Screening Metrics Research Articles

Development and validation of the Normalized Organoid Growth Rate (NOGR) metric in brightfield imaging-based assays

This study focuses on refining growth-rate-based drug response metrics for patient-derived tumor organoid screening using brightfield live-cell imaging. Traditional metrics like Normalized Growth Rate Inhibition (GR) and Normalized Drug Response (NDR) have been used to assess organoid responses to anticancer treatments but face limitations in accurately quantifying cytostatic and cytotoxic effects across varying growth rates. Here, we introduce the Normalized Organoid Growth Rate (NOGR) metric, specifically developed for brightfield imaging-based assays. A label-free image analysis model was applied to segment organoids precisely, track their growth rates over time, and classify viable and dead organoids. Testing eleven phenotypically distinct pancreatic cancer organoid models with five chemotherapeutics demonstrates that the NOGR metric more effectively captures cytostatic and cytotoxic drug effects compared to existing methods. This approach enhances the biological relevance of drug sensitivity assessments on organoids and offers a valuable tool for advancing personalized cancer treatment strategies.

Assessing the Feasibility of Handheld Cameras to Increase Access to Teleretinal Diabetic Retinopathy Screenings in Safety Net Clinics in Los Angeles

Background: Handheld cameras may increase access to teleretinal diabetic retinopathy screenings (TDRS), as they are more economical and nimbler than traditional desktop cameras. However, their use in safety net clinical settings is less understood. Methods: The Los Angeles County Department of Public Health collaborated with the Los Angeles County Department of Health Services (DHS) to pilot the use of handheld cameras in DHS’s TDRS program. This study assessed screening metrics and interviewed TDRS coordinators and photographers about their experiences using these cameras. Results: Handheld cameras were harder to operate and performed less optimally on key screening metrics (e.g., image quality) compared to desktop cameras. However, this challenge appeared to be linked to the selected model rather than to all handheld cameras. The adoption of handheld cameras in DHS was aided by their integration into an existing agency infrastructure that already supported the use of desktop cameras; these operational supports included an established workflow, a data platform, and experienced photographers. Conclusions: The use of handheld cameras for TDRS is a promising practice for assuring equitable access to screenings. Handheld cameras are portable, smaller in size, and lower in cost than desktop cameras. Future projects should invest in and assess the performance of higher-quality models of these cameras, especially their use in safety net clinical settings.

Density Functional Theory Study on Screening and Key Metrics for Non-metallic Oxygen Reduction Catalysts.

This study systematically investigates the oxygen reduction reaction (ORR) catalytic activity of graphene doped with various non-metallic impurities. The non-metal elements include boron (B), silicon (Si), nitrogen (N), phosphorus (P), arsenic (As), oxygen (O), sulfur (S), selenium (Se), tellurium (Te), fluorine (F), chlorine (Cl), bromine (Br), and iodine (I). We found that adsorbates tend to adsorb on positively charged impurity atoms. We identified several substrates with good catalytic activity, all of which have an ORR overpotential of around 0.6 V. We further verified the thermodynamic stability of these substrates and found them to be very stable. We summarized the optimal adsorption energies for ORR intermediates O2H, O, and OH to be -1.9, -3.4, and -2.4 eV, respectively, and validated their reasonableness. Finally, we used simple linear functions to fit the relationship between the adsorption energies of O2H, O, and OH and the charge and magnetic moment of the adsorption site atoms. This model can roughly predict the ORR catalytic activity of doped graphene, facilitating the faster screening of excellent ORR catalysts.

Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis

Integrating artificial intelligence (AI) into mammography screening can support radiologists and improve programme metrics, yet the potential of different strategies for integrating the technology remains understudied. We compared programme-level performance metrics of seven AI integration strategies. We performed a retrospective comparative evaluation of seven strategies for integrating AI into mammography screening using datasets generated from screening programmes in Germany (n=1 657 068), the UK (n=223 603) and Sweden (n=22 779). The commercially available AI model used was Vara version 2.10, trained from scratch on German data. We simulated the performance of each strategy in terms of cancer detection rate (CDR), recall rate, and workload reduction, and compared the metrics with those of the screening programmes. We also assessed the distribution of the stages and grades of the cancers detected by each strategy and the AI model's ability to correctly localise those cancers. Compared with the German screening programme (CDR 6·32 per 1000 examinations, recall rate 4·11 per 100 examinations), replacement of both readers (standalone AI strategy) achieved a non-inferior CDR of 6·37 (95% CI 6·10-6·64) at a recall rate of 3·80 (95% CI 3·67-3·93), whereas single reader replacement achieved a CDR of 6·49 (6·31-6·67), a recall rate of 4·01 (3·92-4·10), and a 49% workload reduction. Programme-level decision referral achieved a CDR of 6·85 (6·61-7·11), a recall rate of 3·55 (3·43-3·68), and an 84% workload reduction. Compared with the UK programme CDR of 8·19, the reader-level, programme-level, and deferral to single reader strategies achieved CDRs of 8·24 (7·82-8·71), 8·59 (8·12-9·06), and 8·28 (7·86-8·71), without increasing recall and while reducing workload by 37%, 81%, and 95%, respectively. On the Swedish dataset, programme-level decision referral increased the CDR by 17·7% without increasing recall and while reducing reading workload by 92%. The decision referral strategies offered the largest improvements in cancer detection rates and reduction in recall rates, and all strategies except normal triaging showed potential to improve screening metrics. Vara.

Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy.

Improved adenoma detection rate (ADR) has been demonstrated with artificial intelligence (AI)-assisted colonoscopy. However, data on the real-world application of AI and its effect on colorectal cancer (CRC) screening outcomes is limited. To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or symptoms. AI software (GI Genius, Medtronic) was implemented into the standard procedure protocol in November 2022. Data was collected on patient demographics, procedure indication, polyp size, location, and pathology. CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up. We evaluated 1008 colonoscopies (278 pre-AI, 255 early post-AI, 285 established post-AI, and 190 late post-AI). The ADR was 38.1% pre-AI, 42.0% early post-AI (P = 0.77), 40.0% established post-AI (P = 0.44), and 39.5% late post-AI (P = 0.77). There were no significant differences in polyp detection rate (PDR, baseline 59.7%), advanced ADR (baseline 16.2%), and non-neoplastic PDR (baseline 30.0%) before and after AI introduction. In patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy. Although the potential of AI in colonoscopy is undisputed, current AI technology may not universally elevate screening metrics across all situations and patient populations. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.

Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer.

Background Retrospective studies have suggested that using artificial intelligence (AI) may decrease the workload of radiologists while preserving mammography screening performance. Purpose To compare workload and screening performance for two cohorts of women who underwent screening before and after AI system implementation. Materials and Methods This retrospective study included 50-69-year-old women who underwent biennial mammography screening in the Capital Region of Denmark. Before AI system implementation (October 1, 2020, to November 17, 2021), all screenings involved double reading. For screenings conducted after AI system implementation (November 18, 2021, to October 17, 2022), likely normal screenings (AI examination score ≤5 before May 3, 2022, or ≤7 on or after May 3, 2022) were single read by one of 19 senior full-time breast radiologists. The remaining screenings were read by two radiologists with AI-assisted decision support. Biopsy and surgical outcomes were retrieved between October 1, 2020, and April 15, 2023, ensuring at least 180 days of follow-up. Screening metrics were compared using the χ2 test. Reading workload reduction was measured as saved screening reads. Results In total, 60 751 and 58 246 women were screened before and after AI system implementation, respectively (median age, 58 years [IQR, 54-64 years] for both cohorts), with a median screening interval before AI of 845 days (IQR, 820-878 days) and with AI of 993 days (IQR, 968-1013 days; P < .001). After AI system implementation, the recall rate decreased by 20.5% (3.09% before AI [1875 of 60 751] vs 2.46% with AI [1430 of 58 246]; P < .001), the cancer detection rate increased (0.70% [423 of 60 751] vs 0.82% [480 of 58 246]; P = .01), the false-positive rate decreased (2.39% [1452 of 60 751] vs 1.63% [950 of 58 246]; P < .001), the positive predictive value increased (22.6% [423 of 1875] vs 33.6% [480 of 1430]; P < .001), the rate of small cancers (≤1 cm) increased (36.6% [127 of 347] vs 44.9% [164 of 365]; P = .02), the rate of node-negative cancers was unchanged (76.7% [253 of 330] vs 77.8% [273 of 351]; P = .73), and the rate of invasive cancers decreased (84.9% [359 of 423] vs 79.6% [382 of 480]; P = .04). The reading workload was reduced by 33.5% (38 977 of 116 492 reads). Conclusion In a population-based mammography screening program, using AI reduced the overall workload of breast radiologists while improving screening performance. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Lee and Friedewald in this issue.

