Prospective evaluation of an EDB-based diagnostic program to be used in patients admitted to hospital with acute chest pain

Jonsbu, J; Aase, O.; Rollag, Arnfinn; Liestøl, Knut; Erikssen, Jan

doi:10.1093/eurheartj/14.4.441

Cited by 25 publications

(10 citation statements)

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“…Twenty-five studies involved academic institutions, three involved community hospitals and five studies were performed in a mixed community and academic setting. A thematic abundance of ESS systems was identified, covering a wide spectrum of clinical areas encompassing acute lung injury [13][14][15], patient-ventilator interaction [16][17][18], identification of seizures [19], rapid patient deterioration [20] nosocomial infection surveillance [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], heart failure [36], life-threatening electrocardiographic changes [37][38][39] and hemodynamic stability monitoring [40][41][42].…”

Section: Resultsmentioning

confidence: 99%

“…The likelihood ratio is particularly important for summarizing diagnostic accuracy and is more directly applicable to the clinical environment [45]. The LLQ section of the graph identifies a subgroup of surveillance systems with enhanced diagnostic capabilities in excluding clinical entities or syndromes [18,21,25,40]. Given a patient with a pretest probability of less than 50%, the application of Bayes' theorem would allow a negative test to reduce post-test probability to less than 1%.…”

Section: Discussionmentioning

confidence: 99%

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Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Kashiouris

O’Horo

Pickering³

et al. 2013

Appl Clin Inform

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SummaryContext: Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making. Objective:To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems. Data Sources: All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012. Study Selection: Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values. Data Extraction: Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created. Results: The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%. Conclusions: There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Kashiouris

O’Horo

Pickering³

et al. 2013

Appl Clin Inform

View full text Add to dashboard Cite

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“…Therefore, expert systems (ES) are widely used in the clinical domain, facilitating the assessment of disease suspicion, its diagnosis and management and, hence, play a significant role in averting medical or clinical mistakes (Bates et al 2001;Menachemi et al 2007;Sim et al 2001;Kawamoto et al 2005). Consequently, the quality of medical healthcare systems improves significantly in the recent days (Jonsbu et al 1993;Lin et al 2006). Various frameworks have been evolved to construct efficient expert systems in the clinical domain with the capability of handling uncertainties.…”

Section: Introductionmentioning

confidence: 99%

A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty

et al. 2017

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Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented.

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“…The applicability of Bayes’ theorem to diagnose patients with acute chest pain has previously been demonstrated in study situations on chest pain databases [1, 2]. The role of computer based decision support systems in clinical practice remains yet to be established.…”

Section: Introductionmentioning

confidence: 99%

“…Estimates of these probability parameters were obtained from a sample population containing anamnestic, clinical and diagnostic data from 918 patients referred to the Central Hospital of Akershus with acute chest pain previously reported [1, 2]. A total of 1,161 probability parameters were estimated from this database and placed in a look-up table for use by Bayes’ theorem.…”

Section: Introductionmentioning

confidence: 99%

Clinical Experience with a Decision Support Computer Program Using Bayes’ Theorem to Diagnose Chest Pain Patients

Aase¹

1999

Cardiology

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A decision support computer program (DSP) was used by the emergency room physician as a diagnostic tool on patients admitted with acute chest pain to guide the referral of these patients either to the Coronary Care Unit (CCU) or general ward. The DSP used Bayes’ theorem on 38 anamnestic and clinical variables to classify patients into one of nine diagnoses. During a six months trial period 32 physicians used the DSP to diagnose 493 patients admitted with acute chest pain. The physicians referred the patients to CCU or general ward based on their clinical judgements, the ECG findings and the diagnostic estimates given by the DSP. The program correctly diagnosed 150 (84%) of 178 patients with acute myocardial infarction and 63 of 112 patients with unstable angina. However, acute ischemic heart disease (acute myocardial infarction or unstable angina) was correctly classified by the DSP for 259 (89%) of 290 patients. By using the DSP, the number of patients unnecessarily referred to CCU was reduced from 35% to 19% and the number of patients in need of CCU observation misallocated to general ward was reduced from 13% to 10%. Thus, use of the DSP in the emergency room on easily available anamnestic and clinical variables may improve referrals to the CCU, optimize therapy and resource use.

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Prospective evaluation of an EDB-based diagnostic program to be used in patients admitted to hospital with acute chest pain

Cited by 25 publications

References 0 publications

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

Diagnostic Performance of Electronic Syndromic Surveillance Systems in Acute Care

A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty

Clinical Experience with a Decision Support Computer Program Using Bayes’ Theorem to Diagnose Chest Pain Patients

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