Attendance in special education (SE) is more common among individuals born preterm than among those born at term. Less is known about school grades of those born preterm in mainstream education (ME), and how these grades predict later educational attainment. This population-based register-linkage study assessed (1) attendance in SE, and then focused on those in ME by assessing (2) school grades at 16 year, (3) completed educational level at 25 year, and (4) school grades as predictors for completed education by gestational age (GA) with full-term birth (39–41 completed weeks) as reference. The sample comprised 223,744 individuals  born in Finland (1/1987–9/1990). Of the sample, 4.9% attended SE. Those born preterm had up to 5.5-fold rates for SE. In ME, those born extremely preterm (EPT) had marginally lower mathematics grades compared with full-term counterparts, whilst those born late preterm or early term had slightly higher grades. Those born EPT or very preterm had lower physical education grades in ME. However, the minor differences in school grades according to GA appear not to translate into educational differences in young adulthood. The associations between school grades at 16 year and completed education at 25 year did not vary by GA. Even more are born at 37–38 weeks (early term): they amount to 16–31% in high-income countries3. Both preterm and early term births impact disease risk and mortality6, and cause considerable population-level economic consequences8. For example, those born before 32 weeks of gestation, or at very  or extremely low-birthweight (< 1000 g) have on average lower educational attainments in childhood14, adolescence16, and young adulthood20 compared with those born at term. However, some contradicting results have been reported among those born very preterm21. When preterm birth is treated as a homogenous group, the study findings of three meta-analyses24 supplemented with two individual studies assessing those born at less than 37 weeks’ gestation26 are similar; individuals born preterm fare worse than their term born counterparts. Additionally, those born preterm30 or at low birth weight16 have more likely special educational needs, as do also moderately- or late preterm33, and even early term34 born individuals.Annually 14.9 million live-born infants worldwide are born preterm (before 37 weeks’ gestation)37 and adulthood40 shows a trend for lower education with declining GA. However, we do not know whether this trend is explained by those who have special educational needs, or whether it is present also among those who attend mainstream education. Moreover, we know relatively little about whether school performance differ by school subject41, and little is known about how school grades predict educational attainment in adulthood. Hence, we had four main aims in assessing the later educational outcomes among those born preterm or early term. We (1) examined whether attending special education varied by GA. We then focused on those attending mainstream education and assessed (2) whether school grades at 16 years and (3) completed educational levels at 25 years varied according to GA. Finally, we examined (4) whether the associations between school grades at 16 years and completed education at 25 years varied by GA.The association between the whole continuum of gestational age (GA) and educational attainments at adolescence43. Individual level register-linkages were done by encrypted personal identity codes (ePIC). The registered persons were not contacted. Based on Finnish and EU legislation, individual consents are not required in research based solely on analyzing pseudonymized register data if the registered persons are not contacted. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa and applicable register authorities approved the study protocol which included the use of register data without need for individual consent, as allowed by Finnish and EU legislation. The study was conducted in accordance with the Declaration of Helsinki.The data sources include six nationwide administrative registers; (1) Finnish Medical Birth Register (MBR), (2) Central Population Register , (3) Register of Congenital Malformations , (4) The Finnish Care Register for Health Care , (5) Statistics Finland’s registers (through December 2015) , and data on completed education (through December 2015), and (6) from the registers of the Social Insurance Institution of Finland . These registers and their validity are described in Supplementary Methods and elsewhereWe identified from the MBR 235,624 index children with a valid PIC  born in Finland between January 1, 1987, and September 30, 1990. After exclusions of a maximum of 26,909 (11.4%) individuals as illustrated in Fig. GA was assessed according to the best clinical estimate  and categorized as follows: extremely preterm; 23–27 full weeks, very preterm; 28–31 weeks, moderately preterm; 32–33 weeks, late preterm; 34–36 weeks, early term; 37–38 weeks, full-term; 39–41 weeks (reference), and post term; 42 weeks. In sensitivity analyses and in the analyses assessing the role of school grades on completed educational level the extremely, very, and moderately preterm groups were combined into ‘early preterm’ group.The covariates were added to the final model as four separate models built on each other. They included information on sex, parental ages, year of birth, parental highest attained educations, smoking in pregnancy, marital status at childbirth, birth order, the birth-weight-standard-deviation score (BWSDS), gestational disorder(s), and child severe medical condition (SMC).The amount of missing data within the covariates included to the models was 0.0% for maternal age (n = 1), 0.0% for birth order (n = 13), 0.0% (n = 14) for maternal education, 0.4% (n = 997) for maternal marital status at childbirth, 1.1% (n = 2530) for paternal age, 1.1% for paternal education (n = 2530), and 1.9% (n = 4253) for maternal smoking in pregnancy. For categorical dummy-coded covariates missing data was coded to a separate category within each covariate, except missing maternal education (n = 14), missing maternal age (n = 1), and missing birth order (n = 13) which were coded to ‘basic or unknown education only’ category, the ‘20–34 year’ category, and to the ‘not first born’ category. Concerning the only continuous covariate, BWSDS, there were no missing values in the analyses.vs. upper secondary less than tertiary vs. lower tertiary or more), paternal highest attained education , (3) smoking in pregnancy (yes vs. no vs. unknown smoking status), marital status at childbirth (married vs. unmarried vs. unknown), the birth order , the BWSDS (continuous), gestational disorder(s) , and (4) severe medical condition (yes vs. no).The covariates were categorized or treated as continuous variable in the four models (1–4) as follows; (1) the sex , maternal age (< 20 year vs. 20–34 year vs. ≥ 35 year) and paternal age (< 20 year vs. 20–34 year vs. ≥ 35 year vs. unknown), the year of birth (1987 vs. 1988 vs. 1989 vs. 1990), (2) maternal highest attained education  based on the birth dates of maternal previous live-born children . Data on disability allowances granted due to specific disability/disabilities or chronic illness originated from the register maintained by SII. Based on that data we formulated an aggregate variable reflecting the severe medical condition of the individual (SMC) before 16 years of age. This variable was computed following Moster et al.39 Major or other medical conditions were considered as present if the index child had received monetary benefit from the SII due to specific disability/disabilities or chronic illness presented in Supplementary Table The data on parental education, paternal age, and the birth dates on such siblings that shared the same mother with the individual included to the study originated from CPR, and were missing for such individuals who have no registered father in the CPR , or lacked a valid maternal personal identity code  respectively. As birth order appear to have an association with one’s intelligence and educational attainmentsbirth weight more than − 2 SDs below the mean/ birth weight more than − 2 SDs) were derived by employing current national growth charts by Sankilampi et al.44 BWSDS as a continuous variable was included in the analysis as a proxy for fetal growth restriction, which appear to influence cognition and school performance48. Data on maternal pregnancy disorders were also drawn from the MBR and supplemented with data drawn from Finnish Care Register for Health Care (CRHC). A maternal pregnancy disorder was considered as present if any of the following three diagnoses appeared within the MBR or CRHC during the index pregnancy : (1) gestational diabetes; International Classification of Diseases, 8th Revision (ICD-8) code 761.10, and International Classification of Diseases, 9th Revision (ICD-9) codes 6480A and 6488A, (2) maternal hypertensive disorder; ICD-8 codes 637.01 and 637.03–637.99, or ICD-9 codes 6420X-6429C, or (3) intrahepatic cholestasis of pregnancy; ICD-8 codes 639.00–639.09, or ICD-9 codes 6467A and 6467X42. We chose to include gestational diabetes, maternal hypertensive disorder, and intrahepatic cholestasis of pregnancy to this composite variable as these conditions are the most common such pregnancy related conditions that are treated and diagnosed at in-hospital specialty care and that are therefore derivable also from the CRCH allowing us to supplement the data originating from MBR.Information on maternal age and marital status at the birth of the child, as well as data on maternal smoking in pregnancy came from the Medical Birth Register (MBR). The information on smoking during index pregnancy is recorded to MBR as it was self-reported at the first antenatal clinic appointment, which usually occurs at 9–10 completed weeks of gestation. The MBR served also as a source for the birth weight and dichotomous information on the sex of the individual. From the data on birth weight combined with the GA and the sex of the infant, the variables on the birth weight standard deviation score, BWSDS, (continuous) and on the smallness for GA status  principally consists of a 9-year compulsory education between 7 and 16 years of age, after which a basic education diploma provides assessments on all school subjects (grading from 4 (fail) to 10 (excellent)). Completing compulsory education later than at 16 years may indicate later school start, longer preschool, repetition of school year, or voluntary additional basic education. The gradings are based on nationally defined criteria but not on standard tests and serve as selection criteria for further education. Of all gradings, the grade point average of all theoretical school subjects  is the selection criterion most often applied by the educational institutions.Compulsory education is divided into mainstream education and special education (see Supplementary