scholarly journals Pregnancy data enable identification of relevant biomarkers and a partial prognosis of autism at birth

2020 ◽  
Author(s):  
Hugues Caly ◽  
Hamed Rabiei ◽  
Perrine Coste-Mazeau ◽  
Sebastien Hantz ◽  
Sophie Alain ◽  
...  
Keyword(s):  
Behavioral Interventions ◽  
Statistical Tests ◽  
False Positive Rate ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Gradient Boosting ◽  
Decision Tree Classification ◽  
Positive Rate ◽  
History Of

AbstractAttempts to extract early biomarkers and expedite detection of Autism Spectrum Disorder (ASD) have been centered on postnatal measures of babies at familial risk. Here, we suggest that it might be possible to do these tasks already at birth relying on ultrasound and biological measurements routinely collected from pregnant mothers and fetuses during gestation and birth. We performed a gradient boosting decision tree classification analysis in parallel with statistical tests on a population of babies with typical development or later diagnosed with ASD. By focusing on minimization of the false positive rate, the cross-validated specificity of the classifier reached to 96% with a sensitivity of 41% and a positive predictive value of 77%. Extracted biomarkers included sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femoral length in the 3rd trimester, white cells in the 3rd trimester, fetal heart rate during labour, newborn feeding and newborn’s temperature difference between birth and one day after. Statistical models revealed that 38% of babies later diagnosed with ASD had significantly larger fetal cephalic perimeter than age matched neurotypical babies, suggesting an in-utero origin of the bigger brains of toddlers with ASD. Results pave the way to use pregnancy follow-up measurements to provide an early prognosis of ASD and implement pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.

scholarly journals Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hugues Caly ◽  
Hamed Rabiei ◽  
Perrine Coste-Mazeau ◽  
Sebastien Hantz ◽  
Sophie Alain ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Behavioral Interventions ◽  
Learning Algorithm ◽  
Statistical Tests ◽  
False Positive Rate ◽  
Supervised Machine Learning ◽  
Fetal Head ◽  
Positive Rate ◽  
History Of

AbstractTo identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

scholarly journals Role of Invasive Urodynamic Studies in Establishing Cauda Equina Syndrome and Postoperative Recovery

2020 ◽  
pp. 219256822097964
Author(s):  
Abhinandan Reddy Mallepally ◽  
Bibhudendu Mohapatra ◽  
Kalidutta Das
Keyword(s):  
Clinical Symptoms ◽  
False Positive Rate ◽  
Cauda Equina ◽  
Postoperative Recovery ◽  
Normal Bladder ◽  
Acontractile Detrusor ◽  
Positive Rate ◽  
Acute Radiculopathy ◽  
Operative Recovery

Study design: Retrospective with prospective follow-up. Objective: Confirming the diagnosis of CES based purely on symptoms and signs is unreliable and usually associated with high false positive rate. A missed diagnosis can permanently disable the patient. Present study aims to determine the relationship between clinical symptoms/ signs (bladder dysfunction) with UDS, subsequently aid in surgical decision making and assessing post-operative recovery. Methods: A prospective follow-up of patients with disc herniation and bladder symptoms from January 2018 to July 2020 was done. All patients underwent UDS and grouped into acontractile, hypocontractile and normal bladder. Data regarding PAS, VAC, GTP, timing to surgery and onset of radiculopathy and recovery with correlation to UDS was done preoperatively and post operatively. Results: 107 patients were studied (M-63/F-44). Patients with PAS present still had acontractile (61%) or hypocontractile (39%) detrusor and with VAC present, 57% had acontractile and 43% hypocontractile detrusors. 10 patients with both PAS and VAC present had acontractile detrusor. 82% patients with acute radiculopathy (<2 days) improved when operated <24 hrs while only 47% showed improvement with chronic radiculopathy. The detrusor function recovered in 66.1% when operated <12 hours, 40% in <12-24 hours of presentation. Conclusion: Adjuvant information from UDS in combination with clinicoradiological findings help in accurate diagnosis even in patients with no objective motor and sensory deficits. Quantitative findings on UDS are consistent with postoperative recovery of patient’s urination power, representing improvement and can be used as a prognostic factor.

scholarly journals Utilizing two-tiered screening for early detection of autism spectrum disorder

