ABSTRACT
Objective
Quality of Life (QoL) has been a multifactorial concerning issue in oncology. We aimed to inspect the pre-operative QoL among patients with craniopharyngioma and to explore the potential correlations between parameters of QoL and clinical indices.
Subjects and methods
We enrolled a total of 109 patients with craniopharyngioma. We utilized Short Form 36 (SF-36), Symptom Check List-90, Generalized Anxiety Disorder Questionnaire scale (GAD7), Patient Health Questionnaire Depression (PHQ9) and Pittsburgh Sleep Quality Index to prospectively evaluated their QoL. Parameters of QoL along with clinical indices were compared among sub-groups divided according to Puget classification. Correlation analyses and regression analyses were performed to detect influential determinants to self-reported wellness.
Results
Patients presented impaired QoL compared with general population ( p < 0.001), as assessed by SF-36. Correlation analyses indicated the detrimental influence resulting from central diabetes insipidus (CDI). Multivariate linear regression unveiled the adverse effect of CDI on Mental Component Summary (coefficient = −13.869, p = 0.007), GAD7 total score (coefficient = 2.072, p = 0.049) as well as PHQ9 total score (coefficient = 3.721, p = 0.001). Multivariate logistic regression verified CDI as a risk factor of developing depressive symptoms (OR = 6.160, p = 0.001).
Conclusion
QoL of patients with craniopharyngioma was remarkably compromised before operation. CDI exerted detrimental influences on patients’ QoL and it might serve as a marker for early identification of patients at risk of depression.
Keywords: Craniopharyngioma, quality of life, depression, central diabetes insipidus
INTRODUCTION
Craniopharyngioma, a type of tumor of low histological malignancy, originates from embryonic malformation of the sellar or suprasellar region. The related clinical manifestations include visual impairment, headache, and hypothalamic/pituitary hormone deficiencies ( 1 ). Besides, craniopharyngiomas involving the hypothalamic area are prone to be associated with diabetes insipidus, obesity, mental disorders, and circadian rhythm disorders ( 2 ). With the advancement of treatment, mortality of these pathological conditions are decreasing and long-term survival becomes feasible ( 3 , 4 ). The overall 10-years survival rate after surgery and/or radiotherapy reaches over 90% in recent reports. However, functional deficits including hormone deficiency, mental disturbance and circadian rhythm disorders remain and continue to be detrimental for overall wellness ( 3 ) in these patients.
Health related QoL (quality of life) has been attached to great significance in the clinical practice. Plenty of studies have focused on the influences brought by craniopharyngiomas ( 1 , 3 , 5 – 18 ). However, the substantial proportion of existing studies are retrospective study, and the scales of prospective researches engaging pre-operative evaluations are relatively small ( 3 , 5 , 17 , 18 ). Lack of information on pre-operative condition and the consequent temporal shift in QoL might render obstacles in working out better clinical practice to improve the long term QoL of these patients. Therefore, it would be crucial to explore the general status of QoL before surgery and identify potential detrimental factors.
We conducted a questionnaire-based survey among pre-operative patients suffering from craniopharyngiomas in a tertiary neurosurgical center from 2019 to 2021. The primary aim was to depict the whole picture of QoL in patients with craniopharyngiomas. The secondary aim was to identify risk factors of poor QoL by correlation and regression analyses in these patients.
SUBJECTS AND METHODS
Participants and methods
Consecutive patients with craniopharyngiomas, diagnosed according to pre-operative MRI (magnetic resonance imaging) and post-operative pathology, were enrolled from the pituitary center of Huashan Hospital, Fudan University from 2018 to 2020. We excluded patients according to the following criteria: 1) failure to meet the inclusion criteria, 2) refusal to participate in the study, 3) incapability to complete the QoL assessment. This study was approved by the institutional review board of Huashan Hospital (KY2015-256) and all participants gave informed consent.
Assessments of quality of life
The evaluation of quality of life in participants was based on self-reported questionnaires collected after admission and before surgery. For kids who were affected by the disease, they would finish the questionnaire with the assistance from their parents. The questionnaires consisted of Short Form 36 (SF-36), Symptom Check List-90 (SCL90), Generalized Anxiety Disorder Questionnaire scale (GAD-7), Patient Health Questionnaire Depression (PHQ-9) and Pittsburgh Sleep Quality Index (PSQI), with detailed rules of each question sheet presented as follows.
The Chinese version of the SF-36, translated from the International Quality of Life Assessment (IQOLA) SF-36 Standard UK Version 1.0, was utilized in the current study ( 19 ). The questionnaire composed of a single item of health transition (HT) and 35 items that could further be divided into 8 subscales: 1) physical function (PF), 2) limitations due to physical health problems (role physical, RP), 3) bodily pain (BP), 4) general health (GH), 5) vitality (VT), 6) social functioning (SF), 7) limitations due to emotional health problems (role emotional, RE), and 8) mental health. The raw scores of HT and 8 subscales were then submitted to z-score transformation and the z-transformed scores of 8 subscales were further aggregated into two summary parameters: physical component summary (PCS) scores and mental component summary (MCS) scores ( 20 – 22 ). Generally, a higher z-transformed score indicated better functional status, while a lower score indicated of a poor QoL ( 22 ).
The SCL-90, a 90-item self-report symptom inventory, was also used in our study to evaluate psychological distress and symptoms of psychopathology. Items in the inventory were clustered into 9 dimensions: Somatization (SOM), Obsessive-Compulsive (OC), Interpersonal Sensitivity (IS), Depression (DEP), Anxiety (ANX), Hostility (HOS), Phobic Anxiety (PHOB), Paranoid Ideation (PAR), and Psychoticism (PSY) ( 23 ). Global Severity Index (GSI), calculated as the mean of all 90 items in SCL-90, was further converted into T-score (mean = 50, SD = 10) to measure composite psychological distress ( 24 ). In general, a higher GSI T-score suggested an increased risk of developing psychological symptoms ( 22 , 25 ).
The Generalized Anxiety Disorder Questionnaire scale (GAD-7) was applied to assess the depressive symptoms ( 26 ). GAD-7 included 7 questions and each question had a score of 0 to 3. The total score ranged from 0 to 21, and a score ≥ 5 was identified as having anxiety symptoms ( 26 , 27 ).
The Patient Health Questionnaire Depression (PHQ-9) was used to detect depressive symptoms ( 28 ). PHQ-9 included 9 questions, and each question had a score of 0 to 3. The total score ranged from 0 to 27, and a score ≥ 5 was identified as having depressive symptoms ( 27 , 28 ).
The Pittsburgh Sleep Quality Index (PSQI) was utilized to assess recent subjective sleep quality. A subject with a total score > 6 points was defined as poor sleep quality, with the higher PSQI score indicating the worse sleep quality ( 29 ).
Norm of Chinese population
The norms of SF-36 and SCL90 in Chinese population were based on the studies conducted by Wang and cols. ( 30 ) and Jin and cols. ( 31 ) respectively.
Measurements of clinical indices
Medical records of each patient were reviewed to collect demographic information (age, gender, education background, marriage status and disability status), anthropometric measurements, medical comorbidities (hypertension and diabetes mellitus), past medical history (disease duration, craniopharyngioma-related surgical history, radiation history and history of epilepsy or psychological disturbance), chief complaints, ophthalmological status, neuro-radiological classification, pathologic subtypes, and pituitary functions.
Anthropometric measurements included height (cm), weight (kg) and body mass index (BMI). BMI was calculated using the following formula: BMI = weight (kg)/height (m2). Individual Z-scores for BMI were calculated using the following formula: Z-score = ([x]−average [x]/SD.), where [x] is the actual individual BMI, average [x] is the mean BMI, and SD is the standard deviation for the mean BMI.
After inspecting the preoperative MRI or computed tomography of the sellar region in all cases, neuro-radiological classification of each participant was determined according to the Puget classification, initially developed to evaluate the extent of hypothalamic involvement in craniopharyngiomas ( 5 ). The Puget classification stratified the participants in accordance to the degree of hypothalamic involvement as follows: Grade 0, no hypothalamic involvement; Grade 1, minor hypothalamic involvement (the tumor abutting or displacing the hypothalamus); and Grade 2, major hypothalamic involvement (the hypothalamus is not identifiable). The Puget classification of each patient was determined by two experienced radiologists (Xiang Zhou and Nidan Qiao).
