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- Blochwitz, Torsten, et al.
(författare)
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Functional Mockup Interface 2.0: The Standard for Tool independent Exchange of Simulation Models
- 2012
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Ingår i: Proceedings of the 9th International Modelica Conference. - : Linköping University Electronic Press. - 9789175198262 ; , s. 173-184
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Konferensbidrag (refereegranskat)abstract
- The Functional Mockup Interface (FMI) is a tool independent standard for the exchange of dynamic models and for co simulation. The first version, FMI 1.0, was published in 2010. Already more than 30 tools support FMI 1.0. In this paper an overview about the recently published version 2.0 of FMI is given that combines the formerly separated interfaces for Model Exchange and Co-Simulation in one standard. Based on the experience on using FMI 1.0, many small details have been improved and new features ease the usability and increase the performance especially for larger models. Additionally, a free FMI compliance checker will become soon available and FMI models from different tools are made available on the web to simplify testing.
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| 2. |
- Meister, Rebecca, et al.
(författare)
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Prediction of perioperative myocardial infarction/injury in high-risk patients after noncardiac surgery
- 2023
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Ingår i: European Heart Journal. - : OXFORD UNIV PRESS. - 2048-8726 .- 2048-8734. ; 12:11, s. 729-739
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Tidskriftsartikel (refereegranskat)abstract
- Aims Perioperative myocardial infarction/injury (PMI) is a surprisingly common yet difficult-to-predict cardiac complication in patients undergoing noncardiac surgery. We aimed to assess the incremental value of preoperative cardiac troponin (cTn) concentration in the prediction of PMI. Methods and results Among prospectively recruited patients at high cardiovascular risk (age >= 65 years or >= 45 years with preexisting cardiovascular disease), PMI was defined as an absolute increase in high-sensitivity cTnT (hs-cTnT) concentration of >= 14 ng/L (the 99th percentile) above the preoperative concentration. Perioperative myocardial infarction/injury was centrally adjudicated by two independent cardiologists using serial measurements of hs-cTnT. Using logistic regression, three models were derived: Model 1 including patient- and procedure-related information, Model 2 adding routinely available laboratory values, and Model 3 further adding preoperative hs-cTnT concentration. Models were also compared vs. preoperative hs-cTnT alone. The findings were validated in two independent cohorts. Among 6944 patients, PMI occurred in 1058 patients (15.2%). The predictive accuracy as quantified by the area under the receiver operating characteristic curve was 0.73 [95% confidence interval (CI) 0.71-0.74] for Model 1, 0.75 (95% CI 0.74-0.77) for Model 2, 0.79 (95% CI 0.77-0.80) for Model 3, and 0.74 for hs-cTnT alone. Model 3 included 10 preoperative variables: age, body mass index, known coronary artery disease, metabolic equivalent >4, risk of surgery, emergency surgery, planned duration of surgery, haemoglobin, platelet count, and hs-cTnT. These findings were confirmed in both independent validation cohorts (n = 722 and n = 966). Conclusion Preoperative cTn adds incremental value above patient- and procedure-related variables as well as routine laboratory variables in the prediction of PMI.
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