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Cardiovascular diseases (CVDs) significantly impact survival and quality of life, highlighting the necessity for prompt and effective prevention and treatment strategies. Healthcare providers rely on guidelines from authoritative bodies like the ESC, AHA, and ACC to manage CVDs. These guidelines incorporate prediction models crucial for evaluating patient outcomes. This study assessed the integration of such models into international clinical practice guidelines. For example, a review of 146 guidelines part of ESC and AHA/ASCVD revealed 17 that described at least 23 distinct models predicting CVD progression, focusing on long-term risk, mortality, disease development likelihood, etc. While these models are referenced as predictive tools in the guidelines, there is a lack of detailed explanation about their development and predictive accuracy within the guidelines. The study emphasizes the need for scientific progress in the guidelines to fully understand these models' applications, advantages, limitations, and outcome calculation methodologies. It recommends that guidelines offer various predictive models with comprehensive usage details, equipping healthcare professionals with a broad spectrum of tools for outcome evaluation and informed decision-making in patient care. The guidelines discussed did not endorse a particular prognostic model but referred to models from other guidelines or studies. Further research is required to provide clear recommendations on using various predictive models in a specific guideline, which will assist healthcare providers in making definitive choices for particular cardiovascular disease (CVD) cases.

Background The proportion of fetal cell-free DNA (cfDNA) in the maternal circulation (i.e., the fetal fraction) is universally measured as a quality parameter in non-invasive prenatal testing (NIPT) for fetal aneuploidies. As fetal cfDNA originates from the placenta, the fetal fraction might reflect placental health. We assessed the added prognostic value of the fetal fraction in the prediction of adverse pregnancy outcomes. Methods We performed a retrospective cohort study of women with singleton pregnancies that opted for NIPT over a 1-year period between June 2018 and June 2019 within the Dutch national implementation study on NIPT for fetal aneuploidies (TRIDENT-2 study). The TRIDENT-2 study data were linked to the Dutch registry of prenatal screening results (Peridos) and the Dutch registry of pregnancy outcomes (Perined). Outcomes included hypertensive disorders of pregnancy (HDP), birthweight 0.05) for extremely sPTB, very sPTB, congenital anomalies, and combined poor neonatal outcome. For outcomes with a statistically

Juvenile Idiopathic Arthritis (JIA) is the most common rheumatic disease of childhood, affecting 16-150 children per 100,000 worldwide, and approximately 60,000 in Europe alone. The disease is an autoinflammatory disease (i.e., the body’s immune system induces inflammatory responses against itself), characterized by joint inflammation of an unknown cause, persisting for at least 6 weeks in at least 1 joint, with an onset before the age of 16 years. If left untreated, joint inflammation can lead to joint destruction and subsequent permanent disability. Therefore, it is important to properly treat JIA with anti-inflammatory drugs. These drugs are designed to suppress inflammatory responses, limiting or completely alleviating joint inflammation. Current treatment recommendations for JIA consist of a step-up approach. This approach recommends starting treatment with the most “simple” form of disease modifying anti-rheumatic drugs (DMARDs), namely non-steroidal anti-inflammatory drugs (NSAIDs) and intra-articular corticosteroid injections. Children who do not respond to either of these treatment options are escalated to more intensive DMARDs, the conventional synthetic DMARDs (csDMARDs). The most frequently prescribed csDMARD is methotrexate (MTX), specifically as oral tablet. If inactive disease with csDMARDs is not achieved within 6 months, treatment is intensified with concomitant biologic DMARDs (bDMARDs). These are more effective, more expensive, and can almost exclusively be prescribed in the form of a subcutaneous injection. Recent studies on long-term disease outcomes and JIA have shown that patients with early attainment of inactive disease exhibit better long-term outcomes. Identification and prediction of which patients will not respond to specific treatment options would be highly valuable for clinical practice. Because MTX is the most frequently prescribed treatment option, this research project focussed on the prediction of MTX nonresponse in children with

