Positive predictive value and screening performance of GoCheck Kids in a primary care university clinic

2020 ◽  
Vol 24 (1) ◽  
pp. 17.e1-17.e5 ◽  
Author(s):  
Megan X. Law ◽  
Mariana Flores Pimentel ◽  
Catherine E. Oldenburg ◽  
Alejandra G. de Alba Campomanes
Keyword(s):  
Primary Care ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Screening Performance

scholarly journals EVALUATING THE PREDICTIVE VALIDITY OF THE NIPISSING DISTRICT DEVELOPMENTAL SCREEN IN PRIMARY CARE SETTINGS AT THE 18-MONTH HEALTH SUPERVISION VISIT

2018 ◽  
Vol 23 (suppl_1) ◽  
pp. e37-e37
Author(s):  
Vinusha Gunaseelan ◽  
Patricia Parkin ◽  
Imaan Bayoumi ◽  
Patricia Jiang ◽  
Alexandra Medline ◽  
...  
Keyword(s):  
Primary Care ◽  
Positive Predictive Value ◽  
Diagnostic Accuracy ◽  
Developmental Delay ◽  
Predictive Validity ◽  
Predictive Value ◽  
Screening Tools ◽  
Developmental Screening ◽  
Research Personnel ◽  
Health Supervision

Abstract BACKGROUND The Canadian Paediatric Society (CPS) recommends that every Canadian physician caring for young children provide an enhanced 18-month well-baby visit including the use of a developmental screening tool, such as the Nipissing District Developmental Screen (NDDS). The Province of Ontario implemented an enhanced 18-month well-baby visit specifically emphasizing the NDDS, which is now widely used in Ontario primary care. However, the diagnostic accuracy of the NDDS in identifying early developmental delays in real-world clinical settings is unknown. OBJECTIVES To assess the predictive validity of the NDDS in primary care for identifying developmental delay and prompting a specialist referral at the 18-month health supervision visit. DESIGN/METHODS This was a prospective longitudinal cohort study enrolling healthy children from primary care practices. Parents completed the 18-month NDDS during their child’s scheduled health supervision visit between January 2012 and February 2015. Using a standardized data collection form, research personnel abstracted data from the child’s health records regarding the child’s developmental outcomes following the 18-month assessment. Data collected included confirmed diagnoses of a development delay, specialist referrals, family history, and interventions. Research personnel were blind to the results of the NDDS. We assessed the diagnostic test properties of the NDDS with a confirmed diagnosis of developmental delay as the criterion measure. The specificity, sensitivity, positive predictive value, and negative predictive value were calculated, with 95% confidence intervals. RESULTS We included 255 children with a mean age of 18.5 months (range, 17.5–20.6) and 139 (55%) were male. 102 (40%) screened positive (1+ flag result on their NDDS). A total of 48 (19%) children were referred, and 23 (9%) had a confirmed diagnosis of a developmental delay (speech and language: 14; gross motor: 4; autism spectrum disorder: 3; global developmental delay: 1; developmental delay: 1). The sensitivity was 74% (95% CI: 52–90%), specificity was 63% (95% CI: 57–70%), positive predictive value was 17% (95% CI:10–25%), and the negative predictive value was 96% (95% CI: 92–99%). CONCLUSION For developmental screening tools, sensitivity between 70%-80% and specificity of 80% have been suggested. The NDDS has moderate sensitivity and specificity in identifying developmental delay at the 18-month health supervision visit. The 1+NDDS flag cut-point may lead to overdiagnosis with more children with typical development being referred, leading to longer wait times for specialist referrals among children in need. Future work includes investigating the diagnostic accuracy of combining the NDDS with other screening tools.

scholarly journals Three Questions to Screen your Patients for Chemical, Food, and Drug Intolerances the Brief Environmental Exposure and Sensitivity Inventory (BREESI)

