Prediction of delirium using data mining: A systematic review.

Delirium remains a significant cause of morbidity, mortality and economic burden to society. "Big data" refers to data of significantly large volume, obtained from a variety of resources, which is created and processed at high velocity. We conducted a systematic review and meta-analysis exploring whether big data could predict the incidence of delirium of patients in the inpatient setting. Medline, Embase, the Cochrane Library, Web of Science, CINAHL, clinicaltrials.gov, who.int and IEEE Xplore were searched using MeSH terms "big data", "data mining", "delirium" and "confusion" up to 30th September 2019. We included both randomised and observational studies. The primary outcome of interest was development of delirium and the secondary outcomes of interest were type of statistical methods used, variables included in the mining algorithms and clinically important outcomes such as mortality and length of hospital stay. The quality of studies was graded using the CHARMs checklist. Six retrospective single centre observational studies were included (n = 178,091), of which 17, 574 participants developed delirium. Studies were of generally of low to moderate quality. The most commonly studied method was random forest, followed by support vector machine and artificial neural networks. The model with best performance for delirium prediction was random forest, with area under receiver operating curve (AUROC) ranging from 0.78 to 0.91. Sensitivity ranged from 0.59 to 0.81 and specificity ranged from 0.73 to 0.92. Our systematic review suggests that machine-learning techniques can be utilised to predict delirium.

Spinal cord compression following traditional confinement massage.

We describe a case of tetraparesis in a 33-year-old woman following neck manipulation performed by a traditional confinement mid-wife. An MRI of the cervical spine revealed a fracture of the second cervical vertebra with atlanto-axial subluxation that resulted in cord compression.

Sleep disturbances in Malaysian patients with Parkinson's disease using polysomnography and PDSS.

BACKGROUND: Sleep disturbances such as sleep fragmentation, sleep disordered breathing (SDB), periodic limb movements (PLM), excessive daytime somnolence (EDS) and insomnia are prevalent in Parkinson's disease (PD). However, studies in the Asian population are limited. METHODS: This was a cross-sectional study involving 46 Malaysians with PD using polysomnography (PSG) and standardized translated Parkinson's disease sleep scale (PDSS). Overnight PSG recordings, UPDRS and PDSS scores, and baseline demographic data were obtained. RESULTS: Data from 44 patients were analysed. Thirty-six patients (81.8%) had PSG-quantified sleep disorders. Twenty-three (52.3%) had sleep fragmentation, 24 (54.6%) had SDB and 14 (32%) had PLM. EDS was present in 9.1%. Insomnia was reported by 31.8%. Patients with sleep fragmentation had significantly higher UPDRS scores and lower PDSS insomnia sub-scores. The UPDRS scores correlated negatively with the TST and sleep efficiency. All patients with EDS had SDB (p=0.056). The PDSS insomnia sub-items correlated with sleep fragmentation on PSG. CONCLUSION: : The prevalence of sleep disorders based on PSG and PDSS in our PD patients was high, the commonest being sleep fragmentation and SDB, while EDS was the least prevalent. Problem specific sub-items of the PDSS were more accurate in predicting the relevant PSG-related changes compared to the PDSS as a whole.