Wheezing Characteristics and Predicting Reactivity to Inhaled ß2-Agonist in Children for Home Medical Care.

Background: Given that wheezing is treated with inhaled ß2-agonists, their effect should be reviewed before the condition becomes severe; however, few methods can currently predict reactivity to inhaled ß2-agonists. We investigated whether preinhalation wheezing characteristics identified by lung sound analysis can predict reactivity to inhaled ß2-agonists. Methods: In 202 children aged 10-153 months, wheezing was identified by auscultation. Lung sounds were recorded for 30 s in the chest region on the chest wall during tidal breathing. We analyzed the wheezing before and after ß2-agonist inhalation. Wheezing was displayed as horizontal bars of intensity defined as a wheeze power band, and the wheezing characteristics (number, frequency, and maximum intensity frequency) were evaluated by lung sound analysis. The participants were divided into two groups: non-disappears (wheezing did not disappear after inhalation) and disappears (wheezing disappeared after inhalation). Wheezing characteristics before ß2-agonist inhalation were compared between the two groups. The characteristics of wheezing were not affected by body size. The number of wheeze power bands of the non-responder group was significantly higher than those of the responder group (P < 0.001). The number of wheeze power bands was a predictor of reactivity to inhaled ß2-agonists, with a cutoff of 11.1. The 95% confidence intervals of sensitivity, specificity, and positive and negative predictive values were 88.8, 42, 44, and 81.1% (P < 0.001), respectively. Conclusions: The number of preinhalation wheeze power bands shown by lung sound analysis was a useful indicator before treatment. This indicator could be a beneficial index for managing wheezing in young children.

Wheeze Recognition Algorithm for Remote Medical Care Device in Children: Validation Study.

BACKGROUND: Since 2020, peoples' lifestyles have been largely changed due to the COVID-19 pandemic worldwide. In the medical field, although many patients prefer remote medical care, this prevents the physician from examining the patient directly; thus, it is important for patients to accurately convey their condition to the physician. Accordingly, remote medical care should be implemented and adaptable home medical devices are required. However, only a few highly accurate home medical devices are available for automatic wheeze detection as an exacerbation sign. OBJECTIVE: We developed a new handy home medical device with an automatic wheeze recognition algorithm, which is available for clinical use in noisy environments such as a pediatric consultation room or at home. Moreover, the examination time is only 30 seconds, since young children cannot endure a long examination time without crying or moving. The aim of this study was to validate the developed automatic wheeze recognition algorithm as a clinical medical device in children at different institutions. METHODS: A total of 374 children aged 4-107 months in pediatric consultation rooms of 10 institutions were enrolled in this study. All participants aged ≥6 years were diagnosed with bronchial asthma and patients ≤5 years had reported at least three episodes of wheezes. Wheezes were detected by auscultation with a stethoscope and recorded for 30 seconds using the wheeze recognition algorithm device (HWZ-1000T) developed based on wheeze characteristics following the Computerized Respiratory Sound Analysis guideline, where the dominant frequency and duration of a wheeze were >100 Hz and >100 ms, respectively. Files containing recorded lung sounds were assessed by each specialist physician and divided into two groups: 177 designated as "wheeze" files and 197 as "no-wheeze" files. Wheeze recognitions were compared between specialist physicians who recorded lung sounds and those recorded using the wheeze recognition algorithm. We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files, and evaluated the influence of age and sex on the wheeze detection sensitivity. RESULTS: Detection of wheezes was not influenced by age and sex. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 96.6%, 98.5%, 98.3%, and 97.0%, respectively. Wheezes were automatically detected, and heartbeat sounds, voices, and crying were automatically identified as no-wheeze sounds by the wheeze recognition algorithm. CONCLUSIONS: The wheeze recognition algorithm was verified to identify wheezing with high accuracy; therefore, it might be useful in the practical implementation of asthma management at home. Only a few home medical devices are available for automatic wheeze detection. The wheeze recognition algorithm was verified to identify wheezing with high accuracy and will be useful for wheezing management at home and in remote medical care.

A wheeze recognition algorithm for practical implementation in children.

