Citations

ABSTRACT An increasing number of cancer patients are benefiting from long term cancer-directed therapy with an ever-expanding arsenal of novel agents from monoclonal antibodies to small molecules and cellular therapies in addition to the mainstay cytotoxic chemotherapy. All these therapies are accompanied by a unique array of adverse events, which include kidney toxicity. In this context, the need for onco-nephrology expertise continues to grow. Oncologists and hematologists collaborate closely with onco-nephrologists to determine: (i) treatment options for special populations based on accurate assessment of patients’ glomerular filtration rate, (ii) supportive therapies for those whose treatment course is complicated by acute kidney injury, and (iii) pharmacologic strategies to continue or restart cancer therapy during kidney dysfunction. Here we outline 10 tips for common clinical scenarios in an outpatient onco-nephrology clinic.

Radiologists are integral members of the interdisciplinary team for patients with mucinous appendiceal neoplasms and can directly affect care by suggesting the diagnosis of pseudomyxoma peritonei, optimizing imaging protocols for evaluating peritoneal implants, and reporting the primary tumor and spilled tumor cells thoroughly.

ImportanceHip fractures present a substantial public health challenge, with projections of more than 500 000 per year by 2040. As such, frailty indices such as the Revised Risk Analysis Index (RAI) and the Modified Five-Item Frailty Index (mFI-5) have been recently investigated as metrics for preoperative risk stratification for these patients.ObjectiveTo understand the accuracy of frailty, as measured by the RAI and the mFI-5, for estimating 30-day mortality following surgically managed hip fractures.Design, Setting, and ParticipantsThis retrospective cross-sectional analysis used the American College of Surgeons’ National Surgical Quality Improvement database. Patients aged 65 years old with surgically managed traumatic hip fracture from 2015 to 2019 were included. Frailty was evaluated using the RAI, a 5-domain scale with 14 weighted variables graded from 0 to 81, and the mFI-5, a 2-domain scale with 5 unweighted variables graded from 0 to 5; for both, a higher score denotes worse frailty. Data collection occurred from May to June 2024.ExposureDiagnosis of hip fracture and undergoing surgical fixation, hemiarthroplasty, or total hip arthroplasty.Main Outcomes and MeasuresThe primary outcome was 30-day mortality. Multivariable regression was conducted to assess the estimating value of frailty scales. Discriminatory accuracy was assessed using a receiver operating characteristic curve and quantified using a C-statistic.ResultsThe cohort consisted of 114 359 patients (70 038 female [69.9%]; median [IQR] age, 84 [77-89] years) with 51 071 prefrail patients (44.7%) according to the mFI-5 and 31 883 very frail patients (27.9%) according to the RAI comprising the largest frailty groups. Increasing frailty status was associated with greater odds ratio (OR) for 30-day mortality for both the mFI-5 (prefrail OR, 1.35 [95% CI, 1.24-1.47]; frail OR, 2.11 [95% CI, 1.94-2.30]; severely frail OR, 3.53 [95% CI, 3.20-3.90]; P < .001 for all) and RAI (normal OR, 1.55 [95% CI, 1.35-1.79]; frail OR, 2.97 [95% CI, 2.59-3.42]; very frail OR, 6.17 [95% CI, 5.38-7.08]; P < .001 for all). The RAI demonstrated superior discriminatory accuracy for 30-day mortality compared with the mFI-5 (area under the receiver operating characteristic curve, 0.73 [95% CI, 0.72-0.73] vs 0.61 [95% CI, 0.60-0.62]; P < .001).Conclusions and RelevanceIn this cross-sectional study of 114 359 patients, the RAI demonstrated superior odds and discriminatory accuracy for estimating 30-day mortality following surgical management of hip fractures. The RAI may be considered as a risk stratification tool for orthopedic surgeons to adjunct surgical planning, thereby reducing postoperative mortality.

The radiotracer [ 68 Ga]Ga-NOTA-CTP was evaluated in healthy and diseased CD1 mice with myocardial infarction following ligation of the left anterior descending artery.

Photon-counting detector CT offers improved assessment of small stents and higher diagnostic accuracy than conventional CT through enhanced spatial resolution and spectral reconstructions.

Abstract Malignant insulinomas are rare pancreatic neuroendocrine tumors characterized by excessive insulin secretion and a high propensity for metastasis, leading to challenging management. This study retrospectively analyzed 57 patients treated for malignant insulinoma at Mayo Clinic sites between 1992 and 2024, focusing on the efficacy of systemic therapies in improving hypoglycemic control and survival outcomes. The most commonly used therapies included somatostatin analogs (SSA), everolimus, capecitabine-temozolomide (CAPTEM), and peptide receptor radionuclide therapy (PRRT). PRRT demonstrated the highest efficacy in controlling hypoglycemia (93%), followed by CAPTEM (68%) and everolimus (62%). SSA, chemotherapy, and streptozocin were less effective, with hypoglycemic improvement seen in 37.5, 33.3, and 28.6% of patients, respectively. Overall survival (OS) was longest with SSA at 84.67 months, followed by CAPTEM at 81.67 months and everolimus at 74.07 months. PRRT demonstrated a median OS of 49.73 months. In contrast, chemotherapy and streptozocin-based therapies had significantly shorter OS times of 15.23 and 8.35 months, respectively. These findings highlight significant variability in systemic therapy efficacy for malignant insulinoma, with PRRT emerging as a promising treatment for refractory hypoglycemia and long survival. Cox regression analysis identified primary tumor resection and a history of benign insulinoma as associated with longer OS. Optimal sequencing of therapies remains unclear, and individualized treatment plans based on hypoglycemic burden and tumor characteristics are critical for improving survival and quality of life in these patients.

