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Clinical Studies, Big Data, and Artificial Intelligence in Medicine

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A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Epidemiology & Public Health".

Deadline for manuscript submissions: closed (27 July 2023) | Viewed by 23242

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Special Issue Editors

Dr. Wisit Cheungpasitporn

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Collection Editor

Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Interests: artificial Intelligence; machine learning; meta-analysis; acute kidney injury; clinical nephrology; kidney transplantation
Special Issues, Collections and Topics in MDPI journals

Dr. Charat Thongprayoon

E-Mail Website
Collection Editor

Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Interests: artificial intelligence; machine Learning; nephrology; acute kidney injury; clinical nephrology; kidney transplantation
Special Issues, Collections and Topics in MDPI journals

Dr. Wisit Kaewput

E-Mail Website
Collection Editor

Department of Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
Interests: machine learning; kidney transplantation; observational studies; statistical analysis; epidemiology
Special Issues, Collections and Topics in MDPI journals

Dr. Pattharawin Pattharanitima

E-Mail Website
Collection Editor

Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand
Interests: clinical nephrology; big data; machine learning; dialysis; hemodialysis; critical care

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence has increasingly played an essential role in diverse areas in medicine, helping clinicians in patient management. In healthcare, artificial intelligence can be utilized to enhance clinical care, such as health monitoring, drug development, management of medical data, disease diagnostics, digital consultation, personalized treatment, analysis of health plans, surgical treatment, and medical treatment. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions.

In this Topical Collection, we inviting researchers and clinicians to submit their invaluable works including original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in medicine and subspecialties that will provide additional knowledge and skills in medicine to improve patient outcomes.

Potential topics include but are not limited to the following:

Challenges and promises of machine learning-based risk prediction modelling in cardiovascular disease;
Machine learning methodologies for identification and triage of heart failure exacerbations;
Systematic reviews with meta-analyses: effect of inflammatory bowel disease therapy on lipid levels;
Outcomes in patients with acute myocardial infarction and history of illicit drug use: a French nationwide analysis;
Alcohol use and development of chronic kidney disease: a nationwide database analysis;
Association of race and poverty with mortality on maintenance dialysis using the United States Renal Data System database;
Prevention of contrast-induced acute kidney injury in patients undergoing cardiovascular procedures—a meta-analysis;
Systematic reviews and meta-analyses of narrow band imaging for non-muscle-invasive bladder cancer;
Systematic reviews and meta-analyses of renal replacement therapy modalities for acute kidney injury.

Dr. Wisit Cheungpasitporn
Dr. Charat Thongprayoon
Dr. Wisit Kaewput
Dr. Pattharawin Pattharanitima
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Medicina is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
machine learning
systematic review
meta-analysis
outcomes
hospital medicine
hospitalization
mortality
predictors
risk factors
survival
deep learning
random forest
neural network
logistic regression
support vector machine
automated machine learning
extreme gradient boosting
gradient boosting machine
decision tree
nationwide

Published Papers (13 papers)

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Research

11 pages, 771 KiB

Open AccessArticle

Hysterectomy for Benign Indications and Risk of Cataract Formation in South Korean Women

by Jae Suk Kim

Medicina 2023, 59(9), 1627; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina59091627 - 08 Sep 2023

Viewed by 785

Abstract

Background and Objectives: Few studies have investigated the relationship between female reproductive hormones, especially estrogen, and the incidence of cataracts. This study sought to evaluate the effects of hysterectomy on the risk of lens opacity in Korean women. Materials and Methods: This retrospective cohort study utilized data collected from 2007 to 2020 for 255,576 postmenopausal women in the Korean National Health Insurance database. Participants were divided into those who did and did not undergo hysterectomy. The hysterectomy group was further divided into two subgroups according to the type of adnexal surgery performed. The prevalence of cataracts was then compared among the control, hysterectomy alone, and hysterectomy with adnexal surgery groups. Results: The control group included 137,999 participants who did not undergo hysterectomy, while the treatment group included 93,719 women who underwent hysterectomy alone or in combination with adnexal surgery. The incidence of cataracts was higher in the control group than in the treatment group, as demonstrated in a 1:1 propensity score-matching analysis adjusted for potential confounding variables. Conclusions: The incidence of cataracts was significantly lower in the group with hysterectomy than in the control group, but the difference was subtle. The current findings may aid in identifying the role of female reproductive hormones in cataract development. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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12 pages, 1688 KiB

