Abstract 19225: Impact of Change in Resuscitation Guidelines on National Out-of-hospital Cardiac Arrest Outcomes: Fulfilled Expectations?

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Shaker M Eid ◽  
Aiham Albaeni ◽  
Rebeca Rios ◽  
May Baydoun ◽  
Bolanle Akinyele ◽  
...  
Keyword(s):  
Time Series ◽  
Cardiac Arrest ◽  
Time Series Data ◽  
Statistical Significance ◽  
Interrupted Time Series ◽  
The United States ◽  
Series Data ◽  
National Database ◽  
Resuscitation Guidelines ◽  
Hospital Cardiac Arrest

Background: The intent of the 5-yearly Resuscitation Guidelines is to improve outcomes. Previous studies have yielded conflicting reports of a beneficial impact of the 2005 guidelines on out-of-hospital cardiac arrest (OHCA) survival. Using a national database, we examined survival before and after the introduction of both the 2005 and 2010 guidelines. Methods: We used the 2000 through 2012 National Inpatient Sample database to select patients ≥18 years admitted to hospitals in the United States with non-traumatic OHCA (ICD-9 CM codes 427.5 & 427.41). A quasi-experimental (interrupted time series) design was used to compare monthly survival trends. Outcomes for OHCA were compared pre- and post- 2005 and 2010 resuscitation guidelines release as follows: 01/2000-09/2005 vs. 10/2005-9/2010 and 10/2005-9/2010 vs. 10/2010-12/2012. Segmented regression analyses of interrupted time series data were performed to examine changes in survival to hospital discharge. Results: For the pre- and post- guidelines periods, 81600, 69139 and 36556 patients respectively survived to hospital admission following OHCA. Subsequent to the release of the 2005 guidelines, there was a statistically significant worsening in survival trends (β= -0.089, 95% CI -0.163 – -0.016, p =0.018) until the release of the 2010 guidelines when a sharp increase in survival was noted which persisted for the period of study (β= 0.054, 95% CI -0.143 – 0.251, p =0.588) but did not achieve statistical significance (Figure). Conclusion: National clinical guidelines developed to impact outcomes must include mechanisms to assess whether benefit actually occurs. The worsening in OHCA survival following the 2005 guidelines is thought provoking but the improvement following the release of the 2010 guidelines is reassuring and worthy of perpetuation.

scholarly journals Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Simon L. Turner ◽  
Amalia Karahalios ◽  
Andrew B. Forbes ◽  
Monica Taljaard ◽  
Jeremy M. Grimshaw ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Method ◽  
Statistical Methods ◽  
Time Series Data ◽  
Statistical Significance ◽  
Empirical Evaluation ◽  
Interrupted Time Series ◽  
Series Data ◽  
Standard Errors ◽  
The Impact

Abstract Background The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. Methods A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. Results From the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. Conclusions The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Development of A Pharmaceutical Early Warning Scoring Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Hsiao-Ko Chang ◽  
Hui-Chih Wang ◽  
Chih-Fen Huang ◽  
Feipei Lai
Keyword(s):  
Time Series ◽  
Cardiac Arrest ◽  
Early Warning ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Vital Signs ◽  
Sliding Window ◽  
Early Warning Score ◽  
Series Data ◽  
Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a pharmaceutical early warning model to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose a new early warning score model for detecting cardiac arrest via pharmaceutical classification and by using a sliding window; we apply learning-based algorithms to time-series data for a Pharmaceutical Early Warning Scoring Model (PEWSM). By treating pharmaceutical features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits, and replenishers and regulators of water and electrolytes. The best AUROC of bits is 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, LSTM yields better performance with time-series data. The proposed PEWSM, which offers 4-hour predictions, is better than the National Early Warning Score (NEWS) in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

Development of A Drug Early Warning System Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Hsiao-Ko Chang ◽  
Hui-Chih Wang ◽  
Chih-Fen Huang ◽  
Feipei Lai
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Cardiac Arrest ◽  
Early Warning ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Vital Signs ◽  
Warning System ◽  
Series Data ◽  
Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a Drug Early Warning System Model (DEWSM), it included drug injections and vital signs as this research important features. We use it to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose this new model for detecting cardiac arrest via drug classification and by using a sliding window; we apply learning-based algorithms to time-series data for a DEWSM. By treating drug features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model, we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits (intravenous therapy), and replenishers and regulators of water and electrolytes (fluid and electrolyte supplement). The best AUROC of bits is 85%, it means the medical expert suggest the drug features: bits, it will affect the vital signs, and then the evaluate this model correctly classified patients with CPR reach 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. It can be seen that the use of new AI technology will achieve better results, currently comparable to the accuracy of traditional common RF, and the LSTM model can be adjusted in the future to obtain better results. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. The National Early Warning Score (NEWS) only focuses on the score of vital signs, and does not include factors related to drug injections. In this study, the experimental results of adding the drug injections are better than only vital signs. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, we use traditional machine learning methods and deep learning (using LSTM method as the main processing time series data) as the basis for comparison of this research. The proposed DEWSM, which offers 4-hour predictions, is better than the NEWS in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

Gendered Funding: United Way Board Composition and the Funding of Women- and Girl-Serving Organizations

2021 ◽  
pp. 089976402110574
Author(s):  
Lauren Dula
Keyword(s):  
Time Series Data ◽  
Critical Mass ◽  
Interrupted Time Series ◽  
The United States ◽  
Women In Leadership ◽  
Representative Bureaucracy ◽  
Series Data ◽  
Leadership Positions ◽  
United Way ◽  
Bureaucracy Theory

Representative bureaucracy theory posits that the passive representation of women in leadership positions will lead to active representation of the concerns of women in general. This article attempts to identify whether this theory plays out on boards of nonprofit funding organizations, specifically United Ways across the United States. Using random effects modeling of interrupted time series data covering 15 years, the findings suggest a small yet significant nonlinear effect of women in leadership positions on boards upon the size of funding for women- and girl-serving organizations. This partially supports representative bureaucracy theory, but raises questions as to why there is a negative representational effect past a certain “critical mass” of women.

