Predictors of unfavorable perinatal outcomes among women with early-onset preeclampsia without severe features: a retrospective cohort study at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital

Predictors of unfavorable perinatal outcomes among women with early-onset preeclampsia without severe features: a retrospective cohort study at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital | 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 Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictors of unfavorable perinatal outcomes among women with early-onset preeclampsia without severe features: a retrospective cohort study at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital Mellese Tsehay, Markos Selamu, Dessalegn Shegute, Binalfew Tsehay, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9431483/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Although the prognosis of preeclampsia without severity features is favorable, its adverse forms are a major cause of perinatal and maternal morbidity and mortality. There is no data on predictors and survival time to develop maternal and perinatal unfavorable outcomes of preeclampsia without severity features and normotensive women in the study area. Objective To determine the incidence and predictors of unfavorable perinatal outcomes of early-onset preeclampsia without severity feature and normotensive pregnant women in Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital,2022 G.C. Methods Hospital-based retrospective cohort study was conducted among 132 exposed and 132 unexposed pregnant women at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital from January 1/2019 to December 30/2021. The incidence of unfavorable perinatal outcomes was calculated from the total adverse outcome to the sample(population) time. Survival probabilities were shown in the Kaplan-Meier graph. Data were entered using the epi data version 4.6 and exported to STATA version 14. Variables having P < 0.25 in bivariate analysis were fitted for multivariate Cox. Results A total of 264 pregnant women were followed for 2305.96 person weeks for perinatal outcomes. The incidence of unfavorable perinatal outcomes was 19.1 per 1000 person-weeks. Gestational age at admission (AHR: 11.81 95%CI:5.94–23.46)), medical illness (AHR: 3.85 95%CI:2.00-7.40), exposure status (AHR:2.64 95%CI:1.20–5.79)) and maternal outcome (AHR: 5.15, 95%CI:2.67–9.92) were predictors of the unfavorable perinatal outcome. Conclusion The Kaplan-Meier survival function graph shows the cumulative survival proportion appears to be much higher in the unexposed group than in the exposed group. There were indicators that were significant for maternal and perinatal outcomes. Outcome Perinatal Preeclampsia Ethiopia Figures Figure 1 Introduction Preeclampsia is defined as the new onset of hypertension and proteinuria in previously normotensive women after 20 weeks of gestational age ( 1 ). Recently the diagnostic criteria have been changed, and Fetal Growth Restriction (FGR) is included ( 2 ). In the past 30 years pathogenesis, pathophysiology, treatment options, predictors of preeclampsia, and associations with risk factors like smoking, stroke, and cardiovascular disease have been the subject of extensive research. Preeclampsia can be divided into two distinct disease entities: early-onset and late-onset preeclampsia. Early onset preeclampsia (EOP) occurs before 34 weeks of pregnancy, whereas late-onset preeclampsia (LOP) occurs at or after 34 weeks of gestation. Even though the presenting characteristics are similar, they are associated with various maternal and fetal outcomes, biochemical markers, heritability, and clinical characteristics ( 3 ). World Health Organisation (WHO) multicountry survey has shown perinatal mortality is three to five times higher in women with preeclampsia/eclampsia syndrome than in women without the disorders ( 4 ). In Ethiopia, the incidence of early-onset preeclampsia without severity feature was 3%, and perinatal mortality was 42.6 per 1000. It was 5.22 and 25.9 times riskier to develop maternal and perinatal complications compared to late-onset preeclampsia ( 5 ). EOP poses a management dilemma. Delivery may benefit the mother but may harm a premature fetus. Expectant management in women with EOP before 34 weeks’ gestation may reduce neonatal complications and stay in a Neonatal Intensive Care Unit (NICU). Although this could aggravate the maternal condition ( 6 , 7 ). Before deciding on outpatient follow-up and management, the International Societ for the Study of Hypertension in Pregnancy (ISSHP) advises admitting all patients with preeclampsia; following assessment in the hospital some women may be managed in a specialized outpatient setting, such as day assessment units in a hospital with appropriate expertise ( 8 , 9 ). There is no data on predictors and survival time to develop maternal and perinatal unfavorable outcomes of preeclampsia without severity features and normotensive women in the study area. Therefore the study aimed to investigate the incidence predictors and survival time of unfavorable perinatal outcomes among preeclampsia without severe features and normotensive pregnant women between the gestational age of 28 to 34 weeks at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital (WCUNEMMCSH), Ethiopia. Methods And Materials Study Setting The study was conducted at WCUNEMMCSH from January 1/2019 to December 30/2021. The Hospital was established in 1984 G.C in Hadiya zone, Hossana town which is located 232 km from Addis Ababa (capital city of Ethiopia) and 192 km from Hawassa (capital city of Southern Nation and Nationalities Regional State) and provides preventive, curative, and rehabilitative services for 3.4 million population with a total of 262917 and 10881 Outpatient Diagnosis (OPD) and Inpatient attendants in 2021 respectively. The hospital had a total of 1172 staff and among them, 588 of them are health professionals. Of 584 Technical staff,269 are administrative staff. The hospital provides different services like an adult Intensive Care Unit, Neonatal Intensive Care Unit, Psychiatry, Ophthalmology, Dental, Medical, Pediatrics, Surgical, Orthopedic, Gynecology, and Obstetrics services. The gynecology and Obstetrics department had nine Gynecologists and Obstetricians, two were females, and 68 midwifery professionals. The department provided different services like delivery, outpatient, ANC, emergency, and inpatient services. There were 15Gynecolgy and 12 Obstetric inpatient beds. The hospital had 4 Laboring coaches and 7Operation room tables. In 2021 a total of 10167 mothers got delivery service. Study Design and Period Hospital-based retrospective cohort study was conducted from June,1/2022 to June,30/2022. Inclusion and Exclusion Criteria All delivered women managed in WUNEMMCSH with the diagnosis of early-onset preeclampsia without severity feature and normotensive pregnant women from January 1/2019 to December 30/2021. Pregnant mothers whose chart was not complete or lost and died on arrival. Sample Size Determination and Sampling Technique The required optimal sample size was determined by using the Epi Info version 7 double population formula with the assumptions of a 95% significance level (2-sided), 5% margin of error, power of 80%, and 1:1 Ratio. Table 1 Sample size estimation of unfavorable perinatal outcome of early-onset preeclampsia and normotensive pregnant women in WUNEMMCSH,2022. Variable CI (%) Power Ratio % Of Unexposed in Outcome % Of Exposed in Outcome Final sample size (10% data incompleteness) Reference Stillbirth 95 80 1 10.2 1.7 268 ( 10 ) Low birth weight 95 80 1 6.1 37.7 59 ( 10 ) Preterm delivery 95 80 1 5.6 40.8 51 ( 10 ) Preterm labor 95 80 1 2.5 43.75 37 ( 11 ) From January 1st, 2019, to December 30th, 2021G.C, there were 858 pregnant mothers managed for early onset preeclampsia without severity feature and 14,070normotensive women. From January 1st, 2019, to December 30th, 2021G.C, pregnant mothers managed for early onset preeclampsia without severity features (exposed group) and normotensives pregnant women (unexposed group) were obtained by simple random sampling technique using computer generated random numbers. Four charts were excluded from the total sample size, two from exposed and unexposed. Data Collection Technique and Tool The data were collected from high-risk registration books, delivery registration books, and patient charts using a well-prepared checklist. The checklist incorporates socio-demographic characteristics, obstetrics history, laboratory results, and maternal and fetal outcomes. Data were collected by trained BSc Midwives under strict supervision. Data Quality and Management The data collection tool was