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PurposeEarly clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.MethodsA systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance.ResultsAfter screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68–0.99 in the ICU, to 0.96–0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance.ConclusionThis systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside.

Background Antibiotic exposure is often inadequate in critically ill patients with severe sepsis or septic shock and this is associated with worse outcomes. Despite markedly altered and rapidly changing pharmacokinetics in these patients, guidelines and clinicians continue to rely on standard dosing schemes. To address this challenge, we developed AutoKinetics, a clinical decision support system for antibiotic dosing. By feeding large amounts of electronic health record patient data into pharmacokinetic models, patient-specific predicted future plasma concentrations are displayed graphically. In addition, a tailored dosing advice is provided at the bedside in real time. To evaluate the effect of AutoKinetics on pharmacometric and clinical endpoints, we are conducting the Right Dose Right Now multicenter, randomized controlled, two-arm, parallel-group, non-blinded, superiority trial. Methods All adult intensive care patients with a suspected or proven infection and having either lactatemia or receiving vasopressor support are eligible for inclusion. Randomization to the AutoKinetics or control group is initiated at the bedside when prescribing at least one of four commonly administered antibiotics: ceftriaxone, ciprofloxacin, meropenem and vancomycin. Dosing advice is available for patients in the AutoKinetics group, whereas patients in the control group receive standard dosing. The primary outcome of the study is pharmacometric target attainment during the first 24 h. Power analysis revealed the need for inclusion of 42 patients per group per antibiotic. Thus, a total of 336 patients will be included, 168 in each group. Secondary pharmacometric endpoints include time to target attainment and fraction of target attainment during an entire antibiotic course. Secondary clinical endpoints include mortality, clinical cure and days free from organ support. Several other exploratory and subgroup analyses are planned. Discussion This is the first randomized controlled trial to assess the effectiveness and safety of bedside data-driven automated antibiotic dosing advice. This is important as adequate antibiotic exposure may be crucial to treat severe sepsis and septic shock. In addition, the trial could prove to be a significant contribution to clinical pharmacometrics and serve as a stepping stone for the use of big data and artificial intelligence in the field. Trial registration Netherlands Trial Register (NTR), NL6501/NTR6689 . Registered on 25 August 2017. European Clinical Trials Database (EudraCT), 2017-002478-37. Registered on 6 November 2017.

The Patient-Reported Outcomes Measurement Information System (PROMIS) is a universally applicable set of instruments, including item banks, short forms and computer adaptive tests (CATs), measuring patient-reported health across different patient populations. PROMIS CATs are highly efficient and the use in practice is considered feasible with little administration time, offering standardized and routine patient monitoring. Before an item bank can be used as CAT, the psychometric properties of the item bank have to be examined. Therefore, the objective was to assess the psychometric properties of the Dutch-Flemish PROMIS Physical Function item bank (DF-PROMIS-PF) in Dutch patients receiving physical therapy. Cross-sectional study. 805 patients >18 years, who received any kind of physical therapy in primary care in the past year, completed the full DF-PROMIS-PF (121 items). Unidimensionality was examined by Confirmatory Factor Analysis and local dependence and monotonicity were evaluated. A Graded Response Model was fitted. Construct validity was examined with correlations between DF-PROMIS-PF T-scores and scores on two legacy instruments (SF-36 Health Survey Physical Functioning scale [SF36-PF10] and the Health Assessment Questionnaire Disability-Index [HAQ-DI]). Reliability (standard errors of theta) was assessed. The results for unidimensionality were mixed (scaled CFI = 0.924, TLI = 0.923, RMSEA = 0.045, 1th factor explained 61.5% of variance). Some local dependence was found (8.2% of item pairs). The item bank showed a broad coverage of the physical function construct (threshold-parameters range: -4.28-2.33) and good construct validity (correlation with SF36-PF10 = 0.84 and HAQ-DI = -0.85). Furthermore, the DF-PROMIS-PF showed greater reliability over a broader score-range than the SF36-PF10 and HAQ-DI. The psychometric properties of the DF-PROMIS-PF item bank are sufficient. The DF-PROMIS-PF can now be used as short forms or CAT to measure the level of physical function of physiotherapy patients.

