Michael W. Kuzniewicz

First-day weight loss predicts eventual weight nadir for breastfeeding newborns

Objective To examine the relationship between high (≥5%) weight loss during the first 24 h after birth and eventual excess weight loss (EWL) of ≥10% of birth weight. Design Retrospective cohort study. Setting Kaiser Permanente Northern California hospitals. Patients 63 096 infants born at ≥36 weeks in 2009–2010, of whom 59 761 (94.5%) had a weight subsequent to birth weight measured at <24 h. Main predictor measure Per cent of birth weight lost by 24 h of age. Main outcome measure Weight nadir, defined as the lowest recorded inpatient or outpatient weight in the first 30 days after birth, expressed as a percentage of birth weight. Results Among infants who breastfed at least once, mean (±SD) weight nadir was 6.3±3.5% below birth weight, and 9.6% of the newborns lost ≥10% of birth weight. Among 2670 infants who lost ≥5% of their birth weight in the first 24 h, 782 (29%) eventually developed EWL, compared with 4840 (8%) of 57 109 infants who lost <5% (p<0.0005). In multivariate analysis, ≥5% first-day weight loss predicted eventual EWL (≥10%) with an OR of 4.06 (95% CI 3.69 to 4.46) after adjusting for gestational age, method of delivery, maternal race/ethnicity and hospital of birth. Conclusions High first-day weight loss predicts eventual weight nadir and can be used to identify infants who might benefit from targeted interventions to support breastfeeding and prevent EWL.

Prevalence and neonatal factors associated with autism spectrum disorders in preterm infants.

OBJECTIVES To determine the prevalence of autism spectrum disorders (ASD) across gestational age, examine the risk of ASD by gestational age controlling for other risk factors, and identify potential risk factors in the neonatal intensive care unit. STUDY DESIGN A retrospective cohort of infants born at ≥ 24 weeks between January 1, 2000, and December 31, 2007 at 11 Kaiser Permanente Northern California hospitals (n = 195,021). ASD cases were defined by a diagnosis made at a Kaiser Permanente ASD evaluation center, by a clinical specialist, or by a pediatrician. Cox proportional hazards regression models were used to evaluate the association between gestational age and ASD as well as potential risk factors in the neonatal intensive care unit and ASD. RESULTS The prevalence of ASD in infants <37 weeks was 1.78% compared with 1.22% in infants born ≥ 37 weeks (P < .001). Compared with term infants, infants born at 24-26 weeks had an adjusted hazard ratio (HR) for a diagnosis of ASD of 2.7 (95% CI 1.5-5.0). Infants born at 27-33 weeks (adjusted HR 1.4, 95% CI 1.1-1.8) and 34-36 weeks (adjusted HR 1.3, 95% CI 1.1-1.4) were also at increased risk. High frequency ventilation and intracranial hemorrhage were associated with ASD in infants < 34 weeks. CONCLUSIONS ASD was ~ 3 times more prevalent in infants <27 weeks compared with term infants. Each week of shorter gestation was associated with an increased risk of ASD. High frequency ventilation and intracranial hemorrhage were associated with ASD among infants <34 weeks.

Stratification of Risk of Early-Onset Sepsis in Newborns ≥34 Weeks’ Gestation

OBJECTIVE: To define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥34 weeks’ gestation. METHODS: We conducted a retrospective nested case-control study that used split validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical milestones, and vital signs during the first 24 hours after birth. Using a combination of recursive partitioning and logistic regression, we developed a risk classification scheme for EOS on the derivation dataset. This scheme was then applied to the validation dataset. RESULTS: Using a base population of 608 014 live births ≥34 weeks’ gestation at 14 hospitals between 1993 and 2007, we identified all 350 EOS cases <72 hours of age and frequency matched them by hospital and year of birth to 1063 controls. Using maternal and neonatal data, we defined a risk stratification scheme that divided the neonatal population into 3 groups: treat empirically (4.1% of all live births, 60.8% of all EOS cases, sepsis incidence of 8.4/1000 live births), observe and evaluate (11.1% of births, 23.4% of cases, 1.2/1000), and continued observation (84.8% of births, 15.7% of cases, incidence 0.11/1000). CONCLUSIONS: It is possible to combine objective maternal data with evolving objective neonatal clinical findings to define more efficient strategies for the evaluation and treatment of EOS in term and late preterm infants. Judicious application of our scheme could result in decreased antibiotic treatment in 80 000 to 240 000 US newborns each year.

