Developing a nomogram for predicting patients’ 24h-fatigue after digestive endoscopy with anesthesia assistance: a cross-sectional study

doi:10.21203/rs.3.rs-4169451/v1

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Research Article

Developing a nomogram for predicting patients’ 24h-fatigue after digestive endoscopy with anesthesia assistance: a cross-sectional study

https://doi.org/10.21203/rs.3.rs-4169451/v1

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Background

Postoperative fatigue (POF) widely exists in patients underwent digestive endoscopy under anesthesia, seriously affecting patients’ postoperative rehabilitation and quality of life, there is few predicted tools for POF. We aim to identify predictors of 24 hours-postoperative fatigue (24h-POF) in patients underwent digestive endoscopy with anesthesia assistance and develop a nomogram.

Methods

This cross-sectional study involved 1306 patients underwent digestive endoscopy in The Second Affiliated Hospital Zhejiang University School of Medicine from May to November in 2023. Christensen Fatigue Scale was used to evaluate POF. Multiple linear regression analysis was utilized to ascertain independent predictors, and a nomogram was developed based on these findings. The area under the curve (AUC), calibration curve and decision curve analysis (DCA) were employed to evaluate the predictive model’s performance.

Results

528 patients (40.43%) reported experiencing 24h-POF. Five modifiable factors (preoperative anxiety, average dosage of propofol, change rates of heart rate, fatigue 30 min after endoscopy, intraoperative hypotension) and nine non-modifiable factors (age, gender, drinker, numbers of comorbidities, degree of education, marital, Buddhist, endoscopic treatment, intraoperative tracheal intubation) were independently associated with 24h-POF in patients undergoing digestive endoscopy. The model exhibited good discrimination with an AUC of 0.865 and 0.891 in training cohort and validation cohort, respectively. The calibration curves and DCA curves demonstrated excellent calibration and clinical practicability.

Conclusion

The nomogram can effectively predict 24h-POF of digestive endoscopic patients, providing valuable assistance for early intervention to expedite patient recovery.

postoperative fatigue

digestive endoscopy

risk factor

nomogram

The principle of fast-track surgery (FTS) aims to reduce hormonal surgical stress response and facilitate patients’ safe and swift passage through the perioperative period ^[1]. Guided by FTS, medical and anesthesia technology have been continually developed and upgraded, thereby advancing the progress of outpatient diagnosis and treatment under anesthesia, and earning unanimous praise from patients ^[2].

However, postoperative fatigue (POF) seriously violates FTS^[3]. POF is characterized by a range of physical and psychological symptoms, which often has a significant influence on the patients, impeding them return to normal daily function and activities^{[4, 5]}. The incidence and duration of POF vary across different surgical procedures, with the most significant impact typically occurring on the first day following the operation ^{[4, 5]}. POF is independently related to ill-health-related quality of life and low work efficiency, which always results in significantly reduction in patients’ self-care ability and negatively affects their study, work, housework and socializing ^[6]. POF always contributes to negative feelings to both patients and care givers^[7]. For outpatients, POF also increases the risk of falling and can even impair motor vehicle driving and instrument operation after discharge ^{[8, 9]}. In consequence, it is of great importance to focus on the POF.

Endoscopic diagnosis and treatment under anesthesia is the fastest-growing outpatient medical procedure in China^{[10, 11]}. The specific preoperative preparation method for endoscopy, which includes fasting and laxative use, can result in physiological blood volume depletion, and further exacerbating POF^[12]. Research indicates that POF occurs in nearly 37.5% patients undergoing digestive endoscopy, yet medical staff have not given it sufficient attention ^{[13, 14]}. Several studies have investigated into the factors related to POF in patients underwent digestive endoscopy. A study involving 158 patients who underwent painless gastrointestinal endoscopy showed a great percentage of change in heart rate before and after anesthesia induction was a risk factor for POF, while being male was found to be a protective factor for POF. Besides, a retrospective study in China reported that appropriately prolonging the waiting time on the day of examination could reduce the 24-hour fatigue after operation for patients undergoing coloscopy under deep sedation levels ^[15]. To our know, there isn’t a unified evaluation tool that has been developed to assess, predict, and manage POF for patients undergoing digestive endoscopy. Furthermore, there are few studies predicted POF using nomogram.

