Patients with chronic low back pain radiating to the leg (CLBPr) are sometimes referred to a specialised pain clinic for a precise diagnosis based, for example, on a diagnostic selective nerve root block. Possible interventions are therapeutic selective nerve root block or pulsed radiofrequency. Central pain sensitisation is not directly assessable in humans and therefore the term ‘human assumed central sensitisation’ (HACS) is proposed. The possible existence and degree of sensitisation associated with pain mechanisms assumed present in the human central nervous system, its role in the chronification of pain and its interaction with diagnostic and therapeutic interventions are largely unknown in patients with CLBPr. The aim of quantitative sensory testing (QST) is to estimate quantitatively the presence of HACS and accumulating evidence suggest that a subset of patients with CLBPr have facilitated responses to a range of QST tests.The aims of this study are to identify HACS in patients with CLBPr, to determine associations with the effect of selective nerve root blocks and compare outcomes of HACS in patients to healthy volunteers.A prospective observational study including 50 patients with CLBPr. Measurements are performed before diagnostic and therapeutic nerve root block interventions and at 4 weeks follow-up. Data from patients will be compared with those of 50 sex-matched and age-matched healthy volunteers. The primary study parameters are the outcomes of QST and the Central Sensitisation Inventory. Statistical analyses to be performed will be analysis of variance.The Medical Research Ethics Committee of the University Medical Center Groningen, Groningen, the Netherlands, approved this study (dossier NL60439.042.17). The results will be disseminated via publications in peer-reviewed journals and at conferences.NTR NL6765. Comparison with sex-matched and age-matched healthy controls.Multimodal assessment of human assumed central and peripheral sensitisation via objective and subjective measures.The order of the quantitative sensory testing measures is not randomised for each patient and each visit and is only randomised per visit.The mean incidence and prevalence of chronic low back pain radiating to the leg (CLBPr) are 9.4 and 17.2 per 1000 person years, respectively.Growing evidence suggests that central pain mechanisms are facilitated in a subset of patients with CLBPr.et al showed that following pRF treatment patients with CLBPr have increased pressure pain thresholds and increased conditioned pain modulation, suggesting a normalisation of the sensory pain profile.11The role of HACS in the development of CLBPr, in the chronification of pain, and the interaction with diagnostic and therapeutical interventions is not clear.The primary objective of this study is to determine if therapeutic selective nerve root block or pulsed radiofrequency in patients with chronic low back pain radiating to one leg changes the outcome of the CSI and the QST measures as proxies for HACS. Secondary objectives are (1) to determine if HACS can be assessed using the outcome of the CSI in patients with chronic low back pain radiating to one leg, (2) to determine if a dSNRB changes the outcome of the CSI and the QST measures as proxies for HACS in patients with chronic low back pain radiating to one leg, and (3) to determine whether CSI and QST measures as proxies for HACS (both pretreatment and post-treatment) among patients differ from those among sex-matched and aged-matched healthy volunteers.A prospective longitudinal observational study about HACS in patients with CLBPr who undergo selective nerve root blocks compared with sex-matched and age-matched healthy volunteers.This study was registered prospectively in the Netherlands Trial Register: Trial NL6765 (first posted 10 January 2018). The trial information in this protocol is in line with the WHO Trial Registration Data Set24Patient recruitment, treatment and measurements take place in the multidisciplinary academic pain center, University Medical Center Groningen (UMCG), Groningen, the Netherlands.Patients were not involved in the development of the research question and the determination of the outcome measures. During recruitment, patients are asked to suggest healthy volunteers whose age and gender match their own to participate in the study.Fifty consecutive adult patients of both sexes with chronic low back pain radiating in the dermatomes L3 to S2 and 50 sex-matched and age-matched healthy volunteers will be included. The inclusion and exclusion criteria are summarised in Patients can retain their ongoing pain medication or discontinue it if they stop feeling pain during the study. Other changes in medication use are not allowed during participation. Moreover, all other pain interventions  during their participation in this study are prohibited. Healthy volunteers are matched with the patients on sex and age (plus or minus 3 years of age). As mentioned, patients are asked to suggest suitable healthy volunteers. If necessary healthy volunteers are also recruited via flyers placed near to the UMCG facilities. Healthy volunteers do not undergo selective nerve root blocks. Patients and healthy volunteers may withdraw from the study at any time for any reason if they wish to do so and without any consequences. The investigator can decide to withdraw a participant from the study for urgent medical reasons.The intervention provided in this study is performed in the same way as the standard intervention provided in the multidisciplinary academic pain centre and follows the guideline for SNRB , they will be compensated for this visit when it is not possible to schedule it with another regular hospital visit. Patients will receive compensation for extra travel costs (€0.19/km). Healthy volunteers will receive 10 euros for their visit plus compensation for travel costs (€0.19/km).Each visit will last no longer than two and a half hours, consisting of 1-hour measurements , 0.5 hour for the treatment and 0.5–1 hour for the assessment of the effect of the treatment (patients). Interventions are performed 1 week apart, and visit 3 will be 4 weeks after the therapeutic intervention. Depending on the number of dSNRBs performed, the total duration of this study for the patient will take between 5 and 7 weeks (3–5 visits). Healthy volunteers will undergo only one visit with an assessment of 1 hour.The first patient was included on 5 June 2018. The planned end date for the inclusion of participants is 1 March 2022.All measurements are performed for study-specific reasons. The primary outcomes are QST measures and the CSI. Secondary outcomes are Pain Catastrophising Scale, Pain Vigilance and Awareness Questionnaire, 36-Item Short-Form Health Survey, Pain Disability Index, Work Ability Index, STarT Back Screening Tool (SBST), NRS and drawing in standard leg images (pain drawing) .Descriptive items include: age (years), sex , weight (kg), height (cm), education, comorbidities, medication and ethnic background.1 or 2 days before each visit, the patient receives an email with a link and is asked to fill out the questionnaires . The queCSI:Pain Catastrophizing Scale:Pain Vigilance and Awareness Questionnaire:32RAND 36-item Health Survey (RAND-36):33Pain Disability Index:36Work Ability Index:42SBST:44NRS for Pain: a pain rating scale where patients are asked to rate their pain at two locations (leg and back), giving a number between 0 and 10 (0=no pain/10=maximum pain imaginable). In the QST measurements, an NRS is also used with a scale from 0 to 100 (0=no pain/100=maximum pain imaginable).Drawing in standard leg images (Pain Drawing): the patient is asked to draw, on an image with standard legs . When the 16 mN von Frey filament’s touch is felt, the von Frey filament’s intensity is reduced until no touch is felt. The intensity then will be increased until the touch is felt again. This test will be repeated at least three times to detect the mechanical detection threshold. The first von Frey filament that cannot be felt is documented.47–49Dynamic mechanical allodynia: dynamic mechanical allodynia is tested using dynamic innocuous stimulus . The innocuous stimulus is administered once and after that three times in a 1–2 cm wiping motion on the skin. The participant is asked to say if something was felt and to rate the NRS-Pain from 0 to 100.50–52Mechanical pain threshold: this test is performed with weighted pinprick stimuli  using the method of limits. Five threshold determinations are made, each with a series of ascending and descending stimulus intensities. The first pinprick that is considered painful is documented.51 53–55Wind-up ratio: in this test, the NRS-Pain score (0–100) experienced for a single pinprick stimuli (256N) is compared with an applied series of repetitive  pinprick stimuli of the same intensity (256N). The wind-up ratio is calculated by dividing the pain intensity rating for the series of stimuli by the pain intensity rating for the single stimulus.Pressure pain threshold: using a handheld pressure algometer , the threshold for pressure-induced pain is assessed by slowly increasing stimulus intensities (5 N/s). The threshold is determined when the pressure becomes painful/more than just the feeling of pressure when the participants say 'now'. The NRS-Pain (0–100) and the applied force (N) at that moment are documented.Cuff algometry (Cuff): during cuff algometry, the participants will be instructed to continuously evaluate the pain using an electronic visual analogue scale .Cuff pain tolerance threshold (PTT): the cuff pressure will be increased by 1 kPa/s on the dominant leg. The participant will be instructed to rate the pain intensity continuously on the electronic visual analogue scale until the tolerance level is reached, and the participant pushes the stop button. When the participant pushes this button, the pressure is released immediately. The PTT is defined as when the participant presses the stop button. A similar assessment will be performed on the non-dominant leg after the cuff temporal summation.Cuff temporal summation: a total of 10 repeated mechanical cuff pressure stimuli at the intensity of PTT will be delivered at 0.5 Hz  to the lower leg. A constant pressure of 1 kPa will be applied between the individual pressure stimuli sets to avoid movement of the cuff. The participants will continuously rate the pain intensity on the electronic visual analogue scale during the 10 repeated stimuli.Conditioned pain modulation: pressure pain threshold will be repeated on the deltoid muscle and the rectus femoris, both on the painful dermatome’s contralateral side. The participant will be asked to put the dominant hand up to the wrist with spread fingers in a bucket filled with ice and cold water (conditioning stimulus). The amount of time (at a maximum of 3 min for safety reasons) that it takes until the participant removes the immersed hand out of the bucket will be noted. Immediately after, pressure pain threshold in the deltoid muscle and the rectus femoris both on the contralateral side of the painful dermatome will be repeated.17 18 20Measurement sites: measures will be taken at 6 different sites in total  and was calculated to be 41 individuals per group. As a safety margin for possible correction for dropout or missing data, we added 20% to result in 50 patients and 50 healthy volunteers.et alThe required sample size to answer the first objective was based on the difference between two dependent means (matched pairs). Mehta et al.For objective 2, the sample size calculation was based on the Wilcoxon-Mann-Whitney test for two groups . The number of participants to compare the patients with the healthy volunteers was calculated based