Axel Junger

The use of an anesthesia information management system for prediction of antiemetic rescue treatment at the postanesthesia care unit.

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® (IMESO GmbH, Hüttenberg, 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 N2O, 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.

Increased Body Mass Index and Peri-operative Risk in Patients Undergoing Non-cardiac Surgery

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.

Using an Anesthesia Information Management System to Prove a Deficit in Voluntary Reporting of Adverse Events in a Quality Assurance Program

Objective.A deficit is suspected in the manual documentation ofadverse events in quality assurance programs in anesthesiology. In order toverify and quantify this, we retrospectively compared the incidence ofmanually recorded perioperative adverse events with automatically detectedevents. Methods.In 1998, data of all anesthetic procedures, includingthe data set for quality assurance of the German Society of Anaesthesiologyand Intensive Care Medicine (DGAI), was recorded online with the AnesthesiaInformation Management System (AIMS) NarkoData4® (Imeso GmbH). SQL(Structured Query Language) queries based on medical data were defined for theautomatic detection of common adverse events. The definition of the SQLstatements had to be in accordance with the definition of the DGAI forperioperative adverse events: A potentially harmful change of parameters ledto therapeutic interventions by an anesthesiologist. Results.During16,019 surgical procedures, anesthesiologists recorded 911 (5.7%) adverseevents manually, whereas 2966 (18.7%) events from the same database weredetected automatically. With the exception of hypoxemia, the incidence ofautomatically detected events was considerably higher than that of manuallyrecorded events. Fourteen and a half percent (435) of all automaticallydetected events were recorded manually. Conclusion.Using automaticdetection, we were able to prove a considerable deficit in the documentationof adverse events according to the guidelines of the German quality assuranceprogram in anesthesiology. Based on the data from manual recording, theresults of the quality assurance of our department match those of othercomparable German departments. Thus, we are of the opinion that manualincident reporting seriously underestimates the true occurrence rate ofincidents. This brings into question the validity of quality assurancecomparisons based on manually recorded data.

Use Of an Anesthesia Information Management System (AIMS) To Evaluate The Physiologic Effects Of Hypnotic Agents Used To Induce Anesthesia

Objective.The aim of this study was to utilize an anesthesiainformation management system (AIMS) in investigating the effects of hypnoticagents used to induce anesthesia on blood pressure, heart rate and arterialoxygen saturation. The characteristics of these agents, known from previousstudies, were compared to the effects documented in this study during routineclinical use. Methods.During the years 1997 and 1998, all relevantdata from anesthetic procedures were recorded online using the automatedanesthesia information system NarkoData. The data from 8,078 generalanesthesia procedures using endotracheal intubation were exported via“structured query language” (SQL) from the AIMS database into astatistics program after excluding children (age < 14), patients whoreceived atropine during induction and procedures with use of extracorporealcirculation. The effects of drug administration on systolic, diastolic andmean arterial blood pressure (SBP, DBP, MBP), heart rate (HR) and arterialoxygen saturation (SpO2) were analyzed prior to induction and at5, 10 and 15 minutes following bolus administration of the hypnotic agent. Thedata were classified into three groups based on the induction agent used:thiopental, etomidate or propofol and further separated into two groups basedon ASA status (ASA ≤ II and ASA > II). The mean and standard deviationswere calculated for each parameter at each point in time. Statisticalcomparisons were performed to determine whether the results for each timepoint differed from the previous time point. Results.There was asignificant decrease in blood pressure (MAP, SBP, DBP) after bolusadministration of all three hypnotics in all of the 8,078 procedures analyzed.The decrease was greater in patients of ASA class > II than in those of ASAclass ≤ II. Propofol caused the greatest drop in blood pressure whereasetomidate caused the least. During the observation period the HR also fell ineach group, except for thiopental where an initial rise of the HR could beobserved. An initial rise of SpO2 was recorded in each group withno differences observed between the individual hypnotics. Conclusions.The effects of the induction hypnotic agents thiopental, etomidate andpropofol on blood pressure and heart rate as documented by an AIMScorresponded to those found in clinical studies. An AIMS with thecorresponding documentation, software and database structure is suitable forcollecting and evaluating data for clinical investigations.

