Key Points Question Can a prediction model for mortality in the intensive care unit be improved by using more laboratory values, vital signs, and clinical text in electronic health records? Findings In this cohort study of 101 196 patients in the intensive care unit, a machine learning–based model using all available measurements of vital signs and laboratory values, plus clinical text, exhibited good calibration and discrimination in predicting in-hospital mortality, yielding an area under the receiver operating characteristic curve of 0.922. Meaning Applying methods from machine learning and natural language processing to information already routinely collected in electronic health records, including laboratory test results, vital signs, and clinical free-text notes, significantly improves a prediction model for mortality in the intensive care unit compared with approaches that use only the most abnormal vital sign and laboratory values.

BackgroundAs patients with congenital heart disease (CHD) are living longer, understanding the comorbidities they develop as they age is increasingly important. However, there are no published population‐based estimates of the comorbidity burden among the US adult patients with CHD.Methods and ResultsUsing the IBM MarketScan commercial claims database from 2010 to 2016, we identified adults aged ≥18 years with CHD and 2 full years of continuous enrollment. These were frequency matched with adults without CHD within categories jointly defined by age, sex, and dates of enrollment in the database. A total of 40 127 patients with CHD met the inclusion criteria (mean [SD] age, 36.8 [14.6] years; and 48.2% were women). Adults with CHD were nearly twice as likely to have any comorbidity than those without CHD (P<0.001). After adjusting for covariates, patients with CHD had a higher prevalence risk ratio for “previously recognized to be common in CHD” (risk ratio, 9.41; 95% CI, 7.99–11.1), “other cardiovascular” (risk ratio, 1.73; 95% CI, 1.66–1.80), and “noncardiovascular” (risk ratio, 1.47; 95% CI, 1.41–1.52) comorbidities. After adjusting for covariates and considering interaction with age, patients with severe CHD had higher risks of previously recognized to be common in CHD and lower risks of other cardiovascular comorbidities than age‐stratified patients with nonsevere CHD. For noncardiovascular comorbidities, the risk was higher among patients with severe than nonsevere CHD before, but not after, the age of 40 years.ConclusionsOur data underscore the unique clinical needs of adults with CHD compared with their peers. Clinicians caring for CHD may want to use a multidisciplinary approach, including building close collaborations with internists and specialists, to help provide appropriate care for the highly prevalent noncardiovascular comorbidities.

BACKGROUND AND OBJECTIVES: Asthma is responsible for ∼1.7 million emergency department (ED) visits annually in the United States. Studies in adults have shown that anxiety and depression are associated with increased asthma-related ED use. Our objective was to assess this association in pediatric patients with asthma. METHODS: We identified patients aged 6 to 21 years with asthma in the Massachusetts All-Payer Claims Database for 2014 to 2015 using International Classification of Diseases, Ninth and 10th Revision codes. We examined the association between the presence of anxiety, depression, or comorbid anxiety and depression and the rate of asthma-related ED visits per 100 child-years using bivariate and multivariable analyses with negative binomial regression. RESULTS: Of 65 342 patients with asthma, 24.7% had a diagnosis of anxiety, depression, or both (11.2% anxiety only, 5.8% depression only, and 7.7% both). The overall rate of asthma-related ED use was 17.1 ED visits per 100 child-years (95% confidence interval [CI]: 16.7-17.5). Controlling for age, sex, insurance type, and other chronic illness, patients with anxiety had a rate of 18.9 (95% CI: 17.0-20.8) ED visits per 100 child-years, patients with depression had a rate of 21.7 (95% CI: 18.3-25.0), and patients with both depression and anxiety had a rate of 27.6 (95% CI: 24.8-30.3). These rates were higher than those of patients who had no diagnosis of anxiety or depression (15.5 visits per 100 child-years; 95% CI: 14.5-16.4; P , .001). CONCLUSIONS: Children with asthma and anxiety or depression alone, or comorbid anxiety and depression, have higher rates of asthma-related ED use compared with those without either diagnosis. WHAT'S KNOWN ON THIS SUBJECT: Studies in adults have shown that anxiety and depression are associated with increased asthma-related emergency department use. However, there is limited literature that addresses this question for children with asthma. WHAT THIS STUDY ADDS: Children with asthma who had anxiety, depression, or comorbid anxiety and depression had higher emergency department use for asthma. Patients with comorbid anxiety and depression had an emergency department visit rate that was 2 times higher than that of patients without anxiety or depression.