Background Tramadol is known to cause fewer adverse events (AEs) than other opioids. However, recent research has raised concerns about various safety issues. Objective We aimed to explore these new AEs related to tramadol using social media and conventional pharmacovigilance data. Methods This study used 2 data sets, 1 from patients’ drug reviews on WebMD (January 2007 to January 2021) and 1 from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS; January 2016 to December 2020). We analyzed 2062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities. To analyze AEs from FAERS, a disproportionality analysis was performed with 3 measures: proportional reporting ratio, reporting odds ratio, and information component. Results From the 869 AEs reported, we identified 125 new signals related to tramadol use not listed on the drug label that satisfied all 3 signal detection criteria. In addition, 20 serious AEs were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients’ symptom descriptions, tramadol-induced pain might also be an unexpected AE. Conclusions This study detected several novel signals related to tramadol use, suggesting newly identified possible AEs. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data.
사례연구: 대구 파티마 병원 폐렴 입원 환자 수에 영향을 미치는 날씨 변수 선택 서론지구 온난화로 인해 전 세계는 날씨가 변하였고, 그에 따라 여러 가지 질병의 발생률도 증가하였다.그 중 폐렴은 국내 질환 중 입원율 1위인 질환으로 폐렴 (pneumonia)에 대한 위험성은 계속 부각되고 있다. 겨울이 되면 폐렴환자가 증가하는 것으로 보아 날씨에 큰 영향을 받는 질환임은 부정할 수 없는 사실이다 (Yim 등, 2012; Kim 등, 2016b). 그렇다면 수 많은 날씨 변수들 중 어떤 종류의 날씨가 폐렴 발병에 영향을 끼치는지 어느 정도 잠복기를 가지는 지를 알아보기 위해 본 연구를 계획하였다. 폐렴은 감염 통로에 따라 community acquired pneumonia (CAP)와 hospital acquired pneumonia (HAP)로 나눌 수 있다. HAP는 날씨와 밀접한 관련이 없는 것으로 판단되어 CAP 환자들로 국한하여 연구를 진 행하였다. 대구 파티마 병원의 폐렴으로 입원한 일별 환자 수와 날씨 자료에 대해 적절한 변수를 선택하 고 일별 환자 수와의 관계를 알아 볼 것이다. 날씨 자료는 습도, 일조량, 일교차, 평균온도, 미세먼지 농 도를 고려하였다 (Lieberman과 Friger, 1999). 포아송 일반화 선형 모형을 사용하였고, 이때 영향을 미 치지 않는 변수로 인한 모형 과적합성과 예측 성능 저하를 피하기 위해 적절한 변수를 선택하여야 한다. 하지만 날씨 변수들은 서로 높은 상관관계를 가지기 때문에 기존의 변수 선택법을 사용하기에 무리가 따 른다. 따라서 벌점화 기법을 적용한 변수 선택법을 통해 실질적으로 입원 환자 수에 영향을 미치는 변수 를 선택하였다. 날씨 변수 134Sohyun Choi · Hag Lae Lee · Chungun Park · Kyeong Eun LeeCase study: Selection of the weather variables related to pneumonia patientsβ Ridge = arg minβ NaiveEN = arg min Zou와 Hastie, 2005):. 136Sohyun Choi · Hag Lae Lee · Chungun Park · Kyeong Eun Lee 벌점함수가 없는 경우는 결국 우도함수를 최대로 하는 추정량이되므로 최대우도추정량이 된다. 포아 송 회귀모형에서 벌점화 회귀 계수 추정량을 구체적으로 살펴보면, 최대우도추정량β MLE , 능형 회귀추 정량β Ridge , 라쏘 회귀추정량β Lasso , 나이브 엘라스틱넷 회귀추정량β NaiveEN 은 각각 다음과 같이 표 현될 수 있다: 결론References Lim, Y., Hong, Y. and Kim. H. (2012). Effects of diurnal temperature range on cardiovascular and respiratory hospital admissions in Korea. Science of the Total Environment, 417, 55-60. Hastie, T., Tibshirani, R. and Wainwright, M. (2015). Statistical learning with sparsity: The lasso and generalizations, Chapman and Hall, London. AbstractThe number of hospital admissions for pneumonia tends to increase annually and even more, pneumonia, the fifth leading causes of death among elder adults, is one of top diseases in terms of hospitalization rate. Although mainly bacteria and viruses cause pneumonia, the weather is also related to the occurrence of pneumonia. The candidate weather variables are humidity, amount of sunshine, diurnal temperature range, daily mean temperatures and density of particles. Due to the delayed occurrence of pneumonia, lagged weather variables are also considered. Additionally, year effects, holiday effects and seasonal effects are considered. We select the related variables that influence the occurrence of pneumonia using penalized generalized linear models.
BACKGROUND Tramadol is known to cause fewer adverse events (AE) than other opioids. However, recent research has raised concerns about various safety issues. OBJECTIVE We aimed to explore these new AE related to tramadol using social media and conventional pharmacovigilance data. METHODS This study used two datasets, one from patients’ drug reviews on WebMD and one from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We analyzed 2,062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities (MedDRA). To analyze AE from FAERS, a disproportionality analysis was performed with three measures: the proportional reporting ratio (PRR), the reporting odds ratio (ROR), and the information component (IC). RESULTS From the 869 AE reported, we identified 125 new signals related to tramadol use not listed on the drug label that satisfied all three signal detection criteria. In addition, 20 serious AE were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients’ symptom descriptions, tramadol-induced pain might also be an unexpected AE. CONCLUSIONS This study detected several novel signals related to tramadol use, suggesting newly identified possible AE. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. CLINICALTRIAL N/A
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