adapted from prior research and adjusted to fit the context. The checklist was written in English. Data collectors and a supervisor received two-day training on the data abstraction checklist and collection method. The supervisor and principal investigator monitored and supervised the data collection process closely. Data Processing and Analysis All collected data were rechecked for completeness, coded, entered using Epi data version 4.6.0.2, and exported to STATA version 14 for analysis. The data was recoded, categorized, and sorted to facilitate analysis. Descriptive statistics were analyzed and presented in tables. Kaplan-Meier survival function among the exposed and unexposed groups was used to compare survival probability between the groups. Then statistically significant difference was checked by the log-rank test. Bivariable analysis was done to see associations between the dependent and independent variables. Variables having a p-value of less than 0.25 were included in the multivariable Cox regression analysis. Those variables having a p-value < 0.05 were considered statistically significant. Proportional-hazard assumption test was used for selecting fitted proportional hazard models. Schoenfeld's residuals test was performed. The global tests were significant, with p > 0.05. The Cox regression model was fitted for analysis. Results Sociodemographic characteristics Most of the women lived in urban 182 (68.9%) (Table 2 ). Table 2 Sociodemographic characteristics among early-onset preeclampsia without severity feature and normotensive pregnant women in WCUNEMMCSH, 2022. Variables Status of Exposure Total Yes(N = 132) No(N = 132) Address Rural 42(31.8%) 40(30.3%) 82(31.1%) Urban 90(68.2%) 92(69.7%) 182(68.9%) Age =35 5(3.8%) 2(1.5%) 7(2.7%) Maternal medical and obstetric factors Multi-Gravidity among exposed and normotensive women was 98 (74.2%) and 83 (62.8%) respectively. Singleton pregnancy was the most common type of pregnancy, 247 (93.6%) of the total. Forty-four (33.3%) of preeclamptic women (exposed group) had a history of medical illness. Of these pre-existing hypertension, 30 (68.2%) and pregestational diabetes mellitus 7 (15.9%) were common comorbidities (Table 3 ). Table 3 Obstetrical and medical history among early-onset preeclampsia without severity feature and normotensive pregnant women in WCUNEMMCSH, 2022. Variables Status of Exposure Total Yes No Gravidity Primigravida 34(25.8%) 49(37.1%) 83(31.4%) Multigravida 98(74.2%) 83(62.9%) 181(68.6%) Parity Nulliparous 5(3.8%) 8(6.1%) 13(4.9%) Multiparous 80(60.6%) 66(50.0%) 146(55.3%) Grand Multiparous 47(35.6%) 58(43.9%) 105(39.8%) Type of pregnancy Singleton 117(88.6%) 130(98.5%) 247(93.6%) Twin 15(11.4%) 2(1.5%) 17(6.4%) History of medical illness Yes 44(33.3%) 2(1.5%) 46(17.4%) No 88(66.7%) 130(98.5%) 82.6% Type of medical illness Pre-existing hypertension 30(68.2%) 1(50%) 31(67.4%) History of Renal Disease 3(6.8%) 1(50%) 4(8.7%) History of Cardiovascular Disease 3(6.8%) 0 3(6.5%) Pre-gestational diabetes mellitus 7(15.9%) 0 7(15.2%) Toxic goiter 1(2.3%) 0 1(2.2%) Were drugs given to decrease Blood Pressure Yes 54(40.9%) 1(0.8%) 55(20.8%) No 78(59.1%) 131(99.2%) 209(79.2%) If yes what was/were the drug/s? Methyldopa 38(70.4%) 0 38(69.1%) Nifedipine 2(3.7%) 0 2(3.6%) Hydralazine 8(14.8%) 0 8(3.0%) MgSo4 4(7.4%) 1(100%) 5(14.5%) Aspirin 2(3.7%) 0 2(3.6%) Perinatal unfavorable outcomes incidence rate Perinatal unfavorable outcome a total of 264 pregnant women were enrolled and followed for a minimum and the maximum follow-up time of 2 and 14.4 weeks respectively giving 2305.96 person-weeks observation. The mean follow-up time was 8.73 weeks. The overall incidence rate of the unfavorable outcome during the follow-up time among pregnant women was 19.1 out of 1000 observations. Of this, the total time at risk for preeclamptic women (exposed group) was 1071.3 and 1234.66 for the unexposed group with an incidence rate of 31.74 per 1000 and 8.12 per 1000 respectively (Table 4 ). Table 4 The incidence rate in person per week of perinatal unfavorable outcome per 1000 among early onset preeclampsia without severity feature and normotensive pregnant women in WCUNEMMCSH,2022. Variables Status of Exposure Total Incidence Rate Yes No Unfavorable perinatal outcomes LBW 12.1 1.6 6.5 IUGR 3.7 2.4 3.0 Low APGAR score 4.7 0.8 2.6 Preterm birth 2.8 0.8 1.7 Stillbirth 3.7 0.8 1.3 IUFD 4.7 0.8 2.6 Death 1.9 0.8 1.3 Survival status of pregnant women for perinatal outcome The Kaplan-Meier survival function graph shows the cumulative survival proportion appears to be much higher in the unexposed group than in the exposed (preeclamptic pregnant women) (Fig. 1 ). A log-rank test was run to determine if there were statistical differences in the survival distribution for the unexposed and exposed groups. The survival distributions were statistically significantly different, χ2( 2 ) = 24.93, p < .0005. The median survival time of pregnant women with preeclampsia was 12.4 weeks. However, the median time for normotensive pregnant women was not reached. predictors of unfavorable perinatal outcome among pregnant women In Bivariable Cox proportional hazard regression type of pregnancy, Antenatal Care (ANC) follow-up, gestational age at admission, history of medical illness, proteinuria, exposure status, and the maternal outcome were a candidate for multivariable analysis (Table 5 ). Table 5 Bivariable Cox regression analysis of predictors of unfavorable perinatal outcome at WCUNEMMCSH, 2022. Variables Category Status CHR (95%CI) p-value Favorable outcome N (%) Unfavorable outcome N (%) Residence Rural 74 (90.2) 8 (9.8) 1.51(0.69, 3.33) 0.303 Urban 155 (85.2) 27 (14.8) 1 Maternal age < 35 207 (84.1) 39 (15.9) 1 ≥ 35 13 (72.2) 5 (27.8) 2.11(0.83, 5.36) 0.12 Gravidity Primigravida 70 (84.3) 13 (15.7) 0.93(0.50, 1.72) 0.680 Multigravida 159 (87.8) 22 (12.2) 1 Parity Nulliparous 76 (80.9) 18 (19.1) 0.93(0.58, 1.49) 0.75 Multiparous 125 (85.0) 22 (15.0) 1 Grand Multiparous 19 (82.6) 4 (17.4) 0.96(0.54, 1.70) Type of pregnancy Singleton 218 (88.3) 29 (11.7) 1 0.005 * Twin 11 (64.7) 6 (35.3) 3.52(1.45, 8.53) Number of ANC =3visits 84 (89.4) 10 (10.6) 1 Gestational age at admission =37 219 (89.0) 27 (11.0) 1 History of medical illness Yes 31 (67.4) 15 (32.6) 3.34(1.78, 6.24) 0.001* No 189 (86.7) 29 (13.3) 1 Proteinuria =2 200 (85.8) 33 (14.2) 1 Exposure status Exposed 98 (74.2) 34 (28.5) 5.00(2.46, 10.18) 0.000* Unexposed 122 (92.4) 10 (7.6) 1 Maternal outcome Favorable 195 (91.1) 19 (8.9) 1 Unfavorable 25 (50.0) 25 (50.0) 7.99(4.37, 14.63) 0.000* In multivariable Cox regression analysis after adjusting the confounders' gestational age at admission (AHR: 11.81 CI:5.94, 23.46)), medical illness (AHR: 3.85 CI:2.00, 7.40), exposure status (AHR:2.64 CI:1.20, 5.79)) and maternal outcome (AHR: 5.15 CI:2.67, 9.92) were found predictors of unfavorable perinatal outcome (Table 6 ). Pregnant women admitted to the hospital before 37 weeks of gestation were 11.81 times hazard to develop unfavorable perinatal outcomes than termly admitted and delivered newborns. Similarly, pregnant women with a history of medical illness were 3.85 times the hazard of unfavorable perinatal outcomes than healthier pregnant women. Pregnant women with unfavorable outcomes were 5.15 times more likely to develop unfavorable perinatal outcomes than pregnant women with favorable outcomes. The hazard of newborns delivered from preeclamptic women was 2.64 times more than the normotensive pregnant women. Table 6 Multivariable Cox regression analysis of predictors of Unfavorable perinatal outcome at WCUNEMMCSH, 2022. Variables Category Status CHR (95%CI) AHR P value Favorable outcome N (%) Unfavorable outcome N (%) Type of pregnancy Singleton 218 (88.3) 29 (11.7) 1 1 0.183 Twin 11 (64.7) 6 (35.3) 3.52(1.45–8.53) 1.82(0.75, 4.40 Number of ANC =3visits 84 (89.4) 10 (10.6) 1 1 Gestational age at admission =37 219 (89.0) 27 (11.0) 1 1 History of medical illness Yes 31 (67.4) 15 (32.6) 3.34(1.78–6.24) 3.85(2.00, 7.40 0.000* No 189 (86.7) 29 (13.3) 1 1 Proteinuria =2 200 (85.8) 33 (14.2) 1 1 Exposure status Exposed 98 (74.2) 34 (28.5) 5.00(2.46–10.18) 2.64(1.20, 5.79) 0.015* Unexposed 122 (92.4) 10 (7.6) 1 1 Maternal outcome Favorable 195 (91.1) 19 (8.9) 1 1 0.000* Unfavorable 25 (50.0) 25 (50.0) 7.99(4.37–14.63) 5.15(2.67, 9.92) Discussion Our study confirmed the presence of significant differences regarding unfavorable perinatal outcomes between preeclamptic and normotensive pregnant women. The overall incidence rate of unfavorable outcomes, LBW, IUGR, Low APGAR score, Preterm birth, stillbirth, IUFD, and Death, during the follow-up time among pregnant women, was 19.1. The incidence rate among the exposed group (preeclamptic women) was 31.74 and 8.12 among the