Patricia Kipnis

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 models 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.

Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system

BACKGROUND Patients with intensive care unit (ICU) transfers from hospital wards have higher mortality than those directly admitted from the emergency department. OBJECTIVE To describe the association between the timing of unplanned ICU transfers and hospital outcomes. DESIGN, SETTING, PATIENTS Evaluation of 6369 early (within 24 hours of hospital admission) unplanned ICU transfer cases and matched directly admitted ICU controls from an integrated healthcare system. Cohorts were matched by predicted mortality, age, gender, diagnosis, and admission characteristics. Hospital mortality of cases and controls were compared based on elapsed time and diagnosis. RESULTS More than 5% of patients admitted through the emergency department experienced an unplanned ICU transfer; the incidence and rates of transfers were highest within the first 24 hours of hospitalization. Multivariable matching produced 5839 (92%) case-control pairs. Median length of stay was higher among cases (5.0 days) than controls (4.1 days, P < 0.01); mortality was also higher among cases (11.6%) than controls (8.5%, P < 0.01). Patients with early unplanned transfers were at an increased risk of death (odds ratio, 1.44; 95% confidence interval, 1.26-1.64; P < 0.01); an increased risk of death was observed even among patients transferred within 8 hours of hospitalization. Hospital mortality differed based on admitting diagnosis categories. While it was higher among cases admitted for respiratory infections and gastrointestinal bleeding, it was not different for those with acute myocardial infarction, sepsis, and stroke. CONCLUSIONS Early unplanned ICU transfers-even within 8 hours of hospitalization-are associated with increased mortality; outcomes vary by elapsed time to transfer and admitting diagnosis.

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-R2 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.

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 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.

A Quantitative, Risk-Based Approach to the Management of Neonatal Early-Onset Sepsis.

Importance Current algorithms for management of neonatal early-onset sepsis (EOS) result in medical intervention for large numbers of uninfected infants. We developed multivariable prediction models for estimating the risk of EOS among late preterm and term infants based on objective data available at birth and the newborn’s clinical status. Objectives To examine the effect of neonatal EOS risk prediction models on sepsis evaluations and antibiotic use and assess their safety in a large integrated health care system. Design, Setting, and Participants The study cohort includes 204 485 infants born at 35 weeks’ gestation or later at a Kaiser Permanente Northern California hospital from January 1, 2010, through December 31, 2015. The study compared 3 periods when EOS management was based on (1) national recommended guidelines (baseline period [January 1, 2010, through November 31, 2012]), (2) multivariable estimates of sepsis risk at birth (learning period [December 1, 2012, through June 30, 2014]), and (3) the multivariable risk estimate combined with the infant’s clinical condition in the first 24 hours after birth (EOS calculator period [July 1, 2014, through December 31, 2015]). Main Outcomes and Measures The primary outcome was antibiotic administration in the first 24 hours. Secondary outcomes included blood culture use, antibiotic administration between 24 and 72 hours, clinical outcomes, and readmissions for EOS. Results The study cohort included 204 485 infants born at 35 weeks’ gestation or later: 95 343 in the baseline period (mean [SD] age, 39.4 [1.3] weeks; 46 651 male [51.0%]; 37 007 white, non-Hispanic [38.8%]), 52 881 in the learning period (mean [SD] age, 39.3 [1.3] weeks; 27 067 male [51.2%]; 20 175 white, non-Hispanic [38.2%]), and 56 261 in the EOS calculator period (mean [SD] age, 39.4 [1.3] weeks; 28 575 male [50.8%]; 20 484 white, non-Hispanic [36.4%]). In a comparison of the baseline period with the EOS calculator period, blood culture use decreased from 14.5% to 4.9% (adjusted difference, −7.7%; 95% CI, −13.1% to −2.4%). Empirical antibiotic administration in the first 24 hours decreased from 5.0% to 2.6% (adjusted difference, −1.8; 95% CI, −2.4% to −1.3%). No increase in antibiotic use occurred between 24 and 72 hours after birth; use decreased from 0.5% to 0.4% (adjusted difference, 0.0%; 95% CI, −0.1% to 0.2%). The incidence of culture-confirmed EOS was similar during the 3 periods (0.03% in the baseline period, 0.03% in the learning period, and 0.02% in the EOS calculator period). Readmissions for EOS (within 7 days of birth) were rare in all periods (5.2 per 100 000 births in the baseline period, 1.9 per 100 000 births in the learning period, and 5.3 per 100 000 births in the EOS calculator period) and did not differ statistically (P = .70). Incidence of adverse clinical outcomes, including need for inotropes, mechanical ventilation, meningitis, and death, was unchanged after introduction of the EOS calculator. Conclusions and Relevance Clinical care algorithms based on individual infant estimates of EOS risk derived from a multivariable risk prediction model reduced the proportion of newborns undergoing laboratory testing and receiving empirical antibiotic treatment without apparent adverse effects.

