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OBJECTIVE Spondylolisthesis is a common operative disease in the United States, but robust predictive models for patient outcomes remain limited. The development of models that accurately predict postoperative outcomes would be useful to help identify patients at risk of complicated postoperative courses and determine appropriate healthcare and resource utilization for patients. As such, the purpose of this study was to develop k-nearest neighbors (KNN) classification algorithms to identify patients at increased risk for extended hospital length of stay (LOS) following neurosurgical intervention for spondylolisthesis. METHODS The Quality Outcomes Database (QOD) spondylolisthesis data set was queried for patients receiving either decompression alone or decompression plus fusion for degenerative spondylolisthesis. Preoperative and perioperative variables were queried, and Mann-Whitney U-tests were performed to identify which variables would be included in the machine learning models. Two KNN models were implemented (k = 25) with a standard training set of 60%, validation set of 20%, and testing set of 20%, one with arthrodesis status (model 1) and the other without (model 2). Feature scaling was implemented during the preprocessing stage to standardize the independent features. RESULTS Of 608 enrolled patients, 544 met prespecified inclusion criteria. The mean age of all patients was 61.9 ± 12.1 years (± SD), and 309 (56.8%) patients were female. The model 1 KNN had an overall accuracy of 98.1%, sensitivity of 100%, specificity of 84.6%, positive predictive value (PPV) of 97.9%, and negative predictive value (NPV) of 100%. Additionally, a receiver operating characteristic (ROC) curve was plotted for model 1, showing an overall area under the curve (AUC) of 0.998. Model 2 had an overall accuracy of 99.1%, sensitivity of 100%, specificity of 92.3%, PPV of 99.0%, and NPV of 100%, with the same ROC AUC of 0.998. CONCLUSIONS Overall, these findings demonstrate that nonlinear KNN machine learning models have incredibly high predictive value for LOS. Important predictor variables include diabetes, osteoporosis, socioeconomic quartile, duration of surgery, estimated blood loss during surgery, patient educational status, American Society of Anesthesiologists grade, BMI, insurance status, smoking status, sex, and age. These models may be considered for external validation by spine surgeons to aid in patient selection and management, resource utilization, and preoperative surgical planning.

OBJECTIVE The purpose of this study was to evaluate the performance of different supervised machine learning algorithms to predict achievement of minimum clinically important difference (MCID) in neck pain after surgery in patients with cervical spondylotic myelopathy (CSM). METHODS This was a retrospective analysis of the prospective Quality Outcomes Database CSM cohort. The data set was divided into an 80% training and a 20% test set. Various supervised learning algorithms (including logistic regression, support vector machine, decision tree, random forest, extra trees, gaussian naïve Bayes, k–nearest neighbors, multilayer perceptron, and extreme gradient boosted trees) were evaluated on their performance to predict achievement of MCID in neck pain at 3 and 24 months after surgery, given a set of predicting baseline features. Model performance was assessed with accuracy, F1 score, area under the receiver operating characteristic curve, precision, recall/sensitivity, and specificity. RESULTS In total, 535 patients (46.9%) achieved MCID for neck pain at 3 months and 569 patients (49.9%) achieved it at 24 months. In each follow-up cohort, 501 patients (93.6%) were satisfied at 3 months after surgery and 569 patients (100%) were satisfied at 24 months after surgery. Of the supervised machine learning algorithms tested, logistic regression demonstrated the best accuracy (3 months: 0.76 ± 0.031, 24 months: 0.773 ± 0.044), followed by F1 score (3 months: 0.759 ± 0.019, 24 months: 0.777 ± 0.039) and area under the receiver operating characteristic curve (3 months: 0.762 ± 0.027, 24 months: 0.773 ± 0.043) at predicting achievement of MCID for neck pain at both follow-up time points, with fair performance. The best precision was also demonstrated by logistic regression at 3 (0.724 ± 0.058) and 24 (0.780 ± 0.097) months. The best recall/sensitivity was demonstrated by multilayer perceptron at 3 months (0.841 ± 0.094) and by extra trees at 24 months (0.817 ± 0.115). Highest specificity was shown by support vector machine at 3 months (0.952 ± 0.013) and by logistic regression at 24 months (0.747 ± 0.18). CONCLUSIONS Appropriate selection of models for studies should be based on the strengths of each model and the aims of the studies. For maximally predicting true achievement of MCID in neck pain, of all the predictions in this balanced data set the appropriate metric for the authors’ study was precision. For both short- and long-term follow-ups, logistic regression demonstrated the highest precision of all models tested. Logistic regression performed consistently the best of all models tested and remains a powerful model for clinical classification tasks.