Autism ◽  
2017 ◽  
Vol 22 (7) ◽  
pp. 881-890 ◽  
Author(s):  
Meena Khowaja ◽  
Diana L Robins ◽  
Lauren B Adamson
Keyword(s):  
Early Detection ◽  
Young Children ◽  
False Positive ◽  
Screening Tool ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Screening Methods ◽  
Positive Rate ◽  
Level 1 ◽  
Level 2

Despite advances in autism screening practices, challenges persist, including barriers to implementing universal screening in primary care and difficulty accessing services. The high false positive rate of Level 1 screening methods presents especially daunting difficulties because it increases the need for comprehensive autism evaluations. This study explored whether two-tiered screening—combining Level 1 (Modified Checklist for Autism in Toddlers, Revised with Follow-Up) and Level 2 (Screening Tool for Autism in Toddlers and Young Children) measures—improves the early detection of autism. This study examined a sample of 109 toddlers who screened positive on Level 1 screening and completed a Level 2 screening measure prior to a diagnostic evaluation. Results indicated that two-tiered screening reduced the false positive rate using published Screening Tool for Autism in Toddlers and Young Children cutoffs compared to Level 1 screening alone, although at a cost to sensitivity. However, alternative Screening Tool for Autism in Toddlers and Young Children scoring in the two-tiered screening improved both positive predictive value and sensitivity. Exploratory analyses were conducted, including comparison of autism symptoms and clinical profiles across screening subsamples. Recommendations regarding clinical implications of two-tiered screening and future areas of research are presented.

scholarly journals Identification of newborns at risk for autism using electronic medical records and machine learning

2019 ◽  
Author(s):  
Rayees Rahman ◽  
Arad Kodesh ◽  
Stephen Z Levine ◽  
Sven Sandin ◽  
Abraham Reichenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Positive Predictive Value ◽  
Electronic Medical Records ◽  
Predictive Value ◽  
False Positive ◽  
Medical Records ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Positive Rate

AbstractImportanceCurrent approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, where most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome.ObjectiveDevelop a machine learning (ML) method predicting the diagnosis of ASD in offspring in a general population sample, using parental electronic medical records (EMR) available before childbirthDesignPrognostic study of EMR data within a single Israeli health maintenance organization, for the parents of 1,397 ASD children (ICD-9/10), and 94,741 non-ASD children born between January 1st, 1997 through December 31st, 2008. The complete EMR record of the parents was used to develop various ML models to predict the risk of having a child with ASD.Main outcomes and measuresRoutinely available parental sociodemographic information, medical histories and prescribed medications data until offspring’s birth were used to generate features to train various machine learning algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross validation, by computing C statistics, sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value, PPV).ResultsAll ML models tested had similar performance, achieving an average C statistics of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85% for predicting ASD in this dataset.Conclusion and relevanceML algorithms combined with EMR capture early life ASD risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.Key pointsQuestionCan autism risk in children be predicted using the pre-birth electronic medical record (EMR) of the parents?FindingsIn this population-based study that included 1,397 children with autism spectrum disorder (ASD) and 94,741 non-ASD children, we developed a machine learning classifier for predicting the likelihood of childhood diagnosis of ASD with an average C statistic of 0.70, sensitivity of 28.63%, specificity of 98.62%, accuracy of 96.05%, false positive rate of 1.37%, and positive predictive value of 45.85%.MeaningThe results presented serve as a proof-of-principle of the potential utility of EMR for the identification of a large proportion of future children at a high-risk of ASD.

scholarly journals Identification of newborns at risk for autism using electronic medical records and machine learning

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Rayees Rahman ◽  
Arad Kodesh ◽  
Stephen Z. Levine ◽  
Sven Sandin ◽  
Abraham Reichenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
General Population ◽  
Electronic Medical Records ◽  
False Positive ◽  
Medical Records ◽  
False Positive Rate ◽  
Autism Spectrum ◽  
Health Maintenance ◽  
C Statistic ◽  
Positive Rate

