\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{Nomogram to predict postoperative complications after cytoreductive
surgery for advanced epithelial ovarian cancer: a multicenter
retrospective cohort study}



\author[1]{Caixia Jiang}%
\author[2]{Yingwei Liu}%
\author[2]{Junying Tang}%
\author[1]{Zhengyu Li}%
\author[3]{Wenjiao Min}%
\affil[1]{Sichuan University West China Second University Hospital Department of Obstetrics and Gynecology}%
\affil[2]{The First Affiliated Hospital of Chongqing Medical University}%
\affil[3]{University of Electronic Science and Technology of China Sichuan Provincial People's Hospital}%


\vspace{-1em}



  
  \date{May 04, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Objective: To establish and externally validate nomograms to predict the
risk of postoperative complications after cytoreductive surgery for
advanced epithelial ovarian cancer (AEOC) patients. Design: A
retrospective cohort study. Setting: Multicenter Population or Sample:
Patients with FIGO stage IIIC-IV epithelial ovarian cancer who underwent
cytoreductive surgery. Methods: Patient preoperative characteristics
were used to predict the risk of postoperative complications by
univariate and multivariate analyses. Nomograms were developed based on
multivariate modeling. Main Outcome Measures: Postoperative overall and
severe complications. Results: Overall, 585 AEOC patients were included
for analysis (training cohort = 426, validation cohort = 159). Among the
clinical variables assessed, modified frailty index (mFI) (OR 1.96 and
2.18), FIGO stage (OR 2.31 and 3.22), and Surgical Complexity Score
(SCS) (OR 1.16 and 1.23) were most strongly associated with the risk of
overall and severe complications, respectively. The final nomograms had
superior internal discrimination with the C-index were 0.74 and 0.78 for
predicting overall and severe complications, respectively, as well as
good consistency and stable calibration. Additionally, the validation
cohort suggested a good external discrimination with the C-index were
0.92 and 0.91 to predict overall and severe complications, respectively.
Conclusions: These two nomograms can help clinicians to predict the risk
of postoperative complications, and instruct clinical decision-making,
as well as perioperative care to prevent adverse outcomes for AEOC
patients. Funding: The Department of Science and Technology of Sichuan
Province (grant number 21PJ050). Keywords: Advanced epithelial ovarian
cancer, Postoperative complications, Cytoreductive surgery, Nomogram%
\end{abstract}%



\sloppy


\textbf{Title page}

\textbf{Title:} \textbf{Nomogram to predict postoperative complications
after cytoreductive surgery for advanced epithelial ovarian cancer: a
multicenter retrospective cohort study}

\textbf{Authors list:} Caixia Jiang\textsuperscript{1} (co-first
author), Yingwei Liu\textsuperscript{2} (co-first author), Junying
Tang\textsuperscript{2}, Zhengyu
Li\textsuperscript{1,3}(co-corresponding author), Wenjiao
Min\textsuperscript{4}(co-corresponding author)

Caixia Jiang and Yingwei Liu contributed equally to this study.

\textbf{Author affiliations:}

\textsuperscript{1} Department of Obstetrics and Gynecology, West China
Second University Hospital, Sichuan University, Chengdu, People's
Republic of China;

\textsuperscript{2} Department of Obstetrics and Gynecology, The First
Affiliated Hospital of Chongqing Medical University, Chongqing, People's
Republic of China;

\textsuperscript{3} Key Laboratory of Obstetrics and Gynecologic and
Pediatric Diseases and Birth Defects of Ministry of Education, West
China Second University Hospital, Sichuan University, Chengdu, People's
Republic of China;

\textsuperscript{4} Psychosomatic Department, Sichuan Provincial
People's Hospital, University of Electronic Science and Technology of
China, Chinese Academy of Sciences Sichuan Translational Medicine
Research Hospital, Chengdu, China

\textbf{Address of co-corresponding author (Zhengyu Li):} postal
address: 610041, Tel: +86 18982151025, Fax: 86-21-85501332, E-mail:

\emph{zhengyuli01@126.com}

\textbf{Address of co-corresponding author (Wenjiao Min):} postal
address: 610000, Tel: +86 -02887394401, Fax: 86-02887394401, E-mail:

\emph{jiaojiao1766@163.com}

\textbf{Running title:} AEOC postoperative complications prediction

\textbf{Abstract}

\textbf{Objective:}To establish and externally validate nomograms to
predict the risk of postoperative complications after cytoreductive
surgery for advanced epithelial ovarian cancer (AEOC) patients.

\textbf{Design:} A retrospective cohort study.

