The Impact of Palliative Care Team Consultation on Quality of Life of Patients with Advanced Cancer in Dutch Hospitals: An Observational Study.

INTRODUCTION: Experimental studies have shown that palliative care team (PCT) involvement can improve quality of life (QoL) and symptom burden of patients with advanced cancer. It is unclear to what extent this effect is sustained in daily practice of hospital care. OBJECTIVE: This observational study aims to investigate the effect of PCT consultation on QoL and symptom burden of hospitalized patients with advanced cancer in daily practice. METHODS: After admission to 1 of 9 participating hospitals, patients with advanced cancer for whom the attending physician answered "no" to the Surprise Question were invited to complete a questionnaire, including the EORTC QLQ-C15-PAL, at 6 points in time, until 3 months after admission. Outcomes were compared between patients who received PCT consultation and patients who did not, taking into account differences in baseline characteristics. RESULTS: A total of 164 patients consented to participate, of whom 32 received PCT consultation. Of these patients, 108 were able to complete a questionnaire at day 14, of whom 19 after receiving PCT consultation. After adjusting for baseline differences, EORTC QLQ-C15-PAL scores for pain, appetite, and emotional functioning at day 14 were more favorable for patients who received a PCT consultation. CONCLUSION: PCT consultation decreased patients' symptom burden and tends to have a positive effect on QoL of hospitalized patients with advanced cancer, even if the PCT is consulted late in the patient's disease trajectory.

Development of a prediction model to target screening for high blood pressure in children.

Targeted screening for childhood high blood pressure may be more feasible than routine blood pressure measurement in all children to avoid unnecessary harms, overdiagnosis or costs. Targeting maybe based e.g. on being overweight, but information on other predictors may also be useful. Therefore, we aimed to develop a multivariable diagnostic prediction model to select children aged 9-10 years for blood pressure measurement. Data from 5359 children in a population-based prospective cohort study were used. High blood pressure was defined as systolic or diastolic blood pressure ≥ 95th percentile for gender, age, and height. Logistic regression with backward selection was used to identify the strongest predictors related to pregnancy, child, and parent characteristics. Internal validation was performed using bootstrapping. 227 children (4.2%) had high blood pressure. The diagnostic model included maternal hypertensive disease during pregnancy, maternal BMI, maternal educational level, parental hypertension, parental smoking, child birth weight standard deviation score (SDS), child BMI SDS, and child ethnicity. The area under the ROC curve was 0.73, compared to 0.65 when using only child overweight. Using the model and a cut-off of 5% for predicted risk, sensitivity and specificity were 59% and 76%; using child overweight only, sensitivity and specificity were 47% and 84%. In conclusion, our diagnostic prediction model uses easily obtainable information to identify children at increased risk of high blood pressure, offering an opportunity for targeted screening. This model enables to detect a higher proportion of children with high blood pressure than a strategy based on child overweight only.

The association between palliative care team consultation and hospital costs for patients with advanced cancer: An observational study in 12 Dutch hospitals.

BACKGROUND: Early palliative care team consultation has been shown to reduce costs of hospital care. The objective of this study was to investigate the association between palliative care team (PCT) consultation and the content and costs of hospital care in patients with advanced cancer. MATERIAL AND METHODS: A prospective, observational study was conducted in 12 Dutch hospitals. Patients with advanced cancer and an estimated life expectancy of less than 1 year were included. We compared hospital care during 3 months of follow-up for patients with and without PCT involvement. Propensity score matching was used to estimate the effect of PCTs on costs of hospital care. Additionally, gamma regression models were estimated to assess predictors of hospital costs. RESULTS: We included 535 patients of whom 126 received PCT consultation. Patients with PCT had a worse life expectancy (life expectancy <3 months: 62% vs. 31%, p < .01) and performance status (p < .01, e.g., WHO status higher than 2:54% vs. 28%) and more often had no more options for anti-tumour therapy (57% vs. 30%, p < .01). Hospital length of stay, use of most diagnostic procedures, medication and other therapeutic interventions were similar. The total mean hospital costs were €8,393 for patients with and €8,631 for patients without PCT consultation. Analyses using propensity scores to control for observed confounding showed no significant difference in hospital costs. CONCLUSIONS: PCT consultation for patients with cancer in Dutch hospitals often occurs late in the patients' disease trajectories, which might explain why we found no effect of PCT consultation on costs of hospital care. Earlier consultation could be beneficial to patients and reduce costs of care.

