Self-assessment of clinical nurse mentors as dimensions of professional development and the capability of developing ethical values among nursing students : A correlational research study

Date of Acceptance: 08/04/2015 The paper presents, in part, the results of a broader non-profit development project entitled “Advance level of knowledge for quality in clinical mentorship — professional ethics and continuously professional development”. The project was financed by the Ministry of Higher Education, Science and Sport of the Republic of Slovenia (contract no. 3211-11-000263, the number of project OP RCV_VS-11-14). The members of the development group of the project were: Brigita Skela-Savič (leader), Karmen Romih, Sanela Pivač, Katja Skinder Savić and Andreja Prebil. The research report for the entire project is available on the online bibliographic database COBIB.si, at the Faculty of Health Care Jesenice and at the Ministry of Higher Education, Science and Sport of the Republic of Slovenia.

Self-assessment of clinical nurse mentors as dimensions of professional development and the capability of developing ethical values among nursing students : A correlational research study

Date of Acceptance: 08/04/2015 The paper presents, in part, the results of a broader non-profit development project entitled “Advance level of knowledge for quality in clinical mentorship — professional ethics and continuously professional development”. The project was financed by the Ministry of Higher Education, Science and Sport of the Republic of Slovenia (contract no. 3211-11-000263, the number of project OP RCV_VS-11-14). The members of the development group of the project were: Brigita Skela-Savič (leader), Karmen Romih, Sanela Pivač, Katja Skinder Savić and Andreja Prebil. The research report for the entire project is available on the online bibliographic database COBIB.si, at the Faculty of Health Care Jesenice and at the Ministry of Higher Education, Science and Sport of the Republic of Slovenia.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Automated Assessment of Image Quality and Dose Attributes in Clinical CT Images

Computed tomography (CT) is a valuable technology to the healthcare enterprise as evidenced by the more than 70 million CT exams performed every year. As a result, CT has become the largest contributor to population doses amongst all medical imaging modalities that utilize man-made ionizing radiation. Acknowledging the fact that ionizing radiation poses a health risk, there exists the need to strike a balance between diagnostic benefit and radiation dose. Thus, to ensure that CT scanners are optimally used in the clinic, an understanding and characterization of image quality and radiation dose are essential.

The state-of-the-art in both image quality characterization and radiation dose estimation in CT are dependent on phantom based measurements reflective of systems and protocols. For image quality characterization, measurements are performed on inserts imbedded in static phantoms and the results are ascribed to clinical CT images. However, the key objective for image quality assessment should be its quantification in clinical images; that is the only characterization of image quality that clinically matters as it is most directly related to the actual quality of clinical images. Moreover, for dose estimation, phantom based dose metrics, such as CT dose index (CTDI) and size specific dose estimates (SSDE), are measured by the scanner and referenced as an indicator for radiation exposure. However, CTDI and SSDE are surrogates for dose, rather than dose per-se.

Currently there are several software packages that track the CTDI and SSDE associated with individual CT examinations. This is primarily the result of two causes. The first is due to bureaucracies and governments pressuring clinics and hospitals to monitor the radiation exposure to individuals in our society. The second is due to the personal concerns of patients who are curious about the health risks associated with the ionizing radiation exposure they receive as a result of their diagnostic procedures.

An idea that resonates with clinical imaging physicists is that patients come to the clinic to acquire quality images so they can receive a proper diagnosis, not to be exposed to ionizing radiation. Thus, while it is important to monitor the dose to patients undergoing CT examinations, it is equally, if not more important to monitor the image quality of the clinical images generated by the CT scanners throughout the hospital.

The purposes of the work presented in this thesis are threefold: (1) to develop and validate a fully automated technique to measure spatial resolution in clinical CT images, (2) to develop and validate a fully automated technique to measure image contrast in clinical CT images, and (3) to develop a fully automated technique to estimate radiation dose (not surrogates for dose) from a variety of clinical CT protocols.

