Cough Counts Research Articles

Longitudinal Cough Frequency Monitoring in Persistent Coughers: Daily Variability and Predictability

PurposeWe determined the cough counts and their variability in subjects with persistent cough for 30 days.MethodsThe Hyfe cough tracker app uses the mobile phone microphone to monitor sounds and recognizes cough with artificial intelligence-enabled algorithms. We analyzed the daily cough counts including the daily predictability rates of 97 individuals who monitored their coughs over 30 days and had a daily cough rate of at least 5 coughs per hour.ResultsThe mean (median) daily cough rates varied from 6.5 to 182 (6.2 to 160) coughs per hour, with standard deviations (interquartile ranges) varying from 0.99 to 124 (1.30 to 207) coughs per hour among all subjects. There was a positive association between cough rate and variability, as subjects with higher mean cough rates (OLS) have larger standard deviations. The accuracy of any given day for predicting all 30 days is the One Day Predictability for that day, defined as the percentage of days when cough frequencies fall within that day’s 95% confidence interval. Overall Predictability was the mean of the 30-One Day Predictability percentages and ranged from 95% (best predictability) to 30% (least predictability).ConclusionThere is substantial within-day and day-to-day variability for each subject with persistent cough recorded over 30 days. If confirmed in future studies, the clinical significance and the impact on the use of cough counts as a primary end-point of cough interventions of this variability need to be assessed.

The Effect of Long-Term Azithromycin on Objective and Subjective Cough in Chronic Respiratory Disease: A Systematic Review and Meta-analysis of Randomised Controlled Trials and Noncomparative Studies

IntroductionAzithromycin is an effective treatment for various respiratory conditions but its effect on cough is poorly understood. We synthesised data from randomised controlled trials (RCTs) and noncomparative studies (NCT) examining its effect on objective and subjective cough.MethodsAfter prospective registration on PROSPERO, we searched MEDLINE, EMBASE, and CENTRAL for both RCTs and NCT trials examining the effect azithromycin on cough in respiratory disease.ResultsWe identified 1240 studies of which 6 (4 RCTs and 2 NCT studies) were included in the meta-analysis, with a total of 275 patients. Azithromycin was associated with significant improvement in Leicester Cough Questionnaire scores at follow-up when compared to baseline scores (SMD = 0.62 [95% CI 0.12 to 1.12], p = 0.01). However, when only RCTs were synthesised, no significant effect was observed (SMD = 0.12 [95% CI − 0.36 to 0.60], p = 0.62). There was no significant reduction in cough severity VAS score (SMD = − 0.39 [95% CI − 0.92 to 0.14], p = 0.15). There was no significant reduction in objective cough count (SMD = − 0.41 [95% CI − 1.04 to 0.32], p = 0.09).ConclusionAzithromycin therapy improves cough-related quality of life in various chronic respiratory diseases; however, there was no significant effect on cough outcomes when only data from RCTs were synthesised. We believe that to accurately identify which patients whose cough would benefit from azithromycin a large-scale clinical trial of patients with a broad spectrum of respiratory diseases, with sufficiently severe cough, should be undertaken with subgroup analysis of individual disease areas.

Inhaled alkaline hypertonic divalent salts reduce refractory chronic cough frequency.

Treatment of chronic cough remains a challenge. We hypothesised that inhaled alkaline hypertonic divalent salts (alkaline HDS) might provide relief for refractory chronic cough by laryngeal and tracheal hydration. We conducted an exploratory, single-blinded, nasal saline-controlled study in 12 refractory chronic cough patients to examine cough suppression efficacy of an alkaline HDS composition (SC001) at pH 8 or pH 9 administered by nasal inhalation. As control, we used nasal saline with the same hand-held pump spray aerosol device. Each subject was monitored continuously using a digital cough monitor watch for 1 week of baseline, 1 week of control treatment and 1 week of active treatment. Baseline daily cough rates ranged from 4 to 34 coughs·h-1 with mean visual analogue score 65±17 pre- and post-baseline testing. Control-adjusted efficacy of cough rate reduction ranged from 15% (p=0.015) (from Day 1) to 23% (p=0.002) (from Day 3). Control-adjusted efficacy was highest with SC001 pH 9 (n=5), ranging from 25% (p=0.03) (from Day 1) to 35% (p=0.02) (from Day 3), and lowest for SC001 pH 8 (n=7), ranging from 9% (p=0.08) (from Day 1) to 16% (p=0.02) (from Day 3). Hourly cough counts and visual analogue score correlated for baseline (r=0.254, p=0.02) and control (r=0.299, p=0.007) monitoring weeks. Treatment improved this correlation (r=0.434, p=0.00006). No adverse events were reported. Alkaline (pH 9) HDS aerosol is a promising treatment for refractory chronic cough and should be further evaluated.