Validation of a score chart to predict the risk of chronic mesenteric ischemia and development of an updated score chart.

Background and objective: The objective of this article is to externally validate and update a recently published score chart for chronic mesenteric ischemia (CMI). Methods: A multicenter prospective cohort analysis was conducted of 666 CMI-suspected patients referred to two Dutch specialized CMI centers. Multidisciplinary consultation resulted in expert-based consensus diagnosis after which CMI consensus patients were treated. A definitive diagnosis of CMI was established if successful treatment resulted in durable symptom relief. The absolute CMI risk was calculated and discriminative ability of the original chart was assessed by the c-statistic in the validation cohort. Thereafter the original score chart was updated based on the performance in the combined original and validation cohort with inclusion of celiac artery (CA) stenosis cause. Results: In 8% of low-risk patients, 39% of intermediate-risk patients and 94% of high-risk patients of the validation cohort, CMI was diagnosed. Discriminative ability of the original model was acceptable (c-statistic 0.79). The total score of the updated chart ranged from 0 to 28 points (low risk 19% absolute CMI risk, intermediate risk 45%, and high risk 92%). The discriminative ability of the updated chart was slightly better (c-statistic 0.80). Conclusion: The CMI prediction model performs and discriminates well in the validation cohort. The updated score chart has excellent discriminative ability and is useful in clinical decision making.

The calibrated model-based concordance improved assessment of discriminative ability in patient clusters of limited sample size.

BACKGROUND: Discriminative ability is an important aspect of prediction model performance, but challenging to assess in clustered (e.g., multicenter) data. Concordance (c)-indexes may be too extreme within small clusters. We aimed to define a new approach for the assessment of discriminative ability in clustered data. METHODS: We assessed discriminative ability of a prediction model for the binary outcome mortality after traumatic brain injury within centers of the CRASH trial. With multilevel logistic regression analysis, we estimated cluster-specific calibration slopes which we used to obtain the recently proposed calibrated model-based concordance (c-mbc) within each cluster. We compared the c-mbc with the naïve c-index in centers of the CRASH trial and in simulations of clusters with varying calibration slopes. RESULTS: The c-mbc was less extreme in distribution than the c-index in 19 European centers (internal validation; n = 1716) and 36 non-European centers (external validation; n = 3135) of the CRASH trial. In simulations, the c-mbc was biased but less variable than the naïve c-index, resulting in lower root mean squared errors. CONCLUSIONS: The c-mbc, based on multilevel regression analysis of the calibration slope, is an attractive alternative to the c-index as a measure of discriminative ability in multicenter studies with patient clusters of limited sample size.

A Predictive Model of Postnatal Surgical Intervention in Children With Prenatally Detected Congenital Anomalies of the Kidney and Urinary Tract.

The aim of this study was to identify predictive factors and develop a model to assess individualized risk of postnatal surgical intervention in patients with antenatal hydronephrosis. This is a retrospective cohort study of 694 infants with prenatally detected congenital anomalies of kidney and urinary tract with a median follow-up time of 37 months. The main event of interest was postnatal surgical intervention. A predictive model was developed using Cox model with internal validation by bootstrap technique. Of 694 patients, 164 (24%) infants underwent surgical intervention in a median age of 7.8 months. Predictors of the surgical intervention in the model were: baseline glomerular filtration rate, associated hydronephrosis, presence of renal damage and the severity of renal pelvic dilatation. The optimism corrected c statistic for the model was 0.84 (95%CI, 0.82-0.87). The predictive model may contribute to identify infants at high risk for surgical intervention. Further studies are necessary to validate the model in patients from other settings.

Autonomy Challenges in Epigenetic Risk-Stratified Cancer Screening: How Can Patient Decision Aids Support Informed Consent?

