High-risk Patients Research Articles

The Effect of Cuproptosis-Related Proteins on Macrophage Polarization in Mesothelioma is Revealed by scRNA-seq.

High invasiveness mesothelioma is a malignant tumor of the peritoneum or pleura. The effect of cuproptosis on mesothelioma (MESO) is still unknown, though. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets were used to identify differential genes linked to cuproptosis in mesothelioma. Multigene features were then created to assess the course of the disease. Use single-cell data and in vitro validation to uncover crucial gene regulation mechanisms. In MESO, we found nine differentially expressed genes linked to cuproptosis. Using univariate Cox and LASSO regression techniques, a 3-gene feature (P < 0.05) was created, showing a good predictive potential for survival time. According to the risk score, patients in the low-risk subset had a considerably greater survival rate than those in the high-risk subset (P = 0). The similar survival pattern and prediction performance are also seen in the validation queue. The findings of the drug sensitivity research indicate that in high-risk patients, vinblastine, paclitaxel, gefitinib, and erlotinib are sensitive medications (P < 0.05). Classical monocytes were identified as core cells connected to cuproptosis by the CellChat results. SLC31A1 is implicated in the positive regulation of M2 macrophage polarization, according to cell subtype analysis and in vitro confirmation. Genes linked to cuproptosis have a major influence on tumor immunity and can predict how MESO will progress.

Biomarker-directed therapy in multiple myeloma.

Multiple myeloma is currently treated with a one-size-fits-all approach despite significant heterogeneity in patient outcomes and disease molecular constitution. A personalised approach would tailor therapy to unique patient or disease characteristics. Well established prognostic biomarkers such as cytogenetic risk and patient frailty status are being evaluated as potential predictive biomarkers. Specifically, treatment intensity can be augmented in high-risk patients or conversely attenuated in those at lower risk or lower ability to withstand treatment toxicities. Alternatively, targeted therapy can be rationally designed to exploit vulnerable pathways in myeloma cells as identified using predictive biomarkers. The t(11;14) translocation, found in approximately 15-20% of myeloma cases, is a leading biomarker for response to BCL-2 inhibitors such as venetoclax. Active research efforts exploring venetoclax combination therapies, as well as new generation BCL-2 inhibitors are underway. Following the development of venetoclax, numerous other cellular pathways are under investigation as candidate predictive biomarkers to rationally inform newer targeted therapies in myeloma.

Evaluating the Safety and Efficacy of a Novel Glaucoma Drainage Device in High-Risk Adult Glaucoma Patients: A One-Year Pilot Study.

Background: We report on the 12-month safety and efficacy outcomes of a new non-valved glaucoma drainage device, the eyePlate-300 (Rheon Medical, Lausanne, Switzerland), in managing refractory glaucoma. Methods: A retrospective review was conducted on consecutive patients over 18 who underwent glaucoma drainage device (GDD) surgery with the eyePlate-300 after a single glaucoma consultation between February 2020 and April 2021, with at least 12 months of documented post-op follow-up. Results: A total of 16 eyes from 15 patients were included. Complete success was observed in 47% of patients and overall success in 83%. The mean IOP decreased from 31.5 mm Hg to 10.7 mm Hg (67% reduction from baseline), and the number of IOP-lowering drops was reduced from 3.1 to 0.7 at one year. The mean BCVA remained stable. No additional IOP-lowering surgeries were required, and no severe sight-threatening complications were noted. Conclusions: The initial one-year results suggest that the eyePlate could be a safe and effective device for reducing IOP in an ethnically diverse refractory glaucoma population. Further follow-up is necessary to determine the long-term safety and efficacy.

The Role of Structured Goals of Care Discussions in Critically Ill Thoracic Surgery Patients.

