Clinical trial NCT03110887: Monitoring Outcome in Neonatal Thrombocytopenia
Official title: Monitoring Outcome in Neonatal Thrombocytopenia
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Neonatal Thrombocytopenia
Intraventricular Hemorrhage
Platelet Transfusion
Keywords: Dynamic prediction model
Lead sponsor: Sanquin-LUMC J.J van Rood Center for Clinical Transfusion Research
Sponsor class: OTHER
Collaborators: name: Leiden University Medical Center
name: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
First posted: 2017-04-12
Enrollment: 700
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Admitted to participating neonatal intensive care unit, gestational age \>34 weeks.

Brief summary: Rationale: Approximately 10% of neonates admitted to neonatal intensive care units develop a major hemorrhage. In an attempt to avert this severe complication various preventive measures have been implemented. One of these is the transfusion of platelets to premature neonates with low platelet counts. However, this practice is not supported by scientific evidence. Most neonates with low platelet counts never experience a major bleeding and platelet transfusions may carry risks of volume overload or infection. Therefore, it is important to treat only those patients that truly benefit from this intervention. We urgently need a scientifically based tool to predict which premature neonates are at risk for major bleeding. A few prediction models do exist, but these only allow assessment of bleeding risk at baseline, and do not correct for changes in clinical status during the admission period. We believe that adding this feature to our prediction model will significantly improve our ability to predict bleeding. The prediction model will also be helpful in developing individualized transfusion guidelines as it helps us to predict which neonates would benefit from prophylactic platelet transfusions.

Main objective: to develop a dynamic prediction model for bleeding in preterm neonates with low platelet counts.

Study design: retrospective observational cohort study.

Study population: neonates with a gestational age at birth of \< 34 weeks admitted to a neonatal intensive care unit (NICU), with a thrombocyte count of less than 50x109/L will be included.

Assessments: only data generated through standard care will be collected. This includes platelet counts, cerebral ultrasounds, information about bleeding and transfusions, and multiple clinical variables.

Main study endpoint: major bleeding during admission

Statistical analyses: dynamic prediction model using landmarking.

Detailed description: 2. INTRODUCTION AND RATIONALE

2.1 The relationship between thrombocytopenia and bleeding A complex relation exists between thrombocytopenia in preterm neonates and bleeding. This is illustrated by a recent observational study that demonstrated that only 9% of neonates with a very low platelet count developed major bleeding.(1) Moreover, patients with normal platelet counts bleed quite frequently, as evidenced by another study in which 25-38% of the patients with an IVH did not have thrombocytopenia.(2,3) Several recent reviews have highlighted that the fact that neonatal bleeding and neonatal thrombocytopenia often occur in the same timeframe, does not necessarily imply a causal relationship.(4,5) It is difficult to identify the role of thrombocytopenia in bleeding, because bleeding is a multifactorial process, and because there are a lot of interactions between bleeding, thrombocytopenia and clinical variables. Moreover, neonatal platelets appear to be different from adult platelets in many ways, which complicates our understanding of the effect of low platelet counts on bleeding risk, or the effect of transfusion of (adult) platelets in the neonatal system.(6)

2.2 potential prognostic variables The most important known risk factor for bleeding is low gestational age.(1) In the specific case of IVH, it is thought that the prematurity of the brain - which has a highly vascularized germinal matrix that can easily be damaged - is an important causal factor. However, several other factors have been associated with an increased risk of bleeding, for example low Apgar scores, acidosis, vaginal birth, need for mechanical ventilation, disordered coagulation,NEC, DIC, indomethacin or surfactant treatment, low oxygen saturation levels, a low Score for Neonatal Acute Physiology (SNAP), and many more. But many of these studies have severe limitations such as lack of multivariate analysis, unclear selection of controls, small numbers and different study populations. Most are also limited to a baseline risk assessment, without a possibility to correct the risk assessment as the clinical status of the child changes. Therefore there is no clarity as to which factors are really relevant in clinical practice when assessing neonatal bleeding in neonates with thrombocytopenia.

2.3 Current prediction models A few prediction models for neonatal bleeding have been published, but to our understanding, none of these is used regularly in clinical practice.(7-9) The main disadvantage of these models is that they only allow assessment of bleeding risk at baseline, and do not correct for changes in clinical status during the admission period. We believe that adding this feature to our prediction model will significantly improve our ability to predict bleeding.

