-
Molecular Metabolism Jun 2024In response to bacterial inflammation, anorexia of acute illness is protective and is associated with the induction of fasting metabolic programs such as ketogenesis....
OBJECTIVE
In response to bacterial inflammation, anorexia of acute illness is protective and is associated with the induction of fasting metabolic programs such as ketogenesis. Forced feeding during the anorectic period induced by bacterial inflammation is associated with suppressed ketogenesis and increased mortality. As ketogenesis is considered essential in fasting adaptation, we sought to determine the role of ketogenesis in illness-induced anorexia.
METHODS
A mouse model of inducible hepatic specific deletion of the rate limiting enzyme for ketogenesis (HMG-CoA synthase 2, Hmgcs2) was used to investigate the role of ketogenesis in endotoxemia, a model of bacterial inflammation, and in prolonged starvation.
RESULTS
Mice deficient of hepatic Hmgcs2 failed to develop ketosis during endotoxemia and during prolonged fasting. Surprisingly, hepatic HMGCS2 deficiency and the lack of ketosis did not affect survival, glycemia, or body temperature in response to endotoxemia. Mice with hepatic ketogenic deficiency also did not exhibit any defects in starvation adaptation and were able to maintain blood glucose, body temperature, and lean mass compared to littermate wild-type controls. Mice with hepatic HMGCS2 deficiency exhibited higher levels of plasma acetate levels in response to fasting.
CONCLUSIONS
Circulating hepatic-derived ketones do not provide protection against endotoxemia, suggesting that alternative mechanisms drive the increased mortality from forced feeding during illness-induced anorexia. Hepatic ketones are also dispensable for surviving prolonged starvation in the absence of inflammation. Our study challenges the notion that hepatic ketogenesis is required to maintain blood glucose and preserve lean mass during starvation, raising the possibility of extrahepatic ketogenesis and use of alternative fuels as potential means of metabolic compensation.
PubMed: 38876267
DOI: 10.1016/j.molmet.2024.101967 -
JMIR Formative Research Jun 2024Journey to 9 Plus (J9) is an integrated reproductive, maternal, neonatal, and child health approach to care that has at its core the goal of decreasing the rate of...
BACKGROUND
Journey to 9 Plus (J9) is an integrated reproductive, maternal, neonatal, and child health approach to care that has at its core the goal of decreasing the rate of maternal and neonatal morbidity and mortality in rural Haiti. For the maximum effectiveness of this program, it is necessary that the data system be of the highest quality. OpenMRS, an electronic medical record (EMR) system, has been in place since 2013 throughout a tertiary referral hospital, the Hôpital Universitaire de Mirebalais, in Haiti and has been expanded for J9 data collection and reporting. The J9 program monthly reports showed that staff had limited time and capacity to perform double charting, which contributed to incomplete and inconsistent reports. Initial evaluation of the quality of EMR data entry showed that only 18% (58/325) of the J9 antenatal visits were being documented electronically at the start of this quality improvement project.
OBJECTIVE
This study aimed to improve the electronic documentation of outpatient antenatal care from 18% (58/325) to 85% in the EMR by J9 staff from November 2020 to September 2021. The experiences that this quality improvement project team encountered could help others improve electronic data collection as well as the transition from paper to electronic documentation within a burgeoning health care system.
METHODS
A continuous quality improvement strategy was undertaken as the best approach to improve the EMR data collection at Hôpital Universitaire de Mirebalais. The team used several continuous quality improvement tools to conduct this project: (1) a root cause analysis using Ishikawa and Pareto diagrams, (2) baseline evaluation measurements, and (3) Plan-Do-Study-Act improvement cycles to document incremental changes and the results of each change.
RESULTS
At the beginning of the quality improvement project in November 2020, the baseline data entry for antenatal visits was 18% (58/325). Ten months of improvement strategies resulted in an average of 89% (272/304) of antenatal visits documented in the EMR at point of care every month.
CONCLUSIONS
The experiences that this quality improvement project team encountered can contribute to the transition from paper to electronic documentation within burgeoning health care systems. Essential to success was having a strong and dedicated nursing leadership to transition from paper to electronic data and motivated nursing staff to perform data collection to improve the quality of data and thus, the reports on patient outcomes. Engaging the nursing team closely in the design and implementation of EMR and quality improvement processes ensures long-term success while centering nurses as key change agents in patient care systems.
