Predictive Testing Research Articles

Combining p-values for Multivariate Predictive Ability Testing

In this paper, we propose an intersection-union test for multivariate forecast accuracy based on the combination of a sequence of univariate tests. The testing framework evaluates a global null hypothesis of equal predictive ability using any number of univariate forecast accuracy tests under arbitrary dependence structures, without specifying the underlying multivariate distribution. An extensive Monte-Carlo simulation exercise shows that our proposed test has very good size and power properties under several relevant scenarios, and performs well in both low- and high-dimensional settings. We illustrate the empirical validity of our testing procedure using a large dataset of 84 daily exchange rates running from 1 January 2011 to 1 April 2021. We show that our proposed test addresses inconclusive results that often arise in practice.

Prädiktive Immunzytochemie beim nicht-kleinzelligen Lungenkarzinom

Die Immunchemie ist eine zeit-, tumorproben- und kosteneffiziente Methode zur Untersuchung prädiktiver Biomarker bei fortgeschrittenen nicht-kleinzelligen Lungenkarzinomen (NSCLC). Die Immunhistochemie (IHC) an Formalin-fixiertem, Paraffin-eingebettetem (FFPE) Tumorgewebe hat sich für den Nachweis der PD-L1-Expression sowie für die ALK-, ROS1- und neuerdings auch für die NTRK-Untersuchung bewährt. Zytologische Proben als Quelle für prädiktive Markeranalysen sind sehr wichtig, da bis zu 40 % aller NSCLC rein zytologisch diagnostiziert werden.Trotz der etablierten Rolle der Zytologie in der Lungenkarzinomdiagnostik wurden keine kommerziellen IHC-Assays für zytologische Proben validiert.Die prädiktive Immunzytochemie (ICC) ist am einfachsten an FFPE-Zellblöcken (CB) durchzuführen, da für FFPE-Histologie standardisierte Protokolle verwendet werden können. CB sind jedoch nicht immer verfügbar.Nicht als CB verarbeitete zytologische Präparate sind weniger standardisiert als histologische Präparate und weisen eine erhebliche präanalytische Variabilität auf. Daher ist eine strenge zytologiespezifische Optimierung, Validierung und Qualitätskontrolle von ICC-Protokollen erforderlich. Unter dieser Voraussetzung ist die prädiktive ICC, die in der Regel an Papanicolaou-gefärbten Zytologien durchgeführt wird, robust und zuverlässig. Dieses wertvolle zytologische Material sollte für prädiktive Biomarkeranalysen genutzt werden, um Patientinnen und Patienten nicht dem unnötigen Risiko einer erneuten Probenentnahme auszusetzen. Diese Übersichtsarbeit beleuchtet präanalytische, analytische und postanalytische Aspekte, die ICC-Ergebnisse beeinflussen können, und fasst die veröffentlichten Daten zur prädiktiven ICC für PD-L1, ALK und ROS1 bei NSCLC zusammen.

Predictive genetic testing for Motor neuron disease: time for a guideline?

Predictive genetic testing for Motor neuron disease: time for a guideline?

Giving Body to the Body: Predictive Genetic Testing and Self-desomatization

Genetics test in predictive medicine seems to take charge of the uniqueness of any human being. Unlike preventive medicine it moves from the theoretical assumption of the knowledge of a specific individual’s genetic structure and potential fragility. However, the attention paid to the gene risks placing the living and experienced body in the shadow. Sometimes, “genetic news” can make the subject in the present act like a sick person without being so, read every event in that direction, and, ultimately, fulfill the prophecy. The article goes beyond the alleged non-exceptionalism of genetic data and discusses the symbolic value that the gene has assumed and its role in reflexivity and self-perception.

INTELLIGENT RESOURCE ALLOCATION MODEL IN HEALTHCARE: A SOLUTION FOR DISTRIBUTED SYSTEMS IN PEDIATRIC PATIENTS’ REFERRAL. A STRUCTURED REVIEW

