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Unpredictable emergency department (ED) admissions challenge healthcare systems, causing resource allocation inefficiencies. This study analyses associations between air pollutants, meteorological factors, and 2,655,861 cause-specific ED admissions from 2014 to 2018 across 12 categories. Generalized additive models were used to assess non-linear associations for each exposure, yielding Incidence Rate Ratios (IRR), while the population attributable fraction (PAF) calculated each exposure's contribution to cause-specific ED admissions. IRRs revealed increased risks of ED admissions for respiratory infections (IRR: 1.06, 95% CI: 1.01-1.11) and infectious and parasitic diseases (IRR: 1.09, 95% CI: 1.03-1.15) during increased rainfall (13.21-16.97 mm). Wind speeds >12.73 km/hr corresponded to increased risks of ED admissions for respiratory infections (IRR: 1.12, 95% CI: 1.03-1.21) and oral diseases (IRR: 1.58, 95% CI: 1.31-1.91). Higher concentrations of air pollutants were associated with elevated risks of cardiovascular disease (IRR: 1.16, 95% CI: 1.05-1.27 for PM10) and respiratory infection-related ED admissions (IRR: 2.78, 95% CI: 1.69-4.56 for CO). Wind speeds >12.5 km/hr were predicted to contribute toward 10% of respiratory infection ED admissions, while mean temperatures >28°C corresponded to increases in the PAF up to 5% for genitourinary disorders and digestive diseases. PM10 concentrations >60 µg/m3 were highly attributable toward cardiovascular disease (PAF: 10%), digestive disease (PAF: 15%) and musculoskeletal disease (PAF: 10%) ED admissions. CO concentrations >0.6 ppm were highly attributable to respiratory infections (PAF: 20%) and diabetes mellitus (PAF: 20%) ED admissions. This study underscores protective effects of meteorological variables and deleterious impacts of air pollutant exposures across the ED admission categories considered.

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This study assessed the cost-effectiveness of continuous positive airway pressure treatment for obstructive sleep apnea in Singapore from a health system perspective. The analysis evaluated a 5-year care pathway using a Markov model, considering per-patient costs of treatment, health system cost savings of obstructive sleep apnea and attributed conditions, the effectiveness measured in disability-adjusted life years with a discount rate of 3% and a weighted 5-year continuous positive airway pressure adherence of 74.1% from Singapore studies. Per-patient costs of treatment were from a large public hospital in Singapore. Efficacy of continuous positive airway pressure treatment, health system costs and disability-adjusted life years were obtained from literature; costs are in US dollars. We conducted probabilistic sensitivity analysis, one-way sensitivity analysis and what-if analysis. Based on a willingness-to-pay threshold of US $50,000 per disability-adjusted life year in USA, continuous positive airway pressure therapy was highly cost-effective, with an incremental cost-effectiveness ratio of $13,822 per disability-adjusted life year averted. Compared with the annual total costs of $856 for patients with continuous positive airway pressure treatment diagnosed by an inpatient sleep study, the total costs for those diagnosed by a home sleep test were $625, resulting in a remarkable 27% reduction per patient per year. One-way sensitivity analysis indicated that costs of treatment, effectiveness of continuous positive airway pressure treatment and adherence had a higher impact on the cost-effectiveness of continuous positive airway pressure therapy. The what-if analysis suggested that for continuous positive airway pressure treatment to be cost-effective, adherence rate should be at least 16.1%. These findings provide valuable insights for policymakers in making informed decisions on funding diagnosis and continuous positive airway pressure therapy within Singapore's healthcare system.

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Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential applications in the medical field are extensive and vary from extracting data from Electronic Health Records -one of its most well-known and frequently exploited uses- to investigating relationships among genetics, biomarkers, drugs, and diseases for the proposal of new medications. NLP can be useful for clinical decision support, patient monitoring, or medical image analysis. Despite its vast potential, the real-world application of NLP is still limited due to various challenges and constraints, meaning that its evolution predominantly continues within the research domain. However, with the increasingly widespread use of NLP, particularly with the availability of large language models, such as ChatGPT, it is crucial for medical professionals to be aware of the status, uses, and limitations of these technologies.

