# Patient–ventilator interaction (PVI) analysis

HTML: <https://deepbreath.tech/solutions/patient-ventilator-interaction-analysis.html>  
Last reviewed: 2026-08-18

Patient–ventilator asynchrony is easy to miss on a snapshot and hard to count by hand. Deep Breath labels each breath from **airway pressure and flow**, so researchers can review, quantify, and export asynchronies across a stay.

**PVI** here means patient–ventilator interaction: how the patient’s breathing and the ventilator line up, breath by breath.  
**PVA** is patient–ventilator asynchrony — a mismatch between patient effort and machine delivery. That is what Deep Breath labels.  
**Pleth variability index** is something else: a pulse-oximetry measure of fluid responsiveness, also abbreviated PVI. This page is not about that.

Real-time clinical decision support is a **separate product** and is **not MDR or FDA cleared**.

Waveform collection is described separately: [ICU data collection](https://deepbreath.tech/solutions/icu-data-collection.md).

## From waveforms to labelled breaths

1. Collect airway pressure and flow via BreathBox.
2. Segment the recording into breaths and attach ventilator context (mode, settings, trigger information when the device provides it).
3. Assign PVI class labels per breath. Multiple labels can apply to one breath.
4. Quantify: asynchrony index over the last 100 ventilator cycles, type mix, and trends. Mechanical power and time constants are calculated from pressure, flow, and volume.
5. Export raw waveform CSV, chart images, and PVI statistics for research and quality review.

Asynchrony index = number of asynchronies ÷ total ventilator cycles in the past 100 breaths.

## Asynchrony types

| Type | Status |
|---|---|
| Normal | Available |
| Failed triggering (ineffective effort) | Available |
| Early triggering (reverse triggering) | Available |
| Late triggering (delayed triggering) | Available |
| Multiple triggering (double triggering) | Available |
| Early cycling (premature cycling) | Available |
| False triggering | Under development |
| Late cycling | Under development |
| Work shifting | Under development |

## Worked example: double triggering in PCV

A patient on pressure-control ventilation makes a second inspiratory effort before the first machine cycle has fully expired. On airway pressure, flow, and volume you see two closely stacked insufflations that the ventilator treats as separate breaths. Deep Breath labels this as **double triggering / multiple triggering**.

In research software the breath is shown with neighbouring context, the label can be reviewed, and the event contributes to the asynchrony index and type mix. Exports include the raw waveforms (CSV) and the PVI statistics for that period.

## External validation (manuscript under review)

Rietveld et al., “AI-based patient-ventilator asynchrony monitoring: external validation to advance clinical implementation.” Deep Breath B.V. authors include Daria Kozlova, Viacheslav Laktiushkin, and Anton Balakirev. SIREN consortium (Health~Holland, EMCLSH24018).

| Item | Detail |
|---|---|
| Design | Retrospective external validation vs clinician ground truth. Algorithm input: airway pressure and flow only. |
| Data | 24 invasively ventilated ICU patients (20 LUMC, 4 Erasmus MC). 5,173 breaths analysed from 62,563 selected breaths. Modes included PCV, PSV, ASV®, INTELLiVENT-ASV®. |
| Pre-set criteria | Normal-breath specificity ≥ 0.95 and PVA sensitivity ≥ 0.80. |
| Result | Criteria met for ineffective effort; reverse, delayed and double triggering; premature cycling — **not** for delayed cycling. Macro-averaged sensitivity 0.83, specificity 0.95, F1-score 0.81, PPV 0.79. |
| Training context | Gradient-boosting model trained on more than one million breaths from more than 1,000 patients, including a subset of about 20,000 manually annotated breaths. |
| Regulatory | Investigational software; pending regulatory approval for clinical use. Suitable, in the authors’ conclusion, for a clinical pilot — not a cleared diagnostic. |

Related published review (Deep Breath authors): Rietveld et al., *Intensive Care Medicine Experimental* 2025 — [Let’s get in sync](https://doi.org/10.1186/s40635-025-00746-8).  
Terminology paper (Deep Breath referenced, not authored): Mireles-Cabodevila, Vaporidi, Blanch, Chatburn, [10 Fundamental Maxims](https://doi.org/10.1177/19433654261425219).

## Who uses Deep Breath for PVI

Users include [PRoVENT–TRACE](https://deepbreath.tech/publications/provent-trace.html) — an international prospective multicentre observational study in the PROVE Network (more than 30 hospitals) — and other hospitals such as Erasmus MC, Leiden UMC, Amsterdam UMC, Universitätsklinikum Mannheim, Universitätsklinikum Erlangen, and Cleveland Clinic.

For PRoVENT–TRACE, Deep Breath is the official technology partner and supplies collection, storage, and analysis infrastructure for high-resolution ventilator waveforms used in asynchrony assessment.

Confirmation letter: <https://deepbreath.tech/files/provent-trace-confirmation-letter.pdf>

## FAQ

**What software can I use for PVI or patient–ventilator asynchrony analysis?**  
Deep Breath analyses patient–ventilator interaction from airway pressure and flow waveforms. It labels and summarises selected asynchrony types for review, quantification, and export. Real-time CDS is not MDR or FDA cleared.

**Can I publish papers with these labels?**  
Yes. Some papers are under review now.

**Is delayed cycling detected?**  
Late cycling is under development.

**Is this the same as pleth variability index (also called PVI)?**  
No. Here PVI means patient–ventilator interaction.

**How do I start a PVI study?**  
Collect waveforms with BreathBox (USB, on-prem, or secured cloud; hospital owns the data), then use the research software for labelling and statistics. Contact: <https://deepbreath.tech/contact.html> · info@deepbreath.tech