ORIGINAL ARTICLE
Comparison of Neonatal Transport Scoring Systems and Transport-Related Mortality Score for Predicting Neonatal Mortality Risk Sumer Sutcuoglu, MD, Tugce Celik, MD, Senem Alkan, MD, Ozkan Ilhan, MD, and Esra Arun Ozer, MD Objectives: To predict the risk of mortality of neonates, birth weight and gestational age were previously used. However, these criteria were considered inadequate; therefore, various scoring systems have been developed in the recent years. The aim of the study was to evaluate the performance of predicting mortality by Mortality Index for Neonatal Transportation (MINT), Score for Neonatal Acute Physiology-Perinatal Extension II (SNAP-PE-II), and Transport Related Mortality Score (TREMS). Methods: All infants transferred to the neonatal intensive care unit between January 1 and December 31, 2011, were included. The scores of SNAP-PE-II, MINT, and TREMS of the all cases were calculated. TREMS is our proposed scoring system and it consists of 5 variables (hypoglycemia, hypoxia, hypercarbia, hypotension, and hypothermia). The scoring systems, SNAP-PE-II, MINT, and TREMS, were compared in terms of mortality risk. Results: A total of 306 newborn infants constituted the study population. The mean gestational age was 33.1 ± 5 weeks and the mean birth weight was 2031.2 ± 1018 g, and 183 (59%) babies were male. The sensitivity of MINT score for predicting mortality was higher than SNAP-PE-II and TREMS. However, specificity was higher in TREMS score. The negative predictive value was highest in MINT score, whereas TREMS has the highest positive predictive value. Conclusions: The TREMS scoring system is a simple scoring system with a high specificity for predicting mortality. Further studies with larger sample size including more centers and newborn infants with diverse clinical problems are needed to assess the validity and reliability of the TREMS scoring system. Key Words: illness severity, neonatal mortality, neonatal transport, scoring systems (Pediatr Emer Care 2015;31: 113–116)
D
espite the reduction in the perinatal mortality rate due to technological and scientific advances in perinatal and neonatal medicine, neonatal mortality is still an important issue for Turkey.1 The first few hours of the newborn after birth are extremely critical in respect to neonatal mortality as well as longterm neurodevelopmental outcome. Sophisticated neonatal transport has improved the safety of transporting newborn infants, but may not substitute for the benefits of in utero transport. However, neonates may need transfer after delivery.2,3 Therefore, transport environment including distances and characteristics of referral centers, transport team composition, training, mode of transport, use of standard protocols, and access to a specialist or a medical control physician is extremely important to transfer
From the Tepecik Teaching and Research Hospital, Neonatal Intensive Care Unit, Izmir, Turkey. Disclosure: The authors declare no conflict of interest. Reprints: Esra Arun Ozer, MD, Tepecik Teaching and Research Hospital, Neonatal Intensive Care Unit, Gaziler Cad. Yenisehir, Izmir, Turkey (e‐mail: [email protected]). Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. ISSN: 0749-5161
the neonates in a safe manner, resulting in a reduced morbidity and mortality. To predict the risk of mortality of neonates, birth weight and gestational age were widely used as the essential indices.4 However, these indices were considered inadequate for a precise prediction of the degree of derangements from physiological normal and they cannot be used as a direct measure of illness severity; therefore, various scoring systems have been developed in the recent years.2,3 Recent scoring systems are used to predict the shortand long-term prognosis of the patient, to evaluate the performance of different units and mortality rates.5 Various scoring systems used in the neonatal period consist of the parameters such as gestational age, birth weight, ethnicity, sex, body temperature, heart rate, blood pressure, blood gases, respiratory status, and diuresis.5,6 Seven variables (Apgar score at 1 minute, birth weight, presence of a congenital anomaly, and infant's age, serum pH, arterial partial pressure of oxygen, and heart rate at the time of the call) are used to generate the Mortality Index for Neonatal Transportation (MINT) score. Score for Neonatal Acute Physiology (SNAP-II) includes 6 physiologic items (blood pressure, temperature, PaO2/FiO2, serum pH, seizure, urine output); to this are added points for birth weight, low Apgar score, and small for gestational age to create a 9-item SNAP-Perinatal Extension-II (SNAP-PE-II). Transport Related Mortality Score (TREMS) is our proposed scoring system and it consists of 5 variables as follows: hypoglycemia, hypoxia, hypercarbia, hypotension, and hypothermia. Maximum points for MINT, SNAP-PE-II, and TREMS scores are 40, 47, and 5, respectively. In all scoring systems that were mentioned, the higher the points, the worse the outcome. The aim of the study was to evaluate the performance of the 3 scoring tools completed on admission to the neonatal intensive care unit (NICU) in predicting neonatal mortality.
