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Physical therapy can prevent functional impairments and improve the quality of life of patients after hospital discharge. However, the effect of early mobilization on patients with a critical illness remains unclear. This study was performed to assess the evidence available regarding the effect of early mobilization on critically ill patients in the intensive care unit (ICU). Electronic databases were searched from their inception to March 21, 2019. Randomized controlled trials (RCTs) comprising critically ill patients who received early mobilization were included. The methodological quality and risk of bias of each eligible trial were assessed using the Cochrane Collaboration tool. Data were extracted using a standard collection form each included study, and processed using the Mantel-Haenszel (M-H) or inverse-variance (I-V) test in the STATA v12.0 statistical software. A total of 1,898 records were screened. Twenty-three RCTs comprising 2,308 critically ill patients were ultimately included. Early mobilization decreased the incidence of ICU-acquired weakness (ICU-AW) at hospital discharge (three studies, 190 patients, relative risk (RR): 0.60, 95% confidence interval (CI) [0.40, 0.90]; p = 0.013, I2 = 0.0%), increased the number of patients who were able to stand (one study, 50 patients, 90% vs. 62%, p = 0.02), increased the number of ventilator-free days (six studies, 745 patients, standardized mean difference (SMD): 0.17, 95% CI [0.02, 0.31]; p = 0.023, I2 = 35.5%) during hospitalization, increased the distance the patient was able to walk unassisted (one study, 104 patients, 33.4 (0-91.4) meters vs. 0 (0-30.4) meters, p = 0.004) at hospital discharge, and increased the discharged-to-home rate (seven studies, 793 patients, RR: 1.16, 95% CI [1.00, 1.34]; p = 0.046). The mortality (28-day, ICU and hospital) and adverse event rates were moderately increased by early mobilization, but the differences were statistically non-significant. However, due to the substantial heterogeneity among the included studies, and the low quality of the evidence, the results of this study should be interpreted with caution. Publication bias was not identified. Early mobilization appears to decrease the incidence of ICU-AW, improve the functional capacity, and increase the number of ventilator-free days and the discharged-to-home rate for patients with a critical illness in the ICU setting.

In the last decade, increasing mental distress and treatment for mental health conditions among youth in North America has paralleled a steep rise in the use of smartphones and social media by children and adolescents. In Ontario, the proportion of teenagers reporting moderate to serious mental distress increased from 24% in 2013, to 34% in 2015 and to 39% in 2017, with parallel increases in health service utilization. Inpatient hospital admissions of children and adolescents for mental health reasons increased substantially across Canada between 2007 and 2014, while admissions for other medical conditions in this age group decreased by 14%.2 Between 2009 and 2014, admissions to hospital for intentional self-harm increased by 110% in Canadian girls. Suicide is now the second leading cause of death for Canadian youth. At the same time, social media use has increased markedly. In the US, the proportion of young people between the ages of 13 and 17 years who have a smartphone has reached 89%, more than doubling over a 6-year period; moreover, 70% of teenagers use social media multiple times per day, up from a third of teens in 2012. The percentage of Ontario's teenagers who reported spending 5 or more hours a day on social media increased from 11% in 2013, to 16% in 2015 and to 20% in 2017. Here, Abi-Jaoude et al examine the evidence that links smartphone and social media use with mental distress and suicidality among adolescents.

To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91). Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Keywords: COVID-2019, Disease severity, Risk factors, Host susceptibility

