Acetaminophen for self-reported sleep problems in an elderly population (ASLEEP): Study protocol of a randomized placebo-controlled double-blind trial

Background: The prevalence of sleep disorders increases with age. Sleep disorders may have serious health implications and may be related to serious underlying diseases. Many older people use hypnotics, like benzodiazepines, although these medications have serious side effects and often lead to habituation. Acetaminophen is one of the most frequently used off-label drugs for sleep disorders, although little is known about its effects. Our objective is to investigate whether acetaminophen is effective in treating self-reported sleep disorders in older people. Methods/Design: Participants, aged 65 years or older (n = 150), who have sleep disorders will be randomized for treatment with either acetaminophen 1000 mg or placebo, once daily at bedtime in a double-blind design. Eligible patients should be able to give informed consent, should not be cognitively impaired (Minimal Mental State Examination (MMSE) score ≥ 20), should not have pain, and should not use acetaminophen on a regular basis because of pain complaints. The study will take three weeks to complete. During these three weeks, the participants register their sleep behavior in a sleep diary. The participants will use the study medication during the second and third week. The primary endpoint will be the self-reported sleep disorders at the end of week three, as measured by means of the Insomnia Severity Index (ISI). To validate these subjective sleep parameters against objectively measured indices of the sleep-wake pattern, we will measure the periods of wakefulness and sleep in a subgroup of participants, using an actigraph worn on the wrist during the entire study period. Discussion: The proposed study will contribute to our knowledge about the treatment of sleep disorders in an older population. There is a need for treatments for sleep disorders without serious adverse effects. Acetaminophen might be a simple and inexpensive alternative for the regimes that are currently used with older people. Trial registration: The Netherlands National Trial Register NTR2747

Prevention of postoperative delirium in elderly patients planned for elective surgery: systematic review and meta-analysis

Introduction: Vulnerable or “frail” patients are susceptible to the development of delirium when exposed to triggers such as surgical procedures. Once delirium occurs, interventions have little effect on severity or duration, emphasizing the importance of primary prevention. This review provides an overview of interventions to prevent postoperative delirium in elderly patients undergoing elective surgery. Methods: A literature search was conducted in March 2018. Randomized controlled trials (RCTs) and before-and-after studies on interventions with potential effects on postoperative delirium in elderly surgical patients were included. Acute admission, planned ICU admission, and cardiac patients were excluded. Full texts were reviewed, and quality was assessed by two independent reviewers. Primary outcome was the incidence of delirium. Secondary outcomes were severity and duration of delirium. Pooled risk ratios (RRs) were calculated for incidences of delirium where similar intervention techniques were used. Results: Thirty-one RCTs and four before-and-after studies were included for analysis. In 19 studies, intervention decreased the incidences of postoperative delirium. Severity was reduced in three out of nine studies which reported severity of delirium. Duration was reduced in three out of six studies. Pooled analysis showed a significant reduction in delirium incidence for dexmedetomidine treatment, and bispectral index (BIS)-guided anaesthesia. Based on sensitivity analyses, by leaving out studies with a high risk of bias, multicomponent interventions and antipsychotics can also significantly reduce the incidence of delirium. Conclusion: Multicomponent interventions, the use of antipsychotics, BIS-guidance, and dexmedetomidine treatment can successfully reduce the incidence of postoperative delirium in elderly patients undergoing elective, non-cardiac surgery. However, present studies are heterogeneous, and high-quality studies are scarce. Future studies should add these preventive methods to already existing multimodal and multidisciplinary interventions to tackle as many precipitating factors as possible, starting in the pre-admission period

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Background and ObjectivesWe sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques.MethodsWe search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes.ResultsWe included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (n = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (n = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (n = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (n = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (n = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (n = 494/522, 94.6% [95% CI 92.4–96.3]).ConclusionOur review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models

From registration to publication: A study on Dutch academic randomized controlled trials

Introduction: Registration of clinical trials has been initiated in order to assess adherence of the reported results to the original trial protocol. This study aimed to investigate the publication rates, timely dissemination of results, and the prevalence of consistency in hypothesis, sample size, and primary endpoint of Dutch investigator-initiated randomized controlled clinical trials (RCTs). Methods: All Dutch investigator-initiated RCTs with a completion date between December 31, 2010, and January 1, 2012, and registered in the Trial Register of The Netherlands database were included. PubMed was searched for the publication of these RCT results until September 2016, and the time to the publication date was calculated. Consistency in hypothesis, sample size, and primary endpoint compared with the registry data were assessed. Results: The search resulted in a total of 168 Dutch investigator-initiated RCTs. In September 2016, the results of 129 (77%) trials had been published, of which 50 (39%) within 2 years after completion of accrual. Consistency in hypothesis with the original protocol was observed in 108 (84%) RCTs; in 71 trials (55%), the planned sample size was reached; and 103 trials (80%) presented the original primary endpoint. Consistency in all three parameters was observe

IMproving PArticipation of patients in Clinical Trials - rationale and design of IMPACT

