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. 2022 Nov 18;4(2):144–148. doi: 10.3168/jdsc.2022-0278

Meta-analysis of the incidence of pregnancy losses in dairy cows at different stages to 90 days of gestation

A Albaaj 1,*, J Durocher 2, SJ LeBlanc 3, S Dufour 1
PMCID: PMC10039243  PMID: 36974208

Graphical Abstract

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Summary: A meta-analysis was performed on a total of 19,723 diagnostic records of pregnancy loss from 46 studies on dairy cows. Pregnancy losses averaged 27%, 13%, 7%, and 2% in the early embryonic (19 to 32 days), late embryonic (30 to 45 days), early fetal (45 to 60 days), and later fetal (60 to 90 days) stages. These results provide a formal synthesis of the incidence of pregnancy losses in dairy cows.

Highlights

  • •

    A meta-analysis was conducted on 19,723 pregnancy diagnosis records in dairy cows.

  • •

    Four periods were defined due to the embryonic development up to 90 days of gestation.

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    Transrectal ultrasound is the dominant method of pregnancy diagnosis.

  • •

    Pregnancy losses ranged from 27% in early embryonic to 2% in later fetal stages.

Abstract

Pregnancy losses are a biological challenge and economically important in dairy herds. A meta-analysis was conducted to quantify losses in 4 periods from 19 to 90 d of pregnancy corresponding to the physiological development of gestation in dairy cows. A total of 19,723 diagnostic records from 46 studies were included. Pregnancy losses averaged 27%, 13%, 7%, and 2% in the early embryonic (19 to 32 d), late embryonic (30 to 45 d), early fetal (45 to 60 d), and later fetal (60 to 90 d) stages. These results provide a formal synthesis of the incidence of pregnancy losses in dairy cows.

Reproductive efficiency is an important determinant of dairy herds' profitability because of its direct link to an optimal milk production cycle and culling. A combination of management practices that achieve a high insemination rate and probability of pregnancy per insemination is critical for reproductive performance (LeBlanc, 2007). This includes prompt and accurate identification of open cows following insemination. However, as fertility and reproductive performance improve through management and genetics, pregnancy and calving rates may be limited by substantial pregnancy losses (PL) (Wiltbank et al., 2016; Ealy and Seekford, 2019). Numerous studies have reported PL at different stages of gestation, documenting greater apparent losses in the first month after practical diagnosis of pregnancy (i.e., from approximately 28 d), with reduced rates of loss later (Santos et al., 2004c). The periods investigated vary among studies, leading to inconsistency in the apparent PL. Pregnancy loss before d 24 is classified as early embryonic loss and those between d 24 and 42 to 50 as late embryonic losses, whereas those detected after d 50 are considered fetal losses (Santos et al., 2004c). Several techniques exist to detect PL including direct (transrectal ultrasonography or palpation) and indirect (measures of pregnancy-specific glycoproteins or progesterone) methods (Romano et al., 2006). Because PL is common, cows correctly diagnosed pregnant between approximately 25 and 60 d of gestation must undergo one or more confirmation tests to confirm the pregnancy status and to identify cows experiencing PL for timely rebreeding. Accordingly, it is important to provide valid benchmark data for farmers, veterinarians, and advisors to identify and investigate elevated rates of PL. Two narrative review articles (Santos et al., 2004c; Wiltbank et al., 2016) summarized a variety of studies that addressed PL in dairy cows. The objective of the present meta-analysis is to further characterize PL in dairy cows by providing an updated and newly quantitative summary estimate of PL during specific periods of gestation in dairy cattle.

