The cost of lung transplantation in the United States: How high is too high?

Objectives: To identify patient and process factors that contribute to the high cost of lung transplantation (LTx) in the perioperative period, which may allow transplant centers to evaluate situations in which transplantation is most cost-effective to inform judicious resource allocation, avoid futile care, and reduce costs. Methods: The MarketScan Research databases were used to identify 582 privately insured patients undergoing single or bilateral LTx between 2013 and 2019. The patients were subdivided into groups by disease etiology using the United Network of Organ Sharing classification system. Multivariable generalized linear models using a gamma distribution with a log link were fit to examine the associations between the etiology of lung disease and costs during the index admission, 3 months before admission, and 3 months after discharge. Results: Our results indicate that the index admission contributed the most to the total transplantation costs compared to the 3 months before admission and after discharge. The regression-adjusted mean index hospitalization cost was 35% higher for patients with pulmonary vascular disease compared to those with obstructive lung disease ($527,156 vs $389,055). The use of extracorporeal membrane oxygenation, mechanical ventilation, and surgical complications in the post-transplantation period were associated with higher costs during the index admission. Surprisingly, age ≥55 was associated with lower costs during the index admission. Conclusions: This analysis identifies pivotal factors influencing the high cost of LTx, emphasizing the significant impact of the index admission, particularly for patients with pulmonary vascular disease. These insights offer transplant centers an opportunity to enhance cost-effectiveness through judicious resource allocation and service bundling, ultimately reducing overall transplantation costs.

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.

Intestinal Conventional Ultrasonography, Contrast-Enhanced Ultrasonography and Magnetic Resonance Enterography in Assessment of Crohn's Disease Activity: A Comparison with Surgical Histopathology Analysis.

BACKGROUND AND AIMS: Contrast-enhanced ultrasonography (CEUS) is a potential interesting method for assessing accurately Crohn's disease (CD) activity. We compared the value of intestinal ultrasonography (US) coupled with contrast agent injection with that of magnetic resonance enterography (MRE) in the assessment of small bowel CD activity using surgical histopathology analysis as reference. METHODS: Seventeen clinically active CD patients (14 women, mean age 33 years) requiring an ileal or ileocolonic resection were prospectively enrolled. All performed a MRE and a US coupled with contrast agent injection (CEUS) less than 8 weeks prior to surgery. Various imaging qualitative and quantitative parameters were recorded and their respective performance to detect disease activity, disease extension and presence of complications was compared to surgical histopathological analysis. RESULTS: The median wall thickness measured by US differed significantly between patients with non-severely active CD (n = 5) and those with severely active CD (n = 12) [7.0 mm, IQR (6.5-9.5) vs 10.0 mm, IQR (8.0-12.0), respectively; p = 0.03]. A non-significant trend was found with MRE with a median wall thickness in severe active CD of 10.0 mm, IQR (8.0-13.7) compared with 8.0 mm, IQR (7.5-10.5) in non-severely active CD (p = 0.07). The area under the ROC curve (AUROC) of the wall thickness assessed by US and MRE to identify patients with or without severely active CD on surgical specimens were 0.85, 95% CI (0.64-1.04), p = 0.03 and 0.80, 95% CI (0.56-1.01), p = 0.07, respectively. Among the parameters derived from the time-intensity curve during CEUS, time to peak and rise time were the two most accurate markers [AUROC = 0.88, 95% CI (0.70-1.04), p = 0.02 and 0.86, 95% CI (0.68-1.04), p = 0.03] to detect patients with severely active CD assessed on surgical specimens. CONCLUSION: The accuracy of intestinal CEUS is close to that of conventional US to detect disease activity. A thickened bowel and shortened time to peak and rise time were the most accurate to identify CD patients with severe histological disease activity.

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.

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.

Ballistocardiogram Based Identity Recognition: Towards Zero-Effort Health Monitoring in an Internet-of-Things (IoT) Environment.

Ballistocardiography (BCG), a measure of body vibrations due to ejection of blood into aorta, has the potential to become a 'zero-effort' cardiovascular health monitoring technology, i.e., a technology that requires little or no engagement on part of the user for its operation. In order for any zero-effort monitoring technology to function without any input from the user, it is important that such a methodology can accurately perform identity recognition and thus continuously provide results and feedback to each user. However, most of the recent work on BCG has focused mainly on the estimation of parameters related to mechanical health and the use of BCG to identify a user has not been explored thoroughly. In this paper, we examine, using discrete cosine transform based features and multi-class linear classifier, the use of BCG heartbeats for identity recognition. We demonstrate from the BCG data of 52 healthy subjects collected using a modified floor tile that an average accuracy of 96.15% can be achieved for correct identification of each subject standing on the tile. Based on these results, we anticipate that such a BCG system, trained for a set of users, can be easily installed at different locations in the house and provide continuous and unobtrusive feedback to users for diagnostic monitoring and quantified-self.

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.).

Validation of the Fitbit One, Garmin Vivofit and Jawbone UP activity tracker in estimation of energy expenditure during treadmill walking and running.

