Altered consciousness mediates the effect of hypernatremia, but not hyponatremia, on mortality among patients observed in the ICU.

Purpose: Dysnatremias - hypernatremia and hyponatremia - may be associated with mortality through their impact on altered consciousness. We examined the mediating effect of decreased consciousness on the relationship between dysnatremia and mortality. Methods: Among 195,568 critically ill patients in the United States contained in the eICU database, we categorized serum sodium into bands of 5mEq/L. Using causal mediation analysis, we compared bands in the hypernatremia and hyponatremia ranges to a reference band of 135-139mEq/L to determine the proportion of mortality mediated by decreased consciousness as determined by the Glasgow Coma Score (GCS). Results: Both hyponatremia (OR [95%CI] for bands: <120mEq/L: 1.58 [1.26-1.97]; 120-<125mEq/L: 1.92 [1.64-2.25]; 125-<130mEq/L: 1.76 [1.60-1.93]; 130-<135mEq/L: 1.32 [1.24-1.41]) and hypernatremia (OR [95%CI] for bands: 140-<145mEq/L: 1.12 [1.05-1.19]; 145-<150mEq/L: 1.89 [1.70-2.11]; ≥150mEq/L: 1.86 [1.57-2.19]) were significantly associated with increased mortality. GCS mediated the effect of hypernatremia on mortality risk (Proportion mediated [95%CI]: 140-144mEq/L: 0.38 [0.23 to 0.89]; 145-149mEq/L: 0.27 [0.22 to 0.34]; ≥150mEq/L: 0.53 [0.41 to 0.81]) but not hyponatremia (proportion mediated 95%CI upper bound <0.05 for all bands). Conclusion: Decreased consciousness mediates the association between increased mortality and hypernatremia, but not hyponatremia. Further studies are needed to explore neurologic mechanisms and directionality in this relationship.

Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

Association of Chloride Ion and Sodium-Chloride Difference With Acute Kidney Injury and Mortality in Critically Ill Patients.

OBJECTIVES: Derangements of chloride ion concentration ([Cl-]) have been shown to be associated with acute kidney injury and other adverse outcomes. For a physicochemical approach, however, chloride ion concentration should be considered with sodium ion concentration. This study aimed to examine the association of chloride ion concentration and the main strong ion difference (difference between sodium ion concentration and chloride ion concentration) during the first 24 hours after admission into ICU with the development of acute kidney injury and mortality. DESIGN: Retrospective analyses using the eICU Collaborative Research Database. SETTING: ICUs in 208 hospitals across the United States between 2014 and 2015. PATIENTS: Critically ill patients who were admitted into the ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 34,801 patients records were analyzed. A multivariable logistic regression analysis for the development of acute kidney injury within 7 days of ICU admission shows that, compared with main strong iron difference 32-34 mEq/as a reference, there were significantly high odds for the development of acute kidney injury in nearly all groups with main strong iron difference more than 34 mEq/L (main strong iron difference = 34-36 mEq/L, odds ratio = 1.17, p = 0.02; main strong iron difference = 38-40 mEq/L, odds ratio = 1.40, p < 0.001; main strong iron difference = 40-42 mEq/L, odds ratio = 1.46, p = 0.001; main strong iron difference > 42 mEq/L, odds ratio = 1.56, p < 0.001). With chloride ion concentration 104-106 mEq/L as a reference, the odds for acute kidney injury were significantly higher only in chloride ion concentration less than or equal to 94 mEq/L and chloride ion concentration 98-100 mEq/L groups. Analyses conducted using inverse probability weighting showed significantly greater odds for ICU mortality in all groups with main strong iron difference greater than 34mEq/L other than the 36-38mEq/L group, as well as in the less than 26-mEq/L group. CONCLUSIONS: Main strong iron difference measured on ICU presentation to the ICU predicts acute kidney injury within 7 days, with low and, in particular, high values representing increased risk. The association between the chloride levels and acute kidney injury is statistically insignificant in models incorporating main strong iron difference, suggesting main strong iron difference is a better predictive marker than chloride on ICU admission.

Assessing team effectiveness and affective learning in a datathon.

BACKGROUND: Datathons are increasingly organized in the healthcare field. The goal is to assemble people with different backgrounds to work together as a team and engage in clinically relevant research or develop algorithms using health-related datasets. Criteria to assess the return of investment on such events have traditionally included publications produced, patents for prediction, classification, image recognition and other types of software, and start-up companies around the application of machine learning in healthcare. Previous studies have not evaluated whether a datathon can promote affective learning and effective teamwork. METHODS: Fifty participants of a health datathon event in São Paulo, Brazil at Hospital Israelita Albert Einstein (HIAE) were divided into 8 groups. A survey with 25 questions, using the Affective Learning Scale and Team-Review Questionnaire, was administered to assess team effectiveness and affective learning during the event. Multivariate regression models and Pearson's correlation tests were performed to evaluate the effect of affective learning on teamwork. RESULTS: Majority of the participants were male 76% (37/49); 32% (16/49) were physicians. The mean score for learning (scale from 1 to 10) was 8.38, while that for relevance of the perceived teamwork was 1.20 (scale from 1 to 5; "1" means most relevant). Pearson's correlation between the learning score and perception of teamwork showed moderate association (r = 0.36, p = 0.009). Five learning and 10 teamwork variables were on average positively graded in the event. The final regression model includes all learning and teamwork variables. Effective leadership was strongly correlated with affective learning (ß = -0.27, p < 0.01, R2 = 75%). Effective leadership, team accomplishment, criticism, individual development and creativity were the variables significantly associated with higher levels of affective learning. CONCLUSION: It is feasible to enhance affective knowledge and the skill to work in a team during a datathon. We found that teamwork is associated with higher affective learning from participants' perspectives. Effective leadership is essential for teamwork and is a significant predictor of learning.