Objective: The American College of Surgeons recommends structured family meetings (FM) for high-risk surgical patients. We hypothesized that goals of care discussions (GOCD) in the form of an FM, multidisciplinary family meeting (MDFM), or palliative care consult (PCC) would be underutilized in imminently dying thoracic surgery patients. Methods: A retrospective chart review at a tertiary academic medical center was performed on all inpatient mortalities and discharges to hospice after any thoracic surgery operation. The utilization of GOCDs was compared between the 2 groups. Secondary outcomes were length-of-stay, comatose status and ventilator dependence during initial GOCD, and timing of code status change. Results: In total, 56 patients met inclusion criteria: 44 of 56 (78.6%) died and 12 of 56 (21.4%) were discharged to hospice. Most patients had a FM (79.5% mortality vs 100% hospice, P = .29) and few had an MDFM (25.0% mortality vs 25.0% hospice, P = 1.00). Patients discharged to hospice were more likely to have a PCC (66.7% vs 31.2%, P = .03) and less likely to be comatose (16.7% vs 59.1%, P = .009) or ventilator dependent during initial GOCD (16.7% vs 70.5%, P = .001). Among patients who died and were DNR-CC (do not resuscitate-comfort care; 37 of 44), 75.7% died the same day of code status change and 67.6% died within 48 h of initial GOCD. Discussion: Although FMs were common, MDFMs were infrequent. Patients discharged to hospice were more likely to have a PCC. Most deaths occurred shortly after initial GOCD and most code status changes occurred on day-of-death. This data suggest an opportunity to improve GOCDs in critically ill thoracic surgery patients.

The role of big data analysis for resources optimization of immunization programmes in Pakistan.

Madam, The significance of Big Data Analysis (BDA) in optimizing immunization programmes in Pakistan emerges within the context of global and regional immunization challenges post the COVID-19 pandemic. Despite recovery efforts, a substantial number of children globally remain under-vaccinated, with 14.3 million zero-dose children in 2022.[1] South Asia, primarily India, has shown notable progress, exceeding pre-pandemic coverage levels.[2] In Pakistan, the Expanded Programme on Immunization (EPI) launched in 1978 aims to combat high under-five mortality rates due to vaccine-preventable diseases.[3] However, vaccine coverage remains suboptimal, with only 66% of children aged 12-23 months receiving basic EPI vaccines.[3] This is compounded by reduced doses administered later in schedules, influenced by limited access, awareness gaps, and insufficient vaccinators, especially in rural areas. [3] Big data analytics can help tackle challenges, improve resource allocation, and optimize immunization in Pakistan. The vital role of BDA in optimizing Pakistan's immunization programmes is rooted in its manifold health benefits. BDA aids disease prevention, identifies health risk factors, and enhances healthcare strategies.[4] Amid health emergencies like COVID-19, BDA leverages information technology to fortify health systems, enhancing decision-making, reducing errors, and conserving resources.[4] BDA's dynamic potential is evident. Its predictive tools swiftly pinpoint high-risk patients, streamlining care and elevating healthcare outcomes.[4] BDA draws insights from vast datasets for informed decisions. A recent study conducted in Sindh, increased vaccinator attendance from 44% to 88%, by factoring in results of BDA of Electronic Immunization Registries into their decision making.[5] BDA of antigen administration during accelerated outreach efforts revealed substantial boosts, compared to routine activities.[5] These findings spurred continued efforts in high-risk areas, showcasing BDA's impact in project acceleration and resource optimization.[5] This case highlights BDA's transformative role, enhancing immunization programs through data-driven insights. To harness the potential of BDA in immunization, Pakistan's healthcare system requires a digital overhaul. This entails prioritizing active patient data collection, storage, and analysis on a national scale. Collected data would facilitate targeted vaccination efforts in vulnerable areas. Storing data supports future research and predictive analysis, offering insights into current measures' efficacy and enabling necessary adjustments. A robust data analysis infrastructure, coupled with healthcare professional training, is essential. Cost-effective data security is vital. Digital registries in tertiary care hospitals and rural centres can aid government analysis. This holistic approach seeks to establish a digital healthcare ecosystem, involve the public, and implement data-driven solutions for improved immunization and overall public health in Pakistan.

Effect of Adding Stem Extension to a Short-Keeled Knee Implant on the Risk of Tibial Loosening: a Historical Cohort Study.