Methods). In special education individual assessment criteria are applied, hence individual grades are non-comparable. Moreover, attending special education may affect eligibility to certain post-compulsory education. In our work an individual was considered to have attended mainstream education if he/she had a diploma indicating no participation in special education in any of the school subjects.The outcomes were: (1) the proportions of individuals in mainstream education, special education, and discontinued compulsory education ; (2) school grades in mainstream education on mathematics, native language , physical education, and the grade point average of all theoretical school subjects; (3) completed educational level at 25 years of age; and (4) the role of school grades on predicting completed educational level at 25 years.49. We trichotomized the highest completed educational level at 25 years according to ISCED ; (1) basic or unknown education only; ISCED level < 3, “low”, or (2) upper secondary, less than tertiary; ISCED 3–5, “intermediate” or (3) lower tertiary or more; ISCED 6–8), “high”, and assessed it by employing multinomial logistic regression models by having intermediate education as a reference.The only continuous variable in our work was birth weight standard deviation score (BWSDS), and its normality was checked by visual inspection i.e., plotting the data for detection of possible non-central or skewed distributions. No major departure from normality was observable. The proportions of individuals in mainstream education, special education and discontinued compulsory education were assessed by multinomial logistic regression, and school grades by ordinary linear regression modelsP values from comparisons of interaction and main-effects-only models were estimated.Several separate regression models were employed when assessing the role of school grades on completed educational level at 25 year. Individuals included in the study were followed up from 16 years until the end of the year they reached the age of 25 years. School grades in three groups  served as potential moderators for the association between GA and education at 25 year , by having full-term category and grade category 7–8 as reference. Multinomial regression models provided Odds Ratios (OR) with 95% confidence intervals (CI). The ORs were considered to differ from 1.0 in a statistically significant manner if the 95% CI did not include 1.0. Interaction In sensitivity analyses we included only the first child of each mother born during the recruiting period.SPSS 27 was the statistical software.Tables In mainstream education 94.9% of the students completed compulsory education within the same year they turned 16 year. Those born preterm completed compulsory education more often at 17 years of age; 7.0% vs. 3.9% in total cohort.P = 0.314)  among extremely preterm individuals to 1.45 (95% CI 1.33–1.59) for those born late preterm and 1.15 (95% CI 1.09–1.21) for those born early term. The ORs for special education in all GA categories attenuated only slightly after adjustment for other covariates than severe medical condition , but GA was not. In models adjusted for all the covariates those born late preterm had slightly lower risk for discontinued compulsory education ) less likely to attain high education at 25 year than those born full-term. In such an unadjusted model the odds were also lower for those born late preterm (OR 0.94 (95% CI 0.89–1.00)). For other preterm GA categories, there were no differences in attaining high education according to the unadjusted model. In the fully adjusted model there were no differences between any of the preterm GA categories and attainment of high education. Those born moderately preterm had, however, an OR of 0.75 (95% CI 0.60–0.94) for low education at 25 year in a fully adjusted model. For other preterm GA categories, there were no differences in attaining low education as compared to those born full-term according to such a model Fig. ab.FigureP values were non-significant in unadjusted models, and in models including all the covariates, i.e. fully adjusted models .We studied over 220,000 individuals and found that those born before 39 completed weeks’ gestation more likely attend special education during compulsory education. Among those in mainstream education, preterm birth appeared to be associated with marginally decreased grades in mathematics, and in physical education, but not in native languages or in theoretical subjects at 16 years of age. Among those who attended mainstream education, gestational age (GA) was only marginally associated with completed educational level at 25 years. Further, the grades at the end of compulsory education predicted the educational level similarly regardless of the GA. Our results indicate that the minor educational differences in mathematics and physical education grades according to GA in compulsory education did not lead to a greater gap in educational attainments later in young adulthood. While parental education was strongly associated with offspring education, these associations between GA and the outcomes were largely similar regardless of parental education.Our study employs a whole population cohort with minimal loss to follow-up. It quantifies educational outcomes at two different time points and assesses the educational trajectories between these two. Register-linkages enabled by PICs provide reliable measures of academic performance at adolescence and young adulthood. Study results focusing on those who attended mainstream education are applicable to most born at suboptimal gestational age; even of those born extremely preterm, more than 70% attended mainstream education.50, which would only have a trivial effect on our estimation. Further, information on emigrations and deaths after April 2012 were inaccessible. The magnitude of the bias caused by this can be considered as minor and lead to slight overestimation of the rates of low education and to minor imprecision in defining the study cohort; Statistics Finland’s aggregate data indicate mortality rate of only 65/100,000 and emigration rate of 808/100,000 among Finns aged 25–34 in 201953. We also lacked data on such pre- and postnatal treatments that were rare during cohort birth years but are common in clinical practice nowadays. Improvements in the care of newborn infants during the recent decades may impact the generalizability of our results to those born at later years. Further, we lacked data on educational attainments at the end of compulsory education for those who did not apply for further education during 2003–2007. However, during the years 2004–2006 only 2.0% of students did not directly apply further education after completing compulsory education54. In addition, the cohort was only followed-up to 25 years age when some young adults may have their higher education still ongoing, whereas most of them would have completed their upper secondary education. Therefore, the results may partially reflect slower tempo in completing higher education. Lastly, the generalizability of the study findings to other national contexts may be affected by the differences between the school systems, especially outside the Nordic countries which share several but not all common traits in their educational systems55. As the gradings at the end of compulsory education in Finland are given by teacher and based on nationally defined criteria but not on standard test, they may be affected by teacher’s personal perceptions and preferences56.As to limitations we lacked data on whether the GA estimation was based on fetal ultrasound or on the last maternal menstrual period (LMP). LMP method may overestimate the GA. As fetal ultrasound was only being introduced in clinical practice in Finland in 1987–1990, the GA estimates may have moved towards a minor underestimation of preterm birth16 but also among all children born before 39 completed weeks of gestation33. In our study, the robustness of the estimates combined with dose–response relationship between the declining GA and increasing rate of special education may indicate a developmental interference component associated to this tendency.Our results align with previous findings on more frequent special educational needs not only among those born most preterm, e.g., at extremely low birth weight24 in particular in mathematics24. These results do not align those of ours, showing only minor differences in mathematics as found in other studies among preterm born population with stringent control of confounders57, and no differences in native language grades by GA. However, as the studies included to these meta-analyses present assessments within a wide age range, have varying definitions for term birth, and especially as the majority of them appear to include also those who attended special education, our study may present more optimistic estimates as compared to theirs. A smaller Finnish study reported similar results to ours: very preterm born individuals without major disabilities performed similarly or better at 16 years as compared to those born at term21. A recent study of over 70,000 adolescents of 16–17 years found no differences in math or English language grades in California among those born moderately to late preterm as compared to those born at term58. In our study the marginal differences in estimates between native language and mathematics may be explained by the fact that some catch up with age in reading skills exists especially among those born at extremely or very low birth weight59, but not to the same extent in mathematics24.Previous meta-analyses on math and reading skills at any age of the individual show that those born preterm fare worse than those born at term35, with definition of 40–41 weeks for full-term birth, imply that grade averages of preterm children at 16 years of age were below those of full-term counterparts in mainstream education. We found essentially no differences in mean grades of theoretical subjects according to the GA. In some GA groups in some of the adjusted models, mean grades were even marginally higher compared with those born at term. Previous work also indicate that the association between declining GA and poorer school performance and lower IQ at adolescence are attributable to factors other than prematurity36, such as parental socioeconomic position37. The results of ours indicating lower grades on physical education among those born preterm may mirror the findings on motor impairments60, lower physical fitness61, and less leisure time physical activity62 among preterm born individuals.As regards to the grades on theoretical subjects combined, we are unaware of previous studies. However, a Swedish register study63. Our study shows that in mainstream education, preterm birth is not associated with higher risk of low education at 25 years, neither to a noticeably extent to lower likelihood for high educational level as