The current standard biomarker for myocardial infarction (MI) is high‐sensitive troponin. Although powerful in clinical setting, search for new markers is warranted as early diagnosis of MI is associated with improved outcomes. Extracellular vesicles (EVs) attracted considerable interest as new blood biomarkers. A training cohort used for diagnostic modelling included 30 patients with STEMI, 38 with stable angina (SA) and 30 matched‐controls. Extracellular vesicle concentration was assessed by nanoparticle tracking analysis. Extracellular vesicle surface‐epitopes were measured by flow cytometry. Diagnostic models were developed using machine learning algorithms and validated on an independent cohort of 80 patients. Serum EV concentration from STEMI patients was increased as compared to controls and SA. EV levels of CD62P, CD42a, CD41b, CD31 and CD40 increased in STEMI, and to a lesser extent in SA patients. An aggregate marker including EV concentration and CD62P/CD42a levels achieved non‐inferiority to troponin, discriminating STEMI from controls (AUC = 0.969). A random forest model based on EV biomarkers discriminated the two groups with 100% accuracy. EV markers and RF model confirmed high diagnostic performance at validation. In conclusion, patients with acute MI or SA exhibit characteristic EV biomarker profiles. EV biomarkers hold great potential as early markers for the management of patients with MI. An independent cohort enrolled at Città della Salute e della Scienza, University of Torino , served as validation cohort. The study protocol was approved by the local ethical committees. All participants gave informed written consent to the study in accordance with the declaration of Helsinki. Peripheral venous blood samples were collected from patients presenting with a diagnosis of STEMI, according to the European Society of Cardiology (ESC) guidelines,2.2Blood was collected in 7 mL heparin‐ and EDTA‐free polypropylene tubes. The first blood tube was discarded. Blood was centrifuged at 1600 g for 15 minutes at 4°C, and supernatant was centrifuged at 3000 g for 20 minutes, 10 000 g for 15 minutes and 20 000 g for 30 minutes to remove intact cells, cellular debris and larger EVs  were separated on SDS Page 4%‐12% gel (BioRad) and transferred onto PVDF membrane. The blot was incubated with the following primary antibody: Rabbit monoclonal anti‐TSG101 (ab125011 Abcam); mouse monoclonal anti‐CD81 (MA5‐17937 Invitrogen); rabbit polyclonal anti‐apolipoprotein A1 (ab33470 Abcam) and rabbit polyclonal anti‐apolipoprotein B48 (ab31992 Abcam).2.4To measure serum particle concentration and diameter, we used NTA with NanoSight LM10  equipped with a 405 nm laser and NTA 2.3 analytic software. EV concentration is shown as particle number/mL .2.5Serum samples underwent bead‐based EV immunocapture and were analysed by FC using MACSPlex human Exosome Kit , as detailed in the Appendix 2.6P‐values < .05 were considered significant.IBM SPSS Statistics 22 (IBM), Python 3.5  and GraphPad PRISM 7.0a were used for statistical analyses. ANOVA with post hoc Bonferroni's test and Kruskal‐Wallis's test was used to compare variables with a normal or non‐normal distribution, respectively. Categorical variables were compared through a chi‐square test. Correlations were evaluated by Pearson's test and regression curve analyses. The analysis of receiver operating characteristics (ROC) curves was used to compare diagnostic performances of selected variables. Multivariate logistic regression analysis was performed to determine odds ratio (ORs). Supervised machine learning algorithms and in particular linear discriminant analysis (LDA) and random forest (RF) models have been exploited in the context of diagnostic modelling for clinical research, as described previously.33.1We analysed 238 serum samples collected from 178 participants from a training cohort (n = 98) and an independent validation cohort (n = 80). The training cohort was divided into 3 groups . Average EV size was similarly increased in STEMI patients, and to a lesser extent in SA patients , whereas SA patients exhibited an increase in small particles only . A heat map of EV surface epitopes in individual participants showed a clear clustering in STEMI patients , with a sensitivity/specificity of 100.0%/87.5% , whereas only STEMI patients exhibited augmented numbers of medium/large‐sized EVs . These results are consistent with the notion that release of microvesicles is induced by cellular stress,The two most highly enriched EV markers in STEMI patients, and to a lesser extent in SA patients, were CD62P, a marker of platelet activation, and CD42a, another platelet‐associated marker.We then looked at the potential diagnostic performance of the above EV markers for STEMI. ROC curve analysis revealed a high performance for CD62P, CD42a, CD41b, CD40 and CD31, with CD62P and CD42a showing non‐inferiority to hs‐troponin. We also aggregated the three most discriminating markers  into a single marker, which achieved 90.0% sensitivity and 93.3% specificity for the diagnosis of STEMI, with non‐inferiority to hs‐troponin, in the training cohort. The aggregate EV marker achieved 100.0% sensitivity at validation. This marker was increased in all STEMI patients with minimally increased hs‐troponin (<50 ng/L) on presentation to the emergency department. Finally, a RF model including the full panel of 37 EV surface epitopes, and a distinct RF model including the five EV markers that were significantly increased in STEMI patients, discriminated these patients and controls with high accuracy, as confirmed in the validation cohort.versus cardiac‐specific hs‐troponin in the diagnosis of patients with acute MI are summarized in Table Unlike previous studies of EV markers in ACS, which focused on single markers, or a few cell type‐specific markers, we characterized, for the first time, a comprehensive panel of markers, which allowed us to identify those markers that achieved highest diagnostic accuracy in STEMI patients. By combining three markers, diagnostic accuracy was higher than that of individual markers and comparable to cardiac‐specific hs‐troponin I. The power of EV biomarkers derives from the enrichment of potential protein markers which otherwise constitute only a very small proportion of the total proteome of body fluids.Clearly, a technological implementation is still required before clinical applications can be envisaged. In this regard, it is worth noting that impressive progress has recently been scored in ultrasensitive detection of circulating exosomes with microfluidic chips. As an example, a 3D‐nanopatterned microfluidic chip allowed for the detection, in 2‐μL plasma samples from ovarian cancer patients, of exosome subpopulations expressing CD24, EpCAM and FR‐α proteins as potential biomarkers for ovarian cancer.EV biomarker profiling is emerging as an important general approach for precision medicine and personalized treatment. This approach could be particularly valuable in the critically ill patient, who may show a non‐specific increase of hs‐troponin.Finally, it should be mentioned that, beyond their diagnostic role, EV‐based biomarkers have predictive value for the prognosis of cardiovascular disease.Regarding methodological issues, we confirmed the technical reproducibility of the flow cytometric assay used in the present study. We also found no major differences between sample ultracentrifugation and SEC, and between 1‐hour or overnight incubation of serum supernatant with capture beads. These observations support the absence of major methodological confounding factors. Both plasma and serum have been used in previous studies; while biobanking of plasma may be preferable for studies involving isolation of EV RNA, serum also has appropriate uses.A few study limitations need to be addressed. Because of the proof‐of‐principle character of the study, we decided to investigate STEMI patients, who exhibit comparatively a clear clinical phenotype and hypothetical large changes in circulating EV. The evaluation of unstable angina or NSTEMI patients could be object of future studies. Though the study cohorts were small, accurate diagnostic models could be developed and validated in larger independent populations. Moreover, while healthy individuals were suitable controls for this pilot study, unselected control populations including patients with chronic CAD, chronic inflammatory diseases, thrombotic diseases, cancer and other disorders would more closely reflect daily clinical practice. This point is exemplified by our data, which showed larger differences in EV markers in STEMI patients versus healthy subjects, compared with STEMI patients versus SA patients.In conclusion, we have shown that circulating EVs in patients with STEMI differ in number, size and surface markers from those in healthy subjects, with SA patients showing somewhat intermediate features. Our findings demonstrate the feasibility of a diagnostic assay for early‐stage acute MI based on EV biomarkers, with a potential usefulness also in chronic CAD patients. The marker profile can be customized to optimize diagnostic accuracy, and the method is amenable to full automation.The authors confirm that there are no conflicts of interest.Jacopo Burrello: Data curation ; Formal analysis ; Investigation ; Writing‐original draft ; Writing‐review & editing . Sara Bolis: Formal analysis ; Methodology ; Visualization . Carolina Balbi: Formal analysis ; Methodology ; Visualization . Alessio Burrello: Data curation ; Formal analysis ; Software . Elena Provasi: Formal analysis ; Methodology ; Visualization . Elena Caporali: Formal analysis ; Investigation ; Visualization . Lorenzo Grazioli Gauthier: Formal analysis ; Investigation ; Visualization . Andrea Peirone: Formal analysis ; Investigation ; Visualization . Fabrizio D'Ascenzo: Formal analysis ; Investigation ; Visualization . Silvia Monticone: Formal analysis ; Investigation ; Visualization . Lucio Barile: Conceptualization ; Funding acquisition ; Investigation ; Methodology ; Visualization . Giuseppe Vassalli: Conceptualization ; Funding acquisition ; Investigation ; Writing‐original draft ; Writing‐review & editing .App S1Click here for additional data file.


