The spine journal : official journal of the North American Spine Society
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The STarT Back Screening Tool (SBST) categorizes risk of future disability in patients with low back pain (LBP). Previous studies evaluating the use of SBST in physical therapy (PT) populations do not reflect the ethnic and socioeconomic diversity occurring in clinical practice and lack statistical power to evaluate factors associated with outcomes within each SBST risk category. ⋯ The SBST tool predicts outcomes of PT in a cohort of patients receiving outpatient PT for LBP. The odds of improvement varied according to baseline disability and SBST risk status. Race, insurance type, and history of previous PT influenced prediction independent of SBST risk status. Incorporating these variables and the interaction between SBST and baseline disability in outcome models has the potential to refine prediction of outcomes after PT.
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In modern clinical research, the accepted minimum follow-up for patient-reported outcome measures (PROMs) after lumbar spine surgery is 24 months, particularly after fusion. Recently, this minimum requirement has been called into question. ⋯ Irrespective of the surgical procedure, 12-month PROMs for functional disability and pain severity accurately reflect those at 24 months. In support of previous literature, our results suggest that 12 months of follow-up may be sufficient for evaluating spinal patient care in clinical practice as well as in research.
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Randomized Controlled Trial
A Prospective Randomized Study to Analyze the Efficacy of Balanced Preemptive Analgesia in Spine Surgery.
Surgical procedures involving the spine are known to cause moderate to severe postoperative pain. Inadequate management of acute pain in the postoperative period results in higher morbidity, and consequently may lead to chronic pain caused by central sensitization. The role of pre-emptive analgesia (PA) and intraoperative analgesia in management of postoperative pain has gained precedence over recent years. Pathophysiology of postoperative pain in spine surgery is unique, as it is a combination of nociceptive, inflammatory, and neuronal stimuli. Blockage of all three stimuli in the perioperative period by pre-emptively administrating a combination of paracetamol (P), ketorolac (K), and pregabalin (PR) might help in adequate management and alleviation of acute postoperative pain. ⋯ Postoperative pain management in spine surgery is maximized if perioperative painful stimuli can be inhibited, which requires adequate blood levels of analgesic, anti-inflammatory, and neuropathic drugs intraoperatively. The employed strategy of preoperative administration of balanced analgesia with a combination of P, K, and PR, each having different mechanisms of action, resulted in lesser pain intensity, allowed better ambulation tolerance, improved functional outcomes and has also reduced the requirement of opioids and duration of hospital stay with no additional complications. Thus, this balanced analgesia administered preoperatively would address the complicated postsurgical pain.
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Randomized Controlled Trial
Prolonged pain reducing effect of sodium hyaluronate-carboxymethyl cellulose solution in the selective nerve root block (SNRB) of lumbar radiculopathy: a prospective, double-blind, randomized controlled clinical trial.
The pattern of linear graph schematized by visual analogue scale (VAS) score displaying pain worsening between 2 days and 2 weeks after selective nerve root block (SNRB) is called rebound pain. ⋯ Compared with conventional cocktail used for SNRB, addition of HA-CMC sol showed effective control of rebound pain at 3 days to 2 weeks after the procedure.
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Models for predicting recovery in traumatic spinal cord injury (tSCI) patients have been developed to optimize care. Several models predicting tSCI recovery have been previously validated, yet recent findings question their accuracy, particularly in patients whose prognoses are the least predictable. ⋯ Previously tested prediction models demonstrated a lower predictive accuracy for AIS B+C than AIS A+D patients. These models were unable to effectively prognosticate AIS A+D patients separately; a failure that was masked when amalgamating the two patient populations. This suggests that former prediction models achieved strong prognostic accuracy by combining AIS classifications coupled with a disproportionately high proportion of AIS A+D patients.