Integration of Patient Characteristics and the Results of Chlamydia Antibody Testing and Hysterosalpingography in the Diagnosis of Tubal Pathology

An Individual Patient Data Meta-analysis

K.A. Broeze; B.C. Opmeer; S.F. Coppus; N. Van Geloven; J.E. Den Hartog; J.A. Land; P.J.Q. Van der Linden; E.H.Y. Ng; J.W. Van der Steeg; P. Steures; F. Van der Veen; B.W. Mol

Disclosures

Hum Reprod. 2012;27(10):2979-2990.

Abstract and Introduction

Abstract

Background Tubal patency tests are routinely performed in the diagnostic work-up of subfertile patients, but it is unknown whether these diagnostic tests add value beyond the information obtained by medical history taking and findings at physical examination. We used individual patient data meta-analysis to assess this question.
Methods We approached authors of primary studies for data sets containing information on patient characteristics and results from tubal patency tests, such as Chlamydia antibody test (CAT), hysterosalpingography (HSG) and laparoscopy. We used logistic regression to create models that predict tubal pathology from medical history and physical examination alone, as well as models in which the results of tubal patency tests are integrated in the patient characteristics model. Laparoscopy was considered to be the reference test.
Results We obtained data from four studies reporting on 4883 women. The duration of subfertility, number of previous pregnancies and a history of previous pelvic inflammatory disease (PID), pelvic surgery or Chlamydia infection qualified for the patient characteristics model. This model showed an area under the receiver operating characteristic curve (AUC) of 0.63 [95% confidence interval (CI) 0.61–0.65]. For any tubal pathology, the addition of HSG significantly improved the predictive performance to an AUC of 0.74 (95% CI 0.73–0.76) (P < 0.001). For bilateral tubal pathology, the addition of both CAT and HSG increased the predictive performance to an AUC of 0.76 (95% CI 0.74–0.79).
Conclusions In the work-up for subfertile couples, the combination of patient characteristics with CAT and HSG results gives the best diagnostic performance for the diagnosis of bilateral tubal pathology.

Introduction

One of the major causes of female subfertility is tubal pathology, with a prevalence of around 30% (Brandes et al., 2010; Evers, 2002). The diagnostic work-up for tubal pathology consists of the performance of several diagnostic tests, of which Chlamydia antibody test (CAT), hysterosalpingography (HSG) and hysterocontrastsonography (HyCoSy) are used most widely.

Presently, numerous studies have been published on the accuracy of tubal patency tests and two previous meta-analyses have reported on the accuracy of CAT and HSG (Swart et al., 1995; Mol et al., 1997a,b). In these meta-analyses, the value of the diagnostic tests was estimated in isolation of the patient's history and physical examination, since the underlying original studies did often not report on these.

The American Society of Reproductive Medicine (ASRM) recommends a careful medical history and physical examination to identify symptoms and signs suggesting a specific cause for subfertility, which can be the focus of subsequent diagnostic evaluation. The National Institute for Clinical Excellence (NICE) guideline advises the use of patient characteristics to decide whether tubal testing should be performed and women without comorbidities should be offered HSG. The guideline of the Dutch Society for Obstetrics and Gynaecology (NVOG) mentions the use of patient characteristics as a first step in the diagnostic strategy (NICE, 2004; NVOG, 2004; ASRM, 2006).

Although these guidelines all recommend medical history and physical examination as a primary evaluation in the fertility work-up, none of them describes whether tubal patency tests have additional value after careful evaluation of the patient characteristics. A few studies on patient characteristics and diagnostic tests have been published since then. One study provided decision rules to express the probability of tubal pathology at the first consultation based on patient characteristics only (Coppus et al., 2007a,b). Another study showed that the addition of CAT to a diagnostic model based on patient characteristics increased the area under the receiver operating characteristic curve (AUC) for the diagnosis of any tubal pathology from 0.65 to 0.70, although not significantly (Coppus et al., 2007a,b). A third study showed that the diagnostic performance of HSG is invariant over several subgroups of patients, suggesting that HSG is able to diagnose both any and bilateral tubal pathology equally in all subfertile women (Broeze et al., 2011a,b). Still, the added value of these tubal patency tests beyond medical history for the diagnosis of tubal pathology has never been properly investigated. This is worrisome, because a diagnostic strategy in which all available information of medical history and physical examination is integrated with the results of tubal patency tests could potentially lead to more cost-effective testing of tubal pathology.