Situation Background Assessment Recommendation (SBAR): Undergraduate Perspectives

Situation Background Assessment and Recommendation (SBAR): Undergraduate Perspectives C Morgan, L Adams, J Murray, R Dunlop, IK Walsh. Ian K Walsh, Centre for Medical Education, Queen’s University Belfast, Mulhouse Building, Royal Victoria Hospital, Grosvenor Road, Belfast BT12 6DP Background and Purpose: Structured communication tools are used to improve team communication quality.1,2 The Situation Background Assessment and Recommendation (SBAR) tool is widely adopted within patient safety.3 SBAR effectiveness is reportedly equivocal, suggesting use is not sustained beyond initial training.4-6 Understanding perspectives of those using SBAR may further improve clinical communication. We investigated senior medical undergraduate perspectives on SBAR, particularly when communicating with senior colleagues. Methodology: Mixed methods data collection was used. A previously piloted questionnaire with 12 five point Lickert scale questions and 3 open questions was given to all final year medical students. A subgroup also participated in 10 focus groups, deploying strictly structured audio-recorded questions. Selection was by convenience sampling, data gathered by open text questions and comments transcribed verbatim. In-vivo coding (iterative, towards data saturation) preceded thematic analysis. Results: 233 of 255 students (91%) completed the survey. 1. There were clearly contradictory viewpoints on SBAR usage. A recurrent theme was a desire for formal feedback and a relative lack of practice/experience with SBAR. 2. Students reported SBAR as having variable interpretation between individuals; limiting use as a shared mental model. 3. Brief training sessions are insufficient to embed the tool. 4. Most students reported SBAR helping effective communication, especially by providing structure in stressful situations. 5. Only 18.5% of students felt an alternative resource might be needed. Sub analysis of the themes highlighted: A. Lack of clarity regarding what information to include and information placement within the acronym, B. Senior colleague negative response to SBAR C. Lack of conciseness with the tool. Discussion and Conclusions: Despite a wide range of contradictory interpretation of SBAR utility, most students wish to retain the resource. More practice opportunities/feedback may enhance user confidence and understanding. References: (1) Leonard M, Graham S, Bonacum D. The human factor: the critical importance of effective teamwork and communication in providing safe care. Quality & Safety in Health Care 2004 Oct;13(Suppl 1):85-90. (2) d'Agincourt-Canning LG, Kissoon N, Singal M, Pitfield AF. Culture, communication and safety: lessons from the airline industry. Indian J Pediatr 2011 Jun;78(6):703-708. (3) Dunsford J. Structured communication: improving patient safety with SBAR. Nurs Womens Health 2009 Oct;13(5):384-390. (4) Compton J, Copeland K, Flanders S, Cassity C, Spetman M, Xiao Y, et al. Implementing SBAR across a large multihospital health system. Jt Comm J Qual Patient Saf 2012 Jun;38(6):261-268. (5) Ludikhuize J, de Jonge E, Goossens A. Measuring adherence among nurses one year after training in applying the Modified Early Warning Score and Situation-Background-Assessment-Recommendation instruments. Resuscitation 2011 Nov;82(11):1428-1433. (6) Cunningham NJ, Weiland TJ, van Dijk J, Paddle P, Shilkofski N, Cunningham NY. Telephone referrals by junior doctors: a randomised controlled trial assessing the impact of SBAR in a simulated setting. Postgrad Med J 2012 Nov;88(1045):619-626.

Colorimetric Assessment of BCR-ABL1 Transcripts in Clinical Samples Via Gold Nanoprobes

Gold nanoparticles functionalized with thiolated oligonucleotides (Au-nanoprobes) have been used in a range of applications for the detection of bioanalytes of interest, from ions to proteins and DNA targets. These detection strategies are based on the unique optical properties of gold nanoparticles, in particular, the intense color that is subject to modulation by modification of the medium dieletric. Au-nanoprobes have been applied for the detection and characterization of specific DNA sequences of interest, namely pathogens and disease biomarkers. Nevertheless, despite its relevance, only a few reports exist on the detection of RNA targets. Among these strategies, the colorimetric detection of DNA has been proven to work for several different targets in controlled samples but demonstration in real clinical bioanalysis has been elusive. Here, we used a colorimetric method based on Au-nanoprobes for the direct detection of the e14a2 BCR-ABL fusion transcript in myeloid leukemia patient samples without the need for retro-transcription. Au-nanoprobes directly assessed total RNA from 38 clinical samples, and results were validated against reverse transcription-nested polymerase chain reaction (RT-nested PCR) and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The colorimetric Au-nanoprobe assay is a simple yet reliable strategy to scrutinize myeloid leukemia patients at diagnosis and evaluate progression, with obvious advantages in terms of time and cost, particularly in low- to medium-income countries where molecular screening is not routinely feasible. Graphical abstract Gold nanoprobe for colorimetric detection of BCR-ABL1 fusion transcripts originating from the Philadelphia chromosome.

