Compensatory Reserve Measurements from 20 Participants in a Simulated Hemorrhage Study

A human research protocol involving 20 participants generated data for evaluating a real-time compensatory reserve measurement (CRM) algorithm. The CRM_RT application, developed in Python, processed photoplethysmography data streamed from a wearable pulse oximeter during simulated hypovolemia in a lower-body negative pressure chamber. The study, authored by Ryan Ortiz and last updated in April 2026, reports an average early prediction time of 18.3 minutes for hypovolemic decompensation.

Use Cases

• Evaluating real-time algorithm performance for predicting hemorrhagic shock based on compensatory reserve measurements.
• Analyzing photoplethysmography waveform data for trends in physiological compensation during simulated blood loss.
• Comparing the predictive capability of a new metric (CRM) against traditional vital signs for triage applications.
• Developing or validating software applications for real-time biomedical data capture and analysis.

Strengths

• Data originates from a controlled human research protocol with 20 participants, reviewed by an Institutional Review Board.
• The real-time application achieved a specific median performance error of -0.95% and an average early prediction time of 18.3 minutes.
• Data was collected using a specified, commercially available wearable sensor (Masimo MightySat Rx).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 672.1 KB, indicating limited scope.

Provenance

Source

figshare, author Ryan Ortiz.

Collection Method

Data collected from 20 participants undergoing simulated hypovolemia in a lower-body negative pressure chamber, with physiological data streamed via Bluetooth from a pulse oximeter.

Time Range

The temporal coverage of the underlying experiment is not specified.

Freshness

Last updated 2026-04-15 05:45:50; freshness should be verified.

Geography

The geographic location of the study is not specified.