Using accelerometer as a diagnostic tool to detect drug-induced parkinsonism (DIP) secondary to first-generation anti-psychotic medications.

OBJECTIVE: The objective of this study is to examine the effectiveness of an accelerometer-based compact system in detecting and quantifying drug-induced parkinsonism (DIP) in patients with schizophrenia. METHOD: A pilot study controlled clinical trial comprising 6 people with schizophrenia and 11 control subjects was conducted at Alfred Health, Melbourne. Participants had their movements assessed using Barnes Akathisia Rating Scale (BARS), Simpson Angus Scale (SAS) and Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) followed by an assessment of gait using three triaxial accelerometers. RESULTS: Median BARS, SAS, MDS-UPDRS III and accelerometer scores were significantly higher for patients with schizophrenia than controls. Accelerometers detected three times more rest tremor than clinical rating scales. Patients with schizophrenia had 70% of their dynamic acceleration at frequencies between 4 and 10 Hz, which is almost twice that observed in the control population (38%). Accelerometer scores were significantly correlated with BARS scores. CONCLUSION: Accelerometers were able to accurately detect patients with DIP better than some clinical rating scale including the SAS. Further larger-scale studies must be conducted to further demonstrate the accuracy of accelerometers in detecting DIP.

Recovery in early stage breast cancer-An Australian longitudinal study.

BACKGROUND: The majority of breast cancer patients will experience some level of emotional distress, with some patients having long-term psychological maladjustment. Personal and social resources play a role in recovery yet the interplay between these factors warrants further examination. This study aimed to investigate the interaction of psychosocial factors impacting women in their breast cancer trajectory, at 2 years or less following diagnosis (stages I-III). DESIGN: A longitudinal cohort study approach was used in this study. METHODS: The sample consisted of n = 49 participants. Data were collected between June 2013 and October 2013 and followed for 12 months across the trajectory of the disease. RESULTS: The mean age was 56.6 years (SD 11.6 years). Most participants had stage I or stage II breast cancer. Time (over three time points-4 weeks, 6 months, and 12 months) after diagnosis was significantly associated with the body image (P = .003) and age (P = .004). CONCLUSION: Older women with breast cancer reported less concern regarding body image than their younger peers. These findings suggest that posttreatment younger women may require access to psychological support posttreatment.

Long-term outcomes of patients admitted to an intensive care unit with intentional self-harm.

Self-harm is one of the most common reasons for admission to an intensive care unit (ICU). While most patients with self-harm survive the ICU admission, little is known about their outcomes after hospital discharge. We conducted a retrospective cohort study of patients in the Barwon region in Victoria admitted to the ICU with self-harm (between 1998 and 2018) who survived to hospital discharge. The primary objective was to determine mortality after hospital discharge, and secondarily estimate relative survival, years of potential life lost, cause of death and factors associated with death. Over the 20-year study period, there were 710 patients in the cohort. The median patient age was 37 years (interquartile range (IQR) 26-48 years). A total of 406 (57%) were female, and 527 (74%) had a prior psychiatric diagnosis. The incidence of ICU admission increased over time (incidence rate ratio 1.05; 95% confidence interval (CI) 1.03-1.06 per annum). There were 105 (15%) patients who died after hospital discharge. Relative survival decreased each year after discharge, with the greatest decrement during the first 12 months. At ten years, relative survival was 0.85 (95% CI 0.81-0.88). The median years of potential life lost was 35 (IQR 22-45). Cause of death was self-harm in 27%, possible self-harm in 32% and medical disease in 41%. The only factors associated with mortality were male sex, older age and re-admission to ICU with self-harm. Further population studies are required to confirm these findings, and to understand what interventions may improve long-term survival in this relatively young group of critically ill patients.

Predicting Early Stage Drug Induced Parkinsonism using Unsupervised and Supervised Machine Learning.

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical practice to effectively diagnose the symptoms at the onset of the disease. In this study, the variations in gait accelerometer data due to the intermittency of tremor at the initial stages is examined. These variations are used to train a logistic regression model to predict subjects with early-stage DIP. The logistic classifier predicts if a subject is a DIP or control with approximately 89% sensitivity and 96% specificity. This paper discusses the algorithm used to extract the features in gait data for training the classifier to predict DIP at the earliest.Clinical Relevance- Diagnosing the disease and the causative drug is vital as the physical health of a patient who is mentally unstable can deteriorate with prolonged usage of the drug. The proposed model helps clinicians to diagnose the disease at the onset of tremors with an accuracy of 93.58%.