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An individual scoring system for the prediction of postpartum anaemia
Annales Franc¸aises d’Anesthe´sie et de Re´animation 32 (2013) e1–e7
Original article
An individual scoring system for the prediction of postpartum anaemia Score individuel pour pre´diction de l’ane´mie du postpartum J. Allary a,c, J.-F. Soubirou a, J. Michel a,c, I. Amiel a,c, V. Silins a,c, C. Brasher a,c, J.-F. Oury b,c, Y. Nivoche a,c, S. Dahmani b,c,d,* a
Department of Anaesthesia, Intensive care and Pain Management, Robert-Debre´ University Hospital, AP–HP, 48, boulevard Serurier, 75019 Paris, France Robert-Debre´ University Hospital, 48, boulevard Serurier, 75019 Paris, France Paris Diderot University (Paris-7), PRES Paris Sorbonne Cite´, 75000 Paris, France d UMR Inserm U 676, Robert-Debre´ University Hospital, 75019 Paris, France b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 April 2012 Accepted 9 November 2012
Background. – Postpartum anaemia (PPA) is a common postpartum complication. The goal of this study was to prospectively construct a predictive score for individual risk of PPA. Patients et method. – We prospectively analyzed factors associated with PPA (< 10 g dL 1 at 48 hours postpartum). Parameters analyzed were demographic data, pregnancy characteristics, delivery and postpartum characteristics. Univariate analysis was performed using Anova or X2; the Cox model was used for multivariate analysis. The scoring system was validated using ROC curve. Results. – Analysis was performed in 475 patients and validation was carried using an additional 95 patients. Multivariate analysis found four factors independently associated with PPA: anaemia during the third trimester of the pregnancy, Southeast Asian ethnic origin, episiotomy and severe postpartum haemorrhage (PPH) identified by the use of sulprostone. According to the score derived from the Cox model, patients were classified as low (22%, score = 0), medium (55%, score = 2 or 3) and high (86%, score > 3) probability of PPA. Using the AUC of the ROC curve for both the first and the validation cohorts (performed on 95 further patients), we recorded AUCs of 72% and 70% respectively. Conclusions. – This study allowed the derivation and validation of a predictive score of PPA. This score might be useful in targeting prophylactic strategies for PPA. Such strategies could include a more active treatment of iron deficiency (increasing oral iron treatment observance or intravenous iron therapy) especially in exposed population, improvement in the prevention and treatment of postpartum haemorrhage and decreasing the use of episiotomy. Future studies must focus on the external validation and generalisation of this scoring system. ß 2012 Socie´te´ franc¸aise d’anesthe´sie et de re´animation (Sfar). Published by Elsevier Masson SAS. All rights reserved.
Keywords: Postpartum anaemia Sulprostone Postpartum haemorrhage Ethnic differences Episiotomy Transfusion
R E´ S U M E´
Mots cle´s : Ane´mie du postpartum Sulprostone He´morragie du postpartum Diffe´rence e´thique E´pisiotomie Transfusion
Introduction. – L’ane´mie du postpartum (APP) est courante apre`s accouchement. L’objectif de ce travail a e´te´ d’e´tudier les facteurs de risques de sa survenue. Patientes et me´thodes. – Il s’agit d’une e´tude prospective observationnelle e´tudiant les facteurs de risques de survenue d’une APP (Hb < 10 g/dL) dans les 48 heures apre`s un accouchement. Les facteurs e´tudie´s e´taient : les donne´es de´mographiques, les caracte´ristiques de la grossesse, de l’accouchement et du postpartum. L’analyse s’est faite par une analyse univarie´e par Anova ou X2 et a e´te´ suivie par une analyse multivarie´e par mode`le de Cox pour la construction du mode`le. Une analyse par courbe ROC a e´te´ utilise´e pour la validation. Re´sultats. – L’analyse a e´te´ re´alise´e sur 475 patientes et la validation sur un autre e´chantillon de 95 patientes. L’analyse multivarie´e a permit de retrouver quatre facteurs inde´pendamment associe´s a` la survenue d’une APP : l’ane´mie du troisie`me trimestre de la gestation, l’origine ge´ographique du sud-est asiatique, l’e´pisiotomie et l’he´morragie du postpartum identifie´e par l’administration de sulprostone. A` partir de ce mode`le, un score a e´te´ e´tabli permettant de classer les patientes en risque faible (22 %,
* Corresponding author. Department of Anaesthesia, Robert-Debre´ Hospital, 48, boulevard Serurier, 75019 Paris, France. E-mail address: [email protected] (S. Dahmani). 0750-7658/$ – see front matter ß 2012 Socie´te´ franc¸aise d’anesthe´sie et de re´animation (Sfar). Published by Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.annfar.2012.11.002
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score = 0), interme´diaire (55 %, score = 2 ou 3) et e´leve´ (86 %, score > 3) d’APP. Les aires sous la courbe de ce score applique´ a` la population de construction et de validation e´taient respectivement de 72 % et 70 %. Conclusion. – Cette e´tude a permis la construction et la validation d’un score pre´dictif de la survenue d’une APP. Il pourrait permettre la mise en place de mesures prophylactiques. Ces mesures pourraient inclure la lutte contre le de´ficit en fer (augmentation de l’observance du traitement oral ou par institution d’un traitement cible´ par fer injectable) dans la population a` risque, une meilleure prise en charge des he´morragies du postpartum, une diminution du recours a` l’e´pisiotomie. De futures e´tudes sont ne´cessaires afin d’assurer la validation externe de ce score. ß 2012 Socie´te´ franc¸aise d’anesthe´sie et de re´animation (Sfar). Publie´ par Elsevier Masson SAS. Tous droits re´serve´s.
1. Introduction Postpartum anaemia (PPA) is a common complication occurring after 35 to 75% of deliveries [1]. This wide range of incidence values varies according to the geographic area where studies were performed and the haemoglobin concentration cut-off values defining anaemia. Many factors have been previously found to be associated with this complication. Of these, postpartum haemorrhage (PPH) is the most important one [2]. However, other factors such as iron deficiency [3], haemoglobinopathy [4], preeclampsia, haemolysis [5], and low socioeconomic status have also been found to influence the incidence of PPA [6]. PPA has been found to negatively impact upon outcomes in both mothers and newborns, since it increases morbidity, postpartum fatigue, stress and depression [7,8]. Anaemia has also been shown to increase preterm delivery and low birth weight [8]. In addition, these phenomena are very likely to alter the mother-newborn relationship, which could result in long-term physical and psychological disabilities for both. Despite the amount of retrospective publications analysing PPA incidence and risk factors of PPA, there have been no prospective risk factor studies. Moreover, no reliable score is available to individually predict PPA occurrence [2,6–10]. The primary aim of this study was to prospectively construct and validate an individual score predicting PPA probability. 2. Patients and methods This study was approved by the Institutional Review Board of Paris North Hospitals, Paris-7 University, AP–HP (N# IRB0006477) and informed consent was obtained from all subjects. The study took place in a tertiary level III (management of complicated pregnancy) university hospital, where 3035 deliveries were performed in 2010. The study included all consecutive parturient delivered in our institution during consecutive 4-month period who accepted to participate in the study. 2.1. Women care policy According to our local protocols, all patients were given folic acid (Speciafoldine1, Sanofi-Aventis, France: 5 mg/d) and iron supplements (Fumafer1, Sanofi-Aventis, France: 66 mg/d, from the first trimester until delivery). This treatment was continued in the postpartum period if serum haemoglobin concentration was less than 10 g dL 1 during the postpartum period. Screening for anaemia and thrombocytopaenia was systematically performed during the first and last trimesters. Clinical evaluation and foetal imaging were performed every trimester. PPH was managed according to recommended protocols. Detection of this complication was based on clinical estimation of an abnormal blood loss, hypotension, tachycardia or altered level of conscience. However, it was not triggered by objective determination of blood losses because of the unusual use of systematic blood collector bag in our institution during the study period. Management of PPH relies in
the first place by intravenous fluid load, active warming, antibioprophylaxis, Oxytocin treatment, speculum examination, bimanual examination of the uterus and manual removal of placental pieces that remain in the uterus, bladder emptying and determination of haemoglobin and platelets levels and haemostasis status (fibrinogen and activated prothrombin time). In a second line, if PPH persists after 30 minutes, sulprostone infusion was stated (Nalador1, Bayer Sante´, Loos, France), intravenous fluid load and transfusion of packed red cell and frozen plasma with a ration 1 to 1 when haemoglobin level fall below 10 g dL 1. In addition to its systematic administration during packed red cell transfusion, frozen fresh plasma was administered when aPT decreased bellow 50%. Platelets and fibrinogen therapy were given according to results of platelets (< 50,000/mm3) and fibrinogen (< 2 g.L 1) [11–15]. Uterine artery embolization is not available in our institution. Consequently, management of uncontrolled PPH was dependant of the haemodynamic status of patients. When patients were stable, they were transferred in structures with an available interventional radiologic team. Otherwise, surgical management of PPH was local and relies on uterus compression sutures, arterial ligations and hysterectomy when facing a life threatening haemorrhage. Except during active PPH, standard care policy for transfusion in our institution is based upon anaemia tolerance and haemoglobin level measured at 48 hours after delivery. For PPA between 7 and 10 g dL 1, patients were transfused where signs of clinical intolerance were observed (defined by tachycardia, asthenia or tachypnoea either at rest or at movement). Otherwise they received intravenous (300 mg twice at 48 hours interval) and subsequent oral iron therapy. Patients were systematically transfused when postpartum haemoglobin was bellow 7 g dL 1. 2.2. Determination of postpartum anaemia Haemoglobin concentrations were measured systematically 48 hours postpartum by ward nurses using the Hemocue 201+ß system (HemoCue AB, A¨ngelholm, Sweden) [16]. According to our local standardized routine protocol, this was considered more easily to perform, less cost effective and less invasive for patients as a systematic generalized screening test in comparison to the laboratory determination of haemoglobin. However, laboratory haemoglobin measurement was systematically performed in cases of low capillary haemoglobin indicating transfusion or after caesarean section. Capillary haemoglobin measurement technique was standardized in all patients. After skin disinfection, a 24-gauge needle puncture was performed on the lateral aspect of the fourth or fifth finger pulp and two samples of blood were analyzed. The haemoglobin level recorded for each patient was the mean of these two measurements. If the two values differed by more than 10%, a third measurement was performed and the mean of the three measurements recorded. According to World Health Organization standards, a haemoglobin level of 11 g dL 1 during the first and third trimesters and 10 g dL 1 in the postpartum period, were considered the thresholds to define anaemia [1,17].