Pituitary function was evaluated by experienced endocrinologists pre-operatively. Hypothalamic-pituitary-adrenal (HPA) axis disturbance was defined by basal serum cortisol level < 3 mg/dL measured at 8 a.m. or peak cortisol level < 18.1 mg/dL after insulin tolerance test or ACTH stimulation test. Hypothalamic-pituitary-thyroid (HPT) axis disturbance was diagnosed based on free T4 level below the reference range combined with a low or normal thyroid-stimulating hormone (TSH). In premenopausal women, hypothalamic-pituitary-gonadal (HPG) axis disturbance was defined by oligomenorrhea or amenorrhea combined with low serum estradiol and inappropriately low or normal follicle-stimulating hormone (FSH) and luteinizing hormone (LH) levels. HPG axis disturbance in postmenopausal women was diagnosed by serum FSH and LH within premenopausal range. In men, HPG axis disturbance was defined as low serum testosterone in conjunction with low gonadotropins. Serum insulin-like growth factor 1 (IGF-1) level was measured, although insulin tolerance test was not performed routinely to detect growth hormone deficiency in this study population. Clinical presentation, urine-specific gravity, urine and serum osmolality, serum sodium level, and the need for desmopressin treatment were comprehensively evaluated for the diagnosis of central diabetes insipidus (CDI) ( 32 ). Water deprivation testing was also performed if necessary. Moreover, all patients with pituitary axis deficits were placed on appropriate hormone replacement except for growth hormone (GH) ( 33 , 34 ). The endocrinological assessment and the associated hormone replacement were initiated in the clinic, approximately 2-3 weeks before ward admission and questionnaire administration. Also, fasting blood glucose (FBG) and serum sodium levels were documented.
Statistical analysis
The Kolmogorov-Smirnov test was used to evaluate the normality and variance uniformity of the data. Continuous variables with normal distribution were presented as means ± SD (standard deviation), while variables with a skewed distribution were expressed as median (interquartile range). Categorical data were described as frequencies along with percentages of the total group. Z-scores of BMI are presented as mean, minimum and maximum value. Participants were divided into subgroups according to different Puget's classifications. Differences in quantitative data between subgroups were tested by Student's t-test or one-way ANOVA analysis. Differences in categorical data were determined by Chi-square analysis or fisher's exact tests as appropriate. Correlations among parameters of questionnaires and clinical indices were calculated via spearman correlation analysis. Multivariate linear or logistic regression analysis was performed to identify factors influencing each QoL as appropriate. A p -value threshold of < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS software 23.0 (SPSS Inc, USA).
RESULTS
Clinical indices of enrolled patients
In total, 109 patients with craniopharyngiomas (CP), confirmed by post-operative pathological examination, were enrolled in this study and the proportion of participants under 18 is 10%. Part of the patients (28, 25.7%) in this study suffered from recurrent craniopharyngioma, the surgical approaches included craniotomy (26, 23.9%) and endoscopic endonasal surgery (2, 1.8%). The patients were further stratified according to Puget classification based on pre-operative MRI, and the schematic demonstration was displayed in Figure 1A . In general, CP patients with hypothalamic involvement (P-1 & 2) presented with an increased age ( p < 0.001) and an elevated BMI ( p = 0.043) than those without hypothalamic involvement, with the remaining parameters being comparable among these groups ( Table 1 ).
Figure 1. Schematic demonstration of Puget classification and divergence between patients and general population in subscales of SF36 and SCL90 ( 30 , 31 ). ( A ) The schematic figure demonstrated hypothalamic area in control and different extents of hypothalamic involvement in Puget classification system, with the arrows denoting the hypothalamic area. ( B ) Comparison of subscales in SF-36 between patients and general population illustrated a generally decreased QoL of patients. ( C ) Comparison of subscales in SCL90 between patients and general population demonstrated higher mean score of somatization along with lower mean scores of interpersonal relationship as well as paranoia in patients. Short Form 36 (SF-36), Symptom Check List-90 (SCL-90), quality of life (QoL). ****, *** and * denote the p value < 0.0001, p value < 0.001 and p value < 0.05.
Table 1. Clinical indices of CP patients.
| Total (n = 109) | P-0 (n = 9) | P-1 (n = 55) | P-2 (n = 45) | Significance | ||
|---|---|---|---|---|---|---|
| Age (year) | 40.0 (28.5-56.0) | 16.0 (12.0-27.0) | 47.0 (30.0-59.0) | 40.0 (29.5-51.5) | <0.001 | |
| Gender | 0.773 | |||||
| Female | 48 (44%) | 5 (55.6%) | 24 (43.6%) | 19 (42.2%) | ||
| Male | 61 (56%) | 4 (44.4%) | 31 (56.4%) | 26 (57.8%) | ||
| BMI (kg/m2) | 24.22 ± 4.27 (0, −2.22, 2.24) | -0.79 (-2.22, 2.24) | 0.06 (-2.10, 2.22) | 0.09 (-1.83, 1.69) | 0.043 | |
| Hypertension | 18 (16.5%) | 1 (11.1%) | 10 (18.2%) | 7 (15.6%) | 0.926 | |
| Diabetes mellitus | 6 (5.5%) | 0 (0%) | 2 (3.6%) | 4 (8.9%) | 0.534 | |
| Disabled | 5 (4.6%) | 0 (0%) | 2 (3.6%) | 3 (6.7%) | 0.778 | |
| Disease duration (year) | 10.0 (2.5-24.0) | 12.0 (3.0-24.0) | 6.0 (2.0-24.0) | 12.0 (3.0-36.0) | 0.461 | |
| Surgical history | 28 (25.7%) | 2 (22.2%) | 13 (23.6%) | 13 (28.9%) | 0.892 | |
| Craniotomy | 26 (23.9%) | 2 (22.2%) | 12 (21.8%) | 12 (26.7%) | ||
| Endoscopic endonasal surgery | 2 (1.8%) | 0 | 1 (1.8%) | 1 (2.2%) | ||
| Radiation history | 5 (4.6%) | 0 (0%) | 2 (3.6%) | 3 (6.7%) | 0.778 | |
| Chief complaint | ||||||
| Visual deterioration | 82 (75.2%) | 5 (55.6%) | 42 (76.4%) | 35 (77.8%) | 0.389 | |
| Headache | 36 (33.0%) | 4 (44.4%) | 15 (27.3%) | 17 (37.8%) | 0.414 | |
| Polyuria | 29 (26.6%) | 2 (22.2%) | 13 (23.6%) | 14 (31.1%) | 0.719 | |
| Amenorrhea | 13 (11.9%) | 0 (0%) | 5 (9.1%) | 8 (17.8%) | 0.273 | |
| Memory disturbance | 1 3(11.9%) | 1 (11.1%) | 3 (5.5%) | 9 (20.0%) | 0.059 | |
| Ophthalmology abnormality | 96 (88.1%) | 6 (66.7%) | 48 (87.3%) | 42 (93.3%) | 0.089 | |
| Epilepsy | 5 (4.6%) | 0 (0%) | 2 (3.6%) | 3 (6.7%) | 0.778 | |
| Psychology disturbance | 4 (3.7%) | 0 (0%) | 2 (3.6%) | 2 (4.4%) | 1.000 | |
| Hormone deficiencies | ||||||
| HPA axis | 25 (22.9%) | 3 (33.3%) | 10 (18.2%) | 12 (26.7%) | 0.391 | |
| HPT axis | 84 (77.1%) | 8 (88.9%) | 43 (78.2%) | 33 (73.3%) | 0.656 | |
| HPG axis | 60 (55.0%) | 6 (66.7%) | 28 (50.9%) | 26 (57.8%) | 0.617 | |
| IGF-1 | 35 (32.1%) | 5 (55.6%) | 13 (23.6%) | 17 (37.8%) | 0.105 | |
| CDI | 38 (34.9%) | 3 (33.3%) | 18 (32.7%) | 17 (37.8%) | 0.908 | |
| Number of pituitary axis deficits | 0.369 | |||||
| 0 | 13 (11.9%) | 1 (11.1%) | 5 (9.1%) | 7 (15.6%) | ||
| 1 | 20 (18.3%) | 1 (11.1%) | 14 (25.5%) | 5 (11.1%) | ||
| 2 | 32 (29.4%) | 1 (11.1%) | 19 (34.5%) | 12 (26.7%) | ||
| 3 | 24 (22.0%) | 3 (33.3%) | 9 (16.4%) | 12 (26.7%) | ||
| 4 | 14 (12.8%) | 2 (22.2%) | 7 (12.7%) | 5 (11.1%) | ||
| 5 | 6 (5.5%) | 1 (11.1%) | 1 (1.8%) | 4 (8.9%) | ||
| FBG (mmol/L) | 5.01 ± 1.24 | 4.61 ± 0.73 | 5.07 ± 1.30 | 5.02 ± 1.25 | 0.594 | |
| FT4 (pmol/L) | 12.30 ± 3.51 | 12.19 ± 4.69 | 12.17 ± 3.76 | 12.47 ± 3.01 | 0.912 | |
| Na (mmol/L) | 142.75 ± 3.18 | 142.89 ± 3.41 | 142.76 ± 3.23 | 142.71 ± 3.15 | 0.988 | |
P-0: Puget grade 0; P-1: Puget grade 1; P-2: Puget grade 2; BMI: body mass index; HPA: hypothalamic-pituitary-adrenal; HPT: hypothalamic-pituitary-thyroid; HPG: hypothalamic-pituitary-gonadal; IGF-1: insulin-like growth factor 1; CDI: central diabetes insipidus; FBG: fasting blood glucose; FT4: free thyroxine; Na: sodium; continuous variables with normal distribution were presented as means ± SD (standard deviation); variables with a skewed distribution were expressed as median (interquartile range); Z-scores of BMI are presented as mean, minimum and maximum value; categorical data were described as frequencies along with percentages of the total group.
