The research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency,... Show moreThe research in this dissertation aims to optimise blood donation processes in the framework of the Dutch national blood bank Sanquin. The primary health risk for blood donors is iron deficiency, which is evaluated based on donors' hemoglobin and ferritin levels. If either of these levels are inadequate, donors are deferred from donation. Deferral due to low hemoglobin levels occurs on-site, meaning that donors have already traveled to the blood bank and then have to return home without donating, which is demotivating for the donor and inefficient for the blood bank. A large part of this dissertation therefore has the objective to develop a prediction model for donors' hemoglobin levels, based on historical measurements and donor characteristics.The prediction model that was developed reduces the deferral rate by approximately 60\% (from 3\% to 1\% for women, and from 1\% to 0.4\% for men), showing the potential of using data to enhance blood bank policy efficiency. Additionally, the model predictions were made explainable, providing the blood bank with insights into why specific predictions are made. These insights increase our understanding of the relationships between donor characteristics and hemoglobin levels. If this prediction model would be implemented in practice, the explanations could also be shared with the donor to help them understand why they are (not) invited to donate, which could also contribute to donor satisfaction and retention.In a collaborative effort with blood banks in Australia, Belgium, Finland and South Africa, the same prediction model was applied on data from each blood bank. Despite differences in blood bank policies and donor demographics, the models found similar associations with the predictor variables in all countries. Differences in performance could mostly be attributed to differences in deferral rates, with blood banks with higher deferral rates obtaining higher model accuracy.Beyond hemoglobin prediction models, additional research questions are explored. One study aims to identify determinants of ferritin levels in donors through repeated measurements, and linking these to environmental variables. Another study involves modeling the pharmacokinetics of antibodies in COVID-19 recovered donors, and finding relationships between patient characteristics, symptoms, and antibody levels over time.In summary, the research in this dissertation shows the potential within the wealth of data collected by blood banks. The proposed data-driven donation strategies not only decrease deferral rates but also increase donor retention and understanding. This comprehensive approach allows Sanquin to provide more personalised feedback to donors regarding their iron status, ultimately optimising the blood donation process and contributing to the overall efficacy of blood banking systems. Show less
In the Innovative Medicine's Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained... Show moreIn the Innovative Medicine's Initiative Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) knee osteoarthritis (OA) study, machine learning models were trained to predict the probability of structural progression (s-score), predefined as >0.3 mm/year joint space width (JSW) decrease and used as inclusion criterion. The current objective was to evaluate predicted and observed structural progression over 2 years according to different radiographic and magnetic resonance imaging (MRI)-based structural parameters. Radiographs and MRI scans were acquired at baseline and 2-year follow-up. Radiographic (JSW, subchondral bone density, osteophytes), MRI quantitative (cartilage thickness), and MRI semiquantitative [SQ; cartilage damage, bone marrow lesions (BMLs), osteophytes] measurements were obtained. The number of progressors was calculated based on a change exceeding the smallest detectable change (SDC) for quantitative measures or a full SQ-score increase in any feature. Prediction of structural progression based on baseline s-scores and Kellgren-Lawrence (KL) grades was analyzed using logistic regression. Among 237 participants, around 1 in 6 participants was a structural progressor based on the predefined JSW-threshold. The highest progression rate was seen for radiographic bone density (39%), MRI cartilage thickness (38%), and radiographic osteophyte size (35%). Baseline s-scores could only predict JSW progression parameters (most P>0.05), while KL grades could predict progression of most MRI-based and radiographic parameters (P<0.05). In conclusion, between 1/6 and 1/3 of participants showed structural progression during 2-year follow-up. KL scores were observed to outperform the machine-learning-based s-scores as progression predictor. The large amount of data collected, and the wide range of disease stage, can be used for further development of more sensitive and successful (whole joint) prediction models. Trial Registration: Clinicaltrials.gov number NCT03883568. Show less
Tohidinezhad, F.; Bontempi, D.; Zhang, Z.; Dingemans, A.M.; Aerts, J.; Bootsma, G.; ... ; Ruysscher, D. de 2023
