Table_1_Validation and comparison of the coding algorithms to identify people with migraine using Japanese claims data.DOCX

PurposeThe study aimed to validate and compare coding algorithms for identifying people with migraine within the Japanese claims database.MethodsThis study used the administrative claim database provided by DeSC Healthcare, Inc., that was linked to the results of an online survey administered to adult users of the health app “kencom®.” The ability of the 12 algorithms to detect migraines using diagnostic records alone or with prescription records was evaluated based on sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). We used a migraine diagnosis judged based on respondents' self-reported symptoms according to the diagnostic criteria of the International Classification of Headache Disorders, version 3 (ICHD-3), as true.ResultsOf the 21,480 individuals, 691 had migraine according to the ICHD-3 criteria. The 12 algorithms had a sensitivity of 5.4–8.8%, specificity of 98.8–99.6%, PPVs of 19.2–32.5%, and NPVs of 96.9–97.0%. Algorithm 9 (migraine diagnostic records more than once AND at least one prescription record for migraine prophylaxis or triptans in the same month as diagnosis) produced the highest PPV, whereas Algorithm 2 (at least one diagnostic record of migraine or tension-type headache) had the highest sensitivity. Similar trends were observed when using the ID-Migraine or 4-item migraine screener, instead of the ICHD-3 criteria, for case ascertainment.ConclusionStrict algorithms, such as Algorithm 9, yielded a higher PPV but a lower sensitivity, and such algorithms may be suitable for studies estimating the relative risk. Conversely, algorithms based on a single diagnostic record, such as Algorithm 2, had a higher sensitivity and may be suitable for studies estimating the prevalence/incidence of disease. Our findings will help select a desirable algorithm for migraine studies using a Japanese claim database.</p

Table_3_Validation and comparison of the coding algorithms to identify people with migraine using Japanese claims data.DOCX

PurposeThe study aimed to validate and compare coding algorithms for identifying people with migraine within the Japanese claims database.MethodsThis study used the administrative claim database provided by DeSC Healthcare, Inc., that was linked to the results of an online survey administered to adult users of the health app “kencom®.” The ability of the 12 algorithms to detect migraines using diagnostic records alone or with prescription records was evaluated based on sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). We used a migraine diagnosis judged based on respondents' self-reported symptoms according to the diagnostic criteria of the International Classification of Headache Disorders, version 3 (ICHD-3), as true.ResultsOf the 21,480 individuals, 691 had migraine according to the ICHD-3 criteria. The 12 algorithms had a sensitivity of 5.4–8.8%, specificity of 98.8–99.6%, PPVs of 19.2–32.5%, and NPVs of 96.9–97.0%. Algorithm 9 (migraine diagnostic records more than once AND at least one prescription record for migraine prophylaxis or triptans in the same month as diagnosis) produced the highest PPV, whereas Algorithm 2 (at least one diagnostic record of migraine or tension-type headache) had the highest sensitivity. Similar trends were observed when using the ID-Migraine or 4-item migraine screener, instead of the ICHD-3 criteria, for case ascertainment.ConclusionStrict algorithms, such as Algorithm 9, yielded a higher PPV but a lower sensitivity, and such algorithms may be suitable for studies estimating the relative risk. Conversely, algorithms based on a single diagnostic record, such as Algorithm 2, had a higher sensitivity and may be suitable for studies estimating the prevalence/incidence of disease. Our findings will help select a desirable algorithm for migraine studies using a Japanese claim database.</p

Table_2_Validation and comparison of the coding algorithms to identify people with migraine using Japanese claims data.DOCX

PurposeThe study aimed to validate and compare coding algorithms for identifying people with migraine within the Japanese claims database.MethodsThis study used the administrative claim database provided by DeSC Healthcare, Inc., that was linked to the results of an online survey administered to adult users of the health app “kencom®.” The ability of the 12 algorithms to detect migraines using diagnostic records alone or with prescription records was evaluated based on sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs). We used a migraine diagnosis judged based on respondents' self-reported symptoms according to the diagnostic criteria of the International Classification of Headache Disorders, version 3 (ICHD-3), as true.ResultsOf the 21,480 individuals, 691 had migraine according to the ICHD-3 criteria. The 12 algorithms had a sensitivity of 5.4–8.8%, specificity of 98.8–99.6%, PPVs of 19.2–32.5%, and NPVs of 96.9–97.0%. Algorithm 9 (migraine diagnostic records more than once AND at least one prescription record for migraine prophylaxis or triptans in the same month as diagnosis) produced the highest PPV, whereas Algorithm 2 (at least one diagnostic record of migraine or tension-type headache) had the highest sensitivity. Similar trends were observed when using the ID-Migraine or 4-item migraine screener, instead of the ICHD-3 criteria, for case ascertainment.ConclusionStrict algorithms, such as Algorithm 9, yielded a higher PPV but a lower sensitivity, and such algorithms may be suitable for studies estimating the relative risk. Conversely, algorithms based on a single diagnostic record, such as Algorithm 2, had a higher sensitivity and may be suitable for studies estimating the prevalence/incidence of disease. Our findings will help select a desirable algorithm for migraine studies using a Japanese claim database.</p

The effects of repeated application of ACh on the blocking action of asperparaline A.