Thus, we aim to identify the predicted factors of patients’ 24h-fatigue after digestive endoscopy with anesthesia assistance and construct a nomogram to predict patients’ 24h-POF early and conveniently.

2.1 Study design and populations

This cross-sectional study was conducted at two endoscopic centers of an affiliated hospital of university from May to November in 2023. Adult patients ASA class I to III undergoing non-emergent digestive endoscopy with anesthesia assistance were included. Patients had psychiatric disorders or language disabilities, or with missing data and aborted procedures were excluded. During the investigation period, those who underwent endoscopic surgery multiple times only had their first result recorded. For sample size, it is recommended to have 10–20 events per variable (EVP) according to estimation of regression coefficients in a predicted model ^[16]. In this study, the model consisted of 15 predictors, suggesting a requirement of at least 150 patients with 24h-POF. Given the prevalence of 24h-POF in digestive endoscopic patients at about 37.5%^{[13, 14]}, the necessary sample size was at least 400. Considering the general ratio of training cohort to validation is 7: 3^[17], the total sample size was least 572. Eventually we included a lager sample size to develop and validate the predictive model.

2.2 Study Protocol

The entire process was conducted in accordance with the standard practices of gastrointestinal endoscopy diagnosis and treatment ^{[18, 19]}. On the day of endoscopy, patients initially checked in at nurse station of endoscopic center, then underwent evaluation by an anesthesiologist and received at least 22G venous access by a nurse. Patients were required to wait until instructed by a nurse to enter the endoscopy room. Then, patients took appropriate position on the examination table and were connected to ECG monitoring with the assistance of a nurse. The anesthesiologist determined the suitable anesthesia protocol and ventilation mode based on the endoscopy’s objectives and the patient’s physical condition. The sedation level of each patient was evaluated by the Ramsay Sedation Scale^[20], when the eyelash reflex and swallowing disappeared, the muscles relaxed and the breathing was stable, the endoscopist began to operate. Following the completion of the endoscopy, patients were transferred to the recovery room, where their vital signs were monitored until they naturally regained consciousness. Subsequently, patients were required to remain under observation for 30 minutes, until their Post-anesthesia discharge scoring system score (PADSS) was ≥ 9 and their vital signs were stable, before he could leave accompanied by his family members or caregivers^{[21, 22]}.

2.3 Data Collection

Data collection was performed independently by three researchers. One researcher collected demographic and health-related behavioral data from patients through face-to-face interviews while they awaited endoscopy. Another researcher collected patients’ general health information and records of endoscopic procedure from the electronic medical record system. A third researcher assessed the patient's 30 min-fatigue after operation(30min-POF) though face-to-face questioning, and 24h-fatigue after operation(24h-POF) via telephone follow-up.

Sociodemographic data included gender, age, occupation, degree of education, marital stage, religion, weight, height, and body mass index (BMI, categorized as underweight if BMI < 18.5 kg/m², normal if between18.5 kg/m² and 23.9 kg/m² and overweight if ≥ 24 kg/m² based on the Chinese population recommendation^[23]). Health-related behavior data encompassed smoking and alcohol consumption in the last six months. General health information covered comorbidities and surgical history. Endoscopic operation data involved tracheal intubation, preoperative waiting time, endoscopic procedure duration, propofol dosage, intraoperative infusion volume, endoscopic treatment(such as polypectomy, mucosal resection, injection sclerotherapy, ultrasound puncture, hemostasis), vital signs (systolic pressure blood pressure(SBP), diastolic blood pressure(DBP), mean arterial pressure(MAP) and heart rate(HR) and percentage change of vital signs (y= | (value during endoscopy - value before endoscopy)/ (value before endoscopy)*100%|+ | (value after endoscopy - value during endoscopy)/ (value during endoscopy)*100%|.

Christensen Fatigue Scale (ChrFS) is a simple, convenient, and validated tool to evaluate POF^[24]. It is categorized as follows: “fit” (1–2 point), experiencing fatigue only due to violent exertion, with normal sleep demand; “slight fatigue” (3–4 point), feeling fatigue particular during household chores, gardening or walking stairs, with normal sleep demand; “moderate fatigue” (5–6 points), ability to manage daily chores with an increased sleep demand; “severe fatigue” (7–8 points), unable to handle daily chores or short walks, with a significantly increased sleep demand; “worst fatigue” (9–10 points), extreme fatigue, even when sitting, with a substantial increase in the need for sleep; a score greater than 2 is defined as fatigue^[25],.