Information of an individual's epigenome can be useful in cancer screening to enable personalised decision making on participation, treatment options and further screening strategies. However, adding this information might result in complex risk predictions on multiple diseases, unsolicited findings and information on (past) environmental exposure and behaviour. This complicates informed consent procedures and may impede autonomous decision-making. In this article we investigate and identify the specific features of epigenetic risk-stratified cancer screening that challenge the current informed consent doctrine. Subsequently we describe current and new informed consent models and the principle of respect for autonomy and argue for a specific informed consent model for epigenetic risk-stratified screening programmes. Next, we propose a framework that guides the development of Patient Decision Aids (PDAs) to support informed consent and promote autonomous choices in the specific context of epigenetic cancer screening programmes.

Childhood prediction models for hypertension later in life: a systematic review.

BACKGROUND: Hypertension, even during childhood, increases the risk of developing atherosclerosis and cardiovascular disease. Therefore, starting prevention of hypertension early in the life course could be beneficial. Prediction models might be useful for identifying children at increased risk of developing hypertension, which may enable targeted primordial prevention of cardiovascular disease. OBJECTIVE: To provide an overview of childhood prediction models for future hypertension. METHODS: Embase and Medline were systematically searched. Studies were included that were performed in the general population, and that reported on development or validation of a multivariable model for children to predict future high blood pressure, prehypertension or hypertension. Data were extracted using the CHARMS checklist for prediction modelling studies. RESULTS: Out of 12 780 reviewed records, six studies were included in which 18 models were presented. Five studies predicted adulthood hypertension, and one predicted adolescent prehypertension/hypertension. BMI and current blood pressure were most commonly included as predictors in the final models. Considerable heterogeneity existed in timing of prediction (from early childhood to late adolescence) and outcome measurement. Important methodological information was often missing, and in four studies information to apply the model in new individuals was insufficient. Reported area under the ROC curves ranged from 0.51 to 0.74. As none of the models were validated, generalizability could not be confirmed. CONCLUSION: Several childhood prediction models for future hypertension were identified, but their value for practice remains unclear because of suboptimal methods, limited information on performance, or the lack of external validation. Further validation studies are indicated.

Dynamic prediction of childhood high blood pressure in a population-based birth cohort: a model development study.

OBJECTIVES: To develop a dynamic prediction model for high blood pressure at the age of 9-10 years that could be applied at any age between birth and the age of 6 years in community-based child healthcare. DESIGN, SETTING AND PARTICIPANTS: Data were used from 5359 children in a population-based prospective cohort study in Rotterdam, the Netherlands. OUTCOME MEASURE: High blood pressure was defined as systolic and/or diastolic blood pressure ≥95th percentile for gender, age and height. Using multivariable pooled logistic regression, the predictive value of characteristics at birth, and of longitudinal information on the body mass index (BMI) of the child until the age of 6 years, was assessed. Internal validation was performed using bootstrapping. RESULTS: 227 children (4.2%) had high blood pressure at the age of 9-10 years. Final predictors were maternal hypertensive disease during pregnancy, maternal educational level, maternal prepregnancy BMI, child ethnicity, birth weight SD score (SDS) and the most recent BMI SDS. After internal validation, the area under the receiver operating characteristic curve ranged from 0.65 (prediction at age 3 years) to 0.73 (prediction at age 5-6 years). CONCLUSIONS: This prediction model may help to monitor the risk of developing high blood pressure in childhood which may allow for early targeted primordial prevention of cardiovascular disease.

Preconception risk of gestational diabetes: Development of a prediction model in nulliparous Australian women.

AIM: To develop a prediction model for preconception identification of women at risk of gestational diabetes mellitus (GDM). METHODS: Data from a prospective cohort, the Australian Longitudinal Study on Women's Health, were used. Nulliparous women aged 18-23 who reported a pregnancy up to age 37-42 were included. Preconception predictors of GDM during a first pregnancy were selected using logistic regression. Regression coefficients were multiplied by a shrinkage factor estimated with bootstrapping to improve prediction in external populations. RESULTS: Among 6504 women, 314 (4.8%) developed GDM during their first pregnancy. The final prediction model included age at menarche, proposed age at future first pregnancy, ethnicity, body mass index, diet, physical activity, polycystic ovary syndrome, and family histories of type 1 or 2 diabetes and GDM. The model showed good discriminative ability with a C-statistic of 0.79 (95% CI 0.76, 0.83) after internal validation. More than half of the women (58%) were classified to be at risk of GDM (>2% predicted risk), with corresponding sensitivity and specificity values of 91% and 43%. CONCLUSIONS: Nulliparous women at risk of GDM in a future first pregnancy can be accurately identified based on preconception lifestyle and health-related characteristics. Further studies are needed to test our model in other populations.

