K111534 · Diatron U.S., Inc. · GKZ · Aug 2, 2012 · Hematology
Device Facts
Record ID
K111534
Device Name
ABACUS 3CP
Applicant
Diatron U.S., Inc.
Product Code
GKZ · Hematology
Decision Date
Aug 2, 2012
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 864.5220
Device Class
Class 2
Attributes
Pediatric
Indications for Use
The Diatron Abacus 3CP System is a quantitative multi-parameter automated hematology analyzer designed for in-vitro-diagnostic use in clinical laboratories for enumeration of the following parameters: WBC, LYM%, LYM#, MID%, MID#, GRA%, GRA#, RBC, HGB, HCT, MCV, MCH, MCHC, RDW, PLT, MPV in K₃EDTA anti-coagulated venous whole blood samples. The Diatron Abacus 3CP is indicated for use to identify patients with hematologic parameters within and outside of established reference ranges.
Device Story
Abacus 3CP is a fully automated, bench-top hematology analyzer for clinical laboratories. It processes K3EDTA anticoagulated venous whole blood samples via manual sampling in open or closed vial modes. The device utilizes impedance technology to count cells (WBC, RBC, PLT) passing through small apertures and a photometric method to measure hemoglobin (HGB). Other parameters (LYM, MID, GRA, HCT, MCV, MCH, MCHC, RDW, MPV) are derived or calculated. The system features a color LCD, foil keypad, and optional built-in printer or external USB printer connectivity. It stores 1000 records with histograms. Data can be exported via USB or RS232 serial link. Healthcare providers use the generated reports to assess patient hematologic status against reference ranges, aiding in clinical decision-making regarding blood health.
Clinical Evidence
No clinical data provided. The submission relied on software and system verification and validation testing performed in accordance with the Class II Special Controls Guidance Document for Automated Differential Cell Counters. Risk and hazard analysis were conducted per ISO 14971.
Technological Characteristics
Bench-top automated hematology analyzer. Sensing: Impedance (WBC, RBC, PLT) and Photometric (HGB). Aperture diameters: 100 µm (WBC), 80 µm (RBC/PLT). Connectivity: USB (slave/flash), RS232. User interface: Color LCD, 29-key foil keypad. Software updateable via USB. Sterilization: Not applicable (in-vitro diagnostic).
Indications for Use
Indicated for use in clinical laboratories to perform quantitative hematology analysis on K₃EDTA anticoagulated venous whole blood samples from patients of all ages (range >2 to >80 years) to identify hematologic parameters within or outside established reference ranges.
Regulatory Classification
Identification
An automated differential cell counter is a device used to identify one or more of the formed elements of the blood. The device may also have the capability to flag, count, or classify immature or abnormal hematopoietic cells of the blood, bone marrow, or other body fluids. These devices may combine an electronic particle counting method, optical method, or a flow cytometric method utilizing monoclonal CD (cluster designation) markers. The device includes accessory CD markers.
Special Controls
*Classification.* Class II (special controls). The special control for this device is the FDA document entitled “Class II Special Controls Guidance Document: Premarket Notifications for Automated Differential Cell Counters for Immature or Abnormal Blood Cells; Final Guidance for Industry and FDA.”
{0}
1
# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY
A. 510(k) Number:
k111534
B. Purpose for Submission:
New device
C. Manufacturer and Instrument Name:
Diatron US Inc., Abacus 3CP
D. Type of Test or Tests Performed:
Quantitative test for WBC, LYM%, LYM#, MID%, MID#, GRA%, GRA#, RBC, HGB, HCT, MCV, MCH, MCHC, RDW, PLT, and MPV parameters.
E. System Descriptions:
1. Device Description:
The Abacus 3 Cap Piercer (Abacus 3CP) is a fully automated, bench top hematology cell counter. It uses the impedance-method for counting cells passing through a small aperture, and measures the hemoglobin content of red blood cells using a photometric method.
The analyzer features a color graphical LCD display module and a foil keypad of 29 keys including 6 software buttons (with icons), 6 function keys (above LCD) and has a START button. The Abacus 3CP instrument requires 100 uL of K₃EDTA anti-coagulated venous whole blood sample and can process 60 samples per hour. The Abacus 3CP hematology analyzer is composed of a Fluidic System, Data Processing System and a Control Panel.
