Background For cancer patients to effectively engage in decision making, they require comprehensive and understandable information regarding treatment options and their associated outcomes. We... Show moreBackground For cancer patients to effectively engage in decision making, they require comprehensive and understandable information regarding treatment options and their associated outcomes. We developed an online prediction tool and supporting communication skills training to assist healthcare providers (HCPs) in this complex task. This study aims to assess the impact of this combined intervention (prediction tool and training) on the communication practices of HCPs when discussing treatment options. Methods We conducted a multicenter intervention trial using a pragmatic stepped wedge design (NCT04232735). Standardized Patient Assessments (simulated consultations) using cases of esophageal and gastric cancer patients, were performed before and after the combined intervention (March 2020 to July 2022). Audio recordings were analyzed using an observational coding scale, rating all utterances of treatment outcome information on the primary outcome-precision of provided outcome information-and on secondary outcomes-such as: personalization, tailoring and use of visualizations. Pre vs. post measurements were compared in order to assess the effect of the intervention. Findings 31 HCPs of 11 different centers in the Netherlands participated. The tool and training significantly affected the precision of the overall communicated treatment outcome information (p = 0.001, median difference 6.93, IQR (-0.32 to 12.44)). In the curative setting, survival information was significantly more precise after the intervention (p = 0.029). In the palliative setting, information about side effects was more precise (p < 0.001). Interpretation A prediction tool and communication skills training for HCPs improves the precision of treatment information on outcomes in simulated consultations. The next step is to examine the effect of such interventions on communication in clinical practice and on patient-reported outcomes. Funding Financial support for this study was provided entirely by a grant from the Dutch Cancer Society (UVA 2014-7000). Copyright (c) 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
IntroductionNosocomial pneumonia has poor prognosis in hospitalized trauma patients. Croce et al. published a model to predict post-traumatic ventilator-associated pneumonia, which achieved high... Show moreIntroductionNosocomial pneumonia has poor prognosis in hospitalized trauma patients. Croce et al. published a model to predict post-traumatic ventilator-associated pneumonia, which achieved high discrimination and reasonable sensitivity. We aimed to externally validate Croce's model to predict nosocomial pneumonia in patients admitted to a Dutch level-1 trauma center.Materials and methodsThis retrospective study included all trauma patients (>= 16y) admitted for > 24 h to our level-1 trauma center in 2017. Exclusion criteria were pneumonia or antibiotic treatment upon hospital admission, treatment elsewhere > 24 h, or death < 48 h. Croce's model used eight clinical variables-on trauma severity and treatment, available in the emergency department-to predict nosocomial pneumonia risk. The model's predictive performance was assessed through discrimination and calibration before and after re-estimating the model's coefficients. In sensitivity analysis, the model was updated using Ridge regression.Results809 Patients were included (median age 51y, 67% male, 97% blunt trauma), of whom 86 (11%) developed nosocomial pneumonia. Pneumonia patients were older, more severely injured, and underwent more emergent interventions. Croce's model showed good discrimination (AUC 0.83, 95% CI 0.79-0.87), yet predicted probabilities were too low (mean predicted risk 6.4%), and calibration was suboptimal (calibration slope 0.63). After full model recalibration, discrimination (AUC 0.84, 95% CI 0.80-0.88) and calibration improved. Adding age to the model increased the AUC to 0.87 (95% CI 0.84-0.91). Prediction parameters were similar after the models were updated using Ridge regression.ConclusionThe externally validated and intercept-recalibrated models show good discrimination and have the potential to predict nosocomial pneumonia. At this time, clinicians could apply these models to identify high-risk patients, increase patient monitoring, and initiate preventative measures. Recalibration of Croce's model improved the predictive performance (discrimination and calibration). The recalibrated model provides a further basis for nosocomial pneumonia prediction in level-1 trauma patients. Several models are accessible via an online tool. Show less