Amsterdam preoperative anxiety and information scale (APAIS) was applied to evaluate preoperative anxieties level of patients undergoing scheduled planned surgery ^[26]. APAIS comprises 6 items, including four anxiety-related items and two information-demand items, designed to be easily comprehensible and quickly answerable. Moreover, the scale is well-recognized by both patients and doctors ^[27]. The researcher evaluated preoperative anxiety levels of patients when they were waiting their endoscopy procedure.

2.4 Ethical approval

Ethical approval was obtained from the research ethics board committee of XX. This study adhered to the principles outlined in the 1975 Declaration of Helsinki. Informed consent was obtained from patients prior to their participation. Confidentiality and voluntary were ensured by patients before they were included. Patients were informed of their right to withdraw from this research at any time.

2.5 Data analysis

The statistical analysis utilized software of the SPSS 25.0 (Chicago, IL, United States) and R 4.3.2 (Foundation for Statistical Computing, Vienna, Austria). Initially, the included patients were randomized into training cohorts and validation cohort at a ratio of 7:3. Next, the descriptive statistics were performed for the research variable in two cohorts: continuous variables were represented as the mean and standard deviations (SDs), while classification data were represented as frequency and percentages. Mann–Whitney U test and χ2 test were employed to estimate differences between the training and validation cohorts. In the training cohort, univariate analysis and multiple linear regression analysis were utilized to identify the predictive factors. As the fatigue scores exhibited a non-normal distribution, the Spearman rank correlation test was applied for continuous variables, the Mann-Whitney test for binary categorical variables, and Kruskal-Wallis test for polytomous variables. Then, variables of statistically significant in univariate analysis were incorporated into multiple linear regression analysis, based on the regression coefficients of the statistically significant variables, a clinical predictive model was established and visualized using nomogram. Subsequently, the model’s discrimination was assessed utilizing the area under the receiver operating characteristic curve (AUC) in both cohorts, with internally validation implemented using 1000 bootstrapped samples in the training cohorts; a higher AUC value closer to 1 represents a better predictive effect, with AUC values > 0.70 generally considered acceptable ^{[28, 29]}. Calibration plots with 1000 bootstrapped resamples and the Hosmer–Lemeshow (HL) test^[30] were employed to evaluate the accuracy and predictive performance of the model. The decision curve analysis (DCA) of the model was utilized for assessment of nomogram’s clinical utility ^[31]. A two-sided P value less than 0.05 was considered statistically significant.

3.1 Baseline data and incidence of 24h-POF

At first, a total of 1500 patients were invited to participate in the survey. However, 194 patients were excluded due to telephone follow-up failure, abortion, and incomplete data, resulting in the inclusion of 1306 patients. Among these patients, the mean age was 53 ± 13 years, 49.8% were female, and 528 patients reported 24h-POF, yielding an incidence of 24h-POF was 40.43% (Fig. 1). The patients were then randomized into a training cohort(n = 914) and a validation cohort(n = 392). The characteristics of all patients were presented in Table 1. We found no significant differences between the training and validation cohorts.

