A Risk Score for Predicting the Incidence of Hemorrhage in Critically Ill Neonates: Development and Validation Study

Abstract

The aim of the study was to develop and validate a prediction model for hemorrhage in critically ill neonates which combines rotational thromboelastometry (ROTEM) parameters and clinical variables. This cohort study included 332 consecutive full-term and preterm critically ill neonates. We performed ROTEM and used the neonatal bleeding assessment tool (NeoBAT) to record bleeding events. We fitted double selection least absolute shrinkage and selection operator logit regression to build our prediction model. Bleeding within 24 hours of the ROTEM testing was the outcome variable, while patient characteristics, biochemical, hematological, and thromboelastometry parameters were the candidate predictors of bleeding. We used both cross-validation and bootstrap as internal validation techniques. Then, we built a prognostic index of bleeding by converting the coefficients from the final multivariable model of relevant prognostic variables into a risk score. A receiver operating characteristic analysis was used to calculate the area under curve (AUC) of our prediction index. EXTEM A10 and LI60, platelet counts, and creatinine levels were identified as the most robust predictors of bleeding and included them into a Neonatal Bleeding Risk (NeoBRis) index. The NeoBRis index demonstrated excellent model performance with an AUC of 0.908 (95% confidence interval [CI]: 0.870–0.946). Calibration plot displayed optimal calibration and discrimination of the index, while bootstrap resampling ensured internal validity by showing an AUC of 0.907 (95% CI: 0.868–0.947). We developed and internally validated an easy-to-apply prediction model of hemorrhage in critically ill neonates. After external validation, this model will enable clinicians to quantify the 24-hour bleeding risk.

Keywords

thromboelastometry - prediction model - hemorrhage - critically ill neonates - Neonatal Bleeding Risk index

