Performance
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8 independent studies. 3,000+ participants.
70M+ real-world glucose values.

Can complications be predicted before they happen? Eight independent evaluations asked. The answer is yes. We started at 60 minutes. Today Endobits holds the longest published prediction horizon in glucose intelligence, and the accuracy to act on it. Predict. Prioritize. Prevent.

Peer-reviewed · JMIR
Based on real-world clinical data · Type 1 & Type 2 mix of patients 43% fewer severe hypos +6 hrs in range daily 250% per-patient income
Sharpest overnight. Above 90% out to hour 11.
Prediction accuracy by forecast horizon · tested on stable Type 1 patients · “accurate” = within 10% of the real glucose value · measured at every hour, not extrapolated
Optimal · nocturnal event window (~1–9 h, during sleep)
Algorithm viability · uncertainty widens
90% accuracy floor · error is tightest (± 1.7%) overnight and widens the further out we forecast Other methods lose accuracy past ~30 minutes, off the left edge of this chart
Hours 1–9 are where it counts: the overnight window, tightest predictions, severe events flagged during sleep. Past hour 11 the curve slips below the 90% floor, shown honestly out to 13 hours, where accuracy falls to 78%.
86% · 97%
Sensitivity · Specificity
85.7% sensitivity · 96.6% specificity across 7.7M real-world forecasts.
Real-world forecasts
Lows, overnight
Seen before they happen
Severe events flagged up to 9 hours ahead, above 90% accuracy, caught before morning.
Optimal window · during sleep
~95%+
Forecast accuracy, real patients
Every forecast within 10% of the real value.
Real-world study · 142 participants
800+ patients
Tested, incl. public benchmarks
From blind pediatric testing to the OhioT1DM benchmark and T1D Exchange registries. Every cohort independent.
Multi-study · public benchmarks · ages 7–80
The evidence
In-silico simulation→Blind testing→Real-world evidence
The same engine, scored independently at every step. Cohorts overlap across studies; each row is an independent evaluation.
Model accuracy independently verified by the National Research Council of Canada under a Data Analytics Centre research partnership. The full verification report ships with the evidence brief.
01Prediction testing
What is being predicted: future glucose values, hours ahead, scored against what the sensor actually recorded. The final row scores event prediction: will this patient cross into a danger range, yes or no.
StudyPatientsData pointsResult
Real-world, live clinical14210.4M95.7% accuracy · +10% time in range
Real-world 1-hour forecasts vs ADA ranges1007.7M94.4% accuracy · 85.7 / 96.6 sens / spec
OhioT1DM public benchmark12401,91693.6% accuracy · 83.6 / 96.1 sens / spec
Performance at scale (Metatron)41233.7MAccuracy held at population scale
External registries · Weinstock + Chase40011.2M~90% accuracy held on the noisiest outside cohorts, ages 7 to 80
BC Children's Hospital · blind80.17M94.9% pediatric accuracy
In-silico (Epsilon Group)30015.5M~98% in Parkes A+B, the clinically safe zones
Latest production model · binary event prediction, ADA ranges14510.5M93% sensitivity · 98.4% specificity (most recent evaluation)
Presented at ADA Scientific Sessions · posters, 78th · 79th · 80th
02Auto-triage & prioritization testingThe workflow product: every patient reviewed, the urgent ones surfaced first
30 min → 3 min
CGM chart review per patient, a 10× efficiency gain in live clinical use
99.6%
Forecasts clinically acceptable (Parkes A+B) · JMIR peer-reviewed · 280 users · 6.9M values
Zero missed nights
The whole panel is re-read continuously. Urgent patients surface themselves, instead of waiting for someone to call
Give your CDCES team superpowers
Not a clinician replaced by an algorithm, or the other way round. A third capability: your specialists' judgment, with the reach to apply it to every patient at once. Endobits triages the full 1,500-patient panel in seconds so the team's hours land where they change outcomes. That reach is what drives the per-patient economics.
CDCES team + Endobits
~ 1.5 seconds
CDCES team alone
~ 5.5 months
See how it works in your clinic →
03Glucotyping & stratification testingSorts patients by their glucose curves, not the average: know who is drifting toward complications, and why
90% sens · 84% acc
Dysglycemia detection: flagging the patients whose patterns need a clinician's eye
4 hours ahead
Nocturnal hypoglycemia warning at 93.5% specificity, before the patient is asleep, not after the ER. Nightly risk classification holds 99% sensitivity across 2,300+ nights, on both Dexcom and Libre data
4 glucotypes
Four recurring glucose curves: stable responders · post-meal spikers · variable/brittle · sustained-elevated, read from everyday CGM data, no glucose-tolerance test needed
2 granted patents
Glucotype classification and intervention-pathway IP. Plus 1 pending: Glucose Atlas
Corroborated in the field
Lancet D&E · staging callPrediabetes and T2D should be staged by CGM-derived metrics, the exact data layer Endobits reads and classifies
Penn Medicine · 19 RCTsGLP-1 agents differ by metabolic profile: precision prescribing needs phenotyping, not a 90-day average
Univ. of Liverpool · n=357,883Higher post-meal glucose → 69% higher Alzheimer's risk, the signal HbA1c can't see
Compared to today's standard of care
CapabilityHuman alone · standard of careHuman + Endobits · real-world testing
Forecast horizon~30 minutes, pump algorithms & apps use linear models that break down beyond that1–9 hours, above 90% to hour 11, the overnight window others can't see
Glucose signalFingersticks: ≤12 readings / month · HbA1c: one lagging 90-day average288 CGM readings / day, analyzed continuously
Overnight lowsUnwitnessed, discovered the next morning, or in the ERFlagged up to 4 hours in advance · 93.5% specificity
Panel review~30 minutes of chart review per patient~3 minutes with automated triage, a 10× gain
Who gets care firstReactive, whoever calls or shows upA predictive priority list: who's heading for trouble, and when
30-minute comparator: pump algorithms (Medtronic 770G, Tandem Basal-IQ, linear-regression forecasting) · workflow timings from live US clinical deployment
Also exercised against ShanghaiT1DM, ABC4D, D1NAMO, Tidepool Loop, OpenAPS and simulator suites · full record available under NDA
Request the full evidence brief → Study protocols, verification reports and the complete metric record. NDA required.

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