253 research outputs found
A review of methods for addressing components of interventions in meta-analysis
Many healthcare interventions are complex, consisting of multiple, possibly interacting, components. Several methodological articles addressing complex interventions in the metaanalytical context have been published. We hereby provide an overview of methods used to evaluate the effects of complex interventions with meta-analytical models. We summarized the methodology, highlighted new developments, and described the benefits, drawbacks, and potential challenges of each identified method. We expect meta-analytical methods focusing on components of several multicomponent interventions to become increasingly popular due to recently developed, easy-to-use, software tools that can be used to conduct the relevant analyses. The different meta-analytical methods are illustrated through two examples comparing psychotherapies for panic disorder. Copyright
Home versus outpatient ultraviolet B phototherapy for mild to severe psoriasis: pragmatic multicentre randomised controlled non-inferiority trial (PLUTO study)
Objective To determine whether ultraviolet B phototherapy at home is equally safe and equally effective as ultraviolet B phototherapy in an outpatient setting for patients with psoriasis
Drug-related readmissions in older hospitalized adults: External validation and updating of OPERAM DRA prediction tool.
BACKGROUND
Drug-related readmissions (DRAs) are defined as rehospitalizations with an adverse drug event as their main or significant contributory cause. DRAs represent a major adverse health burden for older patients. A prediction model which identified older hospitalized patients at high risk of a DRA <1 year was previously developed using the OPERAM trial cohort, a European cluster randomized controlled trial including older hospitalized patients with multimorbidity and polypharmacy. This study has performed external validation and updated the prediction model consequently.
METHODS
The MedBridge trial cohort (a multicenter cluster randomized crossover trial performed in Sweden) was used as a validation cohort. It consisted of 2516 hospitalized patients aged ≥65 years. Model performance was assessed by: (1) discriminative power, assessed by the C-statistic with a 95% confidence interval (CI); (2) calibration, assessed by visual examination of the calibration plot and use of the Hosmer-Lemeshow goodness-of-fit test; and (3) overall accuracy, assessed by the scaled Brier score. Several updating methods were carried out to improve model performance.
RESULTS
In total, 2516 older patients were included in the validation cohort, of whom 582 (23.1%) experienced a DRA <1 year. In the validation cohort, the original model showed a good overall accuracy (scaled Brier score 0.03), but discrimination was moderate (C-statistic 0.62 [95% CI 0.59-0.64]), and calibration showed underestimation of risks. In the final updated model, the predictor "cirrhosis with portal hypertension" was removed and "polypharmacy" was added. This improved the model's discriminative capability to a C-statistic of 0.64 (95% CI 0.59-0.70) and enhanced calibration plots. Overall accuracy remained good.
CONCLUSIONS
The updated OPERAM DRA prediction model may be a useful tool in clinical practice to estimate the risk of DRAs in older hospitalized patients subsequent to discharge. Our efforts lay the groundwork for the future development of models with even better performance
Validation of two age dependent D-dimer cut-off values for exclusion of deep vein thrombosis in suspected elderly patients in primary care: retrospective, cross sectional, diagnostic analysis
Objective To determine whether the use of age adapted D-dimer cut-off values can be translated to primary care patients who are suspected of deep vein thrombosis
Drug-related readmissions in older hospitalized adults: External validation and updating of OPERAM DRA prediction tool
