Nevin Manimala

Retina. 2022 May 23. doi: 10.1097/IAE.0000000000003540. Online ahead of print.

ABSTRACT

PURPOSE: The importance of consistent outpatient follow-up for management of diabetic eye disease has been well-established. The objective of this study was to identify patient factors associated with being lost to follow-up in post-surgical patients after undergoing pars plana vitrectomy for diabetic eye disease.

METHODS: The charts of diabetic patients undergoing pars plana vitrectomy for non-clearing vitreous hemorrhage at an academic medical center by a single surgeon between 2012 and 2019 were reviewed. The rates of loss to follow-up during the postoperative period were compared based on patient distance from clinic and insurance status.

RESULTS: A total of 144 patients met inclusion criteria. 45 patients (31.25%) were lost to follow-up during the three-month postoperative period. The rate of loss to follow-up increased with every postoperative visit and was significantly higher for patients living greater than 30 miles from clinic versus patients living within 30 miles from clinic. There was no statistically significant difference in loss to follow-up based on insurance status.

CONCLUSIONS: Increased distance from clinic presents a challenge to providing safe and effective post-surgical care to diabetic patients. This presents opportunities for co-management or other creative strategies to improve post-surgical follow-up rates for at-risk patients.

PMID:36044683 | DOI:10.1097/IAE.0000000000003540

J Med Internet Res. 2022 May 17. doi: 10.2196/35860. Online ahead of print.

ABSTRACT

BACKGROUND: COVID-19 has been observed to be associated with venous and arterial thrombosis. The inflammatory disease prolongs hospitalization, and preexisting comorbidities can intensify thrombotic burden in COVID-19 patients. However, venous thromboembolism, arterial thrombosis, and other vascular complications may go unnoticed in critical care settings. Early risk stratification is paramount in the COVID-19 patient population for proactive monitoring of thrombotic complications.

OBJECTIVE: This exploratory research seeks to characterize thrombotic complication risk factors associated with COVID-19 using information from Electronic Health Record (EHR) databases and insurance claims databases. The goal is to develop an approach for analysis using real-world data evidence that can be generalized to characterize thrombotic complications and additional conditions in other clinical settings as well, such as pneumonia or acute respiratory distress syndrome in COVID-19 patients or in ICU settings.

METHODS: We extracted deidentified patient data from the insurance claims database, IBM MarketScan, and formulated hypotheses on thrombotic complications in COVID-19 patients with respect to patient demographic and clinical factors using logistic regression. The analysis then verified the hypotheses with deidentified patient data from the Mass General Brigham (MGB) patient EHR database, the Research Patient Data Registry (RPDR). A combination of odds ratios, 95% confidence intervals (CI), and P-values were obtained via the statistical analysis.

RESULTS: The analysis identified significant predictors (with P-value <.001) for thrombotic complications in 184,831 COVID-19 patients out of the millions of records from IBM MarketScan and MGB RPDR. With respect to age groups, patients 60 years old and older had higher odds (4.866 in Marketscan and 6.357 in RPDR) to have thrombotic complications than those under 60 years old. In terms of gender, men were more likely (odds ratio of 1.245 in Marketscan and 1.693 in RPDR) to have thrombotic complications than women. Among the pre-existing comorbidities, heart disease, cerebrovascular diseases, hypertension, and personal history of thrombosis all had significantly higher odds of developing a thrombotic complication. Cancer and obesity had odds greater than 1 as well. The results from RPDR validated the IBM MarketScan findings. They are largely consistent and afford mutual enrichment.

CONCLUSIONS: The analysis approach can work across heterogeneous databases from diverse organizations and thus facilitates collaboration. Searching through millions of patient records, the analysis helped identify factors influencing a phenotype. Use of thrombotic complications in COVID-19 patients is just a case study, and the same design can be used across other disease areas by extracting corresponding disease specific patient data from the databases.

PMID:36044652 | DOI:10.2196/35860

JMIR Res Protoc. 2022 Aug 6. doi: 10.2196/38866. Online ahead of print.

