An Early Warning Score Predicts Risk of Death after In-hospital Cardiopulmonary Arrest in Surgical Patients

In-hospital cardiopulmonary arrest can contribute significantly to publicly reported mortality rates. Systems to improve mortality are being implemented across all specialties. A review was conducted for all surgical patients >18 years of age who experienced a “Code Blue” event between January 1, 2013 and March 9, 2014 at a university hospital. A previously validated Modified Early Warning Score (MEWS) using routine vital signs and neurologic status was calculated at regular intervals preceding the event. In 62 patients, the most common causes of arrest included respiratory failure, arrhythmia, sepsis, hemorrhage, and airway obstruction, but remained unknown in 27 per cent of cases. A total of 56.5 per cent of patients died before hospital discharge. In-hospital death was associated with American Society of Anesthesiologists status ( P = 0.039) and acute versus elective admission ( P = 0.003). Increasing MEWS on admission, 24 hours before the event, the event-day, and a maximum MEWS score on the day of the event increased the odds of death. Max MEWS remained associated with death after multivariate analysis (odds ratio 1.39, P = 0.025). Simple and easy to implement warning scores such as MEWS can identify surgical patients at risk of death after arrest. Such recognition may provide an opportunity for clinical intervention resulting in improved patient outcomes and hospital mortality rates.

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Early warning score using electronically-held data for patients discharged from intensive care units

10.21203/rs.3.rs-52218/v1 ◽

2020 ◽

Author(s):

Marco Pimentel ◽

Alistair Johnson ◽

Julie Darbyshire ◽

Lionel Tarassenko ◽

David Clifton ◽

...

Keyword(s):

Adverse Event ◽

Intensive Care ◽

Intensive Care Units ◽

Early Warning ◽

Vital Sign ◽

Scoring System ◽

Vital Signs ◽

Early Warning Score ◽

Hospital Death ◽

Icu Discharge

Abstract Rationale. Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with early warning score (EWS) systems being used to identify those at risk of deterioration. Objectives. We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWSs) with a risk score of a future adverse event calculated on discharge from ICU.Methods. A modified Delphi process identified common, and candidate variables frequently collected and stored in electronic records as the basis for a ‘static’ score of the patient’s condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital-sign data from the day of hospital discharge, which is combined with the static score and used continuously to quantify and update the patient’s risk of deterioration throughout their hospital stay. Data from two NHS Foundation Trusts (UK) were used to develop and (externally) validate the model.Measurements and Main Results. A total of 12,394 vital-sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4,831 from 136 patients in the validation cohort. Outcome validation of our model yielded an area under the receiver operating characteristic curve (AUROC) of 0.724 for predicting ICU re-admission or in-hospital death within 24h. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (NEWS, 0.653). Conclusion. We showed that a scoring system incorporating data from a patient’s stay in ICU has better performance than commonly-used EWS systems based on vital signs alone.

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

10.2196/preprints.21720 ◽

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.

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The EMS Modified Early Warning Score (EMEWS): A simple count of vital signs as a predictor of out-of-hospital cardiac arrests

Prehospital Emergency Care ◽

10.1080/10903127.2021.1908464 ◽

2021 ◽

pp. 1-10

Author(s):

Brian M Clemency ◽

William Murk ◽

Alexander Moore ◽

Lawrence H. Brown

Keyword(s):

Early Warning ◽

Vital Signs ◽

Early Warning Score ◽

Simple Count

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Glucose as an additional parameter to National Early Warning Score (NEWS) in prehospital setting enhances identification of patients at risk of death: an observational cohort study

Emergency Medicine Journal ◽

10.1136/emermed-2018-208309 ◽

2020 ◽

pp. emermed-2018-208309

Author(s):

Hanna Vihonen ◽

Mitja Lääperi ◽

Markku Kuisma ◽

Jussi Pirneskoski ◽

Jouni Nurmi

Keyword(s):

