*Division of Research, Kaiser Permanente Northern California, Oakland †Department of Applied Mathematics and Statistics, Baskin School of Engineering, University of California, Santa Cruz ‡Kaiser Foundation Health Plan, Management, Information and Analysis, Oakland, CA.
Medical care (Impact Factor: 3.23). 05/2013; 51(5):446-53. DOI: 10.1097/MLR.0b013e3182881c8e
: 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.
"We describe the relationship between patient factors and RBC transfusions in the acute care community hospital setting, taking advantage of the existing research infrastructure of an integrated health care delivery system, Kaiser Permanente Northern California (KPNC). Using data from a comprehensive electronic medical record and an externally validated risk adjustment methodology applicable to all hospitalized patients, we quantified the incremental effect of increasing clinical detail on the likelihood of a patient receiving a RBC transfusion during hospitalization [19-21]. We sought to assess the role of patient comorbidities and severity of illness, in addition to hemoglobin levels, in predicting inpatient RBC transfusion events. "
[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.
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.
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.
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.
BMC Health Services Research 05/2014; 14(1):213. DOI:10.1186/1472-6963-14-213 · 1.71 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Rationale:
Patients with severe sepsis without shock or tissue hypoperfusion face substantial mortality; however, treatment guidelines are lacking.
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.
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.
Annals of the American Thoracic Society 09/2013; 10(5). DOI:10.1513/AnnalsATS.201304-099OC
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