Development and validation of a nomogram for predicting preterm birth among pregnant women who had Antenatal care follow-up at University of Gondar Comprehensive Specialized Hospital using maternal and fetal characteristics: Retrospective follow-up study

Development and validation of a nomogram for predicting preterm birth among pregnant women who had Antenatal care follow-up at University of Gondar Comprehensive Specialized Hospital using maternal and fetal characteristics: Retrospective follow-up study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of a nomogram for predicting preterm birth among pregnant women who had Antenatal care follow-up at University of Gondar Comprehensive Specialized Hospital using maternal and fetal characteristics: Retrospective follow-up study Rewina Tilahun Gessese, Bisrat Misganaw Geremew, Solomon Gedlu Nigatu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4076906/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: - Preterm complications are the leading cause of death in children under the age of 5. Estimating the probability of a pregnant woman being at risk of preterm delivery would help to initiate preventive measures to reduce preterm delivery. The available risk prediction models used non-feasible predictors and did not consider fetal characteristics. This study aimed to develop an easily interpretable nomogram based on maternal and fetal characteristics. Methods : - A retrospective follow-up study was conducted with a total of 1039 pregnant women who were enrolled from June 1, 2021, to June 1, 2022, at the University of Gondar Comprehensive Specialized Hospital. Stata version 17 was used for data analysis. Important predictors were selected by the least absolute shrinkage and selection operator and entered into multivariable logistic regression. Statistically and clinically significant predictors were used for the nomogram’s development. Model performance was assessed by the area under the receiver operating curve (AUROC) and calibration plot. Internal validation was done through the bootstrapping method, and decision curve analysis was performed to evaluate the clinical and public health impacts of the model Result : - The incidence proportion of preterm birth among pregnant women was 14.15% (95%CI: 12.03, 16.27). Antepartum hemorrhage, preeclampsia, polyhydramnios, anemia, human immune virus, malpresentation, premature rupture of membrane, and diabetic mellitus were used to develop a nomogram. The nomogram had a discriminating power AUROC of 0.79 (95% CI: 0.74, 0.83) and 0.78 (95% CI: 0.73, 0.82) on the development and validation sets. The calibration plots exhibited optimal agreement between the predicted and observed values; the Hosmer-Lemeshow test yielded a P-value of 0.602. The decision curve analysis revealed that the nomogram would add net clinical benefits at threshold probabilities less than 0.8. Conclusion: - The developed nomogram had good discriminative performance and good calibration. Using this model could help identify pregnant women at a higher risk of preterm delivery and provide interventions like corticosteroid and progesterone administration, cervical cerclage, and nutritional support. Nomogram Development Validation Preterm birth Ethiopia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Preterm birth (PB) is defined as delivery before completing 37 weeks of gestation [ 1 ]. United Nations categorized preterm birth as iatrogenic and spontaneous preterm birth [ 2 ]. Globally, around 13.4 million neonates were preterm in 2020, and the majority of them occurred in southern Asia and sub-Saharan Africa [ 1 , 3 ]. The burden of preterm birth varies from 10.48 to 23.7% in Africa [ 4 – 7 ], and in Ethiopia, around 320,000 neonates are born prematurely each year. Complications of premature birth are the leading cause of under-five mortality, with nearly 900,000 in 2019 [ 8 ]. In Ethiopia, 23,100 children die due to direct complications of preterm birth [ 9 ]. Surviving premature infants are more susceptible to diseases like respiratory distress syndrome, chronic lung disease, intestinal injury, weakened immunity, cardiovascular issues, cerebral palsy, intellectual disability, hearing and vision loss, high hypertension and cardiovascular disease, and mental health problems [ 10 – 13 ]. Lengthy hospital stays and frequent treatments for premature birth can also cause additional psychological stress and costs for families, as well as increase the strain on the health care system [ 14 , 15 ]. The current clinical practice treats all pregnant women with preterm labor symptoms [ 16 ], which requires them to stay until labor symptoms develop. In addition, it treats all pregnant women with preterm labor symptoms unnecessarily to ensure that the minority of pregnant women who give birth prematurely receive the proper care. However, unnecessary intervention can cause adverse maternal and neonatal outcomes. For example, tocolytics can cause palpitations, pulmonary edema, myocardial infarction, arterial hypotension, chorioamnionitis, and post-operative hemorrhage [ 17 , 18 ], and antenatal corticosteroids are associated with adverse neurodevelopmental outcomes in children [ 19 ]. Given the significant health impact and the potential for prevention through early detection, prognostic models have been developed to identify pregnant women at high risk for preterm birth [ 20 ]. Existing models from developed nations may not be applied as their development is based on predictors like fetal fibrinogen (FN), cervical length, C-reactive protein, and interleukin-6 [ 21 , 22 ], which are not feasible to measure in resource-limited settings like Ethiopia. The previous prediction model from Ethiopia had a very good discriminative performance (AUC = 0.816)[ 23 ], but the model did not account for fetal characteristics like fetal sex, intrauterine growth retardation, and obstetric complications like polyhydramnios and oligohydramnios, which are known risks of preterm birth according to different studies [ 24 – 29 ]. A nomogram is a visual depiction of a statistical prediction model that predicts the likelihood of a specific clinical event in individual patients. It is a user-friendly tool that enables health professionals and individuals to estimate the risk of a certain outcome [ 30 ]. Using a nomogram, clinicians can stratify high-risk pregnancies and provide important interventions like cervical cerclage, steroid, and progesterone administration efficiently [ 31 , 32 ]. Also, this will help to reduce the overtreatment of pregnant women with low risk, which helps to efficiently utilize resources and prevent adverse maternal and neonatal outcomes. Reducing neonatal mortality to as low as 12 per 1,000 live births and under-5 mortality to at least 25 per 1,000 live births is one component of the United Nations Sustainable Development Goal (SDG) and the government of Ethiopia launched different programs to accelerate country’s progress in meeting this goals [ 33 , 34 ]. To achieve this goal, prevention provided for preterm birth can play a crucial role, as it is the leading cause of both under-five and neonatal mortality. Per the knowledge of the researchers, nomograms constructed by combining maternal and fetal characteristics for predicting preterm birth are missed in Ethiopia. Therefore, this study aimed to develop a nomogram by using maternal and fetal characteristics to find practical obstetric tools for predicting preterm birth. Methods Study Design A retrospective follow-up study was conducted from June 1, 2021, to June 1, 2022, at the University of Gondar Comprehensive Specialized Hospital, which is located in Gondar City, located 740 km from Addis Ababa, the capital city of Ethiopia. Under the University of Gondar, a comprehensive specialized hospital. The gynecology and obstetrics department provides ANC services through four different outpatient departments (OPDs), of which one provides service for only high-risk pregnancies. Intern doctors, gynecology and obstetrician resident doctors, and midwifery professionals provide the services. Routinely, physical examinations with laboratory investigations and, within each trimester, ultrasound examinations are provided for pregnant women who visit the services. Theoretical design The occurrence relationship was Probability (preterm birth) = f (APH + preeclampsia + polyhydamnions + HIV + anemia + MABP + PROM) and the domain were pregnant women who had ANC follow-up at university of Gondar Comprehensive Specialized Hospital. Population and eligibility criteria All pregnant women who had ANC follow-up from June 1, 2021, to June 1, 2022, and who delivered a live birth at the University of Gondar Comprehensive and Specialized University Hospital were included and Pregnant women with an unknown or unreliable last normal menstrual period (LNMP) or with no first-trimester ultrasound and pregnant women with multiple pregnancies were excluded. Sample size and sampling techniques The sample size was determined by riley rules using stata “ pmsampsize” which calculates three different sample sizes by using an overall outcome risk or mean outcome value in the target population, the number of candidate predictor parameters, and the anticipated model performance in terms of overall model fit (R 2 ) or c statistics [ 35 ] (Table 1 ). Table 1 Sample size and assumptions for development and validation of nomogram for predicting preterm birth among pregnant women who had ANC follow-up at UGCSH Sample size Shrinkage Parameters R 2 cs Max R 2 cs EPP Criteria 1 508 .9 37 .4658 .545 1.84 Criteria 2 986 .945 37 .4658 .545 3.57 Criteria 3 179 .945 37 .4658 .545 .65 R 2 CS : Cox-Snell R squared statistic, EPP : Event Per Predictor Parameter Table 1 : Sample size and assumptions for development and validation of nomogram for predicting preterm birth The sample size had three different criteria What sample size will produce precise estimate of the overall outcome risk or mean outcome value? What sample size will produce a small require d shrinkage of predictor effects (to minimize potential model over fitting)? What sample size will produce a small optimism in apparent model fit? Where R 2 cs is cox Snell R 2 which can be taken from previous study or estimated from c statistics (AUC) , P is number of parameters (37), C statistics (AUC) which taken from previous study as 0.99 [ 36 ] as we could not find study reporting R 2 cs, Φ incidence from previous study = 0.134 [ 23 ]. Taking the maximum sample size from the above the sample size was 986 and by adding 10% for missing records (charts) it will become 1084. Computer Generated Simple random sampling techniques was used based on sampling frame which was prepared by arranging medical record number order from list of ANC registration book .The details of the sample selection procedure is presented in ( Fig. 1 ) Figure 1 : Flow diagram of sample selection for development and validation of A nomogram for predicting preterm birth Variables of the study The outcome variable was Preterm Birth and the prognostic determinants were Socio demographic factor (Age, residency, marital status), obstetric factors (previous history of abortion, previous history of still birth, previous history of preterm delivery, previous CS, Parity, gravidity, number of gestation, APH, inter pregnancy interval, GDM, polyhydroamnios, Oligohydramnios), Clinical characteristics (haemoglobin level, blood pressure, asthma, thyroid, HIV, UTI, cardiac disease, chronic hypertension, iron and folic supplements), Fetal factor (IUGR, sex of fetus, malpresentation) and Behavioural factor (Smoking). Operation definition Preterm Birth – A delivery before 37 full weeks but after fetal viability (after 28 full weeks), which is calculated by using reliable LMNP or by early ultrasound (less than 22 weeks of Gestation) which are the gold standards for estimating preterm birth and used in our set up [ 37 , 38 ] . Reliable LMNP -It is said if the menses are regular (28 ± 7 days), if she was not on contraception, and if on contraception after 3 months for lactational amenorrhea and oral contraceptives, immediately for implants and an intrauterine contraceptive device (IUCD).