Multiple myeloma (MM) is an incurable plasma-cell neoplasm for which most treatments involve a therapeutic agent combined with dexamethasone. The preclinical combination of lenalidomide with the mTOR inhibitor CCI-779 has displayed synergy in vitro and represents a novel combination in MM.A phase I clinical trial was initiated for patients with relapsed myeloma with administration of oral lenalidomide on days 1 to 21 and CCI-779 intravenously once per week during a 28-day cycle. Pharmacokinetic data for both agents were obtained, and in vitro transport and uptake studies were conducted to evaluate potential drug-drug interactions.Twenty-one patients were treated with 15 to 25 mg lenalidomide and 15 to 20 mg CCI-779. The maximum-tolerated dose (MTD) was determined to be 25 mg lenalidomide with 15 mg CCI-779. Pharmacokinetic analysis indicated increased doses of CCI-779 resulted in statistically significant changes in clearance, maximum concentrations, and areas under the concentration-time curves, with constant doses of lenalidomide. Similar and significant changes for CCI-779 pharmacokinetics were also observed with increased lenalidomide doses. Detailed mechanistic interrogation of this pharmacokinetic interaction demonstrated that lenalidomide was an ABCB1 (P-glycoprotein [P-gp]) substrate.The MTD of this combination regimen was 25 mg lenalidomide with 15 mg CCI-779, with toxicities of fatigue, neutropenia, and electrolyte wasting. Pharmacokinetic and clinical interactions between lenalidomide and CCI-779 seemed to occur, with in vitro data indicating lenalidomide was an ABCB1 (P-gp) substrate. To our knowledge, this is the first report of a clinically significant P-gp-based drug-drug interaction with lenalidomide.
Abstract Background Providers use institutional recommendations, national guidelines, and antibiograms to decide on empiric antibiotics. As local antibiograms are most effective after organisms are known, we sought to use local microbiology and clinical data to develop predictive models for antibiotic coverage prior to identifying the organism. We focused on Gram-negative organisms as they are common urinary pathogens and are often the cause of sepsis originating in the urinary tract. As such, they are important to cover in hospitalized patients with urinary tract infections (UTI). Methods Hospitalized patients, with a diagnosis of UTI and a positive urine culture in the first 48 hours were included. Gram-positive organisms, yeast, and cultures without susceptibilities were excluded. Unknown susceptibilities were filled in using expert-derived rules. Clinical information from electronic health record (EHR) data were extracted on each patient. Penalized logistic regression with 10-fold cross validation was used to develop final models for coverage for five antibiotics (cefazolin, ceftriaxone, ciprofloxacin, cefepime, piperacillin–tazobactam). Final models were chosen based on their discrimination, calibration, and number of predictors, and then tested on a held-out validation dataset. Results Included were 5,096 patients (80% training; 20% validation). Coverage ranged from 65% for cefazolin to 90% for cefepime. Positive blood cultures were present in 544 (11%) with 388 (71%), including a urinary pathogen. In the first 24 hours, 2329 (46%) were hypotensive, 2179 (43%) had a respiratory rate > 22, 2049 (40%) had a WBC > 12, 1079 (21%) were febrile, and 584 (11%) required ICU care. Final model covariates included demographics, antibiotic exposure, prior resistant pathogens, and antibiotic allergies. The five predictive models had a point-estimate for the area under the ROC on the validation set that ranged from 0.70 for ciprofloxacin to 0.73 for ceftriaxone. Conclusion In this cohort of moderate to high acuity hospitalized patients with Gram-negative urinary pathogens, we used EHR data to develop 5 models that predict antibiotic coverage which could be used to support empiric prescribing. These models performed well in a held-out validation set. Disclosures All authors: No reported disclosures.