Introduction: Immunotherapy-induced pneumonitis (IIP) is a serious side-effect which requires accurate diagnosis and management with high-dose corticosteroids. The differ-ential diagnosis between... Show moreIntroduction: Immunotherapy-induced pneumonitis (IIP) is a serious side-effect which requires accurate diagnosis and management with high-dose corticosteroids. The differ-ential diagnosis between IIP and other types of pneumonitis (OTP) remains challenging due to similar radiological patterns. This study was aimed to develop a prediction model to differentiate IIP from OTP in patients with stage IV non-small cell lung cancer (NSCLC) who developed pneumonitis during immunotherapy. Methods: Consecutive patients with metastatic NSCLC treated with immunotherapy in six centres in the Netherlands and Belgium from 2017 to 2020 were reviewed and cause-specific pneumonitis events were identified. Seven regions of interest (segmented lungs and sphe-roidal/cubical regions surrounding the inflammation) were examined to extract the most pre-dictive radiomic features from the chest computed tomography images obtained at pneumonitis manifestation. Models were internally tested regarding discrimination, calibra-tion and decisional benefit. To evaluate the clinical application of the models, predicted labels were compared with the separate clinical and radiological judgements. Results: A total of 556 patients were reviewed; 31 patients (5.6%) developed IIP and 41 pa-tients developed OTP (7.4%). The line of immunotherapy was the only predictive factor in the clinical model (2nd versus 1st odds ratio Z 0.08, 95% confidence interval:0.01-0.77). The best radiomic model was achieved using a 75-mm spheroidal region of interest which showed an optimism-corrected area under the receiver operating characteristic curve of 0.83 (95% confidence interval:0.77-0.95) with negative and positive predictive values of 80% and 79%, respectively. Good calibration and net benefits were achieved for the radiomic model across the entire range of probabilities. A correct diagnosis was provided by the radiomic model in 10 out of 12 cases with non-conclusive radiological judgements. Conclusion: Radiomic biomarkers applied to computed tomography imaging may support cli-nicians making the differential diagnosis of pneumonitis in patients with NSCLC receiving immunotherapy, especially when the radiologic assessment is non-conclusive. 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Fleuren, L.M.; Dam, T.A.; Tonutti, M.; Bruin, D.P. de; Lalisang, R.C.A.; Gommers, D.; ... ; Dutch ICU Data Sharing Covid-19 Co 2021
Introduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients... Show moreIntroduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. Methods We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. Results A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. Conclusion The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records. Show less
Ramspek, C.L.; Teece, L.; Snell, K.I.E.; Evans, M.; Riley, R.D.; Smeden, M. van; ... ; Diepen, M. van 2021
Background: External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing... Show moreBackground: External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing events occur, these competing risks should be accounted for when comparing predicted risks to observed outcomes. Methods: We discuss existing measures of calibration and discrimination that incorporate competing events for time-to-event models. These methods are illustrated using a clinical-data example concerning the prediction of kidney failure in a population with advanced chronic kidney disease (CKD), using the guideline-recommended Kidney Failure Risk Equation (KFRE). The KFRE was developed using Cox regression in a diverse population of CKD patients and has been proposed for use in patients with advanced CKD in whom death is a frequent competing event. Results: When validating the 5-year KFRE with methods that account for competing events, it becomes apparent that the 5-year KFRE considerably overestimates the real-world risk of kidney failure. The absolute overestimation was 10%age points on average and 29%age points in older high-risk patients. Conclusions: It is crucial that competing events are accounted for during external validation to provide a more reliable assessment the performance of a model in clinical settings in which competing risks occur. Show less
Maas, S.C.E.; Vidaki, A.; Teumer, A.; Costeira, R.; Wilson, R.; Dongen, J. van; ... ; Kayser, M. 2021
Background Information on long-term alcohol consumption is relevant for medical and public health research, disease therapy, and other areas. Recently, DNA methylation-based inference of alcohol... Show moreBackground Information on long-term alcohol consumption is relevant for medical and public health research, disease therapy, and other areas. Recently, DNA methylation-based inference of alcohol consumption from blood was reported with high accuracy, but these results were based on employing the same dataset for model training and testing, which can lead to accuracy overestimation. Moreover, only subsets of alcohol consumption categories were used, which makes it impossible to extrapolate such models to the general population. By using data from eight population-based European cohorts (N = 4677), we internally and externally validated the previously reported biomarkers and models for epigenetic inference of alcohol consumption from blood and developed new models comprising all data from all categories. Results By employing data from six European cohorts (N = 2883), we empirically tested the reproducibility of the previously suggested biomarkers and prediction models via ten-fold internal cross-validation. In contrast to previous findings, all seven models based on 144-CpGs yielded lower mean AUCs compared to the models with less CpGs. For instance, the 