OBJECTIVES: To determine the validity of energy expenditure estimation made by the Fitbit One, Garmin Vivofit and Jawbone UP activity trackers during treadmill walking and running. Determining validity of such trackers will inform the interpretation of the data they generate. DESIGN: Cross-sectional study. METHOD: Fourteen adults walked at 0.70, 1.25, 1.80 ms-1 and ran at 2.22, 2.78, 3.33 ms-1 on a treadmill wearing a Fitbit One, Garmin Vivofit and Jawbone UP. Estimation of energy expenditure from each tracker was compared to measurement from indirect calorimetry (criterion). Paired t-tests, correlation coefficients and Bland-Altman plots assessed agreement and proportional bias. Mean percentage difference assessed magnitude of difference between estimated and criterion energy expenditure for each speed. RESULTS: Energy expenditure estimates from the Fitbit One and Garmin Vivofit correlated significantly (p< 0.01; r= 0.702; 0.854) with criterion across all gait speeds (0.70-3.33 ms-1). Fitbit One, Garmin Vivofit and Jawbone UP correlated significantly (p < 0.05; r = 0.729; 0.711; 0.591) with criterion across all walking speeds (0.70-1.80 ms-1). However, only the Garmin Vivofit correlated significantly (p< 0.05; r = 0.346) with energy expenditure estimations from criterion across running speeds (2.22-3.33 ms-1). Bland-Altman plots showed proportional bias for the Fitbit One and Garmin Vivofit. Energy expenditure estimations of single speeds were overestimated by the Fitbit One and underestimated by the Garmin Vivofit. CONCLUSIONS: Energy expenditure reported by the devices distinguished between walking and running, with a general increase as exercise intensity increased. However, the reported energy expenditure from these devices should be interpreted with caution, given their potential bias and error. Practical implications Although devices report the same outcome of EE estimation, they are not equivalent to each other and differ from criterion measurements during walking and running. These devices are not suitable as research measurement tools for recording precise and accurate EE estimates but may be suitable for use in interventions of behaviour change as they provide feedback to user on trends in energy expenditure. If intending to use these devices in studies where precise measurements of energy expenditure are required, researchers need to undertake specific validation and reliability studies prior to interventions and the collection of cross-sectional data.

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.

Population-level evidence for an autoimmune etiology of epilepsy.

IMPORTANCE: Epilepsy is a debilitating condition, often with neither a known etiology nor an effective treatment. Autoimmune mechanisms have been increasingly identified. OBJECTIVE: To conduct a population-level study investigating the relationship between epilepsy and several common autoimmune diseases. DESIGN, SETTING, AND PARTICIPANTS: A retrospective population-based study using claims from a nationwide employer-provided health insurance plan in the United States. Participants were beneficiaries enrolled between 1999 and 2006 (N = 2 518 034). MAIN OUTCOMES AND MEASURES: We examined the relationship between epilepsy and 12 autoimmune diseases: type 1 diabetes mellitus, psoriasis, rheumatoid arthritis, Graves disease, Hashimoto thyroiditis, Crohn disease, ulcerative colitis, systemic lupus erythematosus, antiphospholipid syndrome, Sjögren syndrome, myasthenia gravis, and celiac disease. RESULTS: The risk of epilepsy was significantly heightened among patients with autoimmune diseases (odds ratio, 3.8; 95% CI, 3.6-4.0; P < .001) and was especially pronounced in children (5.2; 4.1-6.5; P < .001). Elevated risk was consistently observed across all 12 autoimmune diseases. CONCLUSIONS AND RELEVANCE: Epilepsy and autoimmune disease frequently co-occur; patients with either condition should undergo surveillance for the other. The potential role of autoimmunity must be given due consideration in epilepsy so that we are not overlooking a treatable cause.

Toward more transparent and reproducible omics studies through a common metadata checklist and data publications.

Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.

Early detection of poor adherers to statins: applying individualized surveillance to pay for performance.

BACKGROUND: Medication nonadherence costs $300 billion annually in the US. Medicare Advantage plans have a financial incentive to increase medication adherence among members because the Centers for Medicare and Medicaid Services (CMS) now awards substantive bonus payments to such plans, based in part on population adherence to chronic medications. We sought to build an individualized surveillance model that detects early which beneficiaries will fall below the CMS adherence threshold. METHODS: This was a retrospective study of over 210,000 beneficiaries initiating statins, in a database of private insurance claims, from 2008-2011. A logistic regression model was constructed to use statin adherence from initiation to day 90 to predict beneficiaries who would not meet the CMS measure of proportion of days covered 0.8 or above, from day 91 to 365. The model controlled for 15 additional characteristics. In a sensitivity analysis, we varied the number of days of adherence data used for prediction. RESULTS: Lower adherence in the first 90 days was the strongest predictor of one-year nonadherence, with an odds ratio of 25.0 (95% confidence interval 23.7-26.5) for poor adherence at one year. The model had an area under the receiver operating characteristic curve of 0.80. Sensitivity analysis revealed that predictions of comparable accuracy could be made only 40 days after statin initiation. When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value. CONCLUSIONS: To preserve their Medicare Star ratings, plan managers should identify or develop effective programs to improve adherence. An individualized surveillance approach can be used to target members who would most benefit, recognizing the tradeoff between improved model performance over time and the advantage of earlier detection.