eAppendix 1:
Data sources
At their first antenatal appointment, pregnant women in Sweden undergo blood typing for ABO blood group and Rhesus D (RhD) status as well as screening for the presence and identification of red blood cell antibodies. These laboratory tests are conducted and recorded at the local blood banks, and together with donation and transfusion data, are assembled in the Scandinavian Donation and
Transfusion database where the proportion of deliveries with maternal screening information was more than 99% of the population from 2003 for all regions who supplied data. The presence or absence of specific antibodies detected during antenatal screening, as well as whether RhD prophylaxis was administered, was determined through a computerized search of free-text laboratory results. This automated data extraction approach is described in detail elsewhere (1) and the accuracy and completeness has been validated.
Our study population of pregnant women was identified from the Medical Birth Register which contains approximately 99% of all births in Sweden from 1973 and includes information on maternal age, parity, multiple or singleton birth, stillbirth and maternal and infant diagnoses classified according to ICD (International Classification of Diseases) codes. The Medical Birth Register also provides information on obstetric history for the same mother.
For information on mothers’ medical history, we used the Swedish hospital inpatient and outpatient registers. The inpatient register has almost complete national coverage of the population from 1987, and records admission and discharge dates, with diagnoses coded using the ICD-9 and -10 systems and procedures coded using the Swedish Classification of Operations and Major Procedures. The outpatient register contains specialist consultations from 2001 and diagnoses are also coded using the ICD-9 or ICD-10 systems.
Variables considered in prediction models
In our models we considered the following predictor variables: maternal blood group (A,B,AB,O), age (in years), maternal place of birth (Sweden, other Nordic countries and non-Nordic countries), parity, an indicator variable for obesity based on a body mass index (BMI) greater than 30kg/m² at the first antenatal visit, smoking status (yes/no) 3 months before pregnancy or at the first antenatal visit. Prior delivery records of the same mother in the Medical Birth Register were used to define the various variables for obstetric history: an indicator variable for diagnosis of preeclampsia or eclampsia ( using ICD-9 code 642 or ICD-10 codes O13, O14 or O15), indicators of previous multiple pregnancy, Caesarean section, amniocentesis, instrumental delivery, perinatal death , number of previous
miscarriages categorized as 0,1,2, 3 or more, and an indicator of a male child on the previous delivery.
In our final model, we combined parity and history of Caesarean section into a three-level variable (primiparous, multiparous with a history of Caesarean section, multiparous with no Caesarean section).
History of RBC transfusion episodes prior to pregnancy or in the first trimester (categorized as 0, 1-2 and 3 or more) was retrieved from the Scandinavian Donation and Transfusion register, with two transfusions considered as belonging to the same transfusion episode if they were given either the same day or on two consecutive days. In the univariate comparisons of alloimmunized and non-immunized pregnancies, we compared the proportions exposed to any red blood cell transfusion prior to the pregnancy, in the first trimester, and in the second or third trimester. Since a transfusion later in
occurred later than a positive screening test. A history of autoimmune disorders was obtained from the patient register using the following ICD codes:
ICD-10 ICD-9
Hemolytic anemia D55, D56, D57, D58, D59 282
Thrombophilia D693 2873
Connective tissue disorder M30 to M36 4460, 71
Other autoimmune disorders E063, E310, K754, E271 2452, 5714, 2554
Statistical Methods for creating the prediction model
A univariate logistic regression analysis of the training data (70% of the population) was used to select predictor variables. Variables associated with the outcome in univariate analyses with a p-value less than 0.2 were considered for the multivariate analysis and retained in the final model on the basis of the minimal AIC using a backwards step-by-step selection.. Implementing this model on the validation set, the probability of alloimmunization was computed for each woman. For the low-risk rule, the cutoff was chosen to select women with an estimated risk below 1/250, i.e., a negative predictive value of more than 99.6. For the high-risk rule, in order to identify pregnancies with a hemolytic disease of the fetus and neonate risk of at least 1/1000, the cut-off that would give a positive predictive value of 2%
was estimated as follows: using the prevalence of .6% of hemolytic jaundice requiring treatment in our population (2) as an estimate of the prevalence of hemolytic disease of the fetus and neonate, and multiplying by the relative risk (approximated by the odds ratio, 9.14) associated with
alloimmunization, the prevalence of hemolytic disease of the fetus and neonate among women immunized with non-D antibodies is 5.5%; for a positive predictive value of 2%, the probability of hemolytic disease of the fetus and neonate = .02 X .055 = .001. These decision rules were evaluated using the test dataset, and their performance compared to the reference decision tree.
Cited:
1. Lee BK, Ploner A, Zhang Z, Gryfelt G, Wikman A, Reilly M. Constructing a population-based research database from routine maternal screening records: a resource for studying alloimmunization in pregnant women. PLoS One. 2011; 6(11):e27619.
2. Lee BK, Le Ray I, Sun JY, Wikman A, Reilly M, Johansson S. Haemolytic and nonhaemolytic neonatal jaundice have different risk factor profiles. Acta Paediatr Oslo Nor 1992. 2016;
105(12):1444-1450.
eTable 1. Performances of the decision rules on the test data set
Reference tree Low-risk rule High-risk rule Percentage of patients positive for
the rule, in the test dataset
55.6% 56.1% 5.9%
Sensitivity %, (CI) 76.3 (71.6-80.4) 77.6 (73-81.7) 20.3 (16.4-24.7) Specificity %, (CI) 44.5 (44.1-44.9) 43.7 (43.3-44.1) 94.3 (94.1-94.4) Negative Predictive Value %, (CI) 99.7 (99.6-99.7) 99.7 (99.6-99.7) 99.5 (99.4-99.5) Positive Predictive Value %, (CI) 0.9 (0.8-1) 0.9 (0.8-1) 2.2 (1.7-2.7)
eFigure 1. Decision algorithm for selecting women for screening.
Total population
Rhesus D status: POS
Previously Ummunized: NO
Apply algorithm:
NEG
YES
No screen Screen
eFigure 2. Receiver Operating Characteristic of the stratified multivariate prediction model applied to the validation data set, yielding an area under the curve (AUC) of 0.64