Open AccessArticle

Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy

by Tae Young Shin, Hyunho Han, Hyun-Seok Min, Hyungjoo Cho, Seonggyun Kim, Sung Yul Park, Hyung Joon Kim, Jung Hoon Kim and Yong Seong Lee

Medicina 2023, 59(8), 1402; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina59081402 - 31 Jul 2023

Cited by 1 | Viewed by 1252

Abstract

Background and Objectives: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN. Methods and Materials: This retrospective study included 785 patients (training set 706, test set 79) from six tertiary referral centers who underwent open or robotic PN. Forty-four perioperative features were used as inputs to train the AI prediction model. XG-Boost and genetic algorithms were used for the final model selection and to determine feature importance. The primary outcome measure was immediate postoperative serum creatinine (Cr) level. The secondary outcome was the incidence of AKI (estimated glomerular filtration rate (eGFR) < 60 mL/h). The average difference between the true and predicted serum Cr levels was considered the mean absolute error (MAE) and was used as a model evaluation parameter. Results: An AI model for predicting immediate postoperative serum Cr levels was selected from 2000 candidates by providing the lowest MAE (0.03 mg/dL). The model-predicted immediate postoperative serum Cr levels correlated closely with the measured values (R² = 0.9669). The sensitivity and specificity of the model for predicting AKI were 85.5% and 99.7% in the training set, and 100.0% and 100.0% in the test set, respectively. The limitations of this study included its retrospective design. Conclusions: Our AI model successfully predicted accurate serum Cr levels and the likelihood of AKI. The accuracy of our model suggests that personalized guidelines to optimize multidisciplinary plans involving pre- and postoperative care need to be developed. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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15 pages, 594 KiB

Open AccessArticle

Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach

by Gerardo Alfonso Perez and Raquel Castillo

Medicina 2023, 59(7), 1218; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina59071218 - 28 Jun 2023

Viewed by 1281

Abstract

Inflammatory bowel disease (IBD) is an illness with increasing prevalence, particularly in emerging countries, which can have a substantial impact on the quality of life of the patient. The illness is rather heterogeneous with different evolution among patients. A machine learning approach is followed in this paper to identify potential genes that are related to IBD. This is done by following a Monte Carlo simulation approach. In total, 23 different machine learning techniques were tested (in addition to a base level obtained using artificial neural networks). The best model identified 74 genes selected by the algorithm as being potentially involved in IBD. IBD seems to be a polygenic illness, in which environmental factors might play an important role. Following a machine learning approach, it was possible to obtain a classification accuracy of 84.2% differentiating between patients with IBD and control cases in a large cohort of 2490 total cases. The sensitivity and specificity of the model were 82.6% and 84.4%, respectively. It was also possible to distinguish between the two main types of IBD: (1) Crohn’s disease and (2) ulcerative colitis. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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11 pages, 2428 KiB

Open AccessArticle

Deep-Learning Model of ResNet Combined with CBAM for Malignant–Benign Pulmonary Nodules Classification on Computed Tomography Images

by Yanfei Zhang, Wei Feng, Zhiyuan Wu, Weiming Li, Lixin Tao, Xiangtong Liu, Feng Zhang, Yan Gao, Jian Huang and Xiuhua Guo

Medicina 2023, 59(6), 1088; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina59061088 - 05 Jun 2023

Cited by 4 | Viewed by 1834

Abstract

Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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13 pages, 1615 KiB

Open AccessArticle

Feasibility and Efficacy of Morning Light Therapy for Adults with Insomnia: A Pilot, Randomized, Open-Label, Two-Arm Study

by Jihyun Yoon, Seokjae Heo, Hyangkyu Lee, Eungyeong Sul, Taehwa Han and Yu-Jin Kwon

Medicina 2023, 59(6), 1066; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina59061066 - 01 Jun 2023

