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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.

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.

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.

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.