Clinical Metrics Research Articles

Revolutionizing Healthcare: How Telemedicine Is Improving Patient Outcomes and Expanding Access to Care.

Telemedicine uses digital technologies to provide healthcare services remotely, greatly improving patient access, especially during crises like the COVID-19 pandemic. This systematic review investigates telemedicine's effects on patient outcomes, access to care, and its role in the evolving healthcare landscape. Relevant studies were identified using MeSH terms and keywords through electronic databases and manual reference list screenings. The selected empirical studies, both quantitative and qualitative, examined telemedicine across various patient populations.The review categorized findings into themes related to patient outcomes and access to care. Telemedicine was found to be a transformative tool in chronic disease management, particularly in diabetes care. Significant improvements in patient health outcomes and cost savings were reported with telemedicine interventions. For example, telehealth platforms enhance diabetes management by increasing patient engagement and improving clinical metrics such as HbA1c levels. Additionally, telehealth services for diabetes-related foot disease(DFD) overcome geographical barriers, providing specialized care and improving patient access and satisfaction. In conclusion, telemedicine significantly improves patient outcomes, access, and satisfaction in chronic disease management, especially diabetes care. By overcoming geographical barriers and enhancing patient engagement, telehealth platforms have the potential to transform global healthcare delivery. Implementing these insights into practice can improve the accessibility and effectiveness of diabetes care worldwide, ensuring equitable and patient-centered healthcare solutions.

Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.

3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning. The goal of this work was to compare three methods of neural network training-a single model trained on all applicator data, fine-tuning the combined model to each applicator, and individual (IDV) applicator models-to determine the optimal method for dose prediction. Models were produced for four applicator types-tandem-and-ovoid (T&O), T&O with 1-7 needles (T&ON), tandem-and-ring (T&R) and T&R with 1-4 needles (T&RN). First, the combined model was trained on 859 treatment plans from 266 cervical cancer patients treated from 2010 onwards. The train/validation/test split was 70%/16%/14%, with approximately 49%/10%/19%/22% T&O/T&ON/T&R/T&RN in each dataset. Inputs included four channels for anatomical masks (high-risk clinical target volume [HRCTV], bladder, rectum, and sigmoid), a mask indicating dwell position locations, and applicator channels for each applicator component. Applicator channels were created by mapping the 3D dose for a single dwell position to each dwell position and summing over each applicator component with uniform dwell time weighting. A 3D Cascade U-Net, which consists of two U-Nets in sequence, and mean squared error loss function were used. The combined model was then fine-tuned to produce four applicator-specific models by freezing the first U-Net and encoding layers of the second and resuming training on applicator-specific data. Finally, four IDV models were trained using only data from each applicator type. Performance of these three model types was compared using the following metrics for the test set: mean error (ME, representing model bias) and mean absolute error (MAE) over all dose voxels and ME of clinical metrics (HRCTV D90% and D2cc of bladder, rectum, and sigmoid), averaged over all patients. A positive ME indicates the clinical dose was higher than predicted. 3D global gamma analysis with the prescription dose as reference value was performed. Dice similarity coefficients (DSC) were computed for each isodose volume. Fine-tuned and combined models showed better performance than IDV applicator training. Fine-tuning resulted in modest improvements in about half the metrics, compared to the combined model, while the remainder were mostly unchanged. Fine-tuned MAE=3.98%/2.69%/5.36%/3.80% for T&O/T&R/T&ON/T&RN, and ME over all voxels=-0.08%/-0.89%/-0.59%/1.42%. ME D2cc were bladder=-0.77%/1.00%/-0.66%/-1.53%, rectum=1.11%/-0.22%/-0.29%/-3.37%, sigmoid=-0.47%/-0.06%/-2.37%/-1.40%, and ME D90=2.6%/-4.4%/4.8%/0.0%. Gamma pass rates (3%/3mm) were 86%/91%/83%/89%. Mean DSCs were 0.92%/0.92%/0.88%/0.91% for isodoses ≤150% of prescription. 3D BT dose was accurately predicted for all applicator types, as indicated by the low MAE and MEs, high gamma scores and high DSCs. Training on all treatment data overcomes challenges with data scarcity in each applicator type, resulting in superior performance than can be achieved by training on IDV applicators alone. This could presumably be explained by the fact that the larger, more diverse dataset allows the neural network to learn underlying trends and characteristics in dose that are common to all treatment applicators. Accurate, applicator-specific dose predictions could enable automated, knowledge-based planning for any cervical brachytherapy treatment.

