scholarly journals Predicting Cardiac Arrest and Respiratory Failure Using Feasible Artificial Intelligence with Simple Trajectories of Patient Data

2019 ◽  
Vol 8 (9) ◽  
pp. 1336 ◽  
Author(s):  
Jeongmin Kim ◽  
Myunghun Chae ◽  
Hyuk-Jae Chang ◽  
Young-Ah Kim ◽  
Eunjeong Park
Keyword(s):  
Artificial Intelligence ◽  
Cardiac Arrest ◽  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Early Warning ◽  
Vital Signs ◽  
Patient Data ◽  
Early Warning Score ◽  
Net Reclassification Improvement ◽  
Fast Pace

We introduce a Feasible Artificial Intelligence with Simple Trajectories for Predicting Adverse Catastrophic Events (FAST-PACE) solution for preparing immediate intervention in emergency situations. FAST-PACE utilizes a concise set of collected features to construct an artificial intelligence model that predicts the onset of cardiac arrest or acute respiratory failure from 1 h to 6 h prior to its occurrence. Data from the trajectory of 29,181 patients in intensive care units of two hospitals includes periodic vital signs, a history of treatment, current health status, and recent surgery. It excludes the results of laboratory data to construct a feasible application in wards, out-hospital emergency care, emergency transport, or other clinical situations where instant medical decisions are required with restricted patient data. These results are superior to previous warning scores including the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS). The primary outcome was the feasibility of an artificial intelligence (AI) model predicting adverse events 1 h to 6 h prior to occurrence without lab data; the area under the receiver operating characteristic curve of this model was 0.886 for cardiac arrest and 0.869 for respiratory failure 6 h before occurrence. The secondary outcome was the superior prediction performance to MEWS (net reclassification improvement of 0.507 for predicting cardiac arrest and 0.341 for predicting respiratory failure) and NEWS (net reclassification improvement of 0.412 for predicting cardiac arrest and 0.215 for predicting respiratory failure) 6 h before occurrence. This study suggests that AI consisting of simple vital signs and a brief interview could predict a cardiac arrest or acute respiratory failure 6 h earlier.

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.

scholarly journals Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital

Critical Care ◽  
2018 ◽  
Vol 22 (1) ◽  
Author(s):  
Mikhail A Dziadzko ◽  
Paul J Novotny ◽  
Jeff Sloan ◽  
Ognjen Gajic ◽  
Vitaly Herasevich ◽  
...  
Keyword(s):  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Early Warning ◽  
Early Warning Score

scholarly journals Implementasi Early Warning Score pada Kejadian Henti Jantung di Ruang Perawatan Rumah Sakit Dr. Hasan Sadikin Bandung yang Ditangani Tim Code Blue Selama Tahun 2017

2019 ◽  
Vol 7 (1) ◽  
pp. 33-41
Author(s):  
Nurul Subhan ◽  
Gezy Weita Giwangkencana ◽  
M. Andy Prihartono ◽  
Doddy Tavianto
Keyword(s):  
Cardiac Arrest ◽  
General Hospital ◽  
Early Warning ◽  
Medical Records ◽  
Vital Signs ◽  
Early Warning Score ◽  
Ward Staff ◽  
Cross Sectional ◽  
Code Blue ◽  
Hospital Cardiac Arrest

Angka kejadian henti jantung di rumah sakit sangat bervariasi. Sebagian besar kasus henti jantung didahului oleh penurunan kondisi pasien yang digambarkan dengan gangguan parameter tanda vital. Keberhasilan Early warning score (EWS) dalam menurunkan angka kejadian henti jantung dipengaruhi oleh implementasi yang baik dari instrumen EWS sesuai dengan pedoman yang ditetapkan. Penelitian ini bertujuan melihat implementasi EWS di RSUP Dr. Hasan Sadikin Bandung. Penelitian bersifat deskriptif dengan desain potong lintang menggunakan data rekam medis pasien henti jantung di ruang perawatan yang ditangani oleh tim Code Blue selama tahun 2017, dan dilakukan pada bulan November 2018. Data EWS 6 jam sebelum dan saat henti jantung, serta tindak lanjut yang dilakukan setelah penilaian EWS dicatat. Didapatkan 87 data rekam medis henti jantung yang memenuhi kriteria inklusi dan tidak termasuk eksklusi. Di antaranya, 72% memiliki catatan EWS lengkap, 9% memiliki catatan EWS tidak lengkap, dan 18% tidak memiliki data EWS. Dari 63 data rekam medis yang memiliki data EWS lengkap hanya 21% yang mendapat tindak lanjut yang sesuai dengan standar prosedur operasional EWS. Simpulan penelitian ini adalah implementasi EWS di ruang rawat inap RSUP Dr. Hasan Sadikin belum cukup memuaskan. Tindak lanjut yang dilakukan setelah penilaian EWS belum sesuai dengan standar prosedur operasional EWS yang berlaku.Implementation of Early Warning Score to Patients with In-Hospital Cardiac Arrest in Dr. Hasan Sadikin General Hospital Managed  by Code Blue Team Incidence of in-hospital cardiac arrest varies greatly around the world. Most in-hospital cardiac arrests are preceded with physiological deteriorations that manifest as alterations in vital signs. The success of early warning score (EWS) in reducing the incidence of cardiac arrest is influenced by the good implementation of EWS instruments by ward staff in accordance with the guidelines The aim of this study was to assess to what degree EWS was implemented at Dr. Hasan Sadikin General Hospital Bandung. This was a cross sectional descriptive study on patients with in-hospital cardiac arrest managed by the code blue team during 2017 that was conducted in November 2018. EWS 6 hour prior to cardiac arrest event, EWS at the event, and action taken upon finding an abnormal value were obtained from medical records.  Eighty seven medical records were included. Of these, 72% medical records had complete EWS data, 9 medical records had incomplete EWS data, and 18% medical records had no EWS recorded. From those 63 medical records with complete EWS recorded, only 21% had been managed correctly according to the EWS guideline. This study concludes that the implementation of EWS in the wards of Dr. Hasan Sadikin General Hospital Bandung has not been completely satisfactorily. Actions taken after EWS assessment are still not accordance with the EWS guideline.