PubMed: 38875702
DOI: 10.2196/55000 -
JMIR AI Nov 2023In 2021, the European Union reported >270,000 excess deaths, including >16,000 in Portugal. The Portuguese Directorate-General of Health developed a deep neural network,...
BACKGROUND
In 2021, the European Union reported >270,000 excess deaths, including >16,000 in Portugal. The Portuguese Directorate-General of Health developed a deep neural network, AUTOCOD, which determines the primary causes of death by analyzing the free text of physicians' death certificates (DCs). Although AUTOCOD's performance has been established, it remains unclear whether its performance remains consistent over time, particularly during periods of excess mortality.
OBJECTIVE
This study aims to assess the sensitivity and other performance metrics of AUTOCOD in classifying underlying causes of death compared with manual coding to identify specific causes of death during periods of excess mortality.
METHODS
We included all DCs between 2016 and 2019. AUTOCOD's performance was evaluated by calculating various performance metrics, such as sensitivity, specificity, positive predictive value (PPV), and F-score, using a confusion matrix. This compared International Statistical Classification of Diseases and Health-Related Problems, 10th Revision (ICD-10), classifications of DCs by AUTOCOD with those by human coders at the Directorate-General of Health (gold standard). Subsequently, we compared periods without excess mortality with periods of excess, severe, and extreme excess mortality. We defined excess mortality as 2 consecutive days with a Z score above the 95% baseline limit, severe excess mortality as 2 consecutive days with a Z score >4 SDs, and extreme excess mortality as 2 consecutive days with a Z score >6 SDs. Finally, we repeated the analyses for the 3 most common ICD-10 chapters focusing on block-level classification.
RESULTS
We analyzed a large data set comprising 330,098 DCs classified by both human coders and AUTOCOD. AUTOCOD demonstrated high sensitivity (≥0.75) for 10 ICD-10 chapters examined, with values surpassing 0.90 for the more prevalent chapters (chapter II-"Neoplasms," chapter IX-"Diseases of the circulatory system," and chapter X-"Diseases of the respiratory system"), accounting for 67.69% (223,459/330,098) of all human-coded causes of death. No substantial differences were observed in these high-sensitivity values when comparing periods without excess mortality with periods of excess, severe, and extreme excess mortality. The same holds for specificity, which exceeded 0.96 for all chapters examined, and for PPV, which surpassed 0.75 in 9 chapters, including the more prevalent ones. When considering block classification within the 3 most common ICD-10 chapters, AUTOCOD maintained a high performance, demonstrating high sensitivity (≥0.75) for 13 ICD-10 blocks, high PPV for 9 blocks, and specificity of >0.98 in all blocks, with no significant differences between periods without excess mortality and those with excess mortality.
CONCLUSIONS
Our findings indicate that, during periods of excess and extreme excess mortality, AUTOCOD's performance remains unaffected by potential text quality degradation because of pressure on health services. Consequently, AUTOCOD can be dependably used for real-time cause-specific mortality surveillance even in extreme excess mortality situations.
PubMed: 38875558
DOI: 10.2196/40965 -
JMIR AI Dec 2023Drug-induced mortality across the United States has continued to rise. To date, there are limited measures to evaluate patient preferences and priorities regarding...
BACKGROUND
Drug-induced mortality across the United States has continued to rise. To date, there are limited measures to evaluate patient preferences and priorities regarding substance use disorder (SUD) treatment, and many patients do not have access to evidence-based treatment options. Patients and their families seeking SUD treatment may begin their search for an SUD treatment facility online, where they can find information about individual facilities, as well as a summary of patient-generated web-based reviews via popular platforms such as Google or Yelp. Web-based reviews of health care facilities may reflect information about factors associated with positive or negative patient satisfaction. The association between patient satisfaction with SUD treatment and drug-induced mortality is not well understood.
OBJECTIVE
The objective of this study was to examine the association between online review content of SUD treatment facilities and drug-induced state mortality.
