RESUMEN
OBJECTIVES: To determine the prevalence of mitral regurgitation (MR) in the U.S. adult population by classifying its mechanisms according to Carpentier's functional class. BACKGROUND: MR is the most common clinically recognizable valvular heart condition in the U.S. affecting 2 to 2.5 million people in 2000. A true estimate of the prevalence of MR in accordance to the functional class and etiology is unavailable. METHODS: We conducted a Medline search regarding prevalence and etiologies of MR. Etiologies were grouped by Carpentier's functional classification, and estimated prevalence numbers were projected to U.S. adult population of 200 million. Moderate-to-severe grades of MR were included. RESULTS: Carpentier type I, including congenital MR and endocarditis, has a prevalence of less than 20 per million. Myxomatous infiltration leading to mitral valve prolapse is the largest group associated with a type II mechanism with 15,000 per million prevalence. Type IIIa includes rheumatic heart disease, systemic lupus erythematosus (SLE), antiphospholipid syndrome (APS), and rare infiltrative and tropical disorders. SLE and APS have a mean prevalence of 10,520 per million. Carpentier IIIb is the largest group leading to MR, which is mostly functional, and includes ischemic cardiomyopathy, left ventricular (LV) dysfunction, and dilated cardiomyopathies. The estimated prevalence of MR in ischemic cardiomyopathy is 7500 to 9000 per million, and in LV dysfunction, 16,250 per million. CONCLUSIONS: The largest number of people with MR is in type IIIb. Certain etiologies show overlap within functional classes due to multiple mechanisms of MR. We attempted to classify etiologies of MR by a functional class to determine the disease burden.
Asunto(s)
Insuficiencia de la Válvula Mitral/clasificación , Insuficiencia de la Válvula Mitral/epidemiología , Humanos , Insuficiencia de la Válvula Mitral/etiología , Prevalencia , Estados Unidos/epidemiologíaRESUMEN
BACKGROUND: Heart rate variability (HRV) changes often reflect autonomic dysfunction with high sensitivity, but the specificity is also low. There are several different methods for measuring HRV, but interpretation is often complex, and the units are not interchangeable. For these reasons, HRV monitoring is not routinely used in many clinical situations. We hypothesized that the specificity of HRV as a screening tool for trauma patients could be improved by controlling some of the confounding influences using multiple logistic regression. METHODS: A prospective observational trial with waiver of consent was performed in 243 healthy student volunteers and 257 trauma patients, in the resuscitation bay and intensive care units of a Level I trauma center, who received computed axial tomography (CT) scans of the head as part of the initial work up. Electrocardiogram results were recorded for 5 minutes. HRV was defined by SD of normal R-R intervals (SDNN5) and by root mean square of successive differences of R-R intervals (RMSSD5). A head CT scan was considered positive (+) if there were abnormalities in the parenchyma (diffuse axonal injury or contusion), vasculature (intraparenchymal, subdural, or epidural hemorrhage), and/or structural or bony components (fractures of the face or cranium). RESULTS: In volunteers, SDNN5 was 73 +/- 15 (M +/- SD) milliseconds, compared with 42 +/- 22, 31 +/- 19, 28 +/- 17, and 12 +/- 8 milliseconds in, CT(-) patients with no sedation (n = 82), CT(-) with sedation (n = 60), CT(+) with no sedation (n = 55), and CT(+) with sedation (n = 60), respectively. The differences between trauma, sedation, and CT categories were significant (all p < 0.001). RMSSD5 differences were similar and also highly significant (all p < 0.001). For both SDNN5 and RMSSD5, in each category, there was wide overlap in the range of values, and strong inverse correlations with heart rate (all p < 0.001). Using multiple logistic regression in a subset with no missing data (n = 194), an index was derived from ln(SDNN5) adjusted for six confounding factors. With a negative predictive value held constant at 0.90, compared with ln(SDNN5) alone, the stepwise addition of heart rate, sedation, age, gender, and blood pressure progressively improved the specificity of the HRV index from 0.56 to 0.77, positive predictive value from 0.55 to 0.68, and efficiency from 0.68 to 0.80. This index was then normalized (0-100 scale) for ease of interpretation. CONCLUSIONS: (1) Several factors alter HRV in patients; (2) when HRV was indexed for some of these factors, its specificity and efficiency were improved for predicting a discrete pathologic state in trauma patients, i.e. (+) or (-) cranial CT scans; (3) the algorithm can incorporate other factors to further refine the diagnostic and/or prognostic ability of HRV as a noninvasive clinical tool; (4) this concept should be applicable to any other HRV measurement technique or outcome.