==== Front ESC Heart FailESC Heart Fail10.1002/(ISSN)2055-5822EHF2ESC Heart Failure2055-5822John Wiley and Sons Inc. Hoboken 10.1002/ehf2.12278EHF212278ESCHF-17-00182Original Research ArticleOriginal Research ArticlesPerformance of the MAGGIC heart failure risk score and its modification with the addition of discharge natriuretic peptides Performance of the MAGGIC score among Japanese patientsM. Sawano et al.Sawano Mitsuaki 1 Shiraishi Yasuyuki 1 Kohsaka Shun sk@keio.jp 1 Nagai Toshiyuki 2 3 9 Goda Ayumi 5 Mizuno Atsushi 4 Sujino Yasumori 6 Nagatomo Yuji 7 8 Kohno Takashi 1 Anzai Toshihisa 2 9 Fukuda Keiichi 1 Yoshikawa Tsutomu 7 1 Department of Cardiology Keio University School of Medicine Tokyo Japan 2 Department of Cardiovascular Medicine National Cerebral and Cardiovascular Center Suita Japan 3 National Heart and Lung Institute, Imperial College London London UK 4 Department of Cardiology St Luke's International Hospital Tokyo Japan 5 Division of Cardiology Kyorin University School of Medicine Tokyo Japan 6 Department of Cardiology Saitama Medical University International Medical Center Saitama Japan 7 Department of Cardiology Sakakibara Heart Institute Tokyo Japan 8 Department of Cardiology National Defense Medical College 9 Department of Cardiovascular Medicine Hokkaido University Graduate School of Medicine * Correspondence to: Shun Kohsaka, Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku‐ku, Tokyo, Japan. Tel: +81 3 5843 6702; Fax: +81 3 5363 3875. Email: sk@keio.jp 09 3 2018 8 2018 5 4 10.1002/ehf2.v5.4610 619 12 10 2017 27 1 2018 01 2 2018 © 2018 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiology.This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.Abstract Aims Predictive models for heart failure patients are widely used in the clinical practice to stratify patients' mortality and enable clinicians to tailor and intensify their approach. However, such models have not been validated internationally. In addition, biomarkers are now frequently measured to obtain prognostic information, and the implications of this practice are not known. In this study, we aimed to validate the model performance of the Meta‐analysis Global Group in Chronic Heart Failure (MAGGIC) score in a Japanese acute heart failure registry and further explore the incremental prognostic value of discharge B‐type natriuretic peptide (BNP) level. Methods and Results In this study, we evaluated the registered data of 2215 consecutive acute HF patients (with 694 119 person‐years follow‐up) from a prospective multicentre registry (the West Tokyo Heart Failure) conducted in Japan from April 2006 to August 2016. The mean age was 73.0 ± 13.0, and 61.2% were male. The MAGGIC score demonstrated modest discrimination (c‐index = 0.71, 95% confidence interval 0.67–0.74) and good calibration (R 2 value = 0.97); there was constant overestimation for 1 year mortality. However, when the BNP level was added to the original MAGGIC variables, the model demonstrated good discrimination (c‐index = 0.74, 95% confidence interval 0.70–0.78) with adequate calibration (R 2 value = 0.91). The modified MAGGIC BNP score was externally validated in a separate Japanese registry (NaDEF) and demonstrated moderate discrimination (c‐index = 0.69, 95% confidence interval 0.65–0.73) and calibration (R 2 value = 0.85). Conclusion The original MAGGIC score performed modestly in Japanese patients, but the addition of discharge BNP level enhanced model performance. The addition of objective biomarkers may result in effective modification of preexisting internationally recognized risk models and aid in multinational comparisons of heart failure patients' outcomes. Heart failurePredictionValidationEast AsiaGrant‐in‐Aid for Scientific Research (C)2359106226461088Sakakibara Clinical Research Grant for Promotion of SciencesHealth Labour Sciences Research Grant14528506 source-schema-version-number2.0component-idehf212278cover-dateAugust 2018details-of-publishers-convertorConverter:WILEY_ML3GV2_TO_NLMPMC version:version=5.4.4 mode:remove_FC converted:03.08.2018 Sawano , M. , Shiraishi , Y. , Kohsaka , S. , Nagai , T. , Goda , A. , Mizuno , A. , Sujino , Y. , Nagatomo , Y. , Kohno , T. , Anzai , T. , Fukuda , K. , and Yoshikawa , T. (2018 ) Performance of the MAGGIC heart failure risk score and its modification with the addition of discharge natriuretic peptides . ESC Heart Failure , 5 : 610 –619 . 