The aim of this study was therefore to determine whether combining results from medical history and physical examination with tubal patency tests does improve the prediction of tubal pathology, and to create a diagnostic score chart that can be applied by clinicians for calculating the probability of both any and bilateral tubal pathology. To do so, we used individual patient data (IPD) meta-analysis, which is a new technology, allowing the study of the relationship between patients' characteristics and test results in a multivariable setting at the level of the individual patient (Broeze et al., 2010).

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Abstract and Introduction
Methods
Results
Discussion
References

Table I. Study characteristics of included studies.
Study	Year	Total number women	Study design	Inclusion criteria	Exclusion criteria	Diagnostic strategy	CAT tests used
Van der Steeg/Steures	2008	3716	Multicentre prospective cohort study	Women referred for subfertility work-up	Previous tubal testing; previous tubal surgery	CAT− → No TT; CAT+→ HSG/DLS	ELISA (Medac); IF (Biomerieux)
Ng	2001	110	Prospective cohort study	Women referred for subfertility work-up	Previous abdominal surgery; severe male factor	CAT → HSG/DLS	MIF (USA)
Van der Linden	2002	395	Prospective cohort study	Women referred for subfertility work-up	Unknown	CAT− → HSG; HSG + → DLS; HSG− → EXP → DLS	IF (Biomerieux)
Den Hartog	2008	659	Prospective cohort study	Women referred for subfertility work-up	Previous pelvic surgery	CAT− → HSG; CAT+→ DLS; HSG+→ DLS; HSG− → EXP → DLS	ELISA (Medac)

TT, tubal testing; HSG, hysterosalpingography; DLS, diagnostic laparoscopy; EXP, expectative management for at least 6 months after HSG.
In the seventh column, the diagnostic strategy of the original study was described when reported in the article. Some studies performed a DLS irrespective of the HSG results. Other studies had a different strategy for HSG-negative women and HSG-positive women. In the same way, the management after CAT was, in some studies, dependent on the obtained results.

Table II. Patient characteristics of included studies.
Study	Female age, mean (5th–95th percentile)	Duration subfertility, median (range)	BMI, mean (5th–95th percentile)	Primary subfertility (%)	HxPID (%)	Previous pelvic surgery (%)	Previous Chlamydia infection (%)	CAT positivity (%)	HSG abnormal (%)
Vd Steeg/Steures^a	32 (25–39)	1.5 (0–18)	24 (19–34)	62	3	18	6	29	18
Ng	32 (25–37)	3.0 (1–12)	21 (18–26)	75	3	n.a.	n.a.	26	38
Vd Linden	32 (25–39)	2.0 (0–13)	n.a.	65	n.a.	n.a.	n.a.	22	21
Den Hartog	31 (24–37)	1.4 (0–9)	n.a.	71	n.a.	n.a.	n.a.	21	21
Pooled data^b	32 (25–39)	1.5 (0–18)	24 (19–33)	64	3	18	6	28	19
Missing data (%)	0.5	18	54	11	35	35	41	27	31

n.a., not available.
^aMulticentre study.
^bOriginal data pooled without adjustments.

Table III. Prevalences and accuracy of included studies.
Study	Prevalence tubal pathology (%)		Accuracy CAT				Accuracy HSG
	Prevalence tubal pathology (%)		Any TP		Bilateral TP		Any TP		Bilateral TP
	Any TP	Bilateral TP	Sens	Spec	Sens	Spec	Sens	Spec	Sens	Spec
Vd Steeg/Steures^a	27	12	51	66	64	65	76	73	62	91
Ng	27	16	53	85	67	83	70	86	n.a.	n.a.
Vd Linden	27	n.a.	68	65	n.a.	n.a.	73	67	n.a.	n.a.
Den Hartog	18	9	56	90	75	88	67	83	n.a.	95
Pooled data^b	25	12	53 (48–58)	72 (69–74)	66 (59–72)	70 (68–73)	75 (69–81)	74 (71–77)	62 (50–71)	91 (89–93)

n.a., not available.
^aMulticentre study.
^bOriginal data pooled before imputation (95% CI).

