External validation of inpatient neonatal mortality prediction models in high-mortality settings (original) (raw)
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International Journal of General Medicine
Background: Early neonatal death is death of infants in the first week of life. And 34% to 92% of neonatal deaths happen within 7 days of postnatal period. Thus, the early neonatal period is the most critical time for an infant, requiring different strategies to prevent mortality. Among strategies, deriving and implementing early warning scores is crucial to predict early neonatal mortality earlier upon hospital admission. Objective: To derive and validate a risk score to predict mortality of early neonates at Felege Hiwot Specialized Hospital neonatal intensive care unit, Bahir Dar, 2021. Methods: The document review was conducted from February 24, to April 08, 2021, on all early neonates admitted to neonatal intensive care unit from January 1, 2018 to December 31, 2020. The total number of early neonates included in the derivation study was 1100. Data were collected by using checklists prepared on EpiCollect5 software. After exporting the data to R version 4.0.5 software, variables with (p < 0.25) from the simple binary regression were entered into a multiple logistic regression model, and significant variables (p < 0.05) were kept in the model. The discrimination and calibration were assessed. The model was internally validated using bootstrapping technique. Results: Admission weight, birth Apgar score, perinatal asphyxia, respiratory distress syndrome, mode of delivery, sepsis, and gestational age at birth remained in the final multiple logistic regression prediction model. The area under curve of receiver operating characteristic curve for early neonatal mortality score was 90.7%. The model retained excellent discrimination under internal validation. The sensitivity, specificity, and positive predictive value, negative predictive value of the model was 89.4%, 82.5%, 55.5%, and 96.9%, respectively. Conclusion: The derived score has an excellent discriminative ability and good prediction performance. This is an important tool for predicting early neonatal mortality in neonatal intensive care units at admission.
Validation of CRIB II for prediction of mortality in premature babies
Indian Pediatrics, 2010
variety of risk adjustment scores have been derived and advocated for use in assessing neonatal mortality(1). Clinical use index for babies (CRIB) score was created to predict mortality for infants born at less than 32 weeks gestation at birth and based upon 6 variables for predicting mortality(2). CRIB with contemporary data has been questioned because it needs data up to 12 hours after admission thus introducing a factor of early treatment bias. It also utilizes FiO 2 which is not a true physiological measure because it is determined by the care team. CRIB II, an improved version of CRIB, was published recently. The new score is meant to improve predictions for smaller, very premature infants and to exclude variables that could be influenced by care given to the infants(3). We conducted this study to validate the efficacy of CRIB II in predicting pre-discharge neonatal mortality in preterm neonates needing intensive care. METHODS The prospective cohort study was conducted at a tertiary care center between October 2005 and June 2006. Study protocol was approved by hospital ethical committee and written informed consent was taken from parents before enrolment in the study. All preterm newborns ≤32 weeks of gestation, born in
BMC Pediatrics, 2020
Background Early warning scores for neonatal mortality have not been designed for low income countries. We developed and validated a score to predict mortality upon admission to a NICU in Ethiopia. Methods We conducted a retrospective case-control study at the University of Gondar Hospital, Gondar, Ethiopia. Neonates hospitalized in the NICU between January 1, 2016 to June 31, 2017. Cases were neonates who died and controls were neonates who survived. Results Univariate logistic regression identified variables associated with mortality. The final model was developed with stepwise logistic regression. We created the Neonatal Mortality Score, which ranged from 0 to 52, from the model’s coefficients. Bootstrap analysis internally validated the model. The discrimination and calibration were calculated. In the derivation dataset, there were 207 cases and 605 controls. Variables associated with mortality were admission level of consciousness, admission respiratory distress, gestational ag...
2020
Background: The scoring systems evaluate neonatal outcomes based on perinatal factors in the Neonatal Intense Course Unit (NICU). Aim: This study aimed to predict mortality risk in preterm neonates for the first time, using the Clinical Risk Index for Babies (CRIB II). Method: This cross-sectional, descriptive-analytical, longitudinal study was conducted on 344 preterm neonates with the gestational age of 23-32 weeks and birth weight of 500-1500 g in a referral center in Tehran, Iran, from winter 2016 to spring 2017. Some neonatal variables were completed within the first 12 h of life, and the final scores were calculated based on CRIB II. Then, the correlation of these variables with mortality outcome was evaluated using logistic regression. Sensitivity, specificity, and positive and negative values were also calculated via SPSS software (version 23). Results: According to the results, 253 (73.57%) neonates, including 122 girls (48%), survived in the first 24 h after birth. The tot...
