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Naderifar E, Tarameshlu M, Salehi R, Ghelichi L, Bordbar A, Moradi N, Lessen Knoll B. A Single-Subject Study to Consider the Premature Infant Oral Motor Intervention Combined with Kinesio-Tape in Premature Infants with Feeding Problems. Med J Islam Repub Iran 2024; 38:38. [PMID: 38978793 PMCID: PMC11230598 DOI: 10.47176/mjiri.38.38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Indexed: 07/10/2024] Open
Abstract
Background The survival rate in premature infants (PIs) has increased, but many have medical and developmental complications. Difficulty with sucking, swallowing, and poor nourishment are common complications. This study aimed to investigate the effects of Kinesio-tape (KT) combined with premature infant oromotor intervention (PIOMI) on feeding efficiency (mean volume intake [%MV]), oromotor skills (Preterm Oral Feeding Readiness Assessment Scale [POFRAS]), and weight gain in PIs. Methods In this single-subject study, 5 PIs with feeding problems were received the PIOMI-KT for 7 consecutive days. The main outcome measure was the POFRAS scale. The %MV and weight gain were the secondary outcome measures. Measurements were taken before treatment (T0), after the 4th session (T1), and after the 7th session (T3). Results The POFRAS scores, %MV, and weight gain improved in all infants after treatment. The maximum and minimum change in level between the baseline and treatment phase was +26 and+16 for POFRAS, +54 and, +34 for %MV, +180, and +100 for weight gain. The treatment trend was upward for all infants and shown by the directions of the slopes indicated by positive values. The feeding problems were resolved in all infants after the 7th treatment session. Conclusion The combination therapy of PIOMI-KT improved feeding function in PIs.
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Affiliation(s)
- Ehsan Naderifar
- Department of Speech and Language Pathology, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Maryam Tarameshlu
- Rehabilitation Research Center, Department of Speech and Language Pathology, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Reza Salehi
- Rehabilitation Research Center, Department of Physiotherapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Leila Ghelichi
- Rehabilitation Research Center, Department of Speech and Language Pathology, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Arash Bordbar
- Department of Pediatrics (Neonatology), Iran University of Medical Sciences, Akbarabadi Teaching Hospital, Tehran, Iran
| | - Negin Moradi
- Musculoskeletal Rehabilitation Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Brenda Lessen Knoll
- School of Nursing, Illinois Wesleyan University, STV Hall, 203 Beecher Street, Bloomington, IL 61702
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Zhang Y, Chen C, Huang L, Liu G, Lian T, Yin M, Zhao Z, Xu J, Chen R, Fu Y, Liang D, Zeng J, Ni J. Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records. JMIR Public Health Surveill 2022; 8:e38182. [PMID: 36422885 PMCID: PMC9732753 DOI: 10.2196/38182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/13/2022] [Accepted: 09/10/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Multimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults. OBJECTIVE This study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults. METHODS Data from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions. RESULTS In total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women. CONCLUSIONS Multimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity.
