Tool For Risk Classification Research Articles

MDRO Colonization Among Nursing Homes Patients: A Risk Classification Tool for Early Identification

Background: Nursing homes (NHs) have high prevalence of multi-drug resistant organisms (MDROs) with rates exceeding those in hospitals. This study proposes quantifying patients’ risk of MDRO colonization and creating a risk profile for the NH patient populations to assist in reducing MDRO burden in health care facilities. Methods: We assessed a risk classification model using data from a prospective cohort study (2018 Pathways study). Patient sample included 783 newly admitted patients followed for up to 180 days and 9,587 samples collected from patients during 2,089 visits. Individual risk factors of MDRO colonization were assessed using unadjusted logistic regression, and patients were risk classified based on number of risk factors. Multivariate regression was performed to obtain odds of MDRO colonization for patient risk groups adjusting for patient age, sex, race, and length of NH stay (LOS). The risk classification tool developed using Pathways data was also tested among a sample of NH residents from Veterans Administration (N=190). Results: The patient sample (Pathways data) was 43.2% male, 37.2% Black with a mean age 74 years. 69.3% were colonized with a MDRO during the study. In unadjusted regression, recent antibiotic use (p<.001), open wounds (p<.05), use of urinary catheter or feeding tube (p<.001), functional disability (p<.001), diabetes (p<.01), and preadmission hospital stay over 14 days (p<.001) were associated with colonization, while Charlson comorbidity score, age, sex, and race were not. In adjusted analysis (c-statistic=0.75), a patient’s colonization risk increased with the number of risk factors (Figure 1), with 47.2% expected colonization among patients with none of the risk factors and 86.7% expected colonization among patients with four or more risk factors. The risk classification model had similar performance among male and female patients, and among Black and White patients (Figure 2). Secondary analysis using data obtained from separate, Veterans Administration facilities provided preliminary validation of the risk scoring tool. The model had acceptable fit (c-statistic=0.71). Veterans with four or more risk factors had 87.8% expected probability of MDRO colonization compared with 39.6% colonization among those without any risk factors. Veterans with less than four risk factors also had higher colonization, but these differences were not statistically significant. Conclusions. Despite system-wide efforts to reduce MDRO burden, prevalence of MDRO colonization remains high in NHs. The risk classification tool can assist in early identification of most vulnerable NH patients to direct targeted interventions such as education, enhanced environmental cleaning, and active surveillance.Disclosure: Lona Mody: NIH, VA, CDC, Kahn Foundation; Honoraria: UpToDate; Contracted Research: Nano-Vibronix

Patients’ voices matter: Translating pretreatment patient-reported outcomes into high and low health-related quality of life provides a new clinical aspect on prognosis and survival in patients with previously untreated diffuse large B‐cell lymphoma.

e23165 Background: Patient-reported health-related quality of life (HRQoL) measures collected prior to treatment initiation have been shown to be prognostic of disease outcomes and survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL), including progression-free survival (PFS) and overall survival (OS), However, these HRQoL measures are usually expressed as numbers from a scale (e.g. from 0 to 100), which limit their ease of clinical utility compared to binary scales (i.e. high vs. low). Using HRQoL measures collected from the phase 3 study GOYA, we aim to convert these measures into binary scales by identifying optimal cutoffs and demonstrate their clinical utility in differentiating patient risks in DLBCL. Methods: HRQoL data from the physical functioning (PF2), global health status (QL2), and fatigue (FA) scales from the EORTC QLQ-C30 questionnaire and the lymphoma subscale (LYMS) from the FACT-LYM questionnaire were used in this analysis. The optimal cutoff points were identified using statistical metrics that evaluate the prognostic value and goodness-of-fit for each assessed cutoff point. The top performing binary HRQoL scale was further assessed in its clinical utility as an addition to the Internal Prognostic Index (IPI), a standard risk classification tool in DLBCL based on 5 binary measures of patient baseline characteristics, to evaluate whether adding the HRQoL scale would potentially improve the risk classification. Results: As shown in the table, while all binary scales can clearly differentiate patient overall survival (demonstrated by the hazard ratios), PF2 numerically exhibited the best performance, and was further assessed as an addition to IPI. After adding PF2 to the base OS model containing only IPI, we observed improvements in model performance metrics, including the concordance index and the goodness-of-fit. In addition, among patients classified as high risk by IPI (n = 185, 3-yr OS: 65% [95% CI 68-72%]), those with low HRQoL (n = 102, 3-yr OS: 56% [95% CI 47-67%]) had a substantially worse OS, suggesting that further refinement of patient-risk defined by IPI may be feasible with HRQoL measures. Conclusions: Our work highlights the potential of the binary HRQoL scales to provide additional prognostic value to IPI, and suggests that patient reported outcomes may be useful in further refining patient risk classification in addition to IPI in untreated DLBCL. [Table: see text]

