Drug-induced anaphylaxis : case/non-case study based on an italian pharmacovigilance database.

OBJECTIVE: To identify the number of cases of anaphylaxis reported in association with different classes of drugs and compare it with other reports contained in the same database. METHODS: The data were obtained from a database containing all of the spontaneous reports of adverse drug reactions (ADRs) coming from the Italian regions of Emilia Romagna, Lombardy and the Veneto, which are the main contributors to the Italian spontaneous surveillance system. The ADRs reported between January 1990 and December 2003 with a causality assessment of certainly, probably or possibly drug related (according to the WHO criteria) were analysed using a case/non-case design. The cases were defined as the reactions already coded by the WHO preferred terms of 'anaphylactic shock' or 'anaphylactoid reaction' (this last term also included anaphylactic reaction) and those with a time of event onset that suggested an allergic reaction and involved at least two of the skin, respiratory, gastrointestinal, CNS or cardiovascular systems; the non-cases were all of the other ADR reports. The frequency of the association between anaphylaxis and the suspected drug in comparison with the frequency of anaphylaxis associated to all of the other drugs was calculated using the ADR reporting odds ratio (ROR) as a measure of disproportionality. RESULTS: Our database contained 744 cases (including 307 cases of anaphylactic shock with 10 deaths) and 27 512 non-cases. The percentage of anaphylaxis cases reported in inpatients was higher than that among outpatients (59.1% vs 40.9%). This distribution is significantly different from that of the other ADR reports that mainly refer to outpatients. After intravenous drug administrations, anaphylactic shock cases were more frequent than anaphylactoid reactions or other ADRs, but more than one-third of these reactions were caused by an oral drug. Blood substitutes and radiology contrast agents had the highest RORs. Among the systemic antibacterial agents, anaphylaxis was disproportionally reported more often for penicillins, quinolones, cephalosporins and glycopeptides, but diclofenac was the only NSAID with a significant ROR. As a category, vaccines had a significantly lower ROR, thus indicating that anaphylaxis is reported proportionally less than other ADRs. CONCLUSIONS: Anaphylaxis is a severe ADR that may also occur with commonly used drugs. It represents 2.7% of all of the ADRs reported in an Italian spontaneous reporting database.     

药品不良反应报告常用信号检测方法应用研究

目的:为指导临床安全、合理用药和进一步评价药品安全风险提供参考。方法:通过收集我区2007-2010年药品不良反应(ADR)监测网络ADR报告8795份,运用频数法中的比例报告(PRR)法、报告比值比(ROR)法和贝叶斯判别可信区间递进神经网络模型(BCPNN)法进行定量检测,对生成的可疑信号进行比较分析,探索以自发呈报系统数据库为基础的定量信号检测方法的运用和科学的ADR信号检测途径。结果与结论:通过对869种Drug-ADR组合和66种Drugs-ADR组合进行3种常用方法信号定量检测,发现对部分已知信号能够检出,PRR法、ROR法结果相近,BCPNN法差别较大,3种方法信号强度各不相同。     

药品不良反应信号检测方法研究

目的：探索适合我国的药品不良反应（ADR）信号检测方法。方法：检索各种ADR信号检测方法有关文献,对其进行研究、分析和比较。结果：目前各国普遍使用的信号检测方法主要有频数法和贝叶斯法,前者主要有比例报告比法、报告比值比法、MHRA法等,后者包括贝叶斯判别可信区间递进神经网络模型与相对比值法等。结论：建议我国信号检测的实施综合采用BCPNN、PRR、ROR、MHRA等多种方法。     

Criteria revision and performance comparison of three methods of signal detection applied to the spontaneous reporting database of a pharmaceutical manufacturer.

