Clinical Impact of Second Opinion Radiology Consultation for Patients With Breast Cancer.

PURPOSE: To assess the incidence and clinical significance of discrepancy in subspecialty interpretation of outside breast imaging examinations for newly diagnosed breast cancer patients presenting to a tertiary cancer center. MATERIALS AND METHODS: This Institutional Review Board-approved retrospective study included patients presenting from July 2016 to March 2017 to a National Cancer Institute-designated comprehensive cancer center for second opinion after breast cancer diagnosis. Outside and second opinion radiology reports of 252 randomly selected patients were compared by two subspecialty breast radiologists to consensus. A peer review score was assigned, modeled after ACR's RADPEERTM peer review metric: 1-agree; 2-minor discrepancy (unlikely clinically significant); 3-moderate discrepancy (may be clinically significant); 4-major discrepancy (likely clinically significant). Among cases with clinically significant discrepancies, rates of clinical management change (management alterations including change in follow-up, neoadjuvant therapy use, and surgical management as a direct result of image review), and detection of additional malignancy were assessed through electronic medical record review. RESULTS: A significant difference in interpretation (scores = 3 or 4) was seen in 41 of 252 cases (16%, 95% confidence interval [CI], 11.7%-20.8%). The difference led to additional workup in 38 of 252 cases (15%, 95% CI 10.6%-19.5%) and change in clinical management in 18 of 252 cases (7.1%, 95% CI 4.0%-10.2%), including 15 of 252 with change in surgical management (6.0%, 95% CI, 3.0%-8.9%). An additional malignancy or larger area of disease was identified in 11 of 252 cases (4.4%, 95% CI, 1.8%-6.9%). CONCLUSION: Discrepancy between outside and second-opinion breast imaging subspecialists frequently results in additional workup for breast cancer patients, changes in treatment plan, and identification of new malignancies.

A Clinical Model for the Early Diagnosis of Acute Pancreatitis in the Emergency Department.

OBJECTIVE: This study aimed to develop a diagnostic model that predicts acute pancreatitis (AP) risk before imaging. METHODS: Emergency department patients with serum lipase elevated to 3 times the upper limit of normal or greater were identified retrospectively (September 1, 2013-August 31, 2015). An AP diagnosis was established by expert review of full hospitalization records. Candidate predictors included demographic and clinical characteristics at presentation. Using a derivation set, a multivariable logistic regression model and corresponding point-based scoring system was developed to predict AP. Discrimination accuracy and calibration were assessed in a separate validation set. RESULTS: In 319 eligible patients, 182 (57%) had AP. The final model (area under curve, 0.92) included 8 predictors: number of prior AP episodes; history of cholelithiasis; no abdominal surgery (prior 2 months); time elapsed from symptom onset; pain localized to epigastrium, of progressively worsening severity, and severity level at presentation; and extent of lipase elevation. At a diagnostic risk threshold of 8 points or higher (≥99%), the model identified AP with a sensitivity of 45%, and a specificity and a positive predictive value of 100%. CONCLUSIONS: In emergency department patients with lipase elevated to 3 times the upper limit of normal or greater, this model helps identify AP risk before imaging. Prospective validation studies are needed to confirm diagnostic accuracy.

Risk Stratification Model: Lower-Extremity Ultrasonography for Hospitalized Patients with Suspected Deep Vein Thrombosis.

BACKGROUND: The Wells score for deep venous thrombosis (DVT) has a high failure rate and low efficiency among inpatients. OBJECTIVE: To create and validate an inpatient-specific risk stratification model to help assess pre-test probability of DVT in hospitalized patients. DESIGN: Prospective cohort study of hospitalized patients undergoing lower-extremity ultrasonography studies (LEUS) for suspected DVT. Demographics, physical findings, medical history, medications, hospitalization, and laboratory and imaging results were collected. Samples were divided into model derivation (patients undergoing LEUS 11/1/2012-12/31/2013) and validation cohorts (LEUS 1/1/2014-5/31/2015). A DVT prediction rule was derived using the recursive partitioning algorithm (decision tree-type approach) and was then validated. PARTICIPANTS: Adult inpatients undergoing LEUS for suspected DVT from November 2012 to May 2015, excluding those with DVT in the prior 3 months, at a 793-bed, urban academic quaternary-care hospital with ~50,000 admissions annually. MAIN MEASURES: The primary outcome was the presence of proximal DVT, and the secondary outcome was the presence of any DVT (proximal or distal). Model sensitivity and specificity for predicting DVT were calculated. KEY RESULTS: Recursive partitioning yielded four variables (previous DVT, active cancer, hospitalization ≥ 6 days, age ≥ 46 years) that optimized the prediction of proximal DVT and yield in the derivation cohort. From this decision tree, we stratified a scoring system using the validation cohort, categorizing patients into low- and high-risk groups. The incidence rates of proximal DVT were 2.9% and 12.0%, and of any DVT were 5.2% and 21.0%, for the low- and high-risk groups, respectively. The AUC for the discriminatory accuracy of the Center for Evidence-Based Imaging (CEBI) score for risk of proximal DVT identified on LEUS was 0.73. Model sensitivity was 98.1% for proximal and 98.1% for any DVT. CONCLUSIONS: In hospitalized adults, specific factors can help clinicians predict risk of DVT, identifying those with low pre-test probability, in whom ultrasonography can be safely avoided.

External validation of risk stratification strategy in the use of renal ultrasonography in the evaluation of acute kidney injury.

BACKGROUND: Per the American College of Radiology Appropriateness Criteria, renal ultrasound is the most appropriate imaging examination to evaluate patients with acute kidney injury. However, recent studies suggest that renal ultrasound may be more selectively performed, which could lead to reductions in the use of medical imaging. OBJECTIVE: Evaluate a published risk stratification prediction model (the Licurse model) for using renal ultrasound in hospitalized patients with acute kidney injury. DESIGN: Prospective, observational cohort study. SETTING: A 793-bed, quaternary care, academic hospital. PATIENTS: All adult hospitalized patients who underwent renal ultrasound for the indication of acute kidney injury. INTERVENTION/EXPOSURE: None. MEASUREMENTS: Primary outcome was rate of hydronephrosis diagnosed on ultrasound. Secondary outcome was rate of hydronephrosis resulting in urologic intervention. RESULTS: Of 778 patients who underwent renal ultrasonography to evaluate acute kidney injury, hydronephrosis was present in 106 (13.6%); urologic intervention was performed in 23 patients (3.0%). The Licurse model had sensitivity of 91.3% (95% confidence interval [CI]: 73.2%-97.6%) for urologic intervention and 93.4% (95% CI: 87.2%-96.8%) for hydronephrosis, respectively. Specificity was low for urologic intervention (23.0% [95% CI: 20.2-26.2]) and hydronephrosis (25.1% [95% CI: 22.0-28.6]). We estimated that for 22.6% of patients, hydronephrosis could be ruled out based on clinical predictors. CONCLUSIONS: We found that the Licurse renal ultrasonography risk stratification model was sufficiently accurate in classifying patients at risk for ureteral obstruction among hospitalized patients with acute kidney injury. Journal of Hospital Medicine 2016;11:763-767. © 2016 Society of Hospital Medicine.