compared to intermediate education.Previous literature on educational attainments in adulthood, mainly also including those who attended special education, illustrates a trend for poorer academic performance with declining GA65. Otherwise, we are not aware of previous studies assessing the trajectories of academic attainments from adolescence to young adulthood, nor across the whole range of GA. We found no differences in the impacts of different school grades on completed educational attainments at 25 years of age according to GA. This indicates that the minor education gap among those born most preterm as compared to those born full-term in mainstream education in compulsory education appear not to amplify in higher education and can be interpreted as supportive information to most families with preterm born children. However, even in mainstream education those born extremely preterm or very preterm still have lower grades in mathematics and physical education, and needing special education is inversely related to decreasing GA across the whole range of gestation from 38 to 23 completed weeks and affects eligibility to post-compulsory education.Previous studies indicate that some positive impact of physical education on later academic achievements may existIn Finland developmental follow-up of all children is provided up to the pre-school age, and speech and occupational therapy are accessible during the kindergarten and pre-school period when needed. Further, in compulsory education support in form of school health care , remedial assistance, student counseling, and visits to school social worker are available, and may have impact on the beneficial educational outcomes those born at suboptimal GA, even in special education.Children born preterm are more likely to attend special education with the highest rates seen at the lowest gestational ages. In mainstream education preterm born individuals have somewhat lower grades in mathematics and physical education, whilst achievement is similar to those born full-term in other school grades. Gestational age is not appreciably associated with educational level at 25 years of age. The school grades in mathematics, native language, physical education, and theoretical subjects, seem to predict completed educational level at 25 years regardless of the gestational age of the individual indicating that educational gap in adolescence appear not to widen in young adulthood.Supplementary Information.


Change in diffusion tensor imaging measures were only consistently associated with dementia conversion in severe SVD.•The diffusion tensor imaging measures PSMD and DSEG required the lowest minimum sample sizes for a hypothetical clinical trial in patients with sporadic cerebral small vessel disease and mild cognitive impairment. DTI is sensitive to white matter (WM) microstructural damage and has been suggested as a surrogate marker for phase 2 clinical trials in cerebral small vessel disease (SVD). The study’s objective is to establish the best way to analyse the diffusion-weighted imaging data in SVD for this purpose. The ideal method would be sensitive to change and predict dementia conversion, but also straightforward to implement and ideally automated. As part of the OPTIMAL collaboration, we evaluated five different DTI analysis strategies across six different cohorts with differing SVD severity.Those 5 strategies were: (1) conventional mean diffusivity WM histogram measure (MD median), (2) a principal component-derived measure based on conventional WM histogram measures (PC1), (3) peak width skeletonized mean diffusivity (PSMD), (4) diffusion tensor image segmentation θ (DSEG θ) and (5) a WM measure of global network efficiency (Geff). The association between each measure and cognitive function was tested using a linear regression model adjusted by clinical markers. Changes in the imaging measures over time were determined. In three cohort studies, repeated imaging data together with data on incident dementia were available. The association between the baseline measure, change measure and incident dementia conversion was examined using Cox proportional-hazard regression or logistic regression models. Sample size estimates for a hypothetical clinical trial were furthermore computed for each DTI analysis strategy.There was a consistent cross-sectional association between the imaging measures and impaired cognitive function across all cohorts. All baseline measures predicted dementia conversion in severe SVD. In mild SVD, PC1, PSMD and Geff predicted dementia conversion. In MCI, all markers except Geff predicted dementia conversion. Baseline DTI was significantly different in patients converting to vascular dementia than to Alzheimer’ s disease. Significant change in all measures was associated with dementia conversion in severe but not in mild SVD. The automatic and semi-automatic measures PSMD and DSEG θ required the lowest minimum sample sizes for a hypothetical clinical trial in single-centre sporadic SVD cohorts.DTI parameters obtained from all analysis methods predicted dementia, and there was no clear winner amongst the different analysis strategies. The fully automated analysis provided by PSMD offers advantages particularly for large datasets. CharactDespite the enormous global burden of SVD in causing stroke, dementia and, disability, there are few effective treatments for the disease . One chaDiffusion tensor imaging (DTI) has been proposed as a promising surrogate marker in SVD trials , 2016. DHowever, DTI analysis can be time consuming, and automated or semi-automated analysis techniques that are robust across different sites and populations are attractive, particularly for clinical trials. Such methods include Peak width of Skeletonized Mean diffusivity (PSMD), a DTI-derived measure based on skeletonization and histogram analysis,  and DiffThe overall aim of the OPTIMAL  collaboration is to find the most efficient way of using MRI measures in clinical trials. A previous OPTIMAL study demonstrated that conventional WM DTI measures such as MD median predicted impaired cognitive function and dementia conversion in SVD and MCI patient cohorts . In this22.1i.St George’s Cognition and Neuroimaging in Stroke (SCANS) included 121 individuals with severe symptomatic SVD defined as a symptomatic lacunar infarct with confluent WMH (Fazekas grade >=2)  Zeestraii.Radboud University Nijmegen Diffusion tensor and Magnetic Resonance Imaging Cohort (RUN DMC) study included 503 predominantly mild symptomatic SVD patients defined as the presence of lacunes and or any WMH on neuroimaging and accompanying stroke, subacute cognitive or motor symptoms iii.PRESERVE multicenter clinical trial with imaging included 111 patients with severe symptomatic SVD defined as a symptomatic lacunar infarct with confluent WMH (Fazekas grade >=2)  Croall iv.HARMONISATION study included 127 patients with mild cognitive impairment (MCI) impaired in at least one cognitive domain of a formal neuropsychological test battery, with or without a history of stroke v.Austrian Stroke Prevention Study (ASPS-Fam), a community population of 382 individuals free of dementia and stroke as well as demonstrating normal neurological function in whom SVD was severity was assessed on MRI vi.CADASIL cohort of 58 patients with monogenic SVD confirmed by genetic testing or skin biopsy .Five prospective longitudinal cohort studies were included  which haFull details of the cohorts are given in 2.2Local ethical approval was obtained for each cohort study and each participant gave written informed consent according to the Declaration of Helsinki.2.3Fluid-attenuated inversion recovery (FLAIR), T1-weighted and diffusion-weighted data were acquired in all cohorts. The imaging and clinical details of all cohorts have been fully described previously and are briefly outlined and referenced below.2.3.1eTable 1. Age-standardized cognitive test scores sensitive in detecting patterns of cognitive impairment in SVD were used to form an index measure of Global Cognition (eTable 2)  . Mini MeTable 2) . DementiTable 2) .2.3.2eTable 1) (eTable 2) . MRI acquisition was based on 1.5-T Siemens Magnetom Avanto MRI machine Table 1) . Age-staTable 2) . MMSE teTable 2) . Dementi . Age-stTable 2) . AlzheimTable 2)  and the Table 2) .2.3.3eTable 3). (eTable 2). Montreal Cognitive Assessment (MOCA) test scores were also employed in this study  at baseline and 2 years (able 3).  Neuropsyable 3).  (eTable is study . No deme2.3.4eTable 1) (eTable 2). Only cognitive measures of patients with MCI were part of this study. MOCA test scores were additionally included in the study (Imaging data were acquired on a 3 T Siemens Magnetom Trio Tim system Table 1) . Baselinhe study . Dementi . Baselihe study  and NINDhe study .2.3.5eTable 1). Only DTI measures at baseline were analysed due to a low sample size on the follow-up timepoint. Dementia conversion as a clinical outcome variable was therefore not included in the analysis. Global Cognition was created based on age-standardised test scores using a principal component analysis (eTable 2) (Magnetic resonance acquisition was performed on a 3 T Tim Trio whole body scanner (Table 2) .2.3.6eTable 1. To measure cognitive function, the Trail-making test–B was used (eTable 2). MMSE test scores were additionally obtained from the patients (Imaging data were based on a 1.5-T GE Signa system in Munich . Furtherpatients . The maipatients . Dementi33.1https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT) , or the co-ordinating site was guided using an identical protocol (ASPS-Fam). Diffusion-weighted image pre-processing was carried out with the eddy correct software from ‘FDT’, FMRIB’s Diffusion Toolbox, (iki/FDT) . In RUN iki/FDT) .3.21.Conventional WM histogram measure MD median (MD median)2.The principal component measure (PC1)3.Peak width skeletonized mean diffusivity (PSMD)4.Diffusion tensor image segmentation θ (DSEG θ)5.Global structural network efficiency measure (Geff)Five different strategies of quantifying DTI parameters were compared in each cohort:All other conventional WM histogram measures included in the analysis were used to compute the PC1 measure.3.2.1MD median and 19 other WM histogram measures requiring manual interventions were computed on the mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AxD) and radial diffusivity (RD) DTI maps. The 5 histogram measures for each of the maps were: Median, normalised peak height (PH), peak value, skew, kurtosis. Hence, 20 histogram measures were computed for each patient.