Background: Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models... Show moreBackground: Clinical prediction models are often not evaluated properly in specific settings or updated, for instance, with information from new markers. These key steps are needed such that models are fit for purpose and remain relevant in the long-term. We aimed to present an overview of methodological guidance for the evaluation (i.e., validation and impact assessment) and updating of clinical prediction models. Methods: We systematically searched nine databases from January 2000 to January 2022 for articles in English with methodological recommendations for the post-derivation stages of interest. Qualitative analysis was used to summarize the 70 selected guidance papers. Results: Key aspects for validation are the assessment of statistical performance using measures for discrimination (e.g., C-statistic) and calibration (e.g., calibration-in-the-large and calibration slope). For assessing impact or usefulness in clinical decision-making, recent papers advise using decision-analytic measures (e.g., the Net Benefit) over simplistic classification measures that ignore clinical consequences (e.g., accuracy, overall Net Reclassification Index). Commonly recommended methods for model updating are recalibration (i.e., adjustment of intercept or baseline hazard and/or slope), revision (i.e., re-estimation of individual predictor effects), and extension (i.e., addition of new markers). Additional methodological guidance is needed for newer types of updating (e.g., meta-model and dynamic updating) and machine learning-based models. Conclusion: Substantial guidance was found for model evaluation and more conventional updating of regression-based models. An important development in model evaluation is the introduction of a decision-analytic framework for assessing clinical usefulness. Consensus is emerging on methods for model updating. Show less
Introduction: This study aims to develop a robust preoperative prediction model for anastomotic leakage (AL) after surgical resection for rectal cancer, based on established risk factors and with... Show moreIntroduction: This study aims to develop a robust preoperative prediction model for anastomotic leakage (AL) after surgical resection for rectal cancer, based on established risk factors and with the power of a large prospective nation-wide population-based study cohort. Materials and methods: A development cohort was formed by using the DCRA (Dutch ColoRectal Audit), a mandatory population-based repository of all patients who undergo colorectal cancer resection in the Netherlands. Patients aged 18 years or older were included who underwent surgical resection for rectal cancer with primary anastomosis (with or without deviating ileostomy) between 2011 and 2019. Anastomotic leakage was defined as clinically relevant leakage requiring reintervention. Multivariable logistic regression was used to build a prediction model and cross-validation was used to validate the model. Results: A total of 13.175 patients were included for analysis. AL was diagnosed in 1319 patients (10%). A deviating stoma was constructed in 6853 patients (52%). The following variables were identified as significant risk factors and included in the prediction model: gender, age, BMI, ASA classification, neo-adjuvant (chemo)radiotherapy, cT stage, distance of the tumor from anal verge, and deviating ileos-tomy. The model had a concordance-index of 0.664, which remained 0.658 after cross-validation. In addition, a nomogram was developed. Conclusion: The present study generated a discriminative prediction model based on preoperatively available variables. The proposed score can be used for patient counselling and risk-stratification before undergoing rectal resection for cancer. (c) 2022 Published by Elsevier Ltd. Show less
Hany, M.; Demerdash, H.M.; Agayby, A.S.S.; Ibrahim, M.; Torensma, B. 2022
Introduction: Obesity is associated with metabolic syndrome (MBS), a cluster of components including central obesity, insulin resistance (IR), dyslipidemia, and hypertension. IR is the major risk... Show moreIntroduction: Obesity is associated with metabolic syndrome (MBS), a cluster of components including central obesity, insulin resistance (IR), dyslipidemia, and hypertension. IR is the major risk factor in the development and progression of type 2 diabetes mellitus in obesity and MBS. Predicting preoperatively whether a patient with obesity would have improved or non-improved IR after bariatric surgery would improve treatment decisions. Methods: A prospective cohort study was conducted between August 2019 and September 2021. We identified pre- and postoperative metabolic biomarkers in patients who underwent laparoscopic sleeve gastrectomy. Patients were divided into two groups: group A (IR < 2.5), with improved IR, and group B (IR >= 2.5), with non-improved IR. A prediction model and receiver operating characteristics (ROC) were used to determine the effect of metabolic biomarkers on IR. Results: Seventy patients with obesity and MBS were enrolled. At 12-month postoperative a