Tag: pubmed

J Bras Nefrol. 2024 Apr 29;46(3):e20240012. doi: 10.1590/2175-8239-JBN-2024-0012en. eCollection 2024.

ABSTRACT

INTRODUCTION: Acute kidney injury (AKI) is an abrupt deterioration of kidney function. The incidence of pediatric AKI is increasing worldwide, both in critically and non-critically ill settings. We aimed to characterize the presentation, etiology, evolution, and outcome of AKI in pediatric patients admitted to a tertiary care center.

METHODS: We performed a retrospective observational single-center study of patients aged 29 days to 17 years and 365 days admitted to our Pediatric Nephrology Unit from January 2012 to December 2021, with the diagnosis of AKI. AKI severity was categorized according to Kidney Disease Improving Global Outcomes (KDIGO) criteria. The outcomes considered were death or sequelae (proteinuria, hypertension, or changes in renal function at 3 to 6 months follow-up assessments).

RESULTS: Forty-six patients with a median age of 13.0 (3.5-15.5) years were included. About half of the patients (n = 24, 52.2%) had an identifiable risk factor for the development of AKI. Thirteen patients (28.3%) were anuric, and all of those were categorized as AKI KDIGO stage 3 (p < 0.001). Almost one quarter (n = 10, 21.7%) of patients required renal replacement therapy. Approximately 60% of patients (n = 26) had at least one sequelae, with proteinuria being the most common (n = 15, 38.5%; median (P25-75) urinary protein-to-creatinine ratio 0.30 (0.27-0.44) mg/mg), followed by reduced glomerular filtration rate (GFR) (n = 11, 27.5%; median (P25-75) GFR 75 (62-83) mL/min/1.73 m2).

CONCLUSIONS: Pediatric AKI is associated with substantial morbidity, with potential for proteinuria development and renal function impairment and a relevant impact on long-term prognosis.

PMID:38748945 | DOI:10.1590/2175-8239-JBN-2024-0012en

Ultrasound Obstet Gynecol. 2024 May 15. doi: 10.1002/uog.27680. Online ahead of print.

ABSTRACT

OBJECTIVE: Our primary aim was to identify radiomic ultrasound features that can distinguish benign from malignant adnexal masses with solid ultrasound morphology, and primary invasive from metastatic solid ovarian masses, and to develop ultrasound-based machine learning models that include radiomics features to discriminate between benign and malignant solid adnexal masses. Our secondary aim was to compare the diagnostic performance of our radiomics models with that of the ADNEX model and subjective assessment by an experienced ultrasound examiner.

METHODS: This is a retrospective observational single center study. Patients with a histological diagnosis of an adnexal tumor with solid morphology at preoperative ultrasound examination performed between 2014 and 2021 were included. The patient cohort was split into training and validation sets with a ratio of 70:30 and with the same proportion of benign and malignant (borderline, primary invasive and metastatic) tumors in the two subsets. The extracted radiomic features belonged to two different families: intensity-based statistical features and textural features. Models to predict malignancy were built based on a random forest classifier, fine-tuned using 5-fold cross-validation over the training set, and tested on the held-out validation set. The variables used in model building were patient’s age, and those radiomic features that were statistically significantly different between benign and malignant adnexal masses (Wilcoxon-Mann-Whitney Test with Benjamini-Hochberg correction for multiple comparisons) and assessed as not redundant based on the Pearson correlation coefficient. We describe discriminative ability as area under the receiver operating characteristics curve (AUC) and classification performance as sensitivity and specificity.

RESULTS: 326 patients were identified and 775 preoperative ultrasound images were analyzed. 68 radiomic features were extracted, 52 differed statistically significantly between benign and malignant tumors in the training set, and 18 features were selected for inclusion in model building. The same 52 radiomic features differed statistically significantly between benign, primary invasive malignant and metastatic tumors. However, the values of the features manifested overlap between primary malignant and metastatic tumors and did not differ statistically significantly between them. In the validation set, 25/98 tumors (25.5%) were benign, 73/98 (74.5%) were malignant (6 borderline, 57 primary invasive, 10 metastases). In the validation set, a model including only radiomics features had an AUC of 0.80, and 78% sensitivity and 76% specificity at its optimal risk of malignancy cutoff (68% based on Youden’s index). The corresponding results for a model including age and radiomics features were 0.79, 86% and 56% (cutoff 60% based on Youden’s method), while those of the ADNEX model were 0.88, 99% and 64% (at 20% malignancy cutoff). Subjective assessment had sensitivity 99% and specificity 72%.