2. Principles of Operation:
It uses the impedance-method for counting cells passing through a small aperture, and measures the hemoglobin content of red blood cells using a photometric method. The Diatron Abacus 3CP System uses electronic sizing to determine three distinct white cell subpopulations. Cells correlating to lymphocytes are included in the small cell subpopulation. Cells correlating to granulocytes are included in the large cell population. The remaining cells correlating to monocytes, basophils, eosinophils, blasts, and other precursor white cells are included in the mid-size cell population. The MCV and RDW values are derived from statistical analysis of the RBC histogram. The HCT, MCH, and MCHC are calculated values. The MPV value is derived from statistical analysis of the PLT histogram.
3. Modes of Operation:
The Abacus 3CP operates in both open and closed sample tube mode.
{1}
2
4. Specimen Identification:
The sample ID can be entered by the operator from a keyboard or hand held barcode reader for a manually processed specimen. For automatically processed specimens, the sample ID can be read from a sample tube barcode or entered into a list for one of the automatic processing list modes.
5. Specimen Sampling and Handling:
Manually presented whole blood K₃EDTA sample tubes must be mixed properly prior to specimen analysis. Sample tubes are placed into a tube adapter inserted on the sample rotor for closed vial or open vial single tube manual sample processing.
6. Calibration:
The Abacus 3CP analyzer calibration is calibrated using a commercial calibrator. The known calibrator parameter values and measured values are used to calculate calibration factors. Diatron recommends performing calibration at installation, when indicated by quality control, after major maintenance or service, and at periodic time intervals as directed by laboratory regulatory agencies.
7. Quality Control:
Diatron recommends the use of CBC-3D controls from R&D Systems to verify performance of the Abacus 3CP. Quality control is performed at intervals established by the laboratory or as required by laboratory accreditation, licensing, or regulatory agencies.
8. Software:
FDA has reviewed applicant’s Hazard Analysis and Software Development processes for this line of product types:
Yes ☐ X ☐ or No ☐
F. Regulatory Information:
1. Regulation section:
21 CFR § 864.5220, Automated differential cell counter
2. Classification:
Class II
3. Product code:
GKZ, Counter, differential cell
4. Panel:
{2}
Hematology (81)
## G. Intended Use:
1. **Indication(s) for Use:**
The Diatron Abacus 3CP System is a quantitative multi-parameter automated hematology analyzer designed for in-vitro-diagnostic use in clinical laboratories for enumeration of the following parameters: WBC, LYM%, LYM#, MID%, MID#, GRA%, GRA#, RBC, HGB, HCT, MCV, MCH, MCHC, RDW, PLT, MPV in K₃EDTA anti-coagulated venous whole blood samples. The Diatron Abacus 3CP is indicated for use to identify patients with hematologic parameters within and outside of established reference ranges.
2. **Special Conditions for Use Statement(s):**
For Prescription Use Only
## H. Substantial Equivalence Information:
1. **Predicate Device Name(s) and 510(k) numbers:**
Abbott CELL-DYN 1800 (k030513)
2. **Comparison with Predicate Device:**
| Similarities | | |
| --- | --- | --- |
| Item | Device Abacus 3CP | Predicate CELL-DYN 1800 |
| Intended Use | The Diatron Abacus 3CP System is a quantitative multi-parameter automated hematology analyzer designed for in vitro diagnostic use in clinical laboratories for enumeration of the following parameters WBC, LYM%, LYM#, MID%, MID#, GRA%, GRA#, RBC, HGB, HCT, MCV, MCH, MCHC, RDW, PLT, MPV in K₃EDTA anticoagulated venous whole blood samples. Diatron Abacus 3CP is indicated for use to identify patients with hematologic parameters within and outside of established reference ranges. | The CELL-DYN 1800 System is an automated, multiparameter hematology analyzer designed to report sixteen parameters relating to the cells of EDTA-anticoagulated blood. |
| IVD Parameters | WBC, RBC, HGB, HCT, MCV, MCH, MCHC, PLT, LYMPH%/#, MID%/#, GRA%/#, RDW, MPV | Same |
{3}
| Item | Device Abacus 3CP | Predicate CELL-DYN 1800 |
| --- | --- | --- |
