Classifications for cesarean section: a systematic review.

Abstract

Rising cesarean section (CS) rates are a major public health concern and cause worldwide debates. To propose and implement effective measures to reduce or increase CS rates where necessary requires an appropriate classification. Despite several existing CS classifications, there has not yet been a systematic review of these. This study aimed to 1) identify the main CS classifications used worldwide, 2) analyze advantages and deficiencies of each system.
Three electronic databases were searched for classifications published 1968-2008. Two reviewers independently assessed classifications using a form created based on items rated as important by international experts. Seven domains (ease, clarity, mutually exclusive categories, totally inclusive classification, prospective identification of categories, reproducibility, implementability) were assessed and graded. Classifications were tested in 12 hypothetical clinical case-scenarios. From a total of 2948 citations, 60 were selected for full-text evaluation and 27 classifications identified. Indications classifications present important limitations and their overall score ranged from 2-9 (maximum grade =14). Degree of urgency classifications also had several drawbacks (overall scores 6-9). Woman-based classifications performed best (scores 5-14). Other types of classifications require data not routinely collected and may not be relevant in all settings (scores 3-8).
This review and critical appraisal of CS classifications is a methodologically sound contribution to establish the basis for the appropriate monitoring and rational use of CS. Results suggest that women-based classifications in general, and Robson's classification, in particular, would be in the best position to fulfill current international and local needs and that efforts to develop an internationally applicable CS classification would be most appropriately placed in building upon this classification. The use of a single CS classification will facilitate auditing, analyzing and comparing CS rates across different settings and help to create and implement effective strategies specifically targeted to optimize CS rates where necessary.

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Classifications for Cesarean Section: A Systematic Review
Maria Regina Torloni1*, Ana Pilar Betran2, Joao Paulo Souza2, Mariana Widmer2, Tomas Allen3, Metin
Gulmezoglu2, Mario Merialdi2
1Department of Obstetrics, Sao Paulo Federal University and Brazilian Cochrane Centre, Sao Paulo, Brazil, 2Department of Reproductive Health and Research, World
Health Organization, Geneva, Switzerland, 3Department of Knowledge Management and Sharing, World Health Organization, Geneva, Switzerland
Abstract
Background: Rising cesarean section (CS) rates are a major public health concern and cause worldwide debates. To propose
and implement effective measures to reduce or increase CS rates where necessary requires an appropriate classification.
Despite several existing CS classifications, there has not yet been a systematic review of these. This study aimed to 1)
identify the main CS classifications used worldwide, 2) analyze advantages and deficiencies of each system.
Methods and Findings: Three electronic databases were searched for classifications published 1968–2008. Two reviewers
independently assessed classifications using a form created based on items rated as important by international experts.
Seven domains (ease, clarity, mutually exclusive categories, totally inclusive classification, prospective identification of
categories, reproducibility, implementability) were assessed and graded. Classifications were tested in 12 hypothetical
clinical case-scenarios. From a total of 2948 citations, 60 were selected for full-text evaluation and 27 classifications
identified. Indications classifications present important limitations and their overall score ranged from 2–9 (maximum
grade=14). Degree of urgency classifications also had several drawbacks (overall scores 6–9). Woman-based classifications
performed best (scores 5–14). Other types of classifications require data not routinely collected and may not be relevant in
all settings (scores 3–8).
Conclusions: This review and critical appraisal of CS classifications is a methodologically sound contribution to establish the
basis for the appropriate monitoring and rational use of CS. Results suggest that women-based classifications in general, and
Robson’s classification, in particular, would be in the best position to fulfill current international and local needs and that
efforts to develop an internationally applicable CS classification would be most appropriately placed in building upon this
classification. The use of a single CS classification will facilitate auditing, analyzing and comparing CS rates across different
settings and help to create and implement effective strategies specifically targeted to optimize CS rates where necessary.
Citation: Torloni MR, Betran AP, Souza JP, Widmer M, Allen T, et al. (2011) Classifications for Cesarean Section: A Systematic Review. PLoS ONE 6(1): e14566.
doi:10.1371/journal.pone.0014566
Editor: Fernando Althabe, Institute for Clinical Effectiveness and Health Policy (IECS), Argentina
Received June 30, 2010; Accepted December 15, 2010; Published January 20, 2011
Copyright: ? 2011 Torloni et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report. The authors were personally salaried by their institutions during the period of writing, though no
specific salary was set aside or given for the writing of this paper.
