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Abstract Background Regular dental l check-ups and good oral hygiene are challenging for nursing home residents, resulting in poor oral health. The interRAI instrument for Long-Term Care Facilities (LTCF) enables caregivers to evaluate residents’ health, including oral health, and to integrate oral care into general care planning. Because the current oral heal1th section in the interRAI instruments does not accurately identify residents who need help with daily oral care or dental referral, the interRAI Oral Health Section (OHS-interRAI) was developed. The OHS-interRAI differs from the current section by including more items, response options and guidelines, photographs, instruction videos, and Collaborative Action Points to alert caregivers when oral care is needed. This study describes and compares residents’ oral health status assessed by caregivers using the current section and the OHS-interRAI. Methods This cross-sectional study includes baseline data of adults aged 65 years or older in Flemish and Dutch nursing homes, collected by professional caregivers (e.g., nurses, nurse aids, therapists). Assessments with the current section dated from October 2016 to January 2023, and with the OHS-interRAI from October 2020 to January 2023. Results InterRAI assessments of 12,476 residents from 158 nursing homes with the current section were compared with those of 1212 residents from 37 nursing homes with the OHS-interRAI. The OHS-interRAI assessments showed more missing data. A higher proportion of oral health problems was detected with the OHS-interRAI compared to the current section for chewing function (13.7% vs. 6.8%), dry mouth (9.8% vs. 7.6%), teeth (22.1% vs. 16.6% ),and gums (7.8% vs. 3.1%). There was no significant difference in the proportion of residents with discomfort or pain in the mouth. Conclusions More missing OHS-interRAI data may be attributed to regulatory decisions on using the interRAI LTCF instrument. Caregivers identified more oral health problems with the OHS-interRAI, which may be due to its additional features, such as photographs and extensive instructions. The Collaborative Action Points included in the OHS-interRAI support continuity of care and enable integration of oral care into general care. Further research is needed to evaluate whether the OHS-interRAI accurately identifies residents who need help with daily oral hygiene or dental referral.

Determining the wood properties is fundamental because these properties are directly related to wood quality. The near-infrared (NIR) spectroscopy technique has been used to determine various properties of wood. However, even with promising results, NIR spectroscopy needs to be further investigated to evaluate the robustness of its estimates. The objective of this study was to develop regression models from NIR spectra to estimate the basic density and the extractive and lignin contents of wood as well as to verify their robustness through independent and cross-validation. NIR spectra were initially obtained through an integration sphere and optical fiber for the transverse and radial faces of solid wood and through an integration sphere for powdered wood. The wood basic density and the extractive and lignin contents were determined by conventional methods in 180 and 143 specimens, respectively. The samples were collected from Eucalyptus urophylla × Eucalyptus grandis clones aged 5 years. The basic density and extractive and lignin content values were correlated with the NIR spectra by a partial least squares regression. The best models for estimating the basic density of the wood were generated from the spectra obtained on the transverse surface, both with the integration sphere pathway and in the optical fiber pathway. For estimating the chemical properties of wood, the best models were developed from the powdered wood via the integration sphere for assessing the extractive content, Klason lignin, acid-soluble lignin content and total lignin. In all the models, the mathematical treatment of the data by the first derivative was essential for better fitting the models and reducing the error. We concluded that NIR spectroscopy was effective for the estimation of basic density and extractive and lignin contents of wood.

The prognosis of patients with mycosis fungoides is variable. As the current literature is scarce and shows mixed results this study investigates the incidence of other primary malignancies in mycosis fungoides patients. A retrospective, nationwide, population- based cohort study was performed with patients with mycosis fungoides between 2000 and 2020 in The Netherlands. All histopathology reports were requested from the Nationwide Network and Registry of Histo- and Cytopathology and screened for other primary malignancies. Lifelong incidence rates were used to compare the incidence of malignancies in mycosis fungoides patients and the general population. In total 1,024 patients were included with a mean follow-up of 10 years (SD 6). A total of 294 cases of other primary malignancies were found with 29% of the mycosis fungoides patients developing at least 1 other primary malignancy. Only cutaneous (odds ratio [OR] 2.54; CI 2.0–3.2) and haematological malignancies (OR 2.62; CI 2.00–3.42) had a statistically significant higher incidence than the Dutch population overall. Mycosis fungoides patients have a significantly increased risk of developing melanomas (OR 2.76; CI 2.11–3.59) and cutaneous squamous cell carcinomas mycosis fungoides (OR 2.34; CI 1.58–3.45). This study shows no association between mycosis fungoides and other solid organ tumours; however, such patients are significantly at risk of developing other haematological and cutaneous malignancies. Clinicians should be aware of this increased risk.