Comparison of quality of life between patients and general population along with internal variance among patients
Compared with general population in China, mean scores of all specific dimensions of SF-36 were significantly decreased in CP ( p < 0.001), except that the mean score in social functioning was comparable between the two groups. In comparison to SCL90 in Chinese norm, mean score in somatization was higher ( p = 0.001) while mean scores in interpersonal relationship ( p < 0.001) as well as paranoia ( p = 0.020) were lower in CP ( Figures 1B and C ). And the details of comparison were provided in Supplementary Table 1 .
Supplementary Table 1. Comparison of parameters in SF-36 and SCL90 between patients and Chinese norm.
| Patients (n = 109) | Norm | Significance | |
|---|---|---|---|
| SF-36 | Wang and cols. (n = 3,214) | ||
| Physical functioning | 82.43 ± 19.73 | 94.02 ± 12.44 | <0.001 |
| Role physical | 47.25 ± 44.53 | 88.79 ± 28.49 | <0.001 |
| Bodily pain | 71.29 ± 22.31 | 88.18 ± 19.02 | <0.001 |
| General health | 55.07 ± 18.97 | 69.74 ± 20.95 | <0.001 |
| Vitality | 61.33 ± 19.59 | 68.92 ± 18.78 | <0.001 |
| Social functioning | 89.68 ± 28.35 | 88.03 ± 16.00 | 0.306 |
| Role emotional | 46.48 ± 43.04 | 89.57 ± 27.95 | <0.001 |
| Mental health | 66.35 ± 17.22 | 77.61 ± 15.85 | <0.001 |
| SCL-90 | Jin and cols. (n = 1,388) | ||
| Somatization | 1.53 ± 0.54 | 1.37 ± 0.48 | 0.001 |
| Compulsion | 1.64 ± 0.56 | 1.62 ± 0.58 | 0.728 |
| Interpersonal relationship | 1.43 ± 0.54 | 1.65 ± 0.51 | <0.001 |
| Depression | 1.55 ± 0.55 | 1.50 ± 0.59 | 0.392 |
| Anxiety | 1.44 ± 0.56 | 1.39 ± 0.43 | 0.254 |
| Hostility | 1.45 ± 0.53 | 1.48 ± 0.56 | 0.589 |
| Phobia | 1.28 ± 0.48 | 1.23 ± 0.41 | 0.227 |
| Paranoia | 1.30 ± 0.45 | 1.43 ± 0.57 | 0.020 |
| Psychoticism | 1.37 ± 0.50 | 1.29 ± 0.42 | 0.059 |
SF-36: Short Form 36; SCL90: Symptom Check List-90; continuous variables with normal distribution were presented as means ± SD (standard deviation).
Among CP with different Puget's classifications, notably enhanced scores in interpersonal relationship ( p = 0.045), phobia ( p = 0.012) and paranoia ( p = 0.024) were detected in patients with no hypothalamus involvement and the remaining indexes in all questionnaires were similar ( Table 2 ).
Table 2. Comparison of QoL among sub-groups of patients.
| P-0 (n = 9) | P-1 (n = 55) | P-2 (n = 45) | Significance | ||
|---|---|---|---|---|---|
| SF-36 | |||||
| Physical functioning | 79.44 ± 29.10 | 82.91 ± 18.95 | 82.44 ± 18.94 | 0.889 | |
| Role physical | 50.00 ± 41.46 | 46.82 ± 45.40 | 47.22 ± 44.98 | 0.981 | |
| Bodily pain point | 76.11 ± 28.41 | 72.36 ± 21.59 | 69.02 ± 22.16 | 0.607 | |
| General health | 51.44 ± 15.42 | 55.27 ± 20.65 | 55.56 ± 17.72 | 0.836 | |
| Vitality | 63.33 ± 16.39 | 61.55 ± 21.56 | 60.67 ± 17.92 | 0.928 | |
| Social functioning | 88.89 ± 24.56 | 89.32 ± 31.95 | 90.28 ± 24.70 | 0.983 | |
| Role emotional | 33.33 (33.33-100) | 66.67 (0-100) | 33.33 (0-66.67) | 0.178 | |
| Mental health | 61.33 ± 18.87 | 66.33 ± 17.97 | 67.38 ± 16.13 | 0.634 | |
| Health transition | 50.00 ± 21.65 | 38.64 ± 26.27 | 35.00 ± 25.23 | 0.271 | |
| PCS | 115.20 ± 29.07 | 114.71 ± 21.85 | 116.41 ± 21.20 | 0.930 | |
| MCS | 110.00 ± 19.33 | 110.79 ± 27.30 | 104.92 ± 24.09 | 0.509 | |
| SCL-90 | |||||
| Somatization | 1.75 (1.33-2.04) | 1.42 (1.17-1.58) | 1.42 (1.08-1.75) | 0.186 | |
| Compulsion | 1.80 (1.40-2.30) | 1.50 (1.20-1.90) | 1.50 (1.10-1.95) | 0.412 | |
| Interpersonal relationship | 1.78 (1.28-2.50) | 1.22 (1.11-1.44) | 1.22 (1.00-1.56) | 0.045 | |
| Depression | 1.85 (1.31-2.38) | 1.31 (1.15-1.69) | 1.38 (1.15-1.73) | 0.186 | |
| Anxiety | 1.50 (1.25-2.40) | 1.20 (1.00-1.70) | 1.20 (1.00-1.55) | 0.107 | |
| Hostility | 1.67 (1.42-2.33) | 1.17 (1.00-1.67) | 1.33 (1.08-1.50) | 0.073 | |
| Phobia | 1.57 (1.21-2.00) | 1.14 (1.00-1.43) | 1.00 (1.00-1.36) | 0.012 | |
| Paranoia | 1.33 (1.33-2.50) | 1.17 (1.00-1.50) | 1.17 (1.00-1.42) | 0.024 | |
| Psychoticism | 1.60 (1.20-1.95) | 1.10 (1.00-1.60) | 1.20 (1.00-1.40) | 0.135 | |
| GSI | 66.46 ± 5.07 | 63.52 ± 5.02 | 62.86 ± 3.09 | 0.080 | |
| PHQ9 total score | 7.8 9± 5.23 | 6.65 ± 6.16 | 6.69 ± 5.22 | 0.829 | |
| Depressive symptoms | 0.471 | ||||
| No | 2 (22.2%) | 25 (45.5%) | 19 (42.2%) | ||
| Yes | 7 (77.8%) | 30 (54.5%) | 26 (57.8%) | ||
| GAD7 total score | 6 (2.5-7) | 3 (0-8) | 4 (1-8) | 0.596 | |
| Anxiety symptoms | 0.499 | ||||
| No | 3 (33.3%) | 30 (54.5%) | 25 (55.6%) | ||
| Yes | 6 (66.7%) | 25 (45.5%) | 20 (44.4%) | ||
| PSQI total score | 7.67 ± 2.50 | 6.93 ± 3.70 | 6.38 ± 3.19 | 0.516 | |
| Sleep disturbance | 0.348 | ||||
| No | 3 (33.3%) | 32 (58.2%) | 27 (60.0%) | ||
| Yes | 6 (66.7%) | 23 (41.8%) | 18 (40.0%) | ||
P-0: Puget grade 0; P-1: Puget grade 1; P-2: Puget grade 2; SF-36: Short Form 36; SCL90: Symptom Check List-90; GAD-7: Generalized Anxiety Disorder Questionnaire scale; PHQ-9: Patient Health Questionnaire Depression; PSQI: Pittsburgh Sleep Quality Index; PCS: physical component summary; MCS: mental component summary; GSI: global severity; continuous variables with normal distribution were presented as means ± SD (standard deviation); variables with a skewed distribution were expressed as median (interquartile range); categorical data were described as frequencies along with percentages of the total group.