Pharmacodynamics, pharmacokinetics and CYP3A4 interaction potential of the selective P2X3 receptor antagonist filapixant: A randomized multiple ascending-dose study in healthy young men.

We report on investigations exploring the P2X3-receptor antagonist filapixant's effect on taste perception and cough-reflex sensitivity and describe its pharmacokinetics, including its CYP3A4-interaction potential. In a randomized, placebo-controlled, double-blind study, 3 × 12 healthy men (18-45 years) were assigned (3:1) to filapixant (20, 80 or 250 mg by mouth) or placebo twice daily over 2weeks. A single dose of midazolam (1mg), a CYP3A4 substrate, was administered with and without filapixant. Assessments included a taste-strips test, a taste questionnaire, cough challenge with adenosine triphosphate, adverse event reports and standard safety assessments. Taste disturbances were observed mainly in the 250-mg group: six of nine participants (67%) in this group reported hypo- or dysgeusia in the questionnaire; eight participants (89%) reported taste-related adverse events. Five participants (56%) had a decrease in overall taste-strips-test scores ≥2 points (point estimate -1.1 points, 90% confidence interval [-3.3; 1.1]). Cough counts increased with adenosine triphosphate concentration but without major differences between treatments. Filapixant exposure increased proportionally to dose. Co-administration of filapixant had no clinically relevant effect on midazolam pharmacokinetics. Area under the concentration-time curve ratios and 90% confidence intervals were within 80-125%. No serious or severe adverse events were reported. Overall, filapixant was safe and well tolerated, apart from mild, transient taste disturbances. Such disturbances occurred more frequently than expected based on (in vitro) receptor-selectivity data, suggesting that other factors than P2X3:P2X2/3 selectivity might also play an important role in this context. The cough-challenge test showed no clear treatment effect. Filapixant has no clinically relevant CYP3A4 interaction potential.

Assessment of Lung Health Status by Analyzing Cough Sound Using Swaasa Artificial Intelligence Technology

Introduction: The crucial function of the respiratory system is the facilitation of blood gas exchange process. Spirometry is the diagnostic tool to detect and differentiate obstructive and restrictive respiratory diseases that impair this vital function. The need for a clinical setup, technical expertise, and patient compliance are certain limitations for utilizing spirometry in remote areas. Swaasa® artificial intelligence (AI) platform, which has a Class B manufacturing license from India’s Central Drugs Standard Control Organization, is a validated “software as a medical device,” which aids in screening and diagnosis of respiratory diseases, unbounded by location, equipment and technical expertise. Objectives: The objectives of the study were to determine the “lung health index” and the “pattern of lung health” conditions among nursing students. Materials and Methods: A cross-sectional study was conducted utilizing the Swaasa® AI platform among students of a nursing college in Central Karnataka in June 2023. Based on audiometric analysis of cough sounds, parameters such as underlying respiratory condition, cough count values, and respiratory symptoms experienced, ‘Swaasa’ AI platform derives pattern of respiratory condition (normal/obstructive/restrictive/mixed), ‘Lung Health Index,’ and presence or absence of lung health risk. Data collected in the Swaasa app were downloaded and analyzed using SPSS.v. 20. Results: Lung health risk was present in 58.2% of participants. Abnormal lung pattern was noted in 21.3% of participants (obstructive: 14.8%, restrictive: 2.5%, and mixed pattern: 4.1%). Conclusion: The Swaasa AI platform was utilized in this study to self-test lung health in real time without the need for a clinical setting. A high lung health index was found among 18% of participants. Studies adopting such cough sound analysis involving a larger population from wider geographic areas have to be conducted to detect and monitor respiratory diseases to increase its usage among the medical community in everyday clinical practice and also in remote areas.

Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors

There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development. For cough detection, healthy individuals wore an ANNE One chest sensor during scripted activity. The final performance of the algorithm achieved an F-1 score of 83.3% in twenty-seven healthy subjects during biomarker validation. In the COVID-19 detection algorithm, individuals at high-risk for developing COVID-19 because of recent exposures received ANNE One sensors and completed daily symptom surveys. An algorithm analyzing vital parameters (heart rate, respiratory rate, cough count, etc.) for early COVID-19 detection was developed. The COVID-19 detection algorithm exhibited a sensitivity of 0.47 and specificity of 0.72 for detecting COVID-19 in 325 individuals with recent exposures. Participants demonstrated high adherence (≥ 4 days of wear per week). ANNE One shows promise for detection of COVID-19. Inclusion of respiratory biomarkers (e.g., cough count) enhanced the algorithm's predictive ability. These findings highlight the potential value of wearable devices in early disease detection and monitoring.

Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Morphine for treatment of cough in idiopathic pulmonary fibrosis (PACIFY COUGH): a prospective, multicentre, randomised, double-blind, placebo-controlled, two-way crossover trial

Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease, with most patients reporting cough. Currently, there are no proven treatments. We examined the use of low dose controlled-release morphine compared with placebo as an antitussive therapy in individuals with idiopathic pulmonary fibrosis. The PACIFY COUGH study is a phase 2, multicentre, randomised, double-blind, placebo-controlled, two-way crossover trial done in three specialist centres in the UK. Eligible patients aged 40-90 years had a diagnosis of idiopathic pulmonary fibrosis within 5 years, self-reported cough (lasting >8 weeks), and a cough visual analogue scale (VAS) score of 30 mm or higher. Patients were randomly assigned (1:1) to placebo twice daily or controlled-release morphine 5 mg orally twice daily for 14 days followed by crossover after a 7-day washout period. Patients were randomised sequentially to a sequence group defining the order in which morphine and placebo were to be given, according to a computer-generated schedule. Patients, investigators, study nurses, and pharmacy personnel were masked to treatment allocation. The primary endpoint was percentage change in objective awake cough frequency (coughs per h) from baseline as assessed by objective digital cough monitoring at day 14 of treatment in the intention-to-treat population, which included all randomised participants. Safety data were summarised for all patients who took at least one study drug and did not withdraw consent. This study was registered at ClinicalTrials.gov, NCT04429516, and has been completed. Between Dec 17, 2020, and March 21, 2023, 47 participants were assessed for eligibility and 44 were enrolled and randomly allocated to treatment. Mean age was 71 (SD 7·4) years, and 31 (70%) of 44 participants were male and 13 (30%) were female. Lung function was moderately impaired; mean forced vital capacity (FVC) was 2·7 L (SD 0·76), mean predicted FVC was 82% (17·3), and mean predicted diffusion capacity of carbon monoxide was 48% (10·9). Of the 44 patients who were randomised, 43 completed morphine treatment and 41 completed placebo treatment. In the intention-to-treat analysis, morphine reduced objective awake cough frequency by 39·4% (95% CI -54·4 to -19·4; p=0·0005) compared with placebo. Mean daytime cough frequency reduced from 21·6 (SE 1·2) coughs per h at baseline to 12·8 (1·2) coughs per h with morphine, whereas cough rates did not change with placebo (21·5 [SE 1·2] coughs per h to 20·6 [1·2] coughs per h). Overall treatment adherence was 98% in the morphine group and 98% in the placebo group. Adverse events were observed in 17 (40%) of 43 participants in the morphine group and six (14%) of 42 patients in the placebo group. The main side-effects of morphine were nausea (six [14%] of 43 participants) and constipation (nine [21%] of 43). One serious adverse event (death) occurred in the placebo group. In patients with cough related to idiopathic pulmonary fibrosis, low dose controlled-release morphine significantly reduced objective cough counts over 14 days compared with placebo. Morphine shows promise as an effective treatment to palliate cough in patients with idiopathic pulmonary fibrosis, and longer term studies should be the focus of future research. The Jon Moulton Charity Trust.

