dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population Patients were included if they were aged ≥18 years with ≥1 diagnosis of hyperlipidemia (ICD-10 code: E78) and ≥1 prescription within a target hyperlipidemia drug class (Supplementary Methods) issued between January 1, 2014 and December 31, 2015 (selection period). Only patients issued with a new prescription for hyperlipidemia during the selection period were included. Patients were also required to have ≥12 months of continuous enrollment in the database before ('pre-index period') and after ('post-index period') the index date without prescription of the index drug class within the pre-index period. The index date was defined as the date of first prescription for a target hyperlipidemia drug class initiated during the selection period; the index drug class was defined as the first therapy within a target drug class prescribed during the selection period.
In situations where followup/enrollment information was missing from the MDV database, patients had to have ≥1 medical claim in each quarter of the pre-index period to allow longitudinal analysis. This analysis reports hyperlipidemia drug treatment patterns, persistence, and adherence data for two sub-cohorts of patients with DM and ASCVD, respectively. Patients in the DM sub-cohort had to have a diagnosis of type 2 DM, an observed Hb1Ac level of > 6. 5%, or ≥1 prescription for a DM medication within the 3 months before the index date. Patients in the ASCVD sub-cohort had to have ≥1 of the following diagnoses within the pre-index period: stroke (ICD10 code: I61, I63, or I64), PAD (ICD10 code: I70), or CHD (ICD10 code: I21, I22, or I24).
Statistical analyses Patient demographics, clinical characteristics, adherence, and 12month continuation rate were analyzed using descriptive statistics. Time to drug discontinuation was determined using Kaplan-Meier methods. The first discontinuation of the index hyperlipidemia drug was the event and patients were censored if they reached the end of follow-up without discontinuation. All analyses were undertaken by Creativ-Ceutical using SAS version 9. 3 (SAS Institute, Cary, NC, USA).
Patients Overall, 185,497 patients in the JMDC database and 704,577 in the MDV database had ≥1 prescription for an agent within a hyperlipidemia drug class of interest between January 1, 2014 and December 31, 2015. For the DM sub-cohort, 11,718 patients (6. 3%) in the JMDC database (UT: 8883; PT: 2835) and 27,746 (3. 9%) in the MDV database (UT: 18,422; PT: 9324) met the eligibility criteria and were included in the analyses (Supplementary Fig. 1 ). For the ASCVD sub-cohort, 4101 patients (2. 2%) in the JMDC database (UT: 2948; PT: 1153) and 14,356 (2. 0%) in the MDV database (UT: 9201; PT: 5155) were included in the analyses (Supplementary Fig. 2 ). The demographics and baseline characteristics of patients are shown in Table  1  for both DM and ASCVD sub-cohorts. Mean duration of follow-up was 717 (JMDC) or 792 (MDV) days in the DM sub-cohort, and 711 (JMDC) or 791 (MDV) days in the ASCVD sub-cohort.
Across both sub-cohorts, compared with patients in the MDV database, patients in the JMDC database were younger, had a higher proportion of men, were prescribed fewer concomitant drugs at the index date, had a lower proportion of PT patients, and had fewer comorbidities. Across both sub-cohorts and databases, UT patients were prescribed fewer concomitant drugs at the index date than PT patients. In the DM sub-cohort, almost all patients had a diagnosis of type 2 DM (JMDC: 95. 6%; MDV: 98. 6%) (Table  1 ). Within this sub-cohort, a lower proportion of JMDC versus MDV patients had concomitant hypertension (53. 4% vs. 73. 2%). Rates of all other recorded hyperlipidemia-related comorbidities were also numerically lower among JMDC versus MDV patients. In the DM sub-cohort, UT patients were less likely to have hypertension than PT patients (JMDC: 49.
2% vs. 66. 5%; MDV: 70. 4% vs. 78. 8%). In the ASCVD sub-cohort, a lower proportion of JMDC versus MDV patients had a diagnosis of stroke (29. 0% vs. 46. 8%; one of the diagnoses used to define ASCVD), type 2 DM (38. 6% vs. 59. 5%), and hypertension (61. 6% vs. 81. 0%) (Table  1 ). Incidences of the other high risk-defining conditions, PAD (45. 8% vs. 46. 1%) and CHD (14. 0% vs. 14. 3%), were similar across the two databases. In this sub-cohort, UT patients were less likely to have comorbid type 2 DM (JMDC: 34. 2% vs. 50. 0%; MDV: 55. 1% vs. 67. 3%), hypertension (JMDC: 58. 2% vs. 70. 5%; MDV: 79. 2% vs. 84. 2%), or PAD (JMDC: 43. 5% vs. 51. 7%; MDV: 41. 1% vs. 55. 1%) than PT patients. UT patients were more likely to have comorbid stroke (JMDC: 31. 0% vs. 23. 9%; MDV: 49. 9% vs. 41. 3%).
