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A Systematic Review of Symptoms for the Diagnosis of Ovarian Cancer Mark H

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A Systematic Review of Symptoms for the Diagnosis of Ovarian Cancer Mark H A Systematic Review of Symptoms for the Diagnosis of Ovarian Cancer Mark H. Ebell, MD, MS, MaryBeth B. Culp, MPH, Taylor J. Radke, MPH Context: Ovarian cancer is common and has significant morbidity and mortality, partly because it is often diagnosed at a late stage. This study sought to determine the accuracy of individual symptoms and combinations of symptoms for the diagnosis of ovarian cancer. Evidence acquisition: MEDLINE was searched, identifying 2,492 abstracts, reviewing 71 articles in full, and ultimately identifying 17 studies published between 2001 and 2014 that met the inclusion criteria. Data were abstracted by two researchers, and quality was assessed using the QUADAS-2 criteria adapted to the study question. Bivariate random effects meta-analysis was used where possible, and heterogeneity and threshold effects were explored using receiver operating character- istic curves. Data were analyzed in 2015. Evidence synthesis: Most studies were at high risk of bias, primarily because of case-control design or differential verification bias. The highest positive likelihood ratios (LRsþ) were found for presence of abdominal mass (LRþ, 30.0); abdominal distension or increased girth (LRþ, 16.0); abdominal or pelvic pain (LRþ, 10.4); abdominal or pelvic bloating (LRþ, 9.3); loss of appetite (LRþ, 9.2); and a family history of ovarian cancer (LRþ, 7.5). No symptoms were helpful at ruling out ovarian cancer when absent. The Ovarian Cancer Symptom Index was validated in five studies and (after excluding one outlier with different inclusion criteria) was 63% sensitive and 95% specific (LRþ, 12.6; LR–, 0.39). Two other symptom scores had not been validated prospectively. Conclusions: Several individual signs and symptoms significantly increase the likelihood of ovarian cancer when present. More work is needed to validate decision rules and develop new decision support tools integrating risk factors, symptoms, and possibly biomarkers to identify women at increased ovarian cancer risk. (Am J Prev Med 2016;50(3):384–394) & 2016 American Journal of Preventive Medicine Context and advanced at the time of diagnosis. Only 25% of women with ovarian cancer are diagnosed at Stage I or II, varian cancer is a common cause of morbidity whereas 58% are stage III and 17% are stage IV. The latter and mortality in women, with approximately stages have 10-year survival rates of 21% and less than 14,000 women dying in the U.S. each year from O 5%, respectively.1 the disease. The lifetime incidence is 1.4% and lifetime Studies of screening in asymptomatic women, includ- mortality is 1.04% in the U.S.1 Risk factors include ing the large Prostate Lung Colon Ovary RCT, have not increasing age, nulliparity, and early menarche or late found a benefit to screening for ovarian cancer using a menopause (all associated with an increasing number of combination of ultrasound and the CA-125 blood test.4 ovulations), as well as the BRCA1 or BRCA2 mutations.2,3 The symptoms of ovarian cancer are often vague and ill Unfortunately, because of the anatomic location of the defined and overlap with symptoms of much more ovaries deep in the pelvis, malignancies are often large common disorders such as dyspepsia, irritable bowel syndrome, menstruation, and menopause. This makes From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia early diagnosis a challenge as well. Address correspondence to: Mark H. Ebell, MD, MS, Professor of The literature searches of previous systematic reviews Epidemiology, 233 Miller Hall, University of Georgia Health Sciences of symptoms of the clinical diagnosis of ovarian cancer, Campus, Athens GA 30602. E-mail: [email protected]. 0749-3797/$36.00 published in 2005 and 2009, are now at least 7 years 5,6 5 http://dx.doi.org/10.1016/j.amepre.2015.09.023 old. Bankhead and colleagues performed a careful 384 Am J Prev Med 2016;50(3):384–394 & 2016 American Journal of Preventive Medicine Published by Elsevier Inc. Downloaded from ClinicalKey.com at University of New Mexico August 15, 2016. For personal use only. No other uses without permission. Copyright ©2016. Elsevier Inc. All rights reserved. Ebell et al / Am J Prev Med 2016;50(3):384–394 385 literature search, but included many case series and discussed and resolved following discussion or after consultation reported ORs rather than sensitivity and specificity. with the third investigator. Data regarding study characteristics, A second systematic review6 also performed a thorough study quality, and information needed to calculate sensitivity and specificity were abstracted in parallel. Any discrepancies or search but was a qualitative review of the literature and conflicts were resolved by the first author. did not abstract quantitative data regarding the accuracy Some studies reported data regarding accuracy for several time of symptoms. Neither systematic review used current periods, such as 6 months, 1 year, or 