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Background Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients’ earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty. Methods We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient’s longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes. Results When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an “always test” strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point. Conclusions A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care. Highlights In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk. We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)–based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs. A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.

How to quickly predict an individual’s behavioral choices is an important issue in the field of human behavior research. Using noninvasive electroencephalography, we aimed to identify neural markers in the prior outcome-evaluation stage and the current option-assessment stage of the chicken game that predict an individual’s behavioral choices in the subsequent decision-output stage. Hierarchical linear modeling-based brain-behavior association analyses revealed that midfrontal theta oscillation in the prior outcome-evaluation stage positively predicted subsequent aggressive choices; also, beta oscillation in the current option-assessment stage positively predicted subsequent cooperative choices. These findings provide electrophysiological evidence for the three-stage theory of decision-making and strengthen the feasibility of predicting an individual’s behavioral choices using neural oscillations.

OBJECTIVE To develop a risk score for obstetric anal sphincter injury (OASI) occurrence among nulliparous women delivering vaginally, based on data available at admission for delivery and as labor progresses. METHODS A retrospective study of all nulliparous women who delivered vaginally between March 2011 and January 2021. Characteristics were compared between OASI and no-OASI groups. Multivariable analyses were performed to identify independent risk factors for OASI occurrence. RESULTS OASI occurred in 453 (1.7%) of 26 081 women who met the inclusion criteria. The following variables were independently associated with OASI: maternal height (adjusted odds ratio [aOR] 0.97, 95% confidence interval (CI) 0.95-0.99), hypertensive disorders (aOR 1.74, 95% CI 1.03-2.95), sonographic fetal weight estimation (aOR 1.00, 95% CI 1.00-1.00), second-stage duration (aOR 1.00, 95% CI 1.00-1.00), occiput posterior position (aOR 2.87, 95% CI 1.79-4.62), and episiotomy performance (aOR 0.63, 95% CI 0.47-0.84). In a risk score based on variables available at admission for delivery, the presence of two factors was associated with 4.3% OASI risk. Upon incorporating intrapartum variables, the presence of two risk factors was associated with 2.9% OASI rate. CONCLUSION A dynamic risk score for OASI occurrence based on data available at admission for delivery and as delivery progresses can assist in counseling regarding OASI risk. A dynamic risk score for obstetric anal sphincter injury occurrence based on data available at admission for delivery and intrapartum was developed.

Background and Objectives: A dynamic prediction model for patients with soft tissue sarcoma of the extremities was previously developed to predict updated overall survival probabilities throughout patient follow‐up. This study updates and externally validates the dynamic model. Methods: Data from 3826 patients with high‐grade extremity soft tissue sarcoma, treated surgically with curative intent were used to update the dynamic PERsonalised SARcoma Care (PERSARC) model. Patients were added to the model development cohort and grade was included in the model. External validation was performed with data from 1111 patients treated at a single tertiary center. Results: Calibration plots show good model calibration. Dynamic C‐indices suggest that the model can discriminate between high‐ and low‐risk patients. The dynamic C‐indices at 0, 1, 2, 3, 4, and 5 years after surgery were equal to 0.697, 0.790, 0.822, 0.818, 0.812, and 0.827, respectively. Conclusion: Results from the external validation show that the dynamic PERSARC model is reliable in predicting the probability of surviving an additional 5 years from a specific prediction time point during follow‐up. The model combines patient‐, treatment‐specific and time‐dependent variables such as local recurrence and distant metastasis to provide accurate survival predictions throughout follow‐up and is available through the PERSARC app.

Aims International early warning scores (EWS) including the additive National Early Warning Score (NEWS) and logistic EWS currently utilise physiological snapshots to predict clinical deterioration. We hypothesised that a dynamic score including vital sign trajectory would improve discriminatory power. Methods Multicentre retrospective analysis of electronic health record data from postoperative patients admitted to cardiac surgical wards in four UK hospitals. Least absolute shrinkage and selection operator-type regression (LASSO) was used to develop a dynamic model (DyniEWS) to predict a composite adverse event of cardiac arrest, unplanned intensive care re-admission or in-hospital death within 24 h. Results A total of 13,319 postoperative adult cardiac patients contributed 442,461 observations of which 4234 (0.96%) adverse events in 24 h were recorded. The new dynamic model (AUC = 0.80 [95% CI 0.78−0.83], AUPRC = 0.12 [0.10−0.14]) outperforms both an updated snapshot logistic model (AUC = 0.76 [0.73−0.79], AUPRC = 0.08 [0.60−0.10]) and the additive National Early Warning Score (AUC = 0.73 [0.70−0.76], AUPRC = 0.05 [0.02−0.08]). Controlling for the false alarm rates to be at current levels using NEWS cut-offs of 5 and 7, DyniEWS delivers a 7% improvement in balanced accuracy and increased sensitivities from 41% to 54% at NEWS 5 and 18% to –30% at NEWS 7. Conclusions Using an advanced statistical approach, we created a model that can detect dynamic changes in risk of unplanned readmission to intensive care, cardiac arrest or in-hospital mortality and can be used in real time to risk-prioritise clinical workload.

In nephrology, a great deal of information is measured repeatedly in patients over time, often alongside data on events of clinical interest. In this introductory article we discuss how these two types of data can be simultaneously analysed using the joint model (JM) framework, illustrated by clinical examples from nephrology. As classical survival analysis and linear mixed models form the two main components of the JM framework, we will also briefly revisit these techniques.

Objective: Low-intensity cognitive behavioural therapy (LiCBT) can help to alleviate acute symptoms of depression and anxiety, but some patients relapse after completing treatment. Little is known regarding relapse risk factors, limiting our ability to predict its occurrence. Therefore, this study aimed to develop a dynamic prediction tool to identify cases at high risk of relapse.\ud \ud Method: Data from a longitudinal cohort study of LiCBT patients was analysed using a machine learning approach (XGBoost). The sample included n=317 treatment completers who were followed-up monthly for 12 months (n=223 relapsed; 70%). An ensemble of XGBoost algorithms was developed in order to predict and adjust the estimated risk of relapse (vs maintained remission) in a dynamic way, at four separate time-points over the course of a patient’s journey.\ud \ud Results: Indices of predictive accuracy in a cross-validation design indicated adequate generalizability (AUC range = 0.72-0.84; PPV range = 71.2%-75.3%; NPV range = 56.0%-74.8%). Younger age, unemployment, (non-)linear treatment responses, and residual symptoms were identified as important predictors.\ud \ud Discussion: It is possible to identify cases at risk of relapse and predictive accuracy improves over time as new information is collected. Early identification coupled with targeted relapse prevention could considerably improve the longer-term effectiveness of LiCBT.