144-CpG heavy versus non-drinkers model gave an AUC of 0.78 +/- 0.06, while the 5 and 23 CpG models achieved 0.83 +/- 0.05, respectively. The transportability of the models was empirically tested via external validation in three independent European cohorts (N = 1794), revealing high AUC variance between datasets within models. For instance, the 144-CpG heavy versus non-drinkers model yielded AUCs ranging from 0.60 to 0.84 between datasets. The newly developed models that considered data from all categories showed low AUCs but gave low AUC variation in the external validation. For instance, the 144-CpG heavy and at-risk versus light and non-drinkers model achieved AUCs of 0.67 +/- 0.02 in the internal cross-validation and 0.61-0.66 in the external validation datasets. Conclusions The outcomes of our internal and external validation demonstrate that the previously reported prediction models suffer from both overfitting and accuracy overestimation. Our results show that the previously proposed biomarkers are not yet sufficient for accurate and robust inference of alcohol consumption from blood. Overall, our findings imply that DNA methylation prediction biomarkers and models need to be improved considerably before epigenetic inference of alcohol consumption from blood can be considered for practical applications. Show less
Background: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to... Show moreBackground: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to identify and compare the main determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity. Methods: Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (classprobability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error) combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were identified by determining variable-importance and correlations between each SL-predicted and observed outcome (rho pred) were calculated. Results: Low to high prediction correlations (rho pred: 0.41-0.91, median=0.73) were found. In the SL, important determinants of psychological distress were age, young age of onset, respiratory rate, participation disability, somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress. Limitations: Limited sample size for machine learning. Conclusions: The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course. Show less
Ramspek, C.L.; Steyerberg, E.W.; Riley, R.D.; Rosendaal, F.R.; Dekkers, O.M.; Dekker, F.W.; Diepen, M. van 2021
Etiological research aims to uncover causal effects, whilst prediction research aims to forecast an outcome with the best accuracy. Causal and prediction research usually require different methods,... Show moreEtiological research aims to uncover causal effects, whilst prediction research aims to forecast an outcome with the best accuracy. Causal and prediction research usually require different methods, and yet their findings may get conflated when reported and interpreted. The aim of the current study is to quantify the frequency of conflation between etiological and prediction research, to discuss common underlying mistakes and provide recommendations on how to avoid these. Observational cohort studies published in January 2018 in the top-ranked journals of six distinct medical fields (Cardiology, Clinical Epidemiology, Clinical Neurology, General and Internal Medicine, Nephrology and Surgery) were included for the current scoping review. Data on conflation was extracted through signaling questions. In total, 180 studies were included. Overall, 26% (n = 46) contained conflation between etiology and prediction. The frequency of conflation varied across medical field and journal impact factor. From the causal studies 22% was conflated, mainly due to the selection of covariates based on their ability to predict without taking the causal structure into account. Within prediction studies 38% was conflated, the most frequent reason was a causal interpretation of covariates included in a prediction model. Conflation of etiology and prediction is a common methodological error in observational medical research and more frequent in prediction studies. As this may lead to biased estimations and erroneous conclusions, researchers must be careful when designing, interpreting and disseminating their research to ensure this conflation is avoided. Show less
Hond, A. de; Raven, W.; Schinkelshoek, L.; Gaakeer, M.; Avest, E. ter; Sir, O.; ... ; Groot, B. de 2021
Objective: Early identification of emergency department (ED) patients who need hospitalization is essential for quality of care and patient safety. We aimed to compare machine learning (ML) models... Show moreObjective: Early identification of emergency department (ED) patients who need hospitalization is essential for quality of care and patient safety. We aimed to compare machine learning (ML) models predicting the hospitalization of ED patients and conventional regression techniques at three points in time after ED registration.Methods: We analyzed consecutive ED patients of three hospitals using the Netherlands Emergency Department Evaluation Database (NEED). We developed prediction models for hospitalization using an increasing number of data available at triage, similar to 30 min (including vital signs) and similar to 2 h (including laboratory tests) after ED registration, using ML (random forest, gradient boosted decision trees, deep neural networks) and multivariable logistic regression analysis (including spline transformations for continuous predictors). Demographics, urgency, presenting