The potential role of RNA sequencing in diagnosing unexplained insensitivity to conventional chemotherapy in pediatric patients with B-cell acute lymphoblastic leukemia

Pediatric B-cell acute lymphoblastic leukemia (B-ALL) is a highly heterogeneous disease. According to large-scale RNA sequencing (RNA-seq) data, B-ALL patients can be divided into more than 10 subgroups. However, many genomic defects associated with resistance mechanisms have not yet been identified. As an individual clinical tool for molecular diagnostic risk classification, RNA-seq and gene expression pattern-based therapy could be potential upcoming strategies. In this study, we retrospectively analyzed the RNA-seq gene expression profiles of 45 children whose molecular diagnostic classifications were inconsistent with the response to chemotherapy. The relationship between the transcriptome and chemotherapy response was analyzed. Fusion gene identification was conducted for the included patients who did not have known high-risk associated fusion genes or gene mutations. The most frequently detected fusion gene pair in the high-risk group was the DHRSX duplication, which is a novel finding. Fusions involving ABL1, LMNB2, NFATC1, PAX5, and TTYH3 at onset were more frequently detected in the high-risk group, while fusions involving LFNG, TTYH3, and NFATC1 were frequently detected in the relapse group. According to the pathways involved, the underlying drug resistance mechanism is related to DNA methylation, autophagy, and protein metabolism. Overall, the implementation of an RNA-seq diagnostic system will identify activated markers associated with chemotherapy response, and guide future treatment adjustments.

Development and validation of a novel risk classification tool for predicting long length of stay in NICU blood transfusion infants

Newborns are as the primary recipients of blood transfusions. There is a possibility of an association between blood transfusion and unfavorable outcomes. Such complications not only imperil the lives of newborns but also cause long hospitalization. Our objective is to explore the predictor variables that may lead to extended hospital stays in neonatal intensive care unit (NICU) patients who have undergone blood transfusions and develop a predictive nomogram. A retrospective review of 539 neonates who underwent blood transfusion was conducted using median and interquartile ranges to describe their length of stay (LOS). Neonates with LOS above the 75th percentile (P75) were categorized as having a long LOS. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to screen variables and construct a risk model for long LOS. A multiple logistic regression prediction model was then constructed using the selected variables from the LASSO regression model. The significance of the prediction model was evaluated by calculating the area under the ROC curve (AUC) and assessing the confidence interval around the AUC. The calibration curve is used to further validate the model’s calibration and predictability. The model’s clinical effectiveness was assessed through decision curve analysis. To evaluate the generalizability of the model, fivefold cross-validation was employed. Internal validation of the models was performed using bootstrap validation. Among the 539 infants who received blood transfusions, 398 infants (P75) had a length of stay (LOS) within the normal range of 34 days, according to the interquartile range. However, 141 infants (P75) experienced long LOS beyond the normal range. The predictive model included six variables: gestational age (GA) (< 28 weeks), birth weight (BW) (< 1000 g), type of respiratory support, umbilical venous catheter (UVC), sepsis, and resuscitation frequency. The area under the receiver operating characteristic (ROC) curve (AUC) for the training set was 0.851 (95% CI 0.805–0.891), and for the validation set, it was 0.859 (95% CI 0.789–0.920). Fivefold cross-validation indicates that the model has good generalization ability. The calibration curve demonstrated a strong correlation between the predicted risk and the observed actual risk, indicating good consistency. When the intervention threshold was set at 2%, the decision curve analysis indicated that the model had greater clinical utility. The results of our study have led to the development of a novel nomogram that can assist clinicians in predicting the probability of long hospitalization in blood transfused infants with reasonable accuracy. Our findings indicate that GA (< 28 weeks), BW(< 1000 g), type of respiratory support, UVC, sepsis, and resuscitation frequency are associated with a higher likelihood of extended hospital stays among newborns who have received blood transfusions.

