At-risk Patients Research Articles

Assessment of Inflammatory Markers (CBC and CRP) and their Impact on Severity and Recurrence of Pneumonia in Under-Five Children

Background: Pneumonia remains a leading cause of morbidity and mortality in children under five. This study evaluates the association between inflammatory markers complete blood count (CBC) parameters and C-reactive protein (CRP) levels and pneumonia severity and recurrence. Methods: A prospective study was conducted with 110 children diagnosed with pneumonia at Dr. Sirajul Islam Medical College & Hospital and Islami Bank Hospital, Motijheel, Dhaka, from January to December 2023. Clinical assessments included demographic data, pneumonia severity classification, and inflammatory marker measurements. Results: The study population comprised 62 males (56.4%) and 48 females (43.6%), with a mean age of 24.6 months. Inflammatory marker analysis revealed significant differences in WBC count, neutrophil percentage, lymphocyte percentage, and CRP levels across mild, moderate, and severe pneumonia groups (p < 0.001). Higher levels of WBC count (14,500 ± 3,500), neutrophils (72.2 ± 8.6%), and CRP (38.5 ± 11.2) were observed in patients with pneumonia recurrence compared to those without recurrence. Multivariate regression analysis identified WBC count, neutrophil count, and CRP levels as significant predictors of pneumonia recurrence. Conclusion: Elevated inflammatory markers, particularly CBC parameters and CRP levels, correlate with increased severity and recurrence of pneumonia in young children. Monitoring these markers can aid in early identification and management of at-risk pediatric patients, potentially improving clinical outcomes. Further research is needed to explore additional biomarkers and their implications in pediatric pneumonia management.

A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes. This study aims to propose detection, initial characterization, and semantic characterization (DIS), a new methodology for analyzingchanges in health outcomes and variables over time while discovering contextual changes for outcomes in large volumes of data. The DIS methodology involves 3 steps: detection, initial characterization, and semantic characterization. Detection uses metrics such as Jensen-Shannon divergence to identify significant data drifts. Initial characterization offers a global analysis of changes in data distribution and predictive feature significance over time. Semantic characterization uses natural language processing-inspired techniques to understand the local context of these changes, helping identify factors driving changes in patient outcomes. By integrating the outcomes from these 3 steps, our results can identify specific factors (eg, interventions and modifications in health care practices) that drive changes in patient outcomes. DIS was applied to the Brazilian COVID-19 Registry and the Medical Information Mart for Intensive Care, version IV (MIMIC-IV) data sets. Our approach allowed us to (1) identify drifts effectively, especially using metrics such as the Jensen-Shannon divergence, and (2) uncover reasons for the decline in overall mortality in both the COVID-19 and MIMIC-IV data sets, as well as changes in the cooccurrence between different diseases and this particular outcome. Factors such as vaccination during the COVID-19 pandemic and reduced iatrogenic events and cancer-related deaths in MIMIC-IV were highlighted. The methodology also pinpointed shifts in patient demographics and disease patterns, providing insights into the evolving health care landscape during the study period. We developed a novel methodology combining machine learningand natural language processing techniques to detect, characterize, and understand temporal shifts in health care data. This understanding can enhance predictive algorithms, improve patient outcomes, and optimize health care resource allocation, ultimatelyimproving the effectiveness of machine learning predictive algorithms applied to health care data. Our methodology can be applied to a variety of scenarios beyond those discussed in this paper.

Identifying at-risk patients for atrial high-rate episodes by machine learning

Abstract Background Current evidence suggests that atrial high rate episodes (AHRE)/subclinical atrial fibrillation (AF) are associated with increasing risk of clinical AF, and subsequently, ischaemic stroke and other adverse cardiovascular events. Purpose To construct a high-performance model by machine learning (ML) for the prediction of AHRE detected by a cardiac implantable electronic device (CIED) capable of atrial sensing in a European cohort. Methods We utilized data from a prospective cohort of patients with CIED. AHRE was defined as an episode lasting at least 5 minutes with atrial rate ≥ 175 beats per minute. We divided the cohort into training and internal validation cohorts in 7:3 and used 5-fold cross-validation in the training cohort to assess model performance and prevent model overfitting. Pre-selected features were imported into eight common ML algorithms to predict AHRE, including light gradient boosting machine (LightGBM), random forest (RF), logistic regression, support vector machine, multiple perceptron, eXtreme gradient boosting machine (XGBoost), Gaussian naïve Bayes, and K nearest neighbours. The importance ranking of the features was interpreted by calculating the SHapley Additive exPlanationation (SHAP) value for each feature within the optimal model. Results A total of 500 consecutive patients with CIEDs (mean age 70.2±13.3; 39.2% female) were included. Of these, 164 patients (32.8%) were found to have AHRE. Among all ML models, RF was the best performing model, which obtained the highest AUC (0.738), recall (0.673) and F1-score (0.584) in the internal validation cohort. The results of SHAP showed that antiplatelet, statin, age, white blood cell count, and cardiomyopathy were the first five important features, indicating that the RF model had good interpretability and reliability. Conclusion Our study is the first to demonstrate in a European cohort the effectiveness and feasibility of ML algorithms in predicting AHRE in patients detected by CIEDs. The best RF models require further external validation to assess generalizability to other cohorts.Figure 1Figure 2

