scholarly journals 145. Comparing Antibiotic Use Across Inpatient Facilities with Different Antibiotic Stewardship Typologies using Machine Learning and Joint Modeling Approach

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S185-S185
Author(s):  
Yue Zhang ◽  
Jincheng Shen ◽  
Tina M Willson ◽  
Edward A Stenehjem ◽  
Tamar F Barlam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Antibiotic Stewardship ◽  
Antibiotic Use ◽  
Analytical Framework ◽  
Intervention Programs ◽  
P Value ◽  
Healthcare Intervention ◽  
Unsupervised Machine Learning ◽  
Implementation Level

Abstract Background Hospital antibiotic stewardship programs (ASP) aim to promote the appropriate use of antimicrobials (including antibiotics) and play a critical role in controlling antibiotic costs and antibiotic-resistant bacterial infection risk, and improving patient outcomes. However, unlike other health care quality improvement intervention programs, the ASP implementation strategies vary among healthcare facilities, and little is known about whether different types of ASP implementation will lead to the shifting of antibiotic drug use from one class to another. Methods We proposed an analytical framework using unsupervised machine learning and joint model approach to 1) develop a typology of ASP strategies in facilities from the Veterans Health Administration, America’s largest integrated health care system; and 2) simultaneously evaluate the impacts of different ASP types on the annual antibiotic use rates across multiple drug classes. The unsupervised machine learning method was used to leverage the structural components in the surveys conducted by the Veteran Affair (VA) Healthcare Analysis and Information group and the Consolidated Framework for Implementation Research experts from Boston University, and reveal the underlying ASP patterns in the VA facilities in 2016. Results We identified 4 groups in the VA facilities in terms of enthusiasm and implementation level of antibiotic control in our ASP typology. We found the facilities with high implementation level and high enthusiasm in ASP and those with high implementation level but low enthusiasm had statistically significant 30% (p-value=0.002) and 22% (p-value=0.031) lower antibiotic use rates in broad-spectrum agents used for community infections, respectively than those with low implementation level and low enthusiasm. However, the facilities with high implementation and high enthusiasm also marginally increased antibiotic use rates in beta-lactam antibiotics (p-value=0.096). Conclusion The developed analytical framework in the study provided an approach to the granular assessment of the impact of the healthcare intervention programs and might be informative for future health service policy development. Disclosures Matthew B. Goetz, MD, Nothing to disclose

scholarly journals Antibiotic stewardship in health care facilities across Gujarat: A cross-sectional descriptive study

2021 ◽  
Vol 6 (3) ◽  
pp. 165-170
Author(s):  
Varshal J. Barot ◽  
Krupa A. Pandya
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Antibiotic Stewardship ◽  
Antibiotic Use ◽  
Descriptive Study ◽  
Health Care Facilities ◽  
P Value ◽  
Cross Sectional ◽  
Dental Practitioners ◽  
Care Facilities

Irrational use of antibiotics is the key contributor to antibiotic resistance. To improve the administration of antibiotics, many programs have been designed at national and international levels; and antibiotic stewardship (ABS) is one of them.The aim of this study was to create awareness and understanding of antibiotic stewardship by estimating its knowledge, attitude and practice (K.A.P) among health care professionals in health care facilities across Gujarat. A cross-sectional descriptive study was carried out among health care professionals in health care facilities across Gujarat. For which a self-administered questionnaire with 15 closed-ended questions with two sections: “Optimal antibiotic use” (no.1-7 questions); and “Responsible antibiotic use” (no.8-15 questions) was disseminated online/ in electronic form. In Dental practitioners, mean scores of knowledge, attitude, practice (K.A.P) regarding “Optimal antibiotic use” and “Responsible antibiotic use” are 6.3682 ± 0.96, 6.2139 ± 1.07, 4.5672 ± 1.51 and 7.1692 ± 1.09, 6.9104 ± 1.25, 5.1443 ± 1.81 respectively.In Medical practitioners, mean scores of knowledge, attitude, practice (K.A.P) regarding “Optimal antibiotic use” and “Responsible antibiotic use” are 6.8201 ± 0.41, 6.7090 ± 0.56, 5.1270 ± 1.62 and 7.6032 ± 0.69, 7.4233 ± 0.82, 5.3492 ± 1.94 respectively.Between the groups, knowledge and attitude regarding “Optimal antibiotic use” and “Responsible antibiotic use” are statistically highly significant (p-value = <0.001). Health care professionals showed higher knowledge as compared to attitude with least practice (K>A>P) regarding antibiotic stewardship in health care facilities across Gujarat. Antibiotic stewardship is fulcrum for the dual face of antibiotics. Equilibrium between individual and societal benefit/risk ratio while making clinical antibiotic decisions will benefit both; individual patients as well as the community.

