Negative Predictive Value Estimates Research Articles

Point-of-care ultrasound for diagnosing extrapulmonary TB.

<sec id="st1"><title>BACKGROUND</title>Despite the high morbidity and mortality globally, standard microbiologic diagnosis for TB requires laboratory infrastructure inaccessible in many resource-limited areas and may be insufficient for identifying extrapulmonary disease. Point-of-care (POC) ultrasound facilitates visualization of extrapulmonary manifestations, permitting laboratory-independent diagnosis, but its diagnostic utility remains unclear.</sec><sec id="st2"><title>METHODS</title>We conducted a systematic review of five online databases for studies reporting ultrasound findings among cases with and without extrapulmonary TB (EPTB). A minimum of two authors independently screened and reviewed each article, and extracted data elements of interest. We conducted a series of univariate meta-analyses using a random-effects model to calculate the pooled effect estimate and 95% confidence interval (CI) for each outcome: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).</sec><sec id="st3"><title>RESULTS</title>Of 279 articles identified, 6 were included. There were 699 cases of EPTB among 1,633 participants. The pooled sensitivity estimate was 0.72 (95% CI 0.57-0.88). The pooled specificity estimate was 0.77 (95% CI 0.63-0.90). The pooled PPV and NPV estimates were respectively 0.67 (95% CI 0.47-0.87) and 0.85 (95% CI 0.77-0.93).</sec><sec id="st4"><title>CONCLUSION</title>POC ultrasound showed modest test characteristics for diagnosing EPTB, which may constitute an improvement over some currently available diagnostics.</sec>.

Computed Tomography Is Predictive of Significant Neurologic Injury in Children Supported on Extracorporeal Membrane Oxygenation.

Computed Tomography Is Predictive of Significant Neurologic Injury in Children Supported on Extracorporeal Membrane Oxygenation.

Validation to correct for outcome misclassification bias.

Validation to correct for outcome misclassification bias.

Screening for Early Detection of Cervical Cancer in Women Living with HIV in Mumbai, India - Retrospective Cohort Study from a Tertiary Cancer Center

Abstract Introduction Women living with human immunodeficiency virus (HIV) have an increased risk of persistent human papillomavirus infection (HPV) of developing cervical cancer precursors and are, therefore, considered at higher risk for cervical cancer. Despite the higher risk, screening for cervical cancer is extremely low among HIV-positive women in India. Objectives Given the limited usefulness of cytology-based screening programs, the current study retrospectively evaluated the comparative performance of visual inspection with 5% acetic acid (VIA), conventional cytology, and human papillomavirus (HPV) testing among HIV-positive women attending the cancer screening clinic at the tertiary cancer center. Materials and Methods Retrospective analysis of 291 HIV-positive women attending cervical cancer screening services in a tertiary cancer center in Mumbai was undertaken. All underwent simultaneous screening with VIA, Pap cytology, and HPV DNA testing, followed by diagnostic colposcopy and histopathology. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to detect cervical intraepithelial neoplasia (CIN) 2/3 on histology were estimated. Results The screen positivity rate for cervical cancer screening by VIA, high-risk HPV DNA, and Pap cytology was 35.7, 34.4, and 6.2% respectively. At the CIN2+ disease threshold, the sensitivity, specificity, PPV, and NPV estimates were 80.00% (59.30–93.17), 68.42% (62.46–73.96), 19.23% (15.46–23.67), 97.33% (94.30–98.77) for VIA; 80.00% (68.78–97.45), 70.68% (64.81–76.08), 22.00% (18.22–26.32), 98.43% (95.58–99.45) for HPV DNA; and 64.00% (42.52–82.03), 98.12% (95.67–99.39), 76.19% (56.13–88.89), 96.67% (94.50–98.00) for cytology (HSIL cutoff). Conclusion The diagnostic performance of VIA and HPV DNA was comparable and better than cytology indicating that VIA as a cost-effective cervical cancer screening test can be incorporated within the services under sexually transmitted diseases /HIV testing and counseling centers within the country.

Misclassification of Neonatal Abstinence Syndrome Surveillance Estimates: Is Considering the Positive Predictive Value Enough?

