Effectiveness of Carboplatin-Prescreening Intradermal Skin Tests to Reduce Unanticipated Immediate Hypersensitivity Reactions: A Comparative Study

BACKGROUND

Carboplatin administration poses a risk of immediate hypersensitivity reactions (IHRs) that tend to increase with repeated administration and are mostly IgE-mediated.

OBJECTIVE

This study evaluated the usefulness of carboplatin-prescreening intradermal skin tests (IDTs).

METHODS

Carboplatin-prescreening IDTs were routinely conducted in patients with a history of receiving six or more carboplatin cycles beginning in January 2021. The primary objective was to assess disparities in the incidence of unanticipated IHRs to carboplatin administration. We compared patients in the intervention group (from 2021 to 2022) and those who did not undergo prescreening IDTs under the same conditions (preintervention group, from 2019 to 2020). Secondary objectives included evaluating the sensitivity and specificity of the prescreening IDT and the incidence of carboplatin IHR according to the number of infusion cycles.

RESULTS

The intervention group was composed of 67 patients who were administered 347 carboplatin cycles whereas the preintervention group included 96 patients who were administered 464 carboplatin cycles. The risk of unanticipated carboplatin IHRs decreased by 83.2% in the intervention group compared with results in the preintervention group (preintervention group, 3.45%, n = 16 vs intervention group, 0.58%, n = 2; P = .005). The prescreening IDT showed a sensitivity and specificity of 77.78% and 99.41%, respectively. The risk of newly developed IHRs based on the number of carboplatin cycles was less than 1% (cycles 1-5), 2.11% (cycle 6), 3.90% (cycles 7-12), 2.90% (cycles 13-18), and 0.74% (cycles 19 and greater), respectively.

CONCLUSIONS

Initiating carboplatin-prescreening IDTs from the seventh cycle on significantly reduced the risk of unanticipated IHRs.

Screening for contamination levels of select organic environmental chemicals in medical supplies used for human specimen collection

Trace-level analysis of environmental chemicals in human specimens can be compromised by contamination introduced during sample collection and storage. Sampling devices and tools can be a source of contamination by plasticizers, additives and antimicrobials, which warrants the need for pre-screening of these products prior to use. In this study, we determined leaching of 121 environmental chemicals in 10% and 100% methanol from 24 types of human specimen collection and storage devices. Cryovials, serum tubes, cups, syringes, transfer pipettes, and gloves -commonly used for the collection of blood, urine, breast milk and stools - were screened for the presence of plasticizers, environmental phenols, and pesticides. Measurable levels of mono-ethyl phthalate (mEP) and triethyl phosphate (TEP) were leached from vials, plastic storage bags, gloves, and diapers, and parabens were leached from collection bottles, at amounts exceeding 100 ng/device. The amount leached from the devices varied depending on the lot numbers of the same product type. Storage time and temperature were found to influence the leaching rate of chemicals, with increased levels observed following prolonged storage and at high temperatures. The study underscores the importance of pre-screening for contamination in devices used for collection and storage of human specimens for biomonitoring studies.

Prescreening for osteoporosis with forearm bone densitometry in health examination population

BACKGROUND

Early detection and timely prophylaxis can retard the progression of osteoporosis. The purpose of this study was to determine the validity of peripheral Dual Energy X-ray Absorptiometry (DXA) test for osteoporosis screening. We examined peripheral bone mineral density (BMD) using AKDX-09 W-I DXA densitometer. Firstly, we acquired BMD data from manufacturer-supplied density-gradient phantoms and 30 volunteers to investigate its accuracy and precision, then we measured BMD for 150 volunteers using both AKDX (left forearm) and Hologic Discovery Wi (left forearm, left hip and L1 - L4 vertebrae) simultaneously. Correlation relationship of BMD results acquired from two instruments was assessed by simple linear regression analysis, the Receiver Operating Characteristic (ROC) curves and Areas Under the Curves (AUCs) were evaluated for the diagnostic value of left forearm BMD measured by AKDX in detecting osteoporosis.

RESULTS

In vitro precision errors of AKDX BMD were 0.40, 0.20, 0.19%, respectively, on low-, medium-, and high-density phantom; in vivo precision was 1.65%. Positive correlation was observed between BMD measured by AKDX and Hologic at the forearm (r = 0.670), L1-L4 (r = 0.430, femoral neck (r = 0.449), and total hip (r = 0.559). With Hologic measured T-score as the gold standard, the sensitivity of AKDX T-score < - 1 for identifying suboptimal bone health was 63.0 and 76.1%, respectively, at the distal one-third radius and at any site, and the specificity was 73.9 and 90.0%, respectively; the AUCs were 0.708 and 0.879. The sensitivity of AKDX T-score ≤ - 2.5 for identifying osteoporosis at the distal one-third radius and at any site was 76.9 and70.4%, respectively, and the specificity was 80.4 and 78.0%, respectively; the AUCs were 0.823 and 0.778.

CONCLUSIONS

Peripheral DXA appears to be a reliable tool for prescreening for osteoporosis.