Comparative Evaluation of Feracrylum and Chlorhexidine in Nonsurgical Periodontal Therapy in Chronic Periodontitis: A Clinicomicrobiological Study

ABSTRACT Background: One common inflammatory illness that affects periodontal tissues is chronic periodontitis. By lowering inflammation and getting rid of periodontal bacteria, nonsurgical periodontal management is essential for treating chronic periodontitis. The objective of this study was to evaluate the effectiveness of chlorhexidine and feracrylum in nonsurgical periodontal management for persistent periodontitis. Techniques: As part of their periodontal therapy, 50 patients with a diagnosis of chronic periodontitis were randomized into two groups: first got chlorhexidine, while other Feracrylum received feracrylum. Microbiological analysis and clinical parameters were measured at baseline and 3 months later. Results: Clinical metrics showed a considerable improvement in both groups. Comparing feracrylum to chlorhexidine, the former showed stronger antibacterial action against periodontal infections. Conclusion: For nonsurgical periodontal management of chronic periodontitis, feracrylum shows similar effectiveness to chlorhexidine. Furthermore, feracrylum has better antibacterial activity against pathogens of the periodontal tissues, indicating that it might be used as a substitute antimicrobial agent in periodontal management.

Measuring pharmacy clinical workload in the emergency department: Current status and a plan for action

BackgroundInpatient pharmacy productivity workload ratios historically encompass dispensing functions including numbers of medication orders verified, dispensed, and billed for and full-time equivalents (FTEs) but do not include pharmacy clinical activities. Clinical pharmacy activities are difficult to capture because they generally require manual documentation and vary among health systems. The emergency medicine pharmacists’ (EMPs) role adds additional complexity to capturing clinical activities given that bedside response and verbal recommendations are difficult to capture outside of manual documentation. It is unknown how many health systems use EMP-specific workload metrics. ObjectivesThe purpose of our survey was to determine the percentage of hospital pharmacies that use EMP-specific workload metrics, to describe workload metrics used by EMPs and to quantify time spent on clinical activities, and to describe methods for implementing EMP metrics. MethodsA 13-item questionnaire was developed, pretested, and sent to EMPs practicing in hospitals in the United States through 2 EMP national committee e-mail listservs. Survey completion online included questions on hospital characteristics, number of EMP FTEs, clinical services provided and time spent on these tasks, and current workload metrics used. ResultsFull responses to the survey were received from 100 pharmacists, with an overall response rate of 9.8%. Workload metrics specific to EMP activities were used by 15.1% of responders. Workload metrics were used but not tailored to EMP activities for 43% of responders, and 41.9% responded that no metrics were used whatsoever. Ten responders provided commentary on types of EMP activities tracked, which included bedside response for critical patients, culture callback, drug information and dosing, time to antibiotic administration, and medication histories. No universal software for metric tracking was identified. ConclusionA survey of EMP clinical services and workload metrics indicated that although 43% of responders use non-EMP-specific metrics, only 15.1% reported workload metrics that capture EMP clinical activities.