Abstract Background The management of persons with multimorbidity challenges healthcare systems tailored to individual diseases. A person-centred care approach is advocated, in particular for persons with multimorbidity. The aim of this study was to describe the co-creation and piloting of a proactive, person-centred chronic care approach for persons with multimorbidity in general practice, including facilitators and challenges for successful implementation. Methods A participatory action research (PAR) approach was applied in 13 general practices employing four subsequent co-creation cycles between 2019 and 2021. The target population included adults with ≥3 chronic conditions. Participating actors were general practitioners (GPs), practice nurses (PNs), patients (target group), the affiliated care cooperation, representatives of a health insurer and researchers. Each cycle consisted of a try-out period in practice and a reflective evaluation through focus groups with healthcare providers, interviews with patients and analyses of routine care data. In each cycle, facilitators, challenges and follow-up actions for the next cycle were identified. Work satisfaction among GPs and PNs was measured pre and at the end of the final co-creation cycle. Results Identified essential steps in the person-centred chronic care approach include (1) appropriate patient selection for (2) an extended person-centred consultation, and (3) personalised goalsetting and follow-up. Key facilitators included improved therapeutic relationships, enhanced work satisfaction for care providers, and patient appreciation of extended time with their GP. Deliberate task division and collaboration between GPs and PNs based on patient, local setting, and care personnel is required. Challenges and facilitators for implementation encompassed a prioritisation tool to support GPs appropriately who to invite first for extended consultations, appropriate remuneration and time to conduct extended consultations, training in delivering person-centred chronic care available for all general practice care providers and an electronic medical record system accommodating comprehensive information registration. Conclusions A person-centred chronic care approach targeting patients with multimorbidity in general practice was developed and piloted in co-creation with stakeholders. More consultation time facilitated better understanding of persons’ situations, their functioning, priorities and dilemma’s, and positively impacted work satisfaction of care providers. Challenges need to be tackled before widespread implementation. Future evaluation on the quadruple aims is recommended.

Abstract Background There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual’s true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one’s chronological age-independent aging divergence ∆. Methods We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold. Results The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging’s association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random. Conclusions Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

Background: : Qualitative research shows barriers in access to healthcare for persons with a migration background, possibly leading to healthcare inequalities. We aimed to compare healthcare use between older community-dwelling persons with a migration background and native Dutch persons with dementia. Methods: : Observational cohort study using electronic health records of NIVEL Primary Care Database (2013–2014) linked to nationwide administrative registries managed by Statistics Netherlands. We included all community-dwelling persons with dementia in the Netherlands aged 65 years and older. Negative binomial and logistic regression analyses were conducted to assess differences in healthcare use outcomes. The largest migrant groups (persons born in Surinam, Turkey and Morocco) were studied separately. Results: : 138,864 persons with dementia were included in the study, whereof 3,991 persons with a migration background and 132,477 native Dutch persons. Moroccan-born persons showed 1.46 higher odds of having an unplanned hospital admission compared to native Dutch persons (95 % Confidence Interval [CI] 1.22–1.74). Persons with a migration background used less professional home care (Odds Ratio [OR] 0.55, 95 %CI 0.51–0.59), and received less home visits from their general practitioner (OR 0.50, 95 %CI 0.43–0.57) than native Dutch persons. Moroccan-born persons had more often an out-of-hours general practitioner consultation compared to native Dutch persons (Incidence Rate Ratio 1.36, 95 %CI 1.18–1.57). Conclusions: : We found significant quantitative differences in healthcare use between persons with a migration background and native Dutch persons with dementia. Further research is needed to gain insight into the reasons of these differences, with consideration of specific migration background.

