Contactless monitoring of respiratory rate (RR) and heart rate (HR) in non-acuity settings: a clinical validity study

Quantification of facial cues for acute illness: a systematic scoping review

IMPORTANCE

The patient's face provides healthcare professionals with important information about the patient's general appearance and clinical condition.

OBJECTIVE

The primary aim of this review is to identify patients' facial cues that healthcare providers can use at the bedside to monitor the clinical condition of acutely ill patients.

EVIDENCE REVIEW

Studies about facial cues for acute illness were systematically searched in PubMed, Embase, Cochrane, and Cumulative Index to Nursing & Allied Health (CINAHL) databases. Studies on vital signs, pain, psychiatric illnesses, animal studies, qualitative studies, case reports, and systematic reviews were excluded. Acute illness was defined as any life-threatening condition or condition that required immediate intervention to prevent serious morbidity, permanent disability, or mortality. An overview of all identified facial cues was created.

FINDINGS

In total, 35 different facial cues were identified in 13 studies. A total of 21 were related to facial appearance, with the most frequently reported cues being closed eyes (2 studies), pale lips (2 studies), parted lips (3 studies), droopy mouth (3 studies), and paler skin tone (2 studies). In addition, 14 facial expression features were identified, characterized primarily by more sad, less happy, and less surprised. Most cues have only been described in a single study without external validation, limiting the generalizability of definitions of these cues and their clinical applicability.

CONCLUSIONS AND RELEVANCE

This systematic scoping review identified 35 facial cues associated with acute illness in patients in the hospital, highlighting the potential of facial observation to enhance clinical assessments. However, the lack of standardization limits applicability in healthcare. Future research should refine the setting of acute illness, develop diverse datasets, and validate the predictive value of facial cues across various populations.

The Challenges and Opportunities in Pediatric Medical Device Innovation: Monitoring Devices

BACKGROUND

Medical device innovation and development for pediatric care lags behind that of adults. With higher technical risks, challenges in accumulating data, smaller market sizes, and limited return on investment, there is less incentive for pediatric device development. Consequently, translation of medical devices specifically designed to improve pediatric care is limited. However, a changing landscape and novel programs may support an expansion of pediatric device development.

METHODS

A keyword-based search was conducted in PubMed; market databases and Food and Drug Administration guidance documents were also used in this review. A total of 18,017 articles underwent a title and abstract review, and 190 articles were reviewed by the expert authors.

RESULTS

Collectively, challenges with evidence generation and business and technical forces have disincentivized innovators from pursuing pediatric device innovation. Innovative programs such as the System of Hospitals for Innovation in Pediatrics-Medical Devices and the existing Pediatric Device Consortia, if strategically leveraged, may foster a robust national pediatric device innovation ecosystem.

CONCLUSIONS

Whereas there is a higher opportunity cost for pediatric device development, leveraging the programs and incentives available in a "pediatric-first" approach may provide effective paths to larger total addressable markets for innovators.

Detection of hypertension using a target spectral camera: a prospective clinical study

Hypertension is a significant contributor to premature mortality, and the regular monitoring of blood pressure (BP) enables the early detection of hypertension and cardiovascular disease. There is an urgent need for the development of highly accurate cuffless BP devices. We examined BP measurements based on a target spectral camera's recordings and evaluated their accuracy. Images of 215 adults' palms and faces were recorded, and BP was measured. The camera captured RGB wavelength data at 640 × 480 pixels and 150 frames per second (fps). These recordings were analyzed to extract pulse transit time (PTT) values between the face and palm, a key parameter for estimating BP. Continuous BP measurements were taken using a CNAPmonitor500 for validation. Three frequency wavelengths were measured from video images. A machine learning model was constructed to determine hypertension, defined as a systolic BP of 130 mmHg or higher or a diastolic BP of 80 mmHg or higher, using the visualized data. The discrimination between hypertension and normal BP was 95.0% accurate within 30 s and 90.3% within 5 s, based on the captured images. The results of heartbeat-by-heartbeat analyses can be used to determine hypertension based on only one second of camera footage or one heartbeat. The data extracted from a video recorded by a target spectral camera enabled accurate hypertension diagnoses, suggesting the potential for simplified BP monitoring.

Agreement between Vital Signs Measured Using Mat-Type Noncontact Sensors and Those from Conventional Clinical Assessment

Vital signs are crucial for assessing the condition of a patient and detecting early symptom deterioration. Noncontact sensor technology has been developed to take vital measurements with minimal burden. This study evaluated the accuracy of a mat-type noncontact sensor in measuring respiratory and pulse rates in patients with cardiovascular diseases compared to conventional methods. Forty-eight hospitalized patients were included; a mat-type sensor was used to measure their respiratory and pulse rates during bed rest. Differences between mat-type sensors and conventional methods were assessed using the Bland-Altman analysis. The mean difference in respiratory rate was 1.9 breaths/min (limits of agreement (LOA): -4.5 to 8.3 breaths/min), and proportional bias existed with significance (r = 0.63, p < 0.05). For pulse rate, the mean difference was -2.0 beats/min (LOA: -23.0 to 19.0 beats/min) when compared to blood pressure devices and 0.01 beats/min (LOA: -11.4 to 11.4 beats/min) when compared to 24-h Holter electrocardiography. The proportional bias was significant for both comparisons (r = 0.49, p < 0.05; r = 0.52, p < 0.05). These were considered clinically acceptable because there was no tendency to misjudge abnormal values as normal. The mat-type noncontact sensor demonstrated sufficient accuracy to serve as an alternative to conventional assessments, providing long-term monitoring of vital signs in clinical settings.

Challenges and prospects of visual contactless physiological monitoring in clinical study

The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.

Continuous Remote Monitoring in Moderate and Severe COVID-19 Patients

Background COVID-19 steadily built up the pressure on healthcare systems worldwide, creating the need for novel methods to alleviate the burden. Continuous remote monitoring of vital parameters reduces morbidity and mortality in hospitals by providing real-time disease data that can be analyzed through web portals. It enables healthcare workers to identify which patients require prompt administration of healthcare. Patients remain under the purview of their doctors and can be notified early if there are any deteriorations in the parameters being monitored. Aims To evaluate the use of remote monitoring in moderate and severe COVID-19 patients and to correlate the Dozee Early Warning Score (DEWS) with severity and outcome in moderate and severe COVID-19 patients. Materials and methods We conducted a prospective study on adult (>18 years old) moderate and severe COVID-19 patients during the second wave of COVID-19. The vitals of the subjects were continuously monitored using Dozee, a contactless remote patient monitoring system enabled with DEWS that reflects the overall patient condition based on respiratory rate (RR), heart rate (HR), and oxygen saturation (SpO2). We assessed the correlation of DEWS with patients' clinical outcomes: deteriorated or recovered. Results Thirty-nine COVID-19 patients were recruited for the study, of whom 29 were discharged after recovery and 10 deteriorated and died. Respiratory rate trend, respiratory rate DEWS, SpO2 DEWS, and total DEWS showed a significant reduction in recovered patients, while the same parameters showed a significant increase followed by consistently high scores in patients who deteriorated and died due to the disease. Total DEWS was proportional to the risk of mortality in a patient. Conclusion We concluded that continuous vitals monitoring and the resulting DEWS in moderate and severe COVID-19 patients were indicators of their improvement or deterioration. DEWS uses continuous remote monitoring of routinely collected vitals (HR, RR, and SpO2) to serve as a predictor of patient outcome.