Utilizing the open-source programming language Python to create interactive Quality Assurance dashboards for diagnostic and screening performance in Cytology

Effective feedback on cytology performance relies on navigating complex laboratory information system data, which is prone to errors and lacks flexibility. As a comprehensive solution, we used the Python programming language to create a dashboard application for screening and diagnostic quality metrics. Data from the 5-year period (2018-2022) were accessed. Versatile open-source Python libraries (user developed program code packages) were used from the first step of LIS data cleaning through the creation of the application. To evaluate performance, we selected 3 gynecologic metrics: the ASC/LSIL ratio, the ASC-US/ASC-H ratio, and the proportion of cytologic abnormalities in comparison to the total number of cases (abnormal rate). We also evaluated the referral rate of cytologists/cytotechnologists (CTs) and the ratio of thyroid AUS interpretations by cytopathologists (CPs). These were formed into colored graphs that showcase individual results in established, color-coded laboratory "goal," "borderline," and "attention" zones based on published reference benchmarks. A representation of the results distribution for the entire laboratory was also developed. We successfully created a web-based test application that presents interactive dashboards with different interfaces for the CT, CP, and laboratory management (https://drkvcsstvn-dashboards.hf.space/app). The user can choose to view the desired quality metric, year, and the anonymized CT or CP, with an additional automatically generated written report of results. Python programming proved to be an effective toolkit to ensure high-level data processing in a modular and reproducible way to create a personalized, laboratory specific cytology dashboard.

Abstract 232: Multi-dimensional patient-derived organoid analysis highly correlates with progression-free survival of pancreatic cancer patients in a retrospective clinical trial

Abstract Patient-derived tumor organoids (PDOs) are promising cancer models, since they preserve the clonal heterogeneity, mutational landscape, and histological architecture of the originating patient tumor1-2. Recent studies have even provided first evidence that PDOs responded similarly to the corresponding patient when treated with the same standard of care therapy, but these studies were only able to predict clinical responses in a subset of patients3,4. This restriction was largely due to the limitation of current analysis methods. The gold-standard analysis method for PDO-based research, CellTiter-Glo 3D,5,6 fails to account for the heterogeneity of PDOs by relying on a bulk lysis approach, and thus only extracts a fraction of the clinically-relevant information that PDOs could provide7. Therefore, we hypothesized that using higher-dimensional analysis methods, will further unlock the predictive performance of PDOs and facilitate translation of research. In this study, we developed an artificial intelligence-driven analysis platform for live-PDO imaging. By combining drug screening metrics with dynamic quantification of organoids on a single-organoid resolution, we unraveled patient-specific tumor heterogeneity and PDAC aggressiveness in response to therapies8. Using a fully characterized a PDAC organoid panel (n=8) we matched our PDO analysis with retrospective clinical patient response to standard of care therapies (gemcitabine-paclitaxel and FOLFIRINOX). Our results demonstrated that our PDO analysis method identified patient-specific sensitives to therapy that were in-line with clinical outcomes. Moreover, our PDO readouts highly correlated with progression-free survival of matched patients (R=0.97), which was a significant improvement to the current, conventional drug response readouts. Taken together, our approach increased the clinical translatability of PDOs by more accurately recapitulating and measuring the complexity of human tumors. This work highlights the potential applications (extendable to other tumor types) and clinical translatability of our approach in drug discovery and clinical care, in the emerging era of personalized medicine.