\textbf{Setting:} Multicenter

\textbf{Population or Sample:} Patients with FIGO stage IIIC-IV
epithelial ovarian cancer who underwent cytoreductive surgery.

\textbf{Methods:} Patient preoperative characteristics were used to
predict the risk of postoperative complications by univariate and
multivariate analyses. Nomograms were developed based on multivariate
modeling.

\textbf{Main Outcome Measures:} Postoperative overall and severe
complications.

\textbf{Results:} Overall, 585 AEOC patients were included for analysis
(training cohort = 426, validation cohort = 159). Among the clinical
variables assessed, modified frailty index (mFI) (OR 1.96 and 2.18),
FIGO stage (OR 2.31 and 3.22), and Surgical Complexity Score (SCS) (OR
1.16 and 1.23) were most strongly associated with the risk of overall
and severe complications, respectively. The final nomograms had superior
internal discrimination with the C-index were 0.74 and 0.78 for
predicting overall and severe complications, respectively, as well as
good consistency and stable calibration. Additionally, the validation
cohort suggested a good external discrimination with the C-index were
0.92 and 0.91 to predict overall and severe complications, respectively.

\textbf{Conclusions:} These two nomograms can help clinicians to predict
the risk of postoperative complications, and instruct clinical
decision-making, as well as perioperative care to prevent adverse
outcomes for AEOC patients.

\textbf{Funding:} The Department of Science and Technology of Sichuan
Province (grant number 21PJ050).

\textbf{Keywords:} Advanced epithelial ovarian cancer, Postoperative
complications, Cytoreductive surgery, Nomogram

\textbf{Tweetable abstract}

Complete resection leaving no residual macroscopic tumor is the most
important prognostic factor in cytoreductive surgery for AEOC, which
could also bring a substantial increase in perioperative morbidity. This
study builds and externally validates the nomograms for predicting the
risk of postoperative complications, which can play a significant role
in risk stratification and clinical decision-making for AEOC patients,
as well as provide a possibility of improving perioperative care to
reduce adverse outcomes.

\textbf{Funding}

Supported by the Department of Science and Technology of Sichuan
Province (grant number 21PJ050).

\textbf{Introduction}

Treatment for patients with advanced epithelial ovarian cancer (AEOC)
includes aggressive cytoreductive surgery followed by platinum-based
chemotherapy{[}1{]}. As we all know, complete resection leaving no
residual macroscopic tumor is a quite important prognostic factor in
AEOC surgery, which could also bring an increase in perioperative
morbidity{[}2{]}. However, not every patient can tolerate the
perioperative complications after maximally cytoreductive surgery,
especially for patients with poor physical status, comorbidities, or
high tumor burden{[}3, 4{]}. Some researchers have already paid
attention to the contradictions between patients' physical tolerance and
tumor resectability, whatever the timing of the procedure, primary or
interval debulking surgery. And previous studies reported that increased
age, advanced International Federation of Gynecology and Obstetrics
(FIGO) stage, and the number of cytoreductive procedures were
independently associated with an increased risk of morbidity and
mortality{[}5-9{]}. It is a known fact that postoperative complications
may lead to prolonged hospital stays and delay in the receipt of
adjuvant chemotherapy, impacting the quality of life and potentially
shortening overall survival of AEOC patients{[}10{]}. A recent
meta-analysis showed that neoadjuvant chemotherapy (NACT) was associated
with less morbidity and no difference in overall or progression free
survival compared to primary debulking surgery (PDS) in advanced ovarian
cancer{[}11{]}. Therefore, balancing the survival benefit of maximally
complete resection with its associated postoperative complications, and
making a preoperative prediction of the risk of postoperative morbidity
is of great importance for identifying those subgroups of patients who
might benefit from alternative treatment approaches such as NACT.

But up to now, a few studies have reported predictive models for
postoperative morbidity or mortality in AEOC surgery with limited
evidences{[}12, 13{]}. The objective of this study is to identify the
influencing factors of adverse postoperative outcomes for AEOC patients
by multicenter retrospective cohort study, and to develop and externally
verify a nomogram for predicting the risk of postoperative
complications.