A mild and green electro‐oxidative protocol to construct aromatic esters from methylarenes and alcohols is herein reported. Importantly, the reaction is free of metals, chemical oxidants, bases, acids, and operates at room temperature. Moreover, the design of the electrolyte was found critical for the oxidation state and structure of the coupling products, a rarely documented effect. This electro‐oxidative coupling process also displays exceptional tolerance of many fragile easily oxidized functional groups such as hydroxy, aldehyde, olefin, alkyne, as well as neighboring benzylic positions. The enantiomeric enrichment of some chiral alcohols is moreover preserved during this electro‐oxidative coupling reaction, making it overall a promising synthetic tool. P beats N: A mild and green electro‐oxidative protocol to construct aromatic esters from methylarenes and alcohols is reported. Importantly, the reaction has a broad functional group tolerance and is free of metals, chemical oxidants, bases, acids, while operating at room temperature. The design of the electrolyte was found critical for the oxidation state and structure of the coupling products. Electro‐oxidative strategies occupy an increasingly important place in the synthetic method toolbox. Indeed, the vast majority of organic building blocks possess numerous C−H bonds, making electro‐oxidation a privileged and arguably still underappreciated tool for the design of step and atom economic coupling reactions. In this context, carboxyl esters are among the most prevalent functional groups encountered in very diverse areas of organic chemistry, from liquid crystal polymers, to cosmetics, pharmaceuticals, artificial fragrances, agrochemicals and food additives.4 or strong acids. Moreover, in those cases, strong chemical oxidants remain indispensable. Thus, the corresponding substrates scopes tend to be limited, in particular toward fragile easily oxidized functional groups such as hydroxy, ether, aldehyde as well as other benzylic positions , and sulfonate anion leading to higher oxidation than tetrafluoroborate.Importantly, the electrolyte seems to have an influence on the electro‐oxidative coupling reaction's outcome. The common ammonium electrolytes favor the formation of acetals Figure . The bess Figure . The besN1 nor P2 were chemically altered during the reaction. Thus, a chemical involvement of the electrolyte can be reasonably excluded. The cyclic voltammetry (CV) profiles of N1 and P2 were then measured with Ferrocene as an internal standard .To investigate the electrolyte's influence, a series of initial mechanistic experiments were conducted. Firstly, in situ NMR of the crude reaction mixture showed that neither representative electrolytes d Figure –d. Interpara position with respect to the methylarene reaction site proved to be important, with para‐methoxy‐ and para‐bromo‐ providing superior results. This particularity allows for remarkable benzylic group tolerance on the other (non‐reactive) positions of the arene substrate . This reaction is also remarkably tolerant to a number of other oxidation sensitive functional groups such as aldehyde (Table 11 c), ethers  and hydroxy groups (Table 16 c). This appreciable redox tolerance toward fragile functional groups highlights the superiority of electro‐oxidative strategies versus chemical oxidants. Other important groups such as furan (Table 14 c), fluoride  and chloride (Table 20 c) are also tolerated in this reaction. Many alcohols have also been found to be effective coupling partners for the reaction (Table 21 c–32 c). For instance, (R)‐(−)‐1‐Methoxy‐2‐propanol (31 b) and (R)‐(+)‐1‐Phenylethanol (32 b) were successfully used in the reaction, while maintaining their enantiomeric enrichment (31 c was obtained with 96 % ee and 32 c with 98 % ee).The electro‐oxidative esterification substrate scope was thereafter investigated. The 1 a, the acetalization was achieved under a low 5 mA current. In other cases, a higher current was needed. In general, the ammonium electrolyte N2 was found to be optimal for electro‐oxidative acetalization. However, some exceptions were noted, such as substrate 2 e which requires phosphonium electrolyte P2.The electro‐oxidative acetalization reaction was thereafter investigated. Acetalization is a useful synthetic technique to protect aldehydes of various multifunctional organic molecules.P2, a variety of aldehydes could be dehydrogenatively coupled with alcohols to furnish the corresponding esters in good yields , including olefins  and alkynes (39 c), making it a useful synthetic method.Next, we established that acetals and aldehydes are also competent substrates in this electro‐oxidative esterification reaction Figure . The dehds Table . This netert‐butyl peroxide, sodium metaperiodate or potassium permanganate, under otherwise identical reaction conditions, none of the three expected oxidation products (1 c–e) could be observed. However, when CAN (ceric ammonium nitrate), or DDQ were used instead of electricity, some oxidation occurred forming the corresponding aldehyde 1 d in 23 % and 8 % NMR yield, respectively  might also occur.The aryl radical cation In summary, we have developed a mild and green electro‐oxidative method to construct esters and acetals from methylarenes and benzaldehydes. This electro‐oxidative method shows special tolerance to many easily oxidized structures such as hydroxys, aldehydes, olefins, alkynes, as well as other benzylic positions making it valuable in synthetic chemistry.The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


The chr7 association remained significant in the presence of the LPA SNV as a covariate. The LPA SNV was also associated with coronary heart disease  in this study (OR (95% CI) = 1.27 (1.13–1.43), p = 5 × 10−5) but the chr7 SNV was not (OR (95% CI) = 1.03 (0.97–1.09), p = .37). Both variants replicated in UK Biobank. Elevated lipoprotein(a) concentrations ([Lp(a)]) and LPA variants associated with elevated [Lp(a)] have previously been associated with CAAD and CHD, including rs10455872. With electronic health record phenotypes in eMERGE and UKB, we replicated a previously known association and identified a novel locus associated with CAAD.Carotid artery atherosclerotic disease (CAAD) is a risk factor for stroke. We used a genome‐wide association (GWAS) approach to discover genetic variants associated with CAAD in participants in the electronic Medical Records and Genomics (eMERGE) Network. We identified adult CAAD cases with unilateral or bilateral carotid artery stenosis and controls without evidence of stenosis from electronic health records at eight eMERGE sites. We performed GWAS with a model adjusting for age, sex, study site, and genetic principal components of ancestry. In eMERGE we found 1793 CAAD cases and 17,958 controls. Two loci reached genome‐wide significance, on chr6 in LPA  = 1.50 (1.30–1.73),  CAADcarotid artery atherosclerosis diseaseCHDcoronary heart diseaseCIMTcarotid initima‐media thicknessCPTcurrent procedural terminologyEHRelectronic health recordseMERGEelectronic Medical Records and GenomicsICDinternational classification of diseaseLp(a)lipoprotein (a) proteinMAFminor allele frequencyNLPnatural language processingPCAprincipal components analysisPPVpositive predictive valueSNVsingle nucleotide variantUKBUnited Kingdom biobank1LPA variants (which are associated with [Lp(a)]  is a major cause of ischemic stroke  Network is a research network that integrates electronic health records (EHR) and genomic data to drive genomic discovery and advance personalized medicine , National Institutes of Health (NIH) since 2007. It includes sites across the United States, with the purpose of linking EHR and genomic data for genomic medicine research. Phenotypes are extracted from EHR by algorithms developed by investigators and applied at each site. It is currently in Phase 3 and includes over 80,000 genotyped participants from nine adults and three pediatric institutions  system at Kaiser Permanente Washington/University of Washington (KPWA/UW) using patients from KPWA. Details of the development of the algorithm and its implementation, including a flow diagram are given in the Supplemental Methods. The algorithm extracts the extent of carotid stenosis from imaging reports. We developed it using the approach previously described for creating eMERGE phenotypes  and all 25 controls (100% control PPV) were validated with chart review at KPWA, and 100% case and control PPV in 20 cases and 20 controls at Marshfield Clinic . We then applied the developed algorithm to adult participants from the Geisinger, Harvard University, KPWA/University of Washington, Marshfield, Mayo, Mt. Sinai Health System, Northwestern, and Vanderbilt University eMERGE sites. Demographics are presented by case status in Table 2.3R2 across all chips, using the R2 values calculated in the MIS imputation. We also calculated minor allele frequency (MAF) among all CAAD analysis participants and within European and African ancestry subgroups. Principal components analysis (PCA) was performed with plink2 PCA approx method .For the GWAS, we used an additive genotype model and logistic regression in PLINK 1.9  to determine if other phenotypes may be associated with this SNP.We performed phenome wide association study  is a population sample of 502,629 participants in the United Kingdom aged 40–69 years at the time of study recruitment , which includes the subclasses of occlusion and stenosis of the right (I65.21), left (I65.22), bilateral (I65.23), or unspecified (I65.29) carotid arteries. All participants without those codes were included as controls. Due to data limitations, individuals with hypercholesterolemia  were not excluded from the UKB analysis. A total of 934 cases and 408,027 controls had complete genotype, phenotype, and covariate data.2.6N = 50,520) or the Affymetrix UK Biobank Axiom Array  with 95% overlap between markers on each array. Haplotype phasing of the data was performed with SHAPEIT2 , European Bioinformatics Institute GWAS catalog (https://ebi.ac.uk/gwas/), and Haploreg .We annotated SNVs passing the genome‐wide significance threshold with Genotype‐Tissue Expression project data , and an intergenic region on chromosome 7 between TYW1 and AUTS2 ” (p = 2 × 10−6), and alters four predicted transcription factor binding motifs: Foxd1_1, Foxf1, Foxj1_2, and Foxo_1. It has entries for association with 16 traits in the GWAS catalog, mostly cardiovascular and lipid phenotypes. Intergenic variant rs6952610 is not a GTEx eQTL, and has no associations in the GWAS catalog. It changes two predicted transcription factor binding motifs: GR_disc1 and GR_known3  but the TYW1–AUTS2 association did not reach genome‐wide significance (p = 7.45 × 10−7). Only 89 (5.0%) cases were of non‐European ancestry, 70 of those were of genetically determined African ancestry. As expected, given the limited power, we found no significant associations in the African ancestry subset (results not shown).We defined ancestry groups via 3.2.3LPA SNV (rs118039278) as a covariate, the TYW1–AUTS2 SNV rs6952610 remained associated (p = 4.3 × 10−8).The addition of covariates other than age, sex, site, and PC1–10 did not substantially change the results. We individually added BMI, CHD, and smoking to the logistic model, as well as combining all covariates in a single model Table . Sample 3.2.4LPA variant was significantly associated with CHD, (OR (95% CI) = 1.27 (1.13–1.43), p = 5.5 × 10−5), whereas the intergenic chr7 SNV was not (OR (95% CI) = 1.03 (0.97–1.09), p = .30).We also evaluated the association of CHD with the putative CAAD loci in the same data set. There were 4199 cases and 11,684 controls, defined by the presence of a CHD diagnosis. The 3.2.5p < 5 × 10−8 in our main analysis in the UKB (Table LPA association replicated (rs10455872 p = 3.4 × 10−6), as did the chr7 SNV (rs6952610 p = .0096). Both were statistically significant at the 5% level, after adjustment for the two contrasts.We tested the six SNVs with KB Table . The LPA3.2.6HDAC9 and TWIST1, and is not near the chr7 SNV in the eMERGE GWAS. The lead SNV on chromosome 1, rs682112, is in an intron of neuron navigator 1 (NAV1). NAV1 is expressed more in the aorta than other GTEx tissues . rs682112 is in the GTEx catalog as an eQTL for NAV1 in Heart—Atrial Appendage  and Esophagus—Mucosa . These did not replicate in the eMERGE data.As a secondary analysis, we performed a GWAS within the UKB with the ICD‐10 defined phenotype. Two loci reach genome‐wide significance, on chromosomes 1 and 7 . The lead SNV on chr7 was rs2526620, which is intergenic between 44.1We found two loci, tagged by rs10455872 and rs6952610, associated with CAAD in the eMERGE Network participants. Both replicated in UKB with a simple ICD‐only phenotype, despite differences in the patient demographics and phenotype definition. UKB is a younger, population‐based cohort with less depth of longitudinal data than most eMERGE sites ]‐associated risk for CAAD varies by race/ethnicity; the burden of risk is higher in individuals with European than African ancestry, but still unclear in Hispanic and Asian populations ] ] have previously been shown to be associated with CAAD ] is associated with CHD risk  codes for a protein that stabilizes codon–anticodon interactions in the ribosome. AUTS2, autism susceptibility candidate 2, is an activator of transcription, has been shown to regulate neuronal migration and have a role in brain development  yielded no phenome‐wide significant results. However, the top four phecodes were “Musculoskeletal symptoms referable to limbs,” “Other disorders of middle ear and mastoid,” “Nephritis and nephropathy with pathological lesion,” and “Cerebral ischemia,” which is a condition caused by insufficient blood flow to the brain. The later result may suggest cerebral ischemia due to CAAD; however, this would require further evidence as it is not statistically significant when the multiple tests are considered. See Figure S3, the Phewas Manhattan plot and Table S3 of the top Phewas results.Unlike 4.4The portable NLP system's built‐in support for local evaluation of NLP performance, combined with centralized modification and re‐distribution of site‐tailored versions of the NLP system, allowed for accurate extraction of quantitative stenosis severity measures from heterogeneously sourced free‐text imaging reports, a known challenge in multisite application of clinical NLP ] is not often clinically measured, we do not have it for these participants. Therefore, we were unable to show that inclusion of this phenotype would eliminate the effect of the genotype, as has been previously reported ] is associated with carotid artery disease and suggests [Lp(a)] is an important therapeutic target for CAAD. The second association, on chr7, is novel and its mechanism of action is unknown.In summary, we identified and replicated two loci associated with CAAD, a risk factor for stroke. The The authors declare that there are no conflict of interests.The following authors made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; were involved in drafting the manuscript or revising it critically for important intellectual content: David R. Crosslin, Elisabeth A. Rosenthal, David S. Carrell, David J. Cronkite, Adam Gordon, Xiaomeng Du, Yatong K. Li, Marc S. Williams, Chunhua Weng, Qiping Feng, Rongling Li, Sarah A. Pendergrass, Hakon Hakonarson, David Fasel, Sunghwan Sohn, Patrick Sleiman, Samuel K. Handelman, Elizabeth Speliotes, Iftikhar J. Kullo, and Eric B. Larson. In addition to the above, the following authors also gave final approval of the version to be published, Melody R. Palmer, Daniel Seung Kim, Ian B. Stanaway, and Gail P. Jarvik.Supporting information.Click here for additional data file.