| Principle of Measurement | Uses the impedance method for counting White Blood Cell (WBC) including Lymphocytes, Monocytes and Neutrophil granulocytes, Red Blood Cell (RBC), and platelet (PLT) passing through a small aperture and uses the photometric method to measure the hemoglobin content of red blood cells. The instrument analyzes whole blood samples from open vials and can be upgraded with a vial piercing option to be used with closed vials. | Same |
| Sample types | Whole Blood (K3EDTA) | Same |
| Sample Container | Open and Closed | Same |
| Sample System | Manual | Same |
| Samples/hour | 60 | Same |
| Sampling Mechanisms | Manual sampling (open tube mode) Closed vial mode Autosampler cap piercing | Same |
| Sample ID | Manual and barcode | Same |
| Methodology | WBC Differential-Optical RBC, PLT- Impedance HGB - Photometric MCV- Derived | Same |
| Aperture Diameter (μm) | WBC 100um RBC/PLT 80um | Same |
| QC data | Maintains QC files; generated Levy-Jennings charts | Same |
| Differences | | |
| --- | --- | --- |
| Item | Device Abacus 3CP | Predicate CELL-DYN 1800 |
| Sample Volume (μL) | Open Vial Mode - 100 μL Closed Vial Mode - 100 μL | Open Vial Mode - 30 μL Closed Vial Mode - 450 μL |
| Calibrator | CBC-Cal Plus Calibrator (k003991) | CELL-DYN 18 Plus Calibrator |
| Quality Control | R&D CBC 3D Cell Control (k843962) | CELL-DYN 18 Plus Controls |
| Reagents | Diatro•Dil-DIFF (diluent) Diatro•Lyse-DIFF( lysing reagent) Diatro•Cleaner (cleaner) | CN-Free Diff Lyse, detergent and lysing reagents |
{4}
5
I. Special Control/Guidance Document Referenced (if applicable):
CLSI EP05-A2 Evaluation of Precision Performance of Quantitative Measurement Methods; Approved Guideline-Second Edition
CLSI EP06-A, Evaluation of the Linearity of Quantitative Measurement Procedures: A Statistical Approach; Approved Guideline
CLSI EP09-A2, Method Comparison and Bias Estimation Using Patient Samples; Approved Guideline-Second Edition
CLSI EP17-A Protocols for the Determination of Limits of Detection and Limits of Quantitation; 1st Edition
CLSI H26-A2 Validation, Verification, and Quality Assurance of Automated Hematology Analyzers; 2nd Edition
CLSI C28-A3 Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; 3rd Edition
CLSI EP07-A2 Interference Testing in Clinical Chemistry; Second Edition
J. Performance Characteristics:
1. Analytical Performance:
a. Accuracy:
Each parameter of the Abacus 3CP was compared to the predicate analyzer, Cell-Dyn 1800. A total of 656 samples were analyzed at three (3) sites in the U.S. Patient demographics include age range from >2 to >80 years of age. Fifty-seven percent (n = 367) samples were normal and 36% (n = 277) had one or more abnormal conditions. In addition, 37.4% were male and 62.6% were female. The correlation analysis was performed using the first replicate of the Abacus 3CP against the first replicate of the Cell-Dyn 1800. Accuracy summary data analysis is provided in the tables below:
Regression Results
| (n = 656) | | | | Intercept 95% CI | | | Slope 95% CI | | Acceptance Criteria |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Parameter | Range | (r) | Intercept | Lower | Upper | Slope | Lower | Upper | |
| WBC | 0.4 - 77.1 | 0.9987 | -0.0285 | -0.1300 | 0.0731 | 0.9866 | 0.9731 | 1.0000 | 0.30 or 6% |
| GRA% | 7.9 - 96.7 | 0.9137 | 1.5956 | -0.7747 | 3.9658 | 0.9749 | 0.9388 | 1.0109 | 3.00 or 10% |
| LYM% | 1.4 - 77.7 | 0.9352 | -1.3378 | -2.5059 | -0.1696 | 1.0523 | 1.0069 | 1.0977 | 3.00 or 10% |
| MID% | 2.2 - 37.8 | 0.8515 | -1.0994 | -1.7549 | -0.4439 | 1.0849 | 1.0181 | 1.1518 | 3.00 or 10% |
| RBC | 0.5 - 7.8 | 0.9965 | -0.1007 | -0.1246 | -0.0769 | 1.0304 | 1.0240 | 1.0367 | 0.15 or 6% |
| HGB | 1.3 - 23.4 | 0.9982 | 0.1915 | 0.1414 | 0.2415 | 0.9869 | 0.9826 | 0.9911 | 0.30 or 6% |
| MCV | 65.6 - 114 | 0.9866 | 1.6424 | 0.5211 | 2.7637 | 0.9845 | 0.9721 | 0.9969 | 1.00 or 6% |
| RDW | 11.8-25.3 | 0.9126 | 0.3061 | -0.4101 | 1.0223 | 0.9737 | 0.9242 | 1.0233 | 0.50 or 6% |
| PLT | 15 - 1168 | 0.9920 | -1.4551 | -5.1098 | 2.1996 | 1.0348 | 1.0185 | 1.0511 | 15 or 8% |
| MPV | 7.3 - 18.2 | 0.8805 | -0.0438 | -0.5992 | 0.5117 | 1.0085 | 0.9543 | 1.0628 | 0.50 or 10% |
Conclusion: The absolute value of the calculated predictive bias of all clinical decision points and range points were below the predictive bias goals for the points determined
{5}
from the acceptance criteria for all parameters. Therefore, the method comparison evaluation is considered to meet its acceptance criteria and all the requirements of the data collection and its evaluation.