Abstract Introduction This paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support. Methods This paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability). Results The linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long‐term care and medication use, that cannot be obtained from single data sources. Conclusions A key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data.

Background & Aims: In the USA, inequal liver transplantation (LT) access exists between patients with and without hepatocellular carcinoma (HCC). Survival benefit considers survival without and with LT and could equalise LT access. We calculated bias-corrected LT survival benefit for patients with(out) HCC who underwent a transplant, based on longitudinal data in a recent United States cohort. Methods: Adult LT candidates with(out) HCC between 2010 and 2019 were included. Waitlist survival over time was contrasted to post-transplant survival, to estimate 5-year survival benefit from the moment of LT. Waitlist survival was modelled with a bias-corrected Cox regression, and post-transplant survival was estimated through Cox proportional hazards regression. Results: Mean HCC survival without LT was always lower than non-HCC waitlist survival. Below model for end-stage liver disease (sodium) (MELD(-Na)) 30, patients with HCC gained more life-years from LT than patients without HCC at the same MELD(-Na) score. Only patients without HCC below MELD(-Na) 9 had negative benefit. Most patients with HCC underwent a transplant below MELD(-Na) 14, and most patients without HCC underwent a transplant above MELD(-Na) 26. Liver function [MELD(-Na), albumin] was the main predictor of 5-year benefit. Therefore, during 5 years, most patients with HCC gained 0.12 to 1.96 years from LT, whereas most patients without HCC gained 2.48 to 3.45 years. Conclusions: On an individual level, performing a transplant in patients with HCC resulted in survival benefit. However, on a population level, benefit was indirectly decreased, as patients without HCC were likely to gain more survival owing to decreased liver function. For patients who underwent a transplant, a constructed online calculator estimates 5-year survival benefit given specific patient characteristics. Survival benefit scores could serve to equalise LT access. Impact and implications: Benefit is a comparison of the survival with and without liver transplantation, and it is important when deciding who should undergo a transplant. Liver function is most important when predicting possible benefit from transplantation. Patients with liver cancer die sooner on the waiting list than similar patients without liver cancer. However, patients with liver cancer more often have better liver function. Most patients without liver cancer derive more benefit from transplantation than patients with liver cancer.

Abstract Background In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presence of CRs. As recently there is a growing interest in applying machine learning (ML) for clinical prediction, these techniques have also been extended to model CRs but literature is limited. Here, our aim is to investigate the potential role of ML versus SM for CRs within non-complex data (small/medium sample size, low dimensional setting). Methods A dataset with 3826 retrospectively collected patients with extremity soft-tissue sarcoma (eSTS) and nine predictors is used to evaluate model-predictive performance in terms of discrimination and calibration. Two SM (cause-specific Cox, Fine-Gray) and three ML techniques are compared for CRs in a simple clinical setting. ML models include an original partial logistic artificial neural network for CRs (PLANNCR original), a PLANNCR with novel specifications in terms of architecture (PLANNCR extended), and a random survival forest for CRs (RSFCR). The clinical endpoint is the time in years between surgery and disease progression (event of interest) or death (competing event). Time points of interest are 2, 5, and 10 years. Results Based on the original eSTS data, 100 bootstrapped training datasets are drawn. Performance of the final models is assessed on validation data (left out samples) by employing as measures the Brier score and the Area Under the Curve (AUC) with CRs. Miscalibration (absolute accuracy error) is also estimated. Results show that the ML models are able to reach a comparable performance versus the SM at 2, 5, and 10 years regarding both Brier score and AUC (95% confidence intervals overlapped). However, the SM are frequently better calibrated. Conclusions Overall, ML techniques are less practical as they require substantial implementation time (data preprocessing, hyperparameter tuning, computational intensity), whereas regression methods can perform well without the additional workload of model training. As such, for non-complex real life survival data, these techniques should only be applied complementary to SM as exploratory tools of model’s performance. More attention to model calibration is urgently needed.

