Nevin Manimala

PLoS Comput Biol. 2022 Jun 15;18(6):e1009486. doi: 10.1371/journal.pcbi.1009486. Online ahead of print.

ABSTRACT

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject’s clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.

PMID:35704658 | DOI:10.1371/journal.pcbi.1009486

PLoS One. 2022 Jun 15;17(6):e0268441. doi: 10.1371/journal.pone.0268441. eCollection 2022.

ABSTRACT

BACKGROUND: Trachoma is the leading infectious disease that leads to blindness worldwide, especially in developing countries. Though Ethiopia had targeted a trachoma elimination program by 2020, the problem worsens, particularly in the Amhara Region. Even though sustained intervention measures are undertaken across the region, it is unclear why trachoma is still a significant public health problem. So, this study assessed the prevalence of active trachoma and associated factors among 1-9 years of age children from model and non-model kebeles in Dangila district Amhara Region, Northwest Ethiopia.

METHODS: A community-based comparative cross-sectional study was conducted from 20th September 2019 to 29th October 2019. A multistage stratified random sampling technique was used to reach 704 children from model and non-model kebeles. Samples were allocated proportionally to model and non-model kebeles. A structured and pretested data collection tool and observational checklist was used to manage the necessary data. Data were coded and entered in Epidata version 4.6, and further analysis was done using SPSS version 20 software. Bivariable and multivariable logistic regression analysis was employed to identify factors associated with active trachoma. Adjusted Odds Ratios (AOR), p-value, and respected Confidence Interval (CI) were used to report the findings.

RESULTS: Seven hundred four children were included in this study, with a response rate of 97.8%. The overall prevalence of active trachoma was 6% (95% CI: 4.5, 8.1). The prevalence of active trachoma among non-model and model Kebele was not significantly different. Still, the prevalence of active trachoma among children from model Kebele were [4.5%, (95% CI: 2.4%, 7.1%)] relatively lower compared with non-model kebeles, [7.6%, 95% CI: (4.9%, 10.9%)]. Moreover, not using latrine (AOR = 4.29, 95% CI: 1.96, 9.34), fly-eye contact (AOR = 2.59, 95% CI: 1.11, 6.03), presence of sleep in eyes (AOR = 2.46, 95% CI: 1.10, 5.47), presence of ocular discharge (AOR = 2.79, 95% CI: 1.30, 6.00), presence of nasal discharges (AOR = 2.67, 95% CI: 1.21, 5.90) and washing faces with soap (AOR = 0.22, 95% CI: 0.07, 0.69) were found significantly associated with the prevalence of active trachoma among children 1-9 years old.

CONCLUSIONS: The prevalence of active trachoma in the model and non-model kebeles was high and did not show a statistical difference. Attention to be given to latrine utilization, washing face with soap, and other personal hygiene activities.

PMID:35704657 | DOI:10.1371/journal.pone.0268441

PLoS One. 2022 Jun 15;17(6):e0269919. doi: 10.1371/journal.pone.0269919. eCollection 2022.

ABSTRACT

BACKGROUND: Non-adherence to insulin therapy is a major global public health issue that has a causal relationship with increased diabetic complications that leads to further increase in the health care cost. However, adherence to insulin therapy and associated factors among diabetic mellitus (DM) patients are still not studied adequately in Ethiopia.

OBJECTIVE: To assess the adherence to insulin therapy and associated factors among type 1 and type 2 diabetic patients on follow-up at Madda Walabu University-Goba Referral Hospital, South East Ethiopia.

METHOD: An institution-based, cross-sectional study was employed among 311 both type 1 and type 2 diabetic patients, Madda Walabu University-Goba Referral Hospital from March 4 to April 30, 2020. Study participants were recruited with simple random sampling method. Adherence to insulin therapy was measured by 8-item Morisky medication adherence scale. Therefore from these 8-items, those who score 6 or more are considered as adherent to insulin therapy. The data were collected through interviewer administered questionnaires by trained graduating class nurse students. The data were entered to Epidata version 3.1, and analyzed with SPSS version 25. Bivariate and multivariable logistic regression analyses were used to identify factors associated with adherence to insulin therapy. Statistical significance were declared at p <0.05.

RESULT: A total of 311 patients participate in the study with response rate of 100%. Among these only 38.9% of them were adherent to insulin therapy with a CI of [33.5, 44.3]. Having glucometer (AOR = 3.88; 95% CI [1.46, 10.35]), regular hospital follow-up (AOR = 3.13; 95% CI [1.12, 8.70]), being knowledgeable (AOR = 3.36; 95% CI [1.53, 7.37]), and favorable attitudes (AOR = 4.55; 95%CI [1.68, 12.34]) were the factor associated with adherence to insulin therapy.