[ 39 ]. Birth interval –interval between last delivery and the present pregnancy and is defined as short when it is less than 2 years, optimal when it is between 3–5 years and long when it is greater than 5 years [ 40 ]. Data collection and procedures A data extraction tool was designed using an electronic data collection tool called Kobo Toolbox, and the data was collected with the Kobocollect version 2022.1.2. This risk prediction model used secondary data sources, which were collected from April 18 up to May 18, 2023, by reviewing the records of pregnant women. Socio-demographic characteristics, past and present obstetric characteristics, fetal characteristics, medical characteristics, and nutritional and behavioral characteristics were collected from ANC records. The data were collected from the date that the pregnant women started ANC follow-up until delivery. Data quality control and assurance The data extraction tool was checked by researchers and gynaecologists prior to data extraction. Preliminary test was be done by taking 5% (54) of the total sample size. A one day training about the objectives of the study, how to use the kobocollect digital data extraction tool, how to access records, data handling and confidentiality of the participants information was given for the data collectors and supervisors. Extracted data was checked for completeness by the investigator on daily basis Missing data management Missing data is handled by multiple imputations chained equations (MICE) after ascertaining the missing type. Missing at random (MAR) was checked by testing the association between the missing variable and other independent variables using a t-test and a Chi-square test value are presented in the supplementary file ( Table S1 Table S1 ). MICE is the preferred multiple imputation method when there are different types of variables and an arbitrary type of pattern. The number of imputations used was 20 [ 41 ]. The number of imputations used was 20, as it was Stata’s recommendation[ 42 ] Finally, sensitivity analysis was performed to compare the imputed data and complete case analysis. In this sensitivity analysis, we did not find a significant difference ( Table S3). Data processing and analysis The data collected with KOBOcollect was exported to Stata 17 for management and further analysis. Descriptive summary statistics (frequency with percentage for categorical variables, mean with standard deviation and median with interquartile range for continuous variables) were used to describe variables. The candidate predictors were selected by least absolute Shrinkage and Selection Operator (LASSO) regression. Selected variables were entered in to multivariable logistic regression and model reduction was performed at a cut-off p -value > 0.15.Significance of the difference between the original and reduced model was assessed by likelihood ratio test and p value for the test was reported. Multicollinearity was assessed by variance inflation factor (VIF) Nomogram development and Validation The risk of having preterm birth was predicted using the predictive nomogram by “ nomolog ” package on Stata. The nomogram was developed by beta coefficient of variables that are remained on the reduced model. The identified predictors were MABP, APH, preeclampsia, DM, polyhydamnions, HIV, anemia and PROM Internal validation was performed using bootstrapping method by the randomly selected bootstrap samples (10000) with replacement to estimate how good the performance of the prediction model developed on the development set would be on a hypothetical set of new Risk Classification by using the nomogram Optimal cutoff point for predicted probability of preterm birth was calculated using the maximum Youden’s index (J) and the pregnant women were categorized as high risk and low risk for delivering preterm neonate based on the cutoff point .The sensitivity, specificity, Positive and negative predictive with their respect 95% confidence interval at the cutoff point was calculated. Model performance and evaluation The performance of a model was assessed by discrimination which was measured by Area under receiver operating characteristic and Calibration refers to the agreement between observed and predictions Stata Package “calibartionbelt” and “PMCALPLOT” were used to plot the calibration plot. The Brier Score was calculated as it measured a composite measure of discrimination and calibration, summarizing performance for a prediction model. The net benefit of the developed model, which is a measure of clinical utility, was represented by decision curve plot. Reporting of the study The analysis and reporting of the study was according to the transparent reporting of multivariable prediction model for individual prognosis or diagnosis (TRIPOD) initiative checklist [ 43 ] Results Characteristics of the study participants Socio demographic characteristics of the participants A total of 1039 pregnant women who had ANC follow up at university of Gondar were included. The median age was 27 years with IQR of 24–35 years and only 13(1.25%) were single (Table 2 ). Table 2 Socio demographic characteristic of pregnant women who had ANC follow-up at UGCSH Characteristics Category Frequency Percent (%) Preterm(Yes) Preterm (No) Age 15–24 293 28.20 35 258 25–34 611 58.81 94 517 > 35 135 12.99 18 117 Residency Urban 936 9.91 128 808 Rural 103 90.09 19 84 Marital Status Married 1,026 98.75 145 881 Unmarried 13 1.25 0 13 Table 2 : Socio demographic characteristic of pregnant women who had ANC follow-up at UGCSH Past and present obstetric characteristics of participants More than half, 611 (58.61%) of the participants of them had history of preterm birth and 199 (32.67%) had history of preterm birth. 46 (7.55%) had history of preterm birth and 199 (32.67%) of them delivered their latest child by CS. Regarding to pregnancy related complications preeclampsia was the highest, 181 (17.42%) had preeclampsia (Table 3 ). Table 3 Past and Present Obstetric characteristics of pregnant women had ANC follow-up at UGCSH Characteristics Category Frequency Percent (%) Preterm(Yes) Preterm(No) Gravidity Prim gravid 375 36.09 53 322 Multigravida 664 63.91 94 570 Parity Nulliparous 428 41.39 57 373 Multiparous 611 58.61 90 519 Birth interval(N = 611)) Short birth interval 155 25.20 29 126 Optimal 359 59.02 41 318 Long interval 97 15.77 21 76 Previous Abortion (N = 664) Yes 150 22.59 26 124 No 514 77.40 68 446 Previous stillbirth(N = 611) Yes 48 7.86 6 42 No 563 92.14 84 479 Previous CS (N = 611) Yes 201 32.89 37 164 No 410 67.11 53 357 Previous preterm birth(N = 611) Yes 46 7.55 21 25 No 565 92.44 69 496 PROM Yes 106 10.20 32 74 No 933 89.80 115 818 Gestational diabetics Yes 88 8.47 11 77 No 951 91.53 136 815 Antepartum Hemorrhage Yes 81 7.80 24 57 No 958 92.20 123 835 Preeclampsia yes 181 17.42 39 142 No 858 82.58 108 750 Polyhydroamnions Yes 51 4.91 19 32 No 988 95.09 128 860 Oligohydramnios Yes 132 12.70 20 112 No 907 87.30 127 780 ANC : Ante Natal Care, CS : Cesarean Section, PROM : Premature Rupture of Membrane Table 3 : Past and Present Obstetric characteristics of pregnant women had ANC follow-up at UGCSH Clinical related characteristics of the of the participants Among the infectious diseases UTI accounts for 13.86% and from the chronic diseases the lowest frequency was recorded on renal diseases only 5 pregnant women were diagnosed to had renal disease (Table 4 ) Table 4 Clinical related characteristics of the of the participants Characteristics Category Frequency Percent (%) Preterm(Yes) Preterm(No) HIV Reactive 43 4.14 14 29 Non-reactive 996 95.86 133 863 Chronic HTN Yes 15 1.44 2 13 No 1024 98.56 145 879 Diabetics mellitus Yes 21 2.02 12 9 No 1018 97.98 135 883 Hemoglobin level Anemic 71 6.83 17 54 Non -anemic 968 93.17 130 838 Thyroid Yes 16 1.54 4 12 No 1,023 98.46 143 880 UTI Yes 144 13.86 32 112 No 895 86.14 115 780 Cardiac disease Yes 8 0.77 0 8 No 1,031 99.23 147 884 Renal disease Yes 1,034 99.52 144 890 No 5 0.48 2 3 Syphilis Yes 6 0.58 0 6 No 1033 99.42 147 886 Asthma Yes 2 0.19 0 2 No 1037 99.81 147 888 UTI : Urinary Tract Infection, HIV : Human immune virus Table 4 : Clinical related characteristics of pregnant women had ANC follow-up at UGCSH Fetal related characteristics About fetal characteristics among the total of 1039 conceived fetus 535(51.49%) of the fetus were with female sex and 125(12.03) of them were diagnosed to had IUGR. In terms of fetal presentation 89 (8.57%) of them were malpresented. Incidence proportion of preterm birth Among the total of 1039 pregnant women, 147 women delivered a preterm neonate which makes the incidence proportion of preterm birth 14.15% (95% CI: 12.03, 16.27) and majority 131(89%) of them the preterm neonate were under category of late preterm (Fig. 2 ) Figure 2 : Incidence proportion of preterm birth among pregnant women who had ANC follow-up at UGCSH Nomogram development Variable selection In this nomogram development socio-demographic, past and present obstetric, medical, behavioral, nutritional and fetal related predictors were and Candidate predictors were selected by LASSO regression. LASSO Based Multivariable Binary Logistic Regression Variables selected by LASSO were Polyhydroamnions, Anemia status, MABP, DM, HIV, APH, IUGR, UTI, Sex of fetus, Number of ANC, Preeclampsia and PROM and were entered in to multivariable logistic regression. Variables were reduced by using P- value (> 0.15), by considering clinical significance. Based on that IUGR, sex of fetus, frequency of ANC and UTI were reduced .Finally Eight variables which were; MABP, preeclampsia, DM, Anemia, PROM, polyhydramnios, HIV and APH remained on the reduced model (Table 5 ) Table 5: LASSO based Multivariable logistic regression for development and validation of nomogram for predicting preterm birth Equation: The probability for risk of preterm using reduced model beta coefficients was:- P(pretermbirth) = 1/1 + exp-(4.32 + 0.74*preeclampsia(yes) + 1.47*polyhydroamnions(yes) + 0.97*anemia(yes) + 1.60*PROM(yes) + 2.16*DM(yes) + 0.72*HIV(yes) + 1.32*APH(yes) + 0.74*MABP(low) + 1.28*Malpresentaion(yes) Table 5 LASSO based Multivariable logistic regression for development and validation of Nomogram for predicting preterm birth among pregnant women who had ANC follow-up at UGCSH (N = 1039) Multivariable Binary Logistic Regression of Original LASSO Based model Multivariable Binary Logistic Regression of