Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool.

BACKGROUND: We previously showed that machine learning-based methodologies of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery and assess case-mix-adjusted performance after benchmark procedures. We extend this methodology to provide interpretable, easily accessible, and actionable hospital performance analysis across all procedures. METHODS: The European Congenital Heart Surgeons Association Congenital Cardiac Database data subset of 172,888 congenital cardiac surgical procedures performed in European centers between 1989 and 2022 was analyzed. OCT models (decision trees) were built predicting hospital mortality (area under the curve [AUC], 0.866), prolonged postoperative mechanical ventilatory support time (AUC, 0.851), or hospital length of stay (AUC, 0.818), thereby establishing case-adjusted benchmarking standards reflecting the overall performance of all participating hospitals, designated as the "virtual hospital." OCT analysis of virtual hospital aggregate data yielded predicted expected outcomes (both aggregate and for risk-matched patient cohorts) for the individual hospital's own specific case-mix, readily available on-line. RESULTS: Raw average rates were hospital mortality, 4.9%; mechanical ventilatory support time, 14.5%; and length of stay, 15.0%. Of 146 participating centers, compared with each hospital's overall case-adjusted predicted hospital mortality benchmark, 20.5% statistically (<90% CI) overperformed and 20.5% underperformed. An interactive tool based on the OCT analysis automatically reveals 14 hospital-specific patient cohorts, simultaneously assessing overperformance or underperformance, and enabling further analysis of cohort strata in any chosen time frame. CONCLUSIONS: Machine learning-based OCT benchmarking analysis provides automatic assessment of hospital-specific case-adjusted performance after congenital heart surgery, not only overall but importantly, also by similar risk patient cohorts. This is a tool for hospital self-assessment, particularly facilitated by the user-accessible online-platform.

Use of artificial intelligence for nonlinear benchmarking of surgical care.

BACKGROUND: Existent methodologies for benchmarking the quality of surgical care are linear and fail to capture the complex interactions of preoperative variables. We sought to leverage novel nonlinear artificial intelligence methodologies to benchmark emergency surgical care. METHODS: Using a nonlinear but interpretable artificial intelligence methodology called optimal classification trees, first, the overall observed mortality rate at the index hospital's emergency surgery population (index cohort) was compared to the risk-adjusted expected mortality rate calculated by the optimal classification trees from the American College of Surgeons National Surgical Quality Improvement Program database (benchmark cohort). Second, the artificial intelligence optimal classification trees created different "nodes" of care representing specific patient phenotypes defined by the artificial intelligence optimal classification trees without human interference to optimize prediction. These nodes capture multiple iterative risk-adjusted comparisons, permitting the identification of specific areas of excellence and areas for improvement. RESULTS: The index and benchmark cohorts included 1,600 and 637,086 patients, respectively. The observed and risk-adjusted expected mortality rates of the index cohort calculated by optimal classification trees were similar (8.06% [95% confidence interval: 6.8-9.5] vs 7.53%, respectively, P = .42). Two areas of excellence and 4 for improvement were identified. For example, the index cohort had lower-than-expected mortality when patients were older than 75 and in respiratory failure and septic shock preoperatively but higher-than-expected mortality when patients had respiratory failure preoperatively and were thrombocytopenic, with an international normalized ratio ≤1.7. CONCLUSION: We used artificial intelligence methodology to benchmark the quality of emergency surgical care. Such nonlinear and interpretable methods promise a more comprehensive evaluation and a deeper dive into areas of excellence versus suboptimal care.

Comparative compositions of grain of tritordeum, durum wheat and bread wheat grown in multi-environment trials.

Three genotypes each of bread wheat, durum wheat and tritordeum were grown in randomized replicated field trials in Andalusia (Spain) for two years and wholemeal flours analysed for a range of components to identify differences in composition. The contents of all components that were determined varied widely between grain samples of the individual species and in most cases also overlapped between the three species. Nevertheless, statistically significant differences between the compositions of the three species were observed. Notably, tritordeum had significantly higher contents of protein, some minerals (magnesium and iron), total phenolics and methyl donors. Tritordeum also had higher levels of total amino acids (but not asparagine) and total sugars, including raffinose. By contrast, bread wheat and tritordeum had similar contents of the two major dietary fibre components in white flour, arabinoxylan and ß-glucan, with significantly lower contents in durum wheat.