−2 images in the DTI sequence) were employed .Using SPM12b , T1-weighted scans were segmented into grey matter, WM and cerebral spinal fluid tissue probability maps (TPM) . MD, FA,employed  and the 3.2.2The PC1 measure was computed using principal component analysis (PCA) on the 20 conventional WM histogram measures . PCA is 3.2.3(PSMD) is a fully automatically computed imaging marker publicly available (http://psmd-marker.com) which involves tensor fitting, skeletonizing the DTI data, applying a custom mask and computing a histogram analysis (1)WM tract skeletonization using tract-based statistics (TBSS) and the FMRIB 1-mm fractional anisotropy template thresholded at an FA value of 0.2 . MD imag2)excluding CSF prone regions employing a custom mask. Brain structures adjacent to the ventricles such as the fornix were removed.3)creating a histogram based on the MD values of voxels within the skeleton. The PSMD measure refers to the difference between the 5th and 95th percentiles of the histogram distribution.The Peak width of skeletonized mean diffusivity analysis . The com3.2.41)excluding the cerebellum using an automated pipeline,2)diffusion tensors fitting using ‘DTIFIT’ , 2003,3)k-median cluster analysis of the diffusion data to characterize each voxel as to belonging to one of the 16 segments that reflect the distinct magnitudes of anisotropy (q) and isotropy (p) microstructural diffusion properties,4)eTable 4.calculating a DSEG summary score, θ, by comparing the volumetric spectrum of the 16 segments between patients in the form of a scalar product, θ, which computes the difference in whole brain diffusion characteristics in subjects compared to a reference brain. Clinical information of the patient's reference brain used in each cohort can be found in DSEG θ is a semiautomatic DTI marker that assigns an unique cerebral diffusion profile to each voxel that is based on the magnitudes of isotropic and anisotropic diffusion metrics . The pro3.2.4.11)for all cohort data 90 brain regions  defined from the AAL parcellation of the cerebral cortex and subcortical nuclei  were emp2)https://www.trackvis.org).employing “DTIFIT”, a diffusion tensor model was fitted to each voxel in all cohort data except for RUN DMC baseline . In RUN 3)stnava.github.io/ANTs/), the standard space template FMRIB58_FA_1mm was registered to the patients FA maps in all cohort data except for RUN DMC at baseline. The AAL atlas was then transformed to the patient’s FA image using the nearest-neighbour interpolation method and the transformation matrix generated by the registration. In RUN DMC at baseline skull stripped T1-weighted images were nonlinearly registered to Montreal Neurological Institute (MNI) 152 template employing the Function MRI of the Brain nonlinear registration tool (FNIRT) (b0-images to T1-weighted subject space was used to register the AAL images to each patient’s diffusion image space with FLIRT (using ANTS ( (FNIRT) . The trath FLIRT .4)whole-brain deterministic tractography using MRtrix  was perf5)www.brain-connectivity-toolbox.net)  (box.net) . The GefBrain networks can be constructed using DTI tractography data, and a measure of global efficiency has been shown to predict dementia in a lacunar stroke cohort . Except 3.3Statistical analyses were carried out using R (version 3.6.3) with two-sided p values and p < 0.05 considered statistically significant. . No mult3.3.1Heatmaps were created reflecting the magnitude of Pearson correlation between the 20 WM DTI histogram measures in each cohort. Using MMSE and MOCA scores, boxplots were created to compare the patients’ profiles of impaired cognitive functions across the different cohorts. The association between the DTI measures and cognition was tested using linear regression while adjusting for age, sex and premorbid IQ or years of education completed. Education was log transformed in HARMONISATION to meet homoscedasticity and linearity assumptions. In the multi-centre PRESERVE study clinical site was added as a confounder into the linear model. In the CADASIL cohort one residual outlier observation was removed from the regression analysis to meet the statistical assumptions. This outlier was due to one DTI observation significantly altering the cross-sectional associations.3.3.2The association between baseline imaging data and later dementia conversion was tested in SCANS, RUN DMC and HARMONISATION using a Cox proportional hazards model . The cli3.3.3N = 64) for the longitudinal analysis of the small changes over time in a population-based sample. In the SCANS cohort with multiple follow-ups, a linear mixed model was fitted for MD Median, PSMD, DSEG θ and Geff . In HARMONISATION, patients who were more cognitively impaired had more consistently lower MOCA test scores than in PRESERVE . In SCANS, RUN DMC and HARMONISATION, patients converting to dementia had overall lower baseline MMSE and MOCA test scores than patients who did not develop dementia . The differences were stronger in SCANS and HARMONISATION than in RUN DMC.4.2. In the sporadic SVD cohorts the PC1′s explained variance ranged between 57.2 and 71.5 % . There were no patterns showing that dementia cases or dementia subtypes would be better represented on the second instead of the first principal component dimension .The percentage of explained variance of PC1 was highest in the CADASIL (84.4 %) group and lowest in ASPS-Fam (40 %) 4.3, eTable 5a-f). There was not a single regression model resulting in the highest explained variance (2Adj. R) and in the best model fit (AIC) across cohorts. In the single-center sporadic SVD cohorts SCANS and RUN DMC the models’ adjusted explained variances (2Adj. R) were similar within each cohort for all parameters . In HARMONISATION 23 patients who had complete baseline DTI measures were diagnosed with dementia within 2 years . The rates of dementia conversion were higher in HARMONISATION than in SCANS or RUN DMC .In SCANS 18 out of 113 patients who had complete baseline DTI imaging measures converted to dementia within 5 years. In RUN DMC 435 patients had complete baseline DTI measures and 50 patients converted to dementia within 9 years . In RUN DMC baseline MD median and DSEG θ were not associated with dementia, MD median: HR (95 % CI) = 1.33 , DSEG θ: HR (95 % CI) = 1.33 . In HARMONISATION baseline Geff did not predict dementia conversion, HR (95 % CI) = 0.79 . The AUCs were overall higher in the sporadic SVD cohorts SCANS and RUN DMC than in the MCI cohort HARMONISATION , MD median , PSMD  and Geff  . Holm-Bonferroni multiple-comparison post hoc tests showed that baseline PC1 (p = 5.4e-05), MD median (p = 4.6e-05) and PSMD (p = 7.9e-04) were significantly higher and baseline Geff (p = 1.6e-04) significantly lower in VD than in the AD dementia subtype. Higher PC1, MD median, PSMD and lower Geff in VD than in AD were consistently found over 3-years intervals in RUN DMC .In RUN DMC there were significant differences between the subtypes for baseline PC1 ( df = 2) . For bas4.4.4eTables 8a-c. Overall, most imaging markers significantly changed over time. PC1 explained less variation of the imaging change data in the sporadic SVD than in the MCI cohort  . In PRESERVE most conventional DTI histogram measures significantly changed and PC1 explained 31.9 % of the variance when running a PCA on all 20 conventional DTI measures (eTable 7). There were no differences between scanner sites for any changes in the 5 imaging measures while accounting for the measure’s baseline value and controlling for the increased Type-1 error rate with the Tukey post hoc test .All imaging measures showed significant change over time except for PSMD and Geff in the multicentre PRESERVE cohort and Geff in HARMONISATION and CADASIL . Changes4.4.5eTable 9). Changes in all imaging measures were associated with dementia conversion in SCANS, but not in RUN DMC (OR (95 % CI) = 1.60 . The association and model fit were strongest in the DSEG θ model in SCANS, HR (95 % CI) = 4.01 , and also resulted in the highest AUC with 0.924 (N = 12), a Firth's Bias-reduced logistic regression was additionally run on the RUN DMC data. In line with previous evidence, no significant association between any changes in DTI and dementia conversion was found (eTable 10).Complete longitudinal imaging data with follow-up dementia conversion were available for SCANS, RUN DMC and HARMONISATION . In SCAN RUN DMC . In HARMth 0.924 . Across 4.4.6Sample sizes for a hypothetical clinical trial with varying durations and varying treatment effect sizes were calculated for the four single imaging measures in the different cohorts and are shown in 5An increasing number of studies have demonstrated that DTI measures of WM damage are associated with cognition, predict future dementia conversion and capture change over time. This has raised awareness of DTI as an attractive surrogate endpoint for clinical trials in SVD. However, how best to analyse DTI data remains uncertain. An ideal analysis technique would combine a simple automated technique, with high sensitivity to change and dementia conversion. In this study, we compared 5 different DTI analysis approaches by comparing associations of each with cognition at baseline, and with future dementia conversion.We were not able to identify a single optimal analysis strategy. Except for the asymptomatic SVD cohort ASPS-Fam, all analysis techniques were significantly associated with global cognition or executive function at baseline. The overall fit of the regression model, measured by AIC, was worse for DSEG and Geff in PRESERVE and better for PSMD in CADASIL. In the 3 studies in which there was imaging and data on future dementia conversion, SCANS, RUN DMC and HARMONISATION, most baseline DTI measures predicted dementia. The area under the curve was larger in the SVD cohorts (SCANS & RUN DMC) compared to the MCI cohort (HARMONISATION). Baseline PC1 and PSMD predicted dementia conversion across all 3 cohorts. DSEG θ performed notably better in the SCANS cohort than in RUN DMC.Differentiating between dementia subtypes in RUN DMC, all baseline DTI measures, except for DSEG, were significantly different between patients converting to VD dementia and those converting to AD dementia. The differences in DTI measure profiles indicated more severe WM ultrastructural damage in those converting to VD. Previous studies also showed different etio-pathogenic profiles of WM microstructure between VD and AD dementia . WhereasThere were significant changes in all DTI metrics over time in all single-center sporadic SVD and MCI cohorts. In the multicentre cohort PRESERVE, the network analysis and PSMD were not sensitive to change. The combined PC1 measure was not a better predictor for dementia conversion, as evidenced by AUC, than one of its underlying single measures, i.e., MD median. Despite the significant changes over time in all DTI measures, consistent associations with dementia conversion were only found in the severe SVD cohort, i.e., SCANS, and not in the 2 cohorts with AD dementia as the most prevalent subtype.https://www.psmd-marker.com/faq/) and this may be a limitation. Another automatic skeletonized measure, called Mean Skeletonized Mean Diffusivity (MSMD), was sensitive to change in PRESERVE . MSMD may therefore offer a better marker than PSMD when using Philips scanners. A further advantage of PSMD is that