BACKGROUND AND OBJECTIVE: Several statistical methods exist for detecting signals of potential adverse drug reactions in spontaneous reporting databases. However, these signal-detection methods were developed using regulatory databases, which contain a far larger number of adverse event reports than the databases maintained by individual pharmaceutical manufacturers. Furthermore, the composition and quality of the spontaneous reporting databases differ between regulatory agencies and pharmaceutical companies. Thus, the signal-detection criteria proposed for regulatory use are considered to be inappropriate for pharmaceutical industry use without modification. The objective of this study was to revise the criteria for signal detection to make them suitable for use by pharmaceutical manufacturers. METHODS: A model comprising 40 drugs and 1000 adverse events was constructed based on a spontaneous reporting database provided by a pharmaceutical company and used in a simulation to investigate appropriate criteria for signal detection. In total, 1000 pseudo datasets were generated with this model, and three statistical methods (proportional reporting ratio [PRR], Bayesian Confidence Propagation Neural Network [BCPNN] and multi-item gamma Poisson shrinker [MGPS]) for signal detection were applied to each dataset. The sensitivity and specificity of each method were evaluated using these pseudo datasets. The optimum critical value for signal detection (i.e. the value that achieved the highest sensitivity with 95% specificity) was identified for each method. The optimum values were also examined with the adverse events classified into two categories according to frequency. The three original detection methods and their revised versions were applied to a real pharmaceutical company database to detect 173 known adverse reactions of four drugs. RESULTS: The 1000 pseudo datasets consisted of an average of 81 862 reports and 11,407 drug-event pairs, including 1192 adverse drug reactions. The sensitivities of PRR, BCPNN and MGPS methods were 49%, 45% and 26%, respectively, whereas their specificities were 95%, 99.6% and 99.99%, respectively; these sensitivities were unacceptably low for pharmaceutical manufacturers, whereas the specificities were acceptable. The highest sensitivity for each method, obtained by changing critical values and maintaining specificity at 95%, was 44%, 62% and 62%, respectively. When adverse events were classified into two categories, sensitivities as high as 75% for regular events and 39% for rare events were achieved with the revised BCPNN method. The critical values of the information component minus two standard deviations (IC - 2SD) index of the revised BCPNN method were greater than -0.7 for regular events and greater than -0.6 for rare events. The revised BCPNN method yielded 51% sensitivity and 89% specificity for the real dataset. CONCLUSION: A lower critical value may be needed when signal-detection methodology is applied to the spontaneous reporting databases of pharmaceutical manufacturers. For example, it is recommended that pharmaceutical manufacturers use the BCPNN method with IC - 2SD criteria of greater than -0.7 for regular events and greater than -0.6 for rare events.     

A comparison of disproportionality analysis methods in national adverse drug reaction databases of China

Objective: Several disproportionality analysis methods are widely used for signal detection. The goal of this study was to compare the concordance of the performance characteristics of these methods in spontaneous reporting system of China.

Methods: Algorithms including reporting odds ratio (ROR), proportional reporting ratio (PRR) and information component (IC), a composite criterion previously used by Medicines and Healthcare Products Regulatory Agency (MHRA) were compared. Kappa coefficient was used as the gauge to test the concordance. Reports received in the year 2004 and 2005 were extracted for analysis in this study.

Results: After data processing, 361,872 reports representing 52,769 combinations were analysed. The analysis generated 24,022, 22,646, 5637 and 5302 signals of disproportionality by PRR, ROR, MHRA and IC, respectively. The kappa coefficient increased with the threshold of number of drug-adverse drug reactions (ADR) combination, and the coefficient exceeded 0.7 when the number of suspected drug–ADR exceeded 2.

Conclusion: This study shows that different measures used are broadly comparable in spontaneous reporting system in China when two or more cases per combination have been collected.     