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2.3. Recorded data The study prospectively and anonymously analyzed factors relevant in the occurrence of PPA, defined as a haemoglobin level strictly bellows 10 g dL 1 48 hours after delivery. This time was considered because of the decreased influence of peripartum fluid loads on haemoglobin level at this time [18]. Considered factors for PPA were: mother’s demographic data (age, weight, ethnic origin, haemoglobin in the first and third trimesters), mother’s socioeconomic status (using the validated EPICES score [19], developed by the French National Health Insurance Institution), pregnancy characteristics (gestate, parity and abnormal placenta insertion), delivery characteristics (duration of labour, mode of delivery, episiotomy) and complications during delivery (severe PPH defined by the use of sulprostone, vaginal or corporeal lesions). An exhaustive list of recorded factors is summarized in Table 1. Patients were excluded from the analysis if any of the above data was lacking. 2.4. Statistical analysis As recommended for score building, a construction and a validation cohort had to be used. The number of patients sample to be included in this study was based on previous studies and was set
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to 500 (corresponding to 2 months of data recording). Consequently, a 3-month data recording was performed as a security in case of discarded patients [20]. A validation cohort of 25% of the constructed cohort was judged necessary for the statistical analysis. Given a stable number of deliveries during the considered period of the study, data from patients included during the first three months were used to construct the score while data from the remaining patients were used for validation purpose. The data obtained were analysed using the statistical package SPSS 15.0 (IBM, Chicago, Illinois, USA). These statistics used mean and standard deviation (SD) or percentages (%) for continuous or categorical variables, respectively. Anova and X2 were used for univariate analysis of continuous and categorical variables respectively. Significant parameters in the univariate analysis were then included in a multivariate Cox model. In order to obtain an easy to use scoring system, two mathematical operations were used. First, partial Cox regression coefficients for significant parameters were divided by the coefficient of the parameter with the highest regression coefficient. These derived coefficients were multiplied by a common number to obtain a rounded (and easy to use) number for each parameter of the score. Second, in order to define the probability of PPA according to this scoring system, ranges of scores were grouped by including in the same group ranges with comparable incidence of
Table 1 Descriptive statistics in the first and validation cohorts. Patient’s characteristics
First cohort n = 475
Validation cohort n = 95
Statistics (X2, P) or P*
Age (years), mean sd Gestational age (weeks), mean sd Gestity (n), mean sd Parity (n), mean sd Ethnical origin Europe, n (%) North African, n (%) Sub-Saharan, n (%) Southeast Asian, n (%) Central Asian, n (%) American, n (%) Caribbean, n (%) EPICES score EPICES quartiles (Q) Quartile 1, n (%) Quartile 2, n (%) Quartile 3, n (%) Quartile 4, n (%) Quartile 5, n (%) Preeclampsia, n (%) Previous Caesarean, n (%) Multiples foetuses, n (%) Abnormal placenta insertion Haemoglobin 1st trimester (g dL 1), mean sd Haemoglobin 1st trimester < 11 g dL 1, n (%) Haemoglobin 3rd trimester (g dL 1), mean sd Haemoglobin 3rd trimester < 11 g dL 1, n (%) Induced delivery Duration of stage 1 (min), mean sd Duration of stage 2 (min), mean sd Instrumental delivery, n (%) Vaginal lesions, n (%) Episiotomy, n (%) Caesarean, n (%) Manual removal of placental, n (%) Bimanual examination of the uterus, n (%) Postpartum sulprostone infusion, n (%) Transfusion, n (%) Intravenous iron therapy Postpartum haemoglobin (g dL 1), mean sd Postpartum haemoglobin < 10 g dL 1, n (%) Duration of hospitalisation (days), mean sd
38 3 38 5 2.6 1.5 21
31 5.5 39 2.7 2.3 1.3 21
NS NS NS NS
166 (34.9) 140 (29.5) 97 (20.4) 37 (7.8) 9 (1.9) 8 (1.7) 18 (3.8) 31 24
40 (42.3) 23 (24.2) 21 (22.1) 5 (5.3) 0 (0) 0 (0) 6 (6.3) 29 26
X2 = 1.7; NS X2 = 1; NS X2 = 0.14; NS X2 = 0.7, NS X2 = 1.8; NS X2 = 1.6; NS X2 = 1.2*; NS P = 0.5
63 (13.3) 111 (23.4) 101 (21.1) 89 (18.7) 111 (23.4) 29 (6) 80 (16.9) 11 (2.7) 13 (2.7) 11.8 1 88 (18.5) 11.5 1.2 124 (26.1) 119 (25.1) 142 122 164 154 82 (17.3) 140 (29.4) 128 (27) 87 (18.3) 146 (30.7) 339 (71.4) 17 (3.5) 11 (2.3) 39 (8) 10 1.6 205 (43.2) 6.2 5.8
22 (23.2) 24 (25.3) 11 (11.6) 14 (14.7) 24 (25.3) 12 (12.4) 17 (17.9) 6 (6.2) 0 (0) 12.4 1 19 (20.6) 11.6 2 27 (29.4) 24 (25.3) 144 115 162 150 17 (16.3) 25 (26.3) 32 (33.7) 19 (20) 29 (30) 61 (64) 3 (3.2) 0 (0) 8 (8) 10 1.4 38 (402) 6.3 3.7
X2 = 6.1; P = 0.01 X2 = 0.1; NS X2 = 4.7; P = 0.02 X2 = 0.8; NS X2 = 0.04*; NS X2 = 5; P = 0.03 X2 = 0.06, NS X2 = 4.3; P = 0.048 X2 = 2.6; NS P = 0.001 X2 = 0.11; NS NS X2 = 0.4; NS X2 = 0.002; NS NS NS X2 = 0.01; NS X2 = 0.4; NS X2 = 1.7; NS X2 = 0.15; NS X2 = 0.002; NS X2 = 2; NS X2 = 0.04; NS X2 = 2.2; NS X2 = 0.02; NS NS X2 = 0.3; NS 0.4
Data are expressed as mean sd or percentages (%). NS: non-significant. Comparisons are displayed as X2 and value of P (when P was bellow 0.05) or as P (when P was bellow 0.05), for X2 and Student-t test, respectively.