Reduced LASSO Based Model Predictor variables Term Preterm Beta coefficient with 95% CI P - value Beta coefficient with 95% CI P value Malpresentation Yes No 835 57 123 32 1.23(0.67 ,1.78) 1.63(.76, 2.51) 0.000 0.000* 1.28(0.73,1.82) 0.00 Preeclampsia Yes No 142 750 39 108 0.63(0.16,1.09) 0 0.008* 0.74(0.28,1.19) 0.003 IUGR Yes No 101 791 30 117 0 .51(0.04,1.06) 0 0.17 Polyhydroamnions Yes No 32 860 19 126 1.56(0.89,2.27) 0 0.000* 1.47((0.78, 2.14) 0.000 Anemia status Anemic Non-Anemic 54 838 17 130 0.93(0.30,1.58) 0 0.004* 0.97(0.33, 1.60) 0.005 Frequency of ANC contact = 4 contact 290 602 56 91 0.36(0.03,0.75) 0 0.67 UTI Yes No 112 780 32 115 0.59(0.09,1.08) 0 0.21 Diabetic Mellitus Yes No 9 883 12 135 2.35(1.38,3.33) 0 0.001* 2.16(1.19,3.14) 0.000 HIV Yes No 29 863 14 133 0.68(0.09,1.27) 0 0.023* 0.720(150,1.3) 0.014 APH Yes No 57 835 24 123 1.13(0.54,.71) 0 0.000* 1.32(0.75,1.99) 0.000 Sex of fetus Male Female 425 467 68 79 0.16(0.22 ,0.5) 0.417 PROM Yes No 74 818 32 115 1.54(1.03,2.09) 0 0.000* 1.60(1.07,2.11) 0.000 Nomogram for predcting preterm birth A total of the eighth variables from the reduced model were used to develop a nomogram. It was characterized by a scale corresponding to each variable, a total score scale and probability scale (Fig. 3 ). Figure 3 : Nomogram for predicting preterm birth Predictor score of the nomogram for predicting preterm birth The minimum score for each variable was 0 and the variable having category with maximum score was DM (10) The Total score for the variables was 46.1 ( Table S4 ). For example let’s take Pregnant women who visits ANC with high MABP, history of Diabetic mellitus, non-anemic, diagnosed to had polyhydramnios, non-reactive to HIV and no PROM, APH, preeclampsia Then the Total score for the given scenario equals to 23.1 and the corresponding probability for this score from the nomogram become 0.91 Discriminative performance of the nomogram The discriminative performance of the nomogram was assessed using area under the curve of ROC and resulted in AUC of 0.79 (95% CI; 0.74, 0.83%). This showed that the nomogram had good level of discriminative performance, implying that it can well discriminative those who deliver preterm neonate and term neonate. Also the nomogram demonstrated good agreement between the actual and the predicted probability from the calibration plot and the Hosmer–Lemeshow resulted a P value of 0.60, which implied that there is an agreement between the predictive probability and the observed probability (Fig. 4 ). Figure 4 : ROC curve of the nomogram on the development set for predicting preterm birth Calibration Plot The nomogram demonstrated good agreement between the actual and the predicted probability from the calibration plot and the Hosmer–Lemeshow goodness test resulted a P value of 0.60, which implied that there is an agreement between the predictive probability and the observed probability (Fig. 5 ). Figure 5 : Calibration plot of the nomogram on the development set for predicting preterm birth Internal validation of the nomogram The area under ROC curve was assessed based on the bootstrapped dataset. The developed nomogram was internally validated, on the bootstrapped data it had AUC of 0.78(CI 0.73, 0.82) and the optimization coefficient was 0.01 which showed that the model is less likely to over fit and less sample dependent (Fig. 6 ). Figure 6 : ROC of the nomogram on the validation data set for predicting preterm birth There is an agreement between the observed and the predicted probability of preterm birth with p value of 0.526 (p value > 0.05) on the bootstrapped data, which confirmed the model is well calibrated on the validation data too (Fig. 7 ) Figure 7 : Calibration of the nomogram on the validation data set for predicting preterm birth Decision Curve analysis Clinical and public health utility of the model was also assessed by decision curve analysis. DCA evaluate whether the model understudy had a higher net benefit than the default strategies (counseling and treating all and none) .This model outperforms the default strategies across threshold probabilities from 0 up to 0.8 (80%) (Fig. 8 ). Figure 8 : Decision Curve analysis of the nomogram for predicting preterm Risk Classification and accuracy measurements of the nomogram at optimal cut of point Optimal cutoff point of predicted probability for risk of preterm birth was calculated using Youden’s index (J) .The maximum Youden’s index was 42.67% and the predicted probability at that Youden’s index was > = 0.12 which was the optimal cutoff point. Using the optimal cutoff point the proportion of pregnant women who were at low risk was 628 (63.44%) and high risk group 411 (39.56%) (Table S5) ( Table S6 ) Discussion This prognostic study used retrospective follow up to develop a practical tool which predicts preterm birth using maternal and fetal characteristics among pregnant women who had ANC follow-up at university of Gondar comprehensive specialized Hospital. By establishing a nomogram, this study developed a preterm risk prediction model to be used in at settings which provides ANC services. This model will help clinicians and pregnant women who utilize ANC services to make clinical and public health decisions. This study showed that the incidence proportion of preterm birth was 14.15% (95% CI: 12.03–16.27), which is in line with studies conducted in Bahirdar (13.4%) [ 23 ], Axum (13.3%) [ 44 ], Butajira (15.5%) [ 45 ] .The similarity may be due to the fact that our country's health care system and services that are provided to mothers are essentially uniform across different parts of the nation and other reason might be due to related levels of socioeconomic status and lifestyle of the respondents. However, the finding is lower than study from Harar (24.9%) [ 46 ],Jimma (25.9%) [ 47 ] and Ghana (37.3%) [ 48 ] the discrepancy might be due to the inclusion of multiple pregnancies in their study as multiple pregnancy increases uterine over destination which initiate preterm labor [ 49 ], time line difference for the study from Jimma and socio demographic difference for the study from Gahanna. Whereas the finding is higher than study from Gondar (4.4)[ 50 ] and Iran (8.2%)[ 51 ]. The difference may be accounted by the difference in health institutions the data were collected and the type of preterm birth assessed by the study. Health centers where the study area for the first study at which high risk pregnancy are referred which might reduce the number of preterm delivery and only spontaneous preterm birth were assessed on the study from Iran . This prediction nomogram was developed by eight predictors and had a good discrimination with an AUC of 0.76% (95% CI: 0.72–0.81) according to diagnostic accuracy classification criteria [ 52 ]. The performance of this nomogram was found to be consistent with other prediction models from Bahirdar using residency, gravidity, APH, PIH, PROM, and hemoglobin level (AUC: 78.96%) [ 23 ] ,study from Canada by combining ovulation induction IVF conception, a history of previous abortions, previous preterm birth, and abnormal pregnancy-associated plasma protein concentrations (AUC: 73.4%) [ 53 ] and from the USA by mass spectrometry-based serum test yielding an AUC of 75% [ 54 ]. This nomogram performed better than a study from Netherlands with an AUC of 63%, which was developed by combining Previous preterm birth, drug abuse, and vaginal bleeding [ 55 ], a study from the USA with a combination of received hydroprogestrone caproate ,cervical length, and cervical funnel with an AUC of 63% [ 56 ], and another study from the USA with a performance of 68.8%, developed by combining age of spouse, smoking, infection, previous CS, prepregnancy weight, race, hypertension ,prenatal care, prepregnancy DM, plurality, education, age, previous preterm birth, and gestational hypertension[ 57 ] .The difference might be due to a difference in the number of predictors used to develop the models, the selection of the study population (only nulliparous was considered in the model from the USA), and the time point difference (early pregnancy was the time for the last study). However, the discriminative performance of the model was lower than a study done in china by combining maternal age, insulin use, and monocyte count with AUC of 88.5% [ 58 ], from Iran by combining cervical length, uterine contractions, rupture of membranes, vaginal bleeding, gestational age, and multiple resulting in AUC of 89.0%[ 59 ], from China by combining gestational Age, magnesium, fundal height, serum inorganic phosphorus, mean platelet volume, waist size, total cholesterol, triglycerides, globulins, and total bilirubin yielding AUC of 88.5 [ 60 ] .The discrepancy might be due to predictor difference ;cervical length ,serum inorganic phosphorus, mean platelet volume are used which were not included in this model as they are not routinely assessed in our country. Another explanation for the difference can be the domain difference the domain for the first study were pregnant women with diabetic mellitus who are already at high risk of delivering preterm birth. The benefit that the developed nomogram would add to clinical practice was also presented in the form of a decision curve analysis. In our study the decision curve analysis showed that there was high net benefit than using treat all or treat none strategies. In our nomogram prediction score, using 0.12 as the cutoff point yields an acceptable level of sensitivity and specificity of 70.19 and 69.48 respectively. This showed that using the nomogram 70.19% of pregnant women were correctly identified as delivering preterm births and 69.48 were correctly identified as delivering term neonates. Also the PPV and NPV was 93% and 70% respectively, implying that the probability of delivering at preterm was 0.93 in subjects who were classified as yes for delivering at preterm and the probability of delivering at term period was 0.7 in a subject who were classified as no by the nomogram. In this study the nomogram yielded a + LR of 2.28 showing that pregnant women who delivered at preterm period were 2.28 more likely to be classified as yes (preterm birth) by the nomogram than women who deliver term neonate on the contrary the –LR was 0.43 implying that using the nomogram the delivering term neonate is 0.43 much less likely to occur in a pregnant women who deliver preterm neonate than those who deliver a term neonate Limitation of the study The findings from this study should be interpreted with the perspective of the following limitations. As a single-site study, it is confined to a single area, which needs external validation before using it in another context and important predictors like BMI, psycho social factors were not considered for model development as the study was retrospective study Conclusion The nomogram was developed from obstetric factor, fetal factors and medical complications which are routinely assessed for pregnant women who visit ANC services. The predictors were APH, preeclampsia, polyhydroamnions, PROM, anemia, DM, HIV and MABP. The nomogram had good discriminating performance power and was well calibrated. It was internally validated by bootstrapping techniques with small optimum coefficients, less likely of over fitting of the model. The nomogram had added