Competing Interests: Academic conflict of interest: Five authors of the present study have published a study on CS in Latin America using one of the
classifications evaluated in the present systematic review. Betran AP, Gulmezoglu AM, Robson M, Merialdi M, Souza JP et al. (2009) WHO global survey on maternal
and perinatal health in Latin America: classifying caesarean sections. Reprod Health 6: 18.
* E-mail: ginecologia@terra.com.br
Introduction
The worldwide rise in cesarean section (CS) rates is becoming a
major public health concern and cause of considerable debate due
to potential maternal and perinatal risks, cost issues and inequity in
access.[1–4] The increase in CS rates observed in many developed
and middle-income countries contrasts sharply with the very low
rates in numerous low-resource settings, along with lack of access to
emergency obstetric care. According to recent data, in Middle
Africa, only 1.8% of all live birth deliveries occur by CS, compared
to 24.3% in North America and 31% and in Central America.[5]
The main determinants of this disparity and specific reasons for the
increase in CS rates in most of the world remain unclear.
In order to propose and implement effective measures to reduce
or increase CS rates where necessary, it is first essential to identify
what groups of women are undergoing CS and investigate the
underlying reasons for trends in different settings. This requires the
use of a classification system that can best monitor and compare
CS rates in a standardized, reliable, consistent and action-oriented
manner. Such a classification system should be applicable
internationally and useful for clinicians and public health
authorities. Ideally, such a system should be simple, clinically
relevant, accountable, replicable and verifiable.[6]
Over the last decades, several CS classification systems have been
created and proposed for different purposes.[6–12] However, to our
knowledge, therehasnotbeen a systematicreviewofthe existingCS
classification systems, analyzing advantages and deficiencies of each
system.Thisgapmotivated thepresentstudy.Webelieve thisreview
is a necessary step in the process of developing a standardized and
internationally accepted methodological framework for monitoring,
auditing, analyzing and comparing CS rates.
The objectives of this study were 1) to identify the main
available classification systems for CS through a systematic review
of the literature, and 2) to analyze qualitatively and compare the
advantages and deficiencies of each system through a pre-defined
comparative framework based on criteria recognized as important
by an international panel of experts.
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Methods
This study has two components: 1) an enquiry to experts about
critical characteristics of a classification for CS, and 2) a systematic
review of the literature to identify and critically appraise available
classifications.
1) Questionnaire to panel of experts
Apanelof46multidisciplinaryinternationalexpertswerecontacted
by email or personally and asked to collaborate with this study by
answering a questionnaire on classifications of CS (Figure S1). They
were asked to grade a total of 18 proposed characteristics of a
classification system for CS from 1 to 9 (1=not important;
9=essential). These characteristics were divided into four main
domains (See Table 1): i) General characteristics, ii) Requirements,
equipment, necessary skills, iii) Use, and iv) Number and content of
categories. Their answers were tabulated in an Excel spreadsheet and
ranked according to frequency.Results fromthis analysisprovided the
basisforthedata-extractionformandassessmentofeachclassification.
2) Systematic Review
Types of studies.
practical (i.e. actually tested in patients) CS classification system or
model was eligible for inclusion in this review, regardless of the
level (e.g. facility, regional, national) in which it was applied. We
included studies regardless of whether or not the main purpose of
the manuscript was to propose a classification (i.e. the classification
could be a secondary outcome in the study).
Type ofparticipants.
Only
classification systems for low-risk or unselected/general obstetric
patients were included.
Any study that described a theoretical or
studies presentingCS
Type of classification systems.
system described in sufficient detail to be understandable and
replicable was accepted. Any system or model that systematically
grouped or organized CS, obstetric populations or other items
(traits, characteristics, variables, attributes) potentially related to the
performance of CS into categories was considered a classification.
Whenever a classification was presented in more than one
publication, data were extracted initially from the original source
and complemented, if necessary, with information presented on
subsequent publications that reported on its use.
Search strategy for identification of studies.
electronic databases were searched (MEDLINE, EMBASE and
LILACS) for articles published from inception to November 26
2008. The search strategy used the following general terms,
expanded and adapted for each database: "classification" or
"taxonomy" or "nomenclature" or "terminology" and "cesarean
section" or "cesarean delivery" or "abdominal delivery" (exact terms
presented in Figure S2).
There were no language or country restrictions. Classic review
articles, textbooks and published letters were also examined for
potentially eligible studies. We checked the references of all articles
chosen for full-text evaluation. Experts were contacted and emails
sent to authors of potentially eligible studies, inquiring about
details, unpublished material and their knowledge of other
relevant studies on CS classification.