Background/Introduction: The social behaviour of some animals have been simulated to design intelligent systems capable of solving complex problems such as those experienced in healthcare. Researchers and medical practitioners are interested in swarm intelligence (SI) in the healthcare domain. However, there have been limited published research on this subject from the perspective of resource allocation especially in line with providing a solution to pediatric patients? referral. Methods: The researchers were able to find 16,870 distinct data using a comprehensive literature review. 979 research were left for detailed review following titles and abstracts were screened. In the end, 90 research were found to meet the requirements. The paper filtered study characteristics of researchers (country of the corresponding author); social variables of authors (number of participants and type of disease or treatment studied); intervention parameters (SI methods models); and outcome characteristics, including patient, practitioner, and health-care system impacts. Results: According to the assessment, there is a developing foundation of expertise. Clinical decision-making, quality healthcare organization and management, predictive medicine, clinical documentation and testing analysis , and patient data and diagnostics are among the topics covered. The highest number of studies came from the United States, China, and the United Kingdom. SI can also enable practitioners in developing diagnoses, forecasting disease spread, connecting patient needs to therapy continuums, and customizing treatment courses, according to the findings. Conclusions:, Several AI applications for healthcare coverage are revealed in the literature, as well as a channel of study results that have not yet been thoroughly explored. with referral services forming minimal areas covered. Pediatric patients? referral completely misses out. SI projects, as per the report, include knowledge and data integrity awareness for content analysis and knowledge-based management. These insights can be used by scientists and health practitioners to better understand and address future SI research in the care industry business.

Data sharing in the age of predictive psychiatry: an adolescent perspective

BackgroundAdvances in genetics and digital phenotyping in psychiatry have given rise to testing services targeting young people, which claim to predict psychiatric outcomes before difficulties emerge. These services raise several...

BIOCHEMICAL PROFILE, BACTERIAL PROFILE, AND ANTIBIOTIC SUSCEPTIBILITY PATTERN OF THE ISOLATES OF NEONATAL SEPTICEMIA IN TERTIARY CARE HOSPITAL, VISAKHAPATNAM

Objectives: The objective of the study was to determine sensitivity, specificity, and predictive values of C-reactive protein (CRP), leukocyte count, platelet count, and blood culture as indicators of neonatal sepsis. Methods: This prospective study was conducted in the Department of Paediatrics, GITAM Institute of Medical sciences and Research, Visakhapatnam during January 2019 to December 2019. The institutional ethics committee had approved the study. Eighty clinically suspected cases of neonatal sepsis constituted material of study. All neonates who were clinically suspected sepsis, categorized into 0–72 h (early onset) or late onset (>3 days) sepsis based on day of presentation. Detailed history and clinical findings were recorded in the pro forma and screened with CRP and various hematological tests with predetermined cutoff values and at the same time blood culture was sent. Concerning a clinical scenario CSF, urine analysis, chest X-ray, and infective focus swabs were all taken. The length of treatment and time spent in the hospital were recorded. The death rate was calculated, and numerous risk variables were evaluated. Results: Intrapartum risk factors such as a prolonged rupture of membrane >24 h, a long labor, several filthy vaginal examinations (>3) before birth, and foul-smelling liquid were all linked to culture-proven newborn sepsis. Any of the two risk factors listed above was a good predictor of sepsis with a positive culture. Neonatal sepsis impacted a higher percentage of low birth weight and preterm babies. The sensitivity and negative predictive value of the I/T ratio and CRP were both high. The most prevalent isolates were Klebsiella species, followed by Staphylococcus aureus and Escherichia coli. While S. aureus was 80% sensitive to Cefotaxime and Amikacin, Klebsiella species and E. coli were only 20% sensitive. All were sensitive to Ofloxacin in range of 75-100%. Septicemia with multiorgan dysfunction was the most common mode of death with 100% risk of mortality. Conclusion: Intraparum risk factors are significantly with culture proven sepsis. All sepsis screening parameters used were statistically significantly associated with culture proven sepsis. I/T ratio and CRP were more sensitivity and had high negative predictive value. CRP can be used as an early and predictable screening test for diagnosing neonatal sepsis.

The Picky Eating Questionnaire and Child-reported Food Preference Questionnaire: Pilot validation in Australian-Indian mothers and children 7-12 years old

