Dataset on neonatal and maternal factors influencing neurodevelopmental outcomes in preterm infants: A study focused on the healthcare context of Mashhad, Iran

This dataset offers an insight into the neurodevelopmental trajectories of preterm infants, encapsulating a wide array of neonatal and maternal factors. The data variables include demographic details alongside a detailed account of maternal health during pregnancy, encompassing aspects and other complications. Furthermore, the dataset documents neonatal health conditions. It also records critical indicators of neonatal health. The dataset is enriched with data on medical interventions and hospitalization details. It also contains information on the mother's drug usage during pregnancy and sonography results. A significant portion of the dataset is dedicated to the developmental assessment of the infants, utilizing the Bayley Scales to evaluate various domains such as cognitive, language, perceptual, fine motor, and coarse motor skills. The data are categorized to denote normal and abnormal outcomes in these domains, providing a detailed view of the developmental progress of the infants. The reuse potential of this dataset is substantial, serving as a rich resource for researchers and clinicians aiming to delve deeper into the multifaceted influences on preterm infant development. It can significantly contribute to the formulation of early intervention strategies, fostering a better understanding and enhancement of developmental outcomes in preterm infants.

This dataset offers an insight into the neurodevelopmental trajectories of preterm infants, encapsulating a wide array of neonatal and maternal factors.The data variables include demographic details alongside a detailed account of maternal health during pregnancy, encompassing aspects and other complications.Furthermore, the dataset documents neonatal health conditions.It also records critical indicators of neonatal health.The dataset is enriched with data on medical interventions and hospitalization details.It also contains information on the mother's drug usage during pregnancy and sonography results.A significant portion of the dataset is dedicated to the developmental assessment of the infants, utilizing the Bayley Scales to evaluate various domains such as cognitive, language, perceptual, fine motor, and coarse motor skills.The data are categorized to denote normal and abnormal outcomes in these domains, providing a detailed view of the developmental progress of the infants.The reuse potential of this dataset is substantial, serving as a rich resource for researchers and clinicians aiming to delve deeper into the multifaceted influences on preterm infant development.It can significantly contribute to the formulation of early intervention strategies, fostering a better un-

Value of the Data
• This dataset is a vital resource for researchers investigating the complex factors affecting preterm infants' developmental outcomes.• The data stands as a facilitative tool for meta-analyses and systematic reviews, aiding researchers in synthesizing existing findings and pinpointing gaps in current knowledge, steering the direction for future focused studies.

Background
The primary motivation for compiling this dataset was to explore the neurodevelopmental outcomes of preterm infants, with a particular focus on the healthcare context of Mashhad, Iran.This endeavor was rooted in a methodological framework that leverages the Bayley Scales of Infant and Toddler Development to assess a range of neonatal and maternal factors.
Our approach was driven by the theoretical understanding that a multitude of factors, including maternal health during pregnancy and neonatal conditions, play a critical role in shaping the developmental trajectories of preterm infants.The dataset, therefore, encompasses detailed demographic information, maternal health data during pregnancy, and neonatal health conditions, among other variables.
This data article complements the original research by providing a granular, data-driven view of the factors influencing neurodevelopmental outcomes in preterm infants.It serves as a resource for researchers and clinicians, offering a multidimensional perspective that underpins and extends the findings of the published research.By presenting the data in its raw, uninterpreted form, this article allows for diverse, independent analyses, thereby adding significant value to the existing research landscape.

Data Description
The dataset contains information on neonatal and maternal factors affecting the neurodevelopmental outcomes of preterm infants.It includes both categorical and continuous variables.The categorical variables represent the occurrence of various conditions and interventions, while the continuous variables represent scores or measurements taken at different time points.The dataset structure is capturing a wide range of variables that influence the health trajectories of neonates and relevant maternal factors.It includes data on pregnancy complications, birth conditions, hospitalization details, and developmental outcomes assessed using the Bayley Scales.The data are collected to ensure an understanding of the neonates' health trajectories and relevant maternal factors, facilitating a detailed analysis of the risk factors affecting neurodevelopmental outcomes in preterm infants [ Table 1 ].It should be noted that the term "NULL" appears frequently in the dataset.In this context, "NULL" signifies missing or unrecorded data for a particular variable.

Study framework
This research was structured as a retrospective cohort study, designed to scrutinize the myriad of neonatal and maternal factors that potentially influence the developmental outcomes of preterm infants [2] .