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Affiliation(s)
- Yan Zhang
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Chao Chen
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Lingfeng Huang
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Gang Liu
- Department of Primary Public Health Promotion, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Tingyu Lian
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Mingjuan Yin
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Zhiguang Zhao
- Administration Office, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Jian Xu
- Department of Elderly Health Management, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Ruoling Chen
- Faculty of Education, Health and Wellbeing, University of Wolverhampton, Wolverhampton, United Kingdom
| | - Yingbin Fu
- Department of Primary Public Health Promotion, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Dongmei Liang
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Jinmei Zeng
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
| | - Jindong Ni
- Precision Key Laboratory of Public Health, School of Public Health, Guangdong Medical University, Dongguan, China
- Institute of Public Health and Wellness, Guangdong Medical University, Dongguan, China
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Haghdoost S, Abdi F, Amirian A. Iranian midwives' awareness and performance of respectful maternity care during labor and childbirth. Eur J Midwifery 2021; 5:59. [PMID: 35083427 PMCID: PMC8711250 DOI: 10.18332/ejm/143873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 10/29/2021] [Accepted: 11/10/2021] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Midwives' perceptions of Respectful Maternity Care (RMC) play an important role in promoting quality of care. This study aimed to explore the awareness and performance of Iranian midwives of RMC during childbirth. METHODS A cross-sectional study was carried out from November to December 2020 to evaluate 130 midwives' awareness and performance of RMC during childbirth at four public hospitals in Urmia province, Iran. Participants were midwives who were working in the labor unit and had at least one year of work experience. The Midwives' Knowledge and Practice Scale on Respectful Maternity Care was used to assess midwives' awareness and performance. The quality assessment of questionnaires was based on the mean for each item. A multivariate linear regression approach was developed to evaluate the relationship between midwives' age, academic education level plus occupational information and their awareness and performance of RMC. RESULTS This study revealed that Iranian midwives had good awareness but fair performance of RMC. The mean scores of the overall awareness and performance of RMC were 36.07±10.13 and 75.47±35.4, respectively. Midwives' performance on two domains was fair including 'Giving emotional support' and 'Providing safe care'. The results of multivariate linear regression analysis showed a significant negative relationship between job satisfaction and midwives' performance on RMC. Also work experience plus a Master's degree in midwifery had positive significant effects on midwives' awareness along with performance on RMC (p<0.05). CONCLUSIONS Promoting respectful maternity care requires essential interpersonal and communication skills and supportive attitudes from midwives.
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Affiliation(s)
- Simin Haghdoost
- Department of Midwifery and Reproductive Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh Abdi
- School of Nursing and Midwifery, Alborz University of Medical Sciences, Karaj, Iran
| | - Azam Amirian
- Department of Midwifery, School of Nursing and Midwifery, Jiroft University of Medical Sciences, Jiroft, Iran
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Alavi A, Razmjoue P, Safari-Moradabadi A, Dadipoor S, Shahsavari S. Maternal predictive factors for preterm birth: A case-control study in Southern Iran. JOURNAL OF EDUCATION AND HEALTH PROMOTION 2021; 10:124. [PMID: 34222499 PMCID: PMC8224520 DOI: 10.4103/jehp.jehp_668_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 08/25/2020] [Indexed: 06/13/2023]
Abstract
BACKGROUND Preterm birth (PTB) is one of the most important factors that increase the risk of chronic diseases and postpartum death in infants. The aim of this study was to determine the maternal factors that affect the birth of preterm infants in the city of Bandar Abbas. MATERIALS AND METHODS This is a case-control study that was performed on 400 preterm infants. Sampling was done by a simple method, and information was gathered by interviewing the mothers and their medical records. Data were collected by SPSS software version 16. To compare risk factors in the two groups, conditional logistic regression was used, and P < 0.05 was considered statistically significant. RESULTS Results showed that factors such as type of delivery (odds ratio [OR] = 3.584, 95% confidence interval [CI]: 1.981-6.485), preeclampsia (OR = 2.688, 95% CI: 1.164-6.207), history of PTB (OR = 4.171, 95% CI: 1.483-11.728), premature rupture of membranes (OR = 3.273, 95% CI: 1.745-6.137), care during prenatal (OR = 0.334, 95% CI: 0.159-0.701), placental abruption (OR = 3.209, 95% CI: 1.209-8.519), placenta previa (OR = 9.333, 95% CI: 2.086-41.770), and cervical insufficiency (OR = 11, 95% CI: 1.381-87.641) were independent risk factors of preterm infant birth. CONCLUSIONS The PTB risk is higher for women with cervical insufficiency, history of placenta previa, and history of preterm. Early recognition and management of these high-risk conditions among pregnant women may lead to a reduction in PTB rates.
