Incidence of Fetal Macrosomia in the Cantonal Hospital Zenica: Retrospective Analysis (2020-2024)

Aim The aim was to determine the incidence and temporal trends of fetal macrosomia at Cantonal Hospital Zenica between 2020 and 2024. Methods A retrospective analysis of all deliveries (n = 11,117) from 2020-2024 was performed. Macrosomia was defined as birth weight ≥4,000 g. Descriptive statistics, trend analysis, and simple linear regression relating total births and macrosomic births were used. Neonatal outcomes included mean birth weight and one‑ and five‑minute Apgar scores; parity, maternal age groups, and mode of delivery were examined. Chi‑square tests assessed yearly distribution changes. Results Of 11,117 deliveries, 1,625 (14.6%) were macrosomic. Annual macrosomia counts declined from 369 (2020) to 295 (2024) while total deliveries fell from 2,478 (2020) to 2,083 (2023) and rose to 2,118 (2024). Mean macrosomic birth weight was 4,263.0 g (annual range 4,254.1-4,270.8 g). Mean one‑minute and five‑minute Apgar scores were 7.89 and 8.80, respectively; 5.91% of neonates had one‑minute Apgar <7 and 1.48% had five‑minute Apgar <7. Primiparas accounted for 38.22% of macrosomic births (621/1,625) and experienced a high cesarean rate (62.32%). Simple linear regression showed a strong association between total and macrosomic births (R = 0.973; R2 = 0.947; p = 0.0052), though interpretation of the model slope requires caution. Yearly changes in parity distribution, maternal age groups, and mode of delivery were not statistically significant (chi‑square tests, all p>0.05). Conclusions Macrosomia constituted a substantial and relatively stable proportion (~14.6%) of births at this referral center, with generally favorable immediate neonatal outcomes and a high cesarean proportion among primiparas. Findings support continued emphasis on antenatal metabolic risk optimization, individualized delivery planning, and maintenance of intrapartum and neonatal emergency preparedness. Study limitations include the short (five‑year) time series and lack of individual covariates (BMI, GDM status, gestational age), which constrain causal inference and adjusted analyses.