OBJECTIVETo provide considerations for reporting and interpretation that can improve assessment of the credibility of exploratory analyses in aetiologic research.DESIGNMini-review of the literature... Show moreOBJECTIVETo provide considerations for reporting and interpretation that can improve assessment of the credibility of exploratory analyses in aetiologic research.DESIGNMini-review of the literature and account of exploratory research principles.SETTINGThis study focuses on a particular type of causal research, namely aetiologic studies, which investigate the causal effect of one or multiple risk factors on a particular health outcome or disease. The mini review included aetiologic research articles published in four epidemiology journals in the first issue of 2021: American Journal of Epidemiology, Epidemiology, European Journal of Epidemiology, and International Journal of Epidemiology, specifically focusing on observational studies of causal risk factors of diseases.MAIN OUTCOME MEASURESNumber of exposure-outcome associations reported, grouped by type of analysis (main, sensitivity, and additional).RESULTSThe journal articles reported many exposure-outcome associations: a mean number of 33 (range 1-120) exposure-outcome associations for the primary analysis, 30 (0-336) for sensitivity analyses, and 163 (0-1467) for additional analyses. Six considerations were discussed that are important in assessing the credibility of exploratory analyses: research problem, protocol, statistical criteria, interpretation of findings, completeness of reporting, and effect of exploratory findings on future causal research.CONCLUSIONSBased on this mini-review, exploratory analyses in aetiologic research were not always reported properly. Six considerations for reporting of exploratory analyses in aetiologic research were provided to stimulate a discussion about their preferred handling and reporting. Researchers should take responsibility for the results of exploratory analyses by clearly reporting their exploratory nature and specifying which findings should be investigated in future research and how. Show less
OBjECTIVE To assess whether percutaneous transforaminal endoscopic discectomy (PTED) is non-inferior to conventional open microdiscectomy in reduction of leg pain caused by lumbar disc herniation.... Show moreOBjECTIVE To assess whether percutaneous transforaminal endoscopic discectomy (PTED) is non-inferior to conventional open microdiscectomy in reduction of leg pain caused by lumbar disc herniation. DESIGN Multicentre randomised controlled trial with non-inferiority design. SETTING Four hospitals in the Netherlands. PARTICIPANTS 613 patients aged 18-70 years with at least six weeks of radiating leg pain caused by lumbar disc herniation. The trial included a predetermined set of 125 patients receiving PTED who were the learning curve cases performed by surgeons who did not do PTED before the trial. INTERVENTIONS PTED (n=179) compared with open microdiscectomy (n=309). MAIN OUTCOME MEASURES The primary outcome was self-reported leg pain measured by a 0-100 visual analogue scale at 12 months, assuming a non-inferiority margin of 5.0. Secondary outcomes included complications, reoperations, self-reported functional status as measured with the Oswestry Disability Index, visual analogue scale for back pain, health related quality of life, and self-perceived recovery. Outcomes were measured until one year after surgery and were longitudinally analysed according to the intention-to - treat principle. Patients belonging to the PTED learning curve were omitted from the primary analyses. RESULTS At 12 months, patients who were randomised to PTED had a statistically significantly lower visual analogue scale score for leg pain (median 7.0, interquartile range 1.0-30.0) compared with patients randomised to open microdiscectomy (16.0, 2.0-53.5) (between group difference of 7.1, 95% confidence interval 2.8 to 11.3). Blood loss was less, length of hospital admission was shorter, and timing of postoperative mobilisation was earlier in the PTED group than in the open microdiscectomy group. Secondary patient reported outcomes such as the Oswestry Disability Index, visual analogue scale for back pain, health related quality of life, and self-perceived recovery, were similarly in favour of PTED. Within one year, nine (5%) in the PTED group compared with 14 (6%) in the open microdiscectomy group had repeated surgery. Per protocol analysis and sensitivity analyses including the patients of the learning curve resulted in similar outcomes to the primary analysis. CONCLUSIONS PTED was non-inferior to open microdiscectomy in reduction of leg pain. PTED resulted in more favourable results for self-reported leg pain, back pain, functional status, quality of life, and recovery. These differences, however, were small and may not reach clinical relevance. PTED can be considered as an effective alternative to open microdiscectomy in treating sciatica. TRIAL REGISTRATION NCT02602093ClinicalTrials.gov NCT02602093. Show less