Clinical and practical requirements of online software for anesthesia documentation : an experience report

The aim of this paper is the presentation of a new version of the anesthesia documentation software, NarkoData, that has been used in routine clinical work in our department as part of an anesthesia information management system (AIMS) since 1995. The performance of this software is presented along with requirements for future development of such a system. The originally used version, NarkoData 3.0, is an online anesthesia documentation software established by the software company ProLogic GmbH. It was primarily developed as a disk-based system for the MacOS operating system (Apple Computer Inc.). Based on our routine experience with the system, a catalogue of requirements was developed that concentrated on improvement in the sequence of work, administration and data management. In 1996, the concepts developed in our department, in close co-operation with medical personnel and the software company, led to a considerable enlargement of the program functions and the subsequent release of a new version of NarkoData. Since 1997, more than 20 000 anesthesia procedures have been recorded annually with this new version at 115 decentralized work stations at our university hospital.

Discriminative power on mortality of a modified Sequential Organ Failure Assessment score for complete automatic computation in an operative intensive care unit.

Objective To evaluate the discriminative power on mortality of a modified Sequential Organ Failure Assessment (SOFA) score and derived measures (maximum SOFA, total maximum SOFA, and delta SOFA) for complete automatic computation in an operative intensive care unit (ICU). Design Retrospective study. Setting Operative ICU of the Department of Anesthesiology and Intensive Care Medicine. Patients Patients admitted to the ICU from April 1, 1999, to March 31, 2000 (n = 524). Data from patients under the age of 18 yrs and patients who stayed <24 hrs were excluded. In the case of patient readmittance, only data from the patient’s last stay was included in the study. Interventions None. Measurements and Main Results The main outcome measure was survival status at ICU discharge. Based on Structured Query Language (SQL) scripts, a modified SOFA score for all patients who stayed in the ICU in 1 yr was calculated for each day in the ICU. Only routine data were used, which were supplied by the patient data management system. Score evaluation was modified in registering unavailable data as being not pathologic and in using a surrogate of the Glasgow Coma Scale. During the first 24 hrs, 459 survivors had an average SOFA score of 4.5 ± 2.1, whereas the 65 deceased patients averaged 7.6 ± 2.9 points. The area under the receiver operating characteristic (ROC) curve was 0.799 and significantly >0.5 (p < .01). A confidence interval (CI) of 95% covers the area (0.739–0.858). The maximum SOFA presented an area under the ROC of 0.922 (CI: 0.879–0.966), the total maximum SOFA of 0.921 (CI: 0.882–0.960), and the delta SOFA of 0.828 (CI: 0.763–0.893). Conclusion Despite a number of differences between completely automated data sampling of SOFA score values and manual evaluation, the technique used in this study seems to be suitable for prognosis of the mortality rate during a patient’s stay at an operative ICU.

User acceptance of an anaesthesia information management system.

Background and objective: This paper describes the user acceptance of an anaesthesia information management system at the University Hospital in Giessen, Germany, after 5 yr of routine use. Methods: A questionnaire with 75 items was distributed to all anaesthesiologists and anaesthetic nurses of the Department of Anaesthesiology. The questions were answered anonymously on a five-point Likert scale. Results: The return rate was 60% (44 physicians and 24 nurses). The results indicated that the system generally met user expectations. The respondents thought that electronic record keeping improved the quality of their work, and they did not want to switch back to paper records. Problems arose with hardware placement and software features, e.g. coding tools for diagnoses and type of surgery. The perceived quality of training strongly influenced user acceptance. Conclusions: Despite the deficits revealed by the survey, the respondents did not want to switch back to manual record keeping. A structured user survey is a useful tool for the development, adaptation and implementation of an anaesthesia information management system. A training strategy that takes the needs of the users into account is recommended.

Review of antibiotic drug use in a surgical ICU: Management with a patient data management system for additional outcome analysis in patients staying more than 24 hours

