Objectives:The current paper explores the psychological predictors of nocebo hyperalgesia and whether the reduction of nocebo hyperalgesia can be predicted by susceptibility to nocebo hyperalgesia... Show moreObjectives:The current paper explores the psychological predictors of nocebo hyperalgesia and whether the reduction of nocebo hyperalgesia can be predicted by susceptibility to nocebo hyperalgesia and psychological characteristics. Methods:Nocebo effects on pressure pain were first experimentally induced in 83 healthy female participants through conditioning with open-label instructions about the pain-worsening function of a sham TENS device to assess susceptibility to nocebo hyperalgesia. Participants were then randomized to 1 out of 2 nocebo-reduction conditions (counterconditioning/extinction) or to continued nocebo-conditioning (control), each combined with open-label instructions about the new sham device function. Dispositional optimism, trait and state anxiety, pain catastrophizing, fear of pain, and body vigilance were assessed at baseline. Results:The results showed that lower optimism and higher trait anxiety were related to a stronger induction of nocebo hyperalgesia. Moreover, a stronger induction of nocebo hyperalgesia and higher trait anxiety predicted a larger nocebo reduction across interventions. Also, nocebo hyperalgesia and optimism moderated the effects of the nocebo-reduction interventions, whereby larger nocebo hyperalgesia and lower optimism were associated with a larger nocebo reduction after counterconditioning, compared with control, and also extinction for larger nocebo hyperalgesia. Discussion:Our findings suggest that open-label conditioning leads to stronger nocebo hyperalgesia when trait anxiety is high and dispositional optimism is low, while these psychological characteristics, along with larger nocebo hyperalgesia, also predict open-label counterconditioning to be an effective nocebo-reduction strategy. Susceptibility to nocebo hyperalgesia, trait anxiety, and dispositional optimism might be indicators of a flexible pain regulatory system. Show less
Background Prediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to... Show moreBackground Prediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to develop prediction models for first-ever cardiovascular event risk in men and women aged 30 to 49 years.Methods and Results We included patients aged 30 to 49 years without cardiovascular disease from a Dutch routine care database. Outcome was defined as first-ever cardiovascular event. Our reference models were sex-specific Cox proportional hazards models based on traditional cardiovascular predictors, which we compared with models using 2 predictor subsets with the 20 or 50 most important predictors based on the Cox elastic net model regularization coefficients. We assessed the C-index and calibration curve slopes at 10 years of follow-up. We stratified our analyses based on 30- to 39-year and 40- to 49-year age groups at baseline. We included 542 141 patients (mean age 39.7, 51% women). During follow-up, 10 767 cardiovascular events occurred. Discrimination of reference models including traditional cardiovascular predictors was moderate (women: C-index, 0.648 [95% CI, 0.645-0.652]; men: C-index, 0.661 [95%CI, 0.658-0.664]). In women and men, the Cox proportional hazard models including 50 most important predictors resulted in an increase in C-index (0.030 and 0.012, respectively), and a net correct reclassification of 3.7% of the events in women and 1.2% in men compared with the reference model.Conclusions Sex-specific electronic health record-derived prediction models for first-ever cardiovascular events in the general population aged <50 years have moderate discriminatory performance. Data-driven predictor selection leads to identification of nontraditional cardiovascular predictors, which modestly increase performance of models. Show less
BackgroundPrediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to... Show moreBackgroundPrediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to develop prediction models for first‐ever cardiovascular event risk in men and women aged 30 to 49 years.Methods and ResultsWe included patients aged 30 to 49 years without cardiovascular disease from a Dutch routine care database. Outcome was defined as first‐ever cardiovascular event. Our reference models were sex‐specific Cox proportional hazards models based on traditional cardiovascular predictors, which we compared with models using 2 predictor subsets with the 20 or 50 most important predictors based on the Cox elastic net model regularization coefficients. We assessed the C‐index and calibration curve slopes at 10 years of follow‐up. We stratified our analyses based on 30‐ to 39‐year and 40‐ to 49‐year age groups at baseline. We included 542 141 patients (mean age 39.7, 51% women). During follow‐up, 10 767 cardiovascular events occurred. Discrimination of reference models including traditional cardiovascular predictors was moderate (women: C‐index, 0.648 [95% CI, 0.645–0.652]; men: C‐index, 0.661 [95%CI, 0.658–0.664]). In women and men, the Cox proportional hazard models including 50 most important predictors resulted in an increase in C‐index (0.030 and 0.012, respectively), and a net correct reclassification of 3.7% of the events in women and 1.2% in men compared with the reference model.ConclusionsSex‐specific electronic health record‐derived prediction models for first‐ever cardiovascular events in the general population aged <50 years have moderate discriminatory performance. Data‐driven predictor selection leads to identification of nontraditional cardiovascular predictors, which modestly increase performance of models. Show less
The medical research literature is abundant with regression analyses that include multiple covariates, so-called multivariable regression models. Despite their common application, the... Show moreThe medical research literature is abundant with regression analyses that include multiple covariates, so-called multivariable regression models. Despite their common application, the interpretation of their results is not always clear or claimed interpretations are not justified. To outline the distinctions between different interpretations, we describe several possible research objectives for which a multivariable regression analysis might be an appropriate way of analyzing the data. In addition, we describe caveats in the interpretation of results of multivariable regression analysis. Show less
Weeda, Y.A.; Kalisvaart, G.M.; Velden, F.H.P. van; Gelderblom, H.; Molen, A.J. van der; Bovee, J.V.M.G.; ... ; Geus-Oei, L.F. de 2022
Gastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms. Tyrosine kinase inhibitor (TKI) therapy is currently part of routine clinical practice for unresectable and metastatic... Show moreGastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms. Tyrosine kinase inhibitor (TKI) therapy is currently part of routine clinical practice for unresectable and metastatic disease. It is important to assess the efficacy of TKI treatment at an early stage to optimize therapy strategies and eliminate futile ineffective treatment, side effects and unnecessary costs. This systematic review provides an overview of the imaging features obtained from contrast-enhanced (CE)-CT and 2-deoxy-2-[F-18]fluoro-D-glucose ([F-18]FDG) PET/CT to predict and monitor TKI treatment response in GIST patients. PubMed, Web of Science, the Cochrane Library and Embase were systematically screened. Articles were considered eligible if quantitative outcome measures (area under the curve (AUC), correlations, sensitivity, specificity, accuracy) were used to evaluate the efficacy of imaging features for predicting and monitoring treatment response to various TKI treatments. The methodological quality of all articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies, v2 (QUADAS-2) tool and modified versions of the Radiomics Quality Score (RQS). A total of 90 articles were included, of which 66 articles used baseline [F-18]FDG-PET and CE-CT imaging features for response prediction. Generally, the presence of heterogeneous enhancement on baseline CE-CT imaging was considered predictive for high-risk GISTs, related to underlying neovascularization and necrosis of the tumor. The remaining articles discussed therapy monitoring. Clinically established imaging features, including changes in tumor size and density, were considered unfavorable monitoring criteria, leading to under- and overestimation of response. Furthermore, changes in glucose metabolism, as reflected by [F-18]FDG-PET imaging features, preceded changes in tumor size and were more strongly correlated with tumor response. Although CE-CT and [F-18]FDG-PET can aid in the prediction and monitoring in GIST patients, further research on cost-effectiveness is recommended. Show less