Acute respiratory distress syndrome (ARDS) survivors experience a high prevalence of cognitive impairment with concomitantly impaired functional status and quality of life, often persisting months after hospital discharge. In this review, we explore the pathophysiological mechanisms underlying cognitive impairment following ARDS, the interrelations between mechanisms and risk factors, and interventions that may mitigate the risk of cognitive impairment. Risk factors for cognitive decline following ARDS include pre-existing cognitive impairment, neurological injury, delirium, mechanical ventilation, prolonged exposure to sedating medications, sepsis, systemic inflammation, and environmental factors in the intensive care unit, which can co-occur synergistically in various combinations. Detection and characterization of pre-existing cognitive impairment imparts challenges in clinical management and longitudinal outcome study enrollment. Patients with brain injury who experience ARDS constitute a distinct population with a particular combination of risk factors and pathophysiological mechanisms: considerations raised by brain injury include neurogenic pulmonary edema, differences in sympathetic activation and cholinergic transmission, effects of positive end-expiratory pressure on cerebral microcirculation and intracranial pressure, and sensitivity to vasopressor use and volume status. The blood-brain barrier represents a physiological interface at which multiple mechanisms of cognitive impairment interact, as acute blood-brain barrier weakening from mechanical ventilation and systemic inflammation can compound existing chronic blood-brain barrier dysfunction from Alzheimer’s-type pathophysiology, rendering the brain vulnerable to both amyloid-beta accumulation and cytokine-mediated hippocampal damage. Although some contributory elements, such as the presenting brain injury or pre-existing cognitive impairment, may be irreversible, interventions such as minimizing mechanical ventilation tidal volume, minimizing duration of exposure to sedating medications, maintaining hemodynamic stability, optimizing fluid balance, and implementing bundles to enhance patient care help dramatically to reduce duration of delirium and may help prevent acquisition of long-term cognitive impairment.

Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one. We used longitudinal data from linked electronic health records of 4.6 million patients aged 18-100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with different risk levels. We first replicated a previously reported Cox proportional hazards (CPH) model for prediction of the risk of the first emergency admission up to 24 months after baseline. This reference model was then compared with 2 machine learning models, random forest (RF) and gradient boosting classifier (GBC). The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions. We then added 13 more variables (marital status, prior general practice visits, and 11 additional morbidities), and also enriched all variables by incorporating temporal information whenever possible (e.g., time since first diagnosis). We also varied the prediction windows to 12, 36, 48, and 60 months after baseline and compared model performances. For internal validation, we used 5-fold cross-validation. When the initial set of variables was used, GBC outperformed RF and CPH, with an area under the receiver operating characteristic curve (AUC) of 0.779 (95% CI 0.777, 0.781), compared to 0.752 (95% CI 0.751, 0.753) and 0.740 (95% CI 0.739, 0.741), respectively. In external validation, we observed an AUC of 0.796, 0.736, and 0.736 for GBC, RF, and CPH, respectively. The addition of temporal information improved AUC across all models. In internal validation, the AUC rose to 0.848 (95% CI 0.847, 0.849), 0.825 (95% CI 0.824, 0.826), and 0.805 (95% CI 0.804, 0.806) for GBC, RF, and CPH, respectively, while the AUC in external validation rose to 0.826, 0.810, and 0.788, respectively. This enhancement also resulted in robust predictions for longer time horizons, with AUC values remaining at similar levels across all models. Overall, compared to the baseline reference CPH model, the final GBC model showed a 10.8% higher AUC (0.848 compared to 0.740) for prediction of risk of emergency admission within 24 months. GBC also showed the best calibration throughout the risk spectrum. Despite the wide range of variables included in models, our study was still limited by the number of variables included; inclusion of more variables could have further improved model performances. The use of machine learning and addition of temporal information led to substantially improved discrimination and calibration for predicting the risk of emergency admission. Model performance remained stable across a range of prediction time windows and when externally validated. These findings support the potential of incorporating machine learning models into electronic health records to inform care and service planning.

Readmissions after hospital discharge are common and costly, but prediction models are poor at identifying patients at high risk of readmission. We evaluated the impact of frailty on readmission or death within 30 days after discharge from general internal medicine wards. We prospectively enrolled patients discharged from 7 medical wards at 2 teaching hospitals in Edmonton. Frailty was defined by means of the previously validated Clinical Frailty Scale. The primary outcome was the composite of readmission or death within 30 days after discharge. Of the 495 patients included in the study, 162 (33%) met the definition of frailty: 91 (18%) had mild, 60 (12%) had moderate, and 11 (2%) had severe frailty. Frail patients were older, had more comorbidities, lower quality of life, and higher LACE scores at discharge than those who were not frail. The composite of 30-day readmission or death was higher among frail than among nonfrail patients (39 [24.1%] v. 46 [13.8%]). Although frailty added additional prognostic information to predictive models that included age, sex and LACE score, only moderate to severe frailty (31.0% event rate) was an independent risk factor for readmission or death (adjusted odds ratio 2.19, 95% confidence interval 1.12–4.24). Frailty was common and associated with a substantially increased risk of early readmission or death after discharge from medical wards. The Clinical Frailty Scale could be useful in identifying high-risk patients being discharged from general internal medicine wards.