Predictive Value of Abacus 3CP vs. Microscopy (Reference Method)
The ability of the Abacus 3CP to flag abnormal samples was evaluated per CLSI H20-A2 in comparison with the reference method (Manual Microscopy). A total of 212 samples were analyzed on the Abacus 3CP in combination with manual differential reviews. The WBC distributional flagging statistical analysis was performed 2 ways and the data are summarized in the tables below:
# Binned manual differential analysis per CLSI H20-A2:
This approach counted the total number of monocytes, eosinophils, basophils, immature granulocytes and blast cells represented in a reference sample and determined whether this total value was outside of the expected reference range for "MID" cells. If that sample was outside the reference range for "MID" cells then it was considered in this analysis to be distributionally positive for the reference sample.
| 3-Part Diff Performance vs Manual Diff (binned manual diff data) per CLSI H20-A2 | | |
| --- | --- | --- |
| | % Positive Agreement | % Negative Agreement |
| Overall | 81.4% | 96.6% |
| Lymph | 86.1% | 98.3% |
| Mid Cells | 33.3% | 97.3% |
| Grans | 81.4% | 97.6% |
# Unbinned manual differential analysis:
This approach counted each cell type, monos, eos, basos, immature grans and blasts, represented in a reference sample and determined whether the individual population was outside of the expected reference range for that particular population. If any individual cell type, monos, eos, basos, immature grans or blasts, was outside of its individual expected reference range, then it would be considered in this analysis to be distributionally positive for "MID" cells for the reference sample.
| 3-Part Diff Performance vs Manual Diff (unbinned manual diff data) | | |
| --- | --- | --- |
| | Sensitivity | Specificity |
| Overall | 58.0% | 97.4% |
| Lymph | 86.1% | 98.3% |
| Mid Cells | 16.7% | 99.1% |
| Grans | 81.4% | 97.6% |
Conclusions: The results of the comparison of the predictive value of the Abacus 3CP versus the manual differential demonstrated performance expected for
{6}
instruments of this class (3-part differential). The overall sensitivity result is attributed to the effect of binning to a 3-part differential and was found to be comparable to other devices reporting a 3-part differential.
# b. Precision/Reproducibility:
# Repeatability
The Abacus 3CP repeatability evaluation was conducted using 23 human blood samples and three levels of control. The samples were selectively chosen to span the analytical measuring range and to be close to clinical decision points on two Abacus 3CP instruments. A minimum of 15 and a maximum of 21 replicates were run for each sample. All repeatability runs passed either their SD or CV criteria for all parameters for both instruments according to the acceptance criteria.
The following tables represent reproducibility and precision. Two replicates of a single lot of commercial low, normal, and high control samples were run twice per day at three different sites for 20 working days. The data from three instruments was analyzed to determine the overall mean and precision for between-site, between-day, within-day, and within-device. Within-day variation represents the variability between AM and PM runs pooled across the 20 days.
