Development, Progression, and Mortality of Suspected Interstitial Lung Disease in COPDGene.

RATIONALE: Some with interstitial lung abnormalities (ILA) have suspected interstitial lung disease (ILD), a subgroup with adverse outcomes. Rates of development and progression of suspected ILD and their effect on mortality are unknown. OBJECTIVES: To determine rates of development and progression of suspected ILD and assess effects of individual ILD and progression criteria on mortality. METHODS: Participants from COPDGene were included. ILD was defined as ILA and fibrosis and/or FVC <80% predicted. Prevalent ILD was assessed at enrollment, incident ILD and progression at 5-year follow-up. CT progression was assessed visually and FVC decline as relative change. Multivariable Cox regression tested associations between mortality and ILD groups. RESULTS: Of 9,588 participants at enrollment, 267 (2.8%) had prevalent ILD. Those with prevalent ILD had 52% mortality after median 10.6 years, which was higher than ILA (33%; HR=2.0; p<0.001). The subgroup of prevalent ILD with fibrosis only had worse mortality (59%) than ILA (HR=2.2; p<0.001). 97 participants with prevalent ILD completed 5-year follow-up: 32% had stable CT and relative FVC decline <10%, 6% FVC decline ≥10% only, 39% CT progression only, and 22% both CT progression and FVC decline ≥10%. Mortality rates were 32%, 50%, 45%, and 46% respectively; those with CT progression only had worse mortality than ILA (HR=2.6; p=0.005). At 5-year follow-up, incident ILD occurred in 168/4,843 participants without prevalent ILD and had worse mortality than ILA (HR=2.5; p<0.001). CONCLUSION: Rates of mortality and progression are high among those with suspected ILD in COPDGene; fibrosis and radiologic progression are important predictors of mortality.

Racial and ethnic associations with interstitial lung disease and healthcare utilization in patients with systemic sclerosis.

OBJECTIVE: Racial and ethnic differences in presentation and outcomes have been reported in systemic sclerosis (SSc) and SSc-interstitial lung disease (ILD). However, prior studies have limited diversity. We aim to evaluate if there are racial/ethnic differences associated with ILD, time intervals between SSc and ILD and with emergency department (ED) visit or hospitalization rates. METHODS: Clinical and sociodemographic variables were extracted for 756 patients with SSc from longitudinal health records in an integrated health-system. Logistic regression models analyzed the association of covariates with ILD and age at SSc-ILD. Healthcare outcomes were analyzed with complementary log-log regression models. RESULTS: Overall, 33.7% of patients in the cohort had an ILD code, with increased odds for Asian (odds ratio [OR], 2.60; 95% confidence interval [CI], 1.29-5.28; p=0.008) compared with White patients. The predicted age in years of SSc-ILD was younger for Hispanic (estimate, -6.5; 95% CI, -13--0.21; p = 0.04) and Black/African American patients (-10; 95% CI -16--4.9; p < 0.001) compared with White patients. Black/African American patients were more likely to have an ILD code before an SSc code (59% compared with 20.6% of White patients), and the shortest interval from SSc to ILD (3 months). Black/African American (HR, 2.59; 95% CI 1.47-4.49; p = 0.001) and Hispanic patients (HR 2.29; 95% CI 1.37- 3.82; p = 0.002) had higher rates of an ED visit. CONCLUSION: We found that odds of SSc-ILD differed by racial/ethnic group, minoritized patients had earlier age of presentation, and greater rates of an ED visit.

Racial and ethnic associations with interstitial lung disease and healthcare utilization in patients with systemic sclerosis.

Rationale: Racial and ethnic differences in presentation and outcomes have been reported in systemic sclerosis (SSc) and SSc-interstitial lung disease (ILD). However, diverse cohorts and additional modeling can improve understanding of risk features and outcomes, which is important for reducing associated disparities. Objectives: To determine if there are racial/ethnic differences associated with SSc-ILD risk and age; time intervals between SSc and ILD, and with emergency department (ED) visit or hospitalization rates. Methods: A retrospective cohort study using electronic health record data from an integrated health system, over a 5.5 year period was conducted using clinical and sociodemographic variables, models were generated with sequential adjustments for these variables. Logistic regression models were used to examine the association of covariates with ILD and age at SSc-ILD. Healthcare outcomes were analyzed with complementary log-log regression models. Results: The cohort included 756 adults (83.6% female, 80.3% non-Hispanic White) with SSc with a mean age of 59 years. Overall, 33.7% of patients in the cohort had an ILD code, with increased odds for Asian (odds ratio [OR], 2.59; 95% confidence interval [CI], 1.29, 5.18; P =.007) compared to White patients. The age in years of patients with SSc-ILD was younger for Hispanic (mean difference, -6.5; 95% CI, -13, -0.21; P = 0.04) and Black/African American patients (-10; 95% CI -16, -4.9; P <0.001) compared to White patients. Black/African American patients were more likely to have an ILD code before an SSc code (59% compared to 20.6% of White patients), and had the shortest interval from SSc to ILD (3 months). Black/African American (HR, 2.59; 95% CI 1.47, 4.49; P =0.001) and Hispanic patients (HR 2.29; 95% CI 1.37, 3.82; P =0.002) had higher rates of an ED visit. Conclusion: In this study, SSc-ILD presentation and outcomes differed by racial/ethnic group (increased odds of SSc-ILD, younger age at SSc-ILD, and preceding diagnosis with respect to SSc, rates of ED visit), some of which was attenuated with adjustment for clinical and sociodemographic characteristics. Differing presentation may be driven by social drivers of health (SDOH), autoantibody profiles, or other key unmeasured factors contributing to susceptibility and severity.