Short-keeled tibial implants may be at higher risk of aseptic loosening. Stem augmentation has been suggested to improve its fixation. We aimed to assess whether this technique could decrease the loosening rate of such tibial implants. We retrospectively studied the knees receiving stemmed versus nonstemmed versions of a short-keeled prosthesis. A total of 932 patients undergoing total knee arthroplasty using a Persona prosthesis with >24-month follow-up (mean 53.7) were included, of whom 212 were at high risk of loosening due to osteoporosis or high body mass index (BMI). The rate of tibial aseptic loosening in high-risk patients and in the whole cohort was compared between those with and without stem extension. Multivariable analysis was conducted to identify the risk factors of loosening. In the whole cohort, in 203 knees (21.8%), a cemented short stem was used, versus 729 knees (78.2%) without. The tibial implant revision rates for loosening were 2.06% and 0.99% for nonstemmed and stemmed cases, respectively (P = 0.551). When the cohort was categorized based on the loosening risk factors, the loosening risk was significantly higher in the high-risk than in the low-risk group, regardless of stem status (log-rank test = 8.1, hazard ratio = 2.17, 95% CI = 1.78, 2.57, P = 0.005). 212 patients were at high risk of loosening, due to high BMI or osteoporosis, among whom the risk of aseptic loosening was higher when a stem was not used (log-rank = 32.1, hazard ratio = 4.95, 95% CI = 4.05, 5.86, P < 0.001). In multivariate analysis, osteoporosis, BMI ≥35, and smoking were correlated with loosening. The failure rate was markedly lower in the stemmed group of the patients at elevated risk of aseptic loosening due to osteoporosis and/or high BMI. In the entire cohort, the risk of loosening in stemmed tibial implants was similar to the nonstemmed group. The findings support a protective role for the stem in patients at high risk of failure of the cemented tibial implant. III therapeutic.

Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma

BackgroundKidney renal clear cell carcinoma (KIRC) represents a significant proportion of renal cell carcinomas and is characterized by high aggressiveness and poor prognosis despite advancements in immunotherapy. Disulfidptosis, a novel cell death pathway, has emerged as a critical mechanism in various cellular processes, including cancer. This study leverages machine learning to identify disulfidptosis-related long noncoding RNAs (DRlncRNAs) as potential prognostic biomarkers in KIRC, offering new insights into tumor pathogenesis and treatment avenues.ResultsOur analysis of data from The Cancer Genome Atlas (TCGA) led to the identification of 431 DRlncRNAs correlated with disulfidptosis-related genes. Five key DRlncRNAs (SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3, and AC108673.3) were used to develop a prognostic model that effectively distinguished between low- and high-risk patients with significant differences in overall survival and progression-free survival. The low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. Conversely, the high-risk group displayed aggressive tumor features and poor immunotherapy outcomes. Validation through qRT‒PCR confirmed the differential expression of these DRlncRNAs in KIRC cells compared to normal kidney cells, underscoring their potential functional significance in tumor biology.ConclusionsThis study established a robust link between disulfidptosis-related lncRNAs and patient prognosis in KIRC, underscoring their potential as prognostic biomarkers and therapeutic targets. The differential expression of these lncRNAs in tumor versus normal tissue further highlights their relevance in KIRC pathogenesis. The predictive model not only enhances our understanding of KIRC biology but also provides a novel stratification tool for precision medicine approaches, improving treatment personalization and outcomes in KIRC patients.

Update on Atypical Femoral Fractures.

➤ Atypical femoral fractures (AFFs) are stress fractures between the lesser trochanter and the metaphyseal flare that are most commonly related to prolonged (3 to 5 years) antiresorptive medication use.➤ An important finding is a visible transverse line in the lateral cortex, known as the "dreaded black line." Complete fractures are transverse and have minimal comminution.➤ Prodromal symptoms including hip, groin, thigh, and knee pain are present in more than one-half of cases and are usually misdiagnosed.➤ Nonoperative treatment for all patients with AFF includes withdrawal of bisphosphonates, assessment for secondary causes of osteoporosis, and optimization of vitamin D and calcium.➤ Incomplete fractures without a visible line can initially be treated nonoperatively with protected weight-bearing.➤ Intramedullary nailing is indicated for complete fractures and incomplete fractures with a visible fracture line. Delayed healing after fixation should be anticipated.➤ Treatment with parathyroid hormone (PTH) analogs (teriparatide and abaloparatide) after AFF prevents other fractures in high-risk patients, but the effect on healing of the fracture is unclear.