Security screening metrics for information-sharing partnerships.

Recent history has shown both the benefits and risks of information sharing among firms. Information is shared to facilitate mutual business objectives. However, information sharing can also introduce security-related concerns that could expose the firm to a breach of privacy, with significant economic, reputational, and safety implications. It is imperative for organizations to leverage available information to evaluate security related to information sharing when evaluating current and potential information-sharing partnerships. The "fine print" or privacy policies of firms can provide a signal of security across a wide variety of firms being considered for new and continued information-sharing partnerships. In this article, we develop a methodology to gauge and benchmark information security policies in the partner-selection process that can help direct risk-based investments in information sharing security. We develop a methodology to collect and interpret firm privacy policies, evaluate characteristics of those policies by leveraging natural language processing metrics and developing benchmarking metrics, and understand how those characteristics relate to one another in information-sharing partnership situations. We demonstrate the methodology on 500 high-revenue firms. The methodology and managerial insights will be of interest to risk managers, information security professionals, and individuals forming information sharing agreements across industries.

Reader bias in breast cancer screening related to cancer prevalence and artificial intelligence decision support—a reader study

ObjectivesThe aim of our study was to examine how breast radiologists would be affected by high cancer prevalence and the use of artificial intelligence (AI) for decision support.Materials and methodThis reader study was based on selection of screening mammograms, including the original radiologist assessment, acquired in 2010 to 2013 at the Karolinska University Hospital, with a ratio of 1:1 cancer versus healthy based on a 2-year follow-up. A commercial AI system generated an exam-level positive or negative read, and image markers. Double-reading and consensus discussions were first performed without AI and later with AI, with a 6-week wash-out period in between. The chi-squared test was used to test for differences in contingency tables.ResultsMammograms of 758 women were included, half with cancer and half healthy. 52% were 40–55 years; 48% were 56–75 years. In the original non-enriched screening setting, the sensitivity was 61% (232/379) at specificity 98% (323/379). In the reader study, the sensitivity without and with AI was 81% (307/379) and 75% (284/379) respectively (p < 0.001). The specificity without and with AI was 67% (255/379) and 86% (326/379) respectively (p < 0.001). The tendency to change assessment from positive to negative based on erroneous AI information differed between readers and was affected by type and number of image signs of malignancy.ConclusionBreast radiologists reading a list with high cancer prevalence performed at considerably higher sensitivity and lower specificity than the original screen-readers. Adding AI information, calibrated to a screening setting, decreased sensitivity and increased specificity.Clinical relevance statementRadiologist screening mammography assessments will be biased towards higher sensitivity and lower specificity by high-risk triaging and nudged towards the sensitivity and specificity setting of AI reads. After AI implementation in clinical practice, there is reason to carefully follow screening metrics to ensure the impact is desired.Key Points• Breast radiologists’ sensitivity and specificity will be affected by changes brought by artificial intelligence.• Reading in a high cancer prevalence setting markedly increased sensitivity and decreased specificity.• Reviewing the binary reads by AI, negative or positive, biased screening radiologists towards the sensitivity and specificity of the AI system.Graphical abstract

Timeliness metrics for screening and preventing TB in household contacts of pulmonary TB patients in Kenya.