Review of meta-analyses on the use of artificial intelligence in radiology

Introduction. Artificial intelligence (AI) is an effective tool for automating routine tasks in radiology. The diagnostic accuracy of AI in detecting various pathologies on medical images has generated considerable interest in the scientific community: the number of studies and meta-analyses has been constantly growing. The abundance of published evidence and the diversity of outcomes necessitate the need to systematize the available publications. The aim of this paper is to conduct an umbrella systematic review of contemporary meta-analyses on the use of AI in radiology.Materials and methods. PubMed was searched for studies published in the English language. Thirty-eight systematic reviews with meta-analyses published between 2021 and 2023 were selected for full-text analysis. The extracted data included the goal, study design, imaging modality, sample size, quality assessment of the included studies, AI diagnostic accuracy estimates, reference method parameters, and clinical efficacy metrics of AI implementation. The methodological quality of included systematic reviews was assessed using the AMSTAR-2 tool.Results. Nearly half (47%) of the included meta-analyses focused on the diagnosis, staging and segmentation of malignancies. Four meta-analyses were related to detection of maxillofacial structures in dentistry, while another four meta-analyses addressed the diagnosis of brain lesions. The diagnosis of COVID-19 and the diagnosis of bone fractures were each covered in three meta-analyses. One meta-analysis was reviewed for each of the following fields: colorectal polyps, pneumothorax, pulmonary embolism, osteoporosis, aneurysms, multiple sclerosis, acute cerebrovascular accident, intracranial hemorrhage, burns, and the risk of intrauterine growth restriction. Thirty-five (92%) meta-analyses assessed the risk of bias. Twenty-eight (80%) meta-analyses utilized QUADAS-2 to assess the risk of bias. 14 out of 28 papers reported low risk of bias (50%); 4 (14%) – moderate; 10 (36%) – high. The major risks were associated with samples that were unbalanced in terms of size and composition, a lack of details about the methods, a low number of prospective studies, and a lack of external validation of the outcomes. The overall results indicate that the diagnostic accuracy of AI is comparable to or even greater than that of radiologists. The mean sensitivity, specificity and area under the ROC curve for AI and radiologists were 85.2%, 89.5%, 93.5% and 84.4%, 90.0%, 92.8%, respectively. However, many studies that compared the diagnostic accuracy of AI and radiologists lack the data on the number and experience of the latter. Only one paper presented results of implementing AI into routine clinical diagnosis.Discussion. AI is capable of reducing the turnaround time for non-urgent examinations. When used to verify the primary interpretation, AI was effective in detecting false-negative results from radiologists. However, the efficacy of detecting false-positive results was inadequate. Our assessment of the quality of systematic reviews with AMSTAR-2 show that the methods of searching, selecting and analyzing literature must be improved and brought to a common standard. The development of a specialized tool for assessing the quality of systematic reviews in the AI implementation is also necessary. Due to high diagnostic accuracy, AI is currently considered a promising tool for optimizing the turnaround time. However, more evidence is needed to study the AI outcomes in routine clinical practice. Furthermore, it is necessary to standardize and improve the quality of research methodology.

Deriving phenotype-representative left ventricular flow patterns by reduced-order modeling and classification

BackgroundExtracting phenotype-representative flow patterns and their associated numerical metrics is a bottleneck in the clinical translation of advanced cardiac flow imaging modalities. We hypothesized that reduced-order models (ROMs) are a suitable strategy for deriving simple and interpretable clinical metrics of intraventricular flow suitable for further assessments. Combined with machine learning (ML) flow-based ROMs could provide new insight to help diagnose and risk-stratify patients. MethodsWe analyzed 2D color-Doppler echocardiograms of 81 non-ischemic dilated cardiomyopathy (DCM) patients, 51 hypertrophic cardiomyopathy (HCM) patients, and 77 normal volunteers (Control). We applied proper orthogonal decomposition (POD) to build patient-specific and cohort-specific ROMs of LV flow. Each ROM aggregates a low number of components representing a spatially dependent velocity map modulated along the cardiac cycle by a time-dependent coefficient. We tested three classifiers using deliberately simple ML analyses of these ROMs with varying supervision levels. In supervised models, hyperparameter grid search was used to derive the ROMs that maximize classification power. The classifiers were blinded to LV chamber geometry and function. We ran vector flow mapping on the color-Doppler sequences to help visualize flow patterns and interpret the ML results. ResultsPOD-based ROMs stably represented each cohort through 10-fold cross-validation. The principal POD mode captured >80 % of the flow kinetic energy (KE) in all cohorts and represented the LV filling/emptying jets. Mode 2 represented the diastolic vortex and its KE contribution ranged from <1 % (HCM) to 13 % (DCM). Semi-unsupervised classification using patient-specific ROMs revealed that the KE ratio of these two principal modes, the vortex-to-jet (V2J) energy ratio, is a simple, interpretable metric that discriminates DCM, HCM, and Control patients. Receiver operating characteristic curves using V2J as classifier had areas under the curve of 0.81, 0.91, and 0.95 for distinguishing HCM vs. Control, DCM vs. Control, and DCM vs. HCM, respectively. ConclusionsModal decomposition of cardiac flow can be used to create ROMs of normal and pathological flow patterns, uncovering simple interpretable flow metrics with power to discriminate disease states, and particularly suitable for further processing using ML.

