When Is the Broken Heart Most Dangerous? Assessing Risk Factors to Predict Inpatient Death in Takotsubo Cardiomyopathy: Analysis of the National Inpatient Sample for 2021
Bibliographic record
Abstract
Background Takotsubo cardiomyopathy (TC) has a similar clinical presentation to acute coronary syndromes (ACS). As the prevalence and influence on clinical decisions of this condition are being increasingly recognized, prognostic factors have yet to be established. We applied known near‐term acute coronary syndrome mortality risk factors to determine their prognostic value in TC. This study aimed to assess the patient characteristics and comorbidities predicting inpatient death from TC. Understanding these risk factors is essential for clinical decision making and improving prognostic assessments. Methods We analyzed the National Inpatient Sample database for 2021. Inclusion criteria were principal diagnosis of TC ( International Classification of Diseases , Tenth Revision [ ICD‐10 ] code I51.81) and age ≥18 years. Different comorbidities, age, and sex were analyzed, and the primary outcome was inpatient death. Univariate logistic regression was used to test the association of each factor with death, and multivariate logistic regression was then used to test for independent predictive value. Results A total of 9109 admissions for TC were identified (10.3% men and 89.7% women) with a mean age of 67 years and an inpatient mortality rate of 2.31%. On univariate regression, age (odds ratio [OR], 1.04; P =0.013), heart failure (OR, 3.2; P <0.001), atrial fibrillation (OR, 3.12; P <0.001), and chronic kidney disease (OR, 3.54; P <0.001) were significant predictors of inpatient death. On multivariate regression, only heart failure (OR, 2.8; P =0.007) and chronic kidney disease (OR, 2.34; P =0.032) were independently associated with inpatient death. Conclusions Preexisting heart failure and a history of chronic kidney disease are poor prognostic factors in patients presenting with TC. Further large‐scale studies are required to validate our findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".