The study assessed whether a "7-1-7" timeliness metric for screening and TB preventive therapy (TPT) could be implemented for household contacts (HHCs) of index patients with bacteriologically confirmed pulmonary TB under routine programmatic settings in Kenya. A longitudinal cohort study conducted among index patients and their HHCs in 12 health facilities, Kiambu County, Kenya. Between January and June 2023, 95% of 508 index patients had their HHCs line-listed within 7 days of initiating anti-TB treatment ("First 7"). In 68% of 1,115 HHCs, screening outcomes were ascertained within 1 day of line-listing ("Next 1"). In 65% of 1,105 HHCs eligible for further evaluation, anti-TB treatment, TPT or a decision for no drugs was made within 7 days of screening ("Second 7"). Altogether, 62% of screened HHCs started TPT during the "7-1-7" period compared with 58% in a historical cohort. Main barriers to TPT uptake were HHCs not consulting clinicians, HHCs being unwilling to initiate TPT and drug shortages. Healthcare workers felt that a timeliness metric was valuable for streamlining HHC management and proposed "3-5-7" as a workable alternative. The national TB programme must generate awareness about TPT, ensure uninterrupted drug supplies and assess whether the "3-5-7" metric can be operationalised.

Assessing the relevance of energy indicators as sustainability screening metrics for the decarbonisation of industrial heat through electrification

Power generation faces the challenge of reducing its greenhouse gas emissions without shifting the burden to other environmental impacts. Energy and exergy-based environmental indicators have proven to some extent to be effective environmental proxies for the current energy mix but the robustness of this approach has barely been discussed in the frame of energy transition scenarios. Hence, the aim of this study is to analyse their relevance in the context of decarbonisation of industrial heat through electrification. To this end, we investigate the potential relationships between two different energy indicators and environmental indicators using life cycle assessment (Ecoinvent database, 16 environmental indicators, 2 energy indicators) for 28 pairs of energy sources shifting for the production of 1 kWh of electricity. For the reference French case, no similar trends across all sources of energy, for given environmental and energy indicators are found neither between all environmental and energy indicators. The extension to other cases is discussed, leading to the same conclusion. These results highlight the need for a multi-criteria assessment to evaluate the impacts of industrial heat in a context of intensive process electrification, which will be country dependent because of the various strategies in achieving net-zero electricity mix.

Exploring Differences in Screening and Enrollment Metrics in Orthopaedic Clinical Trials.

The success of any clinical trial relies heavily on patient recruitment and retention. The purpose of this study was to review screening and enrollment metrics for orthopaedic clinical trials, comparing different patient populations to determine common challenges to recruitment and differences in rates of enrollment. Screening logs and study trackers were manually reviewed for four clinical trials at a single academic institution and included randomized controlled trials (RCTs) and an observational study. Data extracted from these documents included the number of patients screened, number excluded and reasons for exclusion, number enrolled, number of withdrawn and reason. Of the four trials reviewed, the point-of-care diagnostic test had the highest number of patients excluded and the lowest patient refusal rate. Refusal rates were highest in the venous thromboembolism prophylaxis study and enrollment rates were the lowest in the RCT of drug treatments and the highest rate in the observational study. The success of the trial relies on the ability to recruit patients and factors need to be considered when recruiting participants including sample size requirements and inclusion and exclusion criteria. These data provide some insights into the patient recruitment experience at our institution with different patient populations and study types, highlighting key points to be aware of when planning for an orthopaedic clinical trial.

