Formal selection of measures for a composite index of NICU quality of care: Baby-MONITOR

Profit, Jochen; Gould, Jeffrey B.; Zupancic, John A.F.; Stark, Ann R.; Wall, Kristin M.; Kowalkowski, Marc; Mei, Minghua; Pietz, Kenneth; Thomas, Eric J.; Petersen, Laura A.

doi:10.1038/jp.2011.12

Cited by 39 publications

(65 citation statements)

References 24 publications

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“…using rigorous methods, 14 validated the selection, 16 conducted initial data analyses, and developed risk models for individual measures. 17,29 In this study, we aggregated the measures, assessed the composite' s ability to discriminate among NICUs, and conducted extensive sensitivity analyses with regard to weighting and aggregation.…”

Section: Figurementioning

confidence: 99%

“…We have coined the term Baby-MONITOR (Measure Of Neonatal InTensive care Outcomes Research) for the instrument, 14 which we present as a prototype for the next generation of quality assessment. Our primary objective was to test whether the Baby-MONITOR would discriminate global NICU performance on quality of care delivery.…”

Section: (Continued On Last Page)mentioning

confidence: 99%

“…A detailed description of the patient-selection criteria has been published elsewhere. 14 In brief, the goal was to create a sample of VLBW infants that would represent the "common" preterm infant. For this study, 9023 unique VLBW infants cared for at 22 California regional NICUs between 2004 and 2010 met the inclusion criteria.…”

Section: Patientsmentioning

confidence: 99%

“…3,4,15 Preliminary steps in the development of the Baby-MONITOR included development of a theoretical framework 4,15 ; expert-informed selection of its measure components 14,16 ; initial data analysis (1) to investigate the completeness of the data, (2) to develop and test adequate measure definitions and restrictions, and (3) to minimize systematic selection and transfer biases; and construction of risk adjustment models. 17 With these building blocks in place, we standardized and risk-adjusted outcomes, weighted the individual components, and aggregated measures to form a composite indicator.…”

Section: Overviewmentioning

confidence: 99%

“…In sensitivity analyses, we explored a variety of weighting schemes based on expert opinion. Our panel of experts 14 was asked to distribute 100 imaginary dollars across the measures, according to the relative contribution of each measure to overall NICU quality (see Table 1). Mean and median weights derived from this exercise were applied in sensitivity analyses.…”

Section: Weightingmentioning

confidence: 99%

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Baby-MONITOR: A Composite Indicator of NICU Quality

2014

PEDIATRICS

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WHAT'S KNOWN ON THIS SUBJECT:The traditional processfocused approach to quality improvement has not remedied NICUs' inconsistency in quality of care delivery across clinically important measures. Global measurement of quality may induce broad, systems-based improvement, but must be formally studied. WHAT THIS STUDY ADDS:We present a systematically developed and robust composite indicator, the Baby-MONITOR, to assess the quality of care delivered to very low birth weight infants in the NICU setting. abstract BACKGROUND AND OBJECTIVES: NICUs vary in the quality of care delivered to very low birth weight (VLBW) infants. NICU performance on 1 measure of quality only modestly predicts performance on others. Composite measurement of quality of care delivery may provide a more comprehensive assessment of quality. The objective of our study was to develop a robust composite indicator of quality of NICU care provided to VLBW infants that accurately discriminates performance among NICUs. METHODS:We developed a composite indicator, Baby-MONITOR, based on 9 measures of quality chosen by a panel of experts. Measures were standardized, equally weighted, and averaged. We used the California Perinatal Quality Care Collaborative database to perform acrosssectional analysis of care given to VLBW infants between 2004 and 2010. Performance on the Baby-MONITOR is not an absolute marker of quality but indicates overall performance relative to that of the other NICUs. We used sensitivity analyses to assess the robustness of the composite indicator, by varying assumptions and methods. RESULTS: Our sample included 9023 VLBW infants in 22 California regional NICUs. We found significant variations within and between NICUs on measured components of the Baby-MONITOR. Risk-adjusted composite scores discriminated performance among this sample of NICUs. Sensitivity analysis that included different approaches to normalization, weighting, and aggregation of individual measures showed the Baby-MONITOR to be robust (r = 0.89-0.99). CONCLUSIONS:The Baby-MONITOR may be a useful tool to comprehensively assess the quality of care delivered by NICUs. Pediatrics 2014;134:74-82

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Section: Figurementioning

confidence: 99%

Section: (Continued On Last Page)mentioning

confidence: 99%

Section: Patientsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

Section: Weightingmentioning

confidence: 99%

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Baby-MONITOR: A Composite Indicator of NICU Quality

2014

PEDIATRICS

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Perils and Opportunities of Comparative Performance Measurement

Profit¹

2012

Arch Pediatr Adolesc Med

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Correlation of Neonatal Intensive Care Unit Performance Across Multiple Measures of Quality of Care

Profit¹,

Zupancic²,

Gould³

et al. 2013

JAMA Pediatr

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Objectives To examine whether high performance on one measure of quality is associated with high performance on others and to develop a data-driven explanatory model of neonatal intensive care unit (NICU) performance. Design We conducted a cross-sectional data analysis of a statewide perinatal care database. Risk-adjusted NICU ranks were computed for each of 8 measures of quality selected based on expert input. Correlations across measures were tested using the Pearson correlation coefficient. Exploratory factor analysis was used to determine whether underlying factors were driving the correlations. Setting Twenty-two regional NICUs in California. Patients In total, 5445 very low-birth-weight infants cared for between January 1, 2004, and December 31, 2007. Main Outcomes Measures Pneumothorax, growth velocity, health care–associated infection, antenatal corticosteroid use, hypothermia during the first hour of life, chronic lung disease, mortality in the NICU, and discharge on any human breast milk. Results The NICUs varied substantially in their clinical performance across measures of quality. Of 28 unit-level correlations only 6 were significant (P < .05). Correlations between pairs of quality measures were strong (ρ > .5) for 1 pair, moderate (.3 < |ρ| < .5) for 8 pairs, weak (.1 < |ρ| < .3) for 5 pairs and negligible (|ρ| < .1) for 14 pairs. Exploratory factor analysis revealed 4 underlying factors of quality in this sample. Pneumothorax, mortality in the NICU, and antenatal corticosteroid use loaded on factor 1; growth velocity and health care–associated infection loaded on factor 2; chronic lung disease loaded on factor 3; and discharge on any human breast milk loaded on factor 4. Conclusion In this sample, the ability of individual measures of quality to explain overall quality of neonatal intensive care was modest.

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Formal selection of measures for a composite index of NICU quality of care: Baby-MONITOR

Cited by 39 publications

References 24 publications

Baby-MONITOR: A Composite Indicator of NICU Quality

Baby-MONITOR: A Composite Indicator of NICU Quality

Perils and Opportunities of Comparative Performance Measurement

Correlation of Neonatal Intensive Care Unit Performance Across Multiple Measures of Quality of Care

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