2.4 The MONET study The MONET study is a retrospective, multicenter, observational cohort study that assesses risk factors for neonatal bleeding in thrombocytopenic preterm neonates. This population is chosen because these are the neonates that are currently being treated with prophylactic thrombocyte transfusions. The results of this study will allow us to identify neonates within this population that are at high risk of bleeding. These neonates may potentially benefit from different treatment strategies. Also, modifiable risk factors can be further explored as potential targets for preventing neonatal bleeding.

3. OBJECTIVE

To develop a dynamic prediction model for bleeding in preterm neonates with low platelet counts.

4. STUDY DESIGN

Retrospective observational cohort study. Timeframe in which data will be collected is 5 years (2010-2014).

5. STUDY POPULATION

5.1 Population (base) Neonates will be selected based on inclusion and exclusion criteria defined below.

5.2 Inclusion criteria

1. Admission to a neonatal intensive care unit (NICU) in the Netherlands, including postnatal transfers;
2. Gestational age at birth \< 34 weeks;
3. A platelet count of \<50 x109/L;

5.3 Exclusion criteria

A potential subject who meets any of the following criteria will be excluded from participation in this study:

1. Severe congenital malformations;
2. High suspicion of spurious platelet count (e.g. clots in the sample, or very rapid 'recovery' to previous non-severely thrombocytopenic levels);
3. Thrombocytopenia which occurred exclusively in the context of exchange transfusion;
4. Prior admission to a NICU (only first admissions to NICU's will be included. Postnatal transfers from non-NICU's will be included).

5.4 Sample size calculation Data on the frequency of bleeding outcomes in severely thrombocytopenic neonates are available from the PlaNeT-1 survey in which 15/169 or 9% of neonates experienced a major bleed while on study.(1) However, this proportion applies to neonates of all gestational ages. Other studies show incidences in premature neonates of 7-11%, depending on the type of population studied.(3,10,11) Assuming an event rate of approximately 10%, we calculated that for testing 5 variables we will need a sample size of 500 neonates, because we will need approximately 10 events per tested variable. Each year, 4000 neonates are admitted to the neonatal intensive care unit, of which approximately 5% have severe thrombocytopenia. Therefore, we expect 200 eligible neonates each year, and a total sample size of 1000 neonates, which will allow us to include a maximum of 10 variables.

6. METHODS

6.1 Main study endpoint

Major or severe bleeding during admission is the primary outcome. This has been defined as either one of the following:

1. Intraventricular hemorrhage grade 3 (\>50% of ventricle filled with blood)
2. Intraventricular hemorrhage of any grade in combination with parenchymal involvement. Information about dimensions will be collected: maximum diameter of \<1 cm, or 1-2 cm or \>2 cm.
3. Parenchymal hemorrhage (without IVH) visible on ultrasound (contrary to small bleeds visible only on MRI). Information about size will be collected: maximum diameter \<1 cm, 1-2 cm or \>2 cm.
4. Cerebellar hemorrhage visible on ultrasound (contrary to small bleeds visible only on MRI). The maximum diameter of the bleeding will be collected.
5. Other types of intracranial hemorrhage. The maximum diameter will be recorded.
6. Pulmonary hemorrhage, defined as fresh blood from the endotracheal tube in combination with increased ventilatory requirements
7. Any other type of hemorrhage, if major. A bleeding was considered major if it required or was related to either one of the following:

   1. Red cell transfusion
   2. Volume boluses
   3. Need for inotropes (either start of inotrope therapy, or increased dose of current therapy)
   4. Significant drop in blood pressure The first time a major bleed was diagnosed is the endpoint for this study. Date and time of first major bleed will be recorded, if the exact time cannot be retrieved from the medical file, an estimate will be computed based on what is known about the timing (for example, that it occurred in an evening shift, or that it occurred after 16:00).

      6.2 Clinical variables The initial strategy was to select clinical variables through a systematic review of the literature in combination with expert advice. The review was started, but yielded over 8000 abstracts, and over 1000 included full texts, and was therefore considered too large for the scope of this project. However, an overview of the clinical variables assessed in these papers was made, and will be taken into account during the variable selection process. Clinical variables will be selected based on literature in combination with expert advice. Exclusion of a variable will be considered when the variable is not not consistently documented in medical records, when few studies concerning this variable have been published, when multiple published studies show a weak association with bleeding, when a strong interaction with another variable is expected (e.g. birth weight and IUGR), or when the variable is difficult to measure regularly (e.g. blood parameters that need large blood samples).

      6.3 study procedures Data will be collected by study personnel, including research nurses, datamanagers and medical students under supervision of the principal investigator. Data will be collected from the hospital's written or online patient record files, recorded imaging reports and nurses records. Data collection ends when a neonate is transferred out of the neonatal intensive care unit, when a major bleed occurs, or when a neonate dies.

