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BACKGROUND: Multiple studies have considered the necessity of preoperative autologous blood donation before bimaxillary orthognathic osteotomies. In the context of a quality improvement project, this topic was also investigated in our institution. Furthermore, the transfusion practice was analyzed and the correlations between patient, operative variables, and blood loss were studied. STUDY DESIGN AND METHODS: In accordance with the recommendations of the Federal Medical Association and the Federal Ministry for Health and Social Security, a transfusion demand list was compiled using data of 82 patients who underwent bimaxillary orthognathic surgery between 1997 and 2005. The maximum blood loss tolerable without transfusion (MBL) was calculated for each patient on the basis of sex, weight, height, and preoperative hematocrit (Hct). This was compared with the actual transfusion and blood loss data. RESULTS: An autologous blood donation was carried out in 65 of 82 patients (79.3%). Sixty-two of 65 autologous blood donors (95.4%) and 2 of 17 patients (11.8%) without autologous blood donation received transfusion. The actual blood loss did not exceed the calculated MBL in 48 of the 82 cases. Nevertheless, 31 of these 48 patients (64.6%) received transfusions. For patients with a low calculated MBL, only a trend to a higher transfusion rate was observed, although the actual blood loss in these cases more often exceeded the individually calculated MBL (p

Objective: Computers are becoming increasingly visible in operating rooms (OR) and intensive care units (ICU) for use in bedside documentation. Recently, they have been suspected as possibly acting as reservoirs for microorganisms and vehicles for the transfer of pathogens to patients, causing nosocomial infections. The purpose of this study was to examine the microbiological (bacteriological and mycological) contamination of the central unit of computers used in an OR, a surgical and a pediatric ICU of a tertiary teaching hospital. Methods: Sterile swab samples were taken from five sites in each of 13 computers stationed at the two ICUs and 12 computers at the OR. Sample sites within the chassis housing of the computer processing unit (CPU) included the CPU fan, ventilator, and metal casing. External sites were the ventilator and the bottom of the computer tower. Quantitative and qualitative microbiological analyses were performed according to commonly used methods. Results: One hundred and ninety sites were cultured for bacteria and fungi. Analyses of swabs taken at five equivalent sites inside and outside the computer chassis did not find any significant number of potentially pathogenic bacteria or fungi. This can probably be attributed to either the absence or the low number of pathogens detected on the surfaces. Conclusion: Microbial contamination in the CPU of OR and ICU computers is too low for designating them as a reservoir for microorganisms.

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 (P 1 day; yes or no).

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.

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 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 ≥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.

To show that efficiency of operating room times can be improved significantly using rapid changes between operative procedures.Randomized, prospective clinical study.Tertiary care university hospital, elective peripheral trauma-related orthopedic surgery.72 adult, ASA physical status I, II, and III patients scheduled for elective peripheral trauma-related orthopedic surgery requiring general anesthesia.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.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.Anesthesia induction was significantly (p0.01) 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).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.

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.

Summary Objectives: In our department, we have been using an Anesthesia Information Management System (AIMS) for five years. In this study, we tested to what extent data extracted from the AIMS could be suitable for the supervision and time-management of operating rooms. Methods: From 1995 to 1999, all relevant data from 103,264 anesthetic procedures were routinely recorded online with the automatic anesthesia record keeping system NarkoData. The program is designed to record patient related time data, such as the beginning of anesthesia or surgical procedure, on a graphical anesthesia record sheet. The total number of minutes of surgery and anesthesia for each surgical subspecialty per hour/day and day of the year was calculated for each of the more than 40 ORs, amounting to a total of 112 workstations. Results: It was possible to analyze the usage and the utilization of ORs at the hospital for each day of the year since 1997. In addition, annual and monthly evaluations are made available. It is possible to scrutinize data of OR usage from different points of view: queries on the usage of an individual OR, the usage of ORs on certain days or the usage of ORs by a certain surgical subspecialty may be formulated. These data has been used repeatedly in our hospital for decision making in OR management and planning. Conclusions: In assessing the results of our study, it should be considered that the system used is not a specialized OR management tool. Despite these restrictions, the system contains data which can be used for an exact and relevant presentation of OR utilization

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.

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 influence of methods for record keeping on the documentation of vital signs was assessed for the Anesthesia Information Management System (AIMS) NarkoData. We compared manually entered blood-pressure readings with automatically collected data. These data were stored in a database and subsequently evaluated and analyzed. The data sets were split into two groups, ”manual“ and ”automatic“. We evaluated the effect of automatic data collection on the incidence of corrected data, data validity and data variation. Blood-pressure readings of 37,726 data sets were analyzed. We could assess that the method of documentation did influence the data quality. It could not be assessed whether the incorrectness of data during automatic data gathering was caused by artefacts or by the anesthesiologist.