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BACKGROUND: Individuals with chronic lung disease (CLD) are more susceptible to respiratory viral infections; however, significant heterogeneity exists in the literature on CLD and COVID-19 outcomes. Data are lacking on outcomes with newer variants (eg, Omicron) and in vaccinated and boosted populations. RESEARCH QUESTION: What are the outcomes of SARS-CoV-2 infection in individuals with CLD during Delta and Omicron transmission in a highly vaccinated and boosted population-based cohort? STUDY DESIGN AND METHODS: Outcomes of Delta and Omicron SARS-CoV-2 infection in a highly vaccinated and boosted cohort of adult Singaporeans with CLD (including asthma, COPD, bronchiectasis, and pulmonary fibrosis) were contrasted against matched population control participants. Calendar time-scale Cox regressions were used to compare risk of infection, COVID-19-related hospitalizations, and severe COVID-19 disease, adjusting for sociodemographic factors and comorbidities. RESULTS: Overall, 68,782 individual patients with CLD and 534,364 matched population control participants were included. By the end of the Omicron wave, 92.7% of patients with CLD were boosted. Compared with control participants, patients with CLD showed higher risk of SARS-CoV-2 infection, COVID-19-related hospitalization, and severe COVID-19 during both the Delta wave (infection: adjusted hazards ratio [aHR], 1.22 [95% CI, 1.17-1.28]; hospitalization: aHR, 1.76 [95% CI, 1.61-1.92]; severe COVID-19: aHR, 1.75 [95% CI, 1.50-2.05]) and Omicron wave (infection: aHR, 1.15 [95% CI, 1.14-1.17]; hospitalization: aHR, 1.82 [95% CI, 1.74-1.91]; severe COVID-19: aHR, 2.39 [95% CI, 2.18-2.63]). During Omicron, significantly higher risk of infection, hospitalization, and severe COVID-19 was observed among patients with asthma (severe COVID-19: aHR, 1.31 [95% CI, 1.10-1.55]) and COPD (severe COVID-19: aHR, 1.36 [95% CI, 1.12-1.66]) compared with control participants. Severe exacerbation (requiring hospitalization) in the preceding year was associated with higher risk of poorer outcomes (Delta, severe COVID-19: aHR, 9.84 [95% CI, 6.33-15.28]; Omicron, severe COVID-19: aHR, 19.22 [95% CI, 15.35-24.06]). Risk was attenuated in the boosted group, with numerically lower HRs against hospitalization and severe COVID-19 in the four-dose group compared with the three-dose group. INTERPRETATION: Increased risk of COVID-19-related hospitalization and severe COVID-19 was observed among patients with CLD compared with matched population control participants during Delta and Omicron predominance. Boosting attenuated serious COVID-19 outcomes.

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We aimed to compare the concordance of information extracted and the time taken between a large language model (OpenAI's GPT-3.5 Turbo via API) against conventional human extraction methods in retrieving information from scientific articles on diabetic retinopathy (DR). The extraction was done using GPT3.5 Turbo as of October 2023. OpenAI's GPT-3.5 Turbo significantly reduced the time taken for extraction. Concordance was highest at 100% for the extraction of the country of study, 64.7% for significant risk factors of DR, 47.1% for exclusion and inclusion criteria, and lastly 41.2% for odds ratio (OR) and 95% confidence interval (CI). The concordance levels seemed to indicate the complexity associated with each prompt. This suggests that OpenAI's GPT-3.5 Turbo may be adopted to extract simple information that is easily located in the text, leaving more complex information to be extracted by the researcher. It is crucial to note that the foundation model is constantly improving significantly with new versions being released quickly. Subsequent work can focus on retrieval-augmented generation (RAG), embedding, chunking PDF into useful sections, and prompting to improve the accuracy of extraction.

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Objective: Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students' sleep quality. Methods: In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention. Results: Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = -0.51, 95% CI: [-1.55-0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9-59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7-61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep. Conclusions: The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.

