Category: Nevin Manimala

CNS Neurosci Ther. 2022 Jun 1. doi: 10.1111/cns.13873. Online ahead of print.

ABSTRACT

Perioperative neurocognitive disorders (NCDs) refer to neurocognitive abnormalities detected during the perioperative periods, including preexisting cognitive impairment, preoperative delirium, delirium occurring up to 7 days after surgery, delayed neurocognitive recovery, and postoperative NCD. The Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) is the golden standard for diagnosing perioperative NCDs. Given the impracticality of using the DSM-5 by non-psychiatric practitioners, many diagnostic tools have been developed and validated for different clinical scenarios. The etiology of perioperative NCDs is multifactorial and includes predisposing and precipitating factors. Identifying these risk factors is conducive to preoperative risk stratification and perioperative risk reduction. Prevention for perioperative NCDs should include avoiding possible contributors and implementing nonpharmacologic and pharmacological interventions. The former generally includes avoiding benzodiazepines, anticholinergics, prolonged liquid fasting, deep anesthesia, cerebral oxygen desaturation, and intraoperative hypothermia. Nonpharmacologic measures include preoperative cognitive prehabilitation, comprehensive geriatric assessment, implementing fast-track surgery, combined use of regional block, and sleep promotion. Pharmacological measures including dexmedetomidine, nonsteroidal anti-inflammatory drugs, and acetaminophen are found to have beneficial effects. Nonpharmacological treatments are the first-line measures for established perioperative NCDs. Pharmacological treatments are still limited to severely agitated or distressed patients.

PMID:35652170 | DOI:10.1111/cns.13873

Radiol Artif Intell. 2022 Apr 20;4(3):e210115. doi: 10.1148/ryai.210115. eCollection 2022 May.

ABSTRACT

PURPOSE: To present a method that automatically detects, subtypes, and locates acute or subacute intracranial hemorrhage (ICH) on noncontrast CT (NCCT) head scans; generates detection confidence scores to identify high-confidence data subsets with higher accuracy; and improves radiology worklist prioritization. Such scores may enable clinicians to better use artificial intelligence (AI) tools.

MATERIALS AND METHODS: This retrospective study included 46 057 studies from seven “internal” centers for development (training, architecture selection, hyperparameter tuning, and operating-point calibration; n = 25 946) and evaluation (n = 2947) and three “external” centers for calibration (n = 400) and evaluation (n = 16764). Internal centers contributed developmental data, whereas external centers did not. Deep neural networks predicted the presence of ICH and subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and/or epidural hemorrhage) and segmentations per case. Two ICH confidence scores are discussed: a calibrated classifier entropy score and a Dempster-Shafer score. Evaluation was completed by using receiver operating characteristic curve analysis and report turnaround time (RTAT) modeling on the evaluation set and on confidence score-defined subsets using bootstrapping.

RESULTS: The areas under the receiver operating characteristic curve for ICH were 0.97 (0.97, 0.98) and 0.95 (0.94, 0.95) on internal and external center data, respectively. On 80% of the data stratified by calibrated classifier and Dempster-Shafer scores, the system improved the Youden indexes, increasing them from 0.84 to 0.93 (calibrated classifier) and from 0.84 to 0.92 (Dempster-Shafer) for internal centers and increasing them from 0.78 to 0.88 (calibrated classifier) and from 0.78 to 0.89 (Dempster-Shafer) for external centers (P < .001). Models estimated shorter RTAT for AI-prioritized worklists with confidence measures than for AI-prioritized worklists without confidence measures, shortening RTAT by 27% (calibrated classifier) and 27% (Dempster-Shafer) for internal centers and shortening RTAT by 25% (calibrated classifier) and 27% (Dempster-Shafer) for external centers (P < .001).

CONCLUSION: AI that provided statistical confidence measures for ICH detection on NCCT scans reliably detected and subtyped hemorrhages, identified high-confidence predictions, and improved worklist prioritization in simulation.Keywords: CT, Head/Neck, Hemorrhage, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

PMID:35652116 | PMC:PMC9152881 | DOI:10.1148/ryai.210115

Radiol Artif Intell. 2022 May 4;4(3):e210064. doi: 10.1148/ryai.210064. eCollection 2022 May.

