Background: The outcome of neonatal surgery depends on safe anaesthesia, competent surgery and good nursing care. The University of Uyo Teaching Hospital, Uyo, Nigeria, established in February 2008, has specialist ana...Background: The outcome of neonatal surgery depends on safe anaesthesia, competent surgery and good nursing care. The University of Uyo Teaching Hospital, Uyo, Nigeria, established in February 2008, has specialist anaesthetic and surgical manpower. The aim of the study was to determine the outcome and contributing factors to mortality in neonatal surgical emergencies at this new tertiary health institution. Method: It was a retrospective descriptive study of neonates that underwent emergency surgery at the University of Uyo Teaching Hospital between June 2008 and May 2011. Data was obtained from the anaesthetic register, ward admission and discharged register, nurses report books and patient case files. Results: Forty-five neonates were operated upon during the three year period. There were 28 males and 17 females with a male to female ratio of 1.7:1. Forty-four (97.8%) of the neonates were referred to the University of Uyo Teaching Hospital. The mean age and body weight at presentation were 47.5 ± 44.4 hours and 2.65 ± 0.61 kg respectively. The mean interval between admission and surgical intervention was 4.9 ± 6.2 days. Malformations of the gut (40%) and anterior abdominal wall (26.7%) were the major pathologies. The overall mortality following surgery was 62.2%. Case fatality rates ranged from 0% for Hirschprung’s disease to 100% for tracheoesophageal fistula. The immediate causes of death among these neonates were peritonitis from gangrenous gut, hypovolaemia and repeat surgery. Contributing factors to mortality were delivery in unorthodox health facilities, delay in presentation as well as surgical intervention and inefficient postoperative monitoring. Conclusion: Emergency neonatal surgeries at the UUTH are associated with unacceptable high mortality. Reduction in such mortality would require campaign for early presentation, a lot more timely surgical interventions and upgrading of monitoring facili- ties to help in improving perioperative monitoring and care.展开更多
Background: Respiratory distress syndrome (RDS) or hyaline membrane disease (HMD) is the most common cause of neonatal morbidity and mortality in preterm infants. We aimed to determine the frequency of RDS among 3 gro...Background: Respiratory distress syndrome (RDS) or hyaline membrane disease (HMD) is the most common cause of neonatal morbidity and mortality in preterm infants. We aimed to determine the frequency of RDS among 3 groups of preterm infants and the value of some related factors. Methods: A cross-sectional, descriptive analytical investigation was carried out in the NICU ward of Akbarabadi Hospital (Tehran-Iran) during spring 2011. Newborns’ data were collected and assessed by using their hospital medical records. Seventy-three preterm infants with gestational age < 34 weeks were hospitalized in the NICU. All participants were divided into 3 groups: extremely preterm (<28 weeks), very preterm (28 to <32 weeks) and moderate preterm (32 to 34 weeks). Frequency of RDS and some related factors were compared among 3 groups. Results: RDS was observed in 65.6% of all participants;however frequency of RDS was not different between three groups. An inversely correlation was found between gestational age and mortality rate (p = 0.05). In regard to Betamethasone administration prior to birth, this interval was significantly longer in alive neonates in comparison to infants who died (p < 0.05). Conclusion: RDS was frequent in preterm neonates with gestational age < 32 weeks. Time of Betamethasone administration prior to birth can significantly influence on neonatal mortality rate.展开更多
Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardi...Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.展开更多
目的探讨微信实时远程视频评估体系在危重新生儿转运中的应用效果。方法选择2021年3月—2022年10月广西壮族自治区妇幼保健院急诊科转诊的新生儿450例为对象,随机数字表法分为对照组和观察组。对照组150例采用电话沟通方式评估,观察组30...目的探讨微信实时远程视频评估体系在危重新生儿转运中的应用效果。方法选择2021年3月—2022年10月广西壮族自治区妇幼保健院急诊科转诊的新生儿450例为对象,随机数字表法分为对照组和观察组。对照组150例采用电话沟通方式评估,观察组300例采用微信实时远程视频评估体系。观察组交替选择新生儿危重病例评分(neonatal critical case score,NICS)组和新生儿转运生理稳定指数(neonatal transport physiological stability ndex,TRIPS)评估进行危重评分,分为NICS组和TRIPS组两个亚组,各150例。比较两组稳定时间、机械通气时间及平均住院时间、转运不良事件发生率、死亡率及入院7 d内的死亡率。结果两组平均住院时间比较,差异无统计意义(P>0.05);观察组危重新生儿转运中生命体征稳定时间、机械通气时间短于对照组(P<0.05)。观察组根据评分系统不同进行分析,NICS组及TRIPS组生命体征稳定时间、平均住院时间比较,差异无统计意义(P>0.05);NICS组危重新生儿转运中评分所需时间长于TRIPS组(P<0.05);NICS组机械通气时间低于TRIPS组(P<0.05)。两组总转运不良事件发生率比较,差异无统计学意义(P>0.05);观察组入院7 d内死亡率低于对照组(P<0.05)。结论微信实时远程视频评估体系用于危重新生儿转运中可获得良好的效果,且TRIPS评估体系更加便捷、简洁。展开更多
