NeuroPace (NPCE) Total Liabilities (2020 - 2026)
NeuroPace (NPCE) recorded Total Liabilities of $85.11 million in Q2 2026, down 1.8% from $86.68 million a year earlier but up 0.8% from the prior quarter.
NeuroPace (NPCE) Total Liabilities (2020 - 2026) Analysis & Trends
At the end of FY2025, NeuroPace reported Total Liabilities of $86.53 million, down 0.1% from FY2024.
- Annual Total Liabilities has a five-year compound annual growth rate of -15.7% (FY2020 to FY2025).
- Across earlier years, Total Liabilities came in at $86.63 million in FY2024 (-0.4%), $87 million in FY2023 (+9.7%), $79.33 million in FY2022 (+32.1%) and $60.06 million in FY2021 (-70.5%).
- Quarterly Total Liabilities has ranged from $59.55 million in Q3 2021 to $88.74 million in Q3 2025 over the past five years.
- On a year-over-year basis, Total Liabilities has declined for three consecutive quarters, with growth averaging 0.9% over the last eight quarters.
- Peak year-over-year performance for Total Liabilities in the last five years was growth of 32.1% in Q4 2022, against a decline of 70.5% in Q4 2021 at the low end.
- Per Business Quant, the preceding three quarters came in at $84.44 million (Q1 2026), $86.53 million (Q4 2025) and $88.74 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 251.30 Bn | 230.45 Bn | 4.88 Bn | 60.49 Bn |
| 2 | Abbott Laboratories | 174.70 Bn | 145.78 Bn | 7.27 Bn | 57.45 Bn |
| 3 | Danaher | 158.94 Bn | 142.76 Bn | 3.61 Bn | 39.78 Bn |
| 4 | Intuitive Surgical | 145.87 Bn | 125.42 Bn | 1.96 Bn | 2.58 Bn |
| 5 | Medtronic | 111.49 Bn | 77.36 Bn | 6.34 Bn | 42.46 Bn |
| 6 | Stryker | 106.96 Bn | 93.08 Bn | 4.50 Bn | 23.94 Bn |
| 7 | Boston Scientific | 63.62 Bn | 58.63 Bn | 3.85 Bn | 20.04 Bn |
| 8 | Edwards Lifesciences | 50.80 Bn | 34.92 Bn | 1.35 Bn | 3.14 Bn |
| 9 | Becton Dickinson | 49.59 Bn | 46.74 Bn | 2.32 Bn | 26.32 Bn |
| 10 | NeuroPace | 477.90 Mn | 251.15 Mn | 18.91 Mn | 85.11 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 85.11 Mn |
| Mar 31, 2026 | 84.44 Mn |
| Dec 31, 2025 | 86.53 Mn |
| Sep 30, 2025 | 88.74 Mn |
| Jun 30, 2025 | 86.68 Mn |
| Mar 31, 2025 | 85.94 Mn |
| Dec 31, 2024 | 86.63 Mn |
| Sep 30, 2024 | 86.48 Mn |
| Jun 30, 2024 | 84.50 Mn |
| Mar 31, 2024 | 84.51 Mn |
| Dec 31, 2023 | 87.00 Mn |
| Sep 30, 2023 | 82.94 Mn |
| Jun 30, 2023 | 81.01 Mn |
| Mar 31, 2023 | 78.78 Mn |
| Dec 31, 2022 | 79.33 Mn |
| Sep 30, 2022 | 77.07 Mn |
| Jun 30, 2022 | 65.64 Mn |
| Mar 31, 2022 | 66.16 Mn |
| Dec 31, 2021 | 60.06 Mn |
| Sep 30, 2021 | 59.55 Mn |
NeuroPace Total Liabilities API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=total-liabilities&ticker=NPCE&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "NPCE", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=total-liabilities&ticker=NPCE&period=max&api_key=YOUR_API_KEY");
const data = await res.json();