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Table 2 Univariate analysis of factors associated with postpartum anaemia of less than 10 g/dL Patient’s characteristics
Postpartum haemoglobin 10 g dL (n = 205)
Age (years), mean sd Gestationnal age (weeks), mean sd Gestity (n), mean sd Parity (n), mean sd Ethnical origin Europe, n (%) North African, n (%) Sub-Saharan, n (%) Southeast Asian, n (%) Central Asian, n (%) American, n (%) Caribbean, n (%) EPICES score, mean sd EPICES quartiles (Q) Q1, n (%) Q2, n (%) Q3, n (%) Q4, n (%) Q5, n (%) Preeclampsia, n (%) Previous ceasarian, n (%) Multiples foetuses, n (%) Abnormal placenta insertion, n (%) Haemoglobin 1st trimester < 11 g dL 1, n (%) Haemoglobin 3rd trimester < 11 g dL 1, n (%) Induced delivery, n (%) Duration of stage 1 (min), mean sd Duration of stage 2 (min), mean sd Instrumental delivery, n (%) Vaginal lesions, n (%) Episiotomy, n (%) Caesarean, n (%) Bimanual examination of the uterus, n (%) Postpartum sulprostone therapy, n (%)
1
. Postpartum haemoglobin < 10 g dL (n = 270)
1
P 1
31 5 38 3 2.7 1.6 21
31 6 39 3 2.6 1.5 21
NS NS NS NS
96 (35.6) 76 (28.1) 60 (22.2) 12 (4.4) 7 (2.6) 5 (1.9) 14 (5.2) 30 24
70 (34.1) 64 (31.2) 37 (18) 25 (12.2) 2 (1) 3 (1.5) 4 (2) 31 24
X2 = 0.1; X2 = 0.5; X2 = 1.2; X2 = 9.7; X2 = 1.6; X2 = 0.1; X2 = 3.3; NS
36 (13.3) 63 (23.3) 59 (21.9) 50 (18.5) 62 (23) 22 (8.1) 45 (16.7) 7 (2.6) 5 (1.9) 15 (5.6) 39 (14.4) 62 (23) 128 122 146 143 40 (14.8) 85 (31.5) 51 (18.9) 52 (19.3) 189 (70) 2 (0.7)
27 (13.2) 48 (23.4) 42 (20.5) 39 (19) 49 (23.9) 7 (3.4) 35 (17.1) 4 (2) 8 (3.9) 73 (35.6) 85 (1.5) 57 (27.8) 159 120 188 165 42 (20.5) 55 (26.8) 77 (37.6) 35 (17.1) 150 (73.2) 15 (7.3)
X2 = 0.003; NS X2 = 0.001; NS X2 = 0.1; NS X2 = 0.2; NS X2 = 0.06; NS X2 = 4.6; 0.027 X2 = 0.014; NS X2 = 0.2; NS X2 = 1.8; NS X2 = 69.7; 0.001 X2 = 44; 0.001 X2 = 1.4; NS 0.014 0.04* X2 = 2.6; 0.026 X2 = 1.2; NS X2 = 20.6; < 0.0001 X2 = 0.4; NS X2 = 0.6; NS X2 = 14.5; < 0.0001
NS NS NS 0.001 NS NS NS
Data are expressed as mean sd or percentages (%). NS: non-significant. Comparisons are displayed as X2 and value of P (when P was bellow 0.05) or as P (when P was bellow 0.05), for X2 and Student-t test, respectively.
PPA. Incidence of PPA according to the defined score ranges was then calculated for each patient in the validation cohort. Validation of the score was performed using the ROC method with computation of the area under curve (AUC), sensitivity, specificity, positive predicted value and negative predictive value for each level of probability. An AUC of 1.0 indicates that the model discriminates perfectly between patients with different scores, whereas a value of 50% is indicating that the model contains no predictive information [21,22]. The internal validity of the score was considered if the AUC was higher or equal to 70%. 3. Results Five hundred and twenty-seven patients were enrolled in the first cohort, and 102 in the second. Complete data were obtained from 475 (88.4%) and 95 (93.1%) patients for the two cohorts respectively. Patient characteristics for each cohort are described in Tables 1 and 2 respectively. In the construction cohort, anaemia during the first trimester, third trimester, and postpartum period were found
in 88 (18.5%), 124 (26.1%) and 205 (43.2%) patients, respectively. Eleven (2.3%) patients were transfused before the second postpartum day and all were anaemic at 48 hours (Hb level < 10 g dL 1). Among them, six were associated with severe PPH (based on sulprostone administration) and five were not. Seventeen and three patients received sulprostone therapy in the construction and validation cohorts, respectively. No one had to be transferred in another structure for uterine artery embolisation. PPA had no influence on duration of hospitalization (6.3 versus 6.2 days, P > 0.05). Univariate analysis allows determining anaemia during the first and third trimester, Southeast Asian ethnic origin, postpartum use of sulprostone, episiotomy, increased duration of the first and second stage of labor, pregnancy without preeclampsia and instrumental delivery as statistically associated with PPA (Table 2). Multivariate analysis found four risk factors independently associated with the occurrence of PPA: anaemia during the third trimester, Southeast Asian ethnic origin, postpartum use of sulprostone and episiotomy. According to Cox analysis a scoring system was derived. This consists on coding two for anaemia during the third trimester and
Table 3 Cox multivariate analysis with derived hazard ratio (EXP (B)), partial correlation coefficient and coefficient of the model in the score of postpartum anaemia.
Southeast Asian origin Haemoglobin level during the 3rd trimester < 11 g dL 1 Episiotomy Postpartum sulprostone infusion
Exp (B) (Hazard ratio)
95% Confidence interval for Exp (B)
P
Cox Regression Coefficient
Coefficient in the model
1.66 2.25
1.08–2.54 1.69–2.99
0.02 < 0.0001
0.507 0.813
3 2
1.77 1.78
1.3–2.36 1.04–3
< 0.0001 0.034
0.572 0.577
3 3
J. Allary et al. / Annales Franc¸aises d’Anesthe´sie et de Re´animation 32 (2013) e1–e7 Table 4 Calculated percentages of postpartum anaemia in the first and the validation cohort according to the ranges of scores. Score
Construction cohort (%)
Validation cohort (%)
P
0 2 or 3 >3
22 55 86
21.4 52 72
NS NS NS
three for the other three factors (Table 3). According to this classification, anaemia during the third trimester seems statistically less important in the development of PPA than the remaining three others. Individual probabilities for PPA were calculated on the first cohort. Patients were best classified as low (22%, score = 0, no
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predictive factor), medium (55%, score = 2 or 3, one predictive factor) and high (> 86%, score > 3, two to four predictive factors) probability of PPA (Table 4). In this derived cohort, the AUC of the ROC curve of the score for the prediction of PPA was 72 0.024% (95% confidence interval 71.9 to 72.05; Fig. 1). Concerning the validation cohort, it was similar to the construction one except for the Epices scores (quartiles 1 and 3), the incidence of preeclampsia, and the haemoglobin levels during the first trimester of pregnancy (Table 1). However, these differences were unlikely to influence our results while they did not concern predictors or PPA incidence. In addition, they were not clinically relevant for Epices score (the 4th and 5th quartiles defining a low socioeconomic status were not statistically different and incidence of anaemia during the third trimester of pregnancy was similar in the two cohorts). Application of the score defined in the construction cohort to the validation one found probabilities of PPA in the validation cohort of: 21.4%, 52.3% and 72% for score 0, 2 or 3 and > 3, respectively (Table 4). Comparing the individual probability of PPA obtained in the two cohorts, found no significant difference between incidences of PPA between the two cohorts (Table 4). The AUC of the ROC curve for this score in this validation cohort was 70 0.05% (confidence interval 69.9 to 70.1%; Fig. 1).
4. Discussion
Fig. 1. Receiver operating characteristics (ROC) curves of the postpartum anaemia score performed on the constructed and validation cohorts.