基于FAERS的头孢他啶／阿维巴坦致神经系统异常的不良反应信号挖掘与分析

目的：通过对头孢他啶/阿维巴坦（ceftazidime/avibactam, CAZ/AVI）导致神经系统异常相关药物不良反应（adverse drug reactions, ADRs）信号的挖掘分析,为临床安全合理用药提供依据。方法：基于2015年1月至2021年6月美国食品药品监督管理局的不良事件报告系统（FAERS）,采用报告比值比法（reporting odds ratio, ROR）和贝叶斯法判别可信区间产比神经网络法（bayesian confidence propagation neural network, BCPNN）进行数据挖掘,筛选CAZ/AVI给药后导致神经系统异常的相关ADRs,并将其与美罗培南、亚胺培南/西司他丁和头孢他啶进行比较。结果：该研究共收集到CAZ/AVI相关ADRs共694例,其中88例与神经系统异常相关。女性患者比例稍高于男性（54.55% vs.34.09%）,65岁以上患者所占比例较高（15.91%）,用药后中位发病时间为6.5 d。CAZ/AVI与美罗培南和头孢他啶相比,致神经系统异常的风险增加[ROR=1.66（95%CI：1.28~2.13）,IC025=0.46;ROR=1.36（95%CI：1.02~1.81）,IC025=0.21]。CAZ/AVI与美罗培南、亚胺培南/西司他丁和头孢他啶相比,CAZ/AVI发生脑病[ROR=2.73（95%CI：1.85~4.04）,IC025=0.81;ROR=2.26（95%CI：1.46~3.51）,IC025=0.52;ROR=1.81（95%CI：1.15~2.84）,IC025=0.34]、肌阵挛[ROR=4.74（95%CI：2.56~8.81）,IC025=0.97;ROR=4.31（95%CI：2.04~9.10）,IC025=0.63]、昏迷[ROR=2.77（95%CI：1.26~6.08）,IC025=0.57;ROR=2.69（95%CI：1.08~6.71）,IC025=0.39]和癫痫[ROR=1.67（95%CI：1.05~2.67）,IC025=0.30]的风险增加。结论：该研究表明CAZ/AVI具有增加神经系统异常风险的倾向,这些不良反应应引起临床注意,特别是用于有中枢神经系统病史的患者,从而为临床安全用药提供参考,或避免用于高风险人群。     

瑞戈非尼的安全警戒信号检测与评价

目的：运用数据挖掘的方法检测瑞戈非尼的不良反应信号，为临床提供用药参考。方法：采用比例报告比值法（PRR）、报告比值比法（ROR）、英国药品和保健产品管理局的综合标准法（MHRA）和贝叶斯可信区间递进神经网络法（BCPNN）4种数据挖掘方法，对美国食品药品监督管理局（FDA）的不良事件报告系统（FAERS）中2012年第四季度-2018年第三季度共24个季度的数据进行关于瑞戈非尼不良反应的信号检测。结果：4种检测方法检测出重叠的关于瑞戈非尼的不良反应信号136个，存在药品说明书未涵盖的新不良反应信号102个。应特别关注涉及新ADR信号较多且信号中等的肝胆系统。另外，说明书未提及其有生殖系统相关的不良反应，此次检测中有4个信号。结论：通过数据挖掘的信号检测方法，发现瑞戈非尼药品说明书未涉及的新的不良反应信号102个，应引起临床重视。     

Fluvastatin and hepatic reactions: a signal from spontaneous reporting in Italy.

BACKGROUND: Signal detection is a crucial element in recognising new adverse drug reactions (ADRs) as soon as possible. HMG-CoA reductase inhibitors ('statins'), the most potent cholesterol-lowering drugs, are generally well tolerated but can occasionally lead to liver toxicity. Pre- and postmarketing studies on statins revealed an incidence of 0.1-3% elevation in hepatic transaminase levels. However, these elevations are asymptomatic, reversible, dose related or probably due to other causes. Postmarketing studies clearly showed the lack of evidence of hepatotoxicity from statins, apart from some isolated case reports of serious hepatic damage described in the literature. It is still unclear whether serious hepatic reactions are dose related and more frequent than the expected rate in the general population. OBJECTIVE: In this study, the hypothesis that fluvastatin could cause serious liver injuries more than the other statins is investigated, in the light of a quantitative and qualitative signal analysis, drug consumption data and evidence from the literature. METHODS: The Italian Interregional Group of Pharmacovigilance (Gruppo Interregionale di Farmacovigilanza; GIF) is an example of signal detection within the Italian spontaneous ADR reporting system. The GIF database holds reports of suspected ADRs submitted by five Italian pharmacovigilance regional centres. In the GIF database, all reports of suspected ADRs are classified according to the WHO criteria for causality assessment. The reactions are coded according to the WHO Adverse Reaction Terminology and classified as serious or non-serious events on the basis of the WHO Critical Term List. Every 6 months the GIF database is analysed to extract potential signals through a qualitative case-by-case analysis and using a quantitative methodology called proportional reporting ratio (PRR). This methodology permitted us to identify the potential signal 'fluvastatin and hepatic reactions'. RESULTS: At 31 December 2004, the GIF database contained 35 757 reports with an annual reporting rate of 170 reports per million inhabitants. We found a total of 1260 reports of ADRs related to statins, including 178 of hepatic reactions. Sixty-nine reports were attributed to fluvastatin, which showed the highest PRR in comparison with the other statins. Fluvastatin was associated with 33 serious reactions, mainly hepatitis and cholestatic hepatitis. The number of reports of severe hepatotoxicity associated with fluvastatin started to increase from 2002. About half of them did not report other suspected or concomitant drugs and in one third the hepatotoxicity occurred after <1 month of therapy. Twenty-seven out of 33 patients were female, and fluvastatin was administered at 80 mg/day in 81% of cases reporting complete data on drug dosage. CONCLUSION: In the literature, serious hepatic reactions are rarely described in patients taking statins; however, data gathered by GIF suggest that cases of hepatotoxicity are reported more often than expected. In addition, GIF data seem to reveal that fluvastatin is more likely to cause hepatic reactions than the other statins. However, this is a preliminary signal and future evaluations are certainly needed to confirm it and to quantify this possible risk.     