Prediction models for endometrial cancer for the general population or symptomatic women: A systematic review.

OBJECTIVE: To provide an overview of prediction models for the risk of developing endometrial cancer in women of the general population or for the presence of endometrial cancer in symptomatic women. METHODS: We systematically searched the Embase and Pubmed database until September 2017 for relevant publications. We included studies describing the development, the external validation, or the updating of a multivariable model for predicting endometrial cancer in the general population or symptomatic women. RESULTS: Out of 2756 references screened, 14 studies were included. We found two prediction models for developing endometrial cancer in the general population (risk models) and one extension. Eight studies described the development of models for symptomatic women (diagnostic models), one comparison of the performance of two diagnostic models and two external validation. Sample size varied from 60 (10 with cancer) to 201,811 (855 with cancer) women. The age of the women was included as a predictor in almost all models. The risk models included epidemiological variables related to the reproductive history of women, hormone use, BMI, and smoking history. The diagnostic models also included clinical predictors, such as endometrial thickness and recurrent bleeding. The concordance statistic (c), assessing the discriminative ability, varied from 0.68 to 0.77 in the risk models and from 0.73 to 0.957 in the diagnostic models. Methodological information was often limited, especially on the handling of missing data, and the selection of predictors. One risk model and four diagnostic models were externally validated. CONCLUSIONS: Only a few models have been developed to predict endometrial cancer in asymptomatic or symptomatic women. The usefulness of most models is unclear considering methodological shortcomings and lack of external validation. Future research should focus on external validation and extension with new predictors or biomarkers, such as genetic and epigenetic markers.

Development of a clinical prediction model for an international normalised ratio ≥ 4·5 in hospitalised patients using vitamin K antagonists.

Vitamin K antagonists (VKAs) used for the prevention and treatment of thromboembolic disease, increase the risk of bleeding complications. We developed and validated a model to predict the risk of an international normalised ratio (INR) ≥ 4·5 during a hospital stay. Adult patients admitted to a tertiary hospital and treated with VKAs between 2006 and 2010 were analysed. Bleeding risk was operationalised as an INR value ≥4·5. Multivariable logistic regression analysis was used to assess the association between potential predictors and an INR ≥ 4·5 and validated in an independent cohort of patients from the same hospital between 2011 and 2014. We identified 8996 admissions of patients treated with VKAs, of which 1507 (17%) involved an INR ≥ 4·5. The final model included the following predictors: gender, age, concomitant medication and several biochemical parameters. Temporal validation showed a c statistic of 0·71. We developed and validated a clinical prediction model for an INR ≥ 4·5 in VKA-treated patients admitted to our hospital. The model includes factors that are collected during routine care and are extractable from electronic patient records, enabling easy use of this model to predict an increased bleeding risk in clinical practice.

Epigenome-based cancer risk prediction: rationale, opportunities and challenges.

The incidence of cancer is continuing to rise and risk-tailored early diagnostic and/or primary prevention strategies are urgently required. The ideal risk-predictive test should: integrate the effects of both genetic and nongenetic factors and aim to capture these effects using an approach that is both biologically stable and technically reproducible; derive a score from easily accessible biological samples that acts as a surrogate for the organ in question; and enable the effectiveness of risk-reducing measures to be monitored. Substantial evidence has accumulated suggesting that the epigenome and, in particular, DNA methylation-based tests meet all of these requirements. However, the development and implementation of DNA methylation-based risk-prediction tests poses considerable challenges. In particular, the cell type specificity of DNA methylation and the extensive cellular heterogeneity of the easily accessible surrogate cells that might contain information relevant to less accessible tissues necessitates the use of novel methods in order to account for these confounding issues. Furthermore, the engagement of the scientific community with health-care professionals, policymakers and the public is required in order to identify and address the organizational, ethical, legal, social and economic challenges associated with the routine use of epigenetic testing.