Validation studies estimating the positive predictive value (PPV) of neonatal abstinence syndrome (NAS) have consistently suggested overreporting in hospital discharge records. However, few studies estimate the negative predictive value (NPV). Even slightly imperfect NPVs have the potential to bias estimated prevalences of rare outcomes like NAS. Given the challenges in estimating NPV, our objective was to evaluate whether the PPV was sufficient to understand the influence of NAS misclassification bias on conclusions of the NAS prevalence in surveillance research. We used hospital discharge data from the 2016 New Jersey State Inpatient Databases, Healthcare Cost and Utilization Project. We adjusted surveillance data for misclassification using quantitative bias analysis models to estimate the expected NAS prevalence under a range of PPV and NPV bias scenarios. The 2016 observed NAS prevalence was 0.61%. The misclassification-adjusted prevalence estimates ranged from 0.31% to 0.91%. When PPV was assumed to be ≥90%, the misclassification-adjusted prevalence was typically greater than the observed prevalence but the reverse was true for PPV ≤70%. Under PPV 80%, the misclassification-adjusted prevalence was less than the observed prevalence for NPV >99.9% but flipped for NPV <99.9%. When we varied the NPV below 100%, our results suggested that the direction of bias (over or underestimation) was dependent on the PPV, and sometimes dependent on the NPV. However, NPV was important for understanding the magnitude of bias. This study serves as an example of how quantitative bias analysis methods can be applied in NAS surveillance to supplement existing validation data when NPV estimates are unavailable.

Saliva as a gold-standard sample for SARS-CoV-2 detection

Saliva as a gold-standard sample for SARS-CoV-2 detection

Multisite validation of a simple electronic health record algorithm for identifying diagnosed obstructive sleep apnea.

We examined the performance of a simple algorithm to accurately distinguish cases of diagnosed obstructive sleep apnea (OSA) and noncases using the electronic health record (EHR) across six health systems in the United States. Retrospective analysis of EHR data was performed. The algorithm defined cases as individuals with ≥ 2 instances of specific International Classification of Diseases (ICD)-9 and/or ICD-10 diagnostic codes (327.20, 327.23, 327.29, 780.51, 780.53, 780.57, G4730, G4733 and G4739) related to sleep apnea on separate dates in their EHR. Noncases were defined by the absence of these codes. Using chart reviews on 120 cases and 100 noncases at each site (n = 1,320 total), positive predictive value (PPV) and negative predictive value (NPV) were calculated. The algorithm showed excellent performance across sites, with a PPV (95% confidence interval) of 97.1 (95.6, 98.2) and NPV of 95.5 (93.5, 97.0). Similar performance was seen at each site, with all NPV and PPV estimates ≥ 90% apart from a somewhat lower PPV of 87.5 (80.2, 92.8) at one site. A modified algorithm of ≥ 3 instances improved PPV to 94.9 (88.5, 98.3) at this site, but excluded an additional 18.3% of cases. Thus, performance may be further improved by requiring additional codes, but this reduces the number of determinate cases. A simple EHR-based case-identification algorithm for diagnosed OSA showed excellent predictive characteristics in a multisite sample from the United States. Future analyses should be performed to understand the effect of undiagnosed disease in EHR-defined noncases. This algorithm has wide-ranging applications for EHR-based OSA research.

Chronic Disease Case Definitions for Electronic Medical Records: A Canadian Validation Study

ABSTRACTObjectivesCanadians are investing heavily in electronic medical records (EMRs) to inform primary care practice improvements. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is a national practice-based network that has enrolled more than one million patients to date. Accurate CPCSSN EMR data are essential for unbiased research about chronic disease prevention and management. The study purpose was to test the accuracy of chronic disease case definitions in EMR data from one CPCSSN site.&#x0D; ApproachThis study linked CPCSSN EMR data, hospital records, physician billing claims, prescription drug records, and population registration files for the province of Manitoba. Individuals who had at least one encounter with a CPCSSN practice between 1998 and 2012, were at least 18 years of age, and had a minimum of two years of healthcare coverage before and after the study index date were included. Separate cohorts were defined for the following chronic diseases: chronic obstructive pulmonary disease (COPD), depression, diabetes, hypertension, and osteoarthritis. Validated case definitions based on diagnoses in physician and hospital records and prescription drug data were used estimate sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and kappa of each EMR chronic disease case definition.&#x0D; ResultsMore than 74,000 individuals were included in each cohort, except for COPD which had 51,000. Approximately half of each cohort was comprised of urban residents. The average age ranged from 45.9 years for individuals with depression to 65.3 years for individuals with COPD. Hypertension had the highest prevalence (22.0%) in EMR data followed by depression (14.6%). Estimates of agreement (i.e., kappa) for EMR and administrative data ranged from 0.47 for COPD to 0.58 for diabetes. Sensitivity of the EMR data was lowest for COPD (37.4%; 95% CI 36.0-38.8) and highest for diabetes (57.6%; 95% confidence interval [CI] 56.6-58.6). PPV estimates were lowest for osteoarthritis (66.9%; 95% CI 66.0-67.8) and highest for hypertension (78.3%; 95% CI 77.7-78.9). Specificity estimates were consistently above 90% and NPV estimates were always greater than 80%. Validity estimates for the EMR case definitions were associated with demographic and comorbidity characteristics of the study cohorts.&#x0D; ConclusionsValidity of EMR data, when compared to administrative health data, for ascertaining five different chronic diseases was fair to good; it varied with the disease under investigation. Further research is needed to identify methods for improving the accuracy of chronic disease case definitions in EMR data.