Association between clinical ischemic risk and thrombogenicity phenotype and their prognostic prediction in patients undergoing percutaneous coronary intervention

Abstract Introduction The CHADS-P2A2RC score has been developed to provide better prognostic precision and improved selection of at-risk patients with coronary artery disease (CAD). The linkage of this clinical ischemic risk with thrombogenicity and their prognostic implication remain still unknown. Purpose We investigated the values of thrombogenicity indices according to the CHADS-P2A2RC score and their combined prognostic prediction in CAD patients undergoing percutaneous coronary intervention (PCI). Methods In PCI-treated patients (PCI) (n=2,705), clinical ischemic risk was classified with CHADS-P2A2RC score ("high risk": ≥ 4) and thrombogenicity indices were assessed using thromboelastography (TEG). Major adverse cardiovascular events (MACEs: a composite of all-cause death, myocardial infarction, or stroke) and major bleeding were evaluated were monitored for up to 3 years. Results Compared with "low/moderate-risk group" (67.7%), "high-risk group" (32.3%) had higher platelet-fibrin clot strength (PFCS: 67.3±7.9 vs. 65.2±7.2 mm; P < 0.001) and lower fibrinolytic activity (clot lysis at 30 min [LY30]: 0.9±2.1% vs. 1.1±2.4%, P < 0.001]. The "high-risk group" showed higher rates of "high PFCS phenotype (≥ 68 mm)" (35.3% vs. 49.7%; P <0.001), MACE (HRadjusted: 1.47; 95% CI: 1.01-2.13; P = 0.045) and all-cause mortality (HRadjusted: 1.87; 95% CI: 1.14-3.07; P = 0.013) than the "low/moderate-risk group", respectively. Among the "low/moderate-risk group", "high MA phenotype" independently predicted MACE (HRadjusted: 1.51; 95% CI: 1.00-2.27; P = 0.048) and all-cause mortality (HRadjusted: 1.90; 95% CI: 1.06-3.4; P = 0.031). Likewise, this phenotype increased the risks of MACE (HRadjusted: 1.47; 95% CI: 1.01-2.13; p = 0.045) and all-cause mortality (HRadjusted: 1.87; 95% CI: 1.14-3.07; p = 0.013) in the "high-risk group" (Table 1 and Figure 1). Prognostic impact of "high MA phenotype" on major bleeding was not significantly different across the risk-group (HRadjusted: 1.02; 95% CI: 0.39-2.72; P = 0.963 in the "low/moderate-risk group" and HRadjusted: 1.08; 95% CI: 0.50-2.34; P = 0.884 in the "high-risk group", respectively). Conclusion In PCI-treated patients, CHADS-P2A2RC score was significantly associated with a worse clinical outcome and more thrombogenic profile. Regardless of the ischemic-risk group, the level of clot-strength had an addictive effect on predicting the risk of ischemic event, indicating assessment of thrombogenicity indices using TEG may enhance post-PCI risk stratification for personalized antithrombotic therapy.

Blood urea monitoring in heart failure patients - an important predictor of mortality