Early‐onset colorectal cancer (EO‐CRC) is a public health concern. Starting screening at 45 years has been considered, but there is discrepancy in the recommendations. Racial disparities in EO‐CRC incidence and survival are reported; however, racial/ethnic differences in EO‐CRC features that could inform a racial/ethnic‐tailored CRC screening strategy have not been reported. We compared features and survival among Non‐Hispanic White (NHW), Non‐Hispanic Black (NHB), and Hispanics with EO‐CRC.CRC patients from SEER 1973–2010 database were identified, and EO‐CRC was defined as CRC at <50 years. Clinical/pathological features and survival were compared between NHW, NHB, and Hispanics. Cancer‐specific survival (CSS) predictors were assessed in a multivariable Cox proportional hazard model.p < 0.001). EO‐CRC in NHB presents more frequently in females, with well/moderately differentiated, stage IV, and is less likely to present in locations targetable by sigmoidoscopy than NHW . 5‐year CSS was lower in NHB  and Hispanics  than NHW. A regression model among patients with EO‐CRC showed that being NHB or Hispanic were independent predictors for cancer‐specific mortality, after adjusting for gender, grade, stage, and surgery.Of 166,416 patients with CRC, 16,545 (9.9%) had EO‐CRC. The EO‐CRC frequencies in NHB and Hispanics were higher than NHW  has prompted updates in CRC screening guidelines; however, consensus has not been reached yet. Early‐onset colorectal cancer (EO‐CRC) presents more frequently in racial minority groups compared to Non‐Hispanic Whites (NHW). Compared to NHW/Hispanics, EO‐CRC in NHB presents more frequently in females, with a higher rate of well/moderately differentiated, advanced staged and right‐sided tumors. A race‐tailored CRC screening strategy including full colonoscopy in NHB and sigmoidoscopy in NHW and Hispanics could represent a cost‐effective method for screening in young population. Compared to Non‐Hispanic Whites (NHWs), NHBs have a higher incidence rate 22.1Adult patients with newly diagnosed CRC were identified using the Surveillance, Epidemiology and End Results (SEER) database from 1973 to 2010. SEER is a comprehensive database that collects information on cancer incidence, clinical/pathological characteristics, and survival from 20 population‐based cancer registries across the United States, representing approximately 28% of the population.The SEER*Stat 8.15 software was interrogated to locate all cases of CRC adenocarcinoma by histology codes  and anatomical primary site based on the International Classification of Diseases for Oncology–Third Edition (ICD‐O‐3) codes , African American (NHB) and Hispanics were included. Duplicated entries (identified by Set ID number) were located and any second event; representing a second primary or recurrent CRC was excluded.2.2EO‐CRC was defined as CRC diagnosed in patients younger than 50 years old. Clinical characteristics included age at diagnosis (categorized as younger than 45 or between 45 and 49 years), gender and race/ethnicity, whereas pathology features included histological grade (categorized as well/moderate and poor/anaplastic), stage at initial diagnosis (non‐metastatic and metastatic), and tumor location (appendix to anus). We used tumor location to create two additional variables to classify tumors by sidedness (right‐sided: appendix to hepatic flexure and left‐sided: splenic flexure to anus) and to identify tumors targetable by sigmoidoscopy (from sigmoid colon to anus). Data regarding treatment available in SEER included surgical intervention and receipt of radiation. Overall survival (OS) was calculated using vital status and follow‐up time from diagnosis date and cancer‐specific survival (CSS) was calculated using the cause‐specific death classification variable provided in the SEER database.2.3p < 0.05), post‐hoc tests were performed and odds‐ratio with 95% confidence intervals for each comparison are reported. Kaplan–Meier survival analysis and log‐rank tests were used to assess and compare OS and CSS between racial/ethnic groups. To evaluate if race/ethnicity is an independent predictor of CSS, a multivariable Cox proportional hazard model was built including all variables. Given the lack of information about chemotherapy treatment, a second model including only patients diagnosed with stage I CRC was built. Statistical significance was determined by a two‐sided p‐value < 0.05. Analyses were conducted using Stata 15.1 (StataCorp) and SAS 9.4 (SAS Institute).EO‐CRC among patients with CRC in total and by race/ethnicity groups is reported as proportions and compared among groups using chi‐square. Bivariate associations between clinical/pathological characteristics and racial/ethnic group were tested using chi‐square for categorical and Kruskal–Wallis tests for non‐normally distributed continuous variables. When the overall difference between racial/ethnic groups was significant (3p < 0.001) and Hispanics  than NHW.Of all CRC patients in the SEER cancer registry, EO‐CRC was seen in 16,545 (9.9%). The proportion of patients diagnosed with EO‐CRC within each racial/ethnic group differed significantly  (Table 3.1p < 0.001), respectively (Table p < 0.001), whereas no difference was seen between NHW and NHB .The proportion of patients diagnosed at ages younger than 45 versus 45 to 49 years differed between NHW, NHB, and Hispanics .Regarding tumor location distribution, no differences were noted between NHW and Hispanics, while the tumor location pattern varied in NHBs compared to the other racial/ethnic groups. The rectum was the most common location in NHW (30.5%) and Hispanics (28%), whereas it accounted for 21.7% in NHB. In contrast, 25.4% of tumors in NHB were in the cecum and ascending colon, while only 15.9% tumors in NHW and 16.9% in Hispanics were found in these locations. Among 72.3% and 70.6% of tumors that were left‐sided in NHW and Hispanics, respectively; 67.7% and 65.9% were in a location targetable by sigmoidoscopy. In NHB, 60.9% were left‐sided and 54.6% were targetable by sigmoidoscopy  and Hispanics  compared to NHW. Similarly, among 5242 patients diagnosed with EO‐CRC rectal cancer, the frequency of radiation was significantly lower in NHB compared to NHW  whereas no difference was seen among NHW and Hispanics.Among patients with non‐metastatic EO‐CRC, surgery was lower in NHB  and Hispanics   and 19% higher in Hispanics , compared to NHW. A second model, including only patients diagnosed with stage I, showed a non‐significant association between racial/ethnic groups and cancer‐specific death after adjusting for gender, grade, sidedness, surgery, and radiation  and left‐sided tumors (73.7% vs. 60.2%) at 5 years (Table 4This study highlighted racial disparities in the frequency, clinical/pathological characteristics, and survival of patients with EO‐CRC. Consistent with prior reports, we also observed an EO‐CRC frequency of 9.9%;Consistent with differences reported among CRC patients diagnosed at 50 years or older, significant racial disparities were demonstrated in EO‐CRC and identified NHB as a group that suffers disproportionally compared to others. Besides presenting more frequently with metastatic disease compared to other racial/ethnic groups, our data showed that NHB are more likely to have right‐sided tumors than NHW or Hispanics. While nearly two‐thirds of tumors in NHW or Hispanics are localized between the sigmoid colon and rectum, only half of NHBs with EO‐CRC presented with a tumor in these locations. This is an interesting and critical finding that allow us to hypothesize that a racial/ethnic group‐specific CRC screening program, in which NHW and Hispanics younger than 50 could be evaluated with sigmoidoscopy, whereas full colonoscopy could be limited to NHBs, could favorably balance diagnostic yield versus procedure costs and complications. Further studies assessing the cost‐effectiveness of a racial/ethnic‐tailored CRC screening in patients younger than 50 are warranted.Another current discrepancy point is regarding the age to start early CRC screening. While 45 years has already been recommended by some societies, starting at age 40 has also been contemplated.BRAF mutation has shown prognostic value and it does not seem to differ between NHW and NHB.Unfavorable outcomes among NHBs are recognized across several cancer types including CRC;In this study, no differences were evidenced in 1‐year survival across racial/ethnic groups; however, unfavorable outcomes became apparent in minorities at 5 years. We believe this late‐onset differential is suggestive of higher recurrence rates in minorities compared to NHW, which has been reported in other cancer‐registry based studies. Whether this disparity is driven by biological factors or by access to care remains unclear. In this regard, SEER data is limited to make a strong conclusion; however, we hypothesize that access to care plays a pivotal role to explain this differential for two reasons. First, lower surgery rates were evidenced among NHB and Hispanic patient with non‐metastatic disease and lower radiation rates were seen in NHB compared to NHW patients with rectal cancer. Second, while the racial/ethnic group was an independent predictor of cancer‐specific mortality in the multivariable analysis, the association did not hold when the analysis was restricted to patients with stage I only. Since management of patients with stage I disease entails surgery but does not require subsequent, regular visits as chemotherapy treatment does, we consider that access to care would mainly affect the availability of medical therapy, including chemotherapy, in the management of patients with stage II‐IV disease. Further studies are required to test this hypothesis.This study has limitations that should be considered. SEER‐database lacks information about prognostic molecular features and systemic treatment which has proven to improve survival in high‐risk stage II and stage III CRC patients. Also, information regarding treatment is limited to describing whether surgery and/or radiation were given to the patient at any point; whereas information about chemotherapy regimens, the use of biologics, immunotherapy, participation in clinical trials is not provided. The lack of these information impairs the possibility of determining whether biological features or access to treatment are responsible for survival disparities across racial/ethnic groups.In summary, racial disparities are evident in the frequency, clinical/pathological features, and outcomes of patients with EO‐CRC. Based on differences in location between NHB and NHW/Hispanics, we do not believe a “one size fits all approach” is the most cost‐efficient CRC screening strategy. Instead, a race/ethnic group‐specific CRC screening program, in which NHW and Hispanics younger than 50 would be evaluated with sigmoidoscopy, whereas full colonoscopy would be limited to NHBs, could represent a method that would favorably balance diagnosis yield versus procedure costs and complications. Economic studies evaluating a race/ethnic group specific strategy are warranted.To the best of our knowledge, this study is the first to hypothesize that a race‐specific early CRC screening strategy could be considered as an alternative for patients younger than 50 years old. A cost‐effectiveness evaluation of a race‐specific early CRC strategy that would limit full colonoscopies to NHB; while favoring interventions to target the left colon such as sigmoidoscopies in Hispanics and NHW is warranted.The authors report no conflict of interest.