Constructing a prognostic model for colon cancer patients on basis of coagulation genes enriched in cancer-associated fibroblasts to guide personalized immunotherapy

Abstract Objectives Colon cancer is a global health challenge. This research is designed to build a prognostic model that can personalize the guidance of immunotherapy among colon cancer patients. Methods Coagulation-associated prognostic genes which were subsequently integrated into a Least Absolute Shrinkage and Selection Operator algorithm for constructing a prognostic model were identified with the univariate Cox analyses. The potential of coagulation-related risk score (CRRS) in prognosis and immunotherapy outcomes was rigorously assessed. Finally, the cellular origin of genes in the CRRS model was explored with single-cell RNA-seq data, and the biological functions of core genes were further confirmed by cell function experiments. Results Our findings showed the CRRS model usefully classified patients into high-risk and low-risk groups. High-risk patients exhibited worse total survival. A nomogram was subsequently devised, enabling quantitative survival prediction by incorporating CRRS, age, sex, and TNM stage. Moreover, the CRRS model predicted the extent of cancer-associated fibroblasts (CAFs) infiltration. The analysis further indicated diminished immune responsiveness in high-risk patients, and single-cell data analysis pinpointed TIMP1+ CAF as a potential contributor to cancer progression. Conclusions The CRRS model can be adopted as a prognostic device for colon cancer patients and low-risk patients are more suitable for treatment with immune checkpoint inhibitors. TIMP1 secreted by CAF can promote the malignant progression of colon cancer.

Analysis of risk factors and development of a nomogram prediction model for renal tubular acidosis in primary Sjogren syndrome patients

ObjectiveTo investigate the risk factors of renal tubular acidosis (RTA) in patients with primary Sjögren’s syndrome (pSS) and create a personalized nomogram for predicting pSS-RTA patients.MethodData from 99 pSS patients who underwent inpatient treatment at our hospital from January 2012 to January 2024 were retrospectively collected and analyzed. Bootstrap resampling technique, single-factor, and multi-factor logistic regression analyses were used to explore the risk factors for pSS-RTA. A nomogram was developed based on the results of the multivariate logistic model. The model was evaluated through receiver operating characteristic curve, C-index, calibration curve, and decision curve analysis. In addition, we graded the severity of pSS-RTA patients and used univariate analysis to assess the relationship between pSS-RTA severity and risk factors.ResultsA multivariate logistic regression analysis revealed that concurrent thyroid disease, long symptom duration, subjective dry mouth, and positive RF were independent risk factors for pSS-RTA patients. Based on them, a personalized nomogram predictive model was established. With a p-value of 0.657 from the Hosmer-Lemeshow test, the model demonstrated a good fit. The AUC values in the training and validation groups were 0.912 and 0.896, indicating a strong discriminative power of the nomogram. The calibration curves for the training and validation groups closely followed the diagonal line with a slope of 1, confirming the model’s reliable predictive ability. Furthermore, the decision curve analysis showed that the nomogram model had a net benefit in predicting pSS-RTA, emphasizing its clinical value.This study did not find an association between the severity of pSS-RTA and risk factors.DiscussionWe developed a nomogram to predict RTA occurrence in pSS patients, and it is believed to provide a foundation for early identification and intervention for high-risk pSS patients.

