Validation of Decisions for Percutaneous Coronary Intervention Using Smartphone-Based Electrocardiogram Device Spandan: A Cross-Sectional Observational Study
Bibliographic record
Abstract
Background: India bears a high burden of acute coronary syndrome, with younger patients and a high prevalence of ST-elevation myocardial infarction (STEMI). Spandan is, therefore, an attractive smartphone-based electrocardiogram (ECG) device that could allow for potentially early diagnosis as well as enabling timely intervention which may even save lives in resource-poor settings. The study aimed to assess the performance and diagnostic capability of the Spandan smartphone-based ECG device in decision-making for percutaneous coronary intervention (PCI) by analyzing the initial ST-segment elevation, which was compared to a 12-lead ECG as the gold standard (BPL Cardiart ECG Machine). Methods: This was an observational cross-sectional study involving 184 eligible participants with chest pain presenting to the local hospital, in Meerut, Uttar Pradesh, India. The study was conducted for the evaluation of the diagnostic appropriateness of the Spandan ECG device for the detection of ST elevation as compared to standard 12-lead ECGs so that the cardiologists could be more easily guided in their decisions relative to PCI. Patients with the onset of chest pain within or after 120 h and ST elevation above 1 mm in two or more leads were enrolled and patients with dementia, bundle branch block, cardiogenic shock, and ECG artifacts were excluded. The analysis included calculating response characteristics and estimating correlation coefficients and confusion matrix to compare both appraisal methods. Results: The Spandan device performed with good agreement with the gold standard ECG, particularly in the leads II, III, and AVF, with Pearson correlation coefficients close to 1. The ST elevation in the Spandan device showed no statistical difference compared to the 12-lead ECG. The device exhibited a sensitivity of 94% and a positive predictive value of 94% for ST-elevation detection, thus having supportive evidence for possible usefulness for decision-making in PCI. Conclusions: ECG findings, such as that of the smartphone-based device (Spandan Pro ECG, a single channel autoswitched ECG machine), demonstrated comparable accuracy with the gold standard 12-lead ECG for the diagnosis of ST elevation and helped in making clinical decisions in patients requiring PCI, especially in resource-limited settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".