Comparative Compositions of Grain of Bread Wheat, Emmer and Spelt Grown with Different Levels of Nitrogen Fertilisation.

Five cultivars of bread wheat and spelt and three of emmer were grown in replicate randomised field trials on two sites for two years with 100 and 200 kg nitrogen fertiliser per hectare, reflecting low input and intensive farming systems. Wholemeal flours were analysed for components that are suggested to contribute to a healthy diet. The ranges of all components overlapped between the three cereal types, reflecting the effects of both genotype and environment. Nevertheless, statistically significant differences in the contents of some components were observed. Notably, emmer and spelt had higher contents of protein, iron, zinc, magnesium, choline and glycine betaine, but also of asparagine (the precursor of acrylamide) and raffinose. By contrast, bread wheat had higher contents of the two major types of fibre, arabinoxylan (AX) and ß-glucan, than emmer and a higher AX content than spelt. Although such differences in composition may be suggested to result in effects on metabolic parameters and health when studied in isolation, the final effects will depend on the quantity consumed and the composition of the overall diet.

Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

OBJECTIVE: We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) tool in elderly emergency surgery (ES) patients. SUMMARY BACKGROUND DATA: The POTTER tool was derived using a novel Artificial Intelligence (AI)-methodology called optimal classification trees and validated for prediction of ES outcomes. POTTER outperforms all existent risk-prediction models and is available as an interactive smartphone application. Predicting outcomes in elderly patients has been historically challenging and POTTER has not yet been tested in this population. METHODS: All patients ≥65 years who underwent ES in the ACS-NSQIP 2017 database were included. POTTER's performance for 30-day mortality and 18 postoperative complications (eg, respiratory or renal failure) was assessed using c-statistic methodology, with planned sub-analyses for patients 65 to 74, 75 to 84, and 85+ years. RESULTS: A total of 29,366 patients were included, with mean age 77, 55.8% females, and 62% who underwent emergency general surgery. POTTER predicted mortality accurately in all patients over 65 (c-statistic 0.80). Its best performance was in patients 65 to 74 years (c-statistic 0.84), and its worst in patients ≥85 years (c-statistic 0.71). POTTER had the best discrimination for predicting septic shock (c-statistic 0.90), respiratory failure requiring mechanical ventilation for ≥48 hours (c-statistic 0.86), and acute renal failure (c-statistic 0.85). CONCLUSIONS: POTTER is a novel, interpretable, and highly accurate predictor of in-hospital mortality in elderly ES patients up to age 85 years. POTTER could prove useful for bedside counseling and for benchmarking of ES care.

Loss of starch synthase IIIa changes starch molecular structure and granule morphology in grains of hexaploid bread wheat.

Starch synthase III plays a key role in starch biosynthesis and is highly expressed in developing wheat grains. To understand the contribution of SSIII to starch and grain properties, we developed wheat ssIIIa mutants in the elite cultivar Cadenza using in silico TILLING in a mutagenized population. SSIIIa protein was undetectable by immunoblot analysis in triple ssIIIa mutants carrying mutations in each homoeologous copy of ssIIIa (A, B and D). Loss of SSIIIa in triple mutants led to significant changes in starch phenotype including smaller A-type granules and altered granule morphology. Starch chain-length distributions of double and triple mutants indicated greater levels of amylose than sibling controls (33.8% of starch in triple mutants, and 29.3% in double mutants vs. 25.5% in sibling controls) and fewer long amylopectin chains. Wholemeal flour of triple mutants had more resistant starch (6.0% vs. 2.9% in sibling controls) and greater levels of non-starch polysaccharides; the grains appeared shrunken and weighed ~ 11% less than the sibling control which was partially explained by loss in starch content. Interestingly, our study revealed gene dosage effects which could be useful for fine-tuning starch properties in wheat breeding applications while minimizing impact on grain weight and quality.