Table IV. Overview of univariable and multivariable models for prediction of any tubal pathology.
Variables	Univariable			Multivariable patient characteristics model			Patient characteristics with CAT			Patient characteristics with HSG			Patient characteristics with CAT and HSG
Variables	OR	95% CI	P-value	OR	95% CI	P-value	OR	95% CI	P-value	OR	95% CI	P-value	OR	95% CI	P-value
CAT	2.9	2.4–3.6	<0.001				2.5	2.0–3.2	<0.001				2.4	1.8–3.2	<0.001
HSG any TP	6.9	4.1–11	<0.001							6.6	3.9–11	<0.001	6.3	3.6–11	<0.001
Female age	1.0	0.98–1.0	0.45
Duration of subfertility	1.1	1.0–1.2	0.02	1.1	1.0–1.2	0.03	1.1	0.99–1.1	0.10	1.1	0.99–1.2	0.08
BMI	1.0	0.95–1.1	0.96
Secondary subfertility	1.4	1.1–1.7	0.02	1.2	0.95–1.6	0.11
Hx PID	3.4	1.7–6.8	0.002	2.4	1.1–5.1	0.02	1.9	0.90–4.2	0.09	2.4	0.99–6.0	0.05	1.8	0.87–3.9	0.11
Hx Surgery	2.3	1.7–3.1	<0.001	2.2	1.7–2.9	0.002	2.2	1.7–2.9	<0.001	2.0	1.4–2.2	<0.001	2.0	1.4–2.8	0.001
Hx Chlamydia	2.5	1.6–4.0	0.004	2.3	1.4–3.8	<0.001	1.6	1.0–2.7	0.05	2.3	1.3–4.0	<0.001	1.7	0.94–3.1	0.08

Table V. Classification of women according to their probabilities.
Number of women (TP/no TP)	Patient characteristics and HSG model
Number of women (TP/no TP)	<20%	20–30%	30–40%	>40%
Patient characteristics model
<20%	2959 (381/2578)	214 (82/132)	361 (165/196)	0 (0/0)
20–30%	851 (171/680)	71 (22/49)	119 (70/49)	194 (113/81)
30–40%	0 (0/0)	64 (18/46)	0 (0/0)	33 (25/8)
>40%	0 (0/0)	0 (0/0)	8 (3/5)	6 (3/3)

TP, tubal pathology.
Overview of women classified by the patient characteristics model and the patient characteristics and HSG model, based on their probability of tubal pathology, as estimated with the two models. Between the parentheses, numbers of women with and without tubal pathology are shown.

Table VI. Overview of univariable and multivariable models for prediction of bilateral tubal pathology.
Variables	Univariable			Multivariable patient characteristics model			Patient characteristics with CAT			Patient characteristics with HSG			Patient characteristics with CAT and HSG
Variables	OR	95% CI	P-value	OR	95% CI	P-value	OR	95% CI	P-value	OR	95% CI	P-value	OR	95% CI	P-value
CAT	4.6	3.3–6.3	<0.001				3.7	2.6–5.2	<0.001				3.2	2.1–4.8	<0.001
HSG bi TP	8.4	5.5–13	<0.001							10	4.6–21	<0.001	8.7	4.3–18	<0.001
Age	1.0	0.94–1.1	0.91
Duration	1.2	1.0–1.5	0.03	1.3	1.0–1.5	0.02	1.2	0.97–1.4	0.10	1.2	0.96–1.5	0.12
BMI	1.0	0.92–1.1	0.98
Type	1.2	0.94–1.5	0.15
Hx PID	5.0	2.9–8.8	<0.001	3.5	1.9–6.3	<0.001	2.6	1.4–5.0	0.004	2.5	2.2–5.4	0.02	2.0	0.96–4.2	0.06
Hx Surgery	1.6	0.54–4.8	0.35
Hx Chlamydia	3.7	2.6–5.2	<0.001	3.0	2.0–4.4	<0.001	1.9	1.3–2.9	0.002	2.9	1.5–5.6	0.004	1.9	0.93–4.0	0.07

Table VII. Score chart for any and bilateral tubal pathology.
	Any TP	Bilateral TP
Duration (years)
<1	0	1
1–2	1	2
2–4	2	4
>4	3	5
Type of subfertility
Primary	0	0
Secondary	1	0
Previous PID
No	0	0
Yes	6	7
Previous surgery
No	0	0
Yes	7	0
Previous Chlamydia infection
No	0	0
Yes	5	7
CAT
Negative	0	0
Positive	9	12
HSG
Normal	0	0
Abnormal	18	22
Diagnostic summary score

The diagnostic summary score can be calculated by circling and adding the scores for each of the patient characteristics.

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Integration of Patient Characteristics and the Results of Chlamydia Antibody Testing and Hysterosalpingography in the Diagnosis of Tubal Pathology

An Individual Patient Data Meta-analysis

Abstract and Introduction

Abstract

Introduction

Tables

Tables

Tables

Tables

Tables

Tables

Tables

References

Authors and Disclosures

Authors and Disclosures

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