3.2 Identifying 24h-POF predictors

Out of thirty-two variables included in the univariate analysis, twenty-five variables were found to be statistically significant and were subsequently incorporated into multivariate liner regression analysis model, resulting in the identification of fourteen variables of statistical significance. Age (β = 0.196, 95%CI = 0.061 − 0.331, P = 0.013) and complications (β = 0.310, 95%CI = 0.210 − 0.411, P < 0.001) were positively correlated with 24h-POF level. Higher APAIS score (β = 0.736, 95%CI = 0.624 − 0.847, P < 0.001), increased average dosage of propofol by weight(β = 0.128, 95%CI − 0.007–0.249, P = 0.038), greater change rate of heart rate (β = 0.162, 95%CI = 0.090 − 0.234, P < 0.001) and higher 30min-POF scores (β = 0.461, 95%CI = 0.405 − 0.518, P < 0.001) were significantly associated with the higher levels of 24h-POF. Female (β = 0.506, 95%CI = 0.360 − 0.652, P < 0.001) and drinker (β = 0.323, 95%CI = 0.160 − 0.486, P < 0.001) seems to have higher 24h-POF level. Degree of education (β=-0.205, 95%CI=-0.339−-0.071, P < 0.001) was negatively correlated with 24h-POF. Compared to unmarried, divorced, and widowed patients, married patients were linked to lower 24h-POF level (β=-0.882, 95%CI=-1.088−-0.677, P < 0.001). Buddhists had lower 24h-POF level (β=-0.435, 95%CI=-0.602−-0.268, P < 0.001) than non-believers, Christian and Mohammedan. Besides, patients undergoing intraoperative tracheal intubation (β = 0.356, 95%CI = 0.042 − 0.670, P = 0.009), intraoperative hypotension (β = 0.372, 95%CI = 0.213 − 0.530, P < 0.001) and endoscopic treatment (β = 0.177, 95%CI = 0.012 − 0.343, P = 0.031) related with higher 24h-POF level. Table 2 shows the detailed results of univariate analysis and multivariate liner regression analysis. Table 3 shows assignment and dummy variable of classification variables in multivariate regression equation.

3.3.3 Construction of nomogram

A predictive model of fatigue 24h after endoscopy was developed based on the corresponding regression coefficients of each predictors, Z=-2.097 + 0.196 × (Age) + 0.506 × (Gender) + 0.323× (Drinker) + 0.310× (Number of complications) + (-0.205)× (Education) + (-0.882)× (Married)+ (-0.435)× (Buddhist) + 0.177× (Endoscopic treatment) + 0.128 × (Average dosage of propofol by weight) + 0.736× (APAIS) + 0.356× (Intraoperative tracheal intubation) + 0.372× (Intraoperative hypotension) + 0.162× (CR of HR) + 0.461× (30min-POF).

The fourteen independent factors were integrated into a nomogram to predict patient’s 24h-POF level (Fig. 2). The score of each predicted factors can be obtained according to the projection of each factor on the first line of the nomogram, and the total scores can be obtained by adding the scores of each factor. Projecting the total score down to the last line of the nomogram, the corresponding score is the possible 24h-POF score of patients after endoscopic operation. A higher score means a higher level of POF. On the basis of the cut-off value of original scale, all patients were then divided into two sets: fatigue set (> 2) and non-fatigue set (≤ 2).

3.3.4 Validation of nomogram

The AUC calculated based on prediction model in the training cohort was 0.865 (95% CI, 0.840–0.889), while in the validation cohort, it was 0.891(95% CI, 0.857–0.926) (Fig. 3). Internally validation using 1000 bootstrapped samples in training cohorts showed the AUC was 0.865 (95% CI, 0.840–0.889) (Fig. 4). These results demonstrated a high degree of discrimination within the predictive model. The calibration curves of the training and validation cohorts were plotted using 1000 bootstrap resampling approach (Fig. 5), indicated tiny deviation between predicted and actual probabilities. The HL test further confirmed no significant difference between predicted and actual probabilities in the training cohort(P = 0.243) and validation cohort(P = 0.096), suggesting high calibration and accuracy of the nomogram. DCA plotted in Fig. 6 indicated that compared to all-or-nothing strategy, this nomogram is expected to yield increased net benefits.

Fatigue is among the most prevalent symptoms in postoperative patients, severely impacting patients’ postoperative rehabilitation and quality of life. Early prediction of POF as well as taking intervention measures is critical. Our finding showed five modifiable factors (intraoperative anxiety, average dosage of propofol by weight, change rates of HR, fatigue 30 min after endoscopy, and intraoperative hypotension) and nine non-modifiable factors (age, gender, drinker, numbers of comorbidities, degree of education, married, Buddhist, endoscopic treatment, and intraoperative tracheal intubation) were independently associated with severity of fatigue 24h after digestive endoscopy with anesthesia assistance. Taking these factors into account, we constructed and validated a nomogram to enable early prediction of 24h-POF in patients undergoing digestive endoscopy, allowing clinical staff to implement effective strategies for prevention, nursing and intervention before patients leave the hospital.

