Fostering Digital Health in Universities: An Experience of the First Junior Scientific Committee of the Brazilian Congress of Health Informatics.

OBJECTIVES: Digital health (DH) is a revolution driven by digital technologies to improve health. Despite the importance of DH, curricular updates in healthcare university programs are scarce, and DH remains undervalued. Therefore, this report describes the first Junior Scientific Committee (JSC) focusing on DH at a nationwide congress, with the aim of affirming its importance for promoting DH in universities. METHODS: The scientific committee of the Brazilian Congress of Health Informatics (CBIS) extended invitations to students engaged in health-related fields, who were tasked with organizing a warm-up event and a 4-hour session at CBIS. Additionally, they were encouraged to take an active role in a workshop alongside distinguished experts to map out the current state of DH in Brazil. RESULTS: The warm-up event focused on the topic "Artificial intelligence in healthcare: is a new concept of health about to arise?" and featured remote discussions by three professionals from diverse disciplines. At CBIS, the JSC's inaugural presentation concentrated on delineating the present state of DH education in Brazil, while the second presentation offered strategies to advance DH, incorporating viewpoints from within and beyond the academic sphere. During the workshop, participants deliberated on the most crucial competencies for future professionals in the DH domain. CONCLUSIONS: Forming a JSC proved to be a valuable tool to foster DH, particularly due to the valuable interactions it facilitated between esteemed professionals and students. It also supports the cultivation of leadership skills in DH, a field that has not yet received the recognition it deserves.

Gestational age assessed by optical skin reflection in low-birth-weight newborns: Applications in classification at birth.

Introduction: A new medical device was previously developed to estimate gestational age (GA) at birth by processing a machine learning algorithm on the light scatter signal acquired on the newborn's skin. The study aims to validate GA calculated by the new device (test), comparing the result with the best available GA in newborns with low birth weight (LBW). Methods: We conducted a multicenter, non-randomized, and single-blinded clinical trial in three urban referral centers for perinatal care in Brazil and Mozambique. LBW newborns with a GA over 24 weeks and weighing between 500 and 2,500 g were recruited in the first 24 h of life. All pregnancies had a GA calculated by obstetric ultrasound before 24 weeks or by reliable last menstrual period (LMP). The primary endpoint was the agreement between the GA calculated by the new device (test) and the best available clinical GA, with 95% confidence limits. In addition, we assessed the accuracy of using the test in the classification of preterm and SGA. Prematurity was childbirth before 37 gestational weeks. The growth standard curve was Intergrowth-21st, with the 10th percentile being the limit for classifying SGA. Results: Among 305 evaluated newborns, 234 (76.7%) were premature, and 139 (45.6%) were SGA. The intraclass correlation coefficient between GA by the test and reference GA was 0.829 (95% CI: 0.785-0.863). However, the new device (test) underestimated the reference GA by an average of 2.8 days (95% limits of agreement: -40.6 to 31.2 days). Its use in classifying preterm or term newborns revealed an accuracy of 78.4% (95% CI: 73.3-81.6), with high sensitivity (96.2%; 95% CI: 92.8-98.2). The accuracy of classifying SGA newborns using GA calculated by the test was 62.3% (95% CI: 56.6-67.8). Discussion: The new device (test) was able to assess GA at birth in LBW newborns, with a high agreement with the best available GA as a reference. The GA estimated by the device (test), when used to classify newborns on the first day of life, was useful in identifying premature infants but not when applied to identify SGA infants, considering current algohrithm. Nonetheless, the new device (test) has the potential to provide important information in places where the GA is unknown or inaccurate.

Introdução à análise de dados em saúde com Python / Introduction to healthcare data analysis with Python

