Browsing by Subject "Validation"

Background

Melioidosis is a neglected tropical disease that is highly prevalent in Southeast Asia. Misdiagnoses are common as the presenting symptoms are similar to other diseases including upper respiratory infections. When not treated with antibiotics, the disease can lead to severe morbidity or death. Current diagnostics in low- and middle-income countries are often not sensitive nor rapid. Point-of-care rapid diagnostic tests (POC-RDTs) are a potential solution. Few studies have compared the accuracy of POC-RDTs and molecular assays against blood culture. The goal of this study was to conduct such comparisons in detecting Burkholderia pseudomallei infections among infection-suspected patients in Kapit, Sarawak, Malaysia.

Methods

We used an informed consent process as approved by two institutional review boards. In this cross-sectional study, we engaged patients meeting a melioidosis-like case definition that included classical symptoms such as prolonged fever with joint pain and/or abscess. We studied the patients routinely collected clinical specimens with a POC-RDT (Active Melioidosis DetectTM) and a molecular assay compared with the B. pseudomallei bacterial culture for isolation of the bacterial organism.

Results

One hundred patients aged 6 months - 79 years from Kapit Hospital were enrolled in the study from June 12, 2018 to January 8, 2019. Of the 100 sera, 97 urine, and 16 bodily fluid samples (total n= 213) tested with the RDT, 23 samples gave positive results (7 sera, 15 urine, and 1 bodily fluids). Compared to the molecular assay, the POC-RDT had a sensitivity of 40% (95% CI, 5%- 85%), specificity of 94% (95% CI, 87% - 98%), and an accuracy of 90% (95 CI, 82% - 95%) for sera; and a sensitivity of 80% (95% CI, 28%- 99%),a specificity of 65% (95% CI, 55% - 75%), and an accuracy of 87% (95 CI, 77% - 94%) for urine; and a sensitivity of 80% (95% CI, 28%- 99%), a specificity of 65% (95% CI, 55% - 75%), and an accuracy of 81% (95 CI, 54% - 96%) for other bodily fluids. Additionally, when compared to the bacterial culture results, the POC-RDT showed a sensitivity of 38% (95% CI, 9%- 76%), specificity of 95% (95% CI, 88% - 99%), and an accuracy of 90% (95 CI, 82% - 95%) for sera; a sensitivity of 88% (95% CI, 47%- 100%), a specificity of 88% (95% CI, 77% - 95%), and an accuracy of 94% (95 CI, 84% - 98%) for urine; and a sensitivity of 25% (95% CI, 1%- 81%), a specificity of 100% (95% CI, 74% - 100%), and an accuracy of 81% (95 CI, 54% - 96%) for other bodily fluids.

Conclusion

While study enrollment will continue, data from the first 100 participants, suggests the POC-RDT had poor sensitivity, good accuracy, and high specificity in detecting B. pseudomallei infection. Thus far, the POC-RDT assay seems to work better on urine specimens. Due to low sensitivity, the study data do not support recommending POC-RDT strips as a single diagnostic method. However, as the POC-RDT had high specificity when the test is positive it seems appropriate for clinicians to assume the patient is infected and to prescribe specific antimicrobial therapy. While more participant data are needed, it seems likely that the POC-RDT could be useful in helping physicians to begin treatment early with the high specificity that the POC-RDT has exhibited. If paired with an RDT with high sensitivity, this POC-RDT would add a great value to infection management.

Background: Acutely painful conditions, responsible for a large proportion of Emergency Department patients around the world, are inadequately assessed and poorly treated. Routine use of scales to quantify pain is recommended to improve analgesic practice. Currently, no such scale has been validated for use in Kiswahili-speaking patients in Tanzania. The objective of this study was to develop and assess a culturally relevant pain intensity scale for use in injury patients at the Kilimanjaro Christian Medical Center in Moshi, Tanzania. Methods: This was a two-part study, with the initial phase using focus groups to develop a pain scale. The second phase used a convenience sample of injury patients to assess the scale for validity and reliability. Analysis of variance, intra-class correlation coefficients, and Bland-Altman Analysis were used to assess validity and reliability. We used focus groups and surveys to develop a pain scale, which was subsequently tested in injury patients. Results: A 100-point numeric pain scale was developed and tested among 98 injury patients. The intra-class correlation coefficient of scores was 0.97 (95% CI 0.96 - 0.98) and Bland-Altman analysis found that 95% of the differences were between -23.5 and +20.7. Conclusions: Our results suggest that a 100-point numeric rating scale is valid and reliable for use Tanzanian injury patients.