Besides, we have stratified the participants according to the number of pituitary axis deficits ( Supplementary Table 2 ), and found significant intra-subgroups difference in hostility scores ( p = 0.047) while the remaining parameters were comparable. Additionally, we divided the patients according to the status of HPT axis, and lower scores in physical functioning ( p = 0.017) and social functioning ( p = 0.033) were observed in patients with disturbed HPT axis, without significant alterations in other parameters of QoL ( Supplementary Table 3 ).
Table 3. Multivariate linear regression analyses in CP patients identify influential factors associated with MCS, GSI and total scores of GAD7, PHQ9 and PSQI.
| Beta | SE | 95%CI | P value | ||
|---|---|---|---|---|---|
| MCS | |||||
| CDI | -13.869 | 5.021 | -23.827- −3.911 | 0.007 | |
| GSI | |||||
| Headache | 3.007 | 0.798 | 1.423-4.591 | <0.001 | |
| Radiation history | 7.925 | 1.896 | 4.164-11.687 | <0.001 | |
| Higher Puget classification | -2.054 | 0.619 | -3.282- −0.826 | 0.001 | |
| Amenorrhea | 2.480 | 1.162 | 0.175-4.786 | 0.035 | |
| FBG | -0.683 | 0.324 | -1.325- −0.041 | 0.037 | |
| GAD7 total score | |||||
| Headache | 2.229 | 1.070 | 0.106-4.351 | 0.040 | |
| CDI | 2.072 | 1.042 | 0.006-4.138 | 0.049 | |
| PHQ9 total score | |||||
| CDI | 3.721 | 1.062 | 1.614-5.827 | 0.001 | |
| Headache | 2.934 | 1.091 | 0.769-5.098 | 0.008 | |
| PSQI total score | |||||
| Recurrence | -2.287 | 0.779 | -3.833- −0.741 | 0.004 | |
| Radiation history | 4.043 | 1.621 | 0.829-7.258 | 0.014 | |
MCS: mental component summary; GSI: global severity; GAD-7: Generalized Anxiety Disorder Questionnaire scale; PHQ-9: Patient Health Questionnaire Depression; PSQI: Pittsburgh Sleep Quality Index; CDI: central diabetes insipidus; SE: standard error; CI: confidence interval.
Supplementary Table 2. Comparison of QoL among patients with different number of pituitary axis deficits.
| 0 (n = 13) | 1 (n = 20) | 2 (n = 32) | 3 (n = 24) | 4 (n = 14) | 5 (n = 6) | Significance | ||
|---|---|---|---|---|---|---|---|---|
| SF-36 | ||||||||
| Physical functioning | 91.15 ± 8.70 | 83.75 ± 16.85 | 83.28 ± 17.02 | 80.83 ± 27.33 | 74.64 ± 22.91 | 79.17 ± 12.01 | 0.398 | |
| Role physical | 61.54 ± 42.84 | 46.25 ± 44.63 | 44.53 ± 45.67 | 52.08 ± 44.18 | 42.86 ± 47.46 | 25.00 ± 41.83 | 0.650 | |
| Bodily pain point | 76.23 ± 17.43 | 69.80 ± 21.15 | 78.22 ± 22.17 | 67.50 ± 20.69 | 66.86 ± 27.40 | 54.17 ± 22.27 | 0.123 | |
| General health | 57.85 ± 22.37 | 53.00 ± 21.37 | 59.19 ± 15.58 | 55.13 ± 17.60 | 50.14 ± 23.51 | 45.33 ± 12.03 | 0.480 | |
| Vitality | 59.62 ± 20.36 | 63.25 ± 16.57 | 65.16 ± 17.76 | 60.83 ± 20.20 | 56.79 ± 26.21 | 50.83 ± 18.00 | 0.557 | |
| Social functioning | 98.08 ± 20.31 | 93.13 ± 21.64 | 95.70 ± 28.51 | 84.38 ± 31.98 | 81.25 ± 34.23 | 68.75 ± 22.01 | 0.142 | |
| Role emotional | 43.59 ± 43.85 | 58.33 ± 41.71 | 42.71 ± 41.68 | 51.39 ± 43.94 | 42.86 ± 47.91 | 22.22 ± 40.37 | 0.537 | |
| Mental health | 73.23 ± 14.55 | 64.20 ± 18.83 | 68.50 ± 16.78 | 67.67 ± 16.68 | 59.71 ± 19.69 | 57.33 ± 10.63 | 0.240 | |
| Health transition | 38.46 ± 16.51 | 32.50 ± 21.61 | 35.16 ± 25.29 | 39.58 ± 28.47 | 51.79 ± 31.72 | 33.33 ± 25.82 | 0.343 | |
| PCS | 125.34 ± 18.99 | 112.89 ± 20.02 | 118.40 ± 21.69 | 114.07 ± 23.54 | 110.06 ± 26.51 | 104.89 ± 15.27 | 0.332 | |
| MCS | 107.95 ± 25.60 | 113.77 ± 25.97 | 109.62 ± 25.60 | 109.67 ± 23.05 | 103.02 ± 30.19 | 90.62 ± 18.05 | 0.473 | |
| SCL-90 | ||||||||
| Somatization | 1.56 ± 0.78 | 1.46 ± 0.47 | 1.45 ± 0.43 | 1.54 ± 0.47 | 1.75 ± 0.79 | 1.53 ± 0.28 | 0.637 | |
| Compulsion | 1.44 ± 0.40 | 1.63 ± 0.47 | 1.57 ± 0.44 | 1.71 ± 0.65 | 1.84 ± 0.82 | 1.73 ± 0.57 | 0.466 | |
| Interpersonal relationship | 1.23 ± 0.39 | 1.38 ± 0.46 | 1.32 ± 0.37 | 1.45 ± 0.51 | 1.78 ± 0.90 | 1.67 ± 0.54 | 0.056 | |
| Depression | 1.49 ± 0.51 | 1.44 ± 0.36 | 1.50 ± 0.40 | 1.57 ± 0.64 | 1.77 ± 0.88 | 1.74 ± 0.56 | 0.533 | |
| Anxiety | 1.41 ± 0.68 | 1.3 3 ± 0.46 | 1.34 ± 0.34 | 1.50 ± 0.55 | 1.74 ± 0.92 | 1.52 ± 0.52 | 0.297 | |
| Hostility | 1.27 ± 0.27 | 1.47 ± 0.46 | 1.38 ± 0.36 | 1.38 ± 0.47 | 1.86 ± 0.97 | 1.53 ± 0.50 | 0.047 | |
| Phobia | 1.20 ± 0.39 | 1.16 ± 0.28 | 1.23 ± 0.31 | 1.34 ± 0.59 | 1.59 ± 0.76 | 1.24 ± 0.38 | 0.126 | |
| Paranoia | 1.14 ± 0.20 | 1.27 ± 0.39 | 1.21 ± 0.24 | 1.33 ± 0.53 | 1.62 ± 0.77 | 1.44 ± 0.31 | 0.057 | |
| Psychoticism | 1.29 ± 0.52 | 1.24 ± 0.36 | 1.28 ± 0.26 | 1.43 ± 0.48 | 1.70 ± 0.90 | 1.52 ± 0.45 | 0.088 | |
| GSI | 62.60 ± 4.21 | 62.78 ± 3.49 | 62.75 ± 2.67 | 63.77 ± 4.48 | 66.09 ± 7.57 | 64.46 ± 3.85 | 0.203 | |
| PHQ9 total score | 6.08 ± 7.47 | 5.95 ± 5.08 | 6.38 ± 4.39 | 5.88 ± 5.10 | 9.00 ± 7.34 | 11.50 ± 5.89 | 0.172 | |
| Depressive symptoms | 0.595 | |||||||
| No | 8 (61.5%) | 10 (50.0%) | 12 (37.5%) | 12 (50.0%) | 3 (21.4%) | 1 (16.7%) | ||
| Yes | 5 (38.5%) | 10 (50.0%) | 20 (62.5%) | 12 (50.0%) | 11 (78.6%) | 5 (83.3%) | ||
| GAD7 total score | 5.08 ± 6.24 | 4.35 ± 4.70 | 4.78 ± 4.97 | 4.50 ± 4.63 | 6.43 ± 6.27 | 9.83 ± 6.05 | 0.262 | |
| Anxiety symptoms | 0.757 | |||||||
| No | 8 (61.5%) | 12 (60.0%) | 17 (53.1%) | 14 (58.3%) | 6 (42.9%) | 1 (16.7%) | ||