Waldenström macroglobulinemia (WM) is a rare B-cell Non-Hodgkin Lymphoma. There are only few prospective randomized clinical trials to guide treatment recommendations and there is no international consensus on a preferred first line treatment approach. In the recently revised Dutch guideline for WM, we describe recommendations for practice based as much as possible on the known data. Here, we summarize the considerations for first-line treatment based on these Dutch guidelines. Available evidence is summarized, including efficacy and toxicity data. Combinations of Rituximab with chemotherapy, proteasome inhibition or BTK-inhibition are all valid first line treatment options. The Dutch WM working group considers Dexamethasone/Rituximab/Cylofosfamide (DRC) a suitable first-line treatment for many WM patients, given the efficacy, the relatively mild toxicity profile and the extensive experience with this regimen. However, the long-term toxicities of DRC are unclear and need further clarification. Other regimens such as R-bendamustine, R-Bortezomib-dexamethason are also effective options, however with specific toxicities. BTK-inhibitors are not a preferred option in first line for most patients in the Dutch WM guidelines because of the need for longterm treatment and toxicities. Based on patient preferences research, future clinical trials should focus on effective fixed-duration regimens with non-cytotoxic therapies that have a favorable toxicity profile. Further development of (combinations with) BCL-2 inhibititors, novel proteasome inhibitors and BTK-inhibition could be interesting. In addition T-cell-directed treatments including bispecific antibodies as a monotherapy or combined with other novel agents deserve further study in WM.

As a faster, reliable, and low cost technique, applicable to large samplings, near infrared (NIR) spectroscopy technology has been widely applied for high-throughput phenotyping in forest breeding programmes. The aim of this study was to develop multivariate models for estimating the chemical and physical properties of juvenile wood based on NIR signatures of milled wood. Moreover, two approaches, namely, external validation by clone and by age, were tested to validate the model for estimating extractive content. NIR spectra of wood specimens taken from three clones of Eucalyptus urophylla (one to six years old) grown in southern Brazil were used to calibrate and validate models for predicting the wood basic density, total extractives, ash content, holocellulose content, syringyl to guaiacyl ratio (S/G) and elementary components of the wood. PLS-R models were validated by an independent set of wood specimens and presented promising statistics for the estimating wood density (R2p = 0.768), extractives (R2p = 0.912), ash (R2p = 0.936) and carbon (R2p = 0.697) contents from NIR signatures measured in the milled wood of young trees. Furthermore, NIR models for estimating the extractive content of wood were validated using the clones or ages left out of the training sets. Most models presented satisfactory statistics (R2 > 90%) and could be applied to routine laboratory analyses or to select potential trees in Eucalyptus breeding programmes.

Alloreactive donor-derived T-cells play a pivotal role in alloimmune responses after allogeneic hematopoietic stem cell transplantation (alloSCT); both in the relapse-preventing Graft-versus-Leukemia (GvL) effect and the potentially lethal complication Graft-versus-Host-Disease (GvHD). The balance between GvL and GvHD can be shifted by removing T-cells via T-cell depletion (TCD) to reduce the risk of GvHD, and by introducing additional donor T-cells (donor lymphocyte infusions [DLI]) to boost the GvL effect. However, the association between T-cell kinetics and the occurrence of allo-immunological events has not been clearly demonstrated yet. Therefore, we investigated the complex associations between the T-cell kinetics and alloimmune responses in a cohort of 166 acute leukemia patients receiving alemtuzumab-based TCD alloSCT. Of these patients, 62 with an anticipated high risk of relapse were scheduled to receive a prophylactic DLI at 3 months after transplant. In this setting, we applied joint modelling which allowed us to better capture the complex interplay between DLI, T-cell kinetics, GvHD and relapse than traditional statistical methods. We demonstrate that DLI can induce detectable T-cell expansion, leading to an increase in total, CD4+ and CD8+ T-cell counts starting at 3 months after alloSCT. CD4+ T-cells showed the strongest association with the development of alloimmune responses: higher CD4 counts increased the risk of GvHD (hazard ratio 2.44, 95% confidence interval 1.45-4.12) and decreased the risk of relapse (hazard ratio 0.65, 95% confidence interval 0.45-0.92). Similar models showed that natural killer cells recovered rapidly after alloSCT and were associated with a lower risk of relapse (HR 0.62, 95%-CI 0.41-0.93). The results of this study advocate the use of joint models to further study immune cell kinetics in different settings.

