Using Statistical Modeling for Enhanced and Flexible Pharmacovigilance Audit Risk Assessment and Planning

Abstract Background The European Medicines Agency Good Pharmacovigilance Practices (GVP) guidelines provide a framework for pharmacovigilance (PV) audits, including limited guidance on risk assessment methods. Quality assurance (QA) teams of large and medium sized pharmaceutical companies generally conduct annual risk assessments of the PV system, based on retrospective review of data and pre-defined impact factors to plan for PV audits which require a high volume of manual work and resources. In addition, for companies of this size, auditing the entire “universe” of individual entities on an annual basis is generally prohibitive due to sheer volume. A risk assessment approach that enables efficient, temporal, and targeted PV audits is not currently available. Methods In this project, we developed a statistical model to enable holistic and efficient risk assessment of certain aspects of the PV system. We used findings from a curated data set from Roche operational and quality assurance PV data, covering a span of over 8 years (2011–2019) and we modeled the risk with a logistic regression on quality PV risk indicators defined as data stream statistics over sliding windows. Results We produced a model for each PV impact factor (e.g. 'Compliance to Individual Case Safety Report') for which we had enough features. For PV impact factors where modeling was not feasible, we used descriptive statistics. All the outputs were consolidated and displayed in a QA dashboard built on Spotfire®. Conclusion The model has been deployed as a quality decisioning tool available to Roche Quality professionals. It is used, for example, to inform the decision on which affiliates (i.e. pharmaceutical company commercial entities) undergo audit for PV activities. The model will be continuously monitored and fine-tuned to ensure its reliability.

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Terrestrial and Ecological Risk Assessment at US Army Aberdeen Proving Ground Quality Assurance Project Plan, Volume 2 Appendices

10.21236/ada427056 ◽

1995 ◽

Author(s):

ABERDEEN PROVING GROUND MD

Keyword(s):

Risk Assessment ◽

Quality Assurance ◽

Ecological Risk ◽

Ecological Risk Assessment ◽

Us Army ◽

Project Plan ◽

Quality Assurance Project ◽

Aberdeen Proving Ground

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Visual Analysis of Biomarkers Reveals Differences in Lipid Profiles and Liver Enzymes before and after Gastric Sleeve and Bypass

Obesity Facts ◽

10.1159/000510401 ◽

2021 ◽

pp. 1-11

Author(s):

Marijn Marthe Georgine van Berckel ◽

Saskia L.M. van Loon ◽

Arjen-Kars Boer ◽

Volkher Scharnhorst ◽

Simon W. Nienhuijs

Keyword(s):

Bariatric Surgery ◽

Visual Analytics ◽

Biochemical Markers ◽

Visual Analysis ◽

Large Population ◽

High Volume ◽

Cholesterol Concentration ◽

Health State ◽

Data Set ◽

Before And After

Introduction: Bariatric surgery results in both intentional and unintentional metabolic changes. In a high-volume bariatric center, extensive laboratory panels are used to monitor these changes pre- and postoperatively. Consecutive measurements of relevant biochemical markers allow exploration of the health state of bariatric patients and comparison of different patient groups. Objective: The objective of this study is to compare biomarker distributions over time between 2 common bariatric procedures, i.e., sleeve gastrectomy (SG) and gastric bypass (RYGB), using visual analytics. Methods: Both pre- and postsurgical (6, 12, and 24 months) data of all patients who underwent primary bariatric surgery were collected retrospectively. The distribution and evolution of different biochemical markers were compared before and after surgery using asymmetric beanplots in order to evaluate the effect of primary SG and RYGB. A beanplot is an alternative to the boxplot that allows an easy and thorough visual comparison of univariate data. Results: In total, 1,237 patients (659 SG and 578 RYGB) were included. The sleeve and bypass groups were comparable in terms of age and the prevalence of comorbidities. The mean presurgical BMI and the percentage of males were higher in the sleeve group. The effect of surgery on lowering of glycated hemoglobin was similar for both surgery types. After RYGB surgery, the decrease in the cholesterol concentration was larger than after SG. The enzymatic activity of aspartate aminotransferase, alanine aminotransferase, and alkaline phosphate in sleeve patients was higher presurgically but lower postsurgically compared to bypass values. Conclusions: Beanplots allow intuitive visualization of population distributions. Analysis of this large population-based data set using beanplots suggests comparable efficacies of both types of surgery in reducing diabetes. RYGB surgery reduced dyslipidemia more effectively than SG. The trend toward a larger decrease in liver enzyme activities following SG is a subject for further investigation.

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Embed2Detect: temporally clustered embedded words for event detection in social media

Machine Learning ◽

10.1007/s10994-021-05988-7 ◽

2021 ◽

Author(s):

Hansi Hettiarachchi ◽

Mariam Adedoyin-Olowe ◽

Jagdev Bhogal ◽

Mohamed Medhat Gaber

Keyword(s):

Social Media ◽

Event Detection ◽

High Volume ◽

Detection Methods ◽

Word Embeddings ◽

Agglomerative Clustering ◽

Data Set ◽

Social Media Data ◽

Social Media Platforms ◽

Media Data

AbstractSocial media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.

