Skin breakdown: Risk factors, prevention, and treatment

Respiratory Rate Extraction from Neonatal Near-Infrared Spectroscopy Signals

Background: Near-infrared spectroscopy (NIRS) relative concentration signals contain 'noise' from physiological processes such as respiration and heart rate. Simultaneous assessment of NIRS and respiratory rate (RR) using a single sensor would facilitate a perfectly time-synced assessment of (cerebral) physiology. Our aim was to extract respiratory rate from cerebral NIRS intensity signals in neonates admitted to a neonatal intensive care unit (NICU). Methods: A novel algorithm, NRR (NIRS RR), is developed for extracting RR from NIRS signals recorded from critically ill neonates. In total, 19 measurements were recorded from ten neonates admitted to the NICU with a gestational age and birth weight of 38 ± 5 weeks and 3092 ± 990 g, respectively. We synchronously recorded NIRS and reference RR signals sampled at 100 Hz and 0.5 Hz, respectively. The performance of the NRR algorithm is assessed in terms of the agreement and linear correlation between the reference and extracted RRs, and it is compared statistically with that of two existing methods. Results: The NRR algorithm showed a mean error of 1.1 breaths per minute (BPM), a root mean square error of 3.8 BPM, and Bland-Altman limits of agreement of 6.7 BPM averaged over all measurements. In addition, a linear correlation of 84.5% (p < 0.01) was achieved between the reference and extracted RRs. The statistical analyses confirmed the significant (p < 0.05) outperformance of the NRR algorithm with respect to the existing methods. Conclusions: We showed the possibility of extracting RR from neonatal NIRS in an intensive care environment, which showed high correspondence with the reference RR recorded. Adding the NRR algorithm to a NIRS system provides the opportunity to record synchronously different physiological sources of information about cerebral perfusion and respiration by a single monitoring system. This allows for a concurrent integrated analysis of the impact of breathing (including apnea) on cerebral hemodynamics.

Prevention Strategies for Neonatal Skin Injury in the NICU

PURPOSE

The purpose of this article was to determine specific skin injury prevention interventions for neonates in the NICU.

DESIGN

The design was a systematic review.

SAMPLE

PubMed, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Embase, and Scopus were systematically searched to identify quantitative studies identifying skin injury preventions for neonates in the NICU.

OUTCOMES

The outcomes included skin integrity or skin condition.

RESULTS

Nineteen studies were included in the review. Twelve studies included a randomized design. Barriers were the main interventions for the prevention of pressure injury, medical adhesive skin injury, diaper dermatitis, and general skin condition. The types of barriers included hydrocolloids, polyurethane-based dressings, film-forming skin protectant, or emollients. Nonbarrier interventions included rotation between a mask and nasal continuous positive airway pressure (NCPAP) interfaces, utilization of prescribed guidelines to decrease pressure injuries, and use of a lower concentration of chlorhexidine gluconate as a disinfectant.

Neonatal skin assessments and injuries: Nomenclature, workplace culture and clinical opinions-Method triangulation a qualitative study

AIM AND OBJECTIVE

To explore and establish the language, clinical opinions and workplace culture around neonatal skin injury nomenclature. Specifically, what nomenclature is used to describe, define, identity and communicate neonatal skin injuries including (a) terms, (b) locations, (c) associated risks and (d) mechanical forces.

BACKGROUND

Skin injuries are affirmed or denied based on visual assessment with findings reported by language rather than measurements. However, if language or nomenclature is ambiguous, assessments could be misinterpreted effecting healthcare delivery.

DESIGN

Qualitative enquiry including applied discourse analysis and between-method triangulation, within a larger exploratory mixed-methods study.

METHODS

Data were collected over two years from four sources: literature, documents, interviews/focus groups and free text injury assessments. Data analysis included content analysis, selective coding and thematic analysis. The collective data were further explored using discourse analysis and triangulation to achieve collective conclusions about opinions, emotions, feelings, perceptions and workplace cultures. The COREQ checklist provided structure for the reporting of study methods, analysis and findings.

RESULTS

A total of 427 data points were collected from literature, documentation and two clinical data sources. Data convergence revealed that neonatal skin injuries are described by numerous terms with preferences for "injury," "trauma" or "redness." Injuries occur in over 20 anatomical locations and risks for injuries included hospitalisation, specific treatments and prematurity. Essential medical devices, clinical condition, lack of clinician experience and overactive neonates were uniquely associated risks. There was incongruency between sources. The literature and documents empathise pressure as the primary force related to skin injury, while varied forces were identified within interviews, focus groups and free text injury assessments.