Background Although mortality due to critical illness has fallen over decades, the number of patients with long-term functional disabilities has increased, leading to impaired quality of life and significant healthcare costs. As an essential part of the multimodal interventions available to improve outcome of critical illness, optimal nutrition therapy should be provided during critical illness, after ICU discharge, and following hospital discharge. Methods This narrative review summarizes the latest scientific insights and guidelines on ICU nutrition delivery. Practical guidance is given to provide optimal nutrition therapy during the three phases of the patient journey. Results Based on recent literature and guidelines, gradual progression to caloric and protein targets during the initial phase of ICU stay is recommended. After this phase, full caloric dose can be provided, preferably based on indirect calorimetry. Phosphate should be monitored to detect refeeding hypophosphatemia, and when occurring, caloric restriction should be instituted. For proteins, at least 1.3 g of proteins/kg/day should be targeted after the initial phase. During the chronic ICU phase, and after ICU discharge, higher protein/caloric targets should be provided preferably combined with exercise. After ICU discharge, achieving protein targets is more difficult than reaching caloric goals, in particular after removal of the feeding tube. After hospital discharge, probably very high-dose protein and calorie feeding for prolonged duration is necessary to optimize the outcome. High-protein oral nutrition supplements are likely essential in this period. Several pharmacological options are available to combine with nutrition therapy to enhance the anabolic response and stimulate muscle protein synthesis. Conclusions During and after ICU care, optimal nutrition therapy is essential to improve the long-term outcome to reduce the likelihood of the patient to becoming a “victim” of critical illness. Frequently, nutrition targets are not achieved in any phase of recovery. Personalized nutrition therapy, while respecting different targets during the phases of the patient journey after critical illness, should be prescribed and monitored.

Background This study investigated the clinical value of interleukin-6 (IL-6), pentraxin 3 (PTX3), and procalcitonin (PCT) in patients with sepsis and septic shock diagnosed according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Methods Serum levels of IL-6, PTX3, and PCT were measured in 142 enrolled subjects (51 with sepsis, 46 with septic shock, and 45 as controls). Follow-up IL-6 and PTX3 levels were measured in patients with initial septic shock within 24 h of hospital discharge. Optimal cut-off values were determined for sepsis and septic shock, and prognostic values were evaluated. Results Serum IL-6 levels could discriminate sepsis (area under the curve [AUC], 0.83–0.94, P  <  0.001; cut-off value, 52.60 pg/mL, 80.4% sensitivity, 88.9% specificity) from controls and could distinguish septic shock (AUC, 0.71–0.89; cut-off value, 348.92 pg/mL, 76.1% sensitivity, 78.4% specificity) from sepsis. Twenty-eight-day mortality was significantly higher in the group with high IL-6 (≥ 348.92 pg/mL) than in the group with low IL-6 (< 348.92 pg/mL) ( P  = 0.008). IL-6 was an independent risk factor for 28-day mortality among overall patients (hazard ratio, 1.0004; 95% confidence interval, 1.0003–1.0005; p  = 0.024). In septic shock patients, both the initial and follow-up PTX3 levels were consistently significantly higher in patients who died than in those who recovered (initial p  = 0.004; follow-up P  <  0.001). Conclusions The diagnostic and prognostic value of IL-6 was superior to those of PTX3 and PCT for sepsis and septic shock.

Health care-associated infections (HCAIs) are infections that occur while receiving health care, developed in a hospital or other health care facility that first appear 48 hours or more after hospital admission, or within 30 days after having received health care. Multiple studies indicate that the common types of adverse events affecting hospitalized patients are adverse drug events, HCAIs, and surgical complications. The US Center for Disease Control and Prevention identifies that nearly 1.7 million hospitalized patients annually acquire HCAIs while being treated for other health issues and that more than 98,000 patients (one in 17) die due to these. Several studies suggest that simple infection-control procedures such as cleaning hands with an alcohol-based hand rub can help prevent HCAIs and save lives, reduce morbidity, and minimize health care costs. Routine educational interventions for health care professionals can help change their hand-washing practices to prevent the spread of infection. In support of this, the WHO has produced guidelines to promote hand-washing practices among member countries.