Hasil untuk "Diseases of the respiratory system"

H. M. Tran, Feng-Jen Tsai, Yueh-Lun Lee et al.

The impacts of climate change and air pollution on respiratory diseases present significant global health challenges. This review aims to investigate the effects of the interactions between these challenges focusing on respiratory diseases. Climate change is predicted to increase the frequency and intensity of extreme weather events amplifying air pollution levels and exacerbating respiratory diseases. Air pollution levels are projected to rise due to ongoing economic growth and population expansion in many areas worldwide, resulting in a greater burden of respiratory diseases. This is especially true among vulnerable populations like children, older adults, and those with pre-existing respiratory disorders. These challenges induce inflammation, create oxidative stress, and impair the immune system function of the lungs. Consequently, public health measures are required to mitigate the effects of climate change and air pollution on respiratory health. The review proposes that reducing greenhouse gas emissions contribute to slowing down climate change and lessening the severity of extreme weather events. Enhancing air quality through regulatory and technological innovations also helps reduce the morbidity of respiratory diseases. Moreover, policies and interventions aimed at improving healthcare access and social support can assist in decreasing the vulnerability of populations to the adverse health effects of air pollution and climate change. In conclusion, there is an urgent need for continuous research, establishment of policies, and public health efforts to tackle the complex and multi-dimensional challenges of climate change, air pollution, and respiratory health. Practical and comprehensive interventions can protect respiratory health and enhance public health outcomes for all.

Adam P Wagner, Nicholas J Simmonds, Susan C Charman et al.

Introduction Yoga is an emerging exercise choice for people with cystic fibrosis (CF), but evidence of its effect in this population is scarce, with a recent systematic review advocating for further research. Yoga Outcomes Get Assessed in CF (YOGA-CF) is a real-world multicentre randomised controlled trial (RCT) investigating a bespoke CF-specific online 12-week yoga intervention, vers usual care, to determine effectiveness for adults with CF.Methods and analysis A multicentre RCT of adults with CF across the UK. Participants are randomised to usual care or a 12-week online bespoke yoga programme with an expectation of two classes completed weekly. Assessments of lung function, 1 min sit-to-stand, the Cystic Fibrosis Questionnaire-Revised (CFQ-R) and other trial questionnaires are completed preintervention and postintervention (0 and 12 weeks) and after 12 weeks of follow-up (week 24). The primary outcome is the difference in respiratory-related quality of life measured using the CFQ-R before and after yoga/control. Sample size was calculated based on detecting a minimally clinically important difference of 4 for the CFQ-R respiratory domain, with power of 80% and 5% significance level (total target, n=314).Ethics and dissemination Ethics approval gained from the South Yorkshire and Humber Research Ethics Committee (REC) (reference: 23/YH/0270, project ID 303898). Dissemination to involve direct participant feedback and lay webinar, scientific conference presentation and publication in a peer-reviewed journal.Trial registration number NCT06120465.

Muthmainnah Muthmainnah, Galuh Mega Kurnia, Avinka Nugrahani

Introduction With Indonesia ranking top in the Association of Southeast Asian Nations for the number of smokers aged 13–15 years, this study aims to analyze the factors associated with smoking prevention behavior among students of senior high school. Methods This cross-sectional pilot study, conducted in 2022 with 90 samples of grade 10–11 students at SMA Negeri 1 Taman Sidoarjo East Java Indonesia, examined variables such as perceived vulnerability (the belief about the risk of experiencing a health issue), severity (the belief about the seriousness of the health issue), benefits (the belief in the benefit of taking preventive actions), barriers (the perceived obstacles to performing preventive behaviors), self-efficacy (the confidence in one's ability to perform preventive behaviors successfully), and cues to action (factors that trigger the decision to engage) in relation to health behaviors. Data were analyzed using the chi-squared test. Results The chi-squared analysis showed significant associations between several factors and smoking prevention behavior. For perceived susceptibility, 34.4% with high susceptibility had good behavior, and 13.3% had not good behavior (p=0.000). For perceived severity, 33.3% with high severity exhibited good behavior, and 21% had not good behavior (p=0.002). Regarding perceived benefits, 28.9% with high benefits showed good behavior, while 22.6% had not good behavior (p=0.018). Self-efficacy indicated 36.7% with high self-efficacy demonstrated good behavior versus 25.8% with not good behavior (p=0.001). Cues to action revealed that 28.9% with high cues had good behavior, and 18.9% did not have good behavior (p=0.003). No association was found for perceived barriers (p=0.386). Conclusions The level of smoking prevention behavior is influenced by perceived susceptibility, severity, benefits, self-efficacy, and cues to action. Therefore, more intensive and targeted efforts are needed to promote awareness of the dangers of smoking and to enhance adolescents' self-efficacy in preventing smoking.