METHODS
A cross-sectional analysis of online reviews and ratings of Substance Abuse and Mental Health Services Administration (SAMHSA)-designated SUD treatment facilities listed between September 2005 and October 2021 was conducted. The primary outcomes were (1) mean online rating of SUD treatment facilities from 1 star (worst) to 5 stars (best) and (2) average drug-induced mortality rates from the Centers for Disease Control and Prevention (CDC) WONDER Database (2006-2019). Clusters of words with differential frequencies within reviews were identified. A 3-level linear model was used to estimate the association between online review ratings and drug-induced mortality.
RESULTS
A total of 589 SAMHSA-designated facilities (n=9597 reviews) were included in this study. Drug-induced mortality was compared with the average. Approximately half (24/47, 51%) of states had below average ("low") mortality rates (mean 13.40, SD 2.45 deaths per 100,000 people), and half (23/47, 49%) had above average ("high") drug-induced mortality rates (mean 21.92, SD 3.69 deaths per 100,000 people). The top 5 themes associated with low drug-induced mortality included detoxification and addiction rehabilitation services (r=0.26), gratitude for recovery (r=-0.25), thankful for treatment (r=-0.32), caring staff and amazing experience (r=-0.23), and individualized recovery programs (r=-0.20). The top 5 themes associated with high mortality were care from doctors or providers (r=0.24), rude and insensitive care (r=0.23), medication and prescriptions (r=0.22), front desk and reception experience (r=0.22), and dissatisfaction with communication (r=0.21). In the multilevel linear model, a state with a 10 deaths per 100,000 people increase in mortality was associated with a 0.30 lower average Yelp rating (P=.005).
CONCLUSIONS
Lower online ratings of SUD treatment facilities were associated with higher drug-induced mortality at the state level. Elements of patient experience may be associated with state-level mortality. Identified themes from online, organically derived patient content can inform efforts to improve high-quality and patient-centered SUD care.
PubMed: 38875553
DOI: 10.2196/46317 -
JMIR AI Nov 2023Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could...
BACKGROUND
Early warning score systems are widely used for identifying patients who are at the highest risk of deterioration to assist clinical decision-making. This could facilitate early intervention and consequently improve patient outcomes; for example, the National Early Warning Score (NEWS) system, which is recommended by the Royal College of Physicians in the United Kingdom, uses predefined alerting thresholds to assign scores to patients based on their vital signs. However, there is limited evidence of the reliability of such scores across patient cohorts in the United Arab Emirates.
OBJECTIVE
Our aim in this study was to propose a data-driven model that accurately predicts in-hospital deterioration in an inpatient cohort in the United Arab Emirates.
METHODS
We conducted a retrospective cohort study using a real-world data set that consisted of 16,901 unique patients associated with 26,073 inpatient emergency encounters and 951,591 observation sets collected between April 2015 and August 2021 at a large multispecialty hospital in Abu Dhabi, United Arab Emirates. The observation sets included routine measurements of heart rate, respiratory rate, systolic blood pressure, level of consciousness, temperature, and oxygen saturation, as well as whether the patient was receiving supplementary oxygen. We divided the data set of 16,901 unique patients into training, validation, and test sets consisting of 11,830 (70%; 18,319/26,073, 70.26% emergency encounters), 3397 (20.1%; 5206/26,073, 19.97% emergency encounters), and 1674 (9.9%; 2548/26,073, 9.77% emergency encounters) patients, respectively. We defined an adverse event as the occurrence of admission to the intensive care unit, mortality, or both if the patient was admitted to the intensive care unit first. On the basis of 7 routine vital signs measurements, we assessed the performance of the NEWS system in detecting deterioration within 24 hours using the area under the receiver operating characteristic curve (AUROC). We also developed and evaluated several machine learning models, including logistic regression, a gradient-boosting model, and a feed-forward neural network.
RESULTS
In a holdout test set of 2548 encounters with 95,755 observation sets, the NEWS system achieved an overall AUROC value of 0.682 (95% CI 0.673-0.690). In comparison, the best-performing machine learning models, which were the gradient-boosting model and the neural network, achieved AUROC values of 0.778 (95% CI 0.770-0.785) and 0.756 (95% CI 0.749-0.764), respectively. Our interpretability results highlight the importance of temperature and respiratory rate in predicting patient deterioration.
CONCLUSIONS
Although traditional early warning score systems are the dominant form of deterioration prediction models in clinical practice today, we strongly recommend the development and use of cohort-specific machine learning models as an alternative. This is especially important in external patient cohorts that were unseen during model development.