Gastric cancer (GC) is a world health problem and it is the third leading cause of cancer deaths worldwide. The current practice for prognosis assessment in GC is based on radiological and pathological criteria and they may not result in an accurate prognosis. The aim of this study is to evaluate expression and copy number variation of the ADAR gene in advanced GC and clarify its correlation with survival and histopathological characteristics.Forty two patients with stage III and IV GC were included in this study. ADAR gene expression and copy number variation were measured by real-time PCR and Quantitative multiplex fluorescent-PCR, respectively. Survival analysis performed based on the Kaplan–Meier method and Mantel–Cox test.p < 0.01). Also, ADAR expression level in stage IV was higher than stage III. 40% of patients showed amplification in ADAR gene and there was a positive correlation between ADAR copy number and expression. Increased ADAR expression was clearly correlated with poorer survival outcomes and Mantel–Cox test showed statistically significant differences between low and high expression groups (p < 0.0001). ADAR overexpression and amplification were significantly associated with metastasis, size and stage of tumor.ADAR mRNA was significantly overexpressed in the tumor tissues when compared to the adjacent normal tissues (Together, our data indicate that amplification leads to over expression of ADAR and it could be used as a prognostic biomarker for disease progression, especially for the metastatic process in GC. Gastric cancer (GC) currently ranks fourth in cancer incidence worldwide. Gastric adenocarcinoma is the most common histological type of all malignancies originating in the stomach and known as a heterogeneous disease with diverse phenotypes and genotypes. Although in the last few decades its prevalence has declined to reach a plateau, GC still is the third leading cause of cancer deaths worldwide . SurgeryRNA editing is an important form of post-transcriptional processing, which can alter RNA molecules by deamination of adenosine to form inosine. This reaction is catalyzed by a family of enzymes named adenosine deaminase acting on RNA (ADARs) . The humIt is estimated that human transcriptome undergoes over 100 million editing events, which may result in codon changes with the consequence of altered protein function, alternative splicing or affect targeting and maturation of microRNAs . ADAR wiAcute myeloid leukemia was the first cancer that altered mRNA editing was shown to be connected to the disease . ThereafAlthough significant progress has been made in the management and treatment of GC patients, however, further studies should be conducted to promises discoveries of new biomarkers and more innovative and effective treatments for patients with GC. The main end point of our study is to evaluate expression and copy number variation of ADAR (ADAR1) gene in advanced GC and clarify its correlation with overall survival and histopathological characteristics.Fresh frozen tumor and normal adjacent tissue of 42 patients with stage III and IV gastric adenocarcinoma who underwent surgery at the Cancer Institute of Iran were examined in this study. We included only GC patients that did not receive neoadjuvant therapy. The biological materials were provided by the Iran National Tumor Bank, founded by the Cancer Institute of Tehran University of Medical Sciences, for cancer research. Staging were based on the American Joint Committee on Cancer (AJCC) cancer staging manual . All samRNA and DNA were purified from normal and tumoral samples using the All-in-one DNA/RNA/Protein Mini-Preps Kit  following manufacturer’s protocol. The RNA quality was verified by electrophoresis on 1.0% agarose gel. Also, the quantity of isolated RNA and DNA was evaluated by NanoPhotometer .RNA samples were reverse transcribed into complementary deoxyribonucleic acid (cDNA) using Easy™ cDNA Synthesis Kit . The cDNA was synthesized from 1 μg of total RNA with random hexamer and oligo (dT) as reaction primers in a final volume of 20 μl following manufacturer’s protocol.-∆∆Ct method.Obtained cDNA was further amplified by real-time quantitative PCR using RealQ Plus Master Mix Green  based on the kit protocol. The primers used for performing real-time PCR were as follows: for ADAR1 F: 5′AGCTTGGGAACAGGGAATCG3′ and R: 5′CTTCGCAGTCTGGGAGTTGT3′; for GAPDH F: 5′ACACCCACTCCTCCACCTTTG3′ and R: 5′TCCACCACCCTGTTGCTGTAG3′. Real-time PCR mixture contained 10 μl of SYBR green master mix, 1 μl of forward and reverse primers and 2 μl of cDNA. Real-time PCR program for the reaction was based on a holding step at 95 °C for 15 min, followed by 40 cycles of denaturing at 95 °C for 20s, annealing and extending at 60 °C for 40s. Real-time PCR was performed at least three times in the StepOne™ Real-Time PCR System  for each sample and fold change of gene expression was calculated using 2The ADAR gene copy number was quantified by QMF-PCR on a BIOER XP Cycler . The QMF-PCR technique consists of the quantification of fluorescently labeled test and control amplicons, obtained by a single multiplex PCR amplification. Primers were designed for 5′UTR, exonic and 3′UTR of the ADAR gene. Three other genes including BOD1L, AGBL2 and POR were co-amplified as controls. A tail of universal primer was added to forward primers, except for POR gene which the tail was added to reverse primer. The universal primer was labeled with the fluorescent phosphoramidite 6-FAM dye and all the primers were HPLC purified. Multiplex PCR employed the Multiplex TEMPase Master Mix  following the manufacturers recommendations. Primers stock solution was prepared in a 0.5:1:0.5 ratio of forward primer, reverse primer and fluorescently tagged universal primer, respectively. The reaction started with an initial denaturation of 15 min at 95 °C, followed by 30 cycles at 95 °C for 45 s, 59.8 °C for 45 s and 72 °C for 45 s, and a final extension of 15 min at 72 °C. Primer sequences used for QMF-PCR are shown in Table One μl of the PCR products was added to 10 μl of formamide and 0.5 μl of GeneScan-500 LIZ size standard . Then, the PCR product was denatured at 95 °C for 3 min and placed on ice to prevent re-annealing until further analysis. Fragment analyses were performed using a POP7 gel on the ABI 3500 Genetic Analyzer . The results were processed with GeneMarker® software V2.7  to obtain electropherograms for each sample. Each product was identified by its size, and the area under peak were imported into an Excel spreadsheet and the copy number of each amplicon was determined by calculating a dosage quotient (DQ) for each fragment , 17.Biological databases are crucial for exploring the molecular mechanisms of cancer, therefore, basal expression of ADAR mRNA and ADAR copy number in 37 GC cell lines were obtained from cancer cell line Encyclopedia (CCLE) . RNA expTo investigate the relationship between ADAR expression and prognosis of gastric cancer, survival data associated with GC patients were obtained from Kaplan-Meier Plotter . OverallP-value was < 0.05.All statistical analyses were performed using SPSS 18.0  and GraphPad Prism 8  software. Paired T-test was applied in comparisons of ADAR mRNA levels between GC tissue samples and their paired non-tumor counterparts and unpaired T-test was used for comparison between stage III and IV. The Chi-square test was performed to evaluate correlations between ADAR expression or amplification and clinicopathological parameters. Pearson’s correlation coefficient was used to determine the relationship between the expression and CNV of ADAR in GC cell lines. Differences in the OS between the high and low expression groups were estimated and compared by the Kaplan–Meier method with a Mantel–Cox test. Differences were considered significant when the p < 0.01, Fig. p = 0.0225, Fig. Among the 42 tumor specimens tested, average expression of ADAR mRNA was significantly upregulated in the tumor tissues when compared to the adjacent normal tissues of the GS patients p < 0.0, Fig. 1.p < 0.0001) between ADAR copy number and expression  and low expression . This study consisted of 25 patients aged ≥66 years and 17 patients aged < 66 years (P = 0.573). ADAR high expression was significantly associated with metastasis (p = 0.008), stage (p = 0.008), histology (p = 0.015) and size of tumor (p = 0.004). Moreover, amplification was significantly associated with metastasis (p < 0.018), stage (p < 0.018) and size of tumor (p = 0.008) in GC patients and tended to be associated with histology (p = 0.143). Furthermore, no significant association was detected between ADAR gene CNV and its expression and other clinicopathological parameters, including age, gender, regional lymph node metastases, site of primary tumor and Lymphovascular invasion . Median survival of high and low expression groups were 23.2 and 70.2 months, respectively. Furthermore, median survival of patients with different characteristics was obtained. Patients with high expression of ADAR have significantly lower median survival time in most subgroups (Table The GC database used in this study, includes 876 samples from six independent datasets. Kaplan–Meier analysis of this dataset indicated that increased ADAR expression was clearly correlated with poorer survival outcomes Fig. . PatientApplication of molecular biomarkers in clinical setting might improve diagnostic and management strategy of GC patients. To date, some molecular markers, including human epidermal growth factor receptor-2, vascular endothelial growth factor receptor 2, urothelial cancer