Addition of cribriform pattern 4 and intraductal prostatic carcinoma into the CAPRA-S tool improves post-radical prostatectomy patient stratification in a multi-institutional cohort

AimsPre-surgical risk classification tools for prostate cancer have shown better patient stratification with the addition of cribriform pattern 4 (CC) and intraductal prostatic carcinoma (IDC) identified in biopsies. Here, we...

Development and validation of a promising 5-gene prognostic model for pediatric acute myeloid leukemia

Risk classification in pediatric acute myeloid leukemia (P-AML) is crucial for personalizing treatments. Thus, we aimed to establish a risk-stratification tool for P-AML patients and eventually guide individual treatment. A total of 256 P-AML patients with accredited mRNA-seq data from the TARGET database were divided into training and internal validation datasets. A gene-expression-based prognostic score was constructed for overall survival (OS), by using univariate Cox analysis, LASSO regression analysis, Kaplan–Meier (K-M) survival, and multivariate Cox analysis. A P-AML-5G prognostic score bioinformatically derived from expression levels of 5 genes (ZNF775, RNFT1, CRNDE, COL23A1, and TTC38), clustered P-AML patients in training dataset into high-risk group (above optimal cut-off) with shorter OS, and low-risk group (below optimal cut-off) with longer OS (p < 0.0001). Meanwhile, similar results were obtained in internal validation dataset (p = 0.005), combination dataset (p < 0.001), two treatment sub-groups (p < 0.05), intermediate-risk group defined with the Children's Oncology Group (COG) (p < 0.05) and an external Japanese P-AML dataset (p = 0.005). The model was further validated in the COG study AAML1031(p = 0.001), and based on transcriptomic analysis of 943 pediatric patients and 70 normal bone marrow samples from this dataset, two genes in the model demonstrated significant differential expression between the groups [all log2(foldchange) > 3, p < 0.001]. Independent of other prognostic factors, the P-AML-5G groups presented the highest concordance-index values in training dataset, chemo-therapy only treatment subgroups of the training and internal validation datasets, and whole genome-sequencing subgroup of the combined dataset, outperforming two Children's Oncology Group (COG) risk stratification systems, 2022 European LeukemiaNet (ELN) risk classification tool and two leukemic stem cell expression-based models. The 5-gene prognostic model generated by a single assay can further refine the current COG risk stratification system that relies on numerous tests and may have the potential for the risk judgment and identification of the high-risk pediatric AML patients receiving chemo-therapy only treatment.

