Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study
Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharm...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pizzoglio, Véronique [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2011 |
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| Übergeordnetes Werk: |
Enthalten in: European journal of clinical pharmacology - Berlin : Springer, 1968, 68(2011), 5 vom: 06. Dez., Seite 793-799 |
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| Übergeordnetes Werk: |
volume:68 ; year:2011 ; number:5 ; day:06 ; month:12 ; pages:793-799 |
| Links: |
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| DOI / URN: |
10.1007/s00228-011-1178-1 |
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| Katalog-ID: |
SPR002580179 |
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| 245 | 1 | 0 | |a Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study |
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| 520 | |a Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. | ||
| 650 | 4 | |a Pharmacovigilance |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Signal detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Feasibility study |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ahmed, Ismaïl |4 aut | |
| 700 | 1 | |a Auriche, Pascal |4 aut | |
| 700 | 1 | |a Tuber-Bitter, Pascale |4 aut | |
| 700 | 1 | |a Haramburu, Françoise |4 aut | |
| 700 | 1 | |a Kreft-Jaïs, Carmen |4 aut | |
| 700 | 1 | |a Miremont-Salamé, Ghada |4 aut | |
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10.1007/s00228-011-1178-1 doi (DE-627)SPR002580179 (SPR)s00228-011-1178-1-e DE-627 ger DE-627 rakwb eng Pizzoglio, Véronique verfasserin aut Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. Pharmacovigilance (dpeaa)DE-He213 Signal detection (dpeaa)DE-He213 Feasibility study (dpeaa)DE-He213 Ahmed, Ismaïl aut Auriche, Pascal aut Tuber-Bitter, Pascale aut Haramburu, Françoise aut Kreft-Jaïs, Carmen aut Miremont-Salamé, Ghada aut Enthalten in European journal of clinical pharmacology Berlin : Springer, 1968 68(2011), 5 vom: 06. Dez., Seite 793-799 (DE-627)253722829 (DE-600)1459058-X 1432-1041 nnns volume:68 year:2011 number:5 day:06 month:12 pages:793-799 https://dx.doi.org/10.1007/s00228-011-1178-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 68 2011 5 06 12 793-799 |
| spelling |
10.1007/s00228-011-1178-1 doi (DE-627)SPR002580179 (SPR)s00228-011-1178-1-e DE-627 ger DE-627 rakwb eng Pizzoglio, Véronique verfasserin aut Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. Pharmacovigilance (dpeaa)DE-He213 Signal detection (dpeaa)DE-He213 Feasibility study (dpeaa)DE-He213 Ahmed, Ismaïl aut Auriche, Pascal aut Tuber-Bitter, Pascale aut Haramburu, Françoise aut Kreft-Jaïs, Carmen aut Miremont-Salamé, Ghada aut Enthalten in European journal of clinical pharmacology Berlin : Springer, 1968 68(2011), 5 vom: 06. Dez., Seite 793-799 (DE-627)253722829 (DE-600)1459058-X 1432-1041 nnns volume:68 year:2011 number:5 day:06 month:12 pages:793-799 https://dx.doi.org/10.1007/s00228-011-1178-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 68 2011 5 06 12 793-799 |
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10.1007/s00228-011-1178-1 doi (DE-627)SPR002580179 (SPR)s00228-011-1178-1-e DE-627 ger DE-627 rakwb eng Pizzoglio, Véronique verfasserin aut Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. Pharmacovigilance (dpeaa)DE-He213 Signal detection (dpeaa)DE-He213 Feasibility study (dpeaa)DE-He213 Ahmed, Ismaïl aut Auriche, Pascal aut Tuber-Bitter, Pascale aut Haramburu, Françoise aut Kreft-Jaïs, Carmen aut Miremont-Salamé, Ghada aut Enthalten in European journal of clinical pharmacology Berlin : Springer, 1968 68(2011), 5 vom: 06. Dez., Seite 793-799 (DE-627)253722829 (DE-600)1459058-X 1432-1041 nnns volume:68 year:2011 number:5 day:06 month:12 pages:793-799 https://dx.doi.org/10.1007/s00228-011-1178-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 68 2011 5 06 12 793-799 |
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10.1007/s00228-011-1178-1 doi (DE-627)SPR002580179 (SPR)s00228-011-1178-1-e DE-627 ger DE-627 rakwb eng Pizzoglio, Véronique verfasserin aut Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. Pharmacovigilance (dpeaa)DE-He213 Signal detection (dpeaa)DE-He213 Feasibility study (dpeaa)DE-He213 Ahmed, Ismaïl aut Auriche, Pascal aut Tuber-Bitter, Pascale aut Haramburu, Françoise aut Kreft-Jaïs, Carmen aut Miremont-Salamé, Ghada aut Enthalten in European journal of clinical pharmacology Berlin : Springer, 1968 68(2011), 5 vom: 06. Dez., Seite 793-799 (DE-627)253722829 (DE-600)1459058-X 1432-1041 nnns volume:68 year:2011 number:5 day:06 month:12 pages:793-799 https://dx.doi.org/10.1007/s00228-011-1178-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 68 2011 5 06 12 793-799 |
| allfieldsSound |