Prediction of Mortality in Very Premature Infants: A Systematic Review of Prediction Models
PLoS ONE, 2011
Context: Being born very preterm is associated with elevated risk for neonatal mortality. The aim of this review is to give an overview of prediction models for mortality in very premature infants, assess their quality, identify important predictor variables, and provide recommendations for development of future models. Methods: Studies were included which reported the predictive performance of a model for mortality in a very preterm or very low birth weight population, and classified as development, validation, or impact studies. For each development study, we recorded the population, variables, aim, predictive performance of the model, and the number of times each model had been validated. Reporting quality criteria and minimum methodological criteria were established and assessed for development studies. Results: We identified 41 development studies and 18 validation studies. In addition to gestational age and birth weight, eight variables frequently predicted survival: being of average size for gestational age, female gender, non-white ethnicity, absence of serious congenital malformations, use of antenatal steroids, higher 5-minute Apgar score, normal temperature on admission, and better respiratory status. Twelve studies met our methodological criteria, three of which have been externally validated. Low reporting scores were seen in reporting of performance measures, internal and external validation, and handling of missing data. Conclusions: Multivariate models can predict mortality better than birth weight or gestational age alone in very preterm infants. There are validated prediction models for classification and case-mix adjustment. Additional research is needed in validation and impact studies of existing models, and in prediction of mortality in the clinically important subgroup of infants where age and weight alone give only an equivocal prognosis.
Neonatal mortality risk assessment in a neonatal intensive care unit (NICU)
2007
Objective: This study aims to assess the utility of a scoring system as predictor of neonatal mortality rate among the neonates admitted within one year to the neonatal intensive care unit (NICU) of the Children's Medical Center in Tehran, Iran. Material & Methods: Data were gathered from 213 newborns admitted to the NICU from September 2003 to August 2004. In addition to demographic data, Apgar scores at 1 minute and 5 minutes, history and duration of previous hospitalization, initial diagnosis and final diagnosis, and scoring system by using the score for the neonatal acute physiology-perinatal extension II (SNAP-PE II) were carried out within 12 hours after admission to the NICU. All of the parameters were prospectively applied to the admitted newborns. The exclusion criteria were discharge or death in less than 24 hours after NICU admission. Findings: 198 newborn infants met the inclusion criteria. The mean and standard deviation (SD) of the variables including postnatal age, birth weight, SNAP, and finally Apgar scores at 1 minute and 5 minutes of neonates under this study were 7.6 (0.5) days, 2479.8 (29.4) grams, 21.6 (1.1), 7.47(0.08), and 7.71 (0.06), respectively. Twenty five of the 198 patients died (12.6%). Gestational age (P=0.03), birth weight (P=0.02), Apgar score at 5 minutes (0.001), and SNAP-PE II (P=0.04) were significantly related to the mortality rate. By Analyzing through logistic regression to evaluate the predictive value of these variables in relation to the risk of mortality, it was shown that only SNAP-PE II and Apgar score at 5 minutes could significantly predict the neonatal mortality. Conclusion: According to this study SNAP-PE II and Apgar score at 5 minutes can be used to predict mortality among the NICU patients. SNAP-PE II score had the best performance in predicting mortality in this study. More studies with larger samples are suggested to evaluate all of the abovementioned parameters among neonates who are admitted to NICUs countrywide.
BMC Pediatrics
Purpose The study was aimed to assess the prognostic power The Pediatric Risk of Mortality-3 (PRISM-3) and the Pediatric Index of Mortality-3 (PIM-3) to predict in-hospital mortality in a sample of patients admitted to the PICUs. Design and methods The study was performed to include all children younger than 18 years of age admitted to receive critical care in two hospitals, Mashhad, northeast of Iran from December 2017 to November 2018. The predictive performance was quantified in terms of the overall performance by measuring the Brier Score (BS) and standardized mortality ratio (SMR), discrimination by assessing the AUC, and calibration by applying the Hosmer-Lemeshow test. Results A total of 2446 patients with the median age of 4.2 months (56% male) were included in the study. The PICU and in-hospital mortality were 12.4 and 16.14%, respectively. The BS of the PRISM-3 and PIM-3 was 0.088 and 0.093 for PICU mortality and 0.108 and 0.113 for in-hospital mortality. For the entire sa...
Cureus, 2023
Background In India, a significant number of newborns die each year, with Madhya Pradesh having the highest neonatal mortality rate. However, there is a lack of information on factors that can predict neonatal mortality. Objective This study aimed to examine the factors influencing neonatal mortality among neonates admitted to a tertiary care centre's special newborn care unit (SNCU). Methods This retrospective record-based observational study was done at a tertiary care centre, where data from the special newborn care unit (SNCU) from 1st January 2021 to 31st December 2021 was used. We included data of all newborns who were treated in SNCU during the said period and excluded those who got referred or left against medical advice. We abstracted data on age at admission, gender, category, maturity status, birth weight, place of delivery, mode of transport, type of admission, indication of admission, duration of stay and outcome. Qualitative variables were described using frequency and percentage. The chi-square test was used to find out the association of different variables with the outcome, while multivariate logistic regression was conducted to identify risk factors of neonatal mortality. A p-value of <0.05 was considered significant. Results We finalized data of 1052 neonates for analysis. Among them, 846 neonates were successfully discharged while 206 neonates were deceased. The major cause of admission was perinatal asphyxia followed by prematurity. The major cause of mortality in this study was sepsis followed by respiratory distress syndrome, birth asphyxia, and prematurity. Mortality of neonates was significantly associated with maturity status, birth weight, place of delivery, age during admission and duration of stay.