2020 ◽  
Author(s):  
Raymond F Palmer ◽  
Carlos Roberto Jaén ◽  
Roger B. Perales ◽  
Rodolfo Rincon ◽  
Jacqueline Viramontes ◽  
...  
Keyword(s):  
Primary Care ◽  
Positive Predictive Value ◽  
Environmental Exposure ◽  
Health Care Professionals ◽  
Predictive Value ◽  
Primary Care Clinic ◽  
Diagnostic Tools ◽  
Care Clinic ◽  
Predictive Values ◽  
Chemical Intolerance

Abstract Background: The 50-item Quick Environmental Exposure and Sensitivity Inventory (QEESI) is a validated questionnaire used worldwide to assess intolerances to chemicals, foods, and/or drugs and has become the gold standard for assessing chemical intolerance (CI). Despite a reported prevalence of 8-33%, CI often goes undiagnosed in epidemiological studies and routine primary care. To enhance the QEESI’s utility, we developed the Brief Environmental Exposure and Sensitivity Inventory (BREESI) as a 3-item CI screening instrument. We tested the BREESI’s potential to predict whether an individual is likely to respond adversely to structurally unrelated chemicals, foods, and drugs. Methods: We recruited 286 adult participants from a university-based primary care clinic and through online participation. The positive and negative predictive values of the BREESI items were calculated against the full QEESI scores. Results: 90% of participants answering “yes” to all three items on the BREESI were classified as very suggestive of CI based upon the QEESI chemical intolerance and symptom scores both ≥ 40 (positive predictive value = 90%). For participants endorsing two items, 92% were classified as either very suggestive (39%) or Suggestive (53%) of CI (positive predictive value = 87%). Of those endorsing only one item, only 13% were found to be very suggestive of CI. However, 70% were classified as Suggestive. Of those answering “No” to all of the BREESI items, 99% were classified as not suggestive of CI (i.e., negative predictive value = 99%). Conclusions: The BREESI is a versatile screening tool for rapidly determining potential CI, with clinical and epidemiological applications. Together, the validated BREESI and QEESI provide much needed diagnostic tools that will help inform treatment protocols and teach health care professionals about Toxicant Induced Loss of Tolerance – the mechanism driving CI.

The Utility of the Ces-D as a Depression Screening Measure among Low-Income Women Attending Primary Care Clinics

2001 ◽  
Vol 31 (1) ◽  
pp. 25-40 ◽  
Author(s):  
Janet L. Thomas ◽  
Glenn N. Jones ◽  
Isabel C. Scarinci ◽  
Daniel J. Mehan ◽  
Phillip J. Brantley
Keyword(s):  
Primary Care ◽  
Positive Predictive Value ◽  
Low Income ◽  
Predictive Value ◽  
Depression Screening ◽  
Care Practice ◽  
Primary Care Clinics ◽  
Medical Disorders ◽  
Cut Score ◽  
Low Income Women

Objective: Depressive disorders are among the most common medical disorders seen in primary care practice. The Center for Epidemiologic Studies-Depression (CES-D) scale is one of the measures commonly suggested for detecting depression in these clinics. However, to our knowledge, there have been no previous studies examining the validity of the CES-D among low-income women attending primary care clinics. Method: Low-income women attending public primary care clinics ( n = 179, ages 20–77) completed the CES-D and the Diagnostic Interview Schedule for the DSM-IV (DIS-IV). Results: The results supported the validity of the CES-D. The standard cut-score of 16 and above yielded a sensitivity of .95 and specificity of .70 in predicting Major Depressive Disorder (MDD). However, over two-thirds of those who screened positive did not meet criteria for MDD (positive predictive value = .28). The standard cut-score appears valid, but inefficient for depression screening in this population. An elevated cut-score of 34 yielded a higher specificity (.95) and over 50 percent of the patients who screened positive had a MDD (positive predictive value = .53), but at great cost to sensitivity (.45). Conclusion: Results indicated that the CES-D appears to be as valid for low-income, minority women as for any other demographic group examined in the literature. Despite similar validity, the CES-D appears to be inadequate for routine screening in this population. The positive predictive value remains very low no matter which cut-scores are used. The costs of the false positive rates could be prohibitive, especially in similar public primary care settings.