Introduction: In patients with acute respiratory distress syndrome (ARDS), the PaO2/FiO(2) ratio at the time of ARDS diagnosis is weakly associated with mortality. We hypothesized that setting a... Show moreIntroduction: In patients with acute respiratory distress syndrome (ARDS), the PaO2/FiO(2) ratio at the time of ARDS diagnosis is weakly associated with mortality. We hypothesized that setting a PaO2/FiO(2) threshold in 150 mm Hg at 24 h from moderate/severe ARDS diagnosis would improve predictions of death in the intensive care unit (ICU). Methods: We conducted an ancillary study in 1303 patients with moderate to severe ARDS managed with lung-protective ventilation enrolled consecutively in four prospective multicenter cohorts in a network of ICUs. The first three cohorts were pooled (n = 1000) as a testing cohort; the fourth cohort (n = 303) served as a confirmatory cohort. Based on the thresholds for PaO2/FiO(2) (150 mm Hg) and positive end-expiratory pressure (PEEP) (10 cm H2O), the patients were classified into four possible subsets at baseline and at 24 h using a standardized PEEP-FiO(2) approach: (I) PaO2/FiO(2) >= 150 at PEEP < 10, (II) PaO2/FiO(2) > 150 at PEEP >= 10, (III) PaO2/FiO(2) < 150 at PEEP < 10, and (IV) PaO2/FiO(2) < 150 at PEEP >= 10. Primary outcome was death in the ICU. Results: ICU mortalities were similar in the testing and confirmatory cohorts (375/1000, 37.5% vs. 112/303, 37.0%, respectively). At baseline, most patients from the testing cohort (n = 792/1000, 79.2%) had a PaO2/FiO(2) < 150, with similar mortality among the four subsets (p = 0.23). When assessed at 24 h, ICU mortality increased with an advance in the subset: 17.9%, 22.8%, 40.0%, and 49.3% (p < 0.0001). The findings were replicated in the confirmatory cohort (p < 0.0001). However, independent of the PEEP levels, patients with PaO2/FiO(2) < 150 at 24 h followed a distinct 30-day ICU survival compared with patients with PaO2/FiO(2) >= 150 (hazard ratio 2.8, 95% CI 2.2-3.5, p < 0.0001). Conclusions: Subsets based on PaO2/FiO(2) thresholds of 150 mm Hg assessed after 24 h of moderate/severe ARDS diagnosis are clinically relevant for establishing prognosis, and are helpful for selecting adjunctive therapies for hypoxemia and for enrolling patients into therapeutic trials. Show less
Huinink, S.T.; Jong, D.C. de; Nieboer, D.; Thomassen, D.; Steyerberg, E.W.; Dijkgraaf, M.G.W.; ... ; Vries, A.C. de 2022
Background Anti-tumor necrosis factor (TNF) therapy is effective for the treatment of Crohn's disease. Cessation may be considered in patients with a low risk of relapse. We aimed to externally... Show moreBackground Anti-tumor necrosis factor (TNF) therapy is effective for the treatment of Crohn's disease. Cessation may be considered in patients with a low risk of relapse. We aimed to externally validate and update our previously developed prediction model to estimate the risk of relapse after cessation of anti-TNF therapy. Methods We performed a retrospective cohort study in 17 Dutch hospitals. Crohn's disease patients in clinical, biochemical or endoscopic remission were included after anti-TNF cessation. Primary outcome was a relapse necessitating treatment. Discrimination and calibration of the previously developed model were assessed. After external validation, the model was updated. The performance of the updated prediction model was assessed in internal-external validation and by using decision curve analysis. Results 486 patients were included with a median follow-up of 1.7 years. Relapse rates were 35 and 54% after 1 and 2 years. At external validation, the discriminative ability of the prediction model was equal to that found at the development of the model [c-statistic 0.58 (95% confidence interval (CI) 0.54-0.62)], though the model was not well-calibrated on our cohort [calibration slope: 0.52 (0.28-0.76)]. After an update, a c-statistic of 0.60 (0.58-0.63) and calibration slope of 0.89 (0.69-1.09) were reported in internal-external validation. Conclusion Our previously developed and updated prediction model for the risk of relapse after cessation of anti-TNF in Crohn's disease shows reasonable performance. The use of the model may support clinical decision-making to optimize patient selection in whom anti-TNF can be withdrawn. Clinical validation is ongoing in a prospective randomized trial. Show less
