Prediction modelling in the early detection of neonatal sepsis
Tóm tắt
Prediction modelling can greatly assist the health-care professionals in the management of diseases, thus sparking interest in neonatal sepsis diagnosis. The main objective of the study was to provide a complete picture of performance of prediction models for early detection of neonatal sepsis. PubMed, Scopus, CINAHL databases were searched and articles which used various prediction modelling measures for the early detection of neonatal sepsis were comprehended. Data extraction was carried out based on Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Extricate data consisted of objective, study design, patient characteristics, type of statistical model, predictors, outcome, sample size and location. Prediction model Risk of Bias Assessment Tool was applied to gauge the risk of bias of the articles. An aggregate of ten studies were included in the review among which eight studies had applied logistic regression to build a prediction model, while the remaining two had applied artificial intelligence. Potential predictors like neonatal fever, birth weight, foetal morbidity and gender, cervicovaginitis and maternal age were identified for the early detection of neonatal sepsis. Moreover, birth weight, endotracheal intubation, thyroid hypofunction and umbilical venous catheter were promising factors for predicting late-onset sepsis; while gestational age, intrapartum temperature and antibiotics treatment were utilised as budding prognosticators for early-onset sepsis detection. Prediction modelling approaches were able to recognise promising maternal, neonatal and laboratory predictors in the rapid detection of early and late neonatal sepsis and thus, can be considered as a novel way for clinician decision-making towards the disease diagnosis if not used alone, in the years to come.
Tài liệu tham khảo
Zea-Vera A, Ochoa TJ. Challenges in the diagnosis and management of neonatal sepsis. J Trop Pediatr. 2015;61:1–13.
Iroh Tam PY, Bendel CM. Diagnostics for neonatal sepsis: current approaches and future directions. Pediatr Res. 2017;82:574–83.
Gilfillan M, Bhandari V. Neonatal sepsis biomarkers: where are we now? Res Rep Neonatol. 2019;9:9–20.
Yu JC, Khodadadi H, Malik A, Davidson B, Salles ÉDSL, Bhatia J, et al. Innate immunity of neonates and infants. Front Immunol. 2018;9:1759.
Frankenfield J. What is predictive modelling? Investopedia [Internet] 2020. Available from: www.investopedia.com/terms/p/predictive-modeling.asp. Accessed 27 Dec 2020.
Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338:b375.
Royston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: developing a prognostic model. BMJ. 2009;338:604.
Beltempo M, Viel-Thériault I, Thibeault R, Julien AS, Piedboeuf B. C-reactive protein for late-onset sepsis diagnosis in very low birth weight infants. BMC Pediatr. 2018;18:16.
He Y, Du WX, Jiang HY, Ai Q, Feng J, Liu Z, et al. Multiplex cytokine profiling identifies interleukin-27 as a novel biomarker for neonatal early onset sepsis. Shock. 2017;47:140–7.
Yang AP, Liu J, Yue LH, Wang HQ, Yang WJ, Yang GH. Neutrophil CD64 combined with PCT, CRP and WBC improves the sensitivity for the early diagnosis of neonatal sepsis. Clin Chem Lab Med. 2016;54:345–51.
Van der Ham DP, Van Kuijk S, Opmeer BC, Willekes C, Van Beek JJ, Mulder AL, et al. Can neonatal sepsis be predicted in late preterm premature rupture of membranes? Development of a prediction model. Eur J Obstet Gynecol Reprod Biol. 2014;176:90–5.
Adatara P, Afaya A, Salia SM, Afaya RA, Konlan KD, Agyabeng-Fandoh E, et al. Risk factors associated with neonatal sepsis: a case study at a specialist hospital in Ghana. Sci World J. 2019;2019:9369051.
Popowski T, Goffinet F, Maillard F, Schmitz T, Leroy S, Kayem G. Maternal markers for detecting early-onset neonatal infection and chorioamnionitis in cases of premature rupture of membranes at or after 34 weeks of gestation: a two-center prospective study. BMC Pregnancy Childbirth. 2011;11:26.
Stocker M, Daunhawer I, van Herk W, El Helou S, Dutta S, Schuerman F, et al. Machine learning used to compare the diagnostic accuracy of risk factors, clinical signs and biomarkers and to develop a new prediction model for neonatal early-onset sepsis. Pediatr Infect Dis J. 2021. https://doi.org/10.1097/INF.0000000000003344.
Weber EJ, Sanchez LC, Giguère S. Re-evaluation of the sepsis score in equine neonates. Equine Vet J. 2015;47:275–8.
Achten NB, Klingenberg C, Benitz WE, Stocker M, Schlapbach LJ, Giannoni E, et al. Association of use of the neonatal early-onset sepsis calculator with reduction in antibiotic therapy and safety: a systematic review and meta-analysis. JAMA Pediatr. 2019;173:1032–40.
Singh M, Alsaleem M, Gray CP. Neonatal Sepsis. [Updated 2021 Oct 10]. In: StatPearls [Internet] 2021. Available from: www.ncbi.nlm.nih.gov/books/NBK531478/. Accessed 10 Oct 2020.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097.
Moons KG, de Groot JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014;11:e1001744.
Wee L, van Kuijk SMJ, Dankers F, Traverso A, Welch M, Dekker A. Reporting standards and critical appraisal of prediction models. In: Kubben P, Dumontier M, Dekker A, editors. Fundamentals of clinical data science. Cham (CH): Springer; 2019. p. 135–50.
Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019;170:51–8.