Head and neck cancer patients (HNC) treated with radiotherapy often suffer from radiation-induced toxicities, most often xerostomia (dry mouth) and dysphagia (difficulty swallowing). As to reduce the risk of toxicities in these patients, Normal Tissue Complication Probability (NTCP) modeling is used to determine the probability to develop toxicities based on patient and treatment characteristics. Currently, most often supervised logistic regression methods are used in NTCP modeling. However, as the toxicity outcomes that are used in NTCP modeling of HNC are often recorded long after treatment started, ‘unlabeled’ data are also available. In these data the patient and treatment characteristics are recorded, but not yet the toxicity outcomes. Semi-supervised methods are able to incorporate unlabeled data in model development and may thereby gain in predictive performance compared to the current supervised logistic regression models. Here, it will be evaluated how current regression models compare to the semi-supervised method of self-training, and to regression models after multivariate imputation by chain equation (MICE) of the unlabeled data. The models were developed for the most common toxicity outcomes in HNC patients, xerostomia and dysphagia, measured at six months after treatment, in a development cohort of 750 HNC patients. The models were externally validated in a validation cohort of 395 HNC patients. It was found that MICE and self-training did not have a gain in performance in terms of discrimination or calibration at external validation compared to current regression models. Therefore, the addition of unlabeled patient data by using the semi-supervised method of self-training or MICE would not be preferred in current NTCP modeling for HNC patients.

Contains fulltext : 297092.pdf (Publisher’s version ) (Open Access), BACKGROUND: Women with multiple pregnancies are at risk for maternal complications such as preterm birth. Hazardous working conditions, such as physically demanding work and long and irregular working hours, might increase the risk of preterm birth. OBJECTIVE: This study primarily aimed to determine whether certain working conditions up to 20 weeks of pregnancy increase the risk of preterm birth in multiple pregnancies. The secondary objective was to evaluate whether the working conditions of Dutch women with multiple pregnancy have been adjusted to the guidelines of the Netherlands Society of Occupational Medicine. STUDY DESIGN: We performed a prospective cohort study alongside the ProTWIN trial, a multicenter randomized controlled trial that assessed whether cervical pessaries could effectively prevent preterm birth. Women with paid work of >8 hours per week completed questionnaires concerning general health and working conditions between 16 and 20 weeks of pregnancy. Univariable and multivariable logistic regression analyses were performed to identify work-related factors associated with preterm birth (32-36 weeks' gestation) and very preterm birth (<32 weeks' gestation). We analyzed a subgroup of participants who worked for more than half of the week (>28 hours). We calculated the proportion of women who reported work-related factors not in accordance with guidelines. RESULTS: We studied 383 women, of whom 168 (44%) had been randomized to pessary, 142 (37%) to care as usual, and 73 (19%) did not participate in the randomized part of the study. After adjusting for confounding variables, working >28 hours was associated with very preterm birth (n=33; 78%) (adjusted odds ratio, 3.0; 95% confidence interval, 1.1-8.1), and irregular working times were associated with preterm birth (n=26, 17%) (adjusted odds ratio, 2.0; 95% confidence interval, 1.0-4.1) and very preterm birth (n=10; 24%) (adjusted odds ratio, 2.7; 95% confidence interval, 1.0-7.3). Within a subgroup o

Objectives: To identify all prediction models for fetal and neonatal outcomes in pregnancies with preterm manifestations of placental insufficiency (gestational hypertension, pre-eclampsia, HELLP syndrome or fetal growth restriction with its onset before 37 weeks' gestation) and to assess the quality of the models and their performance on external validation. Methods: A systematic literature search was performed in PubMed, Web of Science and EMBASE. Studies describing prediction models for fetal/neonatal mortality or significant neonatal morbidity in patients with preterm placental insufficiency disorders were included. Data extraction was performed using the CHARMS checklist. Risk of bias was assessed using PROBAST. Literature selection and data extraction were performed by two researchers independently. Results: Our literature search yielded 22 491 unique publications. Fourteen were included after full-text screening of 218 articles that remained after initial exclusions. The studies derived a total of 41 prediction models, including four models in the setting of pre-eclampsia or HELLP, two models in the setting of fetal growth restriction and/or pre-eclampsia and 35 models in the setting of fetal growth restriction. None of the models was validated externally, and internal validation was performed in only two studies. The final models contained mainly ultrasound (Doppler) markers as predictors of fetal/neonatal mortality and neonatal morbidity. Discriminative properties were reported for 27/41 models (c-statistic between 0.6 and 0.9). Only two studies presented a calibration plot. The risk of bias was assessed as unclear in one model and high for all other models, mainly owing to the use of inappropriate statistical methods. Conclusions: We identified 41 prediction models for fetal and neonatal outcomes in pregnancies with preterm manifestations of placental insufficiency. All models were considered to be of low methodological quality, apart from one that had