Treatments Regardless of the database population (JMDC or MDV) or sub-cohort (DM or ASCVD), distributions of the index hyperlipidemia drug classes of interest among UT and PT patients were generally comparable (Figs. 1  and 2 ). Among UT patients in the DM sub-cohort, the most common hyperlipidemia drug class prescribed as first-line treatment was a moderate statin (JMDC: 74. 7%, MDV: 77. 5%), followed by a fibrate (JMDC: 12. 6%, MDV: 7. 1%), and a PUFA (JMDC: 6. 0%, MDV: 6. 7%) (Fig. 1A ). Combination therapy was rarely used as the index prescription in UT patients (JMDC: 2. 1%; MDV: 1. 9%). Among PT patients with DM, the most frequently prescribed index drug class was combination therapy (JMDC: 46. 7%; MDV: 53. 6%), followed by a moderate statin (JMDC: 20. 0%, MDV: 14. 7%), and then a fibrate in the JMDC database (10.
7%), or a SICTI (7. 7%) in the MDV database (Fig. 1B ). Combinations prescribed as the index prescription in PT patients most commonly comprised a moderate statin plus a PUFA (JMDC: 14. 3%, MDV: 17. 5%), SICTI (JMDC: 10. 1%, MDV: 11. 6%), or fibrate (JMDC: 5. 4%, MDV: 3. 0%). In the ASCVD sub-cohort, the most common hyperlipidemia drug class prescribed as first-line therapy in UT patients was a moderate statin (JMDC: 75. 4%, MDV: 78. 5%), followed by a PUFA (JMDC: 11. 0%, MDV: 8. 6%), and a fibrate (JMDC: 6. 6%, MDV: 3. 7%) (Fig. 2A ). The most frequent index drug class among ASCVD PT patients was combination therapy (JMDC: 49. 3%; MDV: 53. 3%), followed by a moderate statin (JMDC: 16. 4%, MDV: 15. 0%), and a PUFA (JMDC: 10. 1%, MDV: 8. 8%) (Fig. 2B ). The most common combinations prescribed in PT patients were a moderate statin plus a PUFA (JMDC: 20.
Persistence Kaplan-Meier analysis of time to discontinuation of treatment by hyperlipidemia index drug class during the 12-month follow-up period in the DM (Table  2 , Supplementary Fig. 3 ) and ASCVD (Table  2 , Supplementary Fig. 4 ) sub-cohorts revealed a generally lower probability of continuing treatment among patients in the JMDC database compared with the MDV database. Among UT patients in the DM sub-cohort, the continuation (persistence) rates at 12 months across all hyperlipidemia drugs as monotherapy were 60. 5% (JMDC) and 64. 2% (MDV). The persistence rates across all drugs including combinations were 60. 2% (JMDC) and 63. 8% (MDV). The persistence rate was highest with a moderate statin in both the JMDC (63. 7%) and MDV (65. 7%) databases (Table  2 ). The persistence rate was lowest with an intensive statin in the MDV database (28.
8%); a comparable rate was observed in the JMDC database (28. 9%). Among PT patients in the DM sub-cohort, the persistence rates at 12 months across all drugs as monotherapy only and combinations in the two databases were as follows: monotherapy only, 59. 5% (JMDC) and 64. 2% (MDV); and combinations, 60. 7% (JMDC) and 66. 1% (MDV). Persistence rates ranged from 51. 8% to 72. 2% for all categories except for 'other' hyperlipidemia drugs, where the rates were noticeably lower, both in the JMDC (27. 8%) and the MDV (25. 9%) databases. In the ASCVD sub-cohort, the 12-month persistence rate in UT patients across all hyperlipidemia drugs as monotherapy were 60. 8% (JMDC) and 59. 4% (MDV). The persistence rates across all drugs including combinations were 60. 4% (JMDC) and 59. 1% (MDV). The persistence rate was highest with a moderate statin in both the JMDC (63.