CONCLUSIONS: Even though our radiomics models had discriminative ability inferior to that of the ADNEX model, our results are promising enough to justify continued development of radiomics analysis of ultrasound images of adnexal masses. This article is protected by copyright. All rights reserved.

PMID:38748935 | DOI:10.1002/uog.27680

Am J Manag Care. 2024 May;30(5):237-240. doi: 10.37765/ajmc.2024.89536.

ABSTRACT

OBJECTIVES: To assess initiatives to manage the cost and outcomes of specialty care in organizations that participate in Medicare accountable care organizations (ACOs).

STUDY DESIGN: Cross-sectional analysis of 2023 ACO survey data.

METHODS: Analysis of responses to a 12-question web-based survey from 101 respondents representing 174 ACOs participating in the Medicare Shared Savings Program or the Realizing Equity, Access, and Community Health ACO model in 2023.

RESULTS: Improving specialist alignment was a high priority for 62% of the 101 respondents and a medium priority for 34%. Only 11% reported that employed specialists were highly aligned and 7% reported that contracted specialists were highly aligned. A subset of ACOs reported major efforts to engage specialists in quality improvement projects (38%) and to convene specialists to develop evidence-based care pathways (30%). They also reported supporting primary care physicians through providing specialist directories (44%), specialist e-consults (23%), and sharing specialist cost data (20%). The most common challenges reported were the influence of fee-for-service payment on specialist behavior (58%), lack of data to evaluate specialist performance (53%), and insufficient bandwidth or ACO resources to address specialist alignment (49%).

CONCLUSIONS: Engaging specialists in accountable care is an emerging area for ACOs but one with numerous challenges. Making better data on specialist costs and outcomes available to Medicare ACOs is essential for accelerating progress.

PMID:38748931 | DOI:10.37765/ajmc.2024.89536

Am J Manag Care. 2024 May;30(5):230-236. doi: 10.37765/ajmc.2024.89540.

ABSTRACT

OBJECTIVES: Regular users of the emergency department (ED) include both patients who could be better served in lower-acuity settings and those with high-severity conditions. ED use decreased during the COVID-19 pandemic, but patterns among regular ED users are unknown. To determine the impact of the COVID-19 pandemic on this population, we examined quarterly postpandemic ED utilization among prepandemic regular ED users. Key subgroups included prepandemic ED users with regular visits for (1) low-severity conditions and (2) high-severity conditions.

STUDY DESIGN: An event study design with COVID-19 and historic controls cohorts.

METHODS: We identified 4710 regular ED users at baseline and followed their ED utilization for 7 quarters. We used a generalized estimating equations model to compare the relative quarterly percent difference in ED visit rates between the COVID-19 and historic controls cohorts.

RESULTS: The first postpandemic quarter was associated with the largest decline in ED visits, at -36.0% (95% CI, -42.0% to -29.3%) per regular ED user overall, -52.2% (95% CI, -69.4% to -25.3%) among high-severity users, and -29.6% (95% CI, -39.8% to -17.8%) among low-severity users. However, use did not statistically differ from expected levels after 5 quarters among all regular ED users, 1 quarter among high-severity users, and 3 quarters among regular low-severity users.

CONCLUSIONS: Initial reductions among regular high-severity ED users raise concern for harm from delayed or missed care but did not result in increased high-severity visits later. Nonsustained declines among regular low-severity ED users suggest barriers to and opportunities for redirecting nonurgent ED use to lower-acuity settings.

PMID:38748930 | DOI:10.37765/ajmc.2024.89540

Am J Manag Care. 2024 May;30(5):218-223. doi: 10.37765/ajmc.2024.89539.

ABSTRACT

OBJECTIVES: Most Medicare beneficiaries obtain supplemental insurance or enroll in Medicare Advantage (MA) to protect against potentially high cost sharing in traditional Medicare (TM). We examined changes in Medicare supplemental insurance coverage in the context of MA growth.

STUDY DESIGN: Repeated cross-sectional analysis of the Medicare Current Beneficiary Survey from 2005 to 2019.

