INTRODUCTION
One of the main objectives of primary surgery of the palate in cleft lip and palate
(CLP) is the successful reconstruction of the levator muscle belt to provide a functional
velopharyngeal mechanism for adequate speech production1. The presence of an oronasal fistula is one of the most significant complications
after surgical repair of the palate since its implications can interfere with the
individual’s quality of life. The incidence of residual oronasal fistulas is one factor
that indicates the success of primary surgical repair of the palate2,3,4.
A fistula, as reported by Brosco4,5, is a failure in healing or rupture of the primary surgical palate repair that can
occur anywhere along the cleft closure line. The literature presents conflicting data
regarding the occurrence of fistula6,7,8,9; for example, Salimi et al.10 reported an incidence ranging between 0 and 78%. It is important to understand which
variables (surgical treatment protocols, postoperative complications, speech results
after surgery, and patient characteristics) are associated with fistulas to prevent
and minimize these surgical complications.
The so-called “information age” is characterized by the growing expansion in the volume
of data generated and stored, a phenomenon also reflected in the health area, which
increases the possibility of obtaining important information supporting the decision-making
process11. The patients’ data and the surgery results are available in their medical records
and can be used for clinical studies.
However, many times, the volume of data generated is so large that its use and manual
analysis are difficult, demanding more sophisticated processes, such as, for example,
automated processes, for the manipulation of such data. In this context of the overabundance
of data, data mining emerged as a systematic, interactive, and iterative process of
preparing and automatically extracting knowledge from databases11,12.
In data mining, hypotheses are induced from a set of observed data, such as, for example,
data on patients. Each patient is called an object, and different attributes are stored
on each object (name, identification, gender, age, symptoms, etc.), which correspond
to the different data of that patient. In one of the typical mining tasks, one seeks
to learn ways to predict one of the attributes (this specific attribute of which one
wants to make the prediction is called class or, simply, target attribute or output
attribute). The other attributes that predict the target attribute are called predictors
or input attributes.
From a set of data, we seek to create a model or hypothesis (represented by an algorithm
or set of rules) capable of relating one or more attributes (predictors) to the target
attribute (class). Through an inductive bias, each model identified from data mining
uses a representation to describe the hypothesis induced from the data set.
OBJECTIVE
This work aims to use data mining techniques to automatically extract knowledge about
variables (surgical treatment protocols, postoperative complications, speech results
after surgery, and patient characteristics) associated with the occurrence of oronasal
fistulas in patients with unilateral transforamen incisor cleft (UTIC).
METHOD
The investigation deals with descriptive, quantitative, experimental, and applied
research, approved by the Research Ethics Committee of the Hospital for Rehabilitation
of Craniofacial Anomalies of the University of São Paulo (opinion 1,753,467), carried
out at that institution in September 2021. sample refers to a subset of medical records
of patients with cleft lip and palate participating in a randomized clinical trial
(RCT) with UTIC13.
Data on the occurrence of fistulas were obtained for a total of 466 patients (infants).
These patients were randomized (using a script–code written in a programming language
developed at the University of Florida) to receive different surgical treatment protocols,
including 1) primary cheiloplasty between 3 and 6 months of age with the Millard technique
( M) or Spina (S); 2) early (9 to 12 months) or late (>12 months) palatoplasty; 3)
primary palatoplasty with the von Langenbeck (VL) or Furlow (F) technique; and 4)
to one of four possible surgeons (C1, C2, C3, C4).
Information about the occurrence of fistula after primary palatoplasty was of interest
to the present study. To determine classes in data mining, Spina’s14 classification was used, grouping patients into two groups: SUCCESS (patients without
fistula or with fistula in the pre-incisive foramen region); FAILURE (patients with
fistulas in the post-incisive foramen region or transforamen fistulas). The incisive
foramen marks the limits of the primary palate (central part of the upper lip and
premaxilla) and secondary palate (hard and soft palate).