| Yes | 5 (38.5%) | 8 (40.0%) | 15 (46.9%) | 10 (41.7%) | 8 (57.1%) | 5 (83.3%) | ||
| PSQI total score | 6.23 ± 3.75 | 6.65 ± 3.54 | 7.19 ± 2.81 | 6.88 ± 3.85 | 6.71 ± 3.65 | 5.67 ± 3.72 | 0.919 | |
| Sleep disturbance | 0.829 | |||||||
| No | 6 (46.2%) | 13 (65.0%) | 16 (50.0%) | 13 (54.2%) | 10 (71.4%) | 4 (66.7%) | ||
| Yes | 7 (53.8%) | 7 (35.0%) | 16 (50.0%) | 11 (45.8%) | 4 (28.6%) | 2 (33.3%) | ||
SF-36: Short Form 36; SCL90: Symptom Check List-90; GAD-7: Generalized Anxiety Disorder Questionnaire scale; PHQ-9: Patient Health Questionnaire Depression; PSQI: Pittsburgh Sleep Quality Index; PCS: physical component summary; MCS: mental component summary; GSI: global severity; continuous variables with normal distribution were presented as means ± SD (standard deviation); variables with a skewed distribution were expressed as median (interquartile range); categorical data were described as frequencies along with percentages of the total group.
Supplementary Table 3. Comparison of QoL among patients with different status of HPT axis.
| HPT normal (n = 25) | HPT disturbed (n = 84) | Significance | ||
|---|---|---|---|---|
| SF-36 | ||||
| Physical functioning | 88.40 ± 10.77 | 80.66 ± 21.42 | 0.017 | |
| Role physical | 56.00 ± 45.23 | 44.64 ± 44.26 | 0.265 | |
| Bodily pain point | 73.44 ± 18.57 | 70.66 ± 23.36 | 0.586 | |
| General health | 56.04 ± 21.73 | 54.79 ± 18.21 | 0.773 | |
| Vitality | 59.00 ± 18.43 | 62.02 ± 19.97 | 0.501 | |
| Social functioning | 98.50 ± 20.51 | 87.05 ± 29.90 | 0.033 | |
| Role emotional | 46.67 ± 44.10 | 46.43 ± 42.99 | 0.981 | |
| Mental health | 67.20 ± 17.96 | 66.10 ± 17.09 | 0.780 | |
| Health transition | 38.00 ± 17.85 | 38.10 ± 27.57 | 0.984 | |
| PCS | 121.61 ± 19.08 | 113.61 ± 22.60 | 0.111 | |
| MCS | 107.94 ± 25.83 | 108.41 ± 25.42 | 0.936 | |
| SCL-90 | ||||
| Somatization | 1.54 ± 0.65 | 1.52 ± 0.51 | 0.911 | |
| Compulsion | 1.54 ± 0.47 | 1.67 ± 0.58 | 0.332 | |
| Interpersonal relationship | 1.27 ± 0.40 | 1.47 ± 0.56 | 0.090 | |
| Depression | 1.46 ± 0.46 | 1.58 ± 0.58 | 0.353 | |
| Anxiety | 1.38 ± 0.60 | 1.46 ± 0.55 | 0.564 | |
| Hostility | 1.34 ± 0.36 | 1.48 ± 0.57 | 0.137 | |
| Phobia | 1.23 ± 0.48 | 1.30 ± 0.48 | 0.516 | |
| Paranoia | 1.21 ± 0.37 | 1.33 ± 0.47 | 0.220 | |
| Psychoticism | 1.27 ± 0.46 | 1.41 ± 0.51 | 0.228 | |
| GSI | 62.74 ± 4.05 | 63.71 ± 4.49 | 0.337 | |
| PHQ9 total score | 5.76 ± 6.02 | 7.07 ± 5.57 | 0.313 | |
| Depressive symptoms | 0.112 | |||
| No | 14 (56.0%) | 32 (38.1%) | ||
| Yes | 11 (44.0%) | 52 (61.9%) | ||
| GAD7 total score | 4.68 ± 5.59 | 5.31 ± 5.23 | 0.604 | |
| Anxiety symptoms | 0.218 | |||
| No | 16 (64.0%) | 42 (50.0%) | ||
| Yes | 9 (36.0%) | 42 (50.0%) | ||
| PSQI total score | 6.44 ± 3.54 | 6.86 ± 3.38 | 0.593 | |
| Sleep disturbance | 0.575 | |||
| No | 13 (52.0%) | 49 (58.3%) | ||
| Yes | 12 (48.0%) | 35 (41.7%) | ||
HPT: hypothalamic-pituitary-thyroid; SF-36: Short Form 36; SCL90: Symptom Check List-90; GAD-7: Generalized Anxiety Disorder Questionnaire scale; PHQ-9: Patient Health Questionnaire Depression; PSQI: Pittsburgh Sleep Quality Index; PCS: physical component summary; MCS: mental component summary; GSI: global severity; continuous variables with normal distribution were presented as means ± SD (standard deviation); variables with a skewed distribution were expressed as median (interquartile range); categorical data were described as frequencies along with percentages of the total group.
Correlations between quality of life and clinical indices
To further explore the associations between clinical indices and QoL, we conducted correlation analyses ( Figure 2 ). Regarding SF-36 questionnaire, associations in negative manner could be detected between CDI and general health (r = −0.218, p = 0.023), vitality (r = −0.194, p = 0.043), social functioning (r = −0.206, p = 0.032), role emotional (r = −0.196, p = 0.041), mental health (r = −0.204, p = 0.034), MCS (r = −0.245, p = 0.010) respectively. Additionally, we revealed that social functioning (r = −0.210, p = 0.028) and mental health (r = −0.189, p = 0.049) were negatively associated with the number of pituitary axis deficits. As for SCL-90 questionnaire, headache was remarkably associated with nearly all subscales (except for psychoticism and anxiety) and GSI in a positive manner. Similarly, interpersonal relationship (r = 0.228, p = 0.017), anxiety (r = 0.214, p = 0.025), phobia (r = 0.204, p = 0.033), paranoia (r = 0.200, p = 0.037), psychoticism (r = 0.242, p = 0.011) were positively correlated with the number of pituitary axis deficits. Concerning the other three questionnaires, PHQ9 total scores and PHQ9 classifications were positively associated with CDI (PHQ9 total scores: r = 0.362, p < 0.001; PHQ9 classification: r = 0.313, p = 0.001), meanwhile PHQ9 total score (r = 0.192, p = 0.046) and PHQ9 classification (r = 0.193, p = 0.045) were also positively correlated with the number of pituitary axis deficits. Turning to PSQI, the classification (r = −0.215, p = 0.025) and the total score (r = −0.248, p = 0.009) were negatively related to surgical history.