associated 1, excision repair cross-complementation group 1, B-cell lymphoma-2, and Ki-67 have been proposed to have diagnostic and prognostic value in the management of GC patients , 21. HowWe analyzed mRNA expression level of ADAR in GC tumors and adjacent normal tissues. This revealed that ADAR is remarkably overexpressed in GC tumors. In addition, we compared ADAR expression level in GC stage III and IV. Interestingly, the average expression of ADAR mRNA in stage IV was higher than stage III. Besides, overexpression of ADAR was significantly associated with metastasis, stage, histology and size of tumor, which may indicate that ADAR has a pivotal role in stage III to IV progression of GC. According to ADAR function, overexpression of ADAR could leads to hyper-editing . UnbalanTo determine whether ADAR copy number has variation in tumors, QMF-PCR was conducted. We found that ADAR significantly amplifies in advanced GC. Moreover, our results showed a positive correlation between ADAR gene copy number and its expression at the mRNA level. Correlation between ADAR copy number and ADAR expression has also been reported in breast carcinoma, ovarian adenocarcinoma, lung adenocarcinoma and liver carcinoma . ADAR frAlso, we carried out a correlation test between ADAR expression and copy number change in 37 GC cell lines and statistical analysis revealed that ADAR expression was consistently associated with ADAR copy number. The association of gene copy number with gene expression has also been found in other cancer cell lines. Hyman et al. performed a high-resolution analysis in breast cancer cell lines and showed that 44% of the highly amplified genes showing overexpression and 10.5% of the highly overexpressed genes being amplified . These fAs previously mentioned, ADAR amplification and overexpression in different cancers have been reported , 25. YetCurrently, prognosis of GC patients is primarily determined using depth of wall invasion, lymph node or distant metastasis status and age; however, these prognostic factors are limited in clinical practice, and may not result in an accurate prognosis . To furtThe current study demonstrates that ADAR mRNA is overexpressed in GC through DNA copy number amplification. ADAR overexpression and amplification correlate with the main negative clinicopathological factors such as metastasis, tumor size and stage in GC patients. Furthermore, increased ADAR expression was clearly correlated with poorer survival outcomes. Therefore, ADAR overexpression is not only a biomarker of tumor progression but also contributes to tumor progression. We propose ADAR as useful prognostic markers and therapeutic targets for GS. Doubtless, further investigations are necessary to confirm these primary results.


The aim of this study was to evaluate the validity of self‐reported medication use for hypertension, diabetes, and dyslipidemia by comparison with health insurance claims among employees of large‐sized companies in Japan.Participants were 61 676 participants of 13 large‐sized companies in Japan. Self‐reports on medication use were obtained through web‐ or paper‐based questionnaires conducted at the annual health checkup in fiscal year 2016. Health insurance claims for medication were obtained from corporate health insurance associations from April 1, 2016, to March 31, 2017. Agreement rate, sensitivity, specificity, positive and negative predictive values (PPV and NPV), and kappa statistics of self‐reporting were examined for different reference periods . Subgroup analysis was conducted stratified by sex, age, body mass index, smoking, alcohol drinking, blood pressure, hemoglobin A1c, and low‐density lipoprotein cholesterol.Agreement, sensitivity, specificity, PPV, and NPV were 0.98, 0.90, 0.98, 0.87, and 0.99 for hypertension, 0.99, 0.89, 1.00, 0.89, and 1.00 for diabetes, and 0.98, 0.86, 0.99, 0.83, and 0.99 for dyslipidemia, respectively, between self‐reports and claims data for 3 months. Kappa statistics were highest with the 3‐month reference period of claims data for hypertension, diabetes, and dyslipidemia. No major concordance was observed between the subgroups.This validation of self‐reported medication use for hypertension, diabetes, and dyslipidemia showed almost perfect reliability among employees of large‐sized companies in Japan. Information on whether an individual uses a medication often affects occupational health professionals if the employee is eligible for further interventions, such as health guidance.Medication use is generally assessed using self‐administered questionnaires,In contrast, healthcare insurance claims represent objective data that are considered the “gold standard” in identifying medication use.In this study, we examined the agreement between medication use self‐reported during annual health checkups at large‐sized Japanese companies with prescribing data from health insurance claims as an objective standard for commonly used medications for three conditions, hypertension, diabetes, and dyslipidemia. We also conducted sub‐group analyses to examine whether agreement differed by participant characteristics. To our knowledge, this is one of the largest epidemiological studies of to examine the validation of self‐reported medication use, and the first in an occupational setting in Japan.22.1We used the data from participants of the “the Collabo‐Health Study Group,” established in April 2014, an organization composed of 13 pharmaceutical or manufacturing companies, most of which are listed on the Tokyo Stock Exchange First Section, and their related health insurance unions. Details of this study are reported elsewere.For this study, we obtained data of medication use self‐reports for hypertension, diabetes, or dyslipidemia from May 1, 2016 to March 31, 2017 and pharmacy claims data from the corporate health insurance associations from April 1, 2016 to March 31, 2017. Although we obtained self‐reports from subjects whose health checkup was in April 2016, we did not include them in this study due to differences in validity between the different reference periods for claims data, as described below. For this study, 65 306 participants (50 265 men and 15 041 women) were subject to analysis. Among the study subjects, we excluded those with incomplete data for self‐reports (n = 3630). Although the participation rate of the annual health checkups is considered to be almost 100% in such companies, the questionnaire used a government‐form, which is indicated for patients over 40 years old. Sixty‐two percent of the subjects excluded were under 40 years old. Finally, a total of 61 676 participants (47 362 men and 14 314 women) remained for analysis. The mean  number of the participants of the companies was 4744 .2.2The self‐reports by questionnaire were filled in by employees at the time of the annual health checkup at each workplace. Participants were asked about their current medication use for treatment of hypertension, diabetes, or dyslipidemia , and answered “yes” or “no” to each question. If the participant answered “yes” to either of the questions, the self‐report was determined to be positive.2.3We received pharmacy claims data on medication use from the corporate health insurance associations of all participants from April 1, 2016, to March 31, 2017. Health insurance claims data had an individual code which allows merging with the health checkup data. Medications appropriate for hypertension, diabetes, and dyslipidemia were identified using the code of the National Health Insurance Drug List (NHI code), which is managed by the Ministry of Health, Labour and Welfare, Japan.2.42) was calculated from anthropometric measurement of body weight and height by medical staff. Systolic and diastolic blood pressure (mmHg) were measured by medical staff according to the standard protocol of each health checkup organizations, and if more than one measurement was taken, the lowest blood pressure value was adopted. Hemoglobin A1c (%), and low‐density lipoprotein (LDL) cholesterol was measured using fasting blood samples collected during the health checkup.Additional information collected in the questionnaire during the health checkup included sex (men or women), age (years), smoking (current smoker or not), and alcohol drinking (heavy drinker [consuming more than 46 g of ethanol per day] or not). Body mass index ; past two months, including the month of the health checkup (“2 months”); and past three months, including the month of the health checkup (“3 months”). We also defined the annual fiscal year in which the health checkup occurred (“1 year”). The validity of the self‐reports from questionnaires was assessed by comparison with prescription data from pharmacy insurance claims using the agreement rate  − ), sensitivity, specificity, and positive and negative predictive values (PPV and NPV).2), smoking (current smoker or not), alcohol drinking (heavy drinker or not), blood pressure , hemoglobin A1c , and LDL cholesterol . All analyses were performed using Statistical Analysis System Software Version 9.4 .We also conducted subgroup analyses stratified by sex (men or women), age , BMI .Informed consent: Written informed consent was obtained from each participant.Registry and the registration number of the study/trial: N/A.Animal studies: N/A.The authors have no conflicts of interest directly relevant to the content of this article.KoF, TN, and KM conceived the study; TN, KM, and MN collected the data; KoF, with support from TN and MO, analyzed the data; and KoF led the writing. All authors participated in critically reviewing the paper.