Table 1 presents the information identified in the patient’s records for this study. The
definition of the variables of interest is directly related to some factors, including
1) Surgical treatment protocols (a surgical technique in cheiloplasty and palatoplasty, surgeon, use of surgery modifications
such as relaxing incision and vomer flap, duration of palatoplasty in minutes ); 2)
Patient characteristics (age at palatoplasty, duration of palatoplasty); 3) Post-surgical complications (whether there was an infection in the palatoplasty - at the site -or elsewhere after
the primary palatoplasty; whether there was vomiting or coughing in the postoperative
period of the palatoplasty); 4) Speech results after surgery (whether there was a symptomatic diagnosis of velopharyngeal dysfunction, presence
of hypernasality - recorded in spontaneous or directed conversation); results of nasal
air emission, hypernasality, and hyponasality tests (observed during the repetition
of 10 words); 5) Result of the surgery regarding the occurrence of fistula (SUCCESS or FAILURE). The variables of interest are listed in the “attribute name”
column.
Table 1 - Definition of variables (attributes) of interest for this study.
Variables (Attribute Name) |
Categories (Values) |
Surgical technique in cheiloplasty |
Millard, Spina |
Palatoplasty time |
Early (9-12m), Late (>12m) |
Age at palatoplasty |
months (m) |
Surgical technique in palatoplasty |
Furlow, von Langenbeck |
Surgeon |
C1, C2, C3, C4 |
Relaxing incision |
No incision, unilateral, bilateral |
Vomer flap |
yes, no |
Duration of palatoplasty |
Minutes |
Infection in palatoplasty surgery, at another location |
There was not; at the site of |
Postoperative vomiting Palatoplasty |
yes, no |
Cough after surgery of palatoplasty |
yes, no |
Fever |
yes, no |
Suggestive of velopharyngeal dysfunction |
yes, no |
Hypernasality |
yes, no |
Air emission test |
[1-10] |
Hypernasality test |
[1-10] |
Hyponasality test |
[1-10] |
*Occurrence of fistula |
SUCCESS, FAILURE |
Table 1 - Definition of variables (attributes) of interest for this study.
Variables associated with surgical treatment protocols and patient characteristics
can help indicate whether there is a greater propensity to develop fistulas (even
before the surgical procedure). In contrast, variables related to post-surgical complications
and speech results can be indicative of clinically relevant fistulas after surgery.
In the management of CLP, the SUCCESS of the treatment occurs in the absence of a
fistula and absence of speech disorders. For the present study, a fistula in the region
posterior to the incisive foramen and the presence of velopharyngeal dysfunction were
interpreted as indicative of treatment FAILURE. The guiding question for data mining
involved checking which factors (surgical treatment protocols, patient characteristics,
post-surgical complications, and speech results after surgery) would be associated
with the occurrence or not of fistulas. Therefore, this study aimed to identify whether
some of the analyzed variables could be used as predictors of the occurrence of fistulas
on the palate or as indicators of clinically relevant fistulas after palatoplasty.
To compute the results of the experiment, the algorithm C4.5 (J48) was used, which
generates decision trees to find the relationship between the characteristics considered
and the results of the surgeries, as well as the “Apriori algorithm” (association)
for the rule generation. Decision trees allow variables or attributes to be categorical
(qualitative) or numerical (quantitative). It can be used simultaneously by the model
(which proved adequate considering the different types of variables in the database
used in the investigation ). The Apriori algorithm deals only with qualitative variables.
Both models induce a hypothesis through a model represented by rules (“if...then”).
In this analysis, the variables of interest were treated as attributes in the WEKA
software. Considering a typical mining task, the experiment was divided into four
stages: data pre-processing, feature extraction, classification, and description of
results. The procedure was performed considering the occurrence of a fistula after
palatoplasty as the primary result. Pre-processing was carried out semi-automatically.
Data from the medical records made available in the “.XLS” file format (Excel® spreadsheet)
were converted to the “.ARFF” format (used by WEKA) using the Excel2ArffConverter
software. Before conversion, the attributes were identified as described in Table 1.
RESULTS
Only patients with complete data were selected for analysis, considering the parameters
described in Table 1. After discarding patients with incomplete data for any variables, 222 patients were
selected for analysis. Due to the possibility of bias in the base, it was decided
not to estimate the missing values7. Information on the occurrence of some type of fistula was identified in the medical
records of 222 (47.6%) of the 466 patients studied, and data from these patients were
mined for this article.