CONCLUSIONS

The variety of unique terms, locations and risks for injury indicate the need for updated neonatal skin injury frameworks. If frameworks and policies continue to be created without the empirical knowledge of neonatal clinicians, misrepresentation of neonatal skin injury locations and risk will continue to dominate the literature.

RELEVANCE TO CLINICAL PRACTICE

The recognition and management of neonatal skin injuries are related to language used to describe assessments in the absence of diagnostic confirmation, which has implications for both the neonate and the healthcare team.

Human respiration monitoring using infrared thermography and artificial intelligence

The respiration rate (RR) is the most vital parameter used for the determination of human health. The most widely adopted techniques, used to monitor the RR are contact in nature and face many drawbacks. This paper reports the use of Infrared Thermography, in reliably monitoring the RR in a contact-less and non-invasive way. A thermal camera is used to monitor the variation in nasal temperature during respiration continuously. Further, the nostrils (region of interest) are tracked during head motion and object occlusion, by implementing a computer vision algorithm that makes use of 'Histogram of oriented gradients' and 'Support vector machine' (SVM). The signal to noise ratio (SNR) of the acquired breathing signals is very low; hence they are subjected to appropriate filtering methods. The filters are compared depending on the performance metrics such as SNR and Mean square error. The breaths per minute are obtained without any manual intervention by implementing the 'Breath detection algorithm' (BDA). This algorithm is implemented on 150 breathing signals and its performance is determined by computing the parameters such as Precision, Sensitivity, Spurious cycle rate, and Missed cycle rate values, obtained as 98.6%, 97.2%, 1.4%, and 2.8% respectively. The parameters obtained from the BDA are fed to the k-Nearest Neighbour (k-NN) and SVM classifiers, that determine whether the human volunteers have abnormal or normal respiration, or have Bradypnea (slow breathing), or Tachypnea (fast breathing). The Validation accuracies obtained are 96.25% and 99.5% with Training accuracies 97.75% and 99.4% for SVM and k-NN classifiers respectively. The Testing accuracies of the completely built SVM and k-NN classifiers are 96% and 99%, respectively. The various performance metrics like Sensitivity, Specificity, Precision, G-mean and F-measure are calculated as well, for every class, for both the classifiers. Finally, the Standard deviation values of the SVM and k-NN classifiers are computed and are obtained as 0.022 and 0.007, respectively. It is observed that the k-NN classifier shows a better performance compared to the SVM classifier. The pattern between the data points fed to the classifiers is viewed by making use of the t-Stochastic Neighbor Embedding algorithm. It is noticed from these plots that the separation between the data points belonging to different classes, improves and shows minimal overlap by increasing the perplexity value and number of iterations.

Noncontact Monitoring of Respiratory Rate in Newborn Infants Using Thermal Imaging

Monitoring of respiratory rate (RR) is very important for patient assessment. In fact, it is considered one of the relevant vital parameters in critical care medicine. Nowadays, standard monitoring relies on obtrusive and invasive techniques, which require adhesive electrodes or sensors to be attached to the patient's body. Unfortunately, these procedures cause stress, pain, and frequently damage the vulnerable skin of preterm infants. This paper presents a "black-box" algorithm for remote monitoring of RR in thermal videos. "Black-box" in this context means that the algorithm does not rely on tracking of specific anatomic landmarks. Instead, it automatically distinguishes regions of interest in the video containing the respiratory signal from those containing only noise. To examine its performance and robustness during physiological (phase A) and pathological scenarios (phase B), a study on 12 healthy volunteers was carried out. After a successful validation on adults, a clinical study on eight newborn infants was conducted. A good agreement between estimated RR and ground truth was achieved. In the study involving adult volunteers, a mean root-mean-square error (RMSE) of ( 0.31 ±0.09) breaths/min and ( 3.27 ±0.72) breaths/min was obtained for phase A and phase B, respectively. In the study involving infants, the mean RMSE hovered around ( 4.15 ±1.44) breaths/min. In brief, this paper demonstrates that infrared thermography might become a clinically relevant alternative for the currently available RR monitoring modalities in neonatal care.

