Douglas S. McNair

Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients.

Objective:To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models. Design:Observational cohort study. Setting:A total of 104 intensive care units (ICUs) in 45 U.S. hospitals. Patients:A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data. Interventions:None. Measurements and Main Results:We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to those using APACHE III versions developed 7 and 14 yrs previously. There was little change in discrimination, but aggregate mortality was systematically overestimated as model age increased. When examined across disease, predictive accuracy was maintained for some diagnoses but for others seemed to reflect changes in practice or therapy. Conclusions:APACHE IV predictions of hospital mortality have good discrimination and calibration and should be useful for benchmarking performance in U.S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated.

Intensive care unit length of stay: Benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV*

Objective:To revise and update the Acute Physiology and Chronic Health Evaluation (APACHE) model for predicting intensive care unit (ICU) length of stay. Design:Observational cohort study. Setting:One hundred and four ICUs in 45 U.S. hospitals. Patients:Patients included 131,618 consecutive ICU admissions during 2002 and 2003, of which 116,209 met inclusion criteria. Interventions:None. Measurements and Main Results:The APACHE IV model for predicting ICU length of stay was developed using ICU day 1 patient data and a multivariate linear regression procedure to estimate the precise ICU stay for randomly selected patients who comprised 60% of the database. New variables were added to the previous APACHE III model, and advanced statistical modeling techniques were used. Accuracy was assessed by comparing mean observed and mean predicted ICU stay for the excluded 40% of patients. Aggregate mean observed ICU stay was 3.86 days and mean predicted 3.78 days (p < .001), a difference of 1.9 hrs. For 108 (93%) of 116 diagnoses, there was no significant difference between mean observed and mean predicted ICU stay. The model accounted for 21.5% of the variation in ICU stay across individual patients and 62% across ICUs. Correspondence between mean observed and mean predicted length of stay was reduced for patients with a short (≤1.7 days) or long (≥9.4 days) ICU stay and a low (<20%) or high (>80%) risk of death on ICU day 1. Conclusions:The APACHE IV model provides clinically useful ICU length of stay predictions for critically ill patient groups, but its accuracy and utility are limited for individual patients. APACHE IV benchmarks for ICU stay are useful for assessing the efficiency of unit throughput and support examination of structural, managerial, and patient factors that affect ICU stay.

Detection of Prion Protein in Urine-Derived Injectable Fertility Products by a Targeted Proteomic Approach

Background Iatrogenic transmission of human prion disease can occur through medical or surgical procedures, including injection of hormones such as gonadotropins extracted from cadaver pituitaries. Annually, more than 300,000 women in the United States and Canada are prescribed urine-derived gonadotropins for infertility. Although menopausal urine donors are screened for symptomatic neurological disease, incubation of Creutzfeldt-Jakob disease (CJD) is impossible to exclude by non-invasive testing. Risk of carrier status of variant CJD (vCJD), a disease associated with decades-long peripheral incubation, is estimated to be on the order of 100 per million population in the United Kingdom. Studies showing infectious prions in the urine of experimental animals with and without renal disease suggest that prions could be present in asymptomatic urine donors. Several human fertility products are derived from donated urine; recently prion protein has been detected in preparations of human menopausal gonadotropin (hMG). Methodology/Principal Findings Using a classical proteomic approach, 33 and 34 non-gonadotropin proteins were identified in urinary human chorionic gonadotropin (u-hCG) and highly-purified urinary human menopausal gonadotropin (hMG-HP) products, respectively. Prion protein was identified as a major contaminant in u-hCG preparations for the first time. An advanced prion protein targeted proteomic approach was subsequently used to conduct a survey of gonadotropin products; this approach detected human prion protein peptides in urine-derived injectable fertility products containing hCG, hMG and hMG-HP, but not in recombinant products. Conclusions/Significance The presence of protease-sensitive prion protein in urinary-derived injectable fertility products containing hCG, hMG, and hMG-HP suggests that prions may co-purify in these products. Intramuscular injection is a relatively efficient route of transmission of human prion disease, and young women exposed to prions can be expected to survive an incubation period associated with a minimal inoculum. The risks of urine-derived fertility products could now outweigh their benefits, particularly considering the availability of recombinant products.