10.1002/ehf2.12278 . ==== Body Introduction Heart failure (HF) is a growing epidemic worldwide particularly in countries with a rapidly ageing population.1 In Japan, with over 10.5 million octogenarians at present, the number of HF patients is expected to grow proportionately over the coming decades. To best identify patients at risk and concentrate limited resources, high‐performing internationally validated risk prediction models are necessary.2 Predicting 1 year mortality in HF patients has become extremely important in order to stratify patients that require heart transplantation and ventricular assist devices. Peak oxygen consumption (VO2) values measured during cardiopulmonary exercise testing are considered as an important tool for patient selection in advanced HF patients,3 and its use has been recommended in various guidelines. However, in reality, a significant proportion of these patients are too frail to undergo rigorous exercise testing. In this regard, risk prediction models such as the Seattle Heart Failure Model4 and the Meta‐analysis Global Group in Chronic Heart Failure (MAGGIC) score are particularly useful because their calculation are easy to use in the outpatient clinics or bedsides. The MAGGIC score was developed to predict survival of HF patients after hospitalization; from 39 372 patients with HF, from 30 cohort studies, six of which were clinical trials.5 To date, MAGGIC score has been validated externally in Western countries displaying good overall performance;6, 7 however, its performance in other regions of the world remains largely unknown. Additionally, biomarkers such as B‐type natriuretic peptide (BNP) have become widely used in the management of HF patients. BNP measured at the timing of discharge has been proven to be prognostically important in recent studies.8, 9, 10 In the current study, we aimed to validate the performance of the MAGGIC score and further explore the incremental prognostic value of discharge BNP level in Japanese acute HF patients. Methods Study population In this study, two registries were used as derivation and validation cohorts for evaluating the performance of the prediction models. The derivation cohort was the West Tokyo Heart Failure (WET‐HF) registry, and the validation cohort was the National Cerebral and Cardiovascular center acute DEcompensated heart Failure study (NaDEF). In both registries, acute HF was defined as rapid‐onset HF or a change in the signs and symptoms of HF requiring urgent therapy and hospitalization, based on the Framingham criteria.11 HF patients presenting with coexisting acute coronary syndrome were not included because of exclusion criterion. Individual cardiologists at each institution made the clinical diagnosis of HF. The institutional review boards at each site approved the study protocol, and research was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from each subject before the study. The derivation cohort was the WET‐HF registry: a large, ongoing, prospective, multicenter cohort registry conducted in Tokyo, Japan (East Japan), from June 2005 to August 2016. Details of the registry have been described previously.12, 13, 14, 15 Dedicated clinical research coordinators collected clinical data by reviewing individual patient medical records. Exclusive on‐site auditing by the investigators (Y.S. and S.K.) ensured proper registration of each patient. The objectives and detailed design are provided on the University Hospital Medical Information Network (UMIN000001171). The validation cohort for our study was the NaDEF study: an ongoing, prospective, single‐centre acute HF registry conducted at the National Cerebral and Cardiovascular Center in Osaka, Japan (West Japan), that began from January 2013. The objectives and detailed design are provided on the University Hospital Medical Information Network (UMIN000017024).16, 17 Patient selection and calculation of the MAGGIC score Patients were excluded for missing data required for calculation of the MAGGIC score (Figure 1 ). The 1 year mortality rate was calculated using the MAGGIC score including 13 clinical variables: age, ejection fraction as categorical variable (≥40%, 35–39%, 30–34%, 25–29%, <20%), New York Heart Association (NYHA) functional class, serum creatinine, diabetes, non‐prescription of beta‐blocker, systolic blood pressure (SBP), body mass index, time since diagnosis, current smoker, chronic obstructive pulmonary disease, male gender, and non‐prescription of angiotensin‐converting enzyme inhibitor or angiotensin receptor blockers for the remaining patients. Discharge