Abstract Background Predictive models for empiric antibiotic prescribing often estimate the probability of infection with multidrug-resistant organisms. In this work, we developed models to predict coverage of specific treatment regimens to better target antibiotics to high- and low-risk patients. Methods We established a retrospective cohort of adults admitted to the ICU in a 1,300-bed teaching hospital from November 1, 2011 to June 30, 2016. We included patients with a diagnosis of pneumonia and positive respiratory culture collected during their ICU stay. We collected demographics, comorbidities, and medical history from the electronic health record. We evaluated three penalized regression methods for predicting infection susceptibility to 11 treatment regimens: least absolute selection and shrinkage operator (LASSO), minimax concave penalty (MCP), and smoothly clipped absolute deviation (SCAD). We developed models for susceptibility prediction at two stages of the diagnostic process: for all pathogenic bacteria and for infections with Gram-negative organisms only. We selected final models based on higher area under the receiver operating characteristic (AUROC), acceptable goodness of fit, lower variability of the AUROCs in the cross-validation run, and fewer predictors. Results Among 1,917 cases of pneumonia, 54 different pathogens were identified. The most frequently isolated organisms were: Pseudomonas aeruginosa (16.6%), methicillin-resistant Staphylococcus aureus (16.1%), and Staphylococcus aureus (13.5%). Frequently selected variables included age, Elixhauser score, tracheostomy status, recent antimicrobial use, and prior infection with a carbapenem-resistant organism. All final models used MCP or SCAD methods. Point estimates for the AUROCs in the training set ranged from 0.70 to 0.80, and estimates in the internal validation set ranged from 0.64 to 0.77. Conclusion MCP and SCAD outperformed LASSO. For some regimens, models predicted infection susceptibility with fair accuracy. These models have potential to help antibiotic stewardship efforts to better target appropriate antibiotic use. Disclosures All authors: No reported disclosures.
Background Cervical cancer incidence and mortality rates are high among women from Appalachia, yet data do not exist on human papillomavirus (HPV) prevalence among these women. We examined the prevalence of genital HPV among Appalachian women and identified correlates of HPV detection. Methods We report data from a case-control study conducted between January 2006 and December 2008 as part of the Community Awareness, Resources, and Education (CARE) Project. We examined HPV prevalence among 1116 women (278 women with abnormal Pap tests at study entry [cases], 838 women with normal Pap tests [controls]) from Appalachian Ohio. Analyses used multivariable logistic regression to identify correlates of HPV detection. Results The prevalence of HPV was 43.1% for any HPV type, 33.5% for high-risk HPV types, 23.4% for low-risk HPV types, and 12.5% for vaccine-preventable HPV types. Detection of any HPV type was more common among women who were ages 18–26 (OR = 2.09, 95% CI: 1.26–3.50), current smokers (OR = 1.86, 95% CI: 1.26–2.73), had at least five male sexual partners during their lifetime (OR = 2.28, 95% CI: 1.56–3.33), or had multiple male sexual partners during the last year (OR = 1.98, 95% CI: 1.25–3.14). Similar correlates were identified for detection of a high-risk HPV type. Conclusions HPV was prevalent among Appalachian women, with many women having a high-risk HPV type detected. Results may help explain the high cervical cancer rates observed among Appalachian women and can help inform future cervical cancer prevention efforts in this geographic region.