In this study, we construct and validate an individual scoring system for PPA prediction. This score included four independent risk factors: Southeast Asian ethnic origin, anaemia during the third trimester, severe PPH identified by the use of sulprostone and episiotomy. According to varied statistics [23], incidence of PPA in developed countries, is ranging between 12 and 15.5 [2]. Since the incidence of PPA in our study was 40%, many factors may explain the discrepancy with previously published data. Firstly, the methodology used in our study was prospective, which was not the case in many others that focused on this topic, factor that might underestimate the true incidence of PPA in those previous studies. Secondly, our population was ethnically heterogeneous: 35% of patients were of Caucasian origin, 7.8% of Asian origin, and 48.8% of African origin, which might increased the incidence of pregnancy anaemia in comparison to more homogenous Caucasian populations. Interestingly, despite a greater incidence of haemoglobinopathies (such as sickle cell disease or thalassemias) in African population, these patients were not identified as a risk factor of PPA by our analysis. This might result from a greater attention given to these patients in anaemia prevention consisting in a more active follow-up and education leading to diagnostic of anaemia and increase of iron substitution observance. Our study identified four predictive risk factors for PPA: Southeast Asian ethnic origin, anaemia during the third trimester, severe PPH (defined by the postpartum use of sulprostone) and episiotomy. Concerning Southeast Asian ethnic origin, there are three possible explanations for this risk factor influence upon PPA: lower iron stores in this population in comparison to others, a higher incidence of PPH and the presence of haemoglobinopathies (a and b Thallasemia, Haemoblobin E and Haemoglobin Constant Spring) [24]. Rates of sulprostone administration or episiotomy were not different between different ethnic origins. Consequently, it is unlikely that PPH might be involved in this result. High proportion of PPA observed in Asian population might have involved iron deficit. However, this hypothesis is difficult to confirm in our study while plasma ferritin was not determined. In addition, proportion of patients presenting anaemia during the third trimester was only 16.2% in the Southeast Asian patients while it closes to 27% in other
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ethnic groups (non-significant). Socioeconomic status may also be suspected to contribute to the relationship between Southeast Asian ethnic origin and PPA. Seventy percent of Southeast Asian patients were in the 4th and 5th quartiles (defined as poor socioeconomic status) in comparison to the 40% in others ethnic group. However, this seems unlikely as other socioeconomically disadvantaged populations such as Sub-Saharan African patients were not at greater risk of PPA while 70% of those patients were also classified as low socioeconomic status (in comparison to 35% in other ethnic groups). Furthermore, multivariate analysis did not identify low socioeconomic status as an independent predictor of this complication. Finally, up to 60% of Southeast Asian population presents one or multiple haemoglobinapathy that might account in genesis of PPA in this ethnic group [24]. Anaemia during the third trimester is the second independent predictor of PPA. The threshold chosen to define this risk factor was that defined by the WHO. However, this threshold is not uniformly employed in all studies leading to substantial differences in observed incidences. Furthermore, as mentioned above, the high prevalence of third trimester anaemia may largely explain the high proportion of PPA found in our study. Sulprostone use was also an independent predictor of PPA. Sulprostone is used during severe intrauterine PPH and might represent a surrogate marker of this event, which is well documented as a risk factor of PPA [2]. Interestingly, some classical relevant factors in the genesis of PPA, namely preeclampsia, previous caesarean section, multiple foetuses, and vaginal lesions were not found as predictive of PPA anaemia in our analysis. This might result from the limited study sample that could have underpowered our analyses concerning many factors. This might also result from a more active preventive strategy against PPA applied to these patients. This hypothesis is clearly illustrated concerning preeclampsia. Proportion of anaemia during the third trimester among preeclamptic patients was 10.3% and was statistically less than non-preeclamptic patients (27.1%, P = 0.03). Haemoconcentration observed during severe preeclampsia might also explain the lower incidence of anaemia in this population. Moreover, this might also explain the absence of SubSaharan population as independent risk factor of PPA while 9.1% of this population exhibited preeclampsia while none had this condition in the southeast ethnic group (P = 0.049). The predictive score for PPA has been determined in a derivation cohort. This score was then used in a new sample of patients (validation cohort) in order to test its accuracy. Regarding values of AUC of ROC curves in the derived and validation cohort (72% and 70%, respectively), this score appeared valid (AUC > 70%) and reproducible (AUC were close similar). However, this validation could not account for other populations located in other areas. Consequently, future studies must focus on the multicenter validation of this score in order to allow its generalization. The development of a validated score for PPA prediction is important as it may be used in prevention of this complication. It seems reasonable to propose the use of our score during the third trimester of pregnancy while it allows detection two predictors of PPA: ethnic origin and anaemia during this period. After delivery applying the score allows including postpartum predictors (PPH and episiotomy). Preventive strategies to be considered must include the assessment of iron status (plasma ferritin and plasma transferring saturation determinations), aetiology of anaemia (haemoglobinopathy) and sensitization of patients to the PPA and its risks which might increase the observance of the oral iron therapy given during and after pregnancy [25]. Indeed, the systematic prescription of oral iron therapy to prevent anaemia in our patients appears ineffective in decreasing the incidence of anaemia during the third trimester of pregnancy. This could result
from poor treatment adherence as previously described in the general population. Concerning the administration of intravenous iron therapy, it mandatory more studies in order to validate this preventive strategy using our scoring system in regard to the risk of iron overload in patients with haemoglobinopathies [25,26]. Our study also highlighted the importance of PPH and episiotomy in PPA genesis. The decrease of episiotomy, as recommended by the French College of Gynaecologists and Obstetricians [27] and a more standardized and active diagnosis (systematic quantification of blood loss) and treatment (early treatment based on written consensual protocols) of PPH might also prevent the occurrence of PPA. Interestingly, 20% of patients were recorded as having PPA without any risk factors. This may be explained by poor iron stores at the beginning of pregnancy and increased needs during this period, as previously demonstrated. The increase in the percentage of anaemic patients from the first to the third trimester supports this hypothesis [28]. Homologous transfusion was performed in 2.3% of cases and is comparable to rates observed in other publications [29,30]. Length of hospital stay did not differ significantly between anaemic and non-anaemic patients. This probably reflects local anaemia treatment strategies (transfusion therapy in non tolerant women and not based on haemoglobin level alone) rather than the real impact of anaemia on hospital length of stay. In addition, duration of hospital stay could not be considered as a good marker of PPA while systematic early postpartum follow-up at home is provided in France to prevent unnecessary hospitalisation. Our study suffers from several limitations. Firstly, it was a single-centre investigation. Consequently, external validation is first required before generalization of its use. Secondly, the use of the Hemocue system for haemoglobin determination might have induced misestimation of true haemoglobin values. However, this method has been previously validated in a similar population of pregnant and postpartum women [16,31,32]. In addition, intrapatient variability was decreased by recording the mean of two haemoglobin concentration measurements. Finally, one of the greatest advantages of our cohorts is the ethnic heterogeneity of our population which allows us to investigate this particular factor. However, the effect of haemoglobinopathies, a potential risk factor of PPA frequently observed in the African and southeast ethnic groups, was not investigated in this study. In conclusion, we have prospectively constructed and validated a scoring system that predicts individual probability for PPA in a monocentric study. Based on these results, preventive strategies can be applied in order to decrease its incidence. Further studies should focus on the effects of such a preventive strategies and the external validation of this score and preventive strategies to decrease PPA occurrence. Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. References [1] Reveiz L, Gyte GM, Cuervo LG, Casasbuenas A. Treatments for iron-deficiency anaemia in pregnancy. Cochrane Database Syst Rev 2011;10:CD003094. [2] Bergmann RL, Richter R, Bergmann KE, Dudenhausen JW. Prevalence and risk factors for early postpartum anemia. Eur J Obstet Gynecol Reprod Biol 2010;150:126–31. [3] Casanueva E, Pfeffer F, Drijanski A, Ferna´ndez-Gaxiola AC, Gutie´rrez-Valenzuela V, Rothenberg SJ. Iron and folate status before pregnancy and anemia during pregnancy. Ann Nutr Metab 2003;47:60–3. [4] Jans SMPJ, de Jonge A, Lagro-Janssen ALM. Maternal and perinatal outcomes amongst haemoglobinopathy carriers: a systematic review. Int J Clin Pract 2010;64:1688–98.
J. Allary et al. / Annales Franc¸aises d’Anesthe´sie et de Re´animation 32 (2013) e1–e7 [5] van Gellekom SA, Lindauer-van der Werf G, Hague WM, de Vries JI. Anaemia and haemolysis in pregnancy due to rapid folic acid and vitamin B12 depletion. Neth J Med 2008;66:216–7. [6] Bodnar LM, Scanlon KS, Freedman DS, Siega-Riz AM, Cogswell ME. High prevalence of postpartum anemia among low-income women in the United States. Am J Obstet Gynecol 2001;185:438–43. [7] Scholl TO. Iron status during pregnancy: setting the stage for mother and infant. Am J Clin Nutr 2005;81:1218S–22S. [8] Scholl TO, Hediger ML, Fischer RL, Shearer JW. Anemia vs iron deficiency: increased risk of preterm delivery in a prospective study. Am J Clin Nutr 1992;55:985–8. [9] Scholl TO, Reilly T. Anemia, iron and pregnancy outcome. J Nutr 2000;130: 443S–7S. [10] Mahomed K. Iron supplementation in pregnancy. Cochrane Database Syst Rev 2000;2:CD000117 [Review. Update in: Cochrane Database Syst Rev 2006;3:CD000117]. [11] El-Refaey H, Rodeck C. Post-partum haemorrhage: definitions, medical and surgical management. A time for change. Br Med Bull 2003;67:205–17. [12] Chong YS, Su LL, Arulkumaran S. Current strategies for the prevention of postpartum haemorrhage in the third stage of labour. Curr Opin Obstet Gynecol 2004;16:143–50. [13] Condous GS, Arulkumaran S. Medical and conservative surgical management of postpartum hemorrhage. J Obstet Gynaecol Can 2003;25:931–6. [14] Gu¨lmezoglu AM, Forna F, Villar J, Hofmeyr GJ. Prostaglandins for preventing postpartum haemorrhage. Cochrane Database Syst Rev 2007;18:CD000494 [Review. Update in: Cochrane Database Syst Rev 2012;8:CD000494]. [15] Mignon A, Dreyfus M, Ozier Y. Prise en charge initiale par l’anesthe´siste en cas d’he´morragie du post-partum. J Gynecol Obstet Biol Reprod (Paris) 2004;33: 65–72. [16] Richards NA, Boyce H, Yentis SM. Estimation of blood haemoglobin concentration using the HemoCue during caesarean section: the effect of sampling site. Int J Obstet Anesth 2010;19:67–70. [17] Dodd J, Dare MR, Middleton P. Treatment for women with postpartum irondeficiency anaemia. Cochrabe Database Syst Rev 2004;18:CD004222. [18] Pritchard JA. Changes in blood volume during pregnancy. Anesthesiology 1965;26:393–9.