NYHA class status was imputed as NYHA class II for 187 (8.4%) of the patients with missing data. Data on time since first diagnosis of HF or HF duration were not collected in our current database; thus, patients with previous history of HF hospitalization were regarded as having HF duration of more than 18 months. The study population was divided into six risk categories (Groups 1–6) based on the MAGGIC score according to the original article.5 Figure 1 Study population. Statistical analysis The patient demographics, the predicted cardiovascular death risk according to the MAGGIC score, and the observed mortality rate were compared between those who died during the first year of follow‐up and those who did not by using the Student's t‐tests or the Wilcoxon rank‐sum test for continuous variables and χ2 test for categorical variables. Time‐to‐event data were calculated as the time in days from the date of discharge after initial hospitalization for acute HF to the date of all‐cause death. Cumulative survival was computed according to the Kaplan–Meier method. We fit a Cox regression model with the calculated MAGGIC score as the only independent variable, and we calculated the c‐index, which is regarded as a measure for model discrimination equivalent to the area under curve for binary dependent variables. A c‐index equal or above 0.6 was considered modest, a c‐index above 0.7 was considered reasonable, and a c‐index above 0.80 was considered strong. Receiver operating characteristic curves were computed by using the MAGGIC score as the reference variable and the 1 year observed mortality as the binary reference variable, and the area under the receiver operating characteristic curve were calculated. To measure model calibration, we compared the predicted mortality rate to the observed mortality rate on the calibration plot and evaluated the coefficient of determination (R 2 value). Further, we compared the regression line drawn within the plotted points to the line of unity and calculated the best fit linear regression formula according to the observed mortality rate. The modified MAGGIC score was created by adding the log‐transformed discharge BNP value as an ordinal covariate and incorporated along with the MAGGIC score as independent variables and 1 year mortality rate as the dependent variable. We evaluated model discrimination and calibration similarly as stated previously. Model improvement was assessed by the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Furthermore, the modified MAGGIC score was further evaluated in an independent Japanese HF cohort for external validation of the newly created risk prediction model. Model discrimination and calibration were evaluated similarly as stated previously. All statistical analyses were performed by Stata/IC version 13.1 for Macintosh (StataCorp, College Station, TX). Results Patient demographics Overall, 2215 consecutive acute HF patients (with 694 119 person‐years follow‐up) in the WET‐HF registry were evaluated in this study. In brief, the mean age was 73.1 ± 13.5, 62.3% (n = 1136) were male, mean body mass index was 23.3 ± 4.4, 43.9% had a left ventricular ejection fraction below 40%, mean SBP upon admission was 139 ± 34 mmHg, creatinine level was 127 ± 26 μmol/L, 32.3% (n = 572) had a previous history of HF admissions, 25.5% (n = 459) had an ischaemic aetiology for HF, and the median discharge BNP level was 248 μg/dL (Q1–3, 129–528 μg/dL) (Table 1 and 2). The study patients were divided into those who were alive or dead after 1 year of follow‐up. Patients who died (n = 178) were older, leaner, had a lower SBP, haemogloblin level, and sodium level, higher creatinine, proportion of cardiac resynchronization therapy insertion, HF with ischaemic aetiology, and discharge BNP with longer length of stay during the initial hospitalization. Table 1 Baseline characteristics of patients who died and survived 1 year after discharge Total Dead Alive n = 2215 n = 241 n = 1974 n Mean or % SD n Mean or % SD n Mean or % SD P value Age (years) 2215 73.0 13.0 79 11 73 14 <0.001 Male, % 2215 61.2% 147 61.0% 1209 61.2% 0.94 BMI (kg/m2) 2067 23.5 4.4 222 22.0 4.0 1845 23.6 4.4 <0.001 EF, % 2215 44.7 15.5 241 41.5 4.0 1974 45 15 <0.001 EF <40% 2215 40% 116 48% 767 39% 0.005 Previous heart failure admission 2215 101 41.9% 575 29.1% <0.001 Atrial fibrillation 2086 102 42.3% 885 44.8% 0.87 Medical history, % Diabetes, % 2215 34.7% 92 38.2% 677 34.3% 0.23 Hypertension, % 2210 67.7% 154 63.9% 1,342 