it is freely available. DSEG θ is semi-automated method requiring some user input such as choosing the reference brain. It performed well across datasets and may also be useful for use in clinical trials. However, it is not yet openly available.Since the analysis techniques were similar in their predictive ability, then ease of use and the degree of automation become important factors. PSMD allows for a fully automated analysis without any tissue segmentation, that may require checking and correction, making it suitable for rapid analysis of large data sets. PSMD performed well across all cohorts except for detecting change in the PRESERVE cohort. In the PRESERVE cohort 86% had been scanned on a Philips scanner. The sensitivity of PSMD has been shown to be affected by images from the Philips scanner especially when there was a software update during the study , through to patients with severe symptomatic SVD with confluent WMH (SCANS) and patients with monogenic SVD (CADASIL). The previous OPTIMAL study showed that changes in WM microstructure may contribute to cognitive impairment in all cohorts and in those cohorts in which data on future dementia risk was available predicted dementia . This emIt was notable that in PRESERVE the amount of variance in cognition explained by DTI metrics was lower. This is likely to reflect the multicentre nature of PRESERVE, in which DTI was performed on several different scanners across different sites. In contrast the other studies utilized imaging on a single scanner. To solve these challenges associated with acquisition-related differences of diffusion MRI, a promising DTI harmonization technique has been successfully tested on various SVD cohorts . IdeallyWe used the data from this study to estimate sample size calculations for a trial in SVD. With a treatment effect of 20 %, which would be typical for many intervention studies, sample sizes of a few hundred would be required. Sample sizes were generally low for DSEG θ, and only slightly higher for PSMD. In contrast, the network measure Geff needed a high minimum sample size in all 4 cohorts.The study has several strengths. We compared several different DTI parameters across multiple independent datasets. Analysis methods were evaluated on additional datasets on which they had not been developed. However, it also has limitations. Firstly, different durations and number of follow-up time points were used which may have affected the findings across cohorts with varying SVD severity. Strong associations between DTI changes and dementia conversion were only found in the cohort characterized by more severe sporadic SVD progression, with multiple observational follow-up points. Annual follow-up time points in RUN DMC may have resulted in similar findings as in SCANS. Second, different MRI acquisition parameters were employed for the different cohorts potentially affecting the results. Third, different DTI pre-processing has been applied for different cohorts, nevertheless the results were relatively consistent between the cohorts. The Geff generated from the RUN DMC data differs in scale to that from the other datasets, this is most likely due to the differences in processing between this dataset and those processed centrally. While this may be a limitation, we felt it important to preserve the compatibility between the data published here and in other papers using it. Fourth, these DTI measures were all based on single-shell DTI. However, multi-shell DTI and higher DTI models may be more informative to characterize the WM microstructure, perhaps leading to lower sample sizes.A further consideration is that we included a range of severity of disease pathologies in our study. Some represented severe SVD in whom the predominant pathology which may cause dementia is vascular, and this was reflected in the dementia cases in SCANS being vascular. In others, such as the MCI cohort, the predominant dementia pathology is likely to be AD, although the strong prediction provided by DTI suggests that WM changes are also important. Consistent with this, most dementia cases were AD. Interestingly in RUN DMC in which the inclusion criteria were symptomatic SVD due to stroke, cognitive or motor symptoms, most dementia cases were AD emphasizing that in elderly populations mixed pathology is the major pathology underlying dementia.In conclusion, all DTI approaches at baseline predict future dementia risk in SVD and may be useful surrogate endpoints in clinical trials of SVD. We found no clear difference between different DTI strategies but automated methods such as PSMD offer clear advantages in large studies. PSMD performed well overall except for the PRESERVE dataset, possibly due to difficulties in using it on Philips sequences. DSEG θ performed well overall. Whilst these methods did not show marked improvement over simple histogram methods, they are easier to implement in large clinical studies.This work was funded by a grant from Alzheimer’s Research UK (ARUK-PG2016A-1). Additional support was provided by a Cambridge University- LMU collaborative grant. ME is funded by a Priority Programme Grant from the Stroke Association (2015–02). Infrastructural support was provided by National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre Dementia and Neurodegeneration Theme (146281) and the Cambridge BHF Centre of Research Excellence [RE/18/1/34212]. SB and HSM are supported by a British Heart Foundation Programme Grant (RG/16/4/32218). HSM is also supported by an NIHR Senior Investigator Award. The views expressed are those of the author(s) and not necessarily those of the NHS, NIHR or the Department of Health and Social Care. The funding organisations had no role in any of the following: design and conduct of the study; collection, management, analysis, and interpretation of the data; review, or approval of the manuscript.This work is also supported by BrightFocus foundation (reference no. A2018165F), National Medical Research Council Transition Award (R-608–000-342–213) and Ministry of Education Tier 1 grant (R-608–000-311–114) awarded to SH.FE de Leeuw is supported by a clinical established investigator grant from the Dutch Heart Foundation (2014 T060) and by a VIDI innovational grant from The Netherlands ZonMw (Grant No 016126351). AMT has received a grant from the Junior Staff Member Dutch Heart Foundation (2016 T044). The funders and sponsors played no role in study design or analysis. C Chen is supported by a Singapore Translational Research award from the National Research Council of Singapore.Authors take full responsibility for the data, the analyses and interpretation, and the conduct of the research; full access to all of the data; and the right to publish any and all data. ME and HSM are study guarantors.7All studies were carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. All studies were approved by the ethics committees of the respective institutions. SCANS was approved by the London–Wandsworth ethics committee . RUN DMC was approved by the Medical Review Ethics Committee region Arnhem-Nijmegen (No. 2005/256). HARMONISATION was approved by the National Healthcare Group Domain-Specific Review Board (DSRB reference No. 2010/00017). PRESERVE was approved by the Harrow National Research Ethics Service committee (No: 11/LO/0458). ASPS-Fam was approved by the ethics committee of the Medical University of Graz, Austria (comission registration No. IRB00002556). CADASIL was approved by the LMU medical faculty (No. 299/03).Marco Egle: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Project administration. Saima Hilal: Conceptualization, Investigation, Writing – review & editing. Anil M Tuladhar: Conceptualization, Investigation, Writing – review & editing. Lukas Pirpamer: Conceptualization, Investigation, Writing – review & editing. Steven Bell: Formal analysis, Data curation, Writing – review & editing. Edith Hofer: Investigation, Writing – review & editing. Marco Duering: Conceptualization, Investigation, Software, Writing – review & editing, Funding acquisition. James Wason: Formal analysis, Data curation, Writing – review & editing. Robin G Morris: Conceptualization, Formal analysis, Writing – review & editing. Martin Dichgans: Conceptualization, Investigation, Writing – review & editing, Funding acquisition. Reinhold Schmidt: Conceptualization, Investigation, Writing – review & editing, Funding acquisition. Daniel J Tozer: Conceptualization, Data curation, Software, Writing – review & editing, Supervision. Thomas R. Barrick: Data curation, Software, Writing – review & editing. Christopher Chen: Conceptualization, Investigation, Writing – review & editing, Funding acquisition. Frank-Erik de Leeuw: Conceptualization, Investigation, Writing – review & editing, Funding acquisition. Hugh S Markus: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Intraoperative imaging is a chief asset in neurosurgical oncology, it improves the extent of resection and postoperative outcomes. Imaging devices have evolved considerably, in particular ultrasound (iUS) and magnetic resonance (iMR). Although iUS is regarded as a more economically convenient and yet effective asset, no formal comparison between the efficiency of iUS and iMR in neurosurgical oncology has been performed.A cost-effectiveness analysis comparing two single-center prospectively collected surgical cohorts, classified according to the intraoperative imaging used. iMR (2013-2016) and iUS (2021-2022) groups comprised low- and high-grade gliomas, with a maximal safe resection intention. Units of health gain were gross total resection and equal or increased Karnofsky performance status. Surgical and health costs were considered for analysis. The incremental cost-effectiveness ratio (ICER) was calculated for the two intervention alternatives. The cost-utility graphic and the evolution of surgical duration with the gained experience were also analyzed.50 patients followed an iMR-assisted operation, while 17 underwent an iUS-guided surgery. Gross total resection was achieved in 70% with iMR and in 60% with iUS. Median postoperative Karnofsky was similar in both group (KPS 90). Health costs were € 3,220 higher with iMR, and so were surgical-related costs . The ICER was € 322 per complete resection obtained with iMR, and € 644 per KPS gained or maintained with iMR. When only surgical-related costs were analyzed, ICER was € 198 per complete resection with iMR and € 395 per KPS gained or maintained.This is an unprecedented but preliminary cost-effectiveness analysis of the two most common intraoperative imaging devices in neurosurgical oncology. iMR, although being costlier and time-consuming, seems cost-effective in terms of complete resection rates and postoperative performance status. However, the differences between both techniques are small. Possibly, iMR and iUS are complementary aids during the resection: iUS real-time images assist while advancing towards the tumor limits, informing about the