unexposed group (normotensive pregnant women). In our study, the newborns delivered from preeclamptic women were around three times riskier than the normotensive pregnant women. Unfavorable maternal and perinatal complications were reported by WHO multinational analysis using 29 Countries from Africa, Asia, Latin America, and the Middle East ( 12 ). A study in Sidama reported higher adverse perinatal outcomes in the pre-eclampsia group (61.7%) compared with the normotensive group (37.5%) ( 13 ). This finding was similar to another study conducted in southwest Ethiopia in 2021, which found a higher rate of adverse perinatal outcomes among women with HDPs (64.1%) compared with normotensive women (32.8%) ( 14 ). Another study conducted in the Tigray region in 2020 reported the overall incidence of adverse perinatal outcomes was higher among women with pregnancy-induced hypertension than among normotensive women (66.4% vs 22.2%) ( 10 ). Generally, the above studies confirmed the presence of significant perinatal outcome differences between pregnant women with preeclampsia and normotensive. The incidence of perinatal complications was 24.7% in Ghana( 15 ) and 40.9% at Saint Paul’s Hospital Millennium Medical College ( 5 ). This might be due to the study including preeclamptic women with severity features and participants of Gestational age (28–36 weeks) at Paul’s. A higher low birthweight rate (12.1) was observed in the preeclampsia group compared with the normotensive group. This finding was lower than the findings of the Multicountry Survey in 2014, which found that a higher low birth weight rate was reported among women with pre-eclampsia (26.1%) compared with normotensive women (9.4%) ( 16 ), even studies conducted in Tigray (37.7%), Ghana (24.7%), and India (22.2%) ( 10 , 15 , 17 ). The difference could be due to the study participants, the quality of antenatal care services, management in the study area, and the interventional delivery for preventing further maternal and perinatal morbidity and mortality irrespective of gestational age. The magnitude of the Low APGAR score (4.7) among preeclamptic pregnant women without severity features was higher than the incidence in Public Hospitals in Addis Ababa (2.27%), and lower than the incidence in Yekatit 12 Hospital (22.4%)( 5 , 18 ). This difference may be due to the difference in service provision, and better management of laboring mothers’ and professionals' skills. The incidence rate of Preterm Birth and Stillbirth were 3.7 and 4.7 person week respectively. Preterm Birth is lower than the incidence in Tigray, Ethiopia 40.8%, Ghana (21.7%), India (24.6%) ( 10 , 15 , 17 ), Still Birth (Tigray, Ethiopia 10%, Mettu, Ethiopia 10%, Jimma University medical center 14.4) ( 10 , 19 , 20 ) and even less than (Zimbabwe 5.4%, Ghana 6.8%) ( 15 , 21 ). The difference could be due to better counseling and obstetrical management provision. In our study, Early Neonatal Death (END) was (1.5%). The incidence of early neonatal death was 2.27% at Paul's ( 5 ),3.6% at Tigray ( 10 ), and 3.8% in Ghana( 15 ). This might be due to skilled professionals' presence and instrumental fulfillment of the NICU. The most plausible argument for the emergence of preeclampsia is the defective remodeling of spiral arteries, even though the precise mechanisms underlying the perinatal problems are not yet fully understood. Preterm delivery, LBW, and IUGR are all issues brought on by defective placentation, which impairs uteroplacental blood flow( 22 , 23 ). Based on our study the hazard of developing unfavorable perinatal outcomes of pregnant women admitted to the hospital before 37 weeks of gestation were around 12 times riskier to termly admitted and delivered newborns. Similarly, pregnant women with a history of medical illness were around 4 times the hazard of unfavorable perinatal outcomes than healthier pregnant women. Additionally, pregnant women with unfavorable outcomes were around five times more likely to develop unfavorable perinatal outcomes than pregnant women with favorable outcomes. Similar to our finding a study in Sidama reported that women who were admitted to a hospital early had a higher risk of adverse perinatal outcomes compared with women who were admitted term and near term ( 13 ). These increased perinatal complications might be explained by the progression of pre-eclampsia to severe diseases in those women who developed pre-eclampsia before 34 weeks, which is associated with high preterm birth ( 24 ). Conclusion The incidence of unfavorable perinatal outcomes among preeclamptic pregnant women was higher than among normotensive pregnant women. Preterm gestational age at admission, having a history of medical illness, being preeclamptic, and unfavorable maternal outcomes were found predictors of unfavorable perinatal outcomes. Abbreviations AHR, Adjusted Hazard Ratio; ANC , Antenatal Care; APGAR, Appearance, Pulse, Grimace, Activity, Respiration; CHR, Crude Hazard Ratio; EOP, Early Onset Preeclampsia; HDP, Hypertensive Disorders of Pregnancy; ICU, Intensive Care Unit; ISSHP, International Society for the Study of Hypertension in Pregnancy; IUFD, Intra Uterine Fetal Death; IUGR, Intra Uterine Growth Restriction; Km, Kilometer; LOP, Late Onset Preeclampsia Declarations Acknowledgments We would like to thank Wachemo University College of Medicine and Health Sciences for providing us with the opportunity to carry out this study. Our gratitude also goes to WCUNEMMCSH and data collectors for their valuable contribution to the realization of this study. Authors’ contribution Mellese Tsehay wrote the proposal, participated in data collection, analyzed the data, and drafted the paper. Markos Selamu, Binalfew Tsehay, Dessalegn Shegute, Endale Fikre, Mekdes Folla and Tadele Yohannes approved the proposal, participated in data analysis, and revised the subsequent draft of the paper. All authors read and approved the final manuscript . Funding: No specific funding was provided Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interest: None declared Ethical Consideration Ethical approval for the study was obtained in accordance with the principles of th Declaration of Helsinki. A formal letter of support was secured from Wachemo University, and ethical clearance was granted by Wachemo University Nigist Elleni Mohammed Memorial Comprehensive Specialized Hospital Ethical Review Committee. The study was based on a retrospective review of medical records, and that the requirement for informed consent to participate was waived by the Ethical Review Committee. Respect for participants’ privacy and confidentiality was strictly maintained throughout the study. Personal identifiers, including mothers’ names were not recorded; instead, unique codes were assigned to each record to ensure anonymity. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Authors Details 1 School of Public Health, College of Medicine and Health Sciences, Wachemo University, Hossana, Ethiopia. 2 Central Ethiopia Regional State Health Bureau, Hadyia Zone Health Department, Hossana, Ethiopia 3 School of Medicine, College of Medicine and Health Sciences, Debre Markos University, Debre Markos, Ethiopia. 4 School of Medicine, College of Medicine and Health Sciences, Wachemo University, Hossana, Ethiopia. References Obstetricians, ACo. 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Prevalence of pregnancy induced hypertension and pregnancy outcomes among women seeking maternity services in Harare, Zimbabwe. BMC Cardiovasc Disord. 2015;15(1):1–8. Palatnik A, Grobman WA, Miller ES. Is a history of preeclampsia associated with an increased risk of a small for gestational age infant in a future pregnancy? Am J Obstet Gynecol. 2016;215(3):355. e1-. e6. Bramham K, Briley AL, Seed P, Poston L, Shennan AH, Chappell LC. Adverse maternal and perinatal outcomes in women with previous preeclampsia: a prospective study. Am J Obstet Gynecol. 2011;204(6):512. e1-. e9. Muhe LM, McClure EM, Nigussie AK, Mekasha A, Worku B, Worku A, et al. Major causes of death in preterm infants in selected hospitals in Ethiopia (SIP): a prospective, cross-sectional, observational study. Lancet Global Health. 2019;7(8):e1130–8. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-9431483","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633402640,"identity":"e7a3b9b5-fb51-4469-9062-7f4f51556c2b","order_by":0,"name":"Mellese Tsehay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFAC5sYDEAYPEFeABJgbCGhhbABpkYBoOQPSwkiKFsY2iAheDbrtjQ0HPvyxqZNv7z34mXdebTR/O1DLj4ptOLWYnTnYcHBmW5qEwZlzydK8247nzjjM2MDYc+Y2bi03EhsO8zYcljCQyDEAajmW2wDUwszYhkfL/YcNh//8+S8hP/+N8W/eOcdy5xPUcoOx4TAD2wEJhhs8ZtK8DTW5GwhqOZPYcLC3LVlyw5kcM8s5xw7kbgRqOYjXL8cPH3zw448dv3z7GeMbb2rqcuedB4lU4NaCAph4GA6DGQeIUw8EjD8Y6ohWPApGwSgYBSMHAADYWGRrfDUK4AAAAABJRU5ErkJggg==","orcid":"","institution":"Wachemo University","correspondingAuthor":true,"prefix":"","firstName":"Mellese","middleName":"","lastName":"Tsehay","suffix":""},{"id":633402641,"identity":"caf59bec-dfd3-454d-9c9b-3ab798f5d5a7","order_by":1,"name":"Markos Selamu","email":"","orcid":"","institution":"Wachemo University","correspondingAuthor":false,"prefix":"","firstName":"Markos","middleName":"","lastName":"Selamu","suffix":""},{"id":633402643,"identity":"353fde13-81b8-4d01-a85d-97b4ceb6a4fc","order_by":2,"name":"Dessalegn Shegute","email":"","orcid":"","institution":"Central Ethiopia Regional State Health Bureau, Hadyia Zone Health Department","correspondingAuthor":false,"prefix":"","firstName":"Dessalegn","middleName":"","lastName":"Shegute","suffix":""},{"id":633402650,"identity":"2c936fbf-b805-424f-8686-78432a6fe30c","order_by":3,"name":"Binalfew Tsehay","email":"","orcid":"","institution":"Debre Markos University","correspondingAuthor":false,"prefix":"","firstName":"Binalfew","middleName":"","lastName":"Tsehay","suffix":""},{"id":633402652,"identity":"979614e4-c810-4d31-aa7a-1fb9693d886b","order_by":4,"name":"Endale Fikre","email":"","orcid":"","institution":"Wachemo University","correspondingAuthor":false,"prefix":"","firstName":"Endale","middleName":"","lastName":"Fikre","suffix":""},{"id":633402658,"identity":"7b08679a-b0c9-420b-81b6-cd9a3b873a0f","order_by":5,"name":"Mekdes Folla","email":"","orcid":"","institution":"Wachemo University","correspondingAuthor":false,"prefix":"","firstName":"Mekdes","middleName":"","lastName":"Folla","suffix":""},{"id":633402660,"identity":"8f58679c-009e-43fb-8b52-03557be81ae6","order_by":6,"name":"Tadele Yohannes","email":"","orcid":"","institution":"Wachemo University","correspondingAuthor":false,"prefix":"","firstName":"Tadele","middleName":"","lastName":"Yohannes","suffix":""}],"badges":[],"createdAt":"2026-04-15 23:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9431483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9431483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108419622,"identity":"65eb8f50-0318-4b56-999a-1e7b7e015e4a","added_by":"auto","created_at":"2026-05-04 12:22:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98962,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curve of perinatal outcome for pregnant women by the status of exposure to early onset preeclampsia WUNEMMCSH, 2022, n = 264.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9431483/v1/6c830d76c3b38152ba93d1b5.png"},{"id":108493333,"identity":"a1c16fba-7b94-4192-bd68-3e4ec9370d0d","added_by":"auto","created_at":"2026-05-05 09:59:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":517841,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9431483/v1/75e7b940-602e-4b61-a6e9-3fec5a12ccab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of unfavorable perinatal outcomes among women with early-onset preeclampsia without severe features: a retrospective cohort study at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreeclampsia is defined as the new onset of hypertension and proteinuria in previously normotensive women after 20 weeks of gestational age (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Recently the diagnostic criteria have been changed, and Fetal Growth Restriction (FGR) is included (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the past 30 years pathogenesis, pathophysiology, treatment options, predictors of preeclampsia, and associations with risk factors like smoking, stroke, and cardiovascular disease have been the subject of extensive research. Preeclampsia can be divided into two distinct disease entities: early-onset and late-onset preeclampsia. Early onset preeclampsia (EOP) occurs before 34 weeks of pregnancy, whereas late-onset preeclampsia (LOP) occurs at or after 34 weeks of gestation. Even though the presenting characteristics are similar, they are associated with various maternal and fetal outcomes, biochemical markers, heritability, and clinical characteristics (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWorld Health Organisation (WHO) multicountry survey has shown perinatal mortality is three to five times higher in women with preeclampsia/eclampsia syndrome than in women without the disorders (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Ethiopia, the incidence of early-onset preeclampsia without severity feature was 3%, and perinatal mortality was 42.6 per 1000. It was 5.22 and 25.9 times riskier to develop maternal and perinatal complications compared to late-onset preeclampsia (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEOP poses a management dilemma. Delivery may benefit the mother but may harm a premature fetus. Expectant management in women with EOP before 34 weeks\u0026rsquo; gestation may reduce neonatal complications and stay in a Neonatal Intensive Care Unit (NICU). Although this could aggravate the maternal condition (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBefore deciding on outpatient follow-up and management, the International Societ for the Study of Hypertension in Pregnancy (ISSHP) advises admitting all patients with preeclampsia; following assessment in the hospital some women may be managed in a specialized outpatient setting, such as day assessment units in a hospital with appropriate expertise (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is no data on predictors and survival time to develop maternal and perinatal unfavorable outcomes of preeclampsia without severity features and normotensive women in the study area.\u003c/p\u003e \u003cp\u003eTherefore the study aimed to investigate the incidence predictors and survival time of unfavorable perinatal outcomes among preeclampsia without severe features and normotensive pregnant women between the gestational age of 28 to 34 weeks at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital (WCUNEMMCSH), Ethiopia.\u003c/p\u003e"},{"header":"Methods And Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003eThe study was conducted at WCUNEMMCSH from January 1/2019 to December 30/2021. The Hospital was established in 1984 G.C in Hadiya zone, Hossana town which is located 232 km from Addis Ababa (capital city of Ethiopia) and 192 km from Hawassa (capital city of Southern Nation and Nationalities Regional State) and provides preventive, curative, and rehabilitative services for 3.4\u0026nbsp;million population with a total of 262917 and 10881 Outpatient Diagnosis (OPD) and Inpatient attendants in 2021 respectively. The hospital had a total of 1172 staff and among them, 588 of them are health professionals. Of 584 Technical staff,269 are administrative staff. The hospital provides different services like an adult Intensive Care Unit, Neonatal Intensive Care Unit, Psychiatry, Ophthalmology, Dental, Medical, Pediatrics, Surgical, Orthopedic, Gynecology, and Obstetrics services.\u003c/p\u003e \u003cp\u003eThe gynecology and Obstetrics department had nine Gynecologists and Obstetricians, two were females, and 68 midwifery professionals. The department provided different services like delivery, outpatient, ANC, emergency, and inpatient services.\u003c/p\u003e \u003cp\u003eThere were 15Gynecolgy and 12 Obstetric inpatient beds. The hospital had 4 Laboring coaches and 7Operation room tables.\u003c/p\u003e \u003cp\u003eIn 2021 a total of 10167 mothers got delivery service.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design and Period\u003c/h3\u003e\n\u003cp\u003eHospital-based retrospective cohort study was conducted from June,1/2022 to June,30/2022.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cp\u003eAll delivered women managed in WUNEMMCSH with the diagnosis of early-onset preeclampsia without severity feature and normotensive pregnant women from January 1/2019 to December 30/2021. Pregnant mothers whose chart was not complete or lost and died on arrival.\u003c/p\u003e\n\u003ch3\u003eSample Size Determination and Sampling Technique\u003c/h3\u003e\n\u003cp\u003eThe required optimal sample size was determined by using the Epi Info version 7 double population formula with the assumptions of a 95% significance level (2-sided), 5% margin of error, power of 80%, and 1:1 Ratio.\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 estimation of unfavorable perinatal outcome of early-onset preeclampsia and normotensive pregnant women in WUNEMMCSH,2022.