Introduction In ageing societies, the number of older adults with complex chronic conditions (CCCs) is rapidly increasing. Care for older persons with CCCs is challenging, due to interactions between multiple conditions and their treatments. In home care and nursing homes, where most older persons with CCCs receive care, professionals often lack appropriate decision support suitable and sufficient to address the medical and functional complexity of persons with CCCs. This EU-funded project aims to develop decision support systems using high-quality, internationally standardised, routine care data to support better prognostication of health trajectories and treatment impact among older persons with CCCs.Methods and analysis Real-world data from older persons aged ≥60 years in home care and nursing homes, based on routinely performed comprehensive geriatric assessments using interRAI systems collected in the past 20 years, will be linked with administrative repositories on mortality and care use. These include potentially up to 51 million care recipients from eight countries: Italy, the Netherlands, Finland, Belgium, Canada, USA, Hong Kong and New Zealand. Prognostic algorithms will be developed and validated to better predict various health outcomes. In addition, the modifying impact of pharmacological and non-pharmacological interventions will be examined. A variety of analytical methods will be used, including techniques from the field of artificial intelligence such as machine learning. Based on the results, decision support tools will be developed and pilot tested among health professionals working in home care and nursing homes.Ethics and dissemination The study was approved by authorised medical ethical committees in each of the participating countries, and will comply with both local and EU legislation. Study findings will be shared with relevant stakeholders, including publications in peer-reviewed journals and presentations at national and international meetings.

Background Prediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to develop prediction models for first‐ever cardiovascular event risk in men and women aged 30 to 49 years. Methods and Results We included patients aged 30 to 49 years without cardiovascular disease from a Dutch routine care database. Outcome was defined as first‐ever cardiovascular event. Our reference models were sex‐specific Cox proportional hazards models based on traditional cardiovascular predictors, which we compared with models using 2 predictor subsets with the 20 or 50 most important predictors based on the Cox elastic net model regularization coefficients. We assessed the C‐index and calibration curve slopes at 10 years of follow‐up. We stratified our analyses based on 30‐ to 39‐year and 40‐ to 49‐year age groups at baseline. We included 542 141 patients (mean age 39.7, 51% women). During follow‐up, 10 767 cardiovascular events occurred. Discrimination of reference models including traditional cardiovascular predictors was moderate (women: C‐index, 0.648 [95% CI, 0.645–0.652]; men: C‐index, 0.661 [95%CI, 0.658–0.664]). In women and men, the Cox proportional hazard models including 50 most important predictors resulted in an increase in C‐index (0.030 and 0.012, respectively), and a net correct reclassification of 3.7% of the events in women and 1.2% in men compared with the reference model. Conclusions Sex‐specific electronic health record‐derived prediction models for first‐ever cardiovascular events in the general population aged

Abstract Clofarabine is an active antileukemic drug for subgroups of patients with acute myeloid leukemia (AML). Multi‐state models can provide additional insights to supplement the original intention‐to‐treat analysis of randomized controlled trials (RCT). We re‐analyzed the HOVON102/SAKK30/09 phase III RCT for newly diagnosed AML patients, which randomized between standard induction chemotherapy with or without clofarabine. Using multi‐state models, we evaluated the effects of induction chemotherapy outcomes (complete remission [CR], measurable residual disease [MRD]), and post‐remission therapy with allogeneic stem cell transplantation [alloSCT] on relapse and death. Through the latter a consistent reduction in the hazard of relapse in the clofarabine arm compared to the standard arm was found, which occurred irrespective of MRD status or post‐remission treatment with alloSCT, demonstrating a strong and persistent antileukemic effect of clofarabine. During the time period between achieving CR and possible post‐remission treatment with alloSCT, non‐relapse mortality was higher in patients receiving clofarabine. An overall net benefit of treatment with clofarabine was identified using the composite endpoint current leukemia‐free survival (CLFS). In conclusion, these results enforce and extend the earlier reported beneficial effect of clofarabine in AML and show that multi‐state models further detail the effect of treatment on competing and series of events.