Impact of Universal Bilirubin Screening on Severe Hyperbilirubinemia and Phototherapy Use

OBJECTIVE: The goal was to assess the impact of universal bilirubin screening on severe hyperbilirubinemia and phototherapy use. METHODS: In this retrospective cohort study of 358086 infants of ≥35 weeks and ≥2000 g born between January 1, 1995, and June 30, 2007, we obtained demographic data, bilirubin levels, and codes for inpatient phototherapy from existing databases. We compared the incidence of high total serum bilirubin (TSB) levels and phototherapy before and after implementation of universal screening and examined risk factors for high TSB levels. RESULTS: A total of 38182 infants (10.6%) were born at facilities that had implemented universal bilirubin screening. Compared with infants born at facilities that were not screening, these infants had a 62% lower incidence of TSB levels exceeding the American Academy of Pediatrics exchange guideline (0.17% vs 0.45%; P < .001), received twice the inpatient phototherapy (9.1% vs 4.2%; P < .001), and had slightly longer birth hospitalization lengths of stay (50.9 vs 48.7 hours; P < .001). Of those receiving phototherapy, 56% after initiation of universal screening had TSB levels at which phototherapy was recommended by the guideline, compared with 70% before screening. The adjusted odds ratio for developing TSB levels exceeding the guideline value was 0.28 (95% confidence interval: 0.20–0.40) for those born at a facility using TSB screening and 0.28 (95% confidence interval: 0.19–0.42) for those born at a facility using transcutaneous bilirubin screening. CONCLUSIONS: Universal bilirubin screening was associated with a significantly lower incidence of severe hyperbilirubinemia but also with increased phototherapy use.

Variation in ICU Risk-Adjusted Mortality: Impact of Methods of Assessment and Potential Confounders

BACKGROUND Federal and state agencies are considering ICU performance assessment and public reporting; however, an accurate method for measuring performance must be selected. In this study, we determine whether a substantial variation in ICU mortality performance still exists in modern ICUs, and compare the predictive accuracy, reliability, and data burden of existing ICU risk-adjustment models. METHODS A retrospective chart review of 11,300 ICU patients from 35 California hospitals from 2001 to 2004 was performed. We calculated standardized mortality ratios (SMRs) for each hospital using the mortality probability model III (MPM(0) III), the simplified acute physiology score (SAPS) II, and the acute physiology and chronic health evaluation (APACHE) IV risk-adjustment models. We compared discrimination, calibration, data reliability, and abstraction time for the models. RESULTS Regardless of the model used, there was a large variation in SMRs among the ICUs studied. The discrimination and calibration were adequate for all risk-adjustment models. APACHE IV had the best discrimination (area under the receiver operating characteristic curve [AUC], 0.892) compared to MPM(0) III (AUC, 0.809), and SAPS II (AUC, 0.873; p < 0.001). The models differed substantially in data abstraction times, as follows: MPM(0)III, 11.1 min (95% confidence interval [CI], 8.7 to 13.4); SAPS II, 19.6 min (95% CI, 17.0 to 22.2); and APACHE IV, 37.3 min (95% CI, 28.0 to 46.6). CONCLUSIONS We found substantial variation in the ICU risk-adjusted mortality rates that persisted regardless of the risk-adjustment model. With unlimited resources, the APACHE IV model offers the best predictive accuracy. If constrained by cost and manual data collection, the MPM(0) III model offers a viable alternative without a substantial loss in accuracy.

