Dataset Information

Comparison of neonatal intensive care: Trento area versus Vermont Oxford Network.

ABSTRACT:

Background

S. Chiara hospital is the only neonatal intensive care unit (NICU) in the Province of Trento (Italy). It serves a population of about 460000 people with about 5000 infants per year, admitting the totality of the inborn and outborn VLBWI of the province. The aim of this work is to compare mortality, morbidity and neonatal treatment of the very low birth weight infants (VLBWI) of Trento area with those recorded in the Vermont Oxford Network (VON) during 2004.

Methods

In this retrospective analysis, the rates of complications and related treatments reported in VLBWI admitted in the S. Chiara NICU during the period 2000-2005 were compared with those recorded in the VON in 2004. The analysis included both the total populations and different weight groups.

Results

The frequency of inborn infants was significantly higher in Trento than in VON: 91% vs 84% (MH 8.56; p-value 0.003). The administration of prenatal steroids (82% vs 74%; MH 7.47 and p-value 0.006) and caesarean section were significantly more frequent in the Trento area than in VON. In Trento significantly more VLBWI with BW ConclusionOn the basis of this analysis, a less aggressive therapeutic strategy based on perinatal prevention in global management, such as that employed in Trento area, may be associated with an improvement in clinical outcomes in very low birth weight infants.

SUBMITTER: De Nisi G

PROVIDER: S-EPMC2687545 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Publications

Comparison of neonatal intensive care: Trento area versus Vermont Oxford Network.

De Nisi Giuseppe G Berti Mariarosaria M Malossi Riccardo R Pederzini Fabio F Pedrotti Anna A Valente Alberta A

Italian journal of pediatrics 20090314 1

<h4>Background</h4>S. Chiara hospital is the only neonatal intensive care unit (NICU) in the Province of Trento (Italy). It serves a population of about 460000 people with about 5000 infants per year, admitting the totality of the inborn and outborn VLBWI of the province. The aim of this work is to compare mortality, morbidity and neonatal treatment of the very low birth weight infants (VLBWI) of Trento area with those recorded in the Vermont Oxford Network (VON) during 2004.<h4>Methods</h4>In t ...[more]

PMID: 19490662

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Project description:ImportanceThe Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network (NRN) extremely preterm birth outcome model is widely used for prognostication by practitioners caring for families expecting extremely preterm birth. The model provides information on mean outcomes from 1998 to 2003 and does not account for substantial variation in outcomes among US hospitals.ObjectiveTo update and validate the NRN extremely preterm birth outcome model for most extremely preterm infants in the United States.Design, setting, and participantsThis prognostic study included 3 observational cohorts from January 1, 2006, to December 31, 2016, at 19 US centers in the NRN (derivation cohort) and 637 US centers in Vermont Oxford Network (VON) (validation cohorts). Actively treated infants born at 22 weeks' 0 days' to 25 weeks' 6 days' gestation and weighing 401 to 1000 g, including 4176 in the NRN for 2006 to 2012, 45?179 in VON for 2006 to 2012, and 25?969 in VON for 2013 to 2016, were studied. VON cohorts comprised more than 85% of eligible US births. Data analysis was performed from May 1, 2017, to March 31, 2019.ExposuresPredictive variables used in the original model, including infant sex, birth weight, plurality, gestational age at birth, and exposure to antenatal corticosteroids.Main outcomes and measuresThe main outcome was death before discharge. Secondary outcomes included neurodevelopmental impairment at 18 to 26 months' corrected age and measures of hospital resource use (days of hospitalization and ventilator use).ResultsAmong 4176 actively treated infants in the NRN cohort (48% female; mean [SD] gestational age, 24.2 [0.8] weeks), survival was 63% vs 62% among 3702 infants in the era of the original model (47% female; mean [SD] gestational age, 24.2 [0.8] weeks). In the concurrent (2006-2012) VON cohort, survival was 66% among 45?179 actively treated infants (47% female; mean [SD] gestational age, 24.1 [0.8] weeks) and 70% among 25?969 infants from 2013 to 2016 (48% female; mean [SD] gestational age, 24.1 [0.8] weeks). Model C statistics were 0.74 in the 2006-2012 validation cohort and 0.73 in the 2013-2016 validation cohort. With the use of decision curve analysis to compare the model with a gestational age-only approach to prognostication, the updated model showed a predictive advantage. The birth hospital contributed equally as much to prediction of survival as gestational age (20%) but less than the other factors combined (60%).Conclusions and relevanceAn updated model using well-known factors to predict survival for extremely preterm infants performed moderately well when applied to large US cohorts. Because survival rates change over time, the model requires periodic updating. The hospital of birth contributed substantially to outcome prediction.

Project description:BACKGROUND:?In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS). METHODS:?The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model. RESULTS:?The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p?=?0.03). CONCLUSION:?Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.

Dataset Information

Comparison of neonatal intensive care: Trento area versus Vermont Oxford Network.

Background

Methods

Results

Publications

Comparison of neonatal intensive care: Trento area versus Vermont Oxford Network.

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