Fatigue, a multifaceted subjective sensation, which is manifested as a depletion of energy, stamina, and vitality in everyday life. POF in endoscopic patients is different to general fatigue, as it is directly induced by surgery and volatile^[32]. In endoscopy patients, POF is described as “an unbelievable and unrecoverable loss of vitality that is beyond one’s own control”, and is reported as one of the most challenging symptom to manage^[11]. This study found that 24h-POF in patients underwent endoscopic operation with anesthesia assistance was 40.43%, which is close to previous finding^[13].

This nomogram comprises 14 variables, which have been demonstrated as independent predictors of 24-hour POF in patients undergoing endoscopy through univariate and multiple linear regression analyses, and these variables can be readily obtained in clinical practice. In terms of modifiable factors, the APAIS score was demonstrated to be a strong predictor of POF in this study. A similar study reported that preoperative negative emotions, including anxiety, depression, and anger, were positively associated with the severity of POF in inpatients after surgery^[33-35]. Previous studies have indicated preoperative anxiety can increase the release of proinflammatory factors and stimulate hypothalamus-pituitary-adrenalin (HPA) axis by enhancing the endocrine and immune responses induced by surgery, while dysfunction of HPA axis often exacerbates patients' POF through various signaling pathways^{[36, 37]}. This may offer an explanation for our findings. Next, hypotension deduces blood supply and diminishes oxygen content to the brain, thereby exacerbating postoperative dizziness symptoms^[38]. Then, high change rates of HR, drinker, and large dosage of propofol were related to higher POF level. A greater heart rate variability signifies a heightened stress response, leading to enhanced catabolism within the body ^[39]. From the perspective of central fatigue mechanism, increased catabolism results in elevated tryptophan levels both the peripheral circulation system and brain^[40]. As a precursor of 5-hydroxytryptamine(5-HT), tryptophan converts into more 5-HT, causing central fatigue. Furthermore, the rise in tryptophan levels can stimulate the N-Methyl-D-aspartic acid (NMDA) receptor to produce neurotoxicity, leading to nerve cell destruction and dysfunction ^[41]. Prior studies have indicated that sedation is less effective in heavy drinkers due to abnormal enhancement of the microsomal ethanol oxidizing system (MEOS), accelerating the degradation of drugs ^{[42, 43]}. Therefore, drinkers often require a larger dose of propofol to achieve sedation^[44]. A high dosage of propofol not only induces cardiovascular depression but also over-sedation, inevitably leading to POF ^[45]. Does this imply that a lower average dosage of propofol is better? Clearly not. Researchers have revealed that single administration of propofol at 0.1mg/kg, could promote POF improvement by enhancing gluconeogenesis though CREB/PGC-1a signaling and accelerating FFA β-oxidation in 70% hepatectomy rats, to be emphasized, the dosage of propofol used here was at least 1000 times lower than that of conventional anesthetics^[46]. It is suggested that future research should explore threshold for propofol-induced POF and develop a program foe rational use of propofol, ensuring the patients’ sedation level and maintain the stability of BP and HR during digestive endoscopy. Finally, it was found that 30 min fatigue after endoscopy was an independent predictor of 24h-POF. Studies have reported that aromatherapy, which is easy to implement in clinical settings, can effectively reduce POF of patients^[47].

The older was linked to higher 24h-POF, POF score assessed by ChrFS increases by 0.196 for each year of age in this study, we suppose the normal physiological activities of endocrine-metabolic system in the elderly recover slowly because of the decline of physical function^[9]. But this result was contrary from a Chinese study, the discrepancy may be due to the different sample sizes ^{[15, 48]}. Buddhism has lower POF level than non-believers, positive religious coping styles in Buddhists’ life signified an internalized secure spiritual relationship and well-integrated copying resources on which they could rely across a variety of stress situations ^[49]. Previous study have shown Buddhist beliefs could treat bone pain caused by cancer in a study conducted in Hong Kong, we infer that the Buddhists may also turn to their faith for coping using private prayer, meditation, or Chanting when facing uncontrollable fatigue related to endoscopy ^[50].

