Daily Respiratory Research Analysis

BACKGROUND: Limertinib is a new third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor. This study aimed to prospectively assess the efficacy and safety of limertinib versus gefitinib as a first-line treatment for locally advanced or metastatic non-small-cell lung cancer (NSCLC) with EGFR-sensitising mutation. METHODS: This multicentre, randomised, double-blind, double-dummy, phase 3 trial was done at 56 hospitals in China. Eligible patients were aged ≥18 years with locally advanced or metastatic NSCLC with EGFR-sensitising mutation (exon 19 deletion or exon 21 L858R mutation) detected in tumour tissue samples using the Cobas EGFR Mutation Test at a central laboratory. Patients were randomly assigned (1:1) to receive oral limertinib 80 mg twice a day and gefitinib-matching placebo 250 mg once a day or oral gefitinib 250 mg once a day plus limertinib-matching placebo 80 mg twice a day in 21-day cycles, until disease progression or other discontinuation criteria was met. Random assignment was stratified according to EGFR mutation type (exon 19 deletion or exon 21 L858R mutation) and CNS metastasis (yes or no) using permuted blocks (block size four) through an interactive web-based response system. The primary endpoint was independent central review (ICR)-assessed progression-free survival. All enrolled patients who received at least one dose of study treatment were included in the full analysis set for efficacy analysis. All enrolled patients who received at least one dose of study treatment and one safety assessment were included in the safety set. This study is registered with ClinicalTrials.gov, NCT04143607, and follow-up is ongoing. FINDINGS: Between June 30, 2021, and Sept 22, 2022, 337 patients were enrolled and 168 were randomly assigned to the limertinib group and 169 to the gefitinib group. Patients' median age was 63 years (34-82). 214 (64%) of 337 patients were female and 123 (36%) were male. The median masked ICR-assessed progression-free survival was 20·7 months (95% CI 15·2-22·1) in the limertinib group and 9·7 months (95% CI 8·3-11·1) in the gefitinib group (hazard ratio [HR] 0·44 [95% CI 0·34-0·58]; p<0·0001). Treatment-related adverse events of grade 3 or worse occurred in 42 (25%) of 168 patients in the limertinib group and 42 (25%) of 169 patients in the gefitinib group. Treatment-related serious adverse events occurred in nine (5%) patients and 17 (10%) patients in each group, respectively. Six (4%) patients in the limertinib group died due to adverse events, all of which were considered possibly unrelated to the study drug by investigators. In the gefitinib group, seven (4%) patients died due to adverse events, with three (2%) of those deaths judged as possibly related to the study drug by investigators. Three treatment-related deaths in the gefitinib group were recorded (one case related to pneumonia and two with cause of death unknown). INTERPRETATION: Limertinib showed superior efficacy compared with gefitinib and a manageable safety profile for locally advanced or metastatic NSCLC patients with EGFR-sensitising mutation and should be considered as another first-line treatment option for this patient population. FUNDING: Jiangsu Aosaikang Pharmaceutical. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