BACKGROUND: The detection of wheezes as an exacerbation sign is important in certain respiratory diseases. However, few highly accurate clinical methods are available for automatic detection of wheezes in children. This study aimed to develop a wheeze detection algorithm for practical implementation in children. METHODS: A wheeze recognition algorithm was developed based on wheezes features following the Computerized Respiratory Sound Analysis guidelines. Wheezes can be detected by auscultation with a stethoscope and using an automatic computerized lung sound analysis. Lung sounds were recorded for 30 s in 214 children aged 2 months to 12 years and 11 months in a pediatric consultation room. Files containing recorded lung sounds were assessed by two specialist physicians and divided into two groups: 65 were designated as "wheeze" files, and 149 were designated as "no-wheeze" files. All lung sound judgments were agreed between two specialist physicians. We compared wheeze recognition between the specialist physicians and using the wheeze recognition algorithm and calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files to evaluate the influence of age on the wheeze detection sensitivity. RESULTS: The detection of wheezes was not influenced by age. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 100%, 95.7%, 90.3%, and 100%, respectively. CONCLUSIONS: The wheeze recognition algorithm could identify wheezes in sound files and therefore may be useful in the practical implementation of respiratory illness management at home using properly developed devices.

Wheeze sound characteristics are associated with nighttime sleep disturbances in younger children.

BACKGROUND: Wheezing is a typical symptom of respiratory conditions. Few objective methods are available for predicting sleep disturbance in young children with wheezing. OBJECTIVE: We investigated whether wheezing characteristics, detected by lung-sound analysis, were associated with risk of sleep disturbance. METHODS: We recorded the lung sounds of 66 young children (4-59 months) every morning, for the entire duration of a wheezing episode. On lung-sound analysis, wheezing was displayed as horizontal bars of intensity with corresponding sharp peaks of power. The sharp peak of power was defined as a wheeze band. Wheezing characteristics (e.g., number, frequency, duration, and frequency of maximum intensity of wheeze bands) were analyzed using lung-sound analysis. Patients were divided into 3 groups based on sleep disturbance on the first night after wheezing was recorded: mild group (no sleep disturbance and disappearance of wheezing within 2 days), moderate group (no sleep disturbance but disappearance of wheezing after 3 or more days), and severe group (sleep disturbance and disappearance of wheezing after 3 or more days). Wheezing characteristics on the first morning were compared among the 3 groups based on sleep disturbance on the first night. RESULTS: The highest frequency, the frequency of maximum intensity, and the number of wheeze bands per 30 seconds were significantly higher in the severe group than in the mild group (p < 0.005, p < 0.005, p < 0.001, respectively). The number of wheeze bands per 30 seconds was a predictor of nighttime sleep disturbance, with a cutoff value of 11.1. The sensitivity, specificity, and positive- and negative-predictive values were 100%, 65%, 32%, and 100% (p < 0.001), respectively, with an area under the curve of 0.86 ± 0.05. CONCLUSIONS: The number of wheeze bands per 30 seconds on lung-sound analysis was a useful indicator of risk of prolonged exacerbation.

Effectiveness of a Self-monitoring Device for Urinary Sodium-to-Potassium Ratio on Dietary Improvement in Free-Living Adults: a Randomized Controlled Trial.

BACKGROUND: Reducing the urinary sodium-to-potassium ratio is important for reducing both blood pressure and risk of cardiovascular disease. Among free-living Japanese individuals, we carried out a randomized trial to clarify the effect of lifestyle modification for lowering urinary sodium-to-potassium ratio using a self-monitoring device. METHODS: This was an open, prospective, parallel randomized, controlled trial. Ninety-two individuals were recruited from Japanese volunteers. Participants were randomly allocated into intervention and control groups. A month-long dietary intervention on self-monitoring urinary sodium-to-potassium ratio was carried out using monitors (HEU-001F, OMRON Healthcare Co., Ltd., Kyoto, Japan). All participants had brief dietary education and received a leaflet as usual care. Monitors were handed out to the intervention group, but not to the control group. The intervention group was asked to measure at least one spot urine sodium-to-potassium ratio daily, and advised to lower their sodium-to-potassium ratio toward the target of less than 1. Outcomes included changes in 24-hour urinary sodium-to-potassium ratio, sodium excretion, potassium excretion, blood pressure, and body weight in both groups. RESULTS: Mean measurement frequency of monitoring was 2.8 times/day during the intervention. Changes in urinary sodium-to-potassium ratio were -0.55 in the intervention group and -0.06 in the control group (P = 0.088); respective sodium excretion changes were -18.5 mmol/24 hours and -8.7 mmol/24 hours (P = 0.528); and corresponding potassium excretion was 2.6 mmol/24 hours and -1.5 mmol/24 hours (P = 0.300). No significant reductions were observed in either blood pressure or body weight after the intervention. CONCLUSIONS: Providing the device to self-monitor a sodium-to-potassium ratio did not achieve the targeted reduction of the ratio in "pure self-management" settings, indicating further needs to study an effective method to enhance the synergetic effect of dietary programs and self-monitoring practice to achieve the reduction. However, we cannot deny the possibility of reducing sodium-to-potassium ratio using a self-monitoring device.

