Using enriched observational data to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients.

BACKGROUND: Growth and development in early childhood are associated with rapid physiological changes. We sought to develop and validate age-specific mortality risk adjustment models for hospitalized pediatric patients using objective physiological variables on admission in addition to administrative variables. METHODS: Age-specific laboratory and vital sign variables were crafted for neonates (up to 30 d old), infants/toddlers (1-23 mo), and children (2-17 y). We fit 3 logistic regression models, 1 for each age group, using a derivation cohort comprising admissions from 2000-2001 in 215 hospitals. We validated the models with a separate validation cohort comprising admissions from 2002-2007 in 62 hospitals. We used the c statistic to assess model fit. RESULTS: The derivation cohort comprised 93,011 neonates (0.55% mortality), 46,152 infants/toddlers (0.37% mortality), and 104,010 children (0.40% mortality). The corresponding numbers of admissions (mortality rates) for the validation cohort were 162,131 (0.50%), 33,818 (0.09%), and 73,362 (0.20%), respectively. The c statistics for the 3 models were 0.94, 0.91, and 0.92, respectively, for the derivation cohort and 0.91, 0.86, and 0.93, respectively, for the validation cohort. The relative contributions of physiological versus administrative variables to the model fit were 52% versus 48% (neonates), 93% versus 7% (infants/toddlers), and 82% versus 18% (children). CONCLUSIONS: The thresholds for physiological determinants varied by age. Common physiological variables assessed on admission contributed significantly to predicting mortality for hospitalized pediatric patients. These models may have practical utility in risk adjustment for pediatric outcomes and comparative effectiveness research when physiological data are captured through the electronic medical record.

Predicting the need for mechanical ventilation in acute exacerbations of chronic obstructive pulmonary disease: comparing the CURB-65 and BAP-65 scores.

PURPOSE: Clinicians lack a validated tool for risk stratification for need for mechanical ventilation (MV) in acute exacerbations of chronic obstructive pulmonary disease (AECOPD). We sought to compare 2 risk scores, BAP-65 and CURB-65, at predicting a need for MV in AECOPDs. MATERIALS AND METHODS: We analyzed 34,478 AECOPD admissions to 195 US hospitals (2007). We compared the rates of MV at admission and at any point during hospitalization based on the respective BAP-65 and CURB-65 scores. We compared the accuracy of the 2 scores via the area under the receiver operating characteristic curves. RESULTS: The overall MV rate at admission was 7.9%, and the rate of MV any time equaled 9.3%. Use of MV increased with escalating BAP-65 and CURB-65 scores. The area under the receiver operating characteristic curve for BAP-65 was higher than that for CURB-65 for both early MV, 0.81 (95% confidence interval [CI], 0.80-0.82) vs 0.76 (95% CI, 0.75-0.77), P < .0001, and MV any time, 0.78 (95% CI, 0.77-0.79) vs 0.74 (95% CI, 0.73-0.75), P < .0001. CONCLUSIONS: BAP-65 identifies patients with AECOPD at high risk for need of MV more accurately than does CURB-65. BAP-65 may represent a useful tool for initial MV risk stratification in AECOPD.

Developing and validating a risk score for lower-extremity amputation in patients hospitalized for a diabetic foot infection.

OBJECTIVE: Diabetic foot infection is the predominant predisposing factor to nontraumatic lower-extremity amputation (LEA), but few studies have investigated which specific risk factors are most associated with LEA. We sought to develop and validate a risk score to aid in the early identification of patients hospitalized for diabetic foot infection who are at highest risk of LEA. RESEARCH DESIGN AND METHODS: Using a large, clinical research database (CareFusion), we identified patients hospitalized at 97 hospitals in the U.S. between 2003 and 2007 for culture-documented diabetic foot infection. Candidate risk factors for LEA included demographic data, clinical presentation, chronic diseases, and recent previous hospitalization. We fit a logistic regression model using 75% of the population and converted the model coefficients to a numeric risk score. We then validated the score using the remaining 25% of patients. RESULTS: Among 3,018 eligible patients, 21.4% underwent an LEA. The risk factors most highly associated with LEA (P < 0.0001) were surgical site infection, vasculopathy, previous LEA, and a white blood cell count >11,000 per mm(3). The model showed good discrimination (c-statistic 0.76) and excellent calibration (Hosmer-Lemeshow, P = 0.63). The risk score stratified patients into five groups, demonstrating a graded relation to LEA risk (P < 0.0001). The LEA rates (derivation and validation cohorts) were 0% for patients with a score of 0 and ~50% for those with a score of ≥21. CONCLUSIONS: Using a large, hospitalized population, we developed and validated a risk score that seems to accurately stratify the risk of LEA among patients hospitalized for a diabetic foot infection. This score may help to identify high-risk patients upon admission.