Ho Cheol Kim, Sydney Guthrie, Christopher S. King et al.

Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with a highly variable clinical course. Forced vital capacity (FVC) is widely used as a marker of disease severity and progression, yet its variability and dependence on patient effort raise concerns regarding its reliability. Given these limitations, we investigated the clinical significance of slow vital capacity (SVC) as a potential alternative measure of lung function in IPF. In a retrospective cohort of 89 IPF patients who underwent pulmonary function testing with concomitant SVC measurements, we observed a strong correlation between FVC and SVC (r = 0.973 at baseline, r = 0.978 at follow-up). However, in 99% of cases, SVC values were equal to or exceeded FVC, and follow-up assessments revealed that FVC exhibited greater variability than SVC. Notably, patients with a decrease in SVC demonstrated worse survival outcomes, whereas FVC decline did not show the same prognostic significance. These findings suggest that SVC may provide a more stable and clinically meaningful measure of disease progression in IPF. Moreover, its less effort-dependent nature could improve reproducibility, particularly in patients with advanced diseases. Our study highlights the potential role of SVC as a valuable metric in clinical practice and as an endpoint in future IPF trials. Prospective validation of these findings could further establish SVC as a superior tool for disease monitoring and therapeutic assessment.

M. Camila Melo-Narvaez, Fee Gölitz, Eshita Jain et al.

Abstract Background Human precision-cut lung slices (hPCLS) are a unique platform for functional, mechanistic, and drug discovery studies in the field of respiratory research. However, tissue availability, generation, and cultivation time represent important challenges for their usage. Therefore, the present study evaluated the efficacy of a specifically designed tissue preservation solution, TiProtec, complete or in absence (-) of iron chelators, for long-term cold storage of hPCLS. Methods hPCLS were generated from peritumor control tissues and stored in DMEM/F-12, TiProtec, or TiProtec (-) for up to 28 days. Viability, metabolic activity, and tissue structure were determined. Moreover, bulk-RNA sequencing was used to study transcriptional changes, regulated signaling pathways, and cellular composition after cold storage. Induction of cold storage-associated senescence was determined by transcriptomics and immunofluorescence (IF). Finally, cold-stored hPCLS were exposed to a fibrotic cocktail and early fibrotic changes were assessed by RT-qPCR and IF. Results Here, we found that TiProtec preserves the viability, metabolic activity, transcriptional profile, as well as cellular composition of hPCLS for up to 14 days. Cold storage did not significantly induce cellular senescence in hPCLS. Moreover, TiProtec downregulated pathways associated with cell death, inflammation, and hypoxia while activating pathways protective against oxidative stress. Cold-stored hPCLS remained responsive to fibrotic stimuli and upregulated extracellular matrix-related genes such as fibronectin and collagen 1 as well as alpha-smooth muscle actin, a marker for myofibroblasts. Conclusions Optimized long-term cold storage of hPCLS preserves their viability, metabolic activity, transcriptional profile, and cellular composition for up to 14 days, specifically in TiProtec. Finally, our study demonstrated that cold-stored hPCLS can be used for on-demand mechanistic studies relevant for respiratory research. Graphical Abstract

Robert A. Rigby, Mikis D. Stasinopoulos, Achim Zeileis et al.

We read with interest the above article by Zavorsky (2025, Respiratory Medicine, doi:10.1016/j.rmed.2024.107836) concerning reference equations for pulmonary function testing. The author compares a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), which is the standard adopted by the Global Lung Function Initiative (GLI), with a segmented linear regression (SLR) model, for pulmonary function variables. The author presents an interesting comparison; however there are some fundamental issues with the approach. We welcome this opportunity for discussion of the issues that it raises. The author's contention is that (1) SLR provides "prediction accuracies on par with GAMLSS"; and (2) the GAMLSS model equations are "complicated and require supplementary spline tables", whereas the SLR is "more straightforward, parsimonious, and accessible to a broader audience". We respectfully disagree with both of these points.

