Response to Pieringer and Hellmich: “…Why it may be problematic to conclude that NEWS has a greater ability to discriminate patients at risk of the combined outcome of cardiac arrest, unanticipated ICU admission or death than other EWSs…”

Resuscitation ◽  
2013 ◽  
Vol 84 (6) ◽  
pp. e75-e76
Author(s):  
Gary B. Smith ◽  
David R. Prytherch ◽  
Paul E. Schmidt ◽  
Peter I. Featherstone ◽  
Paul Meredith
Keyword(s):  
At Risk ◽  
Cardiac Arrest ◽  
Icu Admission ◽  
Patients At Risk

Artificial Intelligence in Predicting Cardiac Arrest: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Asma Alamgir ◽  
Osama Mousa 2nd ◽  
Zubair Shah 3rd
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Cardiac Arrest ◽  
Scoping Review ◽  
Pediatric Population ◽  
Data Extraction ◽  
Warning System ◽  
Reference List ◽  
Evaluation Tool ◽  
Patients At Risk

BACKGROUND Cardiac arrest is a life-threatening cessation of heart activity. Early prediction of cardiac arrest is important as it provides an opportunity to take the necessary measures to prevent or intervene during the onset. Artificial intelligence technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS Scoping review was conducted in line with guidelines of PRISMA Extension for Scoping Review (PRISMA-ScR). Scopus, Science Direct, Embase, IEEE, and Google Scholar were searched to identify relevant studies. Backward reference list checking of included studies was also conducted. The study selection and data extraction were conducted independently by two reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. We were able to classify the approach taken by the studies in three different categories - 26 studies predicted cardiac arrest by analyzing specific parameters or variables of the patients while 16 studies developed an AI-based warning system. The rest of the 5 studies focused on distinguishing high-risk cardiac arrest patients from patients, not at risk. 2 studies focused on the pediatric population, and the rest focused on adults (n=45). The majority of the studies used datasets with a size of less than 10,000 (n=32). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (n=38) and the most used algorithm belonged to the neural network (n=23). K-Fold cross-validation was the most used algorithm evaluation tool reported in the studies (n=24). CONCLUSIONS : AI is extensively being used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in changing cardiac medicine for the better. There is a need for more reviews to learn the obstacles of implementing AI technologies in the clinical setting. Moreover, research focusing on how to best provide clinicians support to understand, adapt and implement the technology in their practice is also required.

Identifying patients at risk of futile resuscitation: palliative care indicators in out-of-hospital cardiac arrest

2019 ◽  
pp. bmjspcare-2019-001828
Author(s):  
Mia Cokljat ◽  
Adam Lloyd ◽  
Scott Clarke ◽  
Anna Crawford ◽  
Gareth Clegg
Keyword(s):  
At Risk ◽  
Palliative Care ◽  
Cardiac Arrest ◽  
High Risk ◽  
Low Risk ◽  
Intermediate Risk ◽  
Patients At Risk ◽  
Risk Patients ◽  
Record Review ◽  
Hospital Cardiac Arrest

ObjectivesPatients with indicators for palliative care, such as those with advanced life-limiting conditions, are at risk of futile cardiopulmonary resuscitation (CPR) if they suffer out-of-hospital cardiac arrest (OHCA). Patients at risk of futile CPR could benefit from anticipatory care planning (ACP); however, the proportion of OHCA patients with indicators for palliative care is unknown. This study quantifies the extent of palliative care indicators and risk of CPR futility in OHCA patients.MethodsA retrospective medical record review was performed on all OHCA patients presenting to an emergency department (ED) in Edinburgh, Scotland in 2015. The risk of CPR futility was stratified using the Supportive and Palliative Care Indicators Tool. Patients with 0–2 indicators had a ‘low risk’ of futile CPR; 3–4 indicators had an ‘intermediate risk’; 5+ indicators had a ‘high risk’.ResultsOf the 283 OHCA patients, 12.4% (35) had a high risk of futile CPR, while 16.3% (46) had an intermediate risk and 71.4% (202) had a low risk. 84.0% (68) of intermediate-to-high risk patients were pronounced dead in the ED or ED step-down ward; only 2.5% (2) of these patients survived to discharge.ConclusionsUp to 30% of OHCA patients are being subjected to advanced resuscitation despite having at least three indicators for palliative care. More than 80% of patients with an intermediate-to-high risk of CPR futility are dying soon after conveyance to hospital, suggesting that ACP can benefit some OHCA patients. This study recommends optimising emergency treatment planning to help reduce inappropriate CPR attempts.

