In this study, we assessed to what extent data on the subject of TPPR (transfer, persistence, prevalence, recovery) that are obtained through an older STR typing kit can be used in an activity-level evaluation for a case profiled with a more modern STR kit. Newer kits generally hold more loci and may show higher sensitivity especially when reduced reaction volumes are used, and this could increase the evidential value at the source level. On the other hand, the increased genotyping information may invoke a higher number of contributors in the weight of evidence calculations, which could affect the evidential values as well. An activity scenario well explored in earlier studies [1,2] was redone using volunteers with known DNA profiles. DNA extracts were analyzed with three different approaches, namely using the optimal DNA input for (1) an older and (2) a newer STR typing system, and (3) using a standard, volume-based input combined with replicate PCR analysis with only the newer STR kit. The genotyping results were analyzed for various aspects such as percentage detected alleles and relative peak height contribution for background and the contributors known to be involved in the activity. Next, source-level LRs were calculated and the same trends were observed with regard to inclusionary and exclusionary LRs for persons who had or had not been in direct contact with the sampled areas. We subsequently assessed the impact on the outcome of the activity-level evaluation in an exemplary case by applying the assigned probabilities to a Bayesian network. We infer that data from different STR kits can be combined in the activity-level evaluations.
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BACKGROUND: Conflicting evidence exists on the effectiveness of routinely measured vital signs on the early detection of increased probability of adverse events.PURPOSE: To assess the clinical relevance of routinely measured vital signs in medically and surgically hospitalized patients through a systematic review.DATA SOURCES: MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Cumulative Index to Nursing and Allied Health Literature, and Meta-analysen van diagnostisch onderzoek (in Dutch; MEDION) were searched to January 2013.STUDY SELECTION: Prospective studies evaluating routine vital sign measurements of hospitalized patients, in relation to mortality, septic or circulatory shock, intensive care unit admission, bleeding, reoperation, or infection.DATA EXTRACTION: Two reviewers independently assessed potential bias and extracted data to calculate likelihood ratios (LRs) and predictive values.DATA SYNTHESIS: Fifteen studies were performed in medical (n = 7), surgical (n = 4), or combined patient populations (n = 4; totaling 42,565 participants). Only three studies were relatively free from potential bias. For temperature, the positive LR (LR+) ranged from 0 to 9.88 (median 1.78; n = 9 studies); heart rate 0.82 to 6.79 (median 1.51; n = 5 studies); blood pressure 0.72 to 4.7 (median 2.97; n = 4 studies); oxygen saturation 0.65 to 6.35 (median 1.74; n = 2 studies); and respiratory rate 1.27 to 1.89 (n = 3 studies). Overall, three studies reported area under the Receiver Operator Characteristic (ROC) curve (AUC) data, ranging from 0.59 to 0.76. Two studies reported on combined vital signs, in which one study found an LR+ of 47.0, but in the other the AUC was not influenced.CONCLUSIONS: Some discriminative LR+ were found, suggesting the clinical relevance of routine vital sign measurements. However, the subject is poorly studied, and many studies have methodological flaws. Further rigorous research is needed specifically intended to investigate the clinical relevance of routinely measured vital signs.CLINICAL RELEVANCE: The results of this research are important for clinical nurses to underpin daily routine practices and clinical decision making.
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Background: A pragmatic, stepped wedge trial design can be an appealing design to evaluate complex interventions in real-life settings. However, there are certain pitfalls that need to be considered. This paper reports on the experiences and lessons learned from the conduct of a cluster randomized, stepped wedge trial evaluating the effect of the Hospital Elder Life Program (HELP) in a Dutch hospital setting to prevent older patients from developing delirium. Methods: We evaluated our trial which was conducted in eight departments in two hospitals in hospitalized patients aged 70 years or older who were at risk for delirium by reflecting on the assumptions that we had and on what we intended to accomplish when we started, as compared to what we actually realized in the different phases of our study. Lessons learned on the design, the timeline, the enrollment of eligible patients and the use of routinely collected data are provided accompanied by recommendations to address challenges. Results: The start of the trial was delayed which caused subsequent time schedule problems. The requirement for individual informed consent for a quality improvement project made the inclusion more prone to selection bias. Most units experienced major difficulties in including patients, leading to excluding two of the eight units from participation. This resulted in failing to include a similar number of patients in the control condition versus the intervention condition. Data on outcomes routinely collected in the electronic patient records were not accessible during the study, and appeared to be often missing during analyses. Conclusions: The stepped wedge, cluster randomized trial poses specific risks in the design and execution of research in real-life settings of which researchers should be aware to prevent negative consequences impacting the validity of their results. Valid conclusions on the effectiveness of the HELP in the Dutch hospital setting are hampered by the limited quantity and quality of routine clinical data in our pragmatic trial. Executing a stepped wedge design in a daily practice setting using routinely collected data requires specific attention to ethical review, flexibility, a spacious time schedule, the availability of substantial capacity in the research team and early checks on the data availability and quality.
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