Long-term Results of Hypofractionated Radiotherapy With Intra-prostatic Boosts in Men With Intermediate- and High-risk Prostate Cancer: A Phase II Trial

AimsIn the conventionally fractionated phase III FLAME prostate trial, focal boosts improved local control and biochemical disease-free survival (bDFS). We explored the toxicity and effectiveness of a moderately hypofractionated schedule with focal boosts. Material and methodsBIOPROP20 is a phase II single-arm non-randomised trial for intermediate- to very high-risk localised prostate cancer patients with bulky tumour volumes. Multi-parametric magnetic resonance imaging (MRI) and 18F-choline positron emission tomography-computed tomography (PET-CT) scans were used for staging and boost volume definition. Patients were treated with 60Gy in 20 fractions with a boost dose up to 68Gy. Five patients with positive lymph nodes on the PET-CT scan received radiotherapy to pelvic lymph nodes (45Gy to elective nodes, boosted up to 50Gy to involved nodes). Primary outcomes were acute (≤18 weeks) and late urinary and gastrointestinal toxicity, prospectively recorded up to 5 years with Common Terminology Criteria for Adverse Events v4 (CTCAE). Secondary outcomes were biochemical or clinical progression, metastasis-free survival (MFS), and overall survival (OS). Results61 patients completed radiotherapy with hormone therapy (range: 6–36 months). Cumulative acute and late gastrointestinal toxicity was low at 6.6% and 5.0%, respectively. Cumulative acute and late urinary toxicity was 49.2% and 30.1%, respectively; the prevalence reduced to 5.9% at 5 years. At 5 years: 6 patients had biochemical progression (bDFS: 88.5%; 95% CI: 80.2–97.6%), the MFS was 82.4% (95% CI: 73.0–92.9%), 5 patients died (OS: 91.2%; 95% CI: 84.1–98.9%), one with prostate cancer. The prostate, boost, nodal planning volumes, and the organs at risk (rectum, bowel, urethra, and bladder) met the optimal protocol dose constraints. There was a trend to increased urinary toxicity with increasing urethral (RR: 1.95, 95% CI: 0.73–5.22, p = 0.18), but not bladder dose. ConclusionFocal boosts with a 20 fraction hypofractionated prostate radiotherapy schedule are associated with an acceptable risk of gastrointestinal and urinary toxicity and achieve good cancer control. ClinicalTrials.gov identifierNCT02125175.

Effect of Pravastatin on Placental Expression of Epidermal Growth Factor-like Domain 7 in Early-Onset Pre-Eclampsia: A New Potential Mechanism of Action.

The primary intervention for pre-eclampsia (PE) remains iatrogenic delivery, which can be very preterm and not optimal for the fetus. Although many efforts have been made to prevent and manage PE, there is still a dearth of drugs to treat its pathophysiological progression. Pravastatin (PRA), a hydrophilic statin, has gained interest for the prevention and treatment of PE. The aim of the present study was to evaluate the ability of PRA to modulate factors involved in placentation, such as Epidermal Growth Factor-Like Domain 7 (EGFL7), in human chorionic villous culture from healthy controls and women with PE. A total of 18 women were enrolled: 10 controls and 8 cases. Chorionic villous explants were maintained in culture for 24 h with or without 10 μM Pravastatin, and the expression of EGFL7 and NOTCH1 pathway members was evaluated by qRT-PCR and Western blot analysis. The rationale of the present study was to establish an ex vivo model to identify potential different responses to PRA treatment of chorionic villous explants in order to clarify the molecular mechanism of PRA in the prevention and treatment of PE and to predict whether there are specific clinical conditions that modulate the response to the drug treatment. Within PE patients, two different groups were identified: the high responders, whose villous cultures exhibit significantly increased expressions of the EGFL7 and Notch pathways after PRA incubation; and the low responders, who are high-risk PE patients in which prophylaxis failed to prevent PE and PRA was not able to modulate EGFL7 expression. In conclusion, we identified EGFL7 as a new factor regulated by PRA, placing interest in early discrimination between low- and high- risk women, in which the well-known pharmacological prophylaxis seems to be ineffective, and to explore new potential prevention strategies.

