The elastic-net-regularized classifiers perform reasonably well and are capable of reducing the number of features required by over a thousandfold, with only a modest impact on performance.

Objective Systemic lupus erythematosus (SLE) has among the highest hospital readmission rates among chronic conditions. We sought to identify patient-level, hospital-level, and geographic predictors of 30-day hospital readmissions in SLE. Methods Using hospital discharge databases from 5 geographically dispersed states, we performed a study of all-cause SLE readmissions between 2008 and 2009. We evaluated each hospitalization as a possible index event leading up to a readmission, our primary outcome. We accounted for clustering of hospitalizations within patients and within hospitals and adjusted for hospital case-mix. Using multi-level mixed-effects logistic regression, we examined factors associated with 30-day readmissions and calculated risk-standardized hospital-level and state-level readmission rates. Results We examined 55,936 hospitalizations among 31,903 patients with SLE. 9,244 (16.5%) hospitalizations resulted in readmission within 30 days. In adjusted analyses, age was inversely related to risk of readmission. Black and Hispanic patients were more likely to be readmitted compared to white patients, as were those with Medicare or Medicaid insurance (versus private insurance). Several lupus clinical characteristics, including lupus nephritis, serositis and thrombocytopenia were associated with readmission. Readmission rates varied significantly between hospitals after accounting for patient-level clustering and hospital case mix. There was also geographic variation, with risk-adjusted readmission rates lower in New York and higher in Florida compared to California. Conclusions We found that about 1 in 6 hospitalized patients with SLE were readmitted within 30 days, with higher rates in historically underserved populations. Significant geographic and hospital-level variation in risk-adjusted readmission rates suggests potential for quality improvement.

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.

Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies. These models have differential performance across race and gender groups with inconsistent behavior across studies, potentially resulting in an inequitable distribution of beneficial therapy. In this work, we leverage adversarial learning and a large observational cohort extracted from electronic health records (EHRs) to develop a "fair" ASCVD risk prediction model with reduced variability in error rates across groups. We empirically demonstrate that our approach is capable of aligning the distribution of risk predictions conditioned on the outcome across several groups simultaneously for models built from high-dimensional EHR data. We also discuss the relevance of these results in the context of the empirical trade-off between fairness and model performance.

Importance Commercial virtual visits are an increasingly popular model of care for the management of common, acute illnesses. In commercial virtual visits, patients access a website to be connected synchronously—via videoconference, telephone, or webchat—to a physician with whom they have no prior relationship. There has been no assessment of whether the care delivered through those websites is similar, or whether quality varies among the sites. Objective To assess the variation in quality of care among virtual visit companies. Design We performed an audit study using trained standardized patients. Setting The standardized patients presented to commercial virtual visit companies with six common, acute illnesses (ankle pain, streptococcal pharyngitis, viral pharyngitis, acute rhinosinusitis, low back pain, and recurrent urinary tract infection). Participants The eight commercial virtual visit websites with the highest web traffic. Main Outcome Measures The primary outcomes were completeness of histories and physical examinations, naming the correct diagnosis (versus an incorrect diagnosis or not naming any diagnosis), and adherence to guidelines of key management decisions. Results Standardized patients completed 599 commercial virtual visits from May 2013 to July 2014. Histories and physical examinations were complete in 69.6% (95% confidence interval [CI], 67.7%-71.6%) of virtual visits, diagnoses were correctly named in 76.5% (CI, 72.9%-79.9%), and key management decisions were adherent to guidelines in 54.3% (CI, 50.2%-58.3%). Rates of guideline-adherent care ranged from 34.4% to 66.1% across the eight websites. Variation across websites was significantly greater for viral pharyngitis and acute rhinosinusitis (12.8-82.1%) than for streptococcal pharyngitis and low back pain (74.6-96.5%) or ankle pain and recurrent urinary tract infection (3.4-40.4%). There was no statistically significant variation in guideline adherence by mode of communication (video vs. telephone vs. webchat). Conclusions We found significant variation in quality among companies providing virtual visits for management of common acute illnesses. There was more variation in performance for some conditions than for others, but there was no variation by mode of communication.

SVM-based classifiers can accurately identify procedure status and diagnoses among ICU patients, and including n-gram features improves performance, compared to existing methods.

Background and objectives Anemia guidelines for CKD recommend withholding intravenous iron in the setting of active infection, although no data specifically support this recommendation. This study aimed to examine the association between intravenous iron and clinical outcomes among hemodialysis patients hospitalized for infection.Design, setting, participants, & measurements This was a retrospective observational cohort study using data from the US Renal Data System of 22,820 adult Medicare beneficiaries on in-center hemodialysis who had received intravenous iron in the 14 days preceding their first hospitalization for bacterial infection in 2010. In multivariable analyses, the association between receipt of intravenous iron at any point from the day of hospital admission to discharge and all-cause 30-day mortality, mortality in 2010, length of hospital stay, and readmission for infection or death within 30 days of discharge was evaluated.Results There were 2463 patients (10.8%) who received intravenous iron at any point from the day of admission to discharge. Receipt of intravenous iron was not associated with age, dialysis vintage, or comorbidities. There were 2618 deaths within 30 days of admission and 6921 deaths in 2010 (median follow-up 173 days; 25th and 75th percentiles, 78-271 days). The median length of stay was 7 days (25th and 75th percentiles, 5-12 days). Receipt of intravenous iron was not associated with higher 30-day mortality (odds ratio, 0.86; 95% confidence interval [95% CI], 0.74 to 1.00), higher mortality in 2010 (hazard ratio, 0.92; 95% CI, 0.85 to 1.00), longer mean length of stay (10.1 days [95% CI, 9.7 to 10.5] versus 10.5 days [95% CI, 10.3 to 10.7]; P=0.05), or readmission for infection or death within 30 days of discharge (odds ratio, 1.08; 95% CI, 0.96 to 1.22) compared with no receipt of intravenous iron.Conclusions This analysis does not support withholding intravenous iron upon admission for bacterial infection in hemodialysis patients, although clinical trials are required to make definitive recommendations.

BACKGROUND:Rib fractures are consequential injuries for geriatric patients (age, ≥65 years). Although age and injury patterns drive many rib fracture management decisions, the impact of frailty-which baseline conditions affect rib fracture-specific outcomes-remains unclear for geriatric patients. We aimed to develop and validate the Rib Fracture Frailty (RFF) Index, a practical risk stratification tool specific for geriatric patients with rib fractures. We hypothesized that a compact list of frailty markers can accurately risk stratify clinical outcomes after rib fractures. METHODS:We queried nationwide US admission encounters of geriatric patients admitted with multiple rib fractures from 2016 to 2017. Partitioning around medoids clustering identified a development subcohort with previously validated frailty characteristics. Ridge regression with penalty for multicollinearity aggregated baseline conditions most prevalent in this frail subcohort into RFF scores. Regression models with adjustment for injury severity, sex, and age assessed associations between frailty risk categories (low, medium, and high) and inpatient outcomes among validation cohorts (odds ratio [95% confidence interval]). We report results according to Transparent Reporting of Multivariable Prediction Model for Individual Prognosis guidelines. RESULTS:Development cohort (n = 55,540) cluster analysis delineated 13 baseline conditions constituting the RFF Index. Among external validation cohort (n = 77,710), increasing frailty risk (low [reference group], moderate, high) was associated with stepwise worsening adjusted odds of mortality (1.