2 years of follow-up. When a 7 standards for the assessment of diagnostic study quality, study included information from the same patients about symp- the synthesis of data such as bivariate meta-analysis, or toms during different time periods, the most recent data were used, the reporting of CIs and predictive intervals around as they are most likely to reflect the accuracy of this test in a clinical summary estimates. setting where a patient presents with vague or undifferentiated A previous study8 by this research team performed a symptoms. Also, longer intervals from symptom evaluation to diagnosis may decrease sensitivity (as a symptom may not appear systematic review of the bimanual pelvic exam for the till later in the course of disease). For example, one study9 asked fi diagnosis of ovarian cancer, nding that it lacked about symptoms occurring within 6 months of diagnosis and also sensitivity. In the current systematic review, the results at any time before diagnosis; only the former data were used. of a meta-analysis of individual symptoms and combi- Another10 reported symptoms that occurred during the previous nations of symptoms for the diagnosis of ovarian cancer year (including pre-existing symptoms) as well as only those that are reported. began during the previous year; only the latter were used. These decisions were made to avoid duplication of data and to reflect clinical practice, which would generally value recent symptoms of Evidence Acquisition new onset over older or pre-existing symptoms. Symptoms judged to be evaluating the same thing (e.g., This study followed Preferred Reporting Items for Systematic “increased abdominal girth” and “abdominal distension” or Reviews and Meta-Analyses (PRISMA) guidelines for presentation “ ”“ ” fl abnormal vaginal bleeding, irregular vaginal bleeding, and of the results of a systematic review. The PRISMA ow diagram “postmenopausal bleeding”) were combined. is shown in Appendix A (available online), and the PRISMA checklist is shown in Appendix B (available online). Quality Assessment Inclusion Criteria The QUADAS-2 framework was adapted for this study.7 That Cohort or case-control studies that provided data regarding adaptation is shown in Appendix C (available online). For each of fi symptoms of ovarian cancer, combinations of symptoms, or the 16 items, the de nition was adapted to studies of the medical elements of the medical history present prior to the diagnosis of history for the diagnosis of ovarian cancer. Studies at low risk of ovarian cancer were included. All studies had to provide enough bias in each of the four domains (patient selection, index text, fl information to calculate sensitivity and specificity for at least one reference standard, and ow and timing) were assessed to be at low symptom or symptom score. Studies in children, case reports and risk of bias overall. Those with a high risk of bias in a single case series, studies with o20 patients, studies using something domain were assessed to be at moderate risk of bias, and those with other than histopathology to confirm the diagnosis of ovarian a high risk of bias in two or more domains were assessed to be at cancer, and studies of patients with known ovarian cancer were high risk of bias overall. excluded. Statistical Analysis Search Strategy For each individual sign and symptom in each study, the The following search strategy was used in PubMed: sensitivity; specificity; likelihood ratios (LRs); and their associated (ovarian cancer[tiab] OR ovarian neoplasms[MeSH Terms]) CIs were calculated. If the value of any cell in the 2 X 2 table was AND (symptom[tiab] OR symptoms[tiab] OR abdominal pain zero, a continuity correction of 0.5 was added to each cell to avoid [tiab] OR bloating[tiab]) division by zero when calculating LRs. Bivariate meta-analysis The search strategy was deliberately broad. The bibliographies used the “midas” procedure in Stata, version 13.1, which calculates of two previous systematic reviews5,6 were also reviewed to identify summary estimates of sensitivity, specificity, LRs, and the area additional studies that may have been missed by the initial under the receiver operating characteristic (ROC) curve along with PubMed search. CIs for each value. If bivariate meta-analysis was not possible using this procedure owing to a small number of studies or unstable “ ” Data Abstraction initial estimates, the mada procedure in R, version 3.1.1, was used to perform bivariate meta-analysis. This procedure calculates Each abstract identified by the initial PubMed search was reviewed summary estimates for sensitivity and specificity along with their by two of the investigators, and any article identified as possibly CIs, but does not calculate summary estimates for LRs. When meeting the inclusion criteria was reviewed in full by all three using this procedure, the summary point estimates for sensitivity investigators. Decisions regarding inclusion of a study, subsequent and specificity were used to calculate positive and negative LRs data abstraction, and study quality assessment were always (LRþ and LR–, respectively).
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