Este eBook foi elaborado no contexto do curso de capacitação Introdução à Análise de Dados em Saúde com Python ofertado pelo Centro de Inovação em Inteligência Artificial para Saúde. O curso tem como objetivo introduzir o estudo exploratório de bases de dados de saúde, com a utilização do Python. Neste eBook, procura-se apresentar uma abordagem preliminar à Ciência de Dados, que explora e descreve um conjunto de dados com técnicas da estatística descritiva e inferencial por meio da linguagem de programação Python. O público alvo que pretende-se atingir caracteriza-se por profissionais de saúde, alunos de graduação e pós-graduação, docentes e pesquisadores da área das ciências da saúde, exatas ou demais interessados em utilizar os recursos computacionais para análise de bases de dados em saúde. A linguagem Python tem se destacado como uma ferramenta poderosa para análise de dados em saúde, possuindo uma ampla gama de bibliotecas e recursos, o Python pode ser usado para limpar, processar, analisar e visualizar dados de saúde. Além disso, a comunidade de utilizadores da linguagem Python é muito colaborativa, com muitos recursos disponíveis, incluindo documentação, tutoriais e fóruns de suporte. O conteúdo foi agrupado em conceitos iniciais sobre a utilização dos dados em saúde, introdução ao Python para utilização de dados, conceitos de limpeza e tratamento de dados, aplicação da estatística descritiva com os sumários estatísticos e gráficos, técnicas de amostragens, aplicação da estatística inferencial com os testes de hipótese, de associação, de médias, de medianas e correlações, além de explorar a estilização de gráficos.

Anais do I Simpósio CI-IA Saúde da UFMG / Proceedings of the 1st CI-IA Health Symposium at UFMG

Anualmente o Centro promove um evento com o objetivo principal reunir os pesquisadores, profissionais e alunos, tanto do setor público como privado, para promover um amplo debate de ideias, fundamentos e aplicações relacionados ao uso da Inteligência Artificial na área da Saúde. O evento deste ano contou com 120 participantes e ocorreu no dia 25 de setembro de 2023 no Instituto de Ciências Exatas no Campus Pampulha da Universidade Federal de Minas Gerais. Ao todo, o evento contou com a submissão e avaliação de 26 resumos científicos (short papers) e as revisões de pares foram realizadas com o apoio de 11 revisores voluntários. Ao final da avaliação, foram selecionados para publicação 22 trabalhos, que foram apresentados ao público no formato de pôsteres digitais. Os trabalhos mais bem avaliados foram convidados para apresentação oral no evento e receberam certificados de menção honrosa. A programação do evento contou com mesas redondas onde os projetos de pesquisas apoiados pelo Centro foram apresentados e foram debatidos as competências necessárias para a utilização da Inteligência Artificial na área da Saúde, além da palestra proferida pelo professor Ricardo Cruz-Correia, Universidade do Porto, que abordou os desafios e oportunidades da IA generativa para o ensino e pesquisa em Saúde.

Every year, the Center promotes an event with the main objective of bringing together researchers, professionals and students, both from the public and private sectors, to promote a broad debate of ideas, foundations and applications related to the use of Artificial Intelligence in the area of ​​Health. This year's event featured with 120 participants and took place on September 25, 2023 at the Institute of Exact Sciences in Pampulha Campus of the Federal University of Minas Gerais. In total, the event included the submission and evaluation of 26 scientific summaries (short papers) and the Peer reviews were carried out with the support of 11 volunteer reviewers. At the end of the assessment, they were 22 works were selected for publication, which were presented to the public in poster format digital. The best evaluated works were invited for oral presentation at the event and received certificates of honorable mention. The event's program included round tables where research projects supported by the Center were presented and the necessary skills for using the Artificial Intelligence in the area of ​​Health, in addition to the lecture given by professor Ricardo Cruz-Correia, University of Porto, which addressed the challenges and opportunities of generative AI for teaching and research in Health.

Newborn Skin Maturity Medical Device Validation for Gestational Age Prediction: Clinical Trial.