报告率比例失衡信号检测的实证研究

目的：了解报告率比例法对药物不良反应志愿呈报数据库进行信号筛选和检测效果。方法：江苏省药物不良反应监测中心数据库中2004年所有报告，信号阈值为PRR值≥2，卡方值≥4，至少有3例以上的相关报道。将检测结果与产品说明书进行比较。结果：符合PRRs阈值的Drug-ADR组合共275种，其中115种没有在产品说明书中列出。结论：PRRs方法有助于在药物不良反应数据库中发现新的ADR信号及突出的药物安全性问题，为更深入的研究提供科学依据。     

基于FAERS数据库万古霉素在老年患者中的不良事件信号挖掘

目的 通过分析美国食品药品监督管理局不良事件报告系统（FAERS）数据库,挖掘万古霉素在老年患者的不良事件（ADE）,为临床用药监护提供参考。方法 收集FAERS数据库从2004年至2022年第1季度的ADE报告,使用Open Vigil 2.1数据平台,对65岁以上患者使用万古霉素的ADE进行预处理。采用报告比值比（ROR）法和比例报告比值比（PRR）法对ADE进行挖掘与分析,获得发生频次及信号强度前10位的ADE,并分析前10位的联合用药情况。结果 以万古霉素为首要怀疑药品在65岁以上人群中的ADE报告共2221份,检测到ADE信号2194个,其中445个属于药物的不良反应。按照发生频次排序,ADE分别为药物超敏反应（313例）、急性肾损伤（301例）及发热（296例）等。按照信号强度排序,ADE分别为禽流感（ROR＝4312.79）、细菌性心包炎（ROR＝2985.78）及假丝酵母菌脑膜炎（ROR＝1658.77）。联合用药中,前3位的药物分别为哌拉西林/他唑巴坦151例,庆大霉素119例及美罗培南112例。结论 万古霉素在老年人应用时,应警惕肾毒性、超敏反应及谷浓度,同时应注意联合用药对万古霉素不良反应发生的影响。     

Applying quantitative methods for detecting new drug safety signals in pharmacovigilance national database

OBJECTIVES: To applies three different methods of signal detection to the registered adverse events in Iranian Pharmacovigilance database over the period of 1998-2005. METHODS: All adverse drug reactions (ADRs) reported to Iranian Pharmacovigilance Center (IPC) from March 1998 through January 2005, were used for the analysis. The data were analysed based on three different signal detection methods including reporting odds ratios (RORs), information component (IC) and proportional reporting ratios (PRRs). The signals detected were categorised based on the number of reports per drug-adverse event combination, severity of the event and labelled or unlabelled ADRs. RESULTS: During the study period, 6353 cases of ADR reports describing 11 130 reactions were received by IPC. The dataset involved 4975 drug-adverse event combinations. The count of drug-event combinations was 1, 2 and 3 or more for 3470, 726 and 779 combinations, respectively. According to PRRs, there were 2838, 872 and 488 drug-event combinations known as a signal for the pairs with the reporting frequency of 1, 2 and 3 reports, respectively. The results of estimating RORs showed that 2722, 862 and 481 drug-adverse event combinations were detected to be signal for the pairs with the reporting frequency of 1, 2 and 3 reports, respectively, while measuring IC and IC-2SD detected 1120, 378 and 235 for the same reporting frequencies. Diclofenac-induced paralysis and tramadol-induced severe reactions were the most important signals. CONCLUSION: Applying quantitative signal detection methods to the database of national pharmacovigilance centres is necessary to early detection of drug safety alerts.     