The performance of nomogram to predict 24h-POF in patients undergoing endoscopy was evaluated from discrimination, accuracy and clinal utility. The AUC of both training and validation cohorts were greater than 0.8, the AUC of 1000 bootstrapped samples in training cohorts was greater than 0.8 as well, it showed the predicted model has excellent discrimination in ascertain 24h-POF in endoscopic patients. Additionally, the calibration plot and H-L test (P > 0.05) demonstrated high degree of fit between the actual accuracy and predicted accuracy which indicating the predicted accuracy of the model predictions. DCA plot demonstrated the model’s high clinical utility. Hence, the nomogram could assist medical staff in conducting dynamic assessments, identifying groups with high level of POF, and implementing early measures and interventions, thus improving patients’ quality of postoperative rehabilitation and quality of life.

Although our study conducted a model to predict and validate the 24h-POF in patients undergoing endoscopic treatment under anesthesia, several limitations still need to be considered. Firstly, our research carried out for seven months rather than a whole year, the fatigue feelings may be differed from diverse season or periods. Secondly, Christensen Fatigue Scale is a single-dimensional numeric rating scale that can’t distinguish potential different aspects of POF. Thirdly, our study lacks external validation, so we plan to conduct larger prospective studies to validate and optimize our predicted model.

The current study revealed the incidence of 24h-POF exceeded 40%, with 14 independent predictors identified. Notably, the 24h-POF nomogram developed in this study demonstrated excellent performance in terms of discrimination, accuracy, and clinical utility. This tool may aid healthcare professionals in identifying endoscopy patients who are more likely to experience higher level of 24h-POF, thus providing a scientific basis for devising appropriate strategies and effective measures to prevent and intervene POF, and expediting patient rehabilitation and improving quality of life. Future studies should supplement external verification of nomogram with the data of endoscopic patients in other hospitals and regions. Meantime, it is recommended that a subsequent randomized controlled study be conducted based on the modifiable predictors identified in this paper in order to find the beneficial intervention measures for preventing and ameliorating POF.

Funding

This research was supported by the Medical and Health Research Project of Zhejiang Province, China (ID 2023KY108).

Ethical approval

This study was approved by the research and ethical committee of Second Affiliated Hospital of Zhejiang University, School of Medicine (approval no. 2023-0361). All procedures performed in studies involving human participants were in accordance with the ethical standards of the responsible on human experimentation (institutional and national) committee and with the Helsinki Declaration of 1975, as revised in 2008.

Conflict of Interest Statement

The authors stated no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Acknowledgement

The authors would like to thank all participants of this study for their contribution. We wish to express our heartfelt gratitude to Lai-Juan Chen, a professional nurse of our hospital, who realized the electronic database for endoscopy activity. We express our heartfelt thanks to Hao-Ran Yu for his help in the application of R software as well.

Author Contribution Statements

Bing-Bing Wu contributed to conceptualization and design of the study, patient recruitment, data collection, analysis and interpretation, and manuscript writing. Liang-Yu Fang was involved in patient recruitment, data analysis and interpretation, and manuscript writing, critical revision of the article, and final approval. Dong-Zhou Gu contributed to data interpretation, critical review of results, and manuscript revision. Ying-Ying Jia and Xiao-Dan Lu contributed to data collection, charting and revise the article critically. Xiang-Yun Li assisted with data collection and helped edit the manuscript.