Associations between Anticholinergic Burden and Adverse Health Outcomes in Parkinson Disease

Background Elderly adults should avoid medications with anticholinergic effects since they may increase the risk of adverse events, including falls, delirium, and cognitive impairment. However, data on anticholinergic burden are limited in subpopulations, such as individuals with Parkinson disease (PD). The objective of this study was to determine whether anticholinergic burden was associated with adverse outcomes in a PD inpatient population. Methods Using the Cerner Health Facts® database, we retrospectively examined anticholinergic medication use, diagnoses, and hospital revisits within a cohort of 16,302 PD inpatients admitted to a Cerner hospital between 2000 and 2011. Anticholinergic burden was computed using the Anticholinergic Risk Scale (ARS). Primary outcomes were associations between ARS score and diagnosis of fracture and delirium. Secondary outcomes included associations between ARS score and 30-day hospital revisits. Results Many individuals (57.8%) were prescribed non-PD medications with moderate to very strong anticholinergic potential. Individuals with the greatest ARS score (≥4) were more likely to be diagnosed with fractures (adjusted odds ratio (AOR): 1.56, 95% CI: 1.29–1.88) and delirium (AOR: 1.61, 95% CI: 1.08–2.40) relative to those with no anticholinergic burden. Similarly, inpatients with the greatest ARS score were more likely to visit the emergency department (adjusted hazard ratio (AHR): 1.32, 95% CI: 1.10–1.58) and be readmitted (AHR: 1.16, 95% CI: 1.01–1.33) within 30-days of discharge. Conclusions We found a positive association between increased anticholinergic burden and adverse outcomes among individuals with PD. Additional pharmacovigilance studies are needed to better understand risks associated with anticholinergic medication use in PD.

Towards large-scale sharing of electronic health records of cancer patients

The rising cost of healthcare is one of the major concerns faced by the nation. One way to lower healthcare costs and provide better quality care to patients is through the effective use of Information Technology (IT). Data sharing and collaboration and large-scale management of healthcare data have been identified as important IT challenges to advance the nations healthcare system. In this paper, we present an overview of the software framework called CDN (Collaborative Data Network) that we are developing for large-scale sharing of electronic health records (EHR). In this on-going effort, we focus on sharing EHRs of cancer patients. Cancer is the second leading cause of deaths in the US. CDN is based on the synergistic combination of peer-to-peer technology and the extensible markup language XML and XQuery. We outline the key challenges that arise when sharing evolving, heterogeneous repositories and processing queries across multiple repositories. We present the novel architecture of CDN to overcome these challenges and discuss our plan for implementation, evaluation, and deployment.

External validation and comparison of two variants of the Elixhauser comorbidity measures for all-cause mortality

Assessing prevalent comorbidities is a common approach in health research for identifying clinical differences between individuals. The objective of this study was to validate and compare the predictive performance of two variants of the Elixhauser comorbidity measures (ECM) for inhospital mortality at index and at 1-year in the Cerner Health Facts® (HF) U.S. database. We estimated the prevalence of select comorbidities for individuals 18 to 89 years of age who received care at Cerner contributing health facilities between 2002 and 2011 using the AHRQ (version 3.7) and the Quan Enhanced ICD-9-CM ECMs. External validation of the ECMs was assessed with measures of discrimination [c-statistics], calibration [Hosmer–Lemeshow goodness-of-fit test, Brier Score, calibration curves], added predictive ability [Net Reclassification Improvement], and overall model performance [R2]. Of 3,273,298 patients with a mean age of 43.9 years and a female composition of 53.8%, 1.0% died during their index encounter and 1.5% were deceased at 1-year. Calibration measures were equivalent between the two ECMs. Calibration performance was acceptable when predicting inhospital mortality at index, although recalibration is recommended for predicting inhospital mortality at 1 year. Discrimination was marginally better with the Quan ECM compared the AHRQ ECM when predicting inhospital mortality at index (cQuan = 0.887, 95% CI: 0.885–0.889 vs. cAHRQ = 0.880, 95% CI: 0.878–0.882; p < .0001) and at 1-year (cQuan = 0.884, 95% CI: 0.883–0.886 vs. cAHRQ = 0.880, 95% CI: 0.878–0.881, p < .0001). Both the Quan and the AHRQ ECMs demonstrated excellent discrimination for inhospital mortality of all-causes in Cerner Health Facts®, a HIPAA compliant observational research and privacy-protected data warehouse. While differences in discrimination performance between the ECMs were statistically significant, they are not likely clinically meaningful.

Optimal look back period and summary method for Elixhauser comorbidity measures in a US population-based electronic health record database

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