PubMed: 38875543
DOI: 10.2196/45257 -
JMIR AI Jan 2024Machine learning techniques are starting to be used in various health care data sets to identify frail persons who may benefit from interventions. However, evidence...
Identifying Frailty in Older Adults Receiving Home Care Assessment Using Machine Learning: Longitudinal Observational Study on the Role of Classifier, Feature Selection, and Sample Size.
BACKGROUND
Machine learning techniques are starting to be used in various health care data sets to identify frail persons who may benefit from interventions. However, evidence about the performance of machine learning techniques compared to conventional regression is mixed. It is also unclear what methodological and database factors are associated with performance.
OBJECTIVE
This study aimed to compare the mortality prediction accuracy of various machine learning classifiers for identifying frail older adults in different scenarios.
METHODS
We used deidentified data collected from older adults (65 years of age and older) assessed with interRAI-Home Care instrument in New Zealand between January 1, 2012, and December 31, 2016. A total of 138 interRAI assessment items were used to predict 6-month and 12-month mortality, using 3 machine learning classifiers (random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) and regularized logistic regression. We conducted a simulation study comparing the performance of machine learning models with logistic regression and interRAI Home Care Frailty Scale and examined the effects of sample sizes, the number of features, and train-test split ratios.
RESULTS
A total of 95,042 older adults (median age 82.66 years, IQR 77.92-88.76; n=37,462, 39.42% male) receiving home care were analyzed. The average area under the curve (AUC) and sensitivities of 6-month mortality prediction showed that machine learning classifiers did not outperform regularized logistic regressions. In terms of AUC, regularized logistic regression had better performance than XGBoost, MLP, and RF when the number of features was ≤80 and the sample size ≤16,000; MLP outperformed regularized logistic regression in terms of sensitivities when the number of features was ≥40 and the sample size ≥4000. Conversely, RF and XGBoost demonstrated higher specificities than regularized logistic regression in all scenarios.
CONCLUSIONS
The study revealed that machine learning models exhibited significant variation in prediction performance when evaluated using different metrics. Regularized logistic regression was an effective model for identifying frail older adults receiving home care, as indicated by the AUC, particularly when the number of features and sample sizes were not excessively large. Conversely, MLP displayed superior sensitivity, while RF exhibited superior specificity when the number of features and sample sizes were large.
PubMed: 38875533
DOI: 10.2196/44185 -
JMIR AI Dec 2023Cross-validation remains a popular means of developing and validating artificial intelligence for health care. Numerous subtypes of cross-validation exist. Although...
Cross-validation remains a popular means of developing and validating artificial intelligence for health care. Numerous subtypes of cross-validation exist. Although tutorials on this validation strategy have been published and some with applied examples, we present here a practical tutorial comparing multiple forms of cross-validation using a widely accessible, real-world electronic health care data set: Medical Information Mart for Intensive Care-III (MIMIC-III). This tutorial explored methods such as K-fold cross-validation and nested cross-validation, highlighting their advantages and disadvantages across 2 common predictive modeling use cases: classification (mortality) and regression (length of stay). We aimed to provide readers with reproducible notebooks and best practices for modeling with electronic health care data. We also described sets of useful recommendations as we demonstrated that nested cross-validation reduces optimistic bias but comes with additional computational challenges. This tutorial might improve the community's understanding of these important methods while catalyzing the modeling community to apply these guides directly in their work using the published code.
PubMed: 38875530
DOI: 10.2196/49023 -
Swiss Medical Weekly Jun 2024The mistreatment of older adults is a global and complex problem with varying prevalence. As there are no data on the prevalence of elder mistreatment in European...
AIM OF THE STUDY
The mistreatment of older adults is a global and complex problem with varying prevalence. As there are no data on the prevalence of elder mistreatment in European emergency department populations, we aimed to translate and culturally adapt the Emergency Department Senior Abuse Identification (ED Senior AID) tool for German use, assess the positive screen rate for elder mistreatment with the German version, and compare characteristics of patients who screened positive and negative.
METHODS
To assess the prevalence of elder mistreatment, we created a German version of the ED Senior AID tool. This tool identifies intentional or negligent actions by a caregiver or trusted person that cause harm or risk to an older adult. Then, the German ED Senior AID tool was applied to all consecutively presenting patients aged ≥65 years at our academic emergency department in the Northwest of Switzerland from 25 April to 30 May 2022. Usability was defined as the percentage of patients with completed assessments using the German ED Senior AID tool.
