◽  
Care Process ◽  
Logistic Regression Models ◽  
Therapeutic Decisions ◽  
Ed Visits ◽  
Testing Set

Abstract Background Diagnostic error leads to delays of care and mistaken therapeutic decisions that can cascade in a downward spiral. Thus, it is important to make accurate diagnostic decisions early on in the clinical care process, such as in the emergency department (ED). Clinical data from the Electronic Health Record (EHR) could identify cases where an initial diagnosis appears unusual in context. This capability could be developed into a quality measure for feedback. To that end, we trained a multiclass machine learning classifier to predict infectious disease diagnoses following an ED visit. Methods To train and evaluate our classifier, we sampled ED visits between December 31, 2016, and December 31, 2019, from Veterans Affairs (VA) Corporate Data Warehouse (CDW). Data elements used for prediction included lab orders and results, medication orders, radiology procedures, and vital signs. A multiclass XGBoost classifier was trained to predict one of five infectious disease classes for each ED visit based on the clinical variables extracted from CDW. Our model was trained on an enriched sample of 916,562 ED visits and evaluated on a non-enriched blind testing set of 356,549 visits. We compared our model against an ensemble of univariate Logistic Regression models as a baseline. Our model was trained to predict for an ED visit one of five infectious disease classes or “No Infection”. Labels were assigned to each ED visit based on ICD-9/10-CM diagnosis codes used elsewhere and other structured EHR data associated with a patient between 24 hours prior to an ED visit and 48 hours after. Results Classifier performance varied across each of the five disease classes (Table 1). The classifier achieved the highest F1 and AUC for UTI, the lowest F1 for Sepsis, and the lowest AUC for URI. We compared the average precision, recall and F1 scores of the multiclass XGBoost with the ensemble of Logistic Regression models (Table 2). XGBoost achieved higher scores in all three metrics. Table 1. Classification performance XGBoost testing set performance in each disease class, visits with no labels, and macro average. The infectious disease classes with the highest score in each metric are shown in bold. Table 2. Baseline comparison Macro average scores for XGBoost and baseline classifiers. Conclusion We trained a model to predict infectious disease diagnoses in the Emergency Department setting. Future work will further explore this technique and combine our supervised classifier with additional signs of medical error such as increased mortality or anomalous treatment patterns in order to study medical misdiagnosis. Disclosures All Authors: No reported disclosures

scholarly journals Radiogenomics prediction for MYCN amplification in Neuroblastoma: a hypothesis generating study

2021 ◽  
Author(s):  
Angela Di Giannatale ◽  
Pierluigi Di Paolo ◽  
Davide Curione ◽  
Jacopo Lenkowicz ◽  
Antonio Napolitano ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Pearson Correlation ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Mycn Amplification ◽  
Features Selection ◽  
Logistic Regression Models ◽  
Significant Difference ◽  
Depth Analysis

Background: MYCN amplification represents a powerful prognostic factor in neuroblastoma (NB) and may occasionally account for intratumoral heterogeneity. Radiomics is an emerging field of advanced image analysis that aims to extract a large number of quantitative features from standard radiological images, providing valuable clinical information Procedure: In this retrospective study, we aimed to create a radiogenomics model by correlating computed tomography (CT) radiomics analysis with MYCN status and overall survival (OS). NB lesions were segmented on pre-therapy CT scans and radiomics features subsequently extracted using a dedicated library. Dimensionality reduction/features selection approaches were then used for features procession and logistic regression models have been developed for the considered outcome. Results: Seventy-eight patients were included in this study, 24 presented MYCN amplification. In total, 232 radiomics features were extracted. Eight features were selected through Boruta algorithm and 2 features were lastly chosen through Pearson correlation analysis: mean of voxel intensity histogram (p=0.0082) and zone size non-uniformity (p=0.038). Five-times repeated 3-fold cross-validation logistic regression models yielded an Area Under the Curve (AUC) value of 0.879 on the training and 0.865 on the testing set for MYCN. No statistical significant difference has been observed comparing radiomics predicted and actual OS data. Conclusions: CT based radiomics is able to predict MYCN amplification status and OS in NB, paving the way to the in depth analysis of imaging based biomarkers that could enhance outcomes prediction.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals Effect of prostatic apex shape (Lee types) and urethral sphincter length in preoperative MRI on very early continence rates after radical prostatectomy

2021 ◽  
Author(s):  
Mike Wenzel ◽  
Felix Preisser ◽  
Matthias Mueller ◽  
Lena H. Theissen ◽  
Maria N. Welte ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Regression Models ◽  
Urethral Sphincter ◽  
Anatomical Characteristics ◽  
Logistic Regression Models ◽  
Pad Test ◽  
Type D ◽  
Multivariable Logistic Regression ◽  
Type C

Abstract Purpose To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.