significant improvement in lipid profile, fasting blood glucose, and hormonal biomarkers and a significant reduction in the BMI in all patients (p = 0.008) were visible. HOMA-IR significantly decreased in 57.14% of the patients postoperatively. Significant effects on the change in HOMA-IR >= 2.5 were the variables; preoperative BMI, leptin, ghrelin, leptin/ghrelin ratio (LGr), insulin, and triglyceride with an OR of 1.6,1.82, 1.33, 1.69, 1.77, and 1.82, respectively (p = 0.009 towards p = 0.041). Leptin had the best predictive cutoff value on ROC (86% sensitivity and 92% specificity), whereas ghrelin had the lowest (70% sensitivity and 73% specificity). Conclusion: Preoperative BMI, leptin, ghrelin, LGr, and increased triglycerides have a predictive value on higher postoperative, non-improved patients with HOMA-IR (>= 2.5). Therefore, assessing metabolic biomarkers can help decide on treatment/extra therapy and outcome before surgery. Show less
Endt, V.H.W. van der; Milders, J.; Vries, B.B.L.P. de; Trines, S.A.; Groenwold, R.H.H.; Dekkers, O.M.; ... ; Jong, Y. de 2022
Aims Multiple risk scores to predict ischaemic stroke (IS) in patients with atrial fibrillation (AF) have been developed. This study aims to systematically review these scores, their validations... Show moreAims Multiple risk scores to predict ischaemic stroke (IS) in patients with atrial fibrillation (AF) have been developed. This study aims to systematically review these scores, their validations and updates, assess their methodological quality, and calculate pooled estimates of the predictive performance.Methods and results We searched PubMed and Web of Science for studies developing, validating, or updating risk scores for IS in AF patients. Methodological quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). To assess discrimination, pooled c-statistics were calculated using random-effects meta-analysis. We identified 19 scores, which were validated and updated once or more in 70 and 40 studies, respectively, including 329 validations and 76 updates-nearly all on the CHA(2)DS(2)-VASc and CHADS(2). Pooled c-statistics were calculated among 6 267 728 patients and 359 373 events of IS. For the CHA(2)DS(2)-VASc and CHADS(2), pooled c-statistics were 0.644 [95% confidence interval (CI) 0.635-0.653] and 0.658 (0.644-0.672), respectively. Better discriminatory abilities were found in the newer risk scores, with the modified-CHADS(2) demonstrating the best discrimination [c-statistic 0.715 (0.674-0.754)]. Updates were found for the CHA(2)DS(2)-VASc and CHADS(2) only, showing improved discrimination. Calibration was reasonable but available for only 17 studies. The PROBAST indicated a risk of methodological bias in all studies.Conclusion Nineteen risk scores and 76 updates are available to predict IS in patients with AF. The guideline-endorsed CHA(2)DS(2)-VASc shows inferior discriminative abilities compared with newer scores. Additional external validations and data on calibration are required before considering the newer scores in clinical practice. Show less
Schuijt, H.J.; Smeeing, D.P.J.; Groenwold, R.H.H.; Velde, D. van der; Weaver, M.J. 2022
Introduction: Identification of high-risk hip fracture patients in an early stage is vital for guiding surgical management and shared decision making. To objective of this study was to perform an... Show moreIntroduction: Identification of high-risk hip fracture patients in an early stage is vital for guiding surgical management and shared decision making. To objective of this study was to perform an external international validation study of the U-HIP prediction model for in-hospital mortality in geriatric patients with a hip fracture undergoing surgery. Materials and methods: In this retrospective cohort study, data were used from The American College of Surgeons National Surgical Quality Improvement Program. Patients aged 70 years or above undergoing hip fracture surgery were included. The discrimination (c-statistic) and calibration of the model were investigated. Results: A total of 25,502 patients were included, of whom 618 (2.4%) died. The mean predicted probability of in-hospital mortality was 3.9% (range 0%-55%). The c-statistic of the model was 0.74 (95% CI 0.72-0.76), which was comparable to the c-statistic of 0.78 (95% CI 0.71-0.85) that was found in the development cohort. The calibration plot indicated that the model was slightly overfitted, with a calibrationin-the-large of 0.015 and a calibration slope of 0.780. Within the subgroup of patients aged between 70 and 85, however, the c-statistic was 0.78 (95% CI 0.75-0.81), with good calibration (calibration slope 0.934). Discussion and conclusion: The U-HIP model for in-hospital mortality in geriatric hip fractures was externally validated in a large international cohort, and showed a good discrimination and fair calibration. This model is freely available online and can be used to predict the risk of mortality, identify high-risk patients and aid clinical decision making. (C) 2021 Published by Elsevier Ltd. Show less
