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Aims Improved prognosis of patients with chronic systolic heart failure by treatment with beta-blockers has been shown in several randomized controlled multicentre trials. However, in clinical practice only a part of heart failure patients meet the inclusion criteria of these trials. The present study evaluates whether reduction of mortality by beta-blockers also can be achieved in patients presenting one or more exclusion criteria of the MERIT-HF trial. Methods and results From the Ludwigshafen Heart Failure Registry 675 patients with chronic systolic heart failure consecutively enrolled between January 1995 and June 2004 were divided in two groups either meeting the inclusion criteria of the MERIT-HF trial (‘trial patients’: n =278, 60% treated with beta-blockers) or not (‘non-trial patients’: n =397; 51% treated with beta-blockers). The distribution of the MERIT-HF exclusion criteria in the group of ‘non-trial patients’ was as follows: acute myocardial infarction 9.6%; systolic blood pressure

In this investigation we assessed whether patients receiving spinal anesthesia (SPA) as part of combined spinal-epidural anesthesia (CSE) more often experience relevant hypotension than patients receiving SPA alone. From January 1, 1997, until August 5, 2000, electronic anesthesia records from 1596 patients having received SPA and 1023 patients having received CSE for elective surgery were collected by using a computerized anesthesia record-keeping system. Relevant hypotension was defined as a decrease of mean arterial blood pressure of more than 30% within a 10-min interval and a therapeutic action of the attending anesthesiologist within 20 min after onset. Electronic patient charts were reviewed by using logistic regression with a forward stepwise algorithm to identify independent risk factors that were associated with an increased incidence of hypotension after CSE. Univariate analysis was performed to assess differences in biometric data and relevant risk factors for hypotension between the two procedures. The incidence of relevant hypotension was more frequent with CSE than with SPA alone (10.9% versus 5.0%; P0.001). In the multivariate analysis, arterial hypertension (odds ratio, 1.83; 95% confidence interval, 1.21-2.78) and sensory block heightT6 (odds ratio, 2.81; 95% confidence interval, 1.88-4.22) were found to be factors associated with hypotension in the CSE group. Compared with patients receiving SPA alone, patients undergoing CSE had a significantly more frequent prevalence of arterial hypertension and higher sensory block levels (P0.01) despite smaller amounts of local anesthetics. In this study, patients receiving CSE had an increased risk for relevant hypotension as compared with patients with SPA alone. Part of this effect seems to be due to the procedure alone and not only because this population is at higher risk.This study, based on a large number of patients with a retrospective design by using on-line recorded data, suggests that spinal anesthesia as part of combined spinal-epidural anesthesia may more often lead to relevant hypotension than spinal anesthesia alone. Preexisting arterial hypertension and a sensory block height exceeding T6 are major risk factors for the development of this complication.

This study addresses the question whether K(+) channels are involved in the vasorelaxant effects of 3-(5'-hydroxymethyl-2'-furyl)-1-benzyl-indazole (YC-1 ). In rat aorta, guinea pig aorta, and guinea pig a. carotis, YC-1 inhibited contractions induced by phenylephrine (3 microM) more potently than those induced by K(+)(48 mM). In rat aorta, tetraethylammonium (10 mM), charybdotoxin (0.2 microM), and iberiotoxin (0.1 microM), but not glibenclamide (10 microM), attenuated the relaxant effects of YC-1. In guinea pig a. carotis, YC-1 (30 microM) induced a hyperpolarisation which was antagonised by 1H-[1,2,4]oxadiazolo[4, 3-a]quinoxalin-1-one (ODQ; 50 microM). In rat aorta, YC-1 (30 microM) increased the rate constant of 86Rb-efflux. The effect of YC-1 was potentiated by zaprinast (10 microM), but inhibited by ODQ (50 microM) or charybdotoxin (0.2 microM). In smooth muscle cells from rat aorta, YC-1 (10 microM) increased BK(Ca) channel activity. It is suggested that YC-1-induced vasorelaxation is partially mediated by the activation of K(+) channels.