文摘Background: The outcome of neonatal surgery depends on safe anaesthesia, competent surgery and good nursing care. The University of Uyo Teaching Hospital, Uyo, Nigeria, established in February 2008, has specialist anaesthetic and surgical manpower. The aim of the study was to determine the outcome and contributing factors to mortality in neonatal surgical emergencies at this new tertiary health institution. Method: It was a retrospective descriptive study of neonates that underwent emergency surgery at the University of Uyo Teaching Hospital between June 2008 and May 2011. Data was obtained from the anaesthetic register, ward admission and discharged register, nurses report books and patient case files. Results: Forty-five neonates were operated upon during the three year period. There were 28 males and 17 females with a male to female ratio of 1.7:1. Forty-four (97.8%) of the neonates were referred to the University of Uyo Teaching Hospital. The mean age and body weight at presentation were 47.5 ± 44.4 hours and 2.65 ± 0.61 kg respectively. The mean interval between admission and surgical intervention was 4.9 ± 6.2 days. Malformations of the gut (40%) and anterior abdominal wall (26.7%) were the major pathologies. The overall mortality following surgery was 62.2%. Case fatality rates ranged from 0% for Hirschprung’s disease to 100% for tracheoesophageal fistula. The immediate causes of death among these neonates were peritonitis from gangrenous gut, hypovolaemia and repeat surgery. Contributing factors to mortality were delivery in unorthodox health facilities, delay in presentation as well as surgical intervention and inefficient postoperative monitoring. Conclusion: Emergency neonatal surgeries at the UUTH are associated with unacceptable high mortality. Reduction in such mortality would require campaign for early presentation, a lot more timely surgical interventions and upgrading of monitoring facili- ties to help in improving perioperative monitoring and care.
文摘Background: Respiratory distress syndrome (RDS) or hyaline membrane disease (HMD) is the most common cause of neonatal morbidity and mortality in preterm infants. We aimed to determine the frequency of RDS among 3 groups of preterm infants and the value of some related factors. Methods: A cross-sectional, descriptive analytical investigation was carried out in the NICU ward of Akbarabadi Hospital (Tehran-Iran) during spring 2011. Newborns’ data were collected and assessed by using their hospital medical records. Seventy-three preterm infants with gestational age < 34 weeks were hospitalized in the NICU. All participants were divided into 3 groups: extremely preterm (<28 weeks), very preterm (28 to <32 weeks) and moderate preterm (32 to 34 weeks). Frequency of RDS and some related factors were compared among 3 groups. Results: RDS was observed in 65.6% of all participants;however frequency of RDS was not different between three groups. An inversely correlation was found between gestational age and mortality rate (p = 0.05). In regard to Betamethasone administration prior to birth, this interval was significantly longer in alive neonates in comparison to infants who died (p < 0.05). Conclusion: RDS was frequent in preterm neonates with gestational age < 32 weeks. Time of Betamethasone administration prior to birth can significantly influence on neonatal mortality rate.
文摘Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.
文摘目的探讨微信实时远程视频评估体系在危重新生儿转运中的应用效果。方法选择2021年3月—2022年10月广西壮族自治区妇幼保健院急诊科转诊的新生儿450例为对象,随机数字表法分为对照组和观察组。对照组150例采用电话沟通方式评估,观察组300例采用微信实时远程视频评估体系。观察组交替选择新生儿危重病例评分(neonatal critical case score,NICS)组和新生儿转运生理稳定指数(neonatal transport physiological stability ndex,TRIPS)评估进行危重评分,分为NICS组和TRIPS组两个亚组,各150例。比较两组稳定时间、机械通气时间及平均住院时间、转运不良事件发生率、死亡率及入院7 d内的死亡率。结果两组平均住院时间比较,差异无统计意义(P>0.05);观察组危重新生儿转运中生命体征稳定时间、机械通气时间短于对照组(P<0.05)。观察组根据评分系统不同进行分析,NICS组及TRIPS组生命体征稳定时间、平均住院时间比较,差异无统计意义(P>0.05);NICS组危重新生儿转运中评分所需时间长于TRIPS组(P<0.05);NICS组机械通气时间低于TRIPS组(P<0.05)。两组总转运不良事件发生率比较,差异无统计学意义(P>0.05);观察组入院7 d内死亡率低于对照组(P<0.05)。结论微信实时远程视频评估体系用于危重新生儿转运中可获得良好的效果,且TRIPS评估体系更加便捷、简洁。