\textbf{2. Methods}

\textbf{2.1 Patients}

Patients who present to West China Second University Hospital of Sichuan
University with a chief complaint of a suspected ovarian mass or ovarian
cancer and underwent surgery from Jan. 1, 2018 to Dec. 31, 2020 were
retrospectively enrolled for primary screening. Patients who underwent
cytoreductive surgery for stage IIIC or IV EOC (including primary
peritoneal carcinoma and fallopian tube cancer) were included in this
study. The treatment decision-making for them was based on the Suidan
criteria combined with the Fagotti criteria{[}14, 15{]}. For patients
who had low probability of complete resection were clinical sent to NACT
with later interval debulking surgery (IDS). And patients with the
following characteristics were excluded: recurrent ovarian cancer,
non-EOC, early-stage ovarian cancer, palliative surgery, and accompanied
by other gynecological cancers. After screening according to the
inclusion and exclusion criteria, 426 patients were enrolled into the
training group to establish the nomogram. And the EOC patients who
fulfilled the criteria from The First Affiliated Hospital of Chongqing
Medical University were used for external validation of the nomogram.
The flow chart of the study was shown in \textbf{Figure S1} .

\textbf{2.2 Clinical data extraction}

Clinical data were obtained from electronic medical record review and
telephone follow-up. The modified frailty index (mFI) was used to
evaluate patient's performance status, calculated by adding one point
for each of the 11 following individual variables: diabetes mellitus,
functional status index of 2 or higher, chronic obstructive pulmonary
disease or current pneumonia, congestive heart failure, previous
myocardial infarction, previous percutaneous coronary intervention or
coronary surgery or angina, hypertension requiring medication,
peripheral vascular disease or resting pain, impaired sensorium,
transient ischemic attack or cerebrovascular accident, and
cerebrovascular disease with deficit{[}16{]}. The complexity of surgical
procedures was evaluated based upon the Surgical Complexity Score (SCS),
according to Aletti et al{[}12{]}. We reviewed the intraoperative
information from electronic medical record to get the score. An ordinal
scale was generated, so that patients have been stratified into 3 groups
of surgical complexity: low (score [?] 3), intermediate (score 4-7) and
high (score [?] 8). Residual disease (RD) after surgery was classified
into three categories: R0 if there was no macroscopic residual disease,
measurable or R1 (RD 0.1--1.0 cm), and suboptimal or R2 (RD
\textgreater{} 1 cm) based on the largest residual tumor diameter.
Besides, optimal debulking was defined as the sum total of R0 and R1.
Postoperative 30-day complications were graded using the modified
Accordion classification 0-4 scale as in our previous study{[}17, 18{]},
And the number of overall complications and severe complications of this
study were counted, respectively. In terms of surgical outcomes, the
continuous variables were roughly grouped by median.

\textbf{2.3 External validation and statistical analyses}

The normality test of the measurement data was conducted by using the
Kolmogorov-Smirnov and the Shapiro-Wilks test. Continuous variables were
described in terms of mean (SD) or median (quartile), and categorical
variables were presented as numbers and proportions. Comparisons between
groups were evaluated using Chi-squared test or Fisher exact test for
categorical variables. Univariate logistic regressions analysis were
performed to evaluate the association between predicting variables and
30-day postoperative severe complications and overall complications.
Variables with P [?] 0.10 were incorporated into multivariate analyses to
find out the independent predicting variables. Estimates were given as
odds ratios (ORs) and 95\% confidence intervals (CIs). A nomogram was
developed based on the most valuable predictive factors, and validation
was performed using the bootstrapping correction technique. In order to
prevent overestimation and to achieve a better model performance,
variables with P [?] 0.10 were entered into our prognostic model{[}19{]}.
A nomogram's discrimination is measured via a C-index. With a binary
outcome, the C-index is identical to the area under the curve (AUC) for
a receiver operating characteristic (ROC) curve. The Hosmer-Lemeshow
(H-L) goodness of fit test and calibration curves were used to show
predicted nomogram probabilities compared to the actual probability
across the range of model predictions. All analyses were conducted using
the SPSS version 22.0 for Windows (SPSS Inc., Chicago, IL, USA) and R
version 4.1.3 with the rms packages (https://www.r-project.org/).
Statistical tests were 2-sided, with data followed by P \textless{} 0.05
considered statistically significant.

