Non-pharmaceutical interventions to optimize cancer immunotherapy

The traditional picture of cancer patients as weak individuals requiring maximum rest and protection is beginning to dissolve. Too much focus on the medical side and one’s own vulnerability and mortality might be counterproductive and not doing justice to the complexity of human nature. Unlike cytotoxic and lympho-depleting treatments, immune-engaging therapies strengthen the immune system and are typically less harmful for patients. Thus, cancer patients receiving checkpoint inhibitors are not viewed as being vulnerable per se, at least not in immunological and physical terms. This perspective article advocates a holistic approach to cancer immunotherapy, with an empowered patient in the center, focusing on personal resources and receiving domain-specific support from healthcare professionals. It summarizes recent evidence on non-pharmaceutical interventions to enhance the efficacy of immune checkpoint blockade and improve quality of life. These interventions target behavioral factors such as diet, physical activity, stress management, circadian timing of checkpoint inhibitor infusion, and waiving unnecessary co-medication curtailing immunotherapy efficacy. Non-pharmaceutical interventions are universally accessible, broadly applicable, instantly actionable, scalable, and economically sustainable, creating value for all stakeholders involved. Most importantly, this holistic framework re-emphasizes the patient as a whole and harnesses the full potential of anticancer immunity and checkpoint blockade, potentially leading to survival benefits. Digital therapeutics are proposed to accompany the patients on their mission toward change in lifestyle-related behaviors for creating optimal conditions for treatment efficacy and personal growth

Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study

Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%. Conclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma

Influence of Older Age and Other Risk Factors on Pneumonia Hospitalization in Switzerland in the Pneumococcal Vaccine Era

Background: Pneumococcal pneumonia is a disease of the extremes of age. However, as other traditional risk factors for pneumococcal pneumonia also increase with older age, it is unclear if older age itself should be an indication for pneumococcal vaccination. Therefore, we assessed the effect of age on risk for hospitalization for pneumonia and for pneumococcal pneumonia. Methods: Using a national hospitalization dataset, all patients ≥16 years hospitalized in a Swiss hospital with a diagnosis of pneumonia or pneumococcal pneumonia between 2002 and 2015 were included. Multivariable logistic regression analysis was used to test the association between age (≥50 or ≥65 years) and hospitalization for pneumonia or pneumococcal pneumonia after adjusting for pneumococcal vaccine indications. Similar analyses were performed for effect of age on length of stay (LOS) and mortality. Results: Among a total of 17,619,016 hospitalizations a diagnosis of pneumonia was present in 421,760 (2.4%) and a diagnosis of pneumococcal pneumonia in 21,610 (0.12%). Age ≥50 years (OR: 3.52 and 2.12, respectively; p for both <0.001) and age ≥65 years (OR: 2.98 and 1.80, respectively; p for both <0.001) as well as most Swiss pneumococcal vaccine indications were independent predictors of hospitalization with a pneumonia and pneumococcal pneumonia diagnosis, respectively. Older age with both age cut-offs were associated with increased LOS (≥50 years: aRR: 1.19 and 1.24, respectively; age ≥65 years: aRR: 1.60 and 1.20, respectively; p < 0.001 for all) and mortality (≥50 years: aOR: 4.73 and 2.84, respectively; age ≥65 years: aOR: 2.38 and 2.69, respectively, p < 0.001 for all) in patients with a pneumonia and pneumococcal pneumonia diagnosis, respectively. The effects of pneumococcal vaccine indications decreased with older age. The incidences of hospitalizations with a pneumonia diagnosis and a pneumococcal pneumonia diagnosis increased significantly from the pre-vaccine era to the PCV7 era and the PCV13 era (p for trend for both analyses <0.001). Conclusion: This study confirms the Swiss indications for pneumococcal vaccination as independent risk factors for pneumonia hospitalizations. Older age itself should be considered as an additional vaccine indication. Pneumonia and pneumococcal pneumonia in adults have increased despite pneumococcal vaccination in children

