Sparsity is often a desirable property of statistical models, and various feature selection methods exist so as to yield sparser and interpretable models. However, their application to biomedical text classification, particularly to mortality risk stratification among intensive care unit (ICU) patients, has not been thoroughly studied.To develop and characterize sparse classifiers based on the free text of nursing notes in order to predict ICU mortality risk and to discover text features most strongly associated with mortality.We selected nursing notes from the first 24h of ICU admission for 25,826 adult ICU patients from the MIMIC-II database. We then developed a pair of stochastic gradient descent-based classifiers with elastic-net regularization. We also studied the performance-sparsity tradeoffs of both classifiers as their regularization parameters were varied.The best-performing classifier achieved a 10-fold cross-validated AUC of 0.897 under the log loss function and full L2 regularization, while full L1 regularization used just 0.00025% of candidate input features and resulted in an AUC of 0.889. Using the log loss (range of AUCs 0.889-0.897) yielded better performance compared to the hinge loss (0.850-0.876), but the latter yielded even sparser models.Most features selected by both classifiers appear clinically relevant and correspond to predictors already present in existing ICU mortality models. The sparser classifiers were also able to discover a number of informative - albeit nonclinical - features.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.

Background

Existing risk adjustment models for intensive care unit (ICU) outcomes rely on manual abstraction of patient-level predictors from medical charts. Developing an automated method for abstracting these data from free text might reduce cost and data collection times.

Objective

To develop a support vector machine (SVM) classifier capable of identifying a range of procedures and diagnoses in ICU clinical notes for use in risk adjustment.

Materials and methods

We selected notes from 2001-2008 for 4191 neonatal ICU (NICU) and 2198 adult ICU patients from the MIMIC-II database from the Beth Israel Deaconess Medical Center. Using these notes, we developed an implementation of the SVM classifier to identify procedures (mechanical ventilation and phototherapy in NICU notes) and diagnoses (jaundice in NICU and intracranial hemorrhage (ICH) in adult ICU). On the jaundice classification task, we also compared classifier performance using n-gram features to unigrams with application of a negation algorithm (NegEx).

Results

Our classifier accurately identified mechanical ventilation (accuracy=0.982, F1=0.954) and phototherapy use (accuracy=0.940, F1=0.912), as well as jaundice (accuracy=0.898, F1=0.884) and ICH diagnoses (accuracy=0.938, F1=0.943). Including bigram features improved performance on the jaundice (accuracy=0.898 vs 0.865) and ICH (0.938 vs 0.927) tasks, and outperformed NegEx-derived unigram features (accuracy=0.898 vs 0.863) on the jaundice task.

Discussion

Overall, a classifier using n-gram support vectors displayed excellent performance characteristics. The classifier generalizes to diverse patient populations, diagnoses, and procedures.

Conclusions

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

Objective

Systemic lupus erythematosus (SLE) has one of the highest hospital readmission rates among chronic conditions. This study was undertaken to identify patient-level, hospital-level, and geographic predictors of 30-day hospital readmissions associated with SLE.

Methods

Using hospital discharge databases from 5 geographically dispersed states, we studied all-cause readmission of SLE patients 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 multilevel mixed-effects logistic regression, we examined factors associated with 30-day readmission and calculated risk-standardized hospital-level and state-level readmission rates.

Results

We examined 55,936 hospitalizations among 31,903 patients with SLE. Of these hospitalizations, 9,244 (16.5%) resulted in readmission within 30 days. In adjusted analyses, age was inversely related to risk of readmission. African American and Hispanic patients were more likely to be readmitted than white patients, as were those with Medicare or Medicaid insurance (versus private insurance). Several clinical characteristics of lupus, including nephritis, serositis, and thrombocytopenia, were associated with readmission. Readmission rates varied significantly between hospitals after accounting for patient-level clustering and hospital case mix. We also found geographic variation, with risk-adjusted readmission rates lower in New York and higher in Florida as compared to California.

Conclusion

We found that ~1 in 6 hospitalized patients with SLE were readmitted within 30 days of discharge, with higher rates among historically underserved populations. Significant geographic and hospital-level variation in risk-adjusted readmission rates suggests potential for quality improvement.

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.

Importance

Commercial virtual visits are an increasingly popular model of health 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. To date, whether the care delivered through those websites is similar or quality varies among the sites has not been assessed.

Objective

To assess the variation in the quality of urgent health care among virtual visit companies.

Design, setting, and participants

This audit study used 67 trained standardized patients who presented to commercial virtual visit companies with the following 6 common acute illnesses: ankle pain, streptococcal pharyngitis, viral pharyngitis, acute rhinosinusitis, low back pain, and recurrent female urinary tract infection. The 8 commercial virtual visit websites with the highest web traffic were selected for audit, for a total of 599 visits. Data were collected from May 1, 2013, to July 30, 2014, and analyzed from July 1, 2014, to September 1, 2015.

Main outcomes and measures

Completeness of histories and physical examinations, the correct diagnosis (vs an incorrect or no diagnosis), and adherence to guidelines of key management decisions.

Results

Sixty-seven standardized patients completed 599 commercial virtual visits during the study period. Histories and physical examinations were complete in 417 visits (69.6%; 95% CI, 67.7%-71.6%); diagnoses were correctly named in 458 visits (76.5%; 95% CI, 72.9%-79.9%), and key management decisions were adherent to guidelines in 325 visits (54.3%; 95% CI, 50.2%-58.3%). Rates of guideline-adherent care ranged from 206 visits (34.4%) to 396 visits (66.1%) across the 8 websites. Variation across websites was significantly greater for viral pharyngitis and acute rhinosinusitis (adjusted rates, 12.8% to 82.1%) than for streptococcal pharyngitis and low back pain (adjusted rates, 74.6% to 96.5%) or ankle pain and recurrent urinary tract infection (adjusted rates, 3.4% to 40.4%). No statistically significant variation in guideline adherence by mode of communication (videoconference vs telephone vs webchat) was found.

Conclusions and relevance

Significant variation in quality was found among companies providing virtual visits for management of common acute illnesses. More variation was found in performance for some conditions than for others, but no variation by mode of communication.