Development and validation of a predictive model to predict and manage drug shortages

2021 ◽  
Author(s):  
Ina Liu ◽  
Evan Colmenares ◽  
Casey Tak ◽  
Mary-Haston Vest ◽  
Henry Clark ◽  
...  
Keyword(s):  
Orphan Drug ◽  
Predictive Analytics ◽  
Training Dataset ◽  
Drug Status ◽  
Potential Candidate ◽  
Drug Shortage ◽  
Drug Shortages ◽  
C Statistic ◽  
Testing Dataset ◽  
Therapeutic Class

Abstract Purpose Pharmacy departments across the country are problem-solving the growing issue of drug shortages. We aim to change the drug shortage management strategy from a reactive process to a more proactive approach using predictive data analytics. By doing so, we can drive our decision-making to more efficiently manage drug shortages. Methods Internal purchasing, formulary, and drug shortage data were reviewed to identify drugs subject to a high shortage risk (“shortage drugs”) or not subject to a high shortage risk (“nonshortage drugs”). Potential candidate predictors of drug shortage risk were collected from previous literature. The dataset was trained and tested using 2 methods, including k-fold cross-validation and a 70/30 partition into a training dataset and a testing dataset, respectively. Results A total of 1,517 shortage and nonshortage drugs were included. The following candidate predictors were used to build the dataset: dosage form, therapeutic class, controlled substance schedule (Schedule II or Schedules III-V), orphan drug status, generic versus branded status, and number of manufacturers. Predictors that positively predicted shortages included classification of drugs as intravenous-only, both oral and intravenous, antimicrobials, analgesics, electrolytes, anesthetics, and cardiovascular agents. Predictors that negatively predicted a shortage included classification as an oral-only agent, branded-only agent, antipsychotic, Schedule II agent, or orphan drug, as well as the total number of manufacturers. The calculated sensitivity was 0.71; the specificity, 0.93; the accuracy, 0.87; and the C statistic, 0.93. Conclusion The study demonstrated the use of predictive analytics to create a drug shortage model using drug characteristics and manufacturing variables.

Neues aus der Pharmakotherapie

2010 ◽  
Vol 29 (04) ◽  
pp. 191-198
Author(s):  
G. Kluger ◽  
S. Arnold
Keyword(s):  
Orphan Drug ◽  
Orphan Drug Status ◽  
Drug Status

ZusammenfassungRund ein Drittel aller Patienten mit fokaler Epilepsie ist trotz medikamentöser Behandlung nicht anfallsfrei (1). Insbesondere für diese Patienten mit schwer behandelbaren Epilepsien bieten medikamentöse Neuentwicklungen neue Chancen. Seit 2008 steht in Deutschland für Patienten mit fokaler Epilepsie mit Lacosamid ein Wirkstoff mit einem neuen Wirkungsmechanismus zur Verfügung. 2009 wurde die Medikamentenpalette um Eslicarbazepinacetat erweitert. Beide Substanzen haben in großen randomisierten Doppelblindstudien eine signifikante Reduktion der Anfallshäufigkeit im Vergleich zu Placebo belegen können. Zur Behandlung seltener Erkrankungen können Substanzen mit der Option des “Orphan- Drug”-Status auch nach Untersuchung vergleichsweise geringer Patientenzahlen unter besonderen Auflagen zur Verfügung gestellt werden. Als “Orphan Drug” zur Zusatzbehandlung des Lennox-Gastaut-Syndroms wurde 2007 Rufinamid von der EMEA zugelassen. Bereits seit 2001 ist Stiripentol als “Orphan Drug” von der EMEA zur Zusatzbehandlung des Dravet-Syndromes ausgewiesen und seit 2007 als “Orphan Drug” mit Auflagen für Europa zugelassen. 2008 konnte Stiripentol auch in Deutschland eingeführt werden. In dieser Übersicht sollen die wesentlichen Merkmale der genannten Substanzen dargestellt werden.Da selten auftretende Nebenwirkungen nach der Markteinführung einer Substanz auftreten können, sind weitere Untersuchungen notwendig,um die langfristige Sicherheit der vorgestellten Substanzen zu überprüfen.