Continuous glucose monitoring (CGM) and predicting has the problems of strong time-varying, complex nonlinear, and non-stationary. Given this, one kind of intelligent blood glucose prediction model (Attentional GRU method) is proposed that is based on the gated recurrent unit (GRU) with an attentional mechanism. In this hybrid prediction model, the attentional mechanism gives different weights to the input characteristics of GRU, which makes the prediction model more effective in dealing with short-time series glucose monitoring series as input. It is constructed and compared with simple GRU, long-short memory network (LSTM), and support vector regression (SVM). The dynamic blood glucose monitoring and intelligent prediction experiments for live fish (Scophthalmus Maximus) waterless transportation show that the proposed intelligent algorithm has high accuracy and efficiency than the traditional methods. Through the utilization of this advanced method, the key prediction deviation evaluation (RMSE) was averagely and greatly reduced by 26.1%, 29.2%, 28.5% respectively when compares with GRU, LSTM, SVM within the 60 minutes dynamic prediction. It could provide technical references and a smart support tool for the blood glucose dynamic prediction.

Surface subsidence is a widespread problem frequently caused by underground mining. Accurate surface subsidence prediction is particularly important for protecting buildings and structures above the goaf. Because mining is a dynamic process, surface dynamic movement and deformation can significantly affect buildings and structures. However, dynamic predictions are often highly inaccurate because of the complex nature of dynamic surface movement and deformation. To address this problem, we performed numerical simulations to assess the sensibility of geomechanical parameters to surface movement values. Our results show that: 1) Sensitive factors of surface movement extremum gradually shift from stiffness parameters to strength parameters with increased mining degree; and 2) the sensitivity of the evaluation indicators to the geomechanical parameters is related to the proportion of plastic zones in the mining-affected area. We propose a new dynamic prediction method of surface subsidence and verify the method with a case study from the Yanzhou mine in China. It is proofed that the new method can greatly improve the prediction accuracy of dynamic surface subsidence. The research results are significant for accurately predicting dynamic surface subsidence, protecting ground buildings and structures while promoting sustainable development of mine areas.

Joint models for recurrent and terminal events have not been yet developed for clustered data. The goals of our study are to develop a statistical framework for modelling clustered recurrent and terminal events and to perform dynamic predictions of the terminal event in family studies. We propose a joint nested frailty model for colonoscopy screening visits and colorectal cancer onset in Lynch Syndrome families. The screening and disease processes could each depend on individuals' screening history and other measured covariates and be correlated within families; our approach allows for familial correlations to affect both the visit process and the terminal event and the dependence between the two processes is specified through frailty distributions. We provide dynamic predictions of colorectal cancer risk for an individual conditional on his/her own screening history, his/her family history of screening and disease and other important clinical covariates. We apply our model to 18 Lynch Syndrome families from Newfoundland for individualized dynamic predictions of colorectal cancer risks. We demonstrate that the screening visits are non-ignorable for estimating the disease risks, and the joint nested frailty model improves dynamic prediction accuracies compared to existing joint frailty models after accounting for familial and individual screening and cancer histories.

STUDY QUESTION: How well does a previously developed dynamic prediction model perform in an external, geographical validation in terms of predicting the chances of natural conception at various points in time?SUMMARY ANSWER: The dynamic prediction model performs well in an external validation on a Scottish cohort.WHAT IS KNOWN ALREADY: Prediction models provide information that can aid evidence-based management of unexplained subfertile couples. We developed a dynamic prediction model for natural conception (van Eekelen model) that is able to update predictions of natural conception when couples return to their clinician after a period of unsuccessful expectant management. It is not known how well this model performs in an external population.STUDY DESIGN, SIZE, DURATION: A record-linked registry study including the long-term follow-up of all couples who were considered unexplained subfertile following a fertility workup at a Scottish fertility clinic between 1998 and 2011. Couples with anovulation, uni/bilateral tubal occlusion, mild/severe endometriosis or impaired semen quality according to World Health Organization criteria were excluded.PARTICIPANTS/MATERIALS, SETTING, METHODS: The endpoint was time to natural conception, leading to an ongoing pregnancy (defined as reaching a gestational age of at least 12 weeks). Follow-up was censored at the start of treatment, at the change of partner or at the end of study (31 March 2012). The performance of the van Eekelen model was evaluated in terms of calibration and discrimination at various points in time. Additionally, we assessed the clinical utility of the model in terms of the range of the calculated predictions.MAIN RESULTS AND THE ROLE OF CHANCE: Of a total of 1203 couples with a median follow-up of 1 year and 3 months after the fertility workup, 398 (33%) couples conceived naturally leading to an ongoing pregnancy. Using the dynamic prediction model, the mean probability of natural conception over the course of the first year after the fertility workup was estimated at 25% (observed: 23%). After 0.5, 1 and 1.5 years of expectant management after the completion of the fertility workup, the average probability of conceiving naturally over the next year was estimated at 18% (observed: 15%), 14% (observed: 14%) and 12% (observed: 12%). Calibration plots showed good agreement between predicted chances and the observed fraction of ongoing pregnancy within risk groups. Discrimination was moderate with c statistics similar to those in the internal validation, ranging from 0.60 to 0.64. The range of predicted chances was sufficiently wide to distinguish between couples having a good and poor prognosis with a minimum of zero at all times and a maximum of 55% over the first year after the workup, which decreased to maxima of 43% after 0.5 years, 34% after 1 year and 29% after 1.5 years after the fertility workup.LIMITATIONS, REASONS FOR CAUTION: The model slightly overestimated the chances of conception by ~2-3% points on group level in the first-year post-fertility workup and after 0.5 years of expectant management, respectively. This is likely attributable to the fact that the exact dates of completion of the fertility workup for couples were missing and had to be estimated.WIDER IMPLICATIONS OF THE FINDINGS: The van Eekelen model is a valid and robust tool that is ready to use in clinical practice to counsel couples with unexplained subfertility on their individualized chances of natural conception at various points in time, notably when couples return to the clinic after a period of unsuccessful expectant management.STUDY FUNDING/COMPETING INTEREST(S): This work was supported by a Chief Scientist Office postdoctoral training fellowship in health services research and health of the public research (ref PDF/12/06). There are no conflicts of interest.

BackgroundTacrolimus is given post-kidney transplant to suppress the immune system, and the amount of drug in the body is measured frequently. Higher variability over time may be indicative of poor drug adherence, leading to more adverse events. It is important to account for the variation in Tacrolimus, not just the average change over time.MethodsUsing data from the University of Colorado, we compare methods of assessing how the variability in Tacrolimus influences the hazard of de novo Donor Specific Antibodies (dnDSA), an early warning sign of graft failure. We compare multiple joint models in terms of fit and predictive ability. We explain that the models that account for the individual-specific variability over time have the best predictive performance. These models allowed each patient to have an individual-specific random error term in the longitudinal Tacrolimus model, and linked this to the hazard of dnDSA model.ResultsThe hazard for the variance and coefficient of variation (CV) loading parameter were greater than 1, indicating that higher variability of Tacrolimus had a higher hazard of dnDSA. Introducing the individual-specific variability improved the fit, leading to more accurate predictions about the individual-specific time-to-dnDSA.ConclusionsWe showed that the individual’s variability in Tacrolimus is an important metric in predicting long-term adverse events in kidney transplantation. This is an important step in personalizing the dosage of TAC post-transplant to improve outcomes post-transplant.