ABSTRACT

PURPOSE: To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis.

MATERIALS AND METHODS: In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics.

RESULTS: Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance.

CONCLUSION: Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.

PMID:35652114 | PMC:PMC9152694 | DOI:10.1148/ryai.210064

Cancer Inform. 2022 May 26;21:11769351221100754. doi: 10.1177/11769351221100754. eCollection 2022.

ABSTRACT

The creation of patient-derived cancer organoids represents a key advance in preclinical modeling and has recently been applied to a variety of human solid tumor types. However, conventional methods used to assess in vivo tumor tissue treatment response are poorly suited for the evaluation of cancer organoids because they are time-intensive and involve tissue destruction. To address this issue, we established a suite of 3-dimensional patient-derived glioma organoids, treated them with chemoradiotherapy, stained organoids with non-toxic cell dyes, and imaged them using a rapid laser scanning confocal microscopy method termed “Apex Imaging.” We then developed and tested a fragmentation algorithm to quantify heterogeneity in the topography of the organoids as a potential surrogate marker of viability. This algorithm, SSDquant, provides a 3-dimensional visual representation of the organoid surface and a numerical measurement of the sum-squared distance (SSD) from the derived mass center of the organoid. We tested whether SSD scores correlate with traditional immunohistochemistry-derived cell viability markers (cellularity and cleaved caspase 3 expression) and observed statistically significant associations between them using linear regression analysis. Our work describes a quantitative, non-invasive approach for the serial measurement of patient-derived cancer organoid viability, thus opening new avenues for the application of these models to studies of cancer biology and therapy.

PMID:35652106 | PMC:PMC9150230 | DOI:10.1177/11769351221100754

SSM Popul Health. 2022 May 9;18:101119. doi: 10.1016/j.ssmph.2022.101119. eCollection 2022 Jun.

ABSTRACT

Research has documented important differences in smoking rates across race/ethnicity, gender, and age. Much of the research has either focused on smoking initiation among adolescents or cessation among adults, but little is known about racial/ethnic patterns in intermittent and daily smoking across young and early middle adulthood. We therefore use the life course perspective to identify how racial/ethnic and gender differences in smoking unfold across adulthood. Analyses investigate whether racial/ethnic and gender differences exist in the likelihood of daily smoking in early middle adulthood and whether these disparities persist after the inclusion of adolescent and early midlife sociodemographic characteristics and young adult smoking patterns. Descriptive statistics and multivariate binary logistic regression analyses employ recent data from a nationally representative sample of adults using the National Longitudinal Study of Adolescent to Adult Health (Add Health; N = 8,506). We find evidence that life course patterns of smoking differ across race/ethnicity and gender subgroups. In early middle adulthood (ages 33-44), White women are more likely to smoke daily than Black or Hispanic women. In contrast, there are no significant differences between White and Black men, but White men are more likely to smoke daily than Hispanic men. These racial/ethnic differences are no longer significant for men when previous smoking is controlled, suggesting that early young adult smoking plays an important role in the development of smoking disparities across race/ethnicity. Further, we find that young adult intermittent smoking is associated with daily smoking in early midlife, and this relationship is stronger for Black, compared to White, men and women. Although Black women display lower odds of daily smoking in early midlife compared to White women, they exhibit a higher risk of transitioning from intermittent to daily smoking. These results highlight the importance of considering a greater diversity of life course patterns in smoking across race/ethnicity and gender in future research and policies.

PMID:35652089 | PMC:PMC9149197 | DOI:10.1016/j.ssmph.2022.101119

Front Pediatr. 2022 May 16;10:851033. doi: 10.3389/fped.2022.851033. eCollection 2022.

ABSTRACT

BACKGROUND: Children with medical complexity (CMC) are prone to medical errors and longer hospital stays, while residents do not feel prepared to provide adequate medical care for this vulnerable population. No educational guidance for the training of future pediatric tertiary care specialists outside their field of expertise involving the multidisciplinary care of CMC exists. We investigated pediatric residents past educational needs and challenges to identify key learning content for future training involving care for CMC.