Purpose The aim of this study is to compute and validate a statistical predictive model for the risk of recurrence, defined as regrowth of tumor necessitating salvage treatment, after... Show morePurpose The aim of this study is to compute and validate a statistical predictive model for the risk of recurrence, defined as regrowth of tumor necessitating salvage treatment, after translabyrinthine removal of vestibular schwannomas to individualize postoperative surveillance. Methods The multivariable predictive model for risk of recurrence was based on retrospectively collected patient data between 1995 and 2017 at a tertiary referral center. To assess for internal validity of the prediction model tenfold cross-validation was performed. A 'low' calculated risk of recurrence in this study was set at < 1%, based on clinical criteria and expert opinion. Results A total of 596 patients with 33 recurrences (5.5%) were included for analysis. The final prediction model consisted of the predictors 'age at time of surgery', 'preoperative tumor growth' and 'first postoperative MRI outcome'. The area under the receiver operating curve of the prediction model was 89%, with a C-index of 0.686 (95% CI 0.614-0.796) after cross-validation. The predicted probability for risk of recurrence was low (< 1%) in 373 patients (63%). The earliest recurrence in these low-risk patients was detected at 46 months after surgery. Conclusion This study presents a well-performing prediction model for the risk of recurrence after translabyrinthine surgery for vestibular schwannoma. The prediction model can be used to tailor the postoperative surveillance to the estimated risk of recurrence of individual patients. It seems that especially in patients with an estimated low risk of recurrence, the interval between the first and second postoperative MRI can be safely prolonged. Show less
The relation between prostate-specific antigen (PSA) and other relevant prebiopsy information is often combined in a risk calculator (RC). If the setting for RC use differs from that in which it... Show moreThe relation between prostate-specific antigen (PSA) and other relevant prebiopsy information is often combined in a risk calculator (RC). If the setting for RC use differs from that in which it was developed, there is a risk of making clinical decisions based on incorrect estimates of the absolute risk. The ERSPC-MRI RC predicts clinically significant prostate cancer (csPC; Gleason >= 3 + 4) on targeted and systematic biopsy using information on PSA, digital rectal examination, prostate volume, age, previous negative biopsy, and Prostate Imaging-Recording and Data System score. This calculator was developed on a clinical cohort of 961 men (2012-2017) with a csPC prevalence of 36%. Discrimination was good (area under the receiver operating characteristic curve 0.84). With the increasing use of multiparametric magnetic resonance imaging, we foresee that this RC will also be used for men with a lower a priori likelihood of PC. We investigated the effect of such a scenario on individual risk predictions. A small update of the intercept for the calculator can restore the accuracy to support decision-making with locally valid risk estimates.Patient summary: Decisions on who to refer for a prostate biopsy with its risk of sepsis and overdiagnosis require more than a prostate-specific antigen test. A prediction tool may take other relevant prebiopsy information into account, but may need to be updated to contemporary center-specific settings to provide accurate estimates of the risk of having prostate cancer. (C) 2019 Published by Elsevier B.V. on behalf of European Association of Urology. Show less
Objective: To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at... Show moreObjective: To assess whether the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and a shorter version of this tool can identify clinical prediction models (CPMs) that perform poorly at external validation. Study Design and Setting: We evaluated risk of bias (ROB) on 102 CPMs from the Tufts CPM Registry, comparing PROBAST to a short form consisting of six PROBAST items anticipated to best identify high ROB. We then applied the short form to all CPMs in the Registry with at least 1 validation (n = 556) and assessed the change in discrimination (dAUC) in external validation cohorts (n = 1,147). Results: PROBAST classified 98/102 CPMS as high ROB. The short form identified 96 of these 98 as high ROB (98% sensitivity), with perfect specificity. In the full CPM registry, 527 of 556 CPMs (95%) were classified as high ROB, 20 (3.6%) low ROB, and 9 (1.6%) unclear ROB. Only one model with unclear ROB was reclassified to high ROB after full PROBAST assessment of all low and unclear ROB models. Median change in discrimination was significantly smaller in low ROB models (dAUC -0.9%, IQR -6.2-4.2%) compared to high ROB models (dAUC -11.7%, IQR -33.3-2.6%; P < 0.001). Conclusion: High ROB is pervasive among published CPMs. It is associated with poor discriminative performance at validation, supporting the application of PROBAST or a shorter version in CPM reviews. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http:// creativecommons.org/ licenses/ by- nc- nd/ 4.0/ ) Show less
