Automated CT Analysis of Body Composition as a Frailty Biomarker in Abdominal Surgery.

Importance: Prior studies demonstrated consistent associations of low skeletal muscle mass assessed on surgical planning scans with postoperative morbidity and mortality. The increasing availability of imaging artificial intelligence enables development of more comprehensive imaging biomarkers to objectively phenotype frailty in surgical patients. Objective: To evaluate the associations of body composition scores derived from multiple skeletal muscle and adipose tissue measurements from automated segmentation of computed tomography (CT) with the Hospital Frailty Risk Score (HFRS) and adverse outcomes after abdominal surgery. Design, Setting, and Participants: This retrospective cohort study used CT imaging and electronic health record data from a random sample of adults who underwent abdominal surgery at 20 medical centers within Kaiser Permanente Northern California from January 1, 2010, to December 31, 2020. Data were analyzed from April 1, 2022, to December 1, 2023. Exposure: Body composition derived from automated analysis of multislice abdominal CT scans. Main Outcomes and Measures: The primary outcome of the study was all-cause 30-day postdischarge readmission or postoperative mortality. The secondary outcome was 30-day postoperative morbidity among patients undergoing abdominal surgery who were sampled for reporting to the National Surgical Quality Improvement Program. Results: The study included 48 444 adults; mean [SD] age at surgery was 61 (17) years, and 51% were female. Using principal component analysis, 3 body composition scores were derived: body size, muscle quantity and quality, and distribution of adiposity. Higher muscle quantity and quality scores were inversely correlated (r = -0.42; 95% CI, -0.43 to -0.41) with the HFRS and associated with a reduced risk of 30-day readmission or mortality (quartile 4 vs quartile 1: relative risk, 0.61; 95% CI, 0.56-0.67) and 30-day postoperative morbidity (quartile 4 vs quartile 1: relative risk, 0.59; 95% CI, 0.52-0.67), independent of sex, age, comorbidities, body mass index, procedure characteristics, and the HFRS. In contrast to the muscle score, scores for body size and greater subcutaneous and intermuscular vs visceral adiposity had inconsistent associations with postsurgical outcomes and were attenuated and only associated with 30-day postoperative morbidity after adjustment for the HFRS. Conclusions and Relevance: In this study, higher muscle quantity and quality scores were correlated with frailty and associated with 30-day readmission and postoperative mortality and morbidity, whereas body size and adipose tissue distribution scores were not correlated with patient frailty and had inconsistent associations with surgical outcomes. The findings suggest that assessment of muscle quantity and quality on CT can provide an objective measure of patient frailty that would not otherwise be clinically apparent and that may complement existing risk stratification tools to identify patients at high risk of mortality, morbidity, and readmission.

Development and Validation of an Electronic Health Record-based Score for Triage to Perioperative Medicine.

OBJECTIVE: To develop an electronic health record-based risk model for perioperative medicine (POM) triage and compare this model with legacy triage practices that were based on clinician assessment. SUMMARY OF BACKGROUND DATA: POM clinicians seek to address the increasingly complex medical needs of patients prior to scheduled surgery. Identifying which patients might derive the most benefit from evaluation is challenging. METHODS: Elective surgical cases performed within a health system 2014- 2019 (N = 470,727) were used to develop a predictive score, called the Comorbidity Assessment for Surgical Triage (CAST) score, using split validation. CAST incorporates patient and surgical case characteristics to predict the risk of 30-day post-operative morbidity, defined as a composite of mortality and major NSQIP complications. Thresholds of CAST were then selected to define risk groups, which correspond with triage to POM appointments of different durations and modalities. The predictive discrimination CAST score was compared with the surgeon's assessments of patient complexity and the American Society of Anesthesiologists class. RESULTS: The CAST score demonstrated a significantly higher discrimination for predicting post-operative morbidity (area under the receiver operating characteristic curve 0.75) than the surgeon's complexity designation (0.63; P < 0.001) or the American Society of Anesthesiologists (0.65; P < 0.001) ( Fig. 1 ). Incorporating the complexity designation in the CAST model did not significantly alter the discrimination (0.75; P = 0.098). Compared with the complexity designation, classification based on CAST score groups resulted a net reclassification improvement index of 10.4% ( P < 0.001) ( Table 1 ). CONCLUSION: A parsimonious electronic health record-based predictive model demonstrates improved performance for identifying pre-surgical patients who are at risk than previously-used assessments for POM triage.