Availability of data and material

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Table 1

Characteristics of patients (N = 1306)
	Total	Training cohort (n = 914)	Validation cohort (n = 392)	Statistic value	P value
Age(year)	53.40 ± 13.27	53.44 ± 13.27	53.30 ± 13.30	Z=-0.218	0.828
Male, n%	656(50.2)	445(48.7)	211(53.8)	χ2 = 2.899	0.089
Weight(kg)	63.07 ± 11.66	62.84 ± 11.57	63.61 ± 11.86	Z=-1.049	0.294
Height(cm)	165.37 ± 7.75	165.25 ± 7.59	165.63 ± 8.11	Z=-0.796	0.426
BMI, n%				χ2 = 0.252	0.881
Underweight	88(6.7)	62(6.8)	26(6.6)
Normal	778(59.6)	548(60.0)	230(58.7)
Overweight	440(33.7)	304(33.3)	136(34.7)
Smoker, n%	304(23.3)	201(22.0)	103(26.3)	χ2 = 2.820	0.093
Drinker, n%	338(29.3)	224(24.5)	114(29.1)	χ2 = 2.992	0.084
Hypertension, n%	352(26.9)	255(27.9)	97(24.7)	χ2 = 1.387	0.239
Diabetes, n%	99(7.5)	62(6.8)	37(9.4)	χ2 = 2.761	0.097
Number of complications				χ2 = 3.259	0.515
0	799(61.6)	558(61.1)	240(61.2)
1	388(29.7)	279(30.5)	109(27.8)
2	96(7.4)	61(6.7)	35(8.9)
3	23(1.8)	15(1.6)	8(2.0)
4	1(0.1)	1(0.1)	0(0)
Surgical history, n%	453(34.6)	330(36.1)	123(31.4)	χ2 = 2.706	0.100
Occupation, n%				χ2 = 3.018	0.389
Staff	477(36.5)	333(36.4)	114(36.7)
Freelancer	400(30.6)	288(31.5)	112(28.6)
Student	15(1.1)	8(0.9)	7(1.8)
Retiree	414(31.7)	285(31.2)	129(32.9)
Degree of education, n%				χ2 = 2.828	0.587
Primary school and below	235(18.0)	169(18.5)	66(16.8)
Junior school	284 (21.7)	191(20.9)	93(23.7)
High school or technical secondary school	290(22.2)	204(22.3)	86(21.9)
Undergraduate or junior college	445(34.1)	317(34.7)	128(32.7)
Master or above	52(4.0)	33(3.6)	19(4.8)
Marital stage, n%				χ2 = 0.807	0.668
Married	1146(87.7)	806(88.2)	340(86.7)
Unmarried	63(4.8)	44(4.8)	19(4.8)
Divorced or widowed	97(7.4)	64(7.0)	33(8.4)
Religion, n%				χ2 = 1.007	0.604
Non-believers	1052(80.6)	736(80.5)	316(80.6)
Buddhist	228(17.5)	162(17.7)	66(16.8)
Christian or Mohammedan	26(2.0)	16(1.8)	10(2.6)
Endoscopic treatment, n%	286(21.8)	201(22.0)	85(21.7)	χ2 = 0.015	0.902
Time for endoscopy (min)	18.71 ± 13.73	18.79 ± 14.15	18.52 ± 12.74	-0.188	0.851
Time for waiting(min)	80.05 ± 61.54	80.02 ± 61.48	80.04 ± 61.66	-1.338	0.181
Average dosage of propofol by weight(mg/kg)	4.14 ± 0.75	4.15 ± 0.76	4.13 ± 0.73	Z = 0.284	0.776
Average dosage of propofol by time(mg/min)	18.22 ± 9.94	18.00 ± 9.59	18.73 ± 10.69	Z=-0.658	0.511
Intraoperative Infusion volume(ml)	38.70 ± 78.03	38.71 ± 77.55	38.67 ± 79.23	Z=-0.575	0.565
APAIS score	8.62 ± 1.85	8.69 ± 1.86	8.48 ± 1.82	-1.881	0.060
Intraoperative tracheal intubation, n%	72(5.5)	48(5.3)	24(6.1)	χ2 = 0.399	0.527
Intraoperative hypotension, n%	264(20.2)	178(19.5)	86(21.9)	χ2 = 1.033	0.310
Postoperative hypotension, n%	778(59.6)	533(58.3)	245(62.5)	χ2 = 1.995	0.158
Intraoperative hypoxemia, n%	7(0.7)	6(0.7)	1(0.3)	χ2 = 0.247	0.619
Postoperative hypoxemia, n%	22(1.7)	13(1.4)	9(2.3)	χ2 = 1.264	0.261
Sum of the change rates of vital signs before, during and after endoscopy(%)
SBP	27.46 ± 13.55	26.97 ± 13.39	28.61 ± 13.85	Z=-2.215	0.055
DBP	38.00 ± 22.19	37.45 ± 21.29	39.27 ± 24.13	Z=-0.827	0.408
MAP	28.77 ± 13.98	28.27 ± 13.52	29.94 ± 14.95	Z=-1.594	0.111
HR	22.03 ± 12.19	21.81 ± 12.07	22.53 ± 12.46	Z=-0.700	0.484
POF score in endoscopic day	3.11 ± 1.25	3.11 ± 1.26	3.09 ± 1.25	Z=-0.397	0.691
Abbreviation: BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, MAP mean arterial pressure, HR heart rate

