Retrospective Analysis of Risk Factors in Geriatric Hip Fracture Patients Predictive of Surgical Intensive Care Unit Admission.

INTRODUCTION: Although numerous risk factors and prediction models affecting morbidity and mortality in geriatric hip fracture patients have been previously identified, there are scant published data on predictors for perioperative Surgical Intensive Care Unit (SICU) admission in this patient population. Determining if a patient will need an SICU admission would not only allow for the appropriate allocation of resources and personnel but also permit targeted clinical management of these patients with the goal of improving morbidity and mortality outcomes. The purpose of this study was to identify specific risk factors predictive of SICU admission in a population of geriatric hip fracture patients. Unlike previous studies which have investigated predominantly demographic, comorbidity, and laboratory data, the present study also considered a frailty index and length of time from injury to presentation in the Emergency Department (ED). METHODS: A total of 501 geriatric hip fracture patients admitted to a Level 1 trauma center were included in this retrospective, single-center, quantitative study from January 1, 2019, to December 31, 2022. Using a logistical regression analysis, more than 25 different variables were included in the regression model to identify values predictive of SICU admission. Predictive models of planned versus unplanned SICU admissions were also estimated. The discriminative ability of variables in the final models to predict SICU admission was assessed with receiver operating characteristic curves' area under the curve estimates. RESULTS:  Frailty, serum lactate > 2, and presentation to the ED > 12 hours after injury were significant predictors of SICU admission overall (P = 0.03, 0.038, and 0.05 respectively). Additionally, the predictive model for planned SICU admission had no common significant predictors with unplanned SICU admission. Planned SICU admission significant predictors included an Injury Severity Score (ISS) of 15 and greater, a higher total serum protein, serum sodium <135, systolic blood pressure (BP) under 100, increased heart rate on admission to ED, thrombocytopenia (<120), and higher Anesthesia Society Association physical status classification (ASA) score (P = 0.007, 0.04, 0.05, 0.002, 0.041, 0.05, and 0.005 respectively). Each SICU prediction model (overall, planned, and unplanned) demonstrated sufficient discriminative ability with the area under the curve (AUC) values of 0.869, 0.601, and 0.866 respectively. Finally, mean hospital Length of Stay (LOS) and mortality were increased in SICU admissions when compared to non-SICU admissions. CONCLUSION: Of the three risk factors predictive of SICU admission identified in this study, two have not been extensively studied previously in this patient population. Frailty has been associated with increased mortality and postoperative complications in hip fracture patients, but this is the first study to date to use a novel frailty index specifically designed and validated for use in hip fracture patients. The other risk factor, time from injury to presentation to the ED serves as an indicator for time a hip fracture patient spent without receiving medical attention. This risk factor has not been investigated heavily in the past as a predictor of SICU admissions in this patient population.

Sources of Disparities in Surveillance Mammography Performance and Risk-Guided Recommendations for Supplemental Breast Imaging: A Simulation Study.

BACKGROUND: Surveillance mammography is recommended for all women with a history of breast cancer. Risk-guided surveillance incorporating advanced imaging modalities based on individual risk of a second cancer could improve cancer detection. However, personalized surveillance may also amplify disparities. METHODS: In simulated populations using inputs from the Breast Cancer Surveillance Consortium (BCSC), we investigated race- and ethnicity-based disparities. Disparities were decomposed into those due to primary breast cancer and treatment characteristics, social determinants of health (SDOH) and differential error in second cancer ascertainment by modeling populations with or without variation across race and ethnicity in the distribution of these characteristics. We estimated effects of disparities on mammography performance and supplemental imaging recommendations stratified by race and ethnicity. RESULTS: In simulated cohorts based on 65,446 BCSC surveillance mammograms, when only cancer characteristics varied by race and ethnicity, mammograms for Black women had lower sensitivity compared with the overall population (64.1% vs. 71.1%). Differences between Black women and the overall population were larger when both cancer characteristics and SDOH varied by race and ethnicity (53.8% vs. 71.1%). Basing supplemental imaging recommendations on high predicted second cancer risk resulted in less frequent recommendations for Hispanic (6.7%) and Asian/Pacific Islander women (6.4%) compared with the overall population (10.0%). CONCLUSIONS: Variation in cancer characteristics and SDOH led to disparities in surveillance mammography performance and recommendations for supplemental imaging. IMPACT: Risk-guided surveillance imaging may exacerbate disparities. Decision-makers should consider implications for equity in cancer outcomes resulting from implementing risk-guided screening programs. See related In the Spotlight, p. 1479.

Association between prescribed opioid dose and risk of motor vehicle crashes.