Objective Monochorionic diamniotic twin pregnancies complicated by Type-III selective fetal growth restriction (sFGR) are at high risk of fetal death. The aim of this study was to identify... Show moreObjective Monochorionic diamniotic twin pregnancies complicated by Type-III selective fetal growth restriction (sFGR) are at high risk of fetal death. The aim of this study was to identify predictors of fetal death in these pregnancies. Methods This was an international multicenter retrospective cohort study. Type-III sFGR was defined as fetal estimated fetal weight (EFW) of one twin below the 10(th) percentile and intertwin EFW discordance of >= 25% in combination with intermittent absent or reversed end-diastolic flow in the umbilical artery of the smaller fetus. Predictors of fetal death were recorded longitudinally throughout gestation and assessed in univariable and multivariable logistic regression models. The classification and regression trees (CART) method was used to construct a prediction model of fetal death using significant predictors derived from the univariable analysis. Results A total of 308 twin pregnancies (616 fetuses) were included in the analysis. In 273 (88.6%) pregnancies, both twins were liveborn, whereas 35 pregnancies had single (n = 19 (6.2%)) or double (n = 16 (5.2%)) fetal death. On univariable analysis, earlier gestational age at diagnosis of Type-III sFGR, oligohydramnios in the smaller twin and deterioration in umbilical artery Doppler flow were associated with an increased risk of fetal death, as was larger fetal EFW discordance, particularly between 24 and 32 weeks' gestation. None of the parameters identified on univariable analysis maintained statistical significance on multivariable analysis. The CART model allowed us to identify three risk groups: a low-risk group (6.8% risk of fetal death), in which umbilical artery Doppler did not deteriorate; an intermediate-risk group (16.3% risk of fetal death), in which umbilical artery Doppler deteriorated but the diagnosis of sFGR was made at or after 16 + 5 weeks' gestation; and a high-risk group (58.3% risk of fetal death), in which umbilical artery Doppler deteriorated and gestational age at diagnosis was < 16 + 5 weeks' gestation. Conclusions Type-III sFGR is associated with a high risk of fetal death. A prediction algorithm can help to identify the highest-risk group, which is characterized by Doppler deterioration and early referral. Further studies should investigate the potential benefit of fetal surveillance and intervention in this cohort. (c) 2022 International Society of Ultrasound in Obstetrics and Gynecology. Show less
The role of pathology in patient management has evolved over time from the retrospective review of cells, tissue, and disease ('what happened') to a prospective outlook ('what will happen').... Show moreThe role of pathology in patient management has evolved over time from the retrospective review of cells, tissue, and disease ('what happened') to a prospective outlook ('what will happen'). Examination of a static, two-dimensional hematoxylin and eosin (H&E)-stained tissue slide has traditionally been the pathologist's primary task, but novel ancillary techniques enabled by technological breakthroughs have supported pathologists in their increasing ability to predict disease status and behaviour. Nevertheless, the informational limits of 2D, fixed tissue are now being reached and technological innovation is urgently needed to ensure that our understanding of disease entities continues to support improved individualized treatment options. Here we review pioneering work currently underway in the field of cancer pathology that has the potential to capture information beyond the current basic snapshot. A selection of exciting new technologies is discussed that promise to facilitate integration of the functional and multidimensional (space and time) information needed to optimize the prognostic and predictive value of cancer pathology. Learning how to analyse, interpret, and apply the wealth of data acquired by these new approaches will challenge the knowledge and skills of the pathology community. (c) 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. Show less