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Rationale: Chronic infection and inflammation shapes the airway microbiome in bronchiectasis. Utilizing whole-genome shotgun metagenomics to analyze the airway resistome provides insight into interplay between microbes, resistance genes, and clinical outcomes. Objectives: To apply whole-genome shotgun metagenomics to the airway microbiome in bronchiectasis to highlight a diverse pool of antimicrobial resistance genes: the "resistome," the clinical significance of which remains unclear. Methods: Individuals with bronchiectasis were prospectively recruited into cross-sectional and longitudinal cohorts (n = 280), including the international multicenter cross-sectional Cohort of Asian and Matched European Bronchiectasis 2 (CAMEB 2) study (n = 251) and two independent cohorts, one describing patients experiencing acute exacerbation and a further cohort of patients undergoing Pseudomonas aeruginosa eradication treatment. Sputum was subjected to metagenomic sequencing, and the bronchiectasis resistome was evaluated in association with clinical outcomes and underlying host microbiomes. Measurements and Main Results: The bronchiectasis resistome features a unique resistance gene profile and increased counts of aminoglycoside, bicyclomycin, phenicol, triclosan, and multidrug resistance genes. Longitudinally, it exhibits within-patient stability over time and during exacerbations despite between-patient heterogeneity. Proportional differences in baseline resistome profiles, including increased macrolide and multidrug resistance genes, associate with shorter intervals to the next exacerbation, whereas distinct resistome archetypes associate with frequent exacerbations, poorer lung function, geographic origin, and the host microbiome. Unsupervised analysis of resistome profiles identified two clinically relevant "resistotypes," RT1 and RT2, the latter characterized by poor clinical outcomes, increased multidrug resistance, and P. aeruginosa. Successful targeted eradication in P. aeruginosa-colonized individuals mediated reversion from RT2 to RT1, a more clinically favorable resistome profile demonstrating reduced resistance gene diversity. Conclusions: The bronchiectasis resistome associates with clinical outcomes, geographic origin, and the underlying host microbiome. Bronchiectasis resistotypes link to clinical disease and are modifiable through targeted antimicrobial therapy.

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As one of the most common eye conditions being presented at clinics, acute conjunctivitis puts substantial strain on primary health resources. To reduce this public health burden, it is important to forecast and provide forward guidance to policymakers by estimating conjunctivitis trends, taking into account factors which influence transmission. Using a high-dimensional set of ambient air pollution and meteorological data, this study describes new approaches to point and probabilistic forecasting of conjunctivitis burden which can be readily translated to other infectious diseases. Over the period of 2012 - 2022, we show that simple models without environmental data provided better point forecasts but the more complex models which optimized predictive accuracy and combined multiple predictors demonstrated superior density forecast performance. These results were shown to be consistent over periods with and without structural breaks in transmission. Furthermore, ecological analysis using post-selection inference showed that increases in SO2, O3 surface concentration and total precipitation were associated to increased conjunctivitis attendance. The methods proposed can provide rich and informative forward guidance for outbreak preparedness and help guide healthcare resource planning in both stable periods of transmission and periods where structural breaks in data occur.

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Upper respiratory tract infections (URTIs) represent a large strain on primary health resources. To mitigate URTI transmission and public health burdens, it is important to pre-empt and provide forward guidance on URTI burden, while taking into account various facets which influence URTI transmission. This is so that appropriate public health measures can be taken to mitigate strain on primary care resources. This study describes a new approach to forecasting URTIs which can be used for national public health resource planning. Specifically, using environmental and disease data comprising more than 1000 dimensions, we developed sub-models which optimizes model explainability, in-sample model fit, predictive accuracy and combines many weaker predictors over a 2-month time horizon to generate direct, point forecasts over a 1-8 week ahead forecast horizon. Predictive performance was evaluated using rolling out-of-sample forecast assessment within both periods with/without structural breaks in transmission over the period of 2012-2022. We showed that forecast combinations of 5 other forecasting models had better and more consistent predictive performance than other modelling approaches, over periods with and without structural breaks in transmission dynamics. Furthermore, epidemiological analysis on high dimensional data was enabled using post-selection inference, to show the dynamic association between lower temperature, increases in past relative humidity and absolute humidity and increased URTIs attendance. The methods proposed can be used for outbreak preparedness and guide healthcare resource planning, in both stable periods of transmission and periods where structural breaks in data occur.

