Salim Mezaache1,2, Helene Derumeaux3, Pierre Ferraro4, Pascal Capdepon5,6, Jean-Christophe Steinbach7, Xavier Abballe8, Deborah Palas9, Nabil Saichi10, Karine Desboeuf11, Maryse Lapeyre-Mestre1,2,12, Laurent Sailler1,2,13, Guillaume Moulis1,2,13. 1. UMR 1027 INSERM-University of Toulouse, Toulouse, France. 2. Toulouse University Hospital (CHU de Toulouse), CIC 1436, Toulouse, France. 3. Department of Medical Information, Toulouse University Hospital (CHU de Toulouse), Toulouse, France. 4. Department of Medical Information, Auch Hospital, Auch, France. 5. Department of Medical Information, Tarbes Hospital, Tarbes, France. 6. Department of Medical Information, Lourdes Hospital, Lourdes, France. 7. Department of Medical Information, Castres Hospital, Castres, France. 8. Department of Medical Information, Montauban Hospital, Montauban, France. 9. Department of Medical Information, Albi Hospital, Albi, France. 10. Department of Medical Information, Val d'Ariege Hospital, Foix, France. 11. Department of Medical Information, Lavaur Hospital, Lavaur, France. 12. Department of Medical and Clinical Pharmacology, Toulouse University Hospital (CHU de Toulouse), Toulouse, France. 13. Department of Internal Medicine, Toulouse University Hospital (CHU de Toulouse), Toulouse, France.
Abstract
OBJECTIVES: To evaluate the accuracy of an algorithm identifying newly diagnosed immune thrombocytopenia (ITP) patients in the French national health insurance database (SNIIRAM). METHODS: The source of data was the SNIIRAM of Midi-Pyrenees region (southwest of France, three million inhabitants). Data of patients with at least one ITP code (D69.3 code of the International Classification of Disease, version 10) were extracted between January 1, 2012, and December 31, 2014. We used an algorithm that identifies newly diagnosed primary ITPs. Medical charts of incident ITPs were reviewed. Positive predictive values (PPVs) of identification of true, incident, and primary ITP cases were estimated. RESULTS: Of the 168 patients selected, 161 were true ITP cases yielding a PPV of 95.8% (95% confidence interval-95% CI: 92.8-98.8). Among them, 128 were truly incident according to symptom onset date and 134 according to the diagnosis date yielding PPVs of 79.5% (95% CI: 73.2-85.7) and 83.2% (95% CI: 77.4-89.0), respectively. Median time between estimated diagnosis date by the algorithm and true diagnosis date was 0 days (interquartile range: 0 to 15). CONCLUSIONS: This study showed a very good PPV of this algorithm identifying incident primary ITP patients in the SNIIRAM.
OBJECTIVES: To evaluate the accuracy of an algorithm identifying newly diagnosed immune thrombocytopenia (ITP) patients in the French national health insurance database (SNIIRAM). METHODS: The source of data was the SNIIRAM of Midi-Pyrenees region (southwest of France, three million inhabitants). Data of patients with at least one ITP code (D69.3 code of the International Classification of Disease, version 10) were extracted between January 1, 2012, and December 31, 2014. We used an algorithm that identifies newly diagnosed primary ITPs. Medical charts of incident ITPs were reviewed. Positive predictive values (PPVs) of identification of true, incident, and primary ITP cases were estimated. RESULTS: Of the 168 patients selected, 161 were true ITP cases yielding a PPV of 95.8% (95% confidence interval-95% CI: 92.8-98.8). Among them, 128 were truly incident according to symptom onset date and 134 according to the diagnosis date yielding PPVs of 79.5% (95% CI: 73.2-85.7) and 83.2% (95% CI: 77.4-89.0), respectively. Median time between estimated diagnosis date by the algorithm and true diagnosis date was 0 days (interquartile range: 0 to 15). CONCLUSIONS: This study showed a very good PPV of this algorithm identifying incident primary ITP patients in the SNIIRAM.
Authors: Nicolas H Thurin; Pauline Bosco-Levy; Patrick Blin; Magali Rouyer; Jérémy Jové; Stéphanie Lamarque; Séverine Lignot; Régis Lassalle; Abdelilah Abouelfath; Emmanuelle Bignon; Pauline Diez; Marine Gross-Goupil; Michel Soulié; Mathieu Roumiguié; Sylvestre Le Moulec; Marc Debouverie; Bruno Brochet; Francis Guillemin; Céline Louapre; Elisabeth Maillart; Olivier Heinzlef; Nicholas Moore; Cécile Droz-Perroteau Journal: BMC Med Res Methodol Date: 2021-05-01 Impact factor: 4.615