distance to relevant landmarks and correcting neuronavigation inaccuracy due to brain shift. Yet, at the end of resection, it is the iMR that reliably corroborates whether residual tumor remains. Intraoperative imaging is a major asset in modern neurosurgical oncology which helps the surgeon delineating tumor boundaries and identifying remnants –3. It ulImaging devices have evolved during the last decades, becoming more precise, versatile and accessible. Still, each modality has its own strengths and shortcomings . IntraopFollowing the differential features of intraoperative imaging devices, tertiary neurosurgical centers have been choosing between modalities according to their preferences and prospects. However, a formal comparison of the efficiency between iUS and iMR in the neurosurgical oncology setting has not been performed yet. Hereby, we have evaluated the cost-effectiveness of iUS and iMR for brain tumor resection. Comparing the economic costs and health benefits of these two alternative interventions will provide objective data for decision makers and future investments.The patients included in this retrospective analysis were part of two prospectively collected clinical registries. One cohort was composed of consecutive patients treated from high and low grade gliomas, with a maximal safe resection intention, with the assistance of a low field-iMR . These patients were recruited between June 2013, date of the installation of the device, and June 2016. The data corresponding to this cohort has already been published in this same journal by our group  and a specific neurosurgical probe (bk Craniotomy Transducer N13C5). No other iUS appliances were used, neither 3D reconstructions nor co-registration with the neuronavigation system. Intraoperative contrast agents were not applied. These patients were operated between October 2021, date of acquisition of the device, and May 2022.®) was additionally used to guide the resection. In all the cases, neuronavigation was employed to tailor the craniotomy and to aid with the resection. Patients in which both intraoperative devices were used were excluded from the analysis; they belong to the intermediate time period (2016-2021).Neurophysiologic monitoring was implemented in both cohorts, whenever the surgical team considered it appropriate. In cases with initial suspicion of high-grade glioma, intraoperative fluoresce with 5-aminolevulinic acid . Patients signed an informed consent before surgery (agreeing the use of the low field-iMR and for the academic and scientific use of their anonymized data). The study complies with national legislation in the field of biomedical research, the protection of personal data (15/1999) and the standards of Good Clinical Practice, as well as with the Helsinki Declaration (1975 and 1983 revisions). Patient records were anonymized before analysis.Patients within the iMR and iUS cohorts were operated with conventional microsurgical techniques, including an ultrasonic aspirator and standard neuronavigation. In both groups, neurophysiologic monitoring was employed when the location of the lesion required motor cortical or subcortical mapping. Awake surgery was chosen for language mapping in suitable candidates. Functional criteria for stopping the resection remained unchanged across the duration of the whole study. To reduce the bias inherent to the variable degree of surgeons’ expertise with iUS operation, all interventions were performed by only two surgeons specialized in neurosurgical oncology.The primary outcome was EoR, defined as Gross Total Resection (GTR) if at least 90% of the mass was removed; Near Total Resection (NTR) if at least 80% of the mass was removed; or Partial Resection (PR) if less than 80% of the mass was removed . In highPostoperative complications included hemorrhage , wound infection, new neurological deficits, hydrocephalus, and venous thromboembolic disease. Other variables of interest were demographic (age and gender) and clinical variables (preoperative KPS), histopathological diagnosis, need of re-intervention within the first year, surgical duration, need for intensive care and total hospital length of stay.Economic evaluation consisted of a cost-effectiveness analysis where the two intraoperative imaging techniques were compared, namely the low-field iMR with the iUS. The cost-effectiveness equation explored the incremental cost per unit of health gained with a given device. The effectiveness measures used were maintained or increased postoperative KPS and the EoR, expressed as a dichotomous variable, considering whether GTR was achieved or not achieved. The incremental effectiveness was expressed as the mean difference in the postoperative KPS and as the difference in the percentage of GTR achieved with each technique.Health-related costs included health related variables  and surgical-related variables, namely the operating time (in minutes), the use of prosthesis  and the use of neuronavigation system, the surgical pack and the intraoperative image device. The cost of the imaging device imputed to each patient was inferred as the cost per patient according to all the indications in which iMR or iUS are currently applied to, for the total lifespan of the device. Indications for iMR are intrinsic and extrinsic brain lesions, cavernomas, pituitary macroadenomas and epilepsy surgery, which comprises about 120 surgeries per year in our institution. Indications for iUS include intrinsic and extrinsic brain lesions, hydrocephalus and neurovascular interventions. These account for about 150 surgeries per year. The life cycle of both devices was set at 10 years. Although other health-related costs were described, they did not compute for the cost-effectiveness analysis . Prices were extracted from our institution’s budget and cost of health credits. The same unitary prices were apply to both cohorts, even when they differ in eight years, so as to obtain comparable expenses (euro 2018). Therefore, no discount rates were applied. Costs were expressed as mean cost per patient.The mean incremental cost and mean incremental effectiveness were calculated for each modality. The cost-effectiveness ratio (ICER) was defined as the ratio between the incremental cost and the incremental effectiveness of the two intervention alternatives, as follows:The ICER values of the two intraoperative imaging variants were represented in a cost-utility plane. In this graphic, the north-east corner indicates a more expensive and more effective intervention, whereas the south-east corner indicated a less costly but more effective intervention. Finally, a graphical representation of the evolution of surgical times with the sequentially acquired experience of the surgical team was obtained for both techniques.Calculations were performed using Microsoft Excel XPTM and SPSS (IBM version 23.0). The present analysis followed the Health Economic Evaluation Reporting Standards (CHEERS) guidelines for communicating economic evaluations of health interventions. No statistical tests were conducted as neither hypothesis testing, nor the level of statistical significance were relevant to our analysis.To contextualize our results in terms of efficacy and efficiency, we ran a succinct literature review of the main trials and observational studies reporting the outcomes of the use of iMR and/or iUS for glioma resection. Concretely, we conducted a PubMed search with the words “intraoperative ultrasound” and/or “intraoperative magnetic resonance” and “glioma surgery”. Only studies reporting the rates of gross total resection were included. Small series or series older than 2005 were excluded. Results of the search were summarized in an informative table, along with our own current results, specifying the year of publication, the type of intraoperative imaging device used, the study design, the tumor type included, the sample size, the rates of gross tumor resection and the surgical duration (if available). No statistical analysis was performed to compare between the different studies.A total of 67 patients were included for the analysis: 50 had an iMR assisted surgery and 17 had an iUS guided intervention. Patients in which iUS was only used to obtain a biopsy were excluded from the analysis. A detailed description of the iMR results and cost-effectiveness analysis has already been published by our group . The resBoth cohorts had a male preponderance, a mean age of 50-60 years and an overall good performance status preoperatively (median KPS 90). In both groups, the predominant tumor type was high grade glioma (62% in iMR vs 70% in iUS) Table 1.Surgical resection of tumors assisted with iMR, compared to iUS, provided higher rates of complete resection and lower incidence of postoperative complications Table 2.Conversely, with iUS complete resection was obtained in 60% of cases. Postoperative outcomes were similar in terms of performance status (mean KPS 80), yet morbidity was higher with iUS than with iMR. With iUS there was a 20% complication rate, which included two epidural hematomas, one surgical-cavity hematoma and one surgical-site infection. 11% of iUS-guided cases needed a reintervention due to surgical-related complications Table 2.Mean cost per operation was higher if iMR had been used, ascending to € 5,162, compared to € 3,186 with the iUS. The number of patients requiring ICU and the mean length of hospital stay were also higher in the iMR setting . Therefore, the total health-related costs for each intervention were higher with iMR assisted-surgery  than with iUS guided-surgery  Table 3.Surgical duration was more than double when iMR was used than when iUS was chosen, with an average of 241 minutes more per intervention. Interestingly, the sequential evolution of surgical times was different for the two techniques: While iMR-surgery tended to become nimbler with time, a flat evolution of the iUS-surgery was observed Figure 1The costs of iMR-assisted surgery were higher than with iUS . Meanwhile, the iMR seems more effective at achieving gross total removal of the tumor (mean percentage difference of 10 points). Still, postoperative performance status was similar with both techniques, but iMR showed slightly higher rates of equal or increased postoperative KPS .The results of the cost-effectiveness analysis Table 4 In the cost-effectiveness plane representing the results of iUS compared to the iMR, nearly half of the replicates fall within the north-east corner, indicating a costlier and more effective intervention Figure 2Examples of the intraoperative images used during the interventions can be seen in This is an unprecedented but preliminary cost-effectiveness analysis comparing the two most commonly used intraoperative imaging devices in oncologic neurosurgery. Our results suggest that iMR, although being costlier and time-consuming, seems to be cost-effective comparing to iUS in terms of surgical resection rates, with an ICER of € 322 per GTR