Systematic analysis of off-label and off-guideline cancer therapy usage in a real-world cohort of 165,912 US patients.

Patients with cancer may be given treatments that are not officially approved (off-label) or recommended by guidelines (off-guideline). Here we present a data science framework to systematically characterize off-label and off-guideline usages using real-world data from de-identified electronic health records (EHR). We analyze treatment patterns in 165,912 US patients with 14 common cancer types. We find that 18.6% and 4.4% of patients have received at least one line of off-label and off-guideline cancer drugs, respectively. Patients with worse performance status, in later lines, or treated at academic hospitals are significantly more likely to receive off-label and off-guideline drugs. To quantify how predictable off-guideline usage is, we developed machine learning models to predict which drug a patient is likely to receive based on their clinical characteristics and previous treatments. Finally, we demonstrate that our systematic analyses generate hypotheses about patients' response to treatments.

Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data.

Quantifying the effectiveness of different cancer therapies in patients with specific tumor mutations is critical for improving patient outcomes and advancing precision medicine. Here we perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation-mutation interactions that impact the outcomes of targeted therapies. This work demonstrates how computational analysis of large real-world data generates insights, hypotheses and resources to enable precision oncology.

Machine Learning Prediction of Clinical Trial Operational Efficiency.

Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, and a stringent regulatory environment. Trial designers have historically relied on investigator expertise and legacy norms established within sponsor companies to improve operational efficiency while achieving study goals. As such, data-driven forecasts of operational metrics can be a useful resource for trial design and planning. We develop a machine learning model to predict clinical trial operational efficiency using a novel dataset from Roche containing over 2,000 clinical trials across 20 years and multiple disease areas. The data includes important operational metrics related to patient recruitment and trial duration, as well as a variety of trial features such as the number of procedures, eligibility criteria, and endpoints. Our results demonstrate that operational efficiency can be predicted robustly using trial features, which can provide useful insights to trial designers on the potential impact of their decisions on patient recruitment success and trial duration.

Quality of Life and Healthcare Resource Use in a Real-world Patient Population with Idiopathic Pulmonary Fibrosis: The PROOF Registry.

INTRODUCTION: The PROOF registry is a prospective, observational study that aimed to monitor disease progression in a real-world cohort of patients with idiopathic pulmonary fibrosis (IPF). Here, longitudinal quality-of-life (QoL) outcomes, healthcare resource use (HCRU), and the association between QoL and mortality in patients enrolled in the PROOF registry are presented. METHODS: QoL outcomes (St. George's Respiratory Questionnaire [SGRQ], EuroQoL-5 dimensions-5 levels Health Questionnaire [EQ-5D-5L], EuroQoL-5 dimensions Health Questionnaire [EQ-5D] visual analogue scale [VAS] and cough VAS) and HCRU were collected for all patients. Associations between baseline QoL and mortality were assessed using univariate and multivariate analyses. During multivariate analyses, individual QoL measures were adjusted for the following covariates: age, sex, percent predicted forced vital capacity, percent predicted diffusing capacity of the lungs for carbon monoxide, smoking status, and supplementary oxygen use at registry inclusion. RESULTS: In total, 277 patients were enrolled in the PROOF registry. During the follow-up period, worsening in cough VAS score, SGRQ symptom score, and SGRQ activity score was observed, while EQ-5D VAS, SGRQ total score, and SGRQ impact score remained stable. During univariate analyses, EQ-5D VAS and all SGRQ sub-scores and total score at baseline were associated with mortality; however, during multivariate analyses, only the SGRQ total score, SGRQ impact score, and SGRQ symptom score at baseline were associated with mortality. During the follow-up period, 261 (94.2%) patients required an outpatient consultation (IPF- or non-IPF-related) and there were 182 hospitalizations in total, most of which were respiratory related (66.5%). CONCLUSIONS: The PROOF registry provided valuable, real-world data on the association between baseline QoL and mortality, and longitudinal HCRU and QoL outcomes in patients with IPF over 24 months and identified that SGRQ may be an independent prognostic factor in IPF.