Background Confounding is a common issue in epidemiological research. Commonly used confounder-adjustment methods include multivariable regression analysis and propensity score methods. Although it is common practice to assess the linearity assumption for the exposure-outcome effect, most researchers do not assess linearity of the relationship between the confounder and the exposure and between the confounder and the outcome before adjusting for the confounder in the analysis. Failing to take the true non-linear functional form of the confounder-exposure and confounder-outcome associations into account may result in an under- or overestimation of the true exposure effect. Therefore, this paper aims to demonstrate the importance of assessing the linearity assumption for confounder-exposure and confounder-outcome associations and the importance of correctly specifying these associations when the linearity assumption is violated. Methods A Monte Carlo simulation study was used to assess and compare the performance of confounder-adjustment methods when the functional form of the confounder-exposure and confounder-outcome associations were misspecified (i.e., linearity was wrongly assumed) and correctly specified (i.e., linearity was rightly assumed) under multiple sample sizes. An empirical data example was used to illustrate that the misspecification of confounder-exposure and confounder-outcome associations leads to bias. Results The simulation study illustrated that the exposure effect estimate will be biased when for propensity score (PS) methods the confounder-exposure association is misspecified. For methods in which the outcome is regressed on the confounder or the PS, the exposure effect estimate will be biased if the confounder-outcome association is misspecified. In the empirical data example, correct specification of the confounder-exposure and confounder-outcome associations resulted in smaller exposure effect estimates. Conclusion When attempting to remove bias by adjusting for confounding, misspecification of the confounder-exposure and confounder-outcome associations might actually introduce bias. It is therefore important that researchers not only assess the linearity of the exposure-outcome effect, but also of the confounder-exposure or confounder-outcome associations depending on the confounder-adjustment method used.

This study aimed to explore the real-world representativeness of a prospective registry cohort with active accrual in oncology, applying a representativeness metric that is novel to health care. We used data from the Prospective Observational Cohort Study of Esophageal-Gastric Cancer Patients (POCOP) registry and from the population-based Netherlands Cancer Registry (NCR). We used Representativeness-indicators (R-indicators) and overall survival to investigate the degree to which the POCOP cohort and clinically relevant subgroups were a representative sample compared to the NCR database. Calibration using inverse propensity score weighting was applied to correct differences between POCOP and NCR. The R-indicator of the entire POCOP registry was 0.72 95% confidence interval [0.71, 0.73]. Representativeness of palliative patients was higher than that of potentially curable patients (R-indicator 0.88 [0.85, 0.90] and 0.70 [0.68, 0.71], respectively). Stratification to clinically relevant subgroups based on treatment resulted in higher R-indicators of the respective subgroups. Both after stratification and calibration weighting survival estimates in the POCOP registry were more similar to that in the NCR population. This study demonstrated the assessment of real-world representativeness of patients who participated in a prospective registry cohort and showed that real-world representativeness improved when the variability in treatment was accounted for.

Background To optimize colorectal cancer (CRC) screening and surveillance, information regarding the time-dependent risk of advanced adenomas (AA) to develop into CRC is crucial. However, since AA are removed after diagnosis, the time from AA to CRC cannot be observed in an ethically acceptable manner. We propose a statistical method to indirectly infer this time in a progressive three-state disease model using surveillance data. Methods Sixteen models were specified, with and without covariates. Parameters of the parametric time-to-event distributions from the adenoma-free state (AF) to AA and from AA to CRC were estimated simultaneously, by maximizing the likelihood function. Model performance was assessed via simulation. The methodology was applied to a random sample of 878 individuals from a Norwegian adenoma cohort. Results Estimates of the parameters of the time distributions are consistent and the 95% confidence intervals (CIs) have good coverage. For the Norwegian sample (AF: 78 % , AA: 20 % , CRC: 2 % ), a Weibull model for both transition times was selected as the final model based on information criteria. The mean time among those who have made the transition to CRC since AA onset within 50 years was estimated to be 4.80 years (95% CI: 0; 7.61). The 5-year and 10-year cumulative incidence of CRC from AA was 13.8 % (95 % CI: 7.8 % ;23.8 % ) and 15.4 % (95 % CI: 8.2 % ;34.0 % ), respectively. Conclusions The time-dependent risk from AA to CRC is crucial to explain differences in the outcomes of microsimulation models used for the optimization of CRC prevention. Our method allows for improving models by the inclusion of data-driven time distributions.