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[19] Sass C, Belin S, Chatain C, Moulin JJ, Debout M, Duband S. Social vulnerability is more frequent in victims of interpersonal violence: value of the EPICES score. Presse Med 2009;38:881–92. [20] Bodnar LM, Siega-Riz AM, Arab L, Chantala K, McDonald T. Predictors of pregnancy and postpartum haemoglobin concentrations in low-income women. Public Health Nutr 2004;7:701–11. [21] Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L. The use of receiver operating characteristic curves in biomedical informatics. J Biomed Inform 2005;38:404–15. [22] Akobeng AK. Understanding diagnostic tests 3: receiver operating characteristic curves. Acta Paediatr 2007;96:644–7. [23] McLean E, Cogswell M, Egli I, Wojdyla D, de Benoist B. Worldwide prevalence of anaemia, WHO Vitamin and Mineral Nutrition Information System, 1993– 2005. Public Health Nutr 2009;12:444–54. [24] Fucharoen S, Winichagoon P. Haemoglobinopathies in southeast Asia. Indian J Med Res 2011;134:498–506. [25] Milman N. Prepartum anaemia: prevention and treatment. Ann Hematol 2008;87: 949–59. [26] Breymann C, Gliga F, Bejenariu C, Strizhova N. Comparative efficacy and safety of intravenous ferric carboxymaltose in the treatment of postpartum iron deficiency anemia. Int J Gynaecol Obstet 2008;101:67–73. [27] Reinbold D, E´boue C, Morello R, Lamendour N, Herlicoviez M, Dreyfus M. De l’impact des RPC pour re´duire le taux d’e´pisiotomie. J Gynecol Obstet Biol Reprod (Paris) 2012;41:62–8. [28] Milman N. Iron and pregnancy–a delicate balance. Ann Hematol 2006;85:559–65. [29] Butwick AJ, Aleshi P, Fontaine M, Riley ET, Goodnough LT. Retrospective analysis of transfusion outcomes in pregnant patients at a tertiary obstetric center. Int J Obstet Anesth 2009;18:302–8. [30] So-Osman C, Cicilia J, Brand A, Schipperus M, Berning B, Scherjon S. Triggers and appropriateness of red blood cell transfusions in the postpartum patient–a retrospective audit. Vox Sang 2010;98:65–9. [31] Nkrumah B, Nguah SB, Sarpong N, Dekker D, Idriss A, May J, et al. Hemoglobin estimation by the HemoCue(R) portable hemoglobin photometer in a resource poor setting. BMC Clin Pathol 2011;11. [32] Sari M, de Pee S, Martini E, Herman S, Sugiatmi, Bloem MW, et al. Estimating the prevalence of anaemia: a comparison of three methods. Bull World Health Organ 2001;79:506–11.
Original article
An individual scoring system for the prediction of postpartum anaemia Score individuel pour pre´diction de l’ane´mie du postpartum J. Allary a,c, J.-F. Soubirou a, J. Michel a,c, I. Amiel a,c, V. Silins a,c, C. Brasher a,c, J.-F. Oury b,c, Y. Nivoche a,c, S. Dahmani b,c,d,* a
Department of Anaesthesia, Intensive care and Pain Management, Robert-Debre´ University Hospital, AP–HP, 48, boulevard Serurier, 75019 Paris, France Robert-Debre´ University Hospital, 48, boulevard Serurier, 75019 Paris, France Paris Diderot University (Paris-7), PRES Paris Sorbonne Cite´, 75000 Paris, France d UMR Inserm U 676, Robert-Debre´ University Hospital, 75019 Paris, France b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 April 2012 Accepted 9 November 2012
Background. – Postpartum anaemia (PPA) is a common postpartum complication. The goal of this study was to prospectively construct a predictive score for individual risk of PPA. Patients et method. – We prospectively analyzed factors associated with PPA (< 10 g dL 1 at 48 hours postpartum). Parameters analyzed were demographic data, pregnancy characteristics, delivery and postpartum characteristics. Univariate analysis was performed using Anova or X2; the Cox model was used for multivariate analysis. The scoring system was validated using ROC curve. Results. – Analysis was performed in 475 patients and validation was carried using an additional 95 patients. Multivariate analysis found four factors independently associated with PPA: anaemia during the third trimester of the pregnancy, Southeast Asian ethnic origin, episiotomy and severe postpartum haemorrhage (PPH) identified by the use of sulprostone. According to the score derived from the Cox model, patients were classified as low (22%, score = 0), medium (55%, score = 2 or 3) and high (86%, score > 3) probability of PPA. Using the AUC of the ROC curve for both the first and the validation cohorts (performed on 95 further patients), we recorded AUCs of 72% and 70% respectively. Conclusions. – This study allowed the derivation and validation of a predictive score of PPA. This score might be useful in targeting prophylactic strategies for PPA. Such strategies could include a more active treatment of iron deficiency (increasing oral iron treatment observance or intravenous iron therapy) especially in exposed population, improvement in the prevention and treatment of postpartum haemorrhage and decreasing the use of episiotomy. Future studies must focus on the external validation and generalisation of this scoring system. ß 2012 Socie´te´ franc¸aise d’anesthe´sie et de re´animation (Sfar). Published by Elsevier Masson SAS. All rights reserved.
Keywords: Postpartum anaemia Sulprostone Postpartum haemorrhage Ethnic differences Episiotomy Transfusion
R E´ S U M E´
Mots cle´s : Ane´mie du postpartum Sulprostone He´morragie du postpartum Diffe´rence e´thique E´pisiotomie Transfusion
Introduction. – L’ane´mie du postpartum (APP) est courante apre`s accouchement. L’objectif de ce travail a e´te´ d’e´tudier les facteurs de risques de sa survenue. Patientes et me´thodes. – Il s’agit d’une e´tude prospective observationnelle e´tudiant les facteurs de risques de survenue d’une APP (Hb < 10 g/dL) dans les 48 heures apre`s un accouchement. Les facteurs e´tudie´s e´taient : les donne´es de´mographiques, les caracte´ristiques de la grossesse, de l’accouchement et du postpartum. L’analyse s’est faite par une analyse univarie´e par Anova ou X2 et a e´te´ suivie par une analyse multivarie´e par mode`le de Cox pour la construction du mode`le. Une analyse par courbe ROC a e´te´ utilise´e pour la validation. Re´sultats. – L’analyse a e´te´ re´alise´e sur 475 patientes et la validation sur un autre e´chantillon de 95 patientes. L’analyse multivarie´e a permit de retrouver quatre facteurs inde´pendamment associe´s a` la survenue d’une APP : l’ane´mie du troisie`me trimestre de la gestation, l’origine ge´ographique du sud-est asiatique, l’e´pisiotomie et l’he´morragie du postpartum identifie´e par l’administration de sulprostone. A` partir de ce mode`le, un score a e´te´ e´tabli permettant de classer les patientes en risque faible (22 %,
* Corresponding author. Department of Anaesthesia, Robert-Debre´ Hospital, 48, boulevard Serurier, 75019 Paris, France. E-mail address: [email protected] (S. Dahmani). 0750-7658/$ – see front matter ß 2012 Socie´te´ franc¸aise d’anesthe´sie et de re´animation (Sfar). Published by Elsevier Masson SAS. All rights reserved. http://dx.doi.org/10.1016/j.annfar.2012.11.002
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score = 0), interme´diaire (55 %, score = 2 ou 3) et e´leve´ (86 %, score > 3) d’APP. Les aires sous la courbe de ce score applique´ a` la population de construction et de validation e´taient respectivement de 72 % et 70 %. Conclusion. – Cette e´tude a permis la construction et la validation d’un score pre´dictif de la survenue d’une APP. Il pourrait permettre la mise en place de mesures prophylactiques. Ces mesures pourraient inclure la lutte contre le de´ficit en fer (augmentation de l’observance du traitement oral ou par institution d’un traitement cible´ par fer injectable) dans la population a` risque, une meilleure prise en charge des he´morragies du postpartum, une diminution du recours a` l’e´pisiotomie. De futures e´tudes sont ne´cessaires afin d’assurer la validation externe de ce score. ß 2012 Socie´te´ franc¸aise d’anesthe´sie et de re´animation (Sfar). Publie´ par Elsevier Masson SAS. Tous droits re´serve´s.