Ronghua Zhang, Xiaomeng Li, Xuan Li et al.

Forty-six percent of the world's population resides in rural areas, the majority of whom belong to vulnerable groups. They mainly use cheap solid fuels for cooking and heating, which release a large amount of PM2.5 and cause adverse effects to human health. PM2.5 exhibits urban-rural differences in its health risk to the respiratory system. However, the majority of research on this issue has focused on respiratory diseases induced by atmospheric PM2.5 in urban areas, while rural areas have been ignored for a long time, especially the pathogenesis of respiratory diseases. This is not helpful for promoting environmental equity to aid vulnerable groups under PM2.5 pollution. Thus, this study focuses on rural atmospheric PM2.5 in terms of its chemical components, toxicological effects, respiratory disease types, and pathogenesis, represented by PM2.5 from rural areas in the Sichuan Basin, China (Rural SC-PM2.5). In this study, organic carbon is the most significant component of Rural SC-PM2.5. Rural SC-PM2.5 significantly induces cytotoxicity, oxidative stress, and inflammatory response. Based on multiomics, bioinformatics, and molecular biology, Rural SC-PM2.5 inhibits ribonucleotide reductase regulatory subunit M2 (RRM2) to disrupt the cell cycle, impede DNA replication, and ultimately inhibit lung cell proliferation. Furthermore, this study supplements and supports the epidemic investigation. Through an analysis of the transcriptome and human disease database, it is found that Rural SC-PM2.5 may mainly involve pulmonary hypertension, sarcoidosis, and interstitial lung diseases; in particular, congenital diseases may be ignored by epidemiological surveys in rural areas, including tracheoesophageal fistula, submucous cleft of the hard palate, and congenital hypoplasia of the lung. This study contributes to a greater scientific understanding of the health risks posed by rural PM2.5, elucidates the pathogenesis of respiratory diseases, clarifies the types of respiratory diseases, and promotes environmental equity.

Yanfang Cui, Yanling Xi, Li Li et al.

Air pollution mainly comes from fossil energy consumption (FEC), and it brings great threat to public health. The respiratory system of the elderly is highly susceptible to the effects of air pollution due to the decline in body functions. PM2.5 is a major component of air pollution, so the study of the impact of PM2.5 generated by FEC on the respiratory health of the elderly is of great significance. The existing studies have focused more on the effect of PM2.5 on mortality, and this paper is a useful addition to the existing studies by examining the effect of PM2.5 from FEC on the health of the elderly from the perspective of prevalence. In this paper, the binary Logistic regression model was used to calculate the exposure-response relationship coefficient for respiratory health in older adults using the data in 2018 from the Chinese Longitudinal Healthy Longevity Survey. And referring to the Dynamic Projection model for Emissions in China, the changes in the number of older persons suffering from respiratory diseases due to PM2.5 from FEC in the baseline scenario, the clean air scenario, and the on-time peak-clean air scenario were predicted. The results indicated that: (1) PM2.5 from FEC mainly came from coal; (2) PM2.5 from FEC was detrimental to the respiratory health of the elderly, and older seniors were more affected as they age; (3) In the on-time peak-clean air scenario, the number of elderly people suffering from respiratory diseases due to PM2.5 from FEC was growing at the slowest rate. Based on the above results, this paper raised recommendations for reducing the effect of PM2.5 from FEC on the health of the elderly.

Moez Dawood, Ben Heavner, Marsha M. Wheeler et al.

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

Paridhi Mundra, Manik Sharma, Yashwardhan Chaudhuri et al.

