Trends in red blood cell transfusion and 30-day mortality among hospitalized patients.

BACKGROUND: Blood conservation strategies have been shown to be effective in decreasing red blood cell (RBC) utilization in specific patient groups. However, few data exist describing the extent of RBC transfusion reduction or their impact on transfusion practice and mortality in a diverse inpatient population. STUDY DESIGN AND METHODS: We conducted a retrospective cohort study using comprehensive electronic medical record data from 21 medical facilities in Kaiser Permanente Northern California. We examined unadjusted and risk-adjusted RBC transfusion and 30-day mortality coincident with implementation of RBC conservation strategies. RESULTS: The inpatient study cohort included 391,958 patients who experienced 685,753 hospitalizations. From 2009 to 2013, the incidence of RBC transfusion decreased from 14.0% to 10.8% of hospitalizations; this change coincided with a decline in pretransfusion hemoglobin (Hb) levels from 8.1 to 7.6 g/dL. Decreased RBC utilization affected broad groups of admission diagnoses and was most pronounced in patients with a nadir Hb level between 8 and 9 g/dL (n = 73,057; 50.8% to 19.3%). During the study period, the standard deviation of risk-adjusted RBC transfusion incidence across hospitals decreased by 44% (p < 0.001). Thirty-day mortality did not change significantly with declines in RBC utilization in patient groups previously studied in clinical trials nor in other subgroups. CONCLUSIONS: After the implementation of blood conservation strategies, RBC transfusion incidence and pretransfusion Hb levels decreased broadly across medical and surgical patients. Variation in RBC transfusion incidence across hospitals decreased from 2010 to 2013. Consistent with clinical trial data, more restrictive transfusion practice did not appear to impact 30-day mortality.

Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity.

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.

Risk-adjusting hospital mortality using a comprehensive electronic record in an integrated health care delivery system.

OBJECTIVE: 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. DESIGN: Retrospective cohort study using logistic regression with split validation. PARTICIPANTS: A total of 248,383 patients who experienced 391,584 hospitalizations between January 1, 2008 and August 31, 2011. SETTING: Twenty-one hospitals in an integrated health care delivery system in Northern California. RESULTS: 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. CONCLUSIONS: 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.

Automated identification of pneumonia in chest radiograph reports in critically ill patients.

BACKGROUND: Prior studies demonstrate the suitability of natural language processing (NLP) for identifying pneumonia in chest radiograph (CXR) reports, however, few evaluate this approach in intensive care unit (ICU) patients. METHODS: From a total of 194,615 ICU reports, we empirically developed a lexicon to categorize pneumonia-relevant terms and uncertainty profiles. We encoded lexicon items into unique queries within an NLP software application and designed an algorithm to assign automated interpretations ('positive', 'possible', or 'negative') based on each report's query profile. We evaluated algorithm performance in a sample of 2,466 CXR reports interpreted by physician consensus and in two ICU patient subgroups including those admitted for pneumonia and for rheumatologic/endocrine diagnoses. RESULTS: Most reports were deemed 'negative' (51.8%) by physician consensus. Many were 'possible' (41.7%); only 6.5% were 'positive' for pneumonia. The lexicon included 105 terms and uncertainty profiles that were encoded into 31 NLP queries. Queries identified 534,322 'hits' in the full sample, with 2.7 ± 2.6 'hits' per report. An algorithm, comprised of twenty rules and probability steps, assigned interpretations to reports based on query profiles. In the validation set, the algorithm had 92.7% sensitivity, 91.1% specificity, 93.3% positive predictive value, and 90.3% negative predictive value for differentiating 'negative' from 'positive'/'possible' reports. In the ICU subgroups, the algorithm also demonstrated good performance, misclassifying few reports (5.8%). CONCLUSIONS: Many CXR reports in ICU patients demonstrate frank uncertainty regarding a pneumonia diagnosis. This electronic tool demonstrates promise for assigning automated interpretations to CXR reports by leveraging both terms and uncertainty profiles.

Impact of emergency physician-provided patient education about alternative care venues.

