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Abstract Objective To evaluate the risk factors for postpartum hemorrhage (PPH) according to the Robson Classification in a low-risk maternity hospital. Methods We conducted retrospective cohort study by analyzing the medical records of pregnant women attended in a low-risk maternity hospital, during from November 2019 to November 2021. Variables analyzed were: maternal age, type of delivery, birth weight, parity, Robson Classification, and causes of PPH. We compared the occurrence of PPH between pregnant women with spontaneous (Groups 1 and 3) and with induction of labor (2a and 4a). Chi-square and Student t-tests were performed. Variables were compared using binary logistic regression. Results There were 11,935 deliveries during the study period. According to Robson’s Classification, 48.2% were classified as 1 and 3 (Group I: 5,750/11,935) and 26.1% as 2a and 4a (Group II: 3,124/11,935). Group II had higher prevalence of PPH than Group I (3.5 vs. 2.7%, p=0.028). Labor induction increased the occurrence of PPH by 18.8% (RR: 1.188, 95% CI: 1.02-1.36, p=0.030). Model including forceps delivery [x2(3)=10.6, OR: 7.26, 95%CI: 3.32-15.84, R2 Nagelkerke: 0.011, p<0.001] and birth weight [x2(4)=59.0, OR: 1.001, 95%CI:1.001-1.001, R2 Nagelkerke: 0.033, p<0.001] was the best for predicting PPH in patients classified as Robson 1, 3, 2a, and 4a. Birth weight was poor predictor of PPH (area under ROC curve: 0.612, p<0.001, 95%CI: 0.572-0.653). Conclusion Robson Classification 2a and 4a showed the highest rates of postpartum hemorrhage. The model including forceps delivery and birth weight was the best predictor for postpartum hemorrhage in Robson Classification 1, 3, 2a, and 4a. (PPH lowrisk low hospital 201 2021 age parity Groups . 4a) Chisquare Chi square ttests t tests performed regression 11935 11 935 11,93 period Robsons s 482 48 2 48.2 5,750/11,935 575011935 5 750 261 26 26.1 3,124/11,935. 312411935 3,124/11,935 124 3,124/11,935) 3.5 35 (3. vs 27 7 2.7% p=0.028. p0028 p p=0.028 0 028 p=0.028) 188 18 8 18.8 RR (RR 1188 1.188 95 CI 1.021.36, 102136 1.02 1.36, 02 36 1.02-1.36 p=0.030. p0030 p=0.030 030 p=0.030) x23=10.6, x23106 x x2 =10.6, 10 6 [x2(3)=10.6 OR 726 7.26 95%CI 95CI 3.3215.84, 3321584 3.32 15.84, 32 15 84 3.32-15.84 R Nagelkerke 0011 011 0.011 p<0.001 p0001 001 x24=59.0, x24590 4 =59.0, 59 [x2(4)=59.0 1001 1.001 95%CI1.0011.001, 95CI10011001 95%CI:1.001-1.001 0033 033 0.033 area curve 0612 612 0.612 0.5720.653. 05720653 0.572 0.653 572 653 0.572-0.653) 20 202 1193 93 11,9 48. 5,750/11,93 57501193 75 26. 31241193 3,124/11,93 12 3. (3 2.7 p002 p=0.02 18. 118 1.18 9 021 1.021.36 10213 102 1.0 136 1.36 1.02-1.3 p003 p=0.03 03 x23 x23=10.6 x2310 106 =10.6 [x2(3)=10. 72 7.2 3215 3.3215.84 332158 332 3.3 1584 15.84 3.32-15.8 01 0.01 p<0.00 p000 00 x24 x24=59.0 x2459 590 =59.0 [x2(4)=59. 100 1.00 CI1 95%CI1.0011.001 95CI1001100 95%CI:1.001-1.00 003 0.03 061 61 0.61 5720 0.5720.653 0572065 0572 0.57 0653 0.65 57 65 0.572-0.653 119 11, 5,750/11,9 5750119 3124119 3,124/11,9 ( 2. p00 p=0.0 1.1 1.021.3 1021 1. 13 1.3 1.02-1. x23=10. x231 =10. [x2(3)=10 7. 321 3.3215.8 33215 33 158 15.8 3.32-15. 0.0 p<0.0 x24=59. x245 =59. [x2(4)=59 95%CI1.0011.00 95CI100110 95%CI:1.001-1.0 06 0.6 0.5720.65 057206 057 0.5 065 0.572-0.65 5,750/11, 575011 312411 3,124/11, p0 p=0. 1.021. 1.02-1 x23=10 =10 [x2(3)=1 3.3215. 3321 15. 3.32-15 0. p<0. x24=59 =59 [x2(4)=5 95%CI1.0011.0 95CI10011 95%CI:1.001-1. 0.5720.6 05720 05 0.572-0.6 5,750/11 57501 31241 3,124/11 p=0 1.021 1.02- x23=1 =1 [x2(3)= 3.3215 3.32-1 p<0 x24=5 =5 [x2(4)= 95%CI1.0011. 95CI1001 95%CI:1.001-1 0.5720. 0.572-0. 5,750/1 5750 3124 3,124/1 p= x23= = [x2(3) 3.321 3.32- p< x24= [x2(4) 95%CI1.0011 95CI100 95%CI:1.001- 0.5720 0.572-0 5,750/ 575 312 3,124/ [x2(3 [x2(4 95%CI1.001 95CI10 95%CI:1.001 0.572- 5,750 31 3,124 [x2( 95%CI1.00 95CI1 95%CI:1.00 5,75 3,12 [x2 95%CI1.0 95%CI:1.0 5,7 3,1 [x 95%CI1. 95%CI:1. 5, 95%CI1 95%CI:1