The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution.

Background: When missing data are present in clinical outcomes studies, complete-case analysis (CCA) is often performed, whereby patients with missing data are excluded. While simple, CCA analysis may impart selection bias and reduce statistical power, leading to erroneous statistical results in some cases. However, there exist more rigorous statistical approaches, such as single and multiple imputation, which approximate the associations that would have been present in a full dataset and preserve the study’s power. The purpose of this study is to evaluate how statistical results differ when performed after CCA analysis versus imputation methods. Methods: This simulation study analyzed a sample dataset consisting of 2204 shoulders, with complete datapoints from a larger multicenter total shoulder arthroplasty database. From the sampled dataset of demographics, surgical characteristics, and clinical outcomes, we created five test datasets, ranging from 100 to 2000 shoulders, and simulated 10–50% missingness in the postoperative American Shoulder and Elbow Surgeons (ASES) score and range of motion in four planes in missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) patterns. Missingness in outcomes was remedied using CCA, three single imputation techniques, and two multiple imputation techniques. The imputation performance was evaluated relative to the native complete dataset using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). We also compared the mean and standard deviation (SD) of the postoperative ASES score and the results of multivariable linear and logistic regression to understand the effects of imputation on the study results. Results: The average overall RMSE and MAPE were similar for MCAR (22.6 and 27.2%) and MAR (19.2 and 17.7%) missingness patterns, but were substantially poorer for NMAR (37.5 and 79.2%); the sample size and the percentage of data missingness minimally affected RMSE and MAPE. Aggregated mean postoperative ASES scores were within 5% of the true value when missing data were remedied with CCA, and all candidate imputation methods for nearly all ranges of sample size and data missingness when data were MCAR or MAR, but not when data were NMAR. When data were MAR, CCA resulted in overestimates of the SD. When data were MCAR or MAR, the accuracy of the regression estimate (β or OR) and its corresponding 95% CI varied substantially based on the sample size and proportion of missing data for multivariable linear regression, but not logistic regression. When data were MAR, the width of the 95% CI was up to 300% larger when CCA was used, whereas most imputation methods maintained the width of the 95% CI within 50% of the true value. Single imputation with k-nearest neighbor (kNN) method and multiple imputation with predictive mean matching (MICE-PMM) best-reproduced point estimates and intervariable relationships resembling the native dataset. Availability of correlated outcome scores improved the RMSE, MAPE, accuracy of the mean postoperative ASES score, and multivariable linear regression model estimates. Conclusions: Complete-case analysis can introduce selection bias when data are MAR, and it results in loss of statistical power, resulting in loss of precision (i.e., expansion of the 95% CI) and predisposition to false-negative findings. Our data demonstrate that imputation can reliably reproduce missing clinical data and generate accurate population estimates that closely resemble results derived from native primary shoulder arthroplasty datasets (i.e., prior to simulated data missingness). Further study of the use of imputation in clinical database research is critical, as the use of CCA may lead to different conclusions in comparison to more rigorous imputation approaches.

Background: Total ankle arthroplasty (TAA) using patient-specific instrumentation (PSI) has increased in popularity with evidence for decreased operative duration, fluoroscopy usage, and increased implant placement accuracy. To date, no comparisons have verified the accuracy of PSI in vivo using preoperative and postoperative weightbearing computed tomography (WBCT). Our study aims to supplement the literature by quantifying the accuracy and precision of PSI-guided implant positioning using WBCT. The primary study outcome is to identify positioning deviations in degrees or millimeters in any plane for talus and tibial PSI-guided resections and subsequent implant placement. Secondary outcomes were correlation and regression analyses performed to identify variables that correlate to resection or implant placement deviation, as well as comparison to existing radiography-based PSI techniques. Methods: This was a single-surgeon, single-implant consecutive patient retrospective study where preoperative and postoperative WBCTs were obtained. TAA was performed by the senior author with the same low-profile implant for all cases. Talus and tibial resection analysis was performed in computer-automated fashion on postoperative segmented WBCTs and is described as 3 possible deviations from plan (cut height in millimeters, sagittal slope, varus/valgus deviation). Overall deformity in this group was not severe. Implant placement factors independent of PSI guides are described as center-of-mass translations (mm) and axial rotation (degrees). Desired accuracy for the PSI system was within ±2 mm or ±2 degrees of the preoperative plan. Statistical analysis of data collected included Student t test and linear regression analyses. Results: Thirty patients were included. Four talus implants were excluded per intraoperative surgeon discretion (deviation from PSI plan, use of conventional instruments). All postoperative tibial measurements were within the desired limits, except for mediolateral center of mass positioning ( P = .003) and slope ( P = .013). Two of six talar parameters also diverged from preoperative measurements: axial rotation ( P = .015) and anteroposterior positioning ( P = .002). In addition, no correlations exceeding r = 0.5 were noted between preoperative deformity measurements and postoperative positioning. Conclusion: For the 26 ankles that did not require an intraoperative deviation from PSI plan and/or use of conventional instruments, total ankle replacement performed with PSI using WBCT results in component placement with relatively little deviation from the preoperative plan. In addition, the lack of correlation between preoperative deformity and implant placement suggests that the magnitude of preoperative deformity in this group was not associated with the accuracy of PSI-guided component positioning. Level of Evidence: Level III, retrospective cohort study.