scholarly journals 180. Leveraging the Electronic Medical Record as a Method of Antibiotic Stewardship

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S97-S97
Author(s):  
Christina M Kaul ◽  
Eric Molina ◽  
Donna Armellino ◽  
Mary Ellen Schilling ◽  
Mark Jarrett
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Antibiotic Stewardship ◽  
Antibiotic Use ◽  
Antibiotic Usage ◽  
P Value ◽  
Inpatient Setting ◽  
Post Intervention ◽  
Paired Samples ◽  
Viable Method

Abstract Background Overutilization of antibiotics remains an issue in the inpatient setting. What is more, many protocols geared toward curbing improper antibiotic use rely heavily on resource- and personnel-intensive interventions. Thus, the potential for using the EMR to facilitate antibiotic stewardship remains largely unexplored. Methods We implemented a novel change for ordering certain antibiotics in our EMR: ceftriaxone, daptomycin, ertapenem, imipenem, meropenem, and piperacillin-tazobactam. When ordering one of these antibiotics, providers had to note a usage indication, which assigned a usage duration as per our Antibiotic Stewardship Committee guidelines. Pre-intervention, manual discontinuation was required if a provider did not enter a duration. The intervention was enacted August 2019 in 13 hospitals. Data was collected from January 2018 to February 2020. Antibiotic usage was reported monthly as rate per 1000-patient days. Monthly pre- and post-intervention rates were averaged, respectively. Paired samples t-tests were used to compare pre- and post-intervention rates per unit type per hospital. A p-value of less than 0.05 was considered significant. Units with minimal usage, as defined by a pre- or post-intervention mean of 0, were excluded from analysis. Example of Ordering an Antibiotic Prior to Intervention Example of Ordering an Antibiotic After Intervention Results Ertapenem was noted to have a statistically significant decrease in utilization in seven units at three hospitals. Piperacillin-tazobactam was found to have a decrease in utilization in 19 units at eight hospitals. Daptomycin was found to have a decrease in utilization in one unit. Significant decreases in the utilization of ceftriaxone, imipenem, and meropenem were not noted. Example of Statistically Significant Decreased Utilization in Piperacillin-Tazobactam on a Medical-Surglcal Unit Conclusion Our study showed a statistically significant decrease in use of ertapenem, piperacillin-tazobactam and daptomycin using a simple built-in EMR prompt that curtails provider error. This should allow for an increased ease of integration, as the protocol does not require a host of resources for maintenance. Of note is decreased utilization of piperacillin-tazobactam and ertapenem across multiple hospitals, most notably on the medical and surgical wards. Thus, usage of the EMR without personnel-intensive protocols is a viable method for augmenting antibiotic stewardship in health systems. Disclosures All Authors: No reported disclosures

scholarly journals The Increasing Interest in Antibiotic Stewardship Programs

2020 ◽  
pp. 8-12
Author(s):  
Nehad J. Ahmed ◽  
Mohd. F. Khan
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Antibiotic Stewardship ◽  
Antibiotic Use ◽  
Google Trends ◽  
Prescribing Patterns ◽  
Resistant Organism ◽  
High Morbidity ◽  
Search Term ◽  
The Public

Background: The inappropriate use of antibiotics leads to many adverse effects and also leads to bacterial resistance. A hospital-based program, commonly referred to as antibiotic stewardship programs, is used to improve the usage of antibiotics. This study aims to explore the increasing interest of the public in antibiotic stewardship programs by using data from Google Trends and Twitter. Methodology: A search trends feature that shows how frequently a given search term is entered into Google’s search engine (Google Trends) and a social network site (Twitter) were used. Results: The public and the health care professionals are now more interested in antibiotic use and antibiotic resistance due to the development of more severe infections that were caused by bacteria resisted to many antibiotics which lead to high morbidity and mortality rates. Conclusion: There is a high prevalence of infections caused by multi-drug resistant organism that could lead to more mortality and morbidity rates, as a result the interest in antimicrobial stewardship programs in internet is increased. So it is important to increase the knowledge of health care professionals regarding the appropriate antibiotic use and to encourage them to change their unsuitable prescribing patterns.

scholarly journals P-OGC08 Translational Insights from the Dual ErbB Inhibition in Oesophago-gastric Cancer (DEBIOC) Clinical Trial - A Bioinformatic Analysis