Benchmarking in Congenital Heart Surgery Using Machine Learning-Derived Optimal Classification Trees.

Background: We have previously shown that the machine learning methodology of optimal classification trees (OCTs) can accurately predict risk after congenital heart surgery (CHS). We have now applied this methodology to define benchmarking standards after CHS, permitting case-adjusted hospital-specific performance evaluation. Methods: The European Congenital Heart Surgeons Association Congenital Database data subset (31 792 patients) who had undergone any of the 10 "benchmark procedure group" primary procedures were analyzed. OCT models were built predicting hospital mortality (HM), and prolonged postoperative mechanical ventilatory support time (MVST) or length of hospital stay (LOS), thereby establishing case-adjusted benchmarking standards reflecting the overall performance of all participating hospitals, designated as the "virtual hospital." These models were then used to predict individual hospitals' expected outcomes (both aggregate and, importantly, for risk-matched patient cohorts) for their own specific cases and case-mix, based on OCT analysis of aggregate data from the "virtual hospital." Results: The raw average rates were HM = 4.4%, MVST = 15.3%, and LOS = 15.5%. Of 64 participating centers, in comparison with each hospital's specific case-adjusted benchmark, 17.0% statistically (under 90% confidence intervals) overperformed and 26.4% underperformed with respect to the predicted outcomes for their own specific cases and case-mix. For MVST and LOS, overperformers were 34.0% and 26.4%, and underperformers were 28.3% and 43.4%, respectively. OCT analyses reveal hospital-specific patient cohorts of either overperformance or underperformance. Conclusions: OCT benchmarking analysis can assess hospital-specific case-adjusted performance after CHS, both overall and patient cohort-specific, serving as a tool for hospital self-assessment and quality improvement.

Validation of the artificial intelligence-based trauma outcomes predictor (TOP) in patients 65 years and older.

BACKGROUND: The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient. METHODS: All patients aged 65 years and older in the American College of Surgeons-Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years. RESULTS: A total of 260,505 patients were included. Median age was 77 (71-84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively). CONCLUSION: The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.

Benchmarking cesarean delivery rates using machine learning-derived optimal classification trees.

OBJECTIVE: To establish a case-adjusted hospital-specific performance evaluation tool using machine learning methodology for cesarean delivery. DATA SOURCES: Secondary data were collected from patients between January 1, 2015 and February 28, 2018 using a hospital's "Electronic Data Warehouse" database from Illinois, USA. STUDY DESIGN: The machine learning methodology of optimal classification trees (OCTs) was used to predict cesarean delivery rate by physician group, thereby establishing the case-adjusted benchmarking standards in comparison to the overall hospital cesarean delivery rate. Outcomes of specific patient populations of each participating practice were predicted, as if each were treated in the overall hospital environment. The resulting OCTs estimate physician group expected cesarean delivery outcomes, both aggregate and in specific clinical situations. DATA COLLECTION/EXTRACTION METHODS: Twelve thousand eight hunderd and forty one singleton, vertex, term deliveries, cared for by practices with ≥50 births. PRINCIPAL FINDINGS: The overall rate of cesarean delivery was 18.6% (n = 2384), with a range of 13.3%-33.7% amongst 22 physician practices. An optimal decision tree was used to create a prediction model for the hospital overall, which defined 23 patient cohorts divided by 46 nodes. The model's performance for prediction of cesarean delivery is as follows: area under the curve 0.73, sensitivity 98.4%, specificity 16.1%, positive predictive value 83.7%, negative predictive value 70.6%. Comparisons with the overall hospital's specific-case adjusted benchmark groups revealed that several groups outperformed the overall hospital benchmark, and some practice groups underperformed in comparison to the overall hospital benchmark. CONCLUSIONS: OCT benchmarking can assess physician practice-specific case-adjusted performance, both overall and clinical situation-specific, and can serve as a valuable tool for hospital self-assessment and quality improvement.

Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach.

OBJECTIVE: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence techniques, we sought to develop nonlinear models for predicting outcomes in CHS. METHODS: We built machine learning (ML) models to predict mortality, postoperative mechanical ventilatory support time (MVST), and hospital length of stay (LOS) for patients who underwent CHS, based on data of more than 235,000 patients and 295,000 operations provided by the European Congenital Heart Surgeons Association Congenital Database. We used optimal classification trees (OCTs) methodology for its interpretability and accuracy, and compared to logistic regression and state-of-the-art ML methods (Random Forests, Gradient Boosting), reporting their area under the curve (AUC or c-statistic) for both training and testing data sets. RESULTS: Optimal classification trees achieve outstanding performance across all three models (mortality AUC = 0.86, prolonged MVST AUC = 0.85, prolonged LOS AUC = 0.82), while being intuitively interpretable. The most significant predictors of mortality are procedure, age, and weight, followed by days since previous admission and any general preoperative patient risk factors. CONCLUSIONS: The nonlinear ML-based models of OCTs are intuitively interpretable and provide superior predictive power. The associated risk calculator allows easy, accurate, and understandable estimation of individual patient risks, in the theoretical framework of the average performance of all centers represented in the database. This methodology has the potential to facilitate decision-making and resource optimization in CHS, enabling total quality management and precise benchmarking initiatives.

Trauma outcome predictor: An artificial intelligence interactive smartphone tool to predict outcomes in trauma patients.

BACKGROUND: Classic risk assessment tools often treat patients' risk factors as linear and additive. Clinical reality suggests that the presence of certain risk factors can alter the impact of other factors; in other words, risk modeling is not linear. We aimed to use artificial intelligence (AI) technology to design and validate a nonlinear risk calculator for trauma patients. METHODS: A novel, interpretable AI technology called Optimal Classification Trees (OCTs) was used in an 80:20 derivation/validation split of the 2010 to 2016 American College of Surgeons Trauma Quality Improvement Program database. Demographics, emergency department vital signs, comorbidities, and injury characteristics (e.g., severity, mechanism) of all blunt and penetrating trauma patients 18 years or older were used to develop, train then validate OCT algorithms to predict in-hospital mortality and complications (e.g., acute kidney injury, acute respiratory distress syndrome, deep vein thrombosis, pulmonary embolism, sepsis). A smartphone application was created as the algorithm's interactive and user-friendly interface. Performance was measured using the c-statistic methodology. RESULTS: A total of 934,053 patients were included (747,249 derivation; 186,804 validation). The median age was 51 years, 37% were women, 90.5% had blunt trauma, and the median Injury Severity Score was 11. Comprehensive OCT algorithms were developed for blunt and penetrating trauma, and the interactive smartphone application, Trauma Outcome Predictor (TOP) was created, where the answer to one question unfolds the subsequent one. Trauma Outcome Predictor accurately predicted mortality in penetrating injury (c-statistics: 0.95 derivation, 0.94 validation) and blunt injury (c-statistics: 0.89 derivation, 0.88 validation). The validation c-statistics for predicting complications ranged between 0.69 and 0.84. CONCLUSION: We suggest TOP as an AI-based, interpretable, accurate, and nonlinear risk calculator for predicting outcome in trauma patients. Trauma Outcome Predictor can prove useful for bedside counseling of critically injured trauma patients and their families, and for benchmarking the quality of trauma care.

Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.