on the paper by Blumenstiel The primary data (raw) will be collected on paper and entered into an electronic data capture: OpenClinica . The questionnaires will be assessed via RoQua  as implemented in the electronic patient files . For the healthy volunteers' dummy numbers  will be created to enable them to access and fill in the questionnaires. The data in Open Clinica and RoQua will be combined into SPSS  database.The raw data on paper, in OpenClinica and the data of RoQua, will be anonymised and coded. The data will be stored at the UMCG research drive.Data will be handled confidentially. A participant identification code list will be used to link the data to the participant. The principal investigator will safeguard the key to the code.Data will be accessible for the team of investigators, the Medical Ethical Review Board and the healthcare Inspection. Data will be saved for 15 years. Data will be used for publication, but no participant will be traceable. The handling of personal data will comply with the Dutch Personal Data Protection Act.Data records from the different tests and questionnaires will be collected and merged into one database. SPSS software V.27.0 or higher (IBM) will be used to perform the analyses. Descriptive statistics data will be presented as means±SD when normally distributed and as median and IQR (25–75) when not normally distributed. A significance level of 0.05 is used in all analyses.For the primary objective, to determine treatment effect of the tSNRB and pRF the difference between visit 1a and visit 3 will be analysed for 9 primary measures (CSI and 8 QST measures). This analysis will be performed using a student’s paired t-test in normally distributed data or a Wilcoxon test in non-normally distributed data.For secondary objective 1, to determine if the presence of HACS in patients with CLBPr can be assessed using the CSI, the data from visit 1a will be used.For secondary objective 2, the difference between the latest visit 1 and visit 2 will be used to determine treatment effect of the dSNRB. The 9 primary measures (CSI and 8 QST measures) will be used. The difference will be assessed using a student’s paired t-test or a Wilcoxon test where appropriate.For secondary objective 3, the differences between patients’ visit 1a, versus visit 3 and healthy volunteers are calculated for the questionnaires and QST measurements. The calculated differences  will be assessed using an a student t-test for normally distributed data and a Mann-Whitney U test for non-normally distributed dataThe monitoring will be carried out by research support staff based on GCP monitoring principles. The departmental research office will monitor the trial regularly. This typically includes the following checks:(1)The data collected are consistent with adherence to the trial protocol; (2) case report files are completed by authorised persons; (3) no key data are missing; (4) the data appear to be valid ; (5) review of recruitment rates, withdrawals and losses to follow-up.Experienced specialised pain staff members perform all interventions in the care as usual setting. Study-related additional harms may consist of QST-related pain during and a short time after the measurements. No other harm is expected.The following amendments were made before the inclusion of the fifth patient. Regarding the inclusion criteria, the age limit of 65 was excluded. The visual analogue scale was changed to NRS because NRS is used in care as usual. Before the first healthy volunteer’s inclusion, the number of visits was reduced from 3 to 1. The latest protocol, version 5, is from 6 July 2020. Due to the COVID-19 pandemic, the study was put on hold, and an amendment (version 5) was submitted and approved on 11 August 2020, to prolong the initial inclusion date for the participants to 1 March 2022.Not applicable.https://wwwgroningendatacatalogusnl/. Researchers who provide a methodologically sound proposal can access the data with a signed data access agreement.The deidentified individual clinical trial participant-level data (IPD) will be shared. All individual participant data that underlie the results reported in this article will be shared after deidentification . The study protocol will also be available. The data will be available beginning 9 months following article publication and for a maximum period of 15 years. To make the data findable and accessible for others, we will include a description of the UMCG data catalogue data: 10.1136/bmjopen-2021-052703.supp1Supplementary data


Studies comparing end-of-life care between patients who are high cost users of the healthcare system compared to those who are not are lacking.The objective of this study was to describe and measure the association between high cost user status and several health services outcomes for all adults in Canada who died in acute care, compared to non-high cost users and those without prior healthcare use.We used administrative data for all adults who died in hospital in Canada between 2011 and 2015 to measure the odds of admission to the intensive care unit (ICU), receipt of invasive interventions, major surgery, and receipt of palliative care during the hospitalization in which the patient died. High cost users were defined as those in the top 10% of acute healthcare costs in the year prior to a person’s hospitalization in which they died.Among 252,648 people who died in hospital, 25,264 were high cost users (10%), 112,506 were non-high cost users (44.5%) and 114,878 had no prior acute care use (45.5%). After adjustment for age and sex, high cost user status was associated with a 14% increased odds of receiving an invasive intervention, a 15% increased odds of having major surgery, and an 8% lower odds of receiving palliative care compared to non-high cost users, but opposite when compared to patients without prior healthcare use.Many patients receive aggressive elements of end-of-life care during the hospitalization in which they die and a substantial number do not receive palliative care. Understanding how this care differs between those who were previously high- and non-high cost users may provide an opportunity to improve end of life care for whom better care planning and provision ought to be an equal priority. What is already known about this topic?Patients in the top 10% of healthcare expenditure (“high cost users”) account for 50% of annual healthcare costs and approximately 10%–13% of these costs were devoted to the care of patients in their last year of life.Few studies compare the use of acute care services at the end-of-life according to patients’ prior healthcare use, so the question remains, “how much more likely are the highest cost users getting potentially low-value services?”What this paper addsFifteen to twenty five percent of all patients received more aggressive elements of end-of-life care during the hospitalization in which they died.High cost user status was associated with a 14% increased odds of receiving an invasive intervention (adjusted odds ratio (aOR) 1.14, 95% CI 1.09–1.20), a 15% increased odds of having major surgery , and an 8% lower odds of receiving palliative care .Implications for practiceUnderstanding how this care differs between those who were previously high- and non-high cost users may provide an opportunity to improve end of life care for whom better care planning and provision ought to be an equal priority.13 Almost half of the costs incurred near end-of-life were related to acute care.5The care of high-cost, seriously ill patients at or near the end of life presents a vexing challenge to health care systems, requiring a focus on the delivery of high-quality care in line with their expressed preferences. Prior work demonstrated that the top 10% of healthcare cost users (“high cost users”) account for 50% of annual healthcare costs and approximately 10%–13% of these costs were devoted to the care of patients in their last year of life.613 When asked, the majority of patients report wanting to have their treatment preferences in writing. They also prefer to focus on treatments that provide comfort over survival at the end of life.16 In spite of these findings, more than half of these patients are admitted to intensive care units at the end of life. The delivery of specific clinical interventions such as admission to the intensive care unit (ICU), mechanical ventilation and major surgery at the very end of life may therefore be viewed as of low- or uncertain-value for many patients.In general, end-of-life care that is delivered in the acute care setting is expensive, may be of limited benefit, and is associated with poor quality of life. In the context of end-of-life care, there is evidence that this approach improves the dying experience and controls costs.2022 Healthcare systems are increasingly focused on delivering high-value end-of-life care as the prevalence of chronic disease and its related healthcare expenditure continue to rise. Incorporating palliative care as a component of inpatient care for patients nearing the end of life may improve value by enhancing outcomes such as quality of life and reducing symptom burden. A key function of palliative care teams is the reorientation of healthcare provision to ensure optimal care in line with patients’ and their family’s needs. Although not its intended purpose, palliative care may simultaneously reduce healthcare use and its associated costs in patients opting for less aggressive and generally more expensive care.2329 Yet one third of patients who die in hospital do not access palliative care. One approach to improving value at the end-of-life may be to focus efforts on the specific subgroup of high cost patients at a unique juncture in time—to prevent potentially unwanted aggressive treatment, such as during the hospitalization in which they die.31 Understanding how end of life care differs between those who were previously high- and non-high cost users may provide an opportunity that allows clinicians to focus discussions potential low-value care to ensure care at the end of life is aligned with patient preferences.High-value care is care that reflects patient preferences, is focused on outcomes, and is cost-efficient in achieving those outcomes.The question then remains, “how much more likely are the highest cost users getting potentially low-value services?” Few studies compare the use of acute care services at the end-of-life according to patients’ prior healthcare use. The objective of this study was to describe and measure the association between high cost user status and several health services outcomes for all adults in Canada who died in acute care, compared to non-high cost users and those without prior healthcare use. High cost user status was defined as being in the top 10% of acute care costs in the 12 months prior to hospitalization in which the patient died. Outcomes included the odds of being admitted to an intensive care unit (ICU), of receiving invasive interventions , major surgery and palliative care.We conducted a national retrospective cohort study in Canada, using health administrative data from the Discharge Abstract Database (DAD) between April 1st, 2011 and March 31st, 2015. The DAD is a national database that contains patient-level data for all acute care institutions in Canada (excluding the province of Quebec). All residents of Canada have universal access to hospital care and medically necessary physicians’ services. Canada is a high-income nation with the world’s 10th highest gross domestic product.Our cohort included all Canadian adults who died in hospital between April 1st, 2012 and March 31st, 2015. We excluded people with a hospitalization length of stay ⩾6 months due to the high costs