AbstractObjectiveTo evaluate the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression in pregnant and postpartum women.DesignIndividual participant data ...meta-analysis.Data sourcesMedline, Medline In-Process and Other Non-Indexed Citations, PsycINFO, and Web of Science (from inception to 3 October 2018).Eligibility criteria for selecting studiesEligible datasets included EPDS scores and major depression classification based on validated diagnostic interviews. Bivariate random effects meta-analysis was used to estimate EPDS sensitivity and specificity compared with semi-structured, fully structured (Mini International Neuropsychiatric Interview (MINI) excluded), and MINI diagnostic interviews separately using individual participant data. One stage meta-regression was used to examine accuracy by reference standard categories and participant characteristics.ResultsIndividual participant data were obtained from 58 of 83 eligible studies (70%; 15 557 of 22 788 eligible participants (68%), 2069 with major depression). Combined sensitivity and specificity was maximised at a cut-off value of 11 or higher across reference standards. Among studies with a semi-structured interview (36 studies, 9066 participants, 1330 with major depression), sensitivity and specificity were 0.85 (95% confidence interval 0.79 to 0.90) and 0.84 (0.79 to 0.88) for a cut-off value of 10 or higher, 0.81 (0.75 to 0.87) and 0.88 (0.85 to 0.91) for a cut-off value of 11 or higher, and 0.66 (0.58 to 0.74) and 0.95 (0.92 to 0.96) for a cut-off value of 13 or higher, respectively. Accuracy was similar across reference standards and subgroups, including for pregnant and postpartum women.ConclusionsAn EPDS cut-off value of 11 or higher maximised combined sensitivity and specificity; a cut-off value of 13 or higher was less sensitive but more specific. To identify pregnant and postpartum women with higher symptom levels, a cut-off of 13 or higher could be used. Lower cut-off values could be used if the intention is to avoid false negatives and identify most patients who meet diagnostic criteria.RegistrationPROSPERO (CRD42015024785).
Abstract Objective To systematically review the accuracy of the GAD-7 and GAD-2 questionnaires for identifying anxiety disorders. Methods A systematic review of the literature was conducted to ...identify studies that validated the GAD-7 or GAD-2 against a recognized gold standard diagnosis. Pooled estimates of diagnostic test accuracy were produced using random-effects bivariate metaanalysis. Heterogeneity was explored using the I2 statistic. Results A total of 12 samples were identified involving 5223 participants; 11 samples provided data on the accuracy of the GAD-7 for identifying generalized anxiety disorder (GAD). Pooled sensitivity and specificity values appeared acceptable at a cutoff point of 8 sensitivity: 0.83 (95% CI 0.71–0.91), specificity: 0.84 (95% CI 0.70–0.92) although cutoff scores 7–10 also had similar pooled estimates of sensitivity/specificity. Six samples provided data on the accuracy of the GAD-2 for identifying GAD. Pooled sensitivity and specificity values appeared acceptable at a cutoff of 3 sensitivity: 0.76 (95% CI 0.55–0.89), specificity: 0.81 (95% CI 0.60–0.92). Four studies looked at the accuracy of the questionnaires for identifying any anxiety disorder. Conclusions The GAD-7 had acceptable properties for identifying GAD at cutoff scores 7–10. The GAD-2 had acceptable properties for identifying GAD at a cutoff score of 3. Further validation studies are needed.
Pancreatic cystic neoplasms (PCNs) carry a considerable malignancy risk. Along with main duct dilation, the presence of enhanced mural nodules represents a significant risk factor for malignancy. ...Several articles assessed the role of contrast-enhanced EUS (CE-EUS) for the identification of malignant features in mural nodules. We evaluate the pooled diagnostic performance of CE-EUS for the identification of high-grade dysplasia or invasive carcinoma among mural nodules in PCNs.
A systematic review (Medline, PubMed, EMBASE) and meta-analysis were conducted. Subgroup analysis was used to assess the usefulness of a dedicated contrast-harmonic (CH-EUS). The primary outcome was pooled sensitivity for identification of high-grade dysplasia or invasive carcinoma.
Ten studies (532 patients) were included. Pooled sensitivity of CE-EUS was 88.2% (95% confidence interval CI, 82.7%-92.5%), specificity 79.1% (95% CI, 74.5%-83.3%), and diagnostic accuracy 89.6% (95% CI, 83.4%-95.8%). Eight studies (320 patients) were conducted using CH-EUS: pooled sensitivity increased to 97.0% (95% CI, 92.5%-99.2%), specificity to 90.4% (95% CI, 85.2%-94.2%), and diagnostic accuracy to 95.6% (95% CI, 92.6%-98.7%). At 42% disease prevalence (pretest probability), a positive CH-EUS increased the disease probability to 88%, whereas a negative test decreased the disease probability to 2%. The number needed to diagnose was 1.5 (95% CI, 1.7-1.3) for CE-EUS and just 1.2 (95% CI, 1.3-1.1) for CH-EUS.
This study provided robust evidence on CE-EUS value for the characterization of mural nodules within PCNs. A dedicated contrast-harmonic mode, namely CH-EUS, provided an increased diagnostic yield in the identification and characterization of malignant mural nodules.
