Various injury severity scores exist for trauma; it is known that they do not correlate accurately to military injuries. A promising anatomical scoring system for blast pelvic and perineal injury led to the development of an improved scoring system using machine-learning techniques. An unbiased genetic algorithm selected optimal anatomical and physiological parameters from 118 military cases. A Naïve Bayesian (NB) model was built using the proposed parameters to predict the probability of survival. Ten-fold cross validation was employed to evaluate its performance. Our model significantly out-performed Injury Severity Score (ISS), Trauma ISS, New ISS and the Revised Trauma Score in virtually all areas; Positive Predictive Value 0.8941, Specificity 0.9027, Accuracy 0.9056 and Area Under Curve 0.9059. A two-sample t-test showed that the predictive performance of the proposed scoring system was significantly better than the other systems (p<0.001). With limited resources and the simplest of Bayesian methodologies we have demonstrated that the Naïve Bayesian model performed significantly better in virtually all areas assessed by current scoring systems used for trauma. This is encouraging and highlights that more can be done to improve trauma systems not only for the military, but also in civilian trauma

Background. The advent of value-based conscientiousness and rapid-recovery discharge pathways presents surgeons, hospitals, and payers with the challenge of providing the same total hip arthroplasty episode of care in the safest and most economic fashion for the same fee, despite patient differences. Various predictive analytic techniques have been applied to medical risk models, such as sepsis risk scores, but none have been applied or validated to the elective primary total hip arthroplasty (THA) setting for key payment-based metrics. The objective of this study was to develop and validate a predictive machine learning model using preoperative patient demographics for length of stay (LOS) after primary THA as the first step in identifying a patient-specific payment model (PSPM). Methods. Using 229,945 patients undergoing primary THA for osteoarthritis from an administrative database between 2009– 16, we created a naïve Bayesian model to forecast LOS after primary THA using a 3:2 split in which 60% of the available patient data “built” the algorithm and the remaining 40% of patients were used for “testing.” This process was iterated five times for algorithm refinement, and model performance was determined using the area under the receiver operating characteristic curve (AUC), percent accuracy, and positive predictive value. LOS was either grouped as 1–5 days or greater than 5 days. Results. The machine learning model algorithm required age, race, gender, and two comorbidity scores (“risk of illness” and “risk of morbidity”) to demonstrate excellent validity, reliability, and responsiveness with an AUC of 0.87 after five iterations. Hospital stays of greater than 5 days for THA were most associated with increased risk of illness and risk of comorbidity scores during admission compared to 1–5 days of stay. Conclusions. Our machine learning model derived from administrative big data demonstrated excellent validity, reliability, and responsiveness after primary THA while accurately predicting LOS and identifying two comorbidity scores as key value-based metrics. Predictive data has the potential to engender a risk-based PSPM prior to primary THA and other elective orthopaedic procedures

Lung cancer is the most common cancer diagnosed, the leading cause of cancer-related deaths, and bone metastases occurs in 20–40% of lung cancer patients. They often present symptomatically with pain or skeletal related events (SREs), which are independently associated with decreased survival. Bone modifying agents (BMAs) such as Denosumab or bisphosphonates are routinely used, however no specific guidelines exist from the National Comprehensive Cancer Center or the European Society of Medical Oncologists. Perhaps preventing the formation of guidelines is the lack of a high-quality quantitative synthesis of randomized controlled trial (RCT) data to determine the optimal treatment for the patient important outcomes of 1) Overall survival (OS), 2) Time to SRE, 3) SRE incidence, and 4) Pain Resolution. The objective of this study was to perform the first systematic review and network meta-analysis (NMA) to assess the best BMA for treatment of metastatic lung cancer to bone. We conducted our study in accordance to the PRISMA protocol. We performed a librarian assisted search of MEDLINE, PubMed, EMBASE, and Cochrane Library and Chinese databases including CNKI and Wanfang Data. We included studies that are RCTs reporting outcomes specifically for lung cancer patients treated with a bisphosphonate or Denosumab. Screening, data extraction, risk of bias and GRADE were performed in duplicate. The NMA was performed using a Bayesian probability model with R. Results are reported as relative risks, odds ratios or mean differences, and the I2 value is reported for heterogeneity. We assessed all included articles for risk of bias and applied the novel GRADE framework for NMAs to rate the quality of evidence supporting each outcome. We included 132 RCTs comprising 11,161 patients with skeletal metastases from lung cancer. For OS, denosumab was ranked above zoledronic acid (ZA) and estimated to confer an average of 3.7 months (95%CI: −0.5 – 7.6) increased survival compared to untreated patients. For time to SRE, denosumab was ranked first with an average of 9.1 additional SRE-free months (95%CI: 4.0 – 14.0) compared to untreated patients, while ZA conferred an additional 4.8 SRE-free months (2.4 – 7.0). Patients treated with the combination of Ibandronate and systemic therapy were 2.3 times (95%CI: 1.7 – 3.2) more likely to obtain successful pain resolution, compared to untreated. Meta-regression showed no effect of heterogeneity length of follow-up or pain scales on the observed treatment effects. Heterogeneity in the network was considered moderate for overall survival and time to SRE, mild for SRE incidence, and low for pain resolution. While a generally high risk of bias was observed across studies, whether they were from Western or Chinese databases. The overall GRADE for the evidence underlying our results is High for Pain control and SRE incidence, and Moderate for OS and time to SRE. This study represents the most comprehensive synthesis of the best available evidence guiding pharmacological treatment of bone metastases from lung cancer. Denosumab is ranked above ZA for both overall survival and time to SRE, but both treatments are superior to no treatment. ZA was first among all bisphosphonates assessed for odds of reducing SRE incidence, while the combination of Ibandronate and radionuclide therapy was most effective at significantly reducing pain from metastases. Clinicians and policy makers may use this synthesis of all available RCT data as support for the use of a BMA in MBD for lung cancer