Introduction Patients with lower-leg cast immobilization and patients undergoing knee arthroscopy have an increased risk of venous thrombosis (VT). Guidelines are ambiguous about thromboprophylaxis... Show moreIntroduction Patients with lower-leg cast immobilization and patients undergoing knee arthroscopy have an increased risk of venous thrombosis (VT). Guidelines are ambiguous about thromboprophylaxis use, and individual risk factors for developing VT are often ignored. To assist in VT risk stratification and guide thromboprophylaxis use, various prediction models have been developed. These models depend largely on clinical factors and provide reasonably good C-statistics of around 70%. We explored using protein levels in blood plasma measured by multiplexed quantitative targeted proteomics to predict VT. Our aim was to assess whether a VT risk prediction model based on absolute plasma protein quantification is possible. Methods We used internal standards to quantify proteins in less than 10 mu l plasma. We measured 270 proteins in samples from patients scheduled for knee arthroscopy or with lower-leg cast immobilization. The two prospective POT-(K)CAST trails allow complementary views of VT signature in blood, namely pre and post trauma, respectively. From approximately 3000 patients, 31 patients developed VT who were included and matched with double the number of controls. Results Top discriminating proteins between cases and controls included APOC3, APOC4, APOC2, ATRN, F13B, and F2 in knee arthroscopy patients and APOE, SERPINF2, B2M, F13B, AFM, and C1QC in patients with lower-leg cast. A logistic regression model with cross-validation resulted in C-statistics of 88.1% (95% CI: 85.7-90.6%) and 79.6% (95% CI: 77.2-82.0%) for knee arthroscopy and cast immobilization groups respectively. Conclusions Promising C-statistics merit further exploration of the value of proteomic tests for predicting VT risk upon additional validation. Show less
Boone, S.C.; Smeden, M. van; Rosendaal, F.R.; Cessie, S. le; Groenwold, R.H.H.; Jukema, J.W.; ... ; Mutsert, R. de 2022
Visceral adipose tissue (VAT) is a strong prognostic factor for cardiovascular disease and a potential target for cardiovascular risk stratification. Because VAT is difficult to measure in clinical... Show moreVisceral adipose tissue (VAT) is a strong prognostic factor for cardiovascular disease and a potential target for cardiovascular risk stratification. Because VAT is difficult to measure in clinical practice, we estimated prediction models with predictors routinely measured in general practice and VAT as outcome using ridge regression in 2,501 middle-aged participants from the Netherlands Epidemiology of Obesity study, 2008-2012. Adding waist circumference and other anthropometric measurements on top of the routinely measured variables improved the optimism-adjusted R-2 from 0.50 to 0.58 with a decrease in the root-mean-square error (RMSE) from 45.6 to 41.5 cm(2) and with overall good calibration. Further addition of predominantly lipoprotein-related metabolites from the Nightingale platform did not improve the optimism-corrected R-2 and RMSE. The models were externally validated in 370 participants from the Prospective Investigation of Vasculature in Uppsala Seniors (PIVUS, 2006-2009) and 1,901 participants from the Multi-Ethnic Study of Atherosclerosis (MESA, 2000-2007). Performance was comparable to the development setting in PIVUS (R-2 = 0.63, RMSE = 42.4 cm(2), calibration slope = 0.94) but lower in MESA (R-2 = 0.44, RMSE = 60.7 cm(2), calibration slope = 0.75). Our findings indicate that the estimation of VAT with routine clinical measurements can be substantially improved by incorporating waist circumference but not by metabolite measurements. Show less
Baltussen, J.C.; Welters, M.J.P.; Verdegaal, E.M.E.; Kapiteijn, E.; Schrader, A.M.R.; Slingerland, M.; ... ; Glas, N.A. de 2021
Simple Summary Immune checkpoint inhibitors (ICIs) have revolutionized treatment of advanced melanoma and survival of melanoma patients has radically improved since. However, as durable responses... Show moreSimple Summary Immune checkpoint inhibitors (ICIs) have revolutionized treatment of advanced melanoma and survival of melanoma patients has radically improved since. However, as durable responses after ICIs are only observed in 30-50% of melanoma patients, there is an unmet need to identify predictive biomarkers for response. This systematic review demonstrates the substantial number of publications that have studied a wide variety of possible biomarkers. Covering 177 publications that investigated 128 unique biomarkers, we provide an overview of all studied biomarkers in correlation with response or survival. We highlight blood, tumor and fecal biomarkers that were associated with response to ICIs in multiple studies. Of these, only T-cell inflamed gene expression profiling was predictive for response in a large clinical trial and validated in other studies, thus representing a promising biomarker for clinical practice. Large validation studies are warranted to confirm the predictive utility of other