68.0% 0.22 Stroke, % 2208 12.5% 36 14.9% 240 12.2% 0.23 Current smoker, % 2215 43.0% 102 42.3% 850 43.1% 0.83 COPD, % 2215 5.6% 24 10.0% 99 5.0% 0.002 Length of stay, days 2212 14 (10–22) 241 17 (11–28) 14 (10–22) <0.001 Systolic BP (mmHg) 2209 112 18 239 109 19 1970 112 18 0.02 NYHA class, % 2028 I 188 9% 4 1.7% 184 9.3% II 1312 65% 108 44.8% 1204 61.0% III 497 25% 85 35.3% 412 20.9% IV 31 2% 9 3.7% 22 1.1% <0.001 Haemoglobin (g/L) 2212 12.2 2.2 241 11.0 1.9 1,971 12.3 2.2 <0.001 Creatinine (mg/dL) 2215 1.3 1.4 241 1.7 1.6 1974 1.0 1.3 <0.001 BUN (mg/dL) 2148 26.9 15.4 237 35.7 20 1911 25.7 14 <0.001 Sodium (mmol/L) 2211 138.5 4.5 240 137.7 4 1971 138.6 5 <0.001 BNP (pg/mL) 1400 139 (137–141) 152 532 (242–1157) 1248 246 (123–503) <0.001 Medications, % ACE inhibitor, % 2215 29.3% 650 67 27.8% 583 29.5% 0.58 ARB, % 2215 37.9% 840 65 27.0% 775 39.3% <0.001 Beta‐blocker, % 2215 77.4% 1715 175 72.6% 1540 78.0% 0.058 Aldosterone antagonist, % 2207 36.0% 795 82 34.0% 713 36.1% 0.49 Loop diuretics, % 2215 77.2% 1711 203 84.2% 1508 76.4% 0.004 Digoxin, % 2215 10.9% 241 12 5.0% 170 8.6% 0.053 MAGGIC score 2215 25 (21–29) 241 29 (25–33) 1974 25 (20–28) <0.001 ACE, angiotensin‐converting enzyme; ARB, angiotensin receptor blockers; BMI, body mass index; BP, blood pressure; BUN, blood urea nitrogen; COPD, chronic obstructive pulmonary disease; EF, ejection fraction; MAGGIC, Meta‐analysis Global Group in Chronic Heart Failure; NYHA, New York Heart Association; SD, standard deviation. Table 2 Extent of missing data n % Age (years) 0 0.0 Male, % 0 0.0 BMI (kg/m2) 148 6.7 EF, % 0 0.0 EF < 40% 0 0.0 Previous heart failure admission 0 0.0 Atrial fibrillation 129 5.8 Medical history, % Diabetes, % 0 0.0 Hypertension, % 5 0.2 Stroke, % 7 0.3 Current smoker, % 0 0.0 COPD, % 0 0.0 Length of stay, days 3 0.14 Systolic BP (mmHg) 6 0.3 NYHA class, % 187 8.4 I II III IV Haemoglobin (g/L) 3 0.1 Creatinine (mg/dL) 0 0.0 BUN (mg/dL) 67 3.0 Sodium (mmol/L) 4 0.2 BNP (pg/mL) 815 36.8 Medications, % ACE inhibitor, % 0 0.0 ARB, % 0 0.0 Beta‐blocker, % 0 0.0 Aldosterone antagonist, % 8 0.4 Loop diuretics, % 0 0.0 Digoxin, % 0 0.0 MAGGIC score 0 0.0 ACE, angiotensin‐converting enzyme; ARB, angiotensin receptor blockers; BMI, body mass index; BP, blood pressure; BUN, blood urea nitrogen; COPD, chronic obstructive pulmonary disease; EF, ejection fraction; MAGGIC, Meta‐analysis Global Group in Chronic Heart Failure; NYHA, New York Heart Association. The MAGGIC score was available for 2215 patients with a median score of 25 points with the first (Q1) and third (Q3) interquartile ranging from 21 to 29 points (Figure 2 ). The overall predicted median 1 year mortality rate was 16.0%. Patients who survived (n = 1974) had a median score of 25 (20–28) points (predicted mortality rate, 19.1%), and those who died (n = 241) had a median score of 29 (25–33) points (predicted mortality rate, 22.7%), respectively. A Kaplan–Meier mortality curve has been drawn for the six risk categories divided by the MAGGIC score displaying a distinct difference in 1 year mortality (log‐rank P < 0.001: Figure 3 ). Figure 2 Distribution of the MAGGIC score within the WET‐HF registry. Figure 3 Kaplan–Meier curve of all‐cause mortality divided by MAGGIC risk groups. Within the WET‐HF study population, the MAGGIC score demonstrated modest discrimination (c‐index = 0.71, 95% confidence interval 0.67–0.74) and good calibration (R 2 value = 0.96) (Figure 4 A,B) with a constant overestimation throughout all six risk groups for 1 year mortality (Supporting Information, Figure S1 ). The regression formula drawn linearly on the calibration plot was as follows: Observed1year mortality=−0.0417+0.9095×Predicted1year mortality. Figure 4 Performance of the original and modified MAGGIC score within the WET‐HF registry. When discharge BNP level was added to the MAGGIC score, the modified model demonstrated improved discrimination (c‐index = 0.74, 95% confidence interval 0.70–0.78) and good calibration (R 2 value = 0.91). The AIC and BIC for the original MAGGIC score (AIC = 1413.818, BIC = 1425.224) improved after modification (AIC = 862.454, BIC = 878.1867). The regression formula drawn linearly on the calibration plot was as follows (Figure 4 C,D): Observed1year mortality=0.0032+0.9166×Predicted1year mortality. Finally, the modified MAGGIC score was externally validated in an independent HF registry cohort (NaDEF registry). The modified model demonstrated modest discrimination (c‐index = 0.69; 95% confidence interval, 