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
With increasing age, associations between traditional risk factors (TRFs) and cardiovascular disease (CVD) shift. It is unknown which mid-life risk factors remain relevant predictors for CVD in... Show moreWith increasing age, associations between traditional risk factors (TRFs) and cardiovascular disease (CVD) shift. It is unknown which mid-life risk factors remain relevant predictors for CVD in older people.We systematically searched PubMed and EMBASE on August 16th 2019 for studies assessing predictive ability of > 1 of fourteen TRFs for fatal and non-fatal CVD, in the general population aged 60 + .We included 12 studies, comprising 11 unique cohorts. TRF were evaluated in 2 to 11 cohorts, and retained in 0-70% of the cohorts: age (70%), diabetes (64%), male sex (57%), systolic blood pressure (SBP) (50%), smoking (36%), high-density lipoprotein cholesterol (HDL) (33%), left ventricular hypertrophy (LVH) (33%), total cholesterol (22%), diastolic blood pressure (20%), antihypertensive medication use (AHM) (20%), body mass index (BMI) (0%), hypertension (0%), low-density lipoprotein cholesterol (0%). In studies with low to moderate risk of bias, systolic blood pressure (SBP) (80%), smoking (80%) and HDL cholesterol (60%) were more often retained. Model performance was moderate with C-statistics ranging from 0.61 to 0.77.Compared to middle-aged adults, in people aged 60 + different risk factors predict CVD and current prediction models perform only moderate at best. According to most studies, age, sex and diabetes seem valuable predictors of CVD in old-age. SBP, HDL cholesterol and smoking may also have predictive value. Other blood pressure and cholesterol related variables, BMI, and LVH seem of very limited or no additional value. Without competing risk analysis, predictors are overestimated. Show less
Nemeth, B.; Douillet, D.; Cessie, S. le; Penaloza, A.; Moumneh, T.; Roy, P.M.; Cannegieter, S. 2020
Background: Patients with lower-limb trauma requiring immobilization have an increased risk of venous thromboembolism (VTE). While thromboprophylaxis for all patients seems not effective, targeted... Show moreBackground: Patients with lower-limb trauma requiring immobilization have an increased risk of venous thromboembolism (VTE). While thromboprophylaxis for all patients seems not effective, targeted thromboprophylaxis in high risk patients may be an appropriate alternative. Therefore, we aimed to develop and validate a risk assessment model for VTE risk: the TRiP(cast) score (Thrombosis Risk Prediction following cast immobilization).Methods: In this prediction model study, for development, data were used from the MEGA study (case-control study into the etiology of VTE) and for validation, data from the POT-CAST trial (randomized trial on the effectiveness of thromboprophylaxis following cast immobilization) were used. Model discrimination was calculated by estimating the Area Under the Curve (AUC). For model calibration, observed and predicted risks were assessed.Findings: The TRiP( cast) score includes 14 items; one item for trauma severity (or type), one for type of immobilization and 12 items related to patients' characteristics. Validation analyses showed an AUC of 0.74 (95%CI 0.61-0.87) in the complete dataset (n = 1250) and 0.72 (95%CI 0.60-0.84) in the imputed data set (n = 1435). The calibration plot shows the degree of agreement between the observed and predicted risks (intercept 0.0016 and slope 0.933). Using a cut-off score of 7 points in the POT-CAST trial (incidence 1.6%), the sensitivity, specificity, positive and negative predictive values were 76.1%, 51.2%, 2.5%, and 99.2%, respectively.Interpretation: The TRiP(cast) score provides a helpful tool in daily clinical practice to accurately stratify patients in high versus low-risk categories in order to guide thromboprophylaxis prescribing. To accommodate implementation in clinical practice a mobile phone application has been developed. (C) 2020 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license. (http://creativecommon.org/licenses/by-nc-nd/4.0/) Show less
Pelt, G.W. van; Krol, J.A.; Lips, I.M.; Peters, F.P.; Klaveren, D. van; Boonstra, J.J.; ... ; Slingerland, M. 2020