BACKGROUND: Early access to antenatal care and high-cost technologies for pregnancy dating challenge early neonatal risk assessment at birth in resource-constrained settings. To overcome the absence or inaccuracy of postnatal gestational age (GA), we developed a new medical device to assess GA based on the photobiological properties of newborns' skin and predictive models. OBJECTIVE: This study aims to validate a device that uses the photobiological model of skin maturity adjusted to the clinical data to detect GA and establish its accuracy in discriminating preterm newborns. METHODS: A multicenter, single-blinded, and single-arm intention-to-diagnosis clinical trial evaluated the accuracy of a novel device for the detection of GA and preterm newborns. The first-trimester ultrasound, a second comparator ultrasound, and data regarding the last menstrual period (LMP) from antenatal reports were used as references for GA at birth. The new test for validation was performed using a portable multiband reflectance photometer device that assessed the skin maturity of newborns and used machine learning models to predict GA, adjusted for birth weight and antenatal corticosteroid therapy exposure. RESULTS: The study group comprised 702 pregnant women who gave birth to 781 newborns, of which 366 (46.9%) were preterm newborns. As the primary outcome, the GA as predicted by the new test was in line with the reference GA that was calculated by using the intraclass correlation coefficient (0.969, 95% CI 0.964-0.973). The paired difference between predicted and reference GAs was -1.34 days, with Bland-Altman limits of -21.2 to 18.4 days. As a secondary outcome, the new test achieved 66.6% (95% CI 62.9%-70.1%) agreement with the reference GA within an error of 1 week. This agreement was similar to that of comparator-LMP-GAs (64.1%, 95% CI 60.7%-67.5%). The discrimination between preterm and term newborns via the device had a similar area under the receiver operating characteristic curve (0.970, 95% CI 0.959-0.981) compared with that for comparator-LMP-GAs (0.957, 95% CI 0.941-0.974). In newborns with absent or unreliable LMPs (n=451), the intent-to-discriminate analysis showed correct preterm versus term classifications with the new test, which achieved an accuracy of 89.6% (95% CI 86.4%-92.2%), while the accuracy for comparator-LMP-GA was 69.6% (95% CI 65.3%-73.7%). CONCLUSIONS: The assessment of newborn's skin maturity (adjusted by learning models) promises accurate pregnancy dating at birth, even without the antenatal ultrasound reference. Thus, the novel device could add value to the set of clinical parameters that direct the delivery of neonatal care in birth scenarios where GA is unknown or unreliable. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-027442.

A Mobile Serious Game About the Pandemic (COVID-19 - Did You Know?): Design and Evaluation Study.

BACKGROUND: No treatment for COVID-19 is yet available; therefore, providing access to information about SARS-CoV-2, the transmission route of the virus, and ways to prevent the spread of infection is a critical sanitary measure worldwide. Serious games have advantages in the dissemination of reliable information during the pandemic; they can provide qualified content while offering interactivity to the user, and they have great reach over the internet. OBJECTIVE: This study aimed to develop a serious game with the purpose of providing science-based information on the prevention of COVID-19 and personal care during the pandemic while assessing players' knowledge about COVID-19-related topics. METHODS: The study was conducted with the interdisciplinary collaboration of specialists in health sciences, computing, and design at the Federal University of Minas Gerais, Brazil. The health recommendations were grouped into six thematic blocks, presented in a quiz format. The software languages were based on the progressive web app development methodology with the Ionic framework, JavaScript, HTML5, cascading style sheets, and TypeScript (Angular). Open data reports of how users interact with the serious game were obtained using the Google Analytics application programming interface. The visual identity, logo, infographics, and icons were carefully developed by considering a selection of colors, typography, sounds, and images that are suitable for young audiences. Cards with cartoon characters were introduced at the end of each thematic topic to interact with the player, reinforcing their correct answers or alerting them to the need to learn more about the disease. The players' performance was assessed by the rate of incorrect and correct answers and analyzed by linear correlation coefficient over 7 weeks. The agile SCRUM development methodology enabled quick and daily interactions of developers through a webchat and sequential team meetings. RESULTS: The game "COVID-19-Did You Know?" was made available for free on a public university website on April 1, 2020. The game had been accessed 17,571 times as of September 2020. Dissemination actions such as reports on social media and television showed a temporal correspondence with the access number. The players' error rate in the topic "Mask" showed a negative trend (r=-.83; P=.01) over the weeks of follow-up. The other topics showed no significant trend over the weeks. CONCLUSIONS: The gamification strategy for health education content on the theme of COVID-19 reached a young audience, which is considered to be a priority in the strategy of orientation toward social distancing. Specific educational reinforcement measures were proposed and implemented based on the players' performance. The improvement in the users' performance on the topic about the use of masks may reflect an increase in information about or adherence to mask use over time.

Quality of Pregnancy Dating and Obstetric Interventions During Labor: Retrospective Database Analysis.