Validation of the Feverkidstool and procalcitonin for detecting serious bacterial infections in febrile children.

BackgroundTo validate the Feverkidstool, a prediction model consisting of clinical signs and symptoms and C-reactive protein (CRP) to identify serious bacterial infections (SBIs) in febrile children, and to determine the incremental diagnostic value of procalcitonin.MethodsThis prospective observational study that was carried out at two Dutch emergency departments included children with fever, aged 1 month to 16 years. The prediction models were developed with polytomous logistic regression differentiating "pneumonia" and "other SBIs" from "non-SBIs" using standardized, routinely collected data on clinical signs and symptoms, CRP, and procalcitonin.ResultsA total of 1,085 children were included with a median age of 1.6 years (interquartile range 0.8-3.4); 73 children (7%) had pneumonia and 98 children (9%) had other SBIs. The Feverkidstool showed good discriminative ability in this new population. After adding procalcitonin to the Feverkidstool, c-statistic for "pneumonia" increased from 0.85 (95% confidence interval (CI) 0.76-0.94) to 0.86 (0.77-0.94) and for "other SBI" from 0.81 (0.73-0.90) to 0.83 (0.75- 0.91). A model with clinical features and procalcitonin performed similar to the Feverkidstool.ConclusionThis study confirms the external validity of the Feverkidstool, with CRP and procalcitonin being equally valuable for predicting SBI in our population of febrile children. Our findings do not support routine dual use of CRP and procalcitonin.

Evaluating the impact of prediction models: lessons learned, challenges, and recommendations.

An important aim of clinical prediction models is to positively impact clinical decision making and subsequent patient outcomes. The impact on clinical decision making and patient outcome can be quantified in prospective comparative-ideally cluster-randomized-studies, known as 'impact studies'. However, such impact studies often require a lot of time and resources, especially when they are (cluster-)randomized studies. Before envisioning such large-scale randomized impact study, it is important to ensure a reasonable chance that the use of the prediction model by the targeted healthcare professionals and patients will indeed have a positive effect on both decision making and subsequent outcomes. We recently performed two differently designed, prospective impact studies on a clinical prediction model to be used in surgical patients. Both studies taught us new valuable lessons on several aspects of prediction model impact studies, and which considerations may guide researchers in their decision to conduct a prospective comparative impact study. We provide considerations on how to prepare a prediction model for implementation in practice, how to present the model predictions, and how to choose the proper design for a prediction model impact study.

Angiogenic Markers Predict Pregnancy Complications and Prolongation in Preeclampsia: Continuous Versus Cutoff Values.

To assess the incremental value of a single determination of the serum levels of sFlt-1 (soluble Fms-like tyrosine kinase 1) and PlGF (placental growth factor) or their ratio, without using cutoff values, for the prediction of maternal and fetal/neonatal complications and pregnancy prolongation, 620 women with suspected/confirmed preeclampsia, aged 18 to 48 years, were included in a prospective, multicenter, observational cohort study. Women had singleton pregnancies and a median pregnancy duration of 34 (range, 20-41) weeks. Complications occurred in 118 women and 248 fetuses. The median duration between admission and delivery was 12 days. To predict prolongation, PlGF showed the highest incremental value (R2=0.72) on top of traditional predictors (gestational age at inclusion, diastolic blood pressure, proteinuria, creatinine, uric acid, alanine transaminase, lactate dehydrogenase, and platelets) compared with R2=0.53 for the traditional predictors only. sFlt-1 showed the highest value to discriminate women with and without maternal complications (C-index=0.83 versus 0.72 for the traditional predictors only), and the sFlt-1/PlGF ratio showed the highest value to discriminate fetal/neonatal complications (C-index=0.86 versus 0.78 for the traditional predictors only). Applying previously suggested cutoff values for the sFlt-1/PlGF ratio yielded lower incremental values than applying continuous values. In conclusion, sFlt-1 and PlGF are strong and independent predictors for days until delivery along with maternal and fetal/neonatal complications on top of the traditional criteria. Their use as continuous variables (instead of applying cutoff values for different gestational ages) should now be tested in a prospective manner, making use of an algorithm calculating the risk of an individual woman with suspected/confirmed preeclampsia to develop complications.