Screening and data extraction.
downloaded into Reference ManagerH software version 10. The
citations were organized and duplicates deleted. Two investigators
(MRTandAPB)independentlyscreenedtheresultsoftheelectronic
searches to select potentially relevant citations based on title and
abstracts, according to the criteria defined above. Discrepancies
Any type of CS classification
Three
All citations identified were
Table 1. Questionnaire on characteristics of classifications for caesarean sections: grade given by experts.*
I. General characteristics Grade#
1. Easy to understand8.5 (1.2)
2. Categories clearly defined and unambiguous 8.6 (0.8)
3. Categories mutually exclusive 7.3 (1.9)
4. Categories totally inclusive 6.9 (2.8)
5. Categories identifiable prospectively7.6 (1.5)
6. Reproducible and consistent 8.6 (0.6)
II. Requirement, equipment, skills
7. Guidance on how to deal with cases that have missing information7.7 (1.4)
8. Implementation possible without sophisticated equipment/hardware/software 7.7 (1.8)
9. No extensive training/education needed for data collection 6.9 (2.3)
10. Information necessary for classification easy to obtain/readily available 8.0 (1.5)
11.Minimal burden of data collection7.6 (1.5)
12. Capable of being locally adapted/modified6.9 (2.5)
III. Use
13. Can be incorporated in routine data collection systems (e.g. billing and accounting) 7.4 (1.9)
14. Useful for clinicians8.3 (1.6)
15. Useful for epidemiologists, public health specialists & researchers8.4 (0.9)
16. Useful to change clinical practice 8.5 (0.7)
17. Useful at local and national level 8.2 (1.2)
18. Applicable worldwide8.0 (1.5)
*Each item was rated from 1 to 9 (1=not important; 9=essential).
#Mean (standard deviation).
doi:10.1371/journal.pone.0014566.t001
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were resolved through consensus. When a citation was considered
relevant or when title/abstract was deemed insufficient for decision
on inclusion/exclusion, the full texts were retrieved and evaluated.
All articles selected at first screening were read and abstracted
individually by the two reviewers using a structured data-extraction
form specifically created for this review (Figure S3). Data extracted
were compared and discussed by the two reviewers and a final
extraction form was compiled. Information extracted from each
article included: 1) main purpose of classification, 2) type of study
(theoretical versus clinical), 3) characteristics of study and site
(setting, CS rate, number of cases, inclusion/exclusion criteria), 4)
general characteristics of the system, 5) requirements and skills for
implementation, 6) potential use of the classification, 7) specific
characteristicsoftheclassification,8)main strengths and weaknesses
of the system reported by the authors, and 9) main strengths and
weaknessesof the system as per reviewers. When data in the original
publication were not sufficiently detailed, authors were contacted
for additional information. In order to assure consistency in the
assessment of the classifications over time, the reviewers compared
newly extracted with previously extracted articles and forms.
Semi-qualitative evaluation of classifications.
comparisontablewasconstructeddescribingthemaincharacteristics,
strengths and weaknesses of each classification system. Seven specific
domains (ease of use, clarity, exclusiveness of categories, inclusiveness
of classification, possibility of using classification prospectively,
reproducibility and requirements for implementation) were graded
(2=good;1=median;0=poor).The final grade of eachclassification
ranged from 0 to 14, the higher the grading the better the
classification. Eachclassification
independently by the two reviewers, the answers were compared
and discussed until a consensus was reached.
To assess each classification beyond a theoretical model, we
created a set of 12 different clinical case-scenarios (Figure S4).
A general
wasassessed andscored
After reading and extracting data from each classification system,
the two reviewers independently tested the classification using
these 12 clinical cases. As opposed to the data extraction, the
results of these case scenarios were not compared, reviewed or
discussed between the reviewers since we aimed to assess inter-
rater agreement. Performance of each classification was assessed
by: a) the agreement between the two reviewers in classifying each
case in one of the proposed categories (reproducibility); b) the
possibility of including each of the 12 cases in no more than one of
the categories proposed by the classification (exclusiveness); and c)
the ability to include each of the 12 cases into a specific category
(inclusiveness).
Results
1) Questionnaire to panel of experts
Of the 46 experts contacted, 38 returned the questionnaire on
CS classifications (82% response rate). For each of the first three
domains: (i) general characteristics, (ii) requirements, equipment
and skills, and (iii) use of the classification, the median grade was
either 8 or 9 (over a maximum score of 9). Table 1 presents the
average grade given to each of the questions in these domains.
According to the experts, a CS classification should provide clearly
defined and unambiguous categories, the data needed should be
easy to obtain and it should be useful to help change clinical
practice. Two-thirds of the experts (25/38) answered that ideally, a
classification should have "between 6 and 10" main categories,
while the rest suggested "5 at the most" (data not shown).
