Observational studyClinical interventions in aging2025
Development and Validation of a Diagnostic Nomogram Model for Predicting Cognitive Frailty in Acute Coronary Syndrome.
Observational study in Clinical interventions in aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cognitive frailty (CF) is strongly associated with major adverse cardiovascular events, yet its assessment requires specialized equipment, limiting clinical practicality. This study aimed to develop and validate a nomogram model for predicting CF in patients with acute coronary syndrome (ACS) to enhance early identification and intervention. Methods: Patients with ACS (N=547) were enrolled and randomly split into a training set (70%) and a testing set (30%). The training set was used to construct the nomogram, while the testing set was used for validation. Model performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) to assess discrimination, accuracy, and clinical utility, respectively. Results: The nomogram included six predictors: education level, age, systolic blood pressure (SBP), Charlson Comorbidity Index (CCI), Short Physical Performance Battery (SPPB), and nutritional status. The model demonstrated strong discriminatory power, with an area under the ROC curve of 0.854 (95% CI: 0.741-0.861) in the training cohort and 0.733 (95% CI: 0.500-0.898) in the testing cohort. Calibration analysis confirmed high accuracy, and DCA indicated significant net benefits across both cohorts, supporting its clinical applicability. Conclusion: The nomogram effectively predicts CF in ACS patients by considering education, age, SBP, CCI, SPPB, and nutritional status, serving as a visual aid for healthcare providers to facilitate the early identification and intervention of CF. Future research is needed to validate the nomogram's efficacy in diverse populations and explore standardized assessment methods that enhance its clinical applicability in mitigating CF in ACS patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.