Database size and power to detect safety signals in pharmacovigilance

Most regulatory agencies and pharmaceutical companies focus the majority of their pharmacovigilance on safety signal identification in large databases. GlaxoSmithKline (GSK) has > 100 drugs marketed worldwide. In order to determine which database has the highest statistical power to detect safety signals in three large global databases, ten GSK marketed drugs were randomly selected for review in the three databases. At the time of data lock, the FDA database (Adverse Event Reporting System [AERS]) contained approximately 6.2 million total records of adverse drug reactions (ADRs). The WHO database (VIGIBASE) contained 7.2 million total records of ADRs. GSK's global safety database (OCEANS) contained approximately 2 million total ADRs for all of its marketed drugs. For the ten drugs selected, there was an average of 7566 reports found in AERS, 8661 reports found in VIGIBASE and 15,496 reports in OCEANS. The information from all three databases was used in pairs (AERS/OCEANS; AERS/VIGIBASE; and OCEANS/VIGIBASE) to calculate power using the maximum likelihood estimation. The OCEANS database contained more ADRs for all 10 drugs than AERS. OCEANS also contained more ADRs for 8/10 drugs than VIGIBASE. The highest statistical power to detect safety signals was determined by the pair of databases which had the greatest number of reports for the given drug. Based on this data, it was concluded that the highest power may be achieved by combining those databases with the most drug-specific data. It is also believe that early safety signal detection should involve the use of multiple large global databases because this permits the use of the largest number of reports for a given drug, and that reliance on a single database may reduce statistical power and diversity of ADRs.     

伊伐布雷定的不良反应信号挖掘与评价

目的 利用美国食品药品管理局公共数据开放项目(openFDA)检索伊伐布雷定的不良反应信号并进行评价,为临床用药提供参考.方法 采用比例失衡法中的报告比值比法(ROR)与比例报告比值法(PRR)对美国FDA不良事件报告系统(FAERS)中自2015年4月15日至2020年11月17日的报告进行关于伊伐布雷定的数据挖掘.结果 使用ROR法和PRR法共得到63个信号,其中35个信号的不良反应未在伊伐布雷定的说明书中出现.结论 挖掘和评价基于FAERS数据库获得的伊伐布雷定不良反应相关信号,可为其临床的合理应用提供依据.     

哌柏西利的安全警戒信号检测与评价

目的：运用数据挖掘的方法检测哌柏西利的不良反应信号,为其临床合理应用提供参考。方法：采用比例失衡法中的报告比值比法（ROR）和比例报告比值法（PRR）对美国FDA不良事件报告系统（FAERS）中2015年第一季度至2019年第三季度共19个季度的报告进行关于哌柏西利的数据挖掘。结果：使用ROR法和PRR法均得到哌柏西利的不良反应信号283个,二次筛选并去除考虑肿瘤治疗无效引起的ADR信号后,2种方法得到208个完全重合信号。其中新的不良反应信号159个,主要集中在胃肠系统疾病（30个）、各类检查（26个）、呼吸系统、胸及纵隔疾病（22个）、各类神经系统疾病（15个）和皮肤及皮下组织类疾病（n=14个）。信号强度排名前五中有4个为血液及淋巴系统疾病。结论：通过ROR、PRR信号检测方法,发现哌柏西利药品说明书未涉及的新的不良反应信号159个,建议临床用药时考虑其可能引起的不良反应进行合理用药。     

Use of an entacapone-containing drug combination and risk of death: Analysis of the FDA AERS (FAERS) database