8%) and MDV (61. 4%) databases (Table  2 ). Persistence rates with intensive statins were low in both the JMDC (42. 9%) and MDV (39. 1%) databases, with the lowest persistence rates of 35. 7% (JMDC) and 31. 2% (MDV) observed with 'other' hyperlipidemia drugs. Among PT patients, the persistence rates at 12 months across all drugs in JMDC and MDV were 56. 3% and 61. 5%, respectively, for monotherapy only, and 60. 5% and 65. 1%, respectively, for combinations. Persistence rates for a moderate statin was relatively high compared with other drug classes in both the JMDC (71. 4%) and MDV (70. 1%) databases. Persistence rates at 12 months varied from 46. 9% to 71. 5% in all categories except for 'other' hyperlipidemia drug, where the lowest rates were observed for both JMDC (16. 7%) and MDV (22. 4%) databases.
Adherence Adherence to treatment within different hyperlipidemia drug classes of interest, expressed as PDC in first-line use with a single index drug class, is shown in Table  3  for both DM and ASCVD sub-cohorts. In the DM sub-cohort, for both UT and PT patients in either database (JMDC or MDV), adherence (PDC ≥0. 80) rates were ≥80% across the hyperlipidemia index drug classes, except for an adherence rate of 74. 6% to 'other' hyperlipidemia drugs in UT patients in the JMDC database. In the ASCVD sub-cohort, adherence rates were ≥80% across hyperlipidemia drug classes for UT and PT patients in both databases, except for a rate of 77. 9% with fibrate medications in PT patients in the JDMC database. In both sub-cohorts, adherence rates across index drug classes in UT or PT patients were generally lower in the JMDC database (DM: 74.
6-89. 7%; ASCVD: 77. 9-97. 0%) than the MDV database (DM: 86. 6-97. 0%; ASCVD: 92. 8-97. 6%). Adherence rates across index drug classes were broadly comparable between UT and PT patients within each database in both the DM (JMDC: 74. Intensive statin = atorvastatin ≥20 mg, rosuvastatin ≥10 mg, pitavastatin ≥4 mg; Moderate statin = pravastatin, simvastatin, fulvastatin, atorvastatin < 20 mg, rosuvastatin < 10 mg, pitabastatin < 4 mg; Other = anion exchange resins and nicotinic acid derivatives; JMDC, Japan Medical Data Center; MDV, Medical Data Vision; PT, previously treated; PUFA, polyunsaturated fatty acid; SICTI, small intestine cholesterol transporter inhibitors; UT, untreated.
Discussion This sub-analysis of a retrospective, longitudinal, observational cohort study assessed treatment patterns of, persistence with, and adherence to therapies within hyperlipidemia drug classes in sub-cohorts of patients with DM and ASCVD using medical claims information from the JMDC and MDV administrative claims databases in Japan. The analysis evaluated whether Japanese patients at increased risk of CVD are treated differently to the general hyperlipidemia patient population, and explored how well these patients adhere to and persist with their prescribed treatment(s). Despite all patients in both sub-cohorts having additional cardiovascular risk factors, the overall findings were remarkably similar to those reported for the main hyperlipidemia patient population  [18]. Differences between the two database populations in both sub-cohorts were consistent with those reported previously  [18] , and were reflective of the two distinct insured populations (Supplementary Methods).
Likewise, analysis of hyperlipidemia drug classes used as index therapy in the DM and ASCVD sub-cohorts revealed a similar profile to the main study population  [18]. Approximately three quarters of UT patients in the DM and ASCVD sub-cohorts were prescribed a single moderate statin as first-line index treatment, likely reflecting the fact that most clinical data in at-risk patients relates to moderate statins  [9, [19] [20] [21] [22] [23] [24] [25] [26]. Statins are also recommended by JAS as the first hyperlipidemia drug of choice  [2, 8, 9]. In PT patients, approximately half were prescribed combinations as their index therapy-usually a two-drug regimen including a moderate statin-with 15-20% receiving a single moderate statin. Initial use of an intensive statin was low in both UT (< 1%) and PT (6-7%) patients, reflecting another recently reported analysis using data from the JMDC database  [27].
In contrast to adherence rates, 12-month persistence rates were low and variable across drug classes and databases, which is concerning given the association between poor persistence with hyperlipidemia medications and CVD risk  [11]. Notably, patients who discontinue treatment may not benefit from the time-dependent increase in Intensive statin = atorvastatin ≥20 mg, rosuvastatin ≥10 mg, pitavastatin ≥4 mg; Moderate statin = pravastatin, simvastatin, fulvastatin, atorvastatin < 20 mg, rosuvastatin < 10 mg, pitabastatin < 4 mg; Other = anion exchange resins and nicotinic acid derivatives. ASCVD, atherosclerotic cardiovascular diseases; DM, diabetes mellitus; JMDC, Japan Medical Data Center; MDV, Medical Data Vision; PT, previously treated; PUFA, polyunsaturated fatty acid; SE, standard error; SICTI, small intestine cholesterol transporter inhibitors; UT, untreated.