Abstract Background Assessing calibration—the agreement between estimated risk and observed proportions—is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention. Methods We propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of the cause-specific outcome of interest on the predicted outcome risk of the model whose calibration we want to assess. We provide modifications of the integrated calibration index (ICI), of E50 and of E90, which are numerical calibration metrics, for use with competing risk data. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and when the model was mis-specified and when the incidence of the cause-specific outcome differed between the derivation and validation samples. We illustrated the usefulness of calibration curves and the numerical calibration metrics by comparing the calibration of a Fine-Gray subdistribution hazards regression model with that of random survival forests for predicting cardiovascular mortality in patients hospitalized with heart failure. Results The simulations indicated that the method for constructing graphical calibration curves and the associated calibration metrics performed as desired. We also demonstrated that the numerical calibration metrics can be used as optimization criteria when tuning machine learning methods for competing risk outcomes. Conclusions The calibration curves and numeric calibration metrics permit a comprehensive comparison of the calibration of different competing risk models.

Hypothermic machine perfusion (HMP) provides preservation superior to cold storage and may allow for organ assessment prior to transplantation. Since flavin mononucleotide (FMN) in perfusate has been proposed as a biomarker of organ quality during HMP of donor livers, the aim of this study was to validate FMN as a biomarker for organ quality in the context of HMP preserved kidneys. Perfusate samples (n = 422) from the paired randomised controlled COPE-COMPARE-trial, comparing HMP with oxygenation (HMPO2) versus standard HMP in kidneys, were used. Fluorescence intensity (FI) was assessed using fluorescence spectroscopy (excitation 450nm; emission 500-600nm) and validated by fluorospectrophotometer and targeted liquid chromatography mass spectrometry (LC-MS/MS). Fluorescence intensity (FI)(ex450;em500-600) increased over time during machine perfusion in both groups (p

Background: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor–stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible. Methods: A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations. Results: 37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23–0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures. Conclusion: Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists.

Abstract Background Accurate identification of older persons at risk of unplanned hospital visits can facilitate preventive interventions. Several risk scores have been developed to identify older adults at risk of unplanned hospital visits. It is unclear whether risk scores developed in one country, perform as well in another. This study validates seven risk scores to predict unplanned hospital admissions and emergency department (ED) visits in older home care recipients from six countries. Methods We used the IBenC sample (n = 2446), a cohort of older home care recipients from six countries (Belgium, Finland, Germany, Iceland, Italy and The Netherlands) to validate four specific risk scores (DIVERT, CARS, EARLI and previous acute admissions) and three frailty indicators (CHESS, Fried Frailty Criteria and Frailty Index). Outcome measures were unplanned hospital admissions, ED visits or any unplanned hospital visits after 6 months. Missing data were handled by multiple imputation. Performance was determined by assessing calibration and discrimination (area under receiver operating characteristic curve (AUC)). Results Risk score performance varied across countries. In Iceland, for any unplanned hospital visits DIVERT and CARS reached a fair predictive value (AUC 0.74 [0.68–0.80] and AUC 0.74 [0.67–0.80]), respectively). In Finland, DIVERT had fair performance predicting ED visits (AUC 0.72 [0.67–0.77]) and any unplanned hospital visits (AUC 0.73 [0.67–0.77]). In other countries, AUCs did not exceed 0.70. Conclusions Geographical validation of risk scores predicting unplanned hospital visits in home care recipients showed substantial variations of poor to fair performance across countries. Unplanned hospital visits seem considerably dependent on healthcare context. Therefore, risk scores should be validated regionally before applied to practice. Future studies should focus on identification of more discriminative predictors in order to develop more accurate risk scores.