We conducted a meta-analysis. We began with data from the review articles (Santos et al., 2004c; Wiltbank et al., 2016), including only studies on dairy cows. All published trials were re-examined to confirm the reported PL values and risk periods used. We searched for subsequent publication of the cited unpublished data in the reviews. If not published, estimated PL values were kept as reported in the review paper. We used the PubMed (https://pubmed.ncbi.nlm.nih.gov/) and CAB (https://www.cabdirect.org/cabdirect) search engines in July 2022 using “pregnancy OR fetal OR embryonic AND loss OR death” and “dairy AND cattle OR cow OR bos taurus OR bovine” in the title and abstract for PubMed and in the title for CAB to identify additional data sources as indicated in Table 1. We set 4 risk periods for PL: early (19 to 32 d) and late (30 to 45 d) embryonic periods and early (45 to 60 d) and late (60 to 90 d) fetal periods. These periods were based on the reported data for first and second pregnancy diagnoses, which explains the small overlap between the first 2 risk periods. After eliminating duplicate articles and studies included in the 2 above-mentioned review articles, the literature search returned a total of 142 original research and review articles. Articles with no experimental data on PL or pregnancy diagnoses (n = 58) or in which pregnancy diagnoses spanned 2 or more periods of interest without subdivided data (n = 56) were excluded. Data from trials reporting induced or specifically disease-associated PL were also excluded (n = 14) except when data for control groups were available, in which case only the control group data were used. The complete list of the search results with the reason of exclusion or inclusion in the meta-analysis is available at https://doi.org/10.5683/SP3/4NAUFI. When one of the pregnancy diagnoses occurred within ± 3 d of a defined period the data were attributed to the most relevant subgroup. A list of the studies included in the meta-analysis is in Table 1.

Table 1.

Sources of data included in a meta-analysis on the risk of pregnancy loss in lactating dairy cows

Study Risk period subgroup First pregnancy diagnosis
 Pregnancy confirmation
 Pregnant cows Pregnancy loss Source
Days post- breeding Test Days post- breeding Test
Baez et al. unpublished 19–32 19 ISG-151 27 PAG2 116 0.22 Wiltbank et al. (2016)
Baez et al. unpublished 19–32 19 ISG-15 32 US3 163 0.41 Wiltbank et al. (2016)
Han et al. (2006) 19–32 19 ISG-15 32 US 51 0.33 Wiltbank et al. (2016)
Humblot (2001) 19–32 23 Progesterone 35 PSPB4 1,870 0.23 Literature search (2022)
Monteiro et al. (2014) 19–32 19 ISG-15 32 US 113 0.27 Wiltbank et al. (2016)
Toledo et al. unpublished 19–32 19 ISG-15 27 PAG 172 0.20 Wiltbank et al. (2016)
Toledo et al. unpublished 19–32 19 ISG-15 32 US 172 0.25 Wiltbank et al. (2016)
Alnimer and Lubbadeh (2008)5 30–45 28 US 45 RP6 91 0.33 Literature search (2022)
