Developing a Model to Predict High Health Care Utilization Among Patients in a New York City Safety Net System.

BACKGROUND: Health care facilities use predictive models to identify patients at risk of high future health care utilization who may benefit from tailored interventions. Previous predictive models that have focused solely on inpatient readmission risk, relied on commercial insurance claims data, or failed to incorporate social determinants of health may not be generalizable to safety net hospital populations. To address these limitations, we developed a payer-agnostic risk model for patients receiving care at the largest US safety net hospital system. METHODS: We transformed electronic health record and administrative data from 833,969 adult patients who received care during July 2016-July 2017 into demographic, utilization, diagnosis, medication, and social determinant variables (including homelessness and incarceration history) to predict health care utilization during the following year.We selected the final model by developing and validating multiple classification and regression models predicting 10+ acute days, 5+ acute days, or continuous acute days. We compared a portfolio of performance metrics while prioritizing positive predictive value for patients whose predicted utilization was among the top 1% to maximize clinical utility. RESULTS: The final model predicted continuous number of acute days and included 17 variables. For the top 1% of high acute care utilizers, the model had a positive predictive value of 47.6% and sensitivity of 17.3%. Previous health care utilization and psychosocial factors were the strongest predictors of future high acute care utilization. CONCLUSIONS: We demonstrated a feasible approach to predictive high acute care utilization in a safety net hospital using electronic health record data while incorporating social risk factors.

Surge and Mortality in ICUs in New York City's Public Healthcare System.

OBJECTIVES: To evaluate the impact of ICU surge on mortality and to explore clinical and sociodemographic predictors of mortality. DESIGN: Retrospective cohort analysis. SETTING: NYC Health + Hospitals ICUs. PATIENTS: Adult ICU patients with coronavirus disease 2019 admitted between March 24, and May 12, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Hospitals reported surge levels daily. Uni- and multivariable analyses were conducted to assess factors impacting in-hospital mortality. Mortality in Hispanic patients was higher for high/very high surge compared with low/medium surge (69.6% vs 56.4%; p = 0.0011). Patients 65 years old and older had similar mortality across surge levels. Mortality decreased from high/very high surge to low/medium surge in, patients 18-44 years old and 45-64 (18-44 yr: 46.4% vs 27.3%; p = 0.0017 and 45-64 yr: 64.9% vs 53.2%; p = 0.002), and for medium, high, and very high poverty neighborhoods (medium: 69.5% vs 60.7%; p = 0.019 and high: 71.2% vs 59.7%; p = 0.0078 and very high: 66.6% vs 50.7%; p = 0.0003). In the multivariable model high surge (high/very high vs low/medium odds ratio, 1.4; 95% CI, 1.2-1.8), race/ethnicity (Black vs White odds ratio, 1.5; 95% CI, 1.1-2.0 and Asian vs White odds ratio 1.5; 95% CI, 1.0-2.3; other vs White odds ratio 1.5, 95% CI, 1.0-2.3), age (45-64 vs 18-44 odds ratio, 2.0; 95% CI, 1.6-2.5 and 65-74 vs 18-44 odds ratio, 5.1; 95% CI, 3.3-8.0 and 75+ vs 18-44 odds ratio, 6.8; 95% CI, 4.7-10.1), payer type (uninsured vs commercial/other odds ratio, 1.7; 95% CI, 1.2-2.3; medicaid vs commercial/other odds ratio, 1.3; 95% CI, 1.1-1.5), neighborhood poverty (medium vs low odds ratio 1.6, 95% CI, 1.0-2.4 and high vs low odds ratio, 1.8; 95% CI, 1.3-2.5), comorbidities (diabetes odds ratio, 1.6; 95% CI, 1.2-2.0 and asthma odds ratio, 1.4; 95% CI, 1.1-1.8 and heart disease odds ratio, 2.5; 95% CI, 2.0-3.3), and interventions (mechanical ventilation odds ratio, 8.8; 95% CI, 6.1-12.9 and dialysis odds ratio, 3.0; 95% CI, 1.9-4.7) were significant predictors for mortality. CONCLUSIONS: Patients admitted to ICUs with higher surge scores were at greater risk of death. Impact of surge levels on mortality varied across sociodemographic groups.

Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveillance Using Electronic Health Records.

INTRODUCTION: Electronic health record (EHR) systems provide an opportunity to use a novel data source for population health surveillance. Validation studies that compare prevalence estimates from EHRs and surveys most often use difference testing, which can, because of large sample sizes, lead to detection of significant differences that are not meaningful. We explored a novel application of the two one-sided t test (TOST) to assess the equivalence of prevalence estimates in 2 population-based surveys to inform margin selection for validating EHR-based surveillance prevalence estimates derived from large samples. METHODS: We compared prevalence estimates of health indicators in the 2013 Community Health Survey (CHS) and the 2013-2014 New York City Health and Nutrition Examination Survey (NYC HANES) by using TOST, a 2-tailed t test, and other goodness-of-fit measures. RESULTS: A ±5 percentage-point equivalence margin for a TOST performed well for most health indicators. For health indicators with a prevalence estimate of less than 10% (extreme obesity [CHS, 3.5%; NYC HANES, 5.1%] and serious psychological distress [CHS, 5.2%; NYC HANES, 4.8%]), a ±2.5 percentage-point margin was more consistent with other goodness-of-fit measures than the larger percentage-point margins. CONCLUSION: A TOST with a ±5 percentage-point margin was useful in establishing equivalence, but a ±2.5 percentage-point margin may be appropriate for health indicators with a prevalence estimate of less than 10%. Equivalence testing can guide future efforts to validate EHR data.