Virtual Clinical Trials in PET Imaging for Improved Diagnosis of and Evaluation of Therapies for Cancer

Thesis (Ph.D.)--University of Washington, 2016-08

New tools for cognitive and motor rehabilitation: development and clinical validation

Nervous system disorders are associated with cognitive and motor deficits, and are responsible for the highest disability rates and global burden of disease. Their recovery paths are vulnerable and dependent on the effective combination of plastic brain tissue properties, with complex, lengthy and expensive neurorehabilitation programs. This work explores two lines of research, envisioning sustainable solutions to improve treatment of cognitive and motor deficits. Both projects were developed in parallel and shared a new sensible approach, where low-cost technologies were integrated with common clinical operative procedures. The aim was to achieve more intensive treatments under specialized monitoring, improve clinical decision-making and increase access to healthcare. The first project (articles I – III) concerned the development and evaluation of a web-based cognitive training platform (COGWEB), suitable for intensive use, either at home or at institutions, and across a wide spectrum of ages and diseases that impair cognitive functioning. It was tested for usability in a memory clinic setting and implemented in a collaborative network, comprising 41 centers and 60 professionals. An adherence and intensity study revealed a compliance of 82.8% at six months and an average of six hours/week of continued online cognitive training activities. The second project (articles IV – VI) was designed to create and validate an intelligent rehabilitation device to administer proprioceptive stimuli on the hemiparetic side of stroke patients while performing ambulatory movement characterization (SWORD). Targeted vibratory stimulation was found to be well tolerated and an automatic motor characterization system retrieved results comparable to the first items of the Wolf Motor Function Test. The global system was tested in a randomized placebo controlled trial to assess its impact on a common motor rehabilitation task in a relevant clinical environment (early post-stroke). The number of correct movements on a hand-to-mouth task was increased by an average of 7.2/minute while the probability to perform an error decreased from 1:3 to 1:9. Neurorehabilitation and neuroplasticity are shifting to more neuroscience driven approaches. Simultaneously, their final utility for patients and society is largely dependent on the development of more effective technologies that facilitate the dissemination of knowledge produced during the process. The results attained through this work represent a step forward in that direction. Their impact on the quality of rehabilitation services and public health is discussed according to clinical, technological and organizational perspectives. Such a process of thinking and oriented speculation has led to the debate of subsequent hypotheses, already being explored in novel research paths.

Performance of protective factors assessment in risk prediction for adults: systematic review and meta-analysis

Protective factors are neglected in risk assessment in adult psychiatric and criminal justice populations. This review investigated the predictive efficacy of selected tools that assess protective factors. Five databases were searched using comprehensive terms for records up to June 2014, resulting in 17 studies (n = 2,198). Results were combined in a multilevel meta-analysis using the R (R Core Team, R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing, 2015) metafor package (Viechtbauer, Journal of Statistical Software, 2010, 36, 1). Prediction of outcomes was poor relative to a reference category of violent offending, with the exception of prediction of discharge from secure units. There were no significant differences between the predictive efficacy of risk scales, protective scales, and summary judgments. Protective factor assessment may be clinically useful, but more development is required. Claims that use of these tools is therapeutically beneficial require testing.