Risk Factors of COVID-19 associated mucormycosis in Iranian patients: a multicenter study

BackgroundTo evaluate the demographic, clinical, and prognostic characteristics of patients diagnosed with COVID-19-associated mucormycosis (CAM) in Iranian patients.MethodsThis prospective observational study was conducted in 8 tertiary referral ophthalmology centers in different provinces of Iran during the fifth wave of the COVID-19 pandemic. All patients were subjected to complete history taking and comprehensive ophthalmological examination and underwent standard accepted treatment strategy based on the disease stage.ResultsTwo hundred seventy-four CAM patients (most were males (150, 54.7%)) with a mean age of 56.8 ± 12.44 years were enrolled. Patients with a history of cigarette smoking (Adjusted Odds Ratio (AOR) = 4.36), Intensive Care Unit admission (ICU) (AOR = 16.26), higher stage of CAM (AOR = 2.72), and receiving endoscopic debridement and transcutaneous retrobulbar amphotericin B (AOR = 3.30) had higher odds of mortality. History of taking systemic corticosteroids during COVID-19 was significantly associated with reduced odds of mortality (AOR = 0.16). Generalized Estimating Equations analysis showed that the visual acuity of deceased patients (LogMAR: 3.71, 95% CI: 3.04–4.38) was worse than that of patients who were discharged from the hospital (LogMAR: 2.42, 95% CI: 2.16–2.68) (P < 0.001).ConclusionsThis study highlights significant risk factors for mortality in patients with CAM, such as cigarette smoking, ICU admission, advanced CAM stages, receiving transcutaneous retrobulbar amphotericin B and worser visual acuity. Conversely, a history of systemic corticosteroid use during COVID-19 was linked to reduced mortality. These findings underscore the critical need for early identification and targeted interventions for high-risk CAM patients to improve clinical outcomes.

A novel metric-based approach of scoring early host immune response from oro-nasopharyngeal swabs predicts COVID-19 outcome

Unpredictable fatal outcome of COVID-19 is attributed to dysregulated inflammation. Impaired early adaptive immune response leads to late-stage inflammatory outcome. The purpose of this study was to develop biomarkers for early detection of host immune impairment at first diagnosis from leftover RNA samples, which may in turn identify high risk patients. Leftover RNA samples of COVID-19 patients at first diagnosis were stored. Following prospective follow-up, the samples were shorted and categorized into outcome groups. Impaired adaptive T cell response (severity score) and Impaired IL-10 response (undetectable IL-10 in the presence of high expression of a representative interferon response gene) were determined by RT-PCR based assay. We demonstrate that a T cell response based ‘severity score’ comprising rational combination of Ct values of a target genes’ signature can predict high risk noncomorbid potentially critical COVID-19 patients with a sensitivity of 91% (95% CI 58.7–99.8) and specificity of 92.6% (95% CI 75.7–99) (AUC:0.88). Although inclusion of comorbid patients reduced sensitivity to 77% (95% CI 54.6–92.2), the specificity was still 94% (95% CI 79.8–99.3) (AUC:0.82). The same for ‘impaired IL-10 response’ were little lower to predict high risk noncomorbid patients 64.2% (95% CI 35.1–87.2) and 82% (95% CI 65.5–93.2) respectively. Inclusion of comorbid patients drastically reduce sensitivity and specificity51.6% (95% CI 33.1–69.8) and 80.5% (95% CI 64.0–91.8) respectively. As best of our knowledge this is the first demonstration of a metric-based approach showing the ‘severity score’ as an indicator of early adoptive immune response, could be used as predictor of severe COVID-19 outcome at the time of first diagnosis using the same leftover swab RNA. The work flow could reduce expenditure and reporting time of the prognostic test for an earliest clinical decision ensuring possibility of early rational management.

Evaluation of Machine Learning Algorithms for Pressure Injury Risk Assessment in a Hospital with Limited IT Resources.