Viewed by 1697

Abstract

Background and Objectives: Light therapy (LT) is used as an adjunctive treatment for sleep problems. This study evaluates the impact of LT on sleep quality and sleep-related parameters in patients with sleep disorders. Materials and Methods: We performed a pilot, randomized, open-label clinical trial. Fourteen patients aged 20–60 years with insomnia were randomized into the control and LT groups (1:1 ratio). The LT group was instructed to use a device that provides bright LT (6000 K, 380 lux, wavelength 480 nm) for at least 25 min before 09:00 a.m. for two weeks. A self-reported questionnaire was used to evaluate circadian preference, mood, and sleep-related parameters. We analyzed serum cortisol levels and clock genes’ expression. Results: The Epworth Sleepiness Scale (ESS), insomnia severity index(ISI), and Pittsburgh Sleep Quality index(PSQI) were significantly improved within the LT group only after the two-week period. When comparing the two groups, only the change in ESS was significant (mean difference, control: −0.14 vs. LT: −1.43, p = 0.021) after adjusting for the baseline characteristics. There were no significant differences in serum cortisol or clock genes’ expression. Conclusions: LT can improve daytime sleepiness in patients with sleep disorders; however, further well-designed studies are warranted to confirm its efficacy. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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14 pages, 1576 KiB

Open AccessArticle

Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering

by Charat Thongprayoon, Jing Miao, Caroline C. Jadlowiec, Shennen A. Mao, Michael A. Mao, Pradeep Vaitla, Napat Leeaphorn, Wisit Kaewput, Pattharawin Pattharanitima, Supawit Tangpanithandee, Pajaree Krisanapan, Pitchaphon Nissaisorakarn, Matthew Cooper and Wisit Cheungpasitporn

Medicina 2023, 59(5), 977; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina59050977 - 18 May 2023

Cited by 2 | Viewed by 1666

Abstract

Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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11 pages, 1580 KiB

Open AccessArticle

Hospitalization Duration for Acute Myocardial Infarction: A Temporal Analysis of 18-Year United States Data

by Anusha G. Bhat, Mandeep Singh, Sri Harsha Patlolla, Peter Matthew Belford, David X. Zhao and Saraschandra Vallabhajosyula

Medicina 2022, 58(12), 1846; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina58121846 - 15 Dec 2022

Viewed by 1560

Abstract

Background and objectives: Primary percutaneous coronary intervention (PCI)-related outcomes in acute myocardial infarction (AMI) have improved over time, but there are limited data on the length of stay (LOS) in relation to in-hospital mortality. Materials and Methods: A retrospective cohort of adult AMI admissions was identified from the National Inpatient Sample (2000–2017) and stratified into short (≤3 days) and long (>3 days) LOS. Outcomes of interest included temporal trends in LOS and associated in-hospital mortality, further sub-stratified based on demographics and comorbidities. Results: A total 11,622,528 admissions with AMI were identified, with a median LOS of 3 (interquartile range [IQR] 2–6) days with 49.9% short and 47.3% long LOS, respectively. In 2017, compared to 2000, temporal trends in LOS declined in all AMI, with marginal increases in LOS >3 days and decreases for ≤3 days (median 2 [IQR 1–3]) vs. long LOS (median 6 [IQR 5–9]). Patients with long LOS had lower rates of coronary angiography and PCI, but higher rates of non-cardiac organ support (respiratory and renal) and use of coronary artery bypass grafting. Unadjusted in-hospital mortality declined over time. Short LOS had comparable mortality to long LOS (51.3% vs. 48.6%) (p = 0.13); however, adjusted in-hospital mortality was higher in LOS >3 days when compared to LOS ≤ 3 days (adjusted OR 3.00, 95% CI 2.98–3.02, p < 0.001), with higher hospitalization (p < 0.001) when compared to long LOS. Conclusions: Median LOS in AMI, particularly in STEMI, has declined over the last two decades with a consistent trend in subgroup analysis. Longer LOS is associated with higher in-hospital mortality, higher hospitalization costs, and less frequent discharges to home compared to those with shorter LOS. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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14 pages, 2427 KiB

Open AccessArticle

Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering

by Supawit Tangpanithandee, Charat Thongprayoon, Caroline C. Jadlowiec, Shennen A. Mao, Michael A. Mao, Pradeep Vaitla, Napat Leeaphorn, Wisit Kaewput, Pattharawin Pattharanitima, Pajaree Krisanapan, Pitchaphon Nissaisorakarn, Matthew Cooper and Wisit Cheungpasitporn

Medicina 2022, 58(12), 1831; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina58121831 - 12 Dec 2022