Hospital readmission and healthcare utilization following sepsis in community settings

BACKGROUND Sepsis, the most expensive cause of hospitalization in the United States, is associated with high morbidity and mortality. However, healthcare utilization patterns following sepsis are poorly understood. OBJECTIVE To identify patient-level factors that contribute to postsepsis mortality and healthcare utilization. DESIGN, SETTING, PATIENTS A retrospective study of sepsis patients drawn from 21 community-based hospitals in Kaiser Permanente Northern California in 2010. MEASUREMENTS We determined 1-year survival and use of outpatient and facility-based healthcare before and after sepsis and used logistic regression to identify the factors that contributed to early readmission (within 30 days) and high utilization (≥ 15% of living days spent in facility-based care). RESULTS Among 6344 sepsis patients, 5479 (86.4%) survived to hospital discharge. Mean age was 72 years with 28.9% of patients aged <65 years. Postsepsis survival was strongly modified by age; 1-year survival was 94.1% for <45 year olds and 54.4% for ≥ 85 year olds. A total of 978 (17.9%) patients were readmitted within 30 days; only a minority of all rehospitalizations were for infection. After sepsis, adjusted healthcare utilization increased nearly 3-fold compared with presepsis levels and was strongly modified by age. Patient factors including acute severity of illness, hospital length of stay, and the need for intensive care were associated with early readmission and high healthcare utilization; however, the dominant factors explaining variability-comorbid disease burden and high presepsis utilization-were present prior to sepsis admission. CONCLUSION Postsepsis survival and healthcare utilization were most strongly influenced by patient factors already present prior to sepsis hospitalization.

Length of Stay Predictions: Improvements Through the Use of Automated Laboratory and Comorbidity Variables

Background:Length of stay (LOS) is a common measure of hospital resource utilization. Most methods for risk-adjusting LOS are limited by the use of only administrative data. Recent studies suggest that adding automated clinical data to these models improves performance. Objectives:To evaluate the utility of adding “point of admission” automated laboratory and comorbidity measures—the Laboratory Acute Physiology Score (LAPS) and Comorbidity Point Score (COPS)—to risk adjustment models that are based on administrative data. Methods:We performed a retrospective analysis of 155,474 hospitalizations between 2002 and 2005 at 17 Northern California Kaiser Permanente hospitals. We evaluated the benefit of adding LAPS and COPS in linear regression models using full, trimmed, truncated, and log-transformed LOS, as well as in logistic and generalized linear models. Results:Mean age was 61 ± 19 years; females represented 55.2% of subjects. The mean LOS was 4.5 ± 7.7 days; median LOS was 2.8 days (interquartile range, 1.3–5.1 days). Adding LAPS and COPS to the linear regression model improved R2 by 29% from 0.113 to 0.146. Similar improvements with the inclusion of LAPS and COPS were observed in other regression models. Together, these variables were responsible for >50% of the predictive ability of logistic regression models that identified outliers with longer LOS. Conclusions:The inclusion of automated laboratory and comorbidity data improved LOS predictions in all models, underscoring the need for more widespread adoption of comprehensive electronic medical records.

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

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.

Persistent recurring wheezing in the fifth year of life after laboratory-confirmed, medically attended respiratory syncytial virus infection in infancy

BackgroundRespiratory syncytial virus (RSV) infection in infancy is associated with subsequent recurrent wheezing.MethodsA retrospective cohort study examined children born at ≥32 weeks gestation between 1996–2004. All children were enrolled in an integrated health care delivery system in Northern California and were followed through the fifth year of life. The primary endpoint was recurrent wheezing in the fifth year of life and its association with laboratory-confirmed, medically-attended RSV infection during the first year, prematurity, and supplemental oxygen during birth hospitalization. Other outcomes measured were recurrent wheezing quantified through outpatient visits, inpatient hospital stays, and asthma prescriptions.ResultsThe study sample included 72,602 children. The rate of recurrent wheezing in the second year was 5.6% and fell to 4.7% by the fifth year. Recurrent wheezing rates varied by risk status: the rate was 12.5% among infants with RSV hospitalization, 8% among infants 32–33 weeks gestation, and 18% in infants with bronchopulmonary dysplasia. In multivariate analyses, increasing severity of respiratory syncytial virus infection was significantly associated with recurrent wheezing in year 5; compared with children without RSV infection in infancy, children who only had an outpatient RSV encounter had an adjusted odds ratio of 1.38 (95% CI,1.03–1.85), while children with a prolonged RSV hospitalization had an adjusted odds ratio of 2.59 (95% CI, 1.49–4.50).ConclusionsLaboratory-confirmed, medically attended RSV infection, prematurity, and neonatal exposure to supplemental oxygen have independent associations with development of recurrent wheezing in the fifth year of life.