Predictors of in‐hospital cardiac arrest within 24 h after emergency department triage: A case–control study in urban Thailand

Srivilaithon, Winchana; Amnuaypattanapon, Kumpol; Limjindaporn, Chitlada; Imsuwan, Intanon; Daorattanachai, Kiattichai; Dasanadeba, Ittabud; Siripakarn, Yaowapha

doi:10.1111/1742-6723.13267

Cited by 13 publications

(16 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to gender, the results showed that attendance a BLS course previously had signi cant effect on knowledge level. Similar results were reported from Oman, [13] United Kingdom, [18] Pakistan [15] and Thailand [19]. In this study, the numbers of participants who received training in BLS and had knowledge of CPR (massage rate, massage location, massage depth and massage/ventilation rate) were higher than those who did not receive training.…”

Section: Discussionsupporting

confidence: 88%

“…[23] The knowledge of BLS may decrease in the 6 months following the training as seen in Winchana et al study. [19] Therefore, students should constantly review BLS principles continually and keep up to date with the latest guidelines. Regarding universities ranking, the results showed that universities in Jordan achieved the rst six ranks, while there is only one Syrian university among the top 10 universities.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Awareness of basic life support among medical students in Syria, Iraq and Jordan: a cross sectional study

Shashaa¹,

Alkarrash²,

Kitaz³

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Sudden cardiac arrest is one of the most leading causes for death in all over the world. Basic life support (BLS) training rates vary worldwide, there is a general scarce in the number of Surveys which evaluate knowledge, and awareness about BLS in Middle Eastern countries students especially Jordan, Syria and Iraq. The purpose of this study is to evaluate the awareness of basic life support among medical students.Methods: A Cross sectional web based questionnaire study was conducted in November 2020. The study included 2114 medical students in Syria, Iraq and Jordan. We evaluated BLS skills according to total mean score of correct answers. The participants were classified according to gender, residence, country, University, academic degree, and academic stage to study their effect on the participants’ BLS knowledge. A chi-squared test was used to determine if there were differences between those who attended a basic life support course and those who did not.Results: 1656 of the participants (78.3%) stated that they did not attend a basic life support course. There was a significant difference between the participants from different countries where the mean score in Syria, Jordan and Iraq was 18.3, 24.3 and 18.8 respectively (p < 0.05). The participants were divided into 3 level according to total score; low (0-12), moderate (13-24) and high (25-37). In total, 18.3%, 72.8% and 8.9% of participants had high, intermediate and low level respectively.Conclusion: The overall knowledge of basic life support among medical students is not adequate and need significant improvements. This study showed that an attendance a basic life support course previously had an effect on knowledge level. Hence, there is an urgent need to apply basic life support courses into the pre-clinical stage at universities.

show abstract

Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Awareness of basic life support among medical students in Syria, Iraq and Jordan: a cross sectional study

Shashaa¹,

Alkarrash²,

Kitaz³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A few studies used MTS priority as modeling predictor along with other variables collected at triage, namely for prediction of hospital admissions [39][40][41] and mortality [26,53]. In [39], a logistic regression and an artificial neural network model both yielded an AUROC of 0.86…”

Section: Prior Work In Machine Learning For Risk Stratificationmentioning

confidence: 99%

“…The results for both independent predictors of death within 30 days were: door-toteam times OR of 1.13 (95% CI 1.07-1.18) and team-to-ward times OR of 1.07 (95% CI 1.02-1.13). In [53] a logistic regression model yielded an AUROC of 0.91 (95% CI 0.89-0.93) from information of MTS priority at presentation, age, gender, comorbidities, functional status at presentation, mode of arrival, time of ED visit, type of specialty, physiological parameters at different times, level of consciousness, need for supplement oxygen, need for ventilation assistance, use of vasoactive and inotropic agents and initial laboratory markers in the ED. Other studies were performed for mortality prediction, where MTS priority was not used as modeling predictor.…”

Section: Plos Onementioning

confidence: 99%

Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing

et al. 2020

View full text Add to dashboard Cite

Emergency department triage is the first point in time when a patient's acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome-mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients' chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency-inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Tenfold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model-a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients' age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical

show abstract

“…Early detection and intervention of disease deterioration are the keys to reducing the incidence of preventable in-hospital cardiac arrest (IHCA) [1]. Vital signs have been shown to be accurate predictors of clinical deterioration [2][3][4][5][6]. However, the usefulness of vital signs is affected by the quality of the process of measurement [7,8].…”

Section: Introductionmentioning

confidence: 99%

Factors Predicting Cardiac Arrest in the Intensive Care Unit: A Matched Case-Control Study

Sang

Pan

Zhao

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Early detection and intervention of disease deterioration are the keys to reducing the incidence of preventable intensive care unit cardiac arrest (ICU-CA). We aimed to investigate the ICU-CA predictive factors, including vital signs and laboratory indicators, and to analyze the performance of trends value of those factors on predicting ICU-CA. Methods: We conducted a matched case-control study at Qilu Hospital of Shandong University. Data on adult patients in ICU who suffered a cardiac arrest (CA) were retrospectively collected from 2016 to 2019, including vital signs and laboratory indicators at 48, 36, 24, 12, and 8 hours before ICU-CA. These cases were matched (ward, sex, and admission data) with controls (no ICU-CA) at a 1:2 ratio. Univariable logistic regression was used for statistical comparisons between cases and controls, and multivariate logistic regression was used to investigate the independent associations of indicators and their tendency with ICU-CA at given time points. The area under receiver operating characteristic (AUROC) was used to evaluate the predictive performance on ICU-CA.Results: Of 6164 ICU patients, 1042 patients suffered an ICU-CA during the 3 years. After careful screening, a total of 427 patients were included as the cases in the study, and 790 patients were included as controls. The vital signs and laboratory indicators at 8h before cardiac arrest, such as heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), oxygen saturation (SaO2), hemoglobin (HGB), potassium (K+), sodium (Na+), lactic acid (Lac), and pH all can predict the ICU-CA. The mean value, maximum value, minimum value, and range of these indicators were related to the occurrence of ICU-CA, and the trend values were more accurate than the current value for the variability in laboratory indicators. Conclusions: The ability of trends value of laboratory indicators for predicting ICU-CA was more accurate than the value at given time points for the variability in laboratory indicators. Adding trends of laboratory indicators may increase the accuracy of models designed to detect critical illness in ICU. Trial registration: ClinicalTrials.gov Identifier: NCT04670458.

show abstract

Predictors of in‐hospital cardiac arrest within 24 h after emergency department triage: A case–control study in urban Thailand

Cited by 13 publications

References 19 publications

Awareness of basic life support among medical students in Syria, Iraq and Jordan: a cross sectional study

Awareness of basic life support among medical students in Syria, Iraq and Jordan: a cross sectional study

Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing

Factors Predicting Cardiac Arrest in the Intensive Care Unit: A Matched Case-Control Study

Contact Info

Product

Resources

About