Objective To evaluate the performance of 4 published prognostic models for postoperative onset of nausea and vomiting (PONV) by means of discrimination and calibration and the possible impact of customization on these models. Design Prospective, observational study. Setting Tertiary care university hospital. Patients 748 adult patients (>18 years old) enrolled in this study. Severe obesity (weight > 150 kg or body mass index > 40 kg/m) was an exclusion criterion. Interventions All perioperative data were recorded with an anesthesia information management system. A standardized patient interview was performed on the postoperative morning and afternoon. Measurements Individual PONV risk was calculated using 4 original regression equations by Koivuranta et al, Apfel et al, Sinclair et al, and Junger et al Discrimination was assessed using receiver operating characteristic (ROC) curves. Calibration was tested using Hosmer-Lemeshow goodness-of-fit statistics. New predictive equations for the 4 models were derived by means of logistic regression (customization). The prognostic performance of the customized models was validated using the "leaving-one-out" technique. Main Results Postoperative onset of nausea and vomiting was observed in 11.2% of the specialized patient population. Discrimination could be demonstrated as shown by areas under the receiver operating characteristic curve of 0.62 for the Koivuranta et al model, 0.63 for the Apfel et al model, 0.70 for the Sinclair et al model, and 0.70 for the Junger et al model. Calibration was poor for all 4 original models, indicated by a P value lower than 0.01 in the C and H statistics. Customization improved the accuracy of the prediction for all 4 models. However, the simplified risk scores of the Koivuranta et al model and the Apfel et al model did not show the same efficiency as those of the Sinclair et al model and the Junger et al model. This is possibly a result of having relatively few patients at high risk for PONV in combination with an information loss caused by too few dichotomous variables in the simplified scores. Conclusions The original models were not well validated in our study. An antiemetic therapy based on the results of these scores seems therefore unsatisfactory. Customization improved the accuracy of the prediction in our specialized patient population, more so for the Sinclair et al model and the Junger et al model than for the Koivuranta et al model and the Apfel et al model.

Within surgical departments, a large amount of antibiotics is used for perioperative prophylaxis. Despite the existence of several guidelines and recommendations for administering antibiotic prophylaxis, mistakes still do occur and have an unknown impact on outcome severity. Based on the electronic anaesthesia records of 4304 patients undergoing defined surgical procedures requiring perioperative antibiotic prophylaxis, a matched pairs approach was used to evaluate the impact of inadequate antibiotic prophylaxis on hospital mortality and prolonged length of stay on intensive care. Stepwise regression models were developed to predict the impact of inadequate antibiotic prophylaxis on outcome measures. An inadequate antibiotic prophylaxis was found for a total of 877 cases. 77.9% of cases were successfully matched, leading to 683 cases and controls each. The crude mortality ratio of cases to controls was 1.5 (cases = 21 versus controls = 14; P = 0.19). The case group had a significantly (P0.01) prolonged stay on ICU when analysed as a metric variable. Using logistic regression analysis, we could determine that inadequate antibiotic prophylaxis had no impact on either hospital mortality or prolonged length of stay on ICU (1 day; yes or no).

Background: Increased BMI is a well known risk factor for morbidity and mortality in hospitalized nonsurgical patients. However, the published evidence for a comparable effect in surgical patients is scarce. Methods: This retrospective study was designed to assess the attributable effects of increased BMI (>30 kg/m2) on outcome (hospital mortality, admission to the intensive care unit (ICU), and incidence of intraoperative cardiovascular events (CVE)) in patients undergoing non-cardiac surgery by a computerized anesthesia record-keeping system. The study is based on data-sets of 28,065 patients. Cases were defined as patients with BMI >30; controls (BMI 20-25) were automatically selected according to matching variables (ASA physical status, high risk and urgency of surgery, age and sex) in a stepwise fashion. Differences in outcome measures were assessed using univariate analysis. Stepwise regression models were developed to predict the impact of increased BMI on the different outcome measures. Results: 4,726 patients (16.8%) were found with BMI >30. Matching was successful for 41.5% of the cases, leading to 1,962 cases and controls. The crude mortality rates were 1.1% (cases) vs 1.2% (controls); P =0.50, power=0.88). Admission to ICU was deemed necessary in 6.8% (cases) vs 7.5% (controls), P =0.42, power=0.65, and CVE were detected from the database in 22.3% (cases) vs 21.6% (controls), P =0.30, power=0.60. Using logistic regression analyses, no significant association between higher BMI and outcome measures could be verified. Conclusion: Increased BMI alone was not a factor leading to an increased perioperative risk in non-cardiac surgery. This fact may be due to an elevated level of attention while caring for obese patients.