\textbf{3. Results}

From January 2018 to December 2020, a total of 1182 consecutive patients
with ovarian cancer undergoing surgery were recruited for primary
screening. As shown in \textbf{Figure S1} (Supporting Information), 756
patients were excluded and 426 patients were evaluable for the training
cohort, besides, 159 patients were enrolled into the external validation
cohort. Baseline and operative characteristics of patients in the
training cohort are summarized in \textbf{Table 1} . Overall, all the
patients were no more than four comorbidities, and stratified by mFI
scores of 0-1 and 2-4. In particular, 30 patients (7.04\%) had diabetes
mellitus, 78 patients (18.31\%) had hypertension requiring medication,
71 patients (16.67\%) had peripheral vascular disease or resting pain,
40 patients (9.39\%) suffered from chronic obstructive pulmonary disease
and others had previous percutaneous coronary intervention or coronary
surgery or angina, cerebrovascular accident or functional status index [?]
2. In addition, patients who received IDS (57.98\%) were slightly more
than PDS (42.02\%). Briefly, most patients (66.67\%) in this study
underwent intermediate complexity surgery, and less than 20\% of
patients underwent either low or high complexity surgery. Specifically,
patients who underwent oophorectomy with or without hysterectomy were
included. 409 patients (96.01\%), 308 patients (72.30\%) and 271
patients (63.62\%) had omentectomy, pelvic lymphadenectomy and
paraaortic lymphadenectomy, respectively. 6 patients (1.41\%) and 7
patients (1.64\%) underwent liver and diaphragm surgery, respectively.
Small bowel resection was performed in 8 patients (1.88\%), whereas
large bowel resection with T-T anastomosis was necessary in 55 patients
(12.91\%). Furthermore, 70 patients (16.43\%) and 6 patients (1.41\%)
had colon/rectosigmoid resection and spleen surgery, respectively.
Additionally, most patients (76.06\%) achieved optimal debulking,
whereas less than one-fourth of patients underwent suboptimal resection.

As the most vital evaluation parameters of surgical outcome in this
study, we found that 123 patients (28.87\%) suffered from at least one
30-day postoperative overall complications, and among them, 26 patients
(6.10\%) had severe complications based on the modified Accordion
classification, which including 7 cases of severe pneumonia, 3 cases of
thoracentesis, 9 cases of high-risk pulmonary thromboembolism, 1 case of
septic shock, 1 case of respiratory failure, 3 cases of unplanned
readmission and 2 cases of reoperation. The rest 97 cases of mild and
moderate complications were composed of common pneumonia,
wound/stoma/peritoneal cavity infections, deep vein thrombosis,
moderate-risk pulmonary thromboembolism, hypohepatia, incomplete bowel
obstruction and total parenteral nutrition. Besides, 90-day
postoperative mortality occurred in 1.41\% (6/426), which including 2
cases of bowel obstruction, 2 cases of high-risk pulmonary
thromboembolism, 1 case of respiratory failure and 1 case of septic
shock.

We first examined the factors underlying 30-day postoperative overall
complications. BMI, ASA, mFI, volume of ascites, preoperative albumin,
FIGO stage, NACT and SCS were all correlated with overall complications
in univariate analysis. In multivariate analysis, BMI (odds ratio
{[}OR{]} 1.14; 95\% confidence interval {[}CI{]} 1.05 to 1.24), mFI (OR
1.96; 95\% CI 1.46 to 2.64), FIGO stage (OR 2.31; 95\% CI 1.15 to 4.64),
and SCS (OR 1.16; 95\% CI 1.04 to 1.31) were independent predictors of
overall complications. Then, for 30-day postoperative severe
complications, univariate analysis showed a significant association
between mFI, volume of ascites, preoperative albumin, FIGO stage, NACT,
SCS and occurrence of severe complications. At multivariate analysis,
mFI (OR 2.18; 95\% CI 1.44 to 3.31), FIGO stage (OR 3.22; 95\% CI 1.10
to 9.42), and SCS (OR 1.23; 95\% CI 1.02 to 1.48) were independent
predictors of severe complications. In summarizing, whatever
postoperative overall or severe complications, the most significant
contributors were FIGO stage and mFI, followed by SCS, as shown
in\textbf{Figure 1} .

Additionally, we grouped the patients in the training cohort by overall
and severe complications according to the influencing variables and
surgical outcomes, as shown in \textbf{Table S1} (Supporting
Information). Patients with higher BMI, mFI, FIGO stage, SCS, and who
have not received NACT were more likely to suffer from overall
complications (\emph{P} \textless{} 0.05). For severe complications,
higher mFI, FIGO stage, SCS, and who have not received NACT all
increased the risks of severe complications (\emph{P} \textless{} 0.05).
There were significantly higher percentages of patients with operative
time not less than 300 minutes, intraoperative blood loss not less than
500 ml, and postoperative hospital stays not less than 9 days in the
overall and severe complications populations (P \textless{} 0.05).

We used the best influencing variables from the multivariate analysis to
construct nomograms for predicting postoperative overall and severe
complications. Using the nomograms, points are added accordingly to each
weighted preoperative variable and the point total corresponds to a
predicted probability of overall complications (\textbf{Figure 2A} ) or
severe complications (\textbf{Figure 2B} ).