ABSTRACT

BACKGROUND: At the beginning of 2020, mothers and fathers who experienced perinatal events (from conception to pregnancy and postpartum period) found themselves facing problems relating to the emergency caused by the coronavirus disease 2019 (COVID-19) pandemic, and the associated difficulties for healthcare centers in providing care. In the unexpected and negative event of perinatal loss (miscarriage, stillbirth, neonatal death) more complications occurred. Perinatal loss is a painful and traumatic life experience that causes grief and can cause affective disorders in the parental couple: the baby dies and the couple’s plans for a family are abruptly interrupted. During the COVID-19 pandemic, the limited access to perinatal bereavement care, due to the lockdown measures imposed on medical Healthcare Centers and the social distancing rules to prevent contagion, was an additional risk factor for parental mental health, such as suffering a prolonged and complicated grief.

OBJECTIVE: The main aims of this study are: to investigate the impact of COVID-19 on mothers and fathers who experienced perinatal loss during the pandemic, comparing their perceptions; to evaluate their change over time between first survey administration after bereavement and the second survey, after 6 months; to examine correlations of bereavement with anxiety, depression, couple satisfaction, spirituality and socio-demographic variables; to investigate which psychosocial factors may negatively affect the mourning process and to identify the potential predictors of the development of complicated grief.

METHODS: This longitudinal observational multicenter study is structured according to a mixed methods design, with a quantitative and qualitative section. It will include a sample of parents (mothers and fathers) who experienced perinatal loss during the COVID-19 pandemic from March 2020. There are two phases: a baseline and a follow-up after 6 months.

RESULTS: This protocol was approved by the Ethics Committee of Psychological Research, University of Padova, and by the Institutional Ethics Board of the Spedali Civili of Brescia, Italy. We expect to collect data from 34 or more couples, as determined by our sample size calculation.

CONCLUSIONS: This study will contribute to the understanding of the psychological processes related to perinatal loss and bereavement care during the COVID-19 pandemic. It will provide information useful to prevent the risk of complicated grief and psychopathologies among bereaved parents and to promote perinatal mental health.

PMID:36044641 | DOI:10.2196/38866

Sci Adv. 2022 Sep 2;8(35):eabq2157. doi: 10.1126/sciadv.abq2157. Epub 2022 Aug 31.

ABSTRACT

Genome-scale functional screening accelerates comprehensive assessment of gene function in cells. Here, we have established a genome-scale loss-of-function screening strategy that combined a cytosine base editor with approximately 12,000 parallel sgRNAs targeting 98.1% of total genes in Corynebacterium glutamicum ATCC 13032. Unlike previous data processing methods developed in yeast or mammalian cells, we developed a new data processing procedure to locate candidate genes by statistical sgRNA enrichment analysis. Known and novel functional genes related to 5-fluorouracil resistance, 5-fluoroorotate resistance, oxidative stress tolerance, or furfural tolerance have been identified. In particular, purU and serA were proven to be related to the furfural tolerance in C. glutamicum. A cloud platform named FSsgRNA-Analyzer was provided to accelerate sequencing data processing for CRISPR-based functional screening. Our method would be broadly useful to functional genomics study and strain engineering in other microorganisms.

PMID:36044571 | DOI:10.1126/sciadv.abq2157

Soc Work Public Health. 2022 Aug 31:1-12. doi: 10.1080/19371918.2022.2118924. Online ahead of print.