incurred from long hospital admissions because they would bias the results as de facto high cost users. It is not typical for most patients to remain this long in hospital, and these patients are often waiting for care settings that can address their specific care needs related to their inability to function independently at home. We also excluded those who were <18, those at extremes of age who were >105 years old as these traditionally tend to represent outliers in the data (and comprised less than 0.02% of the eligible patients), or who were non-Canadian residents. hospital frailty risk score, and their use of acute health care services in the 12 months prior to the admission date of the hospitalization in which the patient died.We measured demographic and clinical variables based on data from their prior hospitalizations including age, sex, rural location of residence, comorbidities and chronic conditions,Supplemental Tables S1 and S2). We intentionally used costs over other frequency measures of healthcare use because costs encompass all acute care received and are therefore a more comprehensive surrogate measure of total acute care use.We used the distribution of the costs of patients’ hospitalizations in the 12 months prior to the hospitalization in which the patient died to inform the threshold for defining high user status . RIWs measure the intensity of resource use associated with different medical, diagnostic and surgical procedures, adjusted for patient characteristics and characteristics of the institution such as case-mix adjustments. These RIWs are multiplied by a cost-of-standard-hospital-stay (CSHS) value measured at the provincial level. We adjusted per-admission costs using the Statistics Canada Health Care Consumer Price Index (CPI), using 2014 as a base-year.The total costs of a patient’s prior acute care usage over the 12-months preceding the hospitalization in which the patient died were obtained using validated costing methods.Supplemental Figure S1).We created three groups of patients according to their CPI adjusted per-patient costs associated with their use of acute care in the prior 12 months: high cost users, non-high cost users, and those without prior acute care use. High user status was defined as being in the top decile of acute healthcare costs in the year prior to the hospitalization in which the patient died. We used this threshold based on its prior use in the literature and the distribution of costs among our cohort , major surgery, or palliative care. The specific components of invasive interventions were selected based on a review of the literature and the expert opinions of specialists in internal medicine, palliative care and geriatrics.Supplemental Table S3).3840 We identified the delivery of palliative care using two separate methods. First, we identified a binary measure of the delivery of palliative care comprised of no palliative care vs. palliative care, which could consist of one of three scenarios: (1) when a patient’s most responsible diagnosis (MRDx) or other diagnoses code was “palliative care—Z51.5,” (2) their most responsible physician (MRP) was “palliative care” or they were seen by a palliative care physician during their admission, or (3) a healthcare provider other than a physician delivered palliative care .Major surgical procedures were captured based on a defined set of procedure codes and were categorized according to anatomy: abdominal, cardiac, retroperitoneal, thoracic, and vascular . A patient’s approach to care was classified as delivered with “palliative intent likely” when MRDx and/or MRP were palliative care, “palliative intent unlikely” when a patient had a diagnosis other than MRDx listed as palliative care, and “no palliative intent” when a patient had neither a diagnosis nor MRP listed as palliative care. we intentionally did not adjust for comorbidity because it may be directly related to high user status and may result in multicollinearity in our regression analysis.The association between high user status versus non-high user status or having no prior healthcare use and the odds of receiving aggressive end-of-life care, palliative care or having an approach to care with a palliative intent during the hospitalization in which the patient died was measured using a Generalized Estimating Equation (GEE) framework to model the adjusted impact of high user status on our outcomes. All models considered were adjusted for age and sex and used a compound symmetric working correlation structure, where patient level outcomes were nested within a particular treatment facility. Dichotomous outcomes were modeled using a logistic GEE approach ; whereas, categorical outcomes were modeled using a multinomial GEE approach (approach to care). As the presence of a high burden of chronic disease is recognized as one the six phenotypes of high cost users,All analyses were performed using SAS version 9.4 .There were 290,855 patients who died in hospital during the study period. Of these, 38,207 people were excluded from the study. The final cohort consisted of 252,648 people, 25,264 who were high cost users (10%), 112,506 who were non-high cost users (44.5%) and 114,878 who had no prior acute care use (45.5%) .High cost users were typically younger (median age 74 vs 78 years), had higher hospital frailty risk scores (mean score 3.3 vs 1.8), higher acute healthcare use in the prior 12 months  and more prior exposure to inpatient palliative care (17.8% vs 10.7%) than non-high cost users . A highePatients with no prior healthcare use in the 12 months prior to the hospitalization in which they died were slightly older (mean age 80 years) and a similar proportion were female (47.9%) and lived in rural areas (19.1%), as compared to other the other healthcare user groups.A slightly larger proportion of high cost users had at least one admission to the ICU compared to non-high cost users (27.5% vs 25.2%). However, after adjustment for age and sex, high user status was not associated with ICU admission (unadjusted odds ratio (OR) 1.18, 95% CI 1.11–1.25; adjusted OR (aOR) 1.03, 95% CI 0.98–1.08). A larger proportion of patients without prior healthcare use had at least one admission to the ICU compared to high cost users (35.3%). After adjustment for age and sex, high user status was associated with a lower odds of ICU admission compared to having no prior healthcare use .Among high cost users, 25.7% received at least one invasive intervention, compared with 21.0% of non-high cost users. High user status was associated with a 14% increased odds of receiving an invasive intervention  compared to non-high cost users. A larger proportion of patients without prior healthcare received at least one invasive intervention, compared to high cost users (31.0%). After adjustment for age and sex, high user status was associated with a lower odds of receiving an invasive intervention compared to having no prior healthcare use .A larger proportion of high cost users received major surgery during the hospitalization in which the patient died than non-high cost users, which was associated with a higher odds of having major surgery . A larger proportion of patients without prior healthcare received major surgery compared to high cost users (21.2%). After adjustment for age and sex, high user status was associated with a lower odds of having major surgery compared to having no prior healthcare use .A similar proportion of high- and non-high cost users received palliative care during the hospitalization in which they died (64.1% vs 65.4%). However, after adjustment for age and sex, high user status was associated with a lower odds of receiving any form of palliative care compared to non-high cost users . Among high cost users, 24.6% had an approach to care with “palliative intent likely,” 5.9% with “palliative intent unlikely” and 69.5% with “no palliative intent.” Among non-high cost users, 27.5% had an approach to care with “palliative intent likely,” 6.1% with “palliative intent unlikely” and 66.4% with “no palliative intent.” After adjustment for age and sex, high cost user status was associated with a lower odds of having a palliative approach to care relative to other approaches, compared to non-high cost users  . A smallOverall, the association between high cost user status and both clinical and cost outcomes were stable over the study’s timeframe.Our cohort study of 252,648 adult patients who died in hospital found that people who were previously in the highest decile of acute healthcare expenditure (“high cost users”) were more likely to receive an invasive intervention or major surgery, and less likely to receive palliative care or be treated with a palliative intent than non-high cost users during the hospitalization in which they died. No association was observed between high cost users and admission to ICU, compared to non-high cost users. We found the opposite results when comparing high cost users to patients without prior healthcare use. More broadly, one third of the entire study sample did not receive palliative care during the hospitalization in which they died. Regardless of healthcare user status, clinicians should always attempt to clarify treatment preferences to ensure the care their patients receive is appropriately aligned with them. A careful discussion that weighs the risks and benefits of different approaches to end-of-life care may identify patients who wish to decline more aggressive treatments but who may have otherwise received them.42Our findings demonstrate that patients who were previously high cost users of acute care generally were more likely to receive aggressive elements of care at the end of life compared to non-high cost users but less likely than patients without prior healthcare use. It is unclear whether what we defined as aggressive end-of-life care is a reflection of the underlying treatment preferences of patients or their substitute decision makers, which were not directly measured in this study, or of clinical inertia that sometimes lead to care interventions that worsen quality of life and do not improve survival outcomes. We included blood transfusion as part of care deemed to be invasive because it requires the use of an intravenous and because blood is a finite resource. Although transfusion at the end of life is controversial, it may be useful to improve survival and provide symptom relief in some patients.4 A three-year longitudinal population-based study of high cost users who were in the top 5% of healthcare spending in Ontario, Canada concluded that improving healthcare value may be achieved by focusing efforts on high cost users to avoid potentially low-value care. A recent analysis of wasteful spending in the United States estimated the costs of low-value care at $12.8 billion to $28.6 billion USD. Specifically, the authors suggested that scaling effective interventions that use strategies to reduce overuse, encourage shared decision-making to reduce unnecessary procedures and expand hospice and palliative care access, may help achieve most of these savings. Recent data reports that one third of patients who die in hospital do not access palliative care. Specific disease management programs may also provide benefit, given that high cost users often have a high burden of chronic disease.Similar to prior studies, we found that high cost users accounted for comparable annual acute healthcare costs and costs at the end of life.43 However, there are instances (such as in certain types of cancer for example) whereby the benefits of more aggressive treatments or ICU care is uncertain near the end of life and therefore may be appropriate, especially given the heterogeneity in patient preferences for different