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCI (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Of Unexposed in Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% Of Exposed in Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFinal sample size (10% data incompleteness)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e268\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreterm delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreterm labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\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\u003eFrom January 1st, 2019, to December 30th, 2021G.C, there were 858 pregnant mothers managed for early onset preeclampsia without severity feature and 14,070normotensive women. From January 1st, 2019, to December 30th, 2021G.C, pregnant mothers managed for early onset preeclampsia without severity features (exposed group) and normotensives pregnant women (unexposed group) were obtained by simple random sampling technique using computer generated random numbers.\u003c/p\u003e \u003cp\u003eFour charts were excluded from the total sample size, two from exposed and unexposed.\u003c/p\u003e\n\u003ch3\u003eData Collection Technique and Tool\u003c/h3\u003e\n\u003cp\u003eThe data were collected from high-risk registration books, delivery registration books, and patient charts using a well-prepared checklist. The checklist incorporates socio-demographic characteristics, obstetrics history, laboratory results, and maternal and fetal outcomes. Data were collected by trained BSc Midwives under strict supervision.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Quality and Management\u003c/h2\u003e \u003cp\u003eThe data collection tool was adapted from prior research and adjusted to fit the context. The checklist was written in English. Data collectors and a supervisor received two-day training on the data abstraction checklist and collection method. The supervisor and principal investigator monitored and supervised the data collection process closely.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Processing and Analysis\u003c/h3\u003e\n\u003cp\u003eAll collected data were rechecked for completeness, coded, entered using Epi data version 4.6.0.2, and exported to STATA version 14 for analysis. The data was recoded, categorized, and sorted to facilitate analysis. Descriptive statistics were analyzed and presented in tables. Kaplan-Meier survival function among the exposed and unexposed groups was used to compare survival probability between the groups. Then statistically significant difference was checked by the log-rank test. Bivariable analysis was done to see associations between the dependent and independent variables. Variables having a p-value of less than 0.25 were included in the multivariable Cox regression analysis. Those variables having a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003cp\u003eProportional-hazard assumption test was used for selecting fitted proportional hazard models. Schoenfeld's residuals test was performed. The global tests were significant, with p\u0026thinsp;\u0026gt;\u0026thinsp;0.05. The Cox regression model was fitted for analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e \u003cp\u003eMost of the women lived in urban 182 (68.9%) (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\u003eSociodemographic characteristics among early-onset preeclampsia without severity feature and normotensive pregnant women in WCUNEMMCSH, 2022.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus of Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes(N\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo(N\u0026thinsp;=\u0026thinsp;132)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAddress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82(31.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90(68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92(69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e182(68.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;=18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4(1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125(94.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128(97.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e253(95.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7(2.7%)\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMaternal medical and obstetric factors\u003c/h2\u003e \u003cp\u003eMulti-Gravidity among exposed and normotensive women was 98 (74.2%) and 83 (62.8%) respectively. Singleton pregnancy was the most common type of pregnancy, 247 (93.6%) of the total. Forty-four (33.3%) of preeclamptic women (exposed group) had a history of medical illness. Of these pre-existing hypertension, 30 (68.2%) and pregestational diabetes mellitus 7 (15.9%) were common comorbidities (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\u003eObstetrical and medical history among early-onset preeclampsia without severity feature and normotensive pregnant women in WCUNEMMCSH, 2022.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus of Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGravidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimigravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83(31.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultigravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98(74.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e181(68.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\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=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13(4.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e146(55.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrand Multiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105(39.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117(88.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130(98.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e247(93.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17(6.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHistory of medical illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46(17.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130(98.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eType of medical illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-existing hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31(67.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistory of Renal Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4(8.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistory of Cardiovascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3(6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-gestational diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7(15.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eToxic goiter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1(2.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWere drugs given to decrease Blood Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55(20.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78(59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131(99.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e209(79.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eIf yes what was/were the drug/s?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethyldopa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38(69.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNifedipine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2(3.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydralazine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8(3.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMgSo4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(14.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2(3.6%)\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePerinatal unfavorable outcomes incidence rate\u003c/h2\u003e \u003cp\u003e Perinatal unfavorable outcome a total of 264 pregnant women were enrolled and followed for a minimum and the maximum follow-up time of 2 and 14.4 weeks respectively giving 2305.96 person-weeks observation. The mean follow-up time was 8.73 weeks. The overall incidence rate of the unfavorable outcome during the follow-up time among pregnant women was 19.1 out of 1000 observations. Of this, the total time at risk for preeclamptic women (exposed group) was 1071.3 and 1234.66 for the unexposed group with an incidence rate of 31.74 per 1000 and 8.12 per 1000 respectively (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\u003eThe incidence rate in person per week of perinatal unfavorable outcome per 1000 among early onset preeclampsia without severity feature and normotensive pregnant women in WCUNEMMCSH,2022.