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BACKGROUND: Variable clinical outcomes are reported with fungal sensitisation in chronic obstructive pulmonary disease (COPD), and it remains unclear which fungi and what allergens associate with the poorest outcomes. The use of recombinant as opposed to crude allergens for such assessment is unknown. METHODS: A prospective multicentre assessment of stable COPD (n=614) was undertaken in five hospitals across three countries: Singapore, Malaysia and Hong Kong. Clinical and serological assessment was performed against a panel of 35 fungal allergens including crude and recombinant Aspergillus and non-Aspergillus allergens. Unsupervised clustering and topological data analysis (TDA) approaches were employed using the measured sensitisation responses to elucidate if sensitisation subgroups exist and their related clinical outcomes. RESULTS: Aspergillus fumigatus sensitisation was associated with increased exacerbations in COPD. Unsupervised cluster analyses revealed two "fungal sensitisation" groups. The first was characterised by Aspergillus sensitisation and increased exacerbations, poorer lung function and worse prognosis. Polysensitisation in this group conferred even poorer outcome. The second group, characterised by Cladosporium sensitisation, was more symptomatic. Significant numbers of individuals demonstrated sensitisation responses to only recombinant (as opposed to crude) A. fumigatus allergens f 1, 3, 5 and 6, and exhibited increased exacerbations, poorer lung function and an overall worse prognosis. TDA validated these findings and additionally identified a subgroup within Aspergillus-sensitised COPD of patients with frequent exacerbations. CONCLUSION: Aspergillus sensitisation is a treatable trait in COPD. Measuring sensitisation responses to recombinant Aspergillus allergens identifies an important patient subgroup with poor COPD outcomes that remains overlooked by assessment of only crude Aspergillus allergens.

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In a prior practice and policy article published in Healthcare Science, we introduced the deployed application of an artificial intelligence (AI) model to predict longer-term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home (H2H) program that has been operating since 2017. In this follow on practice and policy article, we further elaborate on Singapore's H2H program and care model, and its supporting AI model for multiple readmission prediction, in the following ways: (1) by providing updates on the AI and supporting information systems, (2) by reporting on customer engagement and related service delivery outcomes including staff-related time savings and patient benefits in terms of bed days saved, (3) by sharing lessons learned with respect to (i) analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants, (ii) balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables, and (iii) the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems, (4) by highlighting how this H2H effort supported broader Covid-19 response efforts across Singapore's public healthcare system, and finally (5) by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards. For the convenience of the reader, some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.

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Neisseria species are frequently identified in the bronchiectasis microbiome, but they are regarded as respiratory commensals. Using a combination of human cohorts, next-generation sequencing, systems biology, and animal models, we show that bronchiectasis bacteriomes defined by the presence of Neisseria spp. associate with poor clinical outcomes, including exacerbations. Neisseria subflava cultivated from bronchiectasis patients promotes the loss of epithelial integrity and inflammation in primary epithelial cells. In vivo animal models of Neisseria subflava infection and metabolipidome analysis highlight immunoinflammatory functional gene clusters and provide evidence for pulmonary inflammation. The murine metabolipidomic data were validated with human Neisseria-dominant bronchiectasis samples and compared with disease in which Pseudomonas-, an established bronchiectasis pathogen, is dominant. Metagenomic surveillance of Neisseria across various respiratory disorders reveals broader importance, and the assessment of the home environment in bronchiectasis implies potential environmental sources of exposure. Thus, we identify Neisseria species as pathobionts in bronchiectasis, allowing for improved risk stratification in this high-risk group.

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Background: Interstitial lung disease (ILD) is associated with poor quality of life (QoL) and high symptom burden. Studies evaluating the benefits of palliative care examined mainly idiopathic pulmonary fibrosis (IPF) patients. We aim to examine the impact of palliative care on a broader group of fibrotic ILD patients. Methods: Single center retrospective cohort study comparing deceased ILD patients who received outpatient palliative care services (palliative-intervention group) against a usual care group. Results: Of 63 subjects, 26 (41%) were in the palliative-intervention group and 37 (59%) in the usual care group. Median time to palliative care referral was 8.6 (IQR .3-21.2) months. Dyspnea-related disability was greater in the palliative-intervention group [mMRC dyspnea score 3.5(IQR 2-4) vs 2(IQR 2-4), P = .039], with more patients requiring long term oxygen therapy (70% vs 30%, P < .001). There was no difference in the median number of hospitalizations or length of stay in the last 6 months of life. Patients in the palliative-intervention group had a higher uptake of advance care planning (ACP) (39% vs 11%, P = .014), lower frequency of intensive care unit (ICU) admissions (5% vs 19%, P = .102) and were prescribed more opioids (96% vs 27%, P < .001) and benzodiazepines (39% vs 14%, P = .022). The palliative-intervention group experienced a longer median survival of 23.9 months (95% confidence interval [CI] 14.1-33.7) compared to the usual group (11.4 months [95% CI 5.4-17.3] (log-rank test: P = .023). Male gender was a strong predictor of 1-year mortality. Conclusions: The palliative-intervention group received earlier pharmacologic intervention for symptom relief. Healthcare utilization was not increased despite greater dyspnea-related disability.

