Case records of the Massachusetts General Hospital. Case 37-2005. A 35-year-old man with cardiac arrest while sleeping.
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Adult↗
Biomedical subjects
Keith A Marill
Publications and source records attributed to Keith A Marill.
Amiodarone is poorly effective for the acute termination of ventricular tachycardia.
STUDY OBJECTIVE: It is hypothesized that intravenous (IV) amiodarone is poorly effective for the acute termination of sustained monomorphic ventricular tachycardia because of the relatively slow onset of its Vaughn-Williams class III effect to prolong myocardial depolarization and the refractory period. This study is designed to determine the effectiveness and safety of IV amiodarone for the termination of sustained monomorphic ventricular tachycardia. METHODS: A retrospective case series was collected at 4 urban university-affiliated hospitals from September 1996 to April 2005 after institutional review board approval with waiver of informed consent. Emergency department (ED) patients treated with IV amiodarone for ventricular tachycardia were identified by ED treatment and hospital pharmacy billing records, International Classification of Diseases, Ninth Revision discharge codes, and ECG characteristics. All consecutive patients who received at least 150 mg amiodarone in 15 minutes or less for spontaneous sustained monomorphic ventricular tachycardia were eligible for inclusion. Sustained monomorphic ventricular tachycardia was defined as a tachycardia with uninterrupted duration or rapid recurrence despite automatic internal cardiac defibrillator therapy for at least 5 minutes before amiodarone treatment, monomorphic morphology, rate greater than 120 beats/min, QRS duration greater than 120 ms, and subsequently determined to be ventricular tachycardia by ECG criteria (eg, atrioventricular dissociation), implanted device interrogation, or formal electrophysiology study. Measured outcomes included sustained termination of ventricular tachycardia within 20 minutes of initiation of amiodarone infusion and any documented adverse effects. Rates of successful termination and adverse effects and their 95% confidence intervals (CIs) were calculated. The presence or average values of potentially confounding predictors in patients with and without ventricular tachycardia termination after amiodarone were also calculated and compared. RESULTS: Thirty-three patients were identified and included. Five patients received electrical therapy within 20 minutes of initiation of amiodarone infusion, and the response to amiodarone was unknown. Twenty-seven of the remaining 28 patients received 150 mg amiodarone, and the rate of successful ventricular tachycardia termination was 8 of 28, 29% (95% CI 13 to 49). Two of 33 patients, 6% (95% CI 1 to 20), required direct current cardioversion for presyncope or hypotension temporally associated with amiodarone treatment. CONCLUSION: IV amiodarone, as currently administered, is relatively safe but ineffective for the acute termination of sustained ventricular tachycardia.
Aged↗
Advanced statistics: linear regression, part II: multiple linear regression.
The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.
Bias↗
The use of a dissected bovine heart to teach cardiac sonography.
OBJECTIVES: To create and test a dissected bovine heart model (BHM) to facilitate the interpretation of cardiac sonography (CS). METHODS: After a pretest and an instructional video on CS, emergency physicians (EPs) were randomized into two groups. Group 1 viewed two-dimensional (2D) anatomic pictures of human hearts. Group 2 examined the BHM and the same anatomic pictures as group 1. The EPs retook the pretest. The differences between the raw pretest and posttest scores of the groups were compared with an unpaired Student's t-test. Multiple linear regression was used to adjust for confounding by variation in education and initial test scores. EPs with previous experience in CS were excluded from the analysis. RESULTS: Thirty-five participants met the inclusion criteria, 16 in group 1 and 19 in group 2. The groups were well balanced with respect to postgraduate year training. The EPs in group 1 had a higher average pretest score of 11.6 versus 8.1 in group 2. Compared with the pretest scores, the average improvements in group 1 and group 2 were 7.6 and 11.3 points, respectively. Group 2 improved an average of 3.7 points (95% confidence interval [95% CI] = 0.7 to 6.7; p = 0.016) more than group 1. After adjusting for confounding by the difference in initial scores, group 2 improved 1.8 (95% CI = -1.1 to 4.8; p = 0.22) more points on average than group 1. CONCLUSIONS: A dissected bovine heart model did not significantly improve the ability of EPs to label structures on static ultrasounds over inspection of static-labeled anatomic pictures alone.
Animals↗
Advanced statistics: linear regression, part I: simple linear regression.
Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.
Biometry↗