Stahlie, E.H.A.; Franke, V.; Zuur, C.L.; Klop, W.M.C.; Hiel, B. van der; Wiel, B.A. van de; ... ; Akkooi, A.C.J. van 2021
Background Talimogene laherparepvec (T-VEC) is a genetically modified herpes simplex type 1 virus and known as an effective oncolytic immunotherapy for injectable cutaneous, subcutaneous and nodal... Show moreBackground Talimogene laherparepvec (T-VEC) is a genetically modified herpes simplex type 1 virus and known as an effective oncolytic immunotherapy for injectable cutaneous, subcutaneous and nodal melanoma lesions in stage IIIB-IVM1a patients. This study set out to identify prognostic factors for achieving a complete response that can be used to optimize patient selection for T-VEC monotherapy. Methods Patients with stage IIIB-IVM1a melanoma, treated with T-VEC at the Netherlands Cancer Institute between 2016-12 and 2020-01 with a follow-up time > 6 months, were included. Data were collected on baseline characteristics, responses and adverse events (AEs). Uni- and multivariable analyses were conducted, and a prediction model was developed to identify prognostic factors associated with CR. Results A total of 93 patients were included with a median age of 69 years, median follow-up time was 16.6 months. As best response, 58 patients (62%) had a CR, and the overall response rate was 79%. The durable response rate (objective response lasting > 6 months) was 51%. Grade 1-2 AEs occurred in almost every patient. Tumor size, type of metastases, prior treatment with systemic therapy and stage (8Th AJCC) were independent prognostic factors for achieving CR. The prediction model includes the predictors tumor size, type of metastases and number of lesions. Conclusions This study shows that intralesional T-VEC monotherapy is able to achieve high complete and durable responses. The prediction model shows that use of T-VEC in patients with less tumor burden is associated with better outcomes, suggesting use earlier in the course of the disease. Show less
Objective: To develop and validate a prediction model for airflow obstruction (AO) in older Chinese.Methods.Design: Multivariable logistic regression analysis in large population cohort of Chinese... Show moreObjective: To develop and validate a prediction model for airflow obstruction (AO) in older Chinese.Methods.Design: Multivariable logistic regression analysis in large population cohort of Chinese aged >50 years.Participants: Model development: 8762 Chinese aged >= 50 years were selected from the early phase recruits to the Guangzhou Biobank Cohort Study (GBCS) (recruited from September 2003 to May 2006). Internal validation: 100 bootstrap samples drawn with replacement from the development sample. External validation: 8395 Chinese aged >= 50 years from later phase GBCS (recruited from September 2006 to January 2008).Outcomes: AO was defined by a forced expiratory volume in 1 s/forced vital capacity ratio < lower limits of normal.Results: 839 (9.6%) and 764 (9.1%) individuals had AO in the development and temporal validation samples respectively. The predictors in the prediction model included sex, age, body mass index groups, smoking status, presence of respiratory symptoms, and history of asthma. Model development and validation was stratified by sex. Model performance including calibration (calibration-in-the-large -0.017 vs. -0.157; and calibration slope 0.88 vs. 1.02), discrimination (C-statistic 0.72 vs. 0.63 with 95% confidence interval 0.69-0.75 vs. 0.62-0.73) and clinical usefulness (decision curve analysis) in the external temporal validation sample were more satisfactory in men than that in women. Prediction models with risk thresholds (13% in men and 7% in women) and easy-to-use nomograms were developed to assess the probability of AO.Conclusion: The diagnostic models based on readily available epidemiologic and clinical information with satisfactory performance can assist physicians to identify older individuals at high risk of AO and may improve the efficiency of spirometry for active case finding. Further validation beyond the Chinese population is warranted. Show less
Roessel, S. van; Strijker, M.; Steyerberg, E.W.; Groen, J.V.; Mieog, J.S.; Groot, V.P.; ... ; Besselink, M.G. 2020