Abstract Background Recent ESC position statement regarding worsening renal failure recommends the use of blood urea monitoring of patients with heart failure with reduced ejection fraction (HFrEF). Although both serum creatinine and blood urea nitrogen are markers for renal dysfunction, blood urea nitrogen levels are also affected by reabsorption in the renal tubules, a process modulated by renin-angiotensin-aldosterone activity, sympathetic nervous activity and arginine-vasopressin activity. Thus, neurohormonal activity is reflected in the BUN/creatinine ratio (bUCR) which effectively ‘normalizes’ BUN for the degree of renal dysfunction, as measured by the creatinine level. Previous studies have demonstrated increased mortality at bUCR >100mmol/l. however, most of the prognostic data has come from patients admitted with acute heart failure. The use of bUCR in patients with chronic HFrHF receiving intensive titration of guideline directed medical therapy (GDMT) has not been studied. Purpose To identify the extent of blood urea monitoring in primary care in HFrEF and whether bUCR remains a significant prognostic indicator despite intensive GDMT. Methods We analysed the electronic health records of all patients referred to the Heart Failure Nurse Service (HFNS) in an NHS population approx. 400,000 in 2022. Inclusion criteria consists of patients with symptomatic heart failure NYHA II-IV with echocardiogram demonstrating HFrEF. Patients were segregated into two cohorts, with a urea/creatinine ratio greater or less than 100mmol/l and analysed. Results Data from 338 patients [mean age 72.3 ± 15.6 years; 218 (64.5%) males; 143 (42.3%) ischaemic cardiomyopathy; 141 (41.7%) AF; 49 (14.5%)] were analysed. Of these, 112 (33.1%) had chronic kidney disease (CKD) with eGFR <60 ml/min/1.73m2 at the time of HF diagnosis and 181 (53.6%) had CKD on discharge from the HFNS. During follow-up, 132 (39%) had further admission with HF and 76 (22.5%) died. Sixty-eight (20.1%) patients did not have blood urea checked despite the availability of serum creatinine. While an eGFR < 60 ml/min/1.73m2 at discharge was associated with a twofold increased risk of death (OR 2.4, 95% CI 1.1-5.3, p=0.029), a bUCR > 100mmol/l at discharge was associated with a four-times greater risk of death (OR 4.2, 95% CI 2.2-7.9, p <0.001). Conclusion Blood urea/creatinine ratio is an important predictor of outcome and can be utilised to help identify at-risk patients with HFrEF despite intensive GDMT.Survival from diagnosis bUCR

Prevalence of depression, anxiety and post-traumatic stress disorder (PTSD) after acute myocardial infarction: a systematic review and meta-analysis

Abstract Background There is increasing recognition that patients experience a greater risk of mental illnesses after an acute myocardial infarction (AMI), with the former being linked to worse post-AMI outcomes. However, the prevalence of these conditions is largely unknown as most studies focus on depression and few on other illnesses such as anxiety and post-traumatic stress disorder (PTSD). Additionally, existing studies mostly involved diagnoses of mental illnesses through patient-reported questionnaires which may carry subjectivity (1). Therefore, we conducted a systematic review and meta-analysis to estimate the prevalence and risk factors of developing depression, anxiety and PTSD after an AMI with the inclusion of only studies with official diagnoses of the mental illnesses. Methods Searches in MEDLINE, EMBASE, and PsycINFO up to January 23, 2023, identified 25 qualifying studies that examined the risk of depression, anxiety and PTSD after AMI, with case definitions based strictly on psychiatrist-administered structured interviews according to the Diagnostic and Statistical Manual for Mental Disorders (DSM) criteria. Meta-analyses of proportions using random-effects models estimated the pooled prevalence of each outcome at the <3-month and >3-month time-points. Heterogeneity was tested using I-squared statistics, if significant heterogeneity was found, subgroup analyses and meta-regression analyses were performed to identify the source of heterogeneity. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and Risk of Bias 2 (RoB2) Tool, and publication bias using the funnel plot and verified by the Egger’s and Begg’s tests. Results A total of 25 studies were included in the meta-analysis. The pooled prevalence of depression post-AMI (20 studies) was 16.70% (95% CI: 12.40%; 22.11%), with pooled prevalence <3-month and >3-month post-AMI at 19.46% (95% CI: 15.47%; 24.19%) and 14.87% (95% CI: 9.55%; 22.43%) respectively. Pooled prevalence of anxiety (7 studies) and PTSD (3 studies) were 11.96% (95% CI: 6.15; 21.96%) and 10.26% (95% CI:5.49;18.36%) respectively. Subgroup analysis showed that the pooled prevalence of both depression and anxiety are significantly higher in the female gender, in those with hypertension, diabetes or hyperlipidemia, and in smokers, while the pooled prevalence of depression is higher in unmarried than married individuals and in patients with a history of depression. Meta regression indicates that history of depression is a significant predictor of prevalence of depression (p= 0.0035, regression coefficient 1.54). Conclusion The prevalence of mental illnesses is high after AMI. Risk factors identified included female gender, hypertension, diabetes, hyperlipidemia, smoking, depression history, as well as one’s social set-up. These findings highlight the importance of screening at-risk patients and early intervention to improve long-term outcomes after AMI.Forest plot of depression prevalenceSubgroup analysis for depression

