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Chen PC, Huang HC, Jeng MJ, Cheng FS. Benefits and Risks of Preprepared Parenteral Nutrition for Early Amino Acid Administration in Premature Infants with Very Low Birth Weight. Pediatr Gastroenterol Hepatol Nutr 2024; 27:246-257. [PMID: 39035403 PMCID: PMC11254649 DOI: 10.5223/pghn.2024.27.4.246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/06/2023] [Accepted: 05/26/2024] [Indexed: 07/23/2024] Open
Abstract
Purpose Administering early parenteral amino acids to very low birth weight (VLBW) premature infants (birth body weight [BBW]<1,500 g) is challenging due to factors such as holidays, cost, and access to sterile compounding facilities. Using advance-prepared parenteral nutrition (PN) may address this issue and should be evaluated for its safety and potential benefits. Methods We extracted data from medical records collected between July 2015 and August 2019. VLBW infants received PN for at least seven days and were split into two groups: the traditional group (n=30), which initially received a glucose solution and then PN on workdays, and the pre-preparation group (n=16), which received advance-prepared PN immediately upon admission to the neonatal intensive care unit. Results The median BBWs of the traditional and pre-preparation groups were 1,180.0 vs. 1,210.0 g. In the initial two days, the pre-preparation group had a significantly higher amino acid intake (2.23 and 2.24 g/kg/d) than the traditional group (0 and 1.78 g/kg/d). The pre-preparation group exhibited greater head circumference growth ratio relative to birth (7th day: 1.21% vs. -3.57%, p=0.014; 21st day: 7.71% vs. 3.31%, p=0.017). No significant differences in metabolic tolerance were observed. Conclusion Advanced preparation of PN can be safely implemented in VLBW preterm infants, offering advantages such as early, higher amino acid intake and improved head circumference growth within the first 21 days post-birth. This strategy may serve as a viable alternative in settings where immediate provision of sterile compounding facilities is challenging.
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Affiliation(s)
- Pin-Chun Chen
- Department of Pharmacy, Taipei City Hospital, Taipei City, Taiwan
- Institute of Emergency and Critical Care Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Hsin-Chung Huang
- Department of Pediatrics, National Taiwan University Hospital, Taipei City, Taiwan
| | - Mei-Jy Jeng
- Institute of Emergency and Critical Care Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
- Department of Pediatrics, Neonatal Medical Care Center, Taipei Veterans General Hospital, Taipei City, Taiwan
- Department of Pediatrics, School of Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan
| | - Feng-Shiang Cheng
- Department of Education and Research, Taipei City Hospital, Taipei City, Taiwan
- Department of Health and Welfare, University of Taipei, Taipei City, Taiwan
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Adeoye IA, Aleka BU, Afolabi RF, Oluwasola TAO. Postpartum length of hospital stay among obstetric patients in Ibadan, Nigeria. BMC Health Serv Res 2024; 24:586. [PMID: 38704565 PMCID: PMC11069136 DOI: 10.1186/s12913-024-11030-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 04/23/2024] [Indexed: 05/06/2024] Open
Abstract
BACKGROUND Postpartum Length of hospital stay (PLOHS) is an essential indicator of the quality of maternal and perinatal healthcare services. Identifying the factors associated with PLOHS will inform targeted interventions to reduce unnecessary hospitalisations and improve patient outcomes after childbirth. Therefore, we assessed the length of hospital stay after birth and the associated factors in Ibadan, Nigeria. METHODS We used the Ibadan Pregnancy Cohort Study (IbPCS) data, and examined the 1057 women who had information on PLOHS the mode of delivery [spontaneous vagina delivery (SVD) or caesarean section (C/S)]. The outcome variable was PLOHS, which was described as the time interval between the delivery of the infant and discharge from the health facility. PLOHS was prolonged if > 24 h for SVD and > 96 h for C/S, but normal if otherwise. Data were analysed using descriptive statistics, a chi-square test, and modified Poisson regression. The prevalence-risk ratio (PR) and 95% confidence interval (CI) are presented at the 5% significance level. RESULTS The mean maternal age was (30.0 ± 5.2) years. Overall, the mean PLOHS for the study population was 2.6 (95% CI: 2.4-2.7) days. The average PLOHS for women who had vaginal deliveries was 1.7 (95%CI: 1.5-1.9) days, whereas those who had caesarean deliveries had an average LOHS of 4.4 (95%CI: 4.1-4.6) days. About a third had prolonged PLOHS: SVD 229 (32.1%) and C/S 108 (31.5%). Factors associated with prolonged PLOHS with SVD, were high income (aPR = 1.77; CI: 1.13, 2.79), frequent ANC visits (> 4) (aPR = 2.26; CI: 1.32, 3.87), and antenatal admission: (aPR = 1.88; CI: 1.15, 3.07). For C/S: maternal age > 35 years (aPR = 1.59; CI: 1.02, 2.47) and hypertensive disease in pregnancy (aPR = 0.61 ; CI: 0.38, 0.99) were associated with prolonged PLOHS. CONCLUSION The prolonged postpartum length of hospital stay was common among our study participants occurring in about a third of the women irrespective of the mode of delivery. Maternal income, advanced maternal age, ANC related issues were predisposing factors for prolonged LOHS. Further research is required to examine providers' perspectives on PLOHS among obstetric patients in our setting.