Long-term treatment with naproxcinod significantly improves skeletal and cardiac disease phenotype in the mdx mouse model of dystrophy.

In Duchenne muscular dystrophy (DMD) patients and the mouse model of DMD, mdx, dystrophin deficiency causes a decrease and mislocalization of muscle-specific neuronal nitric oxide synthase (nNOSµ), leading to functional impairments. Previous studies have shown that nitric oxide (NO) donation associated with anti-inflammatory action has beneficial effects in dystrophic mouse models. In this study, we have systematically investigated the effects of naproxcinod, an NO-donating naproxen derivative, on the skeletal and cardiac disease phenotype in mdx mice. Four-week-old mdx and C57BL/10 mice were treated with four different concentrations (0, 10, 21 and 41 mg/kg) of naproxcinod and 0.9 mg/kg of prednisolone in their food for 9 months. All mice were subjected to twice-weekly treadmill sessions, and functional and behavioral parameters were measured at 3, 6 and 9 months of treatment. In addition, we evaluated in vitro force contraction, optical imaging of inflammation, echocardiography and blood pressure (BP) at the 9-month endpoint prior to sacrifice. We found that naproxcinod treatment at 21 mg/kg resulted in significant improvement in hindlimb grip strength and a 30% decrease in inflammation in the fore- and hindlimbs of mdx mice. Furthermore, we found significant improvement in heart function, as evidenced by improved fraction shortening, ejection fraction and systolic BP. In addition, the long-term detrimental effects of prednisolone typically seen in mdx skeletal and heart function were not observed at the effective dose of naproxcinod. In conclusion, our results indicate that naproxcinod has significant potential as a safe therapeutic option for the treatment of muscular dystrophies.

Selective modulation through the glucocorticoid receptor ameliorates muscle pathology in mdx mice.

The over-expression of NF-κB signalling in both muscle and immune cells contribute to the pathology in dystrophic muscle. The anti-inflammatory properties of glucocorticoids, mediated predominantly through monomeric glucocorticoid receptor inhibition of transcription factors such as NF-κB (transrepression), are postulated to be an important mechanism for their beneficial effects in Duchenne muscular dystrophy. Chronic glucocorticoid therapy is associated with adverse effects on metabolism, growth, bone mineral density and the maintenance of muscle mass. These detrimental effects result from direct glucocorticoid receptor homodimer interactions with glucocorticoid response elements of the relevant genes. Compound A, a non-steroidal selective glucocorticoid receptor modulator, is capable of transrepression without transactivation. We confirm the in vitro NF-κB inhibitory activity of compound A in H-2K(b) -tsA58 mdx myoblasts and myotubes, and demonstrate improvements in disease phenotype of dystrophin deficient mdx mice. Compound A treatment in mdx mice from 18 days of post-natal age to 8 weeks of age increased the absolute and normalized forelimb and hindlimb grip strength, attenuated cathepsin-B enzyme activity (a surrogate marker for inflammation) in forelimb and hindlimb muscles, decreased serum creatine kinase levels and reduced IL-6, CCL2, IFNγ, TNF and IL-12p70 cytokine levels in gastrocnemius (GA) muscles. Compared with compound A, treatment with prednisolone, a classical glucocorticoid, in both wild-type and mdx mice was associated with reduced body weight, reduced GA, tibialis anterior and extensor digitorum longus muscle mass and shorter tibial lengths. Prednisolone increased osteopontin (Spp1) gene expression and osteopontin protein levels in the GA muscles of mdx mice and had less favourable effects on the expression of Foxo1, Foxo3, Fbxo32, Trim63, Mstn and Igf1 in GA muscles, as well as hepatic Igf1 in wild-type mice. In conclusion, selective glucocorticoid receptor modulation by compound A represents a potential therapeutic strategy to improve dystrophic pathology.

Generalizability of Indicators from the New York City Macroscope Electronic Health Record Surveillance System to Systems Based on Other EHR Platforms.

