scholarly journals Long-acting injectable antipsychotic (LAI) prescribing trends during COVID-19 restrictions in Canada: a retrospective observational study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kyle A. McKee ◽  
Candice E. Crocker ◽  
Philip G. Tibbo
Keyword(s):  
Psychotic Disorders ◽  
Public Awareness ◽  
Healthcare Services ◽  
Prescribing Practices ◽  
Patient Level ◽  
Level Data ◽  
Gender And Age ◽  
Prescribing Trends ◽  
Concomitant Medications ◽  
Long Acting

Abstract Background The COVID-19 pandemic has had significant impacts on how mental health services are delivered to patients throughout Canada. The reduction of in-person healthcare services have created unique challenges for individuals with psychotic disorders that require regular clinic visits to administer and monitor long-acting injectable antipsychotic medications. Methods To better understand how LAI usage was impacted, national and provincial patient-level longitudinal prescribing data from Canadian retail pharmacies were used to examine LAI prescribing practices during the pandemic. Prescribing data on new starts of medication, discontinuations of medications, switches between medications, antipsychotic name, concomitant medications, payer plan, gender and age were collected from January 2019 to December 2020 for individuals ≥18-years of age, and examined by month, as well as by distinct pandemic related epochs characterized by varying degrees of public awareness, incidence of COVID-19 infections and public health restrictions. Results National, and provincial level data revealed that rates of LAI prescribing including new starts, discontinuations and switches between LAI products remained highly stable (i.e., no statistically significant differences) throughout the study period. Conclusions Equal numbers of LAI new starts and discontinuations prior to and during the pandemic suggests prescribing of LAI antipsychotics, for those already in care, continued unchanged throughout the pandemic. The observed consistency of LAI prescribing contrasts with other areas of healthcare, such as cardiovascular and diabetes care, which experienced decreases in medication prescribing during the COVID-19 pandemic.

scholarly journals Characterization of breakthrough hemolysis events observed in the phase 3 randomized studies of ravulizumab versus eculizumab in adults with paroxysmal nocturnal hemoglobinuria

Haematologica ◽  
2020 ◽  
Vol 106 (1) ◽  
pp. 230-237 ◽  
Author(s):  
Robert A. Brodsky ◽  
Régis Peffault de Latour ◽  
Scott T. Rottinghaus ◽  
Alexander Röth ◽  
Antonio M. Risitano ◽  
...  
Keyword(s):  
Paroxysmal Nocturnal Hemoglobinuria ◽  
Current Analysis ◽  
First Line ◽  
Phase 3 ◽  
Two Phase ◽  
Patient Level ◽  
Level Data ◽  
Long Acting ◽  
Dosing Intervals

Eculizumab is first-line treatment for paroxysmal nocturnal hemoglobinuria (PNH); however, approximately 11%-27% of patients may experience breakthrough hemolysis (BTH) on approved doses of eculizumab. Ravulizumab, a new long-acting C5 inhibitor with a four-times longer mean half-life than eculizumab, provides immediate, complete, and sustained C5 inhibition over 8-week dosing intervals. In two phase 3 studies, ravulizumab was noninferior to eculizumab (Pinf ≤0.0004) for the BTH endpoint; fewer patients experienced BTH with ravulizumab versus eculizumab in both studies (301 [complement inhibitor-naive patients], 4.0% vs 10.7%; 302 [patients stabilized on eculizumab at baseline], 0% vs 5.1%). In the current analysis, patient-level data were evaluated to assess causes and clinical parameters associated with incidents of BTH reported during the 26-week treatment periods in the ravulizumab phase 3 PNH studies. Of the five BTH events occurring in ravulizumab-treated patients across the studies, none were temporally associated with suboptimal C5 inhibition (free C5 ≥0.5 μg/mL); four (80.0%) were temporally associated with complement-amplifying conditions (CACs). Of the 22 events occurring in eculizumab-treated patients, eleven were temporally associated with suboptimal C5 inhibition, including three events also associated with concomitant infection. Six events were associated with CACs only. Five events were unrelated to free C5 elevation or reported CACs. These results suggest that the immediate, complete, and sustained C5 inhibition achieved through weight-based dosing of ravulizumab reduces the risk of BTH by eliminating BTH associated with suboptimal C5 inhibition in patients with PNH. Clinicaltrials.gov identifiers: Study 301, NCT02946463; Study 302, NCT03056040.