Background: Noninvasive ventilation (NIV) failure is strongly associated with poor prognosis. Nowadays, plenty of mature studies have been proposed to predict early NIV failure (within 48 hours of NIV), however, the prediction for late NIV failure (after 48 hours of NIV) lacks sufficient research. Late NIV failure delays intubation resulting in the increasing mortality of the patients. Therefore, it is of great significance to expeditiously predict the late NIV failure. In order to dynamically predict late NIV failure, we proposed a Time Updated Light Gradient Boosting Machine (TULightGBM) model. Material and Methods: In this work, 5653 patients undergoing NIV over 48 hours were extracted from the database of Medical Information Mart for Intensive Care Ⅲ (MIMIC-Ⅲ) for model construction. The TULightGBM model consists of a series of sub-models which learn clinical information from updating data within 48 hours of NIV and integrates the outputs of the sub-models by the dynamic attention mechanism to predict late NIV failure. The performance of the proposed TULightGBM model was assessed by comparison with common models of logistic regression (LR), random forest (RF), LightGBM, eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and long short-term memory (LSTM). Results: The TULightGBM model yielded prediction results at 8, 16, 24, 36, and 48 hours after the start of the NIV with dynamic AUC values of 0.8323, 0.8435, 0.8576, 0.8886, and 0.9123, respectively. Furthermore, the sensitivity, specificity, and accuracy of the TULightGBM model were 0.8207, 0.8164, and 0.8184, respectively. The proposed model achieved superior performance over other tested models. Conclusions: The TULightGBM model is able to dynamically predict the late NIV failure with high accuracy and offer potential decision support for clinical practice.

Due to the problems related to the numerous factors affecting the spontaneous combustion of goaf coal, such as sudden, uncertain, and dynamic changes, and the fact that the weight of the indexes in the prediction model of the spontaneous combustion risk is difficult to determine, an improved Criteria Importance Through Inter-criteria Correlation (CRITIC) modified Technique for Order of Preference by Similarity to Ideal Solution G2-(TOPSIS) dynamic prediction model of goaf spontaneous combustion was developed. An optimal decision-making model was established by introducing the Euclidean distance function, and the function-driven type G2 weighting method was modified using the differential-driven type weighting method of the CRITIC. In addition, the comprehensive weights of each index were obtained. An update factor was introduced to obtain the dynamic weight, and the primary-secondary relationship of the risk factors affecting the spontaneous combustion of goaf was evaluated. Based on the G2 weighting method, which approximates the driving function principle of the ideal solution ranking method (TOPSIS), a G2-TOPSIS goaf spontaneous combustion risk assessment model was established. The degree of closeness was analyzed and the risk grade of the goaf spontaneous combustion was finally predicted. The sub-model was applied to the goaf of working face 1303 in the Jinniu Coal Mine. It was concluded that the air leakage duration was the dominant factor inducing the risk of the spontaneous combustion of the goaf. The risk grade of spontaneous combustion of the goaf is Class III, and the predicted results are consistent with the actual situation. The improved CRITIC-G2-TOPSIS dynamic model was demonstrated to be scientific in predicting the goaf spontaneous combustion risk, and these research results have important popularization and application value.

BACKGROUND: In kidney transplantation, dynamic prediction of patient and kidney graft survival (DynPG) may help to promote therapeutic alliance by delivering personalized evidence-based information about long-term graft survival for kidney transplant recipients. The objective of the current study is to externally validate the DynPG. METHODS: Based on 6 baseline variables, the DynPG can be updated with any new serum creatinine measure available during the follow-up. From an external validation sample of 1637 kidney recipients with a functioning graft at 1-year posttransplantation from 2 European transplantation centers, we assessed the prognostic performance of the DynPG. RESULTS: As one can expect from an external validation sample, differences in several recipient, donor, and transplantation characteristics compared with the learning sample were observed. Patients were mainly transplanted from deceased donors (91.6% versus 84.8%; P < 0.01), were less immunized against HLA class I (18.4% versus 32.7%; P < 0.01) and presented less comorbidities (62.2% for hypertension versus 82.7%, P < 0.01; 25.1% for cardiovascular disease versus 33.9%, P < 0.01). Despite these noteworthy differences, the area under the ROC curve varied from 0.70 (95% confidence interval [CI], 0.64-0.76) to 0.76 (95% CI, 0.64-0.88) for prediction times at 1 and 6 years posttransplantation respectively, and calibration plots revealed reasonably accurate predictions. CONCLUSIONS: We validated the prognostic capacities of the DynPG in terms of both discrimination and calibration. Our study showed the robustness of the DynPG for informing both the patient and the physician, and its transportability for a cohort presenting different features than the one used for the DynPG development. ispartof: TRANSPLANTATION vol:105 issue:2 pages:396-403 ispartof: location:United States status: published

Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients.Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis.Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction.Results: Of 11,362 patients in MIMIC-IV included in the final cohort, a total of 6,744 (59%) patients developed SIC during sepsis. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869; 95% CI: 0.850–0.886). Coagulation profile and renal function indicators were the most important features for predicting SIC. A compact model was developed with an AUC of 0.854 (95% CI: 0.832–0.872), while the AUCs of Logistic Regression and SIC scores were 0.746 (95% CI: 0.735–0.755) and 0.709 (95% CI: 0.687–0.733), respectively. A cohort of 35,252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837–0.846) and 0.803 (95% CI: 0.798–0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653–0.667) and SIC scores (0.752; 95% CI: 0.747–0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable.Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.

Influenza causes repeat epidemics and huge loss of lives and properties. To predict influenza epidemics, we proposed an infectious disease dynamic prediction model with control variables (SEIR-CV), which considers the characteristics of the influenza epidemic transmission, seasonal impacts, and the intensity changes of control measures over time. The critical parameters of the model were inversed using an adjoint method. When using the surveillance data of the past 15 weeks to invert the parameters, the epidemic in the next 3 weeks in the United States can be accurately predicted. In addition, roll predictions from 26 September 2016 to 27 September 2018 were implemented. The correlation coefficient between the predicted values and the surveillance values was greater than 0.975, and the overall relative error of the predictions was less than 10%. These good model performances demonstrated the practicability and feasibility of SEIR-CV for influenza and corresponding infectious disease prediction.

BackgroundTime-dependent change in the level of biomarkers after stroke is not well understood. We sought to compare fatty acid-binding protein 4 (FABP4), Galectin-3, and soluble ST2 to ascertain for a change in prediction of outcome at admission and 48 h later.MethodsPlasma FABP4, Galectin-3, and soluble ST2 were measured in biospecimens from acute stroke patients at the time of admission ( n = 383) and 48 h later ( n = 244). Functional outcome was assessed at 90 days using the modified Rankin Scale and dichotomized into good (modified Rankin Scale 0–2) and poor outcome (modified Rankin Scale 3–6).ResultsOn admission, elevated levels of each biomarker predicted poor outcome (FABP4: OR 1.92, 95% CI 1.42–2.59, P ConclusionsGalectin-3 performed better when measured on admission, whereas soluble ST2 was predictive at admission and better at 48 h after stroke. The time-dependent differences may reflect the evolving role of these pathways after acute stroke.

