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ObjectiveTo create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.DesignRetrospective cohort study.SettingOne US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.Participants33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19.Main outcome measuresAn ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.Results9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.ConclusionA model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.

BackgroundAlthough specific interventions previously demonstrated benefit in patients with ARDS, use of these interventions is inconsistent, and patient mortality remains high. The impact of variability in center management practices on ARDS mortality rates remains unknown.Research questionWhat is the impact of treatment variability on mortality in patients with moderate to severe ARDS in the United States?Study design and methodsWe conducted a multicenter, observational cohort study of mechanically ventilated adults with ARDS and Pao2 to Fio2 ratio of ≤ 150 with positive end-expiratory pressure of ≥ 5 cm H2O, who were admitted to 29 US centers between October 1, 2016, and April 30, 2017. The primary outcome was 28-day in-hospital mortality. Center variation in ventilator management, adjunctive therapy use, and mortality also were assessed.ResultsA total of 2,466 patients were enrolled. Median baseline Pao2 to Fio2 ratio was 105 (interquartile range, 78.0-129.0). In-hospital 28-day mortality was 40.7%. Initial adherence to lung protective ventilation (LPV; tidal volume, ≤ 6.5 mL/kg predicted body weight; plateau pressure, or when unavailable, peak inspiratory pressure, ≤ 30 mm H2O) was 31.4% and varied between centers (0%-65%), as did rates of adjunctive therapy use (27.1%-96.4%), methods used (neuromuscular blockade, prone positioning, systemic steroids, pulmonary vasodilators, and extracorporeal support), and mortality (16.7%-73.3%). Center standardized mortality ratios (SMRs), calculated using baseline patient-level characteristics to derive expected mortality rate, ranged from 0.33 to 1.98. Of the treatment-level factors explored, only center adherence to early LPV was correlated with SMR.InterpretationSubstantial center-to-center variability exists in ARDS management, suggesting that further opportunities for improving ARDS outcomes exist. Early adherence to LPV was associated with lower center mortality and may be a surrogate for overall quality of care processes. Future collaboration is needed to identify additional treatment-level factors influencing center-level outcomes.Trial registryClinicalTrials.gov; No.: NCT03021824; URL: www.clinicaltrials.gov.

Rationale: Chronic lung allograft dysfunction (CLAD) is the leading cause of death after lung transplant, and azithromycin has variable efficacy in CLAD. The lung microbiome is a risk factor for developing CLAD, but the relationship between lung dysbiosis, pulmonary inflammation, and allograft dysfunction remains poorly understood. Whether lung microbiota predict outcomes or modify treatment response after CLAD is unknown. Objectives: To determine whether lung microbiota predict post-CLAD outcomes and clinical response to azithromycin. Methods: Retrospective cohort study using acellular BAL fluid prospectively collected from recipients of lung transplant within 90 days of CLAD onset. Lung microbiota were characterized using 16S rRNA gene sequencing and droplet digital PCR. In two additional cohorts, causal relationships of dysbiosis and inflammation were evaluated by comparing lung microbiota with CLAD-associated cytokines and measuring ex vivo P. aeruginosa growth in sterilized BAL fluid. Measurements and Main Results: Patients with higher bacterial burden had shorter post-CLAD survival, independent of CLAD phenotype, azithromycin treatment, and relevant covariates. Azithromycin treatment improved survival in patients with high bacterial burden but had negligible impact on patients with low or moderate burden. Lung bacterial burden was positively associated with CLAD-associated cytokines, and ex vivo growth of P. aeruginosa was augmented in BAL fluid from transplant recipients with CLAD. Conclusions: In recipients of lung transplants with chronic rejection, increased lung bacterial burden is an independent risk factor for mortality and predicts clinical response to azithromycin. Lung bacterial dysbiosis is associated with alveolar inflammation and may be promoted by underlying lung allograft dysfunction. [ABSTRACT FROM AUTHOR]