CONCLUSION: This study concluded that adherence to insulin therapy was low in the study area. Having glucometer, regular hospital follow-up, being knowledgeable, and favorable attitudes were the factor associated with adherence to insulin therapy. Attention should be paid to help diabetic patients on acquiring knowledge regarding the need of consistent adherence to insulin therapy and its complications.

PMID:35704654 | DOI:10.1371/journal.pone.0269919

PLoS One. 2022 Jun 15;17(6):e0266945. doi: 10.1371/journal.pone.0266945. eCollection 2022.

ABSTRACT

BACKGROUND: The COVID-19 pandemic in Russia has already resulted in 500,000 excess deaths, with more than 5.6 million cases registered officially by July 2021. Surveillance based on case reporting has become the core pandemic monitoring method in the country and globally. However, population-based seroprevalence studies may provide an unbiased estimate of the actual disease spread and, in combination with multiple surveillance tools, help to define the pandemic course. This study summarises results from four consecutive serological surveys conducted between May 2020 and April 2021 at St. Petersburg, Russia and combines them with other SARS-CoV-2 surveillance data.

METHODS: We conducted four serological surveys of two random samples (May-June, July-August, October-December 2020, and February-April 2021) from adults residing in St. Petersburg recruited with the random digit dialing (RDD), accompanied by a telephone interview to collect information on both individuals who accepted and declined the invitation for testing and account for non-response. We have used enzyme-linked immunosorbent assay CoronaPass total antibodies test (Genetico, Moscow, Russia) to report seroprevalence. We corrected the estimates for non-response using the bivariate probit model and also accounted the test performance characteristics, obtained from independent assay evaluation. In addition, we have summarised the official registered cases statistics, the number of hospitalised patients, the number of COVID-19 deaths, excess deaths, tests performed, data from the ongoing SARS-CoV-2 variants of concern (VOC) surveillance, the vaccination uptake, and St. Petersburg search and mobility trends. The infection fatality ratios (IFR) have been calculated using the Bayesian evidence synthesis model.

FINDINGS: After calling 113,017 random mobile phones we have reached 14,118 individuals who responded to computer-assisted telephone interviewing (CATI) and 2,413 provided blood samples at least once through the seroprevalence study. The adjusted seroprevalence in May-June, 2020 was 9.7% (95%: 7.7-11.7), 13.3% (95% 9.9-16.6) in July-August, 2020, 22.9% (95%: 20.3-25.5) in October-December, 2021 and 43.9% (95%: 39.7-48.0) in February-April, 2021. History of any symptoms, history of COVID-19 tests, and non-smoking status were significant predictors for higher seroprevalence. Most individuals remained seropositive with a maximum 10 months follow-up. 92.7% (95% CI 87.9-95.7) of participants who have reported at least one vaccine dose were seropositive. Hospitalisation and COVID-19 death statistics and search terms trends reflected the pandemic course better than the official case count, especially during the spring 2020. SARS-CoV-2 circulation showed rather low genetic SARS-CoV-2 lineages diversity that increased in the spring 2021. Local VOC (AT.1) was spreading till April 2021, but B.1.617.2 substituted all other lineages by June 2021. The IFR based on the excess deaths was equal to 1.04 (95% CI 0.80-1.31) for the adult population and 0.86% (95% CI 0.66-1.08) for the entire population.

CONCLUSION: Approximately one year after the COVID-19 pandemic about 45% of St. Petersburg, Russia residents contracted the SARS-CoV-2 infection. Combined with vaccination uptake of about 10% it was enough to slow the pandemic at the present level of all mitigation measures until the Delta VOC started to spread. Combination of several surveillance tools provides a comprehensive pandemic picture.

PMID:35704649 | DOI:10.1371/journal.pone.0266945

PLoS One. 2022 Jun 15;17(6):e0269251. doi: 10.1371/journal.pone.0269251. eCollection 2022.

ABSTRACT

Information and communications technology (ICT) has been widely embraced in many developing economies in recent times. Extant research reveals that ICT increases economic growth. Beyond economic growth, improved access to information, markets and economic opportunities via information and communications technology have the potential to influence other dimensions of public welfare. This study quantitatively examines the effects of ICT on selected health and gender dimensions of Pacific Island developing countries’ populations. The results show a statistically significant and positive impact of ICT on health and gender outcomes. Our results are robust with an alternative modeling approach, different control variables, and different measures of health and gender outcomes. We further establish that the health outcome of technology has a valid pass-through of income. The study suggests policy implications for the Pacific and other developing countries striving to enhance the health and gender outcomes of SGDs.