Background Venous thromboembolism (VTE) is a frequent complication of cancer. Elevated D‐dimer is associated with an increased risk of cancer‐associated VTE. Whether changes in D‐dimer over time harbor additional prognostic information that may be exploited clinically for dynamic prediction of VTE is unclear. Objectives To explore the potential role of longitudinal D‐dimer trajectories for personalized prediction of cancer‐associated VTE. Patients/Methods A total of 167 patients with active malignancy were prospectively enrolled (gastrointestinal: n = 59 [35%], lung: n = 56 [34%], brain: n = 50 [30%], others: n = 2 [1%]; metastatic disease: n = 74 [44%]). D‐dimer (median = 0.8 µg/mL [25th‐75th percentile: 0.4‐2.0]) was measured at baseline and during 602 monthly follow‐up visits. Joint models of longitudinal and time‐to‐event data were implemented to quantify the association between D‐dimer trajectories and prospective risk of VTE. Results VTE occurred in 20 patients (250‐day VTE risk = 12.1%, 95% confidence interval [CI], 7.8‐18.5). D‐dimer increased by 34%/month (0.47 µg/mL/month, 95% CI, 0.22‐0.72, P

Background Attenuated decreases in lung function can signal the onset of acute respiratory events known as pulmonary exacerbations (PEs) in children and adolescents with cystic fibrosis (CF). Univariate joint modeling facilitates dynamic risk prediction of PE onset and accounts for measurement error of the lung function marker. However, CF is a multi-system disease and the extent to which simultaneously modeling growth and nutrition markers improves PE predictive accuracy is unknown. Furthermore, it is unclear which routinely collected clinical indicators of growth and nutrition in early life predict PE onset in CF. Methods Using a longitudinal cohort of 17,100 patients aged 6–20 years (US Cystic Fibrosis Foundation Patient Registry; 2003–2015), we fit a univariate joint model of lung-function decline and PE onset and contrasted its predictive performance with a class of multivariate joint models that included combinations of growth markers as additional submodels. Outcomes were longitudinal lung function (forced expiratory volume in 1 s of % predicted), percentiles of body mass index, weight-for-age and height-for-age and PE onset. Relevant demographic/clinical covariates were included in submodels. We implemented a univariate joint model of lung function and time-to-PE and four multivariate joint models including growth outcomes. Results All five joint models showed that declining lung function corresponded to slightly increased risk of PE onset (hazard ratio from univariate joint model: 0.97, P 0.70). None of the growth markers alongside lung function as outcomes in multivariate joint modeling appeared to have an association with hazard of PE. Jointly modeling only lung function and PE onset yielded the most accurate (area under the receiver-operator characteristic curve = 0.75) and precise (narrowest interquartile range) predictions. Dynamic predictions were accurate across forecast horizons (0.5, 1 and 2 years) and precision improved with age. Conclusions Including growth markers via multivariate joint models did not yield gains in prediction performance, compared to a univariate joint model with lung function. Individualized dynamic predictions from joint modeling could enhance physician monitoring of CF disease progression by providing PE risk assessment over a patient’s clinical course.

In recent years, the traditional conceptualization of mental disorders as discrete and static diagnostic entities has been challenged, owing to their pluripotent trajectories, heterotypic course, nonspecific risk factors, and etiological mechanisms. This has motivated new transdiagnostic approaches to predicting, diagnosing, treating, and preventing potentially severe and chronic mental disorders, particularly among young people. Specifically, this has led to the adoption of clinical staging in psychiatry, as well as the emergence of dynamic prediction techniques, which both offer the potential to personalize intervention and advance prevention of mental disorders. Other dimensional approaches have been proposed, including quantitative classifications, although the clinical utility of these has been questioned. This chapter considers transdiagnostic approaches for early intervention and prevention within the context of clinical staging, broad youth models of care, dynamic prediction models, and statistical approaches (dynamical systems theory, network theory, and joint modeling).

A chloride process is especially prone to leakage incidents via vaporizer rupture and pipeline perforation, both of which are usually caused by corrosion. Rupture accidents may not occur frequently but nevertheless can results in explosions, poisoning and other severe consequences. The main factors affecting the corrosion process are the chloride ion concentration and the environmental temperature. In order to study the probability of rupture accidents, this paper first presents experimental research on the corrosion performance of liquid chlorine vaporizers and pipelines made with the steel Q345 and 20G, respectively. Different chloride ion concentrations (1–20 g/L) and environmental temperatures (30–90 °C) were considered in the corrosion experiment. The experiment lasted for 42 days, and the corrosion rate was calculated using weight loss technique. The experimental results show that 20G steel suffers more serious corrosion than Q345 steel and the degree of corrosion is determined by different factors working together. Among these experimental data, the corrosivity data related to 20G steel at the corrosion environment of low temperature with low concentration (30 °C, 1 g/L), as well as at high temperature with high concentration (80 °C, 20 g/L) were selected to establish the corrosion weight loss prediction model, based on which the accumulated wall thickness reduction was further calculated. Finally, a probability prediction model for the corrosive state was established based on Markov chains. Using the dynamic prediction method proposed in this work, it is capable to determine the corrosion situation of the chloride process through predicting the rupture probability before the coming into service of process equipment. Also, these prediction results can provide some guidance for the maintenance and help to reduce the risk of rupture accidents caused by corrosion in a chloride process.

Renal dysfunction is an important component of chronic heart failure (CHF), but its single assessment does not sufficiently reflect clinically silent progression of CHF prior to adverse clinical outcome. Therefore, we aimed to investigate temporal evolutions of glomerular and tubular markers in 263 stable patients with CHF, and to determine if their patient-specific evolutions during this clinically silent period can dynamically predict clinical outcome. We determined the risk of clinical outcome (composite endpoint of Heart Failure hospitalization, cardiac death, Left Ventricular Assist Device placement, and heart transplantation) in relation to marker levels, slopes and areas under their trajectories. In each patient, the trajectories were estimated using repeatedly measured glomerular markers: creatinine/estimated glomerular filtration rate (eGFR), cystatin C (CysC), and tubular markers: urinary N-acetyl-beta-D-glucosaminidase (NAG) and kidney injury molecule (KIM)-1, plasma and urinary neutrophil gelatinase-associated lipocalin (NGAL). During 2.2 years of follow-up, we collected on average 8 urine and 9 plasma samples per patient. All glomerular markers predicted the endpoint (univariable hazard ratio [95% confidence interval] per 20% increase: creatinine: 1.18 [1.07-1.31], CysC: 2.41[ 1.81-3.41], and per 20% eGFR decrease: 1.13[1.05-1.23]). Tubular markers, NAG, and KIM-1 also predicted the endpoint (NAG: 1.06[1.01-1.11] and KIM-1: 1.08[1.04-1.11]). Larger slopes were the strongest predictors (creatinine: 1.57[1.39-1.84], CysC: 1.76 [1.52-2.09], eGFR: 1.59[1.37-1.90], NAG: 1.26[1.11-1.44], and KIM-1: 1.64[1.38-2.05]). Associations persisted after multivariable adjustment for clinical characteristics. Thus, during clinically silent progression of CHF, glomerular and tubular functions deteriorate, but not simultaneously. Hence, patient-specific evolutions of these renal markers dynamically predict clinical outcome in patients with CHF.