The objective of this study was to evaluate prognostic models for quality assurance purposes in predicting automatically detected intraoperative cardiovascular events (CVE) in 58458 patients undergoing noncardiac surgery. To this end, we assessed the performance of two established models for risk assessment in anesthesia, the Revised Cardiac Risk Index (RCRI) and the ASA physical status classification. We then developed two new models. CVEs were detected from the database of an electronic anesthesia record-keeping system. Logistic regression was used to build a complex and a simple predictive model. Performance of the prognostic models was assessed using analysis of discrimination and calibration. In 5249 patients (17.8%) of the evaluation (n = 29437) and 5031 patients (17.3%) of the validation cohorts (n = 29021), a minimum of one CVE was detected. CVEs were associated with significantly more frequent hospital mortality (2.1% versus 1.0%; P0.01). The new models demonstrated good discriminative power, with an area under the receiver operating characteristic curve (AUC) of 0.709 and 0.707 respectively. Discrimination of the ASA classification (AUC 0.647) and the RCRI (AUC 0.620) were less. Neither the two new models nor ASA classification nor the RCRI showed acceptable calibration. ASA classification and the RCRI alone both proved unsuitable for the prediction of intraoperative CVEs.The objective of this study was to evaluate prognostic models for quality assurance purposes to predict the occurrence of automatically detected intraoperative cardiovascular events in 58,458 patients undergoing noncardiac surgery. Two newly developed models showed good discrimination but, because of reduced calibration, their clinical use is limited. The ASA physical status classification and the Revised Cardiac Risk Index are unsuitable for the prediction of intraoperative cardiovascular events.

The aim of this retrospective study was to assess the suitability of routine data gathered with a computerized anesthesia record keeping system in investigating predictors for intraoperative hypoxemia (SpO290%) during one-lung ventilation (OLV) in pulmonary surgery.Over a four-year period data of 705 patients undergoing thoracic surgery (pneumonectomy: 78; lobectomy: 292; minor pulmonary resections: 335) were recorded online using an automated anesthesia record-keeping system. Twenty-six patient-related, surgery-related and anesthesia-related variables were studied for a possible association with the occurrence of intraoperative hypoxemia during OLV. Data were analyzed using univariate and multivariate (logistic regression) analysis (p0.05). The model's discriminative power on hypoxemia was checked with a receiver operating characteristic (ROC) curve. Calibration was tested using the Hosmer-Lemeshow goodness-of-fit test.An intraoperative incidence of hypoxemia during OLV was found in 67 patients (9.5%). Using logistic regression with a forward stepwise algorithm, body-mass-index (BMI, p = 0.018) and preoperative existing pneumonia (p = 0.043) could be detected as independent predictors having an influence on the incidence of hypoxemia during OLV. An acceptable goodness-of-fit could be observed using cross validation for the model (C = 8.21, p = 0.370, degrees of freedom, df 8; H = 3.21, p = 0.350, df 3), the discriminative power was poor with an area under the ROC curve of 0.58 [0.51-0.66].In contrast to conventional performed retrospective studies, data were directly available for analyses without any manual intervention. Due to incomplete information and imprecise definitions of parameters, data of computerized anesthesia records collected in routine are helpful but not satisfactory in evaluating risk factors for hypoxemia during OLV.

Intra-operative tachycardia is a common adverse event, often recorded as an indicator for process quality in quality assurance projects in anaesthesia. This retrospective study is based on data sets of 28,065 patients recorded with a computerised anaesthesia record-keeping system from 23 February 1999 to 31 December 2000 at a tertiary care university hospital. Cases were defined as patients with intra-operative tachycardia; references were automatically selected according to matching variables (high-risk surgery, severe congestive heart failure, severe coronary artery disease, significant carotid artery stenosis and/or history of stroke, renal failure, diabetes mellitus and urgency of surgery) in a stepwise fashion. Main outcome measures were hospital mortality, admission to the intensive care unit (ICU) and prolonged hospital stay. Differences in outcome measures between the matched pairs were assessed by univariate analysis. Stepwise regression models were developed to predict the impact of intra-operative tachycardia on the different outcome measures. In our study 474 patients (1.7%) were found to have had intra-operative tachycardia. Matching was successful for 99.4% of the cases, leading to 471 cases and references. The crude mortality rates for the cases and matched references were 5.5% and 2.5%, respectively (P=0.020). Of all case patients, 22.3% were treated in an ICU, compared to 11.0% of the matched references (P=0.001). Hospital stay was prolonged in 25.1% of the patients with tachycardia compared to 15.1% of the matched references (P=0.001). In this study, patients with intra-operative tachycardia who were undergoing non-cardiac surgery had a greater peri-operative risk, leading to increased mortality, greater frequency of admission to an ICU and prolonged hospital stay.