Relationship Between Discharge Practices and Intensive Care Unit In-hospital Mortality Performance: Evidence of a Discharge Bias

Context:Current intensive care unit performance measures include in-hospital mortality after intensive care unit admission. This measure does not account for deaths occurring after transfer to another hospital or soon after discharge and therefore, may be biased. Objective:Determine how transfer rates to other acute care hospitals and early post-discharge mortality rates impact hospital performance assessments using an in-hospital mortality model. Design, Setting, and Participants:Data were retrospectively collected on 10,502 eligible intensive care unit patients across 35 California hospitals between 2001 and 2004. Measures:We calculated the rates of acute care hospital transfers and early post-discharge mortality (30-day overall mortality—30-day in-hospital mortality) for each hospital. We assessed hospital performance with standardized mortality ratios (SMRs) using the Mortality Probability Model III. Using regression models, we explored the relationship between in-hospital SMRs and the rates of hospital transfers or early post-discharge mortality. We explored the same relationship using a 30-day SMR. Results:In multivariable models, for each 1% increase in patients transferred to another acute care hospital, there was an in-hospital SMR reduction of −0.021 (−0.040−0.001). Additionally, a 1% increase in early post-discharge mortality was associated with an in-hospital SMR reduction of −0.049 (−0.142–0.045). Assessing hospital performance based upon 30-day mortality end point resulted in SMRs closer to 1.0 for hospitals at high and low ends of in-hospital mortality performance. Conclusions:Variations in transfer rates and potentially discharge timing appear to bias in-hospital SMR calculations. A 30-day mortality model is a potential alternative that may limit this bias.

Mortality Probability Model III and Simplified Acute Physiology Score II: Assessing Their Value in Predicting Length of Stay and Comparison to APACHE IV

BACKGROUND To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models. METHODS Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM(0)) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models. RESULTS The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R(2) = 0.422], mortality probability model III at zero hours (MPM(0) III) [R(2) = 0.279], and simplified acute physiology score (SAPS II) [R(2) = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p <or= 0.05) for three, two, and six deciles using APACHE IVrecal, MPM(0) III, and SAPS II, respectively. Plots of the predicted vs the observed LOS ratios of the hospitals revealed a threefold variation in LOS among hospitals with high model correlations. CONCLUSIONS APACHE IV and MPM(0) III were more accurate than SAPS II for the prediction of ICU LOS. APACHE IV is the most accurate and best calibrated model. Although it is less accurate, MPM(0) III may be a reasonable option if the data collection burden or the treatment effect bias is a consideration.

Original ResearchCritical Care MedicineMortality Probability Model III and Simplified Acute Physiology Score II: Assessing Their Value in Predicting Length of Stay and Comparison to APACHE IV

BACKGROUND To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models. METHODS Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM(0)) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models. RESULTS The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R(2) = 0.422], mortality probability model III at zero hours (MPM(0) III) [R(2) = 0.279], and simplified acute physiology score (SAPS II) [R(2) = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p <or= 0.05) for three, two, and six deciles using APACHE IVrecal, MPM(0) III, and SAPS II, respectively. Plots of the predicted vs the observed LOS ratios of the hospitals revealed a threefold variation in LOS among hospitals with high model correlations. CONCLUSIONS APACHE IV and MPM(0) III were more accurate than SAPS II for the prediction of ICU LOS. APACHE IV is the most accurate and best calibrated model. Although it is less accurate, MPM(0) III may be a reasonable option if the data collection burden or the treatment effect bias is a consideration.

A Quantitative, Risk-Based Approach to the Management of Neonatal Early-Onset Sepsis.