| Low Control | WBC | LYM% | MID% | GRA% | RBC | HGB | MCV | RDW | PLT | MPV |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Repeatability SD | 0.058 | 2.194 | 1.658 | 2.387 | 0.05 | 0.087 | 0.299 | 0.024 | 3.283 | 0.285 |
| Repeatability CV | 1.54% | 3.88% | 15.9% | 7.23% | 1.97% | 1.34% | 0.39% | 1.52% | 4.94% | 3.84% |
| Repeatability SD acceptance criteria | 0.18 | 3.1 | 2.00 | 3.50 | 0.11 | 0.20 | 1.00 | 0.4 | 23 | 0.45 |
| Repeatability CV Criteria | 2.70% | 8.0 | 17.0 | 8.0 | 1.70% | 2.00% | 1.70% | 2.50% | 6.00% | 8.70% |
| Repeatability Pass/Fail | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
| Normal Control | WBC | LYM% | MID% | GRA% | RBC | HGB | MCV | RDW | PLT | MPV |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Repeatability SD | 0.127 | 0.74 | 0.438 | 0.805 | 0.09 | 0.159 | 0.373 | 0.25 | 6.344 | 0.19 |
| Repeatability CV | 1.64% | 2.37% | 5.08% | 1.33% | 1.85% | 1.12% | 0.42% | 1.69% | 2.65% | 2.58% |
| Repeatability SD acceptance criteria | 0.18 | 3.1 | 2 | 3.5 | 0.11 | 0.20 | 1.00 | 0.40 | 23 | 0.45 |
| Repeatability CV acceptance criteria | 2.70% | 8.0 | 17.0 | 8.0 | 1.70% | 2.00% | 1.70% | 2.50% | 6.00% | 8.70% |
| Repeatability Pass/Fail | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
{7}
| High Control | WBC | LYM% | MID% | GRA% | RBC | HGB | MCV | RDW | PLT | MPV |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Repeatability SD | 3.48 | 0.672 | 0.347 | 0.656 | 0.107 | 0.259 | 0.345 | 0.189 | 11.028 | 0.134 |
| Repeatability CV | 1.74% | 4.14% | 4.87% | 0.85% | 1.85% | 1.37% | 0.35% | 1.36% | 2.31% | 1.74% |
| Repeatability SD acceptance criteria | 0.18 | 3.1 | 2.0 | 3.50 | 0.11 | 0.20 | 1.00 | 0.40 | 23.00 | 0.45 |
| Repeatability CV acceptance criteria | 2.70% | 8.0% | 17.0% | 8.0% | 1.70% | 2.00% | 1.70% | 2.50% | 6.00% | 8.70% |
| Repeatability Pass/Fail | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
# Reproducibility
The following tables represent reproducibility. Two replicates of a single lot of commercial low, normal, and high control samples were run twice per day at three different sites for 20 working days. The data from three instruments was analyzed to determine the overall mean and precision for between-site, between-day, within-day, and within-device. Within-day variation represents the variability between AM and PM runs pooled across the 20 days. Acceptance criteria for each reproducibility study are represented under within-device CV or SD. Acceptance criteria were met for each parameter.
| Low Control | WBC | LYM% | MID% | GRA% | RBC | HGB | MCV | RDW | PLT | MPV |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Mean (n = 240) | 2.019 | 56.601 | 10.375 | 32.991 | 2.533 | 6.453 | 77.15 | 15.78 | 66.438 | 7.418 |
| Between-Site SD | 0.103 | 0.703 | 0.293 | 1.058 | 0.034 | 0.018 | 0.637 | 0.046 | 1.535 | 0.309 |
| Between-Site CV | 5.10% | 1.24% | 2.82% | 3.21% | 1.34% | 0.28% | 0.83% | 0.29% | 2.31% | 4.17% |
| Between-Day SD | 0.064 | 0 | 0.303 | 0.417 | 0.005 | 0.07 | 0.548 | 0.031 | 2.586 | 0.152 |
| Between-Day CV | 3.17% | 0.00% | 2.92% | 1.26% | 0.20% | 1.08% | 0.71% | 0.20% | 3.89% | 2.05% |
| Within-Day SD | 0.031 | 0 | 0 | 0 | 0.023 | 0.047 | 0.211 | 0.044 | 2.659 | 0.07 |
| Within-Day CV | 1.54% | 0.00% | 0.00% | 0.00% | 0.91% | 0.73% | 0.27% | 0.28% | 0.28% | 0.94% |
| Within-Device SD* | 0.092 | 2.194 | 1.686 | 2.423 | 0.055 | 0.121 | 0.659 | 0.246 | 4.953 | 0.33 |
| Within-Device CV | 4.53% | 3.88% | 16.2% | 7.34% | 2.18% | 1.88% | 0.85% | 1.56% | 7.46% | 4.45% |