785 Reported Restful Sleep Predicting Emotional Distress: Does Exercise (and its modalities) moderate?

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A305-A306
Author(s):  
Jesse Moore ◽  
Ellita Williams ◽  
Collin Popp ◽  
Anthony Briggs ◽  
Judite Blanc ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Aerobic Exercise ◽  
Regression Models ◽  
Regression Analyses ◽  
Logistic Regression Models ◽  
The Past ◽  
Strengthening Exercise ◽  
Waking Up ◽  
The Relationship ◽  
Age And Sex

Abstract Introduction Literature shows that exercise moderates the relationship between sleep and emotional distress (ED.) However, it is unclear whether different types of exercise, such as aerobic and strengthening, affect this relationship differently. We investigated the moderating role of two types of exercise (aerobic and strengthening) regarding the relationship between ED and sleep. Methods Our analysis was based on data from 2018 National Health Interview Survey (NHIS), a nationally representative study in which 2,814 participants provided all data. Participants were asked 1) “how many days they woke up feeling rested over the past week”, 2) the Kessler 6 scale to determine ED (a score &gt;13 indicates ED), and 3) the average frequency of strengthening or aerobic exercise per week. Logistic regression analyses were performed to determine if the reported days of waking up rested predicted level of ED. We then investigated whether strengthening or aerobic exercise differentially moderated this relationship. Covariates such as age and sex were adjusted in the logistic regression models. Logistic regression analyses were performed to determine if subjective reporting of restful sleep predicted level of ED. We investigated whether strengthening exercise or aerobic exercise differentially moderated this relationship. Covariates such as age and sex were adjusted in the logistic regression models. Results On average, participants reported 4.41 restful nights of sleep (SD =2.41), 3.43 strengthening activities (SD = 3.19,) and 8.47 aerobic activities a week (SD=5.91.) We found a significant association between days over the past week reporting waking up feeling rested and ED outcome according to K6, Χ2(1) = -741, p= &lt;.001. The odds ratio signified a decrease of 52% in ED scores for each unit of restful sleep (OR = .48, (95% CI = .33, .65) p=&lt;.001.) In the logistic regression model with moderation, aerobic exercise had a significant moderation effect, Χ2(1) = .03, p=.04, but strengthening exercise did not. Conclusion We found that restful sleep predicted reduction in ED scores. Aerobic exercise moderated this relationship, while strengthening exercise did not. Further research should investigate the longitudinal effects of exercise type on the relationship between restful sleep and ED. Support (if any) NIH (K07AG052685, R01MD007716, K01HL135452, R01HL152453)

scholarly journals Catastrophic Household Expenditure Associated with Out-of-Pocket Healthcare Payments in Spain

2021 ◽  
Vol 18 (3) ◽  
pp. 932
Author(s):  
Samuel López-López ◽  
Raúl del Pozo-Rubio ◽  
Marta Ortega-Ortega ◽  
Francisco Escribano-Sotos
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Regression Models ◽  
Financial Risk ◽  
Household Expenditure ◽  
Financial Protection ◽  
Logistic Regression Models ◽  
Ordered Logistic Regression ◽  
Out Of Pocket Payments ◽  
The Subject

Background. The financial effect of households’ out-of-pocket payments (OOP) on access and use of health systems has been extensively studied in the literature, especially in emerging or developing countries. However, it has been the subject of little research in European countries, and is almost nonexistent after the financial crisis of 2008. The aim of the work is to analyze the incidence and intensity of financial catastrophism derived from Spanish households’ out-of-pocket payments associated with health care during the period 2008–2015. Methods. The Household Budget Survey was used and catastrophic measures were estimated, classifying the households into those above the threshold of catastrophe versus below. Three ordered logistic regression models and margins effects were estimated. Results. The results reveal that, in 2008, 4.42% of Spanish households dedicated more than 40% of their income to financing out-of-pocket payments in health, with an average annual gap of EUR 259.84 (DE: EUR 2431.55), which in overall terms amounts to EUR 3939.44 million (0.36% of GDP). Conclusion. The findings of this study reveal the existence of catastrophic households resulting from OOP payments associated with health care in Spain and the need to design financial protection policies against the financial risk derived from facing these types of costs.

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