Assessing the impact of dip angle on carbon storage in saline reservoirs to aid site selection

Reservoir dip angle and permeability significantly impact CO2 plume migration and the amount of secondary phase trapping in storage formations. There is a fundamental trade-off between up-dip plume migration and secondary trapping that can be advantageous, or disadvantageous, for selecting an optimal CO2 storage site. For example, low permeability reservoirs have limited plume migration up-dip, consequently, residual and solubility trapping are also limited. On the contrary, for highly permeable reservoirs, up-dip migration can be fast and extensive, leading to accelerated residual and solubility trapping. By performing a systematic simulation study of plume migration and secondary trapping in reservoirs with a range of permeabilities and dip angles, we found that having a dip angle of at least 1° and permeability of at least 500mD results in up to 88% of the plume being immobilized 100 years post-injection. In reservoirs with a permeability of at least 500mD, we can optimize the amount of immobilized CO2 while limiting the amount of plume migration. As reservoir dip angles increase up to 2° and permeability increases, CO2 plume migration increases progressively, and up to 8x more mass of CO2 can migrate up-dip versus down-dip. At the same time, CO2 solubility in brine and residual trapping work to decrease the plume volume and can immobilize 28% to 90% of the CO2 plume over 100 years post-injection. Five influential parameters were identified that strongly influence the plume volume during the post-injection period for these dipping reservoirs: CO2 saturation, residual gas saturation, CO2 solubility in brine, CO2 density, and formation permeability. We also investigated the impact of simulation grid resolution, the maximum non-wetting phase saturation, and the pore-size distribution parameter on predicted plume behavior. This work provides simple, reliable relationships between key site screening metrics, reservoir dip angle, and permeability to inform the site selection process and monitoring, measurement, and verification (MMV) design.

MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules

Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without relying on brute-force exploration. However, whether designed by AI or human experts, the molecules need to be accessibly synthesized and biologically evaluated, and the trial-and-error process remains a resources-intensive endeavor. Therefore, AI-based drug design methods face a major challenge of how to prioritize the molecular structures with potential for subsequent drug development. This study indicates that common filtering approaches based on traditional screening metrics fail to differentiate AI-designed molecules. To address this issue, we propose a novel molecular filtering method, MolFilterGAN, based on a progressively augmented generative adversarial network. Comparative analysis shows that MolFilterGAN outperforms conventional screening approaches based on drug-likeness or synthetic ability metrics. Retrospective analysis of AI-designed discoidin domain receptor 1 (DDR1) inhibitors shows that MolFilterGAN significantly increases the efficiency of molecular triaging. Further evaluation of MolFilterGAN on eight external ligand sets suggests that MolFilterGAN is useful in triaging or enriching bioactive compounds across a wide range of target types. These results highlighted the importance of MolFilterGAN in evaluating molecules integrally and further accelerating molecular discovery especially combined with advanced AI generative models.

Evaluating grant proposals: lessons from using metrics as screening device

Abstract Purpose This study examines the effects of using publication-based metrics for the initial screening in the application process for a project leader. The key questions are whether formal policy affects the allocation of funds to researchers with a better publication record and how the previous academic performance of principal investigators is related to future project results. Design/methodology/approach We compared two competitions, before and after the policy raised the publication threshold for the principal investigators. We analyzed 9,167 papers published by 332 winners in physics and the social sciences and humanities (SSH), and 11,253 publications resulting from each funded project. Findings We found that among physicists, even in the first period, grants tended to be allocated to prolific authors publishing in high-quality journals. In contrast, the SSH project grantees had been less prolific in publishing internationally in both periods; however, in the second period, the selection of grant recipients yielded better results regarding awarding grants to more productive authors in terms of the quantity and quality of publications. There was no evidence that this better selection of grant recipients resulted in better publication records during grant realization. Originality This study contributes to the discussion of formal policies that rely on metrics for the evaluation of grant proposals. The Russian case shows that such policy may have a profound effect on changing the supply side of applicants, especially in disciplines that are less suitable for metric-based evaluations. In spite of the criticism given to metrics, they might be a useful additional instrument in academic systems where professional expertise is corrupted and prevents allocation of funds to prolific researchers.

Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload.

To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree.

The conundrum of neuropsychiatric systemic lupus erythematosus: Current and novel approaches to diagnosis.