STUDY OBJECTIVES: To develop and validate a Complete Artificial Intelligence Sleep Report system (CAISR), a system for comprehensive automated sleep analysis, including sleep staging, arousal detection, apnea identification, and limb movement analysis. METHODS: We utilized a large diverse dataset from four cohorts (MGH, MESA, MrOS, SHHS) comprising 25,749 participants to develop CAISR. Following American Academy of Sleep Medicine (AASM) guidelines, CAISR performs four tasks: it stages sleep into five categories (Wake, NREM 1, NREM 2, NREM 3, REM), detects arousals, detects and classifies breathing events (Obstructive Apnea, Central Apnea, Mixed Apnea, Hypopnea, and RERA), and detects limb movements and categorizes them as periodic or isolated. We tested CAISR against multiple datasets independently annotated by multiple experts, including UPenn (69 subjects, 6 experts), BITS (98 subjects, 3 experts), and Stanford (100 subjects, three experts). Sleep staging and arousal detection were accomplished using customized deep neural networks, while breathing event detection and classification and limb movement analysis were accomplished using rule-based signal processing approaches. We quantified CAISR performance with three metrics: Cohen's Kappa, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). To determine whether CAISR performed on par with human experts, we compared expert inter-rater reliability (IRR) with algorithm-expert IRR. RESULTS: The CAISR model showed strong overall performance across the four tasks: sleep staging, arousal detection, apnea detection, and limb movement detection. In sleep staging, the model achieved AUROC values ranging from 0.82 to 0.97 and AUPRC values between 0.63 and 0.90 across the BITS, Stanford, and Penn datasets, indicating high classification accuracy. The Kappa agreement analysis showed that in the BITS and Stanford datasets, CAISR outperformed human experts, with non-overlapping confidence intervals indicating superiority (Kappa values around 0.7 to 0.8 for CAISR vs. experts). In the Penn dataset, the model's performance was comparable to experts, with overlapping confidence intervals suggesting non-inferiority. For arousal detection, the model maintained reliable performance, with AUROC values ranging from 0.83 to 0.94 and AUPRC values from 0.67 to 0.85, and Kappa analysis showing overlapping confidence intervals, indicating comparable performance to experts in both the BITS and Stanford datasets (Kappa values for CAISR around 0.6 to 0.75). In apnea detection, including the detection of obstructive, central, and mixed apnea, the CAISR model achieved competitive results in the BITS dataset with AUROC values between 0.81 and 0.95 and AUPRC values between 0.58 and 0.82, but in the Stanford dataset, it underperformed compared to human experts, as shown by non-overlapping confidence intervals and lower Kappa values (around 0.55 to 0.65). Finally, in limb movement detection, the model demonstrated superior performance in the BITS dataset, with AUROC values of 0.9 to 0.96 and AUPRC values between 0.75 and 0.85, and Kappa analysis indicating significantly higher reliability compared to experts (CAISR Kappa around 0.8, with non-overlapping confidence intervals). In the Stanford dataset, CAISR's performance was comparable to experts, with overlapping confidence intervals suggesting non-inferiority (Kappa values around 0.65 to 0.7). Overall, the CAISR model consistently exhibited high classification performance and reliability across tasks, often matching or surpassing expert-level performance, with particularly strong results in sleep staging and limb detection. CONCLUSIONS: The CAISR model demonstrated high classification accuracy and reliability across sleep staging, arousal, apnea, and limb movement detection tasks, matching or surpassing human expert performance. Human errors and systematic biases in the annotation of micro-events during sleep, such as arousal and apnea detection, likely contributed to variability in expert performance, while the CAISR model showed more consistent results, reducing the impact of these biases and increasing overall reliability across task.

BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. The aim of the KBP-2020 study was to describe survival among patients diagnosed with lung adenocarcinoma in France in 2000, 2010, and 2020, outside academic medical centers. METHODS: We collected prospective data from all patients diagnosed with lung cancer in nonacademic public hospitals in France in 2020. We compared these data with those from similar studies performed in 2000 and 2010 to map the evolution of survival. RESULTS: The KBP-2020 cohort comprised 5015 patients with lung adenocarcinoma. The 3-year overall survival (OS) rate was 38.6%, ranging from 21.3% among patients with metastatic disease at diagnosis to 84.0% for those with stage I disease at diagnosis. The median OS in the overall population more than doubled in 20 years, from 8.5 months in 2000 to 20.7 months in 2020. Female sex, higher performance status, and earlier disease stage were associated with an increased 3-year OS. Patients with metastatic lung adenocarcinoma with CONCLUSIONS: Improvements in the OS of patients with lung adenocarcinoma were seen over 20 years in this setting of nonacademic public hospitals in France. Targeted therapy and immunotherapy were associated with longer OS among patients with metastatic disease. (Le Nouveau Souffle and others; trial registration number, NCT04402099.).