Treatment Options for Mallet Finger:A Review

Background: Mallet finger is a common injury. The aim of this review is to give an overview of the different treatment options of mallet injuries and their indications, outcomes, and potential complications. Methods: A literature-based study was conducted using the PubMed database comprising world literature from January of 1980 until January of 2010. The following search terms were used: "mallet" and "finger." Results: There are many variations in the design of splints; there are, however, only a few studies that compare the type of splints with one another. Splinting appears to be effective in uncomplicated and complicated cases. Equal results have been reported for early and delayed splinting therapy. To internally fixate a mallet finger, many different techniques have been reported; however, none of these studies examined their comparisons in a controlled setting. In chronic mallet injuries, a tenodermodesis followed by splinting or a tenotomy of the central slip is usually performed. If pain and impairment persist despite previous surgical corrective attempts, an arthrodesis of the distal interphalangeal joint should be performed. Conclusions: Uncomplicated cases of mallet injuries are best treated by splinting therapy; cases that do not react to splinting therapy are best treated by surgical interventions. Controversy remains about whether mallet injuries with a larger dislocated bone fragment are best treated by surgery or by external splinting. (Plast. Reconstr. Surg. 126: 1624, 2010.

To What Degree Does Active Cervical Range of Motion Differ Between Patients With Neck Pain, Patients With Whiplash, and Those Without Neck Pain?: A Systematic Review and Meta-Analysis

OBJECTIVES: To quantify differences in active cervical range of motion (aCROM) between patients with neck pain and those without neck pain, in patients with whiplash-associated disorders (WADs) and nontraumatic neck pain, and in patients with acute complaints versus those with chronic complaints. DATA SOURCES: Seven bibliographic databases were searched from inception to April 2015. In addition, a manual search was performed. STUDY SELECTION: Full articles on a numerical comparison of aCROM in patients with neck pain and asymptomatic control persons of similar ages were included. Two reviewers independently selected studies and assessed risk of bias. DATA EXTRACTION: Two reviewers extracted the data. Pooled mean differences of aCROM were calculated using a random-effects model. DATA SYNTHESIS: The search yielded 6261 hits; 27 articles (2366 participants, 13 low risk of bias) met the inclusion criteria. The neck pain group showed less aCROM in all movement directions compared with persons without neck pain. Mean differences ranged from -7.04° (95% CI, -9.70° to -4.38°) for right lateral bending (11 studies) to -89.59° (95% CI, -131.67° to -47.51°) for total aCROM (4 studies). Patients with WADs had less aCROM than patients with nontraumatic neck pain. No conclusive differences in aCROM were found between patients with acute and patients with chronic complaints. CONCLUSIONS: Patients with neck pain have a significantly decreased aCROM compared with persons without neck pain, and patients with WADs have less aCROM than those with nontraumatic neck pain

DeltaScan for the Assessment of Acute Encephalopathy and Delirium in ICU and non-ICU Patients, a Prospective Cross-Sectional Multicenter Validation Study

Objectives: To measure the diagnostic accuracy of DeltaScan: a portable real-time brain state monitor for identifying delirium, a manifestation of acute encephalopathy (AE) detectable by polymorphic delta activity (PDA) in single-channel electroencephalograms (EEGs). Design: Prospective cross-sectional study. Setting: Six Intensive Care Units (ICU's) and 17 non-ICU departments, including a psychiatric department across 10 Dutch hospitals. Participants: 494 patients, median age 75 (IQR:64-87), 53% male, 46% in ICUs, 29% delirious. Measurements: DeltaScan recorded 4-minute EEGs, using an algorithm to select the first 96 seconds of artifact-free data for PDA detection. This algorithm was trained and calibrated on two independent datasets. Methods: Initial validation of the algorithm for AE involved comparing its output with an expert EEG panel's visual inspection. The primary objective was to assess DeltaScan's accuracy in identifying delirium against a delirium expert panel's consensus. Results: DeltaScan had a 99% success rate, rejecting 6 of the 494 EEG's due to artifacts. Performance showed and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.86 (95% CI: 0.83-0.90) for AE (sensitivity: 0.75, 95%CI=0.68-0.81, specificity: 0.87 95%CI=0.83-0.91. The AUC was 0.71 for delirium (95%CI=0.66-0.75, sensitivity: 0.61 95%CI=0.52-0.69, specificity: 72, 95%CI=0.67-0.77). Our validation aim was an NPV for delirium above 0.80 which proved to be 0.82 (95%CI: 0.77-0.86). Among 84 non-delirious psychiatric patients, DeltaScan differentiated delirium from other disorders with a 94% (95%CI: 87-98%) specificity. Conclusions: DeltaScan can diagnose AE at bedside and shows a clear relationship with clinical delirium. Further research is required to explore its role in predicting delirium-related outcomes.</p

DeltaScan for the Assessment of Acute Encephalopathy and Delirium in ICU and non-ICU Patients, a Prospective Cross-Sectional Multicenter Validation Study

Objectives: To measure the diagnostic accuracy of DeltaScan: a portable real-time brain state monitor for identifying delirium, a manifestation of acute encephalopathy (AE) detectable by polymorphic delta activity (PDA) in single-channel electroencephalograms (EEGs). Design: Prospective cross-sectional study. Setting: Six Intensive Care Units (ICU's) and 17 non-ICU departments, including a psychiatric department across 10 Dutch hospitals. Participants: 494 patients, median age 75 (IQR:64-87), 53% male, 46% in ICUs, 29% delirious. Measurements: DeltaScan recorded 4-minute EEGs, using an algorithm to select the first 96 seconds of artifact-free data for PDA detection. This algorithm was trained and calibrated on two independent datasets. Methods: Initial validation of the algorithm for AE involved comparing its output with an expert EEG panel's visual inspection. The primary objective was to assess DeltaScan's accuracy in identifying delirium against a delirium expert panel's consensus. Results: DeltaScan had a 99% success rate, rejecting 6 of the 494 EEG's due to artifacts. Performance showed and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.86 (95% CI: 0.83-0.90) for AE (sensitivity: 0.75, 95%CI=0.68-0.81, specificity: 0.87 95%CI=0.83-0.91. The AUC was 0.71 for delirium (95%CI=0.66-0.75, sensitivity: 0.61 95%CI=0.52-0.69, specificity: 72, 95%CI=0.67-0.77). Our validation aim was an NPV for delirium above 0.80 which proved to be 0.82 (95%CI: 0.77-0.86). Among 84 non-delirious psychiatric patients, DeltaScan differentiated delirium from other disorders with a 94% (95%CI: 87-98%) specificity. Conclusions: DeltaScan can diagnose AE at bedside and shows a clear relationship with clinical delirium. Further research is required to explore its role in predicting delirium-related outcomes.</p