Viewed by 1356

Abstract

Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 ± 13 years) who received dual kidney transplant from pediatric (mean donor age 3 ± 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 ± 9 years) who received dual kidney transplant from adult (mean donor age 59 ± 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI ≥ 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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18 pages, 6151 KiB

Open AccessArticle

Exploring the Potential Use of Wearable Devices as a Prognostic Tool among Patients in Hospice Care

by Yaoru Huang, Muhammad Ashad Kabir, Umashankar Upadhyay, Eshita Dhar, Mohy Uddin and Shabbir Syed-Abdul

Medicina 2022, 58(12), 1824; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina58121824 - 12 Dec 2022

Cited by 2 | Viewed by 2366

Abstract

Background: Smartphones and wearable devices have become a part and parcel of the healthcare industry. The use of wearable technology has already proved its potentials in improving healthcare research, clinical work, and patient care. The real time data allows the care providers to monitor the patients’ symptoms remotely, prioritize the patients’ visits, assist in decision-making, and carry out advanced care planning. Objectives: The primary objective of our study was to investigate the potential use of wearable devices as a prognosis tool among patients in hospice care and palliative care, and the secondary objective was to examine the association between wearable devices and clinical data in the context of patient outcomes, such as discharge and deceased at various time intervals. Methods: We employed a prospective observational research approach to continuously monitor the hand movements of the selected 68 patients between December 2019 and June 2022 via an actigraphy device at hospice or palliative care ward of Taipei Medical University Hospital (TMUH) in Taiwan. Results: The results revealed that the patients with higher scores in the Karnofsky Performance Status (KPS), and Palliative Performance Scale (PPS) tended to live at discharge, while Palliative Prognostic Score (PaP) and Palliative prognostic Index (PPI) also shared the similar trend. In addition, the results also confirmed that all these evaluating tools only suggested rough rather than accurate and definite prediction. The outcomes (May be Discharge (MBD) or expired) were positively correlated with accumulated angle and spin values, i.e., the patients who survived had higher angle and spin values as compared to those who died/expired. Conclusion: The outcomes had higher correlation with angle value compared to spin and ACT. The correlation value increased within the first 48 h and then began to decline. We recommend rigorous prospective observational studies/randomized control trials with many participants for the investigations in the future. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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12 pages, 826 KiB

Open AccessArticle

A Novel Technique for Treatment of Metaphyseal Voids in Proximal Humerus Fractures in Elderly Patients

by Stoyan Hristov, Luke Visscher, Jörg Winkler, Daniel Zhelev, Stoyan Ivanov, Deyan Veselinov, Asen Baltov, Peter Varga, Till Berk, Karl Stoffel, Franz Kralinger and Boyko Gueorguiev

Medicina 2022, 58(10), 1424; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina58101424 - 10 Oct 2022

Cited by 6 | Viewed by 1610

Abstract

Background and Objectives: The treatment of proximal humerus fractures in elderly patients is challenging, with reported high complication rates mostly related to implant failure involving screw cut-out and penetration. Metaphyseal defects are common in osteoporotic bone and weaken the osteosynthesis construct. A novel technique for augmentation with polymethylmethacrylate (PMMA) bone cement was developed for the treatment of patients in advanced age with complex proximal humerus fractures and metaphyseal voids, whereby the cement was allowed to partially cure for 5–7 min after mixing to achieve medium viscosity, and then it was manually placed into the defect through the traumatic lateral window with a volume of 4–6 mL per patient. The aim of this retrospective clinical study was to assess this technique versus autologous bone graft augmentation and no augmentation. Materials and Methods: The outcomes of 120 patients with plated Neer three- and four-part fractures, assigned to groups of 63 cases with no augmentation, 28 with bone graft augmentation and 29 with cement augmentation, were assessed in this study. DASH, CS, pain scores and range of motion were analyzed at 3, 6 and 12 months. Statistical analysis was performed with factors for treatment and age groups, Neer fracture types and follow-up periods, and with the consideration of age as a covariate. Results: DASH and CS improved following cement augmentation at three and six months compared to bone grafting, being significant when correcting for age as a covariate (p ≤ 0.007). While the age group had a significant effect on both these scores with worsened values at a higher age for non-augmented and grafted patients (p ≤ 0.044), this was not the case for cement augmented patients (p ≥ 0.128). Cement augmentation demonstrated good clinical results at 12 months with a mean DASH of 10.21 and mean CS percentage of 84.83% versus the contralateral side, not being significantly different among the techniques (p ≥ 0.372), despite the cement augmented group representing the older population with more four-part fractures. There were no concerning adverse events specifically related to the novel technique. Conclusions: This study has detailed a novel technique for the treatment of metaphyseal defects with PMMA cement augmentation in elderly patients with complex proximal humerus fractures and follow-up to one year, whereby the cement was allowed to partially cure to achieve medium viscosity, and then it was manually placed into the defect through the traumatic lateral window. The results demonstrate clinically equivalent short-term results to 6 months compared to augmentation with bone graft or no augmentation—despite the patient group being older and with a higher rate of more severe fracture patterns. The technique appears to be safe with no specifically related adverse events and can be added in the surgeon’s armamentarium for the treatment of these difficult to manage fractures. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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10 pages, 696 KiB