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Affiliation(s)
- Ikeola A Adeoye
- Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Ibadan, Nigeria.
- Consortium of Advanced Research for Africa (CARTA), Nairobi, Kenya.
| | - Blessing U Aleka
- Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Rotimi F Afolabi
- Department of Epidemiology and Medical Statistics, Faculty of Public Health, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Timothy A O Oluwasola
- Department Obstetrics and Gynaecology, Faculty of Clinical Sciences, College of Medicine, University of Ibadan, Ibadan, Nigeria
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Hibbs AM, Chen Z, Minich NM, Martin RJ, Raffay TM, MacFarlane PM, Di Fiore JM. Association between Intermittent Hypoxemia and NICU Length of Stay in Preterm Infants. Neonatology 2024; 121:327-335. [PMID: 38437802 PMCID: PMC11147692 DOI: 10.1159/000535264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/14/2023] [Indexed: 03/06/2024]
Abstract
INTRODUCTION Length of hospitalization varies widely in preterm infants and can be affected by multiple maternal and neonatal factors including respiratory instability. Therefore, we aimed to determine the association between postnatal intermittent hypoxemia (IH) and prolonged hospitalization. METHODS This prospective single-center cohort study followed infants born at <31 weeks of gestational age through 2 years corrected age with detailed oxygen saturation data captured from days 7 to 30 of age. RESULTS 51/164 (31%) of infants were discharged after 400/7 weeks of corrected gestational age (CGA). A greater average daily number of IH events (OR per 10 events/day 1.33 [95% CI 1.03-1.72]), duration of events (OR per minute 1.14 [1.07-1.21]), and percent time with oxygen saturation <80% (OR per percent 1.88 [1.25-2.85]) on days 7-30 of age were all significantly associated with prolonged hospitalization past 400/7 weeks CGA. In survival analyses, infants with a greater average daily number of IH events (HR per 10 events/day 0.89 [0.81-0.98]), percent time with oxygen saturation <80% (HR per percent 0.79 [0.67-0.94]), and duration of events (HR per minute 0.93 [0.91-0.95]) on days 7-30 of age all had significantly lower probability of earlier discharge. In addition, there was a significant interaction with gestational age; the association between IH and prolonged hospitalization was stronger in more mature infants (p = 0.024). CONCLUSIONS Physiological instability on days 7-30 of age, as manifested by IH, is significantly associated with prolonged hospitalization. IH likely represents both a marker of initial severity of illness and the beginning of biological cascades, leading to prematurity-associated morbidities.