\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=\"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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eStatus of Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eIncidence Rate\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eUnfavorable perinatal outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIUGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow APGAR score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreterm birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIUFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\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=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSurvival status of pregnant women for perinatal outcome\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier survival function graph shows the cumulative survival proportion appears to be much higher in the unexposed group than in the exposed (preeclamptic pregnant women) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A log-rank test was run to determine if there were statistical differences in the survival distribution for the unexposed and exposed groups. The survival distributions were statistically significantly different, χ2(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;24.93, p \u0026lt; .0005. The median survival time of pregnant women with preeclampsia was 12.4 weeks. However, the median time for normotensive pregnant women was not reached.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003epredictors of unfavorable perinatal outcome among pregnant women\u003c/h2\u003e \u003cp\u003eIn Bivariable Cox proportional hazard regression type of pregnancy, Antenatal Care (ANC) follow-up, gestational age at admission, history of medical illness, proteinuria, exposure status, and the maternal outcome were a candidate for multivariable analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariable Cox regression analysis of predictors of unfavorable perinatal outcome at WCUNEMMCSH, 2022.\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=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFavorable outcome N (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnfavorable outcome N (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \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\u003e74 (90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.51(0.69, 3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaternal age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207 (84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11(0.83, 5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGravidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimigravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.50, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultigravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159 (87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\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\u003e76 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93(0.58, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrand Multiparous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.54, 1.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52(1.45, 8.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of ANC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28(1.12, 4.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=3visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGestational age at admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.06(6.49,22.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHistory of medical illness\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\u003e31 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.34(1.78, 6.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProteinuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.17(2.08, 8.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200 (85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.00(2.46, 10.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexposed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e195 (91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003eUnfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.99(4.37, 14.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.000*\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\u003eIn multivariable Cox regression analysis after adjusting the confounders' gestational age at admission (AHR: 11.81 CI:5.94, 23.46)), medical illness (AHR: 3.85 CI:2.00, 7.40), exposure status (AHR:2.64 CI:1.20, 5.79)) and maternal outcome (AHR: 5.15 CI:2.67, 9.92) were found predictors of unfavorable perinatal outcome (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Pregnant women admitted to the hospital before 37 weeks of gestation were 11.81 times hazard to develop unfavorable perinatal outcomes than termly admitted and delivered newborns. Similarly, pregnant women with a history of medical illness were 3.85 times the hazard of unfavorable perinatal outcomes than healthier pregnant women. Pregnant women with unfavorable outcomes were 5.15 times more likely to develop unfavorable perinatal outcomes than pregnant women with favorable outcomes. The hazard of newborns delivered from preeclamptic women was 2.64 times more than the normotensive pregnant women.\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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox regression analysis of predictors of Unfavorable perinatal outcome at WCUNEMMCSH, 2022.\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\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=\"c3\"\u003e \u003cp\u003eFavorable outcome N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnfavorable outcome N (%)\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52(1.45\u0026ndash;8.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.82(0.75, 4.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of ANC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.28(1.12\u0026ndash;4.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22(0.57, 2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=3visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (89.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGestational age at admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.06(6.49\u0026ndash;22.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.81 (5.94,23.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHistory of medical illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.34(1.78\u0026ndash;6.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.85(2.00, 7.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \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\u003eProteinuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.17(2.08\u0026ndash;8.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65(0.78, 3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.183\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;=2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200 (85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExposed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.00(2.46\u0026ndash;10.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.64(1.20, 5.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.015*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexposed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaternal outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.000*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnfavorable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.99(4.37\u0026ndash;14.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.15(2.67, 9.92)\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"},{"header":"Discussion","content":"\u003cp\u003eOur study confirmed the presence of significant differences regarding unfavorable perinatal outcomes between preeclamptic and normotensive pregnant women. The overall incidence rate of unfavorable outcomes, LBW, IUGR, Low APGAR score, Preterm birth, stillbirth, IUFD, and Death, during the follow-up time among pregnant women, was 19.1. The incidence rate among the exposed group (preeclamptic women) was 31.74 and 8.12 among the unexposed group (normotensive pregnant women). In our study, the newborns delivered from preeclamptic women were around three times riskier than the normotensive pregnant women. Unfavorable maternal and perinatal complications were reported by WHO multinational analysis using 29 Countries from Africa, Asia, Latin America, and the Middle East (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). A study in Sidama reported higher adverse perinatal outcomes in the pre-eclampsia group (61.7%) compared with the normotensive group (37.5%) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This finding was similar to another study conducted in southwest Ethiopia in 2021, which found a higher rate of adverse perinatal outcomes among women with HDPs (64.1%) compared with normotensive women (32.8%) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Another study conducted in the Tigray region in 2020 reported the overall incidence of adverse perinatal outcomes was higher among women with pregnancy-induced hypertension than among normotensive women (66.4% vs 22.2%) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Generally, the above studies confirmed the presence of significant perinatal outcome differences between pregnant women with preeclampsia and normotensive.