Left heart chambers reverse remodeling after combined CABG and mitral repair

Slavov, M.; Panayotov, Plamen; Panayotova, Daniela; Peychev, Yavor; Kornovski, Vladimir

doi:10.1186/1749-8090-8-s1-o215

Cited by 1 publication

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“…After concluding that the patient's status is not too deteriorated then the presence of very significant regurgitation through the MV needs to be identified. If present, then the patient is undoubtedly classified in group A, because the lack of MVRepair will be associated with a bad prognosis regarding progression of IMR and left ventricular reverse remodeling [18,19]. In that sense, the following secondary criteria for exclusion from group B can be defined (i.e.…”

Section: Main Algorithm and Identification Of The Group Degree Of Memmentioning

confidence: 99%

Using Fuzzy Sets in Surgical Treatment Selection and Homogenizing Stratification of Patients with Significant Chronic Ischemic Mitral Regurgitation

Nikolova¹,

Panayotov²,

Panayotova³

et al. 2019

IJCIS

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We present three (main one and two auxiliary) fuzzy algorithms to stratify observations in homogenous classes. These algorithms modify, upgrade and fuzzify crisp algorithms from our earlier works on a medical case study to select the most appropriate surgical treatment for patients with ischemic heart disease complicated with significant chronic ischemic mitral regurgitation. Those patients can be treated with either surgical revascularization and mitral valve repair (group A) or with isolated surgical revascularization (group B) depending on their health status. The main algorithm results in a fuzzy partition of patients in two fuzzy sets (groups A and B) through identification of their degrees of membership. The resulting groups are highly nonhomogenous, which impedes subsequent proper comparisons. So, the two auxiliary algorithms further stratify each group into two homogenous subgroups with comparatively preserved medical condition (A 1 and B 1) and with comparatively deteriorated medical condition (A 2 and B 2). Those two algorithms perform fuzzy partition of patients from A and B respectively into A 1 , A 2 , B 1 and B 2 by identifying their conditional degrees of membership to those subgroups. We then utilize the product t-norm to calculate the degree of membership of patients to their respective subgroup as an intersection of two fuzzy sets. We demonstrate how to form fuzzy samples for medical parameters for any subgroup. We also compare the performance of the fuzzy algorithms with their preceding crisp version, as well as with eight Bayesian classifiers. We then assess the quality of classification by modified confusion matrices, summarized further into four criteria. The fuzzy algorithms show total superiority over the other methods, and excellent differentiation of typical patients and outliers. On top, only the fuzzy algorithms provide a measure of how typical a patient is to its subgroup. The fuzzy algorithms clearly outline the role of the Heart Team, which is missing in the Bayesian classifiers.

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Section: Main Algorithm and Identification Of The Group Degree Of Memmentioning

confidence: 99%