Incidence of cough from acute exposure to fine particulate matter (PM2.5) in Madagascar: A pilot study.

Prolonged exposure to fine particulate matter (PM2.5) is a known risk to respiratory health, causing chronic lung impairment. Yet, the immediate, acute effects of PM2.5 exposure on respiratory symptoms, such as cough, are less understood. This pilot study aims to investigate this relationship using objective PM2.5 and cough monitors. Fifteen participants from rural Madagascar were followed for three days, equipped with an RTI Enhanced Children's MicroPEM PM2.5 sensor and a smartphone with the ResApp Cough Counting Software application. Univariable Generalized Estimating Equation (GEE) models were applied to measure the association between hourly PM2.5 exposure and cough counts. Peaks in both PM2.5 concentration and cough frequency were observed during the day. A 10-fold increase in hourly PM2.5 concentration corresponded to a 39% increase in same-hour cough frequency (incidence rate ratio (IRR) = 1.40; 95% CI: 1.12, 1.74). The strength of this association decreased with a one-hour lag between PM2.5 exposure and cough frequency (IRR = 1.21; 95% CI: 1.01, 1.44) and was not significant with a two-hour lag (IRR = 0.93; 95% CI: 0.71, 1.23). This study demonstrates the feasibility of objective PM2.5 and cough monitoring in remote settings. An association between hourly PM2.5 exposure and cough frequency was detected, suggesting that PM2.5 exposure may have immediate effects on respiratory health. Further investigation is necessary in larger studies to substantiate these findings and understand the broader implications.

Performance evaluation of human cough annotators: optimal metrics and sex differences

IntroductionDespite its high prevalence and significance, there is still no widely available method to quantify cough. In order to demonstrate agreement with the current gold standard of human annotation, emerging...

An experimental study of acute toxicity and pharmacology of fermented Platycodonis Radix

This study investigated the acute toxicity of fermented Platycodonis Radix on mice and its effect on coughing in mice infected with Mycoplasma pneumoniae. The maximum dosage(MAD) was used in the acute toxicity experiment on mice to observe the signs of mice. After 14 days, dissection, blood biochemical examination, and pathological tissue section observation were conducted. In the pharmacological experiment of fermented Platycodonis Radix, 60 healthy BALB/c mice, 30 males and 30 females, were randomly divided into a blank group, a model group, a carbetapentane group(0.013 g·kg~(-1)·d~(-1)), and high-, medium-, and low-dose fermented Platycodonis Radix groups(5.2, 2.6, and 1.3 g·kg~(-1)·d~(-1)), with 10 mice in each group. Except for the blank group, the mice in the other five groups underwent model induction by intranasally instilling 20 μL of 1×10~6 CCU M. pneumoniae for 3 days, and the mice in each group were orally administered the corresponding drugs for 7 days. Cough induction experiment was conducted to observe and record the cough latency and total cough count within 3 min for each group. Hematoxylin-eosin(HE) staining and Masson staining were used to observe the pathological changes in lung tissues. Immunohistochemistry was performed to observe the protein expression of transient receptor potential A1(TRPA1), calcitonin gene-related peptide(CGRP), and substance P(SP) in the lung tissues of mice in each group. Real-time fluorescence-based quantitative polymerase chain reaction(qRT-PCR) was used to elucidate the changes in the mRNA levels of cough-related factors TRPA1, CGRP, and SP in mice treated with fermented Platycodonis Radix. No mice died in the acute toxicity experiment, and there were no changes in general behavior and major organ histopathological examinations. Compared with the blank group, there were no statistically significant differences in blood biochemical indexes. In the pharmacological experiment of fermented Platycodonis Radix, compared with the model group, the high-and medium-dose fermented Platycodonis Radix groups showed improved lung tissue structure of mice, with clear structure and regular tissue morphology. The qRT-PCR and immunohistochemical detection showed a decrease in the expression of TRPA1, CGRP, and SP in the fermented Platycodonis Radix groups. Fermented Platycodonis Radix can exert an inhibitory effect on cough by suppressing the expression of TRPA1, CGRP, and SP in lung tissues, thereby identifying the target of the drug.