Process evaluation of a randomised controlled trial - prevention of sickness absence through early identification and rehabilitation of at-risk patients with musculoskeletal disorders (PREVSAM)

BackgroundMusculoskeletal disorders are commonly treated in primary healthcare and may, if not treated adequately, entail a risk for long-term disability and sickness absence. A team-based rehabilitation intervention (PREVention of Sickness Absence for Musculoskeletal disorders, PREVSAM) was evaluated in a randomised controlled trial. The purpose of this study was to evaluate the process of implementing the PREVSAM model in primary care rehabilitation.MethodsThis process evaluation was conducted alongside the trial, collecting quantitative and qualitative data to evaluate how the PREVSAM model was implemented, mechanisms of impact, and contextual factors. Acceptability, feasibility, appropriateness, adaptations, training and support, resources, recruitment, reach, retention, dose, fidelity, and readiness for change were investigated. Qualitative data were collected from healthcare professionals and patients.ResultsEight of 22 invited rehabilitation clinics (36%) and 28 of 54 healthcare professionals (52%) were included in the PREVSAM trial and this process evaluation. Of 507 eligible patients, 261 (51%) were included. Of those, 134 were randomised to the intervention and 129 (96%) were retained. Twelve healthcare professionals and 15 patients participated in the qualitative evaluations. The model’s essential components; individual assessments and structured, team-based rehabilitation with clear division of responsibilities agreed in a joint health plan; were generally delivered according to protocol. The optional components early access to psychological treatment and workplace contact were delivered to a lesser extent. Perceived acceptability, feasibility, and appropriateness of the PREVSAM model were moderate to high. Several contextual barriers, in the form of missing prerequisites, affected the implementation. Qualitative data showed that the model, with its holistic view, was appreciated by both healthcare professionals and patients.ConclusionsThis process evaluation suggests that PREVSAM is acceptable, feasible and appropriate for patients with MSDs reporting psychological risk factors associated with increased risk for sickness absence. While essential components were implemented with fidelity for most patients, optional components were not. This variability reflects the complexity of the model, its mandatory and optional components, contextual barriers, and the person-centred approach meeting individual patient needs. As all model components were not delivered to all patients, the intervention may have been too similar to treatment as usual to detect differences on a group level. A limitation of the study is that half of the participating rehabilitation clinics withdrew prematurely.

The Bidirectional Effects of Periodontal Disease and Oral Dysbiosis on Gut Inflammation in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) flares can lead to excessive morbidity and mortality. This study aimed to determine whether oral dysbiosis/periodontal disease (PD) is common in IBD and is associated with disease activity in IBD. This single-center, prospective, cross-sectional, proof-of-concept, observational study assessed the frequency of periodontal inflammatory disease and interrogated oral and stool microbiota using 16S rRNA gene amplicon sequencing of active-IBD (aIBD), inactive-IBD (iIBD), and healthy controls (HC). Questionnaires assessed diet, alcohol usage, oral hygiene behavior, and disease activity. A subset of participants underwent comprehensive dental examinations to evaluate PD. PD was severer in aIBD subjects than in HC, as aIBD had poorer quality diets (lower Mediterranean diet scores) than iIBD and HC. Significant differences in microbial community structure were observed in unstimulated saliva, stimulated saliva, gingiva, and stool samples, primarily between aIBD and HC. Saliva from aIBD had higher relative abundances of putative oral pathobionts from the genera Streptococcus, Granulicatella, Rothia, and Actinomyces relative to HC, despite similar oral hygiene behaviors between groups. Our study suggests that patients with aIBD have severer periodontal disorders and higher relative abundances of putative "pro-inflammatory" microbiota in their oral cavity, despite normal oral hygiene behaviors. Our data are consistent with the potential presence of an oral-gut inflammatory-axis that could trigger IBD flare-ups in at-risk patients. Routine dental health assessments in all IBD patients should be encouraged as part of the health maintenance of IBD and as a potential strategy to decrease the risk of IBD flares.