There are an unlimited number of ways a person may respond to someone sharing pain-related thoughts or feelings. Understanding what types of responses may result in positive outcomes for individuals with pain is important, yet limited research has been conducted in this area. The purpose of this dissertation was to understand how validation as a response to verbal disclosures about pain influences positive and negative affect, pain intensity, and pain tolerance as compared to other responses. To examine this question, an experimental design with best friend dyads was used. Participants engaged in a pain induction task and were asked to verbally share about their pain, and either their friend or a research assistant delivered validating, neutral, or invalidating responses. Results found that receiving validating was related to greater positive affect and reduced negative affect as compared to receiving in validating responses, and some group differences emerged between participants who received responses from friends as compared to research assistants.

X-ray crystallography 3D atomic models are used in a variety of research areas to understand and manipulate protein structure. Research and application are dependent on the quality of the models. Low-resolution experimental data is a common problem in crystallography which makes solving structures and producing the reliable models that many scientists depend on difficult.

In this work, I develop new, automated tools for validation and correction of low-resolution structures. These tools are gathered under the name CaBLAM, for C-alpha Based Low-resolution Annotation Method. CaBLAM uses a unique, C-alpha-geometry-based parameter space to identify outliers in protein backbone geometry, and to identify secondary structure that may be masked by modeling errors.

CaBLAM was developed in the Python programming language as part of the Phenix crystallography suite and the open CCTBX Project. It makes use of architecture and methods available in the CCTBX toolbox. Quality-filtered databases of high-resolution protein structures, especially the Top8000, were used to construct contours of expected protein behavior for CaBLAM. CaBLAM has also been integrated into the codebase for the Richardson Lab's online MolProbity validation service.

CaBLAM succeeds in providing useful validation feedback for protein structures in the 2.5-4.0A resolution range. This success demonstrates the relative reliability of the C-alpha; trace of a protein in this resolution range. Full mainchain information can be extrapolated from the C-alpha; trace, especially for regular secondary structure elements.

CaBLAM has also informed our approach to validation for low-resolution structures. Moderation of feedback, to reduce validation overload and to focus user attention on modeling errors that are both significant and correctable, is one of our goals. CaBLAM and the related methods that have grown around it demonstrate the progress towards this goal.

Computed Tomography (CT) is a prevalent imaging technique in modern medicine that provides physicians a non-invasive method to evaluate and diagnose various clinical conditions. To aid in diagnosis, it is important to have a high accuracy and reliability in these images. In the first phase of this study, the variability of clinically-relevant imaging biomarkers was analyzed across different scanners and imaging parameters through usage of a customized anthropomorphic chest phantom with several experimental sample inserts. This phantom was scanned across 10 different scanners. Imaging biomarkers were computed for each scan. Intra and inter-scan variability was assessed by computing coefficients of variation and standard deviations of the measurements. It was found that LAA -950 and LAA -856 were the biomarkers with the highest levels of variability, while the majority of other biomarkers had variability less than 10 HU or 10% CV in both inter and intra-scan measurements. No clear trend was found between the variability of the biomarkers and radiation dose (i.e., CTDI).

Traditional assessments of CT technologies are limited in the sense that they work with real patient data and are not efficient. Alternatively, Virtual Imaging Trials (VITs), which use virtual scanners and patients, are more efficient and avoid unnecessary radiation exposure. DukeSim is a CT simulator that has been validated with simple cylindrical phantoms in the past, but not with more clinically-relevant phantoms and conditions. Biomarkers computed from real CT image data were compared to those from simulated CT scans of a computational version of an anthropomorphic chest phantom. Overall, relative percent errors ranged from 0.187% to 18.269%.

Having validated DukeSim in a clinically relevant context, the utility of DukeSim as a VIT tool was shown by investigating the effects of imaging and reconstruction parameters on the clinically relevant biomarkers. It was found that sharper reconstruction kernels and lower tube currents tended to reduce the accuracy of measured biomarkers. These findings will help to spark further studies in virtual imaging, which can help to yield further clinical insights to improve patient health outcomes.

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