Validation of Risk Assessment Models Predicting Venous Thromboembolism in Inpatients with Acute Exacerbation Of Chronic Obstructive Pulmonary Disease: A Multicenter Cohort Study in China.

BACKGROUND: Inpatients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) are at increased risk for venous thromboembolism (VTE); however, the prophylaxis for VTE is largely underused in China. Identifying high-risk patients requiring thromboprophylaxis is critical to reduce the mortality and morbidity associated with VTE. This study aimed to evaluate and compare the validities of the Padua Prediction Score and Caprini risk assessment model (RAM) in predicting the risk of VTE in inpatients with AECOPD in China. METHODS: The inpatients with AECOPD were prospectively enrolled from seven medical centers of China between September 2017 and January 2020. Caprini and Padua scores were calculated on admission, and the incidence of 3-month VTE was investigated. RESULTS: Among the 3,277 eligible patients with AECOPD, 128 patients (3.9%) developed VTE within 3 months after admission. The distribution of the study population by the Caprini risk level was as follows: high, 53.6%; moderate, 43.0%; and low, 3.5%. The incidence of VTE increased by risk level as high, 6.1%; moderate, 1.5%; and low, 0%. According to the Padua RAM, only 10.9% of the study population was classified as high risk and 89.1% as low risk, with the corresponding incidence of VTE of 7.9 and 3.4%, respectively. The Caprini RAM had higher area under curve compared with the Padua RAM (0.713 ± 0.021 vs. 0.644 ± 0.023, p = 0.029). CONCLUSION: The Caprini RAM was superior to the Padua RAM in predicting the risk of VTE in inpatients with AECOPD and might better guide thromboprophylaxis in these patients.

Assessment of respiratory system compliance under pressure control ventilation without an inspiratory pause maneuver.

Objective. The measurement of the static compliance of the respiratory system (Cstat) during mechanical ventilation requires zero end-inspiratory flow. An inspiratory pause maneuver is needed if the zero end-inspiratory flow condition cannot be satisfied under normal ventilation.Approach. We propose a method to measure the quasi-static respiratory compliance (Cqstat) under pressure control ventilation mode without the inspiratory pause maneuver. First, a screening strategy was applied to filter out breaths affected strongly by spontaneous breathing efforts or artifacts. Then, we performed a virtual extrapolation of the flow-time waveform when the end-inspiratory flow was not zero, to allow for the calculation ofCqstatfor each kept cycle. Finally, the outputCqstatwas obtained as the average of the smallest 40Cqstatmeasurements. The proposed method was validated against the gold standardCstatmeasured from real clinical settings and compared with two reported algorithms. The gold standardCstatwas obtained by applying an end-inspiratory pause maneuver in the volume-control ventilation mode.Main results. Sixty-nine measurements from 36 patients were analyzed. The Bland-Altman analysis showed that the bias of agreement forCqstatversus the gold standard measurement was -0.267 ml/cmH2O (95% limits of agreement was -4.279 to 4.844 ml/cmH2O). The linear regression analysis indicated a strong correlation (R2 = 0.90) between theCqstatand gold standard.Significance. The results showed that theCqstatcan be accurately estimated from continuous ventilator waveforms, including spontaneous breathing without an inspiratory pause maneuver. This method promises to provide continuous measurements compliant with mechanical ventilation.

Using Natural Language Processing Techniques to Provide Personalized Educational Materials for Chronic Disease Patients in China: Development and Assessment of a Knowledge-Based Health Recommender System.

BACKGROUND: Health education emerged as an important intervention for improving the awareness and self-management abilities of chronic disease patients. The development of information technologies has changed the form of patient educational materials from traditional paper materials to electronic materials. To date, the amount of patient educational materials on the internet is tremendous, with variable quality, which makes it hard to identify the most valuable materials by individuals lacking medical backgrounds. OBJECTIVE: The aim of this study was to develop a health recommender system to provide appropriate educational materials for chronic disease patients in China and evaluate the effect of this system. METHODS: A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP). RESULTS: The constructed ontology contained 40 classes, 31 object properties, 67 data properties, and 32 individuals. A total of 80 SWRL rules were defined to implement the semantic logic of mapping patient original data to the ontology vector space. The recommender system was implemented as a separate Web service connected with patients' smartphones. According to the evaluation results, our system can achieve a macro precision up to 0.970 for the top 1 recommendation and an overall MAP score up to 0.628. CONCLUSIONS: This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.