Integration of a handheld based anaesthesia rounding system into an anaesthesia information management system.

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.

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 28065 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: 4726 patients (16.8%) were found with BMI >30. Matching was successful for 41.5% of the cases, leading to 1962 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.

Excessive alcohol consumption and perioperative outcome.

BACKGROUND: Excessive alcohol consumption is a well-recognized factor contributing to premature morbidity and mortality. METHODS: This retrospective, matched cohort study was designed to assess the attributable effects of excessive alcohol consumption on outcome in patients undergoing noncardiac surgery. All data of 28,065 patients operated at a tertiary care university hospital were recorded with a computerized anesthesia record-keeping system. Cases were defined as patients with history of excessive alcohol consumption (>30 g alcohol per day). Controls were selected according to matching variables in a stepwise fashion. RESULTS: In our data set, 928 patients (3.3%) were found with a history of excessive alcohol consumption. Matching was successful in 897 patients (97%). The crude mortality rates for the cases were 1.3% and 1.6%, for the matched controls (P=.084, power=0.85). Prolonged length of hospital stay was observed in 38% versus 33% (P=.013, power=0.50), admission to the intensive care unit was deemed necessary in 11% versus 9% (P=.027, power=0.55), and intraoperative cardiovascular events were detected from the database in 22% versus 21% (P=.053, power=0.61). CONCLUSIONS: In this study, history of excessive alcohol consumption alone is not a factor leading to an increased perioperative risk in noncardiac surgery.

Automatic calculation of the nine equivalents of nursing manpower use score (NEMS) using a patient data management system.

OBJECTIVE: 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. DESIGN: Prospective study. SETTING: Operative intensive care unit of a university hospital. PATIENTS: 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. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 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 kappa-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 kappa-statistic showed good results (kappa>0.55) for all NEMS items apart from the item "supplementary ventilatory care". CONCLUSION: 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.

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.

Automatic calculation of a modified APACHE II score using a patient data management system (PDMS).

OBJECTIVE: 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. METHOD: 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. RESULTS: 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 significantly >0.5 (P<0.01) 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). CONCLUSION: 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.

A data model for managing drug therapy within a patient data management system for intensive care units.

The major intent of this article was to describe the design principles of the drug-therapy documentation module of the Patient Data Management System (PDMS) ICUData, in routine use at the intensive care unit (ICU) of the Department of Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. The new drug management system has been in routine use since March 2000. Until 8 January 2001, 1140 patients have been documented using this approach. It could be demonstrated that it was possible to transform the formerly unstructured text-based documentation into a detailed and structured model. The mediated benefit resulted in the automatic calculation of fluid balance. Further, detailed statistical analyses of therapeutic behavior in drug administration are now possible.

Laryngeal mask airway versus endotracheal tube for outpatient surgery: analysis of anesthesia-controlled time.

STUDY OBJECTIVE: To show that efficiency of operating room times can be improved significantly using rapid changes between operative procedures. DESIGN: Randomized, prospective clinical study. SETTING: Tertiary care university hospital, elective peripheral trauma-related orthopedic surgery. PATIENTS: 72 adult, ASA physical status I, II, and III patients scheduled for elective peripheral trauma-related orthopedic surgery requiring general anesthesia. INTERVENTIONS: Patient airways were managed using either a Laryngeal Mask Airway (LMA) or an endotracheal tube (ETT) in the hands of anesthesiologists experienced in both. They were not informed as to the primary intention of the study. All perioperative data, including the preoperative and postoperative outpatient stay at the outpatient surgical ward, were recorded with an anesthesia information management system. MEASUREMENTS: The primary outcome measures were: time needed for anesthesia induction and emergence from anesthesia. All manual recording into the anesthesia information management system during anesthesia was accomplished by nurses who were uninformed as to the aim of the study. MAIN RESULTS: Anesthesia induction was significantly (p < 0.01) shorter using LMAs (means +/- SD, medians, [interquartile ranges]) (LMA: 5.8 +/- 1.5, 5, [5;7] vs. ETT: 7.4 +/- 1.8, 7, [7;8] min), whereas emergence from anesthesia was not different (LMA: 11.8 +/- 3.3, 11, [9;14] vs. ETT: 13.2 +/- 4.8; 12, [10;16] min). CONCLUSION: The clinical relevance of reduced anesthesia induction time using LMA is questionable. The lack of difference in emergence time could be a result of the use of total intravenous anesthesia.