Multimodal opioid‐sparing analgesia is a key component of an enhanced recovery pathway after surgery that aims to improve postoperative recovery. Transcutaneous electrical acupoint stimulation (TEAS) is assumed to alleviate pain and anxiety and to modify the autonomic nervous system. This study aimed to determine the efficacy of TEAS for sedation and postoperative analgesia in lung cancer patients undergoing thoracoscopic pulmonary resection.A total of 80 patients were randomized into two groups: the TEAS group and the sham TEAS combined with general anesthesia group. Postoperative pain levels at six, 24, 48 hours, and one month after surgery were measured using the visual analogue scale (VAS). Bispectral index (BIS) score during the TEAS prior to anesthetic induction, Observer's Assessment of Alertness/Sedation (OAAS) score, sufentanil consumption during postoperative patient‐controlled intravenous analgesia (PCIA), number of total and effective attempts of PCIA pump use, and incidence of postoperative nausea and vomiting were recorded and analyzed statistically.P < 0.01); lower BIS scores at 10, 20, and 30 minutes before induction (P < 0.01); lower levels of postoperative sufentanil consumption; lower number of PCIA attempts and effective rates (P < 0.01); lower incidences of nausea at 0, six, 24, and 48 hours; and lower incidence of vomiting at 24 hours after surgery (P < 0.05). The postoperative OAAS scores were similar between the groups.Patients in the TEAS group had significantly lower VAS scores at six, 24, and 48 hours after surgery (TEAS could be a feasible approach for sedation and postoperative analgesia in thoracoscopic pulmonary resection. The enhanced recovery after surgery (ERAS) pathway has been widely adopted for a broad range of complex surgical procedures to improve the quality of surgical care. As an essential component of most ERAS pathways, multimodal pain management with nonopioid agents or techniques is established to minimize the use of perioperative opioids and to decrease opioid‐related adverse effects  with the goal of improving and expediting patients' recovery after surgery.Acupuncture, a noninvasive and nonpharmacological adjunctive intervention, has been widely applied for sedative and analgesic purposes.To address this question, the present study assessed the effects of TEAS on perioperative analgesia, opioid consumption, recovery, and complications after thoracoscopic pulmonary resection.http://www.chictr.org.cn) under the accession number ChiCTR1800016067.A randomized, double‐blind, placebo‐controlled study with a pre‐ and a post‐test design was conducted in accordance with the Standards for Reporting Interventions in Controlled Trials of Acupuncture recommendations.Patients undergoing VATS pulmonary resection for early‐stage lung cancer between 5 May 2015 and 1 March 2017 by a single surgeon‐anesthesiologist team (Ke‐Neng Chen and Jiheng Chen) at the First Department of Thoracic Surgery, Peking University Cancer Hospital and Institute were consecutively screened and enrolled. Inclusion criteria were as follows: patients aged 18–64 years, American Society of Anesthesiologists status I or II, patients undergoing lung operation for the first time, and no history of previous acupuncture‐drug compound anesthesia. The exclusion criteria for randomization were as follows: American Society of Anesthesiologists status III, IV, or V; recent use of TEAS or acupuncture; severe comorbidities including pre‐existing coagulopathy, hypertension (systolic pressure ≥ 180 mmHg and/or diastolic pressure ≥ 110 mmHg), cardiovascular disease, and diabetes; history of chronic pain; and history of opioid or alcohol abuse. The present study was approved by the Medical Ethics Committee of Peking University Cancer Hospital . Informed consent was obtained from each patient.All patients were informed by a designated anesthesiologist about the methods of TEAS and anesthesia one day before the surgery. All patients fasted without food and water intake before the surgery. After being transferred to the operating room, all patients received an intravenous drip of 0.04–0.06 mg/kg midazolam. The HANS‐200A TEAS device  was connected to bilateral Hegu (LI 4), Neiguan (PC 6), Houxi (SI 3), and Zhigou (TE 6) acupoints. The frequency of TEAS was 2/100 Hz. Electrodes (50 × 50 mm) were then placed over each acupoint with the pore directly over the marked acupoint. Tegaderms were placed over the electrodes and the participants' skin to fix the electrodes during intervention.Patients in the TEAS group received electrical stimulation (10–15 mA) for 30 minutes before anesthetic induction, continuous stimulation (30 mA) throughout the surgical procedure, and intermittent stimulations (10–15 mA) for 30 minutes each at six, 24, and 48 hours after the surgery. Patients in the sham‐TEAS group received 4 mA stimulation before and after the surgery in the same manner as mentioned above. No electrical stimulation was applied during the surgery for patients in the sham‐TEAS group. The sensory threshold of electrical stimulation was around 5 mA.After 30 minutes of TEAS or sham‐TEAS, intravenous induction was started with 0.3–0.5 μg/kg sufentanil, 2 mg/kg propofol, and 0.6–1 mg/kg rocuronium bromide. Patients were intubated with a double‐lumen tube at an appropriate depth. Mechanical ventilation was started after the tube position was confirmed by auscultation and fiberoptic bronchoscopy was performed to reconfirm the tube position. During the procedure, 60% oxygen was maintained with a flow rate of 2 L/min, tidal volume of 6–8 mL/kg, and respiratory rate of 12–15 breaths/minute. Propofol and sufentanil were infused continuously, while rocuronium bromide was administered intermittently for anesthetic maintenance. The bispectral index (BIS) was monitored continuously with a multifunctional electrocardiogram monitor during the surgery. Target‐controlled infusion of propofol was performed and the infusion rate of propofol was adjusted based on the BIS (score 40–60). For the target‐controlled infusion of sufentanil, the infusion rate was adjusted based on hemodynamic parameters. At 30 minutes before the end of the procedure, 10 μg sufentanil was administered for analgesia transition. A patient‐controlled intravenous analgesia (PCIA) pump was connected .The primary endpoint was postoperative visual analogue scale (VAS) scores at six, 24, and 48 hours after surgery and secondary endpoints were BIS scores during the TEAS prior to anesthetic induction, Observer's Assessment of Alertness/Sedation (OAAS) score, sufentanil consumption during postoperative PCIA, number of total and effective PCIA pump attempts, incidence of postoperative nausea and vomiting (PONV), and VAS scores at one month after the surgery.α) of 0.05 and a power of 80%. To account for potential loss to the follow‐up and to enable greater statistical power for secondary analyses, the sample size was increased to 80 patients (40 patients per group).We calculated that 37 patients were required for each group to detect a 25% decrease between the groups, assuming a two‐sided type I error . Continuous variables were presented as mean ± standard deviation and were compared using unpaired Student's Complete datasets were collected for all patients and the data were analyzed Fig . PatientP < 0.001; 24 hours: 2.91 ± 0.2 vs. 5.97 ± 0.56, respectively, P < 0.001; and 48 hours: 1.69 ± 0.66 vs. 2.92 ± 0.38, respectively, P < 0.001).The changes in VAS are depicted in Fig P = 0.07). The BIS scores at 10, 20, and 30 minutes after preinduction TEAS were significantly lower in the TEAS group than those in the sham‐TEAS group  .The cumulative changes in sufentanil consumption during PCIA after surgery are depicted in Fig P < 0.001 and mean effective attempts: 4.58 ± 0.93 vs. 7.48 ± 1.18, respectively, P < 0.001)  , as sample size estimation indicated that 37 patients were required in each group to detect a mean difference of 25% for a study with a type I error of 5% and a type II error of 20%. A 10 mm change in the VAS scores for pain  has previously been found to signify a clinically important difference for acute postoperative pain.We performed a randomized, double‐blind, and placebo‐controlled trial to reduce the probability of bias. We believe that our study was adequately powered to analyze the primary endpoint (difference in the intensity of postoperative pain between the groups compared using the paired Lung surgery guidelines strongly recommend opioid‐sparing analgesia and nausea and vomiting control to improve the quality of ERAS.It is well known that the analgesic mechanism of acupuncture is related to endogenous opioid system activation.There are some limitations to the present study. Since the study had a relatively small sample and a single institutional experience, a prospective multicenter study on TEAS is warranted. Moreover, we were able to assess pain and related outcomes only in the first month after the surgery. A study that follows patients over a period of a few months may yield a more accurate profile of postoperative pain trajectories in this population.In conclusion, the current study demonstrated an association between TEAS use and decreased postoperative pain, decreased use of analgesics, and decreased PONV risk in patients undergoing minimally invasive lung cancer surgery. We recommend this noninvasive and nonpharmacological modality for perioperative analgesia.No authors report any conflict of interest.


Hospital integration among rural districts to concentrate medical resources is one of the main projects of the Japanese government. In advance of this, we experienced hospital integration and screening for chronic kidney disease as definitive risk for end‐stage kidney disease and cardiovascular mortality. After that, high‐risk patients have been appropriately referred from generalists to nephrologists and/or cardiologists without acute deterioration of renal function or cardiac sudden death. Consequently, the concentration of medical resources among such districts is recognized as a major project of the government, which recently designated hospitals for integration.Chronic kidney disease (CKD) is a definitive and common risk factor for not only development of ESKD and requiring chronic dialysis, but also cardiovascular and all‐cause mortality.2 to more than 30 mL/min/1.73 m2. If a subject did not have AKI, he or she was regarded as possibly having CKD.Along with hospital integration in April 2019, we developed a screening system using electronic medical records and establish an algorithm to detect CKD. Among retrospectively obtained data from April 2018 to December 2019, 61 588 creatinine measurements and 40 973 dipstick proteinuria tests were performed in 12 335 men and 9756 women with a mean age of 48 ± 23 (±SD) years. Each participant's worst results for estimated glomerular filtration rate (eGFR) and proteinuria were evaluated. The sequential data from individual patients sometimes contained transient elevations of serum creatinine. CKD is diagnosed based on reduced eGFR persisting for 3 months.2 that is CKD stage 4 or worse  and publicly registered at UMIN‐CTR (#UMIN000039020). Serum creatinine levels were measured as part of routine clinical practice and health check‐ups in Kamisu Saiseikai Hospital, not specifically for this clinical research.1https://www.mhlw.go.jp/content/10800000/000551037.pdf. Accessed June 1, 2020.Website Available from: 2BelloAK, HemmelgarnB, LinM et al Impact of remote location on quality care delivery and relationships to adverse health outcomes in patients with diabetes and chronic kidney disease. Nephrol Dial Transplant. 2012;27(10):3849–55.227593853GoAS, ChertowGM, FanD, McCullochCE, HsuCY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351(13):1296–305.153856564LeveyAS, de JongPE, CoreshJ et al The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report. Kidney Int. 2011;80(1):17–28.211508735ChenTK, KnicelyDH, GramsME. Chronic kidney disease diagnosis and management: a review. JAMA. 2019;322(13):1294–304.31573641Figure S1Click here for additional data file.Supplementary MaterialClick here for additional data file.


