OBJECTIVES: To explore the naturalistic outcome of the schizophrenics, we evaluated key clinical outcome-drop-out rate and readmission rate among the 33 risperidone and 17 conventional antipsychotics(such as haloperidol, chlorpromazine, mesoridazine) treated patients who met DSM-IV diagnostic criteria for schizophrenia at psychiatric department of a university hospital. METHOD: Outcome data was extracted from the charts of 50-schizophrenic patients who were more than 2 years after initiation of treatment with risperidone and conventional anti-psychotics. RESULTS: During over the 2-year period, the drop-out rate of the conventional antipsychotics treated schizophrenics was significantly higher than that of risperidone treated patients. But no significant factors(such as age, education level, duration of illness) were found between these two differently treated groups. Among the risperidone treated patients, the percentage of readmission was 18.2% at 12 months and 45.5% at 24 months. CONCLUSIONS: Reduced drop-out and rehospitalization rate suggest that risperidone was better than conventional antipsychotics among schizophrenic patients in a university hospital. Our data may contribute essential functional outcome information to assist the clinician in long-term, comparative treatment evaluation in 'real' clinical practice.
Antipsychotic Agents* ; Chlorpromazine ; Diagnostic and Statistical Manual of Mental Disorders ; Education ; Haloperidol ; Humans ; Outcome Assessment (Health Care)* ; Risperidone* ; Schizophrenia

OBJECT: Risperidone, unlike other atypical antipsychotics, is thought to elevate prolactin levels. This paper examines the relationship of risperidone-induced hyperprolactinemia and the sexual dysfunctions of the patients in the real clinical practice. METHODS: Forty nine patients (male 22, female 27) with 6 month-over risperidone medication were assigned and serum prolactin was assayed in serum by radioimmunometric assay. In the distinction of sex, six adverse events possibly associated with increased prolactin levels were determined by interviewing the patients (poor erection, ejaculatory dysfunction, galactorrhea, decreased libido, orgasmic dysfunction and obesity in male;amenorrhea, vaginal dryness, galactorrhea, decreased libido, orgasmic dysfunction and obesity in female). RESULTS: In 49 patients, thirty six patients (male 15;56%, female 21;95%) showed hyperprolactinemia and twenty two patients (male 13;48%, female 8;36%) had sexual side-effects. Both risperidone dosage per day and duration were not correlated with prolactin levels and adverse events. There was no significant direct correlation between serum prolactin levels and sexual adverse events. CONCLUSION: The risperidone-associated increase in serum prolactin levels was not significantly directly correlated with the emergence of possible prolactin-related adverse events in the real clinical practice. However, our results suggest that risperidone-induced hyperprolactinemia may play a role in sexual dysfunction of female patients.
Antipsychotic Agents ; Female ; Galactorrhea ; Humans ; Hyperprolactinemia* ; Libido ; Obesity ; Orgasm ; Pregnancy ; Prolactin ; Risperidone

Purpose: This study aimed to develop a fall prediction model for community-dwelling older adults using machine learning. Methods: The present study was conducted with a secondary data analysis that used data from the 2020 national survey of older Koreans. Among 10,097 participants, data 177 were excluded due to incompleteness and 9,920 were included in the final analysis. Because of data imbalance, upsampling was performed to increase the number of individuals who fell. Forty-five independent variables for fall prediction were selected based on the fall risk factors from previous studies and univariate statistical analysis. The data were split into training and testing sets at an 80:20 ratio. Three machine learning algorithms—logistic regression, random forest, and artificial neural network—were used to develop a fall prediction model. Results: The random forest model outperformed the others, with an area under the curve of .91, accuracy of .94, precision of .94, recall of .74, and F1 score of .83. An analysis of feature importance revealed that satisfaction with health condition, visual difficulty, instrumental activities of daily living, performance of 400m walk, and cognitive ability were the top five features for fall prediction. Conclusion The fall prediction model developed using machine learning demonstrated high model performance, implying its suitability for use as a primary screening tool for fall risk. Subjective satisfaction with one’s health should be considered as an important factor in predicting falls in community-dwelling older adults. It is necessary for community health nurses to reinforce positive health awareness by continuous disease management and physical function improvement for older adults to prevent falls.

Purpose: This study aimed to develop a fall prediction model for community-dwelling older adults using machine learning. Methods: The present study was conducted with a secondary data analysis that used data from the 2020 national survey of older Koreans. Among 10,097 participants, data 177 were excluded due to incompleteness and 9,920 were included in the final analysis. Because of data imbalance, upsampling was performed to increase the number of individuals who fell. Forty-five independent variables for fall prediction were selected based on the fall risk factors from previous studies and univariate statistical analysis. The data were split into training and testing sets at an 80:20 ratio. Three machine learning algorithms—logistic regression, random forest, and artificial neural network—were used to develop a fall prediction model. Results: The random forest model outperformed the others, with an area under the curve of .91, accuracy of .94, precision of .94, recall of .74, and F1 score of .83. An analysis of feature importance revealed that satisfaction with health condition, visual difficulty, instrumental activities of daily living, performance of 400m walk, and cognitive ability were the top five features for fall prediction. Conclusion The fall prediction model developed using machine learning demonstrated high model performance, implying its suitability for use as a primary screening tool for fall risk. Subjective satisfaction with one’s health should be considered as an important factor in predicting falls in community-dwelling older adults. It is necessary for community health nurses to reinforce positive health awareness by continuous disease management and physical function improvement for older adults to prevent falls.