Importance Current algorithms for management of neonatal early-onset sepsis (EOS) result in medical intervention for large numbers of uninfected infants. We developed multivariable prediction models for estimating the risk of EOS among late preterm and term infants based on objective data available at birth and the newborn’s clinical status. Objectives To examine the effect of neonatal EOS risk prediction models on sepsis evaluations and antibiotic use and assess their safety in a large integrated health care system. Design, Setting, and Participants The study cohort includes 204 485 infants born at 35 weeks’ gestation or later at a Kaiser Permanente Northern California hospital from January 1, 2010, through December 31, 2015. The study compared 3 periods when EOS management was based on (1) national recommended guidelines (baseline period [January 1, 2010, through November 31, 2012]), (2) multivariable estimates of sepsis risk at birth (learning period [December 1, 2012, through June 30, 2014]), and (3) the multivariable risk estimate combined with the infant’s clinical condition in the first 24 hours after birth (EOS calculator period [July 1, 2014, through December 31, 2015]). Main Outcomes and Measures The primary outcome was antibiotic administration in the first 24 hours. Secondary outcomes included blood culture use, antibiotic administration between 24 and 72 hours, clinical outcomes, and readmissions for EOS. Results The study cohort included 204 485 infants born at 35 weeks’ gestation or later: 95 343 in the baseline period (mean [SD] age, 39.4 [1.3] weeks; 46 651 male [51.0%]; 37 007 white, non-Hispanic [38.8%]), 52 881 in the learning period (mean [SD] age, 39.3 [1.3] weeks; 27 067 male [51.2%]; 20 175 white, non-Hispanic [38.2%]), and 56 261 in the EOS calculator period (mean [SD] age, 39.4 [1.3] weeks; 28 575 male [50.8%]; 20 484 white, non-Hispanic [36.4%]). In a comparison of the baseline period with the EOS calculator period, blood culture use decreased from 14.5% to 4.9% (adjusted difference, −7.7%; 95% CI, −13.1% to −2.4%). Empirical antibiotic administration in the first 24 hours decreased from 5.0% to 2.6% (adjusted difference, −1.8; 95% CI, −2.4% to −1.3%). No increase in antibiotic use occurred between 24 and 72 hours after birth; use decreased from 0.5% to 0.4% (adjusted difference, 0.0%; 95% CI, −0.1% to 0.2%). The incidence of culture-confirmed EOS was similar during the 3 periods (0.03% in the baseline period, 0.03% in the learning period, and 0.02% in the EOS calculator period). Readmissions for EOS (within 7 days of birth) were rare in all periods (5.2 per 100 000 births in the baseline period, 1.9 per 100 000 births in the learning period, and 5.3 per 100 000 births in the EOS calculator period) and did not differ statistically (P = .70). Incidence of adverse clinical outcomes, including need for inotropes, mechanical ventilation, meningitis, and death, was unchanged after introduction of the EOS calculator. Conclusions and Relevance Clinical care algorithms based on individual infant estimates of EOS risk derived from a multivariable risk prediction model reduced the proportion of newborns undergoing laboratory testing and receiving empirical antibiotic treatment without apparent adverse effects.

Incidence, Etiology, and Outcomes of Hazardous Hyperbilirubinemia in Newborns

BACKGROUND AND OBJECTIVES: Total serum bilirubin (TSB) levels ≥30 mg/dL are rare but potentially hazardous. A better understanding of their incidence, causes, and outcomes could help inform preventive efforts. METHODS: We identified infants born ≥35 weeks’ gestational age from 1995–2011 in Kaiser Permanente Northern California (n = 525 409) and examined the medical records of infants with a TSB ≥30 mg/dL to determine etiology and the occurrence of acute bilirubin encephalopathy. We reviewed inpatient and outpatient encounters through 2013 for evidence of sensorineural hearing loss (SNHL) or cerebral palsy (CP). RESULTS: We identified 47 infants with TSB ≥30 mg/dL (8.6 per 100 000 births). In 44 infants (94%), the hyperbilirubinemia occurred after the initial birth hospitalization. The etiology was not identified in 33 (70%). Glucose-6-phosphate dehydrogenase (G6PD) activity was measured in only 25 (53%) of whom 10 (40%) were deficient. Four children had acute bilirubin encephalopathy of whom 2 developed both CP and SNHL, and 1 developed isolated SNHL. These 3 infants all had G6PD deficiency and TSB >40 mg/dL. One additional 35-week infant with TSB 38.2 mg/dL had SNHL. CONCLUSIONS: Hazardous (≥30 mg/dL) hyperbilirubinemia is a rare event. No etiology could be identified from the clinical record in most cases. G6PD deficiency was the leading cause of hazardous hyperbilirubinemia when an etiology was identified, but many were not tested. Chronic, bilirubin-induced neurotoxicity was uncommon and occurred only in the setting of additional risk factors and TSB values well over (>15 mg/dL) the American Academy of Pediatrics exchange transfusion thresholds.