Limited literature has examined parents’ perceptions of children’s pickiness in relation to all the five core food groups (vegetables, legumes/beans; fruits; dairy and alternatives; meat and alternatives; cereals), which is representative of a nutritionally balanced diet and critical for optimal growth and development in children. This study aimed to develop and validate two questionnaires in Australian-Indian mothers and children 7–12 years (N = 482). The core food Picky Eating Questionnaire (PEQ), completed by mothers, identified maternal perceptions of their child’s pickiness. The Child-reported Food Preference Questionnaire (C-FPQ) studied children’s self-reported food preferences. The questionnaires comprised specific food items commonly available in Australia across the five core food groups (PEQ, N = 32; C-FPQ, N = 33) and discretionary foods (C-FPQ, N = 11). Exploratory Factor Analysis identified the initial factor structure, and Confirmatory Factor Analysis provided construct validity. The PEQ observed five constructs, and C-FPQ observed three constructs for food items perceived as picky/non-preferred-green leafy vegetables; other vegetables, pulses/legumes; fruits; wholegrain/wholemeal cereals (PEQ only) and dairy (PEQ only). The PEQ and C-FPQ observed four constructs for food items perceived as not picky/preferred-green vegetables; other vegetables; fruits and nuts, and dairy. C-FPQ also observed savoury and sweet discretionary food constructs. All constructs observed acceptable reliability (test–retest, internal consistency) and validity (convergent, relative, predictive) testing. Mean scores indicated that mothers’ perceptions of pickiness were positively correlated with their children’s report of non-preference. In conclusion, this study pilot validated two questionnaires to examine maternal perceptions of pickiness and children’s self-reported food preferences among Australian-Indians, Australia’s largest ethnic community.

Psychological Impact of Predictive Genetic Testing for Inherited Alzheimer Disease and Frontotemporal Dementia: The IT-DIAfN Protocol.

Aim:Our aim was to evaluate the psychological impact of predictive genetic testing in individuals at-risk for inherited dementia who underwent a structured counseling and testing protocol.Methods:Participants were healthy at-risk relatives from families with at least one affected patient, in whom a disease-associated genetic variant had been ascertained. A comprehensive psychological assessment (personality, anxiety and depression, quality of life, coping strategies, resilience and health-related beliefs) was administered at baseline, at 6 months and 12 months follow-up.Results:Twenty-four participants from 13 families were included. Sixteen participants underwent blood sampling and genetic analysis; 6 resulted to be carriers of pathogenic variants (1 in PSEN1, 1 in PSEN2, 4 in GRN). Carriers showed higher score on the Resilience Scale for Adults (RSA) – social competence, and on Multidimensional Health Locus of Control – internal, than noncarriers (P=0.03 for both). Ten at-risk relatives who completed the follow-up showed improvement in RSA – planned future (P=0.01) with respect to baseline.Discussion:Our case series showed that at-risk individuals undergoing predictive testing showed benefit on personal life and no detrimental impact on a broad range of psychological outcomes. Higher social skills and lower internal health locus of control in carriers may be an early psychological correlate of preclinical dementia.

What's new in molecular genetic pathology 2022: immune checkpoint inhibitor biomarkers and select solid tumors.

Predictive biomarker testing plays a critical role in targeted immuno-oncology, including the use of immune checkpoint inhibitors (ICI) for various solid tumors. Molecular advancements in cancers of the breast, kidney and brain have continued to propel tumor classification and precision therapy.

Population DNA screening for medically actionable disease risk in adults

Population DNA screening for medically actionable disease risk in adults

Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21

SummaryBackgroundMortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021.MethodsAll-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification.FindingsAlthough reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1–19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1–129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71–4·36]), the USA (1·13 million [1·08–1·18]), Russia (1·07 million [1·06–1·08]), Mexico (798 000 [741 000–867 000]), Brazil (792 000 [730 000–847 000]), Indonesia (736 000 [594 000–955 000]), and Pakistan (664 000 [498 000–847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7–378·4] per 100 000) and Mexico (325·1 [301·6–353·3] per 100 000), and was similar in Brazil (186·9 [172·2–199·8] per 100 000) and the USA (179·3 [170·7–187·5] per 100 000).InterpretationThe full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic.FundingBill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom

An Analysis of Agricultural Systems Modelling Approaches and Examples to Support Future Policy Development under Disruptive Changes in New Zealand

Agricultural systems have entered a period of significant disruption due to impacts from change drivers, increasingly stringent environmental regulations and the need to reduce unwanted discharges, and emerging technologies and biotechnologies. Governments and industries are developing strategies to respond to the risks and opportunities associated with these disruptors. Modelling is a useful tool for system conceptualisation, understanding, and scenario testing. Today, New Zealand and other nations need integrated modelling tools at the national scale to help industries and stakeholders plan for future disruptive changes. In this paper, following a scoping review process, we analyse modelling approaches and available agricultural systems’ model examples per thematic applications at the regional to national scale to define the best options for the national policy development. Each modelling approach has specificities, such as stakeholder engagement capacity, complex systems reproduction, predictive or prospective scenario testing, and users should consider coupling approaches for greater added value. The efficiency of spatial decision support tools working with a system dynamics approach can help holistically in stakeholders’ participation and understanding, and for improving land planning and policy. This model combination appears to be the most appropriate for the New Zealand national context.