Background Prediction of motor diagnosis in Huntington's disease (HD) can be improved by incorporating other phenotypic and biological clinical measures in addition to cytosine-adenine-guanine (CAG) repeat length and age. Objective The objective was to compare various clinical and biomarker trajectories for tracking HD progression and predicting motor conversion. Methods Participants were from the PREDICT-HD study. We constructed a mixed-effect model to describe the change of measures while jointly modeling the process with time to HD diagnosis. The model was then used for subject-specific prediction. We employed the time-dependent receiver operating characteristic (ROC) method to assess the discriminating capability of the measures to identify high and low risk patients. The strongest predictor was used to illustrate the dynamic prediction of the disease risk and future trajectories of biomarkers for three hypothetical patients. Results 1078 individuals were included in this analysis. Five longitudinal clinical and imaging measures were compared. The putamen volume had the best discrimination performance with area under the curve (AUC) ranging from 0.74 to 0.82 over time. The total motor score showed a comparable discriminative ability with AUC ranging from 0.69 to 0.78 over time. The model showed that decreasing putamen volume was a significant predictor of motor conversion. A web-based calculator was developed for implementing the methods. Conclusions By jointly modeling longitudinal data with time-to-event outcomes, it is possible to construct an individualized dynamic event prediction model that renews over time with accumulating evidence. If validated, this could be a valuable tool to guide the clinician in predicting age of onset and potentially rate of progression.

STUDY QUESTION: How can we predict chances of natural conception at various time points in couples diagnosed with unexplained subfertility?SUMMARY ANSWER: We developed a dynamic prediction model that can make repeated predictions over time for couples with unexplained subfertility that underwent a fertility workup at a fertility clinic.WHAT IS KNOWN ALREADY: The most frequently used prediction model for natural conception (the 'Hunault model') estimates the probability of natural conception only once per couple, that is, after completion of the fertility workup. This model cannot be used for a second or third time for couples who wish to know their renewed chances after a certain period of expectant management.STUDY DESIGN, SIZE, DURATION: A prospective cohort studying the long-term follow-up of subfertile couples included in 38 centres in the Netherlands between January 2002 and February 2004. Couples with bilateral tubal occlusion, anovulation or a total motile sperm count PARTICIPANTS/MATERIALS, SETTING, METHODS: The primary endpoint was time to natural conception, leading to an ongoing pregnancy. Follow-up time was censored at the start of treatment or at the last date of contact. In developing the new dynamic prediction model, we used the same predictors as the Hunault model, i.e. female age, duration of subfertility, female subfertility being primary or secondary, sperm motility and referral status. The performance of the model was evaluated in terms of calibration and discrimination. Additionally, we assessed the utility of the model in terms of the variability of the calculated predictions.MAIN RESULTS AND THE ROLE OF CHANCE: Of the 4999 couples in the cohort, 1053 (21%) women reached a natural conception leading to an ongoing pregnancy within a mean follow-up of 8 months (5th and 95th percentile: 1-21). Our newly developed dynamic prediction model estimated the median probability of conceiving in the first year after the completion of the fertility workup at 27%. For couples not yet pregnant after half a year, after one year and after one and a half years of expectant management, the median probability of conceiving over the next year was estimated at 20, 15 and 13%, respectively. The model performed fair in an internal validation. The prediction ranges were sufficiently broad to aid in counselling couples for at least two years after their fertility workup.LIMITATIONS, REASONS FOR CAUTION: The dynamic prediction model needs to be validated in an external population.WIDER IMPLICATIONS OF THE FINDINGS: This dynamic prediction model allows reassessment of natural conception chances after various periods of unsuccessful expectant management. This gives valuable information to counsel couples with unexplained subfertility that are seen for a fertility workup.STUDY FUNDING/COMPETING INTERESTS: This study was facilitated by grant 945/12/002 from ZonMW, The Netherlands Organization for Health Research and Development, The Hague, The Netherlands. No competing interests.

Ovarian epithelial cancer is a gynecological tumor with a high risk of recurrence and death. In the clinical diagnosis of ovarian epithelial cancer, CA125 has become an important indicator of disease burden. To account for patient recurrence and death, a proper method is needed to integrate information from biomarkers and recurrence simultaneously. In the past 10 years, many methods have been proposed for joint modeling of longitudinal biomarkers and survival data, but few of them are applicable to longitudinal data and disease processes, including recurrence and death. In this article, we proposed a new joint frailty model based on functional principal component analysis for dynamic prediction of survival probabilities on the total time scale, which took recurrent history and longitudinal data into account simultaneously. The estimation of the joint frailty model is achieved by maximizing the penalized log-likelihood function. The simulation results demonstrated the advantages of our method in both discrimination and accuracy under different scenarios. To indicate the method's practicality, it is applied to an actual dataset of patients with ovarian epithelial cancer to predict survival dynamically using longitudinal data of biomarker CA125 and recurrent history data.

Background The risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD). Methods Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63–1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37–2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526–0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303–0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526–0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515–0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668–0.753 for ECG recorded within 10 years before SCD). Conclusion Short-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

Background: Prognostic nomograms for patients with extremity soft tissue sarcoma (eSTS) typically predict survival or the occurrence of local recurrence or distant metastasis at time of surgery. Our aim was to develop and externally validate a dynamic prognostic nomogram for overall survival in eSTS survivors for use during follow-up. Methods: All primary eSTS patients operated with curative intent between 1994 and 2013 at three European and one Canadian sarcoma centers formed the development cohort. Patients with Fédération Française des Centres de Lutte Contre le Cancer (FNCLCC) grade II and grade III eSTS operated between 2000 and 2016 at seven other European reference centers formed the external validation cohort. We used a landmark analysis approach and a multivariable Cox model to create a dynamic nomogram; the prediction window was fixed at five years. A backward procedure based on the Akaike Information Criterion was adopted for variable selection. We tested the nomogram performance in terms of calibration and discrimination. Findings: The development and validation cohorts included 3740 and 893 patients, respectively. The variables selected applying the backward procedure were patient's age, tumor size and its interaction with landmark time, tumor FNCLCC grade and its interaction with landmark time, histology, and both local recurrence and distant metastasis (as first event) indicator variables. The nomogram showed good calibration and discrimination. Harrell C indexes at different landmark times were between 0.776 (0.761–0.790) and 0.845 (0.823–0.862) in the development series and between 0.675 (0.643–0.704) and 0.810 (0.775–0.844) in the validation series. Interpretation: A new dynamic nomogram is available to predict 5-year overall survival at different times during the first three years of follow-up in patients operated for primary eSTS. This nomogram allows physicians to update the individual survival prediction during follow-up on the basis of baseline variables, time elapsed from surgery and first-event history. Keywords: Dynamic prediction, Landmark analysis, Prognostic nomogram, Sarcoma, Soft tissue sarcoma: Survivors