scholarly journals Patients perspectives on drug shortages in six European hospital settings – a cross sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Darija Kuruc Poje ◽  
Domagoj Kifer ◽  
Isabelle Huys ◽  
Joao Miranda ◽  
Helena Jenzer ◽  
...  
Keyword(s):  
Management Practices ◽  
Healthcare Professionals ◽  
Demographic Data ◽  
General Information ◽  
Basic Knowledge ◽  
Drug Shortage ◽  
Cross Sectional ◽  
Drug Shortages ◽  
Good Communication ◽  
Information Preferences

Abstract Background It is known that drug shortages represent a major challenge for all stakeholders involved in the process, but there is little evidence regarding insights into patients′ awareness and perspectives. This study aimed to investigate the patients-perceived drug shortages experience and their view on outcomes in different European hospital settings. Furthermore, we wanted to explore information preferences on drug shortages. Methods A retrospective, cross sectional, a mixed method study was conducted in six European hospital settings. One hospital (H) from each of this country agreed to participate: Bosnia and Herzegovina (H-BiH), Croatia (H-CR), Germany (H-GE), Greece (H-GR), Serbia (H-SE) and Poland (H-PO). Recruitment and data collection was conducted over 27 months from November 2017 until January 2020. Overall, we surveyed 607 patients which completed paper-based questionnaire. Questions related to: general information (demographic data), basic knowledge on drug shortages, drug shortages experienced during hospitalization and information preferences on drug shortage. Differences between hospital settings were analyzed using Chi-squared test or Fisher’s exact test. For more complex contingency tables, Monte Carlo simulations (N = 2000) were applied for Fisher’s test. Post-hoc hospital-wise analyses were performed using Fisher’s exact tests. False discovery rate was controlled using the Bonferroni method. Analyses were performed using R: a language and environment for statistical computing (v 3.6.3). Results 6 % of patients reported experiences with drug shortages while hospitalized which led to a deterioration of their health. The majority of affected patients were hospitalized at hematology and/or oncology wards in H-BiH, H-PO and H-GE. H-BiH had the highest number of affected patients (18.1 %, N = 19/105, p < 0.001) while the fewest patients were in H-SE (1 %, N = 1/100, p = 0.001). In addition, 82.5 %, (N = 501/607) of respondents wanted to be informed of alternative treatment options if there was a drug shortage without a generic substitute available. Majority of these patients (66.4 %, N = 386/501) prefer to be informed by a healthcare professional. Conclusions Although drug shortages led to serious medical consequences, our findings show that most of the patients did not perceive shortages as a problem. One possible interpretation is that good hospital management practices by healthcare professionals helped to mitigate the perceived impact of shortages. Our study highlights the importance of a good communication especially between patients and healthcare professionals in whom our patients have the greatest trust.

scholarly journals Automatic detection and segmentation of adenomatous colorectal polyps during colonoscopy using Mask R-CNN

2020 ◽  
Vol 15 (1) ◽  
pp. 588-596 ◽  
Author(s):  
Jie Meng ◽  
Linyan Xue ◽  
Ying Chang ◽  
Jianguang Zhang ◽  
Shilong Chang ◽  
...  
Keyword(s):  
Deep Neural Networks ◽  
Alimentary Tract ◽  
Multiple Scale ◽  
Colorectal Polyps ◽  
Training Dataset ◽  
Cad System ◽  
Multicenter Trials ◽  
Testing Dataset ◽  
Adenoma Detection ◽  
Aided Diagnosis

AbstractColorectal cancer (CRC) is one of the main alimentary tract system malignancies affecting people worldwide. Adenomatous polyps are precursors of CRC, and therefore, preventing the development of these lesions may also prevent subsequent malignancy. However, the adenoma detection rate (ADR), a measure of the ability of a colonoscopist to identify and remove precancerous colorectal polyps, varies significantly among endoscopists. Here, we attempt to use a convolutional neural network (CNN) to generate a unique computer-aided diagnosis (CAD) system by exploring in detail the multiple-scale performance of deep neural networks. We applied this system to 3,375 hand-labeled images from the screening colonoscopies of 1,197 patients; of whom, 3,045 were assigned to the training dataset and 330 to the testing dataset. The images were diagnosed simply as either an adenomatous or non-adenomatous polyp. When applied to the testing dataset, our CNN-CAD system achieved a mean average precision of 89.5%. We conclude that the proposed framework could increase the ADR and decrease the incidence of interval CRCs, although further validation through large multicenter trials is required.