biomarkers, thereby further personalizing immunotherapy treatment. Immune checkpoint inhibitors (ICIs) have strongly improved the survival of melanoma patients. However, as durable response to ICIs are only seen in a minority, there is an unmet need to identify biomarkers that predict response. Therefore, we provide a systematic review that evaluates all biomarkers studied in association with outcomes of melanoma patients receiving ICIs. We searched Pubmed, COCHRANE Library, Embase, Emcare, and Web of Science for relevant articles that were published before June 2020 and studied blood, tumor, or fecal biomarkers that predicted response or survival in melanoma patients treated with ICIs. Of the 2536 identified reports, 177 were included in our review. Risk of bias was high in 40%, moderate in 50% and low in 10% of all studies. Biomarkers that correlated with response were myeloid-derived suppressor cells (MDSCs), circulating tumor cells (CTCs), CD8+ memory T-cells, T-cell receptor (TCR) diversity, tumor-infiltrating lymphocytes (TILs), gene expression profiling (GEP), and a favorable gut microbiome. This review shows that biomarkers for ICIs in melanoma patients are widely studied, but heterogeneity between studies is high, average sample sizes are low, and validation is often lacking. Future studies are needed to further investigate the predictive utility of some promising candidate biomarkers. Show less
Simple Summary The objective was to develop and internally validate a predictive model based on preoperative predictors, including geriatric characteristics, for severe postoperative complications... Show moreSimple Summary The objective was to develop and internally validate a predictive model based on preoperative predictors, including geriatric characteristics, for severe postoperative complications after elective surgery for stage I-III CRC in patients >= 70 years. Potential predictors included demographics, comorbidity, tumour location, activities of daily living (ADL), history of falls, malnutrition, risk factors for delirium, use of mobility aid and polypharmacy. The least absolute shrinkage and selection operator (LASSO) method was used for predictor selection and prediction model building. A geriatric model that included gender, previous DVT or pulmonary embolism, COPD/asthma/emphysema, rectal cancer, the use of a mobility aid, ADL assistance, previous delirium and polypharmacy showed satisfactory discrimination with an AUC of 0.69 (95% CI 0.73-0.64); the AUC for the optimism corrected model was 0.65. An eight-item colorectal geriatric model (GerCRC) was developed. After external validation, this risk model has the potential to be used for preoperative (shared) decision-making. Introduction Older patients have an increased risk of morbidity and mortality after colorectal cancer (CRC) surgery. Existing CRC surgical prediction models have not incorporated geriatric predictors, limiting applicability for preoperative decision-making. The objective was to develop and internally validate a predictive model based on preoperative predictors, including geriatric characteristics, for severe postoperative complications after elective surgery for stage I-III CRC in patients >= 70 years. Patients and Methods: A prospectively collected database contained 1088 consecutive patients from five Dutch hospitals (2014-2017) with 171 severe complications (16%). The least absolute shrinkage and selection operator (LASSO) method was used for predictor selection and prediction model building. Internal validation was done using bootstrapping. Results: A geriatric model that included gender, previous DVT or pulmonary embolism, COPD/asthma/emphysema, rectal cancer, the use of a mobility aid, ADL assistance, previous delirium and polypharmacy showed satisfactory discrimination with an AUC of 0.69 (95% CI 0.73-0.64); the AUC for the optimism corrected model was 0.65. Based on these predictors, the eight-item colorectal geriatric model (GerCRC) was developed. Conclusion: The GerCRC is the first prediction model specifically developed for older patients expected to undergo CRC surgery. Combining tumour- and patient-specific predictors, including geriatric predictors, improves outcome prediction in the heterogeneous older population. Show less
Geraedts, V.J.; Koch, M.; Kuiper, R.; Kefalas, M.; Back, T.H.W.; Hilten, J.J. van; ... ; Tannemaat, M.R. 2021