Prediction of Multiple Recurrent Events: A Comparison of Extended Cox Models in Bladder Cancer.

Recurrence of bladder cancer can occur repeatedly in the same patient after treatment of the primary tumor. Models predicting the risk of a next recurrence may inform individualized decision-making on surveillance frequency. We aimed to assess the usefulness of extensions of the Cox proportional hazards model for repeated events in this context. We analyzed 531 Dutch patients with bladder cancer (1990-2012) with information on 7 prespecified predictors at the time of diagnosis of the primary and recurrent tumors. We considered 3 aspects of model variants: how to model time to the repeated events (calendar time, gap time, elapsed time); the number of preceding events (predictor, stratum variable); and the within-subject correlation (ignored in a simple Cox model, robust standard errors in a variance-correction model, random effect in a frailty model). First to fourth recurrences of bladder cancer occurred in 313, 174, 103, and 66 patients, respectively, with median calendar follow-up times of 1.1, 2.5, 3.8, and 4.5 years, respectively. We focused on gap time in the detailed analyses, allowing for clinically meaningful predictions. Variance-correction models may be useful if predictor selection is part of the model development. Frailty models may be useful when within-subject correlation is strong.

FGFR3, TERT and OTX1 as a Urinary Biomarker Combination for Surveillance of Patients with Bladder Cancer in a Large Prospective Multicenter Study.

PURPOSE: Patients with nonmuscle invasive bladder cancer are followed with frequent cystoscopies. In this study FGFR3, TERT and OTX1 were investigated as a diagnostic urinary marker combination during followup of patients with primary nonmuscle invasive bladder cancer. MATERIALS AND METHODS: In this international, multicenter, prospective study 977 patients with nonmuscle invasive bladder cancer were included. A total of 2,496 urine samples were collected prior to cystoscopy during regular visits. Sensitivity was estimated to detect concomitant recurrences. Kaplan-Meier curves were used to estimate the development of future recurrences after urinalysis and a negative cystoscopy. RESULTS: Sensitivity of the assay combination for recurrence detection was 57% in patients with primary low grade, nonmuscle invasive bladder cancer. However, sensitivity was 83% for recurrences that were pT1 or muscle invasive bladder cancer. Of the cases 2% progressed to muscle invasive bladder cancer. Sensitivity for recurrence detection in patients with primary high grade disease was 72% and 7% of them had progression to muscle invasive bladder cancer. When no concomitant tumor was found by cystoscopy, positive urine samples were more frequently followed by a recurrence over time compared to a negative urine sample (58% vs 36%, p <0.001). High stage recurrences were identified within 1 year after a positive urine test and a negative cystoscopy. CONCLUSIONS: Recurrences in patients with primary nonmuscle invasive bladder cancer can be detected by a combination of urine assays. This study supports the value of urinalysis as an alternative diagnostic tool in patients presenting with low grade tumors and as a means to identify high stage tumors earlier.

Validation of prediction models: examining temporal and geographic stability of baseline risk and estimated covariate effects.

BACKGROUND: Stability in baseline risk and estimated predictor effects both geographically and temporally is a desirable property of clinical prediction models. However, this issue has received little attention in the methodological literature. Our objective was to examine methods for assessing temporal and geographic heterogeneity in baseline risk and predictor effects in prediction models. METHODS: We studied 14,857 patients hospitalized with heart failure at 90 hospitals in Ontario, Canada, in two time periods. We focussed on geographic and temporal variation in baseline risk (intercept) and predictor effects (regression coefficients) of the EFFECT-HF mortality model for predicting 1-year mortality in patients hospitalized for heart failure. We used random effects logistic regression models for the 14,857 patients. RESULTS: The baseline risk of mortality displayed moderate geographic variation, with the hospital-specific probability of 1-year mortality for a reference patient lying between 0.168 and 0.290 for 95% of hospitals. Furthermore, the odds of death were 11% lower in the second period than in the first period. However, we found minimal geographic or temporal variation in predictor effects. Among 11 tests of differences in time for predictor variables, only one had a modestly significant P value (0.03). CONCLUSIONS: This study illustrates how temporal and geographic heterogeneity of prediction models can be assessed in settings with a large sample of patients from a large number of centers at different time periods.