ObjectiveTo integrate person imagery from drawing tests in screening for mental disorders through meta-analysis to identify indicators that can effectively predict mental disorders. MethodsA computerized search of CNKI, WanFang Data, VIP, PubMed, Web of Science, and EBSCO databases was conducted to collect studies related to mental disorders and drawing tests, with a search timeframe of the period from the creation of the database to May 8, 2023. Meta-analysis was performed using CMA 3.0 after two researchers independently screened the literature, extracted information, and assessed the risk of bias. ResultsA total of 43 studies were included, with 791 independent effect sizes and 8 444 subjects. Meta-analysis revealed that a total of 29 person imagery traits significantly predicted mental disorders, which could be categorized into 7 types according to the features: absent, bizarre, blackened, simplified, static, detailed, and holistic. The subgroup analysis revealed that the specific indicators of affective disorders included "excessive separation among items", "oversimplified person", "rigid and static person" and "hands behind the back". The specific indicators of thought disorders were "absence of limbs", "absence of facial features" and "disproportionate body proportions". Moreover, there were seven common indicators of mental disorders, including "oversimplified drawing", "very small drawing", "very small person", "weak or intermittent lines", "single line limb", "absence of hands or feet" and "no expression or dullness''. ConclusionThe findings could provide a reference standard for selection and interpretation of drawing indicators, promote standardization of the drawing test, and enhance the accuracy of results in screening for mental disorders.
摘要:目的:探讨重型颅脑损伤后早期精神障碍临床特征及治疗方法,以提高患者的生活质量。方法:对我院48例重型颅脑损伤后早期精神障碍患者进行回顾性分析,观察精神障碍出现的时间、精神障碍的类型及预后及颅脑损伤的部位与精神障碍的关系。结果:重型颅脑损伤后精神障碍主要出现在伤后3周内,多继发于颞叶损伤,其次为额叶。临床上主要有躁狂型、抑郁型、痴呆型、精神分裂性等四型,其中以躁狂型为多见。通过治疗后,lt;1个月精神症状痊愈25例、lt;2个月痊愈13例、治疗gt;2个月仍有精神症状10例。结论:颅脑损伤后精神障碍在原发脑损伤的有效治疗前提下,辅以抗精神障碍药物治疗、心理治疗及高压氧治疗等可取得较好疗效。
The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
ObjectiveTo compare the application of self-made tumble risk factors assessment scale before and after its revision in patients with mental disorder, in order to guide the clinical work. MethodsWe retrospectively analyzed the clinical data of 2 209 patients with mental disorders who were discharged from the hospital between January 1, 2012 and December 31, 2013. All the patients in our hospital underwent the assessment by "table of tumble risk factors for hospitalized patients and nursing measures" within one hour of admission. A total score of 4 or higher meant high tumble risk, and the standardized intervention measures were taken immediately. In 2013, the assessment scale was revised, and binocular vision disorder, low compliance or communication disorders, restlessness were added as risk factors for tumble. The difference among patients with a tumble score of 4 or higher between the year of 2012 and 2013 was compared and analyzed. ResultsIn 2012, 52 patients had a tumble score of 4 or higher, among whom there were 16 males and 36 females; 35 were younger than 65 years old and 17 were older than 65 years. There were 25 patients with organic mental disorders, 10 with spirit obstacle caused by active substance, 12 with schizophrenia, and 5 other cases. In 2013, 154 patients' tumble score was 4 or higher, among whom there were 58 males and 96 females; 142 were younger than 65 years old and 12 were older than 65. Organic mental disorders occurred in 22 patients, 8 had spirit obstacle caused by active substance, 120 had schizophrenia, and there were 4 other cases. In 2013, the number of patients with a tumble score of 4 or higher were significantly more than that in 2012 and young patients with schizophrenia were also significantly more than in 2012 (P<0.05). There were two cases of tumble adverse events, while no adverse events occurred in tumble in 2013. ConclusionCognitive impairment, low compliance, communication barriers and restlessness are high risk factors for tumble in patients with mental disorders. Correct evaluation and early intervention can effectively prevent the occurrence of tumble.
Objective To analyze the risk factors for postoperative cognitive confusion in a surgical intensive care unit. Methods A total of 388 consecutive patients in Surgical Intensive Care Unit of General Hospital of PLA were retrospectively studied. We posed clinical questions according to the patients with older age and large dosage corticosteroid. Using “Postoperative cognitive confusion” and“Intensive Care” as key words, we searched for evidence from MEDLINE (1968-2004). Results We found 3.1% (10/388) of the patients developed postoperative cognitive confusion. Of the 10 postoperative cognitive confusion patients, 9 were over 65 years old. 6.6% (9/136) of the patients (≥ 65 years old) developed postoperative cognitive confusion. While 0.4%(1/252) of the patients (＜65 years old) developed postoperative cognitive confusion. Older age (≥ 65 years old) may induce more postoperative cognitive confusion (P＜0.05). While 7.0% (5/71) of the patients treated by large dose corticosteroids (≥1 000 mg) developed postoperative cognitive confusion. And 1.65% (5/317) of the patients received corticosteroid with large dosage (＜1 000 mg) developed postoperative cognitive confusion. Large dosage corticosteroid (≥1 000 mg) may induce more postoperative cognitive confusion (P＜0.05). Conclusion Older age (≥ 65 years old) and high dose corticosteroid (≥1 000 mg) may be the two main risk factors for postoperative cognitive confusion.
ObjectiveTo systematically review the efficacy of different drugs for patients with methamphetamine-induced psychotic disorders by network meta-analysis.MethodsAn electronical search was conducted in PubMed, The Cochrane Library, Web of Science, EMbase, CNKI, CBM, WanFang Data and VIP databases from inception to October 2016 to collect randomized controlled trials (RCTs) about different drugs for methamphetamine-induced psychotic disorders. Two reviewers independently screened literature, extracted data and assessed the risk bias of included studies, and then RevMan 5.3, R 3.3.2 and JAGS 4.2.0 softwares were used to perform network meta-analysis.ResultsA total of 16 RCTs involving 1 676 patients and 9 kinds of drugs were included. The results of network meta-analysis showed that: compared with the placebo group, olanzapine (OR=28.00, 95%CI 8.10 to 110.00), risperidone (OR=20.00, 95%CI 7.70 to 58.00), quetiapine (OR=30.00, 95%CI 6.60 to 160.00), ziprasidone (OR=28.00, 95%CI 3.70 to 230.00), chlorpromazine (OR=29.00, 95%CI 5.00 to 200.00), aripiprazole (OR=13.00, 95%CI 1.70 to 93.00), haloperidol (OR=19.00, 95%CI 2.10 to 190.00) could significantly improve the psychotic disorders of patients with methamphetamine, respectively, in which quetiapine was the best choice. There were no significant differences between any other pairwise comparisons of these different drugs.ConclusionFor the treatment of psychotic disorders caused by methamphetamine, quetiapine should be of a priority choice, follows by ziprasidone, chlorpromazine, olanzapine, risperidone, aripiprazole or haloperidol in a descending priority. Due to limited quality and quantity of the included studies, more high-quality studies are needed to verify above conclusion.