attained. A similar conclusion is obtained when only surgical-related costs are regarded, with and ICER of € 198 per GTR. On the other hand, when KPS was taken as the unit of health gain, slight differences were found among the two techniques; still, iMR seemed to be cost-effective to the iUS counterpart. Whether the apparent profitability of the iMR is worth the high initial investments required and the longer surgical duration times will depend on the willingness-to-pay threshold of each local healthcare system and the logistics policy of each institution.per se . Meanwhile, a slightly poorer postoperative KPS within the iUS group might be the consequence of a more ambitious approach to resection, by which trying to achieve higher GTR rates there is collateral damage in the form of new neurological deficits .Our economic analysis was performed under two different economic perspectives: one accounted only for the surgical-related costs, and the other one including all the total costs incurred during hospitalization. This strategy was intended to reduce the bias related to the differences in those costs not directly related to the intraoperative image of choice, such as systemic complications and length of stay. For instance, the higher rates of postoperative complications within the iUS-guided group might not be directly related to the imaging devise In both series, the intention of the surgeries was maximal safe resection. In cases of tumors located near eloquent cortical or subcortical structures, neurophysiological monitoring was performed. In both iUS and iMR cohorts there were cases in which resection was halted prematurely due to the proximity of functional areas. However, a plausible explanation for the difference (10%) in GTR between both techniques is a mismatched distribution in the functionally limiting tumor excisions. In fact, in our institution, iMR is used in well-selected candidates, in whom total tumor resection is pursued as a primary goal and in whom the total removal of the tumor seems feasible according to the preoperative planning. On the contrary, iUS is now used as a regular aid for tumor resection, even in cases where a complete removal was only sought up to some extent .Regarding surgical duration, the use of iMR increased operating times to near double those with iUS, a similar magnitude to what had been previously reported . Interes-Although the limited experience with iUS was also concerning at the beginning, the results obtained by our group are in line with previously reported series, Arguably, iMR provides better image resolution, tissue differentiation and wider field of view. These intrinsic characteristics are conceivably responsible for the greater tumor resection rates . NotwithPossibly, iMR and iUS are complementary aids in surgical neuroncology. During the resection, iUS provides real-time information while the surgeon is advancing towards the tumor limits, informing about the distance to relevant landmarks, such are the ventricles or blood vessels, and correcting neuronavigation inaccuracy due to brain shift and deformation , 37. YetLimitations of the present study include the time lapse between the collection of iMR series and iUS series of patients and the heterogeneity of both cohorts. Even when the general management of oncologic patients has not significantly changed over the last decade, advances in neuronavigation and improved experience in neurophysiologic mapping might have acted as bias when comparing the primary and secondary clinical outcomes between the two series. Probably, another source cofounding is the use of gross total resection as the unit of health gain; even when this parameter is of great clinical relevance, its achievement is not only related to the ability of detecting residual tumor. In fact, the surgical aim in this study was a maximal safe resection, and thus safety  might preclude the cautious surgeon from total resection. To add to this variability, it should be noted that provided the iUS is a highly operator-dependent technique, particularly compared to iMR, the reliability of the results regarding surgical duration and quality of resection are strictly linked to the surgeon’s experience and expertise.Finally, certain aspects of the study design should be addressed. The limited sample size, particularly in the iUS group, could be a source of deviation of the global results; however, this study was not intended to prove the superiority of one intraoperative technique over the other. Conversely, this economic evaluation is meant to help in health-related decision making during the set-up of novel operative armamentarium. In fact, the decision process underlying a cost-effectiveness analysis should be based only on the mean net benefits of each intervention irrespective of whether the difference between them is statistically significant , 40. CerIn intracranial oncological procedures, iMR and iUS seem to afford similar results in terms of extent of resection and postoperative performance status; still, the outcomes slightly favor iMR although at a higher relative cost and with longer surgical times. Surgical duration decreases with cumulative experience with iMR, but not so much with the use of iUS, reflecting the obvious differences in the intraoperative workflows between both techniques; while iMR involves the whole surgical team becoming familiarized with patient preparation and device mobilization, iUS relies directly on the surgeon’s ability to simultaneously acquire and interpret the examination images. Possibly, iMR and iUS are complementary aids in neurosurgical oncology: Whilst iUS assists the surgeon with real-time captures while advancing towards the tumor limits, informing about the distance to relevant landmarks and correcting neuronavigation inaccuracy due to brain shift; at the end of the resection, it is the iMR that reliably corroborates whether residual tumor remains.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Comité de Etica de Investigación Clínica, Hospital Clínic de Barcelona, Spain. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.AM, AS, and JG designed and led the present study. JG, AS, and PR developed the theory and performed the computations. AM, AF, and AR collected the clinical and economic data. AM, AS, SG-O, and JG performed the analytical methods and interpreted them. AM drafted the manuscript. JG, AS, and PR critically revised the manuscript. JE gave institutional, material, and logistic support. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The primary outcome measure was the incidence of congenital malformations. The secondary outcome measures were rates of low birth weight (LBW), very low birth weight (VLBW), preterm birth (PTB), very preterm birth (VPTB), and early neonatal death.Birth characteristics for both singletons and twins regarding the sex of infants, gestational age, birth weight, and birth length were comparable between the PPOS group and the GnRH-ant group. Rates of LBW, VLBW, PTB, VPTB, and early neonatal death were also similar. The reanalysis using propensity score matching (PSM) and multivariable logistic regression indicated that the PPOS protocol could not increase the risk of adverse neonatal outcomes compared with the GnRH-ant protocol. Furthermore, no significant difference was observed in the overall incidence of congenital malformations in live-born babies. After PSM and controlling for all confounders, the results remained insignificant with an adjusted odds ratio of 0.66 [95% confidence interval (CI) 0.32–1.34] and 2.43 [95% CI 0.97–6.06], respectively, for singletons and twins.Our study suggests that compared with GnRH-ant treatment for IVF, the PPOS protocol could not produce a negative effect on the newborn population in terms of neonatal outcomes and congenital malformations. Currently, at least five million of infants have been born as a result of ART, approximately accounting for 2% of all births in European countries (With the wide spread of assisted reproductive technology (ART), an increasing number of infertile couples have successfully conceived ountries . Notablyountries . The incountries , 6.Controlled ovarian stimulation (COS) has always been considered to play a critical role in the development of ART. The application of exogenous gonadotropin resulting in supraphysical serum estradiol levels and the subsequently positive feedback at the pituitary body might hence cause a premature luteinizing hormone (LH) and premature luteinizaton . ConsequPreviously, studies have found that progestin (P) secreted during the luteal phase could strongly inhibit LH secretion and estradiol-induced positive feedback effects with no spontaneous LH surge . MoreoveAs one of routine COS procedures, the GnRH-ant protocol could reduce the incidence of OHSS without affecting the pregnancy and neonatal outcomes compared with the GnRHa protocol , 20. HowThis retrospective cohort study was performed in the Department of Assisted Reproduction of the Ninth People’s Hospital of Shanghai Jiao Tong University. The study protocol was approved by the institutional ethics committee of the Ninth Hospital. All participants gave written informed consent after describing the research in detail.All infertile patients  who underwent IVF/ICSI followed by frozen-thawed embryo transfer (FET) using the PPOS or GnRH-ant protocol were recruited for the study during the period between 1 January 2013 and 31 October 2020 in our center. Considering that these factors could be associated with birth defects, women with reported pregnancy-related diseases such as hypertension n = 522), gestational diabetes mellitus n = 1,321), thyroid diseases (n = 35), intrahepatic cholestasis of pregnancy (n = 59), anemia (n = 58), cardiac diseases (n = 13), and inflammatory diseases in pregnancy (n = 32) and combined disorders (n = 172) were excluded  and simultaneously administered with 10 mg of MPA or 100 mg of utrogestan  or 20 mg of dydrogesterone  beginning from the menstrual cycle day 2 or 3 (MC2 or 3) until the trigger day. In the GnRH-ant group, patients were injected daily with 150 or 225 IU hMG intramuscularly from MC2 or 3 onward and 0.25 mg of GnRH-ant  from MC5 onward. Ovarian response was assessed according to the ultrasound monitoring and serum estradiol analysis. When the diameter of one dominant follicle reached 20 mm or at least three follicles reached diameters of 18 mm, the final oocyte maturation was triggered by 0.1 mg of triptorelin  alone or co-triggered with 1,000–5,000 IU of hCG .in vitro by conventional IVF and/or ICSI, depending on the semen parameters. The embryos were further cultured in the Continuous Single Culture  supplemented with 10% serum substitute supplements . The cleavage-stage embryos (day 3) were graded according to the Cummins criteria . Then, the retrieved oocytes were fertilized criteria . Grade Icriteria .Before embryos were thawed on the transfer of day, endometrial preparation for