COVID-19 Vaccination and the Timing of Surgery Following COVID-19 Infection.

OBJECTIVE: To evaluate whether COVID-19 vaccination status or mode of anesthesia modified the temporal harms associated with surgery following coronavirus disease-2019 (COVID-19) infection. BACKGROUND: Surgery shortly after COVID-19 infection is associated with higher rates of complications, leading to recommendations to delay surgery following COVID-19 infection when possible. However, prior studies were based on populations with low or no prevalence of vaccination. METHODS: A retrospective cohort study of patients who underwent scheduled surgery in a health system from January 1, 2018 to February 28, 2022 (N=228,913) was performed. Patients were grouped by time of surgery relative to COVID-19 test positivity: 0 to 4 weeks after COVID-19 ("early post-COVID-19"), 4 to 8 weeks after COVID-19 ("mid post-COVID-19"), >8 weeks after COVID-19 ("late post-COVID-19"), surgery at least 30 days before subsequent COVID-19 ("pre-COVID-19"), and surgery with no prior or subsequent test positivity for COVID-19. RESULTS: Among patients who were not fully vaccinated at the time of COVID-19 infection, the adjusted rate of perioperative complications for the early post-COVID-19 group was significantly higher than for the pre-COVID-19 group (relative risk: 1.55; P =0.05). No significantly higher risk was identified between these groups for patients who were fully vaccinated (0.66; P =1.00), or for patients who were not fully vaccinated and underwent surgery without general anesthesia (0.52; P =0.83). CONCLUSIONS: Surgery shortly following COVID-19 infection was not associated with higher risks among fully vaccinated patients or among patients who underwent surgery without general anesthesia. Further research will be valuable to understand additional factors that modify perioperative risks associated with prior COVID-19 infection.

Decreasing trends in reintervention and readmission after endovascular aneurysm repair in a multiregional implant registry.

OBJECTIVE: As endovascular aortic aneurysm repair (EVAR) matures into its third decade, measures such as long-term reintervention and readmission have become a focus of quality improvement efforts. Within a large United States integrated health care system, we describe time trends in the rates of long-term reinterventions utilization measures. METHODS: Data from a United States multiregional EVAR registry was used to perform a descriptive study of 3891 adults who underwent conventional infrarenal EVAR for infrarenal abdominal aortic aneurysm between 2010 and 2019. Three-year follow-up was 96.7%. Outcomes included 1-, 3-, and 5-year graft revision (defined as a procedure involving placement of a new endograft component), secondary interventions (defined as a procedure necessary for maintenance of EVAR integrity [eg, coil embolization and balloon angioplasty/stenting]), conversion to open, interventions for type II endoleaks alone, and 90-day readmission. Crude cause-specific reintervention probabilities were calculated by operative year using the Aalen-Johansen estimator, with death as a competing risk and December 31, 2020 as the study end date. RESULTS: Excluding interventions for type II endoleak alone, 1-year secondary intervention incidence decreased from 5.9% for EVARs in 2010 to 2.0% in 2019 (P < .001) and 3-year incidence decreased from 7.2% to 3.6% from 2010 to 2017 (P = .03). The 3-year incidences of graft revision (mean incidence, 3.4%) and conversion to open remained fairly stable (mean incidence, 0.6%) over time. The 3-year incidence of interventions for type II endoleak alone also decreased from 3.4% in 2010 to 0.7% in 2017 (P = .01). Ninety-day readmission rates decreased from 19.3% for index EVAR in 2010 to 9.2% in 2019 (P = .03). CONCLUSIONS: Comprehensive data from a multiregional health care system demonstrates decreasing long-term secondary intervention and readmission rates over time in patients undergoing EVAR. These trends are not explained by evolving management of type II endoleaks and suggest improving graft durability, patient selection, or surgical technique. Further study is needed to define implant and anatomic predictors of different types of long-term reintervention.

Predicting early discharge and readmission following pancreaticoduodenectomy [S079].