PMID:35704646 | DOI:10.1371/journal.pone.0269251

JMIR Med Inform. 2022 Jun 15;10(6):e34678. doi: 10.2196/34678.

ABSTRACT

BACKGROUND: New artificial intelligence (AI) tools are being developed at a high speed. However, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely because of the high implementation complexity of AI, legacy IT infrastructure, and unclear business cases, thus complicating AI adoption. Research has recently started to identify the factors influencing AI readiness of organizations.

OBJECTIVE: This study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also assessed the status quo regarding the dissemination of AI tools in hospitals. We focused on IT decision makers, a seldom studied but highly relevant group.

METHODS: We created a web-based survey based on recent AI readiness and implementation literature. Participants were identified through a publicly accessible database and contacted via email or invitational leaflets sent by mail, in some cases accompanied by a telephonic prenotification. The survey responses were analyzed using descriptive statistics.

RESULTS: We contacted 609 possible participants, and our database recorded 40 completed surveys. Most participants agreed or rather agreed with the statement that AI would be relevant in the future, both in Germany (37/40, 93%) and in their own hospital (36/40, 90%). Participants were asked whether their hospitals used or planned to use AI technologies. Of the 40 participants, 26 (65%) answered “yes.” Most AI technologies were used or planned for patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lack of resources (staff, knowledge, and financial). Relevant possible opportunities for using AI were increase in efficiency owing to time-saving effects, competitive advantages, and increase in quality of care. Most AI tools in use or in planning have been developed with external partners.

CONCLUSIONS: Few tools have been implemented in routine care, and many hospitals do not use or plan to use AI in the future. This can likely be explained by missing or unclear business cases or the need for a modern IT infrastructure to integrate AI tools in a usable manner. These shortcomings complicate decision-making and resource attribution. As most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers should assess their hospitals’ readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and readiness factors to determine whether improvements can be made over time. This monitoring is especially important with regard to government-supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to further explore the reasons for slow AI.

PMID:35704378 | DOI:10.2196/34678

JMIR Med Inform. 2022 Jun 15;10(6):e37689. doi: 10.2196/37689.

ABSTRACT

BACKGROUND: Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries.

OBJECTIVE: In this paper, we present a machine learning-based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs).

METHODS: We utilized deidentified anonymized EHRs of 8580 patients with cancer from the Samsung Medical Center in Korea in a longitudinal manner between 2014 and 2019. To build a prediction model based on physical status that would differ between sepsis and nonsepsis patients, we analyzed 2462 laboratory test results and 2266 medication prescriptions using graph network and statistical analyses. The medication relationships and lab test results from each analysis were used as additional learning features to train our predictive model.

RESULTS: Patients with sepsis showed differential medication trajectories and physical status. For example, in the network-based analysis, narcotic analgesics were prescribed more often in the sepsis group, along with other drugs. Likewise, 35 types of lab tests, including albumin, globulin, and prothrombin time, showed significantly different distributions between sepsis and nonsepsis patients (P<.001). Our model outperformed the model trained using only common EHRs, showing an improved accuracy, area under the receiver operating characteristic (AUROC), and F1 score by 11.9%, 11.3%, and 13.6%, respectively. For the random forest-based model, the accuracy, AUROC, and F1 score were 0.692, 0.753, and 0.602, respectively.

CONCLUSIONS: We showed that lab tests and medication relationships can be used as efficient features for predicting sepsis in patients with cancer. Consequently, identifying the risk of sepsis in patients with cancer using EHRs and machine learning is feasible.

PMID:35704364 | DOI:10.2196/37689

JMIR Med Inform. 2022 Jun 15;10(6):e33921. doi: 10.2196/33921.

ABSTRACT

BACKGROUND: Little is known about family member involvement, by relationship status, for patients treated in the intensive care unit (ICU).

OBJECTIVE: Using documentation of family interactions in clinical notes, we examined associations between child and spousal involvement and ICU patient outcomes, including goals of care conversations (GOCCs), limitations in life-sustaining therapy (LLST), and 3-month mortality.

METHODS: Using a retrospective cohort design, the study included a total of 858 adult patients treated between 2008 and 2012 in the medical ICU at a tertiary care center in northeastern United States. Clinical notes generated within the first 48 hours of admission to the ICU were used with standard machine learning methods to predict patient outcomes. We used natural language processing methods to identify family-related documentation and abstracted sociodemographic and clinical characteristics of the patients from the medical record.