METHODS: We determined whether Medicare beneficiaries 65 years and older were enrolled in MA (without Medicaid), TM without supplemental coverage, TM with employer-sponsored supplemental coverage, TM with Medigap, or Medicaid (in TM or MA).

RESULTS: From 2005 to 2019, beneficiaries with TM and supplemental insurance provided by their former (or current) employer declined by approximately half (31.8% to 15.5%) while the share in MA (without Medicaid) more than doubled (13.4% to 35.1%). The decline in supplemental employer-sponsored insurance use was greater for White and for higher-income beneficiaries. Over the same period, beneficiaries in TM without supplemental coverage declined by more than a quarter (13.9% to 10.1%). This decline was largest for Black, Hispanic, and lower-income beneficiaries.

CONCLUSIONS: The rapid rise in MA enrollment from 2005 to 2019 was accompanied by substantial changes in supplemental insurance with TM. Our results emphasize the interconnectedness of different insurance choices made by Medicare beneficiaries.

PMID:38748929 | DOI:10.37765/ajmc.2024.89539

Am J Manag Care. 2024 May;30(5):210-217. doi: 10.37765/ajmc.2024.89538.

ABSTRACT

OBJECTIVE: To examine the association between missed CMS Star Ratings quality measures for medication adherence over 3 years for diabetes, hypertension, and hyperlipidemia medications (9 measures) and health care utilization and relative costs.

STUDY DESIGN: Retrospective cohort study.

METHODS: The study examined eligible patients who qualified for the diabetes, statin, and renin-angiotensin system antagonist medication adherence measures in 2018, 2019, and 2020 and were continuously enrolled in a Medicare Advantage prescription drug plan from 2017 through 2021. A total of 103,900 patients were divided into 4 groups based on the number of adherence measures missed (3 medication classes over 3 years): (1) missed 0 measures, (2) missed 1 measure, (3) missed 2 or 3 measures, and (4) missed 4 or more measures. To achieve a quality measure, patients had to meet the Pharmacy Quality Alliance 80% threshold of proportion of days covered during the calendar year.

RESULTS: The mean age of the cohort was 71.1 years, and 49.9% were female. Compared with patients who missed 0 of 9 adherence measures, those who missed 1 measure, 2 or 3 measures, and 4 or more measures experienced 12% to 26%, 22% to 42%, and 24% to 50% increased risks, respectively, of all-cause and diabetes-related inpatient stays and all-cause and diabetes-related emergency department visits (all P values < .01). Additionally, patients who missed 1, 2 or 3, and 4 or more adherence measures experienced 14%, 19%, and 20% higher monthly medical costs, respectively.

CONCLUSIONS: Missing Star Ratings quality measures for medication adherence was associated with an increased likelihood of health care resource utilization and increased costs for patients taking medications to treat diabetes, hypertension, and hyperlipidemia.

PMID:38748928 | DOI:10.37765/ajmc.2024.89538

Am J Manag Care. 2024 May 1;30(5):e165-e168. doi: 10.37765/ajmc.2024.89544.

ABSTRACT

OBJECTIVES: Given the problematic fragmentation of care for patients with end-stage kidney disease (ESKD), a kidney care organization and an integrated health system within a large accountable care organization partnered to best utilize their individual capabilities to collaborate around their shared patients in a coordinated care approach. Ultimately, the goal of the program is to allow care teams to achieve the triple aim of improving the patient experience, improving clinical outcomes, and reducing the total cost of health care.

STUDY DESIGN: This is a retrospective examination of the first year of the Shared Patient Care Coordination (SPCC) program.

METHODS: The analysis consisted of 2 parts. First, rates of hospitalizations and emergency department visits were compared between the SPCC patients and other patients of the integrated health system who had ESKD but did not participate in SPCC. Second, rates of clinical indicators-central venous catheter (CVC) use, home dialysis, advance care planning, and missed dialysis treatments-were benchmarked vs normative data taken by bootstrap sampling of the kidney care organization’s patient population.

RESULTS: Overall, dialysis patients participating in the SPCC program had a 15% lower rate of hospital admissions than those not participating ( P = .02). Additionally, the bootstrap analysis showed that by the second year, dialysis patients in the program had favorable rates (above the 95th percentile) of CVC use, dialysis treatment absenteeism, and completion of advance care plans.