The ROC curves of the nomograms with internal validation were shown
in\textbf{Figure S2 (A, B)} (Supporting Information). The AUC were 0.75
(95\% CI 0.70-0.80, \emph{P} \textless{} 0.001) and 0.79 (95\% CI
0.70-0.88, \emph{P} \textless{} 0.001) for predicting overall and severe
complications, respectively, indicating that the models had good
distinction. Meanwhile, the bootstrap-corrected C-index were 0.74 (95\%
CI 0.68-0.79) and 0.78 (95\% CI 0.69-0.87) for each model, indicating
good consistency between the prediction and actual observation. Internal
validation of the nomograms showed that the calibration plots are close
to the 45\selectlanguage{ngerman}° line, which indicate good calibration (\textbf{Figure 3 A, B}
).

Lastly, we used the medical data of 159 AEOC patients from The First
Affiliated Hospital of Chongqing Medical University to make an external
validation of the above nomograms. The calibration plots of the
nomograms with external validation were shown in \textbf{Figure 3 (C,
D)} . The C-index were 0.92 (95\% CI 0.87-0.96) and 0.91 (95\% CI
0.80-1.00) for predicting overall and severe complications,
respectively, indicating that the models had good external
discrimination. Moreover, external validation of the nomograms showed
that the calibration plots are near the 45° line, which indicates
acceptable calibration.

\textbf{Discussion}

Overall, this multicenter retrospective cohort study develops the model
and external verifies it effectively, to predict the complications after
cytoreductive surgery (including PDS and IDS) in AEOC. Firstly, we
observed that BMI, mFI, FIGO stage, and SCS were independent predictors
of overall complications, and for severe complications, the independent
predictors were mFI, FIGO stage, NACT, and SCS. Specifically speaking,
FIGO stage and mFI were the most significant contributors for both
overall complications and severe complications, followed by SCS, BMI or
NACT. Secondly, inter-group comparisons showed that higher mFI, FIGO
stage, SCS, and upfront PDS were more likely to suffer from overall and
severe complications. Moreover, in the overall and severe complications
populations, patients may be more likely to suffer other adverse
surgical outcomes. Thirdly, we built two nomograms for predictions of
30-day postoperative overall complications and severe complications,
respectively. Furthermore, strong internal calibration with the C-index
of 0.74 (AUC 0.75) and 0.78 (AUC 0.79) for each model indicated good
distinction and good consistency between the prediction and actual
observation, as well as good calibration demonstrated in the calibration
plots. Fourthly, external validation was performed well in the
validation cohort of patients undergoing cytoreduction. All of the
results we summarized above robustly confirmed that higher mFI, FIGO
stage, SCS, and upfront PDS were significantly positive correlation with
overall and severe complications, and the nomograms developed by those
influencing factors could effectively predict overall and severe
complications for AEOC patients, and also help surgeon to do the most
appropriate clinical decision-making.

More than 70\% of ovarian cancer patients are diagnosed at an advanced
stage. 11.03\% of patients were diagnosed with FIGO IV stage in our
study. Advanced stage means higher tumor load and higher surgical
complexity accordingly, which increased the risk of postoperative
complications. Our results showed that the incidence of postoperative
overall and severe complications in the training cohort were 28.87\% and
6.10\%, respectively. Kumar et al have reported that FIGO stage and
surgical complexity were positively associated with postoperative severe
complications after PDS for advanced ovarian cancer{[}10{]}. Our
findings were consistent with the recent study, we observed that
compared to IIIC stage, AEOC patients with IV stage have about 2-fold
and 3-fold risks of overall complications and severe complications,
respectively. Accumulating studies have presented that R0 is the
ultimate goal of cytoreduction surgery in AEOC. We also noted that AEOC
itself is a severe condition with a high morbidity and mortality rate in
the perioperative period. Therefore, the risk of adverse postoperative
outcomes associated with maximally cytoreduction surgery should be
weighed against its survival benefit{[}3, 18{]}. Here, our present study
had 80.05\% of patients underwent intermediate / high complexity
surgery, and 76.06\% of patients achieved optimal debulking. And we
found that as an independent predictor, SCS was positively correlated
with the occurrence of postoperative overall (OR 1.16) complications and
severe (OR 1.23) complications. The surgery timing for AEOC patients is
a longstanding controversy question within the gynecologic oncology
academic community{[}3, 18{]}. Some gynecologic oncologists held that
upfront complete cytoreduction should remain the standard of care for
advanced ovarian cancer{[}21{]}. However, the others insisted that for
the non-operable patients or low likelihood of achieving complete
cytoreduction, NACT is preferred, as it is associated with lower
postoperative morbidity and mortality{[}22{]}. It is critical and
challenging to screen out the fit patient to receipt suitable treatment.
Our study showed that patients who received NACT followed by IDS
(57.98\%) were slightly more than PDS (42.02\%). More important,
inter-group analysis showed that patients who have not received NACT
were more likely to suffer overall and severe complications, in other
words, upfront PDS was negatively correlated with the postoperative
complications. This is consistent with recent publications{[}11, 22{]}.
The mFI, based on the Canadian Study of Health and Aging Frailty Index,
has been already validated that is useful in the preoperative risk
assessment and prediction of outcomes in patients undergoing
gynecological surgery{[}16{]}. Consistently, our results suggested that
mFI is an independent predictor of both overall (OR 1.96) and severe (OR
2.18) complications{[}23{]}. Besides, we further observed that BMI \selectlanguage{english}[?] 24
could independently predict overall complications. Understandably, the
mild complications in this study were mostly made up of wound infection
and deep vein thrombosis, which can be mainly attributed to being
overweight. Additionally, we also found that patients with overall or
severe complications were more likely to suffer other adverse surgical
outcomes, including more intraoperative blood loss, cannot able to
undergo planned adjuvant chemotherapy, longer operative time, and
postoperative hospital stays.

At present, although there are a few models predicting the postoperative
complications of ovarian cancer, there are also some limitations. Kumar
et al{[}10{]} have demonstrated that age, albumin \textless{} 3.5 g/dL,
surgical complexity, stage, ASA, and BMI influence morbidity and
mortality after debulking surgery. But only PDS patients were included
in the research, so it lacks NACT as a factor for preoperative analysis.
Cham et al{[}13{]} have created a nomogram for patients who are being
considered for primary debulking or neoadjuvant chemotherapy, with an
internal discrimination C-index of 0.71. However, a flaw that cannot be
ignored was the researchers were unable to distinguish whether a
research subject underwent primary or interval cytoreduction and the
stage at the time of diagnosis. In addition, other models have used
single institutional data to determine factors related to postoperative
morbidity and mortality, and without the establishment of nomogram{[}12,
24, 25{]}. We next used the best influencing variables from the
multivariate analysis to construct nomograms for predicting
postoperative overall and severe complications in this study. Our data
suggested that characteristics of the disease (FIGO stage) and physical
fitness and health status (mFI) were the strongest influence factor for
both overall complications and severe complications, followed by SCS,
BMI or NACT. Moreover, the internal validation showed a good distinction
with the C-index were 0.74 and 0.78, respectively, as well as a good
consistency and calibration showed in the results. In addition, the
external validation was independently validated with a C-index
comparable to the training cohort, which indicates good external
discrimination. In consideration of the preoperative timeliness, we
chose to focus on preoperative factors for the nomograms, to maximize
clinical utility for both gynecologic oncology surgeons and patients.
Referring to a previous study, we can get planned procedures through CT
scan or MRI imaging or PET-CT to make SCS determined{[}10{]}. When the
nomogram predicts a high risk of overall complications, such as wound
infection and deep vein thrombosis, clinicians should pay attention to
prevent adverse outcomes after surgery, accordingly, standardized
postoperative care should include postoperative changing dressings in
time and mechanical assisted compression therapy. In the same way, when
the nomogram predicts a high risk of severe complications, especially
those complications with a high fatality rate, the nomogram can help
screen out the unsuitable surgical candidates, as well as contribute to
clinicians and patients to select an appropriate treatment.

Strengths of this study include its multicenter and inclusion of only
FIGO IIIC and IV ovarian cancer, making it relevant for those at the
highest risk for surgical morbidity and mortality. The nomograms have
internal and external validation, which is rarely available in other
studies. One of the limitations of our study is the nature of its
retrospective design, which is vulnerable to selection bias. Moreover,
most patients underwent intermediate complexity surgery, and less than
14\% of patients underwent high complexity surgery, which results in
relatively low surgical risk, and this is similar to the previous
study{[}13{]}.. Additionally, 90-day postoperative mortality in our
study occurred in merely 1.41\%, which is too low to do statistical
analysis. More external validations of these two nomograms in other
settings are necessary.

\textbf{Conclusion}

Cytoreductive surgery for AEOC is at significant risk for the occurrence
of substantial postoperative morbidity. This study suggests several
influencing factors of postoperative complications for AEOC patients via
a multicenter retrospective cohort study, as well as builds and
externally validates the nomograms for predicting the risk of
postoperative complications. These two models are rational and effective
in preoperative prediction, which plays a significant role in risk
stratification and clinical decision-making for AEOC patients, as well
as provides a possibility of improving perioperative care to reduce
adverse outcomes. Broader validation of the model in various surgical
settings is necessary.

\textbf{Acknowledgements}

We sincerely thank for the support by the Sichuan Province Science and
Technology Support Program (grant number 21PJ050).

\textbf{Disclosure of Interests}

The authors declare no conflict of interest, financial, or otherwise.

\textbf{Contribution to Authorship}

The work reported in the paper has been performed by the authors, unless
clearly specified in the text. C.J. collected and statistically analyzed
the medical records, as well as wrote the manuscript. Y.L. and J.T.
collected the medical records. Z.L. and W.M. provided practical
suggestions and critically revised the manuscript. All authors
contributed to the article and approved the submitted version.

\textbf{Details of Ethics Approval}

The study was approved by the Ethics Committee of West China Second
University Hospital of Sichuan University, and the patients' data were
deidentified and handled in accordance with ethical standards. Written
informed consent for retrospective data review was obtained from all
subjects participating in the study.

\textbf{References:}

{[}1{]}. Network, N.C.C., NCCN Clinical Practice Guidelines in Oncology
(NCCN Guidelines) Ovarian Cancer Including Fallopian Tube Cancer and
Primary Peritoneal Cancer. Version 1. 2022.

{[}2{]}. Narasimhulu, D.M., et al., Appropriate triage allows aggressive
primary debulking surgery with rates of morbidity and mortality
comparable to interval surgery after chemotherapy. Gynecologic oncology,
2021. 160(3): p. 681-687.

{[}3{]}. Kumar, A. and W.A. Cliby, Advanced Ovarian Cancer: Weighing the
Risks and Benefits of Surgery. Clin Obstet Gynecol, 2020. 63(1): p.
74-79.

{[}4{]}. Altieri, M., et al., How Should we Perform a Preoperative
Multidimensional Assessment of Elderly Patients with Advanced Ovarian
Cancer? Journal of investigative surgery, 2020: p. 1-2.

{[}5{]}. Benedetti, P.P., et al., Predictors of postoperative morbidity
after cytoreduction for advanced ovarian cancer: Analysis and management
of complications in upper abdominal surgery. Gynecol Oncol, 2015.
137(3): p. 406-11.

{[}6{]}. Patankar, S., et al., Risk stratification and outcomes of women
undergoing surgery for ovarian cancer. Gynecol Oncol, 2015. 138(1): p.
62-9.

{[}7{]}. Di Donato, V., et al., Trends in Mortality After Primary
Cytoreductive Surgery for Ovarian Cancer: A Systematic Review and
Metaregression of Randomized Clinical Trials and Observational Studies.
Ann Surg Oncol, 2017. 24(6): p. 1688-1697.

{[}8{]}. Gerestein, C.G., et al., Prediction of 30-day morbidity after
primary cytoreductive surgery for advanced stage ovarian cancer.
European journal of cancer (1990), 2009. 46(1): p. 102-109.

{[}9{]}. Vizzielli, G., et al., A laparoscopic risk-adjusted model to
predict major complications after primary debulking surgery in ovarian
cancer: A single-institution assessment. Gynecologic Oncology, 2016.
142(1): p. 19-24.

{[}10{]}. Kumar, A., et al., Risk-prediction model of severe
postoperative complications after primary debulking surgery for advanced
ovarian cancer. Gynecol Oncol, 2016. 140(1): p. 15-21.

{[}11{]}. Bartels, H.C., et al., A meta-analysis of morbidity and
mortality in primary cytoreductive surgery compared to neoadjuvant
chemotherapy in advanced ovarian malignancy. Gynecologic Oncology, 2019.
154(3): p. 622-630.

{[}12{]}. Aletti, G.D., et al., Relationship among surgical complexity,
short-term morbidity, and overall survival in primary surgery for
advanced ovarian cancer. American Journal of Obstetrics and Gynecology,
2007. 197(6): p. 676.e1-676.e7.

{[}13{]}. Cham, S., et al., Development and validation of a
risk-calculator for adverse perioperative outcomes for women with
ovarian cancer. Am J Obstet Gynecol, 2019. 220(6): p. 571.e1-571.e8.

{[}14{]}. Suidan, R.S., et al., A multicenter assessment of the ability
of preoperative computed tomography scan and CA-125 to predict gross
residual disease at primary debulking for advanced epithelial ovarian
cancer. Gynecologic Oncology, 2017. 145(1): p. 27-31.

{[}15{]}. Fagotti, A., et al., Introduction of staging laparoscopy in
the management of advanced epithelial ovarian, tubal and peritoneal
cancer: Impact on prognosis in a single institution experience.
Gynecologic Oncology, 2013. 131(2): p. 341-346.

{[}16{]}. George, E.M., et al., Measurement and validation of frailty as
a predictor of outcomes in women undergoing major gynaecological
surgery. BJOG: An International Journal of Obstetrics \& Gynaecology,
2016. 123(3): p. 455-461.

{[}17{]}. Strasberg, S.M., D.C. Linehan and W.G. Hawkins, The Accordion
Severity Grading System of Surgical Complications. Annals of Surgery,
2009. 250(2): p. 177-186.

{[}18{]}. Jiang, C. and Z. Li, Performance validation of the Mayo triage
algorithm applied to individualize surgical management of advanced
epithelial ovarian cancer. Gynecologic Oncology, 2021. 162(2): p.
339-344.

{[}19{]}. Steyerberg, E.W., et al., Prognostic modelling with logistic
regression analysis: a comparison of selection and estimation methods in
small data sets. Stat Med, 2000. 19(8): p. 1059-79.

{[}20{]}. Long Roche, K. and D.S. Chi, Practical guidelines for triage
to neoadjuvant chemotherapy in advanced ovarian cancer: Big risk, big
reward\ldots{}or too much risk? Gynecologic Oncology, 2020. 157(3): p.
561-562.

{[}21{]}. Angeles, M.A., et al., A multivariate analysis of the
prognostic impact of tumor burden, surgical timing and complexity after
complete cytoreduction for advanced ovarian cancer. Gynecologic
Oncology, 2020. 158(3): p. 614-621.

{[}22{]}. Chiofalo, B., et al., Primary debulking surgery vs. interval
debulking surgery for advanced ovarian cancer: review of the literature
and meta-analysis. Minerva Medica, 2019. 110(4).

{[}23{]}. Di Donato, V., et al., Modified fragility index and surgical
complexity score are able to predict postoperative morbidity and
mortality after cytoreductive surgery for advanced ovarian cancer.
Gynecol Oncol, 2021. 161(1): p. 4-10.

{[}24{]}. Stashwick, C., et al., Surgical risk score predicts suboptimal
debulking or a major perioperative complication in patients with
advanced epithelial ovarian, fallopian tube, or primary peritoneal
cancer. International journal of gynecological cancer, 2011. 21(8): p.
1422.

{[}25{]}. Suidan, R.S., et al., Predictive value of the Age-Adjusted
Charlson Comorbidity Index on perioperative complications and survival
in patients undergoing primary debulking surgery for advanced epithelial
ovarian cancer. Gynecol Oncol, 2015. 138(2): p. 246-51.

\textbf{Table/Figure Caption List}

\textbf{Table 1} Patients characteristics in training cohort.

\textbf{Figure 1} (A, C) Univariate and multivariate logistic regression
analysis for the odds of 30-day postoperative overall complications; (B,
D) Univariate and multivariate logistic regression analysis for the odds
of 30-day postoperative severe complications.

\textbf{Figure 2} (A) Nomogram for prediction of 30-day postoperative
overall complications. (B) Nomogram for prediction of 30-day
postoperative severe complications. (Annotation: Points are assigned for
each of the patient characteristics by drawing a line up from the scale
for each predictor to the points bar at the top of the figure. The
points for all predictors are then added to determine the total points.
A patient's predicted probability of 30-day postoperative overall or
severe complications is determined by drawing a line from the total
points bar to the predicted probability bar.)

\textbf{Figure 3} Validation plots for evaluating nomogram calibration.
(1) Internal validation in the training cohort. A: Nomogram for
prediction of overall complications. B: Nomogram for prediction of
severe complications. (2) External verification in the validation
cohort. C: Nomogram for prediction of overall complications. D: Nomogram
for prediction of severe complications.

\textbf{Table S1} Comparisons of the overall and severe complications
between the groups

\textbf{Figure S1} Flow chart of the overall study.

\textbf{Figure S2} ROC curves of the nomograms with the training cohort.
A: Nomogram for prediction of overall complications. B: Nomogram for
prediction of severe complications.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1-forestplot/Figure-1-forestplot}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2-nomograms/Figure-2-nomograms}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3-Calibration/Figure-3-Calibration}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`Table.docx`  available at \url{https://authorea.com/users/493417/articles/575827-nomogram-to-predict-postoperative-complications-after-cytoreductive-surgery-for-advanced-epithelial-ovarian-cancer-a-multicenter-retrospective-cohort-study}

\selectlanguage{english}
\FloatBarrier
\end{document}

