Obesity and meningioma: a US population-based study paired with analysis of a multi-institutional cohort.

OBJECTIVE: Whether obesity is associated with meningioma and the impact of obesity by gender has been debated. The primary objective of this study was to investigate differences in BMI between male and female patients undergoing craniotomy for meningioma and compare those with patients undergoing craniotomy for other intracranial tumors. The secondary objective was to compare meningioma location and progression-free survival (PFS) between obese and nonobese patients in a multi-institutional cohort. METHODS: National data were obtained from the National Surgical Quality Improvement Program (NSQIP) database. Male and female patients were analyzed separately. Patients undergoing craniotomies for meningioma were compared with patients of the same sex undergoing craniotomies for other intracranial tumors. Institutional data from two academic centers were collected for all male and an equivalent number of female meningioma patients undergoing meningioma resection. Multivariate regression controlling for age was used to determine differences in meningioma location. Kaplan-Meier curves and log-rank tests were computed to investigate differences in PFS. RESULTS: From NSQIP, 4163 male meningioma patients were compared with 24,266 controls, and 9372 female meningioma patients were compared with 21,538 controls. Male and female patients undergoing meningioma resection were more likely to be overweight or obese compared with patients undergoing craniotomy for other tumors, with the odds ratio increasing with increasing weight class (all p < 0.0001). In the multi-institutional cohort, meningiomas were more common along the skull base in male patients (p = 0.0123), but not in female patients (p = 0.1246). There was no difference in PFS between obese and nonobese male (p = 0.4104) or female (p = 0.5504) patients. Obesity was associated with increased risk of pulmonary embolism in both male and female patients undergoing meningioma resection (p = 0.0043). CONCLUSIONS: Male and female patients undergoing meningioma resection are more likely to be obese than patients undergoing craniotomy for other intracranial tumors. Obese males are more likely to have meningiomas in the skull base compared with other locations, but this association was not found in females. There was no significant difference in PFS among obese patients. The mechanism by which obesity increases meningioma incidence remains to be determined.

Early Prediction of Poststroke Rehabilitation Outcomes Using Wearable Sensors.

OBJECTIVE: Inpatient rehabilitation represents a critical setting for stroke treatment, providing intensive, targeted therapy and task-specific practice to minimize a patient's functional deficits and facilitate their reintegration into the community. However, impairment and recovery vary greatly after stroke, making it difficult to predict a patient's future outcomes or response to treatment. In this study, the authors examined the value of early-stage wearable sensor data to predict 3 functional outcomes (ambulation, independence, and risk of falling) at rehabilitation discharge. METHODS: Fifty-five individuals undergoing inpatient stroke rehabilitation participated in this study. Supervised machine learning classifiers were retrospectively trained to predict discharge outcomes using data collected at hospital admission, including patient information, functional assessment scores, and inertial sensor data from the lower limbs during gait and/or balance tasks. Model performance was compared across different data combinations and was benchmarked against a traditional model trained without sensor data. RESULTS: For patients who were ambulatory at admission, sensor data improved the predictions of ambulation and risk of falling (with weighted F1 scores increasing by 19.6% and 23.4%, respectively) and maintained similar performance for predictions of independence, compared to a benchmark model without sensor data. The best-performing sensor-based models predicted discharge ambulation (community vs household), independence (high vs low), and risk of falling (normal vs high) with accuracies of 84.4%, 68.8%, and 65.9%, respectively. Most misclassifications occurred with admission or discharge scores near the classification boundary. For patients who were nonambulatory at admission, sensor data recorded during simple balance tasks did not offer predictive value over the benchmark models. CONCLUSION: These findings support the continued investigation of wearable sensors as an accessible, easy-to-use tool to predict the functional recovery after stroke. IMPACT: Accurate, early prediction of poststroke rehabilitation outcomes from wearable sensors would improve our ability to deliver personalized, effective care and discharge planning in the inpatient setting and beyond.

Can Preoperative Laboratory Values Predict Short-term Postoperative Complications and Health Utilization in Patients Undergoing Cranioplasty?