complaints, disease severity and proxies for comorbidity, and complexity were used as covariates. We compared the performance using the area under the ROC curve in independent validation sets from each hospital.Results: We included 172,104 ED patients of whom 66,782 (39 %) were hospitalized. The AUC of the multi-variable logistic regression model was 0.82 (0.78-0.86) at triage, 0.84 (0.81-0.86) at similar to 30 min and 0.83 (0.75-0.92) after similar to 2 h. The best performing ML model over time was the gradient boosted decision trees model with an AUC of 0.84 (0.77-0.88) at triage, 0.86 (0.82-0.89) at similar to 30 min and 0.86 (0.74-0.93) after similar to 2 h.Conclusions: Our study showed that machine learning models had an excellent but similar predictive performance as the logistic regression model for predicting hospital admission. In comparison to the 30-min model, the 2-h model did not show a performance improvement. After further validation, these prediction models could support management decisions by real-time feedback to medical personal. Show less
Background Notwithstanding the firmly established cross-sectional association of happiness with psychiatric disorders and their symptom severity, little is known about their temporal relationships.... Show moreBackground Notwithstanding the firmly established cross-sectional association of happiness with psychiatric disorders and their symptom severity, little is known about their temporal relationships. The goal of the present study was to investigate whether happiness is predictive of subsequent psychiatric disorders and symptom severity (and vice versa). Moreover, it was examined whether changes in happiness co-occur with changes in psychiatric disorder status and symptom severity. Methods In the Netherlands Study of Depression and Anxiety (NESDA), happiness (SRH: Self-Rated Happiness scale), depressive and social anxiety disorder (CIDI: Composite Interview Diagnostic Instrument) and depressive and anxiety symptom severity (IDS: Inventory of Depressive Symptomatology; BAI: Beck Anxiety Inventory; and FQ: Fear Questionnaire) were measured in 1816 adults over a three-year period. Moreover, we focused on occurrence and remittance of 6-month recency Major Depressive Disorder (MDD) and Social Anxiety Disorders (SAD) as the two disorders most intertwined with subjective happiness. Results Interindividual differences in happiness were quite stable (ICC of .64). Higher levels of happiness predicted recovery from depression (OR = 1.41; 95% CI = 1.10-1.80), but not social anxiety disorder (OR = 1.31; 95%CI = .94-1.81), as well as non-occurrence of depression (OR = 2.41; 95%CI = 1.98-2.94) and SAD (OR = 2.93; 95%CI = 2.29-3.77) in participants without MDD, respectively SAD at baseline. Higher levels of happiness also predicted a reduction of IDS depression (sr = - 0.08; 95%CI = -0.10 - -0.04), and BAI (sr = - 0.09; 95%CI = -0.12 - -0.05) and FQ (sr = - 0.06; 95%CI = -0.09 - -0.04) anxiety symptom scores. Conversely, presence of affective disorders, as well as higher depression and anxiety symptom severity at baseline predicted a subsequent reduction of self-reported happiness (with marginal to small sr values varying between -.04 (presence of SAD) to -.17 (depression severity on the IDS)). Moreover, changes in happiness were associated with changes in psychiatric disorders and their symptom severity, in particular with depression severity on the IDS (sr = - 0.46; 95%CI = -.50 - -.42). Conclusions Results support the view of rather stable interindividual differences in subjective happiness, although level of happiness is inversely associated with changes in psychiatric disorders and their symptom severity, in particular depressive disorder and depression severity. Show less
Tio-Coma, M.; Kielbasa, S.M.; Eeden, S.J.F. van den; Mei, H.L.; Roy, J.C.; Wallinga, J.; ... ; Geluk, A. 2021
Background: Leprosy, a chronic infectious disease caused by Mycobacterium leprae, is often late-or misdiag-nosed leading to irreversible disabilities. Blood transcriptomic biomarkers that... Show moreBackground: Leprosy, a chronic infectious disease caused by Mycobacterium leprae, is often late-or misdiag-nosed leading to irreversible disabilities. Blood transcriptomic biomarkers that prospectively predict those who progress to leprosy (progressors) would allow early diagnosis, better treatment outcomes and facilitate interventions aimed at stopping bacterial transmission. To identify potential risk signatures of leprosy, we collected whole blood of household contacts (HC, n=5,352) of leprosy patients, including individuals who were diagnosed with leprosy 4-61 months after sample collection.Methods: We investigated differential gene expression (DGE) by RNA-Seq between progressors before pres-ence of symptoms (n=40) and HC (n=40), as well as longitudinal DGE within each progressor. A prospective leprosy signature was identified using a machine learning approach (Random Forest) and validated using reverse transcription quantitative PCR (RT-qPCR). Findings: Although no significant intra-individual longitudinal variation within leprosy progressors was iden-tified, 1,613 genes were differentially expressed in progressors before diagnosis compared to HC. We identi-fied a 13-gene prospective risk signature with an Area Under the Curve (AUC) of 95.2%. Validation of this RNA-Seq signature in an additional set of progressors (n=43) and HC (n=43) by RT-qPCR, resulted ina final 4 -gene signature, designated RISK4LEP (MT-ND2, REX1BD, TPGS1, UBC) (AUC=86.4%).Interpretation: This study identifies for the first time a prospective transcriptional risk signature in blood pre-dicting development of leprosy 4 to 61 months before clinical diagnosis. Assessment of this signature in con-tacts of leprosy patients can function as an adjunct diagnostic tool to target implementation of interventions to restrain leprosy development. (C) 2021 The Author(s). Published by Elsevier B.V. Show less