Table 2

Univariate analysis and multivariate liner regression analysis of variable in the training set
	Univariate analysis (P value)	Multivariate analysis coefficients		95% CI	P value
Age	< 0.001*	0.196		(0.061,0.331)	0.013*
Gender	< 0.001*	0.506		(0.360,0.652)	< 0.001*
Weight	< 0.001*	0.002		(-0.007,0.011)	0.704
Height	< 0.001*	-0.002		(-0.016,0.012)	0.812
BMI	0.490
Smoker	0.559
Drinker	0.004*	0.323		(0.160,0.486)	< 0.001*
Hypertension	< 0.001*	0.147		(-0.065,0.358)	0.174
Diabetes	0.051
Number of complications	< 0.001*	0.310		(0.210,0.411)	< 0.001*
Surgery history	< 0.001*	0.012		(-0.121,0.146)	0.858
Occupation	0.248
Degree of education	< 0.001*	-0.205		(-0.339, -0.071)	0.004*
Marital stage	< 0.001*	Married	-0.882	(-1.088, -0.677)	< 0.001*
Marital stage	< 0.001*	Unmarried	-0.194	(-0.602, 0.215)	0.352
Religion	0.001*	Christian or Mohammedan	0.173	(-0.301, 0.646)	0.475
Religion	0.001*	Buddhist	-0.435	(-0.602, -0.268)	< 0.001*
Endoscopic treatment	< 0.001*	0.177		(0.012, 0.343)	0.031*
Time for endoscopy (min)	< 0.001*	-0.005		(-0.013, 0.003)	0.236
Time for waiting(min)	0.188
Average dosage of propofol by weight(mg/kg)	< 0.001*	0.128		(0.007, 0.249)	0.038*
Average dosage of propofol by time(mg/min)	< 0.001*	0.003		(-0.005, 0.012)	0.476
Intraoperative Infusion volume(ml)	0.017*	0.00007		(-0.001, 0.001)	0.899
APAIS score	< 0.001*	0.736		(0.624, 0.847)	< 0.001*
Intraoperative tracheal intubation	< 0.001*	0.356		(0.042, 0.670)	0.009*
Intraoperative hypotension	< 0.001*	0.372		(0.213, 0.530)	< 0.001*
Postoperative hypotension	0.334
Intraoperative hypoxemia	0.145
Postoperative hypoxemia	0.844
CR of SBP	0.024*	-0.001		(-0.007, 0.006)	0.88
CR of DBP	0.380
CR of MAP	0.042*	-0.003		(-0.010, 0.003)	0.322
CR of HR	< 0.001*	0.162		(0.090, 0.234)	< 0.001*
30min-POF	< 0.001*	0.461		(0.405, 0.518)	< 0.001*
* p value <0.05 in linear regression

Table 3

Assignment and dummy variable of classification variables in multivariate regression equation
Independent variable	Assignment
Gender	Male = 1, female = 2
Drinker	No = 0, yes = 1
Hypertension	No = 0, yes = 1
Operations history	No = 0, yes = 1
Intraoperative tracheal intubation	No = 0, yes = 1
Intraoperative hypotension	No = 0, yes = 1
Endoscopic treatment	No = 0, yes = 1
Degree of education	Primary school and below = 1
Degree of education	Junior school = 2 High school or technical secondary school = 3 Undergraduate or junior college = 4 Master or above = 5
Religion Non-believers (X1) Buddhist (X2) Christian or Mohammedan X3)	X0 = 1 X2 = 0 X3 = 0(Dummy variable) X1 = 0 X2 = 1 X3 = 0 X1 = 0 X2 = 0 X3 = 1
Martial stage Married(X1) Unmarried(X2) Divorced or widowed(X3)	X1 = 1 X2 = 0 X3 = 0 X1 = 0 X2 = 1 X3 = 0 X1 = 0 X2 = 0 X3 = 0(Dummy variable)

No competing interests reported.

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Developing a nomogram for predicting patients’ 24h-fatigue after digestive endoscopy with anesthesia assistance: a cross-sectional study

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Abstract

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1 Introduction

2 Methods

2.1 Study design and populations

2.2 Study Protocol

2.3 Data Collection

2.4 Ethical approval

2.5 Data analysis

3 Results

3.1 Baseline data and incidence of 24h-POF

4 Discussion

5 Conclusions

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