Usefulness of PREDIC3T Case Type Risk Category in the CRISP Registry

Procedural risk in Congenital Cardiac Catheterization (PREDIC3T) was recently reported as the contemporary procedure-type risk metric by the Congenital Cardiac Catheterization Project on Outcomes (C3PO) registry. The usefulness of this metric has not been evaluated elsewhere. The CRISP registry of Congenital Cardiovascular Interventional Study Consortium (CCISC) data set was analyzed. The study period was 14years (2009 to 2022). The primary outcome was significant adverse event (SAE). Cases were assigned to the 6 PREDIC3T risk categories. Univariate and multivariable logistic regression models were used to evaluate the association between PREDIC3T and the primary outcome. The model discriminative performance was evaluated by the c-statistic. In a total of 64,419 enrolled cases, PREDIC3T case types were assigned in 59,822 cases (93%). The frequency for PREDIC3T category was 0=7,494 (12.5%), 1=16,932 (28.3%), 2=17,023 (28.5%), 3=9,885 (16.5%), 4=4,403 (7.4%), and 5=4,085 (6.8%). SAE was observed in 2,474 cases (4.1%). The SAE rates for category were 0=1.0%, 1=2.3%, 2=4.0%, 3=6.2%, 4=8.2%, and 5=9.0%. In a multivariable model, PREDIC3T case type risk category (odds ratios for category: 0=0.49, 1=1.00, 2=1.40, 3=2.06, 4=2.79, and 5=3.15; p <0.001) were significantly associated with SAE (c-statistic of 0.707) after adjusting for age, preprocedural inotropic support and systemic illness, low systemic saturation, high pulmonary vascular resistance, and the use of general anesthesia. The PREDIC3T case type risk category was associated with the risk of SAE in the CRISP registry data set and appeared to be a useful procedural risk classification tool.

Abstract 147: Is Effect Size of Targeted Temperature Management Different for Pediatric Post-Cardiac Arrest Syndrome According to the Severity?

Introduction: The beneficial effect of target temperature management (TTM) for pediatric post-cardiac arrest syndrome (PCAS) patients is controversial, and there are few studies on the differential effects of TTM according to the risk classification. Objective: The aim of this study was to evaluate the differential effects of TTM for pediatric PCAS patients according to a risk classification tool we previously developed, rCAST. Methods: We used data from the Japanese nationwide out-of-hospital cardiac arrest (OHCA) registry of the Japanese Association for Acute Medicine. We classified eligible pediatric PCAS patients (aged ≤18 years) who achieved return of spontaneous circulation (ROSC) according to the quintile of score points on the rCAST. The effect of TTM for their neurological outcome was evaluated for each severity group. And then, focusing on the severity group that appeared to benefit from TTM, we also evaluated the effect of TTM by the propensity score analysis with several adjustment factors. Good neurological outcome was defined as a Cerebral Performance Categories score or Pediatric Cerebral Performance Categories score ≤2 at 30 days. Results: Among 1,526 OHCA pediatric patients in the registry, data of 307 PCAS patients who achieved ROSC were analyzed. The proportion of patients with a good neurological outcome was higher among those who underwent TTM compared to those who did not in the first to forth quintile (TTM vs non-TTM; 81% [21/26] vs 58% [21/36] [p=0.10] in the first, 11% [2/19] vs 7% [2/31] [p=0.63] in the second, 12% [2/17] vs 0% [0/55] [p=0.053] in the third, and 11% [1/9] vs 0% [0/21] [p=0.30] in the fourth). In the group of the fifth quantile (rCAST ≥ 18.5), no patients showed good neurological outcome regardless of TTM (0% [0/20] vs 0% [0/73]). The propensity score analysis showed that TTM was significantly associated with their good neurological outcome in the patients classified into the first to fourth quantile group (Odds ratio; 1.21 [1.04-1.40], p = 0.014). Conclusions: TTM was significantly associated with a good neurological outcome in the pediatric PCAS patients with rCAST ≤ 18.0. All patients with rCAST ≥ 18.5 showed poor neurological outcomes even if they underwent TTM.

Development of a risk stratification model for pharmaceutical care in HIV patients