| SD acceptance criteria | 0.40 | 3.10 | 2.0 | 3.5 | 0.15 | 0.22 | 1.2 | 0.45 | 27 | 0.5 |
| CV acceptance criteria | 4.00% | 8.00% | 17.0% | 8.0 | 2.50% | 2.40% | 2.00% | 3.00% | 7.00% | 10.0% |
| Pass/Fail | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
{8}
| Normal Control | WBC | LYM% | MID% | GRA% | RBC | HGB | MCV | RDW | PLT | MPV |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Mean (n = 240) | 7.745 | 31.247 | 8.614 | 60.139 | 4.859 | 14.169 | 87.829 | 14.811 | 239.168 | 7.363 |
| Between-Site SD | 0.148 | 0.321 | 0.256 | 0.55 | 0.058 | 0.149 | 0.34 | 0.11 | 3.714 | 0.231 |
| Between-Site CV | 1.91% | 1.03% | 2.97% | 0.91% | 1.19% | 1.05% | 0.39% | 0.74% | 1.55% | 3.14% |
| Between-Day SD | 0.085 | 0.361 | 0 | 0.492 | 0.051 | 0.127 | 0.385 | 0.083 | 4.601 | 0.086 |
| Between-Day CV | 1.10% | 1.16% | 0.00% | 0.82% | 1.05% | 0.90% | 0.44% | 0.56% | 1.92% | 1.17% |
| Within-Day SD | 0.104 | 0.046 | 0.071 | 0.188 | 0.016 | 0.108 | 0.186 | 0 | 3.731 | 0 |
| Within-Day CV | 1.34% | 0.15% | 0.82% | 0.31% | 0.33% | 0.76% | 0.21% | 0.00% | 1.56% | 0.00% |
| Within-Device SD* | 0.185 | 0.824 | 0.443 | 0.962 | 0.105 | 0.23 | 0.567 | 0.263 | 8.68 | 0.209 |
| Within-Device CV | 2.39% | 2.64% | 5.14% | 1.60% | 2.15% | 1.62% | 0.65% | 1.78% | 3.63% | 2.84% |
| SD acceptance criteria | 0.4 | 3.1 | 2 | 3.5 | 0.15 | 0.22 | 1.2 | 0.45 | 27 | 0.5 |
| CV acceptance criteria | 4.00% | 8.0% | 17.0% | 8.0 | 2.50% | 2.40% | 2.00% | 3.00% | 7.00% | 10.0% |
| Pass/Fail | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
{9}
| High Control | WBC | LYM% | MID% | GRA% | RBC | HGB | MCV | RDW | PLT | MPV |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Mean (n = 240) | 20.02 | 16.24 | 7.12 | 76.64 | 5.771 | 18.916 | 98.026 | 13.873 | 477.739 | 7.703 |
| Between-Site SD | 0.679 | 0.160 | 0.313 | 0.279 | 0.131 | 0.190 | 0.175 | 0.074 | 14.952 | 0.234 |
| Between-Site CV | 3.39% | 0.99% | 4.40% | 0.36% | 2.27% | 1.00% | 0.18% | 0.53% | 3.13% | 3.04% |
| Between-Day SD | 0.436 | 0.00 | 0.141 | 0.127 | 0.027 | 0.201 | 0.368 | 0.034 | 6.270 | 0.080 |
| Between-Day CV | 2.18% | 0.00% | 1.98% | 0.17% | 0.47% | 1.06% | 0.38% | 0.25% | 1.31% | 1.04% |
| Within-Day SD | 0.164 | 0.000 | 0.177 | 0.181 | 0.025 | 0.121 | 0.000 | 0082 | 3.257 | 0.086 |
| Within-Day CV | 0.82% | 0.00% | 2.49% | 0.24% | 0.43% | 0.64% | 0.00% | 0.59% | 0.68% | 1.12% |
| Within-Device SD* | 0.582 | 0.672 | 0.414 | 0.692 | 0.113 | 0.35 | 0.504 | 0.209 | 13.098 | 0.178 |
| Within-Device CV | 2.91% | 4.14% | 5.81% | 0.90% | 1.96% | 1.85% | 0.51% | 1.50% | 2.74% | 2.31% |
| SD acceptance criteria | 0.4 | 3.1 | 2.00 | 3.5 | 0.15 | 0.22 | 1.2 | 0.45 | 27 | 0.5 |
| CV acceptance criteria | 4.00% | 8.0% | 17.0% | 8.0% | 2.50% | 2.40% | 2.00% | 3.00% | 7.00% | 10.0% |
| Pass/Fail | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass | Pass |
* As there is only one instrument per site, within-device precision is the same as within-site
# c. Linearity:
The analytical measuring range (AMR) was established according to the protocol in CLSI EP6-A by analyzing dilutions made from manipulated whole human blood or from commercial linearity kit material. In order to demonstrate that each parameter is within the claimed performance, a minimum of 9 proportional dilutions spanning the reportable range were run in triplicate on two Abacus 3CP analyzers. All of the eight linearity runs produced coefficients of determination $(\mathrm{r}^2) > 0.99$ for each measurand. All linearity runs met their respective specifications.