Novel Alzheimer's disease subtypes based on functional brain connectivity in human connectome project

The pathogenesis of Alzheimer's disease (AD) remains unclear, but revealing individual differences in functional connectivity (FC) may provide insights and improve diagnostic precision. A hierarchical clustering-based autoencoder with functional connectivity was proposed to categorize 82 AD patients from the Alzheimer's Disease Neuroimaging Initiative. Compared to directly performing clustering, using an autoencoder to reduce the dimensionality of the matrix can effectively eliminate noise and redundant information in the data, extract key features, and optimize clustering performance. Subsequently, subtype differences in clinical and graph theoretical metrics were assessed. Results indicate a significant inter-subject heterogeneity in the degree of FC disruption among AD patients. We have identified two neurophysiological subtypes: subtype I exhibits widespread functional impairment across the entire brain, while subtype II shows mild impairment in the Limbic System region. What is worth noting is that we also observed significant differences between subtypes in terms of neurocognitive assessment scores associations with network functionality, and graph theory metrics. Our method can accurately identify different functional disruptions in subtypes of AD, facilitating personalized treatment and early diagnosis, ultimately improving patient outcomes.

Expanded perspectives: integrating clinicians' insights for comprehensive patient-reported outcomes in value-based healthcare.

The manuscript explores value-based healthcare (VBHC) and its role in assessing healthcare quality beyond clinical metrics. It identifies four value types: personal, technical, allocative, and societal. Emphasizing the integration of diverse stakeholder perspectives, including patients, families, and clinicians, the study highlights the importance of patient- and family-reported measures (PROMs and PREMs) and clinician input. Clinicians' insights on treatment feasibility and effectiveness are crucial for a holistic understanding of healthcare quality. The manuscript advocates for combining machine learning with participatory approaches to enhance data analysis and continuous quality improvement in VBHC, driving better outcomes for patients and communities.

Improving Diabetic Care Through Education and Innovation.

Background: Despite the innovative advancements in diabetes care, providers may not recognize patients with type 2 diabetes mellitus (T2DM) who qualify for a continuous glucose monitoring (CGM) device or the benefits of a telemedicine program for improving self-management behaviors. Objective: This quality improvement (QI) project aimed to determine if an advanced practice registered nurse (APRN)-led telemedicine program using CGM could improve glycemic control and self-management in patients with insulin-dependent T2DM. Methods: A 6-week telemedicine program was developed and implemented using the CGM's time-in-range to deliver patient-specific education. Clinical metrics were collected at each visit. All patients completed a pre- and postintervention Diabetes Self-Management Questionnaire-Revised (DSMQ-R) survey. Results: A repeated measures analysis of variance revealed that the telemedicine program had a statistically significant impact on time-in-range outcomes, F(2, 14) = 18.203, p < .001. Paired-samples t tests indicate that APRN-led education improved body mass index, t(8) = 4.232, p = .002; decreased systolic blood pressure, t(8) = 2.90, p = .010, and diastolic blood pressure, t(8) = 3.21, p = .007; and increased self-management skills as evidenced by DSMQ-R, t(8) = -5.498, p < .001. Conclusions: This QI project highlights multiple interventions for improving diabetes management in a primary care facility. Implications for Nursing: An APRN-led telemedicine program integrating CGM time-in-range data can improve glycemic control and self-management skills in patients with T2DM who administer insulin.

Baseline health parameters of rhinoceros auklets (Cerorhinca monocerata) using serum protein electrophoresis, acute phase proteins, and biochemistry.

Clinical metrics of baseline health in sentinel seabird species can offer insight into marine ecosystem dynamics, individual and population health, and assist in wildlife rehabilitation and conservation efforts. Protein electrophoresis is useful for detecting changes in acute phase proteins and immunoglobulin levels that may indicate subtle inflammatory responses and/or infectious disease. Serum biochemistry can highlight nutritional status, metabolic derangements, and organ injury and function. However, baseline values for such health parameters are largely unknown for many seabird species. Therefore, the objective of this study is to establish baseline clinical health reference intervals for serum protein electrophoresis, acute phase proteins including serum amyloid A and haptoglobin, and biochemistry parameters in the rhinoceros auklet (Cerorhinca monocerata), a key sentinel species in the North Pacific. From 2013 to 2019, 178 wild, apparently healthy breeding adult rhinoceros auklets were captured across four breeding colonies in British Columbia, Canada (Lucy Island, Pine Island, Triangle Islands, and SGang Gwaay) and from one colony in Washington, United States (Protection Island). Reference intervals were calculated for protein electrophoresis fractions and acute phase proteins (n = 163), and serum biochemistry (n = 35) following established guidelines by the American Society of Veterinary Clinical Pathology. Animals were also assessed for the presence of antibodies to the influenza A virus. Approximately 48% (70/147) of sampled birds were seropositive for influenza A virus, with a prevalence of 50% (6/12) in 2013, 75% (47/63) in 2014, and 24% (17/72) in 2019. This work provides clinical baseline health metrics of a key North Pacific sentinel species to help inform marine ecosystem monitoring, recovery, and rehabilitation efforts in the Pacific Northwest.