frozen embryo transfer (FET) could be performed in the natural cycle, artificial cycle, or mild stimulation cycle. The choice of method of endometrial preparation depends on the maternal infertile characteristics such as menstrual regularity. Then, no more than two thawed embryos were transferred for each patient. Once the pregnancy was achieved, P supplement would be continued until 10 weeks of gestations.All patients who obtained clinical pregnancy were followed up in the form of a telephone interview and recorded every stage of pregnancy until 1 week after delivery. Information including pregnancy-related complications, infant gender, mode of delivery, birth weight and length, birth date and locality, gestational weeks, neonatal diseases, and presence of congenital malformations was included in the standardized questionnaires. Furthermore, detailed reports on pregnancies, deliveries, and neonatal outcomes could be obtained from the gynecologists and pediatricians in charge. For live-born babies with birth defects, case information was gathered by a specially designated nurse to make it clear whether the infants met the definition of the Chinese Birth Defects Monitoring Program.The primary outcome of this study was the incidence of congenital malformations. The secondary outcome measures included low birth weight (LBW), very low birth weight (VLBW), preterm birth (PTB), very preterm birth (VPTB), and early neonatal death rates.Congenital malformations were defined as the structural and functional anomalies as well as genetic defects occurring during pregnancy or at birth, which were classified based on the International Classification of Diseases Q Codes, 10th revision (ICD10: Q00-Q99) , 26. LBWvia Student’s t-test or the Mann–Whitney U test, as appropriate, whereas categorical variables were described as number (percentage) and were compared via the chi-squared test or Fisher’s exact test when appropriate. Statistical significance was defined as a P value <0.05.Sample size calculation was not performed as this was an exploratory retrospective study, and there were no prior data to guide its sample size calculation. Statistical analyses were performed using Statistical Package for Social Sciences  and R statistical programming language . The normality of continuous variables was tested by the histograms and Q–Q plots as well as the Kolmogorov–Smirnov test. Data with normal distribution were presented as the mean ± standard deviation (SD) or as medians for non-normal distribution, and between-group differences were compared vs. GnRH-ant) and LBW, PTB, and congenital malformations after adjusting for the aforementioned confounders. The crude and adjusted odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated.To balance maternal baseline characteristics between two groups, we established a one-to-one propensity score matching (PSM) model using the nearest-neighbor matching algorithm, and after matching, the balance between the two groups was evaluated by the standardized mean difference (<0.1). The potential confounding factors chosen for matching included maternal age, body mass index (BMI), duration of infertility, obstetrical history, cause of infertility, sperm origin, fertilization method, FET endometrial preparation, endometrial thickness, number of transferrable embryos, and embryo stage at transfer. Then, the multivariable logistic regression analysis was further performed to evaluate the possible association between the ovarian stimulation protocol . Moreover, for the treatment with the GnRH-ant protocol, 1,092 ongoing pregnancies could lead to the birth of 1,248 live-born infants . In addition, the selective terminations of pregnancy due to fetal anomalies are listed in vs. 3.01 ± 2.61, P < 0.001). Moreover, the cause of infertility for women in the GnRH-ant group might have significantly contributed more to male factors, endometriosis, and unexplained factor than women in the PPOS group (P = 0.001 <0.05). Furthermore, IVF was applied more frequently in women from the PPOS group than that from the GnRH-ant group , whereas hormone replacement therapy was more preferably used for FET endometrial preparation in women from the GnRH-ant group than that from the PPOS group . Moreover, a significantly fewer proportion of double embryos was transferred in the PPOS group compared with the GnRH-ant group . Regarding the thickness of the endometrium, it was significantly greater in the GnRH-ant group than in the PPOS group . After matching, all maternal baseline characteristics of 1,038 women per group were similarly adjusted, and no significant difference was observed between the two groups concerning all confounding factors.As shown in Congenital malformations were observed in 323 of 15,245 (2.1%) in the PPOS group and 27 of 1,248 (2.2%), with a non-significant difference (P = 0.916 <0.05) as Just as Recently, the PPOS protocol has been suggested to reduce the incidence of OHSS and produce similar pregnancy outcomes compared with conventional protocols. However, the safety about the novel protocol needs to be well settled. As far as we know, our study is the largest retrospective cohort study to investigate the neonatal outcomes and congenital malformations after PPOS treatment compared with GnRH-ant treatment considering and matching the possible influence factors as much as possible to date. We found that PPOS was a safe choice of ovarian stimulation for offspring without compromised neonatal outcomes or increasing the risk of congenital malformations.in vivo and in vitro remained controversial. Fukui et al. also found that adding P to the in vitro culture system could decrease the rate of bovine oocyte maturation  , 34. MorTo further address this issue, a detailed analysis using a retrospective study containing a large amount of samples was run to investigate the safety for the PPOS protocol with regard to the neonatal outcomes and the occurrence of birth defects. In our study, we demonstrated that the neonatal outcomes and the incidence of congenital malformations were always comparable between the PPOS protocol and the GnRH-ant protocol after matching and adjusting for various confounding factors, suggesting that P could not have an adverse effect on the safety for offspring, which was in line with previous studies –17. Our The greatest strength of our study is that we analyze the data with more than 10,000 newborn samples to make the conclusion reliable. However, the minor differences were easy to be significant for the baseline characteristics due to the large sample size of the study, so we have adopted PSM analysis to balance the between-group differences of baseline characteristics, which could reduce the systematic bias. Moreover, the results were confirmed after using multivariable regression analysis controlling for vital confounding factors. In addition, the data of the study were collected in a unified way, and its analysis was performed in a single IVF center including applying for the same IVF procedures and vitrification or thawing procedures to avoid possible errors. Nevertheless, the study also has its limitations. The retrospective cohort study has its inherent defects easily leading to selection bias; therefore, a more rigorous prospective randomized controlled study is required to increase the strength of our study. Furthermore, although we have analyzed the results considering the influential factors as much as possible, there were still some unknown confounders not accounted into the study.In conclusion, our study suggests that the PPOS protocol with a comparison of the GnRH-ant protocol is safe for offspring in terms of newborn outcomes and congenital malformations. In the near future, PPOS treatment might become an appealing option for infertile women. Moreover, the long-term follow-up for the health of babies born after treatment with PPOS is still further needed to testify the safety of this protocol.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the institutional ethics committee of the ninth hospital. The patients/participants provided their written informed consent to participate in this study.XL, YK, and RC conceived of and designed the study. DL collected the data, performed the statistical analysis, and drafted the first version of the manuscript. ZH, QC, and WC helped making interpretation for the data. All authors contributed to the manuscript revision and read and approved the submission of the final version of the manuscript.This research was supported by grants from the National Natural Science Foundation of China (no.81873856), the “Two-hundred Talent” (20191814), Shanghai Health and Family Planning Commission (201940287), and the Shanghai Science and Technology Innovation Fund .We sincerely appreciate the support and cooperation from all staff members of the Department of Assisted Reproduction in Shanghai Ninth People’s Hospital.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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In working ages, sickness absence is strongly related to persons’ health condition. We studied how birth order was associated with receipt of sickness absence, distinguishing mental disorders, musculoskeletal disorders and injuries.A follow-up study based on the entire Finnish population was conducted for sibling groups born 1969-1982, in which each sibling was observed from age 35 years in the period 2004-2018. Focus was on within-family variation in first-time sickness allowance receipt. Stratified Cox regressions were estimated using Stata 16.Each increase in birth order was associated with a higher risk of sickness absence. For mental disorders, the hazard ratio as compared to first borns was 1.03  of second borns, 1.10  of third borns, and 1.52  of fourth or higher borns. Corresponding numbers for musculoskeletal disorders were 1.12 , 1.19  and 1.15 , and for injuries 1.06 , 1.09  and 0.96 , respectively. These associations were notably stronger for women than men, but modestly influenced by mother’s age at birth, educational level, occupation, income and family composition.The study was the first to provide evidence about an interrelation between birth order and sickness absence, utilising diagnose information and family fixed effects methods. A clear birth order pattern was observed for these main causes, but not for a residual group that consisted of other miscellaneous causes. Higher birth order was found associated with a higher risk of sickness absence, and particularly so for mental disorders and muscoloskeletal disorders in women. The underlying mechanisms behind these associations constitute an avenue for future research.• Using sibling fixed effects, the study was the first to analyse if sickness absence by main cause is associated with birth order in working ages.• First-born siblings were the least likely to be sick because of mental or musculoskeletal disorders, and the associations were notably more accentuated for women than men.