Analytics to monitor local impact of the Protecting Access to Medicare Act's imaging clinical decision support requirements.

OBJECTIVE: This study aimed is to: (1) extend the Integrating the Biology and the Bedside (i2b2) data and application models to include medical imaging appropriate use criteria, enabling it to serve as a platform to monitor local impact of the Protecting Access to Medicare Act's (PAMA) imaging clinical decision support (CDS) requirements, and (2) validate the i2b2 extension using data from the Medicare Imaging Demonstration (MID) CDS implementation. MATERIALS AND METHODS: This study provided a reference implementation and assessed its validity and reliability using data from the MID, the federal government's predecessor to PAMA's imaging CDS program. The Star Schema was extended to describe the interactions of imaging ordering providers with the CDS. New ontologies were added to enable mapping medical imaging appropriateness data to i2b2 schema. z-Ratio for testing the significance of the difference between 2 independent proportions was utilized. RESULTS: The reference implementation used 26 327 orders for imaging examinations which were persisted to the modified i2b2 schema. As an illustration of the analytical capabilities of the Web Client, we report that 331/1192 or 28.1% of imaging orders were deemed appropriate by the CDS system at the end of the intervention period (September 2013), an increase from 162/1223 or 13.2% for the first month of the baseline period, December 2011 (P = .0212), consistent with previous studies. CONCLUSIONS: The i2b2 platform can be extended to monitor local impact of PAMA's appropriateness of imaging ordering CDS requirements.

Fecal microbiota transplantation and Clostridioides difficile infection among privately insured patients in the United States.

BACKGROUND: Clostridioides difficile infection (CDI) may be rising in severity in the US over the past decade and its treatment landscape is changing given the recent adoption of fecal microbiota transplantation (FMT) METHODS: We built a retrospective observational cohort using a database of a national care-plan containing medical claims of over 50 million individuals between 2008 and 2019. We used International Classification of Disease (ICD) and prescription data to identify patients with CDI. We estimated trends in disease burden and FMT use, and evaluated complications post FMT using a phenome-wide association approach. RESULTS: We identified 38,396 patients with CDI; the median age was 60 years (IQR 45-74) and 60% were female (n = 23,374). The rate of CDI increased from 33.4 to 69.46 cases per 100,000 person-years between 2008 and 2015, and stabilized from 2015 to 2019 (increase of 4.77 cases per 100,000 person-years per year, 95% CI 3.55-5.98 prior to 2015 vs. 2.01 95% CI - 10.16 to 14.18 after 2015). Of the 7715 patients with recurrent CDI, 407 patients (5%) underwent FMT. Gastrointestinal complications were increased within 1 month post FMT (OR 99.60, p < 0.001). Sepsis was identified in two individuals (0.49% 95% CI 0.05-1.7%) within the first month post FMT. The risk of CDI recurrence significantly decreased post FMT compared with anti-CDI antibiotics in the multivariable model (raw-recurrence rate 9.8% vs 36%, aOR = 0.21, 95% CI 0.12-0.53, p < 0.001). CONCLUSION: We show that FMT is strongly associated with a decrease in CDI recurrence compared with the usual care with generally mild complications for up to 2 years.

Medication Use in the Management of Comorbidities Among Individuals With Autism Spectrum Disorder From a Large Nationwide Insurance Database.