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PURPOSE: Influenza infection is an important cause of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Clinical features predicting influenza PCR positivity are unknown. We aim to identify predictors of influenza PCR positivity in AECOPD. PATIENTS AND METHODS: A retrospective study of AECOPD cases admitted between 1st January 2016 to 30 June 2017 with combined nasal/throat swabs sent for influenza PCR (Xpert Xpress Flu/RSV) within 24 hours of admission was performed. Clinical parameters and investigations within 24 hours of admission were retrieved from electronic medical records. RESULTS: Influenza PCR were sent for 925 AECOPD cases (mean age 75 years, 87.9% male). There were 90 PCR positive cases (68 Influenza A, 22 Influenza B). Influenza PCR positive cases had higher temperatures, higher heart rates, lower white cell and lower eosinophil counts. Age, gender, COPD severity, comorbidities and smoking status were similar in both groups. There were no differences in blood pressure, oxygen status, neutrophil or lymphocyte counts, C reactive protein, procalcitonin or chest X-ray consolidation between groups. Higher temperature, higher heart rate, white cell count in the lowest quartile (Q1 < 8.1 x109/L) and non-eosinophilic exacerbations predicted influenza PCR positivity on univariate logistic regression and these factors remained significant after multivariate adjustment (temperature adjusted odds ratio [adj OR] 1.324 [1.009-1.737], p = 0.043; heart rate adj OR 1.017 [1.004-1.030], p = 0.011; white cell count Q1 adj OR 3.330 [1.690-6.562], p = 0.001; eosinophilic exacerbations adj OR 0.390 [0.202-0.756], p = 0.005). CONCLUSION: Higher temperature, higher heart rate, low white cell count (especially when < 8.1 x109/L) and non-eosinophilic exacerbations are independent predictors of influenza PCR positivity in AECOPD cases.

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BACKGROUND: Allergic bronchopulmonary aspergillosis (ABPA) is associated with frequent exacerbations and poor outcomes in chronic respiratory disease, but remains underdiagnosed. The role of fungal sensitization in bronchiectasis-COPD overlap (BCO) is unknown. RESEARCH QUESTION: What is the occurrence and clinical relevance of Aspergillus sensitization and ABPA in BCO when compared with individuals with COPD or bronchiectasis without overlap? STUDY DESIGN: Prospective, observational, cross-sectional study. METHODS: We prospectively recruited 280 patients during periods of clinical stability with bronchiectasis (n = 183), COPD (n = 50), and BCO (n = 47) from six hospitals across three countries (Singapore, Malaysia, and Scotland). We assessed sensitization responses (as specific IgE) to a panel of recombinant Aspergillus fumigatus allergens and the occurrence of ABPA in relationship to clinical outcomes. RESULTS: Individuals with BCO show an increased frequency and clinical severity of ABPA compared with those with COPD and bronchiectasis without overlap. BCO-associated ABPA is associated with more severe disease, higher exacerbation rates, and lower lung function when compared with ABPA occurring in the absence of overlap. BCO with a severe bronchiectasis severity index (BSI; > 9) is associated significantly with the occurrence of ABPA that is unrelated to underlying COPD severity. CONCLUSIONS: BCO demonstrates a high frequency of ABPA that is associated with a severe BSI (> 9) and poor clinical outcomes. Clinicians should maintain a high index of suspicion for the potential development of ABPA in patients with BCO with high BSI.

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BACKGROUND AND OBJECTIVE: Little is known about the epidemiology and cost of bronchiectasis in Asia. This study describes the disease burden of bronchiectasis in Singapore. METHODS: A nationwide administrative dataset was used to identify hospitalisations with bronchiectasis as a diagnosis. Population statistics and medical encounter data were used to estimate the incidence, mortality, prevalence and direct medical costs associated with bronchiectasis requiring hospitalisation. RESULTS: There were 420 incident hospitalised bronchiectasis patients in 2017, giving an incidence rate of 10.6 per 100 000. Age-standardised incidence declined on average by 2.7% per year between 2007 and 2017. Incidence rates increased strongly with age in both men and women. Tuberculosis was a secondary diagnosis in 37.5% of incident hospitalisations in 2007, but has declined sharply since then. Patient survival was considerably lower in both men (5-year relative survival ratios (RSR) 0.63, 95% CI 0.59-0.66) and women (5-year RSR 0.75, 95% CI 0.72-0.78). The point prevalence of bronchiectasis was 147.1 per 100 000 in 2017, and increased sharply with age, with >1% of people aged ≥75 years having bronchiectasis. Total first-year costs among incident bronchiectasis patients in 2016 varied widely, with a mean±sd USD 7331±8863. Approximately 10% of the patients admitted in 2016 had total first-year costs of more than USD 14 380. CONCLUSION: Bronchiectasis is common and imposes a substantial burden on healthcare costs and survival rates of patients in Singapore.