BACKGROUND: One of the most commonly reported problems of randomised trials is that recruitment is usually slower than expected. Trials will cost more and take longer, thus delaying the use of the results in clinical practice, and incomplete samples imply decreased statistical power and usefulness of its results. We aim to identify barriers and facilitators for successful patient recruitment at the level of the patient, the doctor and the hospital organization as well as the organization and design of trials over a broad range of studies. METHODS/DESIGN: We will perform two cohort studies and a case-control study in the Netherlands. The first cohort study will report on a series of multicenter trials performed in a nationwide network of clinical trials in obstetrics and gynaecology. A questionnaire will be sent to all clinicians recruiting for these trials to identify determinants - aggregated at centre level - for the recruitment rate. In a case control-study nested in this cohort we will interview patients who refused or consented participation to identify factors associated with patients' consent or refusal. In a second cohort study, we will study trials that were prospectively registered in the Netherlands Trial Register. Using a questionnaire survey we will assess whether issues on hospital organization, trial organization, planning and trial design were associated with successful recruitment, i.e. 80% of the predefined number of patients recruited within the planned time. DISCUSSION: This study will provide insight in barriers and facilitators for successful patient recruitment in trials. The results will be used to provide recommendations and a checklist for individual trialists to identify potential pitfalls for recruitment and judge the feasibility prior to the start of the study. Identified barriers and motivators coupled to evidence-based interventions can improve recruitment of patients in clinical trials

Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review

ABSTRACTObjectiveWhile many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.Study design and settingWe included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields (PROSPERO, CRD42019161764). We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies (www.TRIPOD-statement.org). We measured the overall adherence per article and per TRIPOD item.ResultsOur search identified 24 814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0-46.4) of TRIPOD items. No articles fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model’s predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3).ConclusionSimilar to prediction model studies developed using conventional regression-based techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste.What is new?Key findings: Similar to prediction model studies developed using regression techniques, machine learning (ML)-based prediction model studies adhered poorly to the TRIPOD statement, the current standard reporting guideline.What this adds to what is known? In addition to efforts to improve the completeness of reporting in ML-based prediction model studies, an extension of TRIPOD for these type of studies is needed.What is the implication, what should change now? While TRIPOD-AI is under development, we urge authors to follow the recommendations of the TRIPOD statement to improve the completeness of reporting and reduce potential research waste of ML-based prediction model studies.</jats:sec

Quality of Reporting and Study Design of CKD Cohort Studies Assessing Mortality in the Elderly Before and After STROBE:A Systematic Review

BACKGROUND:The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement was published in October 2007 to improve quality of reporting of observational studies. The aim of this review was to assess the impact of the STROBE statement on observational study reporting and study design quality in the nephrology literature. STUDY DESIGN:Systematic literature review. SETTING & POPULATION:European and North American, Pre-dialysis Chronic Kidney Disease (CKD) cohort studies. SELECTION CRITERIA FOR STUDIES:Studies assessing the association between CKD and mortality in the elderly (>65 years) published from 1st January 2002 to 31st December 2013 were included, following systematic searching of MEDLINE & EMBASE. PREDICTOR:Time period before and after the publication of the STROBE statement. OUTCOME:Quality of study reporting using the STROBE statement and quality of study design using the Newcastle Ottawa Scale (NOS), Scottish Intercollegiate Guidelines Network (SIGN) and Critical Appraisal Skills Programme (CASP) tools. RESULTS:37 papers (11 Pre & 26 Post STROBE) were identified from 3621 potential articles. Only four of the 22 STROBE items and their sub-criteria (objectives reporting, choice of quantitative groups and description of and carrying out sensitivity analysis) showed improvements, with the majority of items showing little change between the period before and after publication of the STROBE statement. Pre- and post-period analysis revealed a Manuscript STROBE score increase (median score 77.8% (Inter-quartile range [IQR], 64.7-82.0) vs 83% (IQR, 78.4-84.9, p = 0.05). There was no change in quality of study design with identical median scores in the two periods for NOS (Manuscript NOS score 88.9), SIGN (Manuscript SIGN score 83.3) and CASP (Manuscript CASP score 91.7) tools. LIMITATIONS:Only 37 Studies from Europe and North America were included from one medical specialty. Assessment of study design largely reliant on good reporting. CONCLUSIONS:This study highlights continuing deficiencies in the reporting of STROBE items and their sub-criteria in cohort studies in nephrology. There was weak evidence of improvement in the overall reporting quality, with no improvement in methodological quality of CKD cohort studies between the period before and after publication of the STROBE statement

Prediction models for diagnosis and prognosis of covid-19: : systematic review and critical appraisal

Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity. Funding: LW, BVC, LH, and MDV acknowledge specific funding for this work from Internal Funds KU Leuven, KOOR, and the COVID-19 Fund. LW is a postdoctoral fellow of Research Foundation-Flanders (FWO) and receives support from ZonMw (grant 10430012010001). BVC received support from FWO (grant G0B4716N) and Internal Funds KU Leuven (grant C24/15/037). TPAD acknowledges financial support from the Netherlands Organisation for Health Research and Development (grant 91617050). VMTdJ was supported by the European Union Horizon 2020 Research and Innovation Programme under ReCoDID grant agreement 825746. KGMM and JAAD acknowledge financial support from Cochrane Collaboration (SMF 2018). KIES is funded by the National Institute for Health Research (NIHR) School for Primary Care Research. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. GSC was supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (programme grant C49297/A27294). JM was supported by the Cancer Research UK (programme grant C49297/A27294). PD was supported by the NIHR Biomedical Research Centre, Oxford. MOH is supported by the National Heart, Lung, and Blood Institute of the United States National Institutes of Health (grant R00 HL141678). ICCvDH and BCTvB received funding from Euregio Meuse-Rhine (grant Covid Data Platform (coDaP) interref EMR187). The funders played no role in study design, data collection, data analysis, data interpretation, or reporting.Peer reviewedPublisher PD

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.]