Abstract Background. Current approaches for early identification of individuals at high risk for autism spectrum disorder (ASD) in the general population are limited, and most ASD patients are not identified until after the age of 4. This is despite substantial evidence suggesting that early diagnosis and intervention improves developmental course and outcome. The aim of the current study was to test the ability of machine learning (ML) models applied to electronic medical records (EMRs) to predict ASD early in life, in a general population sample. Methods. We used EMR data from a single Israeli Health Maintenance Organization, including EMR information for parents of 1,397 ASD children (ICD-9/10) and 94,741 non-ASD children born between January 1st, 1997 and December 31st, 2008. Routinely available parental sociodemographic information, parental medical histories, and prescribed medications data were used to generate features to train various ML algorithms, including multivariate logistic regression, artificial neural networks, and random forest. Prediction performance was evaluated with 10-fold cross-validation by computing the area under the receiver operating characteristic curve (AUC; C-statistic), sensitivity, specificity, accuracy, false positive rate, and precision (positive predictive value [PPV]). Results. All ML models tested had similar performance. The average performance across all models had C-statistic of 0.709, sensitivity of 29.93%, specificity of 98.18%, accuracy of 95.62%, false positive rate of 1.81%, and PPV of 43.35% for predicting ASD in this dataset. Conclusions. We conclude that ML algorithms combined with EMR capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.

dtectDx Breast: A serum biomarker test for breast cancer detection used in conjunction with traditional mammography screening.

2013 ◽  
Vol 31 (26_suppl) ◽  
pp. 18-18
Author(s):  
Meredith C. Henderson ◽  
Keri Sweeten ◽  
Sherri Borman ◽  
Christa Corn ◽  
Lindsey Gordon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
False Positive Rate ◽  
Standard Of Care ◽  
Patient Characteristics ◽  
Protein Biomarkers ◽  
Time Period ◽  
Positive Rate ◽  
Previous Clinical Study

18 Background: Provista Diagnostics has developed a test that analyzes serum concentrations of 5 protein biomarkers in order to detect breast cancer. The dtectDx Breast test utilizes a proprietary algorithm that has been described previously (Weber et al. 2010). In this study, it was noted that the algorithm performs best in women under age 50. The aim of this study was to evaluate the performance characteristics of dtectDx Breast in women under age 50 in a commercial setting and compare the results with data from the previous clinical study. Methods: The dtectDx Breast test measures the concentrations of IL-8, IL-12, VEGF, CEA, and HGF via ELISA. These data combined with select patient characteristics and Provista’s proprietary algorithm result in a test value that is characterized as normal or elevated. dtectDx Breast test reports issued for women under age 50 were reviewed from a 3-year time period and prescribing physicians were interviewed regarding follow-up care and outcome measures (largely imaging studies, if warranted). Results: Of the 908 patients, 8 samples were rejected based on serum quality. Of the remaining 900 patients, 121 were reported as elevated (12.7%). In 4 cases, these elevated results were confirmed cases of breast cancer. Of these, 2 patients initially showed no screening evidence of cancer, but upon further evaluation (after receipt of dtectDx Breast results) were diagnosed with breast cancer. dtectDx correctly identified DCIS 66% of the time (n=2). Conclusions: These results describe the use of dtectDx Breast in a clinical setting and confirm that the assay behaves similarly to previously published results (Weber et al 2010). While the false-positive rate is higher than predicted (12.7% vs 6.8%), the assay correctly identified 4 of 4 invasive cancers and 2 of 3 DCIS cases. Since two of the invasive cancer cases were originally not detected via standard screening procedures, the assay has demonstrated important clinical utility when used in conjunction with mammography/standard of care. Here we show that, in the commercial patient population, when combined with standard of care, dtectDx Breast improves the detection of breast cancer in women under 50.

scholarly journals Anencephaly: Do the Pregnancy and Maternal Characteristics Impact the Pregnancy Outcome?

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Isabela Nelly Machado ◽  
Sílvia Dante Martinez ◽  
Ricardo Barini
Keyword(s):  
Survival Time ◽  
Maternal Age ◽  
Statistical Tests ◽  
Maternal Characteristics ◽  
Live Births ◽  
Parity Number ◽  
History Of ◽  
The Mean ◽  
Elective Termination

Objective. To describe the characteristics of obstetric and perinatal outcome of a group of pregnancies complicated by an anencephalic fetus. Methods. Observational study including anencephalic fetuses, divided into groups according to the evolution of pregnancy: elective termination of pregnancy ETP; stillbirths (SBs); live births (LBs), and loss of follow-up. After a univariate description of the sample, some variables were compared using statistical tests. Results. 180 anencephalic fetuses were included. The mean maternal age was 25.3 years. In 71 fetuses (39%) were found additional anomalies. Comparing the groups, no statistical differences in maternal age (), parity (), number of previous abortion (), fetal sex () and additional anomalies () were found. Among those fetuses whose parents opted for continuation of pregnancy (), 20 spontaneous intrauterine deaths occurred (38%) and 33 were live births (62%). The average postnatal survival time was 51 minutes. There was no association between survival time and gestational age () or the presence of additional malformations (). Conclusion. Results presented here could contribute to a better understanding of the natural history of this malformation, allowing obstetricians a more detailed discussion with the families.