Of the 222 patients considered for this study, 98 (44.1%) were female, and 124 (55.9%)
were male. The mean age at primary palatoplasty was 12.8 months (σ=3.2). In this group,
114 (51.3%) received the Millard procedure in primary cheiloplasty, while 108 (48.7%)
received the Spina procedure. One hundred twelve patients (50.4%) received the Furlow
technique in primary palatoplasty, while 110 (49.6%) received von Langenbeck. Of the
patient sample, 182 (81.9%) belonged to the SUCCESS group and 40 (18.1%) to the FAILURE
group.
Through constructing a decision tree, 37 rules were generated from the complete patient
data set. However, in this article, we chose to display only the 5 rules with the
highest value for the coverage metric of each final result of the surgery (SUCCESS
or FAILURE). The coverage metric is the ratio of correctly classified data to the
total sample data for that class. Information about the rule’s accuracy metric (probability
of the final result conditional on the attributes, i.e., the model’s ability to avoid
false positives) was also considered.
The mean accuracy of the rules associated with surgical SUCCESS is 97.26% (σ=4.59).
The five rules together present coverage of about 77.5%; that is, if applied to the
data, they manage to detect 77.5% of the cases of SUCCESS. As for the FAULT class,
the average accuracy of the associated rules is 84.32% (σ=9.40). The coverage of the
five rules is 62.5%, that is, the number of FAILURE cases that the rules can detect
if applied to the database.
The rule with greater coverage and accuracy for predicting a good result indicates
that the main factors involved are: infection (“absence”), hypernasality tests (“≤6”)
and hyponasality (“>9”), and the surgical technique (“von Langenbeck”). The interpretation
of this rule indicates, therefore, that patients submitted to the “von Langenbeck”
procedure, without infection and with hyponasality test results with values greater
than or equal to 9 and hypernasality test with values less than or equal to 6 are
more likely to have obtained SUCCESS as the final result of the surgery. As for FAILURE,
according to the two rules with greater precision and coverage, the factors involved
are related to post-surgical complications and speech results and include infection
(“absence or elsewhere”), hypernasality tests (“greater than 6”), air emission (“greater
than 9”) and fever (“yes”). The rules are shown in Table 2.
Table 2 - Surgery result.
Number |
Rule |
Result (class) |
Coverage |
Precision |
1 |
If “infection=none” and “hypernasality test≤6” and “cough=no” and “surgical technique=von
Langenbeck” and “hyponasality test>9”
|
SUCCESS |
77 |
100% |
2 |
If “infection=none” and “test hypernasality≤6” and “cough=no” and “surgical technique=Furlow”
and “fissure width=regular”
|
SUCCESS |
33 |
96.9% |
3 |
If “infection=none” and “test in hypernasality≤6” and “cough=no” and “surgical technique=Furlow”
and “cleft width=wide” and “surgeon=C3”
|
SUCCESS |
19 |
89.4% |
4 |
If “infection=none” and “test in hypernasality≤6” and “cough=no” and “surgical technique=Furlow”
and “fissure width=wide” and “surgeon=C2”
|
SUCCESS |
7 |
100% |
5 |
If “infection=none” and “test in hypernasality≤6” and “cough=no” and “surgical technique=Furlow”
and “fissure width=wide” and “surgeon=C1” and “relaxing incision=no”
|
SUCCESS |
5 |
100% |
6 |
If “infection=none” and “hypernasality test>6” and “air emission test>9” and “fever=yes” |
FAILURE |
6 |
83.3% |
7 |
If “infection=occurred elsewhere” |
FAILURE |
6 |
83.3% |
8 |
If “infection=none” and “hypernasality test>6” and “air emission test>9” and “fever=no”
and “relaxing incision=bilateral” and “vomit=no” and “surgeon=C3”
|
FAILURE |
5 |
80.0% |
9 |
If “infection=none” and “test hypernasality≤6” and “cough=no” and “surgical technique=Furlow”
and “fissure width=wide” and “surgeon=C4” and “air emission test>2”
|
FAILURE |
4 |
100% |
10 |
If “infection=none” and “test in hypernasality>6” and “air emission test>9” and “fever=no”
and “relaxing incision=no”
|
FAILURE |
4 |
75.0% |
Table 2 - Surgery result.
When analyzing the global performance of the model (generated decision tree) concerning
its predictive capacity, it is observed that it correctly classifies 95.9% of the
patients and incorrectly only 4.1%. Considering each category individually, the model
manages to hit 90.0% of the cases in which a FAILURE result occurs. As for the other
class, the model manages to hit 97.3% of the cases in which a SUCCESS result occurs.
