Marla N. Gardner

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.

Neonatal Sepsis Workups in Infants ≥2000 Grams at Birth: A Population-Based Study

Background. Few data are available on the outcome of neonatal sepsis evaluations in an era when intrapartum antibiotic therapy is common. Methods. We identified all newborns weighing ≥2000 g at birth who were ever evaluated for suspected bacterial infection at 6 Kaiser Permanente hospitals between October 1995 and November 1996, reviewed their records and laboratory data, and tracked them to 1 week after discharge. We analyzed the relationship between key predictors and the presence of neonatal bacterial infection. Results. Among 18 299 newborns ≥2000 g without major congenital anomalies, 2785 (15.2%) were evaluated for sepsis with a complete blood count and/or blood culture. A total of 62 (2.2%) met criteria for proven, probable, or possible bacterial infection: 22 (.8%) had positive cultures and 40 (1.4%) had clinical evidence of bacterial infection. We tracked all but 10 infants (.4%) to 7 days postdischarge. There were 67 rehospitalizations (2.4%; 2 for group B streptococcus bacteremia). Among 1568 infants who did not receive intrapartum antibiotics, initial asymptomatic status was associated with decreased risk of infection (adjusted odds ratio [AOR]: .26; 95% confidence interval [CI]: .11–.63), while chorioamnionitis (AOR: 2.40; 95% CI: 1.15–5.00), low absolute neutrophil count (AOR: 2.84; 95% CI: 1.50–5.38), and meconium-stained amniotic fluid (AOR: 2.23; 95% CI: 1.18–4.21) were associated with increased risk. Results were similar among 1217 infants who were treated, except that maternal chorioamnionitis was not significantly associated with neonatal infection. Conclusions. The risk of bacterial infection in asymptomatic newborns is low. Evidence-based observation and treatment protocols could be defined based on a limited set of predictors: maternal fever, chorioamnionitis, initial neonatal examination, and absolute neutrophil count. Many missed opportunities for treating mothers and infants exist.

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.

Neonatal Assisted Ventilation: Predictors, Frequency, and Duration in a Mature Managed Care Organization

Objectives. Reference data are lacking on the frequency and duration of assisted ventilation in neonates. This information is essential for determining resource needs and planning clinical trials. As mortality becomes uncommon, ventilator utilization is increasingly used as a measure for assessing therapeutic effect and quality of care in intensive care medicine. Valid comparisons require adjustments for differences in a patients baseline risk for assisted ventilation and prolonged ventilator support. The aims of this study were to determine the frequency and length of ventilation (LOV) in preterm and term infants and to develop models for predicting the need for assisted ventilation and length of ventilator support. Methods. We performed a retrospective, population-based cohort study of 77 576 inborn live births at 6 Northern California hospitals with level 3 intensive care nurseries in a group-model managed care organization. The gestational age-specific frequency and duration of assisted ventilation among surviving infants was determined. Multivariable regression was performed to determine predictors for assisted ventilation and LOV. Results. Of 77 576 inborn live births in the study, 11 199 required admission to the neonatal intensive care unit and of these, 1928 survivors required ventilator support. The proportion of infants requiring assisted ventilation and the median LOV decreased markedly with increasing gestational age. In addition to gestational age, admission illness severity, 5-minute Apgar scores, presence of anomalies, male sex, and white race were important predictors for the need for assisted ventilation. The ability of the models to predict need for ventilation was high, and significantly better than birth weight alone with an area under the receiver operating characteristic curve of .90 versus .70 for preterm infants, and .88 versus .50 for term infants. For preterm infants, gestational age, admission illness severity, oxygenation index, anomalies, and small-for-gestational age status were significant predictors for LOV, accounting for 60% of the variance in the length of assisted ventilation. For term infants, oxygenation index and anomalies were significant predictors but only accounted for 29% of the variance. Conclusions. Considerable variation exists in the utilization of ventilator support among infants of closely related gestational age. In addition, a number of medical risk factors influence the need for, and length of, assisted ventilation. These models explain much of the variance in LOV among preterm infants but explain substantially less among term infants. neonatal intensive care, assisted ventilation, Score for Neonatal Acute Physiology, resource consumption, prematurity.

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.

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.

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

BackgroundPrior 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.MethodsFrom 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.ResultsMost 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%).ConclusionsMany 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.

Decreased red blood cell use and mortality in hospitalized patients.

Blood conservation strategies effectively decrease red blood cell (RBC) use in specific patient groups.1-3 However, the impact of RBC transfusion reduction on mortality in a diverse inpatient population remains poorly described. We detail the impact of declining RBC use on 30-day mortality within Kaiser Permanente Northern California (KPNC), an integrated health care delivery system serving 3.5 million members at 21 hospitals.

Maternal and Infant Risk Factors for Neonatal Sepsis: A Comparison between an Inner-City County Hospital and a Health Maintenance Organization

Maternal and Infant Risk Factors for Neonatal Sepsis: A Comparison between an Inner-City County Hospital and a Health Maintenance Organization