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Affiliation(s)
- Anna Maria Hibbs
- Department of Pediatrics, UH Rainbow Babies & Children's Hospital, Case Western Reserve University, Cleveland, Ohio, USA
| | - Zhengyi Chen
- Department of Population and Quantitative Health Sciences, University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio, USA
| | - Nori M Minich
- Department of Pediatrics, UH Rainbow Babies & Children's Hospital, Case Western Reserve University, Cleveland, Ohio, USA
| | - Richard J Martin
- Department of Pediatrics, UH Rainbow Babies & Children's Hospital, Case Western Reserve University, Cleveland, Ohio, USA
| | - Thomas M Raffay
- Department of Pediatrics, UH Rainbow Babies & Children's Hospital, Case Western Reserve University, Cleveland, Ohio, USA
| | - Peter M MacFarlane
- Department of Pediatrics, UH Rainbow Babies & Children's Hospital, Case Western Reserve University, Cleveland, Ohio, USA
| | - Juliann M Di Fiore
- Department of Pediatrics, UH Rainbow Babies & Children's Hospital, Case Western Reserve University, Cleveland, Ohio, USA
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Demirci GM, Kittler PM, Phan HTT, Gordon AD, Flory MJ, Parab SM, Tsai CL. Predicting mental and psychomotor delay in very pre-term infants using machine learning. Pediatr Res 2024; 95:668-678. [PMID: 37500755 PMCID: PMC10899098 DOI: 10.1038/s41390-023-02713-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/25/2023] [Accepted: 06/15/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND Very preterm infants are at elevated risk for neurodevelopmental delays. Earlier prediction of delays allows timelier intervention and improved outcomes. Machine learning (ML) was used to predict mental and psychomotor delay at 25 months. METHODS We applied RandomForest classifier to data from 1109 very preterm infants recruited over 20 years. ML selected key predictors from 52 perinatal and 16 longitudinal variables (1-22 mo assessments). SHapley Additive exPlanations provided model interpretability. RESULTS Balanced accuracy with perinatal variables was 62%/61% (mental/psychomotor). Top predictors of mental and psychomotor delay overlapped and included: birth year, days in hospital, antenatal MgSO4, days intubated, birth weight, abnormal cranial ultrasound, gestational age, mom's age and education, and intrauterine growth restriction. Highest balanced accuracy was achieved with 19-month follow-up scores and perinatal variables (72%/73%). CONCLUSIONS Combining perinatal and longitudinal data, ML modeling predicted 24 month mental/psychomotor delay in very preterm infants ½ year early, allowing intervention to start that much sooner. Modeling using only perinatal features fell short of clinical application. Birth year's importance reflected a linear decline in predicting delay as birth year became more recent. IMPACT Combining perinatal and longitudinal data, ML modeling was able to predict 24 month mental/psychomotor delay in very preterm infants ½ year early (25% of their lives) potentially advancing implementation of intervention services. Although cognitive/verbal and fine/gross motor delays require separate interventions, in very preterm infants there is substantial overlap in the risk factors that can be used to predict these delays. Birth year has an important effect on ML prediction of delay in very preterm infants, with those born more recently (1989-2009) being increasing less likely to be delayed, perhaps reflecting advances in medical practice.
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Affiliation(s)
- Gözde M Demirci
- Computer Science Department, The Graduate Center of the City University of NY, New York, NY, USA
| | - Phyllis M Kittler
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Ha T T Phan
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Anne D Gordon
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Michael J Flory
- Department of Infant Development, NYS Institute for Basic Research in Developmental Disabilities, Staten Island, NY, USA
| | - Santosh M Parab
- Pediatrics, Richmond University Medical Center, Staten Island, NY, USA
| | - Chia-Ling Tsai
- Computer Science Department, Queens College of the City University of NY, Flushing, NY, USA.
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de Boer A, van Beek PE, Andriessen P, Groenendaal F, Hogeveen M, Meijer JS, Obermann-Borst SA, Onland W, Scheepers L(HCJ, Vermeulen MJ, Verweij EJT(J, De Proost L, Geurtzen R. Opportunities and Challenges of Prognostic Models for Extremely Preterm Infants. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1712. [PMID: 37892375 PMCID: PMC10605480 DOI: 10.3390/children10101712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/06/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023]
Abstract
Predicting the short- and long-term outcomes of extremely preterm infants remains a challenge. Multivariable prognostic models might be valuable tools for clinicians, parents, and policymakers for providing accurate outcome estimates. In this perspective, we discuss the opportunities and challenges of using prognostic models in extremely preterm infants at population and individual levels. At a population level, these models could support the development of guidelines for decisions about treatment limits and may support policy processes such as benchmarking and resource allocation. At an individual level, these models may enhance prenatal counselling conversations by considering multiple variables and improving transparency about expected outcomes. Furthermore, they may improve consistency in projections shared with parents. For the development of prognostic models, we discuss important considerations such as predictor and outcome measure selection, clinical impact assessment, and generalizability. Lastly, future recommendations for developing and using prognostic models are suggested. Importantly, the purpose of a prognostic model should be clearly defined, and integrating these models into prenatal counselling requires thoughtful consideration.
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Affiliation(s)
- Angret de Boer
- Department of Neonatology, Amalia Children’s Hospital, Radboud University Medical Center, Geert Grooteplein Zuid 32, 6525 GA Nijmegen, The Netherlands; (P.E.v.B.); (M.H.); (R.G.)