BACKGROUND: Low hematocrit, low albumin, and high creatinine levels have been associated with postoperative morbidity. The present study intends to analyze the effects of preoperative laboratories on medical complications and postoperative health resource utilization in patients undergoing cranioplasty. METHODS: Using data from the American College of Surgeons National Surgical Quality Improvement Program, demographic, clinical, and intraoperative characteristics were collected for each patient who had recorded albumin, hematocrit, or creatinine laboratory values within 90 days of the index cranioplasty. Outcomes analyzed were ≥1 medical complication, ≥1 wound complication, unplanned reoperation, 30-day readmission, and extended hospital stay (>30 d). Outcomes significant on bivariate analyses were evaluated using multivariate logistic regression. Significant outcomes on multivariate analyses were analyzed using receiver operating characteristic curves and Mann-Whitney U tests. RESULTS: The 3 separate cohorts included 1349 patients with albumin, 2201 patients with hematocrit, and 2182 patients with creatinine levels. Upon multivariate analysis, increases in albumin and hematocrit were independently associated with decreased odds of medical complications and extended length of stay. Increases in creatinine were independently associated with increased odds of medical complications. Discriminative cutoff values were identified for albumin and hematocrit. CONCLUSIONS: Preoperative laboratory values were independent predictors of medical complications and health utilization following cranioplasty in this study. Surgical teams can use these findings to optimize preoperative risk stratification.

Repeat Surgery for Vestibular Schwannomas: An Institutional Case Series.

Introduction Vestibular schwannomas (VSs) are treated with microsurgery and/or radiosurgery. Repeat resection is rare, and few studies have reported postoperative outcomes. The objective of this study was to describe clinical characteristics and outcomes in patients undergoing repeat surgery for VS. Methods All adult (≥ 18 years) patients undergoing VS resection between 2003 and 2022 at our institution were retrospectively reviewed to identify patients who underwent repeat surgery of an ipsilateral VS following prior gross-total (GTR) or subtotal resection. Patient, radiographic, and clinical characteristics were reviewed. Primary outcomes were postoperative tumor volume, extent of resection, postoperative cranial nerve deficits, and time to further tumor progression. Results Of 102 patients undergoing VS resection, 6 (5.9%) had undergone repeat surgery. Median (range) follow-up was 20 (5-117) months. Three patients were female. Median age was 56 (36-60) years. Median pre- and postoperative tumor volumes were 8.2 (1.8-28.2) cm 3 and 0.4 (0-3.8) cm 3 . GTR was achieved in two patients. Four patients had higher House-Brackmann scores at last follow-up, but none had tumor progression. Conclusion In this small cohort of patients, repeat resection of recurrent or progressive VS can effectively reduce tumor volume with acceptable perioperative outcomes.

Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy.

When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.

Racial Disparities Following Reconstructive Flap Procedures.

BACKGROUND: Prior reports have highlighted disparities in healthcare access, environmental conditions, and food insecurity between Black and White populations in the United States. However, limited studies have explored racial disparities in postoperative complications, particularly reconstructive flap surgeries. METHODS: Cases of flap reconstruction based on named vascular pedicles were identified in the American College of Surgeons National Surgical Quality Improvement Program database and grouped into 3 time periods: 2005 to 2009, 2010 to 2014, and 2015 to 2019. Logistic regression was used to compare rates of postoperative complications between White and Black patients within each time period while controlling for comorbidities. Data for flap failure was only available from 2005 to 2010. RESULTS: A total of 56,116 patients were included in the study, and 6293 (11.2%) were Black. Black patients were significantly younger than White patients and had increased rates of hypertension, smoking, and diabetes across all years ( P <0.01). Black patients had significantly higher rates of sepsis compared to White patients in all time periods. From 2005 to 2009, Black patients had a significantly higher incidence of flap failure (aOR=2.58, P <0.01), return to the operating room (aOR=1.53, P =0.01), and having any complication (aOR=1.48, P <0.01). From 2010 to 2019, White patients had a higher incidence of superficial surgical site infection. CONCLUSIONS: Surgical complication rates following flap reconstruction based on a named vascular pedicle were higher for Black patients. Limited data on this topic currently exists, indicating that additional research on the drivers of racial disparities is warranted to improve plastic surgery outcomes in Black patients.

