The epidemiological impact of digital and manual contact tracing on the SARS-CoV-2 epidemic in the Netherlands: Empirical evidence.

The Dutch government introduced the CoronaMelder smartphone application for digital contact tracing (DCT) to complement manual contact tracing (MCT) by Public Health Services (PHS) during the 2020-2022 SARS-CoV-2 epidemic. Modelling studies showed great potential but empirical evidence of DCT and MCT impact is scarce. We determined reasons for testing, and mean exposure-testing intervals by reason for testing, using routine data from PHS Amsterdam (1 December 2020 to 31 May 2021) and data from two SARS-CoV-2 rapid diagnostic test accuracy studies at other PHS sites in the Netherlands (14 December 2020 to 18 June 2021). Throughout the study periods, notification of DCT-identified contacts was via PHS contact-tracers, and self-testing was not yet widely available. The most commonly reported reason for testing was having symptoms. In asymptomatic individuals, it was having been warned by an index case. Only around 2% and 2-5% of all tests took place after DCT or MCT notification, respectively. About 20-36% of those who had received a DCT or MCT notification had symptoms at the time of test request. Test positivity after a DCT notification was significantly lower, and exposure-test intervals after a DCT or MCT notification were longer, than for the above-mentioned other reasons for testing. Our data suggest that the impact of DCT and MCT on the SARS-CoV-2 epidemic in the Netherlands was limited. However, DCT impact might be enlarged if app use coverage is improved, contact-tracers are eliminated from the digital notification process to minimise delays, and DCT is combined with self-testing.

[Prognostic factor identified Now what? What we can and cannot learn from prognostic factor research]. / Prognostische factor geïdentificeerd en nu?

Although it is generally known that a (statistical) association between a factor, i.e., determinant or independent variable, and outcome, i.e., dependent variable, does not directly provide evidence of a causal relation, in practice the distinction between associative and causal relationships often becomes fuzzy when interpreting prognostic factor research. We provide suggestions for interpreting the findings of prognostic factor research. It is important to assess the purpose and design of the study, including the statistical analysis. The actual evidence that prognostic factor research can provide is easily overestimated. In particular when associations between factors and outcome are estimated in a multivariable analysis, causal or predictive qualities can easily but wrongfully be attributed to a prognostic factor. It is generally advisable to refrain from judgments on the causal of predictive qualities of a prognostic factor purely based on a prognostic factor study. Findings from prognostic factor research are usually a good starting point for follow-up research, while the direct applicability of such findings in daily medical practice is often limited.

Impact of Modified Transesophageal Echocardiography on Mortality and Stroke after Cardiac Surgery: A Large Cohort Study.

The aim of this study was to investigate the impact of perioperative screening with modified transesophageal echocardiography (A-View method). We compared, in consecutive patients who underwent cardiac surgery between 2006 and 2014, 30-day mortality and in-hospital stroke incidence, operated either with perioperative modified TEE screening (intervention group) or only with conventional TEE screening (control group). Of the 8,605 study patients, modified TEE was applied in 1,391 patients (16.2%). Patients in the intervention group were on average older (71 versus 68 years, p < 0.001) and more often females (31.0% versus 28.0%, p < 0.001) and had a higher predicted mortality (EuroSCORE I: 5.9% versus 4.0%, p < 0.001). The observed 30-day mortality was 2.2% and 2.5% in both groups, respectively, with multivariable and propensity-score adjusted relative risks (RRs) of 0.70 (95% CI: 0.50-1.00, p = 0.05) and 0.67 (95% CI: 0.45-0.98, p = 0.04). In-hospital stroke was 2.9% and 2.1% in both groups, respectively, with adjusted RRs of 1.03 (95% CI: 0.73-1.45) and 1.01 (95% CI: 0.71-1.43). In patients undergoing cardiac surgery, use of perioperative screening for aortic atherosclerosis with modified TEE was associated with lower postoperative mortality, but not stroke, as compared to patients operated on without such screening.

Systematic Review of Health Economic Impact Evaluations of Risk Prediction Models: Stop Developing, Start Evaluating.