Background: The objective of this study was to validate and update the Amsterdam prediction model including tumor grade, lymph node ratio, margin status and adjuvant therapy, for prediction of... Show moreBackground: The objective of this study was to validate and update the Amsterdam prediction model including tumor grade, lymph node ratio, margin status and adjuvant therapy, for prediction of overall survival (OS) after pancreatoduodenectomy for pancreatic cancer.Methods: We included consecutive patients who underwent pancreatoduodenectomy for pancreatic cancer between 2000 and 2017 at 11 tertiary centers in 8 countries (USA, UK, Germany, Italy, Sweden, the Netherlands, Korea, Australia). Model performance for prediction of OS was evaluated by calibration statistics and Uno's C-statistic for discrimination. Validation followed the TRIPOD statement.Results: Overall, 3081 patients (53% male, median age 66 years) were included with a median OS of 24 months, of whom 38% had N2 disease and 77% received adjuvant chemotherapy. Predictions of 3-year OS were fairly similar to observed OS with a calibration slope of 0.72. Statistical updating of the model resulted in an increase of the C-statistic from 0.63 to 0.65 (95% CI 0.64-0.65), ranging from 0.62 to 0.67 across different countries. The area under the curve for the prediction of 3 -year OS was 0.71 after updating. Median OS was 36, 25 and 15 months for the low, intermediate and high risk group, respectively (P < 0.001).Conclusions: This large international study validated and updated the Amsterdam model for survival prediction after pancreatoduodenectomy for pancreatic cancer. The model incorporates readily available variables with a fairly accurate model performance and robustness across different countries, while novel markers may be added in the future. The risk groups and web-based calculator www pancreascalculaior. corn may facilitate use in daily practice and future trials. (C) 2019 Elsevier Ltd, BASO The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved. Show less
The main objective of this thesis was to develop clinically relevant survival models for patients with high-grade soft tissue sarcoma of the extremities, in particular the development and... Show moreThe main objective of this thesis was to develop clinically relevant survival models for patients with high-grade soft tissue sarcoma of the extremities, in particular the development and validation of prediction models for use in clinical practice. The interdisciplinary collaboration between the Mathematical Institute of Leiden University and the Leiden University Medical Center resulted in important contributions to the care of soft tissue sarcoma patients. Show less
Luijken, K.; Wynants, L.; Smeden, M. van; Calster, B. van; Steyerberg, E.W.; Groenwold, R.H.H. 2020
Objectives: The aim of this study was to quantify the impact of predictor measurement heterogeneity on prediction model performance. Predictor measurement heterogeneity refers to variation in the... Show moreObjectives: The aim of this study was to quantify the impact of predictor measurement heterogeneity on prediction model performance. Predictor measurement heterogeneity refers to variation in the measurement of predictor(s) between the derivation of a prediction model and its validation or application. It arises, for instance, when predictors are measured using different measurement instruments or protocols.Study Design and Setting: We examined the effects of various scenarios of predictor measurement heterogeneity in real-world clinical examples using previously developed prediction models for diagnosis of ovarian cancer, mutation carriers for Lynch syndrome, and intrauterine pregnancy.Results: Changing the measurement procedure of a predictor influenced the performance at validation of the prediction models in nine clinical examples. Notably, it induced model miscalibration. The calibration intercept at validation ranged from -0.70 to 1.43 (0 for good calibration), whereas the calibration slope ranged from 0.50 to 1.67 (1 for good calibration). The difference in C-statistic and scaled Brier score between derivation and validation ranged from -0.08 to +0.08 and from -0.40 to +0.16, respectively.Conclusion: This study illustrates that predictor measurement heterogeneity can influence the performance of a prediction model substantially, underlining that predictor measurements used in research settings should resemble clinical practice. Specification of measurement heterogeneity can help researchers explaining discrepancies in predictive performance between derivation and validation setting. (C) 2019 The Authors. Published by Elsevier Inc. Show less