ABSTRACT: Opioid-involved motor vehicle traffic fatalities have increased over the past 2 decades. However, the extent to which prescribed opioids increase the risk of motor vehicle crashes remains uncertain. This study used real-world healthcare claims data to examine the association between prescription opioid dose and motor vehicle crash risk. Using nationwide US commercial insurance claims data for 2010 to 2018, we identified 772,404 adults who received incident, noncancer opioid therapy. We examined associations between daily prescription opioid dose, calculated in morphine milligram equivalents (MME) from filled prescription claims, and risk of motor vehicle crashes, assessed as diagnoses of motor vehicle injuries in claims for emergency visits, inpatient hospitalizations, and ambulance transportation. We estimated associations using a within-individual design, which ruled out all time-stable confounding. We complemented the design with time-varying statistical adjustment for other pharmacotherapies and a negative control pain pharmacotherapy analysis (with incident cyclic antidepressant prescriptions). During 2,150,009 person-years of follow-up, there were 12,123 motor vehicle crashes (5.64 crashes per 1000 person-years). In within-individual comparisons, crash risk was greater during opioid prescription periods involving doses ≤60 MME/day (odds ratio [OR], 3.86; 95% confidence interval [CI], 3.54, 4.21), >60 to 120 MME/day (OR, 5.46; 95% CI, 4.44, 6.73), and >120 MME/day (OR, 3.45; 95% CI, 2.31, 5.15) than during off-treatment periods. The negative control analysis supported the specificity of the results to opioids rather than to other processes associated with pharmacologic pain management. These findings suggest that the receipt of prescription opioids, even at doses ≤60 MME/day, is associated with an increased risk of motor vehicle crashes.

Examining Supervised Machine Learning Methods for Integer Link Weight Prediction Using Node Metadata.

With the goal of understanding if the information contained in node metadata can help in the task of link weight prediction, we investigate herein whether incorporating it as a similarity feature (referred to as metadata similarity) between end nodes of a link improves the prediction accuracy of common supervised machine learning methods. In contrast with previous works, instead of normalizing the link weights, we treat them as count variables representing the number of interactions between end nodes, as this is a natural representation for many datasets in the literature. In this preliminary study, we find no significant evidence that metadata similarity improved the prediction accuracy of the four empirical datasets studied. To further explore the role of node metadata in weight prediction, we synthesized weights to analyze the extreme case where the weights depend solely on the metadata of the end nodes, while encoding different relationships between them using logical operators in the generation process. Under these conditions, the random forest method performed significantly better than other methods in 99.07% of cases, though the prediction accuracy was significantly degraded for the methods analyzed in comparison to the experiments with the original weights.

Rapid changes in illegally manufactured fentanyl products and prices in the United States.

BACKGROUND AND AIMS: Synthetic opioids, mostly illegally manufactured fentanyl (IMF), were mentioned in 60% of United States (US) drug overdose deaths in 2020, with dramatic variation across states that mirrors variation in IMF supply. However, little is known about IMF markets in the United States and how they are changing. Researchers have previously used data from undercover cocaine, heroin, and methamphetamine purchases and seizures to examine how their use and related harms respond to changes in price and availability. This analysis used US Drug Enforcement Administration (DEA) data to address two questions: (i) "To what extent does IMF supply vary over time and geography?" and (ii) "What has happened to the purity-adjusted price of IMF?" METHODS: We developed descriptive statistics and visualizations using data from 66 713 observations mentioning IMF and/or heroin from the DEA's System to Retrieve Information from Drug Evidence (STRIDE; now STARLIMS) from 2013 to 2021. Price regressions were estimated with city-level fixed effects examining IMF-only powder observations with purity and price information at the low-to-medium wholesale level (>1 g to ≤100 g; n = 964). RESULTS: From 2013 to 2021, the share of heroin and/or IMF observations mentioning IMF grew from near zero to more than two-thirds. The share of heroin observations also containing IMF grew from <1% to ~40%. There is important geographic variation: in California, most IMF seizures involved counterfeit tablets, whereas New York and Massachusetts largely involved powder formulation. The median price per pure gram of IMF powder sold at the >10 to ≤100 g level fell by more than 50% from 2016 to 2021; regression analyses suggested an average annual decline of 17% (P < 0.001). However, this price decline appears to have been driven by observations from the Northeast. CONCLUSIONS: Since 2013, the illegally manufactured fentanyl problem in the United States has become more deadly and more diverse.

Identifying and Characterizing Medical Advice-Seekers on a Social Media Forum for Buprenorphine Use.