As respiratory illnesses become more common, it is crucial to quickly and accurately detect them to improve patient care. There is a need for improved diagnostic methods for immediate medical assessments for optimal patient outcomes. This paper introduces VoxMed, a UI-assisted one-step classifier that uses digital stethoscope recordings to diagnose respiratory diseases. It employs an Audio Spectrogram Transformer(AST) for feature extraction and a 1-D CNN-based architecture to classify respiratory diseases, offering professionals information regarding their patients respiratory health in seconds. We use the ICBHI dataset, which includes stethoscope recordings collected from patients in Greece and Portugal, to classify respiratory diseases. GitHub repository: https://github.com/Sample-User131001/VoxMed

Negar Orangi-Fard, Alexandru Bogdan, Hersh Sagreiya

Respiratory diseases impose a significant burden on global health, with current diagnostic and management practices primarily reliant on specialist clinical testing. This work aims to develop machine learning-based algorithms to facilitate at-home respiratory disease monitoring and assessment for patients undergoing continuous positive airway pressure (CPAP) therapy. Data were collected from 30 healthy adults, encompassing respiratory pressure, flow, and dynamic thoraco-abdominal circumferential measurements under three breathing conditions: normal, panting, and deep breathing. Various machine learning models, including the random forest classifier, logistic regression, and support vector machine (SVM), were trained to predict breathing types. The random forest classifier demonstrated the highest accuracy, particularly when incorporating breathing rate as a feature. These findings support the potential of AI-driven respiratory monitoring systems to transition respiratory assessments from clinical settings to home environments, enhancing accessibility and patient autonomy. Future work involves validating these models with larger, more diverse populations and exploring additional machine learning techniques.

Pengfei Zhang, Zhihang Zheng, Shichen Zhang et al.

Compared with invasive examinations that require tissue sampling, respiratory sound testing is a non-invasive examination method that is safer and easier for patients to accept. In this study, we introduce Rene, a pioneering large-scale model tailored for respiratory sound recognition. Rene has been rigorously fine-tuned with an extensive dataset featuring a broad array of respiratory audio samples, targeting disease detection, sound pattern classification, and event identification. Our innovative approach applies a pre-trained speech recognition model to process respiratory sounds, augmented with patient medical records. The resulting multi-modal deep-learning framework addresses interpretability and real-time diagnostic challenges that have hindered previous respiratory-focused models. Benchmark comparisons reveal that Rene significantly outperforms existing models, achieving improvements of 10.27%, 16.15%, 15.29%, and 18.90% in respiratory event detection and audio classification on the SPRSound database. Disease prediction accuracy on the ICBHI database improved by 23% over the baseline in both mean average and harmonic scores. Moreover, we have developed a real-time respiratory sound discrimination system utilizing the Rene architecture. Employing state-of-the-art Edge AI technology, this system enables rapid and accurate responses for respiratory sound auscultation(https://github.com/zpforlove/Rene).

Journal of Healthcare Engineering

[This retracts the article DOI: 10.1155/2021/2621655.].

Jieun Hwang

Introduction Tobacco users are categorized as single, dual, and triple users based on the number of tobacco products (cigarettes, e-cigarettes, and heated tobacco products) used. This study addressed a literature gap by examining how adult Korean tobacco users’ quit attempts/plans differed based on the user type, and the associated psychosocial and subjective health-related factors. Methods We used a questionnaire to examine participants' self-reported health, stress, health concerns, health behavior, tobacco addiction, intentions/plans to quit, and demographic characteristics. Data were analyzed using chi-squared tests, one-way analysis of variance, and multiple linear regression. Results Of the 1288 tobacco users, 55.4%, 28.3%, and 16.4% were single, dual, and triple users, respectively. Self-rated health and stress were lowest among single users and highest among triple users. Most user types had intentions/plans to quit, especially triple users. Quit attempts and plans increased with increasing health behaviors and time elapsed before first tobacco use in the morning, but decreased with higher stress and self-rated addiction. Conclusions Intentions/plans to quit tobacco use varied based on the type of tobacco user. Multiple users had higher self-rated health, plans to quit, and self-reported addiction; they considered themselves healthy or engaged in healthy behaviors to offset problems from tobacco use and used multiple tobacco products to quit smoking. Highly stressed users had fewer plans to quit and used tobacco for stress relief. Thus, the provision of accurate information about tobacco products and stress management is important to promote successful quitting.

Katiuska H. Liendo-Martínez, Stephany I. Briones-Alvarado, Virginia Gallo-González et al.

