Associations between resources and practices of ILD centers and outcomes in patients with idiopathic pulmonary fibrosis: data from the IPF-PRO Registry

Background Performance benchmarks for the management of idiopathic pulmonary fibrosis (IPF) have not been established. We used data from the IPF-PRO Registry, an observational registry of patients with IPF managed at sites across the US, to examine associations between the characteristics of the enrolling sites and patient outcomes. Methods An online survey was used to collect information on the resources, operations, and self-assessment practices of IPF-PRO Registry sites that enrolled ≥ 10 patients. Site variability in 1-year event rates of clinically relevant outcomes, including death, death or lung transplant, and hospitalization, was assessed. Models were adjusted for differences in patient case mix by adjusting for known predictors of each outcome. We assessed whether site-level heterogeneity existed for each patient-level outcome, and if so, we investigated potential drivers of the heterogeneity. Results All 27 sites that enrolled ≥ 10 patients returned the questionnaire. Most sites were actively following > 100 patients with IPF (70.4%), had a lung transplant program (66.7%), and had a dedicated ILD nurse leader (77.8%). Substantial heterogeneity was observed in the event rates of clinically relevant outcomes across the sites. After controlling for patient case mix, there were no outcomes for which the site variance component was significantly different from 0, but the p-value for hospitalization was 0.052. Starting/completing an ILD-related quality improvement project in the previous 2 years was associated with a lower risk of hospitalization (HR 0.60 [95% CI 0.44, 0.82]; p = 0.001). Conclusions Analyses of data from patients with IPF managed at sites across the US found no site-specific characteristics or practices that were significantly associated with clinically relevant outcomes after adjusting for patient case mix. Trial registration ClinicalTrials.gov, NCT01915511. Registered 5 August 2013, https://clinicaltrials.gov/ct2/show/NCT01915511

NUT midline carcinoma as a primary lung tumor treated with anlotinib combined with palliative radiotherapy: a case report

Background NUT (nuclear protein in testis) midline carcinoma (NMC) is a rapidly progressive tumor arising from midline structures. Recent cases have reported that the poor prognosis with a median survival of 6.7 months and a 2 years overall survival of 19% due to limited treatment. Based on the effect of arotinib on inhibiting tumor growth and angiogenesis. We present one patient case treated with anlotinib and radiotherapy. Case presentation Here, we describe a 33-year old patient who complained of cough and chest pain and was diagnosed as a pulmonary NMC through CT scan, FISH and immunohistochemistry. In addition, we initially demonstrated that anlotinib combined with palliative radiotherapy could significantly prevent the tumor growth in a pulmonary NMC. Conclusion The report indicated that anlotinib combined with palliative radiotherapy could inhibit the tumor progression in a pulmonary NMC, which may provide a combined therapy to pulmonary NMC in the future.

DIAGNOSIS OF COVID-19 BASED ON ARTIFICIAL INTELLIGENCE MODELS AND PHYSIOLOGICAL SENSORS: REVIEW

Covid-19 invaded the world very quickly and caused the loss of many lives; maximum emergency was activated all over the world due to its rapid spread. Consequently, it became a huge burden on emergency and intensive care units due to the large number of infected individuals and the inability of the medical staff to deal with patients according to the degree of severity. Covid-19 can be diagnosed based on the artificial intelligence (AI) model. Based on AI, the CT images of the patient’s chest can be analyzed to identify the patient case whether it is normal or he/she has Covid-19. The possibility of employing physiological sensors such as heart rate, temperature, respiratory rate, and SpO2 sensors in diagnosing Covid-19 was investigated. In this paper, several articles which used intelligent techniques and vital signs for diagnosing Covid-19 have been reviewed, classified, and compared. The combination of AI and physiological sensors reading, called AI-PSR, can help the clinician in making the decisions and predicting the occurrence of respiratory failure in Covid-19 patients. The physiological parameters of the Covid-19 patients can be transmitted wirelessly based on a specific wireless technology such as Wi-Fi and Bluetooth to the clinician to avoid direct contact between the patient and the clinician or nursing staff. The outcome of the AI-PSR model leads to the probability of recording and linking data with what will happen later, to avoid respiratory failure, and to help the patient with one of the mechanical ventilation devices.

The Prognostic Utility of Personality Traits Versus Past Psychiatric Diagnoses: Predicting Future Mental Health and Functioning

Past psychiatric diagnoses are central to patient case formulation and prognosis. Recently, alternative classification models such as the Hierarchical Taxonomy of Psychopathology (HiTOP) proposed to assess traits to predict clinically relevant outcomes. In the current study, we directly compared personality traits and past diagnoses as predictors of future mental health and functioning in three independent, prospective samples. Regression analyses found that personality traits significantly predicted future first onsets of psychiatric disorders (change in [∆] R2 = .06–.15), symptom chronicity (∆ R2 = .03–.06), and functioning (∆ R2 = .02–.07), beyond past and current psychiatric diagnoses. Conversely, past psychiatric diagnoses did not provide an incremental prediction of outcomes when personality traits and other concurrent predictors were already included in the model. Overall, personality traits predicted a variety of outcomes in diverse settings beyond diagnoses. Past diagnoses were generally not informative about future outcomes when personality was considered. Together, these findings support the added value of personality traits assessment in case formulation, consistent with the HiTOP model.