Introduction: Cranioplasty is a standard technique for skull defect repair. Restoration of cranial defects is imperative for brain protection and allowing for homeostasis of cerebral spinal fluid within the cranial vault. Calcium phosphate hydroxyapatite (HA) is a synthetic-organic material that is commonly used in cranioplasty. We evaluate a patient series undergoing HA cement cranioplasty with underlying bioresorbable mesh for various cranial defects and propose a preliminary computational model for understanding skull osteointegration. Methods: A retrospective review was performed at the institution for all pediatric patients who underwent HA cement cranioplasty. Seventeen patients were identified, and success of cranioplasty was determined based on clinical and radiographic follow-up. A preliminary computational model was developed using bone growth and scaffold decay equations from previously published literature. The model was dependent on defect size and shape. Patient data were used to optimize the computational model. Results: Seventeen patients were identified with an average age of 6 ± 5.6 years. Average defect size was 11.7 ± 16.8 cm2. Average time to last follow-up computer tomography scan was 10 ± 6 months. Three patients had failure of cranioplasty, all with a defect size above 15 cm2. The computational model developed shows a constant decay rate of the scaffold, regardless of size or shape. The bone growth rate was dependent on the shape and number of edges within the defect. Thus, a star-shaped defect obtained a higher rate of growth than a circular defect because of faster growth rates at the edges. The computational simulations suggest that shape and size of defects may alter success of osteointegration. Conclusion: Pediatric cranioplasty is a necessary procedure for cranial defects with a relatively higher rate of failure than adults. Here, we use HA cement to perform the procedure while creating a preliminary computational model to understand osteointegration. Based on the findings, cranioplasty shape may alter the rate of integration and lead to higher success rates.

1.
Lam
S
,
Kuether
J
,
Fong
A
,
Reid
R
.
Cranioplasty for large-sized calvarial defects in the pediatric population: a review
.
Craniomaxillofac Trauma Reconstr
.
2015 Jun
;
8
(
2
):
159
70
. .
2.
Salam
AA
,
Ibbett
I
,
Thani
N
.
Paediatric cranioplasty: a review
.
Interdiscip Neurosurg
.
2018
;
13
:
59
65
. .
3.
Gregoire
M
,
Orly
I
,
Kerebel
LM
,
Kerebel
B
.
In vitro effects of calcium phosphate biomaterials on fibroblastic cell behavior
.
Biol Cell
.
1987
;
59
(
3
):
255
60
. .
4.
Perez
RA
,
Kim
TH
,
Kim
M
,
Jang
JH
,
Ginebra
MP
,
Kim
HW
.
Calcium phosphate cements loaded with basic fibroblast growth factor: delivery and in vitro cell response
.
J Biomed Mater Res A
.
2013 Apr
;
101
(
4
):
923
31
. .
5.
Shah
AM
,
Jung
H
,
Skirboll
S
.
Materials used in cranioplasty: a history and analysis
.
Neurosurg Focus
.
2014
;
36
(
4
):
E19
. .
6.
Wong
RK
,
Gandolfi
BM
,
St-Hilaire
H
,
Wise
MW
,
Moses
M
.
Complications of hydroxyapatite bone cement in secondary pediatric craniofacial reconstruction
.
J Craniofac Surg
.
2011 Jan
;
22
(
1
):
247
51
. .
7.
Maenhoudt
W
,
Hallaert
G
,
Kalala
JP
,
Baert
E
,
Dewaele
F
,
Bauters
W
,
Hydroxyapatite cranioplasty: a retrospective evaluation of osteointegration in 17 cases
.
Acta Neurochir
.
2018 Nov
;
160
(
11
):
2117
24
.
8.
Moreira-Gonzalez
A
,
Jackson
IT
,
Miyawaki
T
,
Barakat
K
,
DiNick
V
.
Clinical outcome in cranioplasty: critical review in long-term follow-up
.
J Craniofac Surg
.
2003
;
14
(
2
):
144
53
. .
9.
