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Gunter Hempelmann - One of the best experts on this subject based on the ideXlab platform.

  • Computerized Anesthesia Record Keeping in Thoracic Surgery – Suitability of Electronic Anesthesia Records in Evaluating Predictors for Hypoxemia During One-lung Ventilation
    Journal of Clinical Monitoring and Computing, 2002
    Co-Authors: Jochen Sticher, Andreas Jost, Axel Junger, Bernd Hartmann, Matthias Benson, Martin Golinski, Stefan Scholz, Gunter Hempelmann
    Abstract:

    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 (SpO_2 < 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.

  • computerized Anesthesia Record keeping in thoracic surgery suitability of electronic Anesthesia Records in evaluating predictors for hypoxemia during one lung ventilation
    Journal of Clinical Monitoring and Computing, 2002
    Co-Authors: Jochen Sticher, Andreas Jost, Axel Junger, Bernd Hartmann, Matthias Benson, Martin Golinski, Stefan Scholz, Gunter Hempelmann
    Abstract:

    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.

  • An Anesthesia Information Management System (AIMS) as a Tool for Controlling Resource Management of Operating Rooms
    Methods of information in medicine, 2002
    Co-Authors: Axel Junger, M Benson, L. Quinzio, A. Michel, G. Sciuk, Dominik Brammen, Marquardt K, Gunter Hempelmann
    Abstract:

    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 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

Axel Junger - One of the best experts on this subject based on the ideXlab platform.

  • Computerized Anesthesia Record Keeping in Thoracic Surgery – Suitability of Electronic Anesthesia Records in Evaluating Predictors for Hypoxemia During One-lung Ventilation
    Journal of Clinical Monitoring and Computing, 2002
    Co-Authors: Jochen Sticher, Andreas Jost, Axel Junger, Bernd Hartmann, Matthias Benson, Martin Golinski, Stefan Scholz, Gunter Hempelmann
    Abstract:

    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 (SpO_2 < 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.

  • computerized Anesthesia Record keeping in thoracic surgery suitability of electronic Anesthesia Records in evaluating predictors for hypoxemia during one lung ventilation
    Journal of Clinical Monitoring and Computing, 2002
    Co-Authors: Jochen Sticher, Andreas Jost, Axel Junger, Bernd Hartmann, Matthias Benson, Martin Golinski, Stefan Scholz, Gunter Hempelmann
    Abstract:

    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.

  • An Anesthesia Information Management System (AIMS) as a Tool for Controlling Resource Management of Operating Rooms
    Methods of information in medicine, 2002
    Co-Authors: Axel Junger, M Benson, L. Quinzio, A. Michel, G. Sciuk, Dominik Brammen, Marquardt K, Gunter Hempelmann
    Abstract:

    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 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

Robert A Peterfreund - One of the best experts on this subject based on the ideXlab platform.

Michael S Higgins - One of the best experts on this subject based on the ideXlab platform.

  • assessing physiologic data representations for Anesthesia Record keeping
    American Medical Informatics Association Annual Symposium, 2002
    Co-Authors: Lemuel R Waitman, Michael S Higgins
    Abstract:

    Automated acquisition of physiologic data allows flexibility in the presentation of the intraoperative anesthetic Record. Since physiologic data is obtained at greater frequency than the current, handwritten Record there is a question as to which data should be displayed on the intraoperative Record. Too many data points may be distracting and susceptible to artifact while a median value could obscure clinically relevant hemodynamic variability. Multiple representations of a computerized anesthetic Record were created and then evaluated by eleven anesthetists. Data presented at one-minute intervals was preferred over an averaged or median value every five minutes.

  • AMIA - Assessing Physiologic Data Representations for Anesthesia Record-keeping
    2002
    Co-Authors: Lemuel R Waitman, Michael S Higgins
    Abstract:

    Automated acquisition of physiologic data allows flexibility in the presentation of the intraoperative anesthetic Record. Since physiologic data is obtained at greater frequency than the current, handwritten Record there is a question as to which data should be displayed on the intraoperative Record. Too many data points may be distracting and susceptible to artifact while a median value could obscure clinically relevant hemodynamic variability. Multiple representations of a computerized anesthetic Record were created and then evaluated by eleven anesthetists. Data presented at one-minute intervals was preferred over an averaged or median value every five minutes.

Andreas Jost - One of the best experts on this subject based on the ideXlab platform.

  • computerized Anesthesia Record keeping in thoracic surgery suitability of electronic Anesthesia Records in evaluating predictors for hypoxemia during one lung ventilation
    Journal of Clinical Monitoring and Computing, 2002
    Co-Authors: Jochen Sticher, Andreas Jost, Axel Junger, Bernd Hartmann, Matthias Benson, Martin Golinski, Stefan Scholz, Gunter Hempelmann
    Abstract:

    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.

  • Computerized Anesthesia Record Keeping in Thoracic Surgery – Suitability of Electronic Anesthesia Records in Evaluating Predictors for Hypoxemia During One-lung Ventilation
    Journal of Clinical Monitoring and Computing, 2002
    Co-Authors: Jochen Sticher, Andreas Jost, Axel Junger, Bernd Hartmann, Matthias Benson, Martin Golinski, Stefan Scholz, Gunter Hempelmann
    Abstract:

    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 (SpO_2 < 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 use of an Anesthesia information management system for prediction of antiemetic rescue treatment at the postAnesthesia care unit
    Anesthesia & Analgesia, 2001
    Co-Authors: A Junger, B Hartmann, M Benson, E Schindler, G V Dietrich, Andreas Jost, Aida Beyebasse, Gunter Hempelmannn
    Abstract:

    UNLABELLED: 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(R) (IMESO GmbH, Huttenberg, 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 N(2)O, 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. IMPLICATIONS: The aim of this study was to investigate predictors for postoperative nausea and vomiting by using online Anesthesia Records. With the help of computerized data evaluation, 7 of 34 variables could be detected as risk factors. By implementing an automatic score into the Record keeping system, an individual risk calculation could be made possible.