A smartphone-based application for cough counting in patients with acute asthma exacerbation.

While tools exist for objective cough counting in clinical studies, there is no available tool for objective cough measurement in clinical practice. An artificial intelligence (AI)-based cough count system was recently developed that quantifies cough sounds collected through a smartphone application. In this prospective study, this AI-based cough algorithm was applied among real-world patients with an acute exacerbation of asthma. Patients with an acute asthma exacerbation recorded their cough sounds for 7 days (2 consecutive hours during awake time and 5 consecutive hours during sleep) using CoughyTM smartphone application. During the study period, subjects received systemic corticosteroids and bronchodilator to control asthma. Coughs collected by application were counted by both the AI algorithm and two human experts. Subjects also provided self-measured peak expiratory flow rate (PEFR) and completed other outcome assessments [e.g., cough symptom visual analogue scale (CS-VAS), awake frequency, salbutamol use] to investigate the correlation between cough and other parameters. A total of 1,417.6 h of cough recordings were obtained from 24 asthmatics (median age =39 years). Cough counts by AI were strongly correlated with manual cough counts during sleep time (rho =0.908, P<0.001) and awake time (rho =0.847, P<0.001). Sleep time cough counts were moderately to strongly correlated with CS-VAS (rho =0.339, P<0.001), the frequency of waking up (rho =0.462, P<0.001), and salbutamol use at night (rho =0.243, P<0.001). Weak-to-moderate correlations were found between awake time cough counts and CS-VAS (rho =0.313, P<0.001), the degree of activity limitation (rho =0.169, P=0.005), and salbutamol use at awake time (rho =0.276, P<0.001). Neither awake time nor sleep time cough counts were significantly correlated with PEFR. The strong correlation between cough counts using the AI-based algorithm and human experts, and other indicators of patient health status provides evidence of the validity of this AI algorithm for use in asthma patients experiencing an acute exacerbation. Study findings suggest that CoughyTM could be a novel solution for objectively monitoring cough in a clinical setting.

Smartphone-based cough monitoring as a near real-time digital pneumonia biomarker.

Cough represents a cardinal symptom of acute respiratory tract infections. Generally associated with disease activity, cough holds biomarker potential and might be harnessed for prognosis and personalised treatment decisions. Here, we tested the suitability of cough as a digital biomarker for disease activity in coronavirus disease 2019 (COVID-19) and other lower respiratory tract infections. We conducted a single-centre, exploratory, observational cohort study on automated cough detection in patients hospitalised for COVID-19 (n=32) and non-COVID-19 pneumonia (n=14) between April and November 2020 at the Cantonal Hospital St Gallen, Switzerland. Cough detection was achieved using smartphone-based audio recordings coupled to an ensemble of convolutional neural networks. Cough levels were correlated to established markers of inflammation and oxygenation. Cough frequency was highest upon hospital admission and declined steadily with recovery. There was a characteristic pattern of daily cough fluctuations, with little activity during the night and two coughing peaks during the day. Hourly cough counts were strongly correlated with clinical markers of disease activity and laboratory markers of inflammation, suggesting cough as a surrogate of disease in acute respiratory tract infections. No apparent differences in cough evolution were observed between COVID-19 and non-COVID-19 pneumonia. Automated, quantitative, smartphone-based detection of cough is feasible in hospitalised patients and correlates with disease activity in lower respiratory tract infections. Our approach allows for near real-time telemonitoring of individuals in aerosol isolation. Larger trials are warranted to decipher the use of cough as a digital biomarker for prognosis and tailored treatment in lower respiratory tract infections.

