Risk-Adjusting Mortality in the Nationwide Veterans Affairs Healthcare System

BACKGROUND

The US Veterans Affairs (VA) healthcare system began reporting risk-adjusted mortality for intensive care (ICU) admissions in 2005. However, while the VA's mortality model has been updated and adapted for risk-adjustment of all inpatient hospitalizations, recent model performance has not been published. We sought to assess the current performance of VA's 4 standardized mortality models: acute care 30-day mortality (acute care SMR-30); ICU 30-day mortality (ICU SMR-30); acute care in-hospital mortality (acute care SMR); and ICU in-hospital mortality (ICU SMR).

METHODS

Retrospective cohort study with split derivation and validation samples. Standardized mortality models were fit using derivation data, with coefficients applied to the validation sample. Nationwide VA hospitalizations that met model inclusion criteria during fiscal years 2017-2018(derivation) and 2019 (validation) were included. Model performance was evaluated using c-statistics to assess discrimination and comparison of observed versus predicted deaths to assess calibration.

RESULTS

Among 1,143,351 hospitalizations eligible for the acute care SMR-30 during 2017-2019, in-hospital mortality was 1.8%, and 30-day mortality was 4.3%. C-statistics for the SMR models in validation data were 0.870 (acute care SMR-30); 0.864 (ICU SMR-30); 0.914 (acute care SMR); and 0.887 (ICU SMR). There were 16,036 deaths (4.29% mortality) in the SMR-30 validation cohort versus 17,458 predicted deaths (4.67%), reflecting 0.38% over-prediction. Across deciles of predicted risk, the absolute difference in observed versus predicted percent mortality was a mean of 0.38%, with a maximum error of 1.81% seen in the highest-risk decile.

CONCLUSIONS AND RELEVANCE

The VA's SMR models, which incorporate patient physiology on presentation, are highly predictive and demonstrate good calibration both overall and across risk deciles. The current SMR models perform similarly to the initial ICU SMR model, indicating appropriate adaption and re-calibration.

Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure*

OBJECTIVE

To quantify the variability in risk-adjusted mortality and length of stay of Veterans Affairs intensive care units using a computer-based severity of illness measure.

DESIGN

Retrospective cohort study.

SETTING

A stratified random sample of 34 intensive care units in 17 Veterans Affairs hospitals.

PARTICIPANTS

A consecutive sample of 29,377 first intensive care unit admissions from February 1996 through July 1997.

INTERVENTIONS

Standardized mortality ratio (observed/expected deaths) and observed minus expected length of stay (OMELOS) with 95% confidence intervals were estimated for each unit using a hierarchical logistic (standardized mortality ratio) or linear (OMELOS) regression model with Markov Chain Monte Carlo simulation. We adjusted for patient characteristics including age, admission diagnosis, comorbid disease, physiology at admission (from laboratory data), and transfer status.

MEASUREMENTS AND MAIN RESULTS

Mortality across the intensive care units for the 12,088 surgical and 17,289 medical cases averaged 11% (range, 2-30%). Length of stay in the intensive care units averaged 4.0 days (range, mean unit length of stay 3.0-5.9). Standardized mortality ratio of the intensive care units varied from 0.62 to 1.27; the standardized mortality ratio and 95% confidence interval were <1 for four intensive care units and >1.0 for seven intensive care units. OMELOS of the intensive care units ranged from -0.89 to 1.34 days. In a random slope hierarchical model, variation in standardized mortality ratio among intensive care units was similar across the range of severity, whereas variation in length of stay increased with severity. Standardized mortality ratio was not associated with OMELOS (Pearson's r = .13).

CONCLUSIONS

We identified intensive care units whose indicators for mortality and length of stay differ substantially using a conservative statistical approach with a severity adjustment model based on data available in computerized clinical databases. Computerized risk adjustment employing routinely available data may facilitate research on the utility of intensive care unit profiling and analysis of natural experiments to understand process and outcome links and quality efforts.

Parathyroid hormone suppression by intravenous calcitriol: role of phosphate, calcium, race and diabetes

BACKGROUND

Parathyroid hormone (PTH) suppression in patients with end-stage renal disease (ESRD) undergoing maintenance hemodialysis is achieved largely by the use of intravenous calcitriol. Aspects of the utility and efficacy of this therapy remain controversial. It is debated whether oral versus intravenous therapy is more effective. Most existing studies examine the effect of calcitriol in isolation, without adjusting for other factors that might influence PTH levels. Thus, the simultaneous role of factors such as dosing, control of serum calcium and phosphorus, and demographic variables such as age, sex, race, and duration of ESRD is not well understood.

METHODS

We examined the relationship between the administration of calcitriol and PTH suppression in a cohort of hemodialysis patients at a large urban dialysis facility over a period of 30 months. Hemodialysis patients (n = 155) who received at least 3 months of treatment in this facility were included.

RESULTS

Using a time sensitive multiple linear regression modeling technique, we found that second and subsequent PTH levels were positively correlated with black race (P < 0.0001) and serum phosphate (P < 0.03) and strongly negatively correlated with serum calcium (P< 0.0001) and diabetes (P< 0.0039). Drug dose (in micrograms per kilogram per month) was weakly negatively correlated (P < 0.04). Unlike previous studies, we adjusted for the simultaneous confounding influence of demographic and laboratory variables, as well as for drug dose normalized for body weight.

CONCLUSIONS

This analysis suggests that calcitriol therapy in hemodialysis patients is adversely affected by higher phosphate levels and needs to account for such patient characteristics as race and diabetes and such laboratory variables as calcium and phosphate control. Finally, as has been recently suggested by others, the patient's race may require us to aim for different PTH target levels with therapy.