OBJECTIVES The aim of this study was to investigate the performance of the EuroSCORE II over time and dynamics in values of predictors included in the model. METHODS A cohort study was performed using data from the Netherlands Heart Registration. All cardiothoracic surgical procedures performed between 1 January 2013 and 31 December 2019 were included for analysis. Performance of the EuroSCORE II was assessed across 3-month intervals in terms of calibration and discrimination. For subgroups of major surgical procedures, performance of the EuroSCORE II was assessed across 12-month time intervals. Changes in values of individual EuroSCORE II predictors over time were assessed graphically. RESULTS A total of 103 404 cardiothoracic surgical procedures were included. Observed mortality risk ranged between 1.9% [95% confidence interval (CI) 1.6–2.4] and 3.6% (95% CI 2.6–4.4) across 3-month intervals, while the mean predicted mortality risk ranged between 3.4% (95% CI 3.3–3.6) and 4.2% (95% CI 3.9–4.6). The corresponding observed:expected ratios ranged from 0.50 (95% CI 0.46–0.61) to 0.95 (95% CI 0.74–1.16). Discriminative performance in terms of the c-statistic ranged between 0.82 (95% CI 0.78–0.89) and 0.89 (95% CI 0.87–0.93). The EuroSCORE II consistently overestimated mortality compared to observed mortality. This finding was consistent across all major cardiothoracic surgical procedures. Distributions of values of individual predictors varied broadly across predictors over time. Most notable trends were a decrease in elective surgery from 75% to 54% and a rise in patients with no or New York Heart Association I class heart failure from 27% to 33%. CONCLUSIONS The EuroSCORE II shows good discriminative performance, but consistently overestimates mortality risks of all types of major cardiothoracic surgical procedures in the Netherlands.

OBJECTIVE: The aim of this study is to perform a systematic review of available obstetric first trimester prediction models based on maternal characteristics for gestational diabetes mellitus (GDM). METHODS: The review included a comprehensive search of following electronic bibliographic databases: PubMed/MEDLINE from July 1st 2017 until September 30th 2021. Prognostic models published before April 1st 2017 were obtained from a previously conducted reviews. The studies were considered eligible if they met the pre-established criteria as follows: (1) the article must describe either the development or external validation of a prediction model, or an update to a previously developed model; (2) the model in question must contain multiple predictors; (3) the predictors used must be routinely collected in Dutch Obstetric Care; (4) the predictors must be available and/or measured prior to 16 weeks and 0 days of gestation; (5) The study population must comprise pregnant women; (6) the model must be based on weighted risk predictors that have been identified through multivariate analysis; and (7) the model must be used to predict the GDM outcomes. Data was collected through a pre-determined data extraction form, which included specific items related to study type, domain, outcome, development and validation, model performance (measured by AUROC), risk of bias, and applicability. To evaluate the risk of bias and applicability, the prediction model risk of bias assessment tool (PROBAST) was utilized. The systematic review adhered to the PRISMA guidelines for reporting. RESULTS: In this study, 24 studies on GDM prediction models were selected for analysis, with 12 studies retrieved from the latest databases after rigorous selection. The final analysis included 20 models developed for GDM and 57 models externally validated for GDM. The developing models demonstrated AUROC values ranging from 0.64 to 0.88 (mean), but their performance in external validation studies was slightly

