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Summary Background At the University Hospital Giessen, an anesthesia information management system (AIMS) is used for online record keeping of perioperative patient care, but preoperative anaesthesia assessments were still being recorded on paper and subsequently entered into the AIMS. Personal digital assistants (PDAs) seem to be useful instruments to establish a seamless digital anesthesiological documentation. Objectives We decided to implement a solution for direct integration of data gathered during the preoperative assessment into the existing data management infrastructure. Parallel to the development of the system, we surveyed the future users to match their wishes and needs as far as possible. System description A C program embedding the preoperative AIMS’ data fields was developed. Data alignment with the Hospital information system (HIS) is controlled by a Java desktop software. The anaesthesiologist completes the available fields at the patient's bedside following the same algorithm and integrity check as the PC version. Status report Overall, 68% of the surveyed physicians supported the implementation of the system. The PDA solution has been available since May 2002. Data replication into the handheld and integration of mobile collected data into the AIMS generally work without problems. The HIS interconnection software converts the PDA file into the AIMS format for further processing. Discussion The preoperative anaesthetic assessment is a standardised task well suitable for conversion to an electronic data storage medium. Changing from redundant data entry in the OR to direct electronic recording at the patient's bedside seems simply logical. Handheld computers are inexpensive, flexible gadgets to realize this.

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.

The development of the ICUData patient data management system (PDMS) for intensive care units (ICU), by IMESO GmbH, Hüttenberg, Germany, was based on the assumption that processes and therapies at ICU are the most complex with the highest data density compared with those in other wards. Based on experience with the system and on a survey conducted among users at our pain clinic, we evaluated whether the concept of the present software architecture, which sufficiently reproduces processes and data at an ICU, is suitable as a PDMS for general wards. The highly modular and client-centric approach of the PDMS is founded on a message-based communications architecture (HL7). In the beginning of the year 2000, the system was implemented at the pain management clinic (12 beds) of our hospital. To assess its user friendliness, we conducted a survey of medical staff (n=14). From April 1st 2000 to August 31st 2000, all clinical and administrative data of 658 patients at the pain management clinic were recorded with the PDMS. From the start, all users had access to data and information of other connected data management systems of the hospital (e.g. patient administrative data, patient clinical data). Staff members found the system mostly useful, clearly presented, practical, and easy to learn and use. Users were relatively satisfied with stability and performance of the program but mentioned having only limited knowledge of the program's features. The need for external support during a computer crash was rated negatively. Despite the need for further usage training and improved program performance, the software architecture described seems to be a promising starting point for the construction of a PDMS for general wards.

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.

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.

The most recent approach to estimate nursing resources consumption has led to the generation of the Nine Equivalents of Nursing Manpower use Score (NEMS). The objective of this prospective study was to establish a completely automatically generated calculation of the NEMS using a patient data management system (PDMS) database and to validate this approach by comparing the results with those of the conventional manual method. Prospective study. Operative intensive care unit of a university hospital. Patients admitted to the ICU between 24 July 2002 and 22 August 2002. Patients under the age of 16 years, and patients undergoing cardiovascular surgery or with burn injuries were excluded. None. The NEMS of all patients was calculated automatically with a PDMS and manually by a physician in parallel. The results of the two methods were compared using the Bland and Altman approach, the interclass correlation coefficient (ICC), and the κ-statistic. On 20 consecutive working days, the NEMS was calculated in 204 cases. The Bland Altman analysis did not show significant differences in NEMS scoring between the two methods. The ICC (95% confidence intervals) 0.87 (0.84–0.90) revealed a high inter-rater agreement between the PDMS and the physician. The κ-statistic showed good results (κ>0.55) for all NEMS items apart from the item “supplementary ventilatory care”. This study demonstrates that automatical calculation of the NEMS is possible with high accuracy by means of a PDMS. This may lead to a decrease in consumption of nursing resources.

To investigate a fully automated and modified APACHE II score calculation exclusively based on routine data supplied by patient data management system, the ICUData, and to assess the predictive performance of this score using analysis of discrimination and calibration at an operative ICU.SQL scripts (calculation programs) were developed to calculate the scores of 524 patients who stayed at the ICU between April 1st, 1999 and March 31st, 2000. The calculation programs considered unavailable data as 'not pathological'. The main outcome measure was survival status at ICU discharge. The discriminative power on mortality of this modified APACHE II score was checked with a receiver operating characteristic (ROC) curve. Calibration was tested using the Hosmer-Lemeshow goodness-of-fit test.The 459 survivors had an average APACHE score of 17.8+/-5.3. The score of the 65 deceased patients averaged 22.7+/-4.6. The area under the ROC curve of 0.790 was significantly0.5 (P0.01) and had a 95% confidence interval (CI) of 0.712-0.825. The goodness-of-fit test showed a good calibration (H=4.89, P=0.70, dof 7, C=6.96, P=0.541, dof 8).A prediction model based on completely automatically calculated 'modified APACHE II scores' can be constructed using data collected with PDMS. However, due to differences in the patient collective and methods used, the results need validation and can only be partially compared to results from other studies.

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.