METHODS Study Participants This prospective study was approved by the local ethics committee of the Ministry of Health, Izmir Tepecik Teaching and Research Hospital. All infants transferred to the NICU between January 1 and December 31, 2011, were included in the study. Infants with inborn error of metabolism, and outpatient referrals without neonatal transportation were excluded from the study. All infants were transported with ground vehicles by Neonatal Emergency Transport Service of Izmir 112 Emergency Call Center. The Neonatal Emergency Transport Service team consists of a physician and a paramedic who had certification of newborn resuscitation and interhospital transportation. The transport vehicles had equipment for basic newborn care such as an incubator with a mechanical ventilator, medical air and oxygen, as well as emergency medicines and resuscitation tools. Standard care guidelines were followed during the transport. The transport distance varied between 1 and 90 km at maximum and all infants
Pediatric Emergency Care • Volume 31, Number 2, February 2015
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
www.pec-online.com
113
Sutcuoglu et al
Pediatric Emergency Care • Volume 31, Number 2, February 2015
were stabilized before the transport at facilities with pediatricians/ neonatologists.
detected in 92 (30%) patients and hypercarbia was found in 55 (17.9%) newborns. The comparison of mortality-related risk factors in survivors and nonsurvivors was shown in Table 1. Gestational age and birth weight were statistically significant (P < 0.001) in terms of mortality; whereas sex, type of delivery, multiple gestation, using assisted reproductive techniques, CLD, and sepsis were not found statistically significant (P > 0.05). Mean scores of each system were significantly higher in non-survivors (P < 0.001). The correlation between the scoring systems and mortality was shown in Table 2. There was a strong negative correlation in MINT and SNAP-PE-II scoring systems (r = −0.64); whereas there was a medium negative correlation in TREMS (r = −0.50). However, there was a small negative correlation between gestational age and birth weight, and mortality (r = −0.25 and −0.24, respectively). Sensitivity, specificity, and positive and negative predictive values of scoring systems for predicting mortality are shown in Table 3. The sensitivity of MINT score was higher than SNAPPE-II and TREM. However, specificity for predicting mortality was higher in TREMS. The negative predictive value was highest in MINT score, whereas TREMS has the highest positive predictive value along with SNAP-PE-II. The ROC analysis was used to assess the accuracy of the scoring systems for predicting mortality. The AUC calculations from ROC were 0.92 for MINT, whereas 0.84 for SNAP-PE-II and TREMS (Fig. 1).
Demographic and Clinical Data Gestational age, birth weight, sex, birth center, type of delivery, multiple gestation, gestation via assisted reproductive techniques, blood gases, Apgar scores, blood glucose levels, blood pressure, congenital anomalies, and intubation before transportation were recorded. Intracranial hemorrhage, sepsis, and chronic lung disease (CLD) diagnosed during the follow-up were also recorded.
Calculation of Scores of MINT, SNAP-PE-II, and TREMS Scores of SNAP-PE-II, MINT, and TREMS of the all cases during the study period were calculated on admission. TREMS is a proposed scoring system of our study and it consists of 5 variables as follows: hypoglycemia, hypoxia, hypercarbia, hypotension, and hypothermia; the lowest score point being score “0”, and the highest point being score “5.” Hypoglycemia was defined as blood sugar below 45 mg/dL, hypoxia as pulse oximetry measurement of oxygen saturation below 85%, hypercarbia as PCO2 value in venous capillary blood gas analysis greater than or equal to 55 mm Hg, hypotension as gestational and postnatal agedependent blood pressure values below 10th percentiles, and hypothermia as body temperature below 36°C. The scoring systems of SNAP-PE-II, MINT and TREMS were compared with each other in terms of mortality risk.