Time-resolved clinical dose volume metrics, calculations and predictions based on source tracking measurements and uncertainties to aid treatment verification and error detection for HDR brachytherapy—a proof-of-principle study

Objective. High-dose-rate (HDR) brachytherapy lacks routinely available treatment verification methods. Real-time tracking of the radiation source during HDR brachytherapy can enhance treatment verification capabilities. Recent developments in source tracking allow for measurement of dwell times and source positions with high accuracy. However, more clinically relevant information, such as dose discrepancies, is still needed. To address this, a real-time dose calculation implementation was developed to provide more relevant information from source tracking data. A proof-of-principle of the developed tool was shown using source tracking data obtained from a 3D-printed anthropomorphic phantom. Approach. Software was developed to calculate dose-volume-histograms (DVH) and clinical dose metrics from experimental HDR prostate treatment source tracking data, measured in a realistic pelvic phantom. Uncertainty estimation was performed using repeat measurements to assess the inherent dose measuring uncertainty of the in vivo dosimetry (IVD) system. Using a novel approach, the measurement uncertainty can be incorporated in the dose calculation, and used for evaluation of cumulative dose and clinical dose-volume metrics after every dwell position, enabling real-time treatment verification. Main results. The dose calculated from source tracking measurements aligned with the generated uncertainty bands, validating the approach. Simulated shifts of 3 mm in 5/17 needles in a single plan caused DVH deviations beyond the uncertainty bands, indicating errors occurred during treatment. Clinical dose-volume metrics could be monitored in a time-resolved approach, enabling early detection of treatment plan deviations and prediction of their impact on the final dose that will be delivered in real-time. Significance. Integrating dose calculation with source tracking enhances the clinical relevance of IVD methods. Phantom measurements show that the developed tool aids in tracking treatment progress, detecting errors in real-time and post-treatment evaluation. In addition, it could be used to define patient-specific action limits and error thresholds, while taking the uncertainty of the measurement system into consideration.

Gender diversity and healthcare performance: A quantitative analysis from the Italian health system

Unlike many other industries, which are characterized by a more significant presence of men than women, the healthcare sector has a clear majority of women. However, even if at the non-executive level, the percentage of women is extremely higher than that of men, at the executive level, this percentage is completely overturned, generating the so-called glass ceiling effect. Despite extensive research on gender diversity and its impact on financial metrics, few studies have focused on clinical measures. To bridge this research gap, the article analyzes the relationship between gender diversity and healthcare metrics. We developed an econometric model for unbalanced panel data by performing a random effect and a quantile regression analysis, which test the relationship between gender diversity and the average length of stay (LOS), controlling for structural and clinical metrics. We find that, in general, a higher percentage of women in non-executive positions is related to an increase in LOS. Conversely, a higher rate of women in executive positions is related to a lower level of LOS. Empirical evidence supports the relevance of including human resources strategies to increase the number of women at executive managerial positions. However, the study highlights also the necessity to consider how to make the public health sector positions more appealing for men.

The global patient-reported outcomes for multiple sclerosis initiative: bridging the gap between clinical research and care - updates at the 2023 plenary event.

Significant advancements have been achieved in delineating the progress of the Global PROMS (PROMS) Initiative. The PROMS Initiative, a collaborative endeavor by the European Charcot Foundation and the Multiple Sclerosis International Federation, strives to amplify the influence of patient input on MS care and establish a cohesive perspective on Patient-Reported Outcomes (PROs) for diverse stakeholders. This initiative has established an expansive, participatory governance framework launching four dedicated working groups that have made substantive contributions to research, clinical management, eHealth, and healthcare system reform. The initiative prioritizes the global integration of patient (For the purposes of the Global PROMS Initiative, the term "patient" refers to the people with the disease (aka People with Multiple Sclerosis - pwMS): any individual with lived experience of the disease. People affected by the disease/Multiple Sclerosis: any individual or group that is affected by the disease: E.g., family members, caregivers will be also engaged as the other stakeholders in the initiative). insights into the management of MS care. It merges subjective PROs with objective clinical metrics, thereby addressing the complex variability of disease presentation and progression. Following the completion of its second phase, the initiative aims to help increasing the uptake of eHealth tools and passive PROs within research and clinical settings, affirming its unwavering dedication to the progressive refinement of MS care. Looking forward, the initiative is poised to continue enhancing global surveys, rethinking to the relevant statistical approaches in clinical trials, and cultivating a unified stance among 'industry', regulatory bodies and health policy making regarding the application of PROs in MS healthcare strategies.

