Preoperative evaluation : risk management and implementation aspects

In preoperative risk management the anesthesiologist uses diagnostic information to estimate the probability of outcomes and to decide on the anesthetic strategy in a particular patient. The aim of this thesis was explore to what extent simple patient characteristics, particularly obtained from preoperative patient history and physical examination, could contribute to preoperative risk management. Furthermore, the implementation of outpatient preoperative evaluation (OPE) clinics in the Netherlands as well as the effects of OPE in a particular hospital were quantified. Preferably, during OPE, healthy patients are easily distinguished from the remainder using a minimal but optimal set of diagnostic tests. Currently, the literature shows no evidence about the optimal contents of preoperative evaluation. To determine the optimal set of diagnostic tests to appropriately detect existing co-morbidity would require an empirical diagnostic study. In such a study the contribution of each diagnostic test is related to the diagnostic outcome, i.e. presence or absence of significant co-morbidity. To demonstrate how diagnostic research might be used in the clinical setting of an OPE clinic, we studied the diagnostic value of cardiac auscultation to detect VHD. Diagnostic research will decrease redundant information, but requires the a priori definition of what constitutes significant co-morbidity. The anesthesiologist should also have evidence-based knowledge about the probability of perioperative complications and to what extent the anesthetic strategy may alter the complication rate. Prognostic prediction studies aim to estimate the probability of future occurrence of a particular outcome in a particular patient and are also suitable to estimate to what extent the individual risk of a patient can be modified using pre-emptive strategies, such as administering erythropoietin before surgery. Before a prediction model can be implemented in practice, its generalizability (the application to patients from a different but related population) should be estimated. To obtain an estimate of the generalizability, we applied two prediction models on perioperative blood transfusion to a patient population from another hospital. Both models stayed robust and we concluded that they could be implemented in practice. In this context, there will be an important role for information technology: a complication registration system could provide the necessary data for continuous prognostic prediction research, which in turn will provide risk stratification systems for (long-term) morbidity and mortality to be built-in in electronic patient record software used at the OPE clinic. There are several potential benefits of OPE. For example, OPE allows for comprehensive assessment and optimization of the patients health condition without delaying surgery. However, to extract the maximal benefits from OPE the incentives for all those concerned in preoperative patient care, such as anesthesiologists and surgical specialists, must be clear to change existing practice patterns, such as routine admission of patients to the ward the day before surgery. Because widespread implementation of OPE will require an increase in the number of anesthesiologists, the questions arises whether a specially trained anesthetic nurse can screen patients adequately. The partial substitution of the anesthesiologist by a specially trained nurse in a mixed-provider model OPE clinic could have several benefits and might increase the quality and cost-effectiveness of OPE

Assessing the applicability of scoring systems for predicting postoperative nausea and vomiting

We have validated two scoring systems for predicting postoperative nausea and vomiting, derived by Apfel et al. and Koivuranta et al. from 1388 adult inpatients undergoing a wide range of surgical procedures. The predictive accuracy of the scoring systems was evaluated in terms of the ability to discriminate between patients with and without postoperative nausea and vomiting (discrimination) and agreement between observed and predicted outcomes (calibration). Discrimination and calibration were less than expected based on previous reports, with both scoring systems providing risk predictions that were too extreme. The area under the ROC curve was 0.63 for Apfel et al.'s scoring system and 0.66 for Koivuranta et al.'s scoring system. Neither of the scoring systems provided a risk threshold for administering anti-emetic prophylaxis that yielded satisfying results in terms of predictive values, sensitivity and specificity. Hence, in their original forms, the scoring systems do not guarantee accurate prediction of the risk of postoperative nausea and vomiting in other patient populations. Koivuranta et al.'s scoring system appears to be more robust across different population

Internal validation of risk models in clustered data: a comparison of bootstrap schemes

Internal validity of a risk model can be studied efficiently with bootstrapping to assess possible optimism in model performance. Assumptions of the regular bootstrap are violated when the development data are clustered. We compared alternative resampling schemes in clustered data for the estimation of optimism in model performance. A simulation study was conducted to compare regular resampling on only the patient level with resampling on only the cluster level and with resampling sequentially on both the cluster and patient levels (2-step approach). Optimism for the concordance index and calibration slope was estimated. Resampling of only patients or only clusters showed accurate estimates of optimism in model performance. The 2-step approach overestimated the optimism in model performance. If the number of centers or intraclass correlation coefficient was high, resampling of clusters showed more accurate estimates than resampling of patients. The 3 bootstrap schemes also were applied to empirical data that were clustered. The results presented in this paper support the use of resampling on only the clusters for estimation of optimism in model performance when data are clustered. © 2013 © The Author 2013. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected]

