Dataset Information

Weight-for-age distribution and case-mix adjusted outcomes of 14,307 paediatric intensive care admissions.

ABSTRACT:

Aims

To determine whether the paediatric intensive care (PIC) population weight distribution differs from the UK reference population and whether weight-for-age at admission is an independent risk factor for mortality.

Methods

Admission weight-for-age standard deviation scores (SDS) were calculated for all PIC admissions (March 2003-December 2011) to Great Ormond Street Hospital: this is the number of standard deviations (SD) between a child's weight and the UK mean weight-for-age. Categorised into nine SDS groups, standardised mortality ratios (SMR) and logistic regression were used to assess the relationship between weight-for-age at admission and risk-adjusted mortality.

Results

Out of 12,458 admissions, mean weight-for-age was 1.04 SD below the UK reference population mean (p < 0.0001). Based on 942 deaths, risk-adjusted mortality was lowest in those with mild-to-moderately raised weight-for-age (SDS 0.5-2.5) and highest in children with extreme under- or overweight (SDS < -3.5 and SDS > +3.5). Logistic regression indicated that age, gender, ethnicity and weight-for-age are independent risk factors for mortality. South Asian and 'other' ethnicities had significantly increased risk of death compared to children of white and black ethnic origin.

Conclusion

The PIC population mean weight-for-age is significantly lower than the UK reference mean. The extremes of weight-for-age are over-represented, especially underweight. Weight-for-age at admission is an independent risk factor for mortality. A U-shaped association between weight and risk-adjusted mortality exists, with the lowest risk of death in children who are mild-to-moderately overweight. Future studies should determine the impact of malnutrition on risk-adjusted mortality and investigate the aetiology of risk disparities with ethnicity.

SUBMITTER: Prince NJ

PROVIDER: S-EPMC4107282 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:It is important that actual outcomes of care and not surrogate markers, such as process measures, be used to evaluate the quality of inpatient care. Because of the heterogenous composition of patients, risk-adjustment is essential for the objective evaluation of outcomes following inpatient care. Comparative evaluation of risk-adjusted outcomes can be used to identify suboptimal performance and can provide direction for care improvement initiatives.We studied the risk-adjusted outcomes of 6 medical conditions during the inpatient and 90-day post-discharge period to identify the opportunities for care improvement. The Medicare Limited Dataset for 2012 to 2014 was used to identify acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), pneumonia (PNEU), cerebrovascular accidents (CVA), and gastrointestinal hemorrhage (GIH). Stepwise logistic predictive models were developed for the adverse outcomes (AOs) of inpatient deaths, 3-sigma prolonged length-of-stay outliers, 90-day post-discharge deaths, and 90-day readmissions after unrelated events were excluded. Observed and predicted AOs were determined for each hospital with ?75 cases for each of the 6 medical conditions. Z-scores and risk-adjusted AO rates for each hospital permitted comparative analysis of outcomes after adjusting for covariance among the medical conditions.There were a total of 1,811,749 patients from 973 acute care hospitals with the 6 medical conditions. A total of 41% of all patients had ?1 AO events. One or more readmissions were identified in 29.8% of patients. A total of 64 hospitals (6.4%) were 2 standard deviations better than the mean for risk-adjusted outcomes, and 72 (7.4%) were 2 standard deviations poorer. The best performing decile of hospitals had mean AO rates of 35.1% (odds ratio?=?0.766; 95% confidence interval (CI) CI: 0.762-0.771) and the poorest performing decile a mean AO rate of 48.5% (odds ratio?=?1.357; 95% CI: 1.346-1.369). Volume of qualifying cases ranged from 670 to 9314; no association was identified for increased volume of patients (P?<?.40).Risk-adjusted AO rates demonstrated nearly a 14% opportunity for care improvement between top and suboptimal performing hospitals. Hospitals must be able to benchmark objective measurement of outcomes to inform quality initiatives.

Project description:OBJECTIVES:To identify modifiable factors leading to unplanned readmission and characterize differences in adjusted unplanned readmission rates across hospitals. DESIGN:Retrospective cohort study using prospectively collected clinical registry data SETTING:: Pediatric Cardiac Critical Care Consortium clinical registry. PATIENTS:Patients admitted to a pediatric cardiac ICU at Pediatric Cardiac Critical Care Consortium hospitals. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We examined pediatric cardiac ICU encounters in the Pediatric Cardiac Critical Care Consortium registry from October 2013 to March 2016. The primary outcomes were early (< 48?hr from pediatric cardiac ICU transfer) and late (2-7 d) unplanned readmission. Generalized logit models identified independent predictors of unplanned readmission. We then calculated observed-to-expected ratios of unplanned readmission and identified higher-than or lower-than-expected unplanned readmission rates for those with an observed-to-expected ratios greater than or less than 1, respectively, and a 95% CI that did not cross 1. Of 11,301 pediatric cardiac ICU encounters (16 hospitals), 62% were surgical, and 18% were neonates. There were 175 (1.6%) early unplanned readmission, and 300 (2.7%) late unplanned readmission, most commonly for respiratory (31%), or cardiac (28%) indications. In multivariable analysis, unique modifiable factors were associated with unplanned readmission. Although shorter time between discontinuation of vasoactive infusions and pediatric cardiac ICU transfer was associated with early unplanned readmission, nighttime discharge was independently associated with a greater likelihood of late unplanned readmission. Two hospitals had lower-than-expected unplanned readmission in both the early and late categories, whereas two other hospitals were higher-than-expected in both. CONCLUSIONS:This analysis demonstrated time from discontinuation of critical care therapies to pediatric cardiac ICU transfer as a significant, modifiable predictor of unplanned readmission. We identified two hospitals with lower-than-expected adjusted rates of both early and late unplanned readmission, suggesting that their systems are well designed to prevent unplanned readmission. This offers the possibility of disseminating best practices to other hospitals through collaborative learning.