Impact of anti-cancer drugs shortages in oncology and hematology departments in a Moroccan hospital

2021 ◽  
pp. 107815522110082
Author(s):  
Ali Cherif Chefchaouni ◽  
Youssef Moutaouakkil ◽  
Badr Adouani ◽  
Yasmina Tadlaoui ◽  
Jamal Lamsaouri ◽  
...  
Keyword(s):  
Management Plan ◽  
Essential Elements ◽  
Cancer Drugs ◽  
Drug Shortage ◽  
Medical File ◽  
Short Supply ◽  
Drug Shortages ◽  
Non Hodgkin Lymphoma ◽  
Anti Cancer ◽  
The Impact

Introduction Drug shortages have been a growing global problem in recent years. Some of them are of vital necessity and importance for the patient, such as those used to treat pathologies in clinical hematology and oncology departments. The objectives of this study are to determine the impact of anti-cancer drugs shortages on both: treatment and patient in the hematology and oncology departments, to describe the actions that have been put in place to manage the shortages and to survey patients about their perspectives and experiences. Materials and methods It was a prospective, observational study, it took place in the oncology and hematology departments. It was carried out with the help of an operating sheet, which contained two parts: patient and treatment data. This sheet was filled out after the interview with the patient and on the basis of the medical file. Results Of the 101 patients interviewed, 67.3% were impacted by the shortage of drugs. The treated pathology that was most impacted by the rupture was Non-Hodgkin lymphoma (55.8%), vincristine was the drug most responsible for the shortages (34%). Most patients (51.4%) went to a non-local pharmacy to buy the medicine that was in short supply in the hospital. Delayed care was the main impact of the drug shortage (42.6%). As a result of these shortages, the majority of patients (45.6%) were frustrated and anxious about the situation. Conclusion Drug shortages have a profound impact on patient safety, clinical outcomes, quality of treatment, hospital management and other important factors. In-depth collaboration between different health actors and timely communication strategies are essential elements of an effective drug shortage management plan.

Predicting endoscopic remission in Crohn’s disease by the modified multiplier SES-CD (MM-SES-CD)

Gut ◽  
2021 ◽  
pp. gutjnl-2020-323799
Author(s):  
Neeraj Narula ◽  
Emily C L Wong ◽  
Jean-Frederic Colombel ◽  
William J Sandborn ◽  
John Kenneth Marshall ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Assessment Tool ◽  
Training Dataset ◽  
Mucosal Inflammation ◽  
Youden Index ◽  
Active Therapy ◽  
Testing Dataset ◽  
Endoscopic Remission

Background and aimsThe Simple Endoscopic Score for Crohn’s disease (SES-CD) is the primary tool for measurement of mucosal inflammation in clinical trials but lacks prognostic potential. We set to develop and validate a modified multiplier of the SES-CD (MM-SES-CD), which takes into consideration each individual parameter’s prognostic value for achieving endoscopic remission (ER) while on active therapy.MethodsIn this posthoc analysis of three CD clinical trial programmes (n=350 patients, baseline SES-CD ≥ 3 with confirmed ulceration), data were pooled and randomly split into a 70% training and 30% testing cohort. The MM-SES-CD was designed using weights for individual parameters as determined by logistic regression modelling, with 1-year ER (SES-CD < 3) being the dependent variable. A cut point score for low and high probability of ER was determined by using the maximum Youden Index and validated in the testing cohort.ResultsBaseline ulcer size, extent of ulceration and presence of non-passable strictures had the strongest association with 1-year ER as compared with affected surface area, with differential weighting of individual parameters across disease segments being observed during logistic regression. The MM-SES-CD was generated using this weighted regression model and demonstrated strong discrimination for ER in the training dataset (area under the receiver operator curve (AUC) 0.83, 95% CI 0.78 to 0.94) and in the testing dataset (AUC 0.82, 95% CI 0.77 to 0.92). In comparison to the MM-SES-CD scoring model, the original SES-CD score lacks accuracy (AUC 0.60, 95% CI 0.55 to 0.65) for predicting the achievement of ER.ConclusionsWe developed and internally validated the MM-SES-CD as an endoscopic severity assessment tool to predict one-year ER in patients with CD on active therapy.