BACKGROUND: The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of 30-day outcomes in patients undergoing emergency operations. In this study, we sought to assess the performance of POTTER in the emergency general surgery (EGS) population in particular. METHODS: All patients who underwent EGS in the 2017 American College of Surgeons NSQIP database were included. The performance of POTTER in predicting 30-day postoperative mortality, morbidity, and 18 specific complications was assessed using the c-statistic metric. As a subgroup analysis, the performance of POTTER in predicting the outcomes of patients undergoing emergency laparotomy was assessed. RESULTS: A total of 59,955 patients were included. Median age was 50 years and 51.3% were women. POTTER predicted mortality (c-statistic = 0.93) and morbidity (c-statistic = 0.83) extremely well. Among individual complications, POTTER had the highest performance in predicting septic shock (c-statistic = 0.93), respiratory failure requiring mechanical ventilation for 48 hours or longer (c-statistic = 0.92), and acute renal failure (c-statistic = 0.92). Among patients undergoing emergency laparotomy, the c-statistic performances of POTTER in predicting mortality and morbidity were 0.86 and 0.77, respectively. CONCLUSIONS: POTTER is an interpretable, accurate, and user-friendly predictor of 30-day outcomes in patients undergoing EGS. POTTER could prove useful for bedside counseling of patients and their families and for benchmarking of EGS care.

A topical naltrexone formulation for surgical wound healing: A case report.

BACKGROUND: In current guidelines, there is no specific therapeutic recommendation for promoting surgical wound healing. Proper postsurgical wound care regimen can speed up wound healing and prevent abnormal scarring. AIMS: The purpose of this case study was to evaluate the effectiveness of a compounded topical formulation containing naltrexone in managing surgical wound in a patient after Mohs micrographic surgery (MMS). PATIENTS/METHODS: A patient started to apply the topical naltrexone formulation two days after the MMS on his hand. Images of the wound and a Patient Scar Assessment Questionnaire (PSAQ) were used to evaluate the clinical outcomes. RESULTS: The wound completely healed, and the hand function was fully recovered following application of the formulation for 2 weeks. No abnormal scarring was formed, and the scar was only slightly noticeable after 2 months. CONCLUSION: This case study demonstrated the effectiveness of the topical naltrexone formulation in surgical wound management.

Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules.

Importance: Computed tomographic (CT) scanning is the standard for the rapid diagnosis of intracranial injury, but it is costly and exposes patients to ionizing radiation. The Pediatric Emergency Care Applied Research Network (PECARN) rules for identifying children with minor head trauma who are at very low risk of clinically important traumatic brain injury (ciTBI) are widely used to triage CT imaging. Objective: To examine whether optimal classification trees (OCTs), which are novel machine-learning classifiers, improve on PECARN rules' predictive accuracy. Design, Setting, and Participants: A secondary analysis of prospective, publicly available data on emergency department visits for head trauma used by the PECARN group to develop their tool was conducted to derive OCT-based prediction rules for ciTBI in a development cohort and compare their predictive performance vs the PECARN rules in a validation cohort among children who were younger than 2 years and 2 years or older. Data on 42 412 children with head trauma and without severely altered mental status who were examined between June 1, 2004, and September 30, 2006, were gathered from 25 emergency departments in North America participating in PECARN. Data analysis was conducted from September 15, 2016, to December 18, 2018. Main Outcomes and Measures: The outcome was ciTBI, with predictive performance measured by estimating the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for the OCT and the PECARN rules. The OCT and PECARN rules' performance was compared by estimating ratios for each measure. Results: Of the 42 412 children (15 996 [37.7%] girls) included in the analysis, 10 718 were younger than 2 years (25.3%; mean [SD] age, 11.6 [0.6] months) and 31 694 were 2 years or older (74.7%; age, 9.1 [4.9] years). Compared with PECARN rules, OCTs misclassified 0 vs 1 child with ciTBI in the younger and 10 vs 9 children with ciTBI in the older cohort, and correctly identified more children with very low risk of ciTBI in the younger (7605 vs 5701) and older (20 594 vs 18 134) cohorts. In the validation cohorts, compared with the PECARN rules, the OCTs had statistically significantly better specificity (in the younger cohort: 69.3%; 95% CI, 67.4%-71.2% vs 52.8%; 95% CI, 50.8%-54.9%; in the older cohort: 65.6%; 95% CI, 64.5%-66.8% vs 57.6%; 95% CI, 56.4%-58.8%), positive predictive value (odds ratios, 1.54; 95% CI, 1.36-1.74 and 1.23; 95% CI, 1.17-1.30, in younger and older children, respectively), and positive likelihood ratio (risk ratios, 1.54; 95% CI, 1.36-1.74 and 1.23; 95% CI, 1.17-1.30, in younger and older children, respectively). There were no statistically significant differences in the sensitivity, negative predictive value, and negative likelihood ratio between the 2 sets of rules. Conclusions and Relevance: If implemented, OCTs may help reduce the number of unnecessary CT scans, without missing more patients with ciTBI than the PECARN rules.