OBJECTIVES: Interventions that focus on educating patients appear to be the most effective in directing healthcare utilization to more appropriate venues. We sought to evaluate the effects of mailed information and a brief scripted educational phone call from an emergency physician (EP) on subsequent emergency department (ED) utilization by low-risk adults with a recent treat-and-release ED visit. STUDY DESIGN: Patients were randomized into 3 groups for post-ED follow-up: EP phone call with mailed information, mailed information only, and no educational intervention. Each intervention group was compared with a set of matched controls. METHODS: We undertook this study in 6 EDs within an integrated healthcare delivery system. Overall, 9093 patients were identified; the final groups were the phone group (n = 609), mail group (n = 771), and matched control groups for each (n = 1827 and n = 1542, respectively). Analysis was stratified by age (<65 and ≥65 years). Patients were educated about available venues of care delivery for their future medical needs. The primary outcome was the rate of 6-month ED utilization after the intervention compared with the 6-month utilization rate preceding the intervention. RESULTS: Compared with matched controls, subsequent ED utilization decreased by 22% for patients 65 years or older in the phone group (P = .04) and by 27% for patients younger than 65 years in the mail group (P = .03). CONCLUSIONS: ED utilization subsequent to a low-acuity ED visit decreased after a brief post-ED education intervention by an EP explaining alternative venues of care for future medical needs. Response to the method of communication (phone vs mail) varied significantly by patient age.

An electronic order set for acute myocardial infarction is associated with improved patient outcomes through better adherence to clinical practice guidelines.

BACKGROUND: Adherence to evidence-based recommendations for acute myocardial infarction (AMI) remains unsatisfactory. OBJECTIVE: Quantifying association between using an electronic AMI order set (AMI-OS) and hospital processes and outcomes. DESIGN: Retrospective cohort study. SETTING: Twenty-one community hospitals. PATIENTS: A total of 5879 AMI patients were hospitalized between September 28, 2008 and December 31, 2010. MEASUREMENTS: 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. RESULTS: 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. CONCLUSIONS: Use of an electronic order set is associated with increased adherence to evidence-based care and better AMI outcomes.

Risk factors for unplanned transfer to intensive care within 24 hours of admission from the emergency department in an integrated healthcare system.

BACKGROUND: Emergency department (ED) ward admissions subsequently transferred to the intensive care unit (ICU) within 24 hours have higher mortality than direct ICU admissions. DESIGN, SETTING, PATIENTS: Describe risk factors for unplanned ICU transfer within 24 hours of ward arrival from the ED. METHODS: Evaluation of 178,315 ED non-ICU admissions to 13 US community hospitals. We tabulated the outcome of unplanned ICU transfer by patient characteristics and hospital volume. We present factors associated with unplanned ICU transfer after adjusting for patient and hospital differences in a hierarchical logistic regression. RESULTS: There were 4,252 (2.4%) non-ICU admissions transferred to the ICU within 24 hours. Admitting diagnoses most associated with unplanned transfer, listed by descending prevalence were: pneumonia (odds ratio [OR] 1.5; 95% confidence interval [CI] 1.2-1.9), myocardial infarction (MI) (OR 1.5; 95% CI 1.2-2.0), chronic obstructive pulmonary disease (COPD) (OR 1.4; 95% CI 1.1-1.9), sepsis (OR 2.5; 95% CI 1.9-3.3), and catastrophic conditions (OR 2.3; 95% CI 1.7-3.0). Other significant predictors included: male sex, Comorbidity Points Score >145, Laboratory Acute Physiology Score ≥7, arriving on the ward between 11 PM and 7 AM. Decreased risk was found with admission to monitored transitional care units (OR 0.83; 95% CI 0.77-0.90) and to higher volume hospitals (OR 0.94 per 1,000 additional annual ED inpatient admissions; 95% CI 0.91-0.98). CONCLUSIONS: ED patients admitted with respiratory conditions, MI, or sepsis are at modestly increased risk for unplanned ICU transfer and may benefit from better triage from the ED, earlier intervention, or closer monitoring to prevent acute decompensation. More research is needed to determine how intermediate care units, hospital volume, time of day, and sex affect unplanned ICU transfer. Journal of Hospital Medicine 2013. © 2012 Society of Hospital Medicine.

Intra-hospital transfers to a higher level of care: contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS).