Main requirements for an Anesthesia Information Management System (AIMS) are the supply of additional information for the anesthesiologist at his workstation and complete documentation of the anesthetic procedure. With the implementation of an AIMS (NarkoData) and effective user support, the quality of documentation and the information flow at the anesthesia workstation could be increased. Today, more than 20,000 anesthesia procedures are annually recorded with the AIMS at 112 decentralized workstations. The network for data entry and the presentation and evaluation of data, statistics and results directly available at the clinical workstation was made operational.

Summary Objective: Prospective observational study to assess the impact of two differentsampling strategies on the score results of the NEMS, used widely to estimate the amount of nursing workload in an ICU. Methods: NEMS scores of all patients admitted to the surgical ICU over a one-year period were automatically calculated twice a day with a patient data management system for each patient day on ICU using two differentsampling strategies (NEMSindividual: 24-hour intervals starting from the time of admission; NEMS8a.m.: 24-hour intervals starting at 8 a.m.). Results: NEMSindividual and NEMS8a.m. were collected on 3236 patient days; 687 patients were involved. Significantly lower scores were found for the NEMS8a.m. (25.0 ± 8.7) compared to the NEMSindividual (26.1 ± 8.9, p Conclusions: Different sampling strategies produce different score values, especiallydueto the end of stay. This has to betaken into accountwhen using the NEMS in quality assurance projects and multi-center studies.

While developing the patient data management system ICUData in close cooperation with the software company (IMESO GmbH, Huttenberg, Germany), a therapeutic guideline assistance system for empiric antimicrobial therapy in ICU (called “Antibiotic Wizard”) could be introduced and integrated into the existing software. After its introduction into clinical routine, the first version was to be tested, checked for usability and compared to other software products with the help of the IsoMetrics s inventory (based on the EN ISO 9241-10 for computer-assisted workflows). Half a year after introducing the “Antibiotic Wizard” in the ICUs, 40 physicians from different specialties at different levels of training were surveyed in order to detect deficiencies in the use of the program. The results of these surveys were compared to surveys on the word processing software Word® for Windows® (WinWord®) from Microsoft®, the hospital information system IS-H*MED from SAP® (online and paper surveys) and the administrative program, SAP R/3 HR, also from SAP®. Reliabilities (Cronbach’s Alpha) of the subscales ranged from satisfactory (α > 0.70) to good (α > 0.80), except for “Controllability” (α = 0.663) and “Error tolerance” (α = 0.693). Medians for individual subscales ranged between 3.04 (“Error tolerance”) and 3.96 (“Suitability for learning”). The “Antibiotic Wizard” showed significantly better results compared to both IS-H*MED and SAP R/3 HR in the subscales of “Suitability for the task”, “Self-descriptiveness” and “Suitability for learning”. In contrast, “Self-descriptiveness” “Controllability” and “Error tolerance” were significantly worse compared to WinWord®. In generally, the usability of the “Antibiotic Wizard” was deemed good. Some weaknesses were found in the fields of “Error tolerance” and “Controllability”. These problems will be corrected in future versions.

In 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.Based 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.Four 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.Due to reduced calibration, the clinical use of the prediction model is limited.

Due to the increasing number of caesarean sections, we investigated the influence of maternal bradycardia during general and regional anaesthesia on seven standard paediatric outcome parameters using our online recorded data.Data from 1154 women undergoing caesarean section were investigated prospectively. Bradycardia was defined as a heart rate below 60 beats/min. The matched-pairs method was used to evaluate the impact of bradycardia on Apgar scores at 1, 5, and 10 min, umbilical artery pH and base excess, admission to paediatric intensive care unit, and seven-day mortality. Matched references were automatically selected among all patients from the data pool according to anaesthetic technique, sensory block height, urgency, maternal age and body mass index. Stepwise regression models were developed to predict the impact of intra-operative bradycardia on outcome variables with differences between matched pairs assessed using univariate analysis.Bradycardia was found in 146 women (12.7%) for whom a control could be matched in 131 cases (89.7%). Mean 5-minute Apgar score was 9.2+/-1.1 for study patients and 9.3+/-1.1 for controls. pH and base excess were not significantly different between groups. In cases of urgent surgery, neonates had an increased risk of 1.8 (95% CI 1.36-2.44, P0.01) for an Apgar scoreor= 8 at 1 min and a 2.6-fold risk (95% CI 1.64-4.06, P0.01) of umbilical arterial pH ofor= 7.2 compared to infants undergoing non-urgent procedures.Using matched-pairs analysis we were unable to demonstrate that episodes of maternal bradycardia below 60 beats/min were associated with a poorer neonatal outcome regardless of anaesthetic technique.

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).

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 fomites 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.

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.

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.

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).Retrospective study.Operative ICU of the Department of Anesthesiology and Intensive Care Medicine.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 stayed24 hrs were excluded. In the case of patient readmittance, only data from the patient's last stay was included in the study.None.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 significantly0.5 (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).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.