Lena: a Voice-Based Conversational Agent for Remote Patient Monitoring in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. To manage the increasing number of COPD patients and reduce the social and economic burden of treatment, healthcare providers have sought to implement remote patient monitoring (RPM). Screen-based RPM applications, such as filling self-reports on the smartphone or computer, have been shown to increase the quality of life, reduce the frequency and severity of exacerbations, and increase physical activity in patients with COPD. These applications, however, are not without challenges for the elderly target population. They are often used on devices designed by and for a different age group, which makes filling out self-reports prone to error and induces fears of technology malfunctions. Voice-based conversational agents (VCAs) are available on more than 2.5 billion devices and are increasingly present in homes worldwide. Aside from their commercial success, VCAs are also credited with several functionalities, such as hands-free use, that make their adoption in healthcare attractive, especially for the elderly. In this work, we investigate the potential of VCAs for RPM of COPD. Specifically, we designed and evaluated Lena, a single-board computer-based VCA framed as a digital member of the medical team. Lena acts as RPM for the early prediction of COPD exacerbations by asking ten symptom-related questions to determine the patient’s daily health status. This paper presents the patients’ feedback after their interaction with Lena. Patients evaluated the acceptability of the system. Notably, all patients could imagine using the system once a day in the context of a larger study and wished to integrate Lena into their daily routine.ISSN:1613-007

Nocturnal Cough and Sleep Quality to Assess Asthma Control and Predict Attacks

Introduction Objective markers for asthma, that can be measured without extra patient effort, could mitigate current shortcomings in asthma monitoring. We investigated whether smartphone-recorded nocturnal cough and sleep quality can be utilized for the detection of periods with uncontrolled asthma or meaningful changes in asthma control and for the prediction of asthma attacks. Methods We analyzed questionnaire and sensor data of 79 adults with asthma. Data were collected in situ for 29 days by means of a smartphone. Sleep quality and nocturnal cough frequencies were measured every night with the Pittsburgh Sleep Quality Index and by manually annotating coughs from smartphone audio recordings. Primary endpoint was asthma control assessed with a weekly version of the Asthma Control Test. Secondary endpoint was self-reported asthma attacks. Results Mixed-effects regression analyses showed that nocturnal cough and sleep quality were statistically significantly associated with asthma control on a between- and within-patient level (p < 0.05). Decision trees indicated that sleep quality was more useful for detecting weeks with uncontrolled asthma (balanced accuracy (BAC) 68% vs 61%; Δ sensitivity -12%; Δ specificity -2%), while nocturnal cough better detected weeks with asthma control deteriorations (BAC 71% vs 56%; Δ sensitivity 3%; Δ specificity -34%). Cut-offs using both markers predicted asthma attacks up to five days ahead with BACs between 70% and 75% (sensitivities 75 - 88% and specificities 57 - 72%). Conclusion Nocturnal cough and sleep quality have useful properties as markers for asthma control and seem to have prognostic value for the early detection of asthma attacks. Due to the limited study duration per patient and the pragmatic nature of the study, future research is needed to comprehensively evaluate and externally validate the performance of both biomarkers and their utility for asthma self-management

Automatic recognition, segmentation, and sex assignment of nocturnal asthmatic coughs and cough epochs in smartphone audio recordings: observational field study

BACKGROUND: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. OBJECTIVE: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. METHODS: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. RESULTS: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean -0.1 (95% CI -12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI -3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch-based sex classification performed best yielding an accuracy of 83%. CONCLUSIONS: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma

Sputum DNA sequencing in cystic fibrosis: non-invasive access to the lung microbiome and to pathogen details

BACKGROUND: Cystic fibrosis (CF) is a life-threatening genetic disorder, characterized by chronic microbial lung infections due to abnormally viscous mucus secretions within airways. The clinical management of CF typically involves regular respiratory-tract cultures in order to identify pathogens and to guide treatment. However, culture-based methods can miss atypical or slow-growing microbes. Furthermore, the isolated microbes are often not classified at the strain level due to limited taxonomic resolution. RESULTS: Here, we show that untargeted metagenomic sequencing of sputum DNA can provide valuable information beyond the possibilities of culture-based diagnosis. We sequenced the sputum of six CF patients and eleven control samples (including healthy subjects and chronic obstructive pulmonary disease patients) without prior depletion of human DNA or cell size selection, thus obtaining the most unbiased and comprehensive characterization of CF respiratory tract microbes to date. We present detailed descriptions of the CF and healthy lung microbiome, reconstruct near complete pathogen genomes, and confirm that the CF lungs consistently exhibit reduced microbial diversity. Crucially, the obtained genomic sequences enabled a detailed identification of the exact pathogen strain types, when analyzed in conjunction with existing multi-locus sequence typing databases. We also detected putative pathogenicity islands and indicators of antibiotic resistance, in good agreement with independent clinical tests. CONCLUSIONS: Unbiased sputum metagenomics provides an in-depth profile of the lung pathogen microbiome, which is complementary to and more detailed than standard culture-based reporting. Furthermore, functional and taxonomic features of the dominant pathogens, including antibiotics resistances, can be deduced—supporting accurate and non-invasive clinical diagnosis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40168-017-0234-1) contains supplementary material, which is available to authorized users