Factors associated with COVID-19 viral and antibody test positivity and assessment of test concordance: a retrospective cohort study using electronic health records from the USA.

OBJECTIVES: To identify factors associated with COVID-19 test positivity and assess viral and antibody test concordance. DESIGN: Observational retrospective cohort study. SETTING: Optum de-identified electronic health records including over 700 hospitals and 7000 clinics in the USA. PARTICIPANTS: There were 891 754 patients who had a COVID-19 test identified in their electronic health record between 20 February 2020 and 10 July 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: Per cent of viral and antibody tests positive for COVID-19 ('positivity rate'); adjusted ORs for factors associated with COVID-19 viral and antibody test positivity; and per cent concordance between positive viral and subsequent antibody test results. RESULTS: Overall positivity rate was 9% (70 472 of 771 278) and 12% (11 094 of 91 741) for viral and antibody tests, respectively. Positivity rate was inversely associated with the number of individuals tested and decreased over time across regions and race/ethnicities. Antibody test concordance among patients with an initial positive viral test was 91% (71%-95% depending on time between tests). Among tests separated by at least 2 weeks, discordant results occurred in 7% of patients and 9% of immunocompromised patients. Factors associated with increased odds of viral and antibody positivity in multivariable models included: male sex, Hispanic or non-Hispanic black or Asian race/ethnicity, uninsured or Medicaid insurance and Northeast residence. We identified a negative dose effect between the number of comorbidities and viral and antibody test positivity. Paediatric patients had reduced odds (OR=0.60, 95% CI 0.57 to 0.64) of a positive viral test but increased odds (OR=1.90, 95% CI 1.62 to 2.23) of a positive antibody test compared with those aged 18-34 years old. CONCLUSIONS: This study identified sociodemographic and clinical factors associated with COVID-19 test positivity and provided real-world evidence demonstrating high antibody test concordance among viral-positive patients.

Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA.

OBJECTIVES: To develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19. DESIGN: Retrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set. SETTING: Optum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA. PARTICIPANTS: 17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020. MAIN OUTCOME MEASURE: All-cause mortality while hospitalised. RESULTS: The full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May. CONCLUSION: Age was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.

Evaluating eligibility criteria of oncology trials using real-world data and AI.

There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging1-3. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety.

Novel Health Information Technology to Aid Provider Recognition and Treatment of Major Depressive Disorder and Posttraumatic Stress Disorder in Primary Care.

BACKGROUND: Millions of traumatized refugees worldwide have resettled in the United States. For one of the largest, the Cambodian community, having their mental health needs met has been a continuing challenge. A multicomponent health information technology screening tool was designed to aid provider recognition and treatment of major depressive disorder and posttraumatic stress disorder (PTSD) in the primary care setting. METHODS: In a clustered randomized controlled trial, 18 primary care providers were randomized to receive access to a multicomponent health information technology mental health screening intervention, or to a minimal intervention control group; 390 Cambodian American patients empaneled to participating providers were assigned to the providers' randomized group. RESULTS: Electronic screening revealed that 65% of patients screened positive for depression and 34% screened positive for PTSD. Multilevel mixed effects logistic models, accounting for clustering structure, indicated that providers in the intervention were more likely to diagnose depression [odds ratio (OR), 6.5; 95% confidence interval (CI), 1.48-28.79; P=0.013] and PTSD (OR, 23.3; 95% CI, 2.99-151.62; P=0.002) among those diagnosed during screening, relative to the control group. Providers in the intervention were more likely to provide evidence-based guideline (OR, 4.02; 95% CI, 1.01-16.06; P=0.049) and trauma-informed (OR, 15.8; 95% CI, 3.47-71.6; P<0.001) care in unadjusted models, relative to the control group. Guideline care, but not trauma-informed care, was associated with decreased depression at 12 weeks in both study groups (P=0.003), and neither was associated with PTSD outcomes at 12 weeks. CONCLUSIONS: This innovative approach offers the potential for training primary care providers to diagnose and treat traumatized patients, the majority of whom seek mental health care in primary care (ClinicalTrials.gov number, NCT03191929).

