Andreas Jost

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.

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.

Qualitätsdokumentation mit einem Anästhesie-Informations-Management-System (AIMS)

ZusammenfassungZiel der Arbeit: Die Abteilung Anaesthesiologie und Operative Intensivmedizin der Justus-Liebig-Universität Giessen hat sich 1994 für den Aufbau eines Anästhesie-Informations-Management-Systems (AIMS) entschieden, um die bisherige manuelle Papierdokumentation abzulösen. Einzelne Aspekte und Ergebnisse dieses Datenpools sollen mit der Fragestellung vorgestellt werden, wie das System in seiner jetzigen Form geeignet ist, die Qualitätsdokumentation nach den Vorgaben der Deutschen Gesellschaft für Anästhesiologie und Intensivmedizin (DGAI) zu gewährleisten. Methoden: Mit der Narkose-Dokumentations-Software NarkoData 4 (ProLogic GmbH, Erkrath) wurden seit 1997 die Daten von 41.393 Narkosen einschließlich des DGAI-Kerndatensatzes „online” dokumentiert, in einer relationalen Datenbank (Oracle Corporation) archiviert und mit Hilfe des SQL-basierten Programms Voyant (Brossco Systems, Espoo, Finnland) statistisch aufgearbeitet. Beispielhaft für zwei anästhesiologische Verlaufsbeobachtungen (AVB) verglichen wir die durch Mitarbeiter im AIMS dokumentierten AVB-Inzidenzen „Hypotension” und „Übelkeit/Erbrechen” mit dem Auftreten vergleichbarer Ereignisse, die mit Hilfe der erfaßten Onlinedaten wie „Blutdruck” und „Medikamentenapplikationen” während der Narkose erfaßt wurden. Ergebnisse: 1997 lag die Inzidenz der von Hand in das System eingegebenen anästhesiologischen Verlaufsbeobachtungen (AVB) bei 3,6% (mit Schweregrad >II bei 0,9%) und erhöhte sich im Verlauf des Jahres 1998 auf 22,2% (mit Schweregrad >II bei 1,9%). Der prozentuale Anteil der Narkosen mit manuell dokumentierten AVBs war deutlich niedriger als dies bei der Online-Erfassung von Narkosen mit AVB-entsprechenden Ereignissen der Fall war: Für die AVB „Hypotension” fanden wir ein Verhältnis von 1,8% vs. 8.5% und für „Übelkeit/Erbrechen” 4,9% vs. 8,3%. Schlußfolgerung: Die derzeit fast überall praktizierte manuelle AVB-Dokumentation ist nicht vollständig. Im Gegensatz zu manuellen Verfahren stehen innerhalb des AIMS alle erfaßten Daten für Auswertungen zur Verfügung und ermöglichen eine detailliertere und lückenlosere Qualitätsdokumentation. Ob mit diesen Daten tatsächlich Versorgungsqualität beschrieben und gemessen wird, muß durch weitere Untersuchungen evaluiert werden. Weiterhin ist zu überlegen, in Zukunft an die Qualitätsdokumentation bezüglich des Datenrasters und der AVB-Erkennung unterschiedliche Anforderungen in Abhängigkeit vom jeweiligen Dokumentationsverfahren zu stellen (z.B. automatische AVB-Erkennung bei einem AIMS).AbstractObjective: In 1994 the Department of Anaesthesiology and Intensive Care Medicine of the Justus Liebig University of Giessen decided to implement an Anaesthesia Information Management System (AIMS) to replace the previous hand-written documentation on paper. From 1997 until the end of 1998 the data sets of 41,393 anaesthesia procedures were recorded with the help of computers and imported into a data bank. Individual aspects and results of this data pool are presented under the aspect of how the system in its present form is able to guarantee documentation of quality according to the requirements of the German Society of Anaesthesiology and Intensive Care Medicine (DGAI). Methods: Since 1997 information on all anaesthesia procedures has been documented ”online” with the anaesthesia documentation software NarkoData 4 (ProLogic GmbH, Erkrath). The data sets have been stored in a relational data bank (Oracle Corporation) and statistically processed with the help of the SQL-based program Voyant (Brossco Systems, Espoo, Finland). As an example of two adverse perioperative events (AVB) we compared incidences of ”hypotension” and ”nausea/vomiting”, recorded by staff members into the AIMS, with the incidence of comparable events that were recorded with the help of online data during anaesthesia procedures, such as blood pressure and drug application. Since 1998 data recording has been revised constantly in department meetings; advanced training has been given. The results have been analysed critically. Results: In 1997 the incidence of adverse perioperative events entered manually into the system was 3.6% (grade III and higher 0.9%) and increased during 1998 to 22.2% (grade III and higher 1.9%). The frequency of anaesthesia procedures with manually documented AVBs was significantly below the incidence (determined with the help of online data) of comparable events: ”hypotension” (1.8% vs. 8.5%) and ”nausea/vomiting” (4.9% vs. 8.3%). Conclusion: The current documentation of AVBs in almost any hospital is incomplete. In contrast to the hand-written procedure, the AIMS provides recorded data for evaluation and guarantees more detailed and complete quality documentation. In addition, the effort needed for documentation is reduced. Whether these data sets really describe and measure quality or not has to be evaluated. In addition it has to be considered whether different requirements (such as automatic AVB recognition for an AIMS) are advantageous for quality documentation regarding the data raster and the AVB recognition, with respect to different documentation procedures.

Computerized Anesthesia Record Keeping in Thoracic Surgery – Suitability of Electronic Anesthesia Records in Evaluating Predictors for Hypoxemia During One-lung Ventilation

Objective.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 (SpO2 < 90%) during one-lung ventilation (OLV) in pulmonary surgery. Methods.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 (p< 0.05). 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. Results.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]. Conclusions.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.

Corrected incidences of co-morbidities – a statistical approach for risk-assessment in anesthesia using an AIMS

ObjectiveIn anesthesia and intensive care logistic regression analysis are often used to generate predictive models for risk assessment. Strictly seen only independent variables should be represented in such prognostic models. Using anesthesia-information-management-systems a lot of (depending) information is stored in a database during the preoperative ward round. The objective of this study was to evaluate a statistical algorithm to process the different dependent variables without losing the information of each variable on patient’s conditions.MethodBased on data about prognostic models in anesthesia an iterative statistical algorithm was initiated to summarize dependent variables to subscores. Seven subscores out of several preoperative variables were calculated corresponding to the proper incidence and the correlation to the occurrence of intraoperative cardiovascular events was evaluated. After that first step logistic regression was used to build a predictive model out of the seven subscores, 10 patient-related, and two surgery-related variables. Performance of the prognostic model was assessed using analysis of discrimination and calibration.ResultFour out of seven subscores together with age, type and urgency of surgery are represented in the prognostic model to predict the occurrence of intraoperative cardiovascular events. The prognostic model demonstrated good discriminative power with an area under the ROC curve (AUC) of 0.734.ConclusionDue to reduced calibration, the clinical use of the prediction model is limited.