Examining transitions of care among nursing home residents with and without antipsychotic medication use.

OBJECTIVES: This study examines the association between antipsychotic (AP) medication use and care transitions in the nursing home (NH) population. METHODS: This cross-sectional study used data from a 5% random sample of Medicare beneficiaries between 2011 and 2015. Propensity score adjusted negative binomial regression was performed and conditional probabilities of having a first transition from the NH to specific locations were calculated. RESULTS: Among 150,284 eligible beneficiaries, the majority were female (67%), white (84%), and >75 years old (63%). Controlling for resident characteristics, the odds of having any transition was 5% lower among those with AP use [IRR (95% confidence interval (CI))=0.95(0.94-0.96)] relative to those with no AP use. Residents with AP use had higher proportions of transitions to hospital (22.7% vs. 19.5%, p < 0.01), emergency department (19.6% vs. 10.7%, p < 0.01), and different NH (1.5% vs. 0.4%, p < 0.01), and lower proportions of transition to non-healthcare locations compared to those without AP use. CONCLUSIONS: Findings demonstrate that residents with AP use had higher probabilities of transitions to more costly care settings such as the emergency department and hospital compared to those without AP use. Future longitudinal studies will help to inform clinical interventions aimed at improving the quality of care for this population.

Query mandates in prescription drug monitoring programs reduce opioid use among commercially insured patients with cancer.

BACKGROUND: Prescription drug monitoring programs (PDMPs) have been shown to reduce opioid use in the general and noncancer populations. However, evidence of PDMP impacts on patients with cancer remains limited. OBJECTIVE: The aim of the study was to examine the impact of PDMP mandates on individual-level opioid use among patients with cancer. METHODS: This is a retrospective cohort study of patients with newly diagnosed cancer aged 18-65 years in the IQVIA PharMetrics Plus database (IQVIA Inc; nationally representative data of the U.S. commercially insured population in 49 states) between 2013 and 2015. The primary exposure was PDMP rigor (ranked from highest to lowest rigor): provider query + registration, query only, registration only, and unexposed. The study outcomes included (1) prevalent use among all individuals; and among opioid users (2) total days supplied, (3) daily morphine equivalent dose (MED), and (4) cumulative MED. RESULTS: Of the eligible cohort (n=28,353), 37.5% (10,656) received opioids after a cancer diagnosis. The individuals exposed to these mandates were as follows: query + registration: 3899 (13.8%); query only: 3459 (12.2%); registration only: 2764 (9.7%); and no mandates: 18,231 (64.3%). The PDMP mandates had no effect on prevalent opioid use. Compared with unexposed patients, those subject to query mandates-alone or with registration mandates-experienced 12 fewer opioid days supplied and a lower mean cumulative MED (-662 mg and -702 mg, respectively), P < 0.01. Registration-only mandates were associated with 21 days more (P < 0.01) total days supplied and lower daily MED (1.1 mg; P < 0.05) but had no statistically significant effect on cumulative MED (-46 mg, P > 0.05). CONCLUSION: Query mandates are a stronger PDMP tool than registration mandates in reducing opioid days supplied and cumulative MED. Initiatives should target PDMP mandates toward intended patient groups to reduce high-risk opioid use without compromising adequate pain treatment.

Antibiotics Approved for Marketing in Populations Specifically Excluded From Premarketing Trials, 1999-2018.

Approval by the US Food and Drug Administration (FDA) of a drug for a given indication is thought to reassure clinicians, other health care providers, and patients that substantial evidence of effectiveness exists for specific indicated populations (patients and diseases). This study examines whether FDA approval of certain antibiotics should be so reassuring for all patient populations identified in the FDA-approved labels. Specifically, this study compared patient populations covered by FDA-approved labels for 21 novel antibiotics approved between 1999 and 2018 to the patient exclusion and inclusion criteria of pivotal trials that supported those approvals. We found that every FDA-approved label for these antibiotics included at least one identifiable patient population that was explicitly excluded from enrolling in the supporting pivotal trials. Two antibiotics, bedaquiline and ceftazidime-avibactam, were approved for use in populations that were fully excluded from enrolling in registration trials.

Diagnostic performance of magnetic resonance imaging for assessing tumor response in patients with HER2-negative breast cancer receiving neoadjuvant chemotherapy is associated with molecular biomarker profile.