net benefit in clinical practice as it was assured by decision curve analysis across different threshold probabilities. The prediction nomogram was used to risk-stratify pregnant women and identify those who were more likely to have a preterm birth. Following that, high-risk groups might be opted for corticosteroid administration, antibiotic treatment in the event of infection, and other Abbreviations ANC Antenatal Care APH Ante Partum Haemorrhage AUC Area Under the Curve DM Diabetic Mellitus HIV Human Immune Virus LASSO Least Absolute Shrinkage and selection Operator PROM Premature Rupture of Membrane ROC Receiver Operating Characteristic UGCSH University of Gondar Comprehensive Specialized Hospital Declarations Ethics approval and consent to participate Informed consent was waived by institutional Review board of the institute of public health, college of medicine and health science, University of Gondar with reference number /IPH/2520/2023.Official letter was obtained from department of Epidemiology and Biostatics and then provided by the Hospital admission and gynaecology and obstetrics department As this study involved the analysis of pre-existing data, informed consent from participants was not applicable. However, strict confidentiality measures were adhered to throughout the study process to ensure the privacy of individuals' information. Competing interests The authors declare that they have no competing interests Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Funding The authors received no specific funding for this work. Author Contributions RT designed the study, performed analysis and interpretation of data and developed and participated in preparing all versions of the manuscript. BM, SG, SB and TK participated in the analysis process, manuscript drafting and critical revision. All authors have read and agreed to the final version of the manuscript Acknowledgement Not applicable References WHO. WHO preterm fact sheet . 2023; Available from: https://www.who.int/news-room/fact-sheets/detail/preterm-birth. Purisch, S.E. and C. 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WHO, Report of a WHO technical consultation on birth spacing, Geneva, Switzerland. 2007. Bouhlila, D.S. and F. Sellaouti, Multiple imputation using chained equations for missing data in TIMSS: a case study. Large-scale Assessments in Education, 2013. 1 (1): p. 4. Wu, H.-S. Introduction to Multiple Imputation . 2016; Available from: https://www.bgsu.edu/content/dam/BGSU/college-of-arts-and-sciences/center-for-family-and-demographic-research/documents/Workshops/2016-Imputation.pdf. Collins, G.S., et al., Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Annals of internal medicine, 2015. 162 (1): p. 55-63. Aregawi, G., et al., Preterm births and associated factors among mothers who gave birth in Axum and Adwa Town public hospitals, Northern Ethiopia, 2018. BMC Research Notes, 2019. 12 (1): p. 640. Abdo, R.A., et al., Magnitude of Preterm Birth and Its Associated Factors: A Cross-Sectional Study at Butajira Hospital, Southern Nations, Nationalities, and People’s Region, Ethiopia. International Journal of Pediatrics, 2020. 2020 : p. 6303062. Zewde, G.T., Preterm birth and associated factors among mother who gave birth in public health hospitals in harar town eastern Ethiopia 2019. OSP Journal of Health Care and Medicine, 2020. 1 (1): p. 1-3. Molla, I., D. T, and A. Kebebe, Prevalence of Preterm Birth and its Associated Factors among Mothers Delivered in Jimma University Specialized Teaching and Referral Hospital, Jimma Zone, Oromia Regional State, South West Ethiopia. Journal of Women's Health Care, 2017. 06 . Anto, E.O., et al., Prevalence and risk factors of preterm birth among pregnant women admitted at the labor ward of the Komfo Anokye Teaching Hospital, Ghana. Frontiers in Global Women's Health, 2022. 3 : p. 801092. Marcelo Santucci, F., E.N.K.H. Tatiana, and M. Antônio Fernandes, Preterm Birth in Twins , in Multiple Pregnancy , E. Julio, Jr., Editor. 2018, IntechOpen: Rijeka. p. Ch. 9. Gebreslasie, K., Preterm birth and associated factors among mothers who gave birth in Gondar town health institutions. Advances in Nursing, 2016. 2016 . Tehranian, N., M. Ranjbar, and F. Shobeiri, The prevalence and risk factors for preterm delivery in Tehran, Iran. Journal of Midwifery and Reproductive Health, 2016. 4 (2): p. 600-604. Šimundić, A.M., Measures of Diagnostic Accuracy: Basic Definitions. Ejifcc, 2009. 19 (4): p. 203-11. Arabi Belaghi, R., J. Beyene, and S.D. McDonald, Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort. J Perinatol, 2021. 41 (9): p. 2173-2181. Saade, G.R., et al., Development and validation of a spontaneous preterm delivery predictor in asymptomatic women. American Journal of Obstetrics and Gynecology, 2016. 214 (5): p. 633.e1-633.e24. Schaaf, J.M., et al., Development of a prognostic model for predicting spontaneous singleton preterm birth. European Journal of Obstetrics & Gynecology and Reproductive Biology, 2012. 164 (2): p. 150-155. Grobman, W.A., et al., Prediction of Spontaneous Preterm Birth Among Nulliparous Women With a Short Cervix. Journal of Ultrasound in Medicine, 2016. 35 (6): p. 1293-1297. Li, Y., et al., Maternal preterm birth prediction in the United States: a case-control database study. BMC Pediatrics, 2022. 22 (1): p. 547. Huang, Y., et al., Development and validation of nomogram for the prediction of preterm delivery based on patient characteristics and circulating inflammatory cells in patients with gestational diabetes mellitus. Ann Transl Med, 2023. 11 (2): p. 70. Najjarzadeh, M., et al., Validation of a Nomogram for Predicting Preterm Birth in Women With Threatened Preterm Labor: A Prospective Cohort Study in Iranian Tertiary Referral Hospitals. Clin Nurs Res, 2022. 31 (7): p. 1325-1331. Sun, Q., et al., Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record. J Healthc Eng, 2022. 2022 : p. 9635526. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4076906","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287457035,"identity":"57fc6f4e-57bb-4a42-b494-01e9cc3c51f8","order_by":0,"name":"Rewina Tilahun 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UOGCSH\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/6e316a6df0a84ca138c19e35.png"},{"id":54372877,"identity":"1b6816c4-cca4-449c-820a-9eb90291d25a","added_by":"auto","created_at":"2024-04-09 13:24:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17190,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative incidence of preterm birth among pregnant women who had ANC follow-up at UOGCSH\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/8f11f7381648cb87360258a4.png"},{"id":54372875,"identity":"faca32e4-64aa-4dd1-803f-4a64aceac8df","added_by":"auto","created_at":"2024-04-09 13:24:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30350,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting preterm birth among pregnant women who had ANC follow-up at UOGCSH\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/9607cf29c35257a9c0d74145.png"},{"id":54373679,"identity":"1c8fb137-83ac-4095-8a5d-94f171792782","added_by":"auto","created_at":"2024-04-09 13:32:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the nomogram on the development set for predicting preterm birth\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/2e5b809849d0269970c98ef4.png"},{"id":54373680,"identity":"6c26927f-84be-4b2e-a48b-691c4fbb3c12","added_by":"auto","created_at":"2024-04-09 13:32:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28413,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plot of the nomogram on the development set for predicting preterm birth\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/13542b5aeff43a066fe86c08.png"},{"id":54372879,"identity":"0f505f22-6f03-4fa8-b3db-6bdb69def490","added_by":"auto","created_at":"2024-04-09 13:24:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":27574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC and Calibration of the nomogram on the validation data set for predicting preterm birth\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/8d6a3948359cd5f6485940fa.png"},{"id":54373681,"identity":"4f385a09-4810-49bf-9afe-f5f78e63a861","added_by":"auto","created_at":"2024-04-09 13:32:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":30100,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration of the nomogram on the validation data set for predicting preterm birth\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/09f3aaa584912d98d4e15bb5.png"},{"id":54372881,"identity":"a6a30172-bea8-48a7-9c9f-ae4bc1fcd4f7","added_by":"auto","created_at":"2024-04-09 13:24:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":22598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision Curve analysis of the nomogram for predicting preterm\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/59e17d53924ef60530ea4d68.png"},{"id":56190660,"identity":"7e278ba2-087f-4be5-9479-c5d86a7a071a","added_by":"auto","created_at":"2024-05-09 16:32:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1911309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/bf22332f-2ed4-4dfe-99b1-1331d2af8bb4.pdf"},{"id":54372883,"identity":"3f7c224d-ee6a-4767-a70a-bd327c9d69f6","added_by":"auto","created_at":"2024-04-09 13:24:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25710,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4076906/v1/e1f6c860f8a75bdc94b8d41b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a nomogram for predicting preterm birth among pregnant women who had Antenatal care follow-up at University of Gondar Comprehensive Specialized Hospital using maternal and fetal characteristics: Retrospective follow-up study","fulltext":[{"header":"Background","content":"\u003cp\u003ePreterm birth (PB) is defined as delivery before completing 37 weeks of gestation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. United Nations categorized preterm birth as iatrogenic and spontaneous preterm birth [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Globally, around 13.4\u0026nbsp;million neonates were preterm in 2020, and the majority of them occurred in southern Asia and sub-Saharan Africa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The burden of preterm birth varies from 10.48 to 23.7% in Africa [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and in Ethiopia, around 320,000 neonates are born prematurely each year. Complications of premature birth are the leading cause of under-five mortality, with nearly 900,000 in 2019 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In Ethiopia, 23,100 children die due to direct complications of preterm birth [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSurviving premature infants are more susceptible to diseases like respiratory distress syndrome, chronic lung disease, intestinal injury, weakened immunity, cardiovascular issues, cerebral palsy, intellectual disability, hearing and vision loss, high hypertension and cardiovascular disease, and mental health problems [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Lengthy hospital stays and frequent treatments for premature birth can also cause additional psychological stress and costs for families, as well as increase the strain on the health care system [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current clinical practice treats all pregnant women with preterm labor symptoms [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which requires them to stay until labor symptoms develop. In addition, it treats all pregnant women with preterm labor symptoms unnecessarily to ensure that the minority of pregnant women who give birth prematurely receive the proper care. However, unnecessary intervention can cause adverse maternal and neonatal outcomes. For example, tocolytics can cause palpitations, pulmonary edema, myocardial infarction, arterial hypotension, chorioamnionitis, and post-operative hemorrhage [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and antenatal corticosteroids are associated with adverse neurodevelopmental outcomes in children [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the significant health impact and the potential for prevention through early detection, prognostic models have been developed to identify pregnant women at high risk for preterm birth [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Existing models from developed nations may not be applied as their development is based on predictors like fetal fibrinogen (FN), cervical length, C-reactive protein, and interleukin-6 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which are not feasible to measure in resource-limited settings like Ethiopia. The previous prediction model from Ethiopia had a very good discriminative performance (AUC\u0026thinsp;=\u0026thinsp;0.816)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], but the model did not account for fetal characteristics like fetal sex, intrauterine growth retardation, and obstetric complications like polyhydramnios and oligohydramnios, which are known risks of preterm birth according to different studies [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA nomogram is a visual depiction of a statistical prediction model that predicts the likelihood of a specific clinical event in individual patients. It is a user-friendly tool that enables health professionals and individuals to estimate the risk of a certain outcome [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Using a nomogram, clinicians can stratify high-risk pregnancies and provide important interventions like cervical cerclage, steroid, and progesterone administration efficiently [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Also, this will help to reduce the overtreatment of pregnant women with low risk, which helps to efficiently utilize resources and prevent adverse maternal and neonatal outcomes.\u003c/p\u003e \u003cp\u003eReducing neonatal mortality to as low as 12 per 1,000 live births and under-5 mortality to at least 25 per 1,000 live births is one component of the United Nations Sustainable Development Goal (SDG) and the government of Ethiopia launched different programs to accelerate country\u0026rsquo;s progress in meeting this goals [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. To achieve this goal, prevention provided for preterm birth can play a crucial role, as it is the leading cause of both under-five and neonatal mortality.\u003c/p\u003e \u003cp\u003ePer the knowledge of the researchers, nomograms constructed by combining maternal and fetal characteristics for predicting preterm birth are missed in Ethiopia. Therefore, this study aimed to develop a nomogram by using maternal and fetal characteristics to find practical obstetric tools for predicting preterm birth.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eA retrospective follow-up study was conducted from June 1, 2021, to June 1, 2022, at the University of Gondar Comprehensive Specialized Hospital, which is located in Gondar City, located 740 km from Addis Ababa, the capital city of Ethiopia. Under the University of Gondar, a comprehensive specialized hospital. The gynecology and obstetrics department provides ANC services through four different outpatient departments (OPDs), of which one provides service for only high-risk pregnancies. Intern doctors, gynecology and obstetrician resident doctors, and midwifery professionals provide the services. Routinely, physical examinations with laboratory investigations and, within each trimester, ultrasound examinations are provided for pregnant women who visit the services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical design\u003c/h2\u003e \u003cp\u003eThe occurrence relationship was Probability (preterm birth)\u0026thinsp;=\u0026thinsp;f (APH\u0026thinsp;+\u0026thinsp;preeclampsia\u0026thinsp;+\u0026thinsp;polyhydamnions\u0026thinsp;+\u0026thinsp;HIV\u0026thinsp;+\u0026thinsp;anemia\u0026thinsp;+\u0026thinsp;MABP\u0026thinsp;+\u0026thinsp;PROM) and \u003cb\u003ethe domain\u003c/b\u003e were pregnant women who had ANC follow-up at university of Gondar Comprehensive Specialized Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePopulation and eligibility criteria\u003c/h2\u003e \u003cp\u003eAll pregnant women who had ANC follow-up from June 1, 2021, to June 1, 2022, and who delivered a live birth at the University of Gondar Comprehensive and Specialized University Hospital were included and Pregnant women with an unknown or unreliable last normal menstrual period (LNMP) or with no first-trimester ultrasound and pregnant women with multiple pregnancies were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample size and sampling techniques\u003c/h2\u003e \u003cp\u003eThe sample size was determined by riley rules using stata \u0026ldquo;\u003cb\u003epmsampsize\u0026rdquo;\u003c/b\u003e which calculates three different sample sizes by using an overall outcome risk or mean outcome value in the target population, the number of candidate predictor parameters, and the anticipated model performance in terms of overall model fit (R\u003csup\u003e2\u003c/sup\u003e) or c statistics [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample size and assumptions for development and validation of nomogram for predicting preterm birth among pregnant women who had ANC follow-up at UGCSH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShrinkage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e cs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax R\u003csup\u003e2\u003c/sup\u003ecs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEPP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.4658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.4658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.4658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eCS\u003c/b\u003e: Cox-Snell R squared statistic, \u003cb\u003eEPP\u003c/b\u003e: Event Per Predictor Parameter\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eSample size and assumptions for development and validation of nomogram for predicting preterm birth\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe sample size had three different criteria\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat sample size will produce precise estimate of the overall outcome risk or mean outcome value?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat sample size will produce a small require d shrinkage of predictor effects (to minimize potential model over fitting)?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat sample size will produce a small optimism in apparent model fit?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhere \u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003ecs\u003c/b\u003e is cox Snell R\u003csup\u003e2\u003c/sup\u003e which can be taken from previous study or estimated from c statistics (AUC) ,\u003cb\u003eP\u003c/b\u003e is number of parameters (37), C statistics (AUC) which taken from previous study as 0.99 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] as we could not find study reporting R\u003csup\u003e2\u003c/sup\u003ecs, \u003cb\u003eΦ\u003c/b\u003e incidence from previous study\u0026thinsp;=\u0026thinsp;0.134 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Taking the maximum sample size from the above the sample size was 986 and by adding 10% for missing records (charts) it will become 1084. Computer Generated Simple random sampling techniques was used based on sampling frame which was prepared by arranging medical record number order from list of ANC registration book .The details of the sample selection procedure is presented in \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Flow diagram of sample selection for development and validation of A nomogram for predicting preterm birth\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eVariables of the study\u003c/h2\u003e \u003cp\u003eThe outcome variable was Preterm Birth and the prognostic determinants were Socio demographic factor (Age, residency, marital status), obstetric factors (previous history of abortion, previous history of still birth, previous history of preterm delivery, previous CS, Parity, gravidity, number of gestation, APH, inter pregnancy interval, GDM, polyhydroamnios, Oligohydramnios), Clinical characteristics (haemoglobin level, blood pressure, asthma, thyroid, HIV, UTI, cardiac disease, chronic hypertension, iron and folic supplements), Fetal factor (IUGR, sex of fetus, malpresentation) and Behavioural factor (Smoking).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOperation definition\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePreterm Birth \u0026ndash;\u003c/b\u003eA delivery before 37 full weeks but after fetal viability (after 28 full weeks), which is calculated by using reliable LMNP or by early ultrasound (less than 22 weeks of Gestation) which are the gold standards for estimating preterm birth and used in our set up [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003e \u003cb\u003eReliable LMNP\u003c/b\u003e -It is said if the menses are regular (28\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;7 days), if she was not on contraception, and if on contraception after 3 months for lactational amenorrhea and oral contraceptives, immediately for implants and an intrauterine contraceptive device (IUCD).