Abstract P112: Markers of Coagulation and Hemostatic Activation Identify Covid-19 Patients at High Risk for Thrombotic Events

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Darwish Alabyad ◽  
Srikant RANGARAJU ◽  
Michael Liu ◽  
Rajeel Imran ◽  
Christine L Kempton ◽  
...  
Keyword(s):  
At Risk ◽  
Thrombotic Event ◽  
Anticoagulation Therapy ◽  
Composite Endpoint ◽  
Icu Admission ◽  
Vein Thrombosis ◽  
Coagulation Marker ◽  
Patients At Risk ◽  
Thrombotic Complications ◽  
Deep Vein

Introduction: COVID-19 has been associated with venous and arterial thrombotic complications. The objective of our study was to determine whether markers of coagulation and hemostatic activation (MOCHA) on admission could identify COVID-19 patients at risk for thrombotic events. Methods: COVID-19 patients admitted to a tertiary academic healthcare system from April 3, 2020 to July 31, 2020 underwent admission testing of MOCHA profile parameters (plasma d-dimer, prothrombin fragment 1.2, thrombin-antithrombin complex, and fibrin monomer). For this analysis we excluded patients on outpatient anticoagulation therapy preceding admission. Prespecified endpoints monitored during hospitalization included deep vein thrombosis, pulmonary embolism, myocardial infarction, ischemic stroke and access line thrombosis. Results: During the study period, 276 patients were included in the analysis cohort (mean age 59 ± 6.3 years, 47% female, 83% non-white race). Arterial and venous thrombotic events occurred in 43 (16%) patients (see Table). Each coagulation marker was independently associated with the composite endpoint (p<0.05). Admission MOCHA with ≥ 2 abnormalities was associated with the composite endpoint (OR 3.1, 95% CI 1.2-8.3), ICU admission (OR 3.2, 95% CI 1.8-5.5) and intubation (OR 2.8, 95% CI 1.5-5.5). Admission MOCHA with < 2 abnormalities (26% of the cohort) had sensitivity of 88% and a negative predictive value of 93% for an in-hospital endpoint. Conclusion: Admission MOCHA with ≥ 2 abnormalities identified COVID-19 patients at risk for a thrombotic event, ICU admission and intubation while < 2 abnormalities identified a subgroup of patients who were at low risk for thrombotic events. Our results suggest that an admission MOCHA profile can be useful to risk stratify COVID-19 patients. Further studies are needed to determine whether an admission MOCHA profile can guide anticoagulation therapy and improve overall clinical outcomes.

Abstract P193: A Cardiopulmonary Resuscitation Training Program for Family Members of In-Hospital Patients at Risk for Cardiac Arrest

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Marion Leary ◽  
Lori Albright ◽  
Emily B Meshberg ◽  
Noah T Sugerman ◽  
Lance B Becker ◽  
...  
Keyword(s):  
At Risk ◽  
Cardiac Arrest ◽  
Cardiopulmonary Resuscitation ◽  
Family Members ◽  
Care Hospital ◽  
Hospital Patients ◽  
Patients At Risk ◽  
Bystander Cpr ◽  
Self Instruction

Background: Resuscitation from cardiac arrest often depends on prompt cardiopulmonary resuscitation (CPR) from the lay public, yet bystander CPR rates in the US are low. One barrier to bystander CPR delivery is that most arrests occur in the home, where only family members may be available to provide care. Little data exist regarding the ability to target and train family members of “at-risk” patients in CPR. Objective: We sought to implement a CPR video self-instruction (VSI) program for family members of in-hospital patients at risk for cardiac arrest. After training in situ before hospital discharge, we tested the hypothesis that at-risk patient family members would be motivated to secondarily train others in the home after leaving the hospital setting. Methods: Family members of patients hospitalized for cardiac conditions at one tertiary-care hospital between 12/07 and 6/08 who met pre-defined inclusion criteria were offered CPR VSI training requiring 25–30 min. All trainees were assessed for skill competence and video recorded for analysis. Trainees were encouraged to take the VSI kit home, and follow-up surveys were conducted to gauge secondary training of other family members. Results: Among 36 enrollees, mean age (SD) was 50 (13) and 78% of trainees were female; only 17% had been CPR trained within the past 10 years, and 44% had never been trained. Most (67%) of the trainees were either children or spouses of the at-risk hospitalized patients. Most (78%) trainees rated their experience with learning CPR via VSI as “comfortable” or “very comfortable”. During 2 min of CPR skills assessment, mean (SD) chest compression rate was 100 (19), mean percentage (SD) adequate depth was 89% (15%), and mean (SD) time for two breaths was 10.8 (4.6) sec. Follow-up surveys revealed that 33% of recipients performed secondary training at home, with a mean (SD) of 1.8 (1.3) secondary trainees. Conclusions: CPR VSI training for family members of hospitalized cardiac patients may serve as a cost-effective model to disseminate resuscitation skills and allows for secondary training in the home of patients at risk for sudden cardiac arrest.

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