Novel investigational drugs for alopecia areata and future perspectives.

INTRODUCTION: Alopecia areata (AA) is an immune-mediated disease that causes non-scarring hair loss. While acute, solitary patches often spontaneously remit, developing secondary patches or failure of the disease to resolve within 6-12 months predicts a poor prognosis, with an increased risk of alopecia totalis or universalis. Chronic AA increases the risk of depression and suicidality and reduces quality of life. Treatment options for chronic or acute diffuse AA were previously limited to corticosteroids and traditional immunomodulators. Two Janus Kinase (JAK) inhibitors are now approved for the treatment of chronic AA. AREAS COVERED: The results of landmark phase 3 trials for three JAK inhibitors, baricitinib, ritlecitinib, and deuruxolitinib are discussed. Evidence for other JAK inhibitors, biologics, and phosphodiesterase-4 inhibitors are also presented. Therapies currently undergoing clinical trials are listed. EXPERT OPINION: JAK inhibitors are a safe and efficacious treatment of moderate-to-severe AA. Early intervention, regardless of severity, allows for improved treatment efficacy. It is uncertain how long patients should remain on JAK inhibitors; discontinuation often leads to relapse. A black-box warning for JAK inhibitors was extrapolated from safety data in a rheumatoid arthritis cohort; recent meta-analyses of JAK inhibitors used in dermatology cohorts do not demonstrate the same risk profile.

Single ring isolation for atrial fibrillation ablation: Impact of the learning curve.

INTRODUCTION: Although single ring isolation is an accepted strategy for undertaking pulmonary vein (PV) and posterior wall isolation (PWI) during atrial fibrillation (AF) ablation, the learning curve associated with this technique as well as procedural and clinical success rates have not been widely reported. METHODS AND RESULTS: Prospectively collected data from 250 consecutive patients undergoing de novo AF ablation using single ring isolation. PWI was achieved in 212 patients (84.8%) and PV isolation without PWI was achieved in 37 patients (14.4%). Thirty-one cases (12.4%) demonstrated inferior line sparing where PWI was achieved without a continuous posterior wall inferior line. A learning curve was observed, with higher rates of PWI (98% last 50 vs. 82% first 50 cases, p = .016), higher rates of inferior line sparing (20% last 50 vs. 8% first 50 cases, p = .071) and lower ablation times (43.8 min (interquartile range [IQR]: 34.6-57.0 min) last 50 versus. 96.5 min (IQR: 80.8-115.8 min) first 50 cases; p < .001). Three (1.3%) major procedure-related complications were observed. Twelve-month, single-procedure freedom from atrial arrhythmia without drugs was 70.5% (95% confidence interval [CI]: 61.5%-77.7%) and 60.0% (95% CI: 50.2%-68.4%) for paroxysmal and persistent/longstanding persistent AF. Twelve-month multi-procedure freedom from atrial arrhythmia was 92.2% (95%CI: 85.6%-95.9%) and 85.6% (95%CI: 77.2%-91.0%) for paroxysmal and persistent/longstanding persistent AF. CONCLUSION: Employing a single ring isolation approach, PWI can be achieved in most cases. There is a substantial learning curve with higher rates of PWI, reduced ablation times, and higher rates of inferior line sparing as procedural experience grows. Long-term freedom from arrhythmia is comparable to other AF ablation techniques.

Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study.

Machine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets. In the prediction of discharge within the next 2 days, when the algorithm was applied to prospective and external datasets, the area under the receiver operator curve for this task were 0.78 and 0.74, respectively. The performance in the prediction of discharge within the next 7 days was more limited (area under the receiver operator curve 0.68 and 0.67). This study has shown that in prospective and external validation datasets the previously derived deep learning algorithms have demonstrated moderate performance in the prediction of which patients will be discharged within the next 2 days. Future studies may seek to further refine or evaluate the effect of the implementation of such algorithms.

Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study.

The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record. These data were then split into training and testing sets (7-month/1-month train/test split) prior to use in ML analyses aiming to predict discharge within the next 2 days, discharge within the next 7 days and an estimated date of discharge (EDD). Artificial neural networks and logistic regression were effective at predicting discharge within 48 h of a given ward round note. These models achieved an area under the receiver operator curve (AUC) of 0.80 and 0.78, respectively. Prediction of discharge within 7 days of a given note was less accurate, with artificial neural network returning an AUC of 0.68 and logistic regression an AUC of 0.61. The generation of an exact EDD remains inaccurate. This study has shown that repeated estimates of LOS using daily ward round notes and mixed-data inputs are effective in the prediction of general medicine discharges in the next 48 h. Further research may seek to prospectively and externally validate models for prediction of upcoming discharge, as well as combination human-ML approaches for generating EDDs.

Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.

Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with accurate LOS and discharge destination prediction for this patient group. Emergency department triage and doctor notes were retrospectively collected on consecutive general medical and acute medical unit admissions to a single tertiary hospital from a 2-month period in 2019. These data were used to assess the feasibility of predicting LOS and discharge destination using natural language processing and a variety of machine learning models. 313 patients were included in the study. The artificial neural network achieved the highest accuracy on the primary outcome of predicting whether a patient would remain in hospital for > 2 days (accuracy 0.82, area under the received operator curve 0.75, sensitivity 0.47 and specificity 0.97). When predicting LOS as an exact number of days, the artificial neural network achieved a mean absolute error of 2.9 and a mean squared error of 16.8 on the test set. For the prediction of home as a discharge destination (vs any non-home alternative), all models performed similarly with an accuracy of approximately 0.74. This study supports the feasibility of using natural language processing to predict general medical inpatient LOS and discharge destination. Further research is indicated with larger, more detailed, datasets from multiple centres to optimise and examine the accuracy that may be achieved with such predictions.

Specificity and sensitivity of magnetic resonance imaging findings in the diagnosis of progressive supranuclear palsy.

Progressive supranuclear palsy (PSP) is a neurodegenerative condition that can only be diagnosed conclusively on pathological examination. Currently, the diagnosis is based upon the National Institute of Neurological Disorders and Stroke and the Society for PSP criteria. These criteria consist of purely clinical findings. Elements of brain MRI that are being investigated for this role include identifying structural features on conventional MRI, volume changes, signal abnormalities and diffusion changes. The aim of this study is to conduct a systematic search to identify which MRI findings have evidence to support their sensitivity/specificity/accuracy in the diagnosis of PSP. A search was conducted of Pubmed and Medline on July 5th-6th 2016 using the medical subject headings progressive supranuclear palsy and MRI. Seventy articles were identified which assessed the sensitivity/specificity/accuracy of MRI signs for the diagnosis of PSP. There were 13 studies that identified MRI features that had ≥95% sensitivity and specificity for the diagnosis of PSP. Four of these studies identified the magnetic resonance parkinsonism index as highly sensitive and specific. There were only four studies which assessed how effective given MRI features are at predicting the pathological diagnosis of PSP. Several markers, such as the magnetic resonance parkinsonism index, have been demonstrated to be both specific and sensitive for PSP. However, many studies assessing these markers have common weaknesses including small sample size and lacking autopsy correlation.

Domperidone for Hypotension in Parkinson's Disease: A Systematic Review.

BACKGROUND: Domperidone is a proposed treatment of orthostatic hypotension (OH) in Parkinson's disease (PD). However, domperidone use in PD is tempered by concerns regarding QT prolongation and ventricular tachyarrhythmia and sudden cardiac death (VT/SCD). OBJECTIVE: The aim is to identify peer-reviewed studies in which either (1) the effect of domperidone on blood pressure in patients with PD, or (2) the adverse effects associated with domperidone use in PD patients has been reported. METHODS: PubMed, EMBASE, Medline and Scopus were searched using the terms Parkinson's disease and domperidone. RESULTS: Twenty-two articles fulfilled the inclusion criteria. One study was a randomized placebo-controlled trial with domperidone administration independent of the commencement of dopaminergic medications. This study identified a non-statistically significant trend that domperidone may be beneficial for OH in PD. Several studies identified statistically significant differences in BP with domperidone in the setting of initiating dopaminergic medication. There is currently the most evidence to support domperidone use with apomorphine commencement. Studies reporting domperidone adverse effects in PD patients were largely retrospective or cross-sectional. The identified studies demonstrated that domperidone may cause QT prolongation and is associated with increased risk of VT/SCD in PD patients with preexisting cardiac disease. CONCLUSIONS: Domperidone may help to ameliorate OH associated with dopaminergic medications in PD, namely when used in conjunction with apomorphine. When considering whether to use domperidone in PD, factors that should be taken into account include pre-existing heart disease and drug interactions, as well as the impact of OH on mobility, cognition and quality of life.