Background Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine... Show moreBackground Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline.Objective To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS.Methods Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1-year follow-up. A total of 16,674 EEG-features were extracted per patient; a Boruta algorithm selected EEG-features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10-fold cross-validation with Bayesian optimization provided class-differentiation.Results Tweny-five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited.Conclusions Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. (c) 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society Show less
Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who... Show moreBackground: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated.Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model. Show less
Hanna Sawires, R.G.; Schiphuis, J.H.; Wuhrer, M.; Vasen, H.F.A.; Leerdam, M.E. van; Bonsing, B.A.; ... ; Tollenaar, R.A.E.M. 2021
Pancreatic ductal adenocarcinoma (PDAC) is known as a highly aggressive malignant disease. Prognosis for patients is notoriously poor, despite improvements in surgical techniques and new (neo... Show morePancreatic ductal adenocarcinoma (PDAC) is known as a highly aggressive malignant disease. Prognosis for patients is notoriously poor, despite improvements in surgical techniques and new (neo)adjuvant chemotherapy regimens. Early detection of PDAC may increase the overall survival. It is furthermore foreseen that precision medicine will provide improved prognostic stratification and prediction of therapeutic response. In this review, omics-based discovery efforts are presented that aim for novel diagnostic and prognostic biomarkers of PDAC. For this purpose, we systematically evaluated the literature published between 1999 and 2020 with a focus on protein- and protein-glycosylation biomarkers in pancreatic cancer patients. Besides genomic and transcriptomic approaches, mass spectrometry (MS)-based proteomics and glycomics of blood- and tissue-derived samples from PDAC patients have yielded new candidates with biomarker potential. However, for reasons discussed in this review, the validation and clinical translation of these candidate markers has not been successful. Consequently, there has been a change of mindset from initial efforts to identify new unimarkers into the current hypothesis that a combination of biomarkers better suits a diagnostic or prognostic panel. With continuing development of current research methods and available techniques combined with careful study designs, new biomarkers could contribute to improved detection, prognosis, and prediction of pancreatic cancer. Show less
Background: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to... Show moreBackground: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. Methods: PRISMA/RIGHT/ CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. Findings: Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (beta = .29, P = .03) and diagnostic compared to prognostic (beta = .84, p < .0001) and predictive (beta = .87, P = .002) models were associated with increased accuracy. Interpretation: To date, several validated prediction models arc available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap. Show less
In medical research, missing data is common. In acute diseases, such as traumatic brain injury (TBI), even well-conducted prospective studies may suffer from missing data in baseline... Show moreIn medical research, missing data is common. In acute diseases, such as traumatic brain injury (TBI), even well-conducted prospective studies may suffer from missing data in baseline characteristics and outcomes. Statistical models may simply drop patients with any missing values, potentially leaving a selected subset of the original cohort. Imputation is widely accepted by methodologists as an appropriate way to deal with missing data. We aim to provide practical guidance on handling missing data for prediction modeling. We hereto propose a five-step approach, centered around single and multiple imputation: 1) explore the missing data patterns; 2) choose a method of imputation; 3) perform imputation; 4) assess diagnostics of the imputation; and 5) analyze the imputed data sets. We illustrate these five steps with the estimation and validation of the IMPACT (International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury) prognostic model in 1375 patients from the CENTER-TBI database, included in 53 centers across 17 countries, with moderate or severe TBI in the prospective European CENTER-TBI study. Future prediction modeling studies in acute diseases may benefit from following the suggested five steps for optimal statistical analysis and interpretation, after maximal effort has been made to minimize missing data. Show less
