No abstract

No abstract

Dear Editor, While studies have established risk factors for clinical deterioration in coronavirus disease 2019 (COVID-19) patients [1, 2], or attempted to identify phenotypes based on experts opinion [3], identifying sub-phenotypes based on more easily obtained data could help identify patients at highest risk of clinical deterioration and refine inclusion of more homogeneous subpopulations in clinical trials. We here applied an unsupervised, multivariate clustering algorithm using easy-to-obtain clinical variables to identify COVID-19 sub-phenotypes and examined the association with clinical deterioration. This retrospective cohort study was performed among adult COVID-19-positive patients (using real-time reverse transcriptase-polymerase chain reaction assay) with a hospital visit between February 28 and March 26, 2020, at eight teaching hospitals of the Assistance Publique-Hôpitaux de Paris. The Institutional Review Board (IRB) of Ile-de-France VII approved the study and waived the need for informed consent from individual patients (DC 2009/CO-15-000). We selected 22 candidate variables for the clustering analysis including demographic information among 608 patients with available candidate variables, disease history, major clinical symptoms, and medications on the day of positive diagnostic, which represents the final cohort (Supplementary file). We

Opal is the first published example of a full-stack platform infrastructure for an implementation science designed for ML in anesthesia that solves the problem of leveraging ML for clinical decision support. Users interact with a secure online Opal web application to select a desired operating room (OR) case cohort for data extraction, visualize datasets with built-in graphing techniques, and run in-client ML or extract data for external use. Opal was used to obtain data from 29,004 unique OR cases from a single academic institution for pre-operative prediction of post-operative acute kidney injury (AKI) based on creatinine KDIGO criteria using predictors which included pre-operative demographic, past medical history, medications, and flowsheet information. To demonstrate utility with unsupervised learning, Opal was also used to extract intra-operative flowsheet data from 2995 unique OR cases and patients were clustered using PCA analysis and k-means clustering. A gradient boosting machine model was developed using an 80/20 train to test ratio and yielded an area under the receiver operating curve (ROC-AUC) of 0.85 with 95% CI [0.80–0.90]. At the default probability decision threshold of 0.5, the model sensitivity was 0.9 and the specificity was 0.8. K-means clustering was performed to partition the cases into two clusters and for hypothesis generation of potential groups of outcomes related to intraoperative vitals. Opal’s design has created streamlined ML functionality for researchers and clinicians in the perioperative setting and opens the door for many future clinical applications, including data mining, clinical simulation, high-frequency prediction, and quality improvement.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

<abstract> <p>Chronic traumatic encephalopathy (CTE) is a progressive neurodegenerative disease that occurs secondary to repetitive mild traumatic brain injury. Current clinical diagnosis relies on symptomatology and structural imaging findings which often vary widely among those with the disease. The gold standard of diagnosis is post-mortem pathological examination. In this review article, we provide a brief introduction to CTE, current diagnostic workup and the promising research on imaging and fluid biomarker diagnostic techniques. For imaging, we discuss quantitative structural analyses, DTI, fMRI, MRS, SWI and PET CT. For fluid biomarkers, we discuss p-tau, TREM2, CCL11, NfL and GFAP.</p> </abstract>

ObjectiveTo investigate risk factors and subphenotypes associated with long term symptoms and outcomes after hospital admission for covid-19.DesignProspective, multicentre observational study.Setting93 hospitals in France.ParticipantsData from 2187 adults admitted to hospital with covid-19 in France between 1 February 2020 and 30 June 2021.Main outcome measuresPrimary endpoint was the total number of persistent symptoms at six months after hospital admission that were not present before admission. Outcomes examined at six months were persistent symptoms, Hospital Anxiety and Depression Scale, six minute walk test distances, 36-Item Short Form Health Survey scores, and ability to resume previous professional activities and self-care. Secondary endpoints included vital status at six months, and results of standardised quality-of-life scores. Additionally, an unsupervised consensus clustering algorithm was used to identify subphenotypes based on the severity of hospital course received by patients.Results1109 (50.7%) of 2187 participants had at least one persistent symptom. Factors associated with an increased number of persistent symptoms were in-hospital supplemental oxygen (odds ratio 1.12, 95% confidence interval 1 to 1.24), no intensive care unit admission (1.15, 1.01 to 1.32), female sex (1.33, 1.22 to 1.45), gastrointestinal haemorrhage (1.51, 1.02 to 2.23), a thromboembolic event (1.66, 1.17 to 2.34), and congestive heart failure (1.76, 1.27 to 2.43). Three subphenotypes were identified: including patients with the least severe hospital course (based on ventilatory support requirements). Although Hospital Anxiety and Depression Scale scores were within normal values for all groups, patients of intermediate severity and more comorbidities had a higher median Hospital Anxiety and Depression Scale score than did the other subphenotypes. Patients in the subphenotype with most severe hospital course had worse short form-36 scores and were less able to resume their professional activity or care for themselves as before compared with other subphenotypes.ConclusionsPersistent symptoms after hospital admission were frequent, regardless of acute covid-19 severity. However, patients in more severe subphenotypes had a significantly worse functional status and were less likely to resume their professional activity or able to take care of themselves as before.Trial registrationNCT04262921.

Background There is insufficient evidence to guide ventilatory targets in acute brain injury (ABI). Recent studies have shown associations between mechanical power (MP) and mortality in critical care populations. We aimed to describe MP in ventilated patients with ABI, and evaluate associations between MP and clinical outcomes. Methods In this preplanned, secondary analysis of a prospective, multi-center, observational cohort study (ENIO, NCT03400904), we included adult patients with ABI (Glasgow Coma Scale ≤ 12 before intubation) who required mechanical ventilation (MV) ≥ 24 h. Using multivariable log binomial regressions, we separately assessed associations between MP on hospital day (HD)1, HD3, HD7 and clinical outcomes: hospital mortality, need for reintubation, tracheostomy placement, and development of acute respiratory distress syndrome (ARDS). Results We included 1217 patients (mean age 51.2 years [SD 18.1], 66% male, mean body mass index [BMI] 26.3 [SD 5.18]) hospitalized at 62 intensive care units in 18 countries. Hospital mortality was 11% (n = 139), 44% (n = 536) were extubated by HD7 of which 20% (107/536) required reintubation, 28% (n = 340) underwent tracheostomy placement, and 9% (n = 114) developed ARDS. The median MP on HD1, HD3, and HD7 was 11.9 J/min [IQR 9.2–15.1], 13 J/min [IQR 10–17], and 14 J/min [IQR 11–20], respectively. MP was overall higher in patients with ARDS, especially those with higher ARDS severity. After controlling for same-day pressure of arterial oxygen/fraction of inspired oxygen (P/F ratio), BMI, and neurological severity, MP at HD1, HD3, and HD7 was independently associated with hospital mortality, reintubation and tracheostomy placement. The adjusted relative risk (aRR) was greater at higher MP, and strongest for: mortality on HD1 (compared to the HD1 median MP 11.9 J/min, aRR at 17 J/min was 1.22, 95% CI 1.14–1.30) and HD3 (1.38, 95% CI 1.23–1.53), reintubation on HD1 (1.64; 95% CI 1.57–1.72), and tracheostomy on HD7 (1.53; 95%CI 1.18–1.99). MP was associated with the development of moderate-severe ARDS on HD1 (2.07; 95% CI 1.56–2.78) and HD3 (1.76; 95% CI 1.41–2.22). Conclusions Exposure to high MP during the first week of MV is associated with poor clinical outcomes in ABI, independent of P/F ratio and neurological severity. Potential benefits of optimizing ventilator settings to limit MP warrant further investigation.