BACKGROUND: Although health economic evaluations (HEEs) are increasingly common for therapeutic interventions, they appear to be rare for the use of risk prediction models (PMs). OBJECTIVES: To evaluate the current state of HEEs of PMs by performing a comprehensive systematic review. METHODS: Four databases were searched for HEEs of PM-based strategies. Two reviewers independently selected eligible articles. A checklist was compiled to score items focusing on general characteristics of HEEs of PMs, model characteristics and quality of HEEs, evidence on PMs typically used in the HEEs, and the specific challenges in performing HEEs of PMs. RESULTS: After screening 791 abstracts, 171 full texts, and reference checking, 40 eligible HEEs evaluating 60 PMs were identified. In these HEEs, PM strategies were compared with current practice (n = 32; 80%), to other stratification methods for patient management (n = 19; 48%), to an extended PM (n = 9; 23%), or to alternative PMs (n = 5; 13%). The PMs guided decisions on treatment (n = 42; 70%), further testing (n = 18; 30%), or treatment prioritization (n = 4; 7%). For 36 (60%) PMs, only a single decision threshold was evaluated. Costs of risk prediction were ignored for 28 (46%) PMs. Uncertainty in outcomes was assessed using probabilistic sensitivity analyses in 22 (55%) HEEs. CONCLUSIONS: Despite the huge number of PMs in the medical literature, HEE of PMs remains rare. In addition, we observed great variety in their quality and methodology, which may complicate interpretation of HEE results and implementation of PMs in practice. Guidance on HEE of PMs could encourage and standardize their application and enhance methodological quality, thereby improving adequate use of PM strategies.

Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study.

BACKGROUND: The prognosis of early-onset pre-eclampsia (before 34 weeks' gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women. OBJECTIVE: To develop and validate prediction models for outcomes in early-onset pre-eclampsia. DESIGN: Prospective cohort for model development, with validation in two external data sets. SETTING: Model development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies. PARTICIPANTS: Pregnant women with early-onset pre-eclampsia. SAMPLE SIZE: Nine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets. PREDICTORS: The predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey. MAIN OUTCOME MEASURES: The primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications. ANALYSIS: We developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). RESULTS: The PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with anoptimism-adjusted c-statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with a c-statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had a c-statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications. CONCLUSIONS: The PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high- or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation. TRIAL REGISTRATION: Current Controlled Trials ISRCTN40384046. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

Development and validation of prediction models for risk of adverse outcomes in women with early-onset pre-eclampsia: protocol of the prospective cohort PREP study.

BACKGROUND: Early-onset pre-eclampsia with raised blood pressure and protein in the urine before 34 weeks' gestation is one of the leading causes of maternal deaths in the UK. The benefits to the child from prolonging the pregnancy need to be balanced against the risk of maternal deterioration. Accurate prediction models of risks are needed to plan management. METHODS: We aim to undertake a multicentre prospective cohort study (Prediction of Risks in Early onset Pre-eclampsia (PREP)) to develop clinical prediction models in women with early-onset pre-eclampsia, for risk of adverse maternal outcomes by 48 h and by discharge. We will externally validate the models in two independent cohorts with 634 and 216 women. In the secondary analyses, we will assess risk of adverse fetal and neonatal outcomes at birth and by discharge. DISCUSSION: The PREP study will quantify the risk of maternal complications at various time points and provide individualised estimates of overall risk in women with early-onset pre-eclampsia to plan the management. TRIAL REGISTRATION: ISRCTN registry, ISRCTN40384046.

A diagnostic decision rule for management of children with meningeal signs.

In a previous study we devised a diagnostic decision rule to improve management of children with meningeal signs, suspected of having bacterial meningitis. The decision rule aimed to guide decisions on (1) whether a lumbar puncture is necessary in children with meningeal signs, and (2) which children need hospitalisation and empirical antibiotic treatment for bacterial meningitis. In this study we assessed the validity of this rule in an external population of four (paediatric) hospitals in The Netherlands. The decision rule included two scoring algorithms using symptoms, signs and quickly available blood and cerebrospinal fluid (CSF) laboratory tests. To evaluate the discriminative value of both algorithms, the absolute numbers of correctly diagnosed patients and the area under the receiver operator characteristic curve were estimated, and compared with the results from the original population (n = 360). In a 18 month period, we included 226 children, median age 2.2 years, who visited the emergency department with meningeal signs. Bacterial meningitis was present in 25 (11%). Using the scoring algorithms patients could be categorised in groups of increasing risk of bacterial meningitis. The discriminative values of the clinical and CSF algorithm in this new population were similar to those in the original population. In the total population of 586 children with meningeal signs, the rule selected 205 children (35%) who did not need a lumbar puncture and 366 children who did not need empirical treatment (62%). In conclusion, this diagnostic rule performed well in a new population of children with meningeal signs. This diagnostic decision rule is a valuable tool for the clinician when deciding to treat these children for bacterial meningitis and thus improving their management.