Statistical Analyses
DISCUSSION
Statistical analyses were performed using SPSS 18.0 software. Independent samples t test, χ2 test, and Pearson correlation test were used and a P value less than 0.05 was considered statistically significant. To determine the specificity of the scoring systems, “receiver operating characteristic” (ROC) curve and the associated “area under the curve” (AUC) values were calculated. The AUC values are distributed between 0.5 and 1.0. If a parameter has a value of 0.5, it represents that the parameter has no any determinative value, whereas a value of 1.0 represents highly determinative value. To assess model fit, Hosmer-Lemeshow test was used. In addition, the sensitivity and specificity of the cutoff scores were investigated.
RESULTS A total of 306 newborn infants, which were transported to our institute, Izmir Tepecik Teaching and Research Hospital, from remote centers between January 1 and December 31, 2011, constituted the study population. The mean gestational age was 33.1 ± 5 weeks and the mean birth weight was 2031.2 ± 1018 g, and 183 (59%) babies were male. Birth weight of 27 (8.8%) cases was below 750 g, of 34 (11%) cases was 751 to 1000 g, and of 63 (20.5%) cases was 1001 to 1500 g. Ninety-three newborns were delivered vaginally, multiple gestational rate was 9.5% (29) and 8 gestations were achieved by assisted reproductive techniques. Congenital anomalies were present in 52 (17%) cases. The mean duration of hospitalization was 27.9 ± 29.7 days and overall mortality rate was 18.3% (56). The mean scores of MINT, SNAP-PE-II, and TREMS were 6.4 ± 6.3, 8.8 ± 12, and 1.3 ± 1.1, respectively. On admission, 183 (59.8%) cases were hypothermic, 33 (10.7%) cases were hypotensive, and 48 (15.6%) were hypoglycemic. Hypoxia was
114
www.pec-online.com
We proposed a scoring system for the prediction of neonatal mortality based on variables that reflect critical illness and stability and when abnormal are unfavorable conditions for neonatal transport. Although neonatal transport is one of the key components of neonatal-perinatal care in Turkey, transferred newborn infants in this study often experience loss of stability or clinical deterioration, regardless of their characteristics, and this was related to a higher mortality. Therefore, it is critical to optimize care strategies during all neonatal transports.7 The parameters used in scoring systems for predicting mortality in neonates are diverse such as gestational age, birth weight, ethnicity, sex, body temperature, heart rate, blood pressure, blood gas values, mechanical ventilation status, medications, diuresis, and other physiological data.5,6,8,9 However, to ensure an appropriate and reliable prediction, data should be easily obtained, and the cost amount should be low. Moreover, the system must be objective, noninvasive and is expected to be repeated. The timing of these scores may be at the time of referral, time of transport team's first assessment or on admission to NICU over the first 12 hours. Zupancic et al10 collected data for calculation of SNAP-II and SNAPPE-II during the first 12 hours after NICU admission. Similar studies collected data during the first 12 hours.6 In our study, we collected data on admission and during the first 12 hours. In the present study, the mean gestational age was 33.1 ± 5 weeks and the mean birth weight was 2031.2 ± 1018 g. Broughton et al9 reported that the median gestational age was 36 (24-43) weeks and the median birth weight was 2782 (5206140) g out of 2504 infants evaluated with MINT score. Likewise, Lee et al11 assessed the Transport Risk Index of Physiologic Stability (TRIPS) score in 1723 infants and found the mean gestational age 36 ± 5 weeks and the mean birth weight 2607 ± 1010 g. Similar to our study, both studies included all consequent patients regardless of gestational age. However, our study group © 2015 Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
Pediatric Emergency Care • Volume 31, Number 2, February 2015
TREMS for Predicting Neonatal Mortality Risk
TABLE 1. The Comparison of Mortality-Related Risk Factors in Survivors and Nonsurvivors
Gestational age, wk* Birth weight, g* Sex, male, % Type of delivery (normal/sectio) Multiple gestation, % Assisted reproduction techniques, % Congenital anomaly, % Intraventricular hemorrhage, % CLD, % Sepsis, %
Survivors (n = 250)
Nonsurvivors (n = 56)
P
33.6 2152 148 (59) 74/176 24 (10) 8 (3) 30 (12) 42 (17) 31 (12) 68 (27)
30.4 1490 35 (14) 19/37 5 (2) 0 (0) 22 (9) 21 (8) 6 (2) 16 (6)