Clinical decision support systems for Nursing Process (NP-CDSSs) help resolve a critical challenge in nursing decision-making through automating the Nursing Process. NP-CDSSs are more effective when they are linked to Electronic Medical Record (EMR) Data allowing for the computation of Risk Assessment Scores. Braden scale (BS) is a well-known scale used to identify the risk of Hospital-Acquired Pressure Injuries (HAPIs). While BS is widely used, its specificity for identifying high-risk patients is limited. This study develops and evaluates a Machine Learning (ML) model to predict the HAPI risk, leveraging EMR readily available data. Various ML algorithms demonstrated superior performance compared to BS (pooled model AUC/F1-score of 0.85/0.8 vs. AUC of 0.63 for BS). Integrating ML into NP-CDSSs holds promise for enhancing nursing assessments and automating risk analyses even in hospitals with limited IT resources, aiming for better patient safety.

Towards Regional Population Health Management: A Prospective Analysis Using the Adjusted Clinical Groups Classification.

This research seeks to assess the potential of regionally integrated health management for specific sub-populations, including the incorporation of self-management initiatives. It will achieve this by conducting a thorough stratification analysis of hospital data, utilizing the Adjusted Clinical Groups (ACG) classification system. The approach involves a retrospective review of healthcare data spanning five years, which includes patient demographics, health outcomes, and healthcare utilization metrics. We intend to use the ACG method to classify the patient population into pertinent groups that mirror their health requirements and resource use. The insights obtained from this analysis will be used to create a localized adaptation of the Kaiser Permanente Pyramid Model of Care. This adaptation aims to identify the distribution of costs among patients treated in the Rivierenland Hospital. We anticipate that stratifying data with the ACG method will identify distinct multimorbid subgroups. These subgroups will have unique healthcare requirements. Early interventions and customized health management strategies, based on these insights, could enhance health outcomes and resource efficiency for high-risk patients. This analysis will serve as a foundation for constructive discussions with hospital management and clinical staff, fostering a deeper comprehension of the patients' burden of disease. It might also foster multidisciplinary collaboration opportunities between medical specialties as with regional healthcare partners such as general practitioners (GPs), mental health and other long-term care organizations. Moreover, we anticipate that self-care initiatives, supported by customized health information, will encourage increased patient engagement and strategies for enhancing lifestyle improvements. This strategy is expected to enable the personalization of advanced care planning based on individual needs profiles, thereby improving the management of complex and chronic conditions, and encouraging self-care practices. Our anticipated findings highlight the potential benefits of a data-informed approach to advancing healthcare outcomes and present opportunities for future investigations to refine and implement such integrated care models across the region.

Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality. In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45-80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45-80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE. Graphical models, applied on the COPDGene Phase 1 samples (n=8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n=3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n=2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions. Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them. This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.

Nomograms to predict the long-term prognosis for non-metastatic invasive lobular breast carcinoma: a population-based study

Invasive lobular breast carcinoma (ILC) is one potential subset that “clinicopathologic features” can conflict with “long-term outcome” and the optimal management strategy is unknown in such discordant situations. The present study aims to predict the long-term, overall survival (OS) and cancer-specific survival (CSS) of ILC. The clinical information of patients with non-metastatic ILC was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2020. A total of 31451 patients were enrolled and divided into the training cohort (n=22,017) and validation cohort (n=9434). The last follow-up was December, 31, 2020 and the median follow-up period was 99 months (1–203). Age, marriage, estrogen (ER) status, progesterone (PR) status, grade, tumor size, lymph node ratio (LNR) and combined summary (CS) stage were prognostic factors for both OS and CSS of ILC, whereas chemotherapy and radiation were independent protect factors for OS. The nomograms exhibited satisfactory discriminative ability. For the training and validation cohorts, the C-index of the OS nomogram was 0.765 (95% CI 0.762–0.768) and 0.757 (95% CI 0.747–0.767), and the C-index of the CSS nomogram were 0.812 (95% CI 0.804–0.820) and 0.813 (95% CI 0.799–0.827), respectively. Additionally, decision curve analysis (DCA) demonstrated that the nomograms had superior predictive performance than traditional American Joint Committee on Cancer (AJCC) TNM stage. The novel nomograms to predict long-term prognosis based on LNR are reliable tools to predict survival, which may assist clinicians in identifying high-risk patients and devising individual treatments for patients with ILC. Our findings should aid public health prevention strategies to reduce cancer burden. We provide two R/Shiny apps (https://ilc-survival2024.shinyapps.io/osnomogram/; https://ilc-survival2024.shinyapps.io/cssnomogram/) to visualize findings.