\u003c/p\u003e \u003cp\u003eThe incidence of perinatal complications was 24.7% in Ghana(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and 40.9% at Saint Paul\u0026rsquo;s Hospital Millennium Medical College (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This might be due to the study including preeclamptic women with severity features and participants of Gestational age (28\u0026ndash;36 weeks) at Paul\u0026rsquo;s.\u003c/p\u003e \u003cp\u003eA higher low birthweight rate (12.1) was observed in the preeclampsia group compared with the normotensive group. This finding was lower than the findings of the Multicountry Survey in 2014, which found that a higher low birth weight rate was reported among women with pre-eclampsia (26.1%) compared with normotensive women (9.4%) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), even studies conducted in Tigray (37.7%), Ghana (24.7%), and India (22.2%) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The difference could be due to the study participants, the quality of antenatal care services, management in the study area, and the interventional delivery for preventing further maternal and perinatal morbidity and mortality irrespective of gestational age.\u003c/p\u003e \u003cp\u003eThe magnitude of the Low APGAR score (4.7) among preeclamptic pregnant women without severity features was higher than the incidence in Public Hospitals in Addis Ababa (2.27%), and lower than the incidence in Yekatit 12 Hospital (22.4%)(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). This difference may be due to the difference in service provision, and better management of laboring mothers\u0026rsquo; and professionals' skills.\u003c/p\u003e \u003cp\u003eThe incidence rate of Preterm Birth and Stillbirth were 3.7 and 4.7 person week respectively. Preterm Birth is lower than the incidence in Tigray, Ethiopia 40.8%, Ghana (21.7%), India (24.6%) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), Still Birth (Tigray, Ethiopia 10%, Mettu, Ethiopia 10%, Jimma University medical center 14.4) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and even less than (Zimbabwe 5.4%, Ghana 6.8%) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The difference could be due to better counseling and obstetrical management provision.\u003c/p\u003e \u003cp\u003eIn our study, Early Neonatal Death (END) was (1.5%). The incidence of early neonatal death was 2.27% at Paul's (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e),3.6% at Tigray (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and 3.8% in Ghana(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). This might be due to skilled professionals' presence and instrumental fulfillment of the NICU.\u003c/p\u003e \u003cp\u003eThe most plausible argument for the emergence of preeclampsia is the defective remodeling of spiral arteries, even though the precise mechanisms underlying the perinatal problems are not yet fully understood. Preterm delivery, LBW, and IUGR are all issues brought on by defective placentation, which impairs uteroplacental blood flow(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on our study the hazard of developing unfavorable perinatal outcomes of pregnant women admitted to the hospital before 37 weeks of gestation were around 12 times riskier to termly admitted and delivered newborns.\u003c/p\u003e \u003cp\u003eSimilarly, pregnant women with a history of medical illness were around 4 times the hazard of unfavorable perinatal outcomes than healthier pregnant women. Additionally, pregnant women with unfavorable outcomes were around five times more likely to develop unfavorable perinatal outcomes than pregnant women with favorable outcomes.\u003c/p\u003e \u003cp\u003eSimilar to our finding a study in Sidama reported that women who were admitted to a hospital early had a higher risk of adverse perinatal outcomes compared with women who were admitted term and near term (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These increased perinatal complications might be explained by the progression of pre-eclampsia to severe diseases in those women who developed pre-eclampsia before 34 weeks, which is associated with high preterm birth (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe incidence of unfavorable perinatal outcomes among preeclamptic pregnant women was higher than among normotensive pregnant women. Preterm gestational age at admission, having a history of medical illness, being preeclamptic, and unfavorable maternal outcomes were found predictors of unfavorable perinatal outcomes.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eAHR,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAdjusted Hazard Ratio;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eANC\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eAntenatal Care; APGAR, Appearance, Pulse, Grimace, Activity, Respiration; CHR, Crude Hazard Ratio; EOP, Early Onset Preeclampsia; HDP, Hypertensive Disorders of Pregnancy; ICU, Intensive Care Unit; ISSHP, International Society for the Study of Hypertension in Pregnancy; IUFD, Intra Uterine Fetal Death; IUGR, Intra Uterine Growth Restriction; Km, Kilometer; LOP, Late Onset Preeclampsia\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Wachemo University College of Medicine and Health Sciences for providing us with the opportunity to carry out this study. Our gratitude also goes to WCUNEMMCSH and data collectors for their valuable contribution to the realization of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMellese Tsehay wrote the proposal, participated in data collection, analyzed the data, and drafted the paper. Markos Selamu, Binalfew Tsehay, Dessalegn Shegute, Endale Fikre, Mekdes Folla and \u0026nbsp;Tadele Yohannes approved the proposal, participated in data analysis, and revised the subsequent draft of the paper. All authors read and approved the final manuscript\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo specific funding was provided\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\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\u003e\u003cstrong\u003eCompeting interest:\u0026nbsp;\u003c/strong\u003eNone declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Consideration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained in accordance with the principles of th Declaration of Helsinki. A formal letter of support was secured from Wachemo University, and ethical clearance was granted by Wachemo University Nigist Elleni Mohammed Memorial Comprehensive Specialized Hospital Ethical Review Committee.\u003c/p\u003e\n\u003cp\u003eThe study was based on a retrospective review of medical records, and that the requirement for informed consent to participate was waived by the Ethical Review Committee.\u003c/p\u003e\n\u003cp\u003eRespect for participants\u0026rsquo; privacy and confidentiality was strictly maintained throughout the study. Personal identifiers, including mothers\u0026rsquo; names were not recorded; instead, unique codes were assigned to each record to ensure anonymity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eSchool of Public Health,\u0026nbsp;College of Medicine and Health Sciences, Wachemo University, Hossana, Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eCentral Ethiopia Regional State Health Bureau, Hadyia Zone Health Department, Hossana, Ethiopia\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eSchool of Medicine, College of Medicine and Health Sciences, Debre Markos University, Debre Markos, Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eSchool of Medicine, College of Medicine and Health Sciences, Wachemo University, Hossana, Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eObstetricians, ACo. Gynecologists. Task Force on Hypertension in Pregnancy Hypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists\u0026rsquo; task force on hypertension in pregnancy. Obstet Gynecol. 2013;122(5):1122\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWojtowicz A, Zembala-Szczerba M, Babczyk D, Kołodziejczyk-Pietruszka M, Lewaczyńska O, Huras H. Early-and late-onset preeclampsia: a comprehensive cohort study of laboratory and clinical findings according to the new ISHHP criteria. International journal of hypertension. 