Figure 2. Correlation heat map between parameters of QoL and clinical indices of the patients. Correlation analysis between parameters of SF-36, SCL90, GAD7, PHQ9 along with PSQI and clinical indices. Short Form 36 (SF-36), Symptom Check List-90 (SCL90), Generalized Anxiety Disorder Questionnaire scale (GAD-7), Patient Health Questionnaire Depression (PHQ-9), Pittsburgh Sleep Quality Index (PSQI) and quality of life (QoL). The depth of color directly shows the degree of correlation between clinical indices and parameters of QoL. At the same time, correlation significance test was carried out, with * and ** symbolizing p < 0.05 and p < 0.01 respectively.
Identifying independent risk factors for QoL
To further unveil the risk factors of impaired QoL, we conducted regression analyses. CDI (B = −13.869, p = 0.007) was independently associated with MCS in the multivariate linear regression model. Headache (B = 3.007, p < 0.001), radiation history (B = 7.925, p < 0.001), higher Puget classification (B = −2.054, p = 0.001), amenorrhea (B = 2.480, p = 0.035) and FBG (B = −0.683, p = 0.037) were demonstrated to be remarkable determinants of GSI in the multivariate regression model. Headache (B = 2.229, p = 0.040) along with CDI (B = 2.072, p = 0.049) was found to be an influential factor for GAD7. CDI (B = 3.721, p = 0.001) and headache (B = 2.934, p = 0.008) were verified as vital variates for PHQ9. Recurrence (B = −2.287, p = 0.004) and radiation history (B = 4.043, p = 0.014) were verified as vital variates for PSQI. However, no factors were independently associated with PCS in the linear regression model. Details of the identified risk factors in multivariate linear regression analyses were depicted in Table 3 .
GAD7, PHQ9 or PSQI were further dichotomized as having/not anxiety symptoms, depressive symptoms and poor sleeping quality. In the subsequent multivariate logistics regression analysis, headache (OR = 2.395, p = 0.041) were demonstrated to be a significant risk factor of GAD7 classification (developing anxiety symptoms). Recurrence (OR = 0.326, p = 0.038) and CDI (OR = 6.160, p = 0.001) were identified as remarkable risk factors of PHQ9 (developing depressive symptoms). Recurrence (OR = 0.317, p = 0.019) was identified as the remarkable risk factor of PSQI (developing poor sleeping quality). And the detailed parameters of these risk factors were included in Table 4 .
Table 4. Multivariate logistic regression analysis indicated possible factors associated with anxiety symptoms, depressive symptoms and sleep disturbance.
| B | SE | OR | 95%CI | P value | ||
|---|---|---|---|---|---|---|
| GAD7 | ||||||
| Headache | 0.873 | 0.427 | 2.395 | 1.037-5.530 | 0.041 | |
| PHQ9 | ||||||
| Recurrence | -1.122 | 0.540 | 0.326 | 0.113-0.938 | 0.038 | |
| CDI | 1.818 | 0.533 | 6.160 | 2.167-17.513 | 0.001 | |
| PSQI | ||||||
| Recurrence | -1.150 | 0.492 | 0.317 | 0.121-0.830 | 0.019 | |
GAD-7: Generalized Anxiety Disorder Questionnaire scale; PHQ-9: Patient Health Questionnaire Depression; PSQI: Pittsburgh Sleep Quality Index; CDI: central diabetes insipidus; SE: standard error; OR: odds ratio; CI: confidence interval.
DISCUSSION
In the current study, we evaluated the pre-operative QoL within patients with craniopharyngioma. As a consequence, we found that patients with craniopharyngioma presented an impaired QoL compared with general population except for lower mean scores of interpersonal relationship as well as paranoia in patients, probably resulting from lethargy and decreased reactivity subsequent to hypothalamic involvement ( 35 , 36 ). Besides, QoL of sub-groups among these participants were basically comparable. Additional correlation analyses in the entirety indicated the detrimental influence caused by CDI, headache, number of pituitary axis deficits and surgical history. Further multivariate linear regression in participants revealed the adverse effect of CDI, headache and tumor recurrence on quantitative parameters in QoL. And the multivariate logistic regression among all patients confirmed CDI as a risk factor of developing depressive symptoms.
Our study, to the best of our knowledge, illustrated the pre-operative QoL in Chinese people for the first time. Further correlation analyses and regression analyses unfolded the potential negative consequences on QoL associated with CDI, warranting prompt and effective peri-operative management of diabetes insipidus and early identification along with intervention of people prone to poor QoL.
Impaired QoL in CDI has been reported by some researchers ( 37 , 38 ). A patient's quality of life is often impeded as a result of the condition, for example from sleep being disturbed by nocturia ( 39 ). Treating CDI with desmopressin is generally safe and effective and shown to improve patients’ quality of life ( 40 ). However, the results regarding the effect of treatment on QoL in CDI are heterogenous. Patients with CDI often report quality of life issues, despite control of polyuria with desmopressin ( 41 ).
Although the efficacy of CDI as a marker for QoL remains controversial, it can serve as a surrogate marker of hypothalamic involvement. As reported, 80% to 90% of the magnocellular AVP neurons in the hypothalamus must be destroyed to produce polyuria and polydipsia ( 42 ), indicating extensive destruction of the AVP magnocellular neuron cell bodies is needed to induce DI ( 43 ). Thus, the establishment of CDI indicates the extensive destruction of the hypothalamus, providing the neuroanatomical basis for the development of depression. And the current treatment modalities can't reverse the anatomical deconstruction, despite the symptomatic alleviation. Indeed, adults with childhood-onset craniopharyngioma (COCP) with hypothalamic involvement, despite being on hormone replacement including GH, demonstrate the persistent impairment of cognitive function, psychosocial health, QoL and overall social functioning ( 10 , 44 ). Therefore, we propose that CDI might serve as a clinical marker of altered QoL in patients with craniopharyngioma.
The majority of previous researches were retrospective studies involving post-operative QoL in craniopharyngioma, and quantities as well as scales of prospective studies enrolling pre-operative assessment of QoL were relatively small ( 3 , 5 , 17 , 18 ). Compared with previous research, our study also demonstrated a notably impaired QoL before surgery ( 3 ). On the contrary, prior studies indicated that pre-operative hypothalamic involvement in CP patients exerted adverse influences on the post-operative QoL ( 1 , 5 , 8 , 10 , 12 ), while there was no significant divergence in pre-operative QoL among sub-groups of CP patients stratified according to hypothalamic involvement in our study except for higher mean scores of interpersonal relationship, phobia and paranoia in patients without hypothalamic involvement. Also, further regression analyses in CP patients yielded negative association between pre-operative hypothalamic involvement and GSI in SCL90. With regards to the reasons for this discrepancy, we proposed the following factors might explain it. First of all, the hypothalamic involvement might result in lethargy according to previous reports ( 35 , 36 ), thus causing decreased reactivity and subsequent lower scores in interpersonal relationship, phobia and paranoia as well as negative correlation with GSI. Further, the major part of the CP patients in our study had hypothalamic involvement and this imbalanced proportions may introduce bias into the evaluation of association between hypothalamic involvement and QoL. Last but not least, since the bulk of past researches focused exclusively on post-operative QoL, the reported association between pre-operative hypothalamic involvement and impaired QoL might be confounded by ensuing surgical or radiological destruction of hypothalamus and adjacent structures, and therefore masking the relationship between pre-operative hypothalamus involvement and QoL.
The limitations of our study could be summed up as follows. Due to the unequal numbers of patients with different extent of hypothalamus involvement in CP, the current study may introduce certain bias into the comparison and analyses. Also, the lack of paired longitudinal follow-up data on QoL as well as clinical indices would hinder us from exploring the temporal dynamics of QoL and its determinants. Last but not least, although very low levels of IGF-1 are strongly suggestive of GHD, normal IGF-1 concentrations do not exclude GHD at any age ( 45 ). Since GH and IGF-1 are critical regulators of structure and function within the nervous system ( 46 ), a more prevalent untreated GH deficiency in patients of this study might also account for the impaired QoL. Therefore, a larger cohort with balanced proportions of different sub-groups and paired follow-up data is essential.