RESULTS: Most of the 858 patients were White (n=650, 75.8%); 437 (50.9%) were male, 479 (55.8%) were married, and the median age was 68.4 (IQR 56.5-79.4) years. Most patients had documented GOCC (n=651, 75.9%). In adjusted regression analyses, child involvement (odds ratio [OR] 0.81; 95% CI 0.49-1.34; P=.41) and child plus spouse involvement (OR 1.28; 95% CI 0.8-2.03; P=.3) were not associated with GOCCs compared to spouse involvement. Child involvement was not associated with LLST when compared to spouse involvement (OR 1.49; 95% CI 0.89-2.52; P=.13). However, child plus spouse involvement was associated with LLST (OR 1.6; 95% CI 1.02-2.52; P=.04). Compared to spouse involvement, there were no significant differences in the 3-month mortality by family member type, including child plus spouse involvement (OR 1.38; 95% CI 0.91-2.09; P=.13) and child involvement (OR 1.47; 95% CI 0.9-2.41; P=.12).

CONCLUSIONS: Our findings demonstrate that statistical models derived from text analysis in the first 48 hours of ICU admission can predict patient outcomes. Early child plus spouse involvement was associated with LLST, suggesting that decisions about LLST were more likely to occur when the child and spouse were both involved compared to the involvement of only the spouse. More research is needed to further understand the involvement of different family members in ICU care and its association with patient outcomes.

PMID:35704362 | DOI:10.2196/33921

Percept Mot Skills. 2022 Jun 15:315125221108913. doi: 10.1177/00315125221108913. Online ahead of print.

ABSTRACT

The aim of this study was to investigate the effect of young tennis players’ expertise on their postural balance (PB) under sensorial conditions with eyes open (EO) and with eyes closed (EC). Our participants were 75 healthy adolescents aged 15-18 years, divided into three groups based on their skill levels: (a) national tennis players (NAT; n = 25), regional tennis players (REG; n =25), and a control group of non-sport practitioners (CG; n = 25). We recorded center of pressure area and mean velocity on a force platform while participants stood in bipedal and unipedal stances in EO and EC conditions for all three groups. Statistical analyses showed that NAT participants swayed less than CG participants in all conditions and less than REG participants in the bipedal stance with EC and in the unipedal stance, both with EO and EC. Thus, tennis practice/experience may have improved PB in this sample, as high-level tennis players had better PB compared to novices, especially in challenging conditions.

PMID:35704346 | DOI:10.1177/00315125221108913

JAMA Netw Open. 2022 Jun 1;5(6):e2217132. doi: 10.1001/jamanetworkopen.2022.17132.

ABSTRACT

IMPORTANCE: Incident atrial fibrillation (AF) is associated with an increased risk of dementia. However, data on the association between smoking cessation after AF diagnosis and dementia risk are limited.

OBJECTIVE: To evaluate the association between changes in smoking status after AF diagnosis and dementia risk.

DESIGN, SETTING, AND PARTICIPANTS: This nationwide cohort study with 126 252 patients used data from the Korean National Health Insurance Service database, including patients who had a national health checkup examination within 2 years before and after AF diagnosis between January 1, 2010, and December 31, 2016. Based on their smoking status, participants were classified as never smokers, ex-smokers, quit smokers, and current smokers. Ex-smokers were defined as those who had quit smoking before the first examination and remained quit until the second examination. Patients who were current smokers at the first health examination but had quit smoking before the second examination were classed as quit smokers. The index date was the second health examination. Patients were followed up until dementia, death, or the study period ended (December 31, 2017), whichever occurred first. Data were analyzed from January 13, 2020, to March 29, 2022.

EXPOSURES: Smoking cessation after newly diagnosed AF.

MAIN OUTCOMES AND MEASURES: Dementia, including Alzheimer disease and vascular dementia, was the primary outcome. Cox proportional hazards regression model was used to estimate hazard ratios.

RESULTS: A total of 126 252 patients (mean [SD] age, 62.6 [12.0] years; 61.9% men) were included in the analysis. The mean (SD) CHA2DS2-VASc score, which measures the risk of ischemic stroke, was 2.7 (1.7). Smoking status of the total study population was as follows: 65 579 never smokers (51.9%), 34 670 ex-smokers (27.5%), 8919 quit smokers (7.1%), and 17 084 current smokers (13.5%). During a median of 3 years of follow-up, dementia occurred in 5925 patients (1.11 per 1000 person-years). After multivariable adjustment, the risk of quit smokers was significantly lower than that of current smokers (hazard ratio, 0.83 [95% CI, 0.72-0.95]).

CONCLUSIONS AND RELEVANCE: The findings of this cohort study suggest that all types of smoking were associated with a significantly higher risk of dementia in patients with new-onset AF. Smoking cessation after AF diagnosis was associated with a lower risk of dementia than among current smokers. These findings may support promoting smoking cessation to reduce dementia risk in patients with new-onset AF.

PMID:35704317 | DOI:10.1001/jamanetworkopen.2022.17132