Comparison of Neonatal Transport Scoring Systems and Transport-Related Mortality Score for Predicting Neonatal Mortality Risk Sumer Sutcuoglu, MD, Tugce Celik, MD, Senem Alkan, MD, Ozkan Ilhan, MD, and Esra Arun Ozer, MD Objectives: To predict the risk of mortality of neonates, birth weight and gestational age were previously used. However, these criteria were considered inadequate; therefore, various scoring systems have been developed in the recent years. The aim of the study was to evaluate the performance of predicting mortality by Mortality Index for Neonatal Transportation (MINT), Score for Neonatal Acute Physiology-Perinatal Extension II (SNAP-PE-II), and Transport Related Mortality Score (TREMS). Methods: All infants transferred to the neonatal intensive care unit between January 1 and December 31, 2011, were included. The scores of SNAP-PE-II, MINT, and TREMS of the all cases were calculated. TREMS is our proposed scoring system and it consists of 5 variables (hypoglycemia, hypoxia, hypercarbia, hypotension, and hypothermia). The scoring systems, SNAP-PE-II, MINT, and TREMS, were compared in terms of mortality risk. Results: A total of 306 newborn infants constituted the study population. The mean gestational age was 33.1 ± 5 weeks and the mean birth weight was 2031.2 ± 1018 g, and 183 (59%) babies were male. The sensitivity of MINT score for predicting mortality was higher than SNAP-PE-II and TREMS. However, specificity was higher in TREMS score. The negative predictive value was highest in MINT score, whereas TREMS has the highest positive predictive value. Conclusions: The TREMS scoring system is a simple scoring system with a high specificity for predicting mortality. Further studies with larger sample size including more centers and newborn infants with diverse clinical problems are needed to assess the validity and reliability of the TREMS scoring system. Key Words: illness severity, neonatal mortality, neonatal transport, scoring systems (Pediatr Emer Care 2015;31: 113–116)
D
espite the reduction in the perinatal mortality rate due to technological and scientific advances in perinatal and neonatal medicine, neonatal mortality is still an important issue for Turkey.1 The first few hours of the newborn after birth are extremely critical in respect to neonatal mortality as well as longterm neurodevelopmental outcome. Sophisticated neonatal transport has improved the safety of transporting newborn infants, but may not substitute for the benefits of in utero transport. However, neonates may need transfer after delivery.2,3 Therefore, transport environment including distances and characteristics of referral centers, transport team composition, training, mode of transport, use of standard protocols, and access to a specialist or a medical control physician is extremely important to transfer
From the Tepecik Teaching and Research Hospital, Neonatal Intensive Care Unit, Izmir, Turkey. Disclosure: The authors declare no conflict of interest. Reprints: Esra Arun Ozer, MD, Tepecik Teaching and Research Hospital, Neonatal Intensive Care Unit, Gaziler Cad. Yenisehir, Izmir, Turkey (e‐mail: [email protected]). Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. ISSN: 0749-5161
the neonates in a safe manner, resulting in a reduced morbidity and mortality. To predict the risk of mortality of neonates, birth weight and gestational age were widely used as the essential indices.4 However, these indices were considered inadequate for a precise prediction of the degree of derangements from physiological normal and they cannot be used as a direct measure of illness severity; therefore, various scoring systems have been developed in the recent years.2,3 Recent scoring systems are used to predict the shortand long-term prognosis of the patient, to evaluate the performance of different units and mortality rates.5 Various scoring systems used in the neonatal period consist of the parameters such as gestational age, birth weight, ethnicity, sex, body temperature, heart rate, blood pressure, blood gases, respiratory status, and diuresis.5,6 Seven variables (Apgar score at 1 minute, birth weight, presence of a congenital anomaly, and infant's age, serum pH, arterial partial pressure of oxygen, and heart rate at the time of the call) are used to generate the Mortality Index for Neonatal Transportation (MINT) score. Score for Neonatal Acute Physiology (SNAP-II) includes 6 physiologic items (blood pressure, temperature, PaO2/FiO2, serum pH, seizure, urine output); to this are added points for birth weight, low Apgar score, and small for gestational age to create a 9-item SNAP-Perinatal Extension-II (SNAP-PE-II). Transport Related Mortality Score (TREMS) is our proposed scoring system and it consists of 5 variables as follows: hypoglycemia, hypoxia, hypercarbia, hypotension, and hypothermia. Maximum points for MINT, SNAP-PE-II, and TREMS scores are 40, 47, and 5, respectively. In all scoring systems that were mentioned, the higher the points, the worse the outcome. The aim of the study was to evaluate the performance of the 3 scoring tools completed on admission to the neonatal intensive care unit (NICU) in predicting neonatal mortality.