In conclusion, this study demonstrated that QoL of craniopharyngioma was significantly compromised before surgery. Polyuria, surgical history, amenorrhea, headache and CDI were identified to exert detrimental influences on patients’ QoL.
Acknowledgement
we thank for the patients who agreed to participate in this study.
Footnotes
Funding: this work was supported by the following grants: Shanghai Committee of Science and Technology, China (No.17YF426700) to Nidan Qiao; National Project in Promoting the Diagnosis and Treatment of Major Diseases by MDT.
Ethical approval: all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.
REFERENCES
- 1.Eveslage M, Calaminus G, Warmuth-Metz M, Kortmann RD, Pohl F, Timmermann B, et al. The Postoperative Quality of Life in Children and Adolescents with Craniopharyngioma. Dtsch Arztebl Int . 2019 May 03;116(18):321–328. doi: 10.3238/arztebl.2019.0321. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Andrysiak-Mamos E, Sagan K, Sagan L, Sowinska-Przepiera E, Syrenicz A. Cystic lesions of the sellar-suprasellar region – diagnosis and treatment. Endokrynol Pol . 2018;69(2):212–228. doi: 10.5603/EP.2018.0023. [DOI] [PubMed] [Google Scholar]
- 3.Mende KC, Kellner T, Petersenn S, Honegger J, Evangelista-Zamora R, Droste M, et al. Clinical Situation, Therapy, and Follow-Up of Adult Craniopharyngioma. J Clin Endocrinol Metab . 2020 Jan 01;105(1):dgz043. doi: 10.1210/clinem/dgz043. [DOI] [PubMed] [Google Scholar]
- 4.Müller HL, Merchant TE, Warmuth-Metz M, Martinez-Barbera JP, Puget S. Craniopharyngioma. Nat Rev Dis Primers . 2019 Nov 07;5(1):75–75. doi: 10.1038/s41572-019-0125-9. [DOI] [PubMed] [Google Scholar]
- 5.Puget S, Garnett M, Wray A, Grill J, Habrand JL, Bodaert N, et al. Pediatric craniopharyngiomas: classification and treatment according to the degree of hypothalamic involvement. J Neurosurg . 2007 Jan;106(1 Suppl):3–12. doi: 10.3171/ped.2007.106.1.3. [DOI] [PubMed] [Google Scholar]
- 6.Müller HL, Gebhardt U, Teske C, Faldum A, Zwiener I, Warmuth-Metz M, et al. Post-operative hypothalamic lesions and obesity in childhood craniopharyngioma: results of the multinational prospective trial KRANIOPHARYNGEOM 2000 after 3-year follow-up. Eur J Endocrinol . 2011 Jul;165(1):17–24. doi: 10.1530/EJE-11-0158. [DOI] [PubMed] [Google Scholar]
- 7.Ondruch A, Maryniak A, Kropiwnicki T, Roszkowski M, Daszkiewicz P. Cognitive and social functioning in children and adolescents after the removal of craniopharyngioma. Childs Nerv Syst . 2011 Mar;27(3):391–397. doi: 10.1007/s00381-010-1301-0. [DOI] [PubMed] [Google Scholar]
- 8.Müller HL, Gebhardt U, Faldum A, Warmuth-Metz M, Pietsch T, Pohl F, et al. Xanthogranuloma, Rathke's cyst, and childhood craniopharyngioma: results of prospective multinational studies of children and adolescents with rare sellar malformations. J Clin Endocrinol Metab . 2012 Nov;97(11):3935–3943. doi: 10.1210/jc.2012-2069. [DOI] [PubMed] [Google Scholar]
- 9.Erfurth EM, Holmer H, Fjalldal SB. Mortality and morbidity in adult craniopharyngioma. Pituitary . 2013 Mar;16(1):46–55. doi: 10.1007/s11102-012-0428-2. [DOI] [PubMed] [Google Scholar]
- 10.Fjalldal S, Holmer H, Rylander L, Elfving M, Ekman B, Osterberg K, et al. Hypothalamic involvement predicts cognitive performance and psychosocial health in long-term survivors of childhood craniopharyngioma. J Clin Endocrinol Metab . 2013 Aug;98(8):3253–3262. doi: 10.1210/jc.2013-2000. [DOI] [PubMed] [Google Scholar]
- 11.Masson-Cote L, Masucci GL, Atenafu EG, Millar BA, Cusimano M, Croul S, et al. Long-term outcomes for adult craniopharyngioma following radiation therapy. Acta Oncol . 2013 Jan;52(1):153–158. doi: 10.3109/0284186X.2012.685525. [DOI] [PubMed] [Google Scholar]
- 12.Sterkenburg AS, Hoffmann A, Gebhardt U, Warmuth-Metz M, Daubenbüchel AM, Müller HL. Survival, hypothalamic obesity, and neuropsychological/psychosocial status after childhood-onset craniopharyngioma: newly reported long-term outcomes. Neuro Oncol . 2015 Jul;17(7):1029–1038. doi: 10.1093/neuonc/nov044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Yano S, Kudo M, Hide T, Shinojima N, Makino K, Nakamura H, et al. Quality of Life and Clinical Features of Long-Term Survivors Surgically Treated for Pediatric Craniopharyngioma. World Neurosurg . 2016 Jan;85:153–162. doi: 10.1016/j.wneu.2015.08.059. [DOI] [PubMed] [Google Scholar]
- 14.Mehren A, Özyurt J, Zu Klampen P, Boekhoff S, Thiel CM, Müller HL. Self- and informant-rated apathy in patients with childhood-onset craniopharyngioma. J Neurooncol . 2018 Oct;140(1):27–35. doi: 10.1007/s11060-018-2936-z. [DOI] [PubMed] [Google Scholar]
- 15.Hidalgo ET, Orillac C, Kvint S, McQuinn MW, Dastagirzada Y, Phillips S, et al. Quality of life, hypothalamic obesity, and sexual function in adulthood two decades after primary gross-total resection for childhood craniopharyngioma. Childs Nerv Syst . 2020 Feb;36(2):281–289. doi: 10.1007/s00381-019-04161-9. [DOI] [PubMed] [Google Scholar]
- 16.Lucia K, Vajkoczy P, Kaul D, Strasburger CJ, Onken J. Health-Related Quality of Life in Adult Patients with Craniopharyngioma. World Neurosurg . 2021 Oct;154:e46–e53. doi: 10.1016/j.wneu.2021.06.119. [DOI] [PubMed] [Google Scholar]
- 17.Patel KS, Raza SM, McCoul ED, Patrona A, Greenfield JP, Souweidane MM, et al. Long-term quality of life after endonasal endoscopic resection of adult craniopharyngiomas. J Neurosurg . 2015 Sep;123(3):571–580. doi: 10.3171/2014.12.JNS141591. [DOI] [PubMed] [Google Scholar]
- 18.Dhandapani S, Singh H, Negm HM, Cohen S, Souweidane MM, Greenfield JP, et al. Endonasal endoscopic reoperation for residual or recurrent craniopharyngiomas. J Neurosurg . 2017 Feb;126(2):418–430. doi: 10.3171/2016.1.JNS152238. [DOI] [PubMed] [Google Scholar]
- 19.Li L, Wang H, Shen Y. Development and psychometric tests of a Chinese version of the SF-36 Health Survey Scales. Zhonghua Yu Fang Yi Xue Za Zhi . 2002 Mar;36(2):109–113. [PubMed] [Google Scholar]
- 20.Gandek B, Ware JE, Jr, Aaronson NK, Alonso J, Apolone G, Bjorner J, et al. Tests of data quality, scaling assumptions, and reliability of the SF-36 in eleven countries: results from the IQOLA Project. International Quality of Life Assessment. J Clin Epidemiol . 1998 Nov;51(11):1149–1158. doi: 10.1016/s0895-4356(98)00106-1. [DOI] [PubMed] [Google Scholar]