Background: Online communities such as Reddit can provide social support for those recovering from opioid use disorder. However, it is unclear whether and how advice-seekers differ from other users. Our research addresses this gap by identifying key characteristics of r/suboxone users that predict advice-seeking behavior. Objective: The objective of this analysis is to identify and describe advice-seekers on Reddit for buprenorphine-naloxone use using text annotation, social network analysis, and statistical modeling techniques. Methods: We collected 5258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n = 137) or not advice-seeking (n = 65). We also annotated each posting user's buprenorphine-naloxone use status (current versus formerly taking and, if currently taking, whether inducting or tapering versus other stages) and quantified their connectedness using social network analysis. To analyze the relationship between Reddit users' advice-seeking and their social connectivity and medication use status, we constructed four models which varied in their inclusion of explanatory variables for social connectedness and buprenorphine use status. Results: The stepwise model containing "total degree" (p = 0.002), "using: inducting/tapering" (p < 0.001), and "using: other" (p = 0.01) outperformed all other models. Reddit users with fewer connections and who are currently using buprenorphine-naloxone are more likely to seek advice than those who are well-connected and no longer using the medication, respectively. Importantly, advice-seeking behavior is most accurately predicted using a combination of network characteristics and medication use status, rather than either factor alone. Conclusions: Our findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.

Assessing Health and Wellness Outcomes of Medicaid-Enrolled Infants Born to Adolescent Mothers.

OBJECTIVE: To assess the effect of adolescent birth on the health and wellness of these infants within their first year of life. METHODS: Our study focused on 2011 Medicaid births nationwide. The study group (infants born to adolescents, aged 10 to 19 at time of birth) was matched with infants born to adults (aged 20 to 44 at time of birth), based on demographics. Statistical tests (proportion test and Poisson test) were used to compare the outcomes of these two groups to determine if differences were significant. RESULTS: The outcomes assessed were: low birth weight (LBW), substance exposure, foster care, health status, infant mortality, emergency department (ED) visits, and wellness visits. Of the 68,562 infant pairs included in the study, we found statistically significant higher rates of LBW (P ≤ 0·005), infant mortality (P = 0·05), and ED visits (P ≤ 0·005) for infants born to adolescents at the 95% confidence interval. The rate of wellness visits for all infants was well below the recommended amount. Additional differences were found at the race/ethnicity and urbanicity levels. CONCLUSION FOR PRACTICE: Infants born to adolescents had a higher rate of ED visits within the first year of life, however, the increased rates of LBW and mortality for the Medicaid population are not as significant as previous national studies suggest. Analysis of outcomes across stratification helped identify vulnerable populations (i.e. urban infants). Public health programs are urged to examine ED visits in infants born to adolescents among the Medicaid population. Improved health education or phone-based resources could help reduce unnecessary visits and reduce cost.

Leveraging TCGA gene expression data to build predictive models for cancer drug response.

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. RESULTS: We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. CONCLUSIONS: Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.

Medicaid Claims for Contraception Among Women With Medical Conditions After Release of the US Medical Eligibility Criteria for Contraceptive Use.

INTRODUCTION: The US Medical Eligibility Criteria for Contraceptive Use (MEC) identified 20 medical conditions that increase a woman's risk for adverse outcomes in pregnancy. MEC recommends that women with these conditions use long-acting, highly effective contraceptive methods. The objective of our study was to examine provision of contraception to women enrolled in Medicaid who had 1 or more of these 20 medical conditions METHODS: We used Medicaid Analytic Extract claims data to study Medicaid-enrolled women who were of reproductive age in the 2-year period before MEC's release (2008 and 2009) (N = 442,424) and the 2-year period after its release (2011 and 2012) (N = 533,619) for 14 states. We assessed 2 outcomes: provision of family planning management (FPM) and provision of highest efficacy methods (HEMs) for the entire study population and by health condition. The ratio of the after-MEC rate to the before-MEC rate was used to determine significance in MEC's uptake. RESULTS: Outcomes increased significantly from the before-MEC period to the after-MEC period for both FPM (1.06; lower bound confidence interval [CI], 1.05) and HEM (1.37; lower bound CI, 1.36) for a 1-sided hypothesis test. For the 19 of 20 conditions we were able to test for FPM, contraceptive use increased significantly for 12 conditions, with ratios ranging from 1.05 to 2.14. For the 16 of 20 conditions tested for HEM, contraception use increased significantly for all conditions, with ratios ranging from 1.19 to 2.80. CONCLUSION: Provision of both FPM and HEM increased significantly among women with high-risk health conditions from the before-MEC period (2008 and 2009) to the after-MEC period (2011 and 2012). Health policy makers and clinicians need to continue promotion of effective family planning management for women with high-risk conditions.