<p>After recording the control response to ACh at 10 µM, asperparaline A was continuously bath-applied at 30 nM, during which ACh was also applied at 10 µM for 2 s every minute using the U-tube. (A) Traces of the ACh-induced current responses in the presence of 30 nM asperparaline A. (B) Normalized peak current amplitude of the ACh responses recorded during the continuous application of asperparaline A. The peak current amplitude of each response was normalized by that of the response recorded before the application of asperparaline A. Each plot represents the mean ± standard error of the mean of 4 separate experiments.</p

Chemical structure of asperparaline A.

<p>Chemical structure of asperparaline A.</p

Acetylcholine (ACh)-induced currents (A), the effects of blockers (mecamylamine and fipronil) on the ACh- (B), γ-aminobutyric acid (GABA) (C)- and L-glutamate (D)-induced currents and the actions of asperparaline A on the resting-state (E) and neurotransmitter-evoked currents (F–H) in the silkworm (<i>Bombyx mori</i>) larval neurons.

<p>The holding potential was −60 mV. ACh (10 µM), L-glutamate (30 µM) and GABA (30 µM) was applied for 2 s using the U-tube, whereas mecamylamine and fipronil were bath-applied for 1 min prior to co-application with the agonists. In (E), asperparaline A was applied alone at 1 µM for 2 s using the U-tube, whereas in (F–H), it was bath-applied for 1 min prior to co-application with neurotransmitters ACh (F), GABA (G) and L-glutamate (H). Note that both peak and slowly desensitizing current amplitudes of the ACh-evoked response were blocked reversibly, selectively and almost completely by 1 µM asperparaline A (F).</p

Concentration-inhibition curves for asperparaline A in terms of attenuation of the responses to ACh of the silkworm larval neurons.

<p>(A) The ACh-induced responses recorded before and after bath-application of asperparaline A for 1 min prior to co-application with 10 µM ACh. The peak and slowly desensitizing currents are indicated by “a” and “b”, respectively. (B) Concentration-inhibition curves for asperparaline A. Data were normalized to the maximum response to ACh (10 µM). Each plot represents the mean ± the standard error of the mean of 4 experiments. The concentration-inhibition curves were obtained by fitting the data to Eq. (1) (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018354#s2" target="_blank">Materials and Methods</a>). The pIC₅₀ ( = log(1/IC₅₀) values for the peak and slowly desensitizing currents were 7.69±0.02 (n = 4, IC₅₀ = 20.2 nM) and 7.40±0.04 (n = 4, IC₅₀ = 39.6 nM), respectively. These two values are significantly different (<i>p</i><0.05, <i>t</i>-test).</p

Effects of asperparaline A on the ACh-induced responses of chicken α3β4 (A), α4β2 (B) and α7 (C) nAChRs expressed in <i>Xenopus laevis</i> oocytes.

<p>After three successive control applications of ACh, 10 µM asperparaline A was continuously bath-applied and then co-applied with 100 µM ACh. Asperparaline A blocked the ACh-response of α3β4 nAChR by 33.4±3.3% (n = 3), whereas it scarcely influenced the response of α4β2 (n = 4) and α7 (n = 3) nAChRs.</p

Effects of asperparaline A on the concentration-response curve for ACh in the silkworm larval neurons.

<p>The ACh-induced responses were measured at various concentrations in the presence and absence of 100 nM asperparaline A. The concentration-response curves were obtained by fitting the data to Eq. (2) (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0018354#s2" target="_blank">Materials and Methods</a>). The pEC₅₀ ( = log(1/EC₅₀)) values determined in the presence and absence of asperparaline A were 4.98±0.10 (n = 4, EC₅₀ = 10.5 µM) and 4.94±0.04 (n = 7, EC₅₀ = 11.4 µM), respectively. No significant shift in EC₅₀ was observed by the application of asperparaline A.</p

Effects of pre-application on the antagonist action of asperparaline A.

<p>(A) Asperparaline A was co-applied at 30 nM with 10 µM ACh for 2 s without pre-application, or applied for 1, 2 and 5 min prior to co-application with 10 µM ACh. (B) The antagonist action of asperparaline A with and without pre-application for 1, 2 and 5 min. Each bar graph represents the mean ± standard error of the mean (n = 4) of the peak current amplitude of the ACh-induced response normalized by that taken before the application of asperparaline A. The pre-application of asperparaline A significantly enhanced the antagonist action (<i>p</i><0.05, One-way ANOVA, Tukey's test), but there were no significant differences in the blocking action between 1, 2, and 5 min pre-applications.</p