Improving prediction models with new markers: A comparison of updating strategies

Background: New markers hold the promise of improving risk prediction for individual patients. We aimed to compare the performance of different strategies to extend a previously developed prediction model with a new marker. Methods: Our motivating example was the extension of a risk calculator for prostate cancer with a new marker that was available in a relatively small dataset. Performance of the strategies was also investigated in simulations. Development, marker and test sets with different sample sizes originating from the same underlying population were generated. A prediction model was fitted using logistic regression in the development set, extended using the marker set and validated in the test set. Extension strategies considered were re-estimating individual regression coefficients, updating of predictions using conditional likelihood ratios (LR) and imputation of marker values in the development set and subsequently fitting a model in the combined development and marker sets. Sample sizes considered for the development and marker set were 500 and 100, 500 and 500, and 100 and 500 patients. Discriminative ability of the extended models was quantified using the concordance statistic (c-statistic) and calibration was quantified using the calibration slope. Results: All strategies led to extended models with increased discrimination (c-statistic increase from 0.75 to 0.80 in test sets). Strategies estimating a large number of parameters (re-estimation of all coefficients and updating using conditional LR) led to overfitting (calibration slope below 1). Parsimonious methods, limiting the number of coefficients to be re-estimated, or applying shrinkage after model revision, limited the amount of overfitting. Combining the development and marker set using imputation of missing marker values approach led to consistently good performing models in all scenarios. Similar results were observed in the motivating example. Conclusion: When the sample with the new marker information is small, parsimonious methods are required to prevent overfitting of a new prediction model. Combining all data with imputation of missing marker values is an attractive option, even if a relatively large marker data set is available

Predicting biochemical recurrence and prostate cancer-specific mortality after radical prostatectomy: comparison of six prediction models in a cohort of patients with screening- and clinically detected prostate cancer

Objectives To perform a comparison and external validation of three models predicting biochemical recurrence (BCR) and three models predicting prostate cancer (PCa)-specific mortality (PCSM) in a screening setting, i.e. patients with screeningdetected PCa (S-PCa) and in those with clinically detected PCa (C-PCa). Subjects and Methods We retrospectively evaluated 795 men with S-PCa, from the European Randomized Study of Screening for Prostate Cancer, Rotterdam, and 1123 men with C-PCa initially treated with RP. The discriminative ability of the models was assessed according to the area under the curve (AUC) of the receiver-operating characteristic, and calibration was assessed graphically using calibration plots. Results The median (interquartile range [IQR]) follow-up for the SPCa group was 10.4 (6.8–14.3) years and for the C-PCa group it was 8.8 (4.8–12.9) years. A total of 123 men with S-PCa (15%) and 389 men with C-PCa (35%) experienced BCR. Of the men with S-PCa and BCR, 24 (20%) died from PCa and 29 (23%) died from other causes. Of the men with C-PCa and BCR, 68 (17%) died from PCa and 105 (27%) died from other causes. The discrimination of the models predicting BCR or PCSM was higher for men with S-PCa (AUC: BCR 0.77–0.84, PCSM 0.60–0.77) than for the men with C-PCa (AUC: BCR 0.75–0.79, PCSM 0.51–0.68) as a result of the similar patient characteristics of the men with S-PCa in the present study and those of the cohorts used to develop these models. The risk of BCR was typically overestimated, while the risk of PCSM was typically underestimated. Conclusion Prediction models for BCR showed good discrimination and reasonable calibration for both men with S-PCa and men with C-PCa, and even better discrimination for men with SPCa. For PCSM, the ev

Rule-based versus probabilistic selection for active surveillance using three definitions of insignificant prostate cancer

To study whether probabilistic selection by the use of a nomogram could improve patient selection for active surveillance (AS) compared to the various sets of rule-based AS inclusion criteria currently used. We studied Dutch and Swedish patients participating in the European Randomized study of Screening for Prostate Cancer (ERSPC). We explored which men who were initially diagnosed with cT1-2, Gleason 6 (Gleason pattern a parts per thousand currency sign3 + 3) had histopathological indolent PCa at RP [defined as pT2, Gleason pattern a parts per thousand currency sign3 and tumour volume (TV) a parts per thousand currency sign0.5 or TV a parts per thousand currency sign 1.3 ml, and TV no part of criteria (NoTV)]. Rule-based selection was according to the Prostate cancer Research International: Active Surveillance (PRIAS), Klotz, and Johns Hopkins criteria. An existing nomogram to define probability-based selection for AS was refitted for the TV1.3 and NoTV indolent PCa definitions. 619 of 864 men undergoing RP had cT1-2, Gleason 6 disease at diagnosis and were analysed. Median follow-up was 8.9 years. 229 (37 %), 356 (58 %), and 410 (66 %) fulfilled the TV0.5, TV1.3, and NoTV indolent PCa criteria at RP. Discriminating between indolent and significant disease according to area under the curve (AUC) was: TV0.5: 0.658 (PRIAS), 0.523 (Klotz), 0.642 (Hopkins), 0.685 (nomogram). TV1.3: 0.630 (PRIAS), 0.550 (Klotz), 0.615 (Hopkins), 0.646 (nomogram). NoTV: 0.603 (PRIAS), 0.530 (Klotz), 0.589 (Hopkins), 0.608 (nomogram). The performance of a nomogram, the Johns Hopkins, and PRIAS rule-based criteria are comparable. Because the nomogram allows individual trade-offs, it could be a good alternative to rigid rule-based criteria

Comparison of biopsy under-sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies

This study aimed to estimate the rates of biopsy undersampling and progression for four prostate cancer (PCa) active surveillance (AS) cohorts within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium. We used a hidden Markov model (HMM) to estimate factors that define PCa dynamics for men on AS including biopsy under-sampling and progression that are implied by longitudinal data in four large cohorts included in the GAP3 database. The HMM was subsequently used as the basis for a simulation model to evaluate the biopsy strategies previously proposed for each of these cohor

Evaluation of current prediction models for Lynch syndrome: updating the PREMM5 model to identify PMS2 mutation carriers

Until recently, no prediction models for Lynch syndrome (LS) had been validated for PMS2 mutation carriers. We aimed to evaluate MMRpredict and PREMM5 in a clinical cohort and for PMS2 mutation carriers specifically. In a retrospective, clinic-based cohort we calculated predictions for LS according to MMRpredict and PREMM5. The area under the operator receiving characteristic curve (AU

Prostate Magnetic Resonance Imaging, with or Without Magnetic Resonance Imaging-targeted Biopsy, and Systematic Biopsy for Detecting Prostate Cancer: A Cochrane Systematic Review and Meta-analysis

Context: Magnetic resonance imaging (MRI), with or without MRI-targeted biopsy (MRI pathway), is an alternative test to systematic transrectal ultrasonography-guided biopsy in men suspected of having prostate cancer. At present, evidence on which test to use is insufficient to inform detailed evidence-based decision making. Objective: To determine the diagnostic accuracy of the index tests MRI only, MRI-targeted biopsy, MRI pathway, and systematic biopsy, as compared with template-guided biopsy (reference standard), in detecting clinically significant prostat

Clinical consequences of nonadherence to Barrett's esophagus surveillance recommendations: a Multicenter prospective cohort study

Half of Barrett's esophagus (BE) surveillance endoscopies do not adhere to guideline recommendations. In this multicenter prospective cohort study, we assessed the clinical consequences of nonadherence to recommended surveillance intervals and biopsy protocol. Data from BE surveillance patients were collected from endoscopy and pathology reports; questionnaires were distributed among endoscopists. We estimated the association between (non)adherence and (i) endoscopic curability of esophageal adenocarcinoma (EAC), (ii) mortality, and (iii) misclassification of histological diagnosis according to a multistate hidden Markov model. Potential explanatory parameters (patient, facility, endoscopist variables) for nonadherence, related to clinical impact, were analyzed. In 726 BE patients, 3802 endoscopies were performed by 167 endoscopists. Adherence to surveillance interval was 16% for non-dysplastic (ND)BE, 55% for low-grade dysplasia (LGD), and 54% of endoscopies followed the Seattle protocol. There was no evidence to support the following statements: longer surveillance intervals or fewer biopsies than recommended affect endoscopic curability of EAC or cause-specific mortality (P > 0.20); insufficient biopsies affect the probability of NDBE (OR 1.0) or LGD (OR 2.3) being misclassified as high-grade dysplasia/EAC (P > 0.05). Better adherence was associated with older patients (OR 1.1), BE segments <= 2 cm (OR 8.3), visible abnormalities (OR 1.8, all P <= 0.05), endoscopists with a subspecialty (OR 3.2), and endoscopists who deemed histological diagnosis an adequate marker (OR 2.0). Clinical consequences of nonadherence to guidelines appeared to be limited with respect to endoscopic curability of EAC and mortality. This indicates that BE surveillance recommendations should be optimized to minimize the burden of endoscopies.Cellular mechanisms in basic and clinical gastroenterology and hepatolog

Evaluation of current prediction models for Lynch syndrome: updating the PREMM5 model to identify PMS2 mutation carriers

Analysis and support of clinical decision makin

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.

Improved Prostate Cancer Biopsy Grading by Incorporation of Invasive Cribriform and Intraductal Carcinoma in the 2014 Grade Groups

Background: Grade groups (GGs) are an important parameter for therapeutic decision making in prostate cancer (Pca) patients. Invasive cribriform and/or intraductal carcinoma (CR/IDC) has an independent prognostic value for disease outcome, but are not included in the GG limiting their clinical use. Objective: To perform a proof-of-principle study incorporating CR/IDC in the current GG. Design, setting, and participants: All prostate biopsies of 1031 men with screen-detected Pca between 1993 and 2000 were reviewed for the current GG (ranging from 1 to 5) and CR/IDC. The cribriform grade (cGrade) was equal to the GG if CR/IDC was present and GG minus 1 if not. GG1 was cGrade1 if intraductal carcinoma was absent. Intervention: Biopsy review for GG and CR/IDC. A total of 406 patients had received radical prostatectomy (RP), 508 radiotherapy (RT), 108 surveillance, and eight hormonal therapy, and the treatment was unknown for one patient. Outcome measurements and statistical analysis disease-specific survival (DSS), metastasis-free survival (MFS), and biochemical recurrence–free survival (BCRFS) after 15.1 yr (interquartile range 10.9–19.7 yr) follow-up were compared using Harrell's C-statistic. Results and limitations: The biopsy GGs were 486 GG1, 310 GG2, 104 GG3, 64 GG4, and 67 GG5; cGrade distributions were 738 cGrade1, 102 cGrade2, 91 cGrade3, 58 cGrade4, and 42 cGrade5. The cGrade had a better discriminative value than the GG for DSS (C-index 0.79; 95% confidence interval 0.74–0.83 vs 0.76; 0.71–0.82) and MFS (0.79; 0.74–0.84 vs 0.77; 0.72–0.82). The discriminative value for BCRFS after RP and RT was similar for both models. Different diagnostic, such as use of sextant biopsies, and therapeutic strategie