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INTRODUCTION: Acute respiratory distress syndrome (ARDS) in COVID-19 is associated with a high mortality rate, though outcomes of the different lung compliance phenotypes are unclear. We aimed to measure lung compliance and examine other factors associated with mortality in COVID-19 patients with ARDS. METHODS: Adult patients with COVID-19 ARDS who required invasive mechanical ventilation at 8 hospitals in Singapore were prospectively enrolled. Factors associated with both mortality and differences between high (<40mL/cm H2O) and low (<40mL/cm H2O) compliance were analysed. RESULTS: A total of 102 patients with COVID-19 who required invasive mechanical ventilation were analysed; 15 (14.7%) did not survive. Non-survivors were older (median 70 years, interquartile range [IQR] 67-75 versus median 61 years, IQR 52-66; P<0.01), and required a longer duration of ventilation (26 days, IQR 12-27 vs 8 days, IQR 5-15; P<0.01) and intensive care unit support (26 days, IQR 11-30 vs 11.5 days, IQR 7-17.3; P=0.01), with a higher incidence of acute kidney injury (15 patients [100%] vs 40 patients [46%]; P<0.01). There were 67 patients who had lung compliance data; 24 (35.8%) were classified as having high compliance and 43 (64.2%) as having low compliance. Mortality was higher in patients with high compliance (33.3% vs 11.6%; P=0.03), and was associated with a drop in compliance at day 7 (-9.3mL/cm H2O (IQR -4.5 to -15.4) vs 0.2mL/cm H2O (4.7 to -5.2) P=0.04). CONCLUSION: COVID-19 ARDS patients with higher compliance on the day of intubation and a longitudinal decrease over time had a higher risk of death.

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OBJECTIVES: The Integrated Care for Advanced REspiratory Disorders (ICARE) service is a stay-in, post-acute care program for hospitalized patients with chronic, nonmalignant lung diseases. It provides palliative rehabilitation-a novel model integrating functional rehabilitation with early palliative care. We compare reduction of health care utilization among ICARE participants vs matched controls receiving usual care. DESIGN: This is a prospective, propensity score-matched study. Primary outcomes were reduction in hospital readmissions and length of stay and emergency department (ED) visits, comparing the period 6 months before and after ICARE, or 6 months before and after hospital discharge (for controls). Secondary outcomes included 6-minute walking distance (6MWD) and Modified Barthel Index (MBI). SETTING: Participants were matched 1:1 to controls by age, respiratory diagnosis, socioeconomic strata, index hospitalization length of stay, frailty, and recent admissions into intensive care unit or noninvasive ventilation units. METHODS: Multidisciplinary interventions focused on symptom relief, functional rehabilitation, targeted comorbidity management, and postdischarge care coordination. RESULTS: One hundred pairs of patients were matched. Participants were older adults (mean age 73.9 ± 8.2 years) with prolonged index hospitalization (median 12.0 days; interquartile range 7-18). Overall, 57% had high Hospital Frailty Risk Scores and 71% had overlapping respiratory diagnoses, the most common commonest being COPD (89%), followed by interstitial lung disease (54%) and bronchiectasis (28%). Small reductions in health care utilization were observed among controls. ICARE was associated with a further 9.1 ± 19.9 days' reduction in hospitalization length of stay (P < .001), 0.8 ± 1.9 lesser admission (P < .001), and 0.6 ± 2.2 fewer ED visits (P < .02). Participants with longest index hospitalization were observed to have greatest reduction in length of stay. 6MWD and MBI scores improved by 41.0 ± 60.2 m and 12.3 ± 11.6 points, respectively (both P < .001). Greater improvement was observed in patients with lower baseline 6MWD and MBI scores. Prescription of slow-release opioids rose from 9% to 49%. Treatment for anxiety and depression rose from 5% to 19%. CONCLUSIONS AND IMPLICATIONS: Integrating palliative care with postexacerbation functional rehabilitation was associated with short-term reduction in health care utilization, improved functional capacity, and increased treatment of dyspnea, anxiety, and depression.

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