1. Introduction Postpartum anaemia (PPA) is a common complication occurring after 35 to 75% of deliveries [1]. This wide range of incidence values varies according to the geographic area where studies were performed and the haemoglobin concentration cut-off values defining anaemia. Many factors have been previously found to be associated with this complication. Of these, postpartum haemorrhage (PPH) is the most important one [2]. However, other factors such as iron deficiency [3], haemoglobinopathy [4], preeclampsia, haemolysis [5], and low socioeconomic status have also been found to influence the incidence of PPA [6]. PPA has been found to negatively impact upon outcomes in both mothers and newborns, since it increases morbidity, postpartum fatigue, stress and depression [7,8]. Anaemia has also been shown to increase preterm delivery and low birth weight [8]. In addition, these phenomena are very likely to alter the mother-newborn relationship, which could result in long-term physical and psychological disabilities for both. Despite the amount of retrospective publications analysing PPA incidence and risk factors of PPA, there have been no prospective risk factor studies. Moreover, no reliable score is available to individually predict PPA occurrence [2,6–10]. The primary aim of this study was to prospectively construct and validate an individual score predicting PPA probability. 2. Patients and methods This study was approved by the Institutional Review Board of Paris North Hospitals, Paris-7 University, AP–HP (N# IRB0006477) and informed consent was obtained from all subjects. The study took place in a tertiary level III (management of complicated pregnancy) university hospital, where 3035 deliveries were performed in 2010. The study included all consecutive parturient delivered in our institution during consecutive 4-month period who accepted to participate in the study. 2.1. Women care policy According to our local protocols, all patients were given folic acid (Speciafoldine1, Sanofi-Aventis, France: 5 mg/d) and iron supplements (Fumafer1, Sanofi-Aventis, France: 66 mg/d, from the first trimester until delivery). This treatment was continued in the postpartum period if serum haemoglobin concentration was less than 10 g dL 1 during the postpartum period. Screening for anaemia and thrombocytopaenia was systematically performed during the first and last trimesters. Clinical evaluation and foetal imaging were performed every trimester. PPH was managed according to recommended protocols. Detection of this complication was based on clinical estimation of an abnormal blood loss, hypotension, tachycardia or altered level of conscience. However, it was not triggered by objective determination of blood losses because of the unusual use of systematic blood collector bag in our institution during the study period. Management of PPH relies in
the first place by intravenous fluid load, active warming, antibioprophylaxis, Oxytocin treatment, speculum examination, bimanual examination of the uterus and manual removal of placental pieces that remain in the uterus, bladder emptying and determination of haemoglobin and platelets levels and haemostasis status (fibrinogen and activated prothrombin time). In a second line, if PPH persists after 30 minutes, sulprostone infusion was stated (Nalador1, Bayer Sante´, Loos, France), intravenous fluid load and transfusion of packed red cell and frozen plasma with a ration 1 to 1 when haemoglobin level fall below 10 g dL 1. In addition to its systematic administration during packed red cell transfusion, frozen fresh plasma was administered when aPT decreased bellow 50%. Platelets and fibrinogen therapy were given according to results of platelets (< 50,000/mm3) and fibrinogen (< 2 g.L 1) [11–15]. Uterine artery embolization is not available in our institution. Consequently, management of uncontrolled PPH was dependant of the haemodynamic status of patients. When patients were stable, they were transferred in structures with an available interventional radiologic team. Otherwise, surgical management of PPH was local and relies on uterus compression sutures, arterial ligations and hysterectomy when facing a life threatening haemorrhage. Except during active PPH, standard care policy for transfusion in our institution is based upon anaemia tolerance and haemoglobin level measured at 48 hours after delivery. For PPA between 7 and 10 g dL 1, patients were transfused where signs of clinical intolerance were observed (defined by tachycardia, asthenia or tachypnoea either at rest or at movement). Otherwise they received intravenous (300 mg twice at 48 hours interval) and subsequent oral iron therapy. Patients were systematically transfused when postpartum haemoglobin was bellow 7 g dL 1. 2.2. Determination of postpartum anaemia Haemoglobin concentrations were measured systematically 48 hours postpartum by ward nurses using the Hemocue 201+ß system (HemoCue AB, A¨ngelholm, Sweden) [16]. According to our local standardized routine protocol, this was considered more easily to perform, less cost effective and less invasive for patients as a systematic generalized screening test in comparison to the laboratory determination of haemoglobin. However, laboratory haemoglobin measurement was systematically performed in cases of low capillary haemoglobin indicating transfusion or after caesarean section. Capillary haemoglobin measurement technique was standardized in all patients. After skin disinfection, a 24-gauge needle puncture was performed on the lateral aspect of the fourth or fifth finger pulp and two samples of blood were analyzed. The haemoglobin level recorded for each patient was the mean of these two measurements. If the two values differed by more than 10%, a third measurement was performed and the mean of the three measurements recorded. According to World Health Organization standards, a haemoglobin level of 11 g dL 1 during the first and third trimesters and 10 g dL 1 in the postpartum period, were considered the thresholds to define anaemia [1,17].
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2.3. Recorded data The study prospectively and anonymously analyzed factors relevant in the occurrence of PPA, defined as a haemoglobin level strictly bellows 10 g dL 1 48 hours after delivery. This time was considered because of the decreased influence of peripartum fluid loads on haemoglobin level at this time [18]. Considered factors for PPA were: mother’s demographic data (age, weight, ethnic origin, haemoglobin in the first and third trimesters), mother’s socioeconomic status (using the validated EPICES score [19], developed by the French National Health Insurance Institution), pregnancy characteristics (gestate, parity and abnormal placenta insertion), delivery characteristics (duration of labour, mode of delivery, episiotomy) and complications during delivery (severe PPH defined by the use of sulprostone, vaginal or corporeal lesions). An exhaustive list of recorded factors is summarized in Table 1. Patients were excluded from the analysis if any of the above data was lacking. 2.4. Statistical analysis As recommended for score building, a construction and a validation cohort had to be used. The number of patients sample to be included in this study was based on previous studies and was set
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to 500 (corresponding to 2 months of data recording). Consequently, a 3-month data recording was performed as a security in case of discarded patients [20]. A validation cohort of 25% of the constructed cohort was judged necessary for the statistical analysis. Given a stable number of deliveries during the considered period of the study, data from patients included during the first three months were used to construct the score while data from the remaining patients were used for validation purpose. The data obtained were analysed using the statistical package SPSS 15.0 (IBM, Chicago, Illinois, USA). These statistics used mean and standard deviation (SD) or percentages (%) for continuous or categorical variables, respectively. Anova and X2 were used for univariate analysis of continuous and categorical variables respectively. Significant parameters in the univariate analysis were then included in a multivariate Cox model. In order to obtain an easy to use scoring system, two mathematical operations were used. First, partial Cox regression coefficients for significant parameters were divided by the coefficient of the parameter with the highest regression coefficient. These derived coefficients were multiplied by a common number to obtain a rounded (and easy to use) number for each parameter of the score. Second, in order to define the probability of PPA according to this scoring system, ranges of scores were grouped by including in the same group ranges with comparable incidence of
Table 1 Descriptive statistics in the first and validation cohorts. Patient’s characteristics
First cohort n = 475
Validation cohort n = 95
Statistics (X2, P) or P*
Age (years), mean sd Gestational age (weeks), mean sd Gestity (n), mean sd Parity (n), mean sd Ethnical origin Europe, n (%) North African, n (%) Sub-Saharan, n (%) Southeast Asian, n (%) Central Asian, n (%) American, n (%) Caribbean, n (%) EPICES score EPICES quartiles (Q) Quartile 1, n (%) Quartile 2, n (%) Quartile 3, n (%) Quartile 4, n (%) Quartile 5, n (%) Preeclampsia, n (%) Previous Caesarean, n (%) Multiples foetuses, n (%) Abnormal placenta insertion Haemoglobin 1st trimester (g dL 1), mean sd Haemoglobin 1st trimester < 11 g dL 1, n (%) Haemoglobin 3rd trimester (g dL 1), mean sd Haemoglobin 3rd trimester < 11 g dL 1, n (%) Induced delivery Duration of stage 1 (min), mean sd Duration of stage 2 (min), mean sd Instrumental delivery, n (%) Vaginal lesions, n (%) Episiotomy, n (%) Caesarean, n (%) Manual removal of placental, n (%) Bimanual examination of the uterus, n (%) Postpartum sulprostone infusion, n (%) Transfusion, n (%) Intravenous iron therapy Postpartum haemoglobin (g dL 1), mean sd Postpartum haemoglobin < 10 g dL 1, n (%) Duration of hospitalisation (days), mean sd
38 3 38 5 2.6 1.5 21
31 5.5 39 2.7 2.3 1.3 21
NS NS NS NS
166 (34.9) 140 (29.5) 97 (20.4) 37 (7.8) 9 (1.9) 8 (1.7) 18 (3.8) 31 24
40 (42.3) 23 (24.2) 21 (22.1) 5 (5.3) 0 (0) 0 (0) 6 (6.3) 29 26
X2 = 1.7; NS X2 = 1; NS X2 = 0.14; NS X2 = 0.7, NS X2 = 1.8; NS X2 = 1.6; NS X2 = 1.2*; NS P = 0.5
63 (13.3) 111 (23.4) 101 (21.1) 89 (18.7) 111 (23.4) 29 (6) 80 (16.9) 11 (2.7) 13 (2.7) 11.8 1 88 (18.5) 11.5 1.2 124 (26.1) 119 (25.1) 142 122 164 154 82 (17.3) 140 (29.4) 128 (27) 87 (18.3) 146 (30.7) 339 (71.4) 17 (3.5) 11 (2.3) 39 (8) 10 1.6 205 (43.2) 6.2 5.8
22 (23.2) 24 (25.3) 11 (11.6) 14 (14.7) 24 (25.3) 12 (12.4) 17 (17.9) 6 (6.2) 0 (0) 12.4 1 19 (20.6) 11.6 2 27 (29.4) 24 (25.3) 144 115 162 150 17 (16.3) 25 (26.3) 32 (33.7) 19 (20) 29 (30) 61 (64) 3 (3.2) 0 (0) 8 (8) 10 1.4 38 (402) 6.3 3.7
X2 = 6.1; P = 0.01 X2 = 0.1; NS X2 = 4.7; P = 0.02 X2 = 0.8; NS X2 = 0.04*; NS X2 = 5; P = 0.03 X2 = 0.06, NS X2 = 4.3; P = 0.048 X2 = 2.6; NS P = 0.001 X2 = 0.11; NS NS X2 = 0.4; NS X2 = 0.002; NS NS NS X2 = 0.01; NS X2 = 0.4; NS X2 = 1.7; NS X2 = 0.15; NS X2 = 0.002; NS X2 = 2; NS X2 = 0.04; NS X2 = 2.2; NS X2 = 0.02; NS NS X2 = 0.3; NS 0.4
Data are expressed as mean sd or percentages (%). NS: non-significant. Comparisons are displayed as X2 and value of P (when P was bellow 0.05) or as P (when P was bellow 0.05), for X2 and Student-t test, respectively.