Several subgroup analyses were conducted to explore the impact of potential confounders. Overall, 27 studies were eligible. The pooled analyses showed nonsignificant associations between ACEIs/ARBs and death , ICU admission , death/ICU admission , risk of COVID‐19 infection , severe infection , and hospitalization . However, the subgroup analyses indicated significant association between ACEIs/ARBs and hospitalization among USA studies , peer‐reviewed , good quality and studies which reported adjusted measure of effect . Significant differences were found between ACEIs and ARBs with the latter being significantly associated with lower risk of acquiring COVID‐19 infection . In conclusion, high‐quality evidence exists for the effect of ACEIs/ARBs on some COVID‐19 clinical outcomes. For the first time, we provided evidence, albeit of low quality, on interclass differences between ACEIs and ARBs for some of the reported clinical outcomes.Conflicting evidence exists about the effect of angiotensin‐converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs) on COVID‐19 clinical outcomes. We aimed to provide a comprehensive/updated evaluation of the effect of ACEIs/ARBs on COVID‐19‐related clinical outcomes, including exploration of interclass differences between ACEIs and ARBs, using a systematic review/meta‐analysis approach conducted in Medline (OVID), Embase, Scopus, Cochrane library, and medRxiv from inception to 22 May 2020. English studies that evaluated the effect of ACEIs/ARBs among patients with COVID‐19 were included. Studies’ quality was appraised using the Newcastle‐Ottawa Scale. Data were analyzed using the random‐effects modeling stratified by exposure . Heterogeneiity was assessed using I ACE2angiotensin‐converting enzyme 2ACEIsangiotensin‐converting enzyme inhibitorsARBsangiotensin receptor blockers1RATangiotensin receptor 1CVDcardiovascular disease1angiotensin‐converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) might increase susceptibility to COVID‐19 infection and the likelihood of severe and fatal COVID‐19 illness.angiotensin‐converting enzyme 2 (ACE2), an enzyme potentially upregulated by ACEIs/ARBs use, is the viral entry receptor that COVID‐19 uses to enter lung cell,Soon after the report of first clusters of COVID‐19 cases in China in December 2019, concerns were raised among clinicians and investigators that Unsurprisingly, discussions regarding the potential impact of ACEIs/ ARBs have resulted in anxiety, which might cause patients and clinicians to discontinue or stop these medications.We are aware that several observational studies have been conducted to address these concerns. However, these studies have reported conflicting findings which is a concern given the controversies with hydroxychloroquine and lopinavir/ritonavir. For instance, some studies22.1This systematic review and meta‐analysis was conducted and reported in accordance to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement checklist.2.2®Eligibility criteria included original research studies, published in English, with COVID‐19 patients (target population) that reported the effects of ACEIs/ARBs (intervention), in comparison with non‐ACEIs/ARBs use (comparison), on COVID‐19‐related outcomes. No restrictions were placed on the reported outcomes or study types. All records identified from the search strategy were exported from the databases and imported into Covidence2.3Data from the eligible studies were subsequently extracted by two authors  into a spreadsheet including information on the study characteristics  and outcome measures including death, intensive care unit (ICU) admission, risk of COVID‐19 infection, severe COVID‐19 infection, severe pneumonia, hospitalization, hospital discharge, use of ventilators, duration of hospital stay, septic shock, acute kidney injury, cardiac injury, and hospital readmission. Since the need for using ventilators typically necessitates ICU admission, we combined studies that reported ICU admission and ventilator use as a further composite outcome measure. Two authors (NA and LA) independently conducted the assessment of risk of bias using the Newcastle‐Ottawa Scale (NOS) for nonrandomized studies which consists of three domains (selection of participants and control (if applicable), comparability and exposure or outcome),2.4I2 statistic,I2 values were set to zero, hence I2 values ranged between 0% and 100% with 0% indicating lack of heterogeneity, whereas 25%, 50%, and 75% indicating low, moderate and high heterogeneity, respectively.For each study outcome that was reported by more than one study, the results from individual studies were combined statistically using the random‐effects meta‐analysis model, stratified by the level of exposure ; whereas for outcomes which were reported by only one study, narrative synthesis was used. For studies which did not report the summary statistics and measure of effects, we firstly used the reported primary statistics (number of patients with/without the outcomes in both exposed/unexposed group) to calculate the corresponding measure of effects (Odds ratios‐ OR) and their 95% confidence interval (95%CI),2.5None.33.1The literature search identified 452 articles. However, only 27 studies were eligible for inclusion Figure . A totalIn terms of outcomes, nine studies (33.3%) reported three to five COVID‐19‐related outcomes,The majority of the 27 eligible studies were conducted in Asia , followed by nine studies (33.3%) from Europe  and the remaining six (22.3%) from the USA. Furthermore, the reported measure of effects were crude/unadjusted measures in the majority of the studies 3.23.2.1Among pertinent studies, there was insignificant association between mortality and ACEIs/ARBs , ACEIs , or ARBs  Figure , regardl3.2.2The overall pooled analysis of nine studies indicated insignificant association between the risk of acquiring COVID‐19 infection and the use of ACEIs/ARBs , ACEIs , or ARBs  Figure ; Table 43.2.3In a pooled analysis of eight and three studies, there was no signification association between hospitalization, hospital discharge rate and ACEIs/ARBs , ACEIs  or ARBs  Figure . However3.2.4Among pertinent studies, there was no significant association between these outcomes and the use of ACEIs/ARBs , ACEIs , or ARBs  Figures ; Table 33.2.5Zhang et al3.3P = .445).Results from the funnel plot File  and Egge4The pooled analyses in this updated systematic review and meta‐analysis indicated no evidence of any significant association between ACEIs/ARBs and any COVID‐19 related clinical outcomes; however, the subgroup analyses revealed evidence of a negative impact of ACEIs/ARBs use and some COVID‐19‐related clinical outcomes such as higher odds of hospitalization, ICU admission and ventilator use. Contrastingly, a positive impact were observed in terms of lower odds of death/ICU admission, as a composite outcome, and a higher rate of hospital discharge. Furthermore, our study findings, for the first time, showed interclass variations between ACEIs and ARBs effects on COVID‐19 clinical outcomes with low‐quality evidence indicating lower risk of acquiring COVID‐19, less severe COVID‐19 infection, higher rate of ICU admission and ventilator use with ARBs but not ACEIs.Our study findings also showed no significant association between ACEIs/ARBs and mortality, severe COVID‐19 infection, or positive tests for COVID‐19, in agreement with two previously published systematic reviews.On the other hand, our study findings showed some high‐quality evidence on the association of ACEIs/ARBs and higher odds of hospitalization but lower odds of death/ICU admission (as a composite endpoint). A higher odd of hospitalization was observed in the subgroup analyses of studies conducted in the USAangiotensin II‐ AT1R axis, which gets activated by impairment of ACE2 activity as a result of ACE2’s downregulation results from ACE2’s binding with COVID‐19 virus; additionally, the corresponding increase in angiotensin II and angiotensin I, due to ACEIs/ARBs use, would activate the protective axis and hence reduce COVID‐19 viral pathogenicity.Several hypotheses have been suggested to explain the negative and positive effects of ACEIs/ARBs use on COVID‐19 clinical outcomes. The former is thought to be related to ACEIs/ARBs potential ability to upregulate ACE2, the cell entry point for COVID‐19; hence facilitate COVID‐19 cell entry and its subsequent infectivity/pathogenicity4.1We believe this study is the first to provide a systematic, comprehensive and updated evaluation of the effects of ACEIs/ARBs on all the reported COVID‐19‐related clinical outcomes including exploration of interclass differences between ACEIs and ARBs as well as multiple subgroup analyses, although we do acknowledge that some of the subgroup analyses only had 1‐2 studies for some of the studied outcomes such as ICU admission and Death/ICU admission. However, our study has limitations. Since all included studies were observational studies, the effect of confounding including residual confounders cannot be ruled out. There is also the possibility that new studies have been published since our review. However, we included nonpeer‐reviewed articles published in medRxiv to help address this.5There appears to be no evidence of association between ACEIs/ARBs use and a wide range of COVID‐19‐related clinical outcomes. However, good quality evidence exists for ACEIs/ARBs and higher odds of hospitalization, lower odds of death/ICU admission (as composite endpoint); but only low‐quality evidence for higher ICU admission, ventilator use, hospital discharge and lower duration of hospital stay exists. Furthermore, there is evidence, albeit of poor quality, of differences between ACEIs and ARBs with the latter being associated with significantly higher ICU admission but lower COVID‐19 infection risk and severity. Given the continuing controversial and paradoxical clinical studies’ findings and hypotheses, we believe it is necessary to continue to evaluate the effects of ACEIs/ARBs on COVID‐19 clinical outcomes especially as more randomized studies are reported.6http://www.guidetopharmacology.org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY,Key protein targets and ligands in this article are hyperlinked to corresponding entries in Not required.Nothing to declare.Study conception and design: all authors; data collection and management: NA, AL; data analysis and interpretation: AK, BG; manuscript writing and drafting: AK, NA; manuscript reviewing and revising as well as providing constructive criticism and final approval: all authors.File S1Click here for additional data file.File S2Click here for additional data file.File S3Click here for additional data file.File S4Click here for additional data file.File S5AClick here for additional data file.File S5BClick here for additional data file.File S5CClick here for additional data file.File S6Click here for additional data file.File S7Click here for additional data file.File S8Click here for additional data file.File S9Click here for additional data file.File S10Click here for additional data file.


