Antenatal Detection of Abnormal Placental Cord Insertion across Different Trimesters: A Prospective Cohort Study
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Abstract Objectives This article prospectively examines the use of ultrasound for antenatal detection of abnormal placental cord insertion (PCI) and compares the antenatal classification with delivered placental classification. Study Design This prospective cohort study examined 277 singleton pregnancies in a tertiary center. Scans were performed between 10 and 14, 18 and 22, and 32 and 34 weeks where PCI site was identified and its shortest distance to margin measured. Standardized images of delivered placentas were taken and digitally measured. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of antenatal classification compared with delivered placental classification were calculated. Results Abnormal PCI (distance < 2 cm from margin) was confirmed in 30/277 (11%) placentas at delivery. Note that 102/277 (37%) of PCI sites were classified as abnormal in the first trimester (T1), 43/277 (16%) in the second trimester (T2), and 28/277 (10%) in the third trimester (T3). Sensitivity (73%) and specificity (91%) were highest at T2. The PPVs were low (22% in T1, 51% in T2, and 64% in T3) and the NPVs were high (96% in T1 and 97% in both T2 and T3) for all scans. Conclusion Abnormal PCI can be detected antenatally with optimal agreement with postnatal classification in T2. However, the incidence is overestimated at early scans with low PPVs.To the Editor: The predictive value of a diagnostic test is highly dependent upon disease prevalence. Disease prevalence, however, varies widely from patient to patient. Since patients in whom the diagnosis is unclear are the ones most likely to get a diagnostic test, it is helpful to standardize the predictive value of a diagnostic test to a disease prevalence of 50%. Even more useful would be to present predictive values standardized to disease prevalences of 25%, 50%, and 75%. I thank the authors for their thoughtful analysis of how to best present the predictive value of a test. They emphasize that the predictive value of a test varies significantly as the disease prevalence changes. In my previous Letter to the Editor on this topic 1, I proposed that researchers not only present the raw, unadjusted predictive value of a diagnostic test, but also present the predictive value of the test based upon a standardized 50% disease prevalence. What the authors propose, in brief, is that researchers use the Predictive Summary Index 2 which standardizes predictive values based on the estimated disease prevalence in a large population. They suggest that this is a more useful way to determine the overall gain in information from a diagnostic test than my proposal to standardize predictive values to a prevalence of 50%. While using the Predictive Summary Index (PSI) may be useful for making population-based policy decisions, it adds little useful information to practicing clinicians attempting to apply research findings to individual patients. The primary reason for this is because disease prevalence is not a fixed value but varies widely from individual patient to patient. Since diagnostic tests are most frequently ordered when the diagnosis is unclear (ie, the pretest likelihood of disease is around 50%), standardizing predictive values to a prevalence of 50% may be more meaningful to the practicing clinician than using the PSI. For example, after doing a history and physical, I will estimate the likelihood of disease based on a wide range of variables unique to my patient. When the disease of interest is very highly likely, or very unlikely, then additional diagnostic testing is not helpful. On the other hand, if the unique characteristics of my patient do not clearly indicate a specific diagnosis, this is when I order additional diagnostic tests. In this situation, I do not clearly know whether my patient has, or does not have, the disease of interest, ie, my clinical judgment is that the likelihood of disease is in an intermediate range. When my level of diagnostic certainty is no better than a coin flip, what is most useful is the predictive value of a test standardized to a disease prevalence of 50%. Population prevalence can vary widely due to the demographic group(s) included. Was the patient presenting for the first time to a rural family physician, or presenting to a subspecialist at a tertiary care center after an extensive workup? Is the patient male, or female? Diabetic or not diabetic? Prediabetic? How old is the patient? What is the family history? What is the patient's occupation? Where does the patient live? These factors are all taken into account when doing a history and physical. Generating a PSI value for each demographic would not only be nearly impossible, but also confusing and impractical for practicing clinicians to apply. However, if I knew the predictive value of a diagnostic test standardized to disease prevalences of 25%, 50%, and 75%, then I could reasonably estimate its value to the individual patient in front of me. The PSI may be useful when looking at populations; however, standardizing predictive values to a 50% disease prevalence may be more useful to clinicians treating individual patients. Dr. Thomas F. Heston, MD Shoshone Medical Center Kellogg, Idaho
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Background: An elevated lactate level reflects impaired tissue oxygenation and is a predictor of mortality. Studies have shown that the anion gap is inadequate as a screen for hyperlactataemia, particularly in critically ill and trauma patients. A proposed explanation for the anion gap's poor sensitivity and specificity in detecting hyperlactataemia is that the serum albumin is frequently low. This study therefore, sought to compare the predictive values of the anion gap and the anion gap corrected for albumin (cAG) as an indicator of hyperlactataemia as defined by a lactate ⩾2.5 mmol/l. Methods: A retrospective review of 639 sets of laboratory values from a tertiary care hospital. Patients' laboratory results were included in the study if serum chemistries and lactate were drawn consecutively. The sensitivity, specificity, and predictive values were obtained. A receiver operator characteristics curve (ROC) was drawn and the area under the curve (AUC) was calculated. Results: An anion gap ⩾12 provided a sensitivity, specificity, positive predictive value, and negative predictive value of 39%, 89%, 79%, and 58%, respectively, and a cAG ⩾12 provided a sensitivity, specificity, positive predictive value, and negative predictive value of 75%, 59%, 66%, and 69%, respectively. The ROC curves between anion gap and cAG as a predictor of hyperlactataemia were almost identical. The AUC was 0.757 and 0.750, respectively. Conclusions: The sensitivities, specificities, and predictive values of the anion gap and cAG were inadequate in predicting the presence of hyperlactataemia. The cAG provides no additional advantage over the anion gap in the detection of hyperlactataemia.