- 21.Li J, Zhong D, Ye J, He M, Liu X, Zheng H, et al. Rehabilitation for balance impairment in patients after stroke: a protocol of a systematic review and network meta-analysis. BMJ Open . 2019 Jul 19;9(7):e026844. doi: 10.1136/bmjopen-2018-026844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Lin Y, Yu Y, Zeng J, Zhao X, Wan C. Comparing the reliability and validity of the SF-36 and SF-12 in measuring quality of life among adolescents in China: a large sample cross-sectional study. Health Qual Life Outcomes . 2020 Nov 09;18(1):360–360. doi: 10.1186/s12955-020-01605-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Derogatis LR, Lipman RS, Covi L. SCL-90: an outpatient psychiatric rating scale – preliminary report. Psychopharmacol Bull . 1973 Jan;9(1):13–28. [PubMed] [Google Scholar]
- 24.Monteiro F, Canavarro MC, Pereira M. Prevalence and correlates of psychological distress of middle-aged and older women living with HIV. Psychol Health Med . 2017 Oct;22(9):1105–1117. doi: 10.1080/13548506.2017.1281972. [DOI] [PubMed] [Google Scholar]
- 25.Tian F, Li H, Tian S, Yang J, Shao J, Tian C. Psychological symptoms of ordinary Chinese citizens based on SCL-90 during the level I emergency response to COVID-19. Psychiatry Res . 2020 Jun;288:112992–112992. doi: 10.1016/j.psychres.2020.112992. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med . 2006 May 22;166(10):1092–1097. doi: 10.1001/archinte.166.10.1092. [DOI] [PubMed] [Google Scholar]
- 27.Li Z, Hao Y, Han Y, Wu S, Zhu D, Liu M, et al. Prevalence and associated physical symptoms of depressive and anxiety symptoms in neurology outpatient clinic. J Neurol Neurosurg Psychiatry . 2019 Nov;90(11):1286–1287. doi: 10.1136/jnnp-2018-320130. [DOI] [PubMed] [Google Scholar]
- 28.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med . 2001 Sep;16(9):606–613. doi: 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Tsai PS, Wang SY, Wang MY, Su CT, Yang TT, Huang CJ, et al. Psychometric evaluation of the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) in primary insomnia and control subjects. Qual Life Res . 2005 Oct;14(8):1943–1952. doi: 10.1007/s11136-005-4346-x. [DOI] [PubMed] [Google Scholar]
- 30.Rui W, Cheng W, Ma XQ, Zhao YF, Yan XY, Jia H. Health-related quality of life in Chinese people: a population-based survey of five cities in China. Scand J Public Health . 2011 Jun;39(4):410–418. doi: 10.1177/1403494810395817. [DOI] [PubMed] [Google Scholar]
- 31.Jin H, Wu WY, Zhang MY. Primal analysis of Chinese SCL-90 assessment result. Chinese J Nerv Ment Dis . 1986;12(5):260–263. [Google Scholar]
- 32.Arima H, Cheetham T, Christ-Crain M, Cooper DL, Drummond JB, Gurnell M, et al. Changing the name of diabetes insipidus: a position statement of the working group to consider renaming diabetes insipidus. Arch Endocrinol Metab . 2022 Nov 17;66(6):868–870. doi: 10.20945/2359-3997000000528. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Li X, Wu W, Miao Q, He M, Zhang S, Zhang Z, et al. Endocrine and Metabolic Outcomes After Transcranial and Endoscopic Endonasal Approaches for Primary Resection of Craniopharyngiomas. World Neurosurg . 2019 Jan;121:e8–e14. doi: 10.1016/j.wneu.2018.08.092. [DOI] [PubMed] [Google Scholar]
- 34.Wu W, Sun Q, Zhu X, Xiang B, Zhang Q, Miao Q, et al. Risk Factors for Hypothalamic Obesity in Patients with Adult-Onset Craniopharyngioma: A Consecutive Series of 120 Cases. Front Endocrinol (Lausanne) . 2021 Jul 28;12:694213–694213. doi: 10.3389/fendo.2021.694213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Qi S, Pan J, Lu Y, Gao F, Cao Y, Peng J, et al. The impact of the site of origin and rate of tumour growth on clinical outcome in children with craniopharyngiomas. Clin Endocrinol (Oxf) . 2012 Jan;76(1):103–110. doi: 10.1111/j.1365-2265.2011.04172.x. [DOI] [PubMed] [Google Scholar]
- 36.Prieto R, Rosdolsky M, Hofecker V, Barrios L, Pascual JM. Craniopharyngioma treatment: an updated summary of important clinicopathological concepts. Expert Rev Endocrinol Metab . 2020 Jul;15(4):261–282. doi: 10.1080/17446651.2020.1770081. [DOI] [PubMed] [Google Scholar]
- 37.Dekkers OM, Biermasz NR, Smit JW, Groot LE, Roelfsema F, Romijn JA, et al. Quality of life in treated adult craniopharyngioma patients. Eur J Endocrinol . 2006 Mar;154(3):483–489. doi: 10.1530/eje.1.02114. [DOI] [PubMed] [Google Scholar]
- 38.Kendall-Taylor P, Jonsson PJ, Abs R, Erfurth EM, Koltowska-Haggstrom M, Price DA, et al. The clinical, metabolic and endocrine features and the quality of life in adults with childhood-onset craniopharyngioma compared with adult-onset craniopharyngioma. Eur J Endocrinol . 2005 Apr;152(4):557–567. doi: 10.1530/eje.1.01877. [DOI] [PubMed] [Google Scholar]
- 39.Smego AR, Backeljauw P, Gutmark-Little I. Buccally Administered Intranasal Desmopressin Acetate for the Treatment of Neurogenic Diabetes Insipidus in Infancy. J Clin Endocrinol Metab . 2016 May;101(5):2084–2088. doi: 10.1210/jc.2016-1157. [DOI] [PubMed] [Google Scholar]
- 40.Garrahy A, Moran C, Thompson CJ. Diagnosis and management of central diabetes insipidus in adults. Clin Endocrinol (Oxf) . 2019 Jan;90(1):23–30. doi: 10.1111/cen.13866. [DOI] [PubMed] [Google Scholar]
- 41.Nozaki A, Ando T, Akazawa S, Satoh T, Sagara I, Horie I, et al. Quality of life in the patients with central diabetes insipidus assessed by Nagasaki Diabetes Insipidus Questionnaire. Endocrine . 2016 Jan;51(1):140–147. doi: 10.1007/s12020-015-0637-3. [DOI] [PubMed] [Google Scholar]
- 42.Heinbecker P, White HL. Hypothalamico-Hypophysial System and Its Relation to Water Balance in the Dog. Am J Physiol . 1941 Jun;133(3):582–593. doi: 10.1152/ajplegacy.1941.133.3.582. [DOI] [Google Scholar]
- 43.Verbalis JG. Acquired forms of central diabetes insipidus: Mechanisms of disease. Best Pract Res Clin Endocrinol Metab . 2020 Sep;34(5):101449–101449. doi: 10.1016/j.beem.2020.101449. [DOI] [PubMed] [Google Scholar]
- 44.Müller HL, Faldum A, Etavard-Gorris N, Gebhardt U, Oeverink R, Kolb R, et al. Functional capacity, obesity and hypothalamic involvement: cross-sectional study on 212 patients with childhood craniopharyngioma. Klin Padiatr . 2003 Nov-Dec;215(6):310–314. doi: 10.1055/s-2003-45499. [DOI] [PubMed] [Google Scholar]
- 45.Cianfarani S, Liguori A, Boemi S, Maghnie M, Iughetti L, Wasniewska M, et al. Inaccuracy of insulin-like growth factor (IGF) binding protein (IGFBP)-3 assessment in the diagnosis of growth hormone (GH) deficiency from childhood to young adulthood: association to low GH dependency of IGF-II and presence of circulating IGFBP-3 18-kilodalton fragment. J Clin Endocrinol Metab . 2005 Nov;90(11):6028–6034. doi: 10.1210/jc.2005-0721. [DOI] [PubMed] [Google Scholar]
- 46.Ashpole NM, Sanders JE, Hodges EL, Yan H, Sonntag WE. Growth hormone, insulin-like growth factor-1 and the aging brain. Exp Gerontol . 2015 Aug;68:76–81. doi: 10.1016/j.exger.2014.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]