0.65–0.73) and good calibration (R 2 value = 0.85) (Figure 5 ). Similarly, the AIC and BIC for the original MAGGIC score (AIC = 369.0387, BIC = 377.7717) improved after modification (AIC = 344.7423, BIC = 353.4195) within the NaDEF registry, as well. Figure 5 Performance of the original and modified MAGGIC score within the NaDEF registry. Discussion To our knowledge, this is the first reporting of an extensive external validation of the MAGGIC score outside the European states. In this Japanese HF registry, the MAGGIC score demonstrated modest discrimination and calibration with a constant overestimation upon risk prediction of 1 year mortality. To our knowledge, this is the first reporting of an extensive external validation of the MAGGIC score outside the European states. Additionally, when discharge BNP was added to the MAGGIC score, the modified risk model demonstrated improved discrimination without lowering calibration that was further confirmed by external validation in an independent HF registry cohort. These results demonstrate that the original MAGGIC score can modestly predict 1 year mortality, but the addition of BNP greatly enhances its performance, thereby re‐emphasizing the importance of the prognostic impact given by discharge BNP levels in Japanese HF patients. These findings suggest that the addition of objective biomarkers may result in effective modification of preexisting internationally recognized risk prediction models and multinational comparisons of HF patients' outcomes. Previously, we reported the performance of two HF risk models that are widely used in the Western HF community: the Seattle Heart Failure Model4 and the Get With The Guideline–Heart Failure risk score18 within the Japanese HF population. While the Get With The Guideline–Heart Failure risk score was designed to give estimates on short‐term inhospital mortality, the Seattle Heart Failure Model predicts long‐term post‐discharge 1 and 2 year mortality similar to the MAGGIC score. Notably, the Seattle Heart Failure Model demonstrated modest performance with a c‐index of 0.666 and 0.721, respectively, in a subset of 492 Japanese HF patients.19 Although the Seattle Heart Failure Model demonstrated modest performance, it does have its own disadvantages. The calculation of the Seattle Heart Failure Model necessities 20 unique variables such as the use of allopurinol or lymphocyte percentage that are not commonly assessed in modern‐day care. Because the MAGGIC score requires only clinically routinely collected variables and also gives predictions on 1 and 3 year mortality, it has its advantages over the Seattle Heart Failure Model, thus requires rigorous validation in other ethnicities from around the globe. Several distinct differences between the MAGGIC study population and the WET‐HF registry have been notified. Within the 31 studies utilized to create the MAGGIC study, six studies mostly conducted during the 1990s (DIAMOND,20 DIG,21 CHARM,22 and ECHOS23 trials and IN‐CHF24 and HOLA registries25) contributed to over three quarters of the entire meta‐analysis population and were larger or equal to the population size of our current study population. Despite showing a similar distribution in the baseline MAGGIC scores, the observed mean mortality rate was lower than the predicted mortality rate in our database. A similar trend was observed in the external validation European cohort as well.6 Two main reasons could have affected this trend: (i) a disproportionately high score appointed to one or more variables used to calculate the MAGGIC score or (ii) an important prognostic factor not incorporated in the MAGGIC score. An example for the first reason is ejection fraction, and examples for the second reason are renal function (e.g. blood urea nitrogen and/or glomerular filtration rate), serum sodium level, exercise capacity, and frailty.26, 27 Other unmeasured covariates such as the intensity or quality of care after the initial HF hospitalization and patient adherence to essential medications may affect 1 year outcome. Of note, within the MAGGIC meta‐analysis, there was one Japanese HF registry conducted by Tsutsui et al. that consisted 172 HF patients recruited during the year 1997.28 There were notable differences in the baseline demographics such as higher age, higher prevalence of atrial fibrillation, and smokers commonly seen in our current registry; however, the 1 year mortality rate did not differ significantly indicating a better prognosis in