Lifetime Socioeconomic Status and Late-life Health Trajectories: Longitudinal Results From the Mexican Health and Aging Study.

Objective: This article examines the association between childhood and adult socioeconomic status (SES) and late-life health trajectories for older adults in Mexico. Method: Data are from the Mexican Health and Aging Study, a panel survey that began with a nationally representative sample of Mexican adults 50 years and older at baseline (2001), with follow-up in 2003 and 2012. We use a hierarchical repeated measures model to estimate the relationship between SES and depressive symptoms, functional limitations, and self-rated health, respectively. We tested both discrete measures of SES in childhood and adulthood, as well as a combined indicator of lifetime SES. Results: Childhood SES was significantly associated with later-life health trajectories net of adulthood SES indicators. Adult SES was significantly associated with late-life health trajectories, with some differences by gender and outcome. There were significant SES disparities in health outcomes over the 11-year study period. However, there were no significant multiplicative interactions between SES and age, which would have indicated either diminishing or widening SES health disparities with age. Discussion: Socioeconomic disparities in health appear to persist into old age in the Mexican context. Efforts to reduce late-life health disparities in Mexico should target socioeconomic and material conditions across the life course.

Deconstructing Bipolar Disorder and Schizophrenia: A cross-diagnostic cluster analysis of cognitive phenotypes.

BACKGROUND: Bipolar disorder (BD) and schizophrenia (SZ) show substantial overlap. It has been suggested that a subgroup of patients might contribute to these overlapping features. This study employed a cross-diagnostic cluster analysis to identify subgroups of individuals with shared cognitive phenotypes. METHOD: 143 participants (68 BD patients, 39 SZ patients and 36 healthy controls) completed a battery of EEG and performance assessments on perception, nonsocial cognition and social cognition. A K-means cluster analysis was conducted with all participants across diagnostic groups. Clinical symptoms, functional capacity, and functional outcome were assessed in patients. RESULTS: A two-cluster solution across 3 groups was the most stable. One cluster including 44 BD patients, 31 controls and 5 SZ patients showed better cognition (High cluster) than the other cluster with 24 BD patients, 35 SZ patients and 5 controls (Low cluster). BD patients in the High cluster performed better than BD patients in the Low cluster across cognitive domains. Within each cluster, participants with different clinical diagnoses showed different profiles across cognitive domains. LIMITATIONS: All patients are in the chronic phase and out of mood episode at the time of assessment and most of the assessment were behavioral measures. CONCLUSIONS: This study identified two clusters with shared cognitive phenotype profiles that were not proxies for clinical diagnoses. The finding of better social cognitive performance of BD patients than SZ patients in the Lowe cluster suggest that relatively preserved social cognition may be important to identify disease process distinct to each disorder.

The association between self-rated eating habits and dietary behavior in two Latino neighborhoods: Findings from Proyecto MercadoFRESCO.

OBJECTIVE: Latinos are the largest racial and ethnic minority group in the United States and bear a disproportionate burden of obesity related chronic disease. Despite national efforts to improve dietary habits and prevent obesity among Latinos, obesity rates remain high. The objective of this study is to explore the relationship between self-rated dietary quality and dietary behavior among Latinos and how this may vary by socio-demographics to help inform future public health efforts aiming to improve eating habits and obesity rates. DESIGN: Cross-sectional study using a series of chi-square tests, the non-parametric Wilcoxon-Mann-Whitney test and logistic regression to explore self-rated eating habits. SETTING: Two urban, low-income, predominantly Latino neighborhoods in Los Angeles County. SUBJECTS: 1000 adults who self-identified as their household's primary food purchaser and preparer were interviewed from 2012 to 2013. Households were randomly selected based on their proximity to corner stores participating in a project to improve the food environment. RESULTS: Most respondents (59%) report "good" eating habits. Significant associations between "good" eating habits and overall health, fruit and vegetable consumption were observed (p < 0.001). Despite these promising findings, we also find high levels of regular soda and energy-dense food consumption. CONCLUSION: This study revealed a general understanding that healthy dietary habits are associated with fruit and vegetable consumption among Latinos in two urban neighborhoods. However, there is a need for more targeted health promotion and nutrition education efforts on the risks associated with soda and energy-dense food consumption to help improve dietary habits and obesity levels in low-income Latino communities.