Background Conditional survival is the survival probability after already surviving a predefined time period. This may be informative during follow-up, especially when adjusted for tumor... Show moreBackground Conditional survival is the survival probability after already surviving a predefined time period. This may be informative during follow-up, especially when adjusted for tumor characteristics. Such prediction models for patients with resected pancreatic cancer are lacking and therefore conditional survival was assessed and a nomogram predicting 5-year survival at a predefined period after resection of pancreatic cancer was developed. Methods This population-based study included patients with resected pancreatic ductal adenocarcinoma from the Netherlands Cancer Registry (2005-2016). Conditional survival was calculated as the median, and the probability of surviving up to 8 years in patients who already survived 0-5 years after resection was calculated using the Kaplan-Meier method. A prediction model was constructed. Results Overall, 3082 patients were included, with a median age of 67 years. Median overall survival was 18 months (95% confidence interval 17-18 months), with a 5-year survival of 15%. The 1-year conditional survival (i.e. probability of surviving the next year) increased from 55 to 74 to 86% at 1, 3, and 5 years after surgery, respectively, while the median overall survival increased from 15 to 40 to 64 months at 1, 3, and 5 years after surgery, respectively. The prediction model demonstrated that the probability of achieving 5-year survival at 1 year after surgery varied from 1 to 58% depending on patient and tumor characteristics. Conclusions This population-based study showed that 1-year conditional survival was 55% 1 year after resection and 74% 3 years after resection in patients with pancreatic cancer. The prediction model is available via to inform patients and caregivers. Show less
Background: Diagnosing pneumonia can be challenging in general practice but is essential to distinguish from other respiratory tract infections because of treatment choice and outcome prediction.... Show moreBackground: Diagnosing pneumonia can be challenging in general practice but is essential to distinguish from other respiratory tract infections because of treatment choice and outcome prediction. We determined predictive signs, symptoms and biomarkers for the presence of pneumonia in patients with acute respiratory tract infection in primary care.Methods: From March 2012 until May 2016 we did a prospective observational cohort study in three radiology departments in the Leiden-The Hague area, The Netherlands. From adult patients we collected clinical characteristics and biomarkers, chest X ray results and outcome. To assess the predictive value of C-reactive protein (CRP), procalcitonin and midregional pro-adrenomedullin for pneumonia, univariate and multivariate binary logistic regression were used to determine risk factors and to develop a prediction model.Results: Two hundred forty-nine patients were included of whom 30 (12%) displayed a consolidation on chest X ray. Absence of runny nose and whether or not a patient felt ill were independent predictors for pneumonia. CRP predicts pneumonia better than the other biomarkers but adding CRP to the clinical model did not improve classification (- 4%); however, CRP helped guidance of the decision which patients should be given antibiotics.Conclusions: Adding CRP measurements to a clinical model in selected patients with an acute respiratory infection does not improve prediction of pneumonia, but does help in giving guidance on which patients to treat with antibiotics. Our findings put the use of biomarkers and chest X ray in diagnosing pneumonia and for treatment decisions into some perspective for general practitioners. Show less
KleinJan, G.H.; Sikorska, K.; Korne, C.M.; Brouwer, O.R.; Buckle, T.; Tillier, C.; ... ; Poel, H.G. van der 2019
Specific language impairment (SLI), or developmental language disorder, is the most prevalent development disorder in childhood. However, most children with SLI are detected late. Predictive... Show moreSpecific language impairment (SLI), or developmental language disorder, is the most prevalent development disorder in childhood. However, most children with SLI are detected late. Predictive properties of language milestones and risk factors for having SLI later in life were investigated in a nested case-control study. The outcomes showed that from the age of two years, children not meeting language milestones at the norm age are at risk for having SLI at school age. A concise tool was developed to enable young children with SLI to be detected. This uses five language milestones and has acceptable predictive values. Of all investigated risk factors, only maternal age, the place in the birth order and being breastfeed directly after birth had a relationship with having SLI later. It was also established that children with SLI were more likely to be late, not only in reaching language milestones, but also in reaching motor milestones at the norm age. Suggestions are made to improve the early detection of children with SLI using this concise tool. The concise tool is easy to implement in the Dutch healthcare system because it uses data already collected during visits in the well-child healthcare system in the Netherlands. Show less
Loymans, R.J.B.; Debray, T.P.A.; Honkoop, P.J.; Termeer, E.H.; Snoeck-Stroband, J.B.; Schermer, T.R.J.; ... ; Riet, G. ter 2018