types of care near the end of life.4448 The observational nature of our study and the inability to measure a temporal relationship between the receipt of aggressive elements of end-of-life care and the timing of palliative care delivery precludes the ability to establish a causal relationship between the two. However, prior work has demonstrated lower hospital costs to be associated with the early initiation of palliative care.4953 We intentionally did not adjust for co-morbidity as it is directly related to high user status and a high burden of chronic disease is recognized to define a phenotype of high cost users. Moreover, our definition of high user status was based on prior acute care costs and may exclude some high cost users whose costs are related to other care settings. Nonetheless, the objective of our study was to examine acute care use, and our cohort is derived from people who are in the last year of life where nearly half of all healthcare costs are attributable to acute care. We employed a commonly used threshold of the top decile of total prior acute care costs to define user status, but different thresholds may yield slightly different results. However, as clinicians likely use normative representations (i.e. high vs non-high) instead of strictly empirical measures of their patient’s prior healthcare use when determining user status at the time of hospital admission, the exact threshold likely becomes less important. Our costing methods represent the provincial average cost per case of inpatient care weighted for resource intensity and not actual cost at the individual patient level. Canada ranks among one of the wealthiest nations in the world and the generalizability of our findings to middle- and low-income countries is unclear. Finally, although the data in this study is older, we observed stable temporal trends in the association between high-user status and both clinical and cost outcomes, suggesting that these are salient patterns that one can expect to find in the presence of more recent data.Our study has several limitations. The retrospective nature of our study limits our understanding of patients’ preferences for specific types of care interventions and how care decisions were made with their clinicians. It is not possible to establish whether low-value care is being provided without knowing patients’ preferences. We suspect that a substantial proportion of the care delivered in our study was misaligned with patient preferences because the majority of patients prefer comfort focused care over life-prolonging treatments at the end of life and prior work has demonstrated significant discordance between preferences and treatments received. and the continued advancement of artificial intelligence may improve our ability to identify when this is the case.55 Alternatively, having these discussions earlier, especially for older patients with complex medical illness who frequently interact with the healthcare system, may be one strategy to identify, clarify and document patient priorities for care which have been shown to reduce treatment burden and unwanted healthcare.56 Innovative strategies beyond high healthcare use may be required to identify the substantial numbers of non-high cost users in their last year of life who ultimately died in hospital. Our results may therefore highlight an opportunity to focus efforts by clinicians and systems on maximizing value to patients at the end of life, where previous efforts to improve value more broadly have yielded disappointing results.Our study provides a window into the types of care that high cost users may be more likely to receive during the hospitalization in which they die, where clinicians may wish to focus discussions on clarifying preferences surrounding willingness to receive aggressive elements of end-of-life care, especially during situations of uncertainty. One challenge with this is approach is that physicians and patients are faced with care decisions at the time of hospital admission without knowing that it may be their last. However, most patients are hospitalized only once in their last year of life,Many patients receive aggressive elements of end-of-life care during the hospitalization in which they die and a substantial number do not receive palliative care. Understanding how this care differs between those who were previously high- and non-high cost users may provide an opportunity to improve end of life care for whom better care planning and provision ought to be an equal priority.Click here for additional data file.Supplemental material, sj-docx-1-pmj-10.1177_02692163211002045 for Association between high cost user status and end-of-life care in hospitalized patients: A national cohort study of patients who die in hospital by Kieran L Quinn, Amy T Hsu, Christopher Meaney, Danial Qureshi, Peter Tanuseputro, Hsien Seow, Colleen Webber, Rob Fowler, James Downar, Russell Goldman, Raphael Chan, Kimberlyn McGrail and Sarina R Isenberg in Palliative Medicine


Aim of this study is to evaluate whether magnetic particle imaging (MPI) is capable of measuring velocities occurring in the coronary arteries and to compute coronary flow reserve (CFR) in a canonical phantom as a preliminary study.For basic velocity measurements, a circulation phantom was designed containing replaceable glass tubes with three varying inner diameters, matching coronary-vessel diameters. Standardised boluses of superparamagnetic-iron-oxide-nanoparticles were injected and visualised by MPI. Two image-based techniques were competitively applied to calibrate the respective glass tube and to compute the mean velocity: full-duration-at-half-maximum (FDHM) and tracer dilution (TD) method. For CFR-calculation, four necessary settings of the circulation model of a virtual vessel with an inner diameter of 4 mm were generated using differently sized glass tubes and a stenosis model. The respective velocities in stenotic glass tubes were computed without recalibration.rFDHM = 0.869, rTD = 0.796) between techniques, preferably better for 4 mm and 6 mm inner diameter glass tubes. On CFR level MPI-derived CFR-prediction performed considerably inferior with a relative error of 20–44%.On velocity level, comparison showed a good agreement (MPI has the ability to reliably measure coronary blood velocities at rest as well as under hyperaemia and therefore may be suitable for CFR calculation. Calibration-associated accuracy of CFR-measurements has to be improved substantially in further studies. Coronary artery disease (CAD) is accountable for the majority of deaths worldwide. According to estimates, CAD was the underlying cause of every 7th death in the US in 2018 . As a coICA) is measured with a pressure-wire: the pressure ratio between aortic and the concerning post-stenotic coronary vessel under maximal hyperaemic conditions provided by injecting intra-arterial adenosine. A stenosis, labelled with FFRICA ≤ 0.75 has to be treated, mostly by stent implantation or bypass-surgery [Invasive coronary angiography (ICA) currently represents the gold standard for classifying CAD, during which a pressure-derived surrogate of the fractional flow reserve  could be an option, because this three-dimensional imaging modality addresses non-invasive functional imaging like CFR and has the prerequisite for interventional therapy . BasicalGT) and compared to CFRMPI computed via MPI-image evaluation.Hence, the aim of this study is to evaluate MPI’s ability to quantify velocities occurring in coronary vessels towards the aim of functionally characterizing the severity of a stenosis by the CFR. Thus, our approach is to generate realistic coronary flow rates in a closed water circuit and measure the resulting velocities with SPION-based MPI-image evaluation techniques. In a next step, as a preliminary study for CAD, a stenosis is assessed by CFR via flow-meter measurements of the circulation phantom as ground truth  and blood flow at rest . CFR is the factor by which the coronary blood flow can be increased if required (sten) and the theoretical maximal flow (Qmax) as described earlier  or below 0.75 require revascularization in terms of clinical assessment [According to van de Hoef et al. ,24 the crequired . Normal  earlier . Values sessment .In line with this concept and to finally simulate haemodynamically relevant stenoses, requirements to a coronary circulation phantom include coverage of coronary velocities between normal and hyperaemic conditions, a hemodynamically relevant degree of stenosis and a realistic extent of vessel dilation under hyperaemic conditions.In the first step we build up a circulation model filled with water and designed it with coronary specific flow velocities ranging from 20 to 64 cm/s for the experiments. This covers the majority of the stenosis-relevant velocities present in the proximal part of the left anterior descending, left circumflex and right coronary artery in the resting state  as well as during hyperaemia  .The circulation phantom was inserted into a preclinical MPI scanner . Experiment B was intended to be an addition to the flow measurements in experiment A while making as few changes in the experimental setup as possible to avoid additional MPI-related issues. The stenosis phantom was drilled out of plexiglass with a centered minimal lumen of 1.5 mm  to a ‘non-stenotic’ tube (setting 2: 6 mm inner diameter), simulating hyperaemic flow in a healthy and dilated vessel. Finally, a 6 mm inner diameter tube with a stenosis was introduced, representing hyperaemic flow in a dilated and CAD-diseased vessel (setting 3). These three settings enable for calculation of CFR and FFR  of a virTo mimic the physiologic behaviour of the heart, the pump’s electrical power consumption needed for the establishment of hyperaemia in setting 2 was equally applied in setting 3, assuring that the resulting decrease in flow in the diseased vessel is attributed to the introduced stenosis. To simulate a haemodynamically less-relevant stenosis without affecting the delicate MPI setup, the electrical power of the pump was simply doubled (setting 4 = 2 x Setting 3 in hyperaemia). For each setting, at least three standardized SPION boluses (0.5 ml perimag) were injected and monitored via MPI as described in part A.MPI (set3&4), FFRMPI(set3&4)) and compared to the ground truth (flowmeter) (CFRGT(set3&4), FFRGT(set3&4)) as defined in The calculation of CFR and FFR relies on the assumption that normaemic flow in non-stenotic tubes equals the one in stenotic tubes, as autoregulatory processes in the coronaries ensure a baseline flow in healthy as well as in diseased vessels to maintain proper oxygen supply. As a result, the baseline flow presented in setting 1 was referenced for CFR/FFR calculation in stenotic settings. CFR and FFR were calculated for image-derived measurements . The velocities for setting 3 and 4 were calculated using iCF3 and to a System-matrix-FOV of 28×28×14 mm3. The temporal resolution of 21.54 ms in the context of dynamic measurements leads to image stacks with 46.43 images per second. For the x-channel a local receive coil was used in this study. Images were reconstructed by using the established Kaczmarz algorithm [MPI datasets were acquired using 1 mm isotropic 3D settings with a drive field strength of  mT in -direction and a gradient strength of 2.5 T/m. This led to a drive-field-FOV of 22.4×22.4×11.2 mmlgorithm  with a rwww.python.org).The reconstructed MPI images were analysed by a dedicated program written in python 3.6  datasets, in which x, y, z represent the length in flow direction, the height and width, respectively, and t represents the time. A frame is defined as the entirety of all images at the same point of time.At