BACKGROUND: The correct dating of pregnancy is critical to support timely decisions and provide obstetric care during birth. The early obstetric ultrasound assessment before 14 weeks is considered the best reference to assist in determining gestational age (GA), with an accuracy of ±5 to 7 days. However, this information is limited in many settings worldwide. OBJECTIVE: The aim of this study is to analyze the association between the obstetric interventions during childbirth and the quality of GA determination, according to the first antenatal ultrasound assessment, which assisted the calculation. METHODS: This is a hospital-based cohort study using medical record data of 2113 births at a perinatal referral center. The database was separated into groups and subgroups of analyses based on the reference used by obstetricians to obtain GA at birth. Maternal and neonatal characteristics, mode of delivery, oxytocin augmentation, and forceps delivery were compared between groups of pregnancies with GA determination at different reference points: obstetric ultrasound assessment 14 weeks, 20 weeks, and ≥20 weeks or without antenatal ultrasound (suboptimal dating). Ultrasound-based GA information was associated with outcomes between the interest groups using chi-square tests, odds ratios (OR) with 95% CI, or the Mann-Whitney statistical analysis. RESULTS: The chance of nonspontaneous delivery was higher in pregnancies with 14 weeks ultrasound-based GA (OR 1.64, 95% CI 1.35-1.98) and 20 weeks ultrasound-based GA (OR 1.58, 95% CI 1.31-1.90) when compared to the pregnancies with ≥20 weeks ultrasound-based GA or without any antenatal ultrasound. The use of oxytocin for labor augmentation was higher for 14 weeks and 20 weeks ultrasound-based GA, OR 1.41 (95% CI 1.09-1.82) and OR 1.34 (95% CI 1.04-1.72), respectively, when compared to those suboptimally dated. Moreover, maternal blood transfusion after birth was more frequent in births with suboptimal ultrasound-based GA determination (20/657, 3.04%) than in the other groups (14 weeks ultrasound-based GA: 17/1163, 1.46%, P=.02; 20 weeks ultrasound-based GA: 25/1456, 1.71%, P=.048). Cesarean section rates between the suboptimal dating group (244/657, 37.13%) and the other groups (14 weeks: 475/1163, 40.84%, P=.12; 20 weeks: 584/1456, 40.10%, P=.20) were similar. In addition, forceps delivery rates between the suboptimal dating group (17/657, 2.6%) and the other groups (14 weeks: 42/1163, 3.61%, P=.24; 20 weeks: 46/1456, 3.16%, P=.47) were similar. Neonatal intensive care unit admission was more frequent in newborns with suboptimal dating (103/570, 18.07%) when compared with the other groups (14 weeks: 133/1004, 13.25%, P=.01; 20 weeks: 168/1263, 13.30%, P=.01), excluding stillbirths and major fetal malformations. CONCLUSIONS: The present analysis highlighted relevant points of health care to improve obstetric assistance, confirming the importance of early access to technologies for pregnancy dating as an essential component of quality antenatal care.

Prematurity detection evaluating interaction between the skin of the newborn and light: protocol for the preemie-test multicentre clinical trial in Brazilian hospitals to validate a new medical device.

INTRODUCTION: Recognising prematurity is critical in order to attend to immediate needs in childbirth settings, guiding the extent of medical care provided for newborns. A new medical device has been developed to carry out the preemie-test, an innovative approach to estimate gestational age (GA), based on the photobiological properties of the newborn's skin. First, this study will validate the preemie-test for GA estimation at birth and its accuracy to detect prematurity. Second, the study intends to associate the infant's skin reflectance with lung maturity, as well as evaluate safety, precision and usability of a new medical device to offer a suitable product for health professionals during childbirth and in neonatal care settings. METHODS AND ANALYSIS: Research protocol for diagnosis, single-group, single-blinding and single-arm multicenter clinical trial with a reference standard. Alive newborns, with 24 weeks or more of pregnancy age, will be enrolled during the first 24 hours of life. Sample size is 787 subjects. The primary outcome is the difference between the GA calculated by the photobiological neonatal skin assessment methodology and the GA calculated by the comparator antenatal ultrasound or reliable last menstrual period (LMP). Immediate complications caused by pulmonary immaturity during the first 72 hours of life will be associated with skin reflectance in a nested case-control study. ETHICS AND DISSEMINATION: Each local independent ethics review board approved the trial protocol. The authors intend to share the minimal anonymised dataset necessary to replicate study findings. TRIAL REGISTRATION NUMBER: RBR-3f5bm5.