Associations between oxytocin receptor genotypes and social cognitive performance in individuals with schizophrenia.

Individuals with schizophrenia often show substantial deficits in social cognitive abilities, which are strongly associated with social functioning. To advance our understanding of the genetic variation that is associated with social cognitive deficits in schizophrenia, we genotyped 74 schizophrenia outpatients who completed social cognitive performance measures assessing mentalizing, social perception, and emotional intelligence, as well as clinical symptoms. We assessed seven single nucleotide polymorphisms (SNPs) of the oxytocin receptor (OXTR) previously found to show replicable associations with socio-emotional processes. For one of the seven SNPs, rs2268493, the 'T' allele was significantly associated with poorer performance on a composite social cognition index, as well as specific tests of mentalizing and social perception. None of the SNPs were associated with clinical symptoms. Though the sample size is small, these findings provide initial support for the involvement of genetic variants of the OXTR in social cognitive impairments in schizophrenia.

Behavioral approach and avoidance in schizophrenia: an evaluation of motivational profiles.

Schizophrenia is associated with motivational deficits that interfere with a wide range of goal directed activities. Despite their clinical importance, our current understanding of these motivational impairments is limited. Furthermore, different types of motivational problems are commonly seen among individuals within the broad diagnosis of schizophrenia. The goal of the current study was to examine whether clinically meaningful subgroups could be identified based on approach and avoidance motivational tendencies. We measured these tendencies in 151 individuals with schizophrenia. Although prior studies demonstrate elevated BIS sensitivity in schizophrenia at the overall group level, none have explored various combinations of BIS/BAS sensitivities within this disorder. Cluster analyses yielded five subgroups with different combinations of low, moderate, or high BIS and BAS. The subgroups had interpretable differences in clinically rated negative symptoms and self-reported anhedonia/socio-emotional attitudes, which were not detectable with the more commonly used linear BIS/BAS scores. Two of the subgroups had significantly elevated negative symptoms but different approach/avoidance profiles: one was characterized by markedly low BIS, low BAS and an overall lack of social approach motivation; the other had markedly high BIS but moderate BAS and elevated social avoidance motivation. The two subgroups with relatively good clinical functioning showed patterns of BAS greater than BIS. Our findings indicate that there are distinct motivational pathways that can lead to asociality in schizophrenia and highlight the value of considering profiles based on combined patterns of BIS and BAS in schizophrenia.

The association between nutrition facts label utilization and comprehension among Latinos in two east Los Angeles neighborhoods.

BACKGROUND: The Nutrition Facts label can facilitate healthy dietary practices. There is a dearth of research on Latinos' utilization and comprehension of the Nutrition Facts label. OBJECTIVE: To measure use and comprehension of the Nutrition Facts label and to identify correlates among Latinos in East Los Angeles, CA. DESIGN: Cross-sectional interviewer-administered survey using computer-assisted personal interview software, conducted in either English or Spanish in the participant's home. PARTICIPANTS/SETTING: Eligibility criteria were: living in a household within the block clusters identified, being age 18 years or older, speaking English or Spanish, identifying as Latino and as the household's main food purchaser and preparer. Analyses were based on 269 eligible respondents. STATISTICAL ANALYSES PERFORMED: χ(2) test and multivariate logistic regression analysis assessed the associations among the main outcomes and demographics. Multiple imputations addressed missing data. RESULTS: Sixty percent reported using the label; only 13% showed adequate comprehension of the label. Utilization was associated with being female, speaking Spanish, and being below the poverty line. Comprehension was associated with younger age, not being married, and higher education. Utilization was not associated with comprehension. CONCLUSIONS: Latinos who are using the Nutrition Facts label are not correctly interpreting the available information. Targeted education is needed to improve use and comprehension of the Nutrition Facts label to directly improve diet, particularly among males, older Latinos, and those with less than a high school education.