ABSTRACT

The extension of the human lifespan has led to an increase in the proportion of the elderly population worldwide. This situation has also brought the issue of healthy aging to the agenda. The importance of more active participation of elderly individuals in life in the development of health is increasing. Depression and self-efficacy of the elderly people are primarily addressed to support this situation. This study is a randomized controlled intervention study in which evaluating the change in depression and self-efficacy levels of elderly individuals after the empowerment intervention. In the study, which was conducted to improve elderly individuals’ depression and self-efficacy levels, an empowerment intervention consisting of 7 sessions was applied to these individuals. In the sessions, practices were carried out to increase the functionality of the elderly in cognitive, social, emotional, physical and spiritual areas. In this study, 60 elderly individuals (intervention and control groups) who were hospitalized for physical therapy and rehabilitation in a state hospital in Turkey between September 2019 and December 2020 were included. The simple random sampling method was used for sampling. The sample size was determined by G Power analysis. Geriatric depression and self-efficacy scales were used in the study. The study data were analyzed on the IBM SPSS Statistics 25.0 software package. Descriptive statistics were used to calculate descriptive data. Pearson, Chi-Square, and Fisher Exact tests were used to compare the sociodemographic and clinical characteristics of the participants. Paired Samples t-test was used to compare the intervention and the control groups’ pretest and posttest scores. In the study, it was determined that the mean geriatric depression pretest score was 15.43 ± 7.05 in the control group and 14.46 ± 7.21 in the intervention group, and there was no significant difference between the groups’ geriatric depression pretest scores (p = .602). However, it was determined that the mean geriatric depression posttest score was 13.50 ± 9.02 in the control group and 9.23 ± 6.71 in the intervention group, and there was a significant difference between the posttest scores of the groups (p = .042). No significant difference was found between the pretest and posttest geriatric depression scale scores of the control group (t = 1.346; p = .189). The posttest geriatric depression score of the intervention group was significantly lower than the pretest score (t = 5.966; p = .0001). In the study, it was determined that the mean self-efficacy pretest score was 79.63 ± 12.62 in the control group, 75.63 ± 14.20 in the intervention group, and there was no significant difference between the pretest scores of the groups (p = .254). It was determined that the mean self-efficacy posttest score was 83.10 ± 11.35 in the control group and 84.50 ± 14.41 in the intervention group, and there was no significant difference between the posttest scores of the groups (p = .678). The posttest self-efficacy score of the intervention group was found to be significantly higher than the pretest score (p = .001). The empowerment intervention was determined to decrease the elderly individuals’ depression and increase their self-efficacy levels.

PMID:36044559 | DOI:10.1080/19371918.2022.2118924

Sci Robot. 2022 Aug 31;7(69):eabo0235. doi: 10.1126/scirobotics.abo0235. Epub 2022 Aug 31.

ABSTRACT

Learning to combine control at the level of joint torques with longer-term goal-directed behavior is a long-standing challenge for physically embodied artificial agents. Intelligent behavior in the physical world unfolds across multiple spatial and temporal scales: Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals that are defined on much longer time scales and that often involve complex interactions with the environment and other agents. Recent research has demonstrated the potential of learning-based approaches applied to the respective problems of complex movement, long-term planning, and multiagent coordination. However, their integration traditionally required the design and optimization of independent subsystems and remains challenging. In this work, we tackled the integration of motor control and long-horizon decision-making in the context of simulated humanoid football, which requires agile motor control and multiagent coordination. We optimized teams of agents to play simulated football via reinforcement learning, constraining the solution space to that of plausible movements learned using human motion capture data. They were trained to maximize several environment rewards and to imitate pretrained football-specific skills if doing so led to improved performance. The result is a team of coordinated humanoid football players that exhibit complex behavior at different scales, quantified by a range of analysis and statistics, including those used in real-world sport analytics. Our work constitutes a complete demonstration of learned integrated decision-making at multiple scales in a multiagent setting.

PMID:36044556 | DOI:10.1126/scirobotics.abo0235

PLoS One. 2022 Aug 31;17(8):e0273859. doi: 10.1371/journal.pone.0273859. eCollection 2022.

ABSTRACT

BACKGROUND: After the first case of COVID-19 caused by the novel SARS-CoV-2 virus was discovered in Wuhan, China, in December 2019, the disease spread viciously throughout the world. Little is known about the impact of HIV infection on the clinical outcomes of patients co-infected with SARS-CoV-2. Studying the characteristics and outcomes of COVID-19 among HIV-positive patients is key to characterising the risk of morbidity and mortality of HIV-positive patients from COVID-19.

METHODS: In this retrospective cohort study, we included patients admitted to Aga Khan University Hospital, Nairobi, with laboratory-confirmed COVID-19 infection and who had consented to HIV screening. We compared the prevalence and characteristics of HIV patients with those of non-HIV patients and described the results for both groups.

RESULTS: In our sample of 582 patients, the mean age was 49.2 years (SD = 15.2), with 68% of the sample being men. The cumulative HIV prevalence was 3.7%, and the most common symptoms were cough (58.1%), fever (45.2%), difficulty in breathing (36.8%) and general body malaise (23.9%). The most common comorbidities included hypertension (28.5%), diabetes mellitus (26.1%), and heart disease (4.1%). Most participants (228 or 49.5%) had mild COVID-19, and the mortality rate was 5%. Overall, there were no statistically significant differences in demographic characteristics, clinical characteristics, and outcomes between HIV-positive and HIV-negative patients.