Simple Summary Adrenocortical carcinoma is a rare and aggressive cancer. Great variability in clinical course is observed, ranging from patients with extreme long survival to aggressive tumors with... Show moreSimple Summary Adrenocortical carcinoma is a rare and aggressive cancer. Great variability in clinical course is observed, ranging from patients with extreme long survival to aggressive tumors with prompt fatal outcome. This heterogeneity in survival makes it complicated to tailor treatment strategies for an individual patient. Therefore we sought to identify prognostic factors associated with ACC specific mortality. We analyzed the data of 160 ACC patients and developed a clinical prediction model including age, modified European Network for the Study of Adrenal Tumors (mENSAT) stage, and radical resection. This easy-to-use prediction model for ACC-specific mortality has the potential to guide clinical decision making if externally validated. Adrenocortical carcinoma (ACC) has an incidence of about 1.0 per million per year. In general, survival of patients with ACC is limited. Predicting survival outcome at time of diagnosis is a clinical challenge. The aim of this study was to develop and internally validate a clinical prediction model for ACC-specific mortality. Data for this retrospective cohort study were obtained from the nine centers of the Dutch Adrenal Network (DAN). Patients who presented with ACC between 1 January 2004 and 31 October 2013 were included. We used multivariable Cox proportional hazards regression to compute the coefficients for the prediction model. Backward stepwise elimination was performed to derive a more parsimonious model. The performance of the initial prediction model was quantified by measures of model fit, discriminative ability, and calibration. We undertook an internal validation step to counteract the possible overfitting of our model. A total of 160 patients were included in the cohort. The median survival time was 35 months, and interquartile range (IQR) 50.7 months. The multivariable modeling yielded a prediction model that included age, modified European Network for the Study of Adrenal Tumors (mENSAT) stage, and radical resection. The c-statistic was 0.77 (95% Confidence Interval: 0.72, 0.81), indicating good predictive performance. We developed a clinical prediction model for ACC-specific mortality. ACC mortality can be estimated using a relatively simple clinical prediction model with good discriminative ability and calibration. Show less
Barendregt, A.M.; Veldkamp, S.R.; Muller, P.C.E.H.; Geer, A. van de; Aarts, C.; Gulik, E.C. van; ... ; Kuijpers, T.W. 2020
Objective. To study two neutrophil activation markers, myeloid-related protein (MRP) 8/14 and neutrophil elastase (NE), for their ability to predict treatment response and flare in patients with... Show moreObjective. To study two neutrophil activation markers, myeloid-related protein (MRP) 8/14 and neutrophil elastase (NE), for their ability to predict treatment response and flare in patients with JIA.Methods. Using samples from two cohorts (I and II), we determined MRP8/14 and NE levels of 32 (I) and 81 (II) patients with new-onset, DMARD-naive arthritis and compared patients who responded to treatment (defined as fulfilling >= adjusted ACRpedi50 response and/or inactive disease) with non-responders (defined as fulfilling < adjusted ACRpedi50 response and/or active disease) at 6 and 12 months. Secondly, we compared biomarker levels of 54 (I) and 34 (II) patients with clinically inactive disease who did or did not suffer from a flare of arthritis after 6 or 12 months. Receiver operating characteristic analyses were carried out to study the predictive value of MRP8/14 and NE for treatment response and flare.Results. For both cohorts, baseline MRP8/14 and NE levels for patients who did or did not respond to treatment were not different. Also, MRP8/14 and NE levels were not different in patients who did or did not flare. Receiver operating characteristic analysis of MRP8/14 and NE demonstrated areas under the curve <0.7 in both cohorts.Conclusion. In our cohorts, MRP8/14 and NE could not predict treatment response. Also, when patients had inactive disease, neither marker could predict flares. Show less