El uso diagnóstico y terapéutico de la broncoscopia flexible (BF) ha tenido una gran evolución desde que Gustav Killian realizó en 1897 la primera endoscopia traqueal para extraer un cuerpo extraño1. Con el pasar de los años se ha demostrado que es un procedimiento seguro2 con una mortalidad escasa (< 0.1%) siendo sus complicaciones infrecuentes y derivadas principalmente del tipo de técnica, de las propias comorbilidades del paciente y de la sedación3. Dentro de las complicaciones infrecuentes podemos mencionar el neumomediastino y el neumoperitoneo que generalmente se deben a la presencia de una ruptura gástrica. Presentamos el caso de un paciente de 58 años que 15 días tras la realización de una BF, presenta el hallazgo incidental de un neumoperitoneo asintomático sin evidencia de lesión gástrica.

Jegorović Boris, Šipetić-Grujičić Sandra, Ignjatović Svetlana

The occurrence of a new coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), quickly became a global threat after it had spread across the continents in just a few months. Over the next three years, it caused infections in over 646.6 million people and resulted in over 6.6 million deaths. As a novel disease, Coronavirus Disease 19 (COVID-19) became the subject of intensive research. Due to various clinical manifestations of the infection with possible fatal outcomes, it became evident that a finer understanding of COVID-19 pathogenesis, clinical manifestations, and complications is necessary. Investigation of acute-phase reaction as a component of the immune system response to infection can be very helpful. Serum amyloid A (SAA) was investigated for this purpose as one of the acute-phase reactants primarily synthesized by the hepatocytes in response to pro-inflammatory cytokines. It has been found that elevated SAA levels were independent factors for gastrointestinal manifestations and liver injury during COVID-19 but also one of the factors in COVID19-associated coagulopathy. Studies showed that SAA levels positively correlate with disease severity and prognosis. Patients with severe infection demonstrated significantly higher levels of SAA. Higher SAA levels were observed in COVID-19 patients with chronic diseases such as diabetes mellitus, hypertension, cerebrovascular diseases, and obesity, all recognized as independent risk factors for critical disease and poor prognosis. Patients with COVID-19 who died had higher levels of SAA than survivors. This short review will summarize current studies and knowledge about SSA in COVID-19, its role in the pathogenesis of SARS-CoV-2 infection, and its clinical usefulness in COVID-19 patients.

Andrii Voshchepynets, Oleksiy Agapitov, Lynn Wilson et al.

We present the results of processing the effects of the powerful Gamma Ray Burst GRB221009A captured by the charged particle detectors (electrostatic analyzers and solid-state detectors) onboard spacecraft at different points in the heliosphere on October 9, 2022. To follow the GRB221009A propagation through the heliosphere we used the electron and proton flux measurements from solar missions Solar Orbiter and STEREO-A; Earth magnetosphere and the solar wind missions THEMIS and Wind; meteorological satellites POES15, POES19, MetOp3; and MAVEN - a NASA mission orbiting Mars. GRB221009A had a structure of four bursts: less intense Pulse 1 - the triggering impulse - was detected by gamma-ray observatories at 131659 UT (near the Earth); the most intense Pulses 2 and 3 were detected on board all the spacecraft from the list, and Pulse 4 detected in more than 500 s after Pulse 1. Due to their different scientific objectives, the spacecraft, which data was used in this study, were separated by more than 1 AU (Solar Orbiter and MAVEN). This enabled tracking GRB221009A as it was propagating across the heliosphere. STEREO-A was the first to register Pulse 2 and 3 of the GRB, almost 100 seconds before their detection by spacecraft in the vicinity of Earth. MAVEN detected GRB221009A Pulses 2, 3, and 4 at the orbit of Mars about 237 seconds after their detection near Earth. By processing the time delays observed we show that the source location of the GRB221009A was at RA 288.5 degrees, Dec 18.5 degrees (J2000) with an error cone of 2 degrees