METHODS Study Participants This prospective study was approved by the local ethics committee of the Ministry of Health, Izmir Tepecik Teaching and Research Hospital. All infants transferred to the NICU between January 1 and December 31, 2011, were included in the study. Infants with inborn error of metabolism, and outpatient referrals without neonatal transportation were excluded from the study. All infants were transported with ground vehicles by Neonatal Emergency Transport Service of Izmir 112 Emergency Call Center. The Neonatal Emergency Transport Service team consists of a physician and a paramedic who had certification of newborn resuscitation and interhospital transportation. The transport vehicles had equipment for basic newborn care such as an incubator with a mechanical ventilator, medical air and oxygen, as well as emergency medicines and resuscitation tools. Standard care guidelines were followed during the transport. The transport distance varied between 1 and 90 km at maximum and all infants
Pediatric Emergency Care • Volume 31, Number 2, February 2015
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
www.pec-online.com
113
Sutcuoglu et al
Pediatric Emergency Care • Volume 31, Number 2, February 2015
were stabilized before the transport at facilities with pediatricians/ neonatologists.
detected in 92 (30%) patients and hypercarbia was found in 55 (17.9%) newborns. The comparison of mortality-related risk factors in survivors and nonsurvivors was shown in Table 1. Gestational age and birth weight were statistically significant (P < 0.001) in terms of mortality; whereas sex, type of delivery, multiple gestation, using assisted reproductive techniques, CLD, and sepsis were not found statistically significant (P > 0.05). Mean scores of each system were significantly higher in non-survivors (P < 0.001). The correlation between the scoring systems and mortality was shown in Table 2. There was a strong negative correlation in MINT and SNAP-PE-II scoring systems (r = −0.64); whereas there was a medium negative correlation in TREMS (r = −0.50). However, there was a small negative correlation between gestational age and birth weight, and mortality (r = −0.25 and −0.24, respectively). Sensitivity, specificity, and positive and negative predictive values of scoring systems for predicting mortality are shown in Table 3. The sensitivity of MINT score was higher than SNAPPE-II and TREM. However, specificity for predicting mortality was higher in TREMS. The negative predictive value was highest in MINT score, whereas TREMS has the highest positive predictive value along with SNAP-PE-II. The ROC analysis was used to assess the accuracy of the scoring systems for predicting mortality. The AUC calculations from ROC were 0.92 for MINT, whereas 0.84 for SNAP-PE-II and TREMS (Fig. 1).
Demographic and Clinical Data Gestational age, birth weight, sex, birth center, type of delivery, multiple gestation, gestation via assisted reproductive techniques, blood gases, Apgar scores, blood glucose levels, blood pressure, congenital anomalies, and intubation before transportation were recorded. Intracranial hemorrhage, sepsis, and chronic lung disease (CLD) diagnosed during the follow-up were also recorded.
Calculation of Scores of MINT, SNAP-PE-II, and TREMS Scores of SNAP-PE-II, MINT, and TREMS of the all cases during the study period were calculated on admission. TREMS is a proposed scoring system of our study and it consists of 5 variables as follows: hypoglycemia, hypoxia, hypercarbia, hypotension, and hypothermia; the lowest score point being score “0”, and the highest point being score “5.” Hypoglycemia was defined as blood sugar below 45 mg/dL, hypoxia as pulse oximetry measurement of oxygen saturation below 85%, hypercarbia as PCO2 value in venous capillary blood gas analysis greater than or equal to 55 mm Hg, hypotension as gestational and postnatal agedependent blood pressure values below 10th percentiles, and hypothermia as body temperature below 36°C. The scoring systems of SNAP-PE-II, MINT and TREMS were compared with each other in terms of mortality risk.