INTRODUCTION: The New York City (NYC) Macroscope is an electronic health record (EHR) surveillance system based on a distributed network of primary care records from the Hub Population Health System. In a previous 3-part series published in eGEMS, we reported the validity of health indicators from the NYC Macroscope; however, questions remained regarding their generalizability to other EHR surveillance systems. METHODS: We abstracted primary care chart data from more than 20 EHR software systems for 142 participants of the 2013-14 NYC Health and Nutrition Examination Survey who did not contribute data to the NYC Macroscope. We then computed the sensitivity and specificity for indicators, comparing data abstracted from EHRs with survey data. RESULTS: Obesity and diabetes indicators had moderate to high sensitivity (0.81-0.96) and high specificity (0.94-0.98). Smoking status and hypertension indicators had moderate sensitivity (0.78-0.90) and moderate to high specificity (0.88-0.98); sensitivity improved when the sample was restricted to records from providers who attested to Stage 1 Meaningful Use. Hyperlipidemia indicators had moderate sensitivity (≥0.72) and low specificity (≤0.59), with minimal changes when restricting to Stage 1 Meaningful Use. DISCUSSION: Indicators for obesity and diabetes used in the NYC Macroscope can be adapted to other EHR surveillance systems with minimal validation. However, additional validation of smoking status and hypertension indicators is recommended and further development of hyperlipidemia indicators is needed. CONCLUSION: Our findings suggest that many of the EHR-based surveillance indicators developed and validated for the NYC Macroscope are generalizable for use in other EHR surveillance systems.

Can Electronic Health Records Be Used for Population Health Surveillance? Validating Population Health Metrics Against Established Survey Data.

INTRODUCTION: Electronic health records (EHRs) offer potential for population health surveillance but EHR-based surveillance measures require validation prior to use. We assessed the validity of obesity, smoking, depression, and influenza vaccination indicators from a new EHR surveillance system, the New York City (NYC) Macroscope. This report is the second in a 3-part series describing the development and validation of the NYC Macroscope. The first report describes in detail the infrastructure underlying the NYC Macroscope; design decisions that were made to maximize data quality; characteristics of the population sampled; completeness of data collected; and lessons learned from doing this work. This second report, which addresses concerns related to sampling bias and data quality, describes the methods used to evaluate the validity and robustness of NYC Macroscope prevalence estimates; presents validation results for estimates of obesity, smoking, depression and influenza vaccination; and discusses the implications of our findings for NYC and for other jurisdictions embarking on similar work. The third report applies the same validation methods described in this report to metabolic outcomes, including the prevalence, treatment and control of diabetes, hypertension and hyperlipidemia. METHODS: NYC Macroscope prevalence estimates, overall and stratified by sex and age group, were compared to reference survey estimates for adult New Yorkers who reported visiting a doctor in the past year. Agreement was evaluated against 5 a priori criteria. Sensitivity and specificity were assessed by examining individual EHR records in a subsample of 48 survey participants. RESULTS: Among adult New Yorkers in care, the NYC Macroscope prevalence estimate for smoking (15.2%) fell between estimates from NYC HANES (17.7 %) and CHS (14.9%) and met all 5 a priori criteria. The NYC Macroscope obesity prevalence estimate (27.8%) also fell between the NYC HANES (31.3%) and CHS (24.7%) estimates, but met only 3 a priori criteria. Sensitivity and specificity exceeded 0.90 for both the smoking and obesity indicators. The NYC Macroscope estimates of depression and influenza vaccination prevalence were more than 10 percentage points lower than the estimates from either reference survey. While specificity was > 0.90 for both of these indicators, sensitivity was < 0.70. DISCUSSION: Through this work we have demonstrated that EHR data from a convenience sample of providers can produce acceptable estimates of smoking and obesity prevalence among adult New Yorkers in care; gained a better understanding of the challenges involved in estimating depression prevalence from EHRs; and identified areas for additional research regarding estimation of influenza vaccination prevalence. We have also shared lessons learned about how EHR indicators should be constructed and offer methodologic suggestions for validating them. CONCLUSIONS: This work adds to a rapidly emerging body of literature about how to define, collect and interpret EHR-based surveillance measures and may help guide other jurisdictions.

Behavioral and locomotor measurements using an open field activity monitoring system for skeletal muscle diseases.

The open field activity monitoring system comprehensively assesses locomotor and behavioral activity levels of mice. It is a useful tool for assessing locomotive impairment in animal models of neuromuscular disease and efficacy of therapeutic drugs that may improve locomotion and/or muscle function. The open field activity measurement provides a different measure than muscle strength, which is commonly assessed by grip strength measurements. It can also show how drugs may affect other body systems as well when used with additional outcome measures. In addition, measures such as total distance traveled mirror the 6 min walk test, a clinical trial outcome measure. However, open field activity monitoring is also associated with significant challenges: Open field activity measurements vary according to animal strain, age, sex, and circadian rhythm. In addition, room temperature, humidity, lighting, noise, and even odor can affect assessment outcomes. Overall, this manuscript provides a well-tested and standardized open field activity SOP for preclinical trials in animal models of neuromuscular diseases. We provide a discussion of important considerations, typical results, data analysis, and detail the strengths and weaknesses of open field testing. In addition, we provide recommendations for optimal study design when using open field activity in a preclinical trial.