A new index of clinical utility for diagnostic tests

Methods for determining clinical utility

Measuring the clinical utility of a diagnostic test involves evaluating its impact on patient outcomes, clinical decision-making, and healthcare resource utilization. Determining clinical utility requires accessing patient medical history and outcomes data. These studies involve enrolling patients undergoing diagnostic tests and tracking their clinical outcomes. Researchers can determine the test's clinical utility by comparing the outcomes of patients who receive the diagnostic test to those who do not. These outcomes include benefits and harm. The highest level of evidence to support clinical utility determinations may be obtained from clinical trials. However, clinical laboratories are often not involved in clinical trials, and laboratory specialists may not be experienced in conducting such trials. Many established laboratory tests have never had clinical utility determined. Prospective studies assessing a diagnostic test's impact on clinical outcomes may require long-term patient monitoring, which is problematic. This paper presents methods that may be used to assess clinical utility.

Detection of diagnostic and prognostic methylation-based signatures in liquid biopsy specimens from patients with meningiomas

Recurrence of meningiomas is unpredictable by current invasive methods based on surgically removed specimens. Identification of patients likely to recur using noninvasive approaches could inform treatment strategy, whether intervention or monitoring. In this study, we analyze the DNA methylation levels in blood (serum and plasma) and tissue samples from 155 meningioma patients, compared to other central nervous system tumor and non-tumor entities. We discover DNA methylation markers unique to meningiomas and use artificial intelligence to create accurate and universal models for identifying and predicting meningioma recurrence, using either blood or tissue samples. Here we show that liquid biopsy is a potential noninvasive and reliable tool for diagnosing and predicting outcomes in meningioma patients. This approach can improve personalized management strategies for these patients.

Diagnostic accuracy of CO-RADS in patients with suspected Coronavirus Disease-2019: A single center experience

INTRODUCTION

COVID-19 Reporting and Data System (CO-RADS) is a tool for standardizing the reports of patients with suspected or confirmed Sars-CoV-2 infection. We performed a study of the performance of the CO-RADS in a triage scenario of patients in Brazil.

METHODS

Data from 426 Computed Tomography (CT) scans from March 2020 through December 2020 were assessed in an ambidirectional, both retrospective and prospective, for the assessment in one of the six categories of the CO-RADS. We assessed sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR) Youden's index, Positive and Negative Clinical Utility Index (UC + and UC- respectively) and diagnostic odds ratio (DOR). We also plotted Receiver Operating Characteristics (ROC) curve with Area Under the Curve (AUC) for CO-RADS of >4 (4 + 5).

RESULTS

For CO-RADS classification > 4 (4 + 5) considered positive, the AUC obtained was of 0.89 (95% CI of 0.02), sensitivity of 78% (95% CI of 0.3), specificity of 91% (95% CI of 0.3), PPV of 0.92 (95% CI of 0.02), NPV of 0.41 (95% CI of 0.03), PLR of 0.85 (95% CI of 0.2), and NLR of 0.23 (95% CI of 0.02).

CONCLUSION

CO-RADS demonstrated overall good diagnostic performance in stratifying patients with suspected Sars-CoV-2 infection, even those without confirmed laboratorial diagnosis, therefore being useful in a triage scenario with lack of resources.

Dynamic monitoring of carcinoembryonic antigen, CA19-9 and inflammation-based indices in patients with advanced colorectal cancer undergoing chemotherapy

BACKGROUND

The roles of carcinoembryonic antigen (CEA) and carbohydrate antigen (CA19-9) in monitoring the patient response to chemotherapy for metastatic colorectal cancer (mCRC) are not clearly defined, and inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII), have been sparsely investigated for this purpose.

AIM

To aim of this study was to evaluate the relationship between the kinetics of CEA, CA19-9, NLR, LMR, PLR and SII in serum and patient response to chemotherapy estimated by computed tomography (CT) in patients with unresectable mCRC.

METHODS

Patients with mCRC treated with a 1^st-line and 2^nd-line chemotherapy underwent at least 3 whole-body spiral CT scans during response monitoring according to the Response Evaluation Criteria in Solid Tumour 1.1 (RECIST 1.1), and simultaneous determination of CEA, CA19-9, neutrophil, lymphocyte, platelet and monocyte levels was performed. The kinetics of changes in the tumour markers and inflammatory indices were calculated as the percentage change from baseline or nadir, while receiver operating characteristic curves were drawn to select the thresholds to define patients with progressive or responsive disease with the highest sensitivity (Se) and specificity (Sp). The correlation of tumour marker kinetics with inflammatory index changes and RECIST response was determined by univariate and multivariate logistic regression analysis and the clinical utility index (CUI).