Uveitis is characterised as a recurrent inflammation of the eye and an ongoing inflammation can have severe impact on the visual acuity of the patient. The Rotterdam Eye Hospital has been collecting data on every uveitis patient visiting the hospital since 2000. We propose a joint model for the inflammation and visual acuity with the purpose of making dynamic predictions. Dynamic prediction models allow predictions to be updated during the follow-up of the patient based on the patient's disease history. The joint model consists of a submodel for the inflammation, the event history outcome, and one for the visual acuity, the longitudinal outcome. The inflammation process is described with a two-state reversible multistate model, where transition times are interval censored. Correlated log-normal frailties are included in the multistate model to account for the within eye and within patient correlation. A linear mixed model is used for the visual acuity. The joint model is fitted in a two-stage procedure and we illustrate how the model can be used to make dynamic predictions. The performance of the method was investigated in a simulation study. The novelty of the proposed model includes the extension to a multistate outcome, whereas, previously, the standard has been to consider survival or competing risk outcomes. Furthermore, it is usually the case that the longitudinal outcome affects the event history outcome, but in this model, the relation is reversed.

BACKGROUND: Cystic fibrosis (CF) is an inherited, chronic, progressive condition affecting around 10,000 individuals in the United Kingdom and over 70,000 worldwide. Survival in CF has improved considerably over recent decades, and it is important to provide up-to-date information on patient prognosis. METHODS: The UK Cystic Fibrosis Registry is a secure centralized database, which collects annual data on almost all CF patients in the United Kingdom. Data from 43,592 annual records from 2005 to 2015 on 6181 individuals were used to develop a dynamic survival prediction model that provides personalized estimates of survival probabilities given a patient's current health status using 16 predictors. We developed the model using the landmarking approach, giving predicted survival curves up to 10 years from 18 to 50 years of age. We compared several models using cross-validation. RESULTS: The final model has good discrimination (C-indexes: 0.873, 0.843, and 0.804 for 2-, 5-, and 10-year survival prediction) and low prediction error (Brier scores: 0.036, 0.076, and 0.133). It identifies individuals at low and high risk of short- and long-term mortality based on their current status. For patients 20 years of age during 2013-2015, for example, over 80% had a greater than 95% probability of 2-year survival and 40% were predicted to survive 10 years or more. CONCLUSIONS: Dynamic personalized prediction models can guide treatment decisions and provide personalized information for patients. Our application illustrates the utility of the landmarking approach for making the best use of longitudinal and survival data and shows how models can be defined and compared in terms of predictive performance.

The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure could be extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients., Comment: A revised version has been submitted

Background Informing kidney transplant recipients of their prognosis and disease progression is of primary importance in a patient-centred vision of care. By participating in decisions from the outset, transplant recipients may be more adherent to complex medical regimens due to their enhanced understanding. Methods We proposed to include repeated measurements of serum creatinine (SCr), in addition to baseline characteristics, in order to obtain dynamic predictions of the graft failure risk that could be updated continuously during patient follow-up. Adult recipients from the French Données Informatisées et VAlidées en Transplantation (DIVAT) cohort transplanted for the first or second time from a heart-beating or living donor and alive with a functioning graft at 1 year post-transplantation were included. Results The model was composed of six baseline parameters, in addition to the SCr evolution. We validated the dynamic predictions by evaluating both discrimination and calibration accuracy. The area under the receiver operating characteristic curve varied from 0.72 to 0.76 for prediction times at 1 and 6 years post-transplantation, respectively, while calibration plots showed correct accuracy. We also provided an online application tool (https://shiny.idbc.fr/DynPG). Conclusion We have created a tool that, for the first time in kidney transplantation, predicts graft failure risk both at an individual patient level and dynamically. We believe that this tool would encourage willing patients into participative medicine.

Purpose There is increasing interest in personalized prediction of disease progression for soft tissue sarcoma patients. Currently, available prediction models are limited to predictions from time of surgery or diagnosis. This study updates predictions of overall survival at different times during follow-up by using the concept of dynamic prediction. Patients and methods Information from 2232 patients with high-grade extremity soft tissue sarcoma, who underwent surgery at 14 specialized sarcoma centers, was used to develop a dynamic prediction model. The model provides updated 5-year survival probabilities from different prediction time points during follow-up. Baseline covariates as well as time-dependent covariates, such as status of local recurrence and distant metastases, were included in the model. In addition, the effect of covariates over time was investigated and modelled accordingly in the prediction model. Results Surgical margin and tumor histology show a significant time-varying effect on overall survival. The effect of margin is strongest shortly after surgery and diminishes slightly over time. Development of local recurrence and distant metastases during follow-up have a strong effect on overall survival and updated predictions must account for their occurrence. Conclusion The presence of time-varying effects, as well as the effect of local recurrence and distant metastases on survival, suggest the importance of updating predictions during follow-up. This newly developed dynamic prediction model which updates survival probabilities over time can be used to make better individualized treatment decisions based on a dynamic assessment of a patient's prognosis.

It has been an outstanding challenge for global climate models to simulate and predict East Asia summer monsoon (EASM) rainfall. This study evaluated the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern East Asia) but misplaces precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well. The results here suggest that, over East Asia land regions, the skillful rainfall prediction relies on not only model&rsquo, s capability in predicting better summer mean and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.

The outbreak of a novel coronavirus (COVID-19) aroused great public opinion in the Chinese Sina-microblog. To help in designing effective communication strategies during a major public health emergency, we analyze the real data of COVID-19 information and propose a comprehensive susceptible–reading–forwarding–immune (SRFI) model to understand the patterns of key information propagation considering both public contact and participation. We develop the SRFI model, based on the public reading quantity and forwarding quantity that denote contact and participation respectively, and take into account the behavior that users may re-enter another related topic during the attention phase or the participation phase freely. Data fitting using the real data of both reading quantity and forwarding quantity obtained from Chinese Sina-microblog can parameterize the model to make an accurate prediction of the COVID-19 public opinion trend until the next major news item occurs, and the sensitivity analysis provides the basic strategies for communication.