scholarly journals Low Cryptococcus Antigen Titers as Determined by Lateral Flow Assay Should Be Interpreted Cautiously in Patients without Prior Diagnosis of Cryptococcal Infection

2017 ◽  
Vol 55 (8) ◽  
pp. 2472-2479 ◽  
Author(s):  
Marie Dubbels ◽  
Dane Granger ◽  
Elitza S. Theel
Keyword(s):  
False Positive Rate ◽  
Lateral Flow ◽  
Lateral Flow Assay ◽  
Fungal Culture ◽  
Content Type ◽  
Cryptococcal Infection ◽  
Cryptococcal Disease ◽  
Positive Rate ◽  
History Of ◽  
Correlation Prior

ABSTRACTDetection ofCryptococcusantigen (CrAg) is invaluable for establishing cryptococcal disease. Multiple different methods for CrAg detection are available, including a lateral flow assay (LFA). Despite excellent performance of the CrAg LFA, we have observed multiple cases of low-titer (≤1:5) positive CrAg LFA results in patients for whom cryptococcosis was ultimately excluded. To investigate the accuracy of low-titer positive CrAg LFA results, we performed chart reviews for all patients with positive CrAg LFA results between June 2014 and December 2016. During this period, serum and/or cerebrospinal fluid (CSF) samples from 3,969 patients were tested with the CrAg LFA, and 55 patients (1.5%) tested positive. Thirty-eight of those patients lacked a history of cryptococcal disease and were the focus of this study. Fungal culture or histopathology confirmedCryptococcusinfection for 20 patients (52.6%), and CrAg LFA titers in serum and CSF samples ranged from 1:5 to ≥1:2,560. For the 18 patients (47.4%) without culture or histopathological confirmation, the CrAg LFA results were considered true-positive results for 5 patients (titer range, 1:10 to ≥1:2,560), due to clinical improvement with targeted therapy and decreasing CrAg LFA titers. The remaining 13 patients had CrAg LFA titers of 1:2 (n= 11) or 1:5 (n= 2) and were ultimately diagnosed with an alternative condition (n= 11) or began therapy for possible cryptococcosis without improvement (n= 2), leading to an overall CrAg LFA false-positive rate of 34%. We recommend careful clinical correlation prior to establishing a diagnosis of cryptococcal infection for patients with first-time positive CrAg LFA titers of 1:2.

The DSM-5 proposal for attenuated psychosis syndrome: a history

2020 ◽  
Vol 50 (6) ◽  
pp. 920-926
Author(s):  
Peter Zachar ◽  
Michael B. First ◽  
Kenneth S. Kendler
Keyword(s):  
Early Detection ◽  
False Positive Rate ◽  
Field Trials ◽  
Psychotic Symptoms ◽  
Dsm 5 ◽  
Psychosis Risk ◽  
Positive Rate ◽  
History Of ◽  
Attenuated Psychosis Syndrome ◽  
Harmful Use

AbstractThis article narrates a consensus history of the proposal to include diagnostic criteria for a psychosis risk syndrome in the DSM-5, in part, to document what happened, but also to potentially help focus future efforts at clinically useful early detection. The purpose of diagnosing a risk state would be to slow and ideally prevent the development of the full disorder. Concerns about diagnosing a psychosis risk state included a high false positive rate, potentially harmful use of anti-psychotic medication with people who would not transition to psychosis, and stigmatization. Others argued that educating professionals about what ‘risk’ entails could reduce inappropriate treatments. During the revision, the proposal shifted from diagnosing risk to emphasizing current clinical need associated with attenuated psychotic symptoms. Within the community of researchers who studied psychosis risk, people disagreed about whether risk and/or attenuated symptoms should be an official DSM-5 diagnosis. Once it became clear that the DSM-5 field trials did not include enough cases to establish the reliability of the proposed criteria, everyone agreed that the criteria should be put in a section on conditions for further study rather the main section of the DSM-5. We close with recommendations about some practical benchmarks that should be met for including criteria for early detection in the classification system.

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