Decadal Cycles in the Human Cardiovascular System.

Seven of the eight authors of this report each performed physiologic self-surveillance, some around the clock for decades. We here document the presence of long cycles (decadals, including circaundecennians) in the time structure of systolic (S) and diastolic (D) blood pressure (BP) and heart rate (HR). Because of the non-stationary nature in time and space of these and other physiologic and environmental periodic components that, like the wind, can appear and disappear in a given or other geographic location at one or another time, they have been called "Aeolian". The nonlinear estimation of the uncertainties of the periods (τs) of two or more variables being compared has been used to determine whether these components are congruent or not, depending on whether their CIs (95% confidence intervals) overlap or not. Among others, congruence has been found for components with τs clustering around 10 years in us and around us. There is a selective assortment among individuals, variables and cycle characteristics (mean and circadian amplitude and acrophase). Apart from basic interest, like other nonphotic solar signatures such as transyears with periods slightly longer than one year or about 33-year Brückner-Egeson-Lockyer (BEL) cycles, about 10-year and longer cycles present in 7 of 7 self-monitoring individuals are of interest in the diagnosis of Vascular Variability Anomalies (VVAs), including MESOR-hypertension, and others. Some of the other VVAs, such as a circadian overswing, i.e., CHAT (Circadian Hyper-Aplitude-Tension), or an excessive pulse pressure, based on repeated 7-day around-the-clock records, can represent a risk of severe cardiovascular events, greater than that of a high BP. The differential diagnosis of physiologic cycles, infradians (components with a τ longer than 28 hours) as well as circadians awaits the collection of reference values for the infradian parameters of the cycles described herein. Just as in stroke-prone spontaneously hypertensive rats during the weeks after weaning CHAT precedes an elevation of the BP MESOR, a decadal overswing seems to precede the occurrence of high BP in two of the subjects here examined. Only around-the-clock monitoring in health for the collection of reference values will allow on their basis the differential diagnosis of the onsets of a circadian versus a circadecadal overswing in BP and the specification whether, and if so, when to initiate hypotensive non-drug or drug treatment.

Predicting the Crohn's disease activity index from the Harvey-Bradshaw Index.

BACKGROUND: The Crohn's Disease Activity Index (CDAI) was developed in the 1970s to assess the degree of illness in individuals with Crohn's disease and has since been used widely in clinical trials of the condition. The Harvey-Bradshaw Index (HBI) is a simplification of the CDAI, designed to make data collection and computation easier. It is purported, on the basis of a 0.93 correlation coefficient, to give "essentially the same information." However, correlation is an incomplete way to assess sameness, and this study aimed to develop a method for predicting CDAI from HBI values, including relevant prediction limits. MATERIALS AND METHODS: Data used in developing both indexes were combined. Single visits of 224 patients with Crohn's disease were plotted on a scattergram. HBI values seen were integers from 0 through 19. Mean and standard deviation of CDAI were determined for each HBI value that included a sufficient number of patients. Standard deviation of CDAI showed a linear increase with increasing HBI. Therefore, regression of CDAI on HBI was weighted on the inverse of the estimated CDAI standard deviation. RESULTS: Regression predicted a 27-CDAI-unit increase for each HBI unit. Calculated 95% prediction limits were almost straight, diverging lines, bracketing 95% of observations. A table gives central tendency and 95% prediction limits of CDAI for any HBI, as well as key clinical benchmarks. CONCLUSIONS: There is a good but far from perfect relationship between CDAI and HBI. CDAI is preferred for clinical trials; HBI is easier to use.

Identifying patient preoperative risk factors and postoperative adverse events in administrative databases: results from the Department of Veterans Affairs National Surgical Quality Improvement Program.

BACKGROUND: The Department of Veterans Affairs (DVA) National Surgical Quality Improvement Program (NSQIP) employs trained nurse data collectors to prospectively gather preoperative patient characteristics and 30-day postoperative outcomes for most major operations in 123 DVA hospitals to provide risk-adjusted outcomes to centers as quality indicators. It has been suggested that routine hospital discharge abstracts contain the same information and would provide accurate and complete data at much lower cost. STUDY DESIGN: With preoperative risks and 30-day outcomes recorded by trained data collectors as criteria standards, ICD-9-CM hospital discharge diagnosis codes in the Patient Treatment File (PTF) were tested for sensitivity and positive predictive value. ICD-9-CM codes for 61 preoperative patient characteristics and 21 postoperative adverse events were identified. RESULTS: Moderately good ICD-9-CM matches of descriptions were found for 37 NSQIP preoperative patient characteristics (61%); good data were available from other automated sources for another 15 (25%). ICD-9-CM coding was available for only 13 (45%) of the top 29 predictor variables. In only three (23%) was sensitivity and in only four (31%) was positive predictive value greater than 0.500. There were ICD-9-CM matches for all 21 NSQIP postoperative adverse events; multiple matches were appropriate for most. Postoperative occurrence was implied in only 41%; same breadth of clinical description in only 23%. In only four (7%) was sensitivity and only two (4%) was positive predictive value greater than 0.500. CONCLUSION: Sensitivity and positive predictive value of administrative data in comparison to NSQIP data were poor. We cannot recommend substitution of administrative data for NSQIP data methods.