INTRODUCTION: Implementing enhanced recovery after surgery (ERAS) protocols for major abdominal surgery has been shown to decrease length of stay (LOS) and postoperative complications, including mortality and readmission. Little is known to guide which patients undergoing pancreaticoduodenectomy (PD) should be eligible for ERAS protocols. METHODS AND PROCEDURES: A retrospective chart review of all PD performed from 2010 to 2018 within an integrated healthcare system was conducted. A predictive score that ranges from 0 to 4 was developed, with one point assigned to each of the following: obesity (BMI > 30), operating time > 400 min, estimated blood loss (EBL) > 400 mL, low- or high-risk pancreatic remnant (based on the presence of soft gland or small duct). Chi-squared tests and ANOVA were used to assess the relationship between this score and LOS, discharge before postoperative day 7, readmission, mortality, delayed gastric emptying (DGE), and pancreatic leak/fistula. RESULTS: 291 patients were identified. Mean length of stay was 8.5 days in those patients who scored 0 compared to 16.2 days for those who scored 4 (p = 0.001). 30% of patients who scored 0 were discharged before postoperative day 7 compared to 0% of those who scored 4 (p = 0.019). Readmission rates for patients who scored 0 and 4 were 12% and 33%, respectively (p = 0.017). Similarly, postoperative pancreatic fistula occurred in 2% versus 25% in these groups (p = 0.007). CONCLUSION: A simple scoring system using BMI, operating time, EBL, and pancreatic remnant quality can help risk-stratify postoperative PD patients. Those with lower scores could potentially be managed via an ERAS protocol. Patients with higher scores required longer hospitalizations, and adjunctive therapy such as medication and surgical technique to decrease risk of delayed gastric emptying and pancreatic fistula could be considered.

Comparison of Electronic Frailty Metrics for Prediction of Adverse Outcomes of Abdominal Surgery.

Importance: Electronic frailty metrics have been developed for automated frailty assessment and include the Hospital Frailty Risk Score (HFRS), the Electronic Frailty Index (eFI), the 5-Factor Modified Frailty Index (mFI-5), and the Risk Analysis Index (RAI). Despite substantial differences in their construction, these 4 electronic frailty metrics have not been rigorously compared within a surgical population. Objective: To characterize the associations between 4 electronic frailty metrics and to measure their predictive value for adverse surgical outcomes. Design, Setting, and Participants: This retrospective cohort study used electronic health record data from patients who underwent abdominal surgery from January 1, 2010, to December 31, 2020, at 20 medical centers within Kaiser Permanente Northern California (KPNC). Participants included adults older than 50 years who underwent abdominal surgical procedures at KPNC from 2010 to 2020 that were sampled for reporting to the National Surgical Quality Improvement Program. Main Outcomes and Measures: Pearson correlation coefficients between electronic frailty metrics and area under the receiver operating characteristic curve (AUROC) of univariate models and multivariate preoperative risk models for 30-day mortality, readmission, and morbidity, which was defined as a composite of mortality and major postoperative complications. Results: Within the cohort of 37 186 patients, mean (SD) age, 67.9 (female, 19 127 [51.4%]), correlations between pairs of metrics ranged from 0.19 (95% CI, 0.18- 0.20) for mFI-5 and RAI 0.69 (95% CI, 0.68-0.70). Only 1085 of 37 186 (2.9%) were classified as frail based on all 4 metrics. In univariate models for morbidity, HFRS demonstrated higher predictive discrimination (AUROC, 0.71; 95% CI, 0.70-0.72) than eFI (AUROC, 0.64; 95% CI, 0.63-0.65), mFI-5 (AUROC, 0.58; 95% CI, 0.57-0.59), and RAI (AUROC, 0.57; 95% CI, 0.57-0.58). The predictive discrimination of multivariate models with age, sex, comorbidity burden, and procedure characteristics for all 3 adverse surgical outcomes improved by including HFRS into the models. Conclusions and Relevance: In this cohort study, the 4 electronic frailty metrics demonstrated heterogeneous correlation and classified distinct groups of surgical patients as frail. However, HFRS demonstrated the highest predictive value for adverse surgical outcomes.

Resection of a giant mycotic left subclavian pseudoaneurysm.

Subclavian artery pseudoaneurysm due to intravenous drug use is a rare pathologic entity. A 6.6-cm left subclavian artery pseudoaneurysm immediately distal to the origin of the vertebral artery was discovered in a 39-year-old man with neck swelling, bacteremia, and a history of intravenous drug use. The pseudoaneurysm was resected through a median sternotomy and left supraclavicular incision, without reconstruction. This operative approach was opted for given the presence of infection and the ongoing intravenous drug use.