Publication History

Received: 19 April 2020

Accepted: 21 July 2020

Article published online:
24 August 2020

• References

• 1 Radicioni M, Mezzetti D, Del Vecchio A, Motta M. Thromboelastography: might work in neonatology too?. J Matern Fetal Neonatal Med 2012; 25 (Suppl. 04) 18-21
• 2 Ignjatovic V, Kenet G, Monagle P. Perinatal and Paediatric Haemostasis Subcommittee of the Scientific and Standardization Committee of the International Society on Thrombosis and Haemostasis. Developmental hemostasis: recommendations for laboratories reporting pediatric samples. J Thromb Haemost 2012; 10 (02) 298-300
• 3 Mallett SV, Cox DJ. Thrombelastography. Br J Anaesth 1992; 69 (03) 307-313
• 4 Konstantinidi A, Sokou R, Parastatidou S. et al. Clinical application of thromboelastography/thromboelastometry (TEG/TEM) in the neonatal population: a narrative review. Semin Thromb Hemost 2019; 45 (05) 449-457
• 5 Sokou R, Foudoulaki-Paparizos L, Lytras T. et al. Reference ranges of thromboelastometry in healthy full-term and pre-term neonates. Clin Chem Lab Med 2017; 55 (10) 1592-1597
• 6 Sokou R, Giallouros G, Konstantinidi A. et al. Thromboelastometry for diagnosis of neonatal sepsis-associated coagulopathy: an observational study. Eur J Pediatr 2018; 177 (03) 355-362
• 7 Konstantinidi A, Sokou R, Tsantes AG. et al. Thromboelastometry variables in neonates with perinatal hypoxia. Semin Thromb Hemost 2020; 46 (04) 428-434
• 8 Pakvasa MA, Winkler AM, Hamrick SE, Josephson CD, Patel RM. Observational study of haemostatic dysfunction and bleeding in neonates with hypoxic-ischaemic encephalopathy. BMJ Open 2017; 7 (02) e013787
• 9 Baer VL, Lambert DK, Henry E, Christensen RD. Severe thrombocytopenia in the NICU. Pediatrics 2009; 124 (06) e1095-e1100
• 10 Goel R, Josephson CD. Recent advances in transfusions in neonates/infants. F1000 Res 2018; 7: F1000 Faculty Rev–609
• 11 Wikkelsø A, Wetterslev J, Møller AM, Afshari A. Thromboelastography (TEG) or thromboelastometry (ROTEM) to monitor haemostatic treatment versus usual care in adults or children with bleeding. Cochrane Database Syst Rev 2016; 8 (08) CD007871
• 12 Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015; 350: g7594
• 13 Richardson DK, Corcoran JD, Escobar GJ, Lee SK. SNAP-II and SNAPPE-II: simplified newborn illness severity and mortality risk scores. J Pediatr 2001; 138 (01) 92-100
• 14 Venkatesh V, Curley A, Khan R. et al. A novel approach to standardised recording of bleeding in a high risk neonatal population. Arch Dis Child Fetal Neonatal Ed 2013; 98 (03) F260-F263
• 15 Belloni A, Chernozhukov V, Wei Y. Post-selection inference for generalized linear models with many controls. J Bus Econ Stat 2016; 34: 606-619
• 16 Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc 1996; 58: 267-288
• 17 Steyerberg EW, Eijkemans MJ, Habbema JD. Application of shrinkage techniques in logistic regression analysis: a case study. Stat Neerl 2001; 55: 76-88
• 18 Dormann CF, Elith J, Bacher S. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013; 36: 27-46
• 19 Lee YH, Bang H, Kim DJ. How to establish clinical prediction models. Endocrinol Metab (Seoul) 2016; 31 (01) 38-44
• 20 Chakravorty S, Murray N, Roberts I. Neonatal thrombocytopenia. Early Hum Dev 2005; 81 (01) 35-41
• 21 Fustolo-Gunnink SF, Huisman EJ, van der Bom JG. et al. Are thrombocytopenia and platelet transfusions associated with major bleeding in preterm neonates? A systematic review. Blood Rev 2019; 36: 1-9
• 22 Williams MD, Chalmers EA, Gibson BE. Haemostasis and Thrombosis Task Force, British Committee for Standards in Haematology. The investigation and management of neonatal haemostasis and thrombosis. Br J Haem 2002; 119: 295-309
• 23 Luque MJ, Tapia JL, Villarroel L. Neocosur Neonatal Network. et al; A risk prediction model for severe intraventricular hemorrhage in very low birth weight infants and the effect of prophylactic indomethacin. J Perinatol 2014; 34 (01) 43-48
• 24 Heuchan AM, 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 2002; 86: F86-F90
• 25 Singh R, Gorstein SV, Bednarek F, Chou JH, McGowan EC, Visintainer PF. A predictive model for SIVH risk in preterm infants and targeted indomethacin therapy for prevention. Sci Rep 2013; 3: 2539
• 26 Gleissner M, Jorch G, Avenarius S, Kinderheilkunde Z, Magdeburg OU. Risk factors for intraventricular hemorrhage in a birth cohort of 3721 premature infants. J Perinat Med 2000; 28 (02) 104-110
• 27 Fustolo-Gunnink SF, Fijnvandraat K, Putter H. et al. Dynamic prediction of bleeding risk in thrombocytopenic preterm neonates. Haematologica 2019; 104 (11) 2300-2306
• 28 Stanworth SJ. Thrombocytopenia, bleeding, and use of platelet transfusions in sick neonates. Hematology (Am Soc Hematol Educ Program) 2012; 2012: 512-516
• 29 Andreucci VE. Acute Renal Failure. Pathophysiology, Prevention, and Treatment. Boston: Springer US; 1984
• 30 Agarwal B, Gatt A, Riddell A. et al. Hemostasis in patients with acute kidney injury secondary to acute liver failure. Kidney Int 2013; 84 (01) 158-163
• 31 Brenner T, Schmidt K, Delang M. et al. Viscoelastic and aggregometric point-of-care testing in patients with septic shock - cross-links between inflammation and haemostasis. Acta Anaesthesiol Scand 2012; 56 (10) 1277-1290
• 32 Adamzik M, Eggmann M, Frey UH. et al. Comparison of thromboelastometry with procalcitonin, interleukin 6, and C-reactive protein as diagnostic tests for severe sepsis in critically ill adults. Crit Care 2010; 14 (05) R178
• 33 Steyerberg EW, Moons KG, van der Windt DA. PROGRESS Group. et al; Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013; 10 (02) e1001381