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Table 2 Univariate analysis of factors associated with postpartum anaemia of less than 10 g/dL Patient’s characteristics
Postpartum haemoglobin 10 g dL (n = 205)
Age (years), mean sd Gestationnal age (weeks), mean sd Gestity (n), mean sd Parity (n), mean sd Ethnical origin Europe, n (%) North African, n (%) Sub-Saharan, n (%) Southeast Asian, n (%) Central Asian, n (%) American, n (%) Caribbean, n (%) EPICES score, mean sd EPICES quartiles (Q) Q1, n (%) Q2, n (%) Q3, n (%) Q4, n (%) Q5, n (%) Preeclampsia, n (%) Previous ceasarian, n (%) Multiples foetuses, n (%) Abnormal placenta insertion, n (%) Haemoglobin 1st trimester < 11 g dL 1, n (%) Haemoglobin 3rd trimester < 11 g dL 1, n (%) Induced delivery, n (%) Duration of stage 1 (min), mean sd Duration of stage 2 (min), mean sd Instrumental delivery, n (%) Vaginal lesions, n (%) Episiotomy, n (%) Caesarean, n (%) Bimanual examination of the uterus, n (%) Postpartum sulprostone therapy, n (%)
1
. Postpartum haemoglobin < 10 g dL (n = 270)
1
P 1
31 5 38 3 2.7 1.6 21
31 6 39 3 2.6 1.5 21
NS NS NS NS
96 (35.6) 76 (28.1) 60 (22.2) 12 (4.4) 7 (2.6) 5 (1.9) 14 (5.2) 30 24
70 (34.1) 64 (31.2) 37 (18) 25 (12.2) 2 (1) 3 (1.5) 4 (2) 31 24
X2 = 0.1; X2 = 0.5; X2 = 1.2; X2 = 9.7; X2 = 1.6; X2 = 0.1; X2 = 3.3; NS
36 (13.3) 63 (23.3) 59 (21.9) 50 (18.5) 62 (23) 22 (8.1) 45 (16.7) 7 (2.6) 5 (1.9) 15 (5.6) 39 (14.4) 62 (23) 128 122 146 143 40 (14.8) 85 (31.5) 51 (18.9) 52 (19.3) 189 (70) 2 (0.7)
27 (13.2) 48 (23.4) 42 (20.5) 39 (19) 49 (23.9) 7 (3.4) 35 (17.1) 4 (2) 8 (3.9) 73 (35.6) 85 (1.5) 57 (27.8) 159 120 188 165 42 (20.5) 55 (26.8) 77 (37.6) 35 (17.1) 150 (73.2) 15 (7.3)
X2 = 0.003; NS X2 = 0.001; NS X2 = 0.1; NS X2 = 0.2; NS X2 = 0.06; NS X2 = 4.6; 0.027 X2 = 0.014; NS X2 = 0.2; NS X2 = 1.8; NS X2 = 69.7; 0.001 X2 = 44; 0.001 X2 = 1.4; NS 0.014 0.04* X2 = 2.6; 0.026 X2 = 1.2; NS X2 = 20.6; < 0.0001 X2 = 0.4; NS X2 = 0.6; NS X2 = 14.5; < 0.0001
NS NS NS 0.001 NS NS NS
Data are expressed as mean sd or percentages (%). NS: non-significant. Comparisons are displayed as X2 and value of P (when P was bellow 0.05) or as P (when P was bellow 0.05), for X2 and Student-t test, respectively.
PPA. Incidence of PPA according to the defined score ranges was then calculated for each patient in the validation cohort. Validation of the score was performed using the ROC method with computation of the area under curve (AUC), sensitivity, specificity, positive predicted value and negative predictive value for each level of probability. An AUC of 1.0 indicates that the model discriminates perfectly between patients with different scores, whereas a value of 50% is indicating that the model contains no predictive information [21,22]. The internal validity of the score was considered if the AUC was higher or equal to 70%. 3. Results Five hundred and twenty-seven patients were enrolled in the first cohort, and 102 in the second. Complete data were obtained from 475 (88.4%) and 95 (93.1%) patients for the two cohorts respectively. Patient characteristics for each cohort are described in Tables 1 and 2 respectively. In the construction cohort, anaemia during the first trimester, third trimester, and postpartum period were found
in 88 (18.5%), 124 (26.1%) and 205 (43.2%) patients, respectively. Eleven (2.3%) patients were transfused before the second postpartum day and all were anaemic at 48 hours (Hb level < 10 g dL 1). Among them, six were associated with severe PPH (based on sulprostone administration) and five were not. Seventeen and three patients received sulprostone therapy in the construction and validation cohorts, respectively. No one had to be transferred in another structure for uterine artery embolisation. PPA had no influence on duration of hospitalization (6.3 versus 6.2 days, P > 0.05). Univariate analysis allows determining anaemia during the first and third trimester, Southeast Asian ethnic origin, postpartum use of sulprostone, episiotomy, increased duration of the first and second stage of labor, pregnancy without preeclampsia and instrumental delivery as statistically associated with PPA (Table 2). Multivariate analysis found four risk factors independently associated with the occurrence of PPA: anaemia during the third trimester, Southeast Asian ethnic origin, postpartum use of sulprostone and episiotomy. According to Cox analysis a scoring system was derived. This consists on coding two for anaemia during the third trimester and
Table 3 Cox multivariate analysis with derived hazard ratio (EXP (B)), partial correlation coefficient and coefficient of the model in the score of postpartum anaemia.
Southeast Asian origin Haemoglobin level during the 3rd trimester < 11 g dL 1 Episiotomy Postpartum sulprostone infusion
Exp (B) (Hazard ratio)
95% Confidence interval for Exp (B)
P
Cox Regression Coefficient
Coefficient in the model
1.66 2.25
1.08–2.54 1.69–2.99
0.02 < 0.0001
0.507 0.813
3 2
1.77 1.78
1.3–2.36 1.04–3
< 0.0001 0.034
0.572 0.577
3 3
J. Allary et al. / Annales Franc¸aises d’Anesthe´sie et de Re´animation 32 (2013) e1–e7 Table 4 Calculated percentages of postpartum anaemia in the first and the validation cohort according to the ranges of scores. Score
Construction cohort (%)
Validation cohort (%)
P
0 2 or 3 >3
22 55 86
21.4 52 72
NS NS NS
three for the other three factors (Table 3). According to this classification, anaemia during the third trimester seems statistically less important in the development of PPA than the remaining three others. Individual probabilities for PPA were calculated on the first cohort. Patients were best classified as low (22%, score = 0, no
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predictive factor), medium (55%, score = 2 or 3, one predictive factor) and high (> 86%, score > 3, two to four predictive factors) probability of PPA (Table 4). In this derived cohort, the AUC of the ROC curve of the score for the prediction of PPA was 72 0.024% (95% confidence interval 71.9 to 72.05; Fig. 1). Concerning the validation cohort, it was similar to the construction one except for the Epices scores (quartiles 1 and 3), the incidence of preeclampsia, and the haemoglobin levels during the first trimester of pregnancy (Table 1). However, these differences were unlikely to influence our results while they did not concern predictors or PPA incidence. In addition, they were not clinically relevant for Epices score (the 4th and 5th quartiles defining a low socioeconomic status were not statistically different and incidence of anaemia during the third trimester of pregnancy was similar in the two cohorts). Application of the score defined in the construction cohort to the validation one found probabilities of PPA in the validation cohort of: 21.4%, 52.3% and 72% for score 0, 2 or 3 and > 3, respectively (Table 4). Comparing the individual probability of PPA obtained in the two cohorts, found no significant difference between incidences of PPA between the two cohorts (Table 4). The AUC of the ROC curve for this score in this validation cohort was 70 0.05% (confidence interval 69.9 to 70.1%; Fig. 1).