Pushing the bounds of second stage in term nulliparas with a predictive model.

BACKGROUND: Management of the second stage of labor continues to be a clinical challenge with unclear indications for abandoning attempts at spontaneous vaginal delivery. The conflict between diminishing chances of spontaneous vaginal delivery and increasing maternal and neonatal morbidity is difficult to quantify, leading to significant variation in management between providers, and variation in rates of operative vaginal delivery and cesarean birth. OBJECTIVE: The objective of the study was to develop an hourly prediction model for spontaneous vaginal delivery during the second stage of labor in nulliparous women with epidural anesthesia. STUDY DESIGN: This was a secondary analysis of the Consortium for Safe Labor database. The Consortium for Safe Labor collected data from 228,652 patients at 19 hospitals in the United State from 2002 through 2008. Primary outcome was delivery type per hour of second stage: spontaneous vaginal delivery vs operative delivery (operative vaginal and cesarean delivery). Inclusion criteria were term nulliparas with singleton gestations, vertex presentation, and attainment of 10 cm cervical dilation with epidural anesthesia. Exclusion criteria were intrauterine fetal demise, planned cesarean delivery, and major congenital anomalies. An optimal decision tree was used to create a prediction model. A test set was withheld from the data set to perform validation. A risk calculator tool was developed for prediction of spontaneous vaginal birth as well as adverse perinatal outcomes per hour. Adverse maternal outcomes were a composite of postpartum hemorrhage, transfusion, endometritis and third-/fourth-degree laceration. Adverse neonatal outcomes were a composite of neonatal intensive care unit admission, hypoxic ischemic encephalopathy, respiratory distress, seizures, apnea, asphyxia, and shoulder dystocia. RESULTS: The study population included 228,438 deliveries; 26,796 patients met inclusion and exclusion criteria. After removing cases with incomplete data, the study population consisted of 22,299 women, of which 16,593 women had a spontaneous vaginal delivery (74.4%). The number of deliveries at a given hospital per year, fetal position, cervical dilation on admission, chorioamnionitis, augmentation of labor, maternal age, and length of second stage were associated with the odds of spontaneous vaginal delivery. Using the predictors identified, a risk predictor calculator was created, taking into consideration the length of time in the second stage. A receiver-operator characteristic curve was developed to assess the calculator; area under the curve was 0.73. This calculator is available at https://www.pushprescriber.com/. CONCLUSION: Spontaneous vaginal delivery for women with term, cephalic, singleton gestations with epidural anesthesia was associated with several variables. This calculator tool helps facilitate provider decision making and patient counseling about the value of continuing the second stage of labor based on changing rates of success and risks of maternal and neonatal morbidity with time.

Surgical Risk Is Not Linear: Derivation and Validation of a Novel, User-friendly, and Machine-learning-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator.