- Department of Obstetrics and Gynecology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands;
| | - Pauline E. van Beek
- Department of Neonatology, Amalia Children’s Hospital, Radboud University Medical Center, Geert Grooteplein Zuid 32, 6525 GA Nijmegen, The Netherlands; (P.E.v.B.); (M.H.); (R.G.)
- Department of Neonatology, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands; (P.A.); (J.S.M.)
| | - Peter Andriessen
- Department of Neonatology, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands; (P.A.); (J.S.M.)
| | - Floris Groenendaal
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, 3584 EA Utrecht, The Netherlands;
| | - Marije Hogeveen
- Department of Neonatology, Amalia Children’s Hospital, Radboud University Medical Center, Geert Grooteplein Zuid 32, 6525 GA Nijmegen, The Netherlands; (P.E.v.B.); (M.H.); (R.G.)
| | - Julia S. Meijer
- Department of Neonatology, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands; (P.A.); (J.S.M.)
| | - Sylvia A. Obermann-Borst
- Care4Neo, Dutch Neonatal Patient and Parent Advocacy Organization, 3068 JN Rotterdam, The Netherlands; (S.A.O.-B.); (M.J.V.)
| | - Wes Onland
- Department of Neonatology, Emma Children’s Hospital, Amsterdam University Medical Center, 1105 AZ Amsterdam, The Netherlands;
- Amsterdam Reproduction & Development, 1105 AZ Amsterdam, The Netherlands
| | | | - Marijn J. Vermeulen
- Care4Neo, Dutch Neonatal Patient and Parent Advocacy Organization, 3068 JN Rotterdam, The Netherlands; (S.A.O.-B.); (M.J.V.)
- Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Sophia Children’s Hospital, Erasmus Medical Center, 3015 CN Rotterdam, The Netherlands
| | - E. J. T. (Joanne) Verweij
- Department of Obstetrics and Gynecology, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands;
| | - Lien De Proost
- Department of Ethics and Law, Leiden University Medical Centre, 2333 ZA Leiden, The Netherlands;
| | - Rosa Geurtzen
- Department of Neonatology, Amalia Children’s Hospital, Radboud University Medical Center, Geert Grooteplein Zuid 32, 6525 GA Nijmegen, The Netherlands; (P.E.v.B.); (M.H.); (R.G.)
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Khan W, Zaki N, Ahmad A, Bian J, Ali L, Mehedy Masud M, Ghenimi N, Ahmed LA. Infant Low Birth Weight Prediction Using Graph Embedding Features. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1317. [PMID: 36674072 PMCID: PMC9859143 DOI: 10.3390/ijerph20021317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/30/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
Low Birth weight (LBW) infants pose a serious public health concern worldwide in both the short and long term for infants and their mothers. Infant weight prediction prior to birth can help to identify risk factors and reduce the risk of infant morbidity and mortality. Although many Machine Learning (ML) algorithms have been proposed for LBW prediction using maternal features and produced considerable model performance, their performance needs to be improved so that they can be adapted in real-world clinical settings. Existing algorithms used for LBW classification often fail to capture structural information from the tabular dataset of patients with different complications. Therefore, to improve the LBW classification performance, we propose a solution by transforming the tabular data into a knowledge graph with the aim that patients from the same class (normal or LBW) exhibit similar patterns in the graphs. To achieve this, several features related to each node are extracted such as node embedding using node2vec algorithm, node degree, node similarity, nearest neighbors, etc. Our method is evaluated on a real-life dataset obtained from a large cohort study in the United Arab Emirates which contains data from 3453 patients. Multiple experiments were performed using the seven most commonly used ML models on the original dataset, graph features, and a combination of features, respectively. Experimental results show that our proposed method achieved the best performance with an area under the curve of 0.834 which is over 6% improvement compared to using the original risk factors without transforming them into knowledge graphs. Furthermore, we provide the clinical relevance of the proposed model that are important for the model to be adapted in clinical settings.
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Affiliation(s)
- Wasif Khan
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nazar Zaki
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Amir Ahmad
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, USA
| | - Luqman Ali
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Mohammad Mehedy Masud
- Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Nadirah Ghenimi
- Department Family Medicine, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Luai A. Ahmed
- Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
- Zayed Centre for Health Sciences, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
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Kao JH. Highlights. J Formos Med Assoc 2022. [DOI: 10.1016/j.jfma.2022.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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