Influence of Receivables Turnover and Inventory Turnover to Profitability PT Kimia Farma (Persero) Tbk

This study aims to determine the effect of receivables turnover (RTO) and inventory turnover (ITR) on profitability (ROA). The method used in this study is a quantitative method by taking the financial statements of PT Kimia Farma (Persero) Tbk. This study uses descriptive statistical analysis, classical assumption test, predictive multiple regression predictive hypothesis testing and coefficient of determination with SPSS Version 22 software. Receivable Turnover (RTO) partially has no a significant effect where tcount 1.166 < ttable 2.44691 with a significant value of 0.288 > 0.05 on profitability (ROA) and Inventory Turnover (ITR) partially has no a significant effect where tcount 0.149 < ttable 2.44691 with a significant value of 0.887 > 0.05 on profitability (ROA). Simultaneously, Accounts Receivable and Inventory Turnover have no effect where Fcount 1.563 < Ftable 5.14 with a significant level of 0.284 > 0.05.

Public perceptions of predictive testing for rheumatoid arthritis compared to breast cancer and early-onset Alzheimer\u2019s disease: a qualitative study

BackgroundThere is increasing research focus on prediction and prevention of rheumatoid arthritis (RA). Information about risk of RA is increasingly available via direct-to-consumer testing. However, there is limited understanding of public perceptions around predictive testing for RA. This study explores public perceptions of predictive testing for RA in comparison to breast cancer (BC) and early-onset Alzheimer’s disease (AD).MethodsFour focus groups with 21 members of the public were conducted using hypothetical vignettes about predictive testing for each disease. Transcripts of focus group proceedings were analysed inductively using thematic analysis.ResultsThematic analysis of the data produced three key themes: decision-making factors, consequences, and consumer needs. Factors suggested that might influence decision-making about predictive testing included family history, fear, and perceived severity and treatability of the illness. RA was perceived to be less severe and more treatable than BC/AD. Potential consequences of predictive testing across all diseases included lifestyle modification, planning for the future and discrimination by employers or insurers. Predictive testing for RA was perceived to have less potential for negative psychological consequences than other diseases. Participants highlighted that individuals undertaking predictive testing should be signposted to appropriate support services and receive information on the accuracy of predictive testing. It was suggested that strategies to mitigate concerns regarding communication and confidentiality of risk results are required.ConclusionsThe findings of this study reflect public misunderstandings regarding RA that may impact the uptake of and responses to predictive testing, and key informational needs of individuals considering a predictive test. Predictive strategies should be accompanied by awareness-raising initiatives and informational resources.

A Constructive Heuristic for Automated Parallel Tests Assembly

Parallel tests contain different items but have the same measurement properties. They are administered at the same or different time slots and their measurement results must be comparable. The problem of automated parallel tests assembly is studied for a long time, and many (mostly improvement) heuristic solutions are proposed and elaborated in literature. Such approaches frequently suffer from algorithms of unpredictable execution time, forcing the methods to terminate execution when some time limit or solution quality is reached. This paper proposes an efficient method of polynomial complexity, as a complete solution to the automated parallel tests assembly problem. The method uses the idea of Nawaz, Enscore, and Ham constructive heuristic algorithm to reduce the number of examined permutations, originally exploited for solving the permutation flow-shop sequencing problem. We compared the experimental results of the proposed method with two methods based on improvement heuristics that solve the same problem formulation, simulated annealing and variable neighborhood search. The main advantages of the proposed method are predictable execution time and implementation simplicity. Achieved quality of assembled tests, combined with predictable test assembly execution time, may be of particular interest in cases when computational resources for test assembly and administering are overloaded.

