Capillary Refill Technology to Enhance the Accuracy of Peripheral Perfusion Evaluation in Sepsis.

Background: Monitoring of capillary refill time (CRT) is a common bedside assessment used to ascertain peripheral perfusion in a patient for a vast array of conditions. The literature has shown that a change in CRT can be used to recognize life-threatening conditions that cause decreased perfusion, such as sepsis, and aid in resuscitation. The current practice for calculating CRT invites subjectivity and produces a highly variable result. Innovative technology may be able to standardize this process and provide a reliable and accurate value for use in diagnostics and treatment. This study aimed to assess a new technology (DCR by ProMedix Inc.) for rapid, bedside, and noninvasive detection of CRT. Methods: This was a secondary analysis of a prospective observational study evaluating the accuracy of new technology towards CRT-guided diagnosis of sepsis. It was carried out in the adult emergency departments (ED) of an academic tertiary care medical center. Patients seeking care in the ED were determined eligible if they were > 18 years in age and exhibited chief complaints suggestive of possible sepsis. The CRT produced by the technology was compared to the gold standard manual waveform assessment. Results: 218 consecutive subject enrollments were included and multiple measurements were made on each patient. Data with irregular waveforms were excluded. A total of 692 waveforms were evaluated for CRT values by a pair of trained PhD biomedical engineers. The average age of the cohort was 50.62 and 51.4% female. Results showed a Pearson correlation coefficient of 0.91 for the device CRT compared to the CRT gold standard. The Pearson correlation coefficient for the two independent engineering review of the waveform data was 0.98. This device produces accurate, consistent results and eliminates the subjectivity of CRT measurements that is in practice currently.

Variability of Capillary Refill Time among Physician Measurements.

BACKGROUND: The assessment of capillary refill time (CRT) is a common physical examination technique. However, despite its importance and its widespread use, there is little standardization, which can lead to inaccurate assessments. OBJECTIVE: In this article, we assessed how different physicians estimate CRT. We hypothesized that when different physicians are presented with the same recordings of CRT, clinicians will, on average, provide different CRT estimates. METHODS: Using recordings of different fingertip compressions, physicians assessed and documented when capillary refill had returned to normal. Videos were recorded of the fingertips only, with no other identifying markers or subject characteristics provided. Videos were shown at one-quarter speed to allow time for recognition and response to the capillary refill. The primary outcome was physician estimates of CRT for each video recording. RESULTS: An analysis of variance regression revealed significant differences in physician estimates of CRT when examining the same CRT videos from 34 subjects. Further regression analyses reveal the importance of controlling for the physician that is examining the patient when predicting a patient's CRT. CONCLUSIONS: Results indicate that some physicians gave, on average, slower CRT estimates, whereas others gave, on average, faster CRT estimates. Objective approaches and innovations in assessment of capillary refill have the potential to increase the diagnostic accuracy of this important clinical examination finding.

Heart Rate Variability Duration: Expanding the Ability of Wearable Technology to Improve Outpatient Monitoring?

Heart rate variability (HRV) evaluates beat-to-beat interval (BBI) differences and is a suggested marker of the autonomic nervous system with diagnostic/monitoring capabilities in mental health; especially parasympathetic measures. The standard duration for short-term HRV analysis ranges from 24 h down to 5-min. However, wearable technology, mainly wrist devices, have large amounts of motion at times resulting in need for shorter duration of monitoring. The objective of this study was to evaluate the correlation between 1 and 5 min segments of continuous HRV data collected simultaneously on the same patient. Subjects wore a patch electrocardiograph (Cardea Solo, Inc.) over a 1-7 day period. For every consecutive hour the patch was worn, we selected a 5-min, artifact-free electrocardiogram segment. HRV metric calculation was performed to the entire 5-min segment and the first 1-min from this same 5-min segment. There were 492 h of electrocardiogram data collected allowing calculation of 492 5 min and 1 min segments. 1 min segments of data showed good correlation to 5 min segments in both time and frequency domains: root mean square of successive difference (RMSSD) (R = 0.92), high frequency component (HF) (R = 0.90), low frequency component (LF) (R = 0.71), and standard deviation of NN intervals (SDNN) (R = 0.63). Mental health research focused on parasympathetic HRV metrics, HF and RMSSD, may be accomplished through smaller time windows of recording, making wearable technology possible for monitoring.

Heart Rate Variability Analysis: How Much Artifact Can We Remove?

OBJECTIVE: Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology. METHODS: This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject's HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered. RESULTS: Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed. CONCLUSION: Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.