Preoperative mortality risk evaluation in abdominal surgical emergencies: development and internal validation of the NDAR score from a national multicenter audit in Senegal

IntroductionAbdominal surgical emergencies have a high mortality rate. Effective management primarily relies on the early identification of patients at high risk of postoperative complications. The objective of our study was to determine the prognostic factors associated with poor outcomes from abdominal surgical emergencies in Senegal and to establish a predictive score for mortality for preoperative risk evaluation (NDAR (New Death Assessment Risk) score).MethodologyThis was a retrospective national cross-sectional study conducted over one year in 14 regions of Senegal. Adult patients (aged > 15 years) who presented with a traumatic or non-traumatic abdominal surgical emergency were included. The studied variables included clinical and paraclinical data. The variable of interest was death within 30 days of the surgery. Logistic regression was used to identify the factors independently associated with mortality. Risk factors identified after logistic regression analysis were weighted using odds ratio (OR) values rounded to the nearest whole number. The predictive capacity of the score was evaluated by analyzing the ROC (Receiver Operating Characteristic) curve based on the area under the curve (AUC).ResultsA total of 1114 patient records were included, with a mortality rate of 4.4%. Diagnoses were observed in patients included appendicitis in 39.8% of cases (n = 444), followed by peritonitis in 22.3% (n = 249), intestinal obstruction in 18.5% (n = 205), strangulated hernias in 10.5% (n = 117), and abdominal trauma in 6.1%. Logistic regression, established the following scores: age > 40 years (score 2), ASA status grade 2 or higher (score 1), presence of a positive QSIRS score (score 2), diagnosis of peritonitis (score 2), diagnosis of intestinal obstruction (score 1), and the presence of intestinal necrosis (score 3). The score is positive if the total is strictly greater than 5, indicating a 17.7% risk of mortality. This score had a high predictive capacity with an AUC of 0.7397.ConclusionThis study enabled the establishment of a score that allows for the early identification of at-risk patients, even in constrained resource settings, facilitating appropriate perioperative management and timely surgical intervention to reduce the risk of complications. This approach, focused on early recognition of high-risk patients, is crucial for improving clinical outcomes in abdominal surgical emergencies.

Leveraging Digital Health Data to Transform the United Nations Systems for Palestine Refugees for the Post Pandemic Time

ABSTRACT The COVID-19 pandemic presented a grave threat to the continuity of health services that UNRWA provides to 5.9 million Palestine Refugees in the Near East. UNRWA runs 140 primary health care clinics, providing approximately nine million medical consultations a year. During the pandemic, UNRWA’s e-Health system (and other digital health tools) were crucial in maintaining health services. The e-Health system enabled the identification of at-risk patients and transformed UNRWA’s services for sustainability and efficiency. Innovations like telemedicine and two smartphone applications (e-NCD and e-MCH) enhanced service delivery and staff management. To evaluate the effectiveness of digital health integration in UNRWA’s services during and after the pandemic, the team analyzed reports, events, and e-Health data from 2019–2022. Results show that digital tools, like e-NCD and e-MCH applications, helped reduce COVID-19 among Palestine Refugees, enabling remote care and continuous access to essential health services. Digital health has now become essential in UNRWA’s post-pandemic operations. This paper offers a paradigm for future outbreak responses. By harnessing the power of digital health, UNRWA’s e-Health system served as a beacon of hope, demonstrating how innovative approaches can empower patients, enhance health care outcomes, and ensure equitable access to health care services during crisis situations and beyond.

Active Choice Nudge to Increase Screening for Primary Aldosteronism in At-Risk Patients.

Primary aldosteronism (PA) is the most common cause of secondary hypertension, yet screening remains startlingly infrequent. We describe: 1) PA screening practices in a large, diverse health system, 2) the development of a computable phenotype for PA screening, and 3) the design and pilot deployment of an electronic health record (EHR)-based active choice nudge to recommend PA screening. A multidisciplinary team developed a multi-pronged intervention to improve PA screening informed by guidelines, expertise, and multivariable analyses of factors associated with screening. The intervention included EHR-based tools to automatically identify screen-eligible patients, an active choice nudge recommending screening for these patients, and screening result interpretation. The intervention was piloted across two primary care practices for seven months. Screening frequencies were compared to clinics not receiving the intervention. The baseline frequency of screening of eligible patients within one year was 1.4%. Higher mean systolic blood pressure (OR=1.4; p<0.001), more antihypertensive medications (OR=1.3; p=0.002), lower minimum serum potassium (OR=2.0; p=0.001), specialist care (OR=3.0; p<0.001), and Black race (OR=1.5; p=0.001) were associated with a higher likelihood of screening. The refined computable phenotype identified a subcohort with a higher frequency of positive screening (8.6% versus 4.1%; p=0.03). In a pilot study of an active choice nudge, a greater proportion of eligible patients were screened in the intervention clinics (16.4%) than in the non-intervention clinics (1.8%; p<0.001). PA screening rates are low. This pilot study suggests an EHR-based nudge leveraging a precise computable phenotype can dramatically increase appropriate PA screening.