2019;2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymond D, Peterson E. A critical review of early-onset and late-onset preeclampsia. Obstet Gynecol Surv. 2011;66(8):497\u0026ndash;506.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaromssa H, Dwivedi A. Maternal mortality in Ambo Hospital: a five year retrospective review. Ethiop J reproductive health. 2008;2(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelay Tolu L, Yigezu E, Urgie T, Feyissa GT. Maternal and perinatal outcome of preeclampsia without severe features among pregnant women managed at a tertiary referral hospital in urban Ethiopia. PLoS ONE. 2020;15(4):e0230638.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwamy M, Patil K, Nageshu S. Maternal and perinatal outcome during expectant management of severe pre-eclampsia between 24 and 34 weeks of gestation. J Obstet Gynecol India. 2012;62(4):413\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagee L, Yong P, Espinosa V, Cote A, Chen I, Von Dadelszen P. Expectant management of severe preeclampsia remote from term: a structured systematic review. Hypertens Pregnancy. 2009;28(3):312\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTownsend R, O\u0026rsquo;Brien P, Khalil A. Current best practice in the management of hypertensive disorders in pregnancy. Integr blood Press control. 2016;9:79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTranquilli A, Dekker G, Magee L, Roberts J, Sibai B, Steyn W, et al. The classification, diagnosis, and management of the hypertensive disorders of pregnancy: a revised statement from the ISSHP. Elsevier; 2014. pp. 97\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerhe AK, Ilesanmi AO, Aimakhu CO, Mulugeta A. Effect of pregnancy induced hypertension on adverse perinatal outcomes in Tigray regional state, Ethiopia: a prospective cohort study. BMC Pregnancy Childbirth. 2020;20(1):1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThakur A, Dangal G. Fetomaternal outcome in women with pregnancy induced hypertension versus normotensive pregnancy. J Nepal Health Res Counc. 2019;17(4):495\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiroz T, Sanghvi H, Merialdi M, von Dadelszen P. Pre-eclampsia in low and middle income countries. Best Pract Res Clin Obstet Gynecol. 2011;25(4):537\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJikamo B, Adefris M, Azale T, Gelaye KA. Incidence of adverse perinatal outcomes and risk factors among women with pre-eclampsia, southern Ethiopia: a prospective open cohort study. BMJ Paediatrics Open. 2022;6(1):e001567.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaleta DD, Gebremedhin T, Jebena MG. Perinatal outcomes of women with hypertensive disorders of pregnancy in Jimma Medical Center, southwest Ethiopia: Retrospective cohort study. PLoS ONE. 2021;16(8):e0256520.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdu-Bonsaffoh K, Ntumy MY, Obed SA, Seffah JD. Perinatal outcomes of hypertensive disorders in pregnancy at a tertiary hospital in Ghana. BMC Pregnancy Childbirth. 2017;17(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbalos E, Cuesta C, Carroli G, Qureshi Z, Widmer M, Vogel J, et al. Preeclampsia, Eclampsia, and Adverse Maternal and Perinatal Outcomes: A Secondary Analysis of the World Health Organization Multicountry Survey on Maternal and Newborn Health. Obstetric Anesth Digest. 2015;35(1):20\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadal S, Singh LR. Maternal and perinatal outcome in severe pre-eclampsia and eclampsia. World J Pharm Med Res. 2017;3(3):193\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMengistu MD, Kuma T. Feto-maternal outcomes of hypertensive disorders of pregnancy in Yekatit-12 Teaching Hospital, Addis Ababa: a retrospective study. BMC Cardiovasc Disord. 2020;20(1):1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsefa F, Hunde A, Siraneh Y. Outcome of Hypertensive Disorders of Pregnancy and Associated Factors Among Pregnant Women Admitted to Jimma University Medical Center, Southwest Ethiopia. Ethiop J Reproductive Health. 2020;12(01):12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeyom E, Abera M, Tesfaye M, Fentahun N. Maternal and fetal outcome of pregnancy related hypertension in Mettu Karl Referral Hospital, Ethiopia. J ovarian Res. 2015;8(1):1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuti M, Tshimanga M, Notion GT, Bangure D, Chonzi P. Prevalence of pregnancy induced hypertension and pregnancy outcomes among women seeking maternity services in Harare, Zimbabwe. BMC Cardiovasc Disord. 2015;15(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalatnik A, Grobman WA, Miller ES. Is a history of preeclampsia associated with an increased risk of a small for gestational age infant in a future pregnancy? Am J Obstet Gynecol. 2016;215(3):355. e1-. e6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBramham K, Briley AL, Seed P, Poston L, Shennan AH, Chappell LC. Adverse maternal and perinatal outcomes in women with previous preeclampsia: a prospective study. Am J Obstet Gynecol. 2011;204(6):512. e1-. e9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuhe LM, McClure EM, Nigussie AK, Mekasha A, Worku B, Worku A, et al. Major causes of death in preterm infants in selected hospitals in Ethiopia (SIP): a prospective, cross-sectional, observational study. Lancet Global Health. 2019;7(8):e1130\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Outcome, Perinatal, Preeclampsia, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-9431483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9431483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlthough the prognosis of preeclampsia without severity features is favorable, its adverse forms are a major cause of perinatal and maternal morbidity and mortality. There is no data on predictors and survival time to develop maternal and perinatal unfavorable outcomes of preeclampsia without severity features and normotensive women in the study area.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo determine the incidence and predictors of unfavorable perinatal outcomes of early-onset preeclampsia without severity feature and normotensive pregnant women in Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital,2022 G.C.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eHospital-based retrospective cohort study was conducted among 132 exposed and 132 unexposed pregnant women at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital from January 1/2019 to December 30/2021. The incidence of unfavorable perinatal outcomes was calculated from the total adverse outcome to the sample(population) time. Survival probabilities were shown in the Kaplan-Meier graph. Data were entered using the epi data version 4.6 and exported to STATA version 14. Variables having P\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in bivariate analysis were fitted for multivariate Cox.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 264 pregnant women were followed for 2305.96 person weeks for perinatal outcomes. The incidence of unfavorable perinatal outcomes was 19.1 per 1000 person-weeks. Gestational age at admission (AHR: 11.81 95%CI:5.94\u0026ndash;23.46)), medical illness (AHR: 3.85 95%CI:2.00-7.40), exposure status (AHR:2.64 95%CI:1.20\u0026ndash;5.79)) and maternal outcome (AHR: 5.15, 95%CI:2.67\u0026ndash;9.92) were predictors of the unfavorable perinatal outcome.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe Kaplan-Meier survival function graph shows the cumulative survival proportion appears to be much higher in the unexposed group than in the exposed group. There were indicators that were significant for maternal and perinatal outcomes.\u003c/p\u003e","manuscriptTitle":"Predictors of unfavorable perinatal outcomes among women with early-onset preeclampsia without severe features: a retrospective cohort study at Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 12:22:25","doi":"10.21203/rs.3.rs-9431483/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-22T15:33:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-22T15:30:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-20T17:14:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T16:23:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-04-17T13:31:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3fb56e83-5007-40c1-9e62-d3e578ec2f6b","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T12:22:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 12:22:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9431483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9431483","identity":"rs-9431483","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}