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CLINICAL AND PROGNOSTIC FEATURES OF ELDERLY PATIENTS WITH UNSTABLE ANGINA PECTORIS UNDERGOING CONSERVATIVE TREATMENT DEPENDING ON LEFT VENTRICULAR EJECTION FRACTION

2023· article· en· W4382278499 on OpenAlexaboutno aff
Н. Б. Лебедева, Л. К. Исаков, M. N. Sinkovа, Н. И. Тарасов, L. Kuznetsova

Bibliographic record

VenueComplex Issues of Cardiovascular Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEjection fractionHeart failureInternal medicineCardiologyUnstable anginaQuality of life (healthcare)AnginaAcute coronary syndromeCanadian Cardiovascular SocietyCoronary heart diseaseMyocardial infarction

Abstract

fetched live from OpenAlex

Highlights High cardiovascular morbidity and mortality in the Russian Federation and in Kuzbass is mainly due to high number of elderly and senile patients; it simply reflects the current demographic situation in the country. Elderly and senile patients with acute coronary syndrome (ACS) account for 50% of hospitalized patients. In real clinical practice, a significant number of elderly patients with ACS receive conservative treatment, whereas the prognosis in this cohort is determined by the development of recurrent coronary events and the progression of heart failure. Due to small number of patients older than 75-80 years included in randomized clinical trials, there are certain gaps in the management of elderly patients with ACS and heart failure. Obviously, elderly patients require a special approach to patient management, taking into account the complexity of clinical and anamnestic factors affecting the prognosis. Abstract Aim. To study clinical and prognostic features of elderly patients with unstable angina pectoris undergoing conservative treatment depending on left ventricular ejection fraction (LVEF). Methods. 130 elderly patients, with mean age of 82 (77; 89) years, hospitalized for unstable angina with a GRACE score of less than 140 to a vascular center in Kemerovo were included in the study. During hospitalization, standard laboratory and instrumental studies were performed, except coronary angiography. The quality of life was assessed using the EQ-5D 3L questionnaire. After 12 months, patient compliance with treatment recommendations, primary and secondary endpoints, and quality of life were analyzed. Results. All patients were diagnosed with heart failure, 50 (38.5%) patients presented with LV EF less than 40% (the group I), 80 (61.5%) patients presented with LV EF more than 40% (the group II). The groups were comparable in gender, age, presence of multifocal of atherosclerotic disease, prevalence of aortic stenosis, arrhythmias and comorbidities. Women predominated in both groups, and arterial hypertension was noted in all patients. In the group I, a history of myocardial infarction, coronary revascularization, and NYHA FC III were more common (p<0.05). The level of quality of life at discharge was low in both groups: 34.8 (29; 42) and 39.4 (34; 46) points, respectively (p>0.05). Almost all patients were on triple neurohumoral blockade (beta-blockers, renin-angiotensin-aldosterone system inhibitors and mineralocorticoid receptor antagonists) for heart failure and dual antiplatelet therapy. One year later, 85.2% of patients in the group I and 90% of patients in the group II were taking all prescribed medication at low or medium therapeutic doses (without the need for drug titration). The overall mortality in the groups was 46% and 37.5%; cardiovascular deaths accounted for 32% and 30%, respectively (p>0.05). There were no differences in the frequency of endpoints (hospitalization, stroke, acute coronary syndrome, coronary revascularization) between the groups. The level of quality of life remained low in both groups. Conclusion. In patients over 75 years hospitalized for unstable angina, main clinical and anamnestic characteristics, annual prognosis and quality of life do not depend on LV EF, whereas the need for coronary revascularization during the 1 year remains high.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.335
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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