scholarly journals Predicting a diagnosis of ankylosing spondylitis using primary care health records – a machine learning approach

2021 ◽  
Author(s):  
Jonathan Kennedy ◽  
Natasha Kennedy ◽  
Roxanne Cooksey ◽  
Ernest Choy ◽  
Stefan Siebert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Back Pain ◽  
Ankylosing Spondylitis ◽  
General Population ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Principal Component ◽  
Good Prediction ◽  
X Rays

AbstractAnkylosing spondylitis is the second most common cause of inflammatory arthritis. However, a successful diagnosis can take a decade to confirm from symptom onset (via x-rays). The aim of this study was to use machine learning methods to develop a profile of the characteristics of people who are likely to be given a diagnosis of AS in future.The Secure Anonymised Information Linkage databank was used. Patients with ankylosing spondylitis were identified using their routine data and matched with controls who had no record of a diagnosis of ankylosing spondylitis or axial spondyloarthritis. Data was analysed separately for men and women. The model was developed using feature/variable selection and principal component analysis to develop decision trees. The decision tree with the highest average F value was selected and validated with a test dataset.The model for men indicated that lower back pain, uveitis, and NSAID use under age 20 is associated with AS development. The model for women showed an older age of symptom presentation compared to men with back pain and multiple pain relief medications. The models showed good prediction (positive predictive value 70%-80%) in test data but in the general population where prevalence is very low (0.09% of the population in this dataset) the positive predictive value would be very low (0.33%-0.25%).Machine learning can be used to help profile and understand the characteristics of people who will develop AS, and in test datasets with artificially high prevalence, will perform well. However, when applied to a general population with low prevalence rates, such as that in primary care, the positive predictive value for even the best model would be 1.4%. Multiple models may be needed to narrow down the population over time to improve the predictive value and therefore reduce the time to diagnosis of ankylosing spondylitis.

scholarly journals Developing a Primary Care EMR-based Frailty Definition using Machine Learning

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Sylvia Aponte-Hao ◽  
Bria Mele ◽  
Dave Jackson ◽  
Alan Katz ◽  
Charles Leduc ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Laboratory Data ◽  
Screening Tools ◽  
Geriatric Syndrome ◽  
Surveillance Network ◽  
Clinical Frailty Scale ◽  
Definition Of

IntroductionFrailty is a geriatric syndrome that is predictive of heightened vulnerability for disability, hospitalization, and mortality. Annually an estimated 250,000 frail Canadians die, and this estimate is expected to double in the next 40 years, as Canadians grow older. Currently there is no single accepted clinical definition of frailty. Objectives and ApproachThe objective of this study was to develop an operational definition of frailty using machine learning that can be applied to a primary care electronic medical record (EMR) database. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a pan-Canadian network of primary care practices that collect de-identified patient information (such as encounter diagnoses, health conditions, and laboratory data) from EMRs. 780 patients from CPCSSN have were randomly selected and assessed by physicians using the Rockwood Clinical Frailty Scale (as frail or not frail), and their clinical characteristics from CPCSSN used to develop the definition using machine-learning. ResultsA total of 8,044 clinical features were extracted from these tables: billing, problem list, encounter diagnosis, labs, medications and referrals. A chi-squared automatic interaction detector (CHAID) approach was selected as the best approach. The bootstrapping process used a cost matrix that prioritized high sensitivity and positive predictive value. 10-fold cross validation was used for validity measures. Key features factored into the algorithm included: diagnosis of dementia (ICD-9 code 290), medications furosemide and vitamins, and use of key word “obstruction” within the billing table. The validation measures with 95% confidence intervals are as follows: sensitivity of 28% (95% CI: 21% to 36%), specificity of 94% (95% CI: 93% to 96%), positive predictive value of 53% (95% CI: 42% to 64%), negative predictive value of 86% (95% CI: 83% to 88%). Conclusion/ImplicationsNo other primary care specific frailty screening tools have sufficient validity. These results suggest heterogeneous diseases require clearly defined features and potentially more sophisticated algorithms to account for heterogeneity. Further research utilizing continuous features and continuous frailty scores may be more suitable in the creation of a case detection algorithm.