Goldberg O, Amitai N, Chodick G, Bromiker R, Scheuerman O, Ben-Zvi H, et al. Can we improve early identification of neonatal late-onset sepsis? A validated prediction model. J Perinatol. 2020;40:1315–22.
Huang Y, Yu X, Li W, Li Y, Yang J, Hu Z, et al. Development and validation of a nomogram for predicting late-onset sepsis in preterm infants on the basis of thyroid function and other risk factors: mixed retrospective and prospective cohort study. J Adv Res. 2020;24:43–51.
Puopolo KM, Draper D, Wi S, Newman TB, Zupancic J, Lieberman E, et al. Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors. Paediatrics. 2011;128:1155–63.
Escobar GJ, Puopolo KM, Wi S, Turk BJ, Kuzniewicz MW, Walsh EM, et al. Stratification of risk of early-onset sepsis in newborns ≥ 34 weeks’ gestation. Pediatrics. 2014;133:30–6.
López-Martínez F, Núñez-Valdez ER, Lorduy Gomez J, García-Díaz V. A neural network approach to predict early neonatal sepsis. Comput Electr Eng. 2019;76:379–88.
Helguera-Repetto AC, Soto-Ramírez MD, Villavicencio-Carrisoza O, Yong-Mendoza S, Yong-Mendoza A, León-Juárez M, et al. Neonatal sepsis diagnosis decision-making based on artificial neural networks. Front Pediatr. 2020;8:525.
Stanculescu I, Williams CK, Freer Y. Autoregressive hidden Markov models for the early detection of neonatal sepsis. IEEE J Biomed Health Inform. 2014;18:1560–70.
Thakur J, Pahuja S, Pahuja R. Non-invasive prediction model for developing countries to predict SEPSIS in neonates. Biomed Eng Appl Basis Commun. 2019;31:1950001.
Thakur J, Pahuja SK, Pahuja R. Neonatal sepsis prediction model for resource-poor developing countries. 2nd International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech) 2018:1–5.
Fell DB, Hawken S, Wong CA, Wilson LA, Murphy MSQ, Chakraborty P, et al. Using newborn screening analytes to identify cases of neonatal sepsis. Sci Rep. 2017;7:18020.
Achten NB, Klingenberg C, Benitz WE, Stocker M, Schlapbach LJ, Giannoni E, et al. Association of use of the neonatal early-onset sepsis calculator with reduction in antibiotic therapy and safety: a systematic review and meta-analysis. JAMA Paediatr. 2019;173:1032–40.
Deshmukh M, Mehta S, Patole S. Sepsis calculator for neonatal early onset sepsis—a systematic review and meta-analysis. J MaternFetal Neonatal Med. 2021;34:1832–40.
Harder T, Seidel J, Eckmanns T, Weiss B, Haller S. Predicting late-onset sepsis by routine neonatal screening for colonisation by gram-negative bacteria in neonates at intensive care units: a protocol for a systematic review. BMJ Open. 2017;7:e014986.
Liang LD, Kotadia N, English L, Kissoon N, Ansermino JM, Kabakyenga J, et al. Predictors of mortality in neonates and infants hospitalized with sepsis or serious infections in developing countries: a systematic review. Front Pediatr. 2018;6:277.
Seidel J, Haller S, Eckmanns T, Harder T. Routine screening for colonization by Gram-negative bacteria in neonates at intensive care units for the prediction of sepsis: systematic review and meta-analysis. J Hosp Infect. 2018;99:367–80.
Zhang S, Luan X, Zhang W, Jin Z. Platelet-to-lymphocyte and neutrophil-to-lymphocyte ratio as predictive biomarkers for early-onset neonatal sepsis. J Coll Physicians Surg Pak. 2021;30:821–4.
Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med. 2016;35:214–26.
Advantages and Disadvantages of Logistic Regression [Internet]. OpenGenus IQ: Computing Expertise & Legacy. 2021. Available from: https://iq.opengenus.org/advantages-and-disadvantages-of-logistic-regression/. Accessed 25 Nov 2021.
Logistic Regression Pros & Cons - HolyPython.com [Internet]. HolyPython.com. 2021. Available from: https://holypython.com/log-reg/logistic-regression-pros-cons. Accessed 25 Nov 2021.
König IR, Malley JD, Weimar C, Diener HC, Ziegler A. Practical experiences on the necessity of external validation. Stat Med. 2007;26:5499–511.
Chung KC, Song JW. A guide to organizing a multicenter clinical trial. Plast Reconstr Surg. 2010;126:515–23.
Casagranda I, Vendramin C, Callegari T, Vidali M, Calabresi A, Ferrandu G, et al. Usefulness of suPAR in the risk stratification of patients with sepsis admitted to the emergency department. Intern Emerg Med. 2015;10:725–30.
Strengths and weaknesses of hidden Markov models [Internet]. Compbio.soe.ucsc.edu. 2021. Available from: https://compbio.soe.ucsc.edu/html_format_papers/tr-94-24/node11.html. Accessed 25 Nov 2021.
Bleeker SE, Moll HA, Steyerberg EW, Donders AR, Derksen-Lubsen G, Grobbee DE, et al. External validation is necessary in prediction research: a clinical example. J Clin Epidemiol. 2003;56:826–32.
Mayer D, Butler D. Statistical validation. Ecol Model. 1993;68:21–32.
Collins GS, de Groot JA, Dutton S, Omar O, Shanyinde M, Tajar A, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol. 2014;14:40.
Moons KG, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I, Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98:683–90.