BACKGROUND: Patients who experience intra-hospital transfers to a higher level of care (eg, ward to intensive care unit [ICU]) are known to have high mortality. However, these findings have been based on single-center studies or studies that employ ICU admissions as the denominator. OBJECTIVE: To employ automated bed history data to examine outcomes of intra-hospital transfers using all hospital admissions as the denominator. DESIGN: Retrospective cohort study. SETTING: A total of 19 acute care hospitals. PATIENTS: A total of 150,495 patients, who experienced 210,470 hospitalizations, admitted to these hospitals between November 1st, 2006 and January 31st, 2008. MEASUREMENTS: Predictors were age, sex, admission type, admission diagnosis, physiologic derangement on admission, and pre-existing illness burden; outcomes were: 1) occurrence of intra-hospital transfer, 2) death following admission to the hospital, 3) death following transfer, and 4) total hospital length of stay (LOS). RESULTS: A total of 7,868 hospitalizations that began with admission to either a general medical surgical ward or to a transitional care unit (TCU) had at least one transfer to a higher level of care. These hospitalizations constituted only 3.7% of all admissions, but accounted for 24.2% of all ICU admissions, 21.7% of all hospital deaths, and 13.2% of all hospital days. Models based on age, sex, preadmission laboratory test results, and comorbidities did not predict the occurrence of these transfers. CONCLUSIONS: Patients transferred to higher level of care following admission to the hospital have excess mortality and LOS.

Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.

OBJECTIVES: To develop a risk-adjustment methodology that maximizes the use of automated physiology and diagnosis data from the time period preceding hospitalization. DESIGN: : Retrospective cohort study using split-validation and logistic regression. SETTING: Seventeen hospitals in a large integrated health care delivery system. SUBJECTS: Patients (n = 259,699) hospitalized between January 2002 and June 2005. MAIN OUTCOME MEASURES: Inpatient and 30-day mortality. RESULTS: Inpatient mortality was 3.50%; 30-day mortality was 4.06%. We tested logistic regression models in a randomly chosen derivation dataset consisting of 50% of the records and applied their coefficients to the validation dataset. The final model included sex, age, admission type, admission diagnosis, a Laboratory-based Acute Physiology Score (LAPS), and a COmorbidity Point Score (COPS). The LAPS integrates information from 14 laboratory tests obtained in the 24 hours preceding hospitalization into a single continuous variable. Using Diagnostic Cost Groups software, we categorized patients as having up to 40 different comorbidities based on outpatient and inpatient data from the 12 months preceding hospitalization. The COPS integrates information regarding these 41 comorbidities into a single continuous variable. Our best model for inpatient mortality had a c statistic of 0.88 in the validation dataset, whereas the c statistic for 30-day mortality was 0.86; both models had excellent calibration. Physiologic data accounted for a substantial proportion of the model's predictive ability. CONCLUSION: Efforts to support improvement of hospital outcomes can take advantage of risk-adjustment methods based on automated physiology and diagnosis data that are not confounded by information obtained after hospital admission.

Risk adjusting community-acquired pneumonia hospital outcomes using automated databases.

OBJECTIVE: To describe the development and assessment of the Abbreviated Fine Severity Score (AFSS), a simplified version of the Pneumonia Severity Index (PSI) suitable for providing risk-adjusted reports to clinicians caring for patients hospitalized with community-acquired pneumonia. STUDY DESIGN: Retrospective cohort study. METHODS: We defined the AFSS based on data available in administrative and laboratory databases. We downloaded and linked these hospitalization and laboratory data from 2 cohorts (11,030 patients and 6147 patients) hospitalized with community-acquired pneumonia in all Kaiser Permanente Medical Care Program hospitals in northern California. We then assessed the relationship between the AFSS and mortality, length of stay, intensive care unit admission, and the use of assisted ventilation. Using logistic regression analysis, we assessed the performance of the AFSS and determined the area under the receiver operating characteristic curve (c statistic). Using a combination of manual and electronic medical record review, we compared the AFSS with the full PSI in 2 subsets of patients in northern California and Denver, Colorado, whose medical records were manually reviewed. RESULTS: The AFSS compares favorably with the PSI with respect to predicting mortality. It has good discrimination with respect to inhospital (c = 0.74) and 30-day (c = 0.75) mortality. It also correlates strongly with the PSI (r = 0.87 and r = 0.93 in the 2 medical record review subsets). CONCLUSIONS: The AFSS can be used to provide clinically relevant risk-adjusted outcomes reports to clinicians in an integrated healthcare delivery system. It is possible to apply risk-adjustment methods from research settings to operational ones.