In addition, other reasons such as a nocebo effect or down-titration as part of a treatment regimen could have contributed to the high withdrawal rate. Factors related to continuation of statin therapy are complex  [35] , and were not addressed in this study. In our study, persistence rates were low and variable despite consistently high adherence rates, suggesting that patients may not be adequately medicated even with high adherence rates. Of note, adherence rates derived from claims data may be overestimated in Japan, as patients rarely request changes to their prescription before completing the previous course of medication  [36]. The low and variable persistence rates (16. 7-72. 2%) observed in our study are in contrast to those previously reported by Nagar et al. (60. 0-63. 4%) [27].
Patients without hyperlipidemia were included in their study  (31. 6-35. 2%), while our study included only patients diagnosed with hyperlipidemia, who were therefore expected to have greater awareness of their condition; however, this was not reflected in the persistence rates. The strengths and limitations of this analysis have been discussed previously  [18]. In brief, this analysis provides valuable information on many hyperlipidemia patients with continuous enrollment included in two large administrative claims databases. However, the study is limited by its observational nature, strict eligibility criteria, limited followup (12 months), small sample sizes in some sub-groups, and a lack of statistical power to detect differences between sub-groups of interest. There was also an assumption that all patients filled their prescriptions and took their medication.
Additionally, baseline lipid levels and subsequent treatment-related changes, which would have influenced treatment decision-making, were not captured systematically. Further information missing from both databases included reasons for discontinuing treatment, whether patients took extra doses to compensate for forgotten doses, incidence of pill dumping or stockpiling, details of non-reimbursed treatments, and data on the impact of treatment on symptoms/health outcomes. Specific limitations associated with the JMDC database included a lack of information on elderly patients (as beneficiaries were working adults and their family members) and nonvalidated diagnoses. For the MDV database, there is no linkage of data between medical care facilities. Therefore, if a patient receives care in Intensive statin = atorvastatin ≥20 mg, rosuvastatin ≥10 mg, pitavastatin ≥4 mg; Moderate statin = pravastatin, simvastatin, fluvastatin, atorvastatin < 20 mg, rosuvastatin < 10 mg, pitavastatin < 4 mg; Other = anion exchange resins and nicotinic acid derivatives.
ASCVD, atherosclerotic cardiovascular diseases; DM, diabetes mellitus; JMDC, Japan Medical Data Center; MDV, Medical Data Vision; PDC, proportion of days covered (medical possession ratio); PT, previously treated; PUFA, polyunsaturated fatty acid; SD, standard deviation; SICTI, small intestine cholesterol transporter inhibitors; UT, untreated. a PDC ≥0. 80. different institutions, their data will be incomplete. In summary, the results of this retrospective, observational, cohort sub-study indicate that many Japanese patients with hyperlipidemia and DM or ASCVD are prescribed single-agent LLT, most commonly a moderate statin, as their index drug, and therefore may not be receiving adequate treatment to reach their strict lipid management goals. While adherence rates with hyperlipidemia drugs were generally high, persistence rates were low, which is concerning given the link between poor persistence and CVD, particularly in these at-risk patient populations.
Acknowledgements We acknowledge  Midori Kayahara of FireKite , an Ashfield company, part of  UDG Healthcare plc , for writing support during the development of this manuscript, which was funded by  Takeda Pharmaceutical Co. , Ltd. (Tokyo, Japan) , and complied with Good Publication Practice 3 ethical guidelines (Battisti et al. , Ann Intern Med. 2015; 163: 461-464 ).
Introduction Cancer management is challenged by large inter-and intra-patient variabilities in both disease progression and response to treatments. Thus, the quest for accurate and personalized cancer therapies has fostered the development of new technologies enabling multi-type measurements in individual patients and complex drug scheduling. To translate datasets available for an individual patient into personalized therapies and further ensure their precise administration, new mathematical approaches are required. Indeed, systems medicine, that involves the implementation of theoretical approaches in medical research and practice, is critically needed as emphasized in the roadmaps of the Coordinated Action for Systems Medicine (CaSyM) from the European Union (https://www. casym. eu, [1] ) and of the Avicenna action (http://avicenna-isct.