Abstract 970A: Using a radiogenomic approach to classify pancreatic cancer precursors

Abstract Background: Intraductal papillary mucinous neoplasms (IPMNs) are cystic pancreatic cancer precursors incidentally detected by imaging in more than 75,000 Americans each year. There is an unmet need to discover noninvasive approaches to differentiate ‘benign’ IPMNs that can be monitored from ‘malignant’ IPMNs that warrant surgery. We previously developed a blood-based ‘miRNA genomic classifier (MGC)’ that helps predict malignant IPMN pathology. The goal of this study was to evaluate whether novel radiomic features from preoperative computed tomography (CT) scans may improve prediction of IPMN pathology beyond that provided by standard radiologic features, either alone or in combination with the MGC. Methods: Preoperative CT images were obtained for 37 surgically-resected, pathologically-confirmed IPMN cases with matched preoperative miRNA expression data. Images were reviewed for standard radiologic features characterized to be ‘high-risk’ or ‘worrisome’ for malignancy according to consensus guidelines. The region of interest within the pancreas was identified and segmented using a semi-automated algorithm. A total of 112 two-dimensional (2D) quantitative texture features (which measure tumor size, shape, and location) and non-texture features (which measure smoothness, coarseness, and regularity) were extracted. Logistic regression models were used to explore associations between non-redundant radiomic features and IPMN pathology. Principal component analysis was also performed to generate an index score (defined by the first principal component of the most promising radiomic features) that was evaluated for its association with malignant pathology. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the new radiomic features individually and in combination with the MGC were estimated and compared to values obtained for standard radiologic features. Results: The MGC and standard ‘high-risk’ and ‘worrisome’ radiologic features had area under the receiver operating characteristic curve (AUC) values of 0.83, 0.84, and 0.54, respectively. Analysis of 112 extracted preoperative radiomic CT features revealed 14 textural and non-textural features that differentiated malignant from benign IPMNs (P&amp;lt;0.05). Collectively, the 14 radiomic features had an AUC = 0.77. A model that combined radiomic features and the MGC had an AUC = 0.92 and estimates of sensitivity (83%), specificity (89%), PPV (88%), and NPV (85%) that were superior to models not based on these novel data types. Conclusions: Our preliminary findings suggest that clinical decision-making models that integrate novel quantitative radiomic features with a blood-based miRNA classifier may more accurately predict IPMN pathology than models that rely on standard clinical data or worrisome radiologic features alone. Larger, prospective, multi-center studies are planned to explore this topic further. Citation Format: Jennifer B. Permuth, Jung Choi, Yoganand Balarunathan, Jongphil Kim, Dung-Tsa Chen, Kun Jiang, Sonia Orcutt, Lu Chen, Kimberly Quinn, Rodrigo Carvajal, Guillermo Gonzalez-Calderon, Michelle Fournier, Mahmoud Abdalla, Alberto Garcia, Amber Bouton, Danny Yakoub, Suzanne Lechner, Jose Trevino, Nipun Merchant, Robert Gillies, Mokenge Malafa. Using a radiogenomic approach to classify pancreatic cancer precursors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 970A.

Transperineal template prostate-mapping biopsies: an evaluation of different protocols in the detection of clinically significant prostate cancer.

