Retrospective clinical cohort of emergency department patients
The study evaluated the prognostic performance of the Atellica IM TnIH assay for all-cause mortality (ACM) and major adverse cardiac events (MACE) in patients presenting with symptoms suggestive of acute coronary syndrome (ACS).
Prognostic performance; All-cause mortality; MACE; Retrospective cohort; Emergency department
Clinical Evidence
Study Design
Population
Comparator
Key Endpoints
Retrospective cohort study; Follow-up/Duration: 30, 90, 182, and 365 days
Patients presenting to the emergency department with signs and symptoms suggestive of ACS; Sample Size: Up to 2,097 subjects (Cohort C)
Not applicable for this study
All-cause mortality (ACM) and major adverse cardiac events (MACE)
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Prognosis for 30-, 90-, 182-, and 365-day all-cause mortality (ACM) and major adverse cardiac events (MACE)
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Hazard ratios for patients with cTnI > 99th percentile ranged from 1.07 to 2.31 across cohorts and time points.
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Clinical study of patients presenting to the emergency department with signs and symptoms suggestive of ACS (Cohort A: N=874/883; Cohort B: N=1190/1214; Cohort C: N=2064/2097).
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Indications for Use
The Atellica® IM High-Sensitivity Troponin I (TnIH) assay is for in vitro diagnostic use in the quantitative measurement of cardiac troponin I in human serum or plasma (lithium heparin) using the Atellica® IM Analyzer. The assay can be used to aid in the diagnosis of acute myocardial infarction (AMI). The Atellica IM TnIH assay can be used as an aid in prognosis for 30-, 90-, 182-, and 365-day all-cause mortality (ACM) and major adverse cardiac events (MACE) in patients presenting with signs and symptoms suggestive of acute coronary syndrome (ACS). MACE consists of myocardial infarction, urgent revascularization, cardiac death, or heart failure hospitalization.
Device Story
Atellica® IM TnIH assay is a 3-site sandwich immunoassay using direct chemiluminescent technology; inputs are human serum or lithium heparin plasma samples. Solid phase reagent uses streptavidin-coated magnetic particles with biotinylated capture monoclonal antibodies; Lite Reagent uses acridinium ester-labeled recombinant anti-human cTnI sheep Fab. System detects relative light units (RLUs) proportional to cTnI concentration. Used in clinical laboratories on Atellica® IM Analyzer; operated by licensed healthcare professionals. Output is quantitative cTnI concentration; used by clinicians to aid in AMI diagnosis and risk stratification for ACM/MACE in ACS patients. Prognostic utility helps identify patients at higher risk of adverse cardiac events, potentially informing clinical management and monitoring decisions.
Clinical Evidence
Clinical study evaluated prognostic utility in ED patients with ACS symptoms (N=2064). Patients followed for 30, 90, 182, and 365 days for ACM/MACE. Hazard ratios calculated for baseline cTnI levels above vs. below 99th percentile. Results showed higher risk of ACM/MACE for patients with cTnI > 99th percentile. Adjusted hazard ratios for 365-day ACM/MACE were 1.85 (95% CI: 1.39, 2.47) for the total population.
Technological Characteristics
Chemiluminescence sandwich immunoassay; quantitative measurement of cardiac troponin I. Analyzed on Atellica IM Analyzer. Specimen types: serum and lithium heparin plasma. Measuring range: 2.50–25,000.00 pg/mL. Calibration: 2-point. No changes to analytical performance characteristics (precision, linearity, specificity, traceability) since K171566.
Indications for Use
Indicated for quantitative measurement of cardiac troponin I in human serum or lithium heparin plasma to aid in diagnosis of acute myocardial infarction (AMI) and as an aid in prognosis for 30-, 182-, and 365-day all-cause mortality (ACM) and major adverse cardiac events (MACE) in patients presenting with signs and symptoms suggestive of acute coronary syndrome (ACS).
Regulatory Classification
Identification
A creatine phosphokinase/creatine kinase or isoenzymes test system is a device intended to measure the activity of the enzyme creatine phosphokinase or its isoenzymes (a group of enzymes with similar biological activity) in plasma and serum. Measurements of creatine phosphokinase and its isoenzymes are used in the diagnosis and treatment of myocardial infarction and muscle diseases such as progressive, Duchenne-type muscular dystrophy.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.