[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eBirth interval\u003c/b\u003e \u0026ndash;interval between last delivery and the present pregnancy and is defined as short when it is less than 2 years, optimal when it is between 3\u0026ndash;5 years and long when it is greater than 5 years [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData collection and procedures\u003c/h2\u003e \u003cp\u003eA data extraction tool was designed using an electronic data collection tool called Kobo Toolbox, and the data was collected with the Kobocollect version 2022.1.2. This risk prediction model used secondary data sources, which were collected from April 18 up to May 18, 2023, by reviewing the records of pregnant women. Socio-demographic characteristics, past and present obstetric characteristics, fetal characteristics, medical characteristics, and nutritional and behavioral characteristics were collected from ANC records. The data were collected from the date that the pregnant women started ANC follow-up until delivery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData quality control and assurance\u003c/h2\u003e \u003cp\u003eThe data extraction tool was checked by researchers and gynaecologists prior to data extraction. Preliminary test was be done by taking 5% (54) of the total sample size. A one day training about the objectives of the study, how to use the kobocollect digital data extraction tool, how to access records, data handling and confidentiality of the participants information was given for the data collectors and supervisors. Extracted data was checked for completeness by the investigator on daily basis\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMissing data management\u003c/h2\u003e \u003cp\u003eMissing data is handled by multiple imputations chained equations (MICE) after ascertaining the missing type. Missing at random (MAR) was checked by testing the association between the missing variable and other independent variables using a t-test and a Chi-square test value are presented in the supplementary file (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eMICE is the preferred multiple imputation method when there are different types of variables and an arbitrary type of pattern. The number of imputations used was 20 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The number of imputations used was 20, as it was Stata\u0026rsquo;s recommendation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] Finally, sensitivity analysis was performed to compare the imputed data and complete case analysis. In this sensitivity analysis, we did not find a significant difference (\u003cb\u003eTable S3).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData processing and analysis\u003c/h2\u003e \u003cp\u003eThe data collected with KOBOcollect was exported to Stata 17 for management and further analysis. Descriptive summary statistics (frequency with percentage for categorical variables, mean with standard deviation and median with interquartile range for continuous variables) were used to describe variables.\u003c/p\u003e \u003cp\u003eThe candidate predictors were selected by least absolute Shrinkage and Selection Operator (LASSO) regression. Selected variables were entered in to multivariable logistic regression and model reduction was performed at a cut-off p -value\u0026thinsp;\u0026gt;\u0026thinsp;0.15.Significance of the difference between the original and reduced model was assessed by likelihood ratio test and p value for the test was reported. Multicollinearity was assessed by variance inflation factor (VIF)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eNomogram development and Validation\u003c/h2\u003e \u003cp\u003eThe risk of having preterm birth was predicted using the predictive nomogram by \u0026ldquo;\u003cb\u003enomolog\u003c/b\u003e\u0026rdquo; package on Stata. The nomogram was developed by beta coefficient of variables that are remained on the reduced model. The identified predictors were MABP, APH, preeclampsia, DM, polyhydamnions, HIV, anemia and PROM\u003c/p\u003e \u003cp\u003eInternal validation was performed using bootstrapping method by the randomly selected bootstrap samples (10000) with replacement to estimate how good the performance of the prediction model developed on the development set would be on a hypothetical set of new Risk Classification by using the nomogram\u003c/p\u003e \u003cp\u003eOptimal cutoff point for predicted probability of preterm birth was calculated using the maximum Youden\u0026rsquo;s index (J) and the pregnant women were categorized as high risk and low risk for delivering preterm neonate based on the cutoff point .The sensitivity, specificity, Positive and negative predictive with their respect 95% confidence interval at the cutoff point was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel performance and evaluation\u003c/h2\u003e \u003cp\u003eThe performance of a model was assessed by discrimination which was measured by Area under receiver operating characteristic and Calibration refers to the agreement between observed and predictions Stata Package \u0026ldquo;calibartionbelt\u0026rdquo; and \u0026ldquo;PMCALPLOT\u0026rdquo; were used to plot the calibration plot. The Brier Score was calculated as it measured a composite measure of discrimination and calibration, summarizing performance for a prediction model.\u003c/p\u003e \u003cp\u003eThe net benefit of the developed model, which is a measure of clinical utility, was represented by decision curve plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eReporting of the study\u003c/h2\u003e \u003cp\u003eThe analysis and reporting of the study was according to the transparent reporting of multivariable prediction model for individual prognosis or diagnosis (TRIPOD) initiative checklist [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the study participants\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eSocio demographic characteristics of the participants\u003c/h2\u003e \u003cp\u003eA total of 1039 pregnant women who had ANC follow up at university of Gondar were included. The median age was 27 years with IQR of 24\u0026ndash;35 years and only 13(1.25%) were single (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio demographic characteristic of pregnant women who had ANC follow-up at UGCSH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePreterm(Yes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePreterm (No)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eSocio demographic characteristic of pregnant women who had ANC follow-up at UGCSH\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePast and present obstetric characteristics of participants\u003c/h2\u003e \u003cp\u003eMore than half, 611 (58.61%) of the participants of them had history of preterm birth and 199 (32.67%) had history of preterm birth. 46 (7.55%) had history of preterm birth and 199 (32.67%) of them delivered their latest child by CS. Regarding to pregnancy related complications preeclampsia was the highest, 181 (17.42%) had preeclampsia (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePast and Present Obstetric characteristics of pregnant women had ANC follow-up at UGCSH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePreterm(Yes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePreterm(No)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrim gravid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultigravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNulliparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth interval(N\u0026thinsp;=\u0026thinsp;611))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShort birth interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOptimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious Abortion (N\u0026thinsp;=\u0026thinsp;664)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious stillbirth(N\u0026thinsp;=\u0026thinsp;611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious CS (N\u0026thinsp;=\u0026thinsp;611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious preterm birth(N\u0026thinsp;=\u0026thinsp;611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational diabetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntepartum Hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolyhydroamnions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOligohydramnios\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eANC\u003c/b\u003e: Ante Natal Care, \u003cb\u003eCS\u003c/b\u003e: Cesarean Section, \u003cb\u003ePROM\u003c/b\u003e: Premature Rupture of Membrane\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u003cb\u003ePast and Present Obstetric characteristics of pregnant women had ANC follow-up at UGCSH\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eClinical related characteristics of the of the participants\u003c/h2\u003e \u003cp\u003eAmong the infectious diseases UTI accounts for 13.86% and from the chronic diseases the lowest frequency was recorded on renal diseases only 5 pregnant women were diagnosed to had renal disease (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical related characteristics of the of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePreterm(Yes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePreterm(No)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-reactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic HTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetics mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon -anemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e880\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyphilis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eUTI\u003c/b\u003e: Urinary Tract Infection, \u003cb\u003eHIV\u003c/b\u003e: Human immune virus\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: \u003cb\u003eClinical related characteristics of pregnant women had ANC follow-up at UGCSH\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eFetal related characteristics\u003c/h2\u003e \u003cp\u003eAbout fetal characteristics among the total of 1039 conceived fetus 535(51.49%) of the fetus were with female sex and 125(12.03) of them were diagnosed to had IUGR. In terms of fetal presentation 89 (8.57%) of them were malpresented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIncidence proportion of preterm birth\u003c/h2\u003e \u003cp\u003eAmong the total of 1039 pregnant women, 147 women delivered a preterm neonate which makes the incidence proportion of preterm birth 14.15% (95% CI: 12.03, 16.27) and majority 131(89%) of them the preterm neonate were under category of late preterm (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eIncidence proportion of preterm birth among pregnant women who had ANC follow-up at UGCSH\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eNomogram development\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e \u003ch2\u003eVariable selection\u003c/h2\u003e \u003cp\u003eIn this nomogram development socio-demographic, past and present obstetric, medical, behavioral, nutritional and fetal related predictors were and Candidate predictors were selected by LASSO regression.