Mortality, disease progression, and disease burden of acute kidney injury in alcohol use disorder subpopulation.

BACKGROUND: The aim of the study was to quantify the relationship between acute kidney injury (AKI) and alcohol use disorder (AUD). METHODS: We used a large academic medical center and the MIMIC-III databases to quantify AKI disease and mortality burden as well as AKI disease progression in the AUD and non-AUD subpopulations. We used the MIMIC-III dataset to compare two different methods of encoding AKI: ICD-9 codes, and the Kidney Disease: Improving Global Outcomes scheme (KDIGO) definition. In addition to the AUD subpopulation, we also present analyses for the hepatorenal syndrome (HRS) and alcohol-related cirrhosis subpopulations identified via ICD-9/ICD-10 coding. RESULTS: In both the ICD-9 and KDIGO encodings of AKI, the AUD subpopulation had a higher incidence of AKI (ICD-9: 43.3% vs. 37.92% AKI in the non-AUD subpopulations; KDIGO: 48.65% vs. 40.53%) in the MIMIC-III dataset. In the academic dataset, the AUD subpopulation also had a higher incidence of AKI than the non-AUD subpopulation (ICD-9/ICD-10: 12.76% vs. 10.71%). The mortality rate of the subpopulation with both AKI and AUD, HRS, or alcohol-related cirrhosis was consistently higher than that of the subpopulation with only AKI in both datasets, including after adjusting for disease severity using two methods of severity estimation in the MIMIC-III dataset. Disease progression rates were similar for AUD and non-AUD subpopulations. CONCLUSIONS: Our work shows that the AUD patient subpopulation had a higher number of AKI patients than the non-AUD subpopulation, and that patients with both AKI and AUD, HRS, or alcohol-related cirrhosis had higher rates of mortality than the non-AUD subpopulation with AKI.

Pancreatic Neuroendocrine Tumor: Rationale for Centralization in an Integrated Health Care System.

OBJECTIVES: Given the complex surgical management and infrequency of pancreatic neuroendocrine tumor, we hypothesized that treatment at a center of excellence improves survival. METHODS: Retrospective review identified 354 patients with pancreatic neuroendocrine tumor treated between 2010 and 2018. Four hepatopancreatobiliary centers of excellence were created from 21 hospitals throughout Northern California. Univariate and multivariate analyses were performed. The χ2 test of clinicopathologic factors determined which were predictive for overall survival (OS). RESULTS: Localized disease was seen in 51% of patients, and metastatic disease was seen in 32% of patients with mean OS of 93 and 37 months, respectively (P < 0.001). On multivariate survival analysis, stage, tumor location, and surgical resection were significant for OS (P < 0.001). All stage OS for patients treated at designated centers was 80 and 60 months for noncenters (P < 0.001). Surgery was more common across stages at the centers of excellence versus noncenters at 70% and 40%, respectively (P < 0.001). CONCLUSIONS: Pancreatic neuroendocrine tumors are indolent but have malignant potential at any size with management often requiring complex surgeries. We showed survival was improved for patients treated at a center of excellence, where surgery was more frequently utilized.

Centralization of pancreatic cancer treatment within an integrated healthcare system improves overall survival.

BACKGROUND: Higher-volume centers for pancreatic cancer surgeries have been shown to have improved outcomes such as length of stay. We examined how centralization of pancreatic cancer care within a regional integrated healthcare system improves overall survival. METHODS: We conducted a retrospective study of 1621 patients treated for pancreatic cancer from February 2010 to December 2018. Care was consolidated into 4 Centers of Excellence (COE) in surgery, medical oncology, and other specialties. Descriptive statistics, bivariate analysis, Chi-square tests, and Kaplan-Meier analysis were performed. RESULTS: Neoadjuvant chemotherapy use rose from 10% to 31% (p < .001). The median overall survival (OS) improved by 3 months after centralization (p < .001), but this did not reach significance on multivariate analysis. CONCLUSIONS: Our results suggest that in a large integrated healthcare system, centralization improves overall survival and neoadjuvant therapy utilization for pancreatic cancer patients.

Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction.

