Comparison of eight screening tools to detect interactions between herbal supplements and oncology agents

2020 ◽  
Vol 26 (8) ◽  
pp. 1843-1849
Author(s):  
Faisal Shakeel ◽  
Fang Fang ◽  
Kelley M Kidwell ◽  
Lauren A Marcath ◽  
Daniel L Hertz
Keyword(s):  
Drug Interaction ◽  
Positive Predictive Value ◽  
Drug Interactions ◽  
Predictive Value ◽  
Screening Tools ◽  
Herbal Supplements ◽  
Interaction Detection ◽  
Natural Medicine ◽  
Interaction Screening ◽  
Drug Drug Interaction

Introduction Patients with cancer are increasingly using herbal supplements, unaware that supplements can interact with oncology treatment. Herb–drug interaction management is critical to ensure optimal treatment outcomes. Several screening tools exist to detect drug–drug interactions, but their performance to detect herb–drug interactions is not known. This study compared the performance of eight drug–drug interaction screening tools to detect herb–drug interaction with anti-cancer agents. Methods The herb–drug interaction detection performance of four subscription (Micromedex, Lexicomp, PEPID, Facts & Comparisons) and free (Drugs.com, Medscape, WebMD, RxList) drug–drug interaction tools was assessed. Clinical relevance of each herb–drug interaction was determined using Natural Medicine and each drug–drug interaction tool. Descriptive statistics were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value. Linear regression was used to compare performance between subscription and free tools. Results All tools had poor sensitivity (<0.20) for detecting herb–drug interaction. Lexicomp had the highest positive predictive value (0.98) and best overall performance score (0.54), while Medscape was the best performing free tool (0.52). The worst subscription tools were as good as or better than the best free tools, and as a group subscription tools outperformed free tools on all metrics. Using an average subscription tool would detect one additional herb–drug interaction for every 10 herb–drug interactions screened by a free tool. Conclusion Lexicomp is the best available tool for screening herb–drug interaction, and Medscape is the best free alternative; however, the sensitivity and performance for detecting herb–drug interaction was far lower than for drug–drug interactions, and overall quite poor. Further research is needed to improve herb–drug interaction screening performance.

scholarly journals Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database

Pharmaceutics ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 762 ◽  
Author(s):  
Yoshihiro Noguchi ◽  
Tomoya Tachi ◽  
Hitomi Teramachi
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Signal Detection ◽  
Predictive Value ◽  
Drug Event ◽  
Detection Accuracy ◽  
Analysis Model ◽  
Subset Analysis ◽  
Pharmacovigilance Database ◽  
Drug Drug Interaction

Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.

Pharmacokinetic Drug-drug Interaction of Antibiotics Used in Sepsis Care in China

2020 ◽  
Vol 21 ◽  
Author(s):  
Xuan Yu ◽  
Zixuan Chu ◽  
Jian Li ◽  
Rongrong He ◽  
Yaya Wang ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Penicillin G ◽  
Literature Mining ◽  
Human Pharmacokinetics ◽  
P450 Enzyme ◽  
Drug Drug Interaction ◽  
Pharmacokinetic Drug ◽  
Sepsis Care