With the emergence of rich information on biomarkers after treatments, new types of prognostic tools are being developed: dynamic prognostic tools that can be updated at each new biomarker measurement. Such predictions are of interest in oncology where after an initial treatment, patients are monitored with repeated biomarker data. However, in such setting, patients may receive second treatments to slow down the progression of the disease. This paper aims to develop and validate dynamic individual predictions that allow the possibility of a new treatment in order to help understand the benefit of initiating new treatments during the monitoring period. The prediction of the event in the next x years is done under two scenarios: (1) the patient initiates immediately a second treatment, (2) the patient does not initiate any treatment in the next x years. Predictions are derived from shared random-effect models. Applied to prostate cancer data, different specifications for the dependence between the prostate-specific antigen repeated measures, the initiation of a second treatment (hormonal therapy), and the risk of clinical recurrence are investigated and compared. The predictive accuracy of the dynamic predictions is evaluated with two measures (Brier score and prognostic cross-entropy) for which approximated cross-validated estimators are proposed.

Clostridium perfringens type A is a significant public health threat and its spores may germinate, outgrow, and multiply during cooling of cooked meats. This study applies a new C. perfringens growth model in the USDA Integrated Pathogen Modeling Program-Dynamic Prediction (IPMP Dynamic Prediction) Dynamic Prediction to predict the growth from spores of C. perfringens in cooked uncured meat and poultry products using isothermal, dynamic heating, and cooling data reported in the literature. The residual errors of predictions (observation-prediction) are analyzed, and the root-mean-square error (RMSE) calculated. For isothermal and heating profiles, each data point in growth curves is compared. The mean residual errors (MRE) of predictions range from -0.40 to 0.02 Log colony forming units (CFU)/g, with a RMSE of approximately 0.6 Log CFU/g. For cooling, the end point predictions are conservative in nature, with an MRE of -1.16 Log CFU/g for single-rate cooling and -0.66 Log CFU/g for dual-rate cooling. The RMSE is between 0.6 and 0.7 Log CFU/g. Compared with other models reported in the literature, this model makes more accurate and fail-safe predictions. For cooling, the percentage for accurate and fail-safe predictions is between 97.6% and 100%. Under criterion 1, the percentage of accurate predictions is 47.5% for single-rate cooling and 66.7% for dual-rate cooling, while the fail-dangerous predictions are between 0% and 2.4%. This study demonstrates that IPMP Dynamic Prediction can be used by food processors and regulatory agencies as a tool to predict the growth of C. perfringens in uncured cooked meats and evaluate the safety of cooked or heat-treated uncured meat and poultry products exposed to cooling deviations or to develop customized cooling schedules. This study also demonstrates the need for more accurate data collection during cooling.

ObjectivesThis study aimed at developing a dynamic prediction model for patients with Ewing sarcoma (ES) to provide predictions at different follow-up times. During follow-up, disease-related information becomes available, which has an impact on a patient’s prognosis. Many prediction models include predictors available at baseline and do not consider the evolution of disease over time.SettingIn the analysis, 979 patients with ES from the Gesellschaft für Pädiatrische Onkologie und Hämatologie registry, who underwent surgery and treatment between 1999 and 2009, were included.DesignA dynamic prediction model was developed to predict updated 5-year survival probabilities from different prediction time points during follow-up. Time-dependent variables, such as local recurrence (LR) and distant metastasis (DM), as well as covariates measured at baseline, were included in the model. The time effects of covariates were investigated by using interaction terms between each variable and time.ResultsDeveloping LR, DM in the lungs (DMp) or extrapulmonary DM (DMo) has a strong effect on the probability of surviving an additional 5 years with HRs and 95% CIs equal to 20.881 (14.365 to 30.353), 6.759 (4.465 to 10.230) and 17.532 (13.210 to 23.268), respectively. The effects of primary tumour location, postoperative radiotherapy (PORT), histological response and disease extent at diagnosis on survival were found to change over time. The HR of PORT versus no PORT at the time of surgery is equal to 0.774 (0.594 to 1.008). One year after surgery, the HR is equal to 1.091 (0.851 to 1.397).ConclusionsThe time-varying effects of several baseline variables, as well as the strong impact of time-dependent variables, show the importance of including updated information collected during follow-up in the prediction model to provide accurate predictions of survival.

ObjectivesThis study explored the prognostic factors and developed a prediction model for Chinese-American (CA) cervical cancer (CC) patients. We compared two alternative models (the restricted mean survival time (RMST) model and the proportional baselines landmark supermodel (PBLS model, producing dynamic prediction)) versus the Cox proportional hazards model in the context of time-varying effects.Setting and data sourcesA total of 713 CA women with CC and available covariates (age at diagnosis, International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis and radiation) from the Surveillance, Epidemiology and End Results database were included.DesignWe applied the Cox proportional hazards model to analyse the all-cause mortality with the proportional hazards assumption. Additionally, we applied two alternative models to analyse covariates with time-varying effects. The performances of the models were compared using the C-index for discrimination and the shrinkage slope for calibration.ResultsOlder patients had a worse survival rate than younger patients. Advanced FIGO stage patients showed a relatively poor survival rate and low life expectancy. Lymph node metastasis was an unfavourable prognostic factor in our models. Age at diagnosis, FIGO stage and lymph node metastasis represented time-varying effects from the PBLS model. Additionally, radiation showed no impact on survival in any model. Dynamic prediction presented a better performance for 5-year dynamic death rates than did the Cox proportional hazards model.ConclusionsWith the time-varying effects, the RMST model was suggested to explore diagnosis factors, and the PBLS model was recommended to predict a patient’s w-year dynamic death rate.

e23511 Background: Conditional survival provides a dynamic prediction of prognosis for patients surviving a defined period of time after diagnosis. This study aimed to determine the conditional survival and prognostic factors over time among patients with non-metastatic bone osteosarcoma. Methods: We reviewed 714 bone osteosarcoma patients treated from 1985 to 2016. Patients with metastatic disease at diagnosis or limited follow up were excluded, resulting in 587 cases for analysis. Clinical and pathological variables were recorded. Predictive variables included age at diagnosis, gender, previous radiation history, tumor site, tumor size, histologic subtype, histologic grade, resection margin, chemotherapy, and radiation therapy. The multivariate Cox proportional hazards regression was used to analyze prognostic factors of conditional overall survival and progression-free survival at baseline and 5 years after diagnosis. Results: The estimated 5-year conditional overall survival increased from 71.0% (95% CI: 67.5%-75.0%) at baseline to 86.9% (95% CI: 82.6%-90.5%) at 5 years, which means if a patient with non-metastatic bone osteosarcoma survived 5 years, the chance of surviving another 5 years was 86.9%. If the patient was progression-free for 5 years, the 5-year conditional overall survival was even higher, 93.2% (95% CI: 89.5%-96.4%), and the 5-year conditional progression-free survival improved from 57.1% (95% CI: 53.3%-61.0%) at baseline to 91.2% (95% CI: 87.5%-94.6%) at 5 years. Prognostic factors for mortality and disease progression change as survival time increases. At baseline, age (p < 0.001 and p = 0.003), histologic subtype (p < 0.001 and p = 0.001), grade (p < 0.001 and p < 0.001), tumor size (p = 0.002 and p = 0.002), resection margin (p < 0.001 and p < 0.001) and chemotherapy (p = 0.001 and p = 0.001) were predictive of both overall survival and progression-free survival. However, only age (p < 0.001) and histologic subtype (p = 0.015) remained significant for mortality and resection margin (p = 0.001) for disease progression at 5 years. Conclusions: The survival probability of osteosarcoma improves as survival time increases. Estimates of conditional survival can provide useful information for individualized surveillance strategies, risk evaluation, patient counseling, and making clinical decisions.