Assessment of the quality of sample labelling for clinical research

Objective: To assess the quality of the labels for clinical trial samples through current regulations, and to analyze its potential correlation with the specific characteristics of each sample. Method: A transversal multicenter study where the clinical trial samples from two third level hospitals were analyzed. The eleven items from Directive 2003/94/EC, as well as the name of the clinical trial and the dose on the label cover, were considered variables for labelling quality. The influence of the characteristics of each sample on labelling quality was also analyzed. Outcome: The study included 503 samples from 220 clinical trials. The mean quality of labelling, understood as the proportion of items from Appendix 13, was of 91.9%. Out of these, 6.6% did not include the name of the sample in the outer face of the label, while in 9.7% the dose was missing. The samples with clinical trial-type samples presented a higher quality (p < 0.049), blinding reduced their quality (p = 0.017), and identification by kit number or by patient increased it (p < 0.01). The promoter was the variable which introduced the highest variability into the analysis. Conclusions: The mean quality of labelling is adequate in the majority of clinical trial samples. The lack of essential information in some samples, such as the clinical trial code and the period of validity, is alarming and might be the potential source for dispensing or administration errors.

Exposure and risk assessment in occupational exposure to fungi: aspects to consider in highly contaminated settings

Although a clear correlation between levels of fungi in the air and health impacts has not been shown in epidemiological studies, fungi must be regarded as potential occupational health hazards. Fungi can have an impact on human health in four different ways: (1) they can infect humans, (2) they may act as allergens, (3) they can be toxigenic, or (4) they may cause inflammatory reactions. Fungi of concern in occupational hygiene are mostly non-pathogenic or facultative pathogenic (opportunistic) species, but are relevant as allergens and mycotoxins producers. It is known that the exclusive use of conventional methods for fungal quantification (fungal culture) may underestimate the results due to different reasons. The incubation temperature chosen will not be the most suitable for every fungal species, resulting in the inhibition of some species and the favouring of others. Differences in fungi growth rates may also result in data underestimation, since the fungal species with higher growth rates may inhibit others species’ growth. Finally, underestimated data can result from non-viable fungal particles that may have been collected or fungal species that do not grow in the culture media used, although these species may have clinical relevance in the context. Due to these constraints occupational exposure assessment, in setings with high fungal contamination levels, should follow these steps: Apply conventional methods to obtain fungal load information (air and surfaces) regarding the most critical scenario previously selected; Guideline comparation aplying or legal requirements or suggested limits by scientific and/or technical organizations. We should also compare our results with others from the same setting (if there is any); Select the most suitable indicators for each setting and apply conventional-culture methods and also molecular tools. These methodology will ensure a more real characterization of fungal burden in each setting and, consequently, permits to identify further measures regarding assessment of fungal metabolites, and also a more adequate workers health surveillance. The methodology applied to characterize fungal burden in several occupational environments, focused in Aspergillus spp. prevalence, will be present and discussed.

Assessment of laboratory test utilization for HIV/AIDS care in urban ART clinics of Lilongwe, Malawi

Background The 2011 Malawi HIV guidelines promote CD4 monitoring for pre-ART assessment and considering HIVRNA monitoring for ART response assessment, while some clinics used CD4 for both. We assessed clinical ordering practices as compared to guidelines, and determined whether the samples were successfully and promptly processed. Methods We conducted a retrospective review of all patients seen in from August 2010 through July 2011,, in two urban HIV-care clinics that utilized 6-monthly CD4 monitoring regardless of ART status. We calculated the percentage of patients on whom clinicians ordered CD4 or HIVRNA analysis. For all samples sent, we determined rates of successful labprocessing, and mean time to returned results. Results Of 20581 patients seen, 8029 (39%) had at least one blood draw for CD4 count. Among pre-ART patients, 2668/2844 (93.8%) had CD4 counts performed for eligibility. Of all CD4 samples sent, 8082/9207 (89%) samples were successfully processed. Of those, mean time to processing was 1.6 days (s.d 1.5) but mean time to results being available to clinician was 9.3 days (s.d. 3.7). Regarding HIVRNA, 172 patients of 17737 on ART had a blood draw and only 118/213 (55%) samples were successfully processed. Mean processing time was 39.5 days (s.d. 21.7); mean time to results being available to clinician was 43.1 days (s.d. 25.1). During the one-year evaluated, there were multiple lapses in processing HIVRNA samples for up to 2 months. Conclusions Clinicians underutilize CD4 and HIVRNA as monitoring tools in HIV care. Laboratory processing failures and turnaround times are unacceptably high for viral load analysis. Alternative strategies need to be considered in order to meet laboratory monitoring needs.