BackgroundThe increasing number of HIV-patients and their complexity makes it necessary to develop risk classification tools to improve the optimization of resources. ObjectiveTo design a risk-stratified model for pharmaceutical care (PC) in HIV-patients. MethodsA cross-sectional, multicenter study. An expert panel was created by Hospital Pharmacist experienced in PC for HIV-patients. The study was designed in 4 phases. The first phase included a review of literature and the development of a summary of the scientific evidence available. According to their score, patients were stratified into three levels of PC. In the second and third phases, a sample of patients was assessed and data information was recorded. The overall analysis also allowed pharmacists to define the actions to be applied at each level of priority.Finally, each stratification model was applied to a new sample of patients to verify their applicability and usefulness. ResultsAll variables included in the model were weighted in terms of their relative relevance compared to the rest. A sample of 215 patients was evaluated to obtain their score and distribution: Priority-1: score ≥ 32 and 10% of the sample; Priority-2: 18-31.9 and 30%; Priority-3: ≤ 17 and 60%.The PC interventions corresponding to each level of priority were classified into “pharmacotherapeutic monitoring”, “training, education and patient tracking” and “coordination of all the healthcare team members”. ConclusionsThis study supported the design and adaptation of a selection and stratification model for PC in HIV-patients as a tool to identify those who may benefit from priority intervention.

National Early Warning Score-2 for Identification of Patients with Intermediate-High-Risk Pulmonary Embolism.

Consensus statements have proposed the use of the National Early Warning Score 2 (NEWS2) to identify stable patients with acute pulmonary embolism (PE) and an intermediate-high risk of adverse outcomes. We aimed to externally validate NEWS2 and compare it to another predictive score (Bova). Using NEWS2 (cutoff ≥5 and ≥7) and the Bova score (cutoff >4), we classified patients as intermediate-high risk (vs. non-intermediate-high risk), and we compared the test characteristics of these risk classification tools for a complicated course within 30 days after PE diagnosis. We also assessed the validity of NEWS2 for predicting a complicated course by adding the results of echocardiography and troponin testing to the model. Of the 848 enrolled patients, the NEWS2 score ≥5 classified 471 (55.5%) and the Bova score classified 37 (4.4%) as intermediate-high risk. NEWS2 had a significantly lower specificity for a 30-day complicated course than Bova (45.4 vs. 96.3%, respectively; p < 0.001). Using the higher score threshold (≥7), the NEWS2 classified 99 (11.7%) as intermediate-high risk, and the specificity was 88.9% (difference with Bova, 7.4%; p < 0.001). The proportion of patients with intermediate-high risk PE was 2.4% for the combination of a positive troponin testing and echocardiographic right ventricle dysfunction and a positive NEWS2 (score ≥7), while the specificity was 97.8% (difference with Bova, 1.5%; p = 0.07). Bova outperforms NEWS2 for predicting a complicated course among stable patients with PE. Addition of troponin testing and echocardiography improved the specificity of NEWS2, although it was not superior to Bova. CLINICALTRIALS.GOV NUMBER: : NCT02238639.

The Impact of Elevated Troponin Levels on Clinical Outcomes in Patients with Acute Ischemic Stroke: A Systematic Review.

The association between high cardiac troponin (cTn) levels and stroke characteristics and outcomes remains unclear. This systematic review aimed to determine the prevalence and clinical implications of elevated cTn levels in patients with acute ischemic stroke (AIS). We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guidelines. A comprehensive search of PubMed, Google Scholar, Science Direct, and Research Gate databases was conducted to identify relevant studies published in English up to May 2023. This study included all reports on serum cTn levels and AIS. Two independent reviewers assessed study quality and bias using study-specific tools before inclusion. The systematic review included a total of 14 articles with 16906 participants, including one systematic review, one randomized controlled trial (RCT), and 12 observational studies. The results of this systematic review indicate that the prevalence of high cTn levels is averaged at 17.9%, or 1 in 5 individuals, who have an AIS. The review emphasizes the detrimental effects of increased cTn levels on outcomes for in-hospital and all-cause mortality as well as cardiovascular outcomes in patients with AIS. These results demonstrate that serum cTn has the potential to be a useful tool for risk classification and prognostic assessment in individuals with AIS. AIS patients with elevated serum cTn at baseline have an increased risk of mortality. Early and routine evaluation of serum cTn may contribute to the timely detection of co-morbid cardiovascular injury and prevent unfavorable outcomes in patients with AIS.

The deep learning algorithm estimates chest radiograph-based sex and age as independent risk factors for future cardiovascular outcomes.