scholarly journals Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance

2021 ◽  
Author(s):  
Laura Kerschke ◽  
Stefanie Weigel ◽  
Alejandro Rodriguez-Ruiz ◽  
Nico Karssemeijer ◽  
Walter Heindel
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
False Positive ◽  
False Positives ◽  
Arbitration Process ◽  
Screening Performance ◽  
Malignant Lesions

Abstract Objectives To evaluate if artificial intelligence (AI) can discriminate recalled benign from recalled malignant mammographic screening abnormalities to improve screening performance. Methods A total of 2257 full-field digital mammography screening examinations, obtained 2011–2013, of women aged 50–69 years which were recalled for further assessment of 295 malignant out of 305 truly malignant lesions and 2289 benign lesions after independent double-reading with arbitration, were included in this retrospective study. A deep learning AI system was used to obtain a score (0–95) for each recalled lesion, representing the likelihood of breast cancer. The sensitivity on the lesion level and the proportion of women without false-positive ratings (non-FPR) resulting under AI were estimated as a function of the classification cutoff and compared to that of human readers. Results Using a cutoff of 1, AI decreased the proportion of women with false-positives from 89.9 to 62.0%, non-FPR 11.1% vs. 38.0% (difference 26.9%, 95% confidence interval 25.1–28.8%; p < .001), preventing 30.1% of reader-induced false-positive recalls, while reducing sensitivity from 96.7 to 91.1% (5.6%, 3.1–8.0%) as compared to human reading. The positive predictive value of recall (PPV-1) increased from 12.8 to 16.5% (3.7%, 3.5–4.0%). In women with mass-related lesions (n = 900), the non-FPR was 14.2% for humans vs. 36.7% for AI (22.4%, 19.8–25.3%) at a sensitivity of 98.5% vs. 97.1% (1.5%, 0–3.5%). Conclusion The application of AI during consensus conference might especially help readers to reduce false-positive recalls of masses at the expense of a small sensitivity reduction. Prospective studies are needed to further evaluate the screening benefit of AI in practice. Key Points • Integrating the use of artificial intelligence in the arbitration process reduces benign recalls and increases the positive predictive value of recall at the expense of some sensitivity loss. • Application of the artificial intelligence system to aid the decision to recall a woman seems particularly beneficial for masses, where the system reaches comparable sensitivity to that of the readers, but with considerably reduced false-positives. • About one-fourth of all recalled malignant lesions are not automatically marked by the system such that their evaluation (AI score) must be retrieved manually by the reader. A thorough reading of screening mammograms by readers to identify suspicious lesions therefore remains mandatory.

The Validation of the Edinburgh Post-natal Depression Scale on a Community Sample

1990 ◽  
Vol 157 (2) ◽  
pp. 288-290 ◽  
Author(s):  
Lynne Murray ◽  
Andrew D. Carothers
Keyword(s):  
Primary Care ◽  
Positive Predictive Value ◽  
Diagnostic Criteria ◽  
Predictive Value ◽  
Post Partum ◽  
Community Sample ◽  
Depression Scale ◽  
Post Natal Depression ◽  
Large Random Sample ◽  
Sensitivity Specificity

The Edinburgh Post-natal Depression Scale (EPDS) was validated on a community sample of 702 women at six weeks post-partum using Research Diagnostic Criteria for depression. The estimates of sensitivity, specificity and positive predictive value, being based on a large random sample, offer improved guidelines for the use of the EPDS by the primary care team.