Reduction in fluoroquinolone use following introduction of ertapenem into a hospital formulary is associated with improvement in susceptibility of Pseudomonas aeruginosa to group 2 carbapenems: a 10-year study.

We examined the effect of the addition of ertapenem to our hospital formulary on the resistance of nosocomial Pseudomonas aeruginosa to group 2 carbapenems (imipenem, meropenem, and doripenem). This was a retrospective, observational study conducted between 1 January 2000 and 31 January 2009 at a large, tertiary-care hospital. Autoregressive integrated moving average (ARIMA) regression models were used to evaluate the effect of ertapenem use on the susceptibility of Pseudomonas aeruginosa to group 2 carbapenems as well as on the use of the group 2 carbapenems, ciprofloxacin, and other antipseudomonal drugs (i.e., tobramycin, cefepime, and piperacillin-tazobactam). Resistance was expressed as a percentage of total isolates as well as the number of carbapenem-resistant bacterial isolates per 10,000 patient days. Pearson correlation was used to assess the relationship between antibiotic use and carbapenem resistance. Following the addition of ertapenem to the formulary, there was a statistically significant decrease in the percentage of Pseudomonas aeruginosa isolates resistant to the group 2 carbapenems (P = 0.003). Group 2 carbapenem use and the number of carbapenem-resistant Pseudomonas aeruginosa isolates per 10,000 patient days did not change significantly over the time period. There was a large decrease in the use of ciprofloxacin (P = 0.0033), and there was a correlation of ciprofloxacin use with the percentage of isolates resistant to the group 2 carbapenems (ρ = 0.47, P = 0.002). We suspect that the improvement in susceptibility of Pseudomonas aeruginosa to group 2 carbapenems was related to a decrease in ciprofloxacin use.

Sustained reduction in antimicrobial use and decrease in methicillin-resistant Staphylococcus aureus and Clostridium difficile infections following implementation of an electronic medical record at a tertiary-care teaching hospital.

OBJECTIVES: we evaluated the effect of implementation of an electronic medical record (EMR) on the use of antimicrobial agents and on the rates of infections with Clostridium difficile and methicillin-resistant Staphylococcus aureus (MRSA). METHODS: this was a retrospective, observational study conducted between 1 January 2005 and 31 December 2009. Antimicrobial drug use, rates of nosocomial C. difficile infection (CDI) and MRSA infection, the number of medical charts reviewed and number of antimicrobial recommendations made and accepted were compared before and after implementing the EMR utilizing interrupted time-series analysis. RESULTS: compared with the 10 quarters prior to implementing the EMR, there was a 36.6% increase in the number of charts reviewed (P < 0.0001), a 98.1% increase in the number of antimicrobial recommendations made (P < 0.0001) and a 124% increase in the number of recommendations accepted (P < 0.0001). There was a 28.8% decrease in the use of 41 commonly used antibacterial agents (P < 0.0001). Nosocomial CDI decreased by 18.7% (P = 0.07) and nosocomial MRSA infections decreased by 45.2% (P < 0.0001) following implementation of the EMR. CONCLUSIONS: adoption of an EMR facilitated a significant increase in chart reviews and antimicrobial recommendations, which resulted in a sustained decrease in antimicrobial use. There were decreased nosocomial infections with MRSA and a trend towards decreasing CDIs following implementation of the EMR.