Background: The authors examined whether ultrasound sensitivity, specificity, and accuracy in identifying intact repairs or flexor tendon gapping after zone 2 repair are affected by the number of suture strands crossing the repair or gap or imaging modality (static versus dynamic). Methods: A total of 144 fresh-frozen cadaveric digits (thumbs excluded) were randomized to either an intact repair (0-mm gap) or simulated failed repair (4-mm gap), as well as to either a two- or eight-strand locked-cruciate repair of a zone 2 flexor digitorum profundus tendon laceration using 4-0 Fiberwire. Examinations were performed by a blinded musculoskeletal ultrasonographer in static and dynamic modes using an 18-MHz transducer. Gaps were remeasured after scanning, and the final gap width recorded. McNemar exact test was used to determine whether there were differences in sensitivity, specificity, and accuracy affected by modality (static versus dynamic), and chi-square test was used to compare sensitivity, specificity, and accuracy between number of strands (two versus eight) crossing the intact repair or repair gap (≥4 mm). Results: Sensitivity, specificity, and accuracy improved with increased number of suture strands crossing the repair or gap (eight versus two), irrespective of modality (static versus dynamic), and dynamic compared with static scanning modes, irrespective of number of suture strands crossing the repair or gap site. Conclusions: The most sensitive and accurate means of assessing flexor tendon repair integrity and gapping were seen using the dynamic scanning mode. Increased number of suture strands did not negatively affect sensitivity, specificity, or accuracy, regardless of scanning mode (dynamic or static).

The central neural mechanisms affecting sympathetic neuronal discharge strategies supporting homeostatic adjustments in human muscle sympathetic nerve activity (MSNA) remain unclear. This study investigated the hypothesis that central alpha-2 adrenergic mechanisms affect sympathetic action potential (AP) discharge, recruitment, and latency patterns within human MSNA (peroneal nerve microneurography and continuous wavelet transform). An alpha-2 adrenergic agonist (dexmedetomidine) was infused in eight healthy male and female participants (5 females; 28 ± 7years). Data were recorded during a 5-minute resting control condition, a 10-minute intravenous dexmedetomidine loading dose (0.225 μg/kg), and a dexmedetomidine maintenance dose (~0.1 μg/kg/hr). Data are reported as mean (SD) for the 5-minute control condition and 2-minutes of the dexmedetomidine infusion when MSNA bursting activity was demonstrably reduced. Two-tailed paired t-tests were performed. Dexmedetomidine reduced mean pressure (92 ± 7 to 80 ± 8 mmHg; P < 0.01) but did not affect heart rate (61 ± 13 to 60 ± 14 bpm; P = 0.75). Dexmedetomidine reduced integrated MSNA burst frequency (14 ± 6 to 4 ± 3 bursts/min; P < 0.01), sympathetic AP frequency (75 ± 46 to 16 ± 14 AP/min; P < 0.01), APs per burst (5 ± 1 to 4 ± 1 AP/burst; P = 0.01), AP clusters per burst (3.5 ± 0.7 to 3.0 ± 0.7 clusters/burst; P = 0.03), and de-recruited the largest AP clusters (12 ± 3 to 7 ± 2 AP clusters; P < 0.01). Despite de-recruiting large AP clusters with short conduction times, dexmedetomidine elicited shorter sympathetic AP conduction times (1.18 ± 0.12 to 1.14 ± 0.13 s; P = 0.03). Six participants performed a Valsalva Maneuver (20 s, 40 mmHg mouth pressure) during the control condition and during the dexmedetomidine maintenance dose. Data are reported as mean (SD) change (Δ) from the 2-minute period preceding the Valsalva Maneuver to the Valsalva Maneuver for each condition. During the control condition, Valsalva Maneuver increased AP frequency (Δ 311 ± 317 AP/min; P = 0.048), recruited larger previously silent AP clusters (Δ 2 ± 1 AP clusters; P < 0.01), and elicited shorter AP conduction times (Δ ‑0.09 ± 0.07 s; P = 