Geloven, N. van; Giardiello, D.; Bonneville, E.F.; Teece, L.; Ramspek, C.L.; Smeden, M. van; ... ; Steyerberg, E. 2022
Thorough validation is pivotal for any prediction model before it can be advocated for use in medical practice. For time-to-event outcomes such as breast cancer recurrence, death from other causes... Show moreThorough validation is pivotal for any prediction model before it can be advocated for use in medical practice. For time-to-event outcomes such as breast cancer recurrence, death from other causes is a competing risk. Model performance measures must account for such competing events. In this article, we present a comprehensive yet accessible overview of performance measures for this competing eventsetting, including the calculation and interpretation of statistical measures for calibration, discrimination, overall prediction error, and clinical usefulness by decision curve analysis. All methods are illustrated for patients with breast cancer, with publicly available data and R code. Show less
Fu, E.L.; Evans, M.; Carrero, J.J.; Putter, H.; Clase, C.M.; Caskey, F.J.; ... ; Diepen, M. van 2021
OBJECTIVE To identify the optimal estimated glomerular filtration rate (eGFR) at which to initiate dialysis in people with advanced chronic kidney disease.DESIGNNationwide observational cohort... Show moreOBJECTIVE To identify the optimal estimated glomerular filtration rate (eGFR) at which to initiate dialysis in people with advanced chronic kidney disease.DESIGNNationwide observational cohort study.SETTINGNational Swedish Renal Registry of patients referred to nephrologists.PARTICIPANTSPatients had a baseline eGFR between 10 and 20 mL/min/1.73 m(2) and were included between 1 January 2007 and 31 December 2016, with follow-up until 1 June 2017.MAIN OUTCOME MEASURESThe strict design criteria of a clinical trial were mimicked by using the cloning, censoring, and weighting method to eliminate immortal time bias, lead time bias, and survivor bias. A dynamic marginal structural model was used to estimate adjusted hazard ratios and absolute risks for five year all cause mortality and major adverse cardiovascular events (composite of cardiovascular death, non-fatal myocardial infarction, or non-fatal stroke) for 15 dialysis initiation strategies with eGFR values between 4 and 19 mL/min/1.73 m(2) in increments of 1 mL/min/1.73 m(2). An eGFR between 6 and 7 mL/min/1.73 m(2) (eGFR(6-7)) was taken as the reference.RESULTSAmong 10 290 incident patients with advanced chronic kidney disease (median age 73 years; 3739 (36%) women; median eGFR 16.8 mL/min/1.73 m(2)), 3822 started dialysis, 4160 died, and 2446 had a major adverse cardiovascular event. A parabolic relation was observed for mortality, with the lowest risk for eGFR(15-16). Compared with dialysis initiation at eGFR(6-7), initiation at eGFR(15-16) was associated with a 5.1% (95% confidence interval 2.5% to 6.9%) lower absolute five year mortality risk and 2.9% (0.2% to 5.5%) lower risk of a major adverse cardiovascular event, corresponding to hazard ratios of 0.89 (95% confidence interval 0.87 to 0.92) and 0.94 (0.91 to 0.98), respectively. This 5.1% absolute risk difference corresponded to a mean postponement of death of 1.6 months over five years of follow-up. However, dialysis would need to be started four years earlier. When emulating the intended strategies of the Initiating Dialysis Early and Late (IDEAL) trial (eGFR(10-14)v eGFR(5-7)) and the achieved eGFRs in IDEAL (eGFR(7-10)v eGFR(5-7)), hazard ratios for all cause mortality were 0.96 (0.94 to 0.99) and 0.97 (0.94 to 1.00), respectively, which are congruent with the findings of the randomised IDEAL trial.CONCLUSIONSVery early initiation of dialysis was associated with a modest reduction in mortality and cardiovascular events. For most patients, such a reduction may not outweigh the burden of a substantially longer period spent on dialysis. Show less