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eLASSO Based Multivariable Binary Logistic Regression\u003c/h2\u003e \u003cp\u003eVariables selected by LASSO were Polyhydroamnions, Anemia status, MABP, DM, HIV, APH, IUGR, UTI, Sex of fetus, Number of ANC, Preeclampsia and PROM and were entered in to multivariable logistic regression. Variables were reduced by using P- value (\u0026gt;\u0026thinsp;0.15), by considering clinical significance. Based on that IUGR, sex of fetus, frequency of ANC and UTI were reduced .Finally Eight variables which were; MABP, preeclampsia, DM, Anemia, PROM, polyhydramnios, HIV and APH remained on the reduced model (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTable 5: LASSO based Multivariable logistic regression for development and validation of nomogram for predicting preterm birth\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEquation:\u0026nbsp;\u003c/strong\u003eThe probability for risk of preterm using reduced model beta coefficients was:-\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP(pretermbirth)\u0026thinsp;=\u0026thinsp;1/1\u0026thinsp;+\u0026thinsp;exp-(4.32\u0026thinsp;+\u0026thinsp;0.74*preeclampsia(yes)\u0026thinsp;+\u0026thinsp;1.47*polyhydroamnions(yes)\u0026thinsp;+\u0026thinsp;0.97*anemia(yes)\u0026thinsp;+\u0026thinsp;1.60*PROM(yes)\u0026thinsp;+\u0026thinsp;2.16*DM(yes)\u0026thinsp;+\u0026thinsp;0.72*HIV(yes)\u0026thinsp;+\u0026thinsp;1.32*APH(yes)\u0026thinsp;+\u0026thinsp;0.74*MABP(low)\u0026thinsp;+\u0026thinsp;1.28*Malpresentaion(yes)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLASSO based Multivariable logistic regression for development and validation of Nomogram for predicting preterm birth among pregnant women who had ANC follow-up at UGCSH (N\u0026thinsp;=\u0026thinsp;1039)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable Binary Logistic Regression of Original LASSO Based model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariable Binary Logistic Regression of Reduced LASSO Based Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreterm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta coefficient with 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP - value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBeta coefficient with 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalpresentation\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e835\u003c/p\u003e \u003cp\u003e57\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003cp\u003e32\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23(0.67 ,1.78)\u003c/p\u003e \u003cp\u003e1.63(.76, 2.51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28(0.73,1.82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreeclampsia\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63(0.16,1.09)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74(0.28,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIUGR\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003cp\u003e791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 .51(0.04,1.06)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePolyhydroamnions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003cp\u003e860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56(0.89,2.27)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47((0.78, 2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnemia status\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAnemic\u003c/p\u003e \u003cp\u003eNon-Anemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003cp\u003e838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93(0.30,1.58)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97(0.33, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of ANC contact\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u0026lt; 4 contact\u003c/p\u003e \u003cp\u003e\u0026gt;= 4 contact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36(0.03,0.75)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUTI\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59(0.09,1.08)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetic Mellitus\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35(1.38,3.33)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.16(1.19,3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68(0.09,1.27)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.720(150,1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPH\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13(0.54,.71)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32(0.75,1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex of fetus\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e425\u003c/p\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16(0.22 ,0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePROM\u003c/b\u003e\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003cp\u003e818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54(1.03,2.09)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.60(1.07,2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eNomogram for predcting preterm birth\u003c/h2\u003e \u003cp\u003eA total of the eighth variables from the reduced model were used to develop a nomogram. It was characterized by a scale corresponding to each variable, a total score scale and probability scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Nomogram for predicting preterm birth\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003ePredictor score of the nomogram for predicting preterm birth\u003c/h2\u003e \u003cp\u003eThe minimum score for each variable was 0 and the variable having category with maximum score was DM (10) The Total score for the variables was 46.1 (\u003cb\u003eTable S4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFor example let\u0026rsquo;s take\u003c/p\u003e \u003cp\u003ePregnant women who visits ANC with high MABP, history of Diabetic mellitus, non-anemic, diagnosed to had polyhydramnios, non-reactive to HIV and no PROM, APH, preeclampsia\u003c/p\u003e \u003cp\u003eThen the Total score for the given scenario equals to 23.1 and the corresponding probability for this score from the nomogram become 0.91\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eDiscriminative performance of the nomogram\u003c/h2\u003e \u003cp\u003eThe discriminative performance of the nomogram was assessed using area under the curve of ROC and resulted in AUC of 0.79 (95% CI; 0.74, 0.83%). This showed that the nomogram had good level of discriminative performance, implying that it can well discriminative those who deliver preterm neonate and term neonate. Also the nomogram demonstrated good agreement between the actual and the predicted probability from the calibration plot and the Hosmer\u0026ndash;Lemeshow resulted a P value of 0.60, which implied that there is an agreement between the predictive probability and the observed probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e : \u003cb\u003eROC curve of the nomogram on the development set for predicting preterm birth\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eCalibration Plot\u003c/h2\u003e \u003cp\u003eThe nomogram demonstrated good agreement between the actual and the predicted probability from the calibration plot and the Hosmer\u0026ndash;Lemeshow goodness test resulted a P value of 0.60, which implied that there is an agreement between the predictive probability and the observed probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e : \u003cb\u003eCalibration plot of the nomogram on the development set for predicting preterm birth\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInternal validation of the nomogram\u003c/h3\u003e\n\u003cp\u003eThe area under ROC curve was assessed based on the bootstrapped dataset. The developed nomogram was internally validated, on the bootstrapped data it had AUC of 0.78(CI 0.73, 0.82) and the optimization coefficient was 0.01 which showed that the model is less likely to over fit and less sample dependent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e: \u003cb\u003eROC of the nomogram on the validation data set for predicting preterm birth\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThere is an agreement between the observed and the predicted probability of preterm birth with p value of 0.526 (p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05) on the bootstrapped data, which confirmed the model is well calibrated on the validation data too (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e: \u003cb\u003eCalibration of the nomogram on the validation data set for predicting preterm birth\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eDecision Curve analysis\u003c/h2\u003e \u003cp\u003eClinical and public health utility of the model was also assessed by decision curve analysis. DCA evaluate whether the model understudy had a higher net benefit than the default strategies (counseling and treating all and none) .This model outperforms the default strategies across threshold probabilities from 0 up to 0.8 (80%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e : \u003cb\u003eDecision Curve analysis of the nomogram for predicting preterm\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eRisk Classification and accuracy measurements of the nomogram at optimal cut of point\u003c/h2\u003e \u003cp\u003eOptimal cutoff point of predicted probability for risk of preterm birth was calculated using Youden\u0026rsquo;s index (J) .The maximum Youden\u0026rsquo;s index was 42.67% and the predicted probability at that Youden\u0026rsquo;s index was \u0026gt;\u0026thinsp;=\u0026thinsp;0.12 which was the optimal cutoff point. Using the optimal cutoff point the proportion of pregnant women who were at low risk was 628 (63.44%) and high risk group 411 (39.56%) \u003cb\u003e(Table S5)\u003c/b\u003e (\u003cb\u003eTable S6\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis prognostic study used retrospective follow up to develop a practical tool which predicts preterm birth using maternal and fetal characteristics among pregnant women who had ANC follow-up at university of Gondar comprehensive specialized Hospital.\u003c/p\u003e \u003cp\u003eBy establishing a nomogram, this study developed a preterm risk prediction model to be used in at settings which provides ANC services. This model will help clinicians and pregnant women who utilize ANC services to make clinical and public health decisions.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis study showed that the incidence proportion of preterm birth was 14.15% (95% CI: 12.03\u0026ndash;16.27), which is in line with studies conducted in Bahirdar (13.4%) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], Axum (13.3%) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], Butajira (15.5%) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] .The similarity may be due to the fact that our country's health care system and services that are provided to mothers are essentially uniform across different parts of the nation and other reason might be due to related levels of socioeconomic status and lifestyle of the respondents.\u003c/p\u003e\u003cp\u003eHowever, the finding is lower than study from Harar (24.9%) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e],Jimma (25.9%) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and Ghana (37.3%) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] the discrepancy might be due to the inclusion of multiple pregnancies in their study as multiple pregnancy increases uterine over destination which initiate preterm labor [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], time line difference for the study from Jimma and socio demographic difference for the study from Gahanna.\u003c/p\u003e\u003cp\u003eWhereas the finding is higher than study from Gondar (4.4)[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and Iran (8.2%)[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The difference may be accounted by the difference in health institutions the data were collected and the type of preterm birth assessed by the study. Health centers where the study area for the first study at which high risk pregnancy are referred which might reduce the number of preterm delivery and only spontaneous preterm birth were assessed on the study from Iran .