Daily Respiratory Research Analysis

BACKGROUND: People with severe mental illness have a 10-20 year reduced life-expectancy compared with the general population. Respiratory diseases are a main cause of this premature mortality, but no comprehensive meta-analysis of overall and respiratory cause-specific mortality risk in this population exists. We aimed to evaluate the mortality from specific respiratory diseases for people with pooled severe mental illnesses and specific diagnoses, alongside mortality for specific mental disorders. METHODS: For this large-scale random-effects meta-analysis, we searched PubMed, PsycINFO, Embase, Scopus, African Index Medicus, and LILACS from database inception to April 6, 2025, for prospective or retrospective cohort studies published in English. We included studies reporting on patients with schizophrenia spectrum disorder, bipolar disorder, major depressive disorder or depressive episodes, or mixed severe mental illness (defined as at least two among bipolar, depressive, and schizophrenia spectrum disorders). Publications had to include a control group from the general population and quantified reporting. We excluded cross-sectional studies, reviews, systematic reviews, and meta-analyses; studies that did not have respiratory-related mortality data; studies of clustered mixed groups that did not have at least 70% of the patient sample corresponding to our diagnoses, or studies in which the data were not suitable for meta-analysis. The primary outcome was adjusted risk ratio (RR) of overall respiratory disease-related mortality in people with severe mental illness (both pooled and for the specific severe mental disorders) versus the general population control group. Two authors extracted the data using a predetermined data extraction form. The information extracted included first author, country, setting (inpatient, outpatient, or both), data source, design of the study (prospective or retrospective), number of participants and their demographics (sex and mean age), specific severe mental illness and respiratory disease diagnosis, and the RR mortality of each respiratory disease. We assessed the risk of bias in each study using the Newcastle-Ottawa scale and heterogeneity was assessed with a multilevel random-effects meta regression. Individuals with lived experience of mental illness were not involved in the design, analysis, or dissemination of this study. The study was conducted in accordance with PRISMA and was registered with PROSPERO (CRD42024563552). FINDINGS: Our search identified 83 studies that met the eligibility criteria. We included 4 837 720 people with pooled severe mental illness (2 383 821 males [49·3%] and 2 453 899 females [50·7%]; mean age 57·7 years [SD 13·5]). Data on ethnicity or race were insufficiently reported to be included in our study. Our control group comprised 785 538 236 individuals from the general population (382 185 432 [48·7%] males and 403 352 804 [51·3%] females). 57 studies included people with schizophrenia spectrum disorder (2 979 972); 21 included people with bipolar disorder (491 758); 20 included people with major depressive disorder (1 327 642); and ten studies included individuals with mixed severe mental illness (968 326). Across all 83 studies, pooled severe mental illness was associated with significantly higher respiratory-related mortality compared to the general population (RR 2·28 [95% CI 2·02-2·56]). People with schizophrenia spectrum disorder had the highest respiratory-related mortality compared to the general population (RR 2·60 [2·28-2·96]; from 57 studies), followed by bipolar disorder (RR 1·96 [1·57-2·43]; from 21 studies), mixed severe mental illness (RR 1·91 [1·43-2·54]; from ten studies), and major depressive disorder (RR 1·72 [1·39-2·13]; from 20 studies). In the quality assessment, the mean score was 8·7 out of 9 points. 78 included studies (94%) were ranked as good quality (a score of 7-9 on the Newcastle-Ottawa scale) and five studies (6%) were ranked as fair quality (a score of 5-6). INTERPRETATION: Overall and specific respiratory disease mortality risk is significantly higher in individuals with pooled severe mental illnesses and specific severe mental illnesses than in the general population. Programmes for smoking cessation, lung cancer screening, vaccination against respiratory infections, and pulmonary health monitoring in people with severe mental illness should be developed and implemented to address the unmet health needs of this population. FUNDING: Instituto de Salud Carlos III, EU.

BACKGROUND: Alpha-1 antitrypsin deficiency (AATD) is a genetic disorder that causes emphysema from lack of the AAT serpin anti-protease, leading to protease-anti-protease imbalance. Weekly intravenous AAT therapy (augmentation) is the only specific treatment available. Alvelestat is an oral inhibitor of neutrophil elastase (NE) in development as a novel approach to AATD therapy. Here, we tested the safety and mechanistic efficacy of alvelestat in severe AATD. METHODS: We conducted two complementary, double-blind, randomized, placebo-controlled, 12-week trials, incorporating two doses of alvelestat in AATD. ATALANTa investigated 120 mg twice daily, including a subset of participants also receiving augmentation; ASTRAEUS tested 120 mg and 240 mg twice a day without augmentation. Primary and secondary endpoints were the change in blood NE (the putative target) and its activity in AATD (Aα-Val RESULTS: We enrolled 161 participants (63 in ATALANTa and 98 in ASTRAEUS). Blood NE was significantly suppressed in both studies at both doses, with the greatest effect (>90% suppression) at the 240 mg BID dose. There was no effect of 120 mg on disease activity biomarkers, whilst the 240 mg dose demonstrated significant reduction Aα-Val CONCLUSIONS: Alvelestat effectively suppressed NE and its activity at both doses, but only the 240 mg twice daily dose demonstrated relevant efficacy compared to placebo on disease activity biomarkers with a favourable safety profile. These findings support progression of the 240 mg twice daily dose into a clinical endpoint study.

BACKGROUND: Usual interstitial pneumonia (UIP) indicates poor prognosis, and there is significant heterogeneity in the diagnosis of UIP, necessitating an auxiliary diagnostic tool. RESEARCH QUESTION: Can a machine learning (ML) classifier using radiomics features and clinical data accurately identify UIP from patients with interstitial lung disease (ILD)? STUDY DESIGN AND METHODS: This data set from a prospective cohort includes 5,321 sets of high-resolution CT (HRCT) images from 2,901 patients with ILD (male, 63.5%; mean age ± SD, 61.7 ± 10.8 years) across 3 medical centers. Multimodal data, including whole-lung radiomics features on HRCT scan and demographics, smoking, lung function, and comorbidity data, were extracted. An XGBoost and logistic regression were used to design a nomogram predicting UIP or not. The area under the receiver operating characteristic curve (AUC) and Cox regression for all-cause mortality were used to assess the diagnostic performance and prognostic value of models, respectively. RESULTS: A total of 5,213 HRCT image data sets were divided into the training group (n = 3,639), the internal testing group (n = 785), and the external validation group (n = 789). UIP prevalence was 43.7% across the whole data set, with 42.7% and 41.3% for the internal validation set and external validation set, respectively. The radiomics-based classifier had an AUC of 0.790 in the internal testing set and 0.786 for the external validation data set. Integrating multimodal data improved AUCs to 0.802 and 0.794, respectively. The performance of the integration model was comparable with a pulmonologist with > 10 years of experience in ILD. Within 522 patients deceased during a median follow-up period of 3.37 years, the multimodal-based ML model-predicted UIP status was associated with high all-cause mortality risk (hazard ratio, 2.52; P < .001). INTERPRETATION: The classifier combining radiomics and clinical features showed strong performance across varied UIP prevalence. This multimodal-based ML model could serve as an adjunct in the diagnosis of UIP. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; No.: NCT04370158; URL: www. CLINICALTRIALS: gov.