Epidemiology and Outcomes Associated with New Persistent Opioid Use After Transabdominal Operation.

Overall, 3-12% of opioid-naïve patients develop persistent opioid use after surgery. It's still unclear whether persistent opioid use after transabdominal surgery is associated with adverse surgical outcomes. We aimed to assess if new persistent opioid use after transabdominal surgery is associated with increased long-term mortality and readmission rates. Opioid-naïve patients >18 years undergoing transabdominal surgery at Landspitali University Hospital, the only tertiary hospital in Iceland, from 2006-2018 were included. Persistent opioid use was defined as opioid use more than 3 months postoperatively. Inverse probability weighting (IPW) was used to yield balanced study groups accounting for baseline characteristics. Long-term mortality (during median follow-up of 5.2 years) was compared using propensity-weighted Cox regression and readmission within 3-6 months using propensity-weighted logistic regression. Overall, 3923 patients were included (laparoscopy-2680, laparotomy-1243). Rates of new persistent opioid use were 13.0%. Rates were higher after laparotomy than laparoscopy in the crude analysis but not in the propensity-weighted analysis. New persistent opioid use was associated with higher long-term mortality (hazard ratio 1.84, 95% CI 1.41-2.40) and readmission rates (odds ratio 3.24, 95% CI 2.25-4.76). This was consistent for both patients undergoing laparoscopy and laparotomy. Moreover, there were signs of a dose-response relationship, with patients in higher quartiles of postoperative opioid consumption having higher mortality and readmission rates. New persistent opioid use following transabdominal surgery was associated with higher rates of mortality and readmission rates. This calls for increased postoperative support for at-risk patients and increased support during transitions of care for these patients.

Clinical ocular prediction model of postoperative ametropic amblyopia in patients with congenital ectopia lentis.

Despite prompt and appropriate surgical management, a considerable proportion of patients with congenital ectopia lentis (CEL) suffer from postoperative ametropic amblyopia. To predict and identify at-risk patients early, and ensure timely amblyopia treatment, we conducted a thorough investigation into the onset and progression patterns of postoperative amblyopia in patients with CEL. Moreover, an ocular prediction model was constructed for amblyopia. In this prospective cohort study, amblyopia analysis was conducted to reveal the prevalence of postoperative amblyopia at different time points of follow-up. Comparative analysis and logistic regression analysis were performed for the development of an amblyopia prediction model. Receiver Operating Characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA) were used to evaluate the performance of the model. A nomogram was created to determine the probability of postoperative amblyopia. Amblyopia was diagnosed according to the most recent edition of the Amblyopia Preferred Practice Pattern. A total of 889 eyes from 677 patients operated for CEL were enrolled in this study. In the pediatric cohort, the prevalence of amblyopia showed a decreasing trend with follow-up time from 1 month to 3.5 years. A prediction model based on preoperative best-corrected visual acuity (BCVA) and cardiac phenotype was established to predict postoperative amblyopia. For effective individual prediction, a nomogram was created. With great calibration, discrimination, and clinical usefulness, the prediction model demonstrated good performance. The findings underscore that the prevalence of ametropic amblyopia in pediatric CEL patients who underwent lens surgery exhibited a marked decline over time. The prediction model established with preoperative BCVA and cardiac phenotype can provide accurate and individualized predictions of postoperative amblyopia, and it has the potential to assist ophthalmologists in rapidly identifying high-risk patients.

Frailty and its association with readmissions in patients with rheumatoid arthritis: A national readmissions database study.