BACKGROUND: This study aimed to evaluate the influence of hormone receptor (HR) and Ki-67 proliferation markers in predicting the accuracy of magnetic resonance imaging (MRI) for measuring residual tumor size in patients with HER2-negative (HER2(-)) breast cancer receiving neoadjuvant chemotherapy (NAC). PATIENTS AND METHODS: Fifty-four women were studied. Patients received AC (doxorubicin (Adriamycin)/cyclophosphamide) and/or taxane-based regimens. The accuracy of MR-determined clinical complete response (CCR) was compared to pathological complete response (pCR). The size of detectable residual tumor on MRI was correlated with pathologically diagnosed tumor size using the Pearson correlation. RESULTS: MRI correctly diagnosed 16 of the 17 cases of pCR. There were 8 false-negative diagnoses: 7 HR(+) and 1 HR(-). The overall sensitivity, specificity, and accuracy of MRI were 78%, 94%, and 83%, respectively. The positive predictive value was 97% and the negative predictive value was 67%. For MRI vs. pathologically determined tumor size correlation, HR(-) cancers showed a higher correlation (R = 0.79) than did HR(+) cancers (R = 0.58). A worse MRI/pathology size discrepancy was found in HR(+) cancer than in HR(-)cancer (1.6 ± 2.8 cm vs. 0.56 ± 0.9 cm; P = .05). Tumors with low Ki-67 proliferation (< 40%) showed a larger size discrepancy than did those with high Ki-67 proliferation (≥ 40%) (1.2 ± 2.0 cm vs. 0.4 ± 0.8 cm; P = .05). CONCLUSIONS: The results showed that the diagnostic performance of MRI for patients with breast cancer undergoing NAC is associated with a molecular biomarker profile. Among HER2(-)tumors, the accuracy of MRI was worse in HR(+)cancers than in HR(-)cancers and was also worse in low-proliferation tumors than in high-proliferation tumors. These findings may help in surgical planning.

Breast cancer: evaluation of response to neoadjuvant chemotherapy with 3.0-T MR imaging.

PURPOSE: To assess how the molecular biomarker status of a breast cancer, including human epidermal growth factor receptor 2 (HER2), hormone receptors, and the proliferation marker Ki-67 status, affects the diagnosis at 3.0-T magnetic resonance (MR) imaging. MATERIALS AND METHODS: This study was approved by the institutional review board and was HIPAA compliant. Fifty patients (age range, 28-82 years; mean age, 49 years) receiving neoadjuvant chemotherapy were monitored with 3.0-T MR imaging. The longest dimension of the residual cancer was measured at MR imaging and correlated with pathologic findings. Patients were further divided into subgroups on the basis of HER2, hormone receptor, and Ki-67 status. Pathologic complete response (pCR) was defined as when there were no residual invasive cancer cells. The Pearson correlation was used to correlate MR imaging-determined and pathologic tumor size, and the unpaired t test was used to compare MR imaging-pathologic size discrepancies. RESULTS: Of the 50 women, 14 achieved pCR. There were seven false-negative diagnoses at MR imaging. The overall sensitivity, specificity, and accuracy for diagnosing invasive residual disease at MR imaging were 81%, 93%, and 84%, respectively. The mean MR imaging-pathologic size discrepancy was 0.5 cm ± 0.9 (standard deviation) for HER2-positive cancer and 2.3 cm ± 3.5 for HER2-negative cancer (P = .009). In the HER2-negative group, the size discrepancy was smaller for hormone receptor-negative than for hormone receptor-positive cancers (1.0 cm ± 1.1 vs 3.0 cm ± 4.0, P = .04). The size discrepancy was smaller in patients with 40% or greater Ki-67 expression (0.8 cm ± 1.1) than in patients with 10% or less Ki-67 expression (3.9 cm ± 5.1, P = .06). CONCLUSION: The diagnostic accuracy of breast MR imaging is better in more aggressive than in less aggressive cancers. When MR imaging is used for surgical planning, caution should be taken with HER2-negative hormone receptor-positive cancers.

Comparative study of density analysis using automated whole breast ultrasound and MRI.

PURPOSE: The purpose of this study is to compare the measurements of breast density using three-dimensional (3-D) automated whole breast ultrasound (ABUS) and magnetic resonance imaging (MRI). METHODS: In this study, 3-D ABUS and MRI breast images were obtained from 40 patients-bilaterally in 27 patients and unilaterally (due to operation in the contralateral breast) in 13 patients, To differentiate the fibroglandular and fatty tissues in ABUS and MRI images, the fuzzy C-mean classifier was used. Calculated values for percent density and breast volume from the two modalities were compared to and correlated with linear regression analysis. Intraoperator and interoperator variations among eight cases were evaluated to verify the consistency of the density analysis. RESULTS: Mean percent density and breast volume derived from ABUS (17.63 +/- 11.87% and 418.30 +/- 132.97 cm3, respectively) and MRI images (23.79 +/- 16.62% and 544.90 +/- 207.41 cm3) demonstrated good correlation (R = 0.917 and R = 0.884). Intraoperator and interoperator analyses yielded slightly larger coefficients of variation for percent density and breast volume in ABUS compared to MRI. However, the differences were not statistically significant. CONCLUSIONS: ABUS and MRI showed high correlation for breast density and breast volume quantification. Both modalities could provide useful breast density information to physicians.