Post COVID-19 mucormycosis in critical care settings: A prospective cohort study in a tertiary care center in Egypt

BackgroundThe emergence of mucormycosis as a life-threatening fungal infection after the coronavirus disease of 2019 (COVID-19) is a major concern and challenge, but there is limited information on the risk factors for mortality in patients. MethodsWe conducted a prospective cohort study from May 2021 to April 2022 to determine the in-hospital outcomes of post-COVID-19 mucormycosis during the intensive care unit (ICU) stay. The sample of the study was collected as consecutive sampling using all accessible patients in the study period. The Statistical Package for Social Sciences (SPSS), version 25 (IBM, Chicago, Illinois, USA) was used for statistical analysis. ResultsAmong 150 patients with post-COVID-19 mucormycosis, the majority had a primary sinus infection (86.0 %), while 11.3 % had both sinus and ocular infections, and 2.7 % had sinus and cutaneous infections. Around 21 % (n = 31) of patients deceased after staying in the ICU for a median (range) of 45.0 (10.0–145.0) days. The majority of the patients who deceased had pneumonia patches on computed tomography (CT) (90.3 %) while none of the patients who were discharged had pneumonia patches (p < 0.001). The deceased group had higher rates of pulmonary embolism (93.5 %) compared to the surviving groups (21.8 %). In a multivariate Cox regression analysis, the risk of death was higher in older patients above 60 years old (hazard ratio (95 %CI): 6.7 (1.73–15.81)), increase among patient with history of steroid administration (hazard ratio (95 %CI): 5.70 (1.23–10.91)), who had facial cutaneous infection with mucormycosis (hazard ratio (95 %CI): 8.76 (1.78–25.18)), patients with uncontrolled diabetes (hazard ratio (95 %CI): 10.76 (1.78, 65.18)), and total leukocytic count (TLC>10 ×103 mcL) (hazard ratio (95 %CI): 10.03 (3.29–30.61)). ConclusionsIdentifying high-risk patients especially old diabetic patients with corticosteroid administration and detecting their deterioration quickly is crucial in reducing post-COVID-19 mucormycosis mortality rates, and these factors must be considered when developing treatment and quarantine strategies.

Impact of Reclassification of Oncocytic and Follicular Thyroid Carcinoma by the 2022 WHO Classification.

The 2022 WHO Classification categorizes oncocytic (OTC) and follicular thyroid carcinoma (FTC) based on the degree of capsular and vascular invasion into minimally invasive (MI), encapsulated angioinvasive (EA) and widely invasive tumors (WI). While associations with clinical outcomes have been studied extensively in FTC, robust clinical data are lacking for OTC. We aimed to investigate the impact of the reclassification of OTC and FTC by the 2022 WHO Classification on clinical outcomes. All adult OTC and FTC patients treated at the Erasmus MC (the Netherlands) between 2000 and 2016 were retrospectively included. All tumors were extensively revised by two independent pathologists, facilitated by Palga: Dutch Pathology Databank. Kaplan-Meier curves were used to study the association of the 2004 and 2022 WHO Classification with overall survival (OS), disease-specific survival (DSS), recurrence-free survival (RFS) and radioactive iodine (RAI) refractory disease. Fifty-two OTC and 89 FTC patients were included, of which 15 (28.8%) OTC and 34 (38.2%) FTC tumors were reclassified as EAOTC or EAFTC. The 2022 WHO Classification substantially improved risk stratification in both subtypes for DSS, compared with the 2004 edition. Ten-year DSS rates were 100% for MIOTC, 92.9% for EAOTC and 56.5% for WIOTC, compared to 100% (MIOTC) and 64.2% (WIOTC) following the 2004 WHO Classification. For FTC and RAI-refractory disease, similar trends were observed. Classification of OTC and FTC into three subcategories as defined by the 2022 WHO Classification substantially improves discrimination between low, intermediate and high risk patients, especially for DSS and RAI-refractory disease.