Background and purpose: In the last decade, the direct anterior approach (DAA) for total hip arthroplasty (THA) has become more popular in the Netherlands. Therefore, we investigated the learning curve and survival rate of the DAA in primary THA, using data from the Dutch Arthroplasty Register (LROI). Patients and methods: We identified all patients who received a primary THA using the DAA in several high-volume centers in the Netherlands between 2007 and 2019 (n = 15,903). Procedures were ordered per surgeon, using date of operation. Using the procedure number, operations were divided into 6 groups based on the number of previous procedures per surgeon (first 25, 26–50, 51–100, 101–150, 151–200, > 200). Data from different surgeons in different hospitals was pooled together. Revision rates were calculated using a multilevel time-to-event analysis. Results: Patients operated on in group 1–25 (hazard ratio [HR] 1.6; 95% CI 1.1–2.4) and 26–50 (HR 1.6; CI 1.1–2.5) had a higher risk for revision compared with patients operated on in group > 200 THAs. Between 50 and 100 procedures the revision risk was increased (HR 1.3; CI 0.9–1.9), albeit not statistically significant. From 100 procedures onwards the HR for revision was respectively 1.0 (CI 0.6–1.6) and 0.8 (CI 0.5–1.4) for patients in operation groups 101–150 and 151–200. Main reasons for revision were loosening of the stem (29%), periprosthetic infection (19%), and dislocation (16%). Interpretation: We found a 64% increased risk of revision for patients undergoing THA using the DAA for the first 50 cases per surgeon. Between 50 and 100 cases, this risk was 30% increased, but not statistically significant. From 100 cases onwards, a steady state had been reached in revision rate. The learning curve for DAA therefore is around 100 cases.

Patients with Chronic Lymphocytic Leukemia (CLL) have a 29- to 36-fold increased risk of invasive pneumococcal disease (IPD) compared to healthy adults. Therefore, most guidelines recommend vaccination with the 13-valent pneumococcal conjugated vaccine (PCV13) followed 2 months later by the 23-valent polysaccharide vaccine (PPSV23). Because both CLL as well as immunosuppressive treatment have been identified as major determinants of immunogenicity, we aimed to assess the vaccination schedule in untreated and treated CLL patients. We quantified pneumococcal IgG concentrations against five serotypes shared across both vaccines, and against four serotypes unique to PPSV23, before and eight weeks after vaccination. In this retrospective cohort study, we included 143 CLL patients, either treated (n = 38) or naive to treatment (n = 105). While antibody concentrations increased significantly after vaccination, the overall serologic response was low (10.5%), defined as a ≥4-fold antibody increase against ≥70% of the measured serotypes, and significantly influenced by treatment status and prior lymphocyte number. The serologic protection rate, defined as an antibody concentration of ≥1.3 µg/mL for ≥70% of serotypes, was 13% in untreated and 3% in treated CLL patients. Future research should focus on vaccine regimens with a higher immunogenic potential, such as multi-dose schedules with higher-valent T cell dependent conjugated vaccines.

Introduction Anastomotic leakage (AL) is one of the major complications after colorectal surgery. Compromised tissue perfusion at the anastomosis site increases the risk of AL. Several cohort studies have shown that indocyanine green (ICG) combined with fluorescent near-infrared imaging is a feasible and reproducible technique for real-time intraoperative imaging of tissue perfusion, leading to reduced leakage rates after colorectal resection. Unfortunately, these studies were not randomised. Therefore, we propose a randomised controlled trial to assess the value of ICG-guided surgery in reducing AL after colorectal surgery.Methods and analysis A multicentre, randomised controlled clinical trial will be conducted to assess the benefit of ICG-guided surgery in preventing AL. A total of 978 patients scheduled for colorectal surgery will be included. Patients will be randomised between the Fluorescence Guided Bowel Anastomosis group and the Conventional Bowel Anastomosis group. The primary endpoint is clinically relevant AL (defined as requiring active therapeutic intervention or reoperation) within 90 days after surgery. Among the secondary endpoints are 30-day clinically relevant AL, all-cause postoperative complications, all-cause and AL-related mortality, surgical and non-surgical reinterventions, total surgical time, length of hospital stay and all-cause and AL-related readmittance.Ethics and dissemination This protocol has been approved by the Medical Ethical Committee Leiden-Den Haag-Delft (METC-LDD) and is registered at ClinicalTrials.gov and trialregister.nl. The results of this study will be reported through peer-reviewed publications and conference presentations.Trial registration number NCT04712032; NL7502.