first, the signal intensity was summed up for every frame resulting in an intensity over time curve. The influence of an interpolation of the curve in terms of velocity calculation was tested in a controlled setup. Therefore, a virtual bolus shaped as a line (27 pixel × 1 pixel) with increasing intensity from both ends to the centre of the line was simulated. Hereby, the configuration of the simulated bolus matched the one in the reconstructed images as the dilution after injection leads to a distorted shape in longitudinal direction. Applying both algorithms on the simulated bolus showed superior results when using the interpolated curve, especially for higher velocities. A linear interpolation factor of 10 proved to be beneficial, which was then applied to the intensity over time curve. Instead of an advanced background subtraction , the basv can be calculated by using a correction factor c and the size of the FOV in longitudinal direction s as shown in Eq  technique allows for quantification of different signal peaks. It is defined by the time span at which at least half of the maximum signal intensity is present . The velwn in Eq .bT and the tracer iT, the injected volume V, the area under the temperature-time-curve AUC and a specific correction factor k, the cardiac output can be calculated as shown in Eq (The thermodilution method  (TD) is wn in Eq .b equals 0. The injected tracer volume and its initial signal intensity as well as the constant k can be included in the new correction factor ε. The start of the AUC is defined as the frame at which the mean intensity of the initial baseline first crosses the baseline subtracted intensity over time curve :Transferred to our experimental setup, we set the measured property from temperature to signal intensity. As the initial intensity of the FOV has already been subtracted, Tme curve . The endA of the respective glass tubes is known, the velocity can be calculated by using the formula of the volumetric flow rate as seen in Eq  for each measurement and cross-validated by the mean of the complementary correction factors of the concerning series. The error of the correction factor is the standard error of the mean of the remaining correction factors.The iCFs All statistical analyses were performed with IBM SPSS Statistics Version 24 . Categorical variables were expressed as counts and percentages, while continuous data was expressed as mean ± standard deviations. Pearson correlation coefficient was used to quantify the agreement with the reference values. Statistical significance was defined as a p-value <0.05.For each size of the glass tube, six 0.5 ml SPION-boluses were recorded by MPI to acquire image sequences of approximately 3000–9000 frames (64.6s -193.8s) each depending on the expected velocities. In each measurement, the bolus was identifiable in the reconstructed images and the peak intensity was determined. The calculated velocities using both algorithms in comparison to the ground truth (flow-meter) are shown in FDHM = 0.966 and rdilution = 0.911 (p < 0.05). Measurements in the 6 mm diameter glass tube lead to correction factors of rFDHM = 0.975 and rdilution = 0.892 (p < 0.05). The correlation factors of the 10 mm diameter glass tube are rFDHM = 0.632 and rdilution = 0.657 (p > 0.05). In total, the average Pearson correlation coefficient of the FDHM method  for all size of glass tubes is slightly superior to the one of the tracer dilution method .The results of the FDHM method of the 6 mm inner diameter glass tube shows the lowest relative errors of all measurement series (0.8% to 12.5%). For all sizes of glass tubes, the absolute relative error ranges of the tracer dilution method are markedly higher compared to FDHM method. The computed velocities and their respective linear curve fits for the FDHM method and the tracer dilution method in comparison to the reference values are shown in GT = 4.50 as defined by setting 1 and 2  and double hyperaemia (setting 4), the mean relative error amounts to 43.9%. Analogously, copying the mean correction factor of setting 3 to setting 4, the mean relative error yields 20.3%.set3(cset2) = 0.39  and CFR set3(cset2) = 1.74  respectively. For stenosis evaluation in setting 4, applying the same correction factor delivers FFR set4(cset2) = 0.89  and CFRset4(cset2) = 4.02 . Applying the mean correction factor of setting 3 for the measurements in setting 4 provides FFRset4(cset3) = 0.72  and a CFR set4(cset3) = 3.25 .On the CFR-level, using the analogous mean correction factor of setting 2 for measurement of FFR and CFR of stenosis in setting 3 leads to FFRGT = 4.50 by introducing a centered stenosis phantom into the glass tube. By defining four vessel specific settings in the glass tubes . In the first step, the measurement of incremental flow velocities in differently sized glass tubes via MPI as a surrogate for healthy vessels was performed to robustly allow quantification of mean velocities. We chose straight tubes to provide laminar flow which can be described mathematically by analytic models for validation purposes. Therefore, we avoided more complex phantoms like aneurysms or stenoses. Two robust techniques  successfully established in medical applications, were applied to evaluate the bolus velocities, which worked well and showed good agreement for 4 mm and 6 mm diameter glass tubes. We therefore believe to have successfully demonstrated the feasibility of calculating coronary-specific velocities as a proof of concept in MPI. Against this background, we hypothesized that MPI is potentially capable of identifying pathologic haemodynamics in the coronaries, especially CFR. Therefore, we simulated a diseased virtual coronary vessel with a CFRss tubes , the floOur results show that the technique works sufficiently accurate to identify highly haemodynamically relevant stenosis as shown by a MPI-derived CFR value of 1.74 in setting 3. Due to the upgradable precision in CFR calculation, this method is currently unable to distinguish between adjacent conditions requiring treatment or not as demanded by clinical FFR thresholds.This concept currently seems to work when the calibration is performed on the same level of complexity as the subsequent measurement: meaning non-stenotic calibrations will only work sufficiently in non-stenotic tubes and vice versa. Therefore, not only the diameter of the vessel but also the introduction of a stenosis, which eventually leads to a hydrodynamic change, seem to have a significant, still not fully understood influence on the iCF. Therefore, we assume that a thorough assessment of the iCF could be the key for a reliable CFR prediction using MPI. Since up to 60% of participants of FAME II study, eligible for percutaneous coronary intervention (PCI) with pathologic FFR, did not need revascularization in the follow-up period of 2 years , our accin vivo experiments [So far, other studies focused on flow quantification of aneurysm phantoms ,35 or fleriments ,38. Alteeriments  to charaeriments , the velHowever, there are many limitations in our study: Firstly, the correlation for 10 mm diameter glass tube was surprisingly inferior, which may be caused by the increased inhomogeneity during bolus inflow in tubes violating laminar flow conditions with large inner diameter. Increasing the characteristic length, in this case the tube diameter, and increasing the flow rate will both lead to higher Reynolds numbers. As a result, turbulent flow may most likely be present in the 10 mm glass tube at high velocities. Taking into consideration the 25 cm long distance between injection and FOV and the constant bolus volume for all three sizes of glass tubes, this turbulence results in a significant tracer dispersion, which violates our assumption for laminar flow. This is reflected in Due to our simplified study design to develop a reliable tool for intracoronary velocity measurements, there are many general limitations. The straight glass tube used in the experiments represents the maximal simplification of coronary phantoms as it overlooks factors like branching, tortuosity and elasticity of vessel walls as well as pulsatile flow. The introduction of branching and tortuosity will have a considerable impact on the flow conditions inside the phantom, making a computation of the real flow velocity for validation purposes difficult. We therefore used straight and non-branching tubes to allow for a precise computation of reference velocities by using basic fluid mechanics. Furthermore, coronary walls are elastic in nature and not rigid like glass tubes as long as there is no advanced atherosclerotic disease, which would dynamically lower the pressure inside the phantoms leading to different flow conditions. Again, a reliable computation of the reference velocity for calibration purposes would be problematic as well. When introducing pulsatile flow, the SPIONs would fill up the phantom stepwise, but the intensity over time curve will not change its basic configuration, it will rather transform into a superimposed staircase figure of the curve using non-pulsatile flow. As the area under the curve and the full duration at half maximum do not change fundamentally, the algorithms would presumably work under these conditions as well. Since this is a pilot study to evaluate general feasibility of MPI for velocity computation, we chose to design the phantom as simple as possible in order to avoid additional sources of error that would arise when using realistic coronary phantoms. After a thorough assessment of MPI-derived flow characterization and its limitations on the most basic level, more realistic phantoms should be investigated in further studies.Regarding MPI-derived CFR-evaluation, measuring velocities at places in which laminar flow is present is a limitation, too, as real atherosclerotic vessels do not have a long, straight and regular start-up length allowing for laminar conditions at the FOV. Realistically, it would be ideal to place the FOV insight the stenosis to allow for a reliable quantification as long as the imaging modality offers a sufficiently high enough resolution. We decided against this approach, as we wanted to change as few factors as possible compared to the basic flow measurements in experiment A and due to the fact that our MPI-scanner does not provide a mechanism to reliably and reproducibly assign the FOV to the desired location accurate to a millimeter as it would be necessary for our stenotic model.Additionally, the use of water instead of blood is a limitation as well. As a consequence, we were not able to measure flow rates below 20 cm/s because the SPIONs start to sediment at low flow rates and therefore do not reflect the actual water flow anymore. This is a phenomenon that should be addressed when using real blood rather than water as this will most likely have a considerable influence on SPION sedimentation.In sum, this study tries not to simulate a real-world scenario in the clinics like intravenous injection or catheter associated intracoronary injections, but to prove the validity of coronary relevant velocity measurements. Finally, a clinical application of the proposed technique might lead to sensitivity issues of MPI, and other system concepts –44 of MPMPI is supposed to be a promising imaging modality that has the potential to comprehensively assess and treat CAD. As a first step, this preliminary study presented a successful quantification of coronary-specific velocities in simple geometries and an approach for calculation of CFR in canonical stenosis settings as a rough proof of concept. To allow for a reliable MPI-derived CAD assessment, calibration-associated accuracy of CFR-measurements has to be improved substantially in further studies.