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Affiliation(s)
- Azin Alavi
- Fertility and Infertility Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Parisa Razmjoue
- Department of Obstetrics and Gynecology, Shahid Faghihi Hospital, Shiraz, Iran
| | - Ali Safari-Moradabadi
- Department of Public Health, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sakineh Dadipoor
- Mother and Child Welfare Research Center, Faculty of Nursing and Midwifery, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
| | - Saeideh Shahsavari
- Fertility and Infertility Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
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Rouholamin S, Razavi M, Rezaeinejad M, Sepidarkish M. A diagnostic profile on the PartoSure test. Expert Rev Mol Diagn 2020; 20:1163-1170. [PMID: 33175636 DOI: 10.1080/14737159.2020.1848549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Introduction: Preterm birth (PTB) is common, occurring in over 10% of all live births globally, and is increasing worldwide. The limitations of traditional biomarkers of PTB, such as fetal fibronectin (fFN) and phosphorylated insulin-like growth factor-binding protein-1 (phIGFBP-1) have been well demonstrated in the literature. Therefore, augmenting clinical assessment with newer biomarkers, such as placental alpha macroglobulin-1 (PAMG-1); PartoSure, has the potential to improve disease monitoring and the best interventions. Areas covered: The present expert opinion evaluates the utility and limitations of PAMG-1; PartoSure as a biomarker for PTB in light of the current literature. Expert opinion: Although fFN, phIGFBP-1 and PAMG-1; PartoSure test had similar negative predictive value (NPV) and negative likelihood ratio (LR-), the PAMG-1; PartoSure test had the highest specificity, positive predictive value (PPV), and positive likelihood ratio (LR+) across all at-risk pregnant women. Although findings of this review may be encouraging, the PartoSure test should not be interpreted as absolute evidence for prediction of PTB. The PartoSure test result should always be used in conjunction with information available from the clinical evaluation of the pregnant woman and other diagnostic procedures such as cervical examination, assessment of uterine activity, and evaluation of other risk factors.
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Affiliation(s)
- Safoura Rouholamin
- Department of Obstetrics and Gynecology, School of Medicine, Isfahan University of Medical Sciences , Isfahan, Iran
| | - Maryam Razavi
- Pregnancy Health Research Center, Department of Obstetrics and Gynecology, School of Medicine, Zahedan University of Medical Sciences , Zahedan, Iran
| | - Mahroo Rezaeinejad
- Department of Obstetrics and Gynecology, Imam Khomeini Hospital, Tehran University of Medical Sciences , Tehran, Iran
| | - Mahdi Sepidarkish
- Department of Biostatistics and Epidemiology, School of Public Health, Babol University of Medical Sciences , Babol, Iran
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Sufriyana H, Husnayain A, Chen YL, Kuo CY, Singh O, Yeh TY, Wu YW, Su ECY. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis. JMIR Med Inform 2020; 8:e16503. [PMID: 33200995 PMCID: PMC7708089 DOI: 10.2196/16503] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 06/22/2020] [Accepted: 10/24/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Predictions in pregnancy care are complex because of interactions among multiple factors. Hence, pregnancy outcomes are not easily predicted by a single predictor using only one algorithm or modeling method. OBJECTIVE This study aims to review and compare the predictive performances between logistic regression (LR) and other machine learning algorithms for developing or validating a multivariable prognostic prediction model for pregnancy care to inform clinicians' decision making. METHODS Research articles from MEDLINE, Scopus, Web of Science, and Google Scholar were reviewed following several guidelines for a prognostic prediction study, including a risk of bias (ROB) assessment. We report the results based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies were primarily framed as PICOTS (population, index, comparator, outcomes, timing, and setting): Population: men or women in procreative management, pregnant women, and fetuses or newborns; Index: multivariable prognostic prediction models using non-LR algorithms for risk classification to inform clinicians' decision making; Comparator: the models applying an LR; Outcomes: pregnancy-related outcomes of procreation or pregnancy outcomes for pregnant women and fetuses or newborns; Timing: pre-, inter-, and peripregnancy periods (predictors), at the pregnancy, delivery, and either puerperal or neonatal period (outcome), and either short- or long-term prognoses (time interval); and Setting: primary care or hospital. The results were synthesized by reporting study characteristics and ROBs and by random effects modeling of the difference of the