Statistical Analyses
DISCUSSION
Statistical analyses were performed using SPSS 18.0 software. Independent samples t test, χ2 test, and Pearson correlation test were used and a P value less than 0.05 was considered statistically significant. To determine the specificity of the scoring systems, “receiver operating characteristic” (ROC) curve and the associated “area under the curve” (AUC) values were calculated. The AUC values are distributed between 0.5 and 1.0. If a parameter has a value of 0.5, it represents that the parameter has no any determinative value, whereas a value of 1.0 represents highly determinative value. To assess model fit, Hosmer-Lemeshow test was used. In addition, the sensitivity and specificity of the cutoff scores were investigated.
RESULTS A total of 306 newborn infants, which were transported to our institute, Izmir Tepecik Teaching and Research Hospital, from remote centers between January 1 and December 31, 2011, constituted the study population. The mean gestational age was 33.1 ± 5 weeks and the mean birth weight was 2031.2 ± 1018 g, and 183 (59%) babies were male. Birth weight of 27 (8.8%) cases was below 750 g, of 34 (11%) cases was 751 to 1000 g, and of 63 (20.5%) cases was 1001 to 1500 g. Ninety-three newborns were delivered vaginally, multiple gestational rate was 9.5% (29) and 8 gestations were achieved by assisted reproductive techniques. Congenital anomalies were present in 52 (17%) cases. The mean duration of hospitalization was 27.9 ± 29.7 days and overall mortality rate was 18.3% (56). The mean scores of MINT, SNAP-PE-II, and TREMS were 6.4 ± 6.3, 8.8 ± 12, and 1.3 ± 1.1, respectively. On admission, 183 (59.8%) cases were hypothermic, 33 (10.7%) cases were hypotensive, and 48 (15.6%) were hypoglycemic. Hypoxia was
114
www.pec-online.com
We proposed a scoring system for the prediction of neonatal mortality based on variables that reflect critical illness and stability and when abnormal are unfavorable conditions for neonatal transport. Although neonatal transport is one of the key components of neonatal-perinatal care in Turkey, transferred newborn infants in this study often experience loss of stability or clinical deterioration, regardless of their characteristics, and this was related to a higher mortality. Therefore, it is critical to optimize care strategies during all neonatal transports.7 The parameters used in scoring systems for predicting mortality in neonates are diverse such as gestational age, birth weight, ethnicity, sex, body temperature, heart rate, blood pressure, blood gas values, mechanical ventilation status, medications, diuresis, and other physiological data.5,6,8,9 However, to ensure an appropriate and reliable prediction, data should be easily obtained, and the cost amount should be low. Moreover, the system must be objective, noninvasive and is expected to be repeated. The timing of these scores may be at the time of referral, time of transport team's first assessment or on admission to NICU over the first 12 hours. Zupancic et al10 collected data for calculation of SNAP-II and SNAPPE-II during the first 12 hours after NICU admission. Similar studies collected data during the first 12 hours.6 In our study, we collected data on admission and during the first 12 hours. In the present study, the mean gestational age was 33.1 ± 5 weeks and the mean birth weight was 2031.2 ± 1018 g. Broughton et al9 reported that the median gestational age was 36 (24-43) weeks and the median birth weight was 2782 (5206140) g out of 2504 infants evaluated with MINT score. Likewise, Lee et al11 assessed the Transport Risk Index of Physiologic Stability (TRIPS) score in 1723 infants and found the mean gestational age 36 ± 5 weeks and the mean birth weight 2607 ± 1010 g. Similar to our study, both studies included all consequent patients regardless of gestational age. However, our study group © 2015 Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.
Pediatric Emergency Care • Volume 31, Number 2, February 2015
TREMS for Predicting Neonatal Mortality Risk
TABLE 1. The Comparison of Mortality-Related Risk Factors in Survivors and Nonsurvivors
Gestational age, wk* Birth weight, g* Sex, male, % Type of delivery (normal/sectio) Multiple gestation, % Assisted reproduction techniques, % Congenital anomaly, % Intraventricular hemorrhage, % CLD, % Sepsis, %
Survivors (n = 250)
Nonsurvivors (n = 56)
P
33.6 2152 148 (59) 74/176 24 (10) 8 (3) 30 (12) 42 (17) 31 (12) 68 (27)
30.4 1490 35 (14) 19/37 5 (2) 0 (0) 22 (9) 21 (8) 6 (2) 16 (6)