Nighttime continuous contactless smartphone-based cough monitoring for the ward: A validation study.

Clinical deterioration can go unnoticed in hospital wards for hours. Mobile technologies such as wearables and smartphones enable automated, continuous, non-invasive ward monitoring and allow the detection of subtle changes in vital signs. Cough holds great potential for monitoring through mobile technologies on the ward as it is not only a symptom of prevalent respiratory diseases such as asthma, lung cancer, and COVID-19 but also a predictor of acute health deterioration. In past decades, many efforts have been made to develop an automatic cough counting tool. To date, however, there is neither a standardized, sufficiently validated method nor a scalable cough monitor that can be deployed on a consumer-centric device that reports cough counts continuously. These shortcomings limit the tracking of coughing and, consequently, hinder the monitoring of disease progression in prevalent respiratory diseases such as asthma, chronic obstructive pulmonary disease, and COVID-19 in the ward. This exploratory study involves the validation of an automated smartphone-based monitoring system for continuous cough counting in two different modes in the ward. Unlike previous studies that focused on evaluating cough detection models on unseen data, the focus of this work is to validate a holistic smartphone-based cough detection system operating in near real-time. Automated cough counts are measured consistently on-device and on-computer, and compared with cough and non-cough sounds counted manually over eight hours long nocturnal recordings in nine patients with pneumonia in the ward. The proposed cough detection system consists primarily of an Android app running on a smartphone that detects coughs and records sounds, and secondarily of a backend that continuously receives the cough detection information and displays the hourly cough counts. Cough detection is based on an ensemble Convolutional Neural Network developed and trained on asthmatic cough data. In this validation study, a total of 72 hours of recording from nine participants with pneumonia, four of whom were infected with SARS-CoV-2, were analyzed. All recordings were subjected to manual analysis by two blinded raters. The proposed system yielded sensitivity and specificity of 72% and 99% on-device and 82% and 99% on-computer, respectively, for detecting coughs. The mean difference between the automated and human rater cough counts were -1.0, CI 95% [-12.3, 10.2] and -0.9, CI 95% [-6.5, 4.8] coughs per hour within-subject for the on-device and on-computer mode, respectively. The proposed system thus represents a smartphone cough counter that can be used for continuous hourly assessment of cough frequency in the ward.

Comparative Cough Suppression of Chitosan Crab Extract of Uca tangeri and Dihydrocodeine

Cough is an inmate primitive reflex and acts as a part of the body's immune system to protect against foreign materials from the respiratory tract. This study was done to investigate the cough suppression potential of Uca tangeri. A day before the test, guinea pigs were placed individually in a transparent chamber (60 × 36 × 60 cm) for 5 minutes before cough was induced by exposure to 15% citric acid, delivered using an Omron compressor nebulizer (rate of 0.4 ml/minutes and particle size 5?m) for 10 minutes. The animals were then monitored visually within this exposure time for cough; the latency and counts, of which, were taken as the basal values. The animals exhibiting 10 - 20 bouts of cough were selected for the study and fasted overnight but with access to water. The selected animals were randomly allotted to 5 groups (n=5 per group). The animals were treated orally thus: Group 1 was the control group and received 2 ml/kg of normal saline; group 2 received 25 mg/kg dihydrocodeine; Group 3 received 150 mg/kg extract; group 4 received 300 mg/kg extract and group 5 received 600 mg/kg of the extract. An hour after administration, they were re-exposed to citric acid aerosol (as earlier described) and the latency of cough and cough count were recorded. The procedure was repeated at hours 2 and 3 after treatment. Antitussive activity was then evaluated in each guinea-pig as the percentage reduction in the number of coughs also known as percentage suppression of cough and percentage increase in latency of cough. The results revealed that Uca tangeri exhibited a dose dependent percentage increase in cough latency period as well as percentage increase in suppression of cough which was inferior to dihydrocodeine, but significantly greater than normal saline and basal levels.