Background: Drug-related readmissions (DRAs) are defined as rehospitalizations with an adverse drug event as their main or significant contributory cause. DRAs represent a major adverse health burden for older patients. A prediction model which identified older hospitalized patients at high risk of a DRA <1 year was previously developed using the OPERAM trial cohort, a European cluster randomized controlled trial including older hospitalized patients with multimorbidity and polypharmacy. This study has performed external validation and updated the prediction model consequently. Methods: The MedBridge trial cohort (a multicenter cluster randomized crossover trial performed in Sweden) was used as a validation cohort. It consisted of 2516 hospitalized patients aged ≥65 years. Model performance was assessed by: (1) discriminative power, assessed by the C-statistic with a 95% confidence interval (CI); (2) calibration, assessed by visual examination of the calibration plot and use of the Hosmer–Lemeshow goodness-of-fit test; and (3) overall accuracy, assessed by the scaled Brier score. Several updating methods were carried out to improve model performance. Results: In total, 2516 older patients were included in the validation cohort, of whom 582 (23.1%) experienced a DRA <1 year. In the validation cohort, the original model showed a good overall accuracy (scaled Brier score 0.03), but discrimination was moderate (C-statistic 0.62 [95% CI 0.59–0.64]), and calibration showed underestimation of risks. In the final updated model, the predictor “cirrhosis with portal hypertension” was removed and “polypharmacy” was added. This improved the model's discriminative capability to a C-statistic of 0.64 (95% CI 0.59–0.70) and enhanced calibration plots. Overall accuracy remained good. Conclusions: The updated OPERAM DRA prediction model may be a useful tool in clinical practice to estimate the risk of DRAs in older hospitalized patients subsequent to discharge. Our efforts lay the groundwork for the future development of models with even better performance
Drug-related readmissions in older hospitalized adults: External validation and updating of OPERAM DRA prediction tool
Background: Drug-related readmissions (DRAs) are defined as rehospitalizations with an adverse drug event as their main or significant contributory cause. DRAs represent a major adverse health burden for older patients. A prediction model which identified older hospitalized patients at high risk of a DRA <1 year was previously developed using the OPERAM trial cohort, a European cluster randomized controlled trial including older hospitalized patients with multimorbidity and polypharmacy. This study has performed external validation and updated the prediction model consequently. Methods: The MedBridge trial cohort (a multicenter cluster randomized crossover trial performed in Sweden) was used as a validation cohort. It consisted of 2516 hospitalized patients aged ≥65 years. Model performance was assessed by: (1) discriminative power, assessed by the C-statistic with a 95% confidence interval (CI); (2) calibration, assessed by visual examination of the calibration plot and use of the Hosmer–Lemeshow goodness-of-fit test; and (3) overall accuracy, assessed by the scaled Brier score. Several updating methods were carried out to improve model performance. Results: In total, 2516 older patients were included in the validation cohort, of whom 582 (23.1%) experienced a DRA <1 year. In the validation cohort, the original model showed a good overall accuracy (scaled Brier score 0.03), but discrimination was moderate (C-statistic 0.62 [95% CI 0.59–0.64]), and calibration showed underestimation of risks. In the final updated model, the predictor “cirrhosis with portal hypertension” was removed and “polypharmacy” was added. This improved the model's discriminative capability to a C-statistic of 0.64 (95% CI 0.59–0.70) and enhanced calibration plots. Overall accuracy remained good. Conclusions: The updated OPERAM DRA prediction model may be a useful tool in clinical practice to estimate the risk of DRAs in older hospitalized patients subsequent to discharge. Our efforts lay the groundwork for the future development of models with even better performance
A simple and rapid method for assessing similarities among directly observable behavioral effects of drugs: PCP-like effects of 2-amino-5-phosphonovalerate in rats
Directly observable behavioral effects of the N-methyl- D -aspartate (NMDA) receptor antagonist 2-amino-5-phosphonovalerate (AP5) (10–1,000 mg/kg IP, 0.18–5.6 μmol/rat ICV) and of phencyclidine (PCP) (3.2–56 mg/kg IP, 0.032–3.2 mg/rat ICV), ketamine (10–100 mg/kg), amphetamine (1–18 mg/kg), apomorphine (0.1–5.6 mg/kg), chlordiazepoxide (1–100 mg/kg), and pentobarbital (3.2–56 mg/kg) were studied in rats. Pharmacologically specific results were obtained rapidly and reliably, using a cumulative dosing procedure. Cluster analysis grouped the drug treatments, on the basis of their similarities in producing different behavioral activities, into three main clusters; characteristically, stimulant drugs (amphetamine, apomorphine) produced sniffing and gnawing; PCP-like