Bartolome et al. (2005)7 30–45 28 US 45 RP 66 0.20 Literature search (2022)
Cartmill et al. (2001)7 30–45 28 US 45 RP 110 0.43 Literature search (2022)
Cerri et al. (2003) 30–45 30 US 44 US 261 0.13 Santos et al. (2004c)
Chebel et al. (2003a) 30–45 28 US 42 US 195 0.18 Santos et al. (2004c)
Chebel et al. (2003b) 30–45 31 US 45 US 74 0.11 Santos et al. (2004c)
Chebel et al. (2004) 30–45 31 US 45 US 1,465 0.13 Santos et al. (2004c)
Fischer-Tenhagen et al. (2010)5 30–45 27 US 39 RP 160 0.22 Literature search (2022)
Fricke et al. (2016) 30–45 29 US 39 US 1,080 0.08 Literature search (2022)
Galvão et al. (2004) 30–45 27 US 41 US 382 0.18 Santos et al. (2004c)
Juchem et al. (2002) 30–45 28 US 39 US 139 0.11 Santos et al. (2004c)
Kelley et al. (2017) 30–45 34 US 46 RP 122 0.05 Literature search (2022)
López-Gatius et al. (2007) 30–45 35 US 42 US 98 0.05 Literature search (2022)
Middleton and Pursley (2019) 30–45 24 PAG 34 US 104 0.06 Literature search (2022)
Moore et al. (2005) 30–45 28 US 39 RP 381 0.18 Literature search (2022)
Moreira et al. (2001) 30–45 27 US 45 US 139 0.21 Santos et al. (2004c)
Romano et al. (2007) 30–45 30 US 45 US 520 0.10 Literature search (2022)
Romano and Fahning (2013) 30–45 29 US 45 US 347 0.10 Wiltbank et al. (2016)
Romano and Larson (2010) 30–45 28 US 35 US 114 0.07 Literature search (2022)
Santos et al. (2001) 30–45 28 US 45 US 172 0.09 Santos et al. (2004c)
Santos et al. (2004a) 30–45 31 US 45 US 372 0.11 Santos et al. (2004c)
Santos et al. (2004b) 30–45 27 US 41 US 215 0.10 Santos et al. (2004c)
Silke et al. (2002) 30–45 28 US 42 US 705 0.03 Santos et al. (2004c)
Szelényi et al. (2018) 30–45 32 US 46 US 89 0.28 Literature search (2022)
Toledo et al. unpublished 30–45 27 PAG 32 US 172 0.07 Wiltbank et al. (2016)
Vasconcelos et al. (1997) 30–45 28 US 42 US 488 0.11 Santos et al. (2004c)
Zobel et al. (2011) 30–45 32 US 42 US 165 0.18 Literature search (2022)
López-Gatius et al. (2007) 45–60 42 US 63 US 93 0.14 Literature search (2022)
Romano and Fahning (2013) 45–60 46 US 60 US 314 0.03 Wiltbank et al. (2016)
Romano et al. (2007) 45–60 46 US 60 RP 468 0.04 Literature search (2022)
Vasconcelos et al. (1997) 45–60 42 US 56 US 393 0.06 Santos et al. (2004c)
Zobel et al. (2011) 45–60 43 US 53 US 175 0.12 Literature search (2022)
Forar et al. (1996) 60–90 56 RP 80 RP 4,183 0.01 Wiltbank et al. (2016)
Kelley et al. (2017) 60–90 56 US 74 RP 116 0.04 Literature search (2022)
López-Gatius et al. (2004) 60–90 68 US 90 US 1,332 0.02 Wiltbank et al. (2016)
Ricci et al. (2015) 60–90 60 US 90 PAG 48 0.00 Wiltbank et al. (2016)
Romano and Fahning (2013) 60–90 60 US 90 US 306 0.01 Wiltbank et al. (2016)
Silke et al. (2002) 60–90 57 US 84 US 1,661 0.02 Wiltbank et al. (2016)
Vasconcelos et al. (1997) 60–90 56 US 98 US 393 0.03 Wiltbank et al. (2016)
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1