Background Rheumatoid arthritis (RA) is a chronic inflammatory disease of the joints affecting 1% of the world population. It has major impact on patients through disability and associated comorbidities. Current treatment strategies have considerably improved the prognosis, but recent innovations (especially biologic drugs and the new class of so-called “JAK/STAT inhibitors”) have important safety issues and are very costly. Glucocorticoids (GCs) are highly effective in RA, and could reduce the need for expensive treatment with biologic agents. However, despite more than 65 years of clinical experience, there is a lack of studies large enough to adequately document the benefit/harm balance. The result is inappropriate treatment strategies, i.e. both under-use and over-use of GCs, and consequently suboptimal treatment of RA. Methods The GLORIA study is a pragmatic multicentre, 2-year, randomised, double-blind, clinical trial to assess the safety and effectiveness of a daily dose of 5 mg prednisolone or matching placebo added to standard of care in elderly patients with RA. Eligible participants are diagnosed with RA, have inadequate disease control (disease activity score, DAS28 ≥ 2.6), and are ≥ 65 years. The primary outcome measures are the time-averaged mean value of the DAS28 and the occurrence of serious adverse events or adverse events of special interest. During the trial, change in antirheumatic therapy is permitted as clinically indicated, except for GCs. Cost-effectiveness and cost-utility are secondary outcomes. The main challenge is the interpretation of the trial result with two primary endpoints and the pragmatic trial design that allows co-interventions. Another challenge is the definition of safety and the relative lack of power to detect differences between treatment groups. We have chosen to define safety as the number of patients experiencing at least one serious adverse event. We also specify a decision tree to guide our conclusion on the balance of benefit and harm, and our methodology to combat potential confounding caused by co-interventions. Discussion Pragmatic trials minimise impact on daily practice and maximise clinical relevance of the results, but analysis and interpretation of the results is challenging. We expect that the results of this trial are of importance for all rheumatologists who treat elderly patients with RA. Trial registration ClinicalTrials.gov, NCT02585258 . Registered on 20 October 2015.

This study compares the extent of selection error (non-response and coverage error) evoked by the four major contemporary modes of data collection (face to face, telephone, mail and Web) and three sequential mixed mode designs (telephone, mail and Web with face-to-face follow-up) for the case of the Dutch Crime Victimization Survey. Sociodemographic characteristics and target variables from the survey serve as benchmark variables. A special two-wave experimental design allows studying design differences in selection error on Crime Victimization Survey variables independently from differences in measurement error. Despite large differences in response rates, only small or no differences in selection error between the four single-mode designs are found on both types of variable. We observe cases when the error is enlarged or mitigated in the mixed mode designs despite the fact that the designs yielded large response increases. Our results question the use of response rates to motivate the choice of mode and use of mixed mode surveys.

Understanding mechanisms associated with prolonged cognitive health in combination with exceptional longevity might lead to approaches to enable successful aging. To investigate trajectories of cognitive functioning in centenarians across domains, and to examine the association of these trajectories with factors underlying cognitive reserve, physical health, and postmortem levels of Alzheimer disease (AD)-associated neuropathology. This cohort study used neuropsychological test data and postmortem neuropathological reports from Dutch centenarians who were drawn from the 100-plus Study between January 2013 and April 2019. Eligible participants self-reported being cognitively healthy, which was confirmed by a proxy. Data analysis was performed between June 2019 and June 2020. Age, sex, APOE ε genotype, factors of cognitive reserve, physical health, and AD-associated neuropathology (ie, amyloid-β, neurofibrillary tangles, and neuritic plaques). In annual visits (until death or until participation was no longer possible), centenarians underwent an extensive neuropsychological test battery, from which an mean z score of global cognition, memory, executive functions, verbal fluency, visuospatial functions, and attention/processing speed was calculated. Linear mixed models with a random intercept and time as independent variable were used to investigate cognitive trajectories, adjusted for sex, age, education, and vision and hearing capacities. In a second step, linear mixed models were used to associate cognitive trajectories with factors underlying cognitive reserve, physical health at baseline, and AD-associated neuropathology. Of the 1023 centenarians approached, 340 were included in the study. We analyzed 330 centenarians for whom cognitive tests were available at baseline (239 [72.4%] women; median [interquartile range] age of 100.5 [100.2-101.7] years), with a mean (SD) follow-up duration of 1.6 (0.8) years. We observed no decline across investigated cognitive domains, with the exception of a slight decline in memory function (β, -0.10 SD per year; 95% CI, -0.14 to -0.05 SD; P < .001). Cognitive performance was associated with factors of physical health (eg, higher Barthel index: β, 0.37 SD per year; 95% CI, 0.24-0.49; P < .001) and cognitive reserve (eg, higher education: β, 0.41 SD per year; 95% CI, 0.29-0.53; P < .001), but none of these factors were associated with the rate of decline. Neuropathological reports were available for 44 participants. While centenarian brains revealed varying loads of postmortem neuropathological hallmarks of AD, this was not associated with cognitive performance or rate of decline. While we observed a slight vulnerability for decline in memory function, centenarians maintained high levels of performance in all other investigated cognitive domains for up to 4 years despite the presence of risk factors of cognitive decline. These findings suggest that mechanisms of resilience may underlie the prolongation of cognitive health until exceptional ages.