Importance: Although there is no pharmacological treatment for autism spectrum disorder (ASD) itself, behavioral and pharmacological therapies have been used to address its symptoms and common comorbidities. A better understanding of the medications used to manage comorbid conditions in this growing population is critical; however, most previous efforts have been limited in size, duration, and lack of broad representation. Objective: To use a nationally representative database to uncover trends in the prevalence of co-occurring conditions and medication use in the management of symptoms and comorbidities over time among US individuals with ASD. Design, Setting, and Participants: This retrospective, population-based cohort study mined a nationwide, managed health plan claims database containing more than 86 million unique members. Data from January 1, 2014, to December 31, 2019, were used to analyze prescription frequency and diagnoses of comorbidities. A total of 26 722 individuals with ASD who had been prescribed at least 1 of 24 medications most commonly prescribed to treat ASD symptoms or comorbidities during the 6-year study period were included in the analysis. Exposures: Diagnosis codes for ASD based on International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. Main Outcomes and Measures: Quantitative estimates of prescription frequency for the 24 most commonly prescribed medications among the study cohort and the most common comorbidities associated with each medication in this population. Results: Among the 26 722 individuals with ASD included in the analysis (77.7% male; mean [SD] age, 14.45 [9.40] years), polypharmacy was common, ranging from 28.6% to 31.5%. Individuals' prescription regimens changed frequently within medication classes, rather than between classes. The prescription frequency of a specific medication varied considerably, depending on the coexisting diagnosis of a given comorbidity. Of the 24 medications assessed, 15 were associated with at least a 15% prevalence of a mood disorder, and 11 were associated with at least a 15% prevalence of attention-deficit/hyperactivity disorder. For patients taking antipsychotics, the 2 most common comorbidities were combined type attention-deficit/hyperactivity disorder (11.6%-17.8%) and anxiety disorder (13.1%-30.1%). Conclusions and Relevance: This study demonstrated considerable variability and transiency in the use of prescription medications by US clinicians to manage symptoms and comorbidities associated with ASD. These findings support the importance of early and ongoing surveillance of patients with ASD and co-occurring conditions and offer clinicians insight on the targeted therapies most commonly used to manage co-occurring conditions. Future research and policy efforts are critical to assess the extent to which pharmacological management of comorbidities affects quality of life and functioning in patients with ASD while continuing to optimize clinical guidelines, to ensure effective care for this growing population.

Healthcare spending and utilization for pediatric Irritable Bowel Syndrome in a commercially insured population.

BACKGROUND: Pediatric Irritable Bowel Syndrome (IBS) is common and can be associated with disabling gastrointestinal symptoms. Comprehensive data regarding utilization and cost of pediatric IBS are lacking. Our aim was to determine the annual all-cause spending and healthcare utilization in pediatric IBS. METHODS: Cross-sectional cohort study using a national claims database of commercially insured individuals. 932,592 members, age 8-18 years, were included. Members were selected based on PheWas codes and continuous enrollment in 2014. Linear and binomial regression models were used to calculate healthcare spending and compare comorbidities between IBS subjects and controls. KEY RESULTS: 1215 members with claims for IBS (68.4% female) and 931,377 controls (55.7% female) were included. Mean age was 15.03 ± 2.83 (median 16) years in the IBS group and 13.14 ± 3.12 (median 13) years in controls. Mental health and chronic pain comorbidities were more prevalent in the IBS cohort. Healthcare spending: The mean annual all-cause incremental spending of members with IBS was $6,364.60 compared to controls when adjusting for age and gender. Healthcare utilization: Members with IBS had increased healthcare utilization including higher rates of inpatient, outpatient, and emergency room visits, and higher rates of health service utilization including medical care, radiology/laboratory services, surgery, anesthesia, mental health, and physical therapy. General pediatrics was more frequently consulted by controls. All subspecialty consultations, with the exception of dental medicine and endocrinology, were sought more frequently by IBS patients. CONCLUSION: Patients with IBS incur significant annual spending through increased healthcare utilization.

Measuring health-care delays among privately insured patients with tuberculosis in the USA: an observational cohort study.