Characteristics of Asthma-related Nocturnal Cough: A Potential New Digital Biomarker

Introduction The nature of nocturnal cough is largely unknown. It might be a valid marker for asthma control but very few studies characterized it as a basis for better defining its role and its use as clinical marker. This study investigated prevalence and characteristics of nocturnal cough in asthmatics over the course of four weeks. Methods In two centers, 94 adult patients with physician-diagnosed asthma were recruited. Patient-reported outcomes and nocturnal sensor data were collected by a smartphone with a chat-based study app. Results Patients coughed in 53% of 2212 nights (range: 0-345 coughs/night). Median coughs per hour were 0 (IQR 0-1). Nocturnal cough rates showed considerable inter-individual variance. The highest counts were measured in the first 30 min in bed (4.5-fold higher than rest of night). Eighty-six percent of coughs were part of a cough cluster. Clusters consisted of a median of two coughs (IQR 2-4). Nocturnal cough was persistent within patient. Conclusion To the best of the authors' knowledge, this study is the first to describe prevalence and characteristics of nocturnal cough in asthma over a period of one month, demonstrating that it was a prevalent symptom with large variance between patients and high persistence within patients. Cough events in asthmatics were 4.5 times more frequent within the first 30 min in bed indicating a potential role of positional change, and not more frequent during the early morning hours. An important next step will investigate the association between nocturnal cough and asthma control

Smartphone-basierte Hustenerkennung – Auf dem Weg zu einem digitalen Biomarker für chronische Atemwegserkrankungen

Thema: Husten ist das häufigste Symptom, bei dem Personen ärztlichen Rat suchen. Die gewöhnliche Erkältung stellt die bekannteste Ursache dar. Darüber hinaus sind steigende Hustenraten mit einer Verschlechterung des Gesundheitszustands bei Krankheiten wie Asthma und COPD assoziiert. Infolgedessen wurden viele Anstrengungen unternommen, um ein objektives Mass für Husten zu schaffen. Bis heute gibt es jedoch keine standardisierte Methode, und es gibt keinen ausreichend validierten generischen Hustenmonitor, der im Handel erhältlich und klinisch akzeptabel ist. Zielsetzung: Ziel dieses Projekts ist es, die Smartphone-basierte Hustenerkennung über 24 Stunden bei 22 COPD-Patienten zu validieren und die erkannten Hustenzahlen mit menschlichen Annotatoren zu vergleichen. Methode: Für das Android-Betriebssystem wurde eine App zur Hustenerkennung entwickelt. Die Detektion des Hustens basiert auf einen Ensemble-Klassifikator von fünf Convolutional Neural Networks. Die App fungiert auch als Audiorecorder, sodass die Erkennung anschliessend verifiziert werden kann. Zusätzlich werden Hustendetektionen an einen Studienserver gesendet und können in Echtzeit über einen Web-Client verfolgt werden. Ergebnisse: Für das Trainieren von Hustenklassifikationsmodellen wurden Audiodaten von 94 Erwachsenen mit Asthma (57% Frauen, Durchschnittsalter 43 Jahre) verwendet, die über 29 Nächte aufgezeichnet wurden. Insgesamt wurden 704.697 Geräusche benutzt, von denen 30.304 als Husten identifiziert wurden. Der Ensemble-Klassifikator wurde vor der Studie auf dem PC evaluiert und schnitt mit einem Matthews-Korrelationskoeffizienten von 94.4% ab. Ob diese Ergebnisse auf dem Smartphone mit COPD Patienten im Krankenhauszimmer oder zu Hause reproduzierbar sind, wird in diesem Projekt erforscht. Fazit: Smartphone-basierte Hustenerkennung kann einen skalierbaren, kostengünstigen Marker für chronische Atemwegserkrankungen liefern