It remains unknown whether frailty status confers an increased risk of readmission in patients with rheumatoid arthritis (RA). From the 2018 Nationwide Readmissions Database (NRD), we identified adult patients (age ≥ 18years) admitted with a diagnosis of RA between January to June 2018. Utilizing validated Hospital Frailty Score, patients' frailty risk score was calculated at the time of index admission and categorized into frail (score ≥ 5) and non-frail (score < 5) groups. Our primary outcomes of interest were (1) 180- day readmission rate (2) inpatient mortality; secondary outcomes included prolonged length of stay, LOS (LOS ≥ 7days), and costs of hospitalization. Multivariable Cox proportional hazard analysis was performed to evaluate the independent effect of frailty adjusting for confounding variables. 133,187 patients met inclusion criteria, with mean age 67.7years, of whom 64,131 (48.1%) patients were categorized as frail. The rate of readmission was significantly higher in the frail (56.60%) compared to the non-frail group (30.61%). At index hospitalization, frail patients also had significantly higher inpatient mortality compared to non-frail patients (3.36% vs 0.39%, p < 0.005), longer LOS (26.24% vs 7.82%, p < 0.005). On multivariate analysis frailty was independently associated with a 9% increased risk of readmission (adjusted hazard ratio, 1.09; 95% confidence interval, 1.08 - 1.11). People with RA who are frail have higher rates of readmission than those who are not frail. These findings are crucial in identifying at-risk patients with RA and in discharge planning after hospitalization. Key Points • People with RA who are frail have higher rates of readmission than those who are not frail. • Frail RA patients are also at higher risk of hospitalization-related adverse outcomes, including inpatient mortality and longer hospital stay. • Sepsis is the most common cause for readmission identified in frail patients with RA. • These findings suggest that frailty may be a useful metric in identifying patients with RA at an increased risk of adverse health outcomes.

Preventing cardiovascular disease in at-risk patients: Results of a pilot behavioural health programme in general practice

Background The ‘High-Risk Prevention Programme’ (HRPP) involved a six-week health behaviour change programme based in general practices and aimed to address cardiovascular disease (CVD) risk in disadvantaged Irish communities. Objectives This pilot study aimed to establish the HRPP’s likely effectiveness and acceptability to inform the development of a future definitive trial. Methods The HRPP was conducted at six general practices in disadvantaged areas in the Ireland East region. Patients with high CVD risk were recruited by participating practices and were allocated to either a General Practice Nurse (GPN) or Health Promotion Professional (HPP) led programme focusing on positive health behavioural change. Baseline and 12-month follow-up data were collected to capture the HRPP’s likely effectiveness in promoting health outcomes and health behavioural change. Results The HRPP programme was completed by 270 patients. Out of these 270 patients, 245 (90.74%) completed baseline assessments, and 176 (65.19%) completed follow-up assessments at 12 months. Baseline data indicated a high level of CVD risk among patients and follow-up demonstrated positive change in several areas, especially weight (−1.95 kg, p < 0.001), BMI (−0.72, p < 0.001), exercise during the last week ( p <0.001), and consumption of healthy fats in the HPP group (+60%, p < 0.001). Conclusion The HRPP was a much-needed pilot intervention, and positive results were seen in both GPN and HPP arms, especially with regards to weight loss, exercise, and dietary improvements. Future definitive trials of the HRPP are likely to be effective and acceptable in terms of combatting these issues among high-risk patients.

Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings

BackgroundIntegrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system.MethodsWe used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs’ feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances.ResultsThe backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term “high risk” of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs’ workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability.ConclusionsThe final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability.

The Protective Role of Intermedin in Contrast-Induced Acute Kidney Injury: Enhancing Peritubular Capillary Endothelial Cell Adhesion and Integrity Through the cAMP/Rac1 Pathway.

Contrast-induced acute kidney injury (CIAKI) is a common complication with limited treatments. Intermedin (IMD), a peptide belonging to the calcitonin gene-related peptide family, promotes vasodilation and endothelial stability, but its role in mitigating CIAKI remains unexplored. This study investigates the protective effects of IMD in CIAKI, focusing on its mechanisms, particularly the cAMP/Rac1 signaling pathway. Human umbilical vein endothelial cells (HUVECs) were treated with iohexol to simulate kidney injury in vitro. The protective effects of IMD were assessed using CCK8 assay, flow cytometry, ELISA, and Western blotting. A CIAKI rat model was utilized to evaluate renal peritubular capillary endothelial cell injury and renal function through histopathology, immunohistochemistry, immunofluorescence, Western blotting, and transmission electron microscopy. In vitro, IMD significantly enhanced HUVEC viability and mitigated iohexol-induced toxicity by preserving intercellular adhesion junctions and activating the cAMP/Rac1 pathway, with Rac1 inhibition attenuating these protective effects. In vivo, CIAKI caused severe damage to peritubular capillary endothelial cell junctions, impairing renal function. IMD treatment markedly improved renal function, an effect negated by Rac1 inhibition. IMD protects against renal injury in CIAKI by activating the cAMP/Rac1 pathway, preserving peritubular capillary endothelial integrity and alleviating acute renal injury from contrast media. These findings suggest that IMD has therapeutic potential in CIAKI and highlight the cAMP/Rac1 pathway as a promising target for preventing contrast-induced acute kidney injury in at-risk patients, ultimately improving clinical outcomes.