our database reflecting modern‐day HF patients. Natriuretic peptide‐guided treatment of HF has been known to reduce all‐cause mortality in relatively young HF patients and overall reduce HF hospitalizations.29 Although the emergence of other biomarkers such as soluble ST2, growth differentiation factor 15, cystatin‐C, galectin‐3, and high sensitivity C‐reactive protein30 has slightly shifted physicians away from measuring BNP or N terminal proBNP in recent years, the prognostic value of serum natriuretic peptides remains robust, which has been shown in previous studies.31, 32, 33 In this study, we observed a significant additive value of discharge BNP and an overall improvement of the MAGGIC score with this modification. This result points to the importance of BNP measurement upon risk prediction as in fact mentioned in the discussion section by Pocock et al. within the original MAGGIC article.5 Nonetheless, discharge BNP levels should be interpreted with caution because the length of stay is significantly longer in Japan as compared with Western countries. Discharge BNP in Japanese HF patients is likely to reflect patient status after achieving full haemodynamic compensation. Thus, high discharge BNP is likely to indicate a more hazardous state despite rigorous medical attempts to achieve stable status under the Japanese healthcare system. Although the issue of costs associated with BNP measurements remains an issue, its prognostic importance is robust upon predicting post‐discharge mortality beyond racial or ethnic differences.34 With the widespread use of angiotensin receptor neprilysin inhibitor, the use of N terminal‐proBNP over traditional BNP may be more valid in recent days. Nevertheless, our results suggest that future HF risk models ought to incorporate natriuretic peptides as a covariate to meet high performance in various ethnicities. Limitations There are several limitations to our current study. Although more than 2000 patients were evaluated in the current study, this population may not be representative of the entire Japanese population because the registry was conducted within the metropolitan area or its residing districts of Tokyo. Nevertheless, the model performed modestly in a single‐centre NaDEF registry consisted of patients mostly living in Osaka, the second largest city situated in the western part of Japan. Validation of the model may be necessary in other regions or larger sized registries because variation in patient characteristic, treatment, and practice pattern may exist. Second, we inevitably encountered substantial missing data especially for discharge BNP, and the model performance was only assessed for those with complete data without statistical imputation. This may have resulted in overestimation/underestimation of the modified MAGGIC score, although there were no statistical significant differences in the average MAGGIC score between those with and without discharge BNP. Third, we were able to assess model performance for 1 year mortality but not for 3 year mortality because our database was designed to follow up patients up to 2 years. Conclusions The performance of the MAGGIC HF risk score is reasonable in the Japanese HF population over a single year time frame. The addition of discharge BNP significantly enhances model discrimination and calibration that was further confirmed in an external validation cohort. Future HF risk models ought to incorporate BNP as a covariate to meet high performance in various ethnicities. Conflict of interest S.K. received lecture fees from Pfizer Japan Inc. and also received an unrestricted research grant for the Department of Cardiology, Keio University School of Medicine, from Bayer Pharmaceutical Co., Ltd. The authors report no other conflict of interest. Author contributions M.S., Y.S., and S.K. conceived and designed the research and drafted the manuscript; M.S., Y.S., and S.K. analysed and interpreted the data; M.S. performed the statistical analysis; S.K., T.N., T.A., K.F., and T.Y. handled funding and supervision; A.G., A.M., Y.S., Y.N., T.K., and T.Y. made critical revisions of the manuscript for important intellectual content. Funding This study was supported by the Grant‐in‐Aid for Scientific Research (C) (23591062 and 26461088), the Health Labour Sciences Research Grant (14528506), and the Sakakibara Clinical Research Grant for Promotion of Sciences, 2012, 2013, 2014. Supporting information Table S1. 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