METHODS: This was a prospective mixed-methods study at a single pediatric tertiary care center. Qualitative semi-structured interviews with residents were conducted, submitted to thematic content analysis, linked to the American Board of Pediatrics (ABP) general pediatrics content outline, and analyzed with importance performance analysis (IPA). Quantitative validation was focused on key themes of pediatric nephrology within the scope of an online survey among pediatric residents and specialists.

RESULTS: A total of 16 interviews, median duration 69 min [interquartile range IQR 35], were conducted. The 280 listed themes of the ABP general pediatrics content outline were reduced to 165 themes, with 86% (theoretical) knowledge, 12% practical skills, and 2% soft skills. IPA identified 23 knowledge themes to be of high importance where improvement is necessary and deemed fruitful. Quantitative validation among 84 residents and specialists (response rate 55%) of key themes in nephrology yielded high agreement among specialists in pediatric nephrology but low interrater agreement among trainees and “trained” non-nephrologists. The occurrence of themes in the qualitative interviews and their calculated importance in the quantitative survey were highly correlated (tau = 0.57, p = 0.001). Two clusters of high importance for other pediatric specialties emerged together with a contextual cluster of frequent encounters in both in- and outpatient care.

CONCLUSION: Regarding patient safety, this study revealed the heterogeneous aspects and the importance of training future pediatric tertiary care specialists outside their field of expertise involving the multidisciplinary care of CMC. Our results may lay the groundwork for future detailed analysis and development of training boot camps that might be able to aid the improvement of patient safety by decreasing preventable harm by medical errors, especially for vulnerable patient groups, such as CMC in tertiary care pediatrics.

PMID:35652058 | PMC:PMC9149215 | DOI:10.3389/fped.2022.851033

Front Med (Lausanne). 2022 May 16;9:856654. doi: 10.3389/fmed.2022.856654. eCollection 2022.

ABSTRACT

OBJECTIVES: The Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) has been widely utilized to evaluate disease activity in patients with ankylosing spondylitis (AS) by an arbitrary cut-off of ≥4 to indicate high disease activity and initiate biological therapy. The Ankylosing Spondylitis Disease Activity Score (ASDAS) is a new composite index to assess AS disease activity states that have been defined and validated. ASDAS ≥2.1 was selected as a criterion to start biological therapy. The purpose of this study was to estimate the corresponding BASDAI and ASDAS cut-off in a Taiwanese AS cohort.

METHODS: From November 2016 to October 2018, we assessed the ASDAS and the BASDAI regularly and recorded demographic data for 489 AS patients in Taichung Veterans General hospital (TCVGH) using an electronic patient-reported data system linked to electronic medical records. We used receiver operating characteristic curves with Youden’s J statistic to determine the BASDAI values that correspond to ASDAS disease activity cut-offs (i.e., 1.3, 2.1, and 3.5).

RESULTS: In our population, the best trade-off BASDAI values corresponding to ASDAS -C-reactive protein (CRP) 1.3, 2.1, and 3.5 were 2.1, 3.1, and 3.7, respectively. The optimal BASDAI values corresponding to ASDAS-erythrocyte sedimentation rates 1.3, 2.1, and 3.5 were 2.0, 2.6, and 4.8, respectively.

CONCLUSION: We propose a revised BASDAI cut-off based on our data, as BASDAI scores are commonly used globally. A more reasonable, lower BASDAI cut-off to initiate or change biological therapy will bring us closer to better decisions to treat AS patients.

PMID:35652077 | PMC:PMC9149077 | DOI:10.3389/fmed.2022.856654

Contrast Media Mol Imaging. 2022 May 12;2022:3503095. doi: 10.1155/2022/3503095. eCollection 2022.

ABSTRACT

OBJECTIVE: The main objective is to investigate the effect of PDCA circulation management on nursing quality in the day operation room.

METHODS: A retrospective study was performed in 300 patients in the day surgery room. For the control group, 150 patients received routine nursing. For the observation group, 150 patients underwent PDCA circulation nursing management. The scores for nursing quality management, the hospital infection, the detection rate of pathogenic bacteria, the incidence rate of adverse events, the negative emotion of patients, and the satisfaction rate for the day surgery department were recorded and analyzed between two groups.