Plas-Krijgsman, W.G. van der; Boer, A.Z. de; Jong, P. de; Bastiaannet, E.; Bos, F. van den; Mooijaart, S.P.; ... ; Glas, N.A. de 2021
The number of older patients with breast cancer has increased due to the aging of the general population. The use of a geriatric assessment in this population has been advocated in many studies and... Show moreThe number of older patients with breast cancer has increased due to the aging of the general population. The use of a geriatric assessment in this population has been advocated in many studies and guidelines as it can be used to identify high risk populations for early mortality and toxicity. Additionally, geriatric parameters could predict relevant outcome measures. This systematic review summarizes all available evidence on predictive factors for various outcomes (disease-related and survival, toxicity, and patient-reported outcomes), with a special focus on geriatric parameters and patient-reported outcomes, in older patients with breast cancer. Studies were identified through systematic review of the literature published up to September 1st 2019 in the PubMed database and EMBASe. A total of 173 studies were included. Most studies investigated disease-related and survival outcomes (n = 123, 71%). Toxicity was investigated in 40 studies (23%) and a mere 15% (n = 26) investigated patient-reported outcomes. Various measures that can be derived from a geriatric assessment were predictive for survival endpoints. Furthermore, geriatric parameters were among the most frequently found predictors for toxicity and patient-reported outcomes. In conclusion, this study shows that geriatric parameters can predict survival, toxicity, and patient-reported outcomes in older patients with breast cancer. These findings can be used in daily clinical practice to identify patients at risk of early mortality, high risk of treatment toxicity or poor functional outcome after treatment. A minority of studies used relevant outcome measures for older patients, showing the need for studies that are tailored to the older population.(c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/). Show less
There is a pending need for prognostic and predictive biomarkers in the treatment of patients with colorectal cancer.This thesis describes the prognostic and predictive application of the tumor... Show moreThere is a pending need for prognostic and predictive biomarkers in the treatment of patients with colorectal cancer.This thesis describes the prognostic and predictive application of the tumor-stroma ratio (TSR) in colorectal cancer, focusing on expanding current clinical-pathological standards and combining TSR with other diagnostic parameters. The TSR is a microscopy scoring method performed on hematoxylin-eosin stained tissue slides used for routine pathology assessment, and has proven to be a robust prognostic maker. Here, we investigate whether the TSR also exhibits predictive value with regard to adjuvant targeted therapy in stage II and III colon cancer. Moreover, exploring the value of collagen fiber organization in the intratumoral stroma, as well as combining this parameter with the TSR. Finally, expanding the application of the TSR with radiological diagnostics in rectal cancer. Assessing is there is a correlation between TSR and apparent diffusion coefficient values obtained from diagnostically performed MRI-DWI scans, in order to determine if there is potential with regards to neoadjuvant treatment choices or patient follow-up. Show less
Patients with diabetes mellitus have the highest mortality risk within the dialysis population. The presence of chronic kidney disease (CKD) in patients with diabetes is also strongly related to... Show morePatients with diabetes mellitus have the highest mortality risk within the dialysis population. The presence of chronic kidney disease (CKD) in patients with diabetes is also strongly related to impaired quality of life. Research is warranted to prevent progressive diabetic kidney disease, improve quality of life and reduce mortality in this vulnerable population. In order to improve survival, more knowledge about which patients have the highest mortality risk and which risk factors and co-morbid conditions contribute to this increased mortality risk is essential. In this thesis we focussed on clinical aspects of the relation between diabetes mellitus and kidney disease, from hyperfiltration to dialysis. In chapter 2 we assessed many different measures of glucose metabolism and their association with kidney function among Dutch middle-aged adults. In chapter three and four we compared survival of dialysis patients with diabetes mellitus as underlying cause of the renal failure versus dialysis patients with diabetes mellitus as a co-morbid condition only. In chapter five we aimed to develop a prediction model for 1-year mortality in diabetic dialysis patients. Furthermore in chapter six we compared survival after amputation in diabetic dialysis patients to non-diabetic dialysis patients. Show less
