Comparison of Visual Analog Pain Score Reported to Physician vs Nurse in Nonoperatively Treated Foot and Ankle Patients

Background: Patient reported outcome measures (PROMs) are taking a more prominent role in Orthopedics as health care seeks to define treatment outcomes. The Visual Analogue Scale (VAS) is considered a reliable measure of acute pain. A previous study found that operative candidates’ VAS pain score was significantly higher when reported to the surgeon compared to the nurse. This study’s aim is to examine whether this phenomenon occurs in nonoperative patients. We hypothesize that patients’ VAS scores reported to the surgeon and a nurse will be the same Methods: This study is a retrospective cohort of 201 consecutive nonoperative patients treated by a single surgeon. Patients were asked to rate pain intensity by a nurse followed by the surgeon using a horizontal VAS, 0 “no pain” to 10 worst pain”. Differences in reported pain levels were compared with data from the previous cohort of 201 consecutive operative patients. Results: The mean VAS score reported to the nurse was 3.2 whereas the mean VAS score reported to the surgeon was 4.2 (p\u3c.001). The mean difference in VAS scores reported for operative patients was 2.9, whereas the mean difference for nonoperative patients was 1.0 (p \u3c .001). Conclusion: This study found statistically significant differences between VAS scores reported to the surgeon versus the nurse in nonoperative patients which support the trend found in our previous study, where operative patients reported significantly higher scores to the surgeon. The mean difference between reported pain scores is significantly higher for operative patients compared to nonoperative patients

A Resampling Approach For causal Inference On Novel Two-Point Time-Series With Application To Identify Risk Factors For Type-2 Diabetes And Cardiovascular Disease

Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time series structure without a control group, which is driven by an observational routine clinical dataset collected to monitor key risk markers of type-$2$ diabetes (T2D) and cardiovascular disease (CVD). We propose a resampling approach called 'I-Rand' for independently sampling one of the two time points for each individual and making inference on the estimated causal effects based on matching methods. The proposed method is illustrated with data from a service-based dietary intervention to promote a low-carbohydrate diet (LCD), designed to impact risk of T2D and CVD. Baseline data contain a pre-intervention health record of study participants, and health data after LCD intervention are recorded at the follow-up visit, providing a two-point time-series pattern without a parallel control group. Using this approach we find that obesity is a significant risk factor of T2D and CVD, and an LCD approach can significantly mitigate the risks of T2D and CVD. We provide code that implements our method