Can naive Bayes classifier predict infection in a close contact of COVID-19? A comparative test for predictability of the predictive model and healthcare workers in Japan

BackgroundThose who are found in close contact with COVID-19 patients and are also negative by polymerase chain reaction (PCR) test may act without waiting for the incubation period to elapse, can become infectious and spread the infection. MethodsA machine learning model that can evaluate the risk of infection in close contact with COVID-19 patients within the incubation period from the contact status reported from the index case was created using posterior probabilities. To confirm actual predictability, a verification test was conducted on 169 new close contacts, and the machine learning model was compared with four experienced healthcare workers for the predictability. ResultsIn a verification test, 33 of the 169 contacts were infected with COVID-19 during the incubation period, and 13 of 33 were negative on initial PCR test, after that the disease developed and their PCR test became positive. The machine learning model predicted the eventual infection in 12 of 13 patients who had negative results on the initial PCR test. In the verification test, the sensitivity of the machine learning model was 0.879 and the specificity was 0.588. The mean−standard deviation of the sensitivity and the specificity of the four health care workers was 0.568 (0.230) for sensitivity and 0.689 (0.103) for specificity. ConclusionIf it is possible to convey that individual risk of infection, the close contact may take suppressive action during the incubation period regardless of the result of the initial PCR test, thereby preventing secondary spread of infection.

Predictive Biomarker Testing for Lung Cancer: Past and Future Perspectives

Predictive Biomarker Testing for Lung Cancer: Past and Future Perspectives

Three-Dimensional Modelling of Ovarian Cancer: From Cell Lines to Organoids for Discovery and Personalized Medicine.

Ovarian cancer has the highest mortality of all of the gynecological malignancies. There are several distinct histotypes of this malignancy characterized by specific molecular events and clinical behavior. These histotypes have differing responses to platinum-based drugs that have been the mainstay of therapy for ovarian cancer for decades. For histotypes that initially respond to a chemotherapeutic regime of carboplatin and paclitaxel such as high-grade serous ovarian cancer, the development of chemoresistance is common and underpins incurable disease. Recent discoveries have led to the clinical use of PARP (poly ADP ribose polymerase) inhibitors for ovarian cancers defective in homologous recombination repair, as well as the anti-angiogenic bevacizumab. While predictive molecular testing involving identification of a genomic scar and/or the presence of germline or somatic BRCA1 or BRCA2 mutation are in clinical use to inform the likely success of a PARP inhibitor, no similar tests are available to identify women likely to respond to bevacizumab. Functional tests to predict patient response to any drug are, in fact, essentially absent from clinical care. New drugs are needed to treat ovarian cancer. In this review, we discuss applications to address the currently unmet need of developing physiologically relevant in vitro and ex vivo models of ovarian cancer for fundamental discovery science, and personalized medicine approaches. Traditional two-dimensional (2D) in vitro cell culture of ovarian cancer lacks critical cell-to-cell interactions afforded by culture in three-dimensions. Additionally, modelling interactions with the tumor microenvironment, including the surface of organs in the peritoneal cavity that support metastatic growth of ovarian cancer, will improve the power of these models. Being able to reliably grow primary tumoroid cultures of ovarian cancer will improve the ability to recapitulate tumor heterogeneity. Three-dimensional (3D) modelling systems, from cell lines to organoid or tumoroid cultures, represent enhanced starting points from which improved translational outcomes for women with ovarian cancer will emerge.

Feasibility of EBUS-TBNA for histopathological and molecular diagnostics of NSCLC-A retrospective single-center experience.

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive bronchoscopic procedure, well established as a diagnostic modality of first choice for diagnosis and staging of non-small cell lung cancer (NSCLC). The therapeutic decisions for advanced NSCLC require comprehensive profiling of actionable mutations, which is currently considered to be an essential part of the diagnostic process. The purpose of this study was to evaluate the utility of EBUS-TBNA cytology specimen for histological subtyping, molecular profiling of NSCLC by massive parallel sequencing (MPS), as well as for PD-L1 analysis. A retrospective review of 806 EBUS bronchoscopies was performed, resulting in a cohort of 132 consecutive patients with EBUS-TBNA specimens showing NSCLC cells in lymph nodes. Data on patient demographics, radiology features of the suspected tumor and mediastinal engagement, lymph nodes sampled, the histopathological subtype of NSCLC, and performed molecular analysis were collected. The EBUS-TBNA specimen proved sufficient for subtyping NSCLC in 83% and analysis of treatment predictive biomarkers in 77% (MPS in 53%). The adequacy of the EBUS-TBNA specimen was 69% for EGFR gene mutation analysis, 49% for analysis of ALK rearrangement, 36% for ROS1 rearrangement, and 33% for analysis of PD-L1. The findings of our study confirm that EBUS-TBNA cytology aspirate is appropriate for diagnosis and subtyping of NSCLC and largely also for treatment predictive molecular testing, although more data is needed on the utility of EBUS cytology specimen for MPS and PD-L1 analysis.