OPTILIV clinical datasets The OPTILIV trial included 11 colorectal cancer patients with liver metastases (7 men and 4 women with median age of 60). The combination of irinotecan, oxaliplatin and 5-fluorouracil was delivered to patients by Hepatic Artery Infusion (HAI) using the Mélodie pump  [19]. The patients received an intravenous administration of cetuximab 500 mg/m2 over 2 h 30 min on the morning of day 1 which was not modelled. From day 2, chronomodulated HAI of irinotecan (180 mg/m2), oxaliplatin (85 mg/m2) and 5-fluorouracil (2800 mg/m2) were given (Fig  1 ). Irinotecan was delivered as a 6-h sinusoidal infusion starting at 02:00, with a peak at 05:00 on day 2. Oxaliplatin was administered as an 11h 30min sinusoidal infusion beginning at 10:15 with a peak at 16:00 on days 2, 3 and 4.
5-fluorouracil was also delivered as an 11h 30min sinusoidal infusion beginning at 22:15 with peak delivery at 04:00 at night, on days 3, 4 and 5. The superiority of this drug scheduling compared to non-circadian based administration was demonstrated for intravenous administration within several international clinical trials  [15]. Between each drug infusion, there was a glucose serum flush which cleared the tubing. This was a 30-min sinusoidal infusion beginning at 09:45, and then again at 21:45 i. e. at the end of each infusion (Figure  10 ). Plasma pharmacokinetics (PK) data was gathered after the first dose of irinotecan, oxaliplatin and 5-fluorouracil and measured longitudinally for each individual patient. Plasma concentrations of irinotecan and its active metabolite SN38 were determined in mg/ml at the start of infusion, then at 2, 3, 4, 6, 8 h 15 min and 31 h 45 min post HAI onset, for a total of seven time points, including baseline.
Mathematical modelling A pump-to-patient mathematical model was designed as follows, irrespective of the drug delivery device. The drug solutions dynamics from the pump to the patient's blood was modelled using a Partial Differential Equation (PDE) considering time and 1 spatial dimension. This method was chosen as PDEs can take into account both time and space which was key for modelling systems such as pump delivery. The PDE was solved using a backward finite difference method programmed within Python 3. 5. 2 (https://www. python. org/). The drug PK models were based on Ordinary Differential Equations (ODEs) programmed using Python 3. 5. 2 and solved using the odeint function from the scipy library  [36]. PK model parameter estimation involved a weighted least square approach, with conditions also placed on the drug clearance routes.
The minimization of the least square cost function was performed by the Covariance Matrix adaptation Evolution Strategy (CMAES) within Python which has been shown to be successful at handling complex cost function landscapes  [37]. Model goodness of fit was assessed using the sum of square residuals (SSR) and R 2 values. PK model parameter numerical identifiability given the available data was investigated in a two-step process as follows. First, parameter sensitivity regarding the least-square cost function was computed via a global Sobol sensitivity analysis as a necessary condition for identifiability  [38]. This method assesses the relative contributions of each parameter to the variance in the cost function obtained when parameter values are varied, and thus allows for the identification of parameters which have no effect on the cost function and are therefore not identifiable from the available dataset.
This step allowed a first reduction of the PK models. Next, likelihood profiles of parameters of the reduced models were derived following the procedure outlined in  [39]. Additional biological constraints derived from literature were added to ensure numerical identifiability of all parameters. This two-step model design process was undertaken as computing likelihood profiles is associated with a high computational cost. PK models were fit to single-patient plasma PK datasets independently to obtain patient-specific parameter values. Data was available for 10 to 11 patients which was too few to undertake mixed-effect population analysis and to reliably estimate the parameters variance within a patient population  [40, 41]. Sampling points at 6 hours post injection for irinotecan and 11 hours 30 mins post injection for oxaliplatin and 5-fluorouracil theoretically occurred at the same time as the start of the 30 min glucose flush, that is 9:45 for irinotecan and 5-fluorouracil, and 21:45 for oxaliplatin.
Inter-patient variability and patient clustering based on PK parameters Given the relatively small number of patients, the inter-patient variability in parameter values was assessed using a nearly unbiased estimator of coefficient of variation (CV), CV = 1 + 1 4n × σ µ × 100, where µ is the parameter mean, σ the parameter standard deviation and n is the number of patients. Next, fuzzy c-means clustering was used to define patient clusters based on individual PK parameters, for each drug separately. The fuzzy c-means clustering was done using a python library sckit-fuzzy (http://pythonhosted. org/scikit-fuzzy/). The method is based on the determination of cluster centroids and classification of patient parameter vectors into the clusters such that the following quantity is minimised: n i=1 c j=1 w 2 ij (x i -c j ) 2 where nis the number of patients, c is the number of clusters, x i is the parameter vector of patient i, c j is the centroid of cluster j.