Perioperative Outcomes of Total Hip Arthroplasty in Trisomy 21 Patients in Comparison With a Control Population: Analysis From a Large National Sample of 367,894 Patients.

BACKGROUND: Literature on the outcomes of total hip arthroplasty (THA) has established the procedure as a gold standard for hip arthritis. However, postoperative outcomes after THA in specific conditions such as Down's syndrome (DS) have been sparsely described. This large database analysis of over 367,000 patients was aimed at evaluating the immediate postoperative results including morbidity and mortality rates after THA among DS patients and comparing these with a control population. METHODS: Data from the National Inpatient Sample (NIS) database Healthcare Cost and Utilization Project (HCUP) was reviewed retrospectively from 2016 to 2019 on THAs. Among 367,894 patients, 129 were identified with a diagnosis of DS. Complex primaries and revisions were excluded. Demographics, admission details, and perioperative variables including morbidity and mortality rates were compared between DS patients and controls. RESULTS: Patients with DS were younger than the control population (43.3 versus 65.9 years, p=0.002), had a greater preponderance of men, had a lower incidence of smoking and diabetes, and had a relatively higher incidence of non-elective THA. The former also had a longer mean length of stay (LoS) and higher mean costs to healthcare. Two patients with DS died after a THA, making the mortality rate 17-fold higher in DS patients. Higher rates of postoperative anemia (31.8% versus 19.6%, p<0.001), pneumonia (2.3% versus 0.3%), and pulmonary embolism (p=0.0.12) were seen in the DS group. Also seen in the DS group were higher risks of periprosthetic fractures (p=0.020) and periprosthetic joint infections (PJIs) (p=0.047). CONCLUSIONS: Results from a total hip arthroplasty continue to positively transform the lives of patients with end-stage hip arthritis from varying etiologies. In the special cohort of Down's syndrome, a thorough discussion is essential with reference to the relatively higher morbidity and mortality in this group of patients. Documented conversations between patients and their families and healthcare providers should consist of detailed deliberations on the pros and cons of surgery and its potential impacts.

Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.

PURPOSE: This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought to explore the inherent variability found in adolescent sagittal alignment using machine learning to remove bias and determine whether clusters of sagittal alignment exist. METHODS: Multiple semi-supervised machine learning clustering algorithms were applied to 111 normal adolescent sagittal spines. Sagittal parameters for resultant clusters were determined. RESULTS: Machine learning analysis found that the spines did cluster into distinct groups with an optimal number of clusters ranging from 3 to 5. We performed an analysis on both 3 and 5-cluster groups. The 3-cluster groups analysis found good consistency between methods with 96 of 111, while the analysis of 5-cluster groups found consistency with 105 of 111 spines. When assessing for differences in sagittal parameters between the groups for both analyses, there were differences in T4-12 TK, L1-S1 LL, SS, SVA, PI-LL mismatch, and TPA. However, the only parameter that was statistically different for all groups was SVA. CONCLUSIONS: Based on machine learning, the adolescent sagittal spine alignments do cluster into distinct groups. While there were distinguishing features with TK and LL, the most important parameter distinguishing these groups was SVA. Further studies may help to understand these findings in relation to spinal deformities.

High-dimensional proteomics identifies organ injury patterns associated with outcomes in human trauma.

INTRODUCTION: Severe traumatic injury with shock can lead to direct and indirect organ injury; however, tissue-specific biomarkers are limited in clinical panels. We used proteomic and metabolomic databases to identify organ injury patterns after severe injury in humans. METHODS: Plasma samples (times 0, 24, and 72 hours after arrival to trauma center) from injured patients enrolled in two randomized prehospital trials were subjected to multiplexed proteomics (SomaLogic Inc., Boulder, CO). Patients were categorized by outcome: nonresolvers (died >72 hours or required ≥7 days of critical care), resolvers (survived to 30 days and required <7 days of critical care), and low Injury Severity Score (ISS) controls. Established tissue-specific biomarkers were identified through a literature review and cross-referenced with tissue specificity from the Human Protein Atlas. Untargeted plasma metabolomics (Metabolon Inc., Durham, NC), inflammatory mediators, and endothelial damage markers were correlated with injury biomarkers. Kruskal-Wallis/Mann-Whitney U tests with false discovery rate correction assessed differences in biomarker expression across outcome groups (significance; p < 0.1). RESULTS: Of 142 patients, 78 were nonresolvers (median ISS, 30), 34 were resolvers (median ISS, 22), and 30 were low ISS controls (median ISS, 1). A broad release of tissue-specific damage markers was observed at admission; this was greater in nonresolvers. By 72 hours, nine cardiac, three liver, eight neurologic, and three pulmonary proteins remained significantly elevated in nonresolvers compared with resolvers. Cardiac damage biomarkers showed the greatest elevations at 72 hours in nonresolvers and had significant positive correlations with proinflammatory mediators and endothelial damage markers. Nonresolvers had lower concentrations of fatty acid metabolites compared with resolvers, particularly acyl carnitines and cholines. CONCLUSION: We identified an immediate release of tissue-specific biomarkers with sustained elevation in the liver, pulmonary, neurologic, and especially cardiac injury biomarkers in patients with complex clinical courses after severe injury. The persistent myocardial injury in nonresolvers may be due to a combination of factors including metabolic stress, inflammation, and endotheliopathy.

Multi-Omic Admission-Based Prognostic Biomarkers Identified by Machine Learning Algorithms Predict Patient Recovery and 30-Day Survival in Trauma Patients.

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.