| Measurand | Units | AMR |
| --- | --- | --- |
| WBC | 103/μL | 0.95 - 83.45 |
| LYM% | % | 0 - 100 |
| MID% | % | 0 - 40 |
| GRA% | % | 0 - 100 |
| LYM | 103/μL | 0 - 85 |
| MID | 103/μL | 0 - 34 |
{10}
| Measurand | Units | AMR |
| --- | --- | --- |
| GRA | 103/μL | 0 - 85 |
| RBC | 106/μL | 0.44 - 7.74 |
| HGB | g/dL | 1.4 - 23.7 |
| MCV | fl | 50 - 120 |
| PLT | 103/μL | 11 - 975 |
| MPV | fl | 5 - 30 |
# d. Carryover:
Carryover was determined using three Abacus 3CP analyzers by running whole blood specimens with high target values (HTV) of WBC, RBC, HGB, and PLT. Each specimen was run in triplicate followed by three aspirations of whole blood specimens with low target values (LTV). Carryover % was calculated by using the following equation: % Carryover = (HTV1 - LTV3) / (HTV3 - LTV3) x 100. The maximum carryover for WBC, RBC, HGB, and PLT are $1.00\%$ , $0.50\%$ , $0.80\%$ , and $1.00\%$ respectively.
| Abacus 3CP | Carryover | | | |
| --- | --- | --- | --- | --- |
| | WBC | RBC | HGB | PLT |
| Analyzer #1 | 0.09% | -0.03% | -0.31% | 0.03% |
| Analyzer #2 | 0.08% | -0.13% | 0.34% | 0.00% |
| Analyzer #3 | 0.12% | 0.00% | 0.11% | -0.01% |
The carryover measurements for WBC, RBC, HGB and PLT were less than their respective criteria. The overall carryover evaluation is therefore considered passing.
# e. Interfering Substances:
Test samples are created by adding interfering substances to normal samples such as bilirubin and lipids based on CLSI EP07-A2. Other evaluations were made by finding naturally occurring human blood samples with potential interferences such as high WBC, platelet abnormalities and NRBCs. A bias of $5\%$ or more was considered clinically significant in the directly measured parameters. Measurands were not impacted by bilirubin up to $30~\mathrm{mg / dL}$ . The following parameters were affected by interference from NRBC, PLT clumps/large PLT, increased WBC, and lipids (see table below):
{11}
| Parameter | Interference |
| --- | --- |
| WBC | >5 NRBCs/100 WBCs, PLT clumps/large PLTs |
| RBC | WBC Count >50.0 x103/μL |
| MCV | WBC Count >75.0 x103/μL |
| PLT | PLT clumps/large PLTs |
| Hemoglobin | WBC count >50.0 x103/μL, Lipids >270 mg/dL |
| Differential | >5 NRBCs/100 WBCs, PLT clumps/large PLTs |
# f. Background Counts:
Daily start-up background counts were performed on the Abacus 3CP and were verified by each site against the specifications. Start-up background specifications were achieved before data collection. The Abacus 3CP background specifications are as follows:
| Measurand | Background Concentration Limits |
| --- | --- |
| WBC | ≤0.1 x 103cells/μL |
| RBC | ≤0.01 x 106cells/μL |
| HGB | ≤0.3 g/dL |
| PLT | ≤10 x 103cells/μL |
# 2. Other Supportive Instrument Performance Data Not Covered Above:
Studies were conducted to support claims and attributes for whole blood sampling and were designed to evaluate the following:
# a. Sample stability:
The parameters selected for this evaluation are the directly measured parameters (WBC, RBC, HGB, PLT), the optically measured parameters (GRA%, LYM%, MID%) and the derived parameters (MCV, RDW, MPV). A total of 10 normal and six abnormal $\mathrm{K}_3$ EDTA anticoagulated human whole blood samples were analyzed at 0.5 hours after venipuncture and at various time intervals thereafter. The averages of the parameters at each time point were compared to the baseline averages. The results support sample stability at room temperature $(20 - 23^{\circ}\mathrm{C})$ of 7 hours.