Risk prediction is one of the central goals of medicine. However, ultimate prediction-perfectly predicting whether individuals will actually get a disease-is still out of reach for virtually all... Show moreRisk prediction is one of the central goals of medicine. However, ultimate prediction-perfectly predicting whether individuals will actually get a disease-is still out of reach for virtually all conditions. One crucial assumption of ultimate personalized prediction is that individual risks in the relevant sense exist. In the present paper we argue that perfect prediction at the individual level will fail-and we will do so by providing pragmatic, epistemic, conceptual, and ontological arguments. Show less
Souwer, E.T.D.; Bastiaannet, E.; Steyerberg, E.W.; Dekker, J.W.T.; Bos, F. van den; Portielje, J.E.A. 2020
Background: An increasing number of patients with Colorectal Cancer (CRC) is 65 years or older. We aimed to systematically review existing clinical prediction models for postoperative outcomes of... Show moreBackground: An increasing number of patients with Colorectal Cancer (CRC) is 65 years or older. We aimed to systematically review existing clinical prediction models for postoperative outcomes of CRC surgery, study their performance in older patients and assess their potential for preoperative decision making.Methods: A systematic search in Pubmed and Embase for original studies of clinical prediction models for outcomes of CRC surgery. Bias and relevance for preoperative decision making with older patients were assessed using the CHARMS guidelines.Results: 26 prediction models from 25 publications were included. The average age of included patients ranged from 61 to 76. Two models were exclusively developed for 65 and older. Common outcomes were mortality (n = 10), anastomotic leakage (n = 7) and surgical site infections (n = 3). No prediction models for quality of life or physical functioning were identified. Age, gender and ASA score were common predictors; 12 studies included intraoperative predictors. For the majority of the models, bias for model development and performance was considered moderate to high.Conclusions: Prediction models are available that address mortality and surgical complications after CRC surgery. Most models suffer from methodological limitations, and their performance for older patients is uncertain. Models that contain intraoperative predictors are of limited use for preoperative decision making. Future research should address the predictive value of geriatric characteristics to improve the performance of prediction models for older patients. (C) 2020 The Authors. Published by Elsevier Ltd. Show less
This thesis describes the detailed method of scoring the tumor-stroma ratio and the different possibilities to use it in routine clinical diagnostics, for different types of cancer. It can be used... Show moreThis thesis describes the detailed method of scoring the tumor-stroma ratio and the different possibilities to use it in routine clinical diagnostics, for different types of cancer. It can be used not only for prognostic purposes, but it might also be useful for predicting the response on neo-adjuvant therapy. As it is an easy and cheap method, based on routine hematoxylin-eosin stained tissue slides used for daily pathology routine, it can be implemented in clinical diagnostics with little effort. Show less
In this thesis, the transition from a population-based approach to individualized therapy for the prevention of VT following lower-leg cast immobilization and knee arthroscopy is discussed.
Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.Study Design and Setting: We... Show moreObjective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury.Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified.Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study.Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations. (C) 2020 The Authors. Published by Elsevier Inc. Show less
One of the main questions in Ewing sarcoma treatment is to identify low-risk patients that can be treated with less intensive treatment so that toxicity and the occurrence of long-term adverse... Show moreOne of the main questions in Ewing sarcoma treatment is to identify low-risk patients that can be treated with less intensive treatment so that toxicity and the occurrence of long-term adverse effects can be limited while still maintaining high cure rates or to identify those patients for whom treatment is expected to have limited benefit. Furthermore, to identify high-risk patients in which treatment needs to be intensified to improve outcome. Selection of risk groups and adjusted treatment allows for early decision making, will help to improve future outcomes and assists in clinical trial design. Additionally, treatment of Ewing sarcoma is multimodal and surgery, if feasible, is crucial for curative management. However, accurate detection and localization of tumor boundaries, especially in anatomical complex locations such as the pelvic is challenging. Inadequate surgical margins lead to a higher risk of local recurrence which has major impact on oncological outcome. Developments in intra-operative imaging, like CT-based navigation systems and near infrared (NIR)fluorescence guided surgery (FGS) make accurate defining and localization of surgical margins possible. They represent a whole new field of precision medicine and provide new treatment options for patients, thereby improving function outcome and healthcare quality. Show less