BACKGROUND A number of developments have been made in computerized patient data management systems (PDMSs), making them of interest to medical and nursing staff as a means of improving patient care. OBJECTIVES The aim of this study was to assess the capability of a PDMS to record and provide drug-administration data and to investigate whether the PDMS may be used as a means of support for clinical audits and quality control. Furthermore, we assessed whether antibiotic therapy as a surrogate for infections correlates with hospital mortality in patients staying >24 hours in a surgical intensive care unit (SICU). METHODS Because of its medical and economic importance in ICU treatment, we chose to use the field of antibiotic therapy as an example. A PDMS was used in a 14-bed SICU (Department of Anesthesiology, Intensive Care Medicine, and Pain Therapy, University Hospital Giessen, Giessen, Germany) to record relevant patient data, including therapeutic, diagnostic, and nursing actions. During a 15-month period (April 1, 2000 to June 30, 2001), antibiotic drug therapy was electronically analyzed and presented using the anatomic therapeutic chemical (ATC) category for antibacterials (ATC group, J01) with daily defined doses. Furthermore, the correlation of antibiotic therapy with patient outcome (hospital mortality) was tested using logistic regression analysis. RESULTS A total of 2053 patients were treated in the SICU. Of these, 58.0% (1190 patients) received antibiotics (4479 treatment days; 13,145 single doses). Cephalosporins (ATC category, J01DA) were used most frequently (1785 treatment days [39.9% of treatment days]), followed by combinations of penicillins with beta-lactam inhibitors (ATC category, J01CR; 1478 treatment days [33.0%]) and imidazole derivatives (ATC category, J01XD; 667 treatment days [14.9%]). The antibiotic therapy lasted <3 days in 65.6% of cases. In 13.8% of cases, the treatment lasted >1 week. A total of 36.7% of cases were treated with only 1 antibiotic agent, 14.1% were given a combination of 2, and 7.2% were given a combination of > or =3 antibiotic agents. Seven hundred twenty-six patients remained in the SICU for >24 hours; 143 (19.7%) died during their hospital stay; 110 (15.2%) in the SICU. The duration of antibiotic therapy (odds ratio [OR], 1.46) and number of different antibiotic drugs used (OR, 2.15) significantly correlated with hospital mortality. CONCLUSIONS Antibiotic therapy in a SICU can be assessed and analyzed in detail using a PDMS. Furthermore, in this study, the duration of antibiotic therapy and the number of antibiotic agents used correlated with hospital mortality. In further developing PDMSs, it is important for quality-assurance purposes to document the reasons for giving antibiotics and for changing prescriptions. It would also be helpful to integrate certain therapy standards and reminder functions for the duration of therapy in the PDMS.

Intra-operative tachycardia and peri-operative outcome

BackgroundIntra-operative tachycardia is a common adverse event, often recorded as an indicator for process quality in quality assurance projects in anaesthesia.MethodsThis 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.ResultsIn 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).ConclusionsIn 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.

Anästhesie und Schlaf-Apnoe-Syndrom

ZusammenfassungEin Schlaf-Apnoe-Syndrom (SAS) erhöht das Risiko für perioperative Komplikationen bei anästhesierten und sedierten Patienten. Der Anteil an Patienten mittleren Alters mit signifikanten klinischen Symptomen wird bei Männern auf 4%, bei Frauen auf 2% geschätzt. Jedoch sind 80–95% der SAS-Erkrankten nicht diagnostiziert. Zur Identifikation und sicheren perioperativen Betreuung ist ein systematisches Vorgehen zwingend notwendig. Hierbei sollte es sich um ein interdisziplinäres Konzept handeln, das erhöhte Aufmerksamkeit bei dem pflegerischen/ärztlichen Personal, Evaluation der Intubationsverhältnisse, Abklärung kardiopulmonaler Vorerkrankungen, permanente Kontrolle der Atemwege, differenzierten und angepassten Einsatz von Anästhetika sowie konsequentes Monitoring beinhaltet. Der perioperative Einsatz und die Weiterführung einer „Nasal-continuous-positive-airway-pressure- (CPAP-)Therapie“ sowie die postoperative Verlegung in eine entsprechende Überwachungseinheit sind unbedingt zu empfehlen. Insgesamt besteht noch ein erheblicher Bedarf an kontrollierten Studien zum perioperativen Management von SAS-Patienten.AbstractThe perioperative risk for patients with obstructive sleep apnea syndrome and the optimal anaesthesiological management of these patients have not been well elucidated. The prevalence of obstructive sleep apnea with significant symptoms is estimated to be 4% in men and 2% in women. However, in 80–95% of patients this syndrome is not sufficiently diagnosed. Thus identification of patients at risk and a thorough multidisciplinary diagnostic approach are essential for optimal perioperative management. The risk of perioperative complications, like cardiopulmonary compromise, and difficulties in airway management is elevated. The most important aspects of perioperative management include evaluation of intubating conditions, careful search for cardiopulmonary morbidity, permanent control of patient airways, sensible use of anaesthetics, sedatives, and narcotics, and strict monitoring of vital signs. If ambulatory nasal continuous positive airway pressure (CPAP) therapy has been established preoperatively, this should be continued in the perioperative period. Postoperative monitoring should be performed in an intensive care or intermediate care unit. Controlled clinical studies on the best perioperative management of patients with obstructive sleep apnea are urgently required.