Evaluation of Virtual Reality in the Reduction of Pain During Dressing Changes in Patients with Burn Wounds: A pilot study.

Burn injuries are among the most common life-threatening injuries for which medical attention is sought, and are accompanied by intense, severe pain, particularly during treatment. Burn therapy pain management with opioid and non-opioid analgesics is often insufficient when administered alone. Virtual reality (VR) interfaces provide an immersive experience that has demonstrable therapeutic benefits, including distraction from, and reduction of, pain. In this interventional pilot study, we assessed the correlation between VR and passive distraction and pain tolerance during burn wound treatment. This pilot study assessed patients undergoing burn wound dressing changes while receiving both pharmacological and VR intervention at [Removed for masked review]. Questionnaires and evaluation forms were subjectively completed by both patients and medical staff before and after treatment, and clinical metrics were recorded throughout the treatment. Forty-one patients ≥18 years old and 76.9% male that had primarily undergone ≥4 dressing changes before the study were included. Correlations were found between VR engagement during treatment and a decrease in subjective levels of nausea, anxiety, and pain sensation. Furthermore, high levels of VR engagement were correlated with high levels of VR enjoyment. These results suggest that highly engaging and enjoyable VR interfaces may reduce sensations of anxiety and pain in burn patients during dressing changes. Furthermore, these data suggest that VR technology may be applied as an adjunct therapy to pharmacological treatment in the standardization of burn wound care management. Further studies with control groups and larger sample populations are needed for better quantification of these benefits.

Cranial bone thickness and density anomalies quantified from CT images can identify chronic increased intracranial pressure

PurposeThe diagnosis of chronic increased intracranial pressure (IIP)is often based on subjective evaluation or clinical metrics with low predictive value. We aimed to quantify cranial bone changes associated with pediatric IIP using CT images and to identify patients at risk.MethodsWe retrospectively quantified local cranial bone thickness and mineral density from the CT images of children with chronic IIP and compared their statistical differences to normative children without IIP adjusting for age, sex and image resolution. Subsequently, we developed a classifier to identify IIP based on these measurements. Finally, we demonstrated our methods to explore signs of IIP in patients with non-syndromic sagittal craniosynostosis (NSSC).ResultsWe quantified a significant decrease of bone density in 48 patients with IIP compared to 1,018 normative subjects (P < .001), but no differences in bone thickness (P = .56 and P = .89 for age groups 0–2 and 2–10 years, respectively). Our classifier demonstrated 83.33% (95% CI: 69.24%, 92.03%) sensitivity and 87.13% (95% CI: 84.88%, 89.10%) specificity in identifying patients with IIP. Compared to normative subjects, 242 patients with NSSC presented significantly lower cranial bone density (P < .001), but no differences were found compared to patients with IIP (P = .57). Of patients with NSSC, 36.78% (95% CI: 30.76%, 43.22%) presented signs of IIP.ConclusionCranial bone changes associated with pediatric IIP can be quantified from CT images to support earlier diagnoses of IIP, and to study the presence of IIP secondary to cranial pathology such as non-syndromic sagittal craniosynostosis.