INTRODUCTION: Acute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data. METHODS: A CNN prediction system was developed to use EHR data gathered during patients' stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset. RESULTS: On a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset. CONCLUSION: A CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements.

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals.

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. METHODS: Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. RESULTS: 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. CONCLUSIONS: The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS).

PURPOSE: Acute respiratory distress syndrome (ARDS) is a serious respiratory condition with high mortality and associated morbidity. The objective of this study is to develop and evaluate a novel application of gradient boosted tree models trained on patient health record data for the early prediction of ARDS. MATERIALS AND METHODS: 9919 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) data base. XGBoost gradient boosted tree models for early ARDS prediction were created using routinely collected clinical variables and numerical representations of radiology reports as inputs. XGBoost models were iteratively trained and validated using 10-fold cross validation. RESULTS: On a hold-out test set, algorithm classifiers attained area under the receiver operating characteristic curve (AUROC) values of 0.905 when tested for the detection of ARDS at onset and 0.827, 0.810, and 0.790 for the prediction of ARDS at 12-, 24-, and 48-h windows prior to onset, respectively. CONCLUSION: Supervised machine learning predictions may help predict patients with ARDS up to 48 h prior to onset.

Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals.

BACKGROUND: Severe sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat. OBJECTIVE: The purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission. DESIGN: Prospective clinical outcomes evaluation. SETTING: Evaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres. PARTICIPANTS: Analyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay ('sepsis-related' patients). INTERVENTIONS: Machine learning algorithm for severe sepsis prediction. OUTCOME MEASURES: In-hospital mortality, length of stay and 30-day readmission rates. RESULTS: Hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis. CONCLUSIONS: Reductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings. TRIAL REGISTRATION NUMBER: NCT03960203.

Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction.

In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sepsis-Related Organ Failure Assessment (qSOFA), and Modified Early Warning Score (MEWS) using area under the receiver operating characteristic curve (AUROC). For training and testing GBT on data from the same institution, the average AUROCs were 0.96, 0.95, and 0.94 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, GBT AUROCs achieved up to 0.98, 0.96, and 0.96, for 12-, 24-, and 48-hour predictions, respectively. Average AUROC for 48-hour predictions for LR, SVM, MEWS, and qSOFA were 0.85, 0.79, 0.86 and 0.82, respectively. GBT predictions may help identify patients who would benefit from increased clinical care.

Pediatric Severe Sepsis Prediction Using Machine Learning.

Background: Early detection of pediatric severe sepsis is necessary in order to optimize effective treatment, and new methods are needed to facilitate this early detection. Objective: Can a machine-learning based prediction algorithm using electronic healthcare record (EHR) data predict severe sepsis onset in pediatric populations? Methods: EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters for patients between 2-17 years of age, drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016. Results: Pediatric patients (n = 9,486) were identified and 101 (1.06%) were labeled with severe sepsis following the pediatric severe sepsis definition of Goldstein et al. (1). In 4-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.916 for discrimination between severe sepsis and control pediatric patients at the time of onset and AUROC of 0.718 at 4 h before onset. The prediction algorithm significantly outperformed the Pediatric Logistic Organ Dysfunction score (PELOD-2) (p < 0.05) and pediatric Systemic Inflammatory Response Syndrome (SIRS) (p < 0.05) in the prediction of severe sepsis 4 h before onset using cross-validation and pairwise t-tests. Conclusion: This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction through automated monitoring of EHR data for pediatric inpatients, which may enable earlier sepsis recognition and treatment initiation.

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using electronic health records. This study validates a gradient boosted ensemble machine learning tool for sepsis detection and prediction, and compares its performance to existing methods. MATERIALS AND METHODS: Retrospective data was drawn from databases at the University of California, San Francisco (UCSF) Medical Center and the Beth Israel Deaconess Medical Center (BIDMC). Adult patient encounters without sepsis on admission, and with at least one recording of each of six vital signs (SpO2, heart rate, respiratory rate, temperature, systolic and diastolic blood pressure) were included. We compared the performance of the machine learning algorithm (MLA) to that of commonly used scoring systems. Area under the receiver operating characteristic (AUROC) curve was our primary measure of accuracy. MLA performance was measured at sepsis onset, and at 24 and 48 h prior to sepsis onset. RESULTS: The MLA achieved an AUROC of 0.88, 0.84, and 0.83 for sepsis onset and 24 and 48 h prior to onset, respectively. These values were superior to those of SIRS (0.66), MEWS (0.61), SOFA (0.72), and qSOFA (0.60) at time of onset. When trained on UCSF data and tested on BIDMC data, sepsis onset AUROC was 0.89. DISCUSSION AND CONCLUSION: The MLA predicts sepsis up to 48 h in advance and identifies sepsis onset more accurately than commonly used tools, maintaining high performance for sepsis detection when trained and tested on separate datasets.