\u003c/p\u003e\u003cp\u003eThis prediction nomogram was developed by eight predictors and had a good discrimination with an AUC of 0.76% (95% CI: 0.72\u0026ndash;0.81) according to diagnostic accuracy classification criteria [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe performance of this nomogram was found to be consistent with other prediction models from Bahirdar using residency, gravidity, APH, PIH, PROM, and hemoglobin level (AUC: 78.96%) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] ,study from Canada by combining ovulation induction IVF conception, a history of previous abortions, previous preterm birth, and abnormal pregnancy-associated plasma protein concentrations (AUC: 73.4%) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and from the USA by mass spectrometry-based serum test yielding an AUC of 75% [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis nomogram performed better than a study from Netherlands with an AUC of 63%, which was developed by combining Previous preterm birth, drug abuse, and vaginal bleeding [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], a study from the USA with a combination of received hydroprogestrone caproate ,cervical length, and cervical funnel with an AUC of 63% [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and another study from the USA with a performance of 68.8%, developed by combining age of spouse, smoking, infection, previous CS, prepregnancy weight, race, hypertension ,prenatal care, prepregnancy DM, plurality, education, age, previous preterm birth, and gestational hypertension[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] .The difference might be due to a difference in the number of predictors used to develop the models, the selection of the study population (only nulliparous was considered in the model from the USA), and the time point difference (early pregnancy was the time for the last study).\u003c/p\u003e\u003cp\u003eHowever, the discriminative performance of the model was lower than a study done in china by combining maternal age, insulin use, and monocyte count with AUC of 88.5% [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], from Iran by combining cervical length, uterine contractions, rupture of membranes, vaginal bleeding, gestational age, and multiple resulting in AUC of 89.0%[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], from China by combining gestational Age, magnesium, fundal height, serum inorganic phosphorus, mean platelet volume, waist size, total cholesterol, triglycerides, globulins, and total bilirubin yielding AUC of 88.5 [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] .The discrepancy might be due to predictor difference ;cervical length ,serum inorganic phosphorus, mean platelet volume are used which were not included in this model as they are not routinely assessed in our country. Another explanation for the difference can be the domain difference the domain for the first study were pregnant women with diabetic mellitus who are already at high risk of delivering preterm birth.\u003c/p\u003e\u003cp\u003eThe benefit that the developed nomogram would add to clinical practice was also presented in the form of a decision curve analysis. In our study the decision curve analysis showed that there was high net benefit than using treat all or treat none strategies.\u003c/p\u003e\u003cp\u003eIn our nomogram prediction score, using 0.12 as the cutoff point yields an acceptable level of sensitivity and specificity of 70.19 and 69.48 respectively. This showed that using the nomogram 70.19% of pregnant women were correctly identified as delivering preterm births and 69.48 were correctly identified as delivering term neonates. Also the PPV and NPV was 93% and 70% respectively, implying that the probability of delivering at preterm was 0.93 in subjects who were classified as yes for delivering at preterm and the probability of delivering at term period was 0.7 in a subject who were classified as no by the nomogram. In this study the nomogram yielded a\u0026thinsp;+\u0026thinsp;LR of 2.28 showing that pregnant women who delivered at preterm period were 2.28 more likely to be classified as yes (preterm birth) by the nomogram than women who deliver term neonate on the contrary the \u0026ndash;LR was 0.43 implying that using the nomogram the delivering term neonate is 0.43 much less likely to occur in a pregnant women who deliver preterm neonate than those who deliver a term neonate\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003eLimitation of the study\u003c/h2\u003e \u003cp\u003eThe findings from this study should be interpreted with the perspective of the following limitations. As a single-site study, it is confined to a single area, which needs external validation before using it in another context and important predictors like BMI, psycho social factors were not considered for model development as the study was retrospective study\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe nomogram was developed from obstetric factor, fetal factors and medical complications which are routinely assessed for pregnant women who visit ANC services. The predictors were APH, preeclampsia, polyhydroamnions, PROM, anemia, DM, HIV and MABP. The nomogram had good discriminating performance power and was well calibrated. It was internally validated by bootstrapping techniques with small optimum coefficients, less likely of over fitting of the model. The nomogram had added net benefit in clinical practice as it was assured by decision curve analysis across different threshold probabilities.\u003c/p\u003e \u003cp\u003eThe prediction nomogram was used to risk-stratify pregnant women and identify those who were more likely to have a preterm birth. Following that, high-risk groups might be opted for corticosteroid administration, antibiotic treatment in the event of infection, and other\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eANC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eAntenatal Care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eAPH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eAnte Partum Haemorrhage\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetic Mellitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eHIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eHuman Immune Virus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eLASSO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003ePROM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003ePremature Rupture of Membrane\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Receiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.727272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eUGCSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.27272727272727%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity of Gondar Comprehensive Specialized Hospital\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent was waived by\u0026nbsp;\u003c/strong\u003einstitutional Review board of the institute of public health, college of medicine and health science, University of Gondar with reference number /IPH/2520/2023.Official\u0026nbsp;\u0026nbsp;\u0026nbsp;letter was obtained from department of Epidemiology and Biostatics and then provided by the Hospital admission and gynaecology and obstetrics department\u0026nbsp;As this study involved the analysis of pre-existing data, informed consent from participants was not applicable. However, strict confidentiality measures were adhered to throughout the study process to ensure the privacy of individuals\u0026apos; information.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eRT designed the study, performed analysis and interpretation of data and developed and participated in preparing all versions of the manuscript. BM, SG, SB and TK participated in the analysis process, manuscript drafting and critical revision. All authors have read and agreed to the final version of the manuscript\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. \u003cem\u003eWHO preterm fact sheet\u003c/em\u003e. 2023; Available from: https://www.who.int/news-room/fact-sheets/detail/preterm-birth.\u003c/li\u003e\n\u003cli\u003ePurisch, S.E. and C. Gyamfi-Bannerman. \u003cem\u003eEpidemiology of preterm birth\u003c/em\u003e. in \u003cem\u003eSeminars in perinatology\u003c/em\u003e. 2017. Elsevier.\u003c/li\u003e\n\u003cli\u003eOhuma, E., A. Moller, and E. 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Estimating the probability of a pregnant woman being at risk of preterm delivery would help to initiate preventive measures to reduce preterm delivery. The available risk prediction models used non-feasible predictors and did not consider fetal characteristics. This study aimed to develop an easily interpretable nomogram based on maternal and fetal characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: - A retrospective follow-up study was conducted with a total of 1039 pregnant women who were enrolled from June 1, 2021, to June 1, 2022, at the University of Gondar Comprehensive Specialized Hospital. Stata version 17 was used for data analysis. Important predictors were selected by the least absolute shrinkage and selection operator and entered into multivariable logistic regression. Statistically and clinically significant predictors were used for the nomogram’s development. Model performance was assessed by the area under the receiver operating curve (AUROC) and calibration plot. Internal validation was done through the bootstrapping method, and decision curve analysis was performed to evaluate the clinical and public health impacts of the model\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e: - The incidence proportion of preterm birth among pregnant women was 14.15% (95%CI: 12.03, 16.27). Antepartum hemorrhage, preeclampsia, polyhydramnios, anemia, human immune virus, malpresentation, premature rupture of membrane, and diabetic mellitus were used to develop a nomogram. The nomogram had a discriminating power AUROC of 0.79 (95% CI: 0.74, 0.83) and 0.78 (95% CI: 0.73, 0.82) on the development and validation sets. The calibration plots exhibited optimal agreement between the predicted and observed values; the Hosmer-Lemeshow test yielded a P-value of 0.602. The decision curve analysis revealed that the nomogram would add net clinical benefits at threshold probabilities less than 0.8.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: - \u003c/strong\u003eThe developed nomogram had good discriminative performance and good calibration. Using this model could help identify pregnant women at a higher risk of preterm delivery and provide interventions like corticosteroid and progesterone administration, cervical cerclage, and nutritional support.\u003c/p\u003e","manuscriptTitle":"Development and validation of a nomogram for predicting preterm birth among pregnant women who had Antenatal care follow-up at University of Gondar Comprehensive Specialized Hospital using maternal and fetal characteristics: Retrospective follow-up study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 13:24:49","doi":"10.21203/rs.3.rs-4076906/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22f55adb-bb3a-4b50-a716-68d33ab542da","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-09T16:23:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 13:24:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4076906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4076906","identity":"rs-4076906","version":["v1"]},"buildId":"re_ckhLnmML6MCF96OHNJ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}