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Risk Factors for Neonatal Thrombosis in the Neonatal Intensive Care Unit -a Case Control Study

2015· article· en· W2589502557 on OpenAlexaboutno aff
Rukhmi Bhat, Riten Kumar, Soyang Kwon, Karna Murthy, Leif D. Nelin, Paul Monagle, Robert I. Liem

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNeonatal intensive care unitThrombosisPediatricsGestational ageIncidence (geometry)Venous thrombosisLogistic regressionIntensive care unitEmergency medicineIntensive care medicinePregnancySurgeryInternal medicine

Abstract

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Abstract Background Neonates admitted to the neonatal intensive care unit (NICU) are an age group most susceptible to thrombosis in pediatrics. Besides central access devices (CAD), maternal and neonatal factors are reported to be associated with thrombosis. This has not been well investigated because of the relatively rare incidence of thrombosis and the inherent heterogeneity of thrombotic events. An assessment of the impact of individual risk factors is an essential step, as appropriate risk stratification is fundamental to evidence based thromboprophylaxis policy. Objective To identify risk factors associated with thrombosis in sick neonates admitted to the NICU. Methods A case-control study was conducted using the Children's Hospital Neonatal Database (CHND) dataset with neonates admitted to the NICU at Ann and Robert H. Lurie Children's Hospital and Nationwide Children's hospital between Jan 2010 and June 2013. Cases were neonates diagnosed with either arterial or venous thrombosis during their NICU stay and controls were matched to the cases in the same patient pool in a 1:4 ratio on the basis of gestational age and presence or absence of CAD. Neonates with a less than 72-hour stays in the NICU or complex congenital heart defects needing surgical repair as well as re-admission data were excluded. Chi-square tests were performed to compare characteristics as well as potential risk factors between cases and controls. A conditional multivariate logistic regression analysis included potential risk factors with p-value<0.1 in chi-square tests and with clinical relevance. Local IRB approval was obtained at both sites. Results A total of 47 cases were identified in 4,122 NICU patients (11.4 per 1,000 patients). There were 32 (68%) males and 27 (57.5%) preterm neonates with thrombosis. On univariate analysis blood stream infections (BSI) and prolonged mechanical ventilation were significantly more common in cases than controls Table 1. A conditional multivariate analysis showed that prolonged mechanical ventilation was independently associated with higher risk of thrombosis (OR 3.03 [95% CI: 1.29, 7.09], p value 0.01 Table 2). Conclusions The incidence of thrombosis appears to be 5 fold higher than that previously reported in a Canadian registry. After matching for CAD and GA, prolonged mechanical ventilation represents an independent risk factor of thrombosis in neonates. This is the largest study of systematic assessment of risk factors in neonates with mechanical ventilation being reported as a risk factor independent of CAD. Larger multi-centered data should confirm the study results for developing evidence-based risk stratification protocols and thrombosis prevention strategies. Table 1. Comparison of characteristics and potential risk factors between thrombosis cases and controls Patients with thrombosis Patients without thrombosis Variable n (%) n (%) p value Total 47 188 Gender (Male) 32 (68.1) 102 (54.3) 0.09 Gestational age at birth ≤32 weeks 18 (38.3) 72 (38.3) 1.00 33-36 weeks 9 (19.2) 36 (19.2) ≥37 weeks 20 (42.5) 80 (42.6) Birth Weight (gms) 0.51 <2500 22 (46.8) 97 (52.2) ≥2500 25 (53.2) 89 (47.8) Maternal antenatal conditions Chorioamnionitis 2 (4.6) 7 (4.3) 0.95 Diabetes 6 (13.6) 27 (16.7) 0.63 Hypertension 14 (31.8) 39 (24.1) 0.30 Antenatal steroids use 13 (27.7) 65 (35.5) 0.31 CAD type 0.35 No 14 (29.8) 56 (29.8) UAC/UVC 3 (6.4) 24 (12.8) PICC 11 (23.4) 53 (28.2) CC/cutdown/tunnel catheter 0 (0) 3 (1.6) Multiple types 19 (40.4) 52 (27.7) Mechanical ventilation (MV)˃48 hrs 27 (57.4) 68 (36.2) 0.008 Respiratory distress syndrome (RDS) 27 (57.4) 101 (53.7) 0.65 Necrotizing enterocolitis (NEC) 4 (8.5) 19 (10.1) 0.74 Hypoxic ischemic encephalopathy (HIE) 3 (6.4) 5 (2.7) 0.21 Meconium aspiration syndrome (MAS) 1 (2.1) 8 (4.3) 0.50 Blood stream infections (BSI) 9 (19.2) 17 (9.0) 0.048 Central line associated BSI (CLABSI) 2 (22.2) 1 (5.9) 0.27 Abdominal and GI surgery 16 (38.1) 50 (31.1) 0.39 Table 2. Odds ratio of thrombosis cases from a conditional multivariate logistic regression model Predictor Odds ratio 95% confidence interval p value Male gender 1.74 0.88-3.72 0.11 Prolonged mechanical ventilation 3.03 1.29-7.09 0.01 BSI 2.19 0.80-6.01 0.12 Disclosures Liem: Global Blood Therapeutics: Consultancy; Fresenius Kabi: Other: DSMB; NHLBI: Research Funding.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.064
GPT teacher head0.319
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2015
Admission routes1
Has abstractyes

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