The translation and validation of the surgical anxiety questionnaire into the modern standard Arabic language: results from classical test theory and item response theory analyses

BackgroundPreoperative anxiety is commonly found in patients who are waiting for surgery and can lead to negative surgical outcomes. Understanding the sources of surgical anxiety allows healthcare providers to identify at-risk patients and implement psychosocial interventions such as counseling, relaxation techniques, and cognitive‒behavioral therapy to minimize anxiety. Few comprehensive psychiatric measures are available to assess preoperative anxiety in Arabic.ObjectiveOur study aimed to translate, adapt, and validate the Surgical Anxiety Questionnaire (SAQ) into the modern standard Arabic language, also known as Fusha al-Asr Arabic.MethodsTo translate the questionnaire, the research team used the gold standard process of forward translation by two independent translators along with back translation evaluation by four trained medical doctors. A cross-sectional study was conducted using an online survey completed by 208 Arabic speakers (mean age 38 years, 44% women) from four countries. Psychometric analyses, which included internal consistency, test-retest reliability, convergent validity, confirmatory factor analysis, and item response analysis, were performed. Convergent validity tests were performed against the Generalized Anxiety Disorder 2-item Scale (GAD-2), Patient Health Questionnaire-4 (PHQ-2), Perceived Stress Scale 4 (PSS-4), and Arabic version of the Visual Analog Scale for anxiety (VAS-A).ResultsThe mean SAQ of our sample was 19.38 ± 12.63 (possible range 0–68). The Arabic SAQ translation demonstrated excellent internal consistency, with McDonald’s omega and a Cronbach’s alpha of approximately 0.90. The test-retest reliability was also high, with an intraclass coefficient of 0.94. The SAQ showed strong convergent validity against the GAD-2 (r = 0.94, p < 0.01). The SAQ also showed weak-moderate correlations with the PHQ-2 (r = 0.26, p < 0.01), PSS-4 (r = 43, p < 0.01), and VAS-A (r = 0.36, p < 0.01) scores. The original three-factor structure was supported by confirmatory factor analysis, confirming the original structure reported in the original English language version. The results for fitness indices showed acceptable preliminary results (CFI/TLI approximately 0.90), and deleting some items improved the model fit (CFI/TLI > 0.90, RMSEA < 0.08). We suggest retaining the original factorial solution until further validation studies can be conducted. The item response theory (IRT) results identified no items that were excessively difficult or subject to guessing. The multidimensional IRT provided evidence that the SAQ items form a multidimensional scale assessing surgical anxiety that fits the classical model reasonably well.ConclusionThe SAQ has demonstrated acceptable reliability and validity; thus, it is a trustworthy and valid tool for evaluating preoperative anxiety in Arabic speakers. Future research could benefit from using the SAQ in both surgical and psychiatric research.

The Importance of an Interdisciplinary Approach in the Transition of At-Risk Patients From Hospital to Skilled Nursing Facilities: A Case Report

The Importance of an Interdisciplinary Approach in the Transition of At-Risk Patients From Hospital to Skilled Nursing Facilities: A Case Report

The Use of a Single Risk Assessment Tool for Mortality and Numerous Hospital-Acquired Conditions.

Quality assessment organizations leverage numerous patient safety measures to evaluate hospital performance, resulting in significant financial, administrative, and operational burdens on health systems. Low-intensity approaches that allow for reliable risk stratification of patients can reduce the required investment. The Braden score is a routinely performed bedside nursing evaluation validated to assess risk for hospital-acquired pressure injury. We hypothesized that the tool can be used to evaluate risk for other hospital-related adverse outcomes, including mortality, catheter-associated urinary tract infection (CAUTI), and central line-associated blood stream infection (CLABSI). We found that abnormal Braden scores have significant association with numerous adverse outcome measures, including mortality, CLABSI, CAUTI, and iatrogenic hypoglycemia. Because of its frequency of reevaluation, we have found preliminary evidence that leveraging this tool can reduce harm by quickly identifying the most at-risk patients for various types of iatrogenic harm. We conclude that in the face of increasing automation and technical applications, for example, artificial intelligence-driven tools, highly reliable clinician bedside physical examination and evaluation can still have significant, low-cost, and high-value impact in improving patient safety.