We sought to identify factors that are associated with hypotension after the induction of spinal anesthesia (SpA) by using an anesthesia information management system. Hypotension was defined as a decrease of mean arterial blood pressure of more than 30% within a 10-min interval, and relevance was defined as a therapeutic intervention with fluids or pressors within 20 min. From January 1, 1997, to August 5, 2000, data sets from 3315 patients receiving SpA were recorded on-line by using the automatic anesthesia record keeping system NarkoData. Hypotension meeting the predefined criteria occurred in 166 (5.4%) patients. Twenty-nine patient-, surgery-, and anesthesia-related variables were studied by using univariate analysis for a possible association with the occurrence of hypotension after SpA. Logistic regression with a forward stepwise algorithm was performed to identify independent variables (P0.05). The discriminative power of the logistic regression model was checked with a receiver operating characteristic curve. Calibration was tested with the Hosmer-Lemeshow goodness-of-fit test. The univariate analysis identified the following variables to be associated with hypotension after SpA: age, weight, height, body mass index, amount of plain bupivacaine 0.5% used for SpA, amount of colloid infusion before puncture, chronic alcohol consumption, ASA physical status, history of hypertension, urgency of surgery, surgical department, sensory block height of anesthesia, and frequency of puncture. In the multivariate analysis, independent factors for relevant hypotension after SpA consisted of three patient-related variables ("chronic alcohol consumption," odds ratio [OR] = 3.05; "history of hypertension," OR = 2.21; and the metric variable "body mass index," OR = 1.08) and two anesthesia-related variables ("sensory block height," OR = 2.32; and "urgency of surgery," OR = 2.84). The area of 0.68 (95% confidence interval, 0.63-0.72) below the receiver operating characteristic curve was significantly greater than 0.5 (P0.01). The goodness-of-fit test showed a good calibration of the model (H = 4.3, df = 7, P = 0.7; C = 7.3, df = 8, P = 0.51). This study contributes to the identification of patients with a high risk for hypotension after SpA induction, with the risk increasing two- or threefold with each additional risk factor.By using automated data collection, 5 (chronic alcohol consumption, history of hypertension, body mass index, sensory block height, and urgency of surgery) of 29 variables could be detected as having an association with hypotension after spinal anesthesia induction. The knowledge of these risk factors should be useful in increasing vigilance in those patients most at risk for hypotension, in allowing a more timely therapeutic intervention, or even in suggesting the use of alternative methods of spinal anesthesia, such as titrated continuous or small-dose spinal anesthesia.

UNLABELLED We used an anesthesia information management system (AIMS) to devise a score for predicting antiemetic rescue treatment as an indicator for postoperative nausea and vomiting (PONV) in the postanesthesia care unit (PACU). Furthermore, we wanted to investigate whether data collected with an AIMS are suitable for comparable clinical investigations. Over a 3-yr period (January 1, 1997, to December 31, 1999), data sets of 27,626 patients who were admitted postoperatively to the PACU were recorded online by using the automated anesthesia record keeping system NarkoData(R) (IMESO GmbH, Huttenberg, Germany). Ten patient-related, 5 operative, 15 anesthesia-related, and 4 postoperative variables were studied by using forward stepwise logistic regression. Not only can the probability of having PONV in the PACU be estimated from the 3 previously described patient-related (female gender, odds ratio [OR] = 2.45; smoker, OR = 0.53; and age, OR = 0.995) and one operative variables (duration of surgery, OR = 1.005), but 3 anesthesia-related variables (intraoperative use of opioids, OR = 4.18; use of N(2)O, OR = 2.24; and IV anesthesia with propofol, OR = 0.40) are predictive. In implementing an equation for risk calculation into the AIMS, the individual risk of PONV can be calculated automatically. IMPLICATIONS The aim of this study was to investigate predictors for postoperative nausea and vomiting by using online anesthesia records. With the help of computerized data evaluation, 7 of 34 variables could be detected as risk factors. By implementing an automatic score into the record keeping system, an individual risk calculation could be made possible.