RNF5/AGER locus including AGER rs2070600 SNP, which introduces a G82S substitution of AGER, was the most significantly associated with FEV1/FVC. Three other rare missense variants of AGER were further identified. We also found genetic loci with three candidate genes  associated with FeNO levels. Analyses with the BioBank-Japan GWAS resource revealed genetic links of FeNO and asthma-related traits, and existence of common genetic background for allergic diseases and their biomarkers. Our study identified the genetic locus most strongly associated with airway obstruction in the Japanese population and three genetic loci associated with FeNO, an indicator of type 2 airway inflammation in adults.Lung function reflects the ability of the respiratory system and is utilized for the assessment of respiratory diseases. Because type 2 airway inflammation influences lung function, genome wide association studies (GWAS) for lung function would be improved by adjustment with an indicator of the inflammation. Here, we performed a GWAS for lung function with adjustment for exhaled nitric oxide (FeNO) levels in two independent Japanese populations. Our GWAS with genotype imputations revealed that the  RNF5/AGER locus and explore their three-dimensional protein interactions to better investigate the association between these variants and lung function.Yamada, Motoike, Kojima et al. undertake a GWAS study on 19,722 participants from two Japanese populations for lung function with adjustment for fractional exhaled nitric oxide levels to account for airway inflammation. The authors identify variants of the  Lung function is one of the predictors for mortality in the general population3, and is also used for both the diagnosis and assessment of respiratory diseases including chronic obstructive pulmonary disease (COPD). COPD is an important cause of death all over the world5. The development of COPD is mainly influenced by environmental factors, and cigarette smoking is the most common cause of COPD7. However, it has been suggested that the pathogenesis of COPD could also be significantly influenced by genetic factors, because some people develop COPD while others do not, even though both have similar smoking histories11. We, therefore, believe that COPD is one of the best target diseases for developing a personalized healthcare algorithm based on genome information.Measures of lung function such as forced expiratory volume in one second  revealed that the ancestral composition of Japanese populations is different from those of other East Asian populations including Korean and Han Chinese21. This information leads us to the notion that it is worth performing GWAS of lung function for Japanese individuals, as Japanese people are relatively different from the other East Asians in terms of ancestral compositions. Besides, the sizes of lung-function GWAS in East Asia appeared to be rather limited, at best <8900, we wish to perform much larger-scale GWAS than those reported previously23. Further, the clinical characteristics of COPD patients in East Asia including Japan seems to be different from those of the patients in the US and Europe, where most of the previous GWASs were performed29. For instance, proportions of the patients having emphysema-dominant phenotype are greater in Japanese29 and Korean25 COPD populations than those in the US and Europe COPD populations. Frequencies of exacerbation in Japanese and Korean COPD populations are smaller than those in the US and Europe COPD populations28. Based on these reasons, we decided to explore genetic variants associated with the lung function measures in the Japanese population, as it could provide meaningful information for elucidating the genetic vulnerability for pulmonary diseases including COPD in the world.Over the past decade, world-wide multi-centre genome-wide association studies (GWASs) have been performed, and these studies have identified genetic loci that show significant associations with the lung function, COPD affection and COPD-related phenotypes such as emphysema33, which were strategically designed to assess both the long-term impact of the Great East Japan Earthquake on disaster victims and the gene-environmental interactions on the incidence of major common diseases31. Of the two cohort studies, one is a community-based adult cohort study, named the TMM Community-Based Cohort Study (CommCohort Study), while the other is a birth and three-generation cohort study, named the TMM Birth and Three-Generation Cohort Study (BirThree Cohort Study). The latter also aims to assess disease risk throughout a person’s lifespan in multiple generations by means of a long-term follow-up30. The TMM Project aims to establish an integrated biobank, sharing both biological samples and genome-omics information along with routine health examination data34. To this end, we have been conducting a large-scale ethnic-specific SNP array analysis32, through which we are attempting to elucidate the genetic vulnerabilities of common diseases including COPD to enable personalized health care32.The Tohoku Medical Megabank (TMM) has been established in the Miyagi and Iwate Prefectures in Japan. In the Miyagi Prefecture, the Tohoku Medical Megabank Organization (ToMMo) is operating at Tohoku University, while in the Iwate Prefecture, the Iwate TMM Organization is operating at Iwate Medical University. Two genome cohort studies have been conducted by TMM1 and FEV1/FVC) in the two ToMMo independent cohorts from the Miyagi prefecture. We utilized the ToMMo CommCohort Study for the Discovery Stage and ToMMo BirThree Cohort Study for the Validation Stage to elucidate the genetic vulnerabilities for respiratory diseases including COPD. In this study, we wished to perform GWAS for FEV1/FVC, an indicator for airway obstruction, with or without adjustment by the levels of exhaled nitric oxide (FeNO), an indicator of type 2 airway inflammation35. Because type 2 airway inflammation, which involves accumulation of eosinophils, mast cells, basophils, Th2 cells, type 2 innate lymphoid cells (ILC2s) and IgE-producing B cells, and type 2 cytokines , could also influence lung function, especially airway obstruction, we tried to find the genetic factors related to FEV1/FVC regardless of the existence of airway type 2 inflammation. To explore genetic factors related to airway type 2 inflammation, we also performed a large-scaled GWAS for FeNO. To our best knowledge, our GWAS for FeNO is the largest one for adults in terms of the number of subjects.In this study, therefore, we performed GWAS for lung function , we first undertook a genome-wide analysis for 8,587,571 genotyped or imputed genetic variants in the participants of the ToMMo CommCohort Study as the Discovery Stage. We adjusted the FEV1/FVC measures for age, sex, smoking status as covariates and the FEV1 measures for age, sex, height, and smoking status as covariates. We also performed GWAS for FEV1/FVC with or without the adjustment by FeNO, because we wished to examine the effect of the adjustment in terms of exploring the genetic factors for airway obstruction regardless of the existence of airway type 2 inflammation  for FEV1/FVC , a member of the immunoglobulin superfamily of cell surface receptors36. The protective minor T allele of SNP rs2070600 was associated with higher FEV1/FVC (p = 8.1 × 10−24), with the highest mean FEV1/FVC among TT homozygotes at the Discovery Stage , with the highest mean FEV1/FVC among GGAA homozygotes . The results including significantly associated loci were not significantly different between the GWAS with and without the adjustment by the principal components  Analysis V8 Fine-Mapping cis-eQTL data of lung tissue and whole blood  to find out the genes that expressions are related to the haplotypes of identified genetic variants in the two loci. The analyses revealed that the genetic variants at 6p21.32 is related to the expression of the genes including AGER, whereas the genetic variants at 4q22.1 is related to the expression of FAM13A . Two extracellular domains of AGER bind to a homodimer of S100A6 protein, a protein with two EF-hand calcium-binding motifs, forming a tetrameric complex 42. The protective minor T allele of SNP rs2531870 was associated with lower FeNO (p = 1.2 × 10−29), with the lowest mean FeNO among TT homozygotes at the Discovery Stage  box protein 2 (SPSB2). It has been reported that SPSB2 is a negative regulator that recruits an E3 ubiquitin ligase complex and induces polyubiquitination of iNOS, resulting in proteasomal degradation43. The protective allele A of the sentinel SNP rs11064445 at 12p13.31 was associated with lower FeNO (p = 6.2 × 10−17), with the lowest mean FeNO among AA homozygotes at the Discovery Stage , with the highest mean FeNO among TT homozygotes at the Discovery Stage . The results including significantly associated loci were not significantly different between the GWAS with and without the adjustment by the principal components  after Bonferroni correction for the number of pairwise comparisons. In contrast, while pollinosis showed nominal (p < 0.05) significance with FeNO , the disease did not show significant correlation after Bonferroni correction. The risk of asthma did not show significant correlation with FeNO, although there was a tendency of positive correlation between FeNO and asthma .Because FeNO is the biomarker for type 2 airway inflammation and the measurement of FeNO is widely used for a diagnosis of asthma, we examined the genetic correlation between FeNO and asthma using LD score regression (LDSC). To this end, we utilized the BioBank Japan GWAS resource as the traits including the risk of asthma in the Japanese subjects49 , high mobility group protein B1 (HMGB1), and S100 proteins36. The interaction between AGER and the ligands is considered to induce inflammatory responses by the activation of nuclear factor kappa B (NF-κB)51. On note, recent larger-scale meta-analysis of the European