AIM: (1) To identify risk factors for in-hospital cardiac arrest; (2) to formulate activation criteria to alert a clinical response culminating in attendance by a Medical Emergency Team (MET); (3) to evaluate the sensitivity and specificity of the scoring system. METHODS: Quasi-experimental design to determine prevalence of risk factors for cardiac arrest in the hospitalised population. Weighting of risk factors and formulation of activation criteria to alert a graded clinical response. ROC analysis of weighted cumulative scores to determine their sensitivity and specificity. SETTING: An acute 700 bed district general hospital with 32,348 adult admissions in 1999 and a catchment population of around 365,000. SUBJECTS: 118 consecutive adult patients suffering primary cardiac arrest in-hospital and 132 non-arrest patients, randomly selected according to stratified randomisation by gender and age. RESULTS: Risk factors for cardiac arrest include: abnormal respiratory rate (P = 0.013), abnormal breathing indicator (abnormal rate or documented shortness of breath) (P < 0.001), abnormal pulse (P < 0.001), reduced systolic blood pressure (P < 0.001), abnormal temperature (P < 0.001), reduced pulse oximetry (P < 0.001), chest pain (P < 0.001) and nurse or doctor concern (P < 0.001). Multivariate analysis of cardiac arrest cases identified three positive associations for cardiac arrest: abnormal breathing indicator (OR 3.49; 95% CI: 1.69-7.21), abnormal pulse (OR 4.07; 95% CI: 2.0-8.31) and abnormal systolic blood pressure (OR 19.92; 95% CI: 9.48-41.84). Risk factors were weighted and tabulated. The aggregate score determines the grade of clinical response. ROC analysis determined that a score of 4 has 89% sensitivity and 77% specificity for cardiac arrest; a score of 8 has 52% sensitivity and 99% specificity. All patients scoring greater than 10 suffered cardiac arrest. CONCLUSION: Risk factors for cardiac arrest have been identified, quantified and formulated into a table of activation criteria to help predict and avert cardiac arrest by alerting a clinical response. A graded clinical response has resulted in a tool that has both sensitivity and specificity for cardiac arrest.
AIM: (1) To identify risk factors for in-hospital cardiac arrest; (2) to formulate activation criteria to alert a clinical response culminating in attendance by a Medical Emergency Team (MET); (3) to evaluate the sensitivity and specificity of the scoring system. METHODS: Quasi-experimental design to determine prevalence of risk factors for cardiac arrest in the hospitalised population. Weighting of risk factors and formulation of activation criteria to alert a graded clinical response. ROC analysis of weighted cumulative scores to determine their sensitivity and specificity. SETTING: An acute 700 bed district general hospital with 32,348 adult admissions in 1999 and a catchment population of around 365,000. SUBJECTS: 118 consecutive adult patients suffering primary cardiac arrest in-hospital and 132 non-arrestpatients, randomly selected according to stratified randomisation by gender and age. RESULTS: Risk factors for cardiac arrest include: abnormal respiratory rate (P = 0.013), abnormal breathing indicator (abnormal rate or documented shortness of breath) (P < 0.001), abnormal pulse (P < 0.001), reduced systolic blood pressure (P < 0.001), abnormal temperature (P < 0.001), reduced pulse oximetry (P < 0.001), chest pain (P < 0.001) and nurse or doctor concern (P < 0.001). Multivariate analysis of cardiac arrest cases identified three positive associations for cardiac arrest: abnormal breathing indicator (OR 3.49; 95% CI: 1.69-7.21), abnormal pulse (OR 4.07; 95% CI: 2.0-8.31) and abnormal systolic blood pressure (OR 19.92; 95% CI: 9.48-41.84). Risk factors were weighted and tabulated. The aggregate score determines the grade of clinical response. ROC analysis determined that a score of 4 has 89% sensitivity and 77% specificity for cardiac arrest; a score of 8 has 52% sensitivity and 99% specificity. All patients scoring greater than 10 suffered cardiac arrest. CONCLUSION: Risk factors for cardiac arrest have been identified, quantified and formulated into a table of activation criteria to help predict and avert cardiac arrest by alerting a clinical response. A graded clinical response has resulted in a tool that has both sensitivity and specificity for cardiac arrest.
Authors: Haiyan Gao; Ann McDonnell; David A Harrison; Tracey Moore; Sheila Adam; Kathleen Daly; Lisa Esmonde; David R Goldhill; Gareth J Parry; Arash Rashidian; Christian P Subbe; Sheila Harvey Journal: Intensive Care Med Date: 2007-02-22 Impact factor: 17.440
Authors: Amy D Droitcour; Todd B Seto; Byung-Kwon Park; Shuhei Yamada; Alex Vergara; Charles El Hourani; Tommy Shing; Andrea Yuen; Victor M Lubecke; Olga Boric-Lubecke Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2009
Authors: J P Nolan; C D Deakin; J Soar; B W Böttiger; G Smith; M Baubin; B Dirks; V Wenzel Journal: Notf Rett Med Date: 2006-02-01 Impact factor: 0.826