logit area under the receiver operating characteristic curve of each non-LR model compared with the LR model for the same pregnancy outcomes. We also reported between-study heterogeneity by using τ2 and I2. RESULTS Of the 2093 records, we included 142 studies for the systematic review and 62 studies for a meta-analysis. Most prediction models used LR (92/142, 64.8%) and artificial neural networks (20/142, 14.1%) among non-LR algorithms. Only 16.9% (24/142) of studies had a low ROB. A total of 2 non-LR algorithms from low ROB studies significantly outperformed LR. The first algorithm was a random forest for preterm delivery (logit AUROC 2.51, 95% CI 1.49-3.53; I2=86%; τ2=0.77) and pre-eclampsia (logit AUROC 1.2, 95% CI 0.72-1.67; I2=75%; τ2=0.09). The second algorithm was gradient boosting for cesarean section (logit AUROC 2.26, 95% CI 1.39-3.13; I2=75%; τ2=0.43) and gestational diabetes (logit AUROC 1.03, 95% CI 0.69-1.37; I2=83%; τ2=0.07). CONCLUSIONS Prediction models with the best performances across studies were not necessarily those that used LR but also used random forest and gradient boosting that also performed well. We recommend a reanalysis of existing LR models for several pregnancy outcomes by comparing them with those algorithms that apply standard guidelines. TRIAL REGISTRATION PROSPERO (International Prospective Register of Systematic Reviews) CRD42019136106; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136106.
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Affiliation(s)
- Herdiantri Sufriyana
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Medical Physiology, College of Medicine, University of Nahdlatul Ulama Surabaya, Surabaya, Indonesia
| | - Atina Husnayain
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Department of Biostatistics, Epidemiology, and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Ya-Lin Chen
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Chao-Yang Kuo
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Onkar Singh
- Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei, Taiwan
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
| | - Tso-Yang Yeh
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Wei Wu
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Emily Chia-Yu Su
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
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Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019; 110:12-22. [PMID: 30763612 DOI: 10.1016/j.jclinepi.2019.02.004] [Citation(s) in RCA: 812] [Impact Index Per Article: 162.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 01/18/2019] [Accepted: 02/05/2019] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The objective of this study was to compare performance of logistic regression (LR) with machine learning (ML) for clinical prediction modeling in the literature. STUDY DESIGN AND SETTING We conducted a Medline literature search (1/2016 to 8/2017) and extracted comparisons between LR and ML models for binary outcomes. RESULTS We included 71 of 927 studies. The median sample size was 1,250 (range 72-3,994,872), with 19 predictors considered (range 5-563) and eight events per predictor (range 0.3-6,697). The most common ML methods were classification trees, random forests, artificial neural networks, and support vector machines. In 48 (68%) studies, we observed potential bias in the validation procedures. Sixty-four (90%) studies used the area under the receiver operating characteristic curve (AUC) to assess discrimination. Calibration was not addressed in 56 (79%) studies. We identified 282 comparisons between an LR and ML model (AUC range, 0.52-0.99). For 145 comparisons at low risk of bias, the difference in logit(AUC) between LR and ML was 0.00 (95% confidence interval, -0.18 to 0.18). For 137 comparisons at high risk of bias, logit(AUC) was 0.34 (0.20-0.47) higher for ML. CONCLUSION We found no evidence of superior performance of ML over LR. Improvements in methodology and reporting are needed for studies that compare modeling algorithms.
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Affiliation(s)
- Evangelia Christodoulou
- Department of Development & Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000 Belgium
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, OX3 7LD UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Windmill Road, Oxford, OX3 7LD UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2333 ZA The Netherlands
| | - Jan Y Verbakel
- Department of Development & Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000 Belgium; Department of Public Health & Primary Care, KU Leuven, Kapucijnenvoer 33J box 7001, Leuven, 3000 Belgium; Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, Oxford, OX2 6GG UK
| | - Ben Van Calster
- Department of Development & Regeneration, KU Leuven, Herestraat 49 box 805, Leuven, 3000 Belgium; Department of Biomedical Data Sciences, Leiden University Medical Centre, Albinusdreef 2, Leiden, 2333 ZA The Netherlands.