CONCLUSIONS: Enhanced and structured communication between dialysis providers and patient care teams provides a unique opportunity to coordinate patient-centered care and improve patient outcomes.

PMID:38748917 | DOI:10.37765/ajmc.2024.89544

Am J Manag Care. 2024 May 1;30(5):e147-e156. doi: 10.37765/ajmc.2024.89542.

ABSTRACT

OBJECTIVE: Major depressive disorder (MDD) is linked to a 61% increased risk of emergency department (ED) visits and frequent ED usage. Collaborative care management (CoCM) models target MDD treatment in primary care, but how best to prioritize patients for CoCM to prevent frequent ED utilization remains unclear. This study aimed to develop and validate a risk identification model to proactively detect patients with MDD in CoCM at high risk of frequent (≥ 3) ED visits.

STUDY DESIGN: This retrospective cohort study utilized electronic health records from Mayo Clinic’s primary care system to develop and validate a machine learning-based risk identification model. The model predicts the likelihood of frequent ED visits among patients with MDD within a 12-month period.

METHODS: Data were collected from Mayo Clinic’s primary care system between May 1, 2006, and December 19, 2018. Risk identification models were developed and validated using machine learning classifiers to estimate frequent ED visit risks over 12 months. The Shapley Additive Explanations model identified variables driving frequent ED visits.

RESULTS: The patient population had a mean (SD) age of 39.78 (16.66) years, with 30.3% being male and 6.1% experiencing frequent ED visits. The best-performing algorithm (elastic-net logistic regression) achieved an area under the curve of 0.79 (95% CI, 0.74-0.84), a sensitivity of 0.71 (95% CI, 0.57-0.82), and a specificity of 0.76 (95% CI, 0.64-0.85) in the development data set. In the validation data set, the best-performing algorithm (random forest) achieved an area under the curve of 0.79, a sensitivity of 0.83, and a specificity of 0.61. Significant variables included male gender, prior frequent ED visits, high Patient Health Questionnaire-9 score, low education level, unemployment, and use of multiple medications.

CONCLUSIONS: The risk identification model has potential for clinical application in triaging primary care patients with MDD in CoCM, aiming to reduce future ED utilization.

PMID:38748915 | DOI:10.37765/ajmc.2024.89542

Braz J Cardiovasc Surg. 2024 May 15;39(4):e20220453. doi: 10.21470/1678-9741-2022-0453.

ABSTRACT

INTRODUCTION: The analysis of patients submitted to heart surgery at three assessment times has been insufficiently described in the literature.

OBJECTIVE: To analyze chest expansion, maximum inspiratory pressure (MIP), maximum expiratory pressure (MEP), distance traveled on the six-minute walk test (6MWT), and quality of life in the preoperative period, fourth postoperative day (4th PO), and 30th day after hospital discharge (30th-day HD) in individuals submitted to elective heart surgery.

METHODS: A descriptive, analytical, cross-sectional study was conducted with 15 individuals submitted to elective heart surgery between 2016 and 2020 who did not undergo any type of physiotherapeutic intervention in Phase II of cardiac rehabilitation. The outcome variables were difference in chest expansion (axillary, nipple, and xiphoid), MIP, MEP, distance on 6MWT, and quality of life. The assessment times were preoperative period, 4th PO, and 30th-day HD.

RESULTS: Chest expansion diminished between the preoperative period and 4th PO, followed by an increase at 30th-day HD. MIP, MEP, and distance traveled on the 6MWT diminished between the preoperative period and 4th PO, with a return to preoperative values at 30th-day HD. General quality of life improved between the preoperative period and 4th PO and 30th-day HD. An improvement was found in the social domain between the preoperative period and the 30th-day HD.

CONCLUSION: Heart surgery causes immediate physical deficit, but physical functioning can be recovered 30 days after hospital discharge, resulting in an improvement in quality of life one month after surgery.

PMID:38748911 | DOI:10.21470/1678-9741-2022-0453

Clin Chem Lab Med. 2024 May 16. doi: 10.1515/cclm-2024-0124. Online ahead of print.

ABSTRACT

OBJECTIVES: Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control.

METHODS: The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison.

RESULTS: The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example.

CONCLUSIONS: The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

PMID:38748888 | DOI:10.1515/cclm-2024-0124