Development and validation of algorithms to build an electronic health record based cohort of patients with systemic sclerosis.

OBJECTIVES: To evaluate methods of identifying patients with systemic sclerosis (SSc) using International Classification of Diseases, Tenth Revision (ICD-10) codes (M34*), electronic health record (EHR) databases and organ involvement keywords, that result in a validated cohort comprised of true cases with high disease burden. METHODS: We retrospectively studied patients in a healthcare system likely to have SSc. Using structured EHR data from January 2016 to June 2021, we identified 955 adult patients with M34* documented 2 or more times during the study period. A random subset of 100 patients was selected to validate the ICD-10 code for its positive predictive value (PPV). The dataset was then divided into a training and validation sets for unstructured text processing (UTP) search algorithms, two of which were created using keywords for Raynaud's syndrome, and esophageal involvement/symptoms. RESULTS: Among 955 patients, the average age was 60. Most patients (84%) were female; 75% of patients were White, and 5.2% were Black. There were approximately 175 patients per year with the code newly documented, overall 24% had an ICD-10 code for esophageal disease, and 13.4% for pulmonary hypertension. The baseline PPV was 78%, which improved to 84% with UTP, identifying 788 patients likely to have SSc. After the ICD-10 code was placed, 63% of patients had a rheumatology office visit. Patients identified by the UTP search algorithm were more likely to have increased healthcare utilization (ICD-10 codes 4 or more times 84.1% vs 61.7%, p < .001), organ involvement (pulmonary hypertension 12.7% vs 6% p = .011) and medication use (mycophenolate use 28.7% vs 11.4%, p < .001) than those identified by the ICD codes alone. CONCLUSION: EHRs can be used to identify patients with SSc. Using unstructured text processing keyword searches for SSc clinical manifestations improved the PPV of ICD-10 codes alone and identified a group of patients most likely to have SSc and increased healthcare needs.

Hospital-Level Care at Home for Patients With Acute Respiratory Disease: A Descriptive Analysis.

BACKGROUND: Home hospital (HH) care is hospital-level substitutive care delivered at home for acutely ill patients who traditionally would be cared for in the hospital. Despite HH care programs operating successfully for years and scientific evidence of similar or better outcomes compared with bricks-and-mortar care, HH care outcomes in the United States for respiratory disease have not been evaluated. RESEARCH QUESTION: Do outcomes differ between patients admitted to HH care with acute respiratory illness vs those with other acute general medical conditions? STUDY DESIGN AND METHODS: This was a retrospective evaluation of prospectively collected data of patients admitted to HH care (2017-2021). We compared patients requiring admission with respiratory disease (asthma exacerbation [26%], acute exacerbation of COPD [33%], and non-COVID-19 pneumonia [41%]) to all other patients admitted to HH care. During HH care, patients received two nurse and one physician visit daily, IV medications, advanced respiratory therapies, and continuous heart and respiratory rate monitoring. Main outcomes were acute and postacute health care use and safety. RESULTS: We analyzed 1,031 patients; 24% were admitted for respiratory disease. Patients with and without respiratory disease were similar: mean age, 68 ± 17 years, 62% women, and 48% White. Patients with respiratory disease more often were active smokers (21% vs 9%; P < .001). Eighty percent of patients showed an FEV1 to FVC ratio of ≤ 70; 28% showed a severe or very severe obstructive pattern (n = 118). During HH care, patients with respiratory disease showed less health care use: length of stay (mean, 3.4 vs 4.6 days), laboratory orders (median, 0 vs 2), IV medication (43% vs 73%), and specialist consultation (2% vs 7%; P < .001 for all). Ninety-six percent of patients completed the full admission at home with no mortality in the respiratory group. Within 30 days of discharge, both groups showed similar readmission, ED presentation, and mortality rates. INTERPRETATION: HH care is as safe and effective for patients with acute respiratory disease as for those with other acute general medical conditions. If scaled, it can generate significant high-value capacity for health systems and communities, with opportunities to advance the complexity of care delivered.

Cluster analysis of COVID-19 recovery center patients at a clinic in Boston, MA 2021-2022: impact on strategies for access and personalized care.

BACKGROUND: There are known disparities in COVID-19 resource utilization that may persist during the recovery period for some patients. We sought to define subpopulations of patients seeking COVID-19 recovery care in terms of symptom reporting and care utilization to better personalize their care and to identify ways to improve access to subspecialty care. METHODS: Prospective study of adult patients with prior COVID-19 infection seen in an ambulatory COVID-19 recovery center (CRC) in Boston, Massachusetts from April 2021 to April 2022. Hierarchical clustering with complete linkage to differentiate subpopulations was done with four sociodemographic variables: sex, race, language, and insurance status. Outcomes included ICU admission, utilization of supplementary care, self-report of symptoms. RESULTS: We included 1285 COVID-19 patients referred to the CRC with a mean age of 47 years, of whom 71% were female and 78% White. We identified 3 unique clusters of patients. Cluster 1 and 3 patients were more likely to have had intensive care unit (ICU) admissions; Cluster 2 were more likely to be White with commercial insurance and a low percentage of ICU admission; Cluster 3 were more likely to be Black/African American or Latino/a and have commercial insurance. Compared to Cluster 2, Cluster 1 patients were more likely to report symptoms (ORs ranging 2.4-3.75) but less likely to use support groups, psychoeducation, or care coordination (all p < 0.05). Cluster 3 patients reported greater symptoms with similar levels of community resource utilization. CONCLUSIONS: Within a COVID-19 recovery center, there are distinct groups of patients with different clinical and socio-demographic profiles, which translates to differential resource utilization. These insights from different subpopulations of patients can inform targeted strategies which are tailored to specific patient needs.