Estimation of respiratory rate from thermal videos of preterm infants

Studies have demonstrated that respiratory rate (RR) is a good predictor of the patient condition as well as an early marker of patient deterioration and physiological distress. However, it is also referred as "the neglected vital parameter". This is mainly due to shortcoming of current monitoring techniques. Moreover, in preterm infants, the removal of adhesive electrodes cause epidermal stripping, skin disruption, and with it pain. This paper proposes a new algorithm for estimation of RR in thermal videos of moderate preterm infants. It uses the temperature modulation around the nostrils over the respiratory cycle to extract this vital parameter. To compensate movement artifacts the approach incorporates a tracking algorithm. In addition, a new reliable and accurate algorithm for robust estimation of local (breath-to-breath) intervals was included. To evaluate the performance of this approach, thermal recordings of four moderate preterm infants were acquired. Results were compared with RR derived from body surface electrocardiography. The results showed an excellent agreement between thermal imaging and gold standard. On average, the relative error between both monitoring techniques was 3.42%. In summary, infrared thermography may be a clinically relevant alternative to conventional sensors, due to its high thermal resolution and outstanding characteristics.

Camera-Based, Non-Contact, Vital-Signs Monitoring Technology May Provide a Way for the Early Prevention of SIDS in Infants

Sudden infant death syndrome (SIDS) is the unexplained death, usually during sleep, of a baby younger than 1-year-old. Even though researchers have discovered some factors that may put babies at extra risk, SIDS remains unpredictable up until now. One hypothesis is that impaired cardiovascular control may play a role in the underlying mechanism of SIDS. A reduction of heart rate variability (HRV) and progressive decrease in heart rate (HR) have been observed in infants who have later succumbed to SIDS. Many clues indicated the heart could be the final weakness in SIDS. Therefore, continuous monitoring of the dynamic changes within the heart may provide a possible preventive strategy of SIDS. Camera-based photoplethysmography was recently demonstrated as a contactless method to determine HR and HRV. This perspective presents a hypothesis that a camera-based, non-contact, vital-sign monitoring technology, which can indicate abnormal changes or a sudden loss of vital signs in a timely manner, may enable a crucial and low-cost means for the early prevention of SIDS in newborn infants.

Non-contact heart rate monitoring utilizing camera photoplethysmography in the neonatal intensive care unit - a pilot study

BACKGROUND

Presently the heart rate is monitored in the Neonatal Intensive Care Unit with contact sensors: electrocardiogram or pulse oximetry. These techniques can cause injuries and infections, particularly in very premature infants with fragile skin. Camera based plethysmography was recently demonstrated in adults as a contactless method to determine heart rate.

AIM

To investigate the feasibility of this technique for NICU patients and identify challenging conditions.

STUDY DESIGN AND PARTICIPANTS

Video recordings using only ambient light were made of 19 infants at two NICUs in California and The Netherlands. Heart rate can be derived from these recordings because each cardiovascular pulse wave induces minute pulsatile skin color changes, invisible to the eye but measurable with a camera.

RESULTS

In all infants the heart beat induced photoplethysmographic signal was strong enough to be measured. Low ambient light level and infant motion prevented successful measurement from time to time.

CONCLUSIONS

Contactless heart rate monitoring by means of a camera using ambient light was demonstrated for the first time in the NICU population and appears feasible. Better hardware and improved algorithms are required to increase robustness.

Implementation of a protocol for the prevention and management of extravasation injuries in the neonatal intensive care patient

AIM

This project sought to determine nurses' understanding and management of infants with intravenous (IV) therapy. There were three specific aims: • To improve identification and management of extravasation injuries in neonates • To ensure management of extravasation injuries in neonates is classified according to IV extravasation staging guidelines • To develop a protocol that outlined actions required to manage extravasation injuries.

METHODS

This project utilised a pre- and post-implementation audit strategy using the Joanna Briggs Institute (JBI) Getting Research into Practice (GRIP) program. This method has been used to improve clinical practice by utilising an audit, feedback and re-audit sequence. The project was implemented in four stages over a 7-month period from 21 October 2009 to 30 May 2010.

RESULTS

Initially, there was poor compliance with all four criteria, ranging from zero to 63%. The GRIP phase of the project identified five barriers which were addressed throughout this project. These related to education of staff and the development of a protocol for the prevention and management of extravasation injuries in the neonatal population. Following implementation of best practice, the second audit showed a marked improvement in all four criteria, ranging from 70 to 100% compliance.

CONCLUSIONS

Overall, this project has led to improvements in clinical practice in line with current evidence. This has resulted in enhanced awareness of the risks associated with IV therapy and of measures to prevent an injury occurring within this clinical setting.