Chest X-rays (CXRs) convey much illegible physiological information that deep learning model (DLM) has been reported interpreting successfully. Since the electrocardiogram age established by DLM was revealed as a heart biological marker, we hypothesize that CXR age has similar potential to describe the heart and lung states. Therefore, we developed a DLM to predict sex and age through CXR and analyzed its relation with future cardiovascular diseases (CVD). A total of 90,396 CXRs aged 20 to 90 were collected and separated into a development set with 53,102 CXRs and demographic information pairs, a tuning set with 7073 pairs, an internal validation set with 17,364 pairs, and an external validation set with 12,857 pairs. The study trained DLM with development set for estimating age and sex and compared them to actual information. The mean absolute errors of predicted age were 4.803 and 4.313 years in the internal and external validation sets, respectively. The area under the curve of sex analysis was 0.9993 and 0.9988 in the internal and external validation sets, respectively. Patients whose CXR age was 5 years older than chronologic age lead to higher risk of all-cause mortality (hazard ratio (HR): 2.42, 95% confidence interval (CI): 2.00-2.92), cardiovascular (CV)-cause mortality (HR: 7.57, 95% CI: 4.55-12.60), new-onset heart failure (HR: 2.07, 95% CI: 1.56-2.76), new-onset chronic kidney disease (HR: 1.73, 95% CI: 1.46-2.05), new-onset acute myocardial infarction (HR: 1.80, 95% CI: 1.12-2.92), new-onset stroke (HR: 1.45, 95% CI: 1.10-1.90), new-onset coronary artery disease (HR: 1.26, 95% CI: 1.04-1.52), and new-onset atrial fibrillation (HR: 1.43, 95% CI: 1.01-2.02). Using DLM to predict CXR age provided additional information for future CVDs. Older CXR age is an accessible risk classification tool for clinician use.

Using pollution indices to develop a risk classification tool for gold mining contaminated soils

The presence of potentially toxic elements (PTEs) in soil and plants is a risk factor to human well-being and the environment. Soil and leaves from a traditional medicinal plant, Vachellia karroo, known as the sweet thorn plant, were assessed to determine the extent of pollution by PTEs from gold mine tailings in a case study area of Matjhabeng Local Municipality, South Africa. A risk classification tool was developed using the different indicators of pollution quantified to describe the pollution risk at each sampling site. High concentrations of copper, cadmium, lead, nickel, and zinc were measured in the soil, which exceeded the maximum permissible soil quality limits. The enrichment factor and geoaccumulation index results also confirmed heavy soil pollution by copper, nickel, and zinc, with exceptionally heavy pollution by cadmium and selenium. The PTEs in the soil were in an exchangeable form, with the sweet thorn plant accumulating lead and selenium at toxic levels, which poses a risk to the health of the local people as the plant is used for medicinal purposes. The overall pollution risk classification tool of the sampling sites showed that 17% were high-risk areas, 53% were moderate-risk areas, and 20% of the sites were low-risk areas.

Evaluation of an individual anamnesis tool for teaching risk-oriented prevention – a pilot study in undergraduate dental students