Primary Care Plus (PC+) focuses on the substitution of hospital‐based medical care to the primary care setting without moving hospital facilities. The aim of this study was to examine whether population health and experience of care in PC+ could be maintained. Therefore, health‐related quality of life (HRQoL) and experienced quality of care from a patient perspective were compared between patients referred to PC+ and to hospital‐based outpatient care (HBOC).This cohort study included patients from a Dutch region, visiting PC+ or HBOC between December 2014 and April 2018. With patient questionnaires , the HRQoL and experience of care were measured. One‐to‐two nearest neighbour calliper propensity score matching (PSM) was used to control for potential selection bias. Outcomes were compared using marginal linear models and Pearson chi‐square tests.P > .05), indicating no difference in HRQoL development between the groups over time. Regarding experienced quality of care, no differences were found between PC+ and HBOC patients. Only travel time was significantly shorter in the HBOC group (P ≤ .001).One thousand one hundred thirteen PC+ patients were matched to 606 HBOC patients with well‐balanced baseline characteristics (SMDs <0.1). Regarding HRQoL outcomes, no significant interaction terms between time and group were found (Results show equal effects on HRQoL outcomes over time between the groups. Regarding experienced quality of care, only differences in travel time were found. Taken as a whole, population health and quality of care were maintained with PC+ and future research should focus more on cost‐related outcomes. Therefore, the health‐related quality of life (HRQoL) and the experienced quality of care from the patient's perspective are compared between patients referred to PC+ and patients referred directly to hospital‐based outpatient care (HBOC).22.1This cohort study compared patient‐reported HRQoL and the experienced quality of care between patients referred to PC+ and patients referred to HBOC using propensity score matching (PSM). The reporting of this study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.The study is approved by the Medical Research and Ethics Committee of the Maastricht University Medical Centre (METC 14‐4‐136). Informed consent was obtained from all individual participants included in the study.2.2In the Maastricht‐Heuvelland region, located in the southern Netherlands, the primary care organization Care in Development (in Dutch “Zorg in ontwikkeling”), Maastricht University Medical Centre + (Maastricht UMC+), health insurance company VGZ and patient representative foundation “Burgerkracht Limburg” collaborate. In 2014, these organizations developed the PC+ intervention to substitute hospital‐based specialized care with primary care whereby GPs remain responsible for the patient. With two PC+ centres operating according to the same method, GPs within the region are able to refer non‐acute and low‐complex patients to a medical specialist in a neutral primary care setting. Based on the PC+ patients' profiles , GPs' clinical expertise and shared decision‐making, GPs decide whether to refer a patient to PC+. In PC+, the medical specialist examines and/or treats the patient during a maximum of two consultations. Following PC+, the medical specialist refers the patient back to the GP with treatment advice, or, if necessary, refers the patient to HBOC for further diagnosis and/or treatment. Involved specialists are employed in the Maastricht UMC+ and perform PC+ consultations on a regular basis (weekly or biweekly). Like the Maastricht UMC+, the PC+ centres are both located in the city of Maastricht.Besides the assumed benefits of PC+ being more informal and located closer to patients' homes, patients are exempt from paying a mandatory deductible for a consultation. In the Netherlands, GP consultations are fully covered by health insurance but for consulting a medical specialist, a yearly mandatory deductible is levied (€360 in 2014 and €385 since 2016).2.3In 2016, The Maastricht‐Heuvelland region consisted of 55 GP practices caring for a population of about 170 000 people.2.4After referral to PC+ or HBOC by the GP, all eligible patients were recruited by the Transmural Interactive Patient Platform (TIPP) for participation. TIPP plans and registers referrals to medical specialists in either PC+ or HBOC. TIPP informed patients about the study, and if interested, patients' contact details were sent to the research team. The research team then sent an information letter, informed consent and the first questionnaire (T0) to the patient by post or email. Patients were asked to return the informed consent and the questionnaire before the first consultation with the medical specialist. After the first consultation, a second questionnaire was sent within 1 week (T1) and a third questionnaire after 3 months (T2). The inclusion of patients started in December 2014 and continued until April 2018.2.52.5.1Baseline characteristics were collected during T0, including age in years, gender, native country and level of education (low vs medium vs high) Figure . Collect2.5.2To measure generic HRQoL, the EuroQol five‐dimensional questionnaire with five levels (EQ‐5D‐5L), including the EuroQol Visual Analogue Scale (EQ‐VAS) and the Short‐Form Health Survey version 2 (SF‐12v2) were used. Patients' perceptions of a change in their health status was evaluated with the Patient Global Impression of Change (PGIC) seven‐item response scale.The EQ‐5D‐5L is a measure consisting of five questions on mobility, self‐care, pain/discomfort, usual activities and anxiety/depression with five response levels.The SF‐12v2 consists of 12 questions measuring the health status by means of two summary scores; a physical component summary (PCS) and a mental component summary (MCS).With the PGIC scale, patients were able to indicate to what extent their health problem had changed after they consulted the medical specialist, ranging from 1 (very much improved) to 7 (very much worse).2.5.3An influential and often used framework to measure quality of care is that of the Institute of Medicine, stating care must be safe, effective, patient‐centred, timely, efficient and equitable.2.62.6.1Non‐response analysis was performed by comparing respondents with non‐respondents at baseline by patient age, gender and the medical specialty referred to.2.6.2P‐values and standardized mean differences (SMDs). SMDs of <0.1 and P‐values of >.05 indicate minor differences in the mean of a covariate between the two groups and were used to assess the success of matching.Since in this cohort study patients were not‐randomly allocated to treatment, patients being referred to PC+ were expected to differ on covariates to those referred to HBOC.2.6.3The overlap in the distribution of the PS and the balance of baseline variables before and after matching between the PC+ and HBOC groups were described.P‐values were reported. P‐values <.05 were considered as significant. This method takes into account incomplete follow‐up data without any imputation of missing values, and provides valid estimates of treatment effects under the assumption that such data are missing at random.Marginal linear models with an unstructured error covariance structure were applied to analyse the mean change in HRQoL outcome measures. Estimates, standard errors (SEs), 95% confidence intervals (CIs) and P‐values were reported. Additionally, independent t‐tests were used to analyse continuous items; 95% CIs and P‐values were reported. To correct for multiple testing (Type 1 error) the Bonferroni correction was used, whereby the P‐value of .05 was divided by the number of tests.Patients' experiences of care related items were dichotomized before analysing and summarized as “satisfied” vs “unsatisfied” or “yes” vs “no.” Hereafter, they were analysed using Pearson chi‐square tests; counts, percentages and Before taking into account the influence of the PS on the HRQoL and experienced quality of care outcome measures using PSM, the uncorrected effect of “study group” was analysed, with “study group” (PC+ vs HBOC) as the only independent variable.R Studio was used for statistical analyses .2.6.4Baseline characteristics, HRQoL and experience of care outcomes before and after PSM were compared between PC+ and HBOC patients separately for the nine different medical specialties using the same analyses as described above.2.6.5Sensitivity analyses were undertaken to assess the robustness of the results.33.1Figure The characteristics of the 2898 responders and 2637 non‐responders are attached in the Table 3.2Before PSM, the PS for the PC+ group ranged between 0.08 and 0.73; for the HBOC group, the PS ranged between 0.09 and 0.78  (Table P > .05). Furthermore, the EQ‐5D‐5L scores significantly increased over time (T1 and T2) compared to the baseline score before and after PSM (P < .01 or P < .001). Finally, after PSM, the interaction terms between time and group were no longer significant, indicating no difference in the development of EQ‐5D‐5L scores between the groups over time (P > .05).Before PSM, the EQ‐5D‐5L baseline score was significantly lower in the HBOC group (1) Table . After PP < .01). After PSM, the difference at baseline was no longer significant (P > .05). Furthermore, EQ‐VAS scores significantly increased at T1 compared to the baseline score before and after PSM (P < .01). However, no significant interaction terms between time and group were found before and after PSM, indicating no difference in the development of EQ‐VAS scores between the groups over time (P > .05).Regarding EQ‐VAS outcomes, before PSM, the baseline score was significantly lower in the HBOC group compared to the PC+ group . After PSM, the differences at baseline were no longer significant (P > .05). Furthermore, before and after PSM, the PCS score at T2 was significantly higher compared to the baseline score. However, for both PCS and MCS, no significant interaction terms were found before and after PSM, indicating no difference in the development of the PCS and MCS scores between the groups over time (P > .05).Regarding SF12v2 scores, before PSM, the PCS and MCS baseline scores were significantly lower in the HBOC group (P > .05). At T2, the PGIC score was significantly lower compared to the score at T1, both before and after PSM . However, no significant interaction terms between time and group were found before and after PSM, indicating no difference in the development of the PGIC score between the groups over time (P > .05).Finally, the PGIC score at T1 did not differ between the PC+ and HBOC groups  including the 21 items of the CQ‐index. (n = 1741 PC+ group and n = 624 HBOC group). After PSM, 1681 patients were included in the analysis (n = 1094 PC+ group and n = 587 HBOC group).One item in the domain of “communication and collaboration between the GP and medical specialist” was excluded from analysis because of high non‐response before (13.5%) and after (13.1%) PSM. Although, only 1230 patients before and 900 patients after PSM completed the item “shared decision‐making,” this item was not excluded since a high number of patients answered “not applicable.” This was the only item in the questionnaire with this answering option. Including the option “not applicable,” 2320 patients (98.1%) completed this item before PSM and 1659 patients (98.7%) after PSM.P ≤ .001) and they gave significantly higher grades to the medical specialist and the PC+ location they visited  . Furthermore, before PSM, the travel time to the PC+ or HBOC location was significantly shorter in the PC+ group (P ≤ .001). However, after PSM, the travel time was significantly shorter in the HBOC group (P ≤ .001).Before PSM, PC+ patients significantly more often had a waiting time in the waiting room of less than 30 minutes .After one‐to‐one PSM, the PS for the PC+ group ranged between 0.09 and 0.73; for the HBOC group, the PS ranged between 0.10 and 0.74  with block randomization could be a useful technique to achieve balance in the allocation of patients to subgroups and therewith reduce bias.There are several limitations to this study. Although PSM permits a more objective analysis by balancing the study groups with respect to confounders, it only allows for adjustment of measured confounders.In conclusion, this study found equal results on HRQoL and experienced quality of care outcomes between patients referred to PC+ and HBOC. Therefore, it can be concluded that, despite the lack of diagnostic tools, population health and quality of care are maintained in PC+. In future research, there should be more emphasis on cost comparison for patients and for the total health system to demonstrate the potential added value of PC+.The authors declare no conflict of interest.TABLE S1. Baseline Characteristics of Responders and Non‐Responders.Click here for additional data file.TABLE S2. Baseline Characteristics Before and After Propensity Score Matching per Medical Specialty.Click here for additional data file.TABLE S3. Health‐Related Quality of Life Outcomes Before and After Propensity Score Matching per Medical Specialty.Click here for additional data file.TABLE S4. Comparison of Patient Experienced Quality of Care Outcomes Before and After Propensity Score Matching per Medical Specialty.Click here for additional data file.TABLE S5. Baseline Characteristics Before and After Propensity Score Matching.Click here for additional data file.TABLE S6. Health‐Related Quality of Life Outcomes Before and After Propensity Score Matching.Click here for additional data file.TABLE S7. Comparison of Patient Experienced Quality of Care Outcomes Before and After Propensity Score Matching.Click here for additional data file.FIGURE S1. Uncorrected and corrected health‐related quality of life outcomes.Click here for additional data file.FIGURE S2. Overlap of the Propensity Score in the Two Study Groups.Click here for additional data file.