Discriminate Supervised Weighted Scheme for the Classification of Time Series Signals

2021 ◽  
Vol 13 (3) ◽  
pp. 1-16
Author(s):  
Elangovan Ramanujam ◽  
S. Padmavathi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
State Of The Art ◽  
Statistical Significance ◽  
Series Data ◽  
Bag Of Words ◽  
Time Series Classification ◽  
Problem Of Time ◽  
Weighted Matrix

Innovations and applicability of time series data mining techniques have significantly increased the researchers' interest in the problem of time series classification. Several algorithms have been proposed for this purpose categorized under shapelet, interval, motif, and whole series-based techniques. Among this, the bag-of-words technique, an extensive application of the text mining approach, performs well due to its simplicity and effectiveness. To extend the efficiency of the bag-of-words technique, this paper proposes a discriminate supervised weighted scheme to identify the characteristic and representative pattern of a class for efficient classification. This paper uses a modified weighted matrix that discriminates the representative and non-representative pattern which enables the interpretability in classification. Experimentation has been carried out to compare the performance of the proposed technique with state-of-the-art techniques in terms of accuracy and statistical significance.

scholarly journals The Effect of COVID-19 on Domestic Violence and Assaults

2021 ◽  
pp. 073401682110611
Author(s):  
Mustafa Demir ◽  
Suyeon Park
Keyword(s):  
Domestic Violence ◽  
Time Series ◽  
Time Series Data ◽  
Police Department ◽  
Interrupted Time Series ◽  
Series Data ◽  
Data Set ◽  
Calls For Service ◽  
Time Series Analyses ◽  
Significant Change

The purpose of this research was to examine the effect of COVID-19 on four outcomes including calls for service for domestic violence, calls for service for assaults, arrests for domestic violence, and arrests for assaults in Burlington, Vermont. The data for each outcome collected over the time periods January 2012 through May 2021 were obtained from the Burlington Police Department website and then a monthly time-series data set were created. The analyses including an independent samples t-test, a Poisson regression test, and a monthly interrupted time-series analyses (ITSA) were employed to test the effects of COVID-19 on the previously mentioned outcomes. The results of the ITSA showed that in the first month following the onset of the COVID-19 pandemic, domestic violence calls statistically significantly increased, but no statistically significant change was observed in domestic violence arrests, while assault calls and assault arrests statistically significantly decreased. In addition, during COVID-19, there was a statistically significant decreasing trend in domestic violence calls and domestic violence arrests, while there was no statistically significant change in the trends of assault calls and assault arrests. The results suggest that COVID-19 had an immediate as well as a persistent effect on the numbers of domestic violence and assaults. The results and limitations of this study were also discussed.

scholarly journals Comparison of Six Statistical Methods for Interrupted time Series Studies: Empirical Evaluation of 190 Published Series

2020 ◽  
Author(s):  
Simon Turner ◽  
Amalia Karahalios ◽  
Andrew Forbes ◽  
Monica Taljaard ◽  
Jeremy Grimshaw ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Method ◽  
Statistical Methods ◽  
Statistical Significance ◽  
Empirical Evaluation ◽  
Interrupted Time Series ◽  
Series Data ◽  
Standard Errors ◽  
P Values ◽  
The Impact

Abstract Background The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. MethodsA random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. ResultsFrom the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4% to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. ConclusionsThe choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

scholarly journals The Pressing Need to Test for Autocorrelation: Comparison of Repeated Measures ANOVA and Interrupted Time Series Autoregressive Models

2019 ◽  
Vol 9 (2) ◽  
pp. 22-31 ◽  
Author(s):  
Jay Schyler Raadt
Keyword(s):  
Time Series ◽  
Repeated Measures ◽  
Time Series Data ◽  
Longitudinal Research ◽  
Moving Average ◽  
Interrupted Time Series ◽  
Series Data ◽  
Repeated Measures Analysis ◽  
Scientific Results ◽  
Simulated Time

Neglecting to measure autocorrelation in longitudinal research methods such as Repeated Measures (RM) ANOVA produces invalid results. Using simulated time series data varying on autocorrelation, this paper compares the performance of repeated measures analysis of variance (RM ANOVA) to interrupted time series autoregressive integrated moving average (ITS ARIMA) models, which explicitly model autocorrelation. Results show that the number of RM ANOVA signaling an intervention effect increase as autocorrelation increases whereas this relationship is opposite using ITS ARIMA. This calls the use of RM ANOVA for longitudinal educational research into question as well as past scientific results that used this method, exhorting educational researchers to investigate the use of ITS ARIMA.

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