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Hospital acquired pressure injury prediction in surgical critical care patients

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01371-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jenny Alderden ◽

Kathryn P. Drake ◽

Andrew Wilson ◽

Jonathan Dimas ◽

Mollie R. Cummins ◽

...

Keyword(s):

Risk Assessment ◽

Model Performance ◽

Routine Care ◽

Predictor Variables ◽

Classification Algorithms ◽

Data Set ◽

Icu Patients ◽

Pressure Injury ◽

Surgical Icu ◽

Hospital Acquired

Abstract Background Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5–10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. Methods In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. Results Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. Conclusions Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

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Risk assessment of ice dams for water diversion projects based on fuzzy fault trees

Applied Water Science ◽

10.1007/s13201-020-01341-w ◽

2021 ◽

Vol 11 (2) ◽

Author(s):

Mengkai Liu ◽

Xiaoxia Dong ◽

Hui Guo

Keyword(s):

Risk Assessment ◽

Fault Tree ◽

Fault Tree Analysis ◽

Northern China ◽

Indicator System ◽

Water Diversion ◽

Risk Indicators ◽

Scoring Method ◽

Study Results ◽

Dam Risk

AbstractIce dams are among the important risks affecting the operational safety and water conveyance efficiency of water diversion projects in northern China. However, no evaluation indicator system for ice dam risk assessment of water diversion projects has been proposed. Therefore, in this paper, based on the formation mechanism of ice dams, the risk assessment indicator system and the possibility calculation model of ice dams were both proposed for water diversion projects based on the fuzzy fault tree analysis method. The ice dam risk fault tree constructed in this study mainly includes three aspects: ice production, ice transport, and ice submergence conditions. Eighteen basic risk indicators were identified, and 72 minimum cut sets were obtained by using the mountain climb method. Eight risk indicators were determined as the key risk indicators for ice dams, including meteorological conditions, narrowed cross section, sluice incident, erroneous scheduling judgment, ice cover influence, flat bed slope, control structures, and ice flow resistance of piers. Then, the canal from the Fenzhuanghe sluice to the Beijumahe sluice of the Middle Route of the South-to-North Water Diversion Project was taken as the research object. Combined with the expert scoring method, the ice dam risk probability of the canal was determined to be 0.2029 × 10−2, which was defined as a level III risk, which is an occasionally occurring risk. The study results can support ice dam risk prevention and canal system operation in winter for water diversion projects.

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Framework for Colorectal Cancer Health Needs Assessment in North and Central Asian Countries

International Journal of Innovative Research in Medical Science ◽

10.23958/ijirms/vol06-i09/1202 ◽

2021 ◽

Vol 6 (09) ◽

pp. 569-575

Author(s):

Rebecca Pratiti

Keyword(s):

Colorectal Cancer ◽

Risk Assessment ◽

Disease Risk ◽

Population Attributable Risk ◽

National Income ◽

Risk Indicators ◽

Asian Countries ◽

Central Asian ◽

Central Asian Countries ◽

Per Capita

Colorectal cancer (CRC) is the third leading cause for cancer worldwide. Prevalence of CRC is increasing in North and Central Asian Countries (NCAC). European guidelines encourage member countries to allocate resources for primary prevention of CRC through screening. Though, cost-effective screening is becoming a priority. A framework for health priority determination to prioritize CRC screening was developed. Public health websites were accessed to abstract epidemiologic data. The framework included prioritization by absolute risk (incidence, prevalence), relative risk (CRC ranking for national cancer deaths) and population attributable risk for the disease. Risk indicators were identified for the NCAC. Further detailed risk assessment scoring was completed to assess the CRC disease burden. Statistical analysis was performed for correlation. Variables included in risk assessment were population, life expectancy, gross national income per capita, percent GDP spent on health expenditure, total expenditure on health per capita, age standardized mortality to incidence ratio, cancer ranking by incidence and smoking prevalence. Risk assessment showed Kyrgyzstan, Georgia, Belarus and Armenia have more than expected CRC burden. Tajikistan, Turkmenistan and Latvia have lower than expected CRC burden. Conclusion: Identifying high CRC burden countries to prioritize screening is important. Uniform and comparable CRC risk indicators for the region is needed. Health need assessment and priority setting is important for better distribution of resources. Countries with lower risk score may implement preventive policy to reduce CRC risk factors and countries with higher risk could adapt mitigating policy for early diagnosis of CRC.

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Abstract MP052: Neither Insulin Secretion nor Genotype Pattern Modify 12-Month Weight Loss Effects of Healthy Low-Fat vs. Healthy Low-Carbohydrate Diets Among Adults with Obesity

Circulation ◽

10.1161/circ.135.suppl_1.mp052 ◽

2017 ◽

Vol 135 (suppl_1) ◽

Author(s):

Christopher D Gardner ◽

Michelle Hauser ◽

Liana Del Gobbo ◽

John Trepanowski ◽

Joseph Rigdon ◽

...

