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Data from: Placental pathology findings in unexplained pregnancy losses

Cite this dataset

Kliman, Harvey; Thompson, Beatrix; Holzer, Parker (2022). Data from: Placental pathology findings in unexplained pregnancy losses [Dataset]. Dryad.


There are approximately 5 million pregnancies per year in the United States, with 1 million ending in miscarriage (a loss occurring prior to 20 weeks of gestation) and over 20,000 ending in stillbirth at or beyond 20 weeks of gestation. As many as 50% of these losses are unexplained. Our objective was to evaluate the efficacy of expanding the placental pathology diagnostic categories to include the explicit categories of 1) dysmorphic chorionic villi and 2) small placenta to decrease the unexplained fraction. Using a clinical database of 1,256 previously unexplained losses at 6–43 weeks of gestation, the most prevalent abnormality associated with each loss was determined through examination of its placental pathology slides. Of 1,256 cases analyzed from 922 patients, there were 878 (69.9%) miscarriages and 378 (30.1%) antepartum stillbirths. We determined the pathologic diagnoses for 1,150/1,256 (91.6%) of the entire series, 777/878 (88.5%) of the miscarriages (<20 weeks’ gestation), and 373/378 (98.7%) of the stillbirths (≥20 weeks’ gestation). The most common pathologic feature observed in unexplained miscarriages was dysmorphic chorionic villi (757 cases; 86.2%), a marker associated with genetic abnormalities. The most common pathologic feature observed in unexplained stillbirths was a small placenta (128 cases; 33.9%). Our classification system reinforced the utility of placental examination for elucidating potential mechanisms behind pregnancy loss. The improved rate of diagnosis appeared to be the result of filling a gap in previous pregnancy loss classification systems via inclusion of the categories of dysmorphic chorionic villi and small placenta.



A case series of 1,527 singleton pregnancies that ended in loss were identified from our tertiary-care consult service. Cases were excluded if the cause of loss could be elucidated from the clinical records alone, such as the presence of aneuploidies. Available demographic, clinical data, and gross description were abstracted from the clinical records when submitted with the consult request. Hematoxylin and eosin placental slides (no autopsy slides) were reviewed by the senior author (HJK). The analysis of this retrospective case series was approved by the Yale University Human Research Protection Program Institutional Review Board (protocol ID 2000029781).

Excluded cases

Cases with missing pathology slides, or an absence or insufficient number (fewer than five cross sections) of chorionic villi in the placental sample (Fig. 1) were excluded. The second exclusion criterion was an inability to date the clinical gestational age (GA), determined by the patient’s last menstrual period (LMP). In the absence of an LMP, the GA was approximated by chorionic villus histologic criteria [42-44]. The remaining cases in which gestational age could not be reliably estimated were excluded from further analysis. All subsequent references to GA are related to LMP dating.

Pathologic evaluation

The placental pathology of included cases was re-reviewed following the Amsterdam Placental Workshop Group Consensus Statement [45], with the following modifications. This Statement does not include the diagnostic categories of dysmorphic chorionic villi, trophoblast inclusions (TIs), and/or invaginations (Fig. 2). TIs were first described by Boyd and Hamilton in 1964 [46], and later linked specifically to placentas from triploid losses in 1969 [47, 48]. Over time other investigators found that TIs were not a specific marker of triploidy but rather were seen in a wide range of karyotypic and non-karyotypic genetic abnormalities [25, 27-30, 49, 50], and adverse pregnancy outcomes, including stillbirth [20]. Importantly, the frequency of TIs in normal control placentas is very low [51-53]. Therefore, we added dysmorphic chorionic villi (not to be confused with villous dysmaturity [45]) as a diagnostic category, defined as identification of at least one TI and/or multiple invaginations in the examined slides. Additionally, based on normative curves developed by Pinar et al [54], we added the explicit category of small placenta, defined as fixed trimmed disk weight below the 10th percentile for cases ≥20 weeks. Values below the 10th percentile were mathematically extrapolated from the primary Pinar data.

