Computational protocol: Logistic Organ Dysfunction Score (LODS): A reliable postoperative risk management score also in cardiac surgical patients?

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Protocol publication

[…] This study involved evaluation of prospectively collected data from all consecutive adult patients admitted to our ICU after cardiac surgery. Patients admitted between January 1st 2007 and December 31st 2008 were included and the study was approved by the Institutional Review Board of our university (approval no.: 2809-05/10). Only the first admission was considered for patients who were readmitted to the ICU during the study period. Data were collected from the quality control system QIMS 2.0b (University Hospital of Muenster, Germany) and from the intensive care information system COPRA 5.2 (COPRASYSTEM GmbH, Sasbachwalden, Germany), which is interfaced with patient monitors (Philips IntelliVue MP70, Amsterdam, Netherlands), ventilators (Draeger Evita IV, Luebeck, Germany and Hamilton Galileo, Bonaduz, Swizerland), blood gas analyzers (ABL 800Flex Radiometer, Copenhagen, Denmark) and the central laboratories.The attending physician collected the data and calculated LODS values for the first postoperative week. Two assigned medical clerks validated the data collection daily. A senior consultant performed a second periodical validation. There were no missing data. The LODS was calculated daily using the worst value for each variable per day. Outcome was defined as ICU mortality.Statistical analyses were performed with SPSS software version 18 (SPSS Inc, Chicago, IL). Graphics were drawn using SigmaPlot software version 11.0 (Systat Software Inc, San Jose, CA, USA). Continuous scale data are presented as mean ± standard deviation (SD) and were analyzed using the two-tailed Student's t-test for independent samples. A p value of < 0.05 was considered as significant. The LODS performance was assessed with the Hosmer-Lemeshow (HL) goodness-of-fit test to insure the absence of a significant discrepancy between predicted and observed mortality. Calibration was considered good when there was a low X2 value and a high p value (> 0.05). Discrimination (ability of a scoring model to differentiate between survival and death) was evaluated with receiver-operating-characteristic (ROC) curves; the area under the curve (AUC) indicates the discriminative ability of the score, i.e., the ability to discriminate survivors from non-survivors. An AUC of 0.5 (a diagonal line) is equivalent to random chance [], whereas an AUC of 1.0 implies perfect discrimination []. The overall correct classification (OCC) (the ratio of the number of correctly predicted survivors and non-survivors to the total number of patients) values of the score were calculated. All statistical analyses were performed from ICU day 1 (n = 2801) (operative day) until the seventh day (n = 338 patients) only, in order to obtain accurate statistical results with sufficient numbers of patients. […]

Pipeline specifications

Software tools SPSS, SigmaPlot
Application Miscellaneous
Organisms Homo sapiens