A Randomized, Double-Blind, Placebo-Controlled, Parallel-Group Phase 2b Trial of P2X3 Receptor Antagonist Sivopixant for Refractory or Unexplained Chronic Cough

IntroductionTo determine the optimal dose of sivopixant, a highly selective P2X3 receptor antagonist, for refractory or unexplained chronic cough (RCC/UCC).MethodsIn this phase 2b, randomized, double-blind, placebo-controlled, parallel-group, multicenter trial, patients received sivopixant 50, 150, or 300 mg or placebo once daily for 4 weeks. The primary endpoint was a change from baseline in 24-h cough frequency (coughs/h) with sivopixant vs placebo.ResultsOverall, 390/406 randomized patients completed the study. Placebo-adjusted changes in hourly cough count over 24 h were 13.17% (P = 0.3532), − 1.77% (P = 0.8935), and − 12.47% (P = 0.3241) and in cough severity (visual analog scale) were 1.75 mm (P = 0.5854), − 1.21 mm (P = 0.7056), and − 6.55 mm (P = 0.0433) with sivopixant 50, 150, and 300 mg, respectively. Placebo-adjusted changes from baseline in Leicester Cough Questionnaire total scores were − 0.37 (P = 0.4207), − 0.07 (P = 0.8806), and 0.69 (P = 0.1473) with sivopixant 50, 150, and 300 mg, respectively. Additionally, 61.3%, 78.3%, 86.8%, and 71.4% of patients receiving sivopixant 50, 150, and 300 mg and placebo, respectively, reported any improvements in Patient Global Impression of Change. The incidence of treatment-emergent adverse events (TEAEs) was 25.7%, 32.0%, 49.0%, and 20.6% in sivopixant 50, 150, and 300 mg and placebo groups, respectively; all TEAEs in the sivopixant group were mild-to-moderate.ConclusionSivopixant did not demonstrate a statistically significant difference vs placebo in change from baseline in 24-h cough frequency. The dose of 300 mg has potential for RCC/UCC, showing the greatest improvements in cough frequency and patient-reported outcomes and dose-related mild to moderate reversible taste disturbance, although further trials are needed.Clinical Trial RegistrationClinicalTrials.gov identifier NCT04110054; registered September 26, 2019.

Clinical, Laboratory and Tomographic characteristics associated with obesity and BMI at Hospital admission in adult patients with COVID-19: a cross-sectional study

Introduction: The pathological status of obesity can influence COVID-19 from its initial clinical presentation, therefore, the identification of clinical and laboratory parameters most affected in the presence of obesity can contribute to improving the treatment of the disease. Objective: To identify the clinical, laboratory, and tomographic characteristics associated with obesity and BMI at t hospital admission in adult patients with COVID-19. Methods: This is a cross-sectional observational study with a total of 315 participants with COVID-19 confirmed by rt-PCR. The participants were divided into non-Obese (n=203) and Obese (n=112). Physical examinations, laboratory tests, and computed tomography of the chest were performed during the first 2 days of hospitalization. Results: Patients with obesity were younger, and they had higher systolic and diastolic blood pressure, higher frequency of alcoholism, fever, cough, and headache, higher ALT, LDH, and red blood cell count (RBC), hemoglobin, hematocrit, and percentage of lymphocytes. Also, they presented a lower value of leukocyte count and Neutrophil/Lymphocyte Ratio (RNL). The parameters positively correlated with BMI were alcoholism, systolic and diastolic blood pressure, fever, cough, sore throat, number of symptoms, ALT in men, LDH, magnesium, RBC, hemoglobin, hematocrit, and percentage of lymphocytes. The parameters negatively correlated with the BMI were: age and RNL. Conclusion: Several parameters were associated with obesity at hospital admission, revealing better than expected results. However, these results should be interpreted with great caution, as there may be some influence of a phenomenon called the Obesity Paradox that can distort the severity and prognosis of the patient.