Objectives To examine prestroke lifestyle factors associated with poststroke mortality and recovery in older women. Design Longitudinal prospective cohort study. Setting The W omen's H ealth I nitiative ( WHI , clinical trials and observational study), 40 clinical centers in the U nited S tates. Participants WHI participants, women aged 50 to 79, who were stroke‐free at baseline (1993/98), with incident stroke before 2005. Measurements Participants were followed for mortality through 2010. Prestroke characteristics were from the last examination before the stroke event. Annual follow‐up for clinical events ascertained hospitalization for stroke that was subsequently physician adjudicated with medical records. Multivariable regression models were used to analyze factors associated with poststroke mortality and poststroke recovery at hospital discharge (poststroke G lasgow score), adjusting for stroke type. Results Of 3,173 women with incident stroke, 1,111 (35%) died. Individuals who were overweight or obese before stroke had lower poststroke mortality than those who were normal weight (obese: hazard ratio ( HR ) = 0.69, 95% confidence interval ( CI ) = 0.53–0.88; overweight: HR = 0.72, 95% CI = 0.58–0.90); individuals who were underweight before stroke had nonsignificantly greater poststroke mortality ( HR = 2.02, 95% CI = 0.98–4.16, P = .06). Other prestroke factors associated with poststroke mortality included diabetes mellitus ( HR = 1.28, 95% CI = 1.01–1.64), current smoking (vs nonsmoker, HR = 2.13, 95% CI = 1.53–3.00), physical inactivity (vs >150 min of exercise per week, HR = 1.39, 95% CI = 1.09–1.78), and lowest physical function quartile (vs highest, HR = 1.54, 95% CI = 1.18–2.02). Prestroke diabetes mellitus was associated with lower odds of good recovery after stroke (odds ratio ( OR ) = 0.60, 95% CI = 0.44–0.82). Current hormone use before stroke was associated with greater odds of moderate than of severe disability after stroke ( OR = 1.29, 95% CI = 1.00–1.66). Conclusion Potentially modifiable factors before stroke, including smoking, diabetes mellitus, and being underweight, were associated with greater poststroke mortality in older women. Being overweight or obese and physical activity before stroke were associated with lower poststroke mortality in older women.
Women from Appalachian regions of the United States (US) face a number of health related disparities, including mental health and substance misuse. Alcohol misuse is a significant public health concern among women in the US, associated with numerous adverse consequences for the woman, her unborn children, and any children in her care, yet data on correlates of risky alcohol use is limited among women from Appalachian Ohio. The current study examines the prevalence and predictors of risky alcohol use (e.g., heavy episodic drinking) in 2,349 women from 18 clinics in the Community Awareness Resources and Education I (CARE I) study in Appalachian Ohio. Alcohol use history was collected over the past 30 days. Regression models were employed to identify predictors of heavy episodic drinking. Results indicate that 20% of the current sample reported heavy episodic drinking. Being an emerging adult (18-26), single, a current smoker, and reporting a history of four or more partners were independently associated with heavy episodic drinking. Self-identifying as Appalachian was not protective or predictive of heavy episodic drinking. Further research identifying risk factors and enhancing protective factors will inform culturally competent preventive efforts, particularly for emerging adult women from Appalachian Ohio at risk for alcohol misuse and associated morbidities.
INTRODUCTION: Hypertensive diseases of pregnancy complicate approximately 10% of pregnancies. Optimal timing and mode of delivery is unclear for pregnancies complicated by early onset pre-eclampsia with severe features (<34 weeks). This study aims to identify characteristics associated with the patient, fetus, and pregnancy that may affect the likelihood of successful vaginal delivery, which may guide future counseling of these patients. METHODS: This is a retrospective cohort study of women at a large tertiary medical center from 2012-2016. It included a total of 159 patients who delivered at <34 weeks with pregnancies complicated by pre-eclampsia with severe features. Risk factors for failed induction of labor were compared using various statistical methods. We developed and validated a predictive model using a step-wise procedure to calculate a patient's risk for having a failed induction of labor. RESULTS: Of 159 patients who underwent induction of labor, 77 resulted in successful vaginal deliveries. History of prior vaginal delivery, history of preterm labor, and increased parity, were associated with successful induction of labor. Conversely, elevated umbilical artery dopplers, earlier gestational age, lower bishop score, and current smoking were associated with higher rates of cesarean delivery. CONCLUSION: Overall, 48.4% of patients delivered vaginally following induction of labor in the setting of early onset pre-eclampsia with severe features. We developed a predictive model to classify patients with regards to risk for cesarean section, which included the following variables: pregnancy history, bishop score, age, smoking status, obesity. Further research can better elucidate the impact of different characteristics in predicting mode of delivery.
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