Using electronic health information to risk-stratify rates of Clostridium difficile infection in US hospitals.

BACKGROUND: Expanding hospitalized patients' risk stratification for Clostridium difficile infection (CDI) is important for improving patient safety. We applied definitions for hospital-onset (HO) and community-onset (CO) CDI to electronic data from 85 hospitals between January 2007 and June 2008 to identify factors associated with higher HO CDI rates. METHODS: Nonrecurrent CDI cases were identified among adult (≥ 18-year-old) inpatients by a positive C. difficile toxin assay result more than 8 weeks after any previous positive result. Case categories included HO, CO-hospital associated (CO-HA), CO-indeterminate hospital association (CO-IN), and CO-non-hospital associated (CO-NHA). C. difficile testing intensity (CDTI) was defined as the total number of C. difficile tests performed, normalized to the number of patients with at least 1 C. difficile toxin test recorded. We calculated both the incidence density and the prevalence of CDI where appropriate. We fitted a multivariable Poisson model to identify factors associated with higher HO CDI rates. RESULTS: Among 1,351,156 unique patients with 2,022,213 admissions, 9,803 cases of CDI were identified; of these, 50.6% were HO, 17.4% were CO-HA, 9.0% were CO-IN, and 23.0% were CO-NHA. The incidence density of HO was 6.3 per 10,000 patient-days. The prevalence of CO CDI on admission was, per 10,000 admissions, 8.4 for CO-HA, 4.4 for CO-IN, and 11.1 for CO-NHA. Factors associated (P < .0001) with higher HO CDI rates included older age, higher CO-NHA prevalence on admission, and increased CDTI. CONCLUSION: Electronic health information can be leveraged to risk-stratify HO CDI rates by patient age and CO-NHA prevalence on admission. Hospitals should optimize diagnostic testing to improve patient care and measured CDI rates.

Development and validation of a disease-specific risk adjustment system using automated clinical data.

OBJECTIVE: To develop and validate a disease-specific automated inpatient mortality risk adjustment system primarily using computerized numerical laboratory data and supplementing them with administrative data. To assess the values of additional manually abstracted data. METHODS: Using 1,271,663 discharges in 2000-2001, we derived 39 disease-specific automated clinical models with demographics, laboratory findings on admission, ICD-9 principal diagnosis subgroups, and secondary diagnosis-based chronic conditions. We then added manually abstracted clinical data to the automated clinical models (manual clinical models). We compared model discrimination, calibration, and relative contribution of each group of variables. We validated these 39 models using 1,178,561 discharges in 2004-2005. RESULTS: The overall mortality was 4.6 percent (n = 58,300) and 4.0 percent (n = 47,279) for derivation and validation cohorts, respectively. Common mortality predictors included age, albumin, blood urea nitrogen or creatinine, arterial pH, white blood counts, glucose, sodium, hemoglobin, and metastatic cancer. The average c-statistic for the automated clinical models was 0.83. Adding manually abstracted variables increased the average c-statistic to 0.85 with better calibration. Laboratory results displayed the highest relative contribution in predicting mortality. CONCLUSIONS: A small number of numerical laboratory results and administrative data provided excellent risk adjustment for inpatient mortality for a wide range of clinical conditions.

Surgical site infections: Causative pathogens and associated outcomes.

BACKGROUND: Surgical site infections (SSIs) are associated with substantial morbidity, mortality, and cost. Few studies have examined the causative pathogens, mortality, and economic burden among patients rehospitalized for SSIs. METHODS: From 2003 to 2007, 8302 patients were readmitted to 97 US hospitals with a culture-confirmed SSI. We analyzed the causative pathogens and their associations with in-hospital mortality, length of stay (LOS), and cost. RESULTS: The proportion of methicillin-resistant Staphylococcus aureus (MRSA) significantly increased among culture-positive SSI patients during the study period (16.1% to 20.6%, respectively, P < .0001). MRSA (compared with other) infections had higher raw mortality rates (1.4% vs 0.8%, respectively, P=.03), longer LOS (median, 6 vs 5 days, respectively, P < .0001), and higher hospital costs ($7036 vs $6134, respectively, P < .0001). The MRSA infection risk-adjusted attributable LOS increase was 0.93 days (95% confidence interval [CI]: 0.65-1.21; P < .0001), and cost increase was $1157 (95% CI: $641-$1644; P < .0001). Other significant independent risk factors increasing cost and LOS included illness severity, transfer from another health care facility, previous admission (<30 days), and other polymicrobial infections (P < .05). CONCLUSION: SSIs caused by MRSA increased significantly and were independently associated with economic burden. Admission illness severity, transfer from another health care setting, and recent hospitalization were associated with higher mortality, increased LOS, and cost.