Background: The burden of chronic respiratory diseases has changed over the three decades. This study aims to describe the spatiotemporal trends of prevalence, mortality, and disability-adjusted life years (DALY) due to chronic respiratory diseases (CRDs) worldwide during 1990–2019 using data from the Global Burden of Disease Study 2019 (GBD 2019).Methods: The prevalence, mortality, and DALY attributable to CRDs and risk factors from 1990 to 2019 were estimated. We also assessed the driving factors and potentiality for improvement with decomposition and frontier analyses, respectively.Results: In 2019, 454.56 [95% uncertainty interval (UI): 417.35–499.14] million individuals worldwide had a CRD, showing a 39·8% increase compared with 1990. Deaths due to CRDs were 3.97 (95%UI: 3.58–4.30) million, and DALY in 2019 was 103.53 (95%UI: 94.79–112.27) million. Declines by average annual percent change (AAPC) were observed in age-standardized prevalence rates (ASPR) (0.64% decrease), age-standardized mortality rates (ASMR) (1.92%), and age-standardized DALY rates (ASDR) (1.72%) globally and in 5 socio-demographic index (SDI) regions. Decomposition analyses represented that the increase in overall CRDs DALY was driven by aging and population growth. However, chronic obstructive pulmonary disease (COPD) was the leading driver of increased DALY worldwide. Frontier analyses witnessed significant improvement opportunities at all levels of the development spectrum. Smoking remained a leading risk factor (RF) for mortality and DALY, although it showed a downward trend. Air pollution, a growing factor especially in relatively low SDI regions, deserves our attention.Conclusion: Our study clarified that CRDs remain the leading causes of prevalence, mortality, and DALY worldwide, with growth in absolute numbers but declines in several age-standardized estimators since 1990. The estimated contribution of risk factors to mortality and DALY demands the need for urgent measures to improve them.Systematic review registration: http://ghdx.healthdata.org/gbd-results-tool.

Primary intravascular large B-cell lymphoma of the lung: a review and case report

Introduction: Pulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.Methods: Combining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.Results: To confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.Discussion: The experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model’s accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.