INTRODUCTION: Most risk assessment tools assume that the impact of risk factors is linear and cumulative. Using novel machine-learning techniques, we sought to design an interactive, nonlinear risk calculator for Emergency Surgery (ES). METHODS: All ES patients in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) 2007 to 2013 database were included (derivation cohort). Optimal Classification Trees (OCT) were leveraged to train machine-learning algorithms to predict postoperative mortality, morbidity, and 18 specific complications (eg, sepsis, surgical site infection). Unlike classic heuristics (eg, logistic regression), OCT is adaptive and reboots itself with each variable, thus accounting for nonlinear interactions among variables. An application [Predictive OpTimal Trees in Emergency Surgery Risk (POTTER)] was then designed as the algorithms' interactive and user-friendly interface. POTTER performance was measured (c-statistic) using the 2014 ACS-NSQIP database (validation cohort) and compared with the American Society of Anesthesiologists (ASA), Emergency Surgery Score (ESS), and ACS-NSQIP calculators' performance. RESULTS: Based on 382,960 ES patients, comprehensive decision-making algorithms were derived, and POTTER was created where the provider's answer to a question interactively dictates the subsequent question. For any specific patient, the number of questions needed to predict mortality ranged from 4 to 11. The mortality c-statistic was 0.9162, higher than ASA (0.8743), ESS (0.8910), and ACS (0.8975). The morbidity c-statistics was similarly the highest (0.8414). CONCLUSION: POTTER is a highly accurate and user-friendly ES risk calculator with the potential to continuously improve accuracy with ongoing machine-learning. POTTER might prove useful as a tool for bedside preoperative counseling of ES patients and families.

Applied Informatics Decision Support Tool for Mortality Predictions in Patients With Cancer.

PURPOSE: With rapidly evolving treatment options in cancer, the complexity in the clinical decision-making process for oncologists represents a growing challenge magnified by oncologists' disposition of intuition-based assessment of treatment risks and overall mortality. Given the unmet need for accurate prognostication with meaningful clinical rationale, we developed a highly interpretable prediction tool to identify patients with high mortality risk before the start of treatment regimens. METHODS: We obtained electronic health record data between 2004 and 2014 from a large national cancer center and extracted 401 predictors, including demographics, diagnosis, gene mutations, treatment history, comorbidities, resource utilization, vital signs, and laboratory test results. We built an actionable tool using novel developments in modern machine learning to predict 60-, 90- and 180-day mortality from the start of an anticancer regimen. The model was validated in unseen data against benchmark models. RESULTS: We identified 23,983 patients who initiated 46,646 anticancer treatment lines, with a median survival of 514 days. Our proposed prediction models achieved significantly higher estimation quality in unseen data (area under the curve, 0.83 to 0.86) compared with benchmark models. We identified key predictors of mortality, such as change in weight and albumin levels. The results are presented in an interactive and interpretable tool ( www.oncomortality.com ). CONCLUSION: Our fully transparent prediction model was able to distinguish with high precision between highest- and lowest-risk patients. Given the rich data available in electronic health records and advances in machine learning methods, this tool can have significant implications for value-based shared decision making at the point of care and personalized goals-of-care management to catalyze practice reforms.

Minimally invasive lateral transpsoas interbody fusion using a stand-alone construct for the treatment of adjacent segment disease of the lumbar spine: review of the literature and report of three cases.

We describe 3 patients who presented with radiographic signs and clinical symptoms of adjacent segment disease several years after undergoing L4-S1 posterior pedicle screw fusion. All patients underwent successful lateral lumbar interbody fusion (LLIF) at 1-2 levels above their previous constructs, using stand-alone cages, with complete resolution of radiculopathy and a significant improvement in low-back pain. In addition to a thorough analysis of these cases, we review the pertinent literature regarding treatment options for adjacent segment disease and the applications of the lateral lumbar interbody technique.

Denosumab chemotherapy for recurrent giant-cell tumor of bone: a case report of neoadjuvant use enabling complete surgical resection.

Giant-cell tumor of the bone (GCTB) is a rare neoplasm that affects young adults. The tumor is generally benign but sometimes can be locally aggressive. There are no standardized approaches to the treatment of GCTB. Recently, the RANKL inhibitor denosumab has shown activity in this tumor type. We present the case of a young female who presented with locally advanced disease and was successfully managed with the neoadjuvant use of denosumab allowing for surgical resection of the tumor that was previously deemed unresectable. Following surgery, the patient is being managed with continued use of denosumab as 'maintenance,' and she continues to be free of disease. Our case highlights a novel approach for the management of locally advanced and aggressive giant cell tumor of the bone.