Impact of Intraoperative Hypotension During Cardiopulmonary Bypass on Acute Kidney Injury After Coronary Artery Bypass Grafting

Item does not contain fulltextOBJECTIVE: The aim of this study was to investigate whether acute kidney injury (AKI) after coronary artery bypass grafting can be attributed to intraoperative hypotension during cardiopulmonary bypass (IOH-CPB). DESIGN: Retrospective analysis. SETTING: Tertiary-care hospital. PARTICIPANTS: Patients undergoing on-pump coronary artery bypass grafting from June 2011 to January 2014. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: IOH-CPB was defined as blood pressure below several absolute and relative mean arterial pressure (MAP) thresholds and as the area under the curve for absolute MAP thresholds. AKI was defined as an absolute increase in serum creatinine of>/=26 micromol/L within 48 hours or an increase to 150% or more within 7 days of surgery. Poisson regression with robust standard errors both before and after adjustment for confounders was used. Of the 1,891 patients included, 386 (20%) developed AKI. In univariable analysis, all IOH-CPB thresholds defined as a MAP of 50 mmHg or less and as a decrease in MAP of 60% from baseline were associated with a 1.07-to-1.11 times increased risk of AKI per 10 minutes of IOH-CPB (p<0.01). After adjustment for potential confounders, IOH-CPB, irrespective of the definition chosen, was not associated with an increased risk of AKI. CONCLUSIONS: In the authors' study population, univariable analysis showed an association of IOH-CPB with AKI in patients undergoing isolated CABG, but this relationship disappeared after correction for well-known risk factors for AKI

Шантарский архипелаг: основные направления природопользования

BACKGROUND: Short-acting vasopressor agents like phenylephrine or ephedrine can be used during carotid endarterectomy (CEA) to achieve adequate blood pressure (BP) to prevent periprocedural stroke by preserving the cerebral perfusion. Previous studies in healthy subjects showed that these vasopressors also affected the frontal lobe cerebral tissue oxygenation (rSO 2) with a decrease after administration of phenylephrine. This decrease is unwarranted in patients with jeopardized cerebral perfusion, like CEA patients. The study aimed to evaluate the impact of both phenylephrine and ephedrine on the rSO 2 during CEA. METHODS: In this double-blinded randomized controlled trial, 29 patients with symptomatic carotid artery stenosis underwent CEA under volatile general anesthesia in a tertiary referral medical center. Patients were preoperative allocated randomly (1:1) for receiving either phenylephrine (50 µg; n = 14) or ephedrine (5 mg; n = 15) in case intraoperative hypotension occurred, defined as a decreased mean arterial pressure (MAP) ≥ 20% compared to (awake) baseline. Intraoperative MAP was measured by an intra-arterial cannula placed in the radial artery. After administration, the MAP, cardiac output (CO), heart rate (HR), stroke volume, and rSO 2 both ipsilateral and contralateral were measured. The timeframe for data analysis was 120 s before, until 600 s after administration. RESULTS: Both phenylephrine (70 ± 9 to 101 ± 22 mmHg; p < 0.001; mean ± SD) and ephedrine (75 ± 11 mmHg to 122 ± 22 mmHg; p < 0.001) adequately restored MAP. After administration, HR did not change significantly over time, and CO increased 19% for both phenylephrine and ephedrine. rSO 2 ipsilateral and contralateral did not change significantly after administration at 300 and 600 s for either phenylephrine or ephedrine (phenylephrine 73%, 73%, 73% and 73%, 73%, 74%; ephedrine 72%, 73%, 73% and 75%, 74%, 74%). CONCLUSIONS: Within this randomized prospective study, MAP correction by either phenylephrine or ephedrine showed to be equally effective in maintaining rSO 2 in patients who underwent CEA. Clinical Trial Registration ClincalTrials.gov, NCT01451294