4. Discussion
Fig. 1. Receiver operating characteristics (ROC) curves of the postpartum anaemia score performed on the constructed and validation cohorts.
In this study, we construct and validate an individual scoring system for PPA prediction. This score included four independent risk factors: Southeast Asian ethnic origin, anaemia during the third trimester, severe PPH identified by the use of sulprostone and episiotomy. According to varied statistics [23], incidence of PPA in developed countries, is ranging between 12 and 15.5 [2]. Since the incidence of PPA in our study was 40%, many factors may explain the discrepancy with previously published data. Firstly, the methodology used in our study was prospective, which was not the case in many others that focused on this topic, factor that might underestimate the true incidence of PPA in those previous studies. Secondly, our population was ethnically heterogeneous: 35% of patients were of Caucasian origin, 7.8% of Asian origin, and 48.8% of African origin, which might increased the incidence of pregnancy anaemia in comparison to more homogenous Caucasian populations. Interestingly, despite a greater incidence of haemoglobinopathies (such as sickle cell disease or thalassemias) in African population, these patients were not identified as a risk factor of PPA by our analysis. This might result from a greater attention given to these patients in anaemia prevention consisting in a more active follow-up and education leading to diagnostic of anaemia and increase of iron substitution observance. Our study identified four predictive risk factors for PPA: Southeast Asian ethnic origin, anaemia during the third trimester, severe PPH (defined by the postpartum use of sulprostone) and episiotomy. Concerning Southeast Asian ethnic origin, there are three possible explanations for this risk factor influence upon PPA: lower iron stores in this population in comparison to others, a higher incidence of PPH and the presence of haemoglobinopathies (a and b Thallasemia, Haemoblobin E and Haemoglobin Constant Spring) [24]. Rates of sulprostone administration or episiotomy were not different between different ethnic origins. Consequently, it is unlikely that PPH might be involved in this result. High proportion of PPA observed in Asian population might have involved iron deficit. However, this hypothesis is difficult to confirm in our study while plasma ferritin was not determined. In addition, proportion of patients presenting anaemia during the third trimester was only 16.2% in the Southeast Asian patients while it closes to 27% in other
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ethnic groups (non-significant). Socioeconomic status may also be suspected to contribute to the relationship between Southeast Asian ethnic origin and PPA. Seventy percent of Southeast Asian patients were in the 4th and 5th quartiles (defined as poor socioeconomic status) in comparison to the 40% in others ethnic group. However, this seems unlikely as other socioeconomically disadvantaged populations such as Sub-Saharan African patients were not at greater risk of PPA while 70% of those patients were also classified as low socioeconomic status (in comparison to 35% in other ethnic groups). Furthermore, multivariate analysis did not identify low socioeconomic status as an independent predictor of this complication. Finally, up to 60% of Southeast Asian population presents one or multiple haemoglobinapathy that might account in genesis of PPA in this ethnic group [24]. Anaemia during the third trimester is the second independent predictor of PPA. The threshold chosen to define this risk factor was that defined by the WHO. However, this threshold is not uniformly employed in all studies leading to substantial differences in observed incidences. Furthermore, as mentioned above, the high prevalence of third trimester anaemia may largely explain the high proportion of PPA found in our study. Sulprostone use was also an independent predictor of PPA. Sulprostone is used during severe intrauterine PPH and might represent a surrogate marker of this event, which is well documented as a risk factor of PPA [2]. Interestingly, some classical relevant factors in the genesis of PPA, namely preeclampsia, previous caesarean section, multiple foetuses, and vaginal lesions were not found as predictive of PPA anaemia in our analysis. This might result from the limited study sample that could have underpowered our analyses concerning many factors. This might also result from a more active preventive strategy against PPA applied to these patients. This hypothesis is clearly illustrated concerning preeclampsia. Proportion of anaemia during the third trimester among preeclamptic patients was 10.3% and was statistically less than non-preeclamptic patients (27.1%, P = 0.03). Haemoconcentration observed during severe preeclampsia might also explain the lower incidence of anaemia in this population. Moreover, this might also explain the absence of SubSaharan population as independent risk factor of PPA while 9.1% of this population exhibited preeclampsia while none had this condition in the southeast ethnic group (P = 0.049). The predictive score for PPA has been determined in a derivation cohort. This score was then used in a new sample of patients (validation cohort) in order to test its accuracy. Regarding values of AUC of ROC curves in the derived and validation cohort (72% and 70%, respectively), this score appeared valid (AUC > 70%) and reproducible (AUC were close similar). However, this validation could not account for other populations located in other areas. Consequently, future studies must focus on the multicenter validation of this score in order to allow its generalization. The development of a validated score for PPA prediction is important as it may be used in prevention of this complication. It seems reasonable to propose the use of our score during the third trimester of pregnancy while it allows detection two predictors of PPA: ethnic origin and anaemia during this period. After delivery applying the score allows including postpartum predictors (PPH and episiotomy). Preventive strategies to be considered must include the assessment of iron status (plasma ferritin and plasma transferring saturation determinations), aetiology of anaemia (haemoglobinopathy) and sensitization of patients to the PPA and its risks which might increase the observance of the oral iron therapy given during and after pregnancy [25]. Indeed, the systematic prescription of oral iron therapy to prevent anaemia in our patients appears ineffective in decreasing the incidence of anaemia during the third trimester of pregnancy. This could result
from poor treatment adherence as previously described in the general population. Concerning the administration of intravenous iron therapy, it mandatory more studies in order to validate this preventive strategy using our scoring system in regard to the risk of iron overload in patients with haemoglobinopathies [25,26]. Our study also highlighted the importance of PPH and episiotomy in PPA genesis. The decrease of episiotomy, as recommended by the French College of Gynaecologists and Obstetricians [27] and a more standardized and active diagnosis (systematic quantification of blood loss) and treatment (early treatment based on written consensual protocols) of PPH might also prevent the occurrence of PPA. Interestingly, 20% of patients were recorded as having PPA without any risk factors. This may be explained by poor iron stores at the beginning of pregnancy and increased needs during this period, as previously demonstrated. The increase in the percentage of anaemic patients from the first to the third trimester supports this hypothesis [28]. Homologous transfusion was performed in 2.3% of cases and is comparable to rates observed in other publications [29,30]. Length of hospital stay did not differ significantly between anaemic and non-anaemic patients. This probably reflects local anaemia treatment strategies (transfusion therapy in non tolerant women and not based on haemoglobin level alone) rather than the real impact of anaemia on hospital length of stay. In addition, duration of hospital stay could not be considered as a good marker of PPA while systematic early postpartum follow-up at home is provided in France to prevent unnecessary hospitalisation. Our study suffers from several limitations. Firstly, it was a single-centre investigation. Consequently, external validation is first required before generalization of its use. Secondly, the use of the Hemocue system for haemoglobin determination might have induced misestimation of true haemoglobin values. However, this method has been previously validated in a similar population of pregnant and postpartum women [16,31,32]. In addition, intrapatient variability was decreased by recording the mean of two haemoglobin concentration measurements. Finally, one of the greatest advantages of our cohorts is the ethnic heterogeneity of our population which allows us to investigate this particular factor. However, the effect of haemoglobinopathies, a potential risk factor of PPA frequently observed in the African and southeast ethnic groups, was not investigated in this study. In conclusion, we have prospectively constructed and validated a scoring system that predicts individual probability for PPA in a monocentric study. Based on these results, preventive strategies can be applied in order to decrease its incidence. Further studies should focus on the effects of such a preventive strategies and the external validation of this score and preventive strategies to decrease PPA occurrence. Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. References [1] Reveiz L, Gyte GM, Cuervo LG, Casasbuenas A. Treatments for iron-deficiency anaemia in pregnancy. Cochrane Database Syst Rev 2011;10:CD003094. [2] Bergmann RL, Richter R, Bergmann KE, Dudenhausen JW. Prevalence and risk factors for early postpartum anemia. Eur J Obstet Gynecol Reprod Biol 2010;150:126–31. [3] Casanueva E, Pfeffer F, Drijanski A, Ferna´ndez-Gaxiola AC, Gutie´rrez-Valenzuela V, Rothenberg SJ. Iron and folate status before pregnancy and anemia during pregnancy. Ann Nutr Metab 2003;47:60–3. [4] Jans SMPJ, de Jonge A, Lagro-Janssen ALM. Maternal and perinatal outcomes amongst haemoglobinopathy carriers: a systematic review. Int J Clin Pract 2010;64:1688–98.
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