RESULTS
We included 1010 patients aged ≥65 years, of whom 29 (2.9%) screened positive with the ED Senior AID tool. The patients who screened positive were older, more severely cognitively impaired, hospitalised more frequently, and presented with higher frailty scores than those who screened negative. Mortality up to 100 days after presentation was comparable in all patients (p = 0.861), regardless of their screening result. The tool showed good usability, with 73% of assessments completed.
CONCLUSION
This is the first prospective investigation on the prevalence of elder mistreatment in a European emergency department setting. Overall, 2.9% of patients screened positive using a validated screening tool translated into German.
TRIAL REGISTRATION
This study was registered with the National Institute of Health on ClinicalTrials.gov with the registration number NCT05400707.
Topics: Humans; Elder Abuse; Switzerland; Aged; Emergency Service, Hospital; Male; Female; Prospective Studies; Aged, 80 and over; Mass Screening; Prevalence; Geriatric Assessment
PubMed: 38875501
DOI: 10.57187/s.3775 -
Medicine Jun 2024This study examines the relationship between red blood cell distribution width (RDW) and the prognosis of patients undergoing hepatectomy for hepatocellular carcinoma... (Meta-Analysis)
Meta-Analysis
This study examines the relationship between red blood cell distribution width (RDW) and the prognosis of patients undergoing hepatectomy for hepatocellular carcinoma (HCC). Additionally, it explores the potential effect of RDW for the early identification of high-risk patients after surgery, advocating for timely interventions to improve outcomes. A comprehensive literature search was conducted on May 16, 2022, across PubMed (23 studies), Embase (45 studies), the Cochrane Library (1 study), and CNKI (17 studies), resulting in 6 relevant articles after screening. This analysis primarily focused on the postoperative outcomes of patients. Hazard ratios (HRs) and 95% confidence intervals (CIs) were pooled to assess prognosis, with survival indicators including overall survival (OS) and disease-free survival (DFS). All 6 studies reported on OS, and 2 addressed DFS. A total of 1645 patients from 6 studies were included. The pooled analysis revealed that RDW is an independent prognostic factor for both OS (HR = 1.50, I² = 84%, 95% CI = 1.23-1.77, P < .01) and DFS (HR = 2.06, I² = 15%, 95% CI = 1.51-2.82, P < .01). Patients in the high RDW group exhibited significantly poorer OS and DFS compared to those in the low RDW group. RDW is a prognostic factor for HCC patients after surgery. Elevated RDW levels are associated with a poorer prognosis, adversely affecting both OS and DFS. RDW may serve as a valuable marker for stratifying risk and guiding intervention strategies in the postoperative management of HCC patients.
Topics: Humans; Carcinoma, Hepatocellular; Liver Neoplasms; Erythrocyte Indices; Hepatectomy; Prognosis; Female; Disease-Free Survival; Postoperative Period; Male
PubMed: 38875439
DOI: 10.1097/MD.0000000000038475 -
Medicine Jun 2024Malaria remains an endemic public health concern in Africa, significantly contributing to morbidity and mortality rates. The inadequacies of traditional prevention... (Review)
Review
Malaria remains an endemic public health concern in Africa, significantly contributing to morbidity and mortality rates. The inadequacies of traditional prevention measures, like integrated vector management and antimalarial drugs, have spurred efforts to strengthen the development and deployment of malaria vaccines. In addition to existing interventions like insecticide-treated bed nets and artemisinin-based combination therapies, malaria vaccine introduction and implementation in Africa could drastically reduce the disease burden and hasten steps toward malaria elimination. The malaria vaccine rollout is imminent as optimistic results from final clinical trials are anticipated. Thus, determining potential hurdles to malaria vaccine delivery and uptake in malaria-endemic regions of sub-Saharan Africa will enhance decisions and policymakers' preparedness to facilitate efficient and equitable vaccine delivery. A multisectoral approach is recommended to increase funding and resources, active community engagement and participation, and the involvement of healthcare providers.
Topics: Humans; Malaria Vaccines; Malaria; Africa; Vaccination; Africa South of the Sahara
PubMed: 38875411
DOI: 10.1097/MD.0000000000038565