Rationale: Intermediate care (also termed "step-down" or "moderate care") has been proposed as a lower cost alternative to care for patients who may not clearly benefit from intensive care unit admission. Intermediate care units may be appealing to hospitals in financial crisis, including those in rural areas. Outcomes of patients receiving intermediate care are not widely described. Objectives: To examine relationships among rurality, location of care, and mortality for mechanically ventilated patients. Methods: Medicare beneficiaries aged 65 years and older who received invasive mechanical ventilation between 2010 and 2019 were included. Multivariable logistic regression was used to estimate the association between admission to a rural or an urban hospital and 30-day mortality, with separate analyses for patients in general, intermediate, and intensive care. Models were adjusted for age, sex, area deprivation index, primary diagnosis, severity of illness, year, comorbidities, and hospital volume. Results: There were 2,752,492 hospitalizations for patients receiving mechanical ventilation from 2010 to 2019, and 193,745 patients (7.0%) were in rural hospitals. The proportion of patients in rural intermediate care increased from 4.1% in 2010 to 6.3% in 2019. Patient admissions to urban hospitals remained relatively stable. Patients in rural and urban intensive care units had similar adjusted 30-day mortality, at 46.7% (adjusted absolute risk difference −0.1% [95% confidence interval, −0.7% to 0.6%]; P = 0.88). However, adjusted 30-day mortality for patients in rural intermediate care was significantly higher (36.9%) than for patients in urban intermediate care (31.3%) (adjusted absolute risk difference 5.6% [95% confidence interval, 3.7% to 7.6%]; P < 0.001). Conclusions: Hospitalization in rural intermediate care was associated with increased mortality. There is a need to better understand how intermediate care is used across hospitals and to carefully evaluate the types of patients admitted to intermediate care units. [ABSTRACT FROM AUTHOR]

BackgroundCOVID-19 has led to an unprecedented strain on health care facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here, we present the results of an analytical model, Predicting Intensive Care Transfers and Other Unforeseen Events (PICTURE), to identify patients at high risk for imminent intensive care unit transfer, respiratory failure, or death, with the intention to improve the prediction of deterioration due to COVID-19. ObjectiveThis study aims to validate the PICTURE model’s ability to predict unexpected deterioration in general ward and COVID-19 patients, and to compare its performance with the Epic Deterioration Index (EDI), an existing model that has recently been assessed for use in patients with COVID-19. MethodsThe PICTURE model was trained and validated on a cohort of hospitalized non–COVID-19 patients using electronic health record data from 2014 to 2018. It was then applied to two holdout test sets: non–COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to EDI and NEWS scores for head-to-head comparison via area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve. We compared the models’ ability to predict an adverse event (defined as intensive care unit transfer, mechanical ventilation use, or death). Shapley values were used to provide explanations for PICTURE predictions. ResultsIn non–COVID-19 general ward patients, PICTURE achieved an AUROC of 0.819 (95% CI 0.805-0.834) per observation, compared to the EDI’s AUROC of 0.763 (95% CI 0.746-0.781; n=21,740; P

The aim of this Intensive Care Medicine Rapid Practice Guideline (ICM‐RPG) was to provide evidence‐based clinical guidance about the use of higher versus lower oxygenation targets for adult patients in the intensive care unit (ICU). The guideline panel comprised 27 international panelists, including content experts, ICU clinicians, methodologists, and patient representatives. We adhered to the methodology for trustworthy clinical practice guidelines, including the use of the Grading of Recommendations Assessment, Development, and Evaluation approach to assess the certainty of evidence, and used the Evidence‐to‐Decision framework to generate recommendations. A recently published updated systematic review and meta‐analysis constituted the evidence base. Through teleconferences and web‐based discussions, the panel provided input on the balance and magnitude of the desirable and undesirable effects, the certainty of evidence, patients' values and preferences, costs and resources, equity, feasibility, acceptability, and research priorities. The updated systematic review and meta‐analysis included data from 17 randomized clinical trials with 10,248 participants. There was little to no difference between the use of higher versus lower oxygenation targets for all outcomes with available data, including all‐cause mortality, serious adverse events, stroke, functional outcomes, cognition, and health‐related quality of life (very low certainty of evidence). The panel felt that values and preferences, costs and resources, and equity favored the use of lower oxygenation targets. The ICM‐RPG panel issued one conditional recommendation against the use of higher oxygenation targets: "We suggest against the routine use of higher oxygenation targets in adult ICU patients (conditional recommendation, very low certainty of evidence). Remark: an oxygenation target of SpO2 88%–92% or PaO2 8 kPa/60 mmHg is relevant and safe for most adult ICU patients." [ABSTRACT FROM AUTHOR]