Development and validation of a mortality risk-adjustment model for patients hospitalized for exacerbations of chronic obstructive pulmonary disease.

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a leading cause of hospitalization and death. We sought to develop and validate a mortality risk-adjustment model to enhance hospital performance measurement and to support comparative effectiveness research. METHODS: Using a derivation cohort of 69,299 AECOPD admissions in 2005-2006 across 172 hospitals, we developed a logistic regression model with age, sex, laboratory results, vital signs, and secondary diagnosis-based comorbidities as covariates. We converted the model coefficients into a score system and validated it using 33,327 admissions from 2007. We used the c-statistic to assess model fit. RESULTS: In the derivation and validation cohorts, the median (interquartile range) age was 72 (range, 63-79) versus 71 (range, 62-79) years; 45.6% versus 45.9% were male; and in-hospital mortality rates were 3.2% versus 2.9%, respectively. The predicted probability of deaths for individuals ranged from 0.004 to 0.942 versus 0.001 to 0.933, respectively. The relative contribution of variables to the predictive ability of the derivation model was age (18.3%), admission laboratory results (39.9%), vital signs (14.7%), altered mental status (7.1%), and comorbidities (19.9%). The model c-statistic was 0.83 (95% CI: 0.82, 0.84) versus 0.84 (95% CI: 0.83, 0.85), respectively, with good calibration for both cohorts. CONCLUSIONS: A mortality prediction model combining clinical and administrative data that can be obtained from electronic health records demonstrated good discrimination among patients hospitalized for AECOPD. The addition of admission vital signs and laboratory results enhanced clinical validity and could be applied to future comparative effectiveness research and hospital profiling efforts.

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.

Posterior urethral valves masquerading as neuropathic bladder following sacrococcygeal teratoma resection.

Neuropathic bladder may be a co-associated morbidity in newborn babies following resection of a sacrococcygeal teratoma. We report a case of a male newborn showing features of incomplete urinary voiding requiring intermittent catheterisation after operation for bladder emptying. Videourodynamic assessment excluded neuropathic bladder and posterior urethral valves were demonstrated on micturating cystography. Urology outcomes have been excellent following curative valve ablation. This report highlights the crucial importance of being aware of the rare coexistence of lower urinary tract pathology in male babies with sacrococcygeal teratoma. Routine urodynamic assessment should be considered in all children following sacrococcygeal teratoma resection.

Adding Laboratory Data to Hospital Claims Data to Improve Risk Adjustment of Inpatient/30-Day Postdischarge Outcomes.

Numerical laboratory data at admission have been proposed for enhancement of inpatient predictive modeling from administrative claims. In this study, predictive models for inpatient/30-day postdischarge mortality and for risk-adjusted prolonged length of stay, as a surrogate for severe inpatient complications of care, were designed with administrative data only and with administrative data plus numerical laboratory variables. A comparison of resulting inpatient models for acute myocardial infarction, congestive heart failure, coronary artery bypass grafting, and percutaneous cardiac interventions demonstrated improved discrimination and calibration with administrative data plus laboratory values compared to administrative data only for both mortality and prolonged length of stay. Improved goodness of fit was most apparent in acute myocardial infarction and percutaneous cardiac intervention. The emergence of electronic medical records should make the addition of laboratory variables to administrative data an efficient and practical method to clinically enhance predictive modeling of inpatient outcomes of care.

Admission Laboratory Results to Enhance Prediction Models of Postdischarge Outcomes in Cardiac Care.

Predictive modeling for postdischarge outcomes of inpatient care has been suboptimal. This study evaluated whether admission numerical laboratory data added to administrative models from New York and Minnesota hospitals would enhance the prediction accuracy for 90-day postdischarge deaths without readmission (PD-90) and 90-day readmissions (RA-90) following inpatient care for cardiac patients. Risk-adjustment models for the prediction of PD-90 and RA-90 were designed for acute myocardial infarction, percutaneous cardiac intervention, coronary artery bypass grafting, and congestive heart failure. Models were derived from hospital claims data and were then enhanced with admission laboratory predictive results. Case-level discrimination, goodness of fit, and calibration were used to compare administrative models (ADM) and laboratory predictive models (LAB). LAB models for the prediction of PD-90 were modestly enhanced over ADM, but negligible benefit was seen for RA-90. A consistent predictor of PD-90 and RA-90 was prolonged length of stay outliers from the index hospitalization.