Study Protocol: Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project

BackgroundObjective and quantifiable markers are crucial for developing novel therapeutics for mental disorders by 1) stratifying clinically similar patients with different underlying neurobiological deficits and 2) objectively tracking disease trajectory and treatment response. Schizophrenia is often confounded with other psychiatric disorders, especially bipolar disorder, if based on cross-sectional symptoms. Awake and sleep EEG have shown promise in identifying neurophysiological differences as biomarkers for schizophrenia. However, most previous studies, while useful, were conducted in European and American populations, had small sample sizes, and utilized varying analytic methods, limiting comprehensive analyses or generalizability to diverse human populations. Furthermore, the extent to which wake and sleep neurophysiology metrics correlate with each other and with symptom severity or cognitive impairment remains unresolved. Moreover, how these neurophysiological markers compare across psychiatric conditions is not well characterized. The utility of biomarkers in clinical trials and practice would be significantly advanced by well-powered transdiagnostic studies. The Global Research Initiative on the Neurophysiology of Schizophrenia (GRINS) project aims to address these questions through a large, multi-center cohort study involving East Asian populations. To promote transparency and reproducibility, we describe the protocol for the GRINS project.MethodsThe research procedure consists of an initial screening interview followed by three subsequent sessions: an introductory interview, an evaluation visit, and an overnight neurophysiological recording session. Data from multiple domains, including demographic and clinical characteristics, behavioral performance (cognitive tasks, motor sequence tasks), and neurophysiological metrics (both awake and sleep electroencephalography), are collected by research groups specialized in each domain.ConclusionPilot results from the GRINS project demonstrate the feasibility of this study protocol and highlight the importance of such research, as well as its potential to study a broader range of patients with psychiatric conditions. Through GRINS, we are generating a valuable dataset across multiple domains to identify neurophysiological markers of schizophrenia individually and in combination. By applying this protocol to related mental disorders often confounded with each other, we can gather information that offers insight into the neurophysiological characteristics and underlying mechanisms of these severe conditions, informing objective diagnosis, stratification for clinical research, and ultimately, the development of better-targeted treatment matching in the clinic.

Critical care clinical pharmacy value-based metrics: Time to terminate widget counting

Critical care clinical pharmacy value-based metrics: Time to terminate widget counting

The future is now: Implementation of standard of care, preemptive pharmacogenomic testing in patients with gastrointestinal cancers.

1546 Background: Preemptive germline pharmacogenomic (PGx) testing provides insight into the safety of high-risk chemotherapy and effective use of supportive care medications in gastrointestinal (GI) cancers. Advances in clinical practice guidelines and clinician support have accelerated the appetite for PGx testing, particularly for drugs with severe adverse reactions. The objective of this study was to assess operational outcomes associated with end-to-end electronic health record (EHR) integration of standard of care, preemptive multigene PGx testing in patients with GI cancers. Methods: Patients in two UCHealth GI oncology clinics were tested for five PGx genes related to 5-fluorouracil (5-FU, DPYD), irinotecan ( UGT1A1), and supportive care therapies ( CYP2C19, CYP2D6, CYP2C9). The PGx test order was embedded in all GI oncology treatment plans. Patient samples were genotyped, and PGx results were returned as discrete data to the EHR. Automated clinical decision support (CDS) tools provided PGx-guided recommendations at the point of prescribing. We leveraged the EPIS process framework and evaluated operational outcomes such as number of patients tested, lab turnaround time, patients with actionable phenotypes, and number of CDS alerts and clinical actions. Results: The analysis included 89 patients who received results for DPYD/UGT1A1 and 83 patients who received results for supportive care genes. Among samples tested, DPYD/UGT1A1 and supportive care gene results were returned to the EHR in an average of 4.7 ± 2 days (range 1-8) and 17.3 ± 3 days (range 9-24), respectively. The number of patients with abnormal phenotypes, defined as poor metabolizers for UGT1A1 and any non-normal phenotypes for the other genes, were UGT1A1, n=10 (11.2%); DPYD, n=4 (4.5%); CYP2C19, n=43 (51.8%); CYP2D6, n=31 (41.3%; 8 uninterpretable results); and CYP2C9, n=19 (22.9%). This equated to 14/89 (15.7%) and 68/83 (81.9%) patients having an abnormal result for DPYD/UGT1A1 and at least one supportive care gene, respectively. CDS alerts fired for 32 drug-gene interactions for 25 distinct patients. Of the alerts, 7 (21.9%) were for 5-FU or irinotecan, which showed for 7/89 patients (7.9%); 25 (78.1%) were for supportive care medications, which showed for 23/83 patients (27.7%). There were 9 clinical actions taken in response to the PGx alerts, 7 (77.8%) for 5-FU or irinotecan and 2 (22.2%) for supportive care drugs. Appropriate clinical actions were taken for 100% of patients with 5-FU or irinotecan drug-gene interactions. Conclusions: End-to-end EHR integration of PGx testing and prescribing guidance facilitated clinician adoption of preemptive, standard of care multigene PGx testing, with test results driving important medication changes. Future studies will evaluate the impact of PGx testing on clinical outcomes and cost effectiveness metrics in this population.

Predictors of time to diagnosis and treatment: A retrospective cohort study of patients with clinically localized prostate cancer in Nigeria.

e23123 Background: Equitable access to the management of early-stage prostate cancer is important for optimal outcome in the diagnostic and curative treatment pathway of patients. This study aimed to identify the predictors of time to treatment and time to diagnosis in these patients. Methods: This was a cohort study of 110 patients with clinically localized prostate cancer (T1-T3a, N0, M0) in three government-funded tertiary hospitals in Nigeria. Retrospective data on sociodemographic status, co-morbid conditions, clinical, pathological and radiological quality metrics, date of first presentation for cancer-related complaint in the tertiary hospital, date of diagnosis of prostate cancer, and the date of commencement of first cancer-directed treatment were collected. Primary endpoint was the time from diagnosis to treatment and the secondary endpoint was the time from presentation to diagnosis. Predictors were determined by Cox proportional hazards using Stata software, version 18.0 with a p-value of less than 0.05 considered significant. Results: Twelve percent (n = 13) of the patients had a family history of prostate cancer. Fifty-four percent (n = 59) had other co-morbid conditions. Nearly one-third, 29% (n = 32) had retired from their primary occupation and 10% (n = 11) were covered by health insurance. The overall median time to diagnosis was 30 (interquartile range (IQR) 10 - 48) days. The median time to treatment commencement was 32 (IQR 19 - 82) days for ADT; 57 (IQR 50 - 63) days for active surveillance; 45 (IQR 21 - 78) days for radical radiotherapy; and 100 (IQR 0 - 127) days for radical prostatectomy. Diagnostic PSA documentation independently predicted delayed time to diagnosis (adjusted HR = 0.22 with 95% CI [0.05,0.97]) while those with guideline concordant imaging investigations independently predicted longer time to treatment (adjusted HR = 0.63 with 95% CI [0.40,0.99]). Conclusions: The documentation of PSA level and use of guideline concordant imaging investigations were significantly associated with delayed time to care. Studies on interventions to reduce barriers associated with these factors is warranted to strengthen equitable access to care in this setting.

Progress Tracker Breast Cancer Registry: A Breast Cancer Canada initiative.

e23018 Background: Significant gaps exist in the collection of real-world evidence and validated patient-reported outcome measures (PROMs) data from individuals with breast cancer. Variations in treatment and surveillance care across regional jurisdictions present knowledge gaps across diverse populations which remain difficult to study. The PROgress Tracker Breast Cancer Registry, a large, prospective Canadian PROMs cohort study aims to identify care gaps through the lived patient experience to inform patient-centered care and new research directions. Methods: PROgress Tracker is a real-world, longitudinal, non-interventional PROMs registry directed by Breast Cancer Canada, a registered non-profit patient-led organization, and data managed by University of Calgary’s POET program. Launched in October 2023, this registry aims to enroll up to 50,000 adult Canadian participants with a histologic diagnosis of in-situ or invasive breast cancer (stage 0 through IV) within 10 years. A volunteer-led peer-to-peer support model, based in regional and provincial hubs across Canada, facilitates participant recruitment, engagement, and retention. Individuals who self-identify for study inclusion provide informed consent, demographic and socioeconomic profile, and clinical information on their breast cancer diagnosis. Every three months (and up to 10 years) a bilingual digital platform to reduce barriers to access is used to provide all PROgress Tracker participants 8 validated PROMs to capture evolving measures of self-efficacy for wellbeing, symptoms and overall global functioning. Based on treatment, up to 10 additional PROMs to assess the immediate adverse effects and long-term impacts of treatment are collected. PROgress Tracker has the additional capacity to link to external ethics-approved studies to investigate clinical outcome and healthcare utilization metrics for participants consenting to additional studies. Results: In progress. Conclusions: PROgress Tracker represents Canada’s first national PROMs breast cancer registry. Its unique design and implementation by a patient-led research organization helps fill gaps in current knowledge through longitudinal follow up of patients prior to, during, and after cancer care, and from the time from diagnosis through active treatment, long-term survivorship, and/or palliative care. The PROgress Tracker Registry will comprehensively gather the validated real-world data information rarely collected in in the traditional clinical or trial-based research setting using a novel self-enroll design and digital process. Such information is crucial to support evidence-based decision making to inform patient-centered healthcare change. This allows comparison across Canadian jurisdictions, is scalable to other jurisdictions around the world and further explores the utility of electronic PROMs data capture within a diverse patient population.