Six random specimens of daytime casual urine on different days are sufficient to estimate daily sodium/potassium ratio in comparison to 7-day 24-h urine collections.

The objective of this study was to determine the optimal number and type of casual (spot) urine specimens required to estimate an individual's urinary sodium/potassium (Na/K) ratio. A total of 48 participants, 25 men and 23 women, aged between 25 and 59 years, was recruited from healthy volunteers. The Na/K ratio in each casual urine and 7-day 24-h urine sample was measured. Correlation analysis and the quality of agreement by the Bland and Altman method between casual urine and 24-h urine were analyzed. The mean Na/K ratio of 7-day 24-h urine was 4.3. The mean Na/K ratio of six random specimens of daytime (collected between 09 and 17 hours) casual urine correlated most strongly with the Na/K ratio of 7-day 24-h urine (r=0.87). The bias for the mean Na/K ratio between 7-day 24-h urine and daytime casual urine was almost negligible (0.03), and the quality of agreement for the mean of the six random, daytime casual urine specimens on different days was similar to that of the 2-day 24-h urine samples for estimating 7-day 24-h values. Our findings show that the mean Na/K ratio of six random daytime casual urine specimens on different days was a good substitute for the 2-day 24-h urine Na/K ratio.

Determination of the activities of glutamic oxaloacetic transaminase and glutamic pyruvic transaminase in a microfluidic system.

A microfluidic system for the analysis of the activities of glutamic-oxaloacetic transaminase (GOT) and glutamic-pyruvic transaminase (GPT) was fabricated. The device consists of a glass chip with a micro-electrochemical L-glutamate sensor and a polydimethylsiloxane (PDMS) sheet with a Y-shaped micro-flow channel. A sample solution and a substrate solution for the enzymes were introduced from two injection ports at the end of the flow channel. When the flows were stopped, substrates in a solution mixed immediately with either of the enzymes by diffusion in a mixing channel. L-glutamate produced by the enzymatic reaction of GOT or GPT in the flow channel was detected by using the L-glutamate sensor. A distinct current increase was observed immediately after mixing, and the initial slope of the response curve varied in proportion to the activity of GOT or GPT. The relation between the slope of the response curve and the enzyme activity was linear between 7 and 228 U l-1 for GOT and 9 and 250 U l-1 for GPT. The quality of the response curve was improved with an increase in the channel height. The measurement based on the rate analysis in the micro-flow channel facilitated the reduction of the influence of interferents. The influence of the viscosity of the sample solution was also checked for the analysis of real samples. The determination of the enzyme activities was also conducted in a system with micropumps fabricated for a sample injection. Two solutions could be mixed in the mixing channel, and the activity of the enzymes could be measured as in the experiments using microsyringe pumps.

Electrochemical determination of gamma-glutamyl transpeptidase activity and its application to a miniaturized analysis system.

A novel method to determine the activity of gamma-glutamyl transpeptidase (gamma-GTP) was developed. gamma-l-glutamyl-l-glutamate and glycyl-glycine were used as the substrates for gamma-GTP. l-glutamate produced by the enzymatic reaction was measured with an amperometric l-glutamate sensor. Following the mixing of the substrate solution and a sample solution, the current generated on the l-glutamate sensor continued to increase at a constant rate. The method was used to construct a miniaturized analysis system for the determination of gamma-GTP activity. The system consisted of the l-glutamate sensor formed on a glass substrate and a polydimethylsiloxane (PDMS) flow channel. Since the l-glutamate concentration in the solution increased as the solution was mobilized through the flow channel, a constant current increase was observed. The relation between the slope of the response curve and the activity of gamma-GTP was linear between 35 U l(-1) and 659 U l(-1). The rate analysis in the micro flow channel minimized the influence of interferents. The reproducibility of the output of the micro system was found to be good with a relative standard deviation (R.S.D.) of 5.6% at 659 U l(-1). The activities of gamma-GTP in human serum samples were also determined and compared with values obtained with a conventional spectroscopic method. The values obtained by the two methods were consistent with a correlation coefficient of 0.953.