Malignant spinal cord compression: NICE guidance, improvements and challenges

BACKGROUND AND AIM

Malignant spinal cord compression (mSCC) is one of the most serious complications of cancer. Recent NICE guidance has aimed to improve patient pathways and outcomes for patients with mSCC. We have examined the current presentations, management and outcomes for patients with mSCC in West London following the implementation of the NICE guidance.

MATERIALS AND METHODS

The electronic records and clinical notes were reviewed for all patients assessed for confirmed or potential mSCC at Charing Cross Hospital in 2012. Details on the number of referrals, the proportion with confirmed mSCC, the cancer diagnosis, treatment and outcome were analysed.

RESULTS

191 patients were reviewed with 127 (66%) cases of confirmed mSCC. The commonest tumour types were prostate cancer (26 cases), lung cancer (26), breast cancer (21) and kidney cancer (15). 21% of the patients had no previous cancer diagnosis; mSCC was their presenting diagnostic event. Radiotherapy was the predominant management, 24% of the patients had first line surgical treatment. At presentation 62% of patients were either chair or bed bound. Treatment brought important mobility benefits to all patients groups with 20% of the initially chair or bed bound patients leaving the hospital with independent mobility.

CONCLUSION

Enhanced patients pathways with ease of access, rapid assessment and prompt treatment can improve outcomes. Despite these pathways many patients still present with gross motor impairment and over 20% have no previous diagnosis of cancer. Ongoing work to maintain awareness for patients and primary care of the diagnosis and emergency pathways is essential to optimize outcomes.

Degeneration of the extensor muscle group in a surgical low back and leg pain population

The spinal muscles have received great attention in low back pain (LBP) with suggestions of a de-conditioning syndrome. It is not known whether it is a long or short term consequence of LBP. This study explored the cross section area (CSA) and muscle quality of the spinal extensor group in LBP patients referred to a spinal clinic. MRI scans of 100 annoymised patients were retrospectively reviewed; sagittal and transverse (from levels L3-5) images were archived and analysed to determine CSA and levels of fat infiltration of the extensor muscle groups. Forty six patients had spinal stenosis (28 males, 18 females, mean age 66 ± 14.2 years) and 54 had a disc prolapse (28 males, 26 females, mean age 50 ± 12.9 years). CSA was significantly smaller in the stenotics at both L3/4 and L4/5. Patients presenting with leg pain and a disc herniation had a significantly smaller CSA (p < 0.01) at L3/4 and L4/5 levels. Fat infiltration was present in both groups but was significantly greater in the stenotic group (p < 0.01) and was present at a similar degree at all spinal levels. Multiple regression analysis confirmed that reduced CSA was linked to leg pain (p < 0.01) and age was linked with fat infiltration (p < 0.01). Future work should explore the extent of such changes in an age and sex matched control population with no current back pain or sciatica.

Clinical evidence of herb-drug interactions: a systematic review by the natural standard research collaboration

To evaluate the pharmacokinetics and adverse effects of medicinal herbs, as well as clinical evidence of herb-drug interactions.

METHODS

Electronic searches were conducted in multiple databases, including MEDLINE, EMBASE, the Cochrane Library, CINAHL, NAPRALERT, International Pharmaceutical Abstracts, CANCERLIT, CISCOM, and HerbMed. Search terms used included common names, scientific names, and synonyms for the herbs and their primary active constituents. Bibliographies of relevant articles were also searched by hand to obtain additional references. No restrictions were placed on language or quality of publications. All literature collected pertained to adverse effects, pharmacokinetics, and suspected or confirmed cases of herb-drug interactions.

RESULTS

Over 80 herbs or botanicals (including plants, fungi, algae, and common constituents) were identified that had clinically significant interactions with prescription and over-the-counter drugs. Interestingly, herbs beginning with the letter "g" (garlic, ginger, ginkgo, and grapefruit) were among the herbs most commonly involved in herb-drug interactions. Drugs with anticoagulant/antiplatelet activity (e.g. warfarin, aspirin) were frequently implicated in herb-drug interactions, with documented interactions with over 30 herbs and herbal products. Because many herbs have demonstrated adverse effects on the liver, the potential for interaction with hepatotoxic agents (such as acetaminophen) is also significant. Clinical outcomes of reported herb-drug interactions ranged from mild to severe. Of note, fatalities (though rare) have occurred with concomitant ephedra and caffeine use.

CONCLUSION

As herbal products (and dietary supplements in general) continue to grow in popularity, patients and health care providers should be vigilant of potential herb-drug interactions.

Application of artificial neural networks for detection of abnormal fetal heart rate pattern: a comparison with conventional algorithms

Cardiotocography signals were sampled during labour in 53 patients. A recurrent artificial neural network with hidden layer feedback was trained and performance was compared with that of several conventional systems. Correct and false positive rates of all systems tested were calculated. To ensure that the performance of neural networks was not just caused by using different cut-off levels, the threshold used for conventional methods were also adapted and optimised. The correct positives rate of neural networks was between 0.72 and 0.9, and the false positive rate between 0.2 and 0.4. Before optimising, conventional algorithms produced a very low correct positive (0.02-0.5) and a low false positive rate (0.0-0.08). After adjusting the parameters, the tested neural networks still performed better than optimised conventional systems.

Neural networks for recognizing patterns in cardiotocograms

The cardiotocogram (CTG) is commonly used for routine fetal monitoring in the delivery room. A major problem is that the interpretation of the CTG trace requires experienced specialists. In order to avoid long gaps between the detection of a suspicious pattern and the intervention, the CTG has to be checked in short intervals. An automated monitoring system at the obstetric site can reduce such delays. Therefore, an alarm system immediately reporting suspicious events has been built. The focus of our study was put on the question whether AI techniques such as neural networks are suited to the task of recognizing patterns in the CTG trace. In a comparative study, their performance was evaluated against that of conventional methods. The neural networks turned out to provide significantly better results than the tested conventional methods.