Risk-adjusting Hospital Mortality Using a Comprehensive Electronic Record in an Integrated Health Care Delivery System

ArticleinMedical care 51(5):446-53 · May 2013with24 Reads
DOI: 10.1097/MLR.0b013e3182881c8e · Source: PubMed
: Using a comprehensive inpatient electronic medical record, we sought to develop a risk-adjustment methodology applicable to all hospitalized patients. Further, we assessed the impact of specific data elements on model discrimination, explanatory power, calibration, integrated discrimination improvement, net reclassification improvement, performance across different hospital units, and hospital rankings. : Retrospective cohort study using logistic regression with split validation. : A total of 248,383 patients who experienced 391,584 hospitalizations between January 1, 2008 and August 31, 2011. : Twenty-one hospitals in an integrated health care delivery system in Northern California. : Inpatient and 30-day mortality rates were 3.02% and 5.09%, respectively. In the validation dataset, the greatest improvement in discrimination (increase in c statistic) occurred with the introduction of laboratory data; however, subsequent addition of vital signs and end-of-life care directive data had significant effects on integrated discrimination improvement, net reclassification improvement, and hospital rankings. Use of longitudinally captured comorbidities did not improve model performance when compared with present-on-admission coding. Our final model for inpatient mortality, which included laboratory test results, vital signs, and care directives, had a c statistic of 0.883 and a pseudo-R of 0.295. Results for inpatient and 30-day mortality were virtually identical. : Risk-adjustment of hospital mortality using comprehensive electronic medical records is feasible and permits one to develop statistical models that better reflect actual clinician experience. In addition, such models can be used to assess hospital performance across specific subpopulations, including patients admitted to intensive care.
    • "In this study, we utilized advanced measures of comorbid disease burden and severity of illness to risk-adjust patients . We examined the role of these measures, previously validated to assess a patient's mortality risk, in predicting the likelihood of RBC transfusion events [19]. Given the high acuity of illness in newly admitted patients, we chose to examine RBC transfusion within 24 hours of admission in addition to events through hospitalization. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Randomized controlled trial evidence supports a restrictive strategy of red blood cell (RBC) transfusion, but significant variation in clinical transfusion practice persists. Patient characteristics other than hemoglobin levels may influence the decision to transfuse RBCs and explain some of this variation. Our objective was to evaluate the role of patient comorbidities and severity of illness in predicting inpatient red blood cell transfusion events. Methods We developed a predictive model of inpatient RBC transfusion using comprehensive electronic medical record (EMR) data from 21 hospitals over a four year period (2008-2011). Using a retrospective cohort study design, we modeled predictors of transfusion events within 24 hours of hospital admission and throughout the entire hospitalization. Model predictors included administrative data (age, sex, comorbid conditions, admission type, and admission diagnosis), admission hemoglobin, severity of illness, prior inpatient RBC transfusion, admission ward, and hospital. Results The study cohort included 275,874 patients who experienced 444,969 hospitalizations. The 24 hour and overall inpatient RBC transfusion rates were 7.2% and 13.9%, respectively. A predictive model for transfusion within 24 hours of hospital admission had a C-statistic of 0.928 and pseudo-R2 of 0.542; corresponding values for the model examining transfusion through the entire hospitalization were 0.872 and 0.437. Inclusion of the admission hemoglobin resulted in the greatest improvement in model performance relative to patient comorbidities and severity of illness. Conclusions Data from electronic medical records at the time of admission predicts with very high likelihood the incidence of red blood transfusion events in the first 24 hours and throughout hospitalization. Patient comorbidities and severity of illness on admission play a small role in predicting the likelihood of RBC transfusion relative to the admission hemoglobin.
    Full-text · Article · May 2014
  • [Show abstract] [Hide abstract] ABSTRACT: Rationale: Patients with severe sepsis without shock or tissue hypoperfusion face substantial mortality; however, treatment guidelines are lacking. Objectives: To evaluate the association between intravenous fluid resuscitation, lactate clearance, and mortality in patients with "intermediate" lactate values of 2 mmol/L or greater and less than 4 mmol/L. Measurements and main results: This was a retrospective study of 9,190 patients with sepsis with intermediate lactate values. Interval changes between index lactate values and those at 4, 8, and 12 hours were calculated with corresponding weight-based fluid volumes. Outcomes included lactate change and mortality. Repeat lactate tests were completed in 94.7% of patients within 12 hours. Hospital and 30-day mortality were 8.2 and 13.3%, respectively, for patients with lactate clearance; they were 18.7 and 24.7%, respectively, for those without lactate clearance. Each 10% increase in repeat lactate values was associated with a 9.4% (95% confidence interval [CI] = 7.8-11.1%) increase in the odds of hospital death. Within 4 hours, patients received 32 (± 18) ml/kg of fluid. Each 7.5 ml/kg increase was associated with a 1.3% (95% CI = 0.6-2.1%) decrease in repeat lactate. Across an unrestricted range, increased fluid was not associated with improved mortality. However, when limited to less than 45 ml/kg, additional fluid was associated with a trend toward improved survival (odds ratio = 0.92; 95% CI = 0.82-1.03) that was statistically significant among patients with highly concordant fluid records. Conclusions: Early fluid administration, below 45 ml/kg, was associated with modest improvements in lactate clearance and potential improvements in mortality. Further study is needed to define treatment strategies in this prevalent and morbid group of patients with sepsis.
    Article · Sep 2013
  • [Show abstract] [Hide abstract] ABSTRACT: Adherence to evidence-based recommendations for acute myocardial infarction (AMI) remains unsatisfactory. Quantifying association between using an electronic AMI order set (AMI-OS) and hospital processes and outcomes. Retrospective cohort study. Twenty-one community hospitals. A total of 5879 AMI patients were hospitalized between September 28, 2008 and December 31, 2010. We ascertained whether patients were treated using the AMI-OS or individual orders (a la carte). Dependent process variables were use of evidence-based care; outcome variables were mortality and rehospitalization. Use of individual and combined therapies improved outcomes (eg, 50% lower odds of 30-day mortality for patients with ≥3 therapies). The 3531 patients treated using the AMI-OS were more likely to receive evidence-based therapies (eg, 50% received 5 different therapies vs 36% a la carte). These patients had lower 30-day mortality (5.7% vs 8.5%) than the 2348 treated using a la carte orders. Although AMI-OS patients' predicted mortality risk was lower (3.2%) than that of a la carte patients (4.8%), the association of improved processes and outcomes with the use of the AMI-OS persisted after risk adjustment. For example, after inverse probability weighting, the relative risk for inpatient mortality in the AMI-OS group was 0.67 (95% confidence interval: 0.52-0.86). Inclusion of use of recommended therapies in risk adjustment eliminated the benefit of the AMI-OS, highlighting its mediating effect on adherence to evidence-based treatment. Use of an electronic order set is associated with increased adherence to evidence-based care and better AMI outcomes. Journal of Hospital Medicine 2014. © 2014 Society of Hospital Medicine.
    Article · Mar 2014
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