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Dipyridamole
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Negative Predictive Value of Breast Imaging in Patients with Palpable LesionsFerris M. HallAudio Available | Share
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The usefulness of diagnostic tests, that is their ability to detect a person with disease or exclude a person without disease, is usually described by terms such as sensitivity, specificity, positive predictive value and negative predictive value. In this article, the first of the series, a simple, practical explanation of these concepts is provided and their use and misuse discussed. It is explained that while sensitivity and specificity are important measures of the diagnostic accuracy of a test, they are of no practical use when it comes to helping the clinician estimate the probability of disease in individual patients. Predictive values may be used to estimate probability of disease but both positive predictive value and negative predictive value vary according to disease prevalence. It would therefore be wrong for predictive values determined for one population to be applied to another population with a different prevalence of disease.Sensitivity and specificity are important measures of the diagnostic accuracy of a test but cannot be used to estimate the probability of disease in an individual patient. Positive and negative predictive values provide estimates of probability of disease but both parameters vary according to disease prevalence.
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To the Editor.—
The measures of accuracy in clinical screening or diagnosis relate to the intrinsic ability of a procedure to identify correctly those persons with disease (sensitivity) and those persons without disease (specificity).1In a recent article entitled "The Sensitivity and Specificity of Clinical Diagnostics During Five Decades,"2the authors use theterms clinical accuracy for a positive diagnosis and clinical accuracy for a negative diagnosisand equate them by definition to the positive predictive value (the proportion of positive tests or diagnoses that correctly identify the presence of disease) and the negative predictive value (the proportion of negative tests that correctly identify the absence of disease), respectively. Unlikesensitivity and specificity, the predictive values are not properties inherent to the procedure itself, but vary with the prevalence of disease in the population tested. For example, the authors state that "the accuracy of the clinical diagnosisPositive predicative value
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Objective To evaluate morphologic abnormalities warnings of the system XE-2100 hematology analyzer.Methods Manual stained differentiations were performed on 200 morphologic normal samples and 200 morphologic abnormal samples.Results The analyzer test showed the sensitivity to morphologic abnormalities was 99.34%,the specificity was 80.24%,the positive predictive value was 75.50% and the negative predictive value was 99.50%.The sensitivity to immature granulocytes was 95.31%,the specificity was 78.27%,the positive predictive value was 45.52% and the negative predictive value was 98.87%.The sensitivity to variant lymphocyte was 63.64%,the specificity was 96.47%,the positive predictive value was 83.58% and the negative predictive value was 90.39%.Conclusion The sample is warned by the system XE-2100 hematology analyzer must be manually differentiated.
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To assess the usefulness of HELISAL in the diagnosis of Helicobacter pylori infection by comparing it with ELISA, JATROX and histopathologic findings.Randomized prospective study.Sixty-one patients, thirty-three males and twenty-eight females, 18-73 years old, submitted to esophagogastroduodenoscopy.The sensitivity of HELISAL when compared to ELISA test was 60.8%, the specificity 73.3%, the positive predictive value 87.5%, the negative predictive value 37.9%, and the kappa index 0.26. When compared to histopathologic test: sensitivity 60.9%, specificity 65%, positive predictive value 78.1%, negative predictive value 44.7%, kappa 0.28. When compared to JATROX, sensitivity 57.7%, specificity 62.5%, positive predictive value 81.2%, negative predictive value 34.4%, kappa 0.21.The sensitivity of HELISAL test is lower than that of other compared tests, and the negative predictive value is very low. The specificity and the positive predictive value are higher than the sensitivity. The kappa index shows a very low concordance.
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ResultsNDS evaluates PPN signals through the thermal, painful and vibratory sensation, and the Achilles reflex.Compared to MNSI, which evaluates the PPN through the appearance of the feet, presence of ulcers, vibratory sensitivity, monofilament and Achilles reflex, NDS had a sensitivity of 50%, specificity of 93%, PPV of 78%, NPV of 79% and accuracy of 79%, according to Figure 1.
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One hundred two consecutive patients undergoing surgical treatment for rectal cancer were examined by means of endorectal ultrasound (US) for staging before surgery. Eighty-one of these patients also underwent staging with computed tomography (CT). The diagnostic sensitivity of endorectal US in detection of tumor extension into fat was 67%; specificity, 77%; positive predictive value, 73%; and negative predictive value, 72%. The sensitivity of CT for this finding was 53%; specificity, 53%; positive predictive value, 56%; and negative predictive value, 50%. The sensitivity of endorectal US in detection of lymph node infiltration was 50%; specificity, 92%; positive predictive value, 68%; and negative predictive value, 84%. For this finding the sensitivity and negative predictive value, 76%. These findings suggest that endorectal US may be as accurate as CT, or more so, in the preoperative staging of rectal cancer.
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