2) Systematic Review
The search strategy yielded 1076 citations in the Medline and
EMBASE and 1872 in LILACS. A total of 60 were selected for
full-text evaluation (Figure 1). A total of 20 relevant studies were
Figure 1. Flow chart of the process of identifying and selecting classifications.
doi:10.1371/journal.pone.0014566.g001
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retrieved and 27 different classifications were included (Table 2);
one study[4] presented three classifications and two studies [13,14]
presented two classifications each. These 27 classifications were
grouped into 4 general types, according to the main unit being
classified: indication (N=12),[4,7,15–24] degree of urgency
(N=5),[13,14,25,26]
and other systems (N=6).[4,13,30–32] Table 2 presents the
main characteristics and performance of the 27 classifications,
the overall score obtained and the results of the 12 case
scenarios.
womancharacteristics(N=4)[6,27–29]
Table 2. Main characteristics of 27 classifications for caesarean section and results from the 12 case-scenarios.
Main Characteristics*Case-scenarios (N=12)#
ClassificationsEasy1
Clarity2
Mutually
exclusive3
Totally
inclusive4
Prospective
Identif.
categories5
Reprod-
ucibilty6
Impleme-
ntability7
Overall
score
(max.
14)
%
disagr-
eement
between
raters
% cases
class-
ified
in . .1
category
% cases
not
included
in any
category
Indication based
Althabe 2004 [15]22021119 1788
Anderson 1984 [7]20220129880
Calvo 2009 [16]22021018 58580
Prytherch 2007 [22]2200102733858
RCOG 2001 (a) [4]200210274250 0
NICE 2004 [20]21001127---
Gregory 1994 [18]10201116 2580
Nico 1990 [21]11021005 8317 0
Stanton 2008 [23]20001025 5058 8
Unmet needs
network 2000[24]
20001025 42 2833
Cisse 1998 [17]10001024 8342 42
Kushtagi 2008 [19]10000012
Degree of
urgency based
Van Dillen 2009 (a) [14]20221029 3300
Nicopoullos 2003
(a) [13]
21120028 67428
Lucas 2000 [26]2012002758 420
Van Dillen 2009
(b) [14]
20201027 170 17
Huissoud 2009 [25]20200026 5017 25
Women based
Robson 2001 [6]2222222 14000
Denk 2006 [28]2222221 13880
Cleary 1996 [27]2220222 12800
Lieberman 1998 [29]1001111533 250
Other types
RCOG 2001 (b) [4]21002128---
RCOG 2001 (c) [4] 210011227---
Nicopoullos 2003
(b) [13]
21-0-115---
ICD 10 1992 [32]1012001550 80
WHO 2004 [30] 20020015 4280
Guidotti 2008 [31]20001003---
Code: 2=good, =regular, 0= poor, ; - = not applicable.
1-Easy: how much effort or time it takes to understand main concepts, logic and rules of the classification.
2-Clarity: clear, objective, precise and unambiguous definitions given for each category.
3- Mutually exclusive: each unit being classified by the system (e.g. woman or CS) can only be placed in a single of the existing categories.
4- Totally inclusive: Each and every unit being classified can be placed in at least one of the categories.
5- Prospective identification of categories: allows classification of the patient into one of the categories before she is taken to the operating theater.
6- Reproducibility: probability that the same case would be classified in the same category by different raters.
7- Implementability: human and material requirements needed to introduce and maintain the classification in continuous use.
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Table 3 shows the main general strengths and weaknesses of
each the 4 general types of classifications. Outlines of each of the
27 classifications are provided in Figure S5.
Indication based classifications.