The Novel Antipsychotic Drug Cariprazine and Cognition Enhancing Drugs: Indications for Their Use as an Add-on Therapy in Schizophrenia

2020 ◽  
Vol 26 ◽  
Author(s):  
Felix-Martin Werner ◽  
Rafael Coveñas
Keyword(s):  
Clinical Trials ◽  
Maintenance Therapy ◽  
Antipsychotic Drug ◽  
Cognitive Functions ◽  
Antipsychotic Drugs ◽  
Psychotic Disorders ◽  
Therapeutic Effects ◽  
Treatment Resistant ◽  
Long Acting ◽  
Novel Antipsychotic

Background: Schizophrenia and schizoaffective disorder are treated with antipsychotic drugs. Some patients show treatment-resistant forms of psychotic disorders and, in this case, they can be treated with clozapine. In these patients and based on previous reviews on novel antipsychotic drugs, it is important to know whether an add-on therapy with new drugs can ameliorate the positive and negative schizophrenic scale (PANSS) total score. Objective: The aim of this review is to suggest an appropriate treatment for patients with treatment-resistant forms of psychotic disorders. A combination of current available antipsychotic drugs with novel antipsychotic or modulating drugs might improve negative schizophrenic symptoms and cognitive function and thereby social functioning and quality of life. Results: The mechanisms of action, the therapeutic effects and the pharmacokinetic profiles of novel antipsychotic drugs such as cariprazine, brexipiprazole and lumateperone are up-dated. Published case reports of patients with treatmentresistant psychoses are also discussed. These patients were treated with clozapine but a high PANSS total score was observed. Only an add-on therapy with cariprazine improved the score and, above all, negative schizophrenic symptoms and cognitive functions. To ensure a constant antipsychotic drug concentration, long-acting injectable antipsychotic drugs may be a choice for a maintenance therapy in schizophrenia. New modulating drugs, such as receptor positive allosteric modulators (N-methyl-D-aspartate receptor; subtype 5 of the metabotropic glutamatergic receptor) and encenicline, an alpha7 nicotinic cholinergic receptor agonist, are being investigated in preclinical and clinical trials. Conclusion: In clinical trials, patients with treatment-resistant forms of psychosis should be examined to know whether a combination therapy with clozapine and a novel antipsychotic drug can ameliorate the PANSS total score. In schizophrenia, long-acting injectable antipsychotic drugs are a safe and tolerable maintenance therapy. In further clinical studies, it should be investigated whether patients with treatment-resistant forms of psychoses can improve negative schizophrenic symptoms and cognitive functions by an add-on therapy with cognition enhancing drugs.

scholarly journals Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning

2021 ◽  
Vol 09 (02) ◽  
pp. E233-E238
Author(s):  
Rajesh N. Keswani ◽  
Daniel Byrd ◽  
Florencia Garcia Vicente ◽  
J. Alex Heller ◽  
Matthew Klug ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Medical Center ◽  
Academic Medical Center ◽  
Procedure Time ◽  
Image Capture ◽  
Common Procedure ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

Abstract Background and study aims Storage of full-length endoscopic procedures is becoming increasingly popular. To facilitate large-scale machine learning (ML) focused on clinical outcomes, these videos must be merged with the patient-level data in the electronic health record (EHR). Our aim was to present a method of accurately linking patient-level EHR data with cloud stored colonoscopy videos. Methods This study was conducted at a single academic medical center. Most procedure videos are automatically uploaded to the cloud server but are identified only by procedure time and procedure room. We developed and then tested an algorithm to match recorded videos with corresponding exams in the EHR based upon procedure time and room and subsequently extract frames of interest. Results Among 28,611 total colonoscopies performed over the study period, 21,170 colonoscopy videos in 20,420 unique patients (54.2 % male, median age 58) were matched to EHR data. Of 100 randomly sampled videos, appropriate matching was manually confirmed in all. In total, these videos represented 489,721 minutes of colonoscopy performed by 50 endoscopists (median 214 colonoscopies per endoscopist). The most common procedure indications were polyp screening (47.3 %), surveillance (28.9 %) and inflammatory bowel disease (9.4 %). From these videos, we extracted procedure highlights (identified by image capture; mean 8.5 per colonoscopy) and surrounding frames. Conclusions We report the successful merging of a large database of endoscopy videos stored with limited identifiers to rich patient-level data in a highly accurate manner. This technique facilitates the development of ML algorithms based upon relevant patient outcomes.

scholarly journals Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Chuan Hong ◽  
Everett Rush ◽  
Molei Liu ◽  
Doudou Zhou ◽  
Jiehuan Sun ◽  
...  
Keyword(s):  
Feature Selection ◽  
Electronic Health Record ◽  
Large Scale ◽  
Knowledge Extraction ◽  
Knowledge Map ◽  
Health Record ◽  
Clinical Knowledge ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

AbstractThe increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

Sign in / Sign up

Export Citation Format

Share Document