The efficacy of remote ischemic preconditioning (RIPC) in high-risk cardiac surgery is uncertain. In this study, 96 adults undergoing high-risk cardiac surgery were randomised to RIPC (3 cycles of 5 min of upper-limb ischemia induced by inflating a blood pressure cuff to 200 mmHg with 5 min of reperfusion) or control. Main endpoints were plasma high-sensitivity troponin T (hsTNT) levels at 6 and 12 h, worst post-operative acute kidney injury (AKI) based on RIFLE criteria, and noradrenaline duration. hsTNT levels were log-normally distributed and higher with RIPC than control at 6-h post cross-clamp removal [810 ng/ml (IQR 527-1,724) vs. 634 ng/ml (429-1,012); ratio of means 1.41 (99.17% CI 0.92-2.17); P=0.04] and 12 h [742 ng/ml (IQR 427-1,700) vs. 514 ng/ml (IQR 356-833); ratio of means 1.56 (99.17% CI 0.97-2.53); P=0.01]. After adjustment for baseline confounders, the ratio of means of hsTNT at 6 h was 1.23 (99.17% CI 0.88-1.72; P=0.10) and at 12 h was 1.30 (99.17% CI 0.92-1.84; P=0.05). In the RIPC group, 35/48 (72.9%) had no AKI, 5/48 (10.4%) had AKI risk, and 8/48 (16.7%) had either renal injury or failure compared to the control group where 34/48 (70.8%) had no AKI, 7/48 (14.6%) had AKI risk, and 7/48 (14.6%) had renal injury or failure (Chi-squared 0.41; two degrees of freedom; P = 0.82). RIPC increased post-operative duration of noradrenaline support [21 h (IQR 7-45) vs. 9 h (IQR 3-19); ratio of means 1.70 (99.17% CI 0.86-3.34); P=0.04]. RIPC does not reduce hsTNT, AKI, or ICU-support requirements in high-risk cardiac surgery.

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ANALYSES of ex post security price data have in general been concerned with (1) the behavior of stock market prices [2,5,9,12], (2) evaluation of portfolio selection models [ 1,8,10,11 ], and (3) security-stock market performance [6,7 ]. In this paper ex post annual holding period returns (HPRs) are used to test methods for allocating financial resources among portfolio assets (risk free bonds and securities) taking cognizance of the character of security prices, portfolio diversification and portfolio leverage (borrowing or lending). We assume that the objective of portfolio management is to maximize long-run wealth. This is done by maximizing the compound annual average return, that is geometric mean return, on net worth. Hence, we test the effectiveness of various portfolio building rules on the geometric mean return, G, by using past holding period returns to represent probability beliefs of investors faced with building portfolios.We are using ex post distributions of HPRs to test portfolio building rules. This involves two very important assumptions: (1) we assume perfect knowledge as to the statistics of these distributions over time and across securities but no knowledge of the individual HPRs, and (2) we assume that these statistics remain constant over time so that the probability beliefs do not change as actual results become available over time. We are attempting to show the outcome of several portfolio rules given specified statistics for distributions of HPRs. For example, we will use ex post data to test the intuitively appealing hypothesis that it would be generally better to reallocate portfolios at the beginning of each investment period rather than to buy and hold, since reallocation would reduce holdings in stocks that had performed well to date (and thus will perform relatively poorer over the subsequent periods) and increase holdings that have had relatively poor performance (but will wind up with an average performance approximately equal to the other stocks in the portfolio.)"