The correlations found using the Apriori algorithm were obtained using the support
(minimum of 60%) and confidence (minimum of 90%) metrics. The objective was to find
frequent (high support value) rules in the database with a high degree of confidence
(directly related to rule validity). Four rules were found with an average confidence
of 90.75% (σ=0.5) and an average support of 69.45% (σ=0.49), which meet the requirements
above, as shown in Table 3.
Table 3 - Rules with high support and confidence values.
Characteristics |
Result |
Support |
Confidence |
Absence of cough and infection without suggestive of dysfunction velopharyngeal |
SUCCESS |
69.8% |
91.0% |
Absence of cough and infection with absent hypernasality |
SUCCESS |
69.8% |
91.0% |
Absence of cough and infection without suggestive of dysfunction velopharyngeal with
absent hypernasality
|
SUCCESS |
69.8% |
91.0% |
Absence of cough and infection and no fever |
SUCCESS |
68.4% |
90.0% |
absence of cough |
FAILURE |
77.5% |
100.0% |
absence of infection |
FAILURE |
77.5% |
100.0% |
Furlow’s surgical technique |
FAILURE |
72.5% |
100.0% |
Use of vomer flap |
FAILURE |
70.0% |
100.0% |
absence of vomiting |
FAILURE |
67.5% |
100.0% |
Absence of cough and infection |
FAILURE |
67.5% |
100.0% |
Table 3 - Rules with high support and confidence values.
Considering only the 40 patients in the FAILURE group, the results show the six rules
found with a minimum support of 67.5% and a minimum confidence of 100% (Table 3). The rules have average support of 72.08% for this group. In the SUCESSO group,
the model indicates the absence of post-surgical intercurrences (cough and infection)
and speech results with absent hypernasality. Patients in the FAILURE group also had
no cough and no infection.
Table 4 summarizes the relationship between the duration of the palatoplasty and the result
regarding the occurrence of fistulas. It is observed that surgery times vary from
25 to 140 minutes.
Table 4 - Relationship between duration of palatoplasty and classes (SUCCESS and FAILURE).
Duration: Minutes |
No |
Average |
Standard deviation |
Minimum |
Maximum |
Duration of palatoplasty – All groups |
222 |
65.62 |
24.43 |
25 |
140 |
Duration of palatoplasty (group SUCCESS) |
182 |
62.57 |
22.89 |
25 |
125 |
Duration of palatoplasty (group FAILURE) |
40 |
79.5 |
26.62 |
25 |
140 |
Table 4 - Relationship between duration of palatoplasty and classes (SUCCESS and FAILURE).
The algorithms allow data-based exploration of non-linear relationships and interactions
between many variables, generating easy interpretation models. However, as a weakness
of the method, the unbalance between the two groups (SUCCESS and FAILURE) and the
full use of the sample for the induction of the models can be pointed out, which can
cause overfitting of the data, impairing the extrapolation of the findings ( rules)
to other databases.
DISCUSSION
Specifically, concerning fistulas, the rules found with a high degree of precision
and coverage can show useful standards on which variables, among surgical treatment
protocols, patient characteristics, speech results after surgery, and post-surgical
intercurrences, are determinant for the success or failure of the palatoplasty. The
opportunity to adopt data mining on patients undergoing palatoplasty can provide a
better understanding of the specificities that may occur with the group of patients,
thus expanding the professional’s knowledge in identifying the conduct to be adopted.
In this specific study, the visibility given to some factors (Table 1) allows health professionals to identify patterns of association of variables, with
the proper analysis of this set of discoveries, which can give meaning to diagnostic
and therapeutic actions. In the same way, as in other previous studies, this investigation
opted for combining different types of data mining tasks to carry out the experiment
or identify patterns15,16,17,18,19.
Despite the initial availability of data referring to 466 patients, we chose to use
222 (considering only the complete ones). This may have limited the rules obtained
and not have evidenced other associations of the factors related to the final results
of the palatoplasty. This decision follows the guidelines of other works20. The entire database can be used for future studies, as some algorithms can deal
with missing data11.
