Patient preference for chronic obstructive pulmonary disease (COPD) treatment inhalers: a discrete choice experiment in France

Objectives: Understanding inhaler preferences may contribute to improving adherence in COPD patients and improving long-term outcomes. This study aims to identify and quantify preferences for convenience-related inhaler attributes in French moderate-to-severe COPD patients, with discrete choice experiment (DCE) methodology.

Methods: Attributes were defined from a literature search, clinician and patient interviews: shape, dose insertion, dose preparation, dose release, dose confirmation, dose counter and reusability. An online DCE was conducted in respondents with self-reported COPD stage 2–4 recruited through a panel. The study questionnaire included twelve choice scenarios per respondent and questions on patient characteristics, treatment and disease severity. Statistical analyses used a mixed logit regression model with random effects. Utility scores were estimated for four types of inhalers: Inhaler A – soft mist inhaler; Inhaler B – reusable soft mist inhaler; Inhaler C – multi-dose dry powder inhaler; and Inhaler D – single dose dry powder inhaler.

Results: The study was completed by 153 patients (50 females); respondents were 50.4?years old on average; 13 different inhaler devices were reported. The most preferred inhaler is L-shaped, has dose preparation with capsule insertion and a dose counter, and is reusable. Inhaler profiles A and B had the highest utilities (mean of 1.2533 and 0.9578 respectively) compared to inhaler C (0.6315) and D (0.2200).

Conclusions: This study showed statistically significant results that the strongest drivers of preference in French users of inhalation devices for COPD are shape, dose counter and reusability. Convenience-related characteristics are important to patients and should be taken into account by clinicians prescribing these devices.     

Structural changes of tumor necrosis factor alpha associated with membrane insertion and channel formation.

Low pH enhances tumor necrosis factor alpha (TNF)-induced cytolysis of cancer cells and TNF-membrane interactions that include binding, insertion, and ion-channel formation. We have also found that TNF increases Na+ influx in cells. Here, we examined the structural features of the TNF-membrane interaction pathway that lead to channel formation. Fluorometric studies link TNF's acid-enhanced membrane interactions to rapid but reversible acquisition of hydrophobic surface properties. Intramembranous photolabeling shows that (i) protonation of TNF promotes membrane insertion, (ii) the physical state of the target bilayer affects the kinetics and efficiency of TNF insertion, and (iii) binding and insertion of TNF are two distinct events. Acidification relaxes the trimeric structure of soluble TNF so that the cryptic carboxyl termini, centrally located at the base of the trimer cone, become susceptible to carboxypeptidase Y. After membrane insertion, TNF exhibits a trimeric configuration in which the carboxyl termini are no longer exposed; however, the proximal salt-bridged Lys-11 residues as well as regional surface amino acids (Glu-23, Arg-32, and Arg-44) are notably more accessible to proteases. The sequenced cleavage products bear the membrane-restricted photoreactive probe, proof that surface-cleaved TNF has an intramembranous disposition. In summary, the trimer's structural plasticity is a major determinant of its channel-forming ability. Channel formation occurs when cracked or partially splayed trimers bind and penetrate the bilayer. Reannealing leads to a slightly relaxed trimeric structure. The directionality of bilayer penetration conforms with x-ray data showing that receptor binding to the monomer interfaces of TNF poises the tip of the trimeric cone directly above the target cell membrane.     

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk

BackgroundUsing novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis. MethodThe study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell’s C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia. ResultsA total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3–3.6). External validation indicated strong performance on unseen data (Harrell’s C 0.85, 95% CI 0.84–0.86), an increase of 0.06 from the original model. ConclusionsUsing NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.