Informação e decisão em saúde I / Health information and decision I

Caro estudante da disciplina Informação e Decisão em Saúde I (IDS I), seja muito bem-vindo! O objetivo principal desta disciplina é apresentar os conceitos básicos e as melhores práticas sobre o registro, o gerenciamento e a utilização da informação clínica como peça fundamental para apoiar a atenção à saúde, com foco na sua importância para tomada de decisão. Você vai aprender sobre a importância da informação clínica e a sua relação direta com os registros sobre a saúde das pessoas. Aspectos como qualidade, segurança, acessibilidade e padronização dos dados coletados durante a assistência podem impactar diretamente na qualidade do serviço prestado à saúde. Assim como as demais tecnologias aplicadas à saúde, a Tecnologia de Informação e Comunicação (TIC) tem assumido papel cada vez mais relevante nas atividades cotidianas do profissional e das instituições onde atua. Destaca-se pelo seu potencial de transformar a prestação do cuidado, tornando-a mais segura e efetiva.

Dear student of the discipline Information and Decision in Health I (IDS I), you are very welcome! The main objective of this course is to present the basic concepts and best practices on the recording, management and use of clinical information as a fundamental part to support health care, focusing on its importance for decision-making. You will learn about the importance of clinical information and its direct relationship to people's health records. Aspects such as quality, safety, accessibility and standardization of data collected during care can directly impact the quality of the health service provided. Like other technologies applied to health, Information and Communication Technology (ICT) has assumed an increasingly important role in the daily activities of professionals and institutions where they work. It stands out for its potential to transform the provision of care, making it safer and more effective.

Avaliação do impacto da informatização dos registros sobre o parto na produção do conhecimento científico, em um hospital universitário / Impact assessment in the production of scientific knowledge from the computerization of records of birth in a university hospital

O objetivo deste estudo foi investigar o impacto do uso de um sistema computadorizado de registro médico no número e qualidade de publicações científicas, em um hospital universitário. Para essa análise foram consideradas comparativamente as produções de trabalhos científicos de professores dos Departamentos de Ginecologia e Obstetrícia de duas universidades federais brasileiras. Na primeira delas comparou-se a produção de sete anos antes com a de três anos depois da implantação de um sistema eletrônico de monitoramento da qualidade da assistência obstétrica, o SISMater®.A outra, com registro em prontuário de papel, serviu como controle. Houve uma queda geral de 5% na produção/professor/ano na maternidade com sistema eletrônico, enquanto que no controle a variação no mesmo período foi elevação de 55%. Não foram encontradas evidências de que a informatização de parte dos registros médicos nesta maternidade tenha resultado em impacto imediato na produção científica publicada. Há ainda expressivos desafios para que o uso dos sistemas eletrônicos de informação na saúde possam beneficiar diretamente a disponibilidade e qualidade de dados deforma a fomentar diretamente o avanço da ciência médica em instituições acadêmicas de ensino e pesquisa.

The aim of this study is to investigate the impact of the use of computerized medical record systems in increasing the number and quality of scientific publications in a public university in Brazil. For this analysis were compared the teachers productions of the Departments of Gynecology and Obstetrics of two Federal Universities of Minas Gerais, before and after the implementation of electronic medical records in hospitals of their respective teaching hospitals. The results of this study demonstrated that the computerization of medical records in the maternity ward had no immediate results in the production of scientific articles published. Among the causes that may be related to this fact can be highlighted the lack of government investment in research and the need for adaptation of health professionals to the use of health technologies.

[Association between risk pregnancy and route of delivery with maternal and neonatal outcomes]. / Associacao entre risco gestacional e tipo de parto com as repercussoes maternas e neonatais.

PURPOSE: To analyze the relationships among gestational risk, type of delivery and immediate maternal and neonatal repercussions. METHODS: A retrospective cohort study based on secondary data was conducted in a university maternity hospital. A total of 1606 births were analyzed over a 9-month period. Epidemiological, clinical, obstetric and neonatal characteristics were compared according to the route of delivery and the gestational risk characterized on the basis of the eligibility criteria for high clinical risk. The occurrence of maternal and neonatal complications during hospitalization was analyzed according to gestational risk and cesarean section delivery using univariate and multivariate logistic analysis. RESULTS: The overall rate of cesarean sections was 38.3%. High gestational risk was present in 50.2% of births, mainly represented by hypertensive disorders and fetal malformations. The total incidence of cesarean section, planned cesarean section or emergency cesarean section was more frequent in pregnant women at gestational high risk (p<0.001). Cesarean section alone did not influence maternal outcome, but was associated with poor neonatal outcome (OR 3.4; 95%CI 2.7-4.4). Gestational high risk was associated with poor maternal and neonatal outcome (OR 3.8; 95%CI 1.3-8.7 and OR 17.5; 95%CI 11.6-26.3, respectively). In multivariate analysis, the ratios were maintained, although the effect of gestational risk has determined a reduction in the OR of the type of delivery alone from 3.4 (95%CI 2.7-4.4) to 1.99 (95%CI 1.5-2.6) for adverse neonatal outcome. CONCLUSION: Gestational risk was the main factor associated with poor maternal and neonatal outcome. Cesarean delivery was not directly associated with poor maternal outcome but increased the chances of unfavorable neonatal outcomes.