BackgroundA comprehensive medical history is needed to establish and ensure a high standard in dental care; however, it is challenging to draw clinical consequences on the variety of potential diseases and medications, especially for dental students. Aim of this observational study was to investigate, whether undergraduate dental students using an analog anamnesis tool for risk classification would be more confident and have more knowledge in risk classification than other students in the same year of study.MethodsA cohort of 48 fifth year dental students was included and allocated into two groups based on their curriculum-related division (group A: n = 25, group B: n = 23). Group A received a teaching event and provision of an analog anamnesis tool for risk classification; group B received neither a teaching event nor the anamnesis tool. At baseline and after two weeks (follow-up), questionnaires regarding self-perceived confidence with risk classification, questions on different disease, medications and lifestyle factors and a task with 15 medical histories of prepared patient cases were applied. The data was statistically analyzed using Mann–Whitney or Wilcoxon test.ResultsIn group comparison of the differences between baseline and follow-up regarding self-perceived confidence, significantly higher improvement was noted in group A compared to group B for all questions (p < 0.05). With regard to knowledge, the group comparison revealed that the differences in all of the four tasks were significantly higher in group A compared to group B (pi ≤ 0.01). Thereby, the different tasks in group A differed between baseline and follow-up as follows: Risk of complications: 49.04 ± 13.59 vs. 67.96 ± 17.22, p < 0.01, Risk of oral diseases: 48.77 ± 13.57 vs. 63.44 ± 16.78, p = 0.01, Indication of antibiotic prophylaxis: 75.70 ± 13.45 vs. 87.97 ± 10.37, p < 0.01 and the Medical history task on 15 patient cases: 58.45 ± 4.74 vs. 71.47 ± 9.54, p < 0.01.ConclusionThe applied analog anamnesis tool supported an increase in students´ confidence with issues related to at-risk patients alongside with their knowledge in risk classification. The applied anamnesis tool can be recommended for improving teaching of risk management for undergraduate dental students.

Climate stress impacts on livestock health: Implications for farming livelihoods and animal disease in Karnataka, India

AbstractUnderstanding the impact of climate change on livestock health is critical to safeguarding global food supplies, economies, and farming livelihoods. We evidence, through exploration of secondary data informed by a rapid ethnographic assessment of farming livelihoods in Karnataka, India, that both precipitation and vapour pressure are key climate variables relating to outbreaks of haemorrhagic septicaemia (HS), anthrax (AX), and black quarter (BQ) across the Indian state of Karnataka. We then developed a risk classification tool that assesses how disease risk varies in Karnataka at present and in possible future scenarios. Temperature and maximum temperature are negatively correlated with HS, AX, and BQ, indicating that regions experiencing a cool (but still hot) climate with increasingly wetter, more humid conditions are at high risk of future outbreaks. Principal component analyses revealed the southwest India monsoon and winter periods to be the most strongly correlated with HS, AX, and BQ outbreaks. Vapour pressure, a proxy for humidity, has a positive relationship with these specific livestock diseases. Certain environmental conditions increase the incidence of some bacterial diseases and conditions that mimic their symptoms, and thus also risk driving up the use of antibiotics and the emergence of antimicrobial resistance (AMR) in regions under stress. This methodology can be replicated to investigate other diseases and regions, as long as the climate and epidemiological data cover similar time periods. This evidence highlights the need for greater synergies between climate change and One Health research and policy.One Health Impact StatementWe have taken a transdisciplinary approach, which expands out of One Health to include climate science and participatory research with farmers and livestock industry consultants, to investigate the relationship between factors related to climate (surface temperatures, rainfall, humidity) and outbreaks of livestock-related bacterial diseases. This is especially relevant to the One Health approach as it attempts to integrate findings between not only the science of disease but also the science of climate change as a driver of disease, and address problems that could arise within the public and private sectors (local farming, livestock health, government policy etc.). Providing spatial context to climate-associated disease risk across the Indian state of Karnataka will benefit: local farmers who may already be practising, or are transitioning to, more intensive livestock farming; policymakers; and private sector companies who are planning for future investments. Such expansion needs to be undertaken with full awareness of potentially heightened disease risk. Our transdisciplinary approach is borne from the observations of famers’ lived experiences of challenges to their livelihoods and facilitates the use of climate datasets that may not have been primarily collected for, or used by, disease-related studies to map long-term epidemiological risk. This demonstrates the pragmatic impact that such transdisciplinary projects can have, by providing interpretations of observed risks to animal health (highlighted by social scientists during engagement with practitioner communities) that Earth Scientists were then able to quantify, proving links that would otherwise not have been evidenced. The use of disease data sourced from local institutions, including the Government of India and research laboratories, can inform plans for the application of pragmatic solutions to local challenges by local farmers who are primarily impacted by the challenges highlighted in the research.