Stefini
R
,
Esposito
G
,
Zanotti
B
,
Iaccarino
C
,
Fontanella
MM
,
Servadei
F
.
Use of “custom made” porous hydroxyapatite implants for cranioplasty: postoperative analysis of complications in 1549 patients
.
Surg Neurol Int
.
2013
;
4
:
12
. .
10.
Canullo
L
,
Genova
T
,
Gross Trujillo
E
,
Pradies
G
,
Petrillo
S
,
Muzzi
M
,
Fibroblast interaction with different abutment surfaces: in vitro study
.
Int J Mol Sci
.
2020
;
21
(
6
):
1919
.
11.
Bidan
CM
,
Wang
FM
,
Dunlop
JW
.
A three-dimensional model for tissue deposition on complex surfaces
.
Comput Methods Biomech Biomed Engin
.
2013 Oct
;
16
(
10
):
1056
70
. .
12.
Pang
D
,
Tse
HH
,
Zwienenberg-Lee
M
,
Smith
M
,
Zovickian
J
.
The combined use of hydroxyapatite and bioresorbable plates to repair cranial defects in children
.
J Neurosurg
.
2005 Jan
;
102
(
1 Suppl
):
36
43
. .
13.
Rumpler
M
,
Woesz
A
,
Dunlop
JW
,
van Dongen
JT
,
Fratzl
P
.
The effect of geometry on three-dimensional tissue growth
.
J R Soc Interface
.
2008 Oct 6
;
5
(
27
):
1173
80
. .
14.
Dunlop
JWC
,
Fischer
FD
,
Gamsjäger
E
,
Fratzl
P
.
A theoretical model for tissue growth in confined geometries
.
J Mech Phys Solids
.
2010
;
58
(
8
):
1073
87
.
15.
Gamsjager
E
,
Bidan
CM
,
Fischer
FD
,
Fratzl
P
,
Dunlop
JW
.
Modelling the role of surface stress on the kinetics of tissue growth in confined geometries
.
Acta Biomater
.
2013 Mar
;
9
(
3
):
5531
43
.
16.
Knychala
J
,
Bouropoulos
N
,
Catt
CJ
,
Katsamenis
OL
,
Please
CP
,
Sengers
BG
.
Pore geometry regulates early stage human bone marrow cell tissue formation and organisation
.
Ann Biomed Eng
.
2013 May
;
41
(
5
):
917
30
. .
17.
Guyot
Y
,
Papantoniou
I
,
Chai
YC
,
Van Bael
S
,
Schrooten
J
,
Geris
L
.
A computational model for cell/ECM growth on 3D surfaces using the level set method: a bone tissue engineering case study
.
Biomech Model Mechanobiol
.
2014 Nov
;
13
(
6
):
1361
71
. .
18.
Laycock
B
,
Nikolic
M
,
Colwell
JM
,
Gauthier
E
,
Halley
P
,
Bottle
S
,
Lifetime prediction of biodegradable polymers
.
Prog Polym Sci
.
2017
;
71
:
144
89
.
19.
Habraken
WJ
,
Wolke
JG
,
Mikos
AG
,
Jansen
JA
.
Injectable PLGA microsphere/calcium phosphate cements: physical properties and degradation characteristics
.
J Biomater Sci Polym Ed
.
2006
;
17
(
9
):
1057
74
. .
20.
Gopferich
A
.
Polymer bulk erosion
.
Macromolecules
.
1997
;
30
(
9
):
2598
604
. .
21.
An
J
,
Liao
H
,
Kucko
NW
,
Herber
RP
,
Wolke
JG
,
van den Beucken
JJ
,
Long-term evaluation of the degradation behavior of three apatite-forming calcium phosphate cements
.
J Biomed Mater Res A
.
2016 May
;
104
(
5
):
1072
81
.
22.
Winslow
RL
,
Trayanova
N
,
Geman
D
,
Miller
MI
.
Computational medicine: translating models to clinical care
.
Sci Transl Med
.
2012 Oct 31
;
4
(
158
):
158rv11
. .
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