Knowledge-based decision support for patient monitoring in cardioanesthesia

Schecke, Thomas; Langen, Manfred; Popp, H.-J.; Rau, G.; Käsmacher, H.; Kalff, G

doi:10.1007/bf01145897

Cited by 20 publications

(6 citation statements)

References 18 publications

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“…InCare had a rule-based system that used multi-variable and trend based analysis of physiological data to detect events. Similarly, Schecke et al [28] designed a knowledge-based decision support system for patient monitoring in cardio anesthesia. The medications used and progress of the surgery was fed into the system manually by one of the members of the surgical staff.…”

Section: Related Workmentioning

confidence: 99%

“…We define a medically significant event as any event that affects or is a part of the surgical procedure. Many systems [27,20,28] have been built that monitor physiological parameters of a patient and signal alarming conditions. Healthcare providers use these alarms as cues as it is not possible to maintain a constant vigil over the patients' health status.…”

Section: Introductionmentioning

confidence: 99%

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A Pervasive Computing System for the Operating Room of the Future

et al. 2007

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We describe a prototype Context Aware Perioperative Information System to capture and interpret data in an operating room of the future. The captured data is used to construct the context of the surgical procedure and detect medically significant events. Such events, and other state information, are used to automatically construct an Electronic Medical Encounter Record (EMR). The EMR records and correlates significant medical data and video streams with an inferred higher-level event model of the surgery. Information from sensors such as Radio Frequency Identification (RFID) tags provides basic context information including the presence of medical staff, devices, instruments and medication in the operating room (OR). Patient monitoring systems and sensors such as pulse oximeters and anesthesia machines provide continuous streams of physiological data. These low level data streams are processed to generate higher-level primitive events, such as a nurse entering the OR. A hierarchical knowledge-based event detection system correlates primitive events, patient data and workflow data to infer high-level events, such as the onset of anesthesia. The resulting EMR provides medical staff with a permanent record of the surgery that can be used for subsequent evaluation and training. The system can also be used to detect potentially significant errors. It seeks to automate some of the tasks done by nursing staff today that detracts from their ability to attend to the patient.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Pervasive Computing System for the Operating Room of the Future

et al. 2007

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“…They can, however, not predict the individual outcome in a given patient. Techniques derived from artificial intelligence [22,23] , which improve individual risk prediction in cardiology [24][25][26][27] , cardiac surgery [28,29] or other fields of medicine [30][31][32][33][34] are gaining increasing importance. The present study is a continuation of a previous study, also performed within the INTERVENT project [35] , which was focused on procedural complications that appeared during or shortly after interventional procedures while the patient was still in the catheterization laboratory.…”

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confidence: 99%

A prognostic computer model to individually predict post-procedural complications in interventional cardiology; the INTERVENT Project

Budde

Haude²,

Höpp³

et al. 1999

European Heart Journal

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AimsThe purpose of this part of the INTERVENT project was (1) to redefine and individually predict post-procedural complications associated with coronary interventions, including alternative/adjunctive techniques to PTCA and (2) to employ the prognostic INTERVENT computer model to clarify the structural relationship between (pre)-procedural risk factors and post-procedural outcome. Methods and ResultsIn a multicentre study, 2500 data items of 455 consecutive patients (mean age: 61·1 8·3 years; 33-84 years) undergoing coronary interventions at three university centres were analysed. 80·4% of the patients were male, 16·7% had unstable angina, and 5·1%/10·1% acute/subacute myocardial infarction. There were multiple or multivessel stenoses in 16·0%, vessel bending >90 in 14·5%, irregular vessel contours in 65·0%, moderate calcifications in 20·9%, moderate/severe vessel tortuosity in 53·2% and a diameter stenosis of 90%-99% in 44·4% of cases. The in-lab (out-of-lab) complications were: 0·4% (0·9%) death, 1·8% (0·2%) abrupt vessel closure with myocardial infarction and 5·5% (4·0) haemodynamic disorders. ConclusionComputer algorithms derived from artificial intelligence were able to predict the individual risk of these post-procedural complications with an accuracy of >95% and to explain the structural relationship between risk factors and post-procedural complications. The most important prognostic factors were: heart failure (NYHA class), use of adjunctive/alternative techniques (rotablation, atherectomy, laser), acute coronary ischaemia, pre-existent cardiac medication, stenosis length, stenosis morphology (calcification), gender, age, amount of contrast agent and smoker status. Pre-medication with aspirin or other cardiac medication had a beneficial effect. Techniques, such as laser angioplasty or atherectomy were predictors for postprocedural complications. Single predictors alone were not able to describe the individual outcome completely. (Eur Heart J 1999; 20: 354-363)

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“…Die bisher im Helmhoilz-Institut verfolgten Ansätze zur Überwachung der Hämodynamik basieren auf zwei wesentlichen Darstellungen des Herz-Kreislaufzustandes eines Patienten: (1) eine prozeßorientierte Darstellung des Zustandes am Beispiel des Anästhesie-Informationssystems AIS [1] sowie (2) eine abstrakte Profilogrammdarstellung auf der Basis einer linguistischen Beschreibung der Schlußfolgerungsschritte eines Anästhesisten (am Beispiel des intelligenten Alarmsystems A ES) [2,3]. Beide Techniken haben spezifische Vor-und Nachteile.…”

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