To assess the signal of death associated with the use of an entacapone-containing drug combination in the FDA Adverse Event Reporting System (FAERS) database.Reports of death events submitted between January 2004 and December 2010 were retrieved and analysed by the reporting odds ratio (ROR). The ROR of case/non-case reports of death associated with an entacapone-containing drug combination was compared with the levodopa/carbidopa combination using the FDA AERS database.Eighty-seven reports linked the entacapone-containing drug combination to death, compared to 27 reports of death linking the levodopa/carbidopa combination. The ROR was statistically significant for the association between deaths with the use of an entacapone-containing drug combination (1.86 [95% CI 1.50–2.31]). In contrast, the ROR of death associated with the combination of levodopa and carbidopa was not statistically significant (0.89 [95% CI 0.61–1.30)].Based on analysing reports in the FAERS database, there is a risk of death with the use of an entacapone-containing drug combination. These results generated a signal of death with the use of this drug. However, epidemiological studies are required to confirm this association.     

基于FDA不良事件数据库和指定医疗事件对氯喹和羟氯喹不良反应信号的检测与评价

目的： 挖掘和评价新型冠状病毒肺炎治疗方案（试行第七版）中加入的药物"磷酸氯喹"和《上海市2019冠状病毒病综合救治专家共识》中推荐加入的"硫酸羟氯喹"上市后的安全信号,为临床合理用药提供参考。方法： 检索美国FDA不良事件报告系统（FDA adverse event reporting system,FAERS）数据库2004年1月1日-2019年12月31日收录以"氯喹"及"羟氯喹"为怀疑对象的不良事件（adverse drug events,ADEs）报告,提取排名前200位药物不良反应（adverse drug reaction,ADR）报告进行指定医疗事件（designated medical event,DME）筛选,采用报告比值比法（ROR）和比例报告比值比法（PRR）检测ADR信号,重点评估和比较出现DME的系统器官分类（SOC）中的安全信号,并对DME进行分析。结果： 提取FAERS数据库得到氯喹与羟氯喹ADEs报告数量分别为1 128例和29 639例;氯喹严重不良事件（serious adverse event,SAE）占比57.89%,羟氯喹占比26.60%。经DME筛选,共涉及7种SOC,其中眼部疾病与呼吸系统相关ADR中,羟氯喹检出的信号较多;皮肤和皮下组织类疾病,2种药物信号检出数量大致相等;心脏、血液及肝胆系统相关ADR中,信号主要集中在氯喹;耳部系统相关ADR中,信号检出较少。另外,氯喹检出的7种DME中尖端扭转型室速信号值最高,羟氯喹检出的4种DME中中毒性表皮坏死松解症信号值最高。结论： 基于真实世界的ADR信号检测有助于新冠疫情中氯喹与羟氯喹的安全性评价,降低临床用药风险。     

基于VAERS的轮状病毒疫苗不良反应信号挖掘和数据分析

目的:挖掘、评价轮状病毒疫苗上市后药物不良反应信号,为临床安全用药提供参考.方法:采用报告比值比法、比例报告比值比法、贝叶斯法对美国疫苗不良反应报告系统中的轮状病毒疫苗不良反应信号进行挖掘和分析.结果:纳入以轮状病毒疫苗为首要怀疑药品的不良反应(adverse drug reaction,ADR)报告15 417例,经...     

基于FDA不良事件报告系统度拉糖肽安全信号的检测与分析

目的：检测和分析度拉糖肽的安全信号,为度拉糖肽的临床合理用药提供参考。方法：采用报告比值法（ROR）和比例报告比法（PRR）对美国FDA不良事件报告系统（FAERS）上报的度拉糖肽的药物不良反应（ADR）信号进行数据挖掘和分析。结果：在纳入的72 189例报告中,女性（52.2%）略多于男性（42.9%）,年龄主要集中在41~80岁,利用ROR法和PRR法筛选后获得145个共同信号,经系统器官分类（SOC）后主要集中在胃肠疾病、一般疾病和给药部位反应,其中运动亢进性心脏综合征（PRR=269.8,ROR=270.0）、剂量不足（PRR=144.2,ROR=148.9）、糖尿病肌萎缩（PRR=95.4,ROR=95.5）、BMI异常（PRR=70.9,ROR=70.9）和梗阻性胰腺炎（PRR=52.2,ROR=52.2）为中等强度信号,其余为弱信号。与甲状腺肿瘤（降钙素水平升高、甲状腺肿块、甲状腺良性肿瘤、甲状腺髓样癌）、胰腺炎（血淀粉酶升高、血脂肪酶升高、胰腺炎、急性胰腺炎、梗阻性胰腺炎、水肿性胰腺炎）和胰腺肿瘤（胰腺肿块、胰腺肿瘤、胰腺癌、胰腺导管内乳头状黏液瘤）相关的指标和疾病,均是度拉糖肽的安全信号。结论：度拉糖肽可能会增加胰腺炎、甲状腺和胰腺肿瘤的发病风险,此类不良反应的高风险人群应慎用。     