Diagnosis of cerebral hemorrhage before the era of computed tomography

2013 ◽  
Vol 154 (44) ◽  
pp. 1743-1746
Author(s):  
Gergely Hofgárt ◽  
Rita Szepesi ◽  
Bertalan Vámosi ◽  
László Csiba
Keyword(s):  
Computed Tomography ◽  
Cerebrospinal Fluid ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Hemorrhagic Stroke ◽  
Cranial Computed Tomography ◽  
Cerebral Stroke ◽  
Daily Routine ◽  
Stroke Patients ◽  
The Difference

Introduction: During the past decades there has been a great progress in neuroimaging methods. Cranial computed tomography is part of the daily routine now and its use allows a fast diagnosis of parenchymal hemorrhage. However, before the availability of computed tomography the differentiation between ischemic and hemorrhagic stroke was based on patient history, physical examination, percutan angiography and cerebrospinal fluid sampling, and the clinical utility could be evaluated by autopsy of deceased patients. Aim: The authors explored the diagnostic performance of cerebrospinal fluid examination for the diagnosis of ischemic and hemorrhagic stroke. Method: Data of 200 deceased stroke patients were retrospectively evaluated. All patients had liquor sampling at admission and all of them had brain autopsy. Results: Bloody or yellowish cerebrospinal fluid at admission had a positive predictive value of 87.5% for hemorrhagic stroke confirmed by autopsy, while clear cerebrospinal fluid had positive predictive value of 90.7% for ischemic stroke. Patients who had clear liquor, but autopsy revealed hemorrhagic stroke had higher protein level in the cerebrospinal fluid, but the difference was not statistically significant (p = 0.09). Conclusions: The results confirm the importance of pathological evaluation of the brain in cases deceased from cerebral stroke. With this article the authors wanted to salute for those who contributed to the development of the Hungarian neuropathology. In this year we remember the 110th anniversary of the birth, and the 60th anniversary of the death of professor Kálmán Sántha. Professor László Molnár would be 90 years old in 2013. Orv. Hetil., 154 (44), 1743–1746.

VALUE OF COMBINATION OF HAP SCORE AND BISAP SCORE IN PREDICTING SEVERITY IN PATIENTS OF ACUTE PANCREATITIS

2019 ◽  
pp. 96-100
Author(s):  
Thi Ngoc Suong Le ◽  
Pham Chi Tran ◽  
Van Huy Tran
Keyword(s):  
Acute Pancreatitis ◽  
Positive Predictive Value ◽  
Roc Curve ◽  
Predictive Value ◽  
Prognostic Value ◽  
Acute Inflammation ◽  
Clinical Symptoms ◽  
Mortality Rates ◽  
Multiple Organ ◽  
Central Hospital

Acute pancreatitis (AP) is an acute inflammation of the pancreas, usually occurs suddenly with a variety of clinical symptoms, complications of multiple organ failure and high mortality rates. Objectives: To determine the value of combination of HAP score and BISAP score in predicting the severity of acute pancreatitis of the Atlanta 2012 Classification. Patients and Methods: 75 patients of acute pancreatitis hospitalized at Hue Central Hospital between March 2017 and July 2018; HAP and BISHAP score is calculated within the first 24 hours. The severity of AP was classified by the revised Atlanta criteria 2012. Results: When combining the HAP and BISAP scores in predicting the severity of acute pancreatitis, the area under the ROC curve was 0,923 with sensitivity value was 66.7%, specificity value was 97.1%; positive predictive value was 66.7%, negative predictive value was 97.1%. Conclusion: The combination of HAP and BISAP scores increased the sensitivity, predictive value, and prognostic value in predicting the severity of acute pancreatitis of the revised Atlanta 2012 classification in compare to each single scores. Key words: HAPscore, BiSAP score, acute pancreatitis, predicting severity

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