Background: Many antibiotics have a high potential for having an interaction with drugs, as perpetrator and/or victim, in critically ill patients, and particularly in sepsis patients. Methods: The aim of this review is to summarize the pharmacokinetic drug-drug interaction (DDI) of 45 antibiotics commonly used in sepsis care in China. Literature mining was conducted to obtain human pharmacokinetics/dispositions of the antibiotics, their interactions with drug metabolizing enzymes or transporters, and their associated clinical drug interactions. Potential DDI is indicated by a DDI index > 0.1 for inhibition or a treated-cell/untreated-cell ratio of enzyme activity being > 2 for induction. Results: The literature-mined information on human pharmacokinetics of the identified antibiotics and their potential drug interactions is summarized. Conclusion: Antibiotic-perpetrated drug interactions, involving P450 enzyme inhibition, have been reported for four lipophilic antibacterials (ciprofloxacin, erythromycin, trimethoprim, and trimethoprim-sulfamethoxazole) and three lipophilic antifungals (fluconazole, itraconazole, and voriconazole). In addition, seven hydrophilic antibacterials (ceftriaxone, cefamandole, piperacillin, penicillin G, amikacin, metronidazole, and linezolid) inhibit drug transporters in vitro. Despite no reported clinical PK drug interactions with the transporters, caution is advised in the use of these antibacterials. Eight hydrophilic antibacterials (all β-lactams; meropenem, cefotaxime, cefazolin, piperacillin, ticarcillin, penicillin G, ampicillin, and flucloxacillin), are potential victims of drug interactions due to transporter inhibition. Rifampin is reported to perpetrate drug interactions by inducing CYP3A or inhibiting OATP1B; it is also reported to be a victim of drug interactions, due to the dual inhibition of CYP3A4 and OATP1B by indinavir. In addition, three antifungals (caspofungin, itraconazole, and voriconazole) are reported to be victims of drug interactions because of P450 enzyme induction. Reports for other antibiotics acting as victims in drug interactions are scarce.

scholarly journals A machine learning approach for predicting suicidal thoughts and behaviours among college students

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Melissa Macalli ◽  
Marie Navarro ◽  
Massimiliano Orri ◽  
Marie Tournier ◽  
Rodolphe Thiébaut ◽  
...  
Keyword(s):  
Mental Health ◽  
College Students ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Screening Tools ◽  
Receiver Operating Curve ◽  
Depression Symptoms ◽  
Suicidal Thoughts ◽  
One Year

AbstractSuicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013–2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviours. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviours in a community sample of college students.

scholarly journals Evaluation of the Performance of Drug-Drug Interaction Screening Software in Community and Hospital Pharmacies

2006 ◽  
Vol 12 (5) ◽  
pp. 383-389 ◽  
Author(s):  
Jacob Abarca ◽  
Lisa R. Colon ◽  
Victoria S. Wang ◽  
Daniel C. Malone ◽  
John E. Murphy ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Hospital Pharmacies ◽  
Interaction Screening ◽  
Drug Drug Interaction ◽  
Screening Software

scholarly journals EVALUATING THE PREDICTIVE VALIDITY OF THE NIPISSING DISTRICT DEVELOPMENTAL SCREEN IN PRIMARY CARE SETTINGS AT THE 18-MONTH HEALTH SUPERVISION VISIT

2018 ◽  
Vol 23 (suppl_1) ◽  
pp. e37-e37
Author(s):  
Vinusha Gunaseelan ◽  
Patricia Parkin ◽  
Imaan Bayoumi ◽  
Patricia Jiang ◽  
Alexandra Medline ◽  
...  
Keyword(s):  
Primary Care ◽  
Positive Predictive Value ◽  
Diagnostic Accuracy ◽  
Developmental Delay ◽  
Predictive Validity ◽  
Predictive Value ◽  
Screening Tools ◽  
Developmental Screening ◽  
Research Personnel ◽  
Health Supervision