      7. SAFETY REPORTING

      MONET is an observational study, therefore we consider reporting of adverse events or serious adverse events not applicable for this study.

      8. STATISTICAL ANALYSIS

      The development of the prediction model will take place in cooperation with several experts, including prof. dr. J.G. van der Bom, professor in clinical transfusion medicine, and prof dr. H. Putter, professor of medical statistics and an expert in dynamic prediction modelling. We will develop a dynamic prediction model using landmarking, as described elsewhere.(12) A statistical analysis plan will be written and signed prior to any analysis. Variables to be included in the model will be chosen prior to the statistical analyses.

      9. ETHICAL CONSIDERATIONS

      9.1 Regulation statement This study will be conducted according to the principles of the Declaration of Helsinki (version 59, oct 2008). The Medical Research Involving Human Subjects Act (WMO) does not apply.

      9.2 Recruitment and consent Informed consent does not need to be obtained for this study, as only retrospective data are being collected.

      10. ADMINISTRATIVE ASPECTS, MONITORING AND PUBLICATION

      10.1 Handling and storage of data and documents Data will be held and processed in accordance with the Dutch Personal Data Protection Act. All study data will be held securely. It will not be disclosed to third parties. All staff working on the study owe a duty of confidentiality to the participants. Manual records will be held securely (for example in locked filing cabinets). Electronic records will be held on a secure network requiring user ID and password access. A fully anonymised data set will be used for data analysis. Individuals will not be identifiable from the results of the study. Neonates enrolled in the MONET study will receive a unique study code. Data will be send to the study manager and entered into a MONET database.

      10.2 Monitoring and Quality Assurance Because of the retrospective, observational nature of this study, monitoring was not deemed necessary.

      10.3 Public disclosure and publication policy The study will be registered in the website of the Dutch National Competent Authority, the 'Centrale Commissie Mensgebonden Onderzoek' (CCMO) and a public study registry. The results from the MONET study will be analyzed and published as soon as possible in peer-reviewed international scientific journals and presented at scientific meetings, unless the study was terminated prematurely and did not yield sufficient data for a publication. The responsibility for presentations and/or publications belongs to the investigators. No restriction regarding the public disclosure and publication of the research data have been, or will be made by the funding agencies. The final publication of the study results will be written by the principal investigators and the co-investigators. A draft manuscript will be submitted for review to all co-authors. Results will also be published in a PhD-thesis. Authors of the main manuscript will include the Principal Investigator, the co-investigators and, investigators who have included evaluable patients in the study. Others who have made a significant contribution to the study may also be included as author, or otherwise will be included in the acknowledgement.

