Showing papers by "Richard S. Johannes published in 2013"
TL;DR: Patients with HO-CDI incur additional attributable mortality, LOS, and cost burden compared with patients with similar primary clinical condition, exposure risk, lead time of hospitalization, and baseline characteristics.
Abstract: Objective. To determine the attributable in-hospital mortality, length of stay (LOS), and cost of hospital-onset Clostridium difficile infection (HO-CDI).Design. Propensity score matching.Setting. Six Pennsylvania hospitals (2 academic centers, 1 community teaching facility, and 3 community nonteaching facilities) contributing data to a clinical research database.Patients. Adult inpatients between 2007 and 2008.Methods. We defined HO-CDI in adult inpatients as a positive C. difficile toxin assay result from a specimen collected more than 48 hours after admission and more than 8 weeks following any previous positive result. We developed an HO-CDI propensity model and matched cases with noncases by propensity score at a 1∶3 ratio. We further restricted matching within the same hospital, within the same principal disease group, and within a similar length of lead time from admission to onset of HO-CDI.Results. Among 77,257 discharges, 282 HO-CDI cases were identified. The propensity score–matched rate was 90...
78 citations
TL;DR: 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.
Abstract: 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.
32 citations
TL;DR: 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.
Abstract: 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.
6 citations