Outcomes following surgical resection of trigeminal schwannomas: a systematic review and meta-analysis.

Although typically benign, trigeminal schwannomas (TS) may require surgical resection when large or symptomatic and can cause significant morbidity. This study aims to summarize the literature and synthesize outcomes following surgical resection of TS. A systematic review was performed according to PRISMA guidelines. Data extracted included patient and tumor characteristics, surgical approaches, and postoperative outcomes. Odds ratios (OR) with corresponding 95% confidence intervals (CI) were used for outcome analysis. The initial search yielded 1838 results, of which 26 studies with 974 patients undergoing surgical resection of TS were included. The mean age was 42.9 years and 58.0% were female. The mean tumor diameter was 4.7 cm, with Samii type A, B, C, and D tumors corresponding to 33.4%, 15.8%, 37.2%, and 13.6%, respectively. Over a mean symptom duration of 29 months, patients presented with trigeminal hypesthesia (58.7%), headache (32.8%), trigeminal motor weakness (22.8%), facial pain (21.3%), ataxia (19.4%), diplopia (18.7%), and visual impairment (12.0%). Surgical approaches included supratentorial (61.4%), infratentorial (15.0%), endoscopic (8.6%), combined/staged (5.3%), and anterior (5.7%) or posterior (4.0%) petrosectomy. Postoperative improvement of facial pain (83.9%) was significantly greater than trigeminal motor weakness (33.0%) or hypesthesia (29.4%). The extent of resection (EOR) was reported as gross total (GTR), near total, and subtotal in 77.7%, 7.7%, and 14.6% of cases, respectively. Over a mean follow-up time of 62.6 months, recurrence/progression was noted in 7.4% of patients at a mean time to recurrence of 44.9 months. Patients with GTR had statistically significantly lower odds of recurrence/progression (OR: 0.07; 95% CI: 0.04-0.15) compared to patients with non-GTR. This systematic review and meta-analysis report patient outcomes following surgical resection of TS. EOR was found to be an important predictor of the risk of recurrence. Facial pain was more likely to improve postoperatively than facial hypesthesia. This work reports baseline rates of post-operative complications across studies, establishing benchmarks for neurosurgeons innovating and working to improve surgical outcomes for TS patients.

Validating the VS-5 Score for Predicting Outcomes After Vestibular Schwannoma Resection in an Institutional Cohort.

BACKGROUND: The VS-5 index was recently proposed to predict complications, nonroutine discharge, length of stay (LOS), and cost after vestibular schwannoma (VS) resection. The VS-5 ranges from 0-17.86, and a score ≥2 was proposed as being predictive of postoperative adverse events. We sought to determine whether the VS-5 is predictive of nonroutine discharge and length of stay in an institutional cohort. METHODS: This is a retrospective study of 100 patients undergoing VS resection. For each patient, a VS-5 score was calculated. Bivariate analyses were conducted to determine differences in postoperative outcomes between high- and low-risk subgroups. Area under the receiver operating characteristic curve sensitivity/specificity analysis using Youden's Index was conducted to evaluate the optimal cutoff. RESULTS: Fifty-one (51%) patients were classified as high risk (VS-5 ≥ 2). Patients with VS-5 ≥ 2 had higher frequency of nonroutine discharge (22% vs. 4%, P = 0.0150) and no significant difference in postoperative LOS. The area under the receiver operating characteristic curve for predicting nonroutine discharge was 0.78 ± 0.15 (P < 0.0001). The optimal cutoff for nonroutine discharge was ≥6, higher than the published cutoff of ≥ 2. The new cutoff was predictive of nonroutine discharge (47% vs. 6%, P = 0 < 0.0001) and LOS (6 [3-11] days vs. 3 [1-28] days, P = 0.0001). CONCLUSIONS: The VS-5 frailty index predicted nonroutine discharge but not LOS. Youden's index indicates that a cutoff of 6, not 2, is optimal for predicting nonroutine discharge and LOS.

