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BackgroundCOVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. ObjectiveTo overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. MethodsWe selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. ResultsIn the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. ConclusionsOur new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

Various electrocardiographic changes occur during sepsis, but data on the clinical importance of a low QRS voltage in sepsis are still limited. We aimed to evaluate the association between low QRS voltage identified early in sepsis and mortality in patients with sepsis. Between September 2019 and December 2020, all consecutive adult patients diagnosed with sepsis in the emergency room or general ward at Samsung Medical Center were enrolled. Patients without a 12-lead electrocardiogram recorded within 48 h of recognition of sepsis were excluded. In 432 eligible patients, 12-lead electrocardiogram was recorded within the median of 24 min from the first recognition of sepsis, and low QRS voltage was identified in 115 (26.6%) patients. The low QRS group showed more severe organ dysfunction and had higher levels of N-terminal pro-brain natriuretic peptide. The hospital mortality was significantly higher in the low QRS voltage group than in the normal QRS voltage group (49.6% vs. 28.1%, p < 0.001). Similarly, among the 160 patients who required intensive care unit admission, significantly more patients in the low QRS group died in the intensive care unit (35.9% vs. 18.2%, p = 0.021). Low QRS voltage was associated with increased hospital mortality in patients with sepsis. [ABSTRACT FROM AUTHOR]

Background Delirium is a common complication of older people in hospitals, rehabilitation and long-term facilities. Objective To assess the worldwide use of validated delirium assessment tools and the presence of delirium management protocols. Design Secondary analysis of a worldwide one-day point prevalence study on World Delirium Awareness Day, 15 March 2023. Setting Cross-sectional online survey including hospitals, rehabilitation and long-term facilities. Methods Participating clinicians reported data on delirium, the presence of protocols, delirium assessments, delirium-awareness interventions, non-pharmacological and pharmacological interventions, and ward/unit-specific barriers. Results Data from 44 countries, 1664 wards/units and 36 048 patients were analysed. Validated delirium assessments were used in 66.7% (n  = 1110) of wards/units, 18.6% (n  = 310) used personal judgement or no assessment, and 10% (n  = 166) used other assessment methods. A delirium management protocol was reported in 66.8% (n  = 1094) of wards/units. The presence of protocols for delirium management varied across continents, ranging from 21.6% (on 21/97 wards/units) in Africa to 90.4% (235/260) in Australia, similar to the use of validated delirium assessments with 29.6% (29/98) in Africa to 93.5% (116/124) in North America. Wards/units with a delirium management protocol [ n  = 1094/1664, 66.8%] were more likely to use a validated delirium test than those without a protocol [odds ratio 6.97 (95% confidence interval 5.289–9.185)]. The presence of a delirium protocol increased the chances for valid delirium assessment and, likely, evidence-based interventions. Conclusion Wards/units that reported the presence of delirium management protocols had a higher probability of using validated delirium assessments tools to assess for delirium. [ABSTRACT FROM AUTHOR]

This study determined whether compared to conventional mechanical ventilation (MV), extracorporeal membrane oxygenation (ECMO) is associated with decreased hospital mortality or fibrotic changes in patients with COVID-19 acute respiratory distress syndrome. A cohort of 72 patients treated with ECMO and 390 with conventional MV were analyzed (February 2020–December 2021). A target trial was emulated comparing the treatment strategies of initiating ECMO vs no ECMO within 7 days of MV in patients with a PaO2/FiO2 < 80 or a PaCO2 ≥ 60 mmHg. A total of 222 patients met the eligibility criteria for the emulated trial, among whom 42 initiated ECMO. ECMO was associated with a lower risk of hospital mortality (hazard ratio [HR], 0.56; 95% confidence interval [CI] 0.36–0.96). The risk was lower in patients who were younger (age < 70 years), had less comorbidities (Charlson comorbidity index < 2), underwent prone positioning before ECMO, and had driving pressures ≥ 15 cmH2O at inclusion. Furthermore, ECMO was associated with a lower risk of fibrotic changes (HR, 0.30; 95% CI 0.11–0.70). However, the finding was limited due to relatively small number of patients and differences in observability between the ECMO and conventional MV groups. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to develop a new warning score for cancer patients who are at risk of getting worse in the hospital. Cancer patients can have serious problems due to their treatment or cancer itself. A quick response system is used to find and treat these patients. This study used a deep-learning method to create a warning score based on the changes in vital signs. The warning score, called Can-EWS, was better than the existing score, called MEWS, in predicting and preventing bad outcomes. Can-EWS also had fewer false alarms than MEWS. This study could help improve the safety and care of cancer patients in the hospital. Background: Cancer patients who are admitted to hospitals are at high risk of short-term deterioration due to treatment-related or cancer-specific complications. A rapid response system (RRS) is initiated when patients who are deteriorating or at risk of deteriorating are identified. This study was conducted to develop a deep learning-based early warning score (EWS) for cancer patients (Can-EWS) using delta values in vital signs. Methods: A retrospective cohort study was conducted on all oncology patients who were admitted to the general ward between 2016 and 2020. The data were divided into a training set (January 2016–December 2019) and a held-out test set (January 2020–December 2020). The primary outcome was clinical deterioration, defined as the composite of in-hospital cardiac arrest (IHCA) and unexpected intensive care unit (ICU) transfer. Results: During the study period, 19,739 cancer patients were admitted to the general wards and eligible for this study. Clinical deterioration occurred in 894 cases. IHCA and unexpected ICU transfer prevalence was 1.77 per 1000 admissions and 43.45 per 1000 admissions, respectively. We developed two models: Can-EWS V1, which used input vectors of the original five input variables, and Can-EWS V2, which used input vectors of 10 variables (including an additional five delta variables). The cross-validation performance of the clinical deterioration for Can-EWS V2 (AUROC, 0.946; 95% confidence interval [CI], 0.943–0.948) was higher than that for MEWS of 5 (AUROC, 0.589; 95% CI, 0.587–0.560; p < 0.001) and Can-EWS V1 (AUROC, 0.927; 95% CI, 0.924–0.931). As a virtual prognostic study, additional validation was performed on held-out test data. The AUROC and 95% CI were 0.588 (95% CI, 0.588–0.589), 0.890 (95% CI, 0.888–0.891), and 0.898 (95% CI, 0.897–0.899), for MEWS of 5, Can-EWS V1, and the deployed model Can-EWS V2, respectively. Can-EWS V2 outperformed other approaches for specificities, positive predictive values, negative predictive values, and the number of false alarms per day at the same sensitivity level on the held-out test data. Conclusions: We have developed and validated a deep learning-based EWS for cancer patients using the original values and differences between consecutive measurements of basic vital signs. The Can-EWS has acceptable discriminatory power and sensitivity, with extremely decreased false alarms compared with MEWS. [ABSTRACT FROM AUTHOR]