0.02). Compared to the control condition, Valsalva Maneuver performed during the dexmedetomidine maintenance dose condition elicited lesser increases in AP frequency (Δ 116 ± 189 AP/min; P vs. control = 0.02) and lesser recruitment of larger previously silent AP clusters (Δ 0 ± 1 AP clusters; P vs. control = 0.02). Dexmedetomidine did not affect the latency reduction elicited by Valsalva Maneuver (Δ ‑0.07 ± 0.14 s; P vs. control = 0.61). These data suggest that human central alpha-2 adrenergic mechanisms affect: i) sympathetic postganglionic neuronal discharge and recruitment patterns, and ii) synaptic delays within the sympathetic neurocircuitry influencing AP latency. This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Institutes of Health (NIH). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Background: Hemoglobin, the primary oxygen-carrying protein in humans, provides the intermediate link between pulmonary oxygen uptake and tissue oxygen consumption. Oxygen transport is greatly influenced by the hemoglobin-oxygen affinity, which is commonly characterized by the metric P50 - defined as the oxygen tension at which 50% of hemoglobin is saturated. Humans with rare hemoglobin mutations causing a low P50 (high hemoglobin-oxygen affinity) have demonstrated remarkable preservation of exercise tolerance at high altitude conditions (~3,000m). However, the influence of a low P50 on V̇O2max at extreme altitudes (>5,500m) remains largely unexamined. To examine the dependence of V̇O2max on P50 and altitude, we developed a computational model of oxygen uptake and utilization. We hypothesized that a low P50 would result in a better maintained V̇O2max at extreme altitudes compared to conditions of normal P50 and high P50 (low hemoglobin-oxygen affinity). Methods: We created a model that couples pulmonary oxygen uptake with systemic oxygen utilization to estimate V̇O2max as a function of P50, hemoglobin concentration, and altitude. Fixed values for cardiac output and tissue oxygen demand for V̇O2max at sea level were assigned in accordance with experimental data. The pulmonary oxygen uptake model assumes a single blood compartment exposed to alveolar gas, from which the arterial oxygen tension may be estimated from venous input. Using the alveolar gas equation, we interpolated respiratory parameters from data obtained during human sojourn to the summit of Everest. The systemic oxygen utilization model uses arterial input parameters along with Michaelis-Menten kinetics to compute oxygen consumption. The Fick principle was used to determine the venous oxygen tension, which was assumed to approximate tissue oxygen tension. From these values, systemic oxygen extraction and V̇O2max were determined as a function of P50, hemoglobin concentration, and altitude. Results: We present the results for several cases of P50 (low, normal, and high) and hemoglobin concentrations as a function of altitude. For a low P50, the model demonstrated a greater arterial oxygen saturation, greater oxygen content, and lower systemic extraction at extreme altitudes compared to values determined for cases of normal and high P50. Additionally, a low P50 led to better maintenance of V̇O2max at ~8,850m (~38% decrease from sea-level V̇O2max) compared to values determined for normal P50 and high P50 (~53% and ~67% decrease from sea-level V̇O2max, respectively, P<0.05). Conclusion: This model demonstrates the importance of P50 in the determination of V̇O2max at various altitudes. At low altitudes, a low P50 does not confer an advantage in terms of oxygen utilization, likely due to diffusive oxygen limitations. However, at high and extreme altitudes, a greater convective oxygen transport associated with a low P50 likely outweighs impairments in oxygen diffusivity. This project was supported by the National Institutes of Health R-35-HL139854. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Background: Our understanding of the risk factors for and effectiveness of prophylactic measures against shoulder periprosthetic joint infections (PJIs) continues to evolve. This study’s objective was to study patient characteristics, procedural characteristics, and various infection prophylactic measures and their effects on the risk of shoulder PJI after primary arthroplasty. Methods: Nine hundred and ninety-eight patients in a longitudinally maintained, single-institution shoulder arthroplasty database who had at least 2 years of clinical follow-up were retrospectively reviewed. Patient and procedural characteristics were collected. Perioperative variables, including the use of intraoperative antibiotics, topical antibiotics, antibiotic-containing irrigation solution, and a postoperative drain, were collected. Patients who developed shoulder PJI were compared with those without shoulder PJI to identify any association with patient or procedural characteristics. Results: Of the 998 patients, 20 (2.0%) met the criteria for shoulder PJI. Cutibacterium was identified as the causative organism in 19 (95%) of 20 culture-positive reoperations. On univariate analysis, patients in the PJI group were more likely to be younger (p < 0.001), to be male (p = 0.014), to have commercial insurance (p = 0.003), to use alcohol (p = 0.048), and to have had a ream-and-run or hemiarthroplasty procedure (p = 0.005). On multivariable analysis, older age was independently associated with a lower risk of PJI (odds ratio [OR] per year = 0.95; 95% confidence interval [CI], 0.91 to 0.99; p = 0.014). Reverse total shoulder arthroplasty (OR, 10.32; 95% CI, 0.92 to 116.33; p = 0.059) and hemiarthroplasty (OR, 8.59; 95% CI, 0.86.30 to 85.50; p = 0.067) trended toward a higher risk of PJI. Conclusions: Younger patients and patients undergoing procedures other than anatomic total shoulder arthroplasty are at higher risk for shoulder PJI. The majority of culture-positive reoperations were a result of Cutibacterium species. Level of Evidence: Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

Pharmacogenetics can improve clinical outcomes by reducing adverse drug effects and enhancing therapeutic efficacy for commonly used drugs that treat a wide range of cardiovascular diseases. One of the major barriers to the clinical implementation of cardiovascular pharmacogenetics is limited education on this field for current healthcare providers and students. The abundance of pharmacogenetic literature underscores its promise, but it can also be challenging to learn such a wealth of information. Moreover, current clinical recommendations for cardiovascular pharmacogenetics can be confusing because they are outdated, incomplete, or inconsistent. A myriad of misconceptions about the promise and feasibility of cardiovascular pharmacogenetics among healthcare providers also has halted clinical implementation. Therefore, the main goal of this tutorial is to provide introductory education on the use of cardiovascular pharmacogenetics in clinical practice. The target audience is any healthcare provider (or student) with patients that use or have indications for cardiovascular drugs. This tutorial is organized into the following 6 steps: (1) understand basic concepts in pharmacogenetics; (2) gain foundational knowledge of cardiovascular pharmacogenetics; (3) learn the different organizations that release cardiovascular pharmacogenetic guidelines and recommendations; (4) know the current cardiovascular drugs/drug classes to focus on clinically and the supporting evidence; (5) discuss an example patient case of cardiovascular pharmacogenetics; and (6) develop an appreciation for emerging areas in cardiovascular pharmacogenetics. Ultimately, improved education among healthcare providers on cardiovascular pharmacogenetics will lead to a greater understanding for its potential in improving outcomes for a leading cause of morbidity and mortality.