Another limitation related to the base is the fact that the two classes considered
are unbalanced; however, as they reflect the real situation in which SUCCESS results
are more common than FAILURES, it was decided to maintain the natural proportion of
the data. This presence of majority classes much more frequently than other minority
classes makes algorithms respond well to majority classes to the detriment of minority
ones. In future works, the experiment can be repeated using random resampling techniques
of the data in order to generate balanced sets21.
The fact that the entire database was used for induction and testing of the model
may generate a bias to fit the data. Any mining method is subject to generating a
model that overfits itself to the data on which it was induced (overfitting) but cannot
generalize the learned knowledge, not obtaining a good performance when confronted
with data from another base. However, this approach was chosen as the purpose of this
experiment is not to induce a model to automate the classification process of surgeries
but rather to extract rules that can be evaluated by humans, evidencing useful patterns.
The analysis of Table 2 indicates that the SUCCESS results are associated with post-surgical complications
such as the absence of infection and cough; in addition, the patients presented a
hypernasality test below or equal to 6 (on a scale that goes up to 10). In the case
of large fissures associated with the Furlow surgical technique, in addition to the
complications highlighted, the surgeon’s factor influences the final result.
In the case of the FAILURE results, the presence of infection seems to be an important
factor; however, it is not decisive. Due to the similarity between rules 9, 3, and
4 (Table 2), the decisive factor for obtaining a FAILURE result is linked to the surgeon. Under
the same conditions, surgeons C2 and C3 obtained SUCCESSFUL results; however, surgeon
C4 obtained SUCCESS in only 50% of the surgeries, which may indicate the influence
of the surgeon factor. In the case of speech results, values of hypernasality tests
greater than 6 are indicative of a possible FAILURE.
In the same way as the rules of the decision tree, the rules presented by the model
induced by the Apriori algorithm must be evaluated by a professional to validate them
against reality. The Apriori algorithm does not deal with quantitative attributes,
only with categorical ones, which requires excluding some attributes or even their
transformation to non-numerical data (discretization process); this strategy was used
in some processing carried out in this work. Thus, to avoid this limitation in future
work, other algorithms may be experimented with, such as AprioriTid, SETM, and AprioriHybrid22.
The analysis of Table 3 indicates that, in general, the absence of post-surgical complications (infection
and fever) and speech results with absent hypernasality, as well as patients without
suggestive of velopharyngeal dysfunction, present SUCCESS after primary palatoplasty.
Concerning surgical procedures, there are indications that the Furlow technique and
the Vomer flap are frequent in the FAILURE group. Observations such as the absence
of cough, vomiting, or infection alone cannot be used as parameters to rule out a
possible FAILURE.
The analysis of Table 4 shows that a palatoplasty in the group of patients who had a result of FAILURE lasts
an average of 79.5 minutes; for the group of patients with SUCCESS results, the average
drops to 62.57 minutes. There are indications, therefore, that longer surgeries tend
to cause worse results.
Finally, it is recognized that this study offers only a punctual perspective of reality
through the analysis of models induced by data mining techniques in the considered
database since it reveals only a few factors associated with the results of palatoplasty
from the point of view of the mining algorithms, with the need for validation by health
professionals.
CONCLUSION
Data analysis revealed that the absence of some post-surgical complications (fever,
cough, infection) together with speech results after surgery (hypernasality, suggestive
of velopharyngeal dysfunction) and with characteristics associated with surgical treatment
protocols (technique, the flap of the vomer, surgeon) could help to predict the success
or failure of the palatoplasty.
1. Hospital de Reabilitação de Anomalias Craniofaciais, Universidade de São Paulo,
Programa de Pós-Graduação em Ciências da Reabilitação, Bauru, SP, Brazil.
2. Hospital de Reabilitação de Anomalias Craniofaciais, Universidade de São Paulo,
Programa de Pós-Doutorado, Bauru, SP, Brazil
3. Hospital de Reabilitação de Anomalias Craniofaciais, Universidade de São Paulo,
Departamento de Cirurgia Plástica, Bauru, SP, Brazil
4. Faculdade de Odontologia de Bauru, Universidade de São Paulo, Programa de Pós-Graduação
em Fonoaudiologia, Bauru, SP, Brazil
Corresponding author: Patrick Pedreira Silva R. Silvio Marchione, 3-20, Vila Nova, Cidade Universitaria, Bauru, SP, Brazil. Zip
code: 17012-900 E-mail: patrickpsilva@alumni.usp.br