SGLT2抑制剂不良反应信号的挖掘与评价

目的:挖掘和评价钠-葡萄糖共转运蛋白2(SGLT2)抑制剂卡格列净、达格列净、恩格列净上市后的不良反应(ADR)信号,为临床合理用药提供参考。方法:采用比例报告比法(PRR)和报告比值比法(ROR)对2013年第2季度至2020年第3季度美国FDA不良事件报告系统自发呈报系统中接收的卡格列净、达格列净、恩格列净等3种SGLT2抑制剂的ADR进行信号挖掘,分析ADR报告中对应患者的基本信息(包括性别、年龄、上报年份、上报国家、严重ADR)和安全警告信号。结果:收集到的6029375份ADR报告中,SGLT2抑制剂为伴随和怀疑药物的ADR报告有43807份,其中卡格列净ADR报告19301份、达格列净ADR报告10960份、恩格列净ADR报告13546份。除性别未知和年龄缺失的ADR报告外,纳入报告患者的性别分布均衡,主要集中在50~75岁范围内,上报年份主要在2018年,主要上报国家为美国,以“住院或住院时间延长”为主要的严重ADR。共挖掘得到ADR信号573个,累及系统26个,主要集中在代谢与营养类疾病、内分泌失调、肾脏及泌尿系统疾病、感染及侵扰类疾病等方面。卡格列净、达格列净、恩格列净ADR频数排序前10位的主要ADR信号共14个,达格列净、恩格列净的ADR信号中强度最强的信号都依次为酮症酸中毒(PRR值分别为119.64、140.11,ROR值的95%CI下限分别为148.28、178.78)和真菌感染(PRR值分别为47.76、34.77,ROR值的95%CI下限分别为50.69、36.28);而卡格列净除上述2个信号较强外,截趾(PRR值为489.79,ROR值的95%CI下限为520.15)和骨髓炎(PRR值为61.42,ROR值的95%CI下限为65.38)的信号也较强。结论:SGLT2抑制剂在代谢与营养类疾病、内分泌失调、肾脏及泌尿系统疾病、感染及侵扰类疾病方面的安全风险较高。达格列净、卡格列净、恩格列净易引起酮症酸中毒、真菌感染等ADR,卡格列净还易引起截趾、骨髓炎等ADR。     

The reporting odds ratio and its advantages over the proportional reporting ratio

PURPOSE: The proportional reporting ratio (PRR) is the proportion of spontaneous reports for a given drug that are linked to a specific adverse outcome, divided by the corresponding proportion for all or several other drugs. The PRR is similar to the proportional mortality ratio (PMR), an old epidemiologic measure calculated from death registries and constructed in similar fashion to the PRR. The PMR has important deficiencies, however, which the PRR shares. Miettinen and Wang demonstrated that the PMR could be improved by reformulating it as an odds ratio and applying the principles of a case-control study to the measure. In this paper, we review the problem with the PRR and show how the corresponding odds ratio represents an improvement over the PRR. METHODS: The method used is discussion and illustration by way of a hypothetical example. RESULTS: The PRR does not estimate relative risk. If, however, a spontaneous report database is viewed as source data for a case-control study, the reporting odds ratio (ROR) can be used to estimate relative risk. Treating the data as source data for a case-control study allows for further reduction of bias by the judicious choice of controls. CONCLUSIONS: Calculating the ROR in spontaneous report databases offers advantages over the PRR. It allows for estimation of the relative risk, and focuses attention on which people or reports should be included or excluded from the control series, permitting more deliberate elimination of biases. It also highlights the inherent weaknesses in spontaneous report data, which become more evident in light of the usual principles of control selection in case-control studies.