Abstract BACKGROUND The Canadian Paediatric Society (CPS) recommends that every Canadian physician caring for young children provide an enhanced 18-month well-baby visit including the use of a developmental screening tool, such as the Nipissing District Developmental Screen (NDDS). The Province of Ontario implemented an enhanced 18-month well-baby visit specifically emphasizing the NDDS, which is now widely used in Ontario primary care. However, the diagnostic accuracy of the NDDS in identifying early developmental delays in real-world clinical settings is unknown. OBJECTIVES To assess the predictive validity of the NDDS in primary care for identifying developmental delay and prompting a specialist referral at the 18-month health supervision visit. DESIGN/METHODS This was a prospective longitudinal cohort study enrolling healthy children from primary care practices. Parents completed the 18-month NDDS during their child’s scheduled health supervision visit between January 2012 and February 2015. Using a standardized data collection form, research personnel abstracted data from the child’s health records regarding the child’s developmental outcomes following the 18-month assessment. Data collected included confirmed diagnoses of a development delay, specialist referrals, family history, and interventions. Research personnel were blind to the results of the NDDS. We assessed the diagnostic test properties of the NDDS with a confirmed diagnosis of developmental delay as the criterion measure. The specificity, sensitivity, positive predictive value, and negative predictive value were calculated, with 95% confidence intervals. RESULTS We included 255 children with a mean age of 18.5 months (range, 17.5–20.6) and 139 (55%) were male. 102 (40%) screened positive (1+ flag result on their NDDS). A total of 48 (19%) children were referred, and 23 (9%) had a confirmed diagnosis of a developmental delay (speech and language: 14; gross motor: 4; autism spectrum disorder: 3; global developmental delay: 1; developmental delay: 1). The sensitivity was 74% (95% CI: 52–90%), specificity was 63% (95% CI: 57–70%), positive predictive value was 17% (95% CI:10–25%), and the negative predictive value was 96% (95% CI: 92–99%). CONCLUSION For developmental screening tools, sensitivity between 70%-80% and specificity of 80% have been suggested. The NDDS has moderate sensitivity and specificity in identifying developmental delay at the 18-month health supervision visit. The 1+NDDS flag cut-point may lead to overdiagnosis with more children with typical development being referred, leading to longer wait times for specialist referrals among children in need. Future work includes investigating the diagnostic accuracy of combining the NDDS with other screening tools.

Automated screening tool for Subcutaneous Implantable Defibrillator in Brugada syndrome has a high eligibility rate which is predicted by simple electrocardiographic parameters

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Marelli ◽  
D Kukavica ◽  
A Mazzanti ◽  
T Chargeishvili ◽  
A Trancuccio ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Brugada Syndrome ◽  
Predictive Value ◽  
Screening Tool ◽  
Screening Tools ◽  
Molecular Medicine ◽  
Funding Source ◽  
S Wave ◽  
High Positive Predictive Value ◽  
Automated Screening

Abstract Background Manual electrocardiographic (ECG) screening tools for the use of subcutaneous cardiac defibrillator (S-ICD) have been associated with high ineligibility rates in Brugada syndrome patients (BrS). Although recent works identified ECG parameters for S-ICD eligibility in general population, automated screening tool (AST) for S-ICD eligibility have not even been assessed in large series of patients with BrS. Purpose This study evaluates the AST-derived eligibility rates for an S-ICD in patients with BrS, and ECG parameters associated with S-ICD eligibility. Methods Screening for S-ICD eligibility was performed using AST in 194 consecutive patients with BrS. Eligibility was defined when at least one of the three vectors was acceptable both in supine and standing position. Twelve-lead ECGs were registered during the screening. ECG parameters associated with AST eligibility were identified using multivariable logistical regression. Results Our study population consisted of 194 patients, with male preponderance (n=165/194; 85%); and were 43±12 years old at the time of screening. Majority of patients presented a spontaneous type 1 pattern during screening (n=128/194; 66%), with an average pattern height of 3±3 mm. Remarkably, 93% of patients passed the screening with AST. No differences in eligibility rates in terms of gender (93% males vs. 93% females eligible; p=1) and age (48±9 years non-eligible vs. 42±12 eligible; p=0.07) existed. Notably, our eligibility rate was 2.5 times higher than rates reported in literature when using manual screening tools (p=0.023). Independent 12-lead ECG parameters (Table) associated with AST eligibility were duration of S wave &lt;80 ms in aVF and R/T ratio ≥3 in lead II (Figure), which have a high positive predictive value (97% and 99%, respectively) for screening eligibility. Conclusions Most BrS patients (93%) are eligible for S-ICD when AST is used. S wave &lt;80 ms in aVF, and R/T ratio ≥3 in lead II have a high positive predictive value for S-ICD eligibility. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): The Italian Ministry of Research and University Dipartimenti di Eccellenza 2018–2022 grant to the Molecular Medicine Department (University of Pavia)

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