      11. REFERENCES
      1. Stanworth SJ, Clarke P, Watts T, Ballard S, Choo L, Morris T, et al. Prospective, observational study of outcomes in neonates with severe thrombocytopenia. Pediatrics \[Internet\]. 2009 Nov \[cited 2013 May 27\];124(5):e826-34. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19841111
      2. von Lindern JS, Hulzebos C V, Bos AF, Brand A, Walther FJ, Lopriore E. Thrombocytopaenia and intraventricular haemorrhage in very premature infants: a tale of two cities. Arch Dis Child Fetal Neonatal Ed \[Internet\]. 2012 Sep \[cited 2014 Oct 1\];97(5):F348-52. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22933094
      3. von Lindern JS, van den Bruele T, Lopriore E, Walther FJ. Thrombocytopenia in neonates and the risk of intraventricular hemorrhage: a retrospective cohort study. BMC Pediatr \[Internet\]. BioMed Central Ltd; 2011 Jan \[cited 2013 Jun 15\];11(1):16. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3045959\&tool=pmcentrez\&rendertype=abstract
      4. Stanworth SJ. Thrombocytopenia, bleeding, and use of platelet transfusions in sick neonates. Hematology Am Soc Hematol Educ Program \[Internet\]. 2012 Jan;2012:512-6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23233627
      5. Gunnink SF, Vlug R, Fijnvandraat K, van der Bom JG, Stanworth SJ, Lopriore E. Neonatal thrombocytopenia: etiology, management and outcome. Expert Rev Hematol \[Internet\]. 2014 Jun \[cited 2014 Jun 12\];7(3):387-95. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24665958
      6. Sola-Visner M. Platelets in the neonatal period: developmental differences in platelet production, function, and hemostasis and the potential impact of therapies. Hematology Am Soc Hematol Educ Program \[Internet\]. 2012 Jan;2012(1):506-11. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23233626
      7. Singh R, Visintainer P. Predictive models for severe intraventricular hemorrhage in preterm infants. J Perinatol \[Internet\]. Nature Publishing Group; 2014;34(10):802-802. Available from: http://www.nature.com/doifinder/10.1038/jp.2014.152
      8. Heuchan a M, Evans N, Henderson Smart DJ, Simpson JM. Perinatal risk factors for major intraventricular haemorrhage in the Australian and New Zealand Neonatal Network, 1995-97. Arch Dis Child Fetal Neonatal Ed \[Internet\]. 2002 Mar;86(2):F86-90. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1721387\&tool=pmcentrez\&rendertype=abstract
      9. Luque MJ, Tapia JL, Villarroel L, Marshall G, Musante G, Carlo W, et al. A risk prediction model for severe intraventricular hemorrhage in very low birth weight infants and the effect of prophylactic indomethacin. J Perinatol \[Internet\]. Nature Publishing Group; 2014 Jan \[cited 2014 Sep 20\];34(1):43-8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24113396
      10. Murray N a, Howarth LJ, McCloy MP, Letsky E a, Roberts I a G. Platelet transfusion in the management of severe thrombocytopenia in neonatal intensive care unit patients. Transfus Med \[Internet\]. 2002 Feb;12(1):35-41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11967135
      11. Honohan A, Van't Ende E, Hulzebos C, Lopriore E, Van't Verlaat E, Govaert P, et al. Posttransfusion platelet increments after different platelet products in neonates: a retrospective cohort study. Transfusion \[Internet\]. 2013 Feb 26 \[cited 2013 Jul 1\];53(12):3100-9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23441721
      12. Fontein DBY, Klinten Grand M, Nortier JWR, Seynaeve C, Meershoek-Klein Kranenbarg E, Dirix LY, et al. Dynamic prediction in breast cancer: Proving feasibility in clinical practice using the TEAM trial. Ann Oncol. 2015;26(6):1254-62.

      12. APPENDIX 1: names and contact information of participating NICUs

      NICU University Medical Center Groningen (UMCG) Local investigator: Chris Hulzebos, neonatologist

   c.v.hulzebos@umcg.nl

   NICU Isala Klinieken Zwolle (Isala) Local investigator: Esther d'Haens, neonatologist e.j.haens@isala.nl

   NICU University Medical Center Utrecht (UMCU) Local investigator: Daniël Vijlbrief, neonatologist d.c.Vijlbrief@umcutrecht.nl

   NICU Academic Medical Center Amsterdam (AMC) Wes Onland, neonatologist w.onland@amc.uva.nl

   NICU Leiden University Medical Center (LUMC) Local investigator: Enrico Lopriore, neonatologist e.lopriore@lumc.nl

   NICU Maxima Medical Center Veldhoven (MMC) Peter Andriessen, neonatologist p.andriessen@mmc.nl

   NICU Erasmus University Medical Center Rotterdam (Erasmus MC) Andre Kroon, neonatologist

   a.a.kroon@erasmusmc.nl

Eligibility criteria: Inclusion Criteria:

* gestational age \<34 weeks at birth
* platelet count \<50x10\^9/L

Exclusion Criteria:

* readmission to NICU (only first admissions are included. Postnatal transfers from non-NICU hospitals are included)
* major congenital malformations
* high suspicion of spurious platelet count
* thrombocytopenia exclusively in the context of exchange transfusion

Primary outcomes:
measure: Major hemorrhage
timeFrame: T0 = time of first platelet count <50. End of study = major bleed, death or discharge. Datacollection between 01-01-2010 and 31-12-2014
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
name: Karin Fijnvandraat, MD PhD prof
role: PRINCIPAL_INVESTIGATOR
affiliation: Leiden University Medical Center
name: Enrico Lopriore, MD PhD prof
role: PRINCIPAL_INVESTIGATOR
affiliation: Sanquin Blood Supply Foundation, department of clinical transfusion research, Leiden, the Netherlands
name: Anske van der Bom, MD PhD prof
role: PRINCIPAL_INVESTIGATOR
References:
citation: Fustolo-Gunnink SF, Fijnvandraat K, Putter H, Ree IM, Caram-Deelder C, Andriessen P, d'Haens EJ, Hulzebos CV, Onland W, Kroon AA, Vijlbrief DC, Lopriore E, van der Bom JG. Dynamic prediction of bleeding risk in thrombocytopenic preterm neonates. Haematologica. 2019 Nov;104(11):2300-2306. doi: 10.3324/haematol.2018.208595. Epub 2019 Feb 28.