Analysis of a COVID-19 Prescreening Process in an Outpatient Clinic at a University Hospital during the COVID-19 Pandemic

BACKGROUND

To minimize nosocomial infection against coronavirus disease 2019 (COVID-19), most hospitals conduct a prescreening process to evaluate the patient or guardian of any symptoms suggestive of COVID-19 or exposure to a COVID-19 patient at entrances of hospital buildings. In our hospital, we have implemented a two-level prescreening process in the outpatient clinic: an initial prescreening process at the entrance of the outpatient clinic (PPEO) and a second prescreening process is repeated in each department. If any symptoms or epidemiological history are identified at the second level, an emergency code is announced through the hospital's address system. The patient is then guided outside through a designated aisle. In this study, we analyze the cases missed in the PPEO that caused the emergency code to be applied.

METHODS

All cases reported from March 2020 to April 2021 were analyzed retrospectively. We calculated the incidence of cases missed by the PPEO per 1,000 outpatients and compared the incidence between first-time hospital visitors and those visiting for the second time or more; morning and afternoon office hours; and days of the week.

RESULTS

During the study period, the emergency code was applied to 449 cases missed by the PPEO. Among those cases, 20.7% were reported in otorhinolaryngology, followed by 11.6% in gastroenterology, 5.8% in urology, and 5.8% in dermatology. Fever was the most common symptom (59.9%), followed by cough (19.8%). The incidence of cases per 1,000 outpatients was significantly higher among first-time visitors than among those visiting for the second time or more (1.77 [confidence interval (CI), 1.44-2.10] vs. 0.59 [CI, 0.52-0.65], respectively) (P < 0.001).

CONCLUSION

Fever was the most common symptom missed by the PPEO, and otorhinolaryngology and gastroenterology most frequently reported missed cases. Cases missed by the PPEO were more likely to occur among first-time visitors than returning visitors. The results obtained from this study can provide insights or recommendations to other healthcare facilities in operating prescreening processes during the COVID-19 pandemic.

Comparison of ELISA- and SIMOA-based quantification of plasma Aβ ratios for early detection of cerebral amyloidosis

BACKGROUND

Blood-based amyloid biomarkers may provide a non-invasive, cost-effective and scalable manner for detecting cerebral amyloidosis in early disease stages.

METHODS

In this prospective cross-sectional study, we quantified plasma Aβ1-42/Aβ1-40 ratios with both routinely available ELISAs and novel SIMOA Amyblood assays, and provided a head-to-head comparison of their performances to detect cerebral amyloidosis in a nondemented elderly cohort (n = 199). Participants were stratified according to amyloid-PET status, and the performance of plasma Aβ1-42/Aβ1-40 to detect cerebral amyloidosis was assessed using receiver operating characteristic analysis. We additionally investigated the correlations of plasma Aβ ratios with amyloid-PET and CSF Alzheimer's disease biomarkers, as well as platform agreement using Passing-Bablok regression and Bland-Altman analysis for both Aβ isoforms.

RESULTS

ELISA and SIMOA plasma Aβ1-42/Aβ1-40 detected cerebral amyloidosis with identical accuracy (ELISA: area under curve (AUC) 0.78, 95% CI 0.72-0.84; SIMOA: AUC 0.79, 95% CI 0.73-0.85), and both increased the performance of a basic demographic model including only age and APOE-ε4 genotype (p ≤ 0.02). ELISA and SIMOA had positive predictive values of respectively 41% and 36% in cognitively normal elderly and negative predictive values all exceeding 88%. Plasma Aβ1-42/Aβ1-40 correlated similarly with amyloid-PET for both platforms (Spearman ρ = - 0.32, p <  0.0001), yet correlations with CSF Aβ1-42/t-tau were stronger for ELISA (ρ = 0.41, p = 0.002) than for SIMOA (ρ = 0.29, p = 0.03). Plasma Aβ levels demonstrated poor agreement between ELISA and SIMOA with concentrations of both Aβ1-42 and Aβ1-40 measured by SIMOA consistently underestimating those measured by ELISA.

CONCLUSIONS

ELISA and SIMOA demonstrated equivalent performances in detecting cerebral amyloidosis through plasma Aβ1-42/Aβ1-40, both with high negative predictive values, making them equally suitable non-invasive prescreening tools for clinical trials by reducing the number of necessary PET scans for clinical trial recruitment.

TRIAL REGISTRATION

EudraCT 2009-014475-45 (registered on 23 Sept 2009) and EudraCT 2013-004671-12 (registered on 20 May 2014, https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-004671-12/BE ).

Prescreening for European Prevention of Alzheimer Dementia (EPAD) trial-ready cohort: impact of AD risk factors and recruitment settings

BACKGROUND

Recruitment is often a bottleneck in secondary prevention trials in Alzheimer disease (AD). Furthermore, screen-failure rates in these trials are typically high due to relatively low prevalence of AD pathology in individuals without dementia, especially among cognitively unimpaired. Prescreening on AD risk factors may facilitate recruitment, but the efficiency will depend on how these factors link to participation rates and AD pathology. We investigated whether common AD-related factors predict trial-ready cohort participation and amyloid status across different prescreen settings.