Many Neurology Readmissions Are Nonpreventable.

INTRODUCTION: Reducing unplanned hospital readmissions has become a national focus due to the Centers for Medicare and Medicaid Services' (CMS) penalties for hospitals with high rates. A first step in reducing unplanned readmission is to understand which patients are at high risk for readmission, which readmissions are planned, and how well planned readmissions are currently captured in comparison to patient-level chart review. METHODS: We examined all 5455 inpatient neurology admissions over a 2-year period to University of California San Francisco Medical Center and Johns Hopkins Hospital via chart review. We collected information such as patient age, procedure codes, diagnosis codes, all-payer diagnosis-related group, observed length of stay (oLOS), and expected length of stay. We performed multivariate logistic modeling to determine predictors of readmission. Discharge summaries were reviewed for evidence that a subsequent readmission was planned. RESULTS: A total of 353 (6.5%) discharges were readmitted within 30 days. Fifty-five (15.6%) of the 353 readmissions were planned, most often for a neurosurgical procedure (41.8%) or immunotherapy (23.6%). Only 8 of these readmissions would have been classified as planned using current CMS methodology. Patient age (odds ratio [OR] = 1.01 for each 10-year increase, P < .001) and estimated length of stay (OR = 1.04, P = .002) were associated with a greater likelihood of readmission, whereas index admission oLOS was not. CONCLUSIONS: Many neurologic readmissions are planned; however, these are often classified by current CMS methodology as unplanned and penalized accordingly. Modifications of the CMS lists for potentially planned neurological and neurosurgical procedures and for acute discharge neurologic diagnoses should be considered.

What Influences How Patients Rate Their Hospital After Total Hip Arthroplasty?

BACKGROUND: Centers for Medicare and Medicaid Services are now using results from patient satisfaction surveys, such as Press Ganey, for reimbursement. It is unknown what factors influence scores on satisfaction surveys in post-total hip arthroplasty (THA) patients. The purpose of this study was to evaluate what influences these scores in THA patients. Specifically, we aimed to evaluate: (1) how pain control affects the patients' perception of their orthopedist, nursing staff, and overall hospital satisfaction; (2) the individual impact of these factors on overall hospital satisfaction after THA; and (3) the impact of lengths of stay, age, body mass index (BMI), and American Society of Anesthesiology (ASA) scores on overall satisfaction. METHODS: To assess whether pain management influences patients' perception of the orthopedist, a correlation analysis was performed between pain control and perception of their doctor. Similar analyses were performed to determine the relationship between pain management and patients' perception of their treating nurse, as well as overall satisfaction. A multiple regression analysis was performed to determine which of the aforementioned factors have the greatest impact on overall satisfaction. To determine the impact of length of stay on overall hospital satisfaction, a correlation analysis was performed between these 2 variables. Similar analyses were performed for age, BMI, and ASA scores. RESULTS: Patients' perception of pain control was significantly positively correlated with the perception of their orthopedist, nurse, and overall hospital satisfaction. Multiple regression analysis demonstrated that patients' perception of nurses and orthopedists yielded a significantly positive influence on overall hospital satisfaction. A significant negative correlation existed between lengths of stay and hospital satisfaction. There were no significant correlations between age, BMI, and ASA scores and overall hospital rating. CONCLUSION: Post-THA patients associate pain management with hospital satisfaction, as well as their perception of their treating nurses and orthopedists. Overall satisfaction was most impacted by patients' perception of their nurse, followed by their orthopedist. In addition, there was an association between shorter length of stay and higher overall satisfaction. These results are of paramount importance because by recognizing factors that affect scores on satisfaction surveys, orthopedic surgeons can direct efforts to improve post-THA satisfaction and optimize reimbursements.