A small proportion of women develop complications during the preterm period of pregnancy (the period before 37 weeks of gestation). An increase in blood pressure (hypertension) in combination with organ failure is one of these complications, also known as HELLP syndrome or preeclampsia. In some pregnancies, this increase in blood pressure goes hand in hand with a growth restriction of the fetus due to placental insufficiency. A high blood pressure (with or without accompanying organ failure) can be dangerous for mother whereas placental insufficiency can result in chronic brain damage of the fetus or even stillbirth. Consequently, health of both mother and child is sometimes best served by medically-induced preterm labor. To inform couples about the risks for the newborn of medically-induced preterm labor (such as serious breathing problems or dying due to prematurity) versus the chance of stillbirth when continuing pregnancy, risk estimates of these undesirable pregnancy outcomes are required. Such estimates can be made using prediction models, mathematical models containing information about the mother, fetus and current course of pregnancy. Using these risk estimates, couples together with their healthcare professionals can come to a decision on medical management. The aim of this research project was to summarize all available prediction models on adverse outcomes of the fetus or newborn for women dealing with complications of an high blood pressure in the preterm period of pregnancy. To this end, a systematic search of medical literature was performed. After model identification, the quality of the models and their robustness when using the models in external patient populations (for instance, patients with the same medical problem in another hospital or country) was studied using standardized tools and checklists by two independent researchers. Information on the performance of the prediction models was studied, which can be expressed using statistical formula

Contains fulltext : 249327.pdf (Publisher’s version ) (Open Access)

Background: Photon-based radiation therapy is important in the treatment of head-and-neck cancer (HNC). However, potential post-radiogenic complications may have a long-lasting negative impact on the patient's quality of life. The current study focused on the presence of laryngeal edema 6 months after irradiation of HNC and had three main objectives: (I) external validation of the normal tissue complication probability (NTCP)-model published by Rancati based on the equivalent uniform dose (EUD), (II) development of new NTCP-models using a large training cohort, (III) external validation of the new models with an independent validation data set. Methods: The training cohort consisted of n=750 and the validation cohort of n=395 patients. For both cohorts, a sub-cohort including patients with non-laryngeal HNC was formed (n=416 and n=227 participants, respectively). Multiple imputation was executed to compensate for missing values. Rancati’s model performance was assessed with both the training and the validation cohort, determining the discrimination (area-under-the-curve, AUC) and calibration parameters (calibration intercept and slope). Following, candidate predictors for new models were selected, their correlation and multicollinearity was checked and the required sample size was calculated. New models were set up with stepwise regression (p<0.20), considering different (sub-)cohorts and candidate predictors to comprehensively answer the research question. The performance of the new models was analyzed and cross-validation (cv) was executed to account for optimism. Finally, external validation of the new models was performed. Results: External validation of Rancati’s model revealed a poor calibration and moderate model performance with the training data set (AUC=0.66; 95%-confidence interval (CI): 0.60-0.71). Analysis of 11 different NTCP-models showed that the model consisting of dose values (= mean dose to the glottic and supraglottic area = GLOTTIC DMEAN and SUP

Aims: Proton therapy is a radiation technique that yields less dose in normal tissues than photon therapy. In the Netherlands, proton therapy is reimbursed if the reduced dose to normal tissues is predicted to translate into a prespecified reduction in toxicity, based on nationally approved validated models. The aim of this paper is to present the development of a national indication protocol for proton therapy (NIPP) for model-based selection of breast cancer patients and to report on first clinical experiences. Materials and methods: A national proton therapy working group for breast cancer (PWG-BC) screened the literature for prognostic models able to estimate the individual risk of specific radiation-induced side-effects. After critical appraisal and selection of suitable models, a NIPP for breast cancer was written and subjected to comments by all stakeholders. The approved NIPP was subsequently introduced to select breast cancer patients who would benefit most from proton therapy. Results: The model of Darby et al. (N Engl J Med 2013; 368:987–82) was the only model fulfilling the criteria prespecified by the PWG-BC. The model estimates the relative risk of an acute coronary event (ACE) based on the mean heart dose. The absolute lifetime risk of ACE <80 years was calculated by applying this model to the Dutch absolute incidence of ACE for female and male patients, between 40 and 70 years at breast cancer radiotherapy, with/without cardiovascular risk factors. The NIPP was approved for reimbursement in January 2019. Based on a threshold value of a 2% absolute lower risk on ACE for proton therapy compared with photons, 268 breast cancer patients have been treated in the Netherlands with proton therapy between February 2019 and January 2021. Conclusion: The NIPP includes a model that allows the estimation of the absolute risk on ACE <80 years based on mean heart dose. In the first 2 years, 268 breast cancer patients have been treated with proton therapy i