drugs (PCP, ketamine) produced locomotion, sniffing, swaying and falling; sedative drugs (pentobarbital, chlordiazepoxide) produced loss of righting. The behavioral effects of ICV administration of AP5 were more similar to the effects of PCP-like drugs than to the effects of either stimulant or sedative drugs, thus supporting the hypothesis that the behavioral effects of PCP-like drugs may result from reduced neurotransmission at excitatory synapses utilizing NMDA preferring receptors. The present procedure is simple, rapid and may provide a useful approach in the classification of behaviorally active drugs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46445/1/213_2004_Article_BF00518181.pd
Discovery of early-stage biomarkers for diabetic kidney disease using ms-based metabolomics (FinnDiane study)
Diabetic kidney disease (DKD) is a devastating complication that affects an estimated third of patients with type 1 diabetes mellitus (DM). There is no cure once the disease is diagnosed, but early treatment at a sub-clinical stage can prevent or at least halt the progression. DKD is clinically diagnosed as abnormally high urinary albumin excretion rate (AER). We hypothesize that subtle changes in the urine metabolome precede the clinically significant rise in AER. To test this, 52 type 1 diabetic patients were recruited by the FinnDiane study that had normal AER (normoalbuminuric). After an average of 5.5 years of follow-up half of the subjects (26) progressed from normal AER to microalbuminuria or DKD (macroalbuminuria), the other half remained normoalbuminuric. The objective of this study is to discover urinary biomarkers that differentiate the progressive form of albuminuria from non-progressive form of albuminuria in humans. Metabolite profiles of baseline 24 h urine samples were obtained by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS) to detect potential early indicators of pathological changes. Multivariate logistic regression modeling of the metabolomics data resulted in a profile of metabolites that separated those patients that progressed from normoalbuminuric AER to microalbuminuric AER from those patients that maintained normoalbuminuric AER with an accuracy of 75% and a precision of 73%. As this data and samples are from an actual patient population and as such, gathered within a less controlled environment it is striking to see that within this profile a number of metabolites (identified as early indicators) have been associated with DKD already in literature, but also that new candidate biomarkers were found. The discriminating metabolites included acyl-carnitines, acyl-glycines and metabolites related to tryptophan metabolism. We found candidate biomarkers that were univariately significant different. This study demonstrates the potential of multivariate data analysis and metabolomics in the field of diabetic complications, and suggests several metabolic pathways relevant for further biological studies
Glutathione S-transferase M1-null genotype as risk factor for SOS in oxaliplatin-treated patients with metastatic colorectal cancer
Background: Oxaliplatin is used as a neo-adjuvant therapy in hepatic colorectal carcinoma metastasis. This treatment has significant side effects, as oxaliplatin is toxic to the sinusoidal endothelial cells and can induce sinusoidal obstruction syndrome (SOS), which is related to decreased overall survival. Glutathione has an important role in the defence system, catalysed by glutathione S-transferase (GST), including two non-enzyme producing polymorphisms (GSTM1-null and GSTT1-null). We hypothesise that patients with a non-enzyme producing polymorphism have a higher risk of developing toxic injury owing to oxaliplatin. Methods: In the nontumour-bearing liver, the presence of SOS was studied histopathologically. The genotype was determined by a semi-nested PCR. Results: Thirty-two of the 55 (58%) patients showed SOS lesions, consisting of 27% mild, 22% moderate and 9% severe lesions. The GSTM1-null genotype was present in 25 of the 55 (46%). Multivariate analysis showed that the GSTM1-null genotype significantly correlated with the presence of (moderate-severe) SOS (P=0.026). Conclusion: The GSTM1-null genotype is an independent risk factor for SOS. This finding allows us, in association with other risk factors, to conceive a potential risk profile predicting whether the patient is at risk of developing SOS, before starting oxaliplatin, and subsequently might result in adjustment of treatment
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