CONCLUSIONS: There was a 3.7% prevalence of HIV in COVID-19 positive patients. Demographic characteristics and clinical outcomes were similar between the two groups. Future studies should seek to achieve larger samples, include multiple study sites and conduct subgroup analyses based on the immunologic status of HIV-positive patients.

PMID:36044517 | DOI:10.1371/journal.pone.0273859

IEEE J Biomed Health Inform. 2022 Aug 31;PP. doi: 10.1109/JBHI.2022.3203345. Online ahead of print.

ABSTRACT

Given that depression is one of the most prevalent mental illnesses, developing effective and unobtrusive diagnosis tools is of great importance. Recent work that screens for depression with text messages leverage models relying on lexical category features. Given the colloquial nature of text messages, the performance of these models may be limited by formal lexicons. We thus propose a strategy to automatically construct alternative lexicons that contain more relevant and colloquial terms. Specifically, we generate 36 lexicons from fiction, forum, and news corpuses. These lexicons are then used to extract lexical category features from the text messages. We utilize machine learning models to compare the depression screening capabilities of these lexical category features. Out of our 36 constructed lexicons, 14 achieved statistically significantly higher average F1 scores over the pre-existing formal lexicon and basic bag-of-words approach. In comparison to the pre-existing lexicon, our best performing lexicon increased the average F1 scores by 10%. We thus confirm our hypothesis that less formal lexicons can improve the performance of classification models that screen for depression with text messages. By providing our automatically constructed lexicons, we aid future machine learning research that leverages less formal text.

PMID:36044503 | DOI:10.1109/JBHI.2022.3203345

IEEE Trans Neural Netw Learn Syst. 2022 Aug 31;PP. doi: 10.1109/TNNLS.2022.3201160. Online ahead of print.

ABSTRACT

In this brief, the state estimation problem is investigated for a class of randomly delayed artificial neural networks (ANNs) subject to probabilistic saturation constraints (PSCs) and non-Gaussian noises under the redundant communication channels. A series of mutually independent Bernoulli distributed white sequences are introduced to govern the random occurrence of the time delays, the saturation constraints, and the transmission channel failures. A comprehensive redundant-channel-based communication mechanism is constructed to attenuate the phenomenon of packet dropouts so as to enhance the quality of data transmission. To compensate for the influence of randomly occurring time delays, the corresponding occurrence probability is exploited in the process of particle generation. In addition, an explicit expression of the likelihood function is established based on the statistical information to account for the impact of PSCs and redundant channels. By virtue of the modified operations of particle propagation and weight update, a particle-filter-based state estimation algorithm is proposed with mild restriction on the system type. Finally, an illustrative example with Monte Carlo simulations is provided to demonstrate the effectiveness of the developed state estimation scheme.

PMID:36044499 | DOI:10.1109/TNNLS.2022.3201160

IEEE Trans Med Imaging. 2022 Aug 31;PP. doi: 10.1109/TMI.2022.3203309. Online ahead of print.

ABSTRACT

Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. The most popular CNN-based methods are optimised using pixel wise loss functions, ignorant of the spatially extended features that characterise anatomy. Therefore, whilst sharing a high spatial overlap with the ground truth, inferred CNN-based segmentations can lack coherence, including spurious connected components, holes and voids. Such results are implausible, violating anticipated anatomical topology. In response, (single-class) persistent homology-based loss functions have been proposed to capture global anatomical features. Our work extends these approaches to the task of multi-class segmentation. Building an enriched topological description of all class labels and class label pairs, our loss functions make predictable and statistically significant improvements in segmentation topology using a CNN-based post-processing framework. We also present (and make available) a highly efficient implementation based on cubical complexes and parallel execution, enabling practical application within high resolution 3D data for the first time. We demonstrate our approach on 2D short axis and 3D whole heart CMR segmentation, advancing a detailed and faithful analysis of performance on two publicly available datasets.

PMID:36044487 | DOI:10.1109/TMI.2022.3203309