Open AccessArticle

Decreasing Significance of Early Allograft Dysfunction with Rising Use of Nonconventional Donors

by Stephanie Ohara, Elizabeth Macdonough, Lena Egbert, Abigail Brooks, Blanca Lizaola-Mayo, Amit K. Mathur, Bashar Aqel, Kunam S. Reddy and Caroline C. Jadlowiec

Medicina 2022, 58(6), 821; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina58060821 - 17 Jun 2022

Cited by 3 | Viewed by 1670

Abstract

Background and Objectives: Early allograft dysfunction (EAD) is considered a surrogate marker for adverse post-liver transplant (LT) outcomes. With the increasing use of nonconventional donors, EAD has become a more frequent occurrence. Given this background, we aimed to assess the prevalence and impact of EAD in an updated cohort inclusive of both conventional and nonconventional liver allografts. Materials and Methods: Perioperative and one-year outcomes were assessed for a total of 611 LT recipients with and without EAD from Mayo Clinic Arizona. EAD was defined as the presence of one or more of the following: bilirubin > 10 mg/dL on day 7, INR > 1.6 on day 7, or ALT and/or AST > 2000 IU/L within the first 7 days of LT. Results: Within this cohort, 31.8% of grafts (n = 194) came from donation after circulatory death (DCD) donors, 17.7% (n = 108) were nationally shared, 16.4% (n = 100) were allocated as post-cross clamp, and 8.7% contained moderate steatosis. EAD was observed in 52.2% (n = 321) of grafts in the study cohort (79% in DCD grafts and 40% in DBD grafts). EAD grafts had higher donor risk index (DRI) scores (1.9 vs. 1.6, p < 0.0001), were more likely to come from DCD donors (48% vs. 13.8%, p < 0.0001), were regionally allocated (p = 0.003), and had higher cold ischemia times (median 6.0 vs. 5.5 h, p = 0.001). Primary nonfunction events were rare in both groups (1.3% vs. 0.3%, p = 0.22). Post-LT acute kidney injury occurred at a similar frequency in recipients with and without EAD (43.6% vs. 30.3%, p = 0.41), and there were no differences in ICU (median 2 vs. 1 day, p = 0.60) or hospital (6 vs. 5 days, p = 0.24) length of stay. For DCD grafts, the rate of ischemic cholangiopathy was similar in the two groups (14.9% EAD vs. 17.5% no EAD, p = 0.69). One-year patient survival for grafts with and without EAD was 96.0% and 94.1% (HR 1.2, 95% CI 0.7–1.8; p = 0.54); one-year graft survival was 92.5% and 92.1% (HR 1.0, 95% CI 0.7–1.5; p = 0.88). Conclusions: In this cohort, EAD occurred in 52% of grafts. The occurrence of EAD, however, did not portend inferior outcomes. Compared to those without EAD, recipients with EAD had similar post-operative outcomes, as well as one-year patient and graft survival. EAD should be managed supportively and should not be viewed as a deterrent to utilization of non-ideal grafts. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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10 pages, 631 KiB

Open AccessArticle

Increased Body Fat and Organic Acid Anions Production Are Associated with Larger Kidney Size in ADPKD

by Adriana dos Santos Dutra, Fernanda Guedes Rodrigues, Daniel Ribeiro da Rocha, Larissa Collis Vendramini, Ana Cristina Carvalho de Matos and Ita Pfeferman Heilberg

Medicina 2022, 58(2), 152; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina58020152 - 19 Jan 2022

