Biomedical subjects

Andrew A Kramer

Publications and source records attributed to Andrew A Kramer.

5 recordsLinked to original sources

PubMed2007

Transferring critically ill patients out of hospital improves the standardized mortality ratio: a simulation study.

BACKGROUND: Transferring critically ill patients to other acute care hospitals may artificially impact benchmarking measures. We sought to quantify the effect of out-of-hospital transfers on the standardized mortality ratio (SMR), an outcome-based measure of ICU performance. METHODS: We performed a cohort study and Monte Carlo simulation using data from 85 ICUs participating in the acute physiology and chronic health evaluation (APACHE) clinical information system from 2002 to 2003. The SMR (observed divided by expected hospital mortality) was calculated for each ICU using APACHE IV risk adjustment. A set number of patients was randomly assigned to be transferred out alive rather than experience their original outcome. The SMR was recalculated, and the mean simulated SMR was compared to the original. RESULTS: The mean (+/- SD) baseline SMR was 1.06 +/- 0.19. In the simulation, increasing the number of transfers by 2% and 6% over baseline decreased the SMR by 0.10 +/- 0.03 and 0.14 +/- 0.03, respectively. At a 2% increase, 27 ICUs had a decrease in SMR of > 0.10, and two ICUs had a decrease in SMR of > 0.20. Transferring only one additional patient per month was enough to create a bias of > 0.1 in 27 ICUs. CONCLUSIONS: Increasing the number of acute care transfers by a small amount can significantly bias the SMR, leading to incorrect inference about ICU quality. Sensitivity to the variation in hospital discharge practices greatly limits the use of the SMR as a quality measure.

APACHE↗

PubMed2006

Hospital volume and the outcomes of mechanical ventilation.

BACKGROUND: An increased volume of patients is associated with improved survival in numerous high-risk medical and surgical conditions. The relationship between the number of patients admitted (hospital volume) and outcome among patients with critical illnesses is unknown. METHODS: We analyzed data from 20,241 nonsurgical patients receiving mechanical ventilation at 37 acute care hospitals in the Acute Physiology and Chronic Health Evaluation clinical information system from 2002 through 2003. Multivariate analyses were performed to adjust for the severity of illness and other differences in the case mix. RESULTS: An increase in hospital volume was associated with improved survival among patients receiving mechanical ventilation in the intensive care unit (ICU) and in the hospital. Admission to a hospital in the highest quartile according to volume (i.e., >400 patients receiving mechanical ventilation per year) was associated with a 37 percent reduction in the adjusted odds of death in the ICU as compared with admission to hospitals in the lowest quartile (< or =150 patients receiving mechanical ventilation per year, P<0.001). In-hospital mortality was similarly reduced (adjusted odds ratio, 0.66; 95 percent confidence interval, 0.52 to 0.83; P<0.001). A typical patient in a hospital in a low-volume quartile would have an adjusted in-hospital mortality of 34.2 percent as compared with 25.5 percent in a hospital in a high-volume quartile. Among survivors, there were no significant trends in the length of stay in the ICU or the hospital. CONCLUSIONS: Mechanical ventilation of patients in a hospital with a high case volume is associated with reduced mortality. Further research is needed to determine the mechanism of the relationship between volume and outcome among patients with a critical illness.

APACHE↗

PubMed2006

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.

APACHE↗

PubMed2006

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 (< or =1.7 days) or long (> or =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.

APACHE↗

PubMed2005

Predictive mortality models are not like fine wine.

The authors of a recent paper have described an updated simplified acute physiology score (SAPS) II mortality model developed on patient data from 1998 to 1999. Hospital mortality models have a limited range of applicability. SAPS II, Acute Physiology, Age, and Chronic Health Evaluation (APACHE) III, and mortality probability model (MPM)-II, which were developed in the early 1990s, have shown a decline in predictive accuracy as the models age. The deterioration in accuracy is manifested by a decline in the models' calibration. In particular, mortality tends to get over predicted when older models are applied to more contemporary data, which in turn leads to 'grade inflation' when benchmarking intensive care unit (ICU) performance. Although the authors claim that their updated SAPS II can be used for benchmarking ICU performance, it seems likely that this model might already be out of calibration for patient data collected in 2005 and beyond. Thus, the updated SAPS II model may be interesting for historical purposes, but it is doubtful that it can be an accurate tool for benchmarking data from contemporary populations.

Benchmarking↗