To determine whether modified transperineal template prostate-mapping (TTPM) biopsy protocols, altering the template or the biopsy density, have sensitivity and negative predictive value (NPV) equal to full 5-mm TTPM. Retrospective analysis of an institutional registry including treatment-naïve men undergoing 5-mm TTPM biopsy analysed in a 20-zone fashion. The value of three modified strategies was assessed by comparing the information provided by selected zones against full 5-mm TTPM. Strategy 1, did not consider the findings of anterior areas; strategies 2 and 3 simulated a reduced biopsy density by excluding intervening zones. A bootstrapping technique was used to calculate reliable estimates of sensitivity and NPV of these three strategies for the detection of clinically significant disease (maximum cancer core length ≥4 mm and/or Gleason score ≥3 + 4). In all, 391 men with a median (interquartile range, IQR) age of 62 (58-67) years were included. The median (IQR) PSA level and PSA density were 6.9 (4.8-10) ng/mL and 0.17 (IQR 0.12-0.25) ng/mL/mL, respectively. A median (IQR) of 6 (2-9) cores out of 48 (33-63) taken per man were positive for prostate cancer. No cancer was detected in 67 men (17%), whilst low-, intermediate- and high-risk disease was identified in 78 (20%), 80 (21%), and 166 (42%), respectively. Strategy 1, 2 and 3 had sensitivities of 78% [95% confidence interval (CI) 73-84%], 85% (95% CI 80-90%) and 84% (95% CI 79-89%), respectively. The NPVs of the three strategies were 73% (95% CI 67-80%), 80% (95% CI 74-86%) and 79% (95% CI 72-84%), respectively. Altering the template or decreasing sampling density has a substantial negative impact on the ability of TTPM biopsy to exclude clinically significant disease. This should be considered when modified TTPM biopsy strategies are used to select men for tissue-preserving approaches, and when modified TTPM are used to validate new diagnostic tests.

Bivariate meta-analysis of predictive values of diagnostic tests can be an alternative to bivariate meta-analysis of sensitivity and specificity

ObjectiveMeta-analysis of predictive values is usually discouraged because these values are directly affected by disease prevalence, but sensitivity and specificity sometimes show substantial heterogeneity as well. We propose a bivariate random-effects logitnormal model for the meta-analysis of the positive predictive value (PPV) and negative predictive value (NPV) of diagnostic tests. Study Design and SettingTwenty-three meta-analyses of diagnostic accuracy were reanalyzed. With separate models, we calculated summary estimates of the PPV and NPV and summary estimates of sensitivity and specificity. We compared these summary estimates, the goodness of fit of the two models, and the amount of heterogeneity of both approaches. ResultsThere were no substantial differences in the goodness of fit or amount of heterogeneity between both models. The median absolute difference between the projected PPV and NPV from the summary estimates of sensitivity and specificity and the summary estimates of PPV and NPV was 1% point (interquartile range, 0–2% points). ConclusionA model for the meta-analysis of predictive values fitted the data from a range of systematic reviews equally well as meta-analysis of sensitivity and specificity. The choice for either model could be guided by considerations of the design used in the primary studies and sources of heterogeneity.

Interval Estimates for Predictive Values in Diagnostic Testing with Three Outcomes

in Diagnostic Testing with Three Outcomes Scott Clark, Lauren Mondin, Courtney Weber and Jessica Winborn Advisor: Melinda Miller Holt Abstract: In disease testing, patients and doctors are interested in estimates for positive predictive value (PPV) and negative predictive value (NPV). The PPV of a test is the probability that a patient actually has the disease, given a positive test result. The NPV is the probability that a patient actually does not have the disease, given a negative test result. Here we consider diagnostic tests in which the disease state remains uncertain, so the uncertain predictive value (UPV) is also of interest. UPV is the probability that, given an uncertain test result, follow-up testing will remain inconclusive. We derive classical Wald-type and Bayesian interval estimates of PPV, NPV, and UPV. Performance of these intervals is compared through simulation studies of interval coverage and width.

How Well Does CT Predict the Need for Laparotomy in Hemodynamically Stable Patients With Penetrating Abdominal Injury? A Review and Meta-Analysis

The objective of our study was to determine how well CT predicts the need for laparotomy in hemodynamically stable patients with penetrating abdominal injury. We reviewed MEDLINE articles published from January 1994 through June 2008. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy were calculated for each source and collectively using a meta-analysis. Of 180 relevant studies, five were included in the meta-analysis. Pooled weighted estimates of sensitivity, specificity, NPV, PPV, and accuracy were 94.90%, 95.38%, 98.62%, 84.51%, and 94.70%, respectively. CT in patients with penetrating abdominal trauma has high sensitivity, specificity, NPV, and accuracy, but has lower PPV in determining the need for laparotomy. It follows that CT is an indispensable tool in predicting the need for laparotomy in these patients but still has room for improvement.