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.