The acceptance criteria are found in the following table:
| | WBC | LYM% | MID% | GRA% | RBC | HGB | MCV | RDW | PLT | MPV |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Absolute | 0.3 | 3.0 | 3.0 | 3.0 | 0.2 | 0.3 | 1.0 | 0.5 | 15.0 | 0.5 |
| Percent | 6% | 10% | 10% | 10% | 6% | 6% | 6% | 6% | 8% | 10% |
# b. Comparison of open and closed vial sampling:
A total of $50\mathrm{K}_3$ EDTA anticoagulated human blood samples spanning the reportable range were used in the study. The specimens were run in closed vial cap pierce mode (selected as the reference as per CLSI H26-A2) and compared to
{12}
the open vial mode and to the Autosampler automatic processing mode, on two instruments. The absolute value of the calculated predictive bias at all range and clinical decision points for all parameters were less than the bias goals calculated from the absolute and percent criteria for the parameter. Therefore, the overall mode-to-mode evaluation is considered to meet its specifications.
The predictive bias acceptance criteria are found in the following table:
| | WBC | LYM% | MID% | GRA% | RBC | HGB | MCV | RDW | PLT | MPV |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Absolute | 0.3 | 3.0 | 3.0 | 3.0 | 0.15 | 0.3 | 1.0 | 0.5 | 15.0 | 0.5 |
| Percent | 6% | 10% | 10% | 10% | 6% | 6% | 6% | 6% | 8% | 10% |
# c. Reference ranges:
Evaluation of reference ranges for the Abacus 3CP instrument was conducted using a data set of 120 normal human whole blood samples collected at the US site. Of these 120 samples, 60 were from female patients and 60 were from male patients. The established reference ranges are represented in the table below.
| Parameter | Units | Lower limit | Upper limit |
| --- | --- | --- | --- |
| WBC | 103/μL | 4.0 | 11.7 |
| LYM | % | 10.8 | 45.40 |
| MID | % | 1.8 | 17.0 |
| GRA | % | 44.0 | 80.9 |
| LYM | 103/μL | 0.8 | 3.3 |
| MID | 103/μL | 0.3 | 1.7 |
| GRA | 103/μL | 2.3 | 8.8 |
| RBC | 106/μL | F-2.76 M-3.27 | F-5.59 M-5.74 |
| HGB | g/dL | F-8.8 M-10.1 | F-15.5 M-16.5 |
| HCT | % | F-26.1 M-30.6 | F-47.4 M-49.6 |
| MCV | fl | 76.4 | 102.0 |
| MCH | pg | 23.3 | 36.1 |
| MCHC | g/dL | 29.7 | 36.8 |
| RDWcv | % | 111.3 | 16.7 |
| PLT | 103/μL | 97 | 390 |
| MPV | fL | 7.5 | 13.1 |
# d. Determination of lower limits of detection and quantitation:
The lower limit of detection (LLoD) and the lower limit of quantitation (LLoQ) of WBC, PLT, RBC and HGB were quantified as per CLSI H26-A2 section 5.8. To determine LLoD, 12 blank runs were collected daily over five days (total 60 blank runs) on two instruments using alternating operators. The values of WBC, PLT, RBC and HGB for these blank runs are analyzed to determine the LLoD. Once
{13}
the LLoD is calculated, five low level specimens were run over a period of five days. One specimen was run each day using alternating operators. Twelve replicates per specimen were collected for a total of 60 replicates. The CVs of each dilution of WBC and PLT were analyzed to determine the LLoQ of each. Results were as follows:
| Item | WBC | PLT | RBC | HGB |
| --- | --- | --- | --- | --- |
| LLoD | 0.0748 | 5.6138 | 0.000 | 0.3192 |
| LLoQ | 0.1645 | 7.3450 | 0.4016 | 0.3192 |
# K. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10.
# L. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
Predicate graph will load when search results are available.
Embedding visualization will load when search results are available.
PDF viewer will load when search results are available.
Loading panels...
Select an item from Submissions
Click any panel, subpart, regulation, product code, or device to see details here.
Section Matches
Results will appear here.
Product Code Matches
Results will appear here.
Special Control Matches
Results will appear here.
Loading collections...
Loading
My Alerts
You will receive email notifications based on the filters and frequency you set for each alert.
Sort by:
Create Alert
Search Filters
Agent Token
Create a read-only bearer token for Claude, ChatGPT, or other agents that can call HTTP APIs.
Copy this now. It will not be shown again.
Connected apps
Apps you authorized through browser sign-in. Disconnecting revokes their access immediately.
Learn the FDA Browser
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.