At Risk ◽

Blood Glucose ◽

Regression Model ◽

Early Warning ◽

Prehospital Setting ◽

Early Warning Score ◽

Calibration Model ◽

Risk Of Death ◽

Patients At Risk ◽

National Early Warning Score

BackgroundTo determine if prehospital blood glucose could be added to National Early Warning Score (NEWS) for improved identification of risk of short-term mortality.MethodsRetrospective observational study (2008–2015) of adult patients seen by emergency medical services in Helsinki metropolitan area for whom all variables for calculation of NEWS and a blood glucose value were available. Survival of 24 hours and 30 days were determined. The NEWS parameters and glucose were tested by multivariate logistic regression model. Based on ORs we formed NEWSgluc model with hypoglycaemia (≤3.0 mmol/L) 3, normoglycaemia 0 and hyperglycaemia (≥11.1 mmol/L) 1 points. The scores from NEWS and NEWSgluc were compared using discrimination (area under the curve), calibration (Hosmer-Lemeshow test), likelihood ratio tests and reclassification (continuous net reclassification index (cNRI)).ResultsData of 27 141 patients were included in the study. Multivariable regression model for NEWSgluc parameters revealed a strong association with glucose disturbances and 24-hour and 30-day mortality. Likelihood ratios (LRs) for mortality at 24 hours using a cut-off point of 15 were for NEWSgluc: LR+ 17.78 and LR− 0.96 and for NEWS: LR+ 13.50 and LR− 0.92. Results were similar at 30 days. Risks per score point estimation and calibration model showed glucose added benefit to NEWS at 24 hours and at 30 days. Although areas under the curve were similar, reclassification test (cNRI) showed overall improvement of classification of survivors and non-survivors at 24 days and 30 days with NEWSgluc.ConclusionsIncluding glucose in NEWS in the prehospital setting seems to improve identification of patients at risk of death.

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

Journal of Health Services Research & Policy ◽

10.1177/1355819617720955 ◽

2017 ◽

Vol 22 (4) ◽

pp. 236-242 ◽

Cited By ~ 8

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.

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Use of the Modified Early Warning Score and Serum Lactate to Prevent Cardiopulmonary Arrest in Hematology-Oncology Patients

American Journal of Medical Quality ◽

10.1177/1062860613508305 ◽

2013 ◽

Vol 29 (6) ◽

pp. 530-537 ◽

Cited By ~ 7

Author(s):

Robert S. Young ◽

Barbara H. Gobel ◽

Mark Schumacher ◽

Jungwha Lee ◽

Charlotta Weaver ◽

...

Keyword(s):

Early Warning ◽

Cardiopulmonary Arrest ◽

Serum Lactate ◽

Early Warning Score ◽

Oncology Patients

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Inability to Walk Predicts Death among Adult Patients in Hospitals in Malawi

Emergency Medicine International ◽

10.1155/2019/6586891 ◽

2019 ◽

Vol 2019 ◽

pp. 1-5

Author(s):

Raphael Kazidule Kayambankadzanja ◽

Carl Otto Schell ◽

Grace Nsanjama ◽

Isaac Mbingwani ◽

Samson Kwazizira Mndolo ◽

...

Keyword(s):

Predictive Value ◽

Vital Signs ◽

Predictive Performance ◽

Early Warning Score ◽

Walking Ability ◽

Patient Death ◽

Predictive Values ◽

Risk Of Death ◽

Close Observation ◽

National Early Warning Score

Objective. Vital signs are often used in triage, but some may be difficult to assess in low-resource settings. A patient’s ability to walk is a simple and rapid sign that requires no equipment or expertise. This study aimed to determine the predictive performance for death of an inability to walk among hospitalized Malawian adults and to compare its predictive value with the vital signs-based National Early Warning Score (NEWS). Methods. It is a prospective cohort study of adult in-patients on selected days in two hospitals in Malawi. Patients were asked to walk five steps with close observation and their vital signs were assessed. Sensitivities, specificities, and predictive values for in-patient death of an inability to walk were calculated and an inability to walk was compared with NEWS. Results. Four-hundred and forty-three of the 1094 participants (40.5%) were unable to walk independently. In this group, 70 (15.8 %) died in-hospital compared to 16 (2.5%) among those who could walk: OR 7.4 (95% CI 4.3-13.0 p<0.001). Inability to walk had a sensitivity for death of 81.4%, specificity of 63.0%, positive predictive value (PPV) of 15.8%, and negative predictive value (NPV) of 97.5%. NEWS>6 had sensitivity 70.9%, specificity 70.6%, PPV 17.1%, and NPV 96.6%. An inability to walk had a fair concordance with NEWS>6 (kappa 0.21). Conclusion. Inability to walk predicted mortality as well as NEWS among hospitalized adults in Malawi. Patients who were able to walk had a low risk of death. Walking ability could be considered an additional vital sign and may be useful for triage.

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