details of the 12 classifications that belong to this category. Four
[19,20,23,24] of the twelve indication classifications presented only
theoreticalmodels. Theother
18,21,22] were tested on actual patients in studies with sample
sizes ranging from 498 to 454,668 deliveries and CS rates from 0.6
to 25%. Only three classifications [15,16,22] provided clearly
defined and unambiguous categories. For example, Althabe et
al[15] proposed a CS classification along with a guideline
containing specific, precise and clear definitions for indications
such as dystocia, acute intrapartum fetal distress and several
maternal indications, whereas Anderson’s[7] classification also
used these same terms but did not provide any details or
parameters on how to decide that this was indeed the indication
for the CS. Therefore, Althabe’s classification was considered clear
in its definition of categories, while Anderson’s was considered
unclear. On the other hand, Anderson’s classification provided
clear hierarchical rules on how to classify a woman with more than
one indication for CS (for e.g. a case with previous CS and
dystocia), while Althabe’s classification did not provide instructions
on how to deal with such cases, which could theoretically be
classified in more than one category. This lead us to grade the
system proposed by Anderson as being mutually exclusive, while
Althabe’s classification scored poorly on this characteristic. Only
two classifications offered mutually exclusive categories[7,18] and
five were totally inclusive.[4,7,15,16,21], meaning that each and
every possible indication could be placed in at least one of the
categoriesprovidedby the
classifications were judged easy to implement.[4,7,17,20,22–24]
None of the classifications allowed prospective identification for all
categories and in two classifications[7,19] less than half of the
Table 4 presents the main
eight classifications[4,7,15–
authors. Overhalfofthese
categories could be prospectively identifiable. This refers to the
possibility of including a woman into one of the existing indication
categories provided by the authors before she is actually taken to
theoperatingtheater. Two
Anderson’s[7,15] obtained the best overall grade for this group
of classifications (9 out of a maximum of 14 points).
Urgency based classifications.
characteristics of these classifications. All five classifications based
on degree of urgency had been tested in real life, in studies with
sample sizes ranging from 18 to 407 cases in settings with CS rates
ranging from 17.7% to 27%. All were judged easy to understand
and implement. Three had mutually exclusive categories,[14,25]
andtwoweretotallyinclusive.[13,14,26]
classifications allowed prospective identification for all categories
and in two, less than half of the categories proposed could be
prospectively identifiable. Van Dillen’s classification[14] obtained
the best overall grade (9 out of a possible maximum of 14)
(Table 2).
Woman-based classifications.
characteristics of all four women-based classifications. These were
tested in real life, with samples ranging from 2876 to 222,013 births,
in settings with CS rates ranging from 7.9% to 31%. Three
classifications presented mutually exclusive categories,[6,27,28] two
were totally inclusive,[6,28] and two were judged very easy to
implement.[6,27] Although the 10-group (Robson’s) classification[6]
received the maximum grade in this type of classification, the 8-
group (Denk)[28] and the case-mix (Cleary)[27] classifications also
obtained high grades (Table 2).
Other types of classifications.
classificationsarepresentedon
classifications was just a theoretical model that was not tested in
real life;[31] the other five were tested in studies involving from 137
to 32,222 cases in settings with CS rates ranging from 23% to 35%.
These classifications proposed from 3 to 21 main categories and up
classifications,Althabeand
Table 5 presents the main
None ofthe
Table 6 presents the main
The six other types of
Table7.Oneofthese
Table 3. Main types of Classification Systems for cesarean section: general strengths and weaknesses.
Name
and main questionStrengthsWeaknesses
Indication
WHY
Information usually routinely collected in any maternity,
therefore it is easy to implement.
Allows to look at the contribution of:
N maternal vs fetal indications
N absolute vs relative indications
No clear uniform definitions for common indications (e.g. fetal distress, failure
to progress, dystocia).
Poor reproducibility unless clear diagnostic definitions are given and rules on
hierarchy of classification (for cases with .1 indication)
Categories are not mutually exclusive (could be .1 primary indication)
Not totally inclusive (unless large number or "Other indications" category exist)
"Other Indications" category makes data analysis difficult
Not very useful to change clinical practice
Degree of urgency
WHEN
Conceptually easy, almost intuitive
Could improve communication between professionals
(obstetricians, anesthesiologists, nurses) and ultimately
improve maternal-perinatal outcomes
Does not provide clear definitions for each of the categories
Poor reproducibility unless clear definitions are given and staff is trained
Cut-offs proposed (time to delivery) are subjective and not evidence-based.
Not very useful to change clinical practice
Limited utility for policy makers, epidemiologists, public health specialists
Patient characteristics
WHO
Conceptually easy and clearly defined categories
Information routinely collected in most maternities,
easy to implement
Mutually exclusive and most are totally inclusive
Good reproducibility
Prospective, allows modifications in clinical practice
Tested in different countries and in large datasets
Does not look at the reason for performing CS on that woman
The case-mix ones are not totally inclusive; they analyze only a portion of all
women delivering by CS at a facility
Other systems
WHERE, HOW,
BY WHOM and
combinations
Address important but neglected details often
overlooked
that could compromise clinical outcomes and should
receive more investment
Offer valuable info for administrators and policy makers
Some need adjustment, improvement, clearer definitions
Several are just theoretical models and have not been tested in real life
Some of the data required not usually collected in most maternities; would
require some effort to be implemented; limited utility for clinicians
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