Valve Replacement Surgery Complicated by Acute Renal Failure-Predictors of Early Mortality

The Assessment of Risk in Cardiothoracic Intensive Care (ARCtIC): prediction of hospital mortality after admission to cardiothoracic critical care

The models used to predict outcome after adult general critical care may not be applicable to cardiothoracic critical care. Therefore, we analysed data from the Case Mix Programme to identify variables associated with hospital mortality after admission to cardiothoracic critical care units and to develop a risk-prediction model. We derived predictive models for hospital mortality from variables measured in 17,002 patients within 24 h of admission to five cardiothoracic critical care units. The final model included 10 variables: creatinine; white blood count; mean arterial blood pressure; functional dependency; platelet count; arterial pH; age; Glasgow Coma Score; arterial lactate; and route of admission. We included additional interaction terms between creatinine, lactate, platelet count and cardiac surgery as the admitting diagnosis. We validated this model against 10,238 other admissions, for which the c index (95% CI) was 0.904 (0.89-0.92) and the Brier score was 0.055, while the slope and intercept of the calibration plot were 0.961 and -0.183, respectively. The discrimination and calibration of our model suggest that it might be used to predict hospital mortality after admission to cardiothoracic critical care units.

Ensuring comparisons of health-care providers are fair: development and validation of risk prediction models for critically ill patients

Background

National clinical audit has a key role in ensuring quality in health care. When comparing outcomes between providers, it is essential to take the differing case mix of patients into account to make fair comparisons. Accurate risk prediction models are therefore required.

Objectives

To improve risk prediction models to underpin quality improvement programmes for the critically ill (i.e. patients receiving general or specialist adult critical care or experiencing an in-hospital cardiac arrest).

Design

Risk modelling study nested within prospective data collection.

Setting

Adult (general/specialist) critical care units and acute hospitals in the UK.

Participants

Patients admitted to an adult critical care unit and patients experiencing an in-hospital cardiac arrest attended by the hospital-based resuscitation team.

Interventions

None.

Main outcome measures

Acute hospital mortality (adult critical care); return of spontaneous circulation (ROSC) greater than 20 minutes and survival to hospital discharge (in-hospital cardiac arrest).

Data sources

The Case Mix Programme (adult critical care) and National Cardiac Arrest Audit (in-hospital cardiac arrest).

Results

The current Intensive Care National Audit & Research Centre (ICNARC) model was externally validated using data for 29,626 admissions to critical care units in Scotland (2007–9) and outperformed the Acute Physiology And Chronic Health Evaluation (APACHE) II model in terms of discrimination (c-index 0.848 vs. 0.806) and accuracy (Brier score 0.140 vs. 0.157). A risk prediction model for cardiothoracic critical care was developed using data from 17,002 admissions to five units (2010–12) and validated using data from 10,238 admissions to six units (2013–14). The model included prior location/urgency, blood lactate concentration, Glasgow Coma Scale (GCS) score, age, pH, platelet count, dependency, mean arterial pressure, white blood cell (WBC) count, creatinine level, admission following cardiac surgery and interaction terms, and it had excellent discrimination (c-index 0.904) and accuracy (Brier score 0.055). A risk prediction model for admissions to all (general/specialist) adult critical care units was developed using data from 155,239 admissions to 232 units (2012) and validated using data from 90,017 admissions to 216 units (2013). The model included systolic blood pressure, temperature, heart rate, respiratory rate, partial pressure of oxygen in arterial blood/fraction of inspired oxygen, pH, partial pressure of carbon dioxide in arterial blood, blood lactate concentration, urine output, creatinine level, urea level, sodium level, WBC count, platelet count, GCS score, age, dependency, past medical history, cardiopulmonary resuscitation, prior location/urgency, reason for admission and interaction terms, and it outperformed the current ICNARC model for discrimination and accuracy overall (c-index 0.885 vs. 0.869; Brier score 0.108 vs. 0.115) and across unit types. Risk prediction models for in-hospital cardiac arrest were developed using data from 14,688 arrests in 122 hospitals (2011–12) and validated using data from 7791 arrests in 143 hospitals (2012–13). The models included age, sex (for ROSC > 20 minutes), prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between rhythm and location. Discrimination for hospital survival exceeded that for ROSC > 20 minutes (c-index 0.811 vs. 0.720).

Limitations

The risk prediction models developed were limited by the data available within the current national clinical audit data sets.

Conclusions

We have developed and validated risk prediction models for cardiothoracic and adult (general and specialist) critical care units and for in-hospital cardiac arrest.

Future work

Future development should include linkage with other routinely collected data to enhance available predictors and outcomes.

Funding details

The National Institute for Health Research Health Services and Delivery Research programme.

Selection of artificial valve for the patients on hemodialysis

The selection of artificial valve for the hemodialysis patients is still controversial. Initially ACC/AHA guideline recommended using mechanical valve because of concern on the durability of bioprosthesis after replacement on the dialysis patients; however, revised guideline deleted that recommendation. Although many reports recognized rapid deterioration of bioprosthesis mainly due to calcification after valve replacement, there is no difference on survival between both types of valve. The recently conducted meta-analysis reported the same conclusions. Actually the long-term survival of the dialysis patients is poorer than that of non-dialysis people; however, it differs according to the etiology of renal failure. For example, the long-term survival of the non-diabetic patients seems longer than that of diabetic patients requiring longer durability of artificial valve. According to ACC/AHA guideline and the meta-analysis, surgeon should not hesitate to use bioprosthetic valve; however, surgeon should consider stratification of the dialysis patients by prediction for the long survival of each patient.

Trends in cardiac surgery-associated acute renal failure in the United States: a disproportionate increase after heart transplantation

Acute renal failure (ARF) is common after cardiac surgery and more frequent after complex cardiac surgery. While the incidence of ARF is increasing after coronary artery bypass graft (CABG) surgery, trends in other forms of cardiac surgery remain unclear. We investigated the trend of ARF in various cardiac procedures and compared patterns using CABG surgery as a reference group. The study population consisted of discharges from the Nationwide Inpatient Sample from 1988 to 2003, grouped according to surgery as: CABG, CABG with mitral valve, CABG with other valve, valve alone, and heart transplant. Standard diagnostic codes were used to identify ARF among discharges. Multivariable regression was used to determine trends in ARF among various procedures with CABG as a reference group. The incidence of ARF increased in all five groups (p < 0.001) over the 16-year period. The ARF incidence was highest in the heart transplant group (17%). Compared to the CABG population, patients following heart transplantation developed ARF at higher rates during the study period. In contrast, while ARF increased over time in other groups, the rates of rise were slower than in CABG patients. Among heart surgery procedures, ARF incidence is highest in heart transplantation. The incidence of ARF is also increasing at a faster rate in this group of patients in contrast to other procedure groups when compared to CABG surgery. The disproportionate increase in ARF burden after heart transplantation is a concern due to its strong association with chronic kidney disease and mortality.