Developing a personalized risk prediction model of death is fundamental for improving patient care and touches on the realm of personalized medicine. The increasing availability of genomic information and large-scale meta-analytic data sets for clinicians has motivated the extension of traditional survival prediction based on the Cox proportional hazards model. The aim of our paper is to develop a personalized risk prediction formula for death according to genetic factors and dynamic tumour progression status based on meta-analytic data. To this end, we extend the existing joint frailty-copula model to a model allowing for high-dimensional genetic factors. In addition, we propose a dynamic prediction formula to predict death given tumour progression events possibly occurring after treatment or surgery. For clinical use, we implement the computation software of the prediction formula in the joint.Cox R package. We also develop a tool to validate the performance of the prediction formula by assessing the prediction error. We illustrate the method with the meta-analysis of individual patient data on ovarian cancer patients.

Drug abuse is a serious problem in south of China. Because of drug abuse, many people lose their family, cause, even their lives. Chinese government has tried its best to fight against drug abuse and trafficking all along. However, drug abuse is still a serious problem in Yunnan, Guizhou and some other places. This article proposes a dynamic prediction model which can predict the change in number of drug related people. We adjust some parameters that are adjustable and get prediction results under different parameters. Through the analysis of these data, we get some useful information, and these information can provide data base for government decision makers to develop anti-drug specific action.

Background Cancer remains the second major cause of death in the United States over the last decade. Chemotherapy is a core component of nearly every cancer treatment plan. Chemotherapy-Induced Nausea and Vomiting (CINV) are the two most dreadful and unpleasant side-effects of chemotherapy for cancer patients. Several patient-specific factors affect the risk of CINV. However, none of the guidelines consider those factors. Not all of the patients have the similar emetic risk of CINV. Despite the improvements in CINV management, as many as two-thirds of chemotherapy patients still experience some degree of CINV. As a result, physicians use their personal experiences for CINV treatment, which leads to inconsistent managements of CINV. Objective The overall objective of this study is to improve the prevention of CINV using precise, personalized and evidence-based antiemetic treatment before chemotherapy. In CINV prediction, one of the interesting factors is that CINV has two distinct and complex pathophysiologic phases: acute and delayed. In addition, the risk factors and their associations are different for different emetogenic chemotherapies (e.g., low, moderate, and high). There are six contexts considering the combination of phases and emetogenicity levels. This will require the creation of six different models. Instead, our objective was to describe a single framework named “prediction engine” that can perform prediction query without losing the sensitivity to each context. The prediction engine discovers how the patient-related variables and the emetogenecity of chemotherapy are associated with the risk of CINV for each phase. Methods This was a single-center retrospective study. The data were collected by retrospective record review from the electronic medical record system used at the University of Missouri Ellis Fischel Cancer Center. An association rule-based dynamic and context-sensitive Prediction Engine has been developed. Physicians receive feedback about CINV risks of patients from the CINV decision support system based on patient-specific factors. Results The prediction performance of the system outperformed many popular prediction methods and all the results of CINV risk prediction published in the literature. Best prediction performance was achieved using the rule-ranking approach. The accuracy, sensitivity, and specificity were 87.85 %, 87.54 %, and 88.2 %, respectively. Conclusions The system used the patient-specific risk factors for making personalized treatment recommendations for CINV. It solved a real clinical problem that will shorten the gap between clinical practices and evidence-based guidelines for CINV management leading to the practice of personalized and precise treatment recommendation, better life quality of patient, and reduced healthcare cost. The approach presented in this article can be applied to any other clinical predictions.

Background Standard survival analysis fails to give insight into what happens to a patient after a first outcome event (like first relapse of a disease). Multi-state models are a useful tool for analyzing survival data when different treatments and results (intermediate events) can occur. Aim of this study was to implement a multi-state model on data of patients with rectal cancer to illustrate the advantages of multi-state analysis in comparison to standard survival analysis. Methods We re-analyzed data from the RCT FOGT-2 study by using a multi-state model. Based on the results we defined a high and low risk reference patient. Using dynamic prediction, we estimated how the survival probability changes as more information about the clinical history of the patient becomes available. Results A patient with stage UICC IIIc (vs UICC II) has a higher risk to develop distant metastasis (DM) or both DM and local recurrence (LR) if he/she discontinues chemotherapy within 6 months or between 6 and 12 months, as well as after the completion of 12 months CTx with HR 3.55 (p = 0.026), 5.33 (p = 0.001) and 3.37 (p

Considerable research has been conducted seeking risk factors and constructing prediction models for transition to psychosis in individuals at ultra-high risk (UHR). Nearly all such research has only employed baseline predictors, i.e. data collected at the baseline time point, even though longitudinal data on relevant measures such as psychopathology have often been collected at various time points. Dynamic prediction, which is the updating of prediction at a post-baseline assessment using baseline and longitudinal data accumulated up to that assessment, has not been utilized in the UHR context. This study explored the use of dynamic prediction and determined if it could enhance the prediction of frank psychosis onset in UHR individuals. An emerging statistical methodology called joint modelling was used to implement the dynamic prediction. Data from the NEURAPRO study (n = 304 UHR individuals), an intervention study with transition to psychosis study as the primary outcome, were used to investigate dynamic predictors. Compared with the conventional approach of using only baseline predictors, dynamic prediction using joint modelling showed significantly better sensitivity, specificity and likelihood ratios. As dynamic prediction can provide an up-to-date prediction for each individual at each new assessment post entry, it can be a useful tool to help clinicians adjust their prognostic judgements based on the unfolding clinical symptomatology of the patients. This study has shown that a dynamic approach to psychosis prediction using joint modelling has the potential to aid clinicians in making decisions about the provision of timely and personalized treatment to patients concerned.

Purpose – This paper aims to clarify the predicting and compensating method of aeroplane assembly. It proposes modeling the process of assembly. The paper aims to solve the precision assembly of aeroplane, which includes predicting the assembly variation and compensating the assembly errors. Design/methodology/approach – The paper opted for an exploratory study using the state space theory and small displacement torsor theory. The assembly variation propagation model is established. The experiment data are obtained by a real small aeroplane assembly process. Findings – The paper provides the predicting and compensating method for aeroplane assembly accuracy. Originality/value – This paper fulfils an identified need to study how the assembly variation propagates in the assembly process.