Changes in the Demographic Distribution of Chicago Gun-Homicide Decedents From 2015-2021: Violent Death Surveillance Cross-sectional Study.

BACKGROUND: Homicide is one of the 5 leading causes of death in the United States for persons aged 1 to 44 years. In 2019, 75% of US homicides were by gun. Chicago has a gun-homicide rate 4 times the national average, and 90% of all homicides are by gun. The public health approach to violence prevention calls for a 4-step process, beginning with defining and monitoring the problem. Insight into the characteristics of gun-homicide decedents can help frame next steps, including identifying risk and protective factors, developing prevention and intervention strategies, and scaling effective responses. Although much is known about gun homicide because it is a long-standing, entrenched public health problem, it is useful to monitor trends to update ongoing prevention efforts. OBJECTIVE: This study aimed to use public health surveillance data and methods to describe changes in the race/ethnicity, sex, and age of Chicago gun-homicide decedents from 2015-2021, in the context of year-to-year variation and an overall increase in the city's gun-homicide rate. METHODS: We calculated the distribution of gun-related homicide deaths by 6 race/ethnicity and sex groups (non-Hispanic Black female, non-Hispanic White female, Hispanic female, non-Hispanic Black male, non-Hispanic White male, and Hispanic male), age in years, and age by age group. We used counts, percentages, and rates per 100,000 persons to describe the distribution of deaths among these demographic groups. Comparisons of means and column proportions with tests of significance set at P≤.05 were used to describe changes in the distribution of gun-homicide decedents over time by race-ethnicity-sex and age groups. The comparison of mean age by race-ethnicity-sex group is done using 1-way ANOVA with significance set at P≤.05. RESULTS: The distribution of gun-homicide decedents in Chicago by race/ethnicity and sex groups had been relatively stable from 2015 to 2021 with 2 notable exceptions: a more than doubling of the proportion of gun-homicide decedents who were non-Hispanic Black female (3.6% in 2015 to 8.2% in 2021) and an increase of 3.27 years in the mean age of gun-homicide decedents. The increase in mean age coincided with a decrease in the proportion of non-Hispanic Black male gun-homicide decedents between the ages of 15-19 and 20-24 years and, conversely, an increase in the proportion of non-Hispanic Black male gun-homicide decedents aged 25-34 years. CONCLUSIONS: The annual gun-homicide rate in Chicago had been increasing since 2015 with year-to-year variation. Continued monitoring of trends in the demographic makeup of gun-homicide decedents is necessary to provide the most relevant and timely information to help shape violence prevention efforts. We detected several changes that suggest a need for increased outreach and engagement marketed toward non-Hispanic Black female and non-Hispanic Black male individuals between the ages of 25-34 years.

Using Machine Learning and Deep Learning Algorithms to Predict Postoperative Outcomes Following Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: A retrospective cohort study from a multisite academic medical center. OBJECTIVE: To construct, evaluate, and interpret a series of machine learning models to predict outcomes related to inpatient health care resource utilization for patients undergoing anterior cervical discectomy and fusion (ACDF). SUMMARY OF BACKGROUND DATA: Reducing postoperative health care utilization is an important goal for improving the delivery of surgical care and serves as a metric for quality assessment. Recent data has shown marked hospital resource utilization after ACDF surgery, including readmissions, and ED visits. The burden of postoperative health care use presents a potential application of machine learning techniques, which may be capable of accurately identifying at-risk patients using patient-specific predictors. METHODS: Patients 18-88 years old who underwent ACDF from 2011 to 2021 at a multisite academic center and had preoperative lab values within 3 months of surgery were included. Outcomes analyzed included 90-day readmissions, postoperative length of stay, and nonhome discharge. Four machine learning models-Extreme Gradient Boosted Trees, Balanced Random Forest, Elastic-Net Penalized Logistic Regression, and a Neural Network-were trained and evaluated through the Area Under the Curve estimates. Feature importance scores were computed for the highest-performing model per outcome through model-specific metrics. RESULTS: A total of 1026 cases were included in the analysis cohort. All machine learning models were predictive for outcomes of interest, with the Random Forest algorithm consistently demonstrating the strongest average area under the curve performance, with a peak performance of 0.84 for nonhome discharge. Important features varied per outcome, though age, body mass index, American Society of Anesthesiologists classification >2, and medical comorbidities were highly weighted in the studied outcomes. CONCLUSIONS: Machine learning models were successfully applied and predictive of postoperative health utilization after ACDF. Deployment of these tools can assist clinicians in determining high-risk patients. LEVEL OF EVIDENCE: III.