ancestry GWAS for FEV1/FVC revealed that the p-value of the AGER locus is the lowest one20, suggesting that loss of AGER function may affect FEV1/FVC similarly in individuals both of European and Japanese ancestry.Our GWAS revealed that the genetic locus including AGER is associated with FEV1/FVC measures53 as well as the risk of COPD19. In contrast, to the best of our knowledge, the association of RNF5 rs41315238 with FEV1/FVC has not been identified in previous GWAS for FEV1/FVC and the risk of COPD. However, the linkage disequilibrium analysis suggests that RNF5 rs41315238 is in near complete linkage disequilibrium with AGER rs2070600. Therefore, our observation also suggests that the locus 6p21.32 including these sentinel SNPs are important for lung function as previously reported53.At this 6p21.32 locus, we observed two top variants, an exonic nonsynonymous SNP rs2070600 in AGER and intronic insertion/deletion rs41315238 in RNF5 at the Discovery Stage. It has been well reported that rs2070600 in AGER is protective and associated with higher FEV1/FVC, suggesting that this nonsynonymous variant is functional. Three-dimensional structure analysis of AGER also suggests that a G82S substitution induced by the rs2070600 affects the molecular function of AGER. Since our present GWAS study clearly identified that AGER is an influential molecule for lung function in the Japanese population, we further explored the presence of other nonsynonymous variants of AGER that influence molecular and biological functions of the protein in the Japanese population using our genomic allele frequency panel, 4.7KJPN39. We consider that those common variants in GWAS to rare variants in the whole-genome reference panel approach provide important information. Indeed, we have identified three rare missense variants of AGER. The protein structure analysis further revealed that one of the variants, R244H, is likely to inhibit the dimerization and signalling of AGER, because the structural analysis clearly indicated that this mutation affects the interaction between AGER and its ligand, S100A6. Functional and biological analyses of this variant in the laboratory as well as in COPD patients remain to reveal the precise mechanism of the R244H substitution in the pathogenesis of COPD.Our GWAS showed that the minor T allele of nonsynonymous exonic SNP rs2070600 in AGER loci containing rs2070600 is located in the human leukocyte antigen (HLA) or major histocompatibility complex (MHC) region. The MHC region contains both HLA genes and the genes related to inflammation and immune responses. The AGER is located in the MHC class III region that does not harbour genes involved in antigen presentation. However, because it is between class I and class II regions and all MHC genes possess haplotype transmission, it is possible that the variants in AGER could be in linkage disequilibrium (LD) in some MHC haplotypes that might be related to the phenotypes including lung function and the risk of COPD.The 54. The study for AGER G82S polymorphism introduced by rs2070600 on rheumatoid arthritis (RA), a representative autoimmune and inflammatory disease, showed that G82S showed the association with the risk of RA. However, after correction for the presence/absence of HLA DRB1*0401, this association was lost, indicating that rs2070600 is not associated with RA independently of HLA DRB1*55.The GWAS to identify susceptibility loci for adult asthma in the Japanese population have revealed that the SNP rs404860 in the MHC class III region has a significant association with the risk of adult asthma and this SNP shows relatively weak LD with rs20706001/FVC or the risk of COPD. We rather surmise that following studies may be pertinent. Investigation on the relation between plasma AGER levels and AGER genetic variants in COPD patients revealed that rs2070600 is associated with circulating sAGER levels56. AGER rs2070600 polymorphism influenced the expression of both AGER mRNA and protein57. These studies suggest that the rs2070600 polymorphism influences the expression and function of AGER, directly influencing the lung function and the risk of COPD, but AGER rs2070600 polymorphism does not influence lung function and COPD through indirect effects in terms of the linkage with MHC haplotypes.To our best knowledge, there was no direct or strong evidence that shows the pathogenic linkage between rs2070600 and MHC haplotype in terms of the FEV1/FVC with FeNO adjustment did not show significant difference from that without the adjustment. The reason why FeNO, an indicator of type 2 inflammation, did not influence the GWAS for FEV1/FVC is unclear at present. One plausible explanation is that the type 2 airway inflammation is reversible and may not be strongly correlated to the airway obstruction in steady state.We performed GWAS for lung function with the adjustment by FeNO level, because we wished to identify genetic factors related to lung function irrespective to the existence of type 2 inflammation. However, the result of GWAS for FEV58. It is interesting to note that the genetic locus at 17q11.2 is also identified as an associated locus for FeNO, whereas the other two loci are identified in this study are not identified in the study. Similarly, our GWAS did not find significant association of 17q21.1 with FeNO, which is identified in the previous children GWAS58. We surmise that the difference between children and adults or the difference of ethnicity may serve as reasons for this inconsistency. Alternatively, the difference may be attributable to the sample size, as the sample size of our Discovery GWAS is much larger than that of the paediatric GWAS.Our GWAS study identified three genetic loci  associated with the levels of FeNO in adult populations. A GWAS study for FeNO in children has been reportedNOS2, SPSB2, and RIPOR2. Importantly, the functions of the three proteins encoded by these genes are clearly involved in the production of nitric oxide. Firstly, NOS2 encodes iNOS, which is an enzyme responsible for exhaled NO production in normal and asthmatic subjects59. IL-4 and IL-13, which are produced by inflammatory cells including type 2 helper T cells during allergic inflammation including asthma, are known to induce the expression of iNOS in airway epithelial cells61. Secondly, SPSB2 is an adopter protein that recruits an E3 ubiquitin ligase complex and induces polyubiquitination and degradation of iNOS43, so that SPSB2 works as a negative regulator for the production of NO. Importantly, cis-eQTL analysis has revealed that the protective allele A of the sentinel SNP rs11064445 at 12p13.31, which is associated with lower FeNO in our GWAS, is significantly related to higher expression of mRNA for SPSB2 that accelerates iNOS degradation. This clearly shows that this genetic variant upregulates the expression of a negative regulator of iNOS, which reduces the level of FeNO in human subjects. Thirdly, RIPOR2 is also called FAM65B, which negatively regulates the activation and proliferation of effector T cells45, which produce the cytokines that induce iNOS in airway epithelial cells. It has been well documented that increased production of NO causes nitrosative stress, nitrosylation of redox-sensitive thiols, and is intimately involved in the pathogenesis of respiratory diseases including COPD and asthma65. Taken together, we propose that the genetic variants identified in these three genes influence the phenotypes of respiratory diseases including COPD and asthma.Both the regional association plot and the cis-eQTL analyses suggest that the probable candidate genes at these loci are To explore whether there exists a common genetic background between asthma and FeNO, a biomarker for type 2 airway inflammation and widely used for the diagnosis of asthma, we further performed analyses of genetic correlation between FeNO and the risk of asthma as well as the traits related to asthma. The analyses showed the tendency of a positive correlation between FeNO and the risk of asthma. We surmise that the lack of significant correlation between FeNO and asthma in this analysis reflects the limitation of the sample size. This hypothesis is further supported by the finding that there are significant correlations between FeNO and blood eosinophil count. Moreover, two loci out of the 11 loci that showed the association with FeNO in our GWAS are found to be overlapped with the risk of asthma. Alternative explanation for the observation is to assume possible difference of genetic background between FeNO and the risk of asthma, but we interpret the latter as remote.67.Finally, we must discuss the limitations in our study. First, the size of the population, especially for the Validation Stage, is relatively small, which may have caused a lower repeat ratio of the genetic loci in this study. We realize that we need to recruit more participants. Nonetheless, it is important to note that we have identified a significant number of repeated loci, including, to the best of our knowledge, newly identified loci for FeNO. Second, we need to confirm whether the identified genetic variants actually alter the functions and/or expressions of the gene products. We are planning fine expression analyses as well as loss-of-function analyses for these variants. Especially, the generation of a mouse line with an AGER rare variant mutation is now ongoing. In this regard, we would like to note that these genetic variants are significantly associated with the measures of lung function or FeNO, and the functions of target genes are reported to be involved in the pathogenesis of emphysema or the synthesis of NOAGER as the most influential locus associated with FEV1/FVC in the adult population in Japan. The three-dimensional protein structure analysis of AGER revealed that the SNP is likely to affect the molecular function of the receptor. Further analyses of AGER using our genomic allele reference panel identified three other rare missense