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ES Journal of Cardiology

ISSN: 2768-0533

Takotsubo Syndrome - A New Algorithm with Insights in Susceptibility, Evolution, and Aftermath

  • Research Article

  • John Pantazopoulos MD* and Stavros Zinonos, PhD
  • Myocardial Infarction Data Acquisition System [MIDAS] Study Group, USA
  • *Corresponding author: John Pantazopoulos, MD, Rutgers Robert Wood Johnson Medical School, Cardiovascular Institute, 125 Paterson Street, CAB-4180, New Brunswick, NJ.
  • Received: June 02, 2020; Accepted: June 28, 2020; Published: July 04, 2020

Abstract

Background: Takotsubo syndrome [TTS] at presentation, raises significant questions of differential diagnosis.

Objectives: To develop an algorithm todistinguish TTS from acute myocardial infarction [MI] , based on comorbidities and conditions at presentation, derived from MIDAS, a New Jersey statewide database.

Methods: International classification of diseases-9 [ICD-9] codes as primary admission diagnoses were: for TTS 429.83 [747 patients] and for acute anterolateral wall MI [ALWMI ] 410.01 [4118 patients].

Six clinical characteristics were identified: mitral valve disorder, disorders of magnesium metabolism, other chest pain, acute systolic heart failure, anxiety disorder, and other primary cardiomyopathy.

A logistic classification of above allowed for a more direct way to incorporate a weighting scheme to score each of the six factors and demographic information, to classify patients as having either TTS or ALWMI.

Results: The rate of TTS stabilized at around 2% of all MI’s,female and white race were higher and event rates for heart failure and cardiovascular deaths were lower. The results for the logistic classification using the 6 prespecified features, race, sex, and age were: for TTS; sensitivity 0.84, specificity 0.71, precision rate 0.36, and correct classification rate 0.73. For MI; negative predictive value was 0.96.

Conclusions: The algorithm of above noted comorbidities and conditions at presentation is useful in differential diagnosis, points to potential mechanisms and may explain recurrences of TTS. It is derived from a non-selective database with no apparent bias and utilizes a bottom up approach.

Keywords

Mitral valve prolapse syndrome/ Genes/ Pathways