RESULTS: Compared with the control group, the scores for nursing quality management and the satisfaction rate for the day surgery department were significantly increased (all P < 0.05), while the hospital infection, the detection rate of pathogenic bacteria, HAMA scores, HAMD scores, and the incidence rate of adverse events were obviously decreased (all P < 0.05). Significantly statistical differences were observed between the two groups.

CONCLUSION: PDCA circulation nursing management in the day operating room could optimize the nursing quality management, improve the satisfaction rate of the operating room, reduce the negative emotions of patients, and prevent adverse events in time, with lower hospital infections.

PMID:35652037 | PMC:PMC9119782 | DOI:10.1155/2022/3503095

Contracept Reprod Med. 2022 Jun 2;7(1):9. doi: 10.1186/s40834-022-00175-y.

ABSTRACT

BACKGROUND: Family planning for HIV-positive women has numerous advantages. However, the need of family planning utilization is challenged by women’s nonautonomous decision-making power. Therefore, this study aimed to examine the level and associated factors of decision-making power to utilize family planning among HIV-positive married women.

METHODS: A facility-based cross-sectional study was conducted from March to June 2020 among 363 HIV-positive married women on ART, using systematic random sampling technique. Logistic regression analysis was used to identify variables that affect women’s decision-making power on family planning utilization. Statistical significance was declared at p-value < 0.05 with 95% confidence interval and strength of association was reported by adjusted odds ratio.

RESULTS: Overall 55.2% (95% CI: 49.9-60.5) of the women had decision-making power on family planning utilization. Women’s having good knowledge (AOR: 2.87, 95% CI: 1.52-5.40), favorable attitude (AOR: 1.96, 95% CI: 1.13-3.38), women’s getting family planning counseling in ART clinics (AOR: 2.04, 95% CI: 1.16-3.59), women who get integration service of FP and ART (AOR: 1.83, 95% CI:1.07-3.12) were factors independently associated with women decision-making power on family planning utilization.

CONCLUSION: Decision-making power to utilize family planning among married HIV-positive women was low. Factors like poor knowledge about family planning, dissatisfaction with family planning service, not getting counseling about family planning in ART clinics, and not receiving family planning service in ART clinics were independently associated with women’s decision-making power on family planning. Infrastructure linked with the health facility, knowledge, and attitudinal factors should all be combined in future family planning programs.

PMID:35650651 | DOI:10.1186/s40834-022-00175-y

Diagn Progn Res. 2022 Jun 2;6(1):10. doi: 10.1186/s41512-022-00124-y.

ABSTRACT

BACKGROUND: There is substantial interest in the adaptation and application of so-called machine learning approaches to prognostic modelling of censored time-to-event data. These methods must be compared and evaluated against existing methods in a variety of scenarios to determine their predictive performance. A scoping review of how machine learning methods have been compared to traditional survival models is important to identify the comparisons that have been made and issues where they are lacking, biased towards one approach or misleading.

METHODS: We conducted a scoping review of research articles published between 1 January 2000 and 2 December 2020 using PubMed. Eligible articles were those that used simulation studies to compare statistical and machine learning methods for risk prediction with a time-to-event outcome in a medical/healthcare setting. We focus on data-generating mechanisms (DGMs), the methods that have been compared, the estimands of the simulation studies, and the performance measures used to evaluate them.

RESULTS: A total of ten articles were identified as eligible for the review. Six of the articles evaluated a method that was developed by the authors, four of which were machine learning methods, and the results almost always stated that this developed method’s performance was equivalent to or better than the other methods compared. Comparisons were often biased towards the novel approach, with the majority only comparing against a basic Cox proportional hazards model, and in scenarios where it is clear it would not perform well. In many of the articles reviewed, key information was unclear, such as the number of simulation repetitions and how performance measures were calculated.

CONCLUSION: It is vital that method comparisons are unbiased and comprehensive, and this should be the goal even if realising it is difficult. Fully assessing how newly developed methods perform and how they compare to a variety of traditional statistical methods for prognostic modelling is imperative as these methods are already being applied in clinical contexts. Evaluations of the performance and usefulness of recently developed methods for risk prediction should be continued and reporting standards improved as these methods become increasingly popular.

PMID:35650647 | DOI:10.1186/s41512-022-00124-y