Limb Necrosis in the Setting of Vasopressor Use.

BACKGROUND: It remains poorly understood why only some hemodynamically unstable patients who receive aggressive treatment with vasopressor medications develop limb necrosis. OBJECTIVE: To determine the incidence of limb necrosis and the factors associated with it following high-dose vasopressor therapy. METHODS: A retrospective case-control medical records review was performed of patients aged 18 to 89 years who received vasopressor therapy between 2012 and 2021 in a single academic medical center. The study population was stratified by the development of limb necrosis following vasopressor use. Patients who experienced necrosis were compared with age- and sex-matched controls who did not experience necrosis. Demographic information, comorbidities, and medication details were recorded. RESULTS: The incidence of limb necrosis following vasopressor administration was 0.25%. Neither baseline demographics nor medical comorbidities differed significantly between groups. Necrosis was present in the same limb as the arterial catheter most often for femoral catheters. The vasopressor dose administered was significantly higher in the necrosis group than in the control group for ephedrine (P = .02) but not for the other agents. The duration of therapy was significantly longer in the necrosis group than in the control group for norepinephrine (P = .001), epinephrine (P = .04), and ephedrine (P = .01). The duration of vasopressin administration did not differ significantly between groups. CONCLUSION: The findings of this study suggest that medication-specific factors, rather than patient and disease characteristics, should guide clinical management of necrosis in the setting of vasopressor administration.

Role of Operating Room Size on Air Quality in Primary Total Hip Arthroplasty.

BACKGROUND: Airborne biologic particles (ABPs) can be measured intraoperatively to evaluate operating room (OR) sterility. Our study examines the role of OR size on air quality and ABP count in primary total hip arthroplasty (THA). METHODS: We analyzed primary THA procedures done within 2 ORs measuring 278 ft2 and 501 ft2 at a single academic institution from April 2019 to June 2020. Temperature, humidity, and ABP count per minute were recorded with a particle counter intraoperatively and cross-referenced with surgical data from the electronic health records using procedure start and end times. Descriptive statistics were used to evaluate differences in variables. P-values were calculated using t-test and chi-squared test. RESULTS: A total of 116 primary THA cases were included: 18 (15.5%) in the "small" OR and 98 (84.5%) in the "large" OR. Between-group comparisons revealed significant differences in temperature (small OR: 20.3 ± 1.23 C versus large OR: 19.1 ± 0.85 C, P < .0001) and relative humidity (small OR: 41.1 ± 7.24 versus large OR: 46.9 ± 7.56, P < .001). Significant percent decreases in ABP rates for particles measuring 2.5 um (-125.0%, P = .0032), 5.0 um (-245.0%, P = .00078), and 10.0 um (-413.9%, P = .0021) were found in the large OR. Average time spent in the OR was significantly longer in the large OR (174 ± 33 minutes) compared to the small OR (151 ± 14 minutes) (P = .00083). CONCLUSION: Temperature and humidity differences and significantly lower ABP counts were found in the large compared to the small OR despite longer average time spent in the large OR, suggesting the filtration system encounters less particle burden in larger rooms. Further research is needed to determine the impact this may have on infection rates.

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.

There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable sensors enable non-invasive monitoring of human movement in applied situations, current commercial wearables do not estimate tissue-level loading on structures inside the body. Here we explore the feasibility of using wearable sensors to estimate tibial bone force during running. First, we used lab-based data and musculoskeletal modeling to estimate tibial force for ten participants running across a range of speeds and slopes. Next, we converted lab-based data to signals feasibly measured with wearables (inertial measurement units on the foot and shank, and pressure-sensing insoles) and used these data to develop two multi-sensor algorithms for estimating peak tibial force: one physics-based and one machine learning. Additionally, to reflect current running wearables that utilize running impact metrics to infer musculoskeletal loading or injury risk, we estimated tibial force using a commonly measured impact metric, the ground reaction force vertical average loading rate (VALR). Using VALR to estimate peak tibial force resulted in a mean absolute percent error of 9.9%, which was no more accurate than a theoretical step counter that assumed the same peak force for every running stride. Our physics-based algorithm reduced error to 5.2%, and our machine learning algorithm reduced error to 2.6%. Further, to gain insights into how force estimation accuracy relates to overuse injury risk, we computed bone damage expected due to a given loading cycle. We found that modest errors in tibial force translated into large errors in bone damage estimates. For example, a 9.9% error in tibial force using VALR translated into 104% error in estimated bone damage. Encouragingly, the physics-based and machine learning algorithms reduced damage errors to 41% and 18%, respectively. This study highlights the exciting potential to combine wearables, musculoskeletal biomechanics and machine learning to develop more accurate tools for monitoring musculoskeletal loading in applied situations.