METHODS

We monitored the prescreening in four cohorts linked to the European Prevention of Alzheimer Dementia (EPAD) Registry (n = 16,877; mean ± SD age = 64 ± 8 years). These included a clinical cohort, a research in-person cohort, a research online cohort, and a population-based cohort. Individuals were asked to participate in the EPAD longitudinal cohort study (EPAD-LCS), which serves as a trial-ready cohort for secondary prevention trials. Amyloid positivity was measured in cerebrospinal fluid as part of the EPAD-LCS assessment. We calculated participation rates and numbers needed to prescreen (NNPS) per participant that was amyloid-positive. We tested if age, sex, education level, APOE status, family history for dementia, memory complaints or memory scores, previously collected in these cohorts, could predict participation and amyloid status.

RESULTS

A total of 2595 participants were contacted for participation in the EPAD-LCS. Participation rates varied by setting between 3 and 59%. The NNPS were 6.9 (clinical cohort), 7.5 (research in-person cohort), 8.4 (research online cohort), and 88.5 (population-based cohort). Participation in the EPAD-LCS (n = 413 (16%)) was associated with lower age (odds ratio (OR) age = 0.97 [0.95-0.99]), high education (OR = 1.64 [1.23-2.17]), male sex (OR = 1.56 [1.19-2.04]), and positive family history of dementia (OR = 1.66 [1.19-2.31]). Among participants in the EPAD-LCS, amyloid positivity (33%) was associated with higher age (OR = 1.06 [1.02-1.10]) and APOE ɛ4 allele carriership (OR = 2.99 [1.81-4.94]). These results were similar across prescreen settings.

CONCLUSIONS

Numbers needed to prescreen varied greatly between settings. Understanding how common AD risk factors link to study participation and amyloid positivity is informative for recruitment strategy of studies on secondary prevention of AD.

Statistical considerations for testing an AI algorithm used for prescreening lung CT images

Artificial intelligence, as applied to medical images to detect, rule out, diagnose, and stage disease, has seen enormous growth over the last few years. There are multiple use cases of AI algorithms in medical imaging: first-reader (or concurrent) mode, second-reader mode, triage mode, and more recently prescreening mode as when an AI algorithm is applied to the worklist of images to identify obvious negative cases so that human readers do not need to review them and can focus on interpreting the remaining cases. In this paper we describe the statistical considerations for designing a study to test a new AI prescreening algorithm for identifying normal lung cancer screening CTs. We contrast agreement vs. accuracy studies, and retrospective vs. prospective designs. We evaluate various test performance metrics with respect to their sensitivity to changes in the AI algorithm's performance, as well as to shifts in reader behavior to a revised worklist. We consider sample size requirements for testing the AI prescreening algorithm.

Clinical research in small genomically stratified patient populations

The paradigm of early drug development in cancer is shifting from 'histology-oriented' to 'molecularly oriented' clinical trials. This change can be attributed to the vast amount of tumour biology knowledge generated by large international research initiatives such as The Cancer Genome Atlas (TCGA) and the use of next generation sequencing (NGS) techniques developed in recent years. However, targeting infrequent molecular alterations entails a series of special challenges. The optimal molecular profiling method, the lack of standardised biological thresholds, inter- and intra-tumor heterogeneity, availability of enough tumour material, correct clinical trials design, attrition rate, logistics or costs are only some of the issues that need to be taken into consideration in clinical research in small genomically stratified patient populations. This article examines the most relevant challenges inherent to clinical research in these populations. Moreover, perspectives from the Academia point of view are reviewed as well as initiatives to be taken in forthcoming years.

Development and validation of osteoporosis prescreening model for Iranian postmenopausal women

Background

Studies have indicated that the commonly used osteoporosis prescreening tools are not appropriate for use in every nation. This study was designed to develop and validate a prescreening model for bone mineral densitometry among Iranian postmenopausal women.

Methods

From 13613 individuals who were referred for bone mineral densitometry in Shariati hospital in Tehran, 8644 postmenopausal women were considered for the study after excluding men and premenopausal women. Questionnaires regarding the risk factors for osteoporosis were filled for each individual. Bone mineral density at the lumbar vertebrae (L2-L4), femoral neck and total femur was measured by dual X-ray absorptiometry. Using holdout validation, the study sample was divided into two parts; training set (5705) and test set (2939). Logistic regression analysis was performed on the training set. A scoring model was developed and tested in the test set.

Results

Based on the training set, a seven-variable model named OPMIP (Osteoporosis Prescreening Model for Iranian Postmenopausal women) was developed with C statistics (area under curve) of 0.72. Using a cut-off of -2.5 for the model, the sensitivity, specificity, positive predictive value and negative predictive value were 72%, 59.5%, 64% and 69% respectively. The model performance was tested in the test set. OPMIP correctly classified 67.10% of cases with a sensitivity and specificity of 73.2% and 61%.

Conclusions

In order to appropriately refer patients for a bone mineral densitometry, OPMIP can be used as a prescreening tool in Iranian Postmenopausal women.