variants of AGER and one of them, R244H, appears to be critical for the dimerization and signalling of AGER. We also identified three genetic loci associated with the FeNO levels. The candidate genes in these loci are NOS2, SPSB2 and RIPOR2, which are all involved in the production of NO. We believe that these findings will contribute to a better understanding of the pathogenesis of respiratory diseases, especially COPD and asthma. Further investigations in the laboratory as well as in patients could elucidate the pathogenic role of the genes and their variants identified in this study.In summary, our large-scale GWAS study with Discovery and Validation Stages has identified the SNP rs2070600 in All the studies related to the TMM project were approved by the ethics committee of the Tohoku University School of Medicine and the ethics committee of the ToMMo in Tohoku University. Participants in this study were recruited from the Miyagi prefecture, from 1 June 2017 until 2 July 2019. Written informed consent was obtained from each participant when they were enroled in the cohort studies.1 measures, FEV1/FVC measures, FeNO levels, phenotype information for covariates, and the genotype data of 14,061 Japanese individuals from ToMMo CommCohort Study for the Discovery Stage and those for 5661 Japanese individuals from ToMMo BirThree Cohort Study as an independent dataset for the Validation Stage. The FEV1, FEV1/FVC, and FeNO levels were measured in the medical examination for the cohort participants.In our GWAS for lung function and FeNO, we used FEV1 and FVC using HI-801 . All test results were regularly checked by trained physicians in a blind manner, and invalid data were excluded from the data set. Similarly, we measured FeNO using NIOX VERO . Participants were measured for both respiratory functions and FeNO. We further checked the collected data to determine whether there were outliers or systematic differences among the measured assessment centres or measured seasons.We measured the respiratory function parameters including FEV68. The Japonica Array (v2) analysis including genotype imputation were conducted as described69 with IMPUTE2 (ver. 2.3.2)70 using a phased reference panel of 3552 Japanese individuals from cohort studies of the TMM project71. For quality control, we excluded plates with an average call rate <0.95 and removed samples with DishQC metric <0.82 or step1 call rate <0.97 before batch genotyping. After batch genotyping, we removed samples with a call rate <0.95, or having unusually high IBD values with many other samples. We also excluded variants with a p-value of the Hardy-Weinberg equilibrium (HWE) test <1.00 × 10−5, minor allele frequency (MAF) < 0.01, or missing rate >0.01 in each batch. We merged the imputed genotype datasets for the 19 batches using QCTOOL (v2.0.4) (https://www.well.ox.ac.uk/gav/qctool/) with the following options: -omit-chromosome -compare-variants-by position,allele -flip-to-match-cohort1. As a result, we obtained an imputed genotype dataset in Oxford BGEN format for 90,565 Japanese individuals with 54,034,112 variants. We also obtained a direct genotype dataset in PLINK BED format for the 90,565 individuals with 659,326 variants by merging the genotype datasets before the imputation for the 19 batches.To prepare direct and imputed genotype datasets, cohort participants were genotyped with Affymetrix Axiom Japonica Array (v2) separately in 19 batches. We set the number of plates for each batch to 50 according to the guidelines for Axiom array data genotypingp-value of HWE test <1.00 × 10−5, MAF < 0.01, or missing rate >0.05 for imputed and >0.01 for direct genotype, and IMPUTE2 info score <0.8 for imputed dataset. The number of remaining variants in each dataset for GWAS is 8,587,571 in the Discovery Stage and 8,595,665 in the Validation Stage.From our Japonica Array analysis, we extracted 14,115 individuals who were from CommCohort Study and were without any missing information of the phenotype and covariate values considered in our GWAS for the Discovery Stage. We also extracted 5672 individuals who were from the BirThree Cohort Study and were without any missing information of the phenotype and covariate values, for the Validation Stage. We excluded 47 individuals from the Discovery Stage and nine individuals from the Validation Stage due to discrepancies in reported gender. We also excluded seven individuals from the Discovery Stage and two individuals from the Validation Stage to avoid pairs of individuals with pi-hat value >0.75. We calculated pi-hat values for the exclusion using the direct genotype dataset and PLINK (v1.90b5.1) with the following options:-maf 0.05, -hwe 0.05 -geno 0.01 -indep-pairwise 1500 150 0.03 -genome. We again applied an SNP quality control step to the imputed and direct genotype datasets for the Discovery and Validation Stages to exclude variants with 72 for linear-mixed model analysis in order to test the additive genetic effect of SNPs on the FEV1, FEV1/FVC, and FeNO level for autosomal chromosomes. In our GWAS analysis, biases from the population stratification as well as familial and cryptic relatedness were controlled by considering the genetic correlation matrix in the linear-mixed model73. To reduce the skewness and kurtosis of the distribution of FEV1, FEV1/FVC, and FeNO, Box-Cox transformation was applied to these data using the R package . The following information was used as covariates for the adjustment: (i) age, sex, height, and smoking status for FEV1; (ii) age, sex, smoking status, and FeNO level for FEV1/FVC; and (iii) age, height, and smoking status for the FeNO level. Required information for BOLT-LMM other than the genotype and phenotype data were: LDscoresFile: East Asian LD Scores , geneticMapFile: genetic_map_hg19.txt.gz; and modelSnps: text files including the information of 214,837 and 215,346 SNPs that exist both in the file for East Asian LD Scores and the non-imputed genotype dataset for the Discovery and Validation Stages, respectively. In the GWAS for each phenotype, we gave the direct genotype dataset with -bfile option and the imputed genotype dataset with -bgenFile option to BOLT-LMM. We merged the results for the direct genotype dataset and the results for the imputed genotype dataset by overwriting the latter results with former results for variants existing in both of them.We used BOLT-LMM v2.3.2p-value of 5 × 10−8 as the genome-wide significance level to identify the genetic locus associated with the trait at Discovery Stage GWAS. To validate potential associations between the trait and the genetic locus suggested at Discovery Stage, we utilized both p-value of 5 × 10−8 and p-value of 5 × 10−5 as the genome-wide suggestive level at Validation Stage GWAS of which size of participants is smaller than that of Discovery Stage. Based on these two-stage analyses, we set up three tiers according to the certainty. Tier 1 loci had p < 5 × 10−8 at both Discovery Stage and Validation Stage. Tier 2 loci had p < 5 × 10−8 at the Discovery Stage and 5 × 10−8 ≤ p < 5 × 10−5 at the Validation Stage with consistent directions of effect. Tier 3 loci had p < 5 × 10−8 at Discovery Stage but their p was more than 5 × 10−5 at the Validation Stage.We utilized 1/FVC, FEV1, FeNO, and the following traits by applying LDSC74 to their GWAS summary statistics: asthma, COPD, pollinosis, atopic dermatitis, drug eruption, eosinophil count, and BMI. GWAS summary statistics of FEV1/FVC, FEV1, and FeNO were obtained from our GWAS results, while those of the remaining traits in Japanese population were obtained from the BioBank Japan GWAS resource48 (https://humandbs.biosciencedbc.jp/en/hum0014-v21). Note that, for obtaining the GWAS summary statistics of FEV1/FVC and FEV1 for LDSC, FeNO was not included in the covariates. As the LD scores, which was also required in LDSC, we used East Asian LD Scores from 1000 Genomes Project . We excluded variants that were not included in the East Asian LD Scores, with MAF < 0.01, or with the imputation quality score (INFO metric or Rsq) <0.9, from the calculation of LDSC.We calculated genetic correlations for all the pair of FEVhttps://gtexportal.org/home/). We selected ‘Lung’ and ‘Whole Blood’ as the relevant tissues for our GWAS traits among the 54 types of tissues, and limited the use of the eQTL results for those two tissues. We selected the highly confident eQTL associated variants for each gene from the significantly associated eQTL variants based on the posterior probabilities of causal variant statuses estimated by CAVIAR75, a method for identifying causal variants from GWAS and eQTL results. The posterior probabilities by CAVIAR were pre-computed in the GTEx V8 Analysis dataset, and we set the threshold of the posterior probabilities for the extraction to 0.05. For each signal in an intergenic region, we then selected genes for which there existed at least one highly confident eQTL associated variant located within 1 kb distance from one of genomewide-significant variants comprising the signal and identified them as the genes whose expression changes were possibly caused by the signal.We explored functions of the identified signals in intergenic regions by the detection of genes whose expression changes were associated with those signals based on the expression quantitative trait locus (eQTL) data. We used the Genotype-Tissue Expression (GTEx) V8 Analysis dataset as the eQTL data, which contains eQTL results for 54 types of tissues and is available in GTEx Portal (We calculated two linkage disequilibrium LD measures, r^2 and D’, using PLINK 1.90b5.1 for variants in chr6:32120000–32180000, a neighbouring region of rs10947233, rs41315238, and rs2070600. These LD measures were calculated from the genotype data of Discovery Stage.Further information on research design is available in the Transparent Peer Review FileSupplementary InformationDescription of Supplementary Data FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary