Contrast Between Traditional Regression Model and AI in Predicting Prolonged Stay Stay After Head and Neck Tumors
Conditions: Head and Neck Cancer
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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Clinical study protocol
Project name: Comparison of traditional regression model
and AI in predicting prolonged ICU stay after head and neck
tumors
Sponsor: Sun Yat-sen Memorial Hospital of Sun Yat-sen
University
Version number: V1.0
Version date: 2024.3.21
Project leader: Yang Zhengfei
Scheme signature confirmation:

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一、Abstract of the study protocol
Title: Comparison of traditional regression model and AI to predict
prolonged ICU stay after head and neck tumors
Brief description of the study: This experiment is a cohort observational
study. By establishing a cohort of patients with head and neck tumors after
surgery, we compared the prediction effect of AI
and traditional
prediction models on whether patients with ICU can be transferred to ICU
within 24 hours after head and neck tumors after surgery. First,
retrospective analysis of postoperative patients with head and neck
tumors, patients' medical records were collected, and the patients were
divided into training group and validation group according to 7:3, divided
into 2 groups according to whether the ICU stay time was greater than 24
hours, and the prediction model of postoperative ICU stay time of head
and neck tumors over 24 hours was established after using LASSO regression
to select variables. At the same time, the data was cleaned, the AI was
trained, and the efficacy was compared by ROC. After the establishment
of prediction model and AI training, the patients included in the cohort
were evaluated by prediction model and AI evaluation immediately after
transfer to the ICU, which predicted the possibility of transferring out
of the ICU within 24 hours.
Study jects: All patients with head and neck tumors
Outcome measures: The patient was transferred out of the ICU
Sample size: Following the principle of 10 times the sample size, the
included sample size is more than 10 times of the final variable, and 500
patients are currently expected to be included.
2. Research background (including the introduction of the disease, the treatment status
at home and abroad, the significance of the research, etc.)
Head and neck tumor incidence ranks the sixth [1] in the global tumor incidence.

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The incidence of head and neck tumors is increasing in China and is expected to reach
3.99 [2] per 100,000 people by 2049. Surgical resection has been identified as the
primary treatment for oral cancer [3]. Because of the complex anatomy of the oral
region, limited surgical space, and the need for simultaneous flap reconstruction to
protect the integrity of the oral and maxillofacial systems, about 80% of patients
undergoing oral cancer surgery will be transferred to the intensive care unit (ICU)
after the operation, [4].
Previous studies found that prolonged ICU stay was associated with poorer
prognosis and quality of life, causing a substantial disease and economic burden of [4].
ICU stay> 24 h implies a higher risk of nosocomial infection [5-7] and worsen the
prognosis [8] in oral cancer patients. Previous studies found that ICU admission
delayed oral feeding time and negatively correlated with 5-year survival [8,9].
Therefore, it is important to identify oral cancer patients with a long ICU after
surgery.
Most patients with head and neck tumors have postoperative ICU stay time
between 24 and 72 hours [10-12]. In a retrospective study of the UK National
Intensive Care database, the median stay of patients undergoing head and neck cancer
surgery was found in the ICU of 24 hours (IQR 18.8-46 hours) [13]. Meanwhile,
according to our clinical experience, we believe that the ICU stay time of
postoperative head and neck tumor patients should be within 24 hours is reasonable.
Currently, the study of ICU stay time after head and neck cancer surgery has
focused on demographic characteristics, surgical methods, blood transfusion and
physiological scores of [8,9,14]. Previous studies have evaluated some risk factors
associated with prolonged length of stay (LOS). Poor nutritional status and unhealthy
lifestyle habits are associated with prolonged ICU stay in [15,16]. Furthermore,
surgery-related factors such as intraoperative blood transfusion and flap
reconstruction
methods
were
[5,17]
associated
with
LOS
in
patients
undergoing surgery for head and neck tumours.

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However, there are few studies on the effect of laboratory results on ICU stay
time after head and neck cancer surgery. Consideration of laboratory findings as
potential predictors of ICU stay is warranted. These measurements include liver and
renal function indicators, electrolyte levels and lactate levels, etc. Developing a
predictive model for identifying patients with a prolonged ICU stay (> 24 hours) after
head and neck cancer surgery is of great clinical importance. We plan to use the
patient's clinical data to build a prediction model and AI prediction model to predict
whether the head and neck tumor can be transferred out of the ICU within 24 hours
after surgery, compare the efficacy of AI with the traditional prediction model, and
subsequently validate his efficacy in a prospective cohort.
Head and neck tumor incidence ranks the sixth [1] in the global tumor incidence.
The incidence of head and neck tumors is increasing in China and is expected to reach
3.99 [2] per 100,000 people by 2049. Surgical resection has been identified as the
primary treatment for oral cancer [3]. Because of the complex anatomy of the oral
region, limited surgical space, and the need for simultaneous flap reconstruction to
protect the integrity of the oral and maxillofacial systems, about 80% of patients
undergoing oral cancer surgery will be transferred to the intensive care unit (ICU)
after the operation, [4].
Previous studies found that prolonged ICU stay was associated with poorer
prognosis and quality of life, causing a substantial disease and economic burden of [4].
ICU stay> 24 h implies a higher risk of nosocomial infection [5-7] and worsen the
prognosis [8] in oral cancer patients. Previous studies found that ICU admission
delayed oral feeding time and negatively correlated with 5-year survival [8,9].
Therefore, it is important to identify oral cancer patients with a long ICU after
surgery.
Most patients with head and neck tumors have postoperative ICU stay time
between 24 and 72 hours [10-12]. In a retrospective study of the UK National
Intensive Care database, the median stay of patients undergoing head and neck cancer
surgery was found in the ICU of 24 hours (IQR 18.8-46 hours) [13]. Meanwhile,

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according to our clinical experience, we believe that the ICU stay time of
postoperative head and neck tumor patients should be within 24 hours is reasonable.
Currently, the study of ICU stay time after head and neck cancer surgery has
focused on demographic characteristics, surgical methods, blood transfusion and
physiological scores of [8,9,14]. Previous studies have evaluated some risk factors
associated with prolonged length of stay (LOS). Poor nutritional status and unhealthy
lifestyle habits are associated with prolonged ICU stay in [15,16]. Furthermore,
surgery-related factors such as intraoperative blood transfusion and flap
reconstruction
methods
were
[5,17]
associated
with
LOS
in
patients
undergoing surgery for head and neck tumours.
However, there are few studies on the effect of laboratory results on ICU stay
time after head and neck cancer surgery. Consideration of laboratory findings as
potential predictors of ICU stay is warranted. These measurements include liver and
renal function indicators, electrolyte levels and lactate levels, etc. Developing a
predictive model for identifying patients with a prolonged ICU stay (> 24 hours) after
head and neck cancer surgery is of great clinical importance. We plan to use the
patient's clinical data to build a prediction model and AI prediction model to predict
whether the head and neck tumor can be transferred out of the ICU within 24 hours
after surgery, compare the efficacy of AI with the traditional prediction model, and
subsequently validate his efficacy in a prospective cohort.
3. Study objective (clear objective)
3.1.1 Main Purpose
Comparing the prediction accuracy of AI and traditional prediction models
for ICU stay time after head and neck tumors.
3.1.2 Secondary Purpose
Explore the construction method and influencing factors of ICU stay time
prediction model after head and neck cancer surgery.
4. research design

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This study is a cohort observational study by establishing a retrospective
cohort of patients transferred to ICU for head and neck tumors from January
2022 to December 2023, which is exempt from informed consent. Screen the
variables to establish the traditional prediction model and train the AI, and
then compare the accuracy of the traditional prediction model and the AI. After
the validation on the basis of retrospective study of the traditional model and
the prediction of AI for head and neck tumor patients postoperative ICU stay
time, prospectively into the head and neck tumor postoperative ICU patients,
the patient in the ICU to sign the waste specimen use consent, through
prospective studies to verify the AI in ICU stay time prediction superiority.
5. Research protocol and technical route (including the calculation of study sample
size, inclusion, exclusion, withdrawal criteria, evaluation indicators and methods of
study results, statistical analysis plan, informed consent, ethics, etc.)
Sample size: Following the principle of 10 times the sample size, the
included sample size is more than 10 times of the final variable. Currently,
500 retrospective patients and 200 prospective patients are expected to
be included.
Selection criteria
Patients after head and neck tumors, older than 18 years.
Exclusion criteria
1 Patients transferred to ICU twice after head and neck tumors
2. Patients with unplanned transfer to the ICU
Evaluation indicators and methods of the results: compare the effectiveness of the
model using accuracy or ROC

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Statistical analysis plan: data description of statistical analysis, analysis methods of
primary / secondary indicators / safety indicators, statistical school positive methods,
control of bias, stratification / subgroup / sensitivity analysis, etc
All data were statistically analyzed using SPSS 25.0 and R languages, and P <0.05
was considered statistically significant.
The measurement data conforming to the normal distribution are described by the
mean standard deviation ± (x ± s); the measurement data that do not conform to
the normal distribution are described by the median (M) and interquartile spacing
(P25-P75); the counting data are described by frequency and percentage (%); the
lacking measurement data are filled by mean, and the missing counting data is filled
by mean.
Comparison of differences between groups: t-test was used for the measurement data
meeting the normal distribution, rank sum test for the measurement data, χ 2 test,
Fisher exact probability method or continuous corrected chi-square test for the
counting data.
Informed consent: Exemption from informed consent for retrospective part. Signed
informed consent for transfer to ICU.
VI. Safety evaluation (including the definition and assessment of adverse events and
serious adverse events)
There is no direct interventional operation in this study, and the risks
mainly include possible errors in data collection and management, as well
as patient privacy protection issues.
Vii. Data collection and management
All data are retrieved and cleaned after export from the big data system.
All data are retrieved by the database software. The relevant data are as
follows:
Baseline data:

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Patients collected from the electronic medical records of admission
baseline data: age, gender, Body Mass Index, body mass index (BMI), past
history [including oral cancer treatment (radiotherapy, chemotherapy,
immunotherapy), previous basic disease history (hypertension, diabetes
history)],
personal
history
(including
smoking
history,
drinking
history), family history of oral cancer, tumor site;
Pre-operative indicators:
①
Clinical
Assessment
Scale:
Nutritional
Risk
Screening
Scale
(Nutritional Risk Screening-2002, NRS-2002) (see Supplementary Table 1);
②Preoperative laboratory examination results: blood routine [white
blood cell count (10
9/ L), hemoglobin (g / L), platelets (10
9/ L),
neutrophil count (10
9/ L), lymphocyte count (10
9/ L), monocyte cell count
(10
9/ L), red blood cell volume (fL), the ratio of neutrophil count to
lymphocyte count (Neutrophil-to-Lymphocyte Ratio, NLR), the ratio of
lymphocyte count to monocyte count (Lymphocyte-to-Monocyte Ratio, LMR)],
Coagulation function [plasma prothrombin time (s), fibrinogen (g / L),
activated partial thromboplastin time (s), thrombin time (s), D-dimer
(mg/L)], Liver function [glutamate aminotransferase (U / L), glutamate
aminotransferase (U / L), albumin (g / L)), Renal function (creatinine
(u mol/L)], Blood electrolytes [potassium ion (mmol/L), sodium ion
(mmol/L), calcium ion (mmol/L), chloride ion (mmol/L)];
Procedure-related indicators:
Operation duration (h), flap type (none, fibula, femur, pectoralis major),
scope of cervical lymph node dissection (none, unilateral, bilateral);
Index of postoperative monitoring:
①Clinical assessment scale: Acute Physiological and Chronic Health
Assessment II score (Acute Physiology and Chronic Health Evaluation II,
APACHE II), Sequential Organ failure score (Sequential Organ Failure
Assessment, SOFA), Caprini venous thrombosis risk score;

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②Related indicators of ICU monitoring: time of mechanical ventilation,
body temperature [mean body temperature in the first 24h after transfer
to ICU (℃), highest body temperature (℃) in the first 24h after transfer
to ICU, minimum body temperature (℃)], arterial pressure [mean arterial
pressure (mmHg) in the first 24h after transfer to ICU, highest mean
arterial pressure (mmHg) in the first 24h after transfer to ICU, lowest
mean arterial pressure (mmHg) in the first 24h after transfer to ICU];
③First postoperative laboratory examination: blood routine [white
blood cell count (10
9/ L), hemoglobin (g / L), platelets (10
9/ L),
neutrophil count (10
9/ L), lymphocyte count (10
9/ L), monocyte cell count
(10
9/ L), red blood cell volume (fL), NLR, LMR], Coagulation function
[plasma prothrombin time (s), fibrinogen (g / L), activated partial
thromboplastin time (s), thrombin time (s), D-dimer (mg/L)], Liver
function
[glutamate
aminotransferase
(U
/
L),
glutamate
transaminotransferase
(U
/
L),
albumin
(g
/
L)],
Renal
function
[creatinine (u mol/L)), Blood electrolytes (potassium ion (mmol/L),
sodium ion (mmol/L), calcium ion (mmol/L), chloride ion (mmol/L)]; Blood
gas analysis [pH, partial pressure of carbon dioxide (kPa), partial
pressure of oxygen (kPa), lactic acid (mmol/L)].
八、Quality management plan (please introduce relevant measures to ensure project
quality and progress)
Data processing by special person, in addition, data review by special personnel to
determine the authenticity of the data, each data processing steps are retained to
ensure the repeatability of the experiment.
IX. Pre-assessment and risk control plan of project risk benefits (Please describe the
risks and benefits of the researchers, subjects and the medical institution; if there are
risks, please introduce the measures and feasibility of risk control.）
By comparing the ICU stay time of head and neck cancer, it is expected

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to improve the efficiency of postoperative management of head and neck
cancer patients and reduce the waste of medical resources.
X. References
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