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.

scholarly journals Impact of the level of sickness on higher mortality in emergency medical admissions to hospital at weekends

2017 ◽  
Vol 22 (4) ◽  
pp. 236-242 ◽  
Author(s):  
Mohammed Mohammed ◽  
Muhammad Faisal ◽  
Donald Richardson ◽  
Robin Howes ◽  
Kevin Beatson ◽  
...  
Keyword(s):  
Early Warning ◽  
Odds Ratio ◽  
Vital Signs ◽  
Early Warning Score ◽  
Emergency Admissions ◽  
Clinical Assessments ◽  
Admission Time ◽  
Emergency Medical ◽  
Acute Hospitals ◽  
National Early Warning Score

Objective Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p < 0.001) with a higher mortality (weekend: 706/11,332 6.23% vs. weekday: 2039/35,785 5.70%; odds ratio = 1.10, 95% CI 1.01 to 1.20, p = 0.04) which was no longer seen after adjusting for the index National Early Warning Score (odds ratio = 0.99, 95% CI 0.90 to 1.09, p = 0.87). Index National Early Warning Score was recorded sooner (−0.45 h, 95% CI −0.52 to −0.38, p < 0.001) for weekend admissions. Conclusions Emergency medical admissions at the weekend with electronic National Early Warning Score recorded within 24 h are sicker, have earlier clinical assessments, and after adjusting for the severity of their sickness, do not appear to have a higher mortality compared to weekday admissions. A larger definitive study to confirm these findings is needed.

scholarly journals Novel approaches to ultrasonography of the lung and pleural space: where are we now?

Breathe ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 100-111 ◽  
Author(s):  
Daniel Lichtenstein
Keyword(s):  
Pulmonary Embolism ◽  
Cardiac Arrest ◽  
Acute Respiratory Distress Syndrome ◽  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Critically Ill ◽  
Distress Syndrome ◽  
Lung Ultrasound ◽  
List Type ◽  
Interstitial Syndrome

This review article is an update of what should be known for practicing basic lung ultrasound in the critically ill (LUCI) and is also of interest for less critical disciplines (e.g. pulmonology). It pinpoints on the necessity of a professional machine (not necessarily a sophisticated one) and probe. It lists the 10 main signs of LUCI and some of the main protocols made possible using LUCI: the BLUE protocol for a respiratory failure, the FALLS protocol for a circulatory failure, the SESAME protocol for a cardiac arrest and the investigation of a ventilated acute respiratory distress syndrome patient, etc. It shows how the field has been fully standardised to avoid confusion.Key pointsA simple ultrasonography unit is fully adequate, with minimal filters, and provides a unique probe for integrating the lung into a holistic, whole-body approach to the critically ill.Interstitial syndrome is strictly defined. Its clinical relevance in the critically ill is standardised for defining haemodynamic pulmonary oedema, pneumonia and pulmonary embolism.Pneumothorax is strictly and sequentially defined by the A′-profile (at the anterior wall in a supine or semirecumbent patient, abolished lung siding plus the A-line sign) and then the lung point.The BLUE protocol integrates lung and venous ultrasound findings for expediting the diagnosis of acute respiratory failure, following pathophysiology, allowing prompt diagnosis of pneumonia, haemodynamic pulmonary oedema, exacerbated chronic obstructive pulmonary disease or asthma, pulmonary embolism or pneumothorax, even in clinically challenging presentations.Educational aimsTo understand that the use of lung ultrasound, although long standardised, still needs educational efforts for its best use, a suitable machine, a suitable universal probe and an appropriate culture.To be able to use a terminology that has been fully standardised to avoid any confusion of useless wording.To understand the logic of the BLUE points, three points of interest enabling expedition of a lung ultrasound examination in acute respiratory failure.To be able to cite, in the correct hierarchy, the seven criteria of the B-line, then those of interstitial syndrome.To understand the sequential thinking when making ultrasound diagnosis of pneumothorax.To be able to use the BLUE protocol for building profiles of pneumonia (or acute respiratory distress syndrome) and understand their limitations.To understand that lung ultrasound can be used for the direct analysis of an acute respiratory failure (the BLUE protocol), an acute circulatory failure (the FALLS protocol) and even a cardiac arrest (SESAME protocol), following a pathophysiological approach.To understand that the first sequential target in the SESAME protocol (search first for pneumothorax in cardiac arrest) can also be used in countless more quiet settings of countless disciplines, making lung ultrasound in the critically ill cost-, time- and radiation-saving.To be able to perform a BLUE protocol in challenging patients, understanding how the best lung ultrasound can be obtained from bariatric or agitated, dyspnoeic patients.

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