ISG-15 = interferon-stimulated gene.

2

PAG = pregnancy-associated glycoproteins.

3

US = ultrasonography.

4

PSPB = pregnancy-specific protein B.

5

Only data from control groups were included.

6

RP = rectal palpation.

7

Not identified in the search but cited by a paper found in the search and met the inclusion criteria.

A meta-analysis was conducted to calculate the summary percentage of PL through the effect sizes estimated by each trial using the Meta package (Balduzzi et al., 2019) of R (version 4.2.1; R Foundation for Statistical Computing). A fixed and random effects model for meta-analysis of single proportions was built to calculate the pooled effect size using the number of lost pregnancies (events) and the number of pregnant cows at first diagnosis (total), using the inverse variance method. The inconsistency of results among trials was quantified using both Cochran's Q test and the I2 statistic. Greater I2 values denote variation in true effect sizes and indicate heterogeneity in the data (Higgins et al., 2003). A forest plot was used to display the pooled risks of PL and their 95% CI within each period of interest and for the whole period (19 to 90 d). Funnel and influential case diagnostics were conducted at the subgroup level to analyze publication bias to assess balance between results of large and small studies (results not shown). These analyses were conducted on previously collected data and do not contain any experiments with animal subjects, so did not require IACUC approval.

The data included 46 data sets derived from 2 review articles supplemented with 14 additional publications. Transrectal ultrasound was the dominant method of pregnancy diagnosis, whereas pregnancy-specific glycoprotein measurements were used in some studies. The meta-analysis (Figure 1) showed that during the early embryonic stage, PL averaged 27% (95% CI = 21 to 33%) and was highly variable as indicated by a prediction interval of 13 to 53%. One study (Humblot, 2001) measured progesterone at 23 d to classify pregnancy, which is of dubious accuracy; this was also one of the larger studies in the data set with 1,870 cows, increasing its weight in the meta-analysis. Nevertheless, its estimated PL aligned with the others in the 19 to 32 d risk period (Table 1). Other studies in the same risk period used quantification of interferon-stimulated gene (ISG) 15 for the first classification of pregnancy, which may produce approximately 15% false-negative results (Wiltbank et al., 2016). The incidence of PL decreased in the subsequent periods and was estimated at 13% (95% CI 10 to 16%), 7% (95% CI 4 to 11%), and 2% (95% CI 1 to 3%) for the late embryonic, early fetal, and later fetal periods, respectively. The I2 statistic was greater than 50% for all periods, indicating substantial heterogeneity among studies within a period. Combined, using 19,723 dairy cows diagnosed pregnant between 19 and 90 d, we obtained a summary estimate of PL of 10% over the whole period. There was substantial heterogeneity of the data [I2 (95% CI) = 96.4% (95.8% to 97.0%); Q statistics: χ2 = 1,265.64, df = 32, P < 0.0001]. However, the overall PL might underestimate the real value because none of the studies included in this meta-analysis cover the entire period to 90 d. For instance, PL between d 28 and 110 of gestation was 12.3% (Fernandez-Novo et al., 2020) and 12.7% between d 29 and 90 (Romano and Fahning, 2013). The funnel plots showed no evidence of publication bias. Three data points appeared to be possible outliers, but removing them from the data set did not lead to significant changes in the results of the meta-analysis.

Figure 1.

Figure 1

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Forest plot of the incidence of pregnancy loss in dairy cows in different stages of gestation. Events: number of lost pregnancies. Total: number of pregnant cows at the first pregnancy diagnosis. Weight (common): weights to all studies based entirely on the amount of information captured by each study. Weight (random): weights assigned under random effects. IV, Fixed + Random, 95% CI: inverse variance of the summary estimate of pregnancy losses of fixed effect and random effects model (represented by squares) and their 95% CI (represented by the upper and lower limits of the line connected to the square). The size of the square reflects the relative weighting of the study to the overall effect size estimate. The vertical lines represent the overall random (dotted) and fixed (dashed) effect size estimate. The diamonds at the bottom represent the 95% CI for the subgroup and overall estimates. Tau2: the variance of the distribution of true effect sizes. Chi2: Cochran's Q test for heterogeneity. I2: percentage of variation among studies that is due to heterogeneity rather than chance.

The objective of this meta-analysis was to quantify PL in dairy cows based on a synthesis of available data. Despite the slight overlaps, the risk periods for PL in this meta-analysis generally align with the physiological periods of pregnancy development and loss described by Wiltbank et al. (2016). Our random effects model provides the average risk of PL across all studies within a risk period and underlines the high between-study heterogeneity (i.e., the value of I2 indicates variability in risk estimates as a consequence of true differences between trials rather than due to chance). Ultrasonography and rectal palpation represent the most common and practical methods of pregnancy diagnosis in the field, and ultrasonography from 28 d after insemination is widely used as a reference test. However, as quantified in this analysis, there is meaningful PL during the first month after early pregnancy diagnosis (i.e., from 28 to 60 d), which supports the need for confirmatory pregnancy tests when early pregnancy diagnosis methods are used. Because pregnancy diagnosis from 28 d is widely practiced and because PL diminishes after 60 d, numerous studies have pregnancy diagnosis and confirmation performed at ~30 and ~60 d, but these were excluded using our criteria based on reproductive biology. From a physiological point of view, we believe that the distinction at 45 d should be made. This point represents the end of the differentiation stage and the start of the fetal period from d 45 of pregnancy to parturition (López-Gatius, 2021).

The benefits of a meta-analysis include providing benchmark values of the effect size or risk, increasing statistical power by combining larger quantities of data than available in any one study, and quantifying heterogeneity among studies. If sufficient comparable covariate data are available, meta-regression can identify sources of variation in outcomes. For PL, however, there are numerous causes of these PL that were previously summarized and discussed (Wiltbank et al., 2016), but many of which are difficult to study, which probably interact, and which were not reported consistently in the studies included here. Therefore, additional research is needed on the mechanisms of PL and methods to attribute cause(s) of PL in specific cases. The current meta-analysis summarizes PL in the first trimester gestation and highlights the high variability of PL in dairy cows during the early embryonic period. The benchmark risks quantified here can inform monitoring of PL in dairy herds to identify problems and successes. Study of mechanisms of PL in specific periods will support development of methods to reduce pregnancy loss.

Notes

This study received no external funding.

These analyses were conducted on previously collected data and do not contain any experiments with animal subjects, so did not require IACUC approval.

The authors have not stated any conflicts of interest.

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