Identifying a nonacute cord accident required evidence of cord compression, as manifested by: 1) the presence of squamous metaplasia [55-57] on the umbilical cord surface (Fig. 3A); 2) fetal hypoxia defined as an abnormal increase in fetal nucleated RBCs [58]; 3) and thrombosis within the fetal circulation [59]. A loss was only identified as being caused by an infection when a fetal inflammatory response was observed, evidenced by either fetal neutrophil migration through the fetal chorionic plate vessels and/or through the umbilical cord vessels (funisitis) (Fig. 3B) [60]. A maternal inflammatory response alone, as evidenced by maternal neutrophils migrating into and through either the chorionic plate or external membranes, was not sufficient to identify a loss as being caused by an infection. Maternal immunologic rejection was identified when significant numbers of maternal T-cells infiltrated the chorionic villi (chronic villitis, Fig. 3C) [61-64], or monocytes filled the intervillous space (chronic histiocytic intervillositis; CHI) [65-67]. Abruption occurred when a clear, well-developed fibrin clot was adherent to the maternal surface of the placenta [68]. Fetal maternal hemorrhage was identified when intervillous fibrin forming layered lines of Zahn (indicative of blood clot formation in flowing blood [69]) was admixed with blood containing nucleated red blood cells (indicative of a fetal bleeding source) (Fig. 3E) [70, 71]. In contrast, massive perivillous fibrin (a manifestation of maternal intervillous blood thrombosis [72-74]) was identified when the intervillous space was largely filled with fibrin (Fig. 3F).

Classification system

After pathologic examination, we identified the most prevalent abnormality associated with the loss. First, any clear and marked case of abruption, cord accident, or fetal bleed was assigned. Next, we identified all cases with evidence of thrombosis or fetal inflammatory response.

After losses associated with the above five abnormalities were identified, the remaining cases with a placental weight <10th percentile for its corresponding gestational age were categorized as a small placenta and sorted into four etiologic sub-categories: small placenta with evidence of maternal immunologic rejection, small placenta with dysmorphic chorionic villi, small placenta with evidence of uteroplacental insufficiency, or small placenta with no other pathologic findings.

Next, remaining cases with indication of maternal immunologic rejection were classified. Cases that showed dysmorphic chorionic villi with no other etiology were then assigned. The remaining “other” defined abnormalities included viral stigmata revealed on pathologic examination [75, 76], uteroplacental insufficiency without a concomitantly small placenta [77], maternal and/or fetal sickle cell disease (Fig. 3D) [78], premature inappropriate maternal perfusion prior to 8 weeks of gestation [79], complete mole [27], and severe intraamniotic fluid infection without apparent fetal inflammatory responses [80]. Cases revealing no pathologic findings remained unexplained.

Statistical analysis

We displayed the distribution of pregnancy losses across gestational age and associated abnormalities using kernel density estimation [81, 82]. This can be thought of as a smoothed version of a histogram, where each individual data point is replaced with a Gaussian and the total density plot is the sum of all such Gaussians. We found that when these Gaussians had a spread of 1.5 gestational days, there was a good balance between under- and over-smoothing of the data. For each individual category, all corresponding gestational ages were used to create a density estimate of that associated abnormality. Then, to account for how some abnormalities occur more frequently than others, we multiplied the density of each cause by the proportion of cases with that associated abnormality.

To analyze the frequencies of small and large placentas in our series, we converted placental weight percentiles to z-scores, allowing us to visualize this loss cohort against the standard z-score distribution of placentas from normal term or uncomplicated preterm deliveries [54].

We conducted an analysis of patients with multiple losses to investigate whether their associated abnormalities were correlated. More precisely, the null hypothesis to be tested was that the abnormality identified in the second loss was not related to that of the first loss. We tested this against the alternative hypothesis that the abnormality identified in the second loss was the same as that of the first. To perform this hypothesis test, we used a permutation test [83]. Specifically, we randomly shuffled the order of all second losses and calculated what proportion of them matched the findings in the unshuffled first loss causes. Repeating this 500,000 times via computer algorithm gave an estimate of the distribution for the proportion of matching abnormalities when the null hypothesis was true.

Statistical analysis was performed using R version 4.0.4 (R Foundation for Statistical Computing, Vienna, Austria) and the Python packages of Scikit-learn [84] and Matplotlib [85].


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