Keyword(s):

Weight Loss ◽

Insulin Secretion ◽

Energy Restriction ◽

Risk Indicators ◽

Oral Glucose ◽

Multilocus Genotype ◽

Low Fat ◽

Data Set ◽

Low Carbohydrate ◽

Glucose Dynamics

Background: Dietary modification remains an essential component of successful weight loss strategies. No one dietary strategy has been determined to be superior to others for the general population. Studies that contrast reducing dietary fat vs. carbohydrate report consistently high within-group variability in dietary adherence and weight loss. Previous research by our group and others suggest that insulin-glucose dynamics or genotype patterns may modify diet effects. Objective: To determine if within-group weight loss variability on a Healthy Low-Fat (HLF) vs. a Healthy Low Carbohydrate (HLC) diet can be attributed to underlying factors such as insulin-glucose dynamics (i.e., insulin resistance and secretion) or genotype pattern. We hypothesized the above factors would be effect modifiers of HLF and HLC diets on 12-month weight loss. Methods: Generally healthy, non-diabetic adults, 18-50 years, BMI 28-40 kg/m 2 , were randomized to HLF or HLC with no specific prescribed energy restriction for 12 months (n=609). Health educators delivered the intervention in 22 1-hr group classes. Data were collected at 0, 3, 6, & 12 months. Dietary intake was assessed by three 24-hour recalls/time point. Clinical data includes: 75-g glucose oral glucose tolerance tests (insulin concentration at 30 minutes [Ins-30], a measure of insulin secretion), genotyping (3-SNP multilocus genotype: Low-Fat Genotype vs. Low-Carb Genotype, UK Biobank Axiom® array), body composition (DXA), resting energy expenditure (indirect calorimetry), epigenetics, proteomics, subcutaneous adipose tissue, microbiota, and standard CVD risk indicators. Results: At 12 months participants collectively lost 6,559 lbs. Retention was 79%, with equal dropout between arms. Range of weight change in both diet arms was ~80 lbs (-60 to +20 lbs). Macronutrient distribution at 12 months was 48% vs. 30% carbohydrate, 29% vs. 45% fat, and 21% vs. 23% protein for HLF and HLC, respectively. Both groups reported achieving and maintaining an average ~500 kcal deficit relative to baseline. Weight loss was similar for HLF vs. HLC: -12.1 ± 1.1 lbs vs. -13.8 ± 1.0 lbs, mean ± SEM. Neither Ins-30 (p for interaction = 0.84) nor genotype pattern (p for interaction = 0.20) modified the effect of diet on 12-month weight loss. Conclusions: Despite substantial weight loss, high within-group variability, and strong dietary differentiation between groups, neither baseline Ins-30 nor genotype pattern modified the effect of diet on 12-month weight loss. Focus on a healthy diet in both diet arms is novel in the context of many previous Low-Fat vs. Low-Carb studies and may have diminished expected effect modification. The extensive data set collected will be used to explore this and other potential explanatory factors.

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Consideration of Non-stationarity in the Development of Emergency Situations for the Quantitative Risk Assessment of Accidents at Hazardous Production Facilities of Oil and Product Pipelines

Occupational Safety in Industry ◽

10.24000/0409-2961-2021-2-89-96 ◽

2021 ◽

pp. 89-96

Author(s):

A.M. Sverchkov ◽

Keyword(s):

Risk Assessment ◽

Fire Hazard ◽

Ignition Time ◽

Quantitative Risk Assessment ◽

Risk Indicators ◽

Oil Pipeline ◽

Emergency Situations ◽

External Conditions ◽

Hazardous Production ◽

Real Practice

It is proposed to use the new approach to assessing quantitative risk indicators. This approach allows to consider the temporal non-stationarity of the number of processes, including the development of an accident and the spatial movements of people. The greatest uncertainty in the risk analysis with an explosive and fire hazard component is not the frequency of initiating events used, but, for example, data on the probability of ignition. The range of variation of this probability is about two orders of magnitude (relatively speaking, from 1 % to 100 %), and the criteria and factors that determine the choice of this value are not always clearly defined. The paper proposes an approach that considers the probability of ignition as a dependence on the time that passed after the start of emergency depressurization. Knowing this dependence, it is possible to consider several scenarios with different ignition time after the start of the release and assign certain consequences and probabilities to each scenario. Moreover, it is possible for each single scenario on a specific piece of equipment (pipeline section) to obtain non-stationary, namely time-varying potential risk fields. The example of an accident on the oil pipeline is considered, the risk indicators of such an accident are calculated, it is shown that the risks can change over time, namely they are non-stationary characteristics. Further, this fact is transformed into the development of theoretical foundations for quantitative risk assessment, considering the non-stationarity of various processes occurring during emergency situations arising during the operation of equipment, individual behavior of people and changes in external conditions. The results obtained show the importance of considering the changes that occur during an emergency on the main oil and product pipelines. It is concluded that the proposed approach allows to reduce the conservatism of assessments provided by traditional methods. In real practice this approach can reasonably reduce the risk indicators by several times, sometimes by orders of magnitude.

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