Wearable Sensors Improve Prediction of Post-Stroke Walking Function Following Inpatient Rehabilitation.

OBJECTIVE: A primary goal of acute stroke rehabilitation is to maximize functional recovery and help patients reintegrate safely in the home and community. However, not all patients have the same potential for recovery, making it difficult to set realistic therapy goals and to anticipate future needs for short- or long-term care. The objective of this study was to test the value of high-resolution data from wireless, wearable motion sensors to predict post-stroke ambulation function following inpatient stroke rehabilitation. METHOD: Supervised machine learning algorithms were trained to classify patients as either household or community ambulators at discharge based on information collected upon admission to the inpatient facility (N=33-35). Inertial measurement unit (IMU) sensor data recorded from the ankles and the pelvis during a brief walking bout at admission (10 meters, or 60 seconds walking) improved the prediction of discharge ambulation ability over a traditional prediction model based on patient demographics, clinical information, and performance on standardized clinical assessments. RESULTS: Models incorporating IMU data were more sensitive to patients who changed ambulation category, improving the recall of community ambulators at discharge from 85% to 89-93%. CONCLUSIONS: This approach demonstrates significant potential for the early prediction of post-rehabilitation walking outcomes in patients with stroke using small amounts of data from three wearable motion sensors. CLINICAL IMPACT: Accurately predicting a patient's functional recovery early in the rehabilitation process would transform our ability to design personalized care strategies in the clinic and beyond. This work contributes to the development of low-cost, clinically-implementable prognostic tools for data-driven stroke treatment.

Community Versus Hospital Opioid-Related Overdose Deaths in Illinois.

OBJECTIVE: To better understand approaches to reducing mortality from the opioid epidemic, we analyzed in-hospital versus community opioid-related overdose deaths in Illinois. METHODS: We used data from the Statewide Unintentional Drug Overdose Reporting System (July 2017 through December 2018) to identify deaths that occurred in hospitals and communities (ie, homes or public spaces). We used census tract-level data for 34 Illinois counties to create bivariate mapping by overdose death rates. We used logistic regression to analyze the association of demographic and overdose characteristics with the likelihood of death in a hospital versus a community. RESULTS: During the study period, 2833 opioid-related overdose deaths occurred in 24 Illinois counties, 655 (23.1%) of which occurred in the hospital; of 2178 community deaths, 1888 (86.7%) occurred in the same census tract as the decedent's recorded residence and 1285 (59.0%) occurred in the decedent's home. Non-Hispanic Black people were 1.63 (95% CI, 1.27-2.10) times more likely than non-Hispanic White people to die in a hospital. Decedents from suburban Cook County and other Chicago suburban counties were significantly more likely to die in the hospital than decedents from Chicago or other Illinois counties. Documentation of a previous overdose, history of opioid use, and having bystanders present were significantly associated with hospital deaths. Evidence of a rapid overdose, fentanyl present, or prescription opioids were significantly associated with deaths in a community. CONCLUSIONS: The high number of opioid-related overdose deaths in the community illustrates the need to decriminalize illicit drug use and facilitate treatment seeking. Establishing supervised safe consumption sites may have the biggest effect in reducing the number of opioid-related overdose deaths.