PAciFy Cough\u2014a multicentre, double-blind, placebo-controlled, crossover trial of morphine sulphate for the treatment of pulmonary Fibrosis Cough

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive disease that leads to lung scarring. Cough is reported by 85% of patients with IPF and can be a distressing symptom with a significant impact on patients’ quality of life. There are no proven effective therapies for IPF-related cough. Whilst morphine is frequently used as a palliative agent for breathlessness in IPF, its effects on cough have never been tested. PAciFy Cough is a multicenter, double-blind, placebo-controlled, crossover trial of morphine sulphate for the treatment of cough in IPF.MethodsWe will recruit 44 subjects with IPF prospectively from three interstitial lung disease units in the UK, namely the Royal Brompton Hospital, Manchester University NHS Foundation Trust (MFT) and Aintree University Hospital NHS Foundation Trust. Patients will be randomised (1:1) to either placebo twice daily or morphine sulphate 5 mg twice daily for 14 days. They will then crossover after a 7-day washout period. The primary endpoint is the percent change in daytime cough frequency (coughs per hour) from baseline as assessed by objective cough monitoring at day 14 of treatment.DiscussionThis multicentre, randomised trial will assess the effect of opioids on cough counts and cough associated quality of life in IPF subjects. If proven to be an effective intervention, it represents a readily available treatment for patients.Trial registrationThe study was approved by the UK Medicines and Healthcare Regulatory Agency (Ref: CTA 21268/0224/001-0001 – EUDRACT 2019-003571-19 – Protocol Number RBH2019/001) on 08 April 2020, in compliance with the European Clinical Trials Directive and the Medicines for Human Use (Clinical Trials) Regulations 2004 and its subsequent amendments. The study was provided with ethical approval by the London Brent Research Ethics Committee (Ref: 20/LO/0368) on 21 May 2020 and is registered with clinicaltrials.gov (NCT04429516) on 12 June 2020, available at https://clinicaltrials.gov/ct2/show/NCT04429516

Phase 2B Study of Inhaled RVT-1601 for Chronic Cough in Idiopathic Pulmonary Fibrosis: A Multicenter, Randomized, Placebo-controlled Study (SCENIC Trial).

Rationale: Chronic cough remains a major and often debilitating symptom for patients with idiopathic pulmonary fibrosis (IPF). In a phase 2A study, inhaled RVT-1601 (cromolyn sodium) reduced daytime cough and 24-hour average cough counts in patients with IPF. Objectives: To determine the efficacy, safety, and optimal dose of inhaled RVT-1601 for the treatment of chronic cough in patients with IPF. Methods: In this multicenter, randomized, placebo-controlled phase 2B study, patients with IPF and chronic cough for ⩾8 weeks were randomized (1:1:1:1) to receive 10, 40, and 80 mg RVT-1601 three times daily or placebo for 12 weeks. The primary endpoint was change from baseline to end of treatment in log-transformed 24-hour cough count. Key secondary endpoints were change from baseline in cough severity and cough-specific quality of life. Safety was monitored throughout the study. Measurements and Main Results: The study was prematurely terminated owing to the impact of the coronavirus disease (COVID-19) pandemic. Overall, 108 patients (mean age 71.0 years, 62.9% males) received RVT-1601 10 mg (n = 29), 40 mg (n = 25), 80 mg (n = 27), or matching placebo (n = 27); 61.1% (n = 66) completed double-blind treatment. No statistically significant difference was observed in the least-square mean change from baseline in log-transformed 24-hour average cough count, cough severity, and cough-specific quality of life score between the RVT-1601 groups and the placebo group. The mean percentage change from baseline in 24-hour average cough count was 27.7% in the placebo group. Treatment was generally well tolerated. Conclusions: Treatment with inhaled RVT-1601 (10, 40, and 80 mg three times a day) did not provide benefit over placebo for the treatment of chronic cough in patients with IPF.

Development and technical validation of a smartphone-based pediatric cough detection algorithm.

IntroductionCoughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone‐based algorithm that objectively and automatically counts cough sounds of children.MethodsThe training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0–14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone‐based algorithm during various conditions.ResultsThe final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual‐ and automated cough counts in the validation dataset was 0.97 (p < .001). The intra‐ and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5–1 m from the audio source.ConclusionThis novel smartphone‐based pediatric cough detection application can be used for longitudinal follow‐up in clinical care or as digital endpoint in clinical trials.