10.1007/s00228-011-1178-1 doi (DE-627)SPR002580179 (SPR)s00228-011-1178-1-e DE-627 ger DE-627 rakwb eng Pizzoglio, Véronique verfasserin aut Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag 2011 Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. Pharmacovigilance (dpeaa)DE-He213 Signal detection (dpeaa)DE-He213 Feasibility study (dpeaa)DE-He213 Ahmed, Ismaïl aut Auriche, Pascal aut Tuber-Bitter, Pascale aut Haramburu, Françoise aut Kreft-Jaïs, Carmen aut Miremont-Salamé, Ghada aut Enthalten in European journal of clinical pharmacology Berlin : Springer, 1968 68(2011), 5 vom: 06. Dez., Seite 793-799 (DE-627)253722829 (DE-600)1459058-X 1432-1041 nnns volume:68 year:2011 number:5 day:06 month:12 pages:793-799 https://dx.doi.org/10.1007/s00228-011-1178-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 68 2011 5 06 12 793-799 |
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Enthalten in European journal of clinical pharmacology 68(2011), 5 vom: 06. Dez., Seite 793-799 volume:68 year:2011 number:5 day:06 month:12 pages:793-799 |
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Enthalten in European journal of clinical pharmacology 68(2011), 5 vom: 06. Dez., Seite 793-799 volume:68 year:2011 number:5 day:06 month:12 pages:793-799 |
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Pizzoglio, Véronique @@aut@@ Ahmed, Ismaïl @@aut@@ Auriche, Pascal @@aut@@ Tuber-Bitter, Pascale @@aut@@ Haramburu, Françoise @@aut@@ Kreft-Jaïs, Carmen @@aut@@ Miremont-Salamé, Ghada @@aut@@ |
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The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. 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Pizzoglio, Véronique |
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Pizzoglio, Véronique misc Pharmacovigilance misc Signal detection misc Feasibility study Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study |
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Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study Pharmacovigilance (dpeaa)DE-He213 Signal detection (dpeaa)DE-He213 Feasibility study (dpeaa)DE-He213 |
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Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study |
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Pizzoglio, Véronique Ahmed, Ismaïl Auriche, Pascal Tuber-Bitter, Pascale Haramburu, Françoise Kreft-Jaïs, Carmen Miremont-Salamé, Ghada |
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implementation of an automated signal detection method in the french pharmacovigilance database: a feasibility study |
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Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study |
| abstract |
Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. © Springer-Verlag 2011 |
| abstractGer |
Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. © Springer-Verlag 2011 |
| abstract_unstemmed |
Purpose In France, early detection of adverse effects does not currently involve any automatic signal detection method. The present objective was to assess the feasibility and measure the potential benefit of the incorporation of an automatic signal detection tool ($ GPS_{pH0} $) in the French pharmacovigilance system. Methods $ GPS_{pH0} $ was first applied to the data collected from 1 January 2000 to 31 December 2008 and then to the data collected from 1 January 2000 to 31 March 2009. A total of 1,414 original signals were detected. They were shared out for further expertise among 32 centres, i.e. the 31 Regional Pharmacovigilance Centres and the French medicine agency (AFSSAPS) pharmacovigilance department. Results The participating centres (n = 28) analysed 1,292 signals in May 2009. Overall, 277 signals whether known or unknown were thus considered worth following up. Half of the other 893 categorised signals were “well-known” (35.7%) and non-interpretable/non-pertinent signals (36.6%); 4% were not categorised because of a lack of time. Analysis of the signals was time-consuming, but the working time estimated by the participants was highly variable (median time: 6 h; minimum: 2 h maximum: 26 h). Conclusions The results of this study are in favour of the integration of an automated signal detection tool to complement the current pharmacovigilance activities. The Anatomic Therapeutic Chemical for drug classification poses difficulties in many situations; the international proprietary name might be more efficient. The variability observed in the time needed for analysis suggests that a standardised methodology should be employed. Overall, the findings of this prospective study will contribute to refining the signal management procedure to be implemented in the future. © Springer-Verlag 2011 |
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Implementation of an automated signal detection method in the French pharmacovigilance database: a feasibility study |
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https://dx.doi.org/10.1007/s00228-011-1178-1 |
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Ahmed, Ismaïl Auriche, Pascal Tuber-Bitter, Pascale Haramburu, Françoise Kreft-Jaïs, Carmen Miremont-Salamé, Ghada |
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Ahmed, Ismaïl Auriche, Pascal Tuber-Bitter, Pascale Haramburu, Françoise Kreft-Jaïs, Carmen Miremont-Salamé, Ghada |
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10.1007/s00228-011-1178-1 |
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| score |
7.3964148 |