Associacao entre risco gestacional e tipo de parto com as repercussoes maternas e neonatais / Association between risk pregnancy and route of delivery with maternal and neonatal outcomes

OBJETIVO: Avaliar as relações entre risco gestacional, tipo de parto e suas repercussões maternas e neonatais imediatas. MÉTODOS: Análise retrospectiva de coorte em base de dados secundários, em maternidade de hospital universitário. Foram considerados 1606 partos no período de nove meses. Características epidemiológicas, clínicas, obstétricas e neonatais foram comparadas em função da via de parto e do risco gestacional, caracterizado conforme os critérios de elegibilidade de alto risco clínico. A ocorrência de complicações maternas e neonatais durante a internação foi analisada em função do risco gestacional e parto cesariano. Para isto, análise logística univariada e multivariada foram empregadas. RESULTADOS: A taxa global de cesarianas foi de 38,3%. O alto risco gestacional esteve presente em 50,2% dos partos, representado principalmente pelos distúrbios hipertensivos e as malformações fetais. A ocorrência total de cesarianas, cesarianas anteparto ou intraparto foi mais frequente em gestantes de elevado risco gestacional (p<0,001]. A cesariana, isoladamente, não influenciou o resultado materno, mas associou-se ao resultado neonatal desfavorável (OR 3,4; IC95% 2,7-4,4). O alto risco gestacional associou-se ao resultado materno e neonatal desfavorável (OR 3,8; IC95% 1,6-8,7 e OR 17,5; IC95% 11,6-26,3, respectivamente) Na análise multivariada, essas relações de risco se mantiveram, embora o efeito do risco gestacional tenha determinado uma redução no OR do tipo de parto isoladamente de 3,4 (IC95% 2,66-4,4) para 1,99 (IC95% 1,5-2,6) para o resultado neonatal desfavorável. CONCLUSÃO: O risco gestacional foi o principal fator associado ao resultado materno e neonatal desfavorável. A cesariana não influenciou diretamente ...

PURPOSE: To analyze the relationships among gestational risk, type of delivery and immediate maternal and neonatal repercussions. METHODS: A retrospective cohort study based on secondary data was conducted in a university maternity hospital. A total of 1606 births were analyzed over a 9-month period. Epidemiological, clinical, obstetric and neonatal characteristics were compared according to the route of delivery and the gestational risk characterized on the basis of the eligibility criteria for high clinical risk. The occurrence of maternal and neonatal complications during hospitalization was analyzed according to gestational risk and cesarean section delivery using univariate and multivariate logistic analysis. RESULTS: The overall rate of cesarean sections was 38.3%. High gestational risk was present in 50.2% of births, mainly represented by hypertensive disorders and fetal malformations. The total incidence of cesarean section, planned cesarean section or emergency cesarean section was more frequent in pregnant women at gestational high risk (p<0.001). Cesarean section alone did not influence maternal outcome, but was associated with poor neonatal outcome (OR 3.4; 95%CI 2.7-4.4). Gestational high risk was associated with poor maternal and neonatal outcome (OR 3.8; 95%CI 1.3-8.7 and OR 17.5; 95%CI 11.6-26.3, respectively). In multivariate analysis, the ratios were maintained, although the effect of gestational risk has determined a reduction in the OR of the type of delivery alone from 3.4 (95%CI 2.7-4.4) to 1.99 (95%CI 1.5-2.6) for adverse neonatal outcome. CONCLUSION: Gestational risk was the main factor associated with poor maternal and neonatal outcome. Cesarean delivery was not directly associated with poor maternal outcome but increased the chances of unfavorable neonatal outcomes. .