Primary plasma cell leukemia (pPCL) is a rare and challenging malignancy. There are limited data regarding optimum transplant approaches. We therefore undertook a retrospective analysis from 1998-2014 of 751 patients with pPCL undergoing one of four transplant strategies; single autologous transplant (single auto), single allogeneic transplant (allo-first) or a combined tandem approach with an allogeneic transplant following an autologous transplant (auto-allo) or a tandem autologous transplant (auto-auto). To avoid time bias, multiple analytic approaches were employed including Cox models with time-dependent covariates and dynamic prediction by landmarking. Initial comparisons were made between patients undergoing allo-first (n=70) versus auto-first (n=681), regardless of a subsequent second transplant. The allo-first group had a lower relapse rate (45.9%, 95% confidence interval [95% CI]: 33.2-58.6 vs. 68.4%, 64.4-72.4) but higher non-relapse mortality (27%, 95% CI: 15.9-38.1 vs. 7.3%, 5.2-9.4) at 36 months. Patients who underwent allo-first had a remarkably higher risk in the first 100 days for both overall survival and progression-free survival. Patients undergoing auto-allo (n=122) had no increased risk in the short term and a significant benefit in progression-free survival after 100 days compared to those undergoing single auto (hazard ratio [HR]=0.69, 95% CI: 0.52- 0.92; P=0.012). Auto-auto (n=117) was an effective option for patients achieving complete remission prior to their first transplant, whereas in patients who did not achieve complete remission prior to transplantation our modeling predicted that auto-allo was superior. This is the largest retrospective study reporting on transplantation in pPCL to date. We confirm a significant mortality risk within the first 100 days for allo-first and suggest that tandem transplant strategies are superior. Disease status at time of transplant influences outcome. This knowledge may help to guide clinical decisions on transplant strategy.

Early number skills represent critical milestones in children's cognitive development and are shaped over years of interacting with quantities and numerals in various contexts. Several connectionist computational models have attempted to emulate how certain number concepts may be learned, represented, and processed in the brain. However, these models mainly used highly simplified inputs and focused on limited tasks. We expand on previous work in two directions: First, we train a model end-to-end on video demonstrations in a synthetic environment with multimodal visual and language inputs. Second, we use a more holistic dataset of 35 tasks, covering enumeration, set comparisons, symbolic digits, and seriation. The order in which the model acquires tasks reflects input length and variability, and the resulting trajectories mostly fit with findings from educational psychology. The trained model also displays symbolic and non-symbolic size and distance effects. Using techniques from interpretability research, we investigate how our attention-based model integrates cross-modal representations and binds them into context-specific associative networks to solve different tasks. We compare models trained with and without symbolic inputs and find that the purely non-symbolic model employs more processing-intensive strategies to determine set size.

IntroductionResilience incorporates the presence of a positive response to some type of stressor. To properly explore resilience, it is important to systematically identify relevant stressors. We aimed to identify (combinations of) stressors with the strongest relationship with observer-reported and self-reported mood outcomes in older residents of long-term care facilities (LTCFs) in The Netherlands.Materials and MethodsWe included 4,499 older (≥60) residents of 40 LTCFs who participated in the Dutch InterRAI-LTCF cohort between 2005 and 2018. The association of possible stressors (single stressors, number of stressors, and combinations of two stressors) in this population with observer-reported (Depression Rating Scale) and self-reported mood outcomes was analyzed using multilevel tobit models and logistic regressions.ResultsMajor life stressor [“experiences that (threatened to) disrupt(ed) a person's daily routine and imposed some degree of readjustment”] and conflict with other care recipients and/or staff were most strongly associated with both mood outcomes. Furthermore, conflict was a particularly prevalent stressor (24%). Falls, fractures, and hospital visits were more weakly or not associated at all. Overall, the associations were similar for the mood outcomes based on observer-report and self-report, although there were some differences. Multiple stressors were more strongly associated with both mood outcomes than one stressor.ConclusionMajor life stressor and conflict emerged as important stressors for resilience research within the psychological domain in LTCF residents. Further (longitudinal) research is necessary to determine the directionality and relevance of the strong association of conflict with mood for LTCF practice.