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Peng X, Lv Y, Feng G, Peng Y, Li Q, Song W, Ni X. Algorithm on age partitioning for estimation of reference intervals using clinical laboratory database exemplified with plasma creatinine. Clin Chem Lab Med 2019; 56:1514-1523. [PMID: 29672263 DOI: 10.1515/cclm-2017-1095] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 01/31/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND We describe an algorithm to determine age-partitioned reference intervals (RIs) exemplified for creatinine using data collection from the clinical laboratory database. METHODS The data were acquired from the test results of creatinine of 164,710 outpatients aged <18 years in Beijing Children's Hospital laboratories' databases between January 2016 and December 2016. The tendency of serum creatinine with age was examined visually using box plot by gender first. The age subgroup was divided automatically by the decision tree method. Subsequently, the statistical tests of the difference between subgroups were performed by Harris-Boyd and Lahti methods. RESULTS A total of 136,546 samples after data cleaning were analyzed to explore the partition of age group for serum creatinine from birth to 17 years old. The suggested age partitioning of RIs for creatinine by the decision tree method were for eight subgroups. The difference between age subgroups was demonstrated to be statistically significant by Harris-Boyd and Lahti methods. In addition, the results of age partitioning for RIs estimation were similar to the suggested age partitioning by the Canadian Laboratory Initiative in Pediatric Reference Intervals study. Lastly, a suggested algorithm was developed to provide potential methodological considerations on age partitioning for RIs estimation. CONCLUSIONS Appropriate age partitioning is very important for establishing more accurate RIs. The procedure to explore the age partitioning using clinical laboratory data was developed and evaluated in this study, and will provide more opinions for designing research on establishment of RIs.
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Affiliation(s)
- Xiaoxia Peng
- Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, P.R. China
| | - Yaqi Lv
- Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, P.R. China.,Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, P.R. China
| | - Guoshuang Feng
- Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, P.R. China
| | - Yaguang Peng
- Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, P.R. China
| | - Qiliang Li
- Department of Clinical Laboratory Center, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, P.R. China
| | - Wenqi Song
- Department of Clinical Laboratory Center, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, No. 56 Nanlishi Road, Beijing, 100045, P.R. China
| | - Xin Ni
- Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, P.R. China.,Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck, Surgery, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, No. 56 Nanlishi Road, Beijing, 100045, P.R. China, Phone: +86-010-59617132
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Kim YS. Analysis of Spontaneous Preterm Labor and Birth and Its Major Causes Using Artificial Neural Network. J Korean Med Sci 2019; 34:e131. [PMID: 31020818 PMCID: PMC6484176 DOI: 10.3346/jkms.2019.34.e131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 04/19/2019] [Indexed: 11/20/2022] Open
Affiliation(s)
- Yun Sook Kim
- Department of Obstetrics and Gynecology, Soonchunhyang University College of Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Korea.
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Li G, Zhou X, Liu J, Chen Y, Zhang H, Chen Y, Liu J, Jiang H, Yang J, Nie S. Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province. PLoS Negl Trop Dis 2018; 12:e0006262. [PMID: 29447165 PMCID: PMC5831639 DOI: 10.1371/journal.pntd.0006262] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 02/28/2018] [Accepted: 01/23/2018] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND In order to better assist medical professionals, this study aimed to develop and compare the performance of three models-a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model-to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province. METHODOLOGY/PRINCIPAL FINDINGS Schistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient's outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity. CONCLUSIONS/SIGNIFICANCE Predictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC.
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Affiliation(s)
- Guo Li
- Department of Epidemiology and Health Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Xiaorong Zhou
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Jianbing Liu
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Yuanqi Chen
- Department of Mathematics, Wuhan University, Wuhan, Hubei, China
| | - Hengtao Zhang
- Department of Mathematics, Wuhan University, Wuhan, Hubei, China
| | - Yanyan Chen
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Jianhua Liu
- Yichang Center for Disease Control and Prevention, Yichang, Hubei, China
| | - Hongbo Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Junjing Yang
- Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, China
| | - Shaofa Nie
- Department of Epidemiology and Health Statistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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