Realizing a Realtime Shared Patient Chart using a Universal Message Forwarding Architecture.

The goal of this paper is to describe the clinical needs and the informational methodology which led to the realization of a realtime shared patient chart. It is an integral part of the communications infrastructure of the Patient Data Management System (PDMS) ICUData which is in routine use at the intensive care unit (ICU) of the Department for Anesthesiology and Intensive Care Medicine at the University Hospital of Giessen, Germany, since February 1999. ICUData utilizes a four tier system architecture consisting of modular clients, message forwarders, application servers and a relational database management system. All layers communicate with health level seven messages. The innovative aspect of this architecture consists of the interposition of a message forwarder layer which allows for instant exchange of patient data between the clients without delays caused by database access. This works even in situations with high workload as in patient monitoring. Therefore a system with many workstations acts a blackboard for patient data allowing shared access under realtime conditions. Realized first as an experimental feature, it has been embraced by the clinical users and served well during the documentation of more than 18000 patient stays.

The incidence and risk factors for hypotension after spinal anesthesia induction: an analysis with automated data collection.

UNLABELLED: 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 (P < 0.05). 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 (P < 0.01). 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. IMPLICATIONS: 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.

The incidence and prediction of automatically detected intraoperative cardiovascular events in noncardiac surgery.

UNLABELLED: 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%; P < 0.01). 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. IMPLICATIONS: 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.

Computerize 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.

Differing incidences of relevant hypotension with combined spinal-epidural anesthesia and spinal anesthesia.

UNLABELLED: 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%; P < 0.001). In the multivariate analysis, arterial hypertension (odds ratio, 1.83; 95% confidence interval, 1.21-2.78) and sensory block height >T6 (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 (P < 0.01) 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. IMPLICATIONS: 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.

Intra-operative tachycardia and peri-operative outcome.

BACKGROUND: Intra-operative tachycardia is a common adverse event, often recorded as an indicator for process quality in quality assurance projects in anaesthesia. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Computer keyboard and mouse as a reservoir of pathogens in an intensive care unit.

OBJECTIVE: User interfaces of patient data management systems (PDMS) in intensive care units (ICU), like computer keyboard and mouse, may serve as reservoirs for the transmission of microorganisms. Pathogens may be transferred via the hands of personnel to the patient causing nosocomial infections. The purpose of this study was to examine the microbial contamination of computer user interfaces with potentially pathogenic microorganisms, compared with other fomites in a surgical intensive care unit of a tertiary teaching hospital. METHODS: Sterile swab samples were received from patient's bedside computer keyboard and mouse, and three other sites (infusion pumps, ventilator, ward round trolley) in the patient's room in a 14 bed surgical intensive care unit at a university hospital. At the central ward samples from keyboard and mouse of the physician's workstation, and control buttons of the ward's intercom and telephone receiver were obtained. Quantitative and qualitative bacteriological sampling occurred during two periods of three months each on eight nonconsecutive days. RESULTS: In all 14 patients' rooms we collected a total of 1118 samples: 222 samples from keyboards and mice, 214 from infusion pumps and 174 from the ward's trolley. From the central ward 16 samples per formites were obtained (computer keyboard and mouse at the physician's workstation and the ward's intercom and telephone receiver). Microbacterial analysis from samples in patients' rooms yielded 26 contaminated samples from keyboard and mouse (5.9%) compared with 18 positive results from other fomites within patients' rooms (3.0%; p < 0.02). At the physician's computer terminal two samples obtained from the mouse (6.3%) showed positive microbial testing whereas the ward's intercom and telephone receiver were not contaminated (p = 0.15). CONCLUSIONS: The colonization rate for computer keyboard and mouse of a PDMS with potentially pathogenic microorganisms is greater than that of other user interfaces in a surgical ICU. These fomites may be additional reservoirs for the transmision of microorganisms and become vectors for cross-transmission of nosocomial infections in the ICU setting.

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.