2021 ◽  
Vol 108 (Supplement_9) ◽  
Author(s):  
Enya Scanlon ◽  
Anita Lavery ◽  
Leanne Stevenson ◽  
Chloe Kennedy ◽  
Ryan Byrne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gastric Cancer ◽  
Clinical Trial ◽  
Differential Expression ◽  
Single Gene ◽  
Oesophageal Adenocarcinoma ◽  
P Value ◽  
Hallmarks Of Cancer ◽  
Unsupervised Machine Learning ◽  
Clinical Observations

Abstract Background Oesophageal Adenocarcinoma (OAC) incidence in the Western-world has increased markedly over 30 years. 5-year survival rates for patients remains below 20% with dismal response to neo-adjuvant or perioperative chemotherapy for operable tumours. The Dual ErbB Inhibition in Oesophago-gastric Cancer (DEBIOC) clinical trial assessed efficacy of combined oxaliplatin and capecitabine (Xelox) with dual ErbB inhibitor AZD8931 in providing additional benefit to operable patients compared to Xelox alone. We utilised a bioinformatic approach combing Almac Clara-T Transcriptional Discovery software with unsupervised machine learning methods to unveil translational clinical potential and biological insights from DEBIOC patient biopsy and resection specimens. Methods Using microarrays of DEBIOC patient specimens with documented clinical observations, we combined unsupervised machine learning techniques with state-of-the-art Almac Clara-T software to assess transcriptional changes between treatment types regarding the 10 hallmarks of cancer, characterised by representative gene-expression signatures and scores. These methods were employed to identify possible mechanisms of treatment resistance, evaluate changes in the tumour-microenvironment and determine clinically significant molecular subgroups in OAC. Differential expression and pathway analytics were used to describe signalling dissimilarities between clusters from unsupervised analysis and phenotypes respective to hallmarks of cancer, with alignment of sensitivities to single-gene drug targets for subgroups of interest. Results Unsupervised clustering analysis of biopsy specimens, resulted in the identification of two robust subgroups pre-treatment in OAC, determined to be significantly associated with the prediction of Mandard Score (Tumour Regression Grade 1-5) post-treatment (fishers exact p < 0.05). Differential expression analysis revealed distinguishing biology between subtypes and noted increased ErbB signalling in non-responding patients in addition to increased PI3K signalling, highlighting a potential mechanism of resistance to dual ErbB inhibition (nominal p-value <0.05, FDR p-value <0.2). Semi-supervised clustering revealed hallmark-specific-phenotypes associated with clinical observations including lymph node involvement, EGFR FISH classification, vascular invasion and progression events at BH adjusted p-values <0.05. Conclusions Our analysis has revealed translational insights into possible mechanisms of drug resistance as well as cancer hallmark-specific phenotypes significantly associated with clinico-pathological factors during the DEBIOC clinical trial. Continued analysis into resulting phenotypes and clusters combined with the alignment of single gene drug target sensitivities is anticipated to reveal novel molecular pathways driving phenotypic differences in an effort to further inform biological understanding and improve treatment response and survival outcomes in OAC patients. 

scholarly journals Inclusion of vulnerable groups in health policies: Regional policies on health priorities in Africa

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Margie Schneider ◽  
Arne Henning Eide ◽  
Mutamad Amin ◽  
Malcom MacLachlan ◽  
Hasheem Mannan
Keyword(s):  
Health Care ◽  
Analytical Framework ◽  
Vulnerable Groups ◽  
Health Priorities ◽  
African Union ◽  
Core Concept ◽  
Vulnerable Group ◽  
Policy Documents ◽  
Core Concepts ◽  
Implementation Level

Background: If access to equitable health care is to be achieved for all, policy documents must mention and address in some detail different needs of groups vulnerable to not accessing such health care. If these needs are not addressed in the policy documents, there is little chance that they will be addressed at the stage of implementation.Objectives: This paper reports on an analysis of 11 African Union (AU) policy documents to ascertain the frequency and the extent of mention of 13 core concepts in relation to 12 vulnerable groups, with a specific focus on people with disabilities.Method: The paper applied the EquiFrame analytical framework to the 11 AU policy documents. The 11 documents were analysed in terms of how many times a core concept was mentioned and the extent of information on how the core concept should be addressed at the implementation level. Each core concept mention was further analysed in terms of the vulnerable group in referred to.Results: The analysis of regional AU policies highlighted the broad nature of the reference made to vulnerable groups, with a lack of detailed specifications of different needs of different groups. This is confirmed in the highest vulnerable group mention being for ‘universal’. The reading of the documents suggests that vulnerable groups are homogeneous in their needs, which is not the case. There is a lack of recognition of different needs of different vulnerable groups in accessing health care.Conclusion: The need for more information and knowledge on the needs of all vulnerable groups is evident. The current lack of mention and of any detail on how to address needs of vulnerable groups will significantly impair the access to equitable health care for all.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

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