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Abstract

Background and Objectives: A high body mass index (BMI) is associated with the progression of autosomal dominant polycystic kidney disease (ADPKD). However, body fat (BF), which is another adiposity marker, has not yet been studied. Excessive weight may promote elevation in the endogenous synthesis of organic acid (OA) anions. Accordingly, we aimed to investigate the possible association of the aforementioned markers with kidney volume and renal function in patients with ADPKD. Materials and Methods: We conducted a retrospective cohort study of adult ADPKD outpatients involving clinical, serum, and urinary laboratorial data and body composition assessments retrieved from their medical records. BF was estimated by skinfold thickness (mm) on the non-dominant arm and was considered as normal or high for each sex. Total kidney volume (TKV) and height-adjusted volume (htTKV) were measured by magnetic resonance imaging. The annual estimated glomerular filtration rate (eGFR) slope was analyzed during a median follow-up time of 6 (5.0–7.0) years to calculate rapid progression (decline in renal function ≥2.5 mL/min/year over 5 years). Results: A total of 104 patients were included (41.9 ± 11.9 years old, 38.5% men), with 62.5% of the patients classified as high BF. The High BF group presented higher levels of OA, glycosylated hemoglobin (HbA1c), C-reactive protein (CRP), 24 h urinary sodium (UNa), and htTKV, and lower eGFR than those with a normal BF. In the multivariate linear regression, the associated variables with TKV were high BF, OA and BMI (std. β 0.47, p < 0.05; std. β 0.36, p = 0.001; std. β 0.25, p = 0.01, respectively). In the binary logistic regression, when adjusted for potential confounders, UNa was the only parameter associated with an increased risk of eGFR decline ≥2.5 mL/min/year (OR 1.02, 95% CI 1.01–1.03, p = 0.02). Conclusions: Increased body fat and endogenous production of organic acid anions are associated with larger kidney size in ADPKD but not with a decline in renal function. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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Open AccessArticle

Clinical Association between Gout and Parkinson’s Disease: A Nationwide Population-Based Cohort Study in Korea

by Ji Hyoun Kim, In Ah Choi, Aryun Kim and Gilwon Kang

Medicina 2021, 57(12), 1292; https://0-doi-org.brum.beds.ac.uk/10.3390/medicina57121292 - 24 Nov 2021

Cited by 8 | Viewed by 2550

Abstract

Background and Objectives: This retrospective cohort study aimed to investigate the association between gout and Parkinson’s disease (PD) in Korea. Materials and Methods: Overall, 327,160 patients with gout and 327,160 age- and sex-matched controls were selected from the Korean National Health Insurance Service (NHIS) database. PD incidence was evaluated by reviewing NHIS records during the period from 2002 to 2019. Patients with a diagnosis of gout (International Classification of Diseases-10 (ICD-10), M10) who were prescribed medications for gout, including colchicine, allopurinol, febuxostat, and benzbromarone for at least 90 days were selected. Patients with PD who were assigned a diagnosis code (ICD-G20) and were registered in the rare incurable diseases (RID) system were extracted. Results: During follow-up, 912 patients with gout and 929 control participants developed PD. The incidence rate (IR) of overall PD (per 1000 person-years) was not significantly different between both groups (0.35 vs. 0.36 in gout and control groups, respectively). The incidence rate ratio (IRR) was 0.98 (95% CI: 0.89–1.07). The cumulative incidence of PD was not significantly different between the groups. No association between gout and PD was identified in univariate analysis (HR = 1.00, 95% CI: 0.91–1.10, p = 0.935). HR increased significantly with old age (HR = 92.08, 198, and 235.2 for 60–69 years, 70–79 years, and over 80 years, respectively), female sex (HR = 1.21, 95% CI: 1.07–1.37, p = 0.002), stroke (HR = 1.95, 95% CI: 1.76–2.16, p < 0.001), and hypertension (HR = 1.16, 95% CI: 1.01–1.34, p = 0.04). Dyslipidemia exhibited an inverse result for PD (HR = 0.6, 95% CI: 0.52–0.68, p < 0.001). Conclusions: This population-based study did not identify an association between gout and PD. Age, female sex, stroke, and hypertension were identified as independent risk factors for PD, and dyslipidemia demonstrated an inverse result for PD. Full article

(This article belongs to the Special Issue Clinical Studies, Big Data, and Artificial Intelligence in Medicine)

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