Background Chronic respiratory diseases (CRD) are common among patients with coronavirus disease 2019 (COVID-19). Objectives We sought to determine the association between CRD (including disease overlap) and the clinical outcomes of COVID-19. Methods Data of diagnoses, comorbidities, medications, laboratory results and clinical outcomes were extracted from the national COVID-19 reporting system. CRD was diagnosed based on ICD-10 codes. The primary endpoint was the composite outcome of needing invasive ventilation, admission to intensive care unit, or death within 30 days after hospitalization. The secondary endpoint was death within 30 days after hospitalization. Results We included 39,420 laboratory-confirmed patients from the electronic medical records as of May 6th, 2020. Any CRD and CRD overlap was present in 2.8% and 0.2% of patients, respectively. COPD was most common (56.6%), followed by bronchiectasis (27.9%) and asthma (21.7%). COPD-bronchiectasis overlap was the most common combination (50.7%), followed by COPD-asthma (36.2%) and asthma-bronchiectasis overlap (15.9%). After adjustment for age, sex and other systemic comorbidities, patients with COPD (OR: 1.71, 95% CI: 1.44-2.03) and asthma (OR: 1.45, 95%CI: 1.05-1.98), but not bronchiectasis, were more likely to reach to the composite endpoint compared with those without at day 30 after hospitalization. Patients with CRD were not associated with a greater likelihood of dying from COVID-19 compared to those without. Patients with CRD overlap did not have a greater risk of reaching the composite endpoint compared to those without. Conclusion CRD was associated with the risk of reaching the composite endpoint, but not death, of COVID-19.

Vinicius C. Iamonti, Gerson F. Souza, Antonio A. M. Castro et al.

Limited information is available regarding the role of anaerobic metabolism capacity on GOLD 1 and 2 COPD patients during upper limb exercise. We aimed to compare the upper limb anaerobic power capacity, blood lactate concentration, cardiovascular and respiratory responses, in male COPD patients versus healthy subjects during the 30-s Wingate anaerobic test (WAnT). The rate of fatigue and time constant of the power output decay (τ, tau) were also calculated and a regression analysis model was built to assess the predictors of τ in these patients. Twenty-four male COPD patients (post-bronchodilator FEV1 73.2 ± 15.3% of predicted) and 17 healthy subjects (FEV1 103.5 ± 10.1% of predicted) underwent the WAnT. Measurements were performed at rest, at the end of the WAnT, and during 3′ and 5′ of recovery time. Peak power (p = 0.04), low power (p = 0.002), and mean power output (p = 0.008) were significantly lower in COPD patients than in healthy subjects. Power output decreased exponentially in both groups, but at a significantly faster rate (p = 0.007) in COPD patients. The time constant of power decay was associated with resistance (in ohms) and fat-free mass (r2 = 0.604, adjusted r2 = 0.555, and p = 0.002). Blood lactate concentration was significantly higher in healthy subjects at the end of the test, as well as during 3′ and 5′ of recovery time (p < 0.01). Compared with healthy subjects, COPD patients with GOLD 1 and 2 presented lower upper limb anaerobic capacity and a faster rate of power output decrease during a maximal intensity exercise. Also, the WAnT proved to be a valid tool to measure the upper limb anaerobic capacity in these patients.

Johannes Leiner, Vincent Pellissier, Sebastian König et al.

Abstract Background Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach. Methods Administrative data (dataset randomly split 75%/25% for model training/testing) from years 2016–2019 of 86 German Helios hospitals was retrospectively analyzed. Inpatient SARI cases were defined by ICD-codes J09-J22. Three ML algorithms were evaluated and its performance compared to generalized linear models (GLM) by computing receiver operating characteristic area under the curve (AUC) and area under the precision-recall curve (AUPRC). Results The dataset contained 241,988 inpatient SARI cases (75 years or older: 49%; male 56.2%). In-hospital mortality was 11.6%. AUC and AUPRC in the testing dataset were 0.83 and 0.372 for GLM, 0.831 and 0.384 for random forest (RF), 0.834 and 0.382 for single layer neural network (NNET) and 0.834 and 0.389 for extreme gradient boosting (XGBoost). Statistical comparison of ROC AUCs revealed a better performance of NNET and XGBoost as compared to GLM. Conclusion ML algorithms for predicting in-hospital mortality were trained and tested on a large real-world administrative dataset of SARI patients and showed good discriminatory performances. Broad application of our models in clinical routine practice can contribute to patients’ risk assessment and quality management.