Participant Cohort
The study population included 89 preterm infants initially admitted to the NICU of Ghaem Hospital, Mashhad, between December 2016 and January 2020.These subjects were later reevaluated to assess their developmental status.
Inclusion Criteria Age greater than 2 months at the time of developmental assessment.
Complete medical records available for review.Availability of Bayley Scales of Infant and Toddler Development scores.Exclusion Criteria Incomplete medical records.Presence of severe congenital anomalies affecting neurodevelopment.Data collection was a multi-step process, focusing on different facets of neonatal and maternal health:

Methodology of data acquisition
The data acquisition process was orchestrated in several phases, each focusing on different aspects of neonatal and maternal health: Neonatal Data: This phase involved the collection of data pertaining to the neonates' gender, chronological age, and corrected age, a metric fine-tuned based on the gestational age at birth.
Maternal Data: This segment was dedicated to the exhaustive documentation of maternal aspects, encompassing a spectrum of complications encountered during pregnancy, inclusive of Diabetes Mellitus, preeclampsia, and hypothyroidism.Additionally, data on maternal drug usage and abnormal ultrasound findings during the gestational period were collated.
Birth Metrics: This segment focused on gathering data related to the neonates' birth conditions, encapsulating metrics such as birth weight, head circumference at birth, and the nature of labor, supplemented by Apgar scores documented at one and five minutes post-birth.
Hospitalization Chronicles: This phase chronicled an array of data points during the neonates' hospitalization period, encompassing medical interventions and complications encountered, thereby forming a rich repository of data for analysis.

Analytical framework
The analytical framework adopted for this study was grounded in the utilization of descriptive statistics, non-parametric tests, and binary logistic regression methodologies.Leveraging the capabilities of SPSS V.26 and R programming software, an analysis was undertaken to decipher patterns and correlations within the data.

Instrumentation
Central to the evaluation of neurodevelopmental outcomes is the utilization of the Bayley Scales of Infant and Toddler Development, a prominent instrument in pediatric assessment.This tool facilitates a granular analysis across a spectrum of developmental domains.
The Bayley Test stands as a pivotal tool extensively employed in scrutinizing the developmental trajectory in infants and toddlers [3] .It encompasses a broad spectrum of developmental aspects, including cognitive, motor, and linguistic capabilities, thereby establishing itself as a cornerstone in exhaustive developmental research.The test is structured around a series of tasks that scrutinize a child's motor proficiencies and cognitive abilities, alongside their comprehension and articulation of language.This deep-dive analysis furnished by the Bayley Test furnishes a panorama of a child's developmental stage, pinpointing potential areas of delay or concern.Consequently, it serves as a catalyst in formulating intervention strategies, amplifying the efficacy of early intervention services for children who necessitate additional developmental support [4][5][6] .
Furthermore, to offer a well-rounded perspective on the developmental milestones achieved by the subjects, results were delineated using two distinct parameters.Initially, each domain of the Bayley scale underwent an analysis to ascertain the Z-score, providing a statistical insight into a score's relation to the group's mean score (7).
Moreover, these Z-scores were subsequently categorized into "normal'' and "abnormal'' classifications to foster a clearer understanding of the developmental statuses.A Z-score of −1 or below in any specific domain was identified as a marker of potential developmental delays of varying degrees, labeled as "abnormal''.In contrast, scores exceeding −1 were denoted as "normal", signifying a regular developmental pathway.This bifurcated parameter approach not only facilitates a statistical evaluation but also offers a clinically pertinent interpretation of the developmental advancements in the neonates, thereby enriching the depth and practicality of the research outcomes.

Software utilization
Statistical analyses were performed using the R studio (version 3.5.3,R Core Team, 2019) and SPSS version 26 (SPSS Inc., Chicago, Illinois, USA).

Sample size determination
The initial phase of the study was orchestrated within the confines of the NICU at Ghaem hospital, Mashhad, characterized by a controlled environment conducive to neonatal care.
Using the data obtained from the study conducted by Sung Ho Ahn ( 2 ), where the prevalence of cognitive, language, and motor delays in preterm children was found to be 38%, 26%, and 35% respectively, the sample size was calculated considering an alpha of 0.05 and d = 0.3p for these outcomes, resulting in respective sample sizes of 70, 122, and 80.The calculations were as follows: Alpha ( α): 0.05, Critical value (Z1-α/2): 1.96, Margin of error (d): 0.078, Proportion (p): 0.26, Sample size (n): 122

Limitations
The primary limitation encountered during this study was the extensive number of variables that were scrutinized, which necessitated a substantial allocation of resources, both in terms of budget and personnel.This, unfortunately, restricted us from expanding the sample size to a more extensive cohort, which could have potentially offered a more insight into the developmental trajectories of preterm infants.

Ethics Statement
This research was conducted in strict adherence to the principles outlined in the Declaration of Helsinki.All procedures involving human participants were reviewed and approved by the Dataset link: development preterm infant (Original data) Keywords: Bayley scales of infant and toddler development Neonatal intensive care unit (NICU) Intrauterine growth restriction (IUGR) Bronchopulmonary dysplasia (BPD) Gestational age Pneumothorax a b s t r a c t

Table 1
variables in dataset.