BACKGROUND: A high index of suspicion is needed to initiate appropriate testing for tuberculosis due to its protean symptoms, yet health-care providers in low-incidence settings are becoming less familiar with the disease as rates decline. We aimed to estimate delays in tuberculosis diagnosis and treatment at the US national level between 2008 and 2016. METHODS: In this retrospective observational cohort study, we repurposed private insurance claims data provided by Aetna (Connecticut, USA), to measure health-care delays in tuberculosis diagnosis in the USA in 2008-16. Active tuberculosis was determined by diagnosis codes and the filling of anti-tuberculosis treatment prescriptions. Health-care delays were defined as the duration between the first health-care visit for a tuberculosis symptom and the initiation of anti-tuberculosis treatment. We assessed if delays varied over time, and by patient and system variables, using multivariable regression. We estimated household tuberculosis transmission and respiratory complications after treatment initiation. FINDINGS: We confirmed 738 active tuberculosis cases (incidence 1·45 per 100 000 person-years) with a median health-care delay of 24 days (IQR 10-45). Multivariable regression analysis showed that longer delays were associated with older age (8·4% per 10 year increase [95% CI 4·0 to 13·1]; p<0·0086) and non-HIV immunosuppression (19·2% [15·1 to 60·0]; p=0·0432). Presenting with three or more symptoms was associated with a shorter delay (-22·5% [-39·1 to -2·0]; p=0·0415), relative to presenting with one symptom, as did use of chest imaging (-24·9% [-37·9 to -8·9]; p<0·0098), tuberculosis nucleic acid amplification tests (-19·2% [-32·7 to -3·1]; p=0·0241), and care by a tuberculosis specialist provider (-17·2% [-33·1 to -22·3]; p<0·0087). Longer delays were associated with an increased rate of respiratory complications even after controlling for patient characteristics, and an increased rate of secondary tuberculosis among dependents. INTERPRETATION: In the USA, the median health-care delay for privately insured patients with tuberculosis exceeds WHO-recommended levels of 21 days (3 weeks). The results suggest the need for health-care provider education on best practices in tuberculosis diagnosis, including the use of molecular tests and the maintenance of a high index of suspicion for the disease. FUNDING: US National Institutes of Health.

A fast divide-and-conquer sparse Cox regression.

We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size $n_0$ is exceedingly large and the covariate dimension $p$ is not small but $n_0\gg p$. The proposed algorithm achieves computational efficiency through a one-step linear approximation followed by a least square approximation to the partial likelihood (PL). These sequences of linearization enable us to maximize the PL with only a small subset and perform penalized estimation via a fast approximation to the PL. The algorithm is applicable for the analysis of both time-independent and time-dependent survival data. Simulations suggest that the proposed DAC algorithm substantially outperforms the full sample-based estimators and the existing DAC algorithm with respect to the computational speed, while it achieves similar statistical efficiency as the full sample-based estimators. The proposed algorithm was applied to extraordinarily large survival datasets for the prediction of heart failure-specific readmission within 30 days among Medicare heart failure patients.

Scalability and cost-effectiveness analysis of whole genome-wide association studies on Google Cloud Platform and Amazon Web Services.

OBJECTIVE: Advancements in human genomics have generated a surge of available data, fueling the growth and accessibility of databases for more comprehensive, in-depth genetic studies. METHODS: We provide a straightforward and innovative methodology to optimize cloud configuration in order to conduct genome-wide association studies. We utilized Spark clusters on both Google Cloud Platform and Amazon Web Services, as well as Hail (http://doi.org/10.5281/zenodo.2646680) for analysis and exploration of genomic variants dataset. RESULTS: Comparative evaluation of numerous cloud-based cluster configurations demonstrate a successful and unprecedented compromise between speed and cost for performing genome-wide association studies on 4 distinct whole-genome sequencing datasets. Results are consistent across the 2 cloud providers and could be highly useful for accelerating research in genetics. CONCLUSIONS: We present a timely piece for one of the most frequently asked questions when moving to the cloud: what is the trade-off between speed and cost?

Estimates of healthcare spending for preterm and low-birthweight infants in a commercially insured population: 2008-2016.

The growth in healthcare spending is an important topic in the United States, and preterm and low-birthweight infants have some of the highest healthcare expenditures of any patient population. We performed a retrospective cohort study of spending in this population using a large, national claims database of commercially insured individuals. A total of 763,566 infants with insurance coverage through Aetna, Inc. for the first 6 months of post-natal life were included, and received approximately $8.4 billion (2016 USD) in healthcare services. Infants with billing codes indicating preterm status (<37 weeks, n = 50,511) incurred medical expenditures of $76,153 on average, while low-birthweight status (<2500 g) was associated with average spending of $114,437. Infants born at 24 weeks gestation (n = 418) had the highest per infant average expenditures of $603,778. Understanding the drivers of variation in costs within gestational age and birthweight bands is an important target for future studies.

Big Data in the Assessment of Pediatric Medication Safety.

