Clinical prediction models

Objective!#!The aim of this study was to evaluate the validity of a semiautomated volumetric approach (5DCNS+) for the detailed assessment of the fetal brain in a clinical setting.!##!Methods!#!Stored 3D volumes of > 1100 consecutive 2nd and 3rd trimester pregnancies (range 15-36 gestational weeks) were analyzed using a workflow-based volumetric approach 5DCNS+, enabling semiautomated reconstruction of diagnostic planes of the fetal central nervous system (CNS). All 3D data sets were examined for plane accuracy, the need for manual adjustment, and fetal-maternal characteristics affecting successful plane reconstruction. We also examined the potential of these standardized views to give additional information on proper gyration and sulci formation with advancing gestation.!##!Results!#!Based on our data, we were able to show that gestational age with an OR of 1.085 (95% CI 1.041-1.132) and maternal BMI with an OR of 1.022 (95% CI 1.041-1.054) only had a slight impact on the number of manual adjustments needed to reconstruct the complete volume, while maternal age and fetal position during acquisition (p = 0.260) did not have a significant effect. For the vast majority (958/1019; 94%) of volumes, using 5DCNS+ resulted in proper reconstruction of all nine diagnostic planes. In less than 1% (89/9171 planes) of volumes, the program failed to give sufficient information. 5DCNS+ was able to show the onset and changing appearance of CNS folding in a detailed and timely manner (lateral/parietooccipital sulcus formation seen in < 65% at 16-17 gestational weeks vs. 94.6% at 19 weeks).!##!Conclusions!#!The 5DCNS+ method provides a reliable algorithm to produce detailed, 3D volume-based assessments of fetal CNS integrity through a standardized reconstruction of the orthogonal diagnostic planes. The method further gives valid and reproducible information regarding ongoing cortical development retrieved from these volume sets that might aid in earlier in utero recognition of subtle structural CNS anomalies

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

Procalcitonin to guide taking blood cultures in the intensive care unit; a cluster-randomized controlled trial

Objectives: We aimed to study the safety and efficacy of procalcitonin in guiding blood cultures taking in critically ill patients with suspected infection. Methods: We performed a cluster-randomized, multi-centre, single-blinded, cross-over trial. Patients suspected of infection in whom taking blood for culture was indicated were included. The participating intensive care units were stratified and randomized by treatment regimen into a control group and a procalcitonin-guided group. All patients included in this trial followed the regimen that was allocated to the intensive care unit for that period. In both groups, blood was drawn at the same moment for a procalcitonin measurement and blood cultures. In the procalcitonin-guided group, blood cultures were sent to the department of medical microbiology when the procalcitonin was>0.25 ng/mL. The main outcome was safety, expressed as mortality at day 28 and day 90. Results: The control group included 288 patients and the procalcitonin-guided group included 276 patients. The 28- and 90-day mortality rates in the procalcitonin-guided group were 29% (80/276) and 38% (105/276), respectively. The mortality rates in the control group were 32% (92/288) at day 28 and 40% (115/288) at day 90. The intention-to-treat analysis showed hazard ratios of 0.85 (95% CI 0.62-1.17) and 0.89 (95% CI 0.67-1.17) for 28-day and 90-day mortality, respectively. The results were deemed non-inferior because the upper limit of the 95% CI was below the margin of 1.20. Conclusion: Applying procalcitonin to guide blood cultures in critically ill patients with suspected infection seems to be safe, but the benefits may be limited. Trial registration: . ClinicalTrials.gov identifier: ID . NCT01847079. Registered on 24 April 2013, retrospectively registered

Geographic and temporal validity of prediction models: different approaches were useful to examine model performance

AbstractObjectiveValidation of clinical prediction models traditionally refers to the assessment of model performance in new patients. We studied different approaches to geographic and temporal validation in the setting of multicenter data from two time periods.Study Design and SettingWe illustrated different analytic methods for validation using a sample of 14,857 patients hospitalized with heart failure at 90 hospitals in two distinct time periods. Bootstrap resampling was used to assess internal validity. Meta-analytic methods were used to assess geographic transportability. Each hospital was used once as a validation sample, with the remaining hospitals used for model derivation. Hospital-specific estimates of discrimination (c-statistic) and calibration (calibration intercepts and slopes) were pooled using random-effects meta-analysis methods. I2 statistics and prediction interval width quantified geographic transportability. Temporal transportability was assessed using patients from the earlier period for model derivation and patients from the later period for model validation.ResultsEstimates of reproducibility, pooled hospital-specific performance, and temporal transportability were on average very similar, with c-statistics of 0.75. Between-hospital variation was moderate according to I2 statistics and prediction intervals for c-statistics.ConclusionThis study illustrates how performance of prediction models can be assessed in settings with multicenter data at different time periods

Are children with prolonged fever at a higher risk for serious illness? : A prospective observational study

Funding Information: This project received funding from the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No 668303). The research was supported by the National Institute for Health Research Biomedical Research Centres at Imperial College London, Newcastle Hospitals NHS Foundation Trust and Newcastle University. RGN was funded by NIHR ACL award (ACL-2018-021-007). Funding Information: This project received funding from the European Union's Horizon 2020 research and innovation programme (Grant Agreement No 668303). The research was supported by the National Institute for Health Research Biomedical Research Centres at Imperial College London, Newcastle Hospitals NHS Foundation Trust and Newcastle University. RGN was funded by NIHR ACL award (ACL-2018- 021-007). Publisher Copyright: © 2023 Author(s) (or their employer(s)).Objectives: To describe the characteristics and clinical outcomes of children with fever ≥5 days presenting to emergency departments (EDs). Design: Prospective observational study. Setting: 12 European EDs. Patients: Consecutive febrile children 0.90, but were observed infrequently (range: 0.4%-17%). Absence of warning signs was not sufficiently reliable to rule out SBI (sensitivity 0.92 (95% CI 0.87-0.95), negative likelihood ratio (LR) 0.34 (0.22-0.54)). CRP <20 mg/L was useful for ruling out SBI (negative LR 0.16 (0.11-0.24)). There were 66 cases (1.7%) of non-infectious serious illnesses, including 21 cases of Kawasaki disease (0.6%), 28 inflammatory conditions (0.7%) and 4 malignancies. Conclusion: Children with prolonged fever have a higher risk of SBI, warranting a careful clinical assessment and diagnostic workup. Warning signs of SBI occurred infrequently but, if present, increased the likelihood of SBI. Although rare, clinicians should consider important non-infectious causes of prolonged fever.Peer reviewe

Performance of IMPACT, CRASH and Nijmegen models in predicting six month outcome of patients with severe or moderate TBI: An external validation study

Background: External validation on different TBI populations is important in order to assess the generalizability of prognostic models to different settings. We aimed to externally validate recently developed models for prediction of six month unfavourable outcome and six month mortality. Methods: The International Neurotrauma Research Organization - Prehospital dataset (INRO-PH) was collected within an observational study between 2009-2012 in Austria and includes 778 patients with TBI of GCS < = 12. Three sets of prognostic models were externally validated: the IMPACT core and extended models, CRASH basic models and the Nijmegen models developed by Jacobs et al - all for prediction of six month unfavourable outcome and six month mortality. The external validity of the models was assessed by discrimination (Area Under the receiver operating characteristic Curve, AUC) and calibration (calibration statistics and plots). Results: Median age in the validation cohort was 50 years and 44% had an admission GSC motor score of 1-3. Six-month mortality was 27%. Mortality could better be predicted (AUCs around 0.85) than unfavourable outcome (AUCs around 0.80). Calibration plots showed that the o