scholarly journals A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video)

2021 ◽  
Vol 11 ◽  
Author(s):  
Dehua Tang ◽  
Jie Zhou ◽  
Lei Wang ◽  
Muhan Ni ◽  
Min Chen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Diagnostic Accuracy ◽  
Diagnostic Performance ◽  
Training Dataset ◽  
Deep Convolutional Neural Networks ◽  
Testing Dataset ◽  
Kappa Value ◽  
The Mean ◽  
Gastric Cancer Patients ◽  
Novel Model

Background and AimsPrediction of intramucosal gastric cancer (GC) is a big challenge. It is not clear whether artificial intelligence could assist endoscopists in the diagnosis.MethodsA deep convolutional neural networks (DCNN) model was developed via retrospectively collected 3407 endoscopic images from 666 gastric cancer patients from two Endoscopy Centers (training dataset). The DCNN model’s performance was tested with 228 images from 62 independent patients (testing dataset). The endoscopists evaluated the image and video testing dataset with or without the DCNN model’s assistance, respectively. Endoscopists’ diagnostic performance was compared with or without the DCNN model’s assistance and investigated the effects of assistance using correlations and linear regression analyses.ResultsThe DCNN model discriminated intramucosal GC from advanced GC with an AUC of 0.942 (95% CI, 0.915–0.970), a sensitivity of 90.5% (95% CI, 84.1%–95.4%), and a specificity of 85.3% (95% CI, 77.1%–90.9%) in the testing dataset. The diagnostic performance of novice endoscopists was comparable to those of expert endoscopists with the DCNN model’s assistance (accuracy: 84.6% vs. 85.5%, sensitivity: 85.7% vs. 87.4%, specificity: 83.3% vs. 83.0%). The mean pairwise kappa value of endoscopists was increased significantly with the DCNN model’s assistance (0.430–0.629 vs. 0.660–0.861). The diagnostic duration reduced considerably with the assistance of the DCNN model from 4.35s to 3.01s. The correlation between the perseverance of effort and diagnostic accuracy of endoscopists was diminished using the DCNN model (r: 0.470 vs. 0.076).ConclusionsAn AI-assisted system was established and found useful for novice endoscopists to achieve comparable diagnostic performance with experts.

scholarly journals Emotion Detection using Social Media and Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4491-4494
Author(s):  
Mr. Bhavar Shivam S.
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Social Networking Sites ◽  
Training Dataset ◽  
Emotion Detection ◽  
Pos Tagging ◽  
Testing Dataset ◽  
Svm Algorithm ◽  
The Right ◽  
Negative Sentiment

Today we do a lot of things online from shopping to data sharing on social networking sites. Social networking (SNS) is good for releasing stress and depression by sharing one’s thoughts. Thus, emotion detection has become a hot trend to day. But there is a problem in analyzing emotions on a SNS like twitter as it generates lakhs of tweets each day and it is hard to keep track of the emotion behind each tweet as it is impossible for a human being to read and decide the emotions behind tweets. So, to help understand behind the texts in a SNS site we thought of designing a project which will keep track of the tweets and predict the right emotion behind the tweets whether they have a positive or a negative sentiment behind them. This thought of project can be achieved by a integration of SNS with NLP and machine learning together. For SNS we will use Twitter as it generates a lot of data which is accessible freely using an API. First, we will enter a keyword and fetch tweets from the twitter. Then stop words will be removed from these tweets using NLTK stop words database. Then the tweets will be passed for POS tagging and only right form of grammatical words will be kept and others will be removed. Then we create a training dataset with two types positive and negative. Then SVM algorithm will be trained using this training dataset. Then each tweet will be passed to the SVM as testing dataset which in turn will return classification of each tweet as a whole in two classes positive and negative. Thus, our application will be helpful in recognizing emotion behind a tweet.

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