Recognising neuropsychiatric involvement by systemic lupus erythematosus (SLE) is of growing importance, however many barriers to this exist at multiple levels of our currently available diagnostic algorithms that may ultimately delay its diagnosis and subsequent treatment. The heterogeneous and non-specific clinical syndromes, serological and cerebrospinal fluid (CSF) markers and neuroimaging findings that often do not mirror disease activity, highlight important research gaps in the diagnosis of neuropsychiatric SLE (NPSLE). Formal neuropsychological assessments or the more accessible screening metrics may also help improve objective recognition of cognitive or mood disorders. Novel serum and CSF markers, including autoantibodies, cytokines and chemokines have also shown increasing utility as part of diagnosis and monitoring, as well as in distinguishing NPSLE from SLE patients without SLE-related neuropsychiatric manifestations. Novel neuroimaging studies also expand upon our existing strategy by quantifying parameters that indicate microarchitectural integrity or provide an assessment of neuronal function. Some of these novel markers have shown associations with specific neuropsychiatric syndromes, suggesting that future research move away from considering NPSLE as a single entity but rather into its individually recognized neuropsychiatric manifestations. Nevertheless, it is likely that a composite panel of these investigations will be needed to better address the gaps impeding recognition of neuropsychiatric involvement by SLE.

The Role of Psychosocial Screening in Patient Selection for Spine Surgery: A Review.

The purpose of this review is to provide a brief history of previous attempts at developing patient screening tools and to further examine the definitions of these psychological concepts, relevance to clinical outcomes, and implications for spine surgeons during preoperative patient assessments. A literature review was performed by 2 independent researchers to identify original manuscripts related to spine surgery and novel psychological concepts. The history of presurgical psychology screening was also studied, and definitions of frequently utilized metrics were detailed. Seven manuscripts were identified that utilized psychological metrics for preoperative risk assessments and correlated outcomes with these scores. The metrics most frequently used in the literature included resilience, patient activation, grit, and self-efficacy. Current literature favors resilience and patient activation as important metrics for preoperative patient screening. Available studies demonstrate significant associations between these character traits and patient outcomes. Further research is warranted to investigate the roles of preoperative psychological screening to optimize patient selection in spine surgery. The purpose of this review is to provide clinicians with a reference for available psychosocial screening tools and their relevance to patient selection. This review also serves to guide future research directions given the importance of this topic.

Objective cataract grading methods and expected contrast sensitivity reestablishment with multifocal intraocular lenses.

To determine the cut-off points for age and metrics provided by cataract grading objective systems for which a reestablishment in contrast sensitivity(CS) is expected after multifocal intraocular lens(MIOL) implantation. One hundred seven subjects were included in this retrospective analysis carried out during the screening for presbyopia and cataract surgery. Monocular distance corrected contrast sensitivity defocus curve (CSDC) and visual acuity were measured, and crystalline lens sclerosis was graded with three objective metrics: Ocular Scatter Index(OSI), Dysfunctional Lens Index(DLI) and Pentacam Nucleus Staging(PNS). A CS value of 0.8 logCS at far distance, following published literature on this matter, was selected to compute the cut-off that maximized the detection of eyes exceeding this value at the preoperative screening, either for age or objective metrics. The CDCS showed a stronger correlation than CDVA with objective grading methods, whereas all objective metrics were significantly correlated between them (p < 0.05). The cut-offs for age, OSI, DLI and PNS were ≤ 62, ≤ 1.25, ≥ 7.67 and ≤ 1, respectively. The OSI provided the highest area under the receiver operating characteristic curve (0.85) followed by the age (0.84), DLI (0.74) and PNS (0.63). Surgeons conducting clear lens exchange should communicate the possible distance CS loss after surgery with MIOL implantation according to the previous described cut-offs points. Age in combination with any cataract grading objective system is recommended to detect possible inconsistencies.