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Summary Background Men with high serum prostate specific antigen usually undergo transrectal ultrasound-guided prostate biopsy (TRUS-biopsy). TRUS-biopsy can cause side-effects including bleeding, ...pain, and infection. Multi-parametric magnetic resonance imaging (MP-MRI) used as a triage test might allow men to avoid unnecessary TRUS-biopsy and improve diagnostic accuracy. Methods We did this multicentre, paired-cohort, confirmatory study to test diagnostic accuracy of MP-MRI and TRUS-biopsy against a reference test (template prostate mapping biopsy TPM-biopsy). Men with prostate-specific antigen concentrations up to 15 ng/mL, with no previous biopsy, underwent 1·5 Tesla MP-MRI followed by both TRUS-biopsy and TPM-biopsy. The conduct and reporting of each test was done blind to other test results. Clinically significant cancer was defined as Gleason score ≥4 + 3 or a maximum cancer core length 6 mm or longer. This study is registered on ClinicalTrials.gov , NCT01292291. Findings Between May 17, 2012, and November 9, 2015, we enrolled 740 men, 576 of whom underwent 1·5 Tesla MP-MRI followed by both TRUS-biopsy and TPM-biopsy. On TPM-biopsy, 408 (71%) of 576 men had cancer with 230 (40%) of 576 patients clinically significant. For clinically significant cancer, MP-MRI was more sensitive (93%, 95% CI 88–96%) than TRUS-biopsy (48%, 42–55%; p<0·0001) and less specific (41%, 36–46% for MP-MRI vs 96%, 94–98% for TRUS-biopsy; p<0·0001). 44 (5·9%) of 740 patients reported serious adverse events, including 8 cases of sepsis. Interpretation Using MP-MRI to triage men might allow 27% of patients avoid a primary biopsy and diagnosis of 5% fewer clinically insignificant cancers. If subsequent TRUS-biopsies were directed by MP-MRI findings, up to 18% more cases of clinically significant cancer might be detected compared with the standard pathway of TRUS-biopsy for all. MP-MRI, used as a triage test before first prostate biopsy, could reduce unnecessary biopsies by a quarter. MP-MRI can also reduce over-diagnosis of clinically insignificant prostate cancer and improve detection of clinically significant cancer. Funding PROMIS is funded by the UK Government Department of Health, National Institute of Health Research–Health Technology Assessment Programme, (Project number 09/22/67). This project is also supported and partly funded by UCLH/UCL Biomedical Research Centre and The Royal Marsden and Institute for Cancer Research Biomedical Research Centre and is coordinated by the Medical Research Council Clinical Trials Unit (MRC CTU) at UCL. It is sponsored by University College London (UCL).
We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, ...producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
IgG4-related disease (IgG4-RD) can cause fibroinflammatory lesions in nearly any organ. Correlation among clinical, serological, radiological and pathological data is required for diagnosis. This ...work was undertaken to develop and validate an international set of classification criteria for IgG4-RD. An international multispecialty group of 86 physicians was assembled by the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR). Investigators used consensus exercises; existing literature; derivation and validation cohorts of 1879 subjects (1086 cases, 793 mimickers); and multicriterion decision analysis to identify, weight and test potential classification criteria. Two independent validation cohorts were included. A three-step classification process was developed. First, it must be demonstrated that a potential IgG4-RD case has involvement of at least one of 11 possible organs in a manner consistent with IgG4-RD. Second, exclusion criteria consisting of a total of 32 clinical, serological, radiological and pathological items must be applied; the presence of any of these criteria eliminates the patient from IgG4-RD classification. Third, eight weighted inclusion criteria domains, addressing clinical findings, serological results, radiological assessments and pathological interpretations, are applied. In the first validation cohort, a threshold of 20 points had a specificity of 99.2% (95% CI 97.2% to 99.8%) and a sensitivity of 85.5% (95% CI 81.9% to 88.5%). In the second, the specificity was 97.8% (95% CI 93.7% to 99.2%) and the sensitivity was 82.0% (95% CI 77.0% to 86.1%). The criteria were shown to have robust test characteristics over a wide range of thresholds. ACR/EULAR classification criteria for IgG4-RD have been developed and validated in a large cohort of patients. These criteria demonstrate excellent test performance and should contribute substantially to future clinical, epidemiological and basic science investigations.
Large-scale sequencing of RNA from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states
. However, current short-read single-cell RNA-sequencing ...methods have limited ability to count RNAs at allele and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells
. Here we introduce Smart-seq3, which combines full-length transcriptome coverage with a 5' unique molecular identifier RNA counting strategy that enables in silico reconstruction of thousands of RNA molecules per cell. Of the counted and reconstructed molecules, 60% could be directly assigned to allelic origin and 30-50% to specific isoforms, and we identified substantial differences in isoform usage in different mouse strains and human cell types. Smart-seq3 greatly increased sensitivity compared to Smart-seq2, typically detecting thousands more transcripts per cell. We expect that Smart-seq3 will enable large-scale characterization of cell types and states across tissues and organisms.
Accurate information regarding prognosis is fundamental to optimal clinical care. The best approach to assess patient prognosis relies on prediction models that simultaneously consider a number of ...prognostic factors and provide an estimate of patients’ absolute risk of an event. Such prediction models should be characterized by adequately discriminating between patients who will have an event and those who will not and by adequate calibration ensuring accurate prediction of absolute risk. This Users’ Guide will help clinicians understand the available metrics for assessing discrimination, calibration, and the relative performance of different prediction models. This article complements existing Users’ Guides that address the development and validation of prediction models. Together, these guides will help clinicians to make optimal use of existing prediction models.
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