RESULTS

A total of 102 patients with mCRC treated with chemotherapy were included. Progressive disease (PD), defined as a CEA increase of 25.52%, resulted in an Se of 80.3%, an Sp of 84%, a good CUI negative [CUI (Ve-)] value of 0.75 and a good fraction correct (FC) value of 81.2; at a CEA cut-off of -60.85% with an Se of 100% and an Sp of 35.7% for PD, CT could be avoided in 25.49% of patients. The 21.49% CA19-9 cut-off for PD had an Se of 66.5%, an Sp of 87.4%, an acceptable CUI (Ve-) value of 0.65 and an acceptable FC value of 75. An NLR increase of 11.5% for PD had an Se of 67% and an Sp of 66%; a PLR increase of 5.9% had an Se of 53% and an Sp of 69%; an SII increase above -6.04% had an Se of 72% and an Sp of 63%; and all had acceptable CUI (Ve-) values at 0.55. In the univariate logistic regression analysis, CEA (P < 0.001), CA19-9 (P < 0.05), NLR (P < 0.05), PLR (P < 0.05) and SII (P < 0.05) were important predictors of tumour progression, but in the multivariate logistic regression analysis, CEA was the only independent predictor of PD (P < 0.05).

CONCLUSION

CEA is a useful marker for monitoring the chemotherapy response of patients with unresectable mCRC and could replace a quarter of CT examinations. CA19-9 has poorer diagnostic characteristics than CEA but could be useful in some clinical circumstances, particularly when CEA is not increased. Dynamic changes in the inflammatory indices NLR, PLR and SII could be promising for further investigation as markers of the chemotherapy response.

Diagnostic Accuracy with Total Adenosine Deaminase as a Biomarker for Discriminating Pleural Transudates and Exudates in a Population-Based Cohort Study

Background

An initial step in the evaluation of patients with pleural effusion syndrome (PES) is to determine whether the pleural fluid is a transudate or an exudate.

Objectives

To investigate total adenosine deaminase (ADA) as a biomarker to classify pleural transudates and exudates.

Methods

An assay of total ADA in pleural fluids (P-ADA) was observed using a commercial kit in a population-based cohort study.

Results

157 pleural fluid samples were collected from untreated individuals with PES due to several causes. The cause most prevalent in transudate samples (21%, n = 33/157) was congestive heart failure (79%, 26/33) and that among exudate samples (71%, n = 124/157) was tuberculosis (28.0%, 44/124). There was no significant difference in the proportion of either sex between the transudate and exudate groups. The median values of P-ADA were significantly different (P < 0.0001) between both total exudates (18.4 U/L; IQR, 9.85-41.4) and exudates without pleural tuberculosis (11.0 U/L; IQR, 7.25-19.75) and transudates (6.85; IQR, 2.67-11.26). For exudates, the AUC was 0.820 (95% CI, 0.751-0.877; P < 0.001), with excellent discrimination. The optimum cut-off point in the ROC curve was determined as the level that provided the maximum positive likelihood ratio (PLR; 14.64; 95% CI, 2.11-101.9) and was22.0 U/L. For transudates, the AUC was 0.8245 (95% CI, 0.7470-0.9020; P < 0.0001). Internal validation of the AUC after 1000 resamples was evaluated with a tolerance minor than 2%. The clinical utility was equal to 92% (95% CI, 0.84 to 0.96, P < 0.05).

Conclusions

P-ADA is a useful biomarker for distinguishing pleural exudates from transudates.

Diagnosing empty iron stores in women: unbound iron binding capacity (UIBC) versus soluble transferrin receptor (sTFR)

Unbound iron binding capacity (UIBC) is more accurate than total iron binding capacity (TIBC) and percent transferrin saturation in diagnosing empty iron stores. It is unknown whether UIBC is more or less accurate than soluble transferrin receptor (sTFR). We obtained public-use data from the U.S. National Health and Nutrition Examination Survey (NHANES) 2005-2006 to compare the accuray of UIBC and sTFR in diagnosing empty iron stores in 2337 women aged 12-49 years. We grouped the women according to CRP less than 5 mg/L and pregnancy (four groups) and used three definitions of empty iron stores: Serum ferritin less than 10, 15, and 20 µg/L. Receiver operating characteristic (ROC) curve analysis was used to estimate the diagnostic accuracy. UIBC showed a better diagnostic accuracy than sTFR in all groups and definitions of empty iron stores, except in nonpregnant women with CRP at least 5 mg/L when empty iron stores were defined as ferritin less than 10 and 15 µg/L. Two differences reached statistical significance: In nonpregnant women without inflammation the area under the ROC curve for UIBC was 0.830 compared to 0.793 for sTFR (p = .007) when empty iron stores were defined as ferritin less than 20 µg/L. The corresponding figures for pregnant women without inflammation were 0.843 for UIBC and 0.739 for sTFR (p = .003). In conclusion, UIBC is a more accurate test than sTFR in diagnosing empty iron stores in women without inflammation.