Slieker, R.C.; Heijden, A.A.W.A. van der; Siddiqui, M.K.; Langendoen-Gort, M.; Nijpels, G.; Herings, R.; ... ; Beulens, J.W.J. 2021
OBJECTIVES To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. DESIGN Systematic... Show moreOBJECTIVES To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. DESIGN Systematic review and external validation. DATA SOURCES PubMed and Embase. ELIGIBILITY CRITERIA Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. METHODS Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell's C statistic). RESULTS 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. CONCLUSIONS This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42020192831. Show less
Blum, M.R.; Sallevelt, B.T.G.M.; Spinewine, A.; O'Mahony, D.; Moutzouri, E.; Feller, M.; ... ; Rodondi, N. 2021
OBJECTIVETo examine the effect of optimising drug treatment on drug related hospital admissions in older adults with multimorbidity and polypharmacy admitted to hospital.DESIGNCluster randomised... Show moreOBJECTIVETo examine the effect of optimising drug treatment on drug related hospital admissions in older adults with multimorbidity and polypharmacy admitted to hospital.DESIGNCluster randomised controlled trial.SETTING110 clusters of inpatient wards within university based hospitals in four European countries (Switzerland, Netherlands, Belgium, and Republic of Ireland) defined by attending hospital doctors.PARTICIPANTS2008 older adults (>= 70 years) with multimorbidity (>= 3chronic conditions) and polypharmacy (>= 5 drugs used long term).INTERVENTIONClinical staff clusters were randomised to usual care or a structured pharmacotherapy optimisation intervention performed at the individual level jointly by a doctor and a pharmacist, with the support of a clinical decision software system deploying the screening tool of older person's prescriptions and screening tool to alert to the right treatment (STOPP/START) criteria to identify potentially inappropriate prescribing.MAIN OUTCOME MEASUREPrimary outcome was first drug related hospital admission within 12 months.RESULTS2008 older adults (median nine drugs) were randomised and enrolled in 54 intervention clusters (963 participants) and 56 control clusters (1045 participants) receiving usual care. In the intervention arm, 86.1% of participants (n=789) had inappropriate prescribing, with a mean of 2.75 (SD 2.24) STOPP/START recommendations for each participant. 62.2% (n=491) had >= 1 recommendation successfully implemented at two months, predominantly discontinuation of potentially inappropriate drugs. In the intervention group, 211 participants (21.9%) experienced a first drug related hospital admission compared with 234 (22.4%) in the control group. In the intention-to-treat analysis censored for death as competing event (n=375, 18.7%), the hazard ratio for first drug related hospital admission was 0.95 (95% confidence interval 0.77 to 1.17). In the per protocol analysis, the hazard ratio for a drug related hospital admission was 0.91 (0.69 to 1.19). The hazard ratio for first fall was 0.96 (0.79 to 1.15; 237 v263 first falls) and for death was 0.90 (0.71 to 1.13; 172 v 203 deaths).CONCLUSIONSInappropriate prescribing was common in older adults with multimorbidity and polypharmacy admitted to hospital and was reduced through an intervention to optimise pharmacotherapy, but without effect on drug related hospital admissions. Additional efforts are needed to identify pharmacotherapy optimisation interventions that reduce inappropriate prescribing and improve patient outcomes. Show less
Wynants, L.; Calster, B. van; Bonten, M.M.J.; Collins, G.S.; Debray, T.P.A.; Vos, M. de; ... ; Smeden, M. van 2020
OBJECTIVETo review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis... Show moreOBJECTIVETo review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia.DESIGNRapid systematic review and critical appraisal.DATA SOURCESPubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020.STUDY SELECTIONStudies that developed or validated a multivariable covid-19 related prediction model.DATA EXTRACTIONAt least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).RESULTS2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed.CONCLUSIONPrediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Show less
Engelaer, F.M.; Bodegom, D. van; Westendorp, R.G.J. 2012