Purpose: This study aimed to develop a fall prediction model for community-dwelling older adults using machine learning. Methods: The present study was conducted with a secondary data analysis that used data from the 2020 national survey of older Koreans. Among 10,097 participants, data 177 were excluded due to incompleteness and 9,920 were included in the final analysis. Because of data imbalance, upsampling was performed to increase the number of individuals who fell. Forty-five independent variables for fall prediction were selected based on the fall risk factors from previous studies and univariate statistical analysis. The data were split into training and testing sets at an 80:20 ratio. Three machine learning algorithms—logistic regression, random forest, and artificial neural network—were used to develop a fall prediction model. Results: The random forest model outperformed the others, with an area under the curve of .91, accuracy of .94, precision of .94, recall of .74, and F1 score of .83. An analysis of feature importance revealed that satisfaction with health condition, visual difficulty, instrumental activities of daily living, performance of 400m walk, and cognitive ability were the top five features for fall prediction. Conclusion The fall prediction model developed using machine learning demonstrated high model performance, implying its suitability for use as a primary screening tool for fall risk. Subjective satisfaction with one’s health should be considered as an important factor in predicting falls in community-dwelling older adults. It is necessary for community health nurses to reinforce positive health awareness by continuous disease management and physical function improvement for older adults to prevent falls.

Purpose: This study aimed to develop a fall prediction model for community-dwelling older adults using machine learning. Methods: The present study was conducted with a secondary data analysis that used data from the 2020 national survey of older Koreans. Among 10,097 participants, data 177 were excluded due to incompleteness and 9,920 were included in the final analysis. Because of data imbalance, upsampling was performed to increase the number of individuals who fell. Forty-five independent variables for fall prediction were selected based on the fall risk factors from previous studies and univariate statistical analysis. The data were split into training and testing sets at an 80:20 ratio. Three machine learning algorithms—logistic regression, random forest, and artificial neural network—were used to develop a fall prediction model. Results: The random forest model outperformed the others, with an area under the curve of .91, accuracy of .94, precision of .94, recall of .74, and F1 score of .83. An analysis of feature importance revealed that satisfaction with health condition, visual difficulty, instrumental activities of daily living, performance of 400m walk, and cognitive ability were the top five features for fall prediction. Conclusion The fall prediction model developed using machine learning demonstrated high model performance, implying its suitability for use as a primary screening tool for fall risk. Subjective satisfaction with one’s health should be considered as an important factor in predicting falls in community-dwelling older adults. It is necessary for community health nurses to reinforce positive health awareness by continuous disease management and physical function improvement for older adults to prevent falls.

OBJECTIVES: While most neurological models of schizophrenia have focused on cerebral functions, cerebellar abnormality, especially in cerebellar vermis has been repeatedly reported in schizophrenic patients from brain imaging and lesion studies. And cerebellar vermis has been implicated for adaptive control of saccadic eye movements, which has not been studied in schizophrenics to our knowledge. METHOD: We investigated saccadic adaptation using double-step paradigm in 5 patients with a DSM-IV diagnosis of schizophrenia and 5 age and sex-matched normal controls. Gaze and head movements were recorded with scleral search coil method in head-free condition. RESULTS: Time course of adaptation in schizophrenics was similar to that of normal control but it never reached to the complete level of adaptation seen in control, and accordingly gaze gain (gaze amplitude/target amplitude) was low even after 600 adaptive trials. Head contribution to gaze saccade was relatively low and time to peak head velocity was longer in schizophrenics. CONCLUSIONS: Our results suggested that schizophrenic patients used a different strategy of gaze control and some schizophrenics might have cerebellar abnormality. Variability across patients in adaptation parameters need to be further investigated in combination with cerebellar volumetry. This study was supported by "In-san Schizophrenia Research Grant" from the Research Institute of Korean Neuropsychiatric Association in 1997.
Academies and Institutes ; Cerebellum ; Diagnosis ; Diagnostic and Statistical Manual of Mental Disorders ; Eye Movements* ; Head ; Head Movements ; Humans ; Models, Neurological ; Neuroimaging ; Saccades ; Schizophrenia

Delusional parasitosis is characterized by the unshakeable belief of being infested with tiny (microscopic) insects. Patients spend much time trying to get rid of the bugs and suffer from these symptoms. Patients prefer to go to dermatologists because they have a strong conviction over the presence of a somatic disease and do not accept any psychiatric advice for their complaints. 'Folie a deux' or shared psychotic disorder (SPD) is a relatively rare syndrome, which has long attracted clinical attention. Delusional parasitosis is associated in 5-15% of SPD and can run within a family. We experienced delusional parasitosis as 'Folie a Deux' between a mother and her son and successfully treated them through early psychiatric intervention. We believe that attention should be drawn to DP with SPD.
Adult ; Delusions/*diagnosis/*psychology ; Ectoparasitic Infestations/psychology ; Female ; Human ; Male ; Middle Aged ; Paranoid Disorders/diagnosis/psychology ; Shared Paranoid Disorder/*diagnosis/*psychology ; Social Isolation

Posterior reversible encephalopathy syndrome (PRES) is a newly described adverse effect possibly associated with gonadotropin-releasing hormone (GnRH) agonist therapy. We report a case of PRES after 2 doses of depot GnRH agonists in a 44-year-old woman with a huge myoma uteri and iron-deficiency anemia. Brain magnetic resonance imaging showed high signal lesions in both occipital lobes on fluid-attenuated inversion-recovery (FLAIR) images, compatible with PRES. After treatment with anticonvulsant, she recovered both radiographically and clinically. The association between PRES and GnRH agonist use is still enigmatic, and thus should be further clarified.
Adult ; Anemia, Iron-Deficiency ; Brain ; Brain Diseases ; Female ; Gonadotropin-Releasing Hormone* ; Humans ; Leuprolide ; Magnetic Resonance Imaging ; Myoma ; Occipital Lobe ; Posterior Leukoencephalopathy Syndrome* ; Uterus