Big data (BD) in pediatric medication safety research provides many opportunities to improve the safety and health of children. The number of pediatric medication and device trials has increased in part because of the past 20 years of US legislation requiring and incentivizing study of the effects of medical products in children (Food and Drug Administration Modernization Act of 1997, Pediatric Rule in 1998, Best Pharmaceuticals for Children Act of 2002, and Pediatric Research Equity Act of 2003). There are some limitations of traditional approaches to studying medication safety in children. Randomized clinical trials within the regulatory context may not enroll patients who are representative of the general pediatric population, provide the power to detect rare safety signals, or provide long-term safety data. BD sources may have these capabilities. In recent years, medical records have become digitized, and cell phones and personal devices have proliferated. In this process, the field of biomedical science has progressively used BD from those records coupled with other data sources, both digital and traditional. Additionally, large distributed databases that include pediatric-specific outcome variables are available. A workshop entitled "Advancing the Development of Pediatric Therapeutics: Application of 'Big Data' to Pediatric Safety Studies" held September 18 to 19, 2017, in Silver Spring, Maryland, formed the basis of many of the ideas outlined in this article, which are intended to identify key examples, critical issues, and future directions in this early phase of an anticipated dramatic change in the availability and use of BD.

Systemic nature of spinal muscular atrophy revealed by studying insurance claims.

OBJECTIVE: We investigated the presence of non-neuromuscular phenotypes in patients affected by Spinal Muscular Atrophy (SMA), a disorder caused by a mutation in the Survival of Motor Neuron (SMN) gene, and whether these phenotypes may be clinically detectable prior to clinical signs of neuromuscular degeneration and therefore independent of muscle weakness. METHODS: We utilized a de-identified database of insurance claims to explore the health of 1,038 SMA patients compared to controls. Two analyses were performed: (1) claims from the entire insurance coverage window; and (2) for SMA patients, claims prior to diagnosis of any neuromuscular disease or evidence of major neuromuscular degeneration to increase the chance that phenotypes could be attributed directly to reduced SMN levels. Logistic regression was used to determine whether phenotypes were diagnosed at significantly different rates between SMA patients and controls and to obtain covariate-adjusted odds ratios. RESULTS: Results from the entire coverage window revealed a broad spectrum of phenotypes that are differentially diagnosed in SMA subjects compared to controls. Moreover, data from SMA patients prior to their first clinical signs of neuromuscular degeneration revealed numerous non-neuromuscular phenotypes including defects within the cardiovascular, gastrointestinal, metabolic, reproductive, and skeletal systems. Furthermore, our data provide evidence of a potential ordering of disease progression beginning with these non-neuromuscular phenotypes. CONCLUSIONS: Our data point to a direct relationship between early, detectable non-neuromuscular symptoms and SMN deficiency. Our findings are particularly important for evaluating the efficacy of SMN-increasing therapies for SMA, comparing the effectiveness of local versus systemically delivered therapeutics, and determining the optimal therapeutic treatment window prior to irreversible neuromuscular damage.

Pre-existing autoimmune disease and the risk of immune-related adverse events among patients receiving checkpoint inhibitors for cancer.

INTRODUCTION: Patients with pre-existing autoimmune diseases have been excluded from clinical trials of immune checkpoint inhibitors (ICIs) for cancer. Real-world evidence is necessary to understand ICI safety in this population. METHODS: Patients treated with ICIs from 2011 to 2017 were identified using data from a large health insurer. Outcomes included time to (1) any hospitalization; (2) any hospitalization with an irAE diagnosis; and (3) outpatient corticosteroid treatment. The key exposure was pre-existing autoimmune disease, ascertained within 12 months before starting ICI treatment, and defined either by strict criteria (one inpatient or two outpatient claims at least 30 days apart) or relaxed criteria only (any claim, without meeting strict criteria). RESULTS: Of 4438 ICI-treated patients, pre-existing autoimmune disease was present among 179 (4%) by strict criteria, and another 283 (6%) by relaxed criteria only. In multivariable models, pre-existing autoimmune disease by strict criteria was not associated with all-cause hospitalization (HR 1.27, 95% CI 0.998-1.62), but it was associated with hospitalization with an irAE diagnosis (HR 1.81, 95% CI 1.21-2.71) and with corticosteroid treatment (HR 1.93, 95% CI 1.35-2.76). Similarly, pre-existing autoimmune disease by relaxed criteria only was not associated with all-cause hospitalization (HR 1.11, 95% CI 0.91-1.34), but was associated with hospitalization with an irAE diagnosis (HR 1.46, 95% CI 1.06-2.01) and corticosteroid treatment (HR 1.46, 95% CI 1.13-1.88). CONCLUSION: Pre-existing autoimmune disease was not associated with time to any hospitalization after initiating ICI therapy, but it was associated with a modest increase in hospitalizations with irAE diagnoses and with corticosteroid treatment.

Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease.

BACKGROUND: Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added. METHODS: We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care. RESULTS: A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes. CONCLUSIONS: The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.).

Genetic Misdiagnoses and the Potential for Health Disparities.

BACKGROUND: For more than a decade, risk stratification for hypertrophic cardiomyopathy has been enhanced by targeted genetic testing. Using sequencing results, clinicians routinely assess the risk of hypertrophic cardiomyopathy in a patient's relatives and diagnose the condition in patients who have ambiguous clinical presentations. However, the benefits of genetic testing come with the risk that variants may be misclassified. METHODS: Using publicly accessible exome data, we identified variants that have previously been considered causal in hypertrophic cardiomyopathy and that are overrepresented in the general population. We studied these variants in diverse populations and reevaluated their initial ascertainments in the medical literature. We reviewed patient records at a leading genetic-testing laboratory for occurrences of these variants during the near-decade-long history of the laboratory. RESULTS: Multiple patients, all of whom were of African or unspecified ancestry, received positive reports, with variants misclassified as pathogenic on the basis of the understanding at the time of testing. Subsequently, all reported variants were recategorized as benign. The mutations that were most common in the general population were significantly more common among black Americans than among white Americans (P<0.001). Simulations showed that the inclusion of even small numbers of black Americans in control cohorts probably would have prevented these misclassifications. We identified methodologic shortcomings that contributed to these errors in the medical literature. CONCLUSIONS: The misclassification of benign variants as pathogenic that we found in our study shows the need for sequencing the genomes of diverse populations, both in asymptomatic controls and the tested patient population. These results expand on current guidelines, which recommend the use of ancestry-matched controls to interpret variants. As additional populations of different ancestry backgrounds are sequenced, we expect variant reclassifications to increase, particularly for ancestry groups that have historically been less well studied. (Funded by the National Institutes of Health.).

Driving Innovation in Health Systems through an Apps-Based Information Economy.

Healthcare data will soon be accessible using standard, open software interfaces. Here, we describe how these interfaces could lead to improved healthcare by facilitating the development of software applications (apps) that can be shared across physicians, health care organizations, translational researchers, and patients. We provide recommendations for next steps and resources for the myriad stakeholders. If challenges related to efficacy, accuracy, utility, safety, privacy, and security can be met, this emerging apps model for health information technology will open up the point of care for innovation and connect patients at home to their healthcare data.

Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study.

Natural language processing tools allow the characterization of sentiment--that is, terms expressing positive and negative emotion--in text. Applying such tools to electronic health records may provide insight into meaningful patient or clinician features not captured in coded data alone. We performed sentiment analysis on 2,484 hospital discharge notes for 2,010 individuals from a psychiatric inpatient unit, as well as 20,859 hospital discharges for 15,011 individuals from general medical units, in a large New England health system between January 2011 and 2014. The primary measures of sentiment captured intensity of subjective positive or negative sentiment expressed in the discharge notes. Mean scores were contrasted between sociodemographic and clinical groups in mixed effects regression models. Discharge note sentiment was then examined for association with risk for readmission in Cox regression models. Discharge notes for individuals with greater medical comorbidity were modestly but significantly lower in positive sentiment among both psychiatric and general medical cohorts (p<0.001 in each). Greater positive sentiment at discharge was associated with significantly decreased risk of hospital readmission in each cohort (~12% decrease per standard deviation above the mean). Automated characterization of discharge notes in terms of sentiment identifies differences between sociodemographic groups, as well as in clinical outcomes, and is not explained by differences in diagnosis. Clinician sentiment merits investigation to understand why and how it reflects or impacts outcomes.