Success of the acute renal angina index in the early prediction of acute kidney injury in the emergency department

ABSTRACT Introduction It is mentioned that the acute renal angina index (aRAI), a new concept, can be used in emergency departments to calculate and accurately predict the risk of developing acute kidney injury (AKI). The aims of the study included: to evaluate the predictive performance of the aRAI (AKI risk classification tool) in predicting AKI in the pediatric emergency department. Method Patients who met the criteria for systemic inflammatory response syndrome were examined. AKI was defined with creatinine N1.5× baseline 24–72 hours after hospitalization. aRAI and original RAI scores were calculated for patients and were shown as renal angina positive (RA+) above a population-derived threshold. The performance of aRAI in predicting AKI compared to changes in creatinine and original RAI was evaluated. Results In total, 241 eligible subjects were enrolled. The median age of the patients was 17 months (min–max 1–192). AKI developed in 60 (24.8%) of the patients. According to the aRAI, 76 (31.5%) of 241 patients were RA(+). The aRAI had an NPV of 1.00 and an AUC of 0.948 (0.914–0.983) for the prediction of AKI. Sensitivity was 95% for the aRAI as compared to 48% for an elevation in SCr noted to be at least two times greater than the baseline while in the PED and 61% for original RAI. Conclusions The aRAI is easily computable, does not depend on complex computational or derivation methods, and is universally accessible. We confirm and extend the findings of previous study reporting the performance of RAI for early prediction of AKI.

Risk Stratification for the Rate and Location of Residual Bladder Tumor for the Decision of Re-Transurethral Resection of Bladder Tumor.

IntroductionTo assess the rate and location of residual tumor in re-transurethral resection of bladder tumor (re-TURBT) and develop a risk stratification tool to assist clinicians in making treatment decisions.Patients and MethodsThe data of 144 patients with high-risk bladder cancer who received re-TURBT were retrospectively reviewed. The rate and location of residual tumors was recorded. Logistic regression was performed to explore risk factors for residual tumors, and a risk classification tool was developed.ResultsAmong the 144 patients, the rates of residual tumor and tumor location at the base of the primary tumor were 22.2% and 10.4%, respectively. Non-urothelial carcinoma subspecialist, piecemeal resection and the absence of detrusor muscle in the first specimen were defined as risk factors. Patients were categorized into low-, intermediate-, and high-risk groups according to the number of risk factors. The rate of residual tumor in the high-risk group was significantly higher than that in the low- and intermediate-risk groups (50% vs. 7.8%, P=0.001; 50% vs. 18.6%, P=0.002). Moreover, high-risk patients benefitted more from a second resection at the base of the primary tumor due to the high rate of residual tumor located at this site than low- and intermediate-risk patients (23.5% vs. 2.0%, P=0.002; 23.5% vs. 10.2%, P=0.083).ConclusionsRisk stratification based on the subspecialist category, operative method, and presence or absence of detrusor muscle in the first specimen could help identify patients who benefit from re-TURBT and second resection the base of the primary tumor.

MORTALITY OF ACUTE MYOCARDIAL INFARCTION IN RELATION WITH TIMI RISK SCORE

Background: The Thrombolysis in Myocardial Infarction (TIMI) risk score is said to be an important factor in predicting mortality risk in fibrinolysis-eligible STEMI patients. An attempt was made to assess the situation by comparing risk stratification based on the TIMI score with the hospital outcome of such individuals. Methods: 145 STEMI patients were included in this srudy , TIMI risk scores were calculated and analysed vis-Ã -vis various relevant parameters.. Based on their TIMI scores, the patients were placed into three risk groups: low-risk,moderate-risk, and high-risk. All patients received standard anti-ischemic medication, were thrombolyzed, monitored in the ICCU, and monitored throughout their hospital stay for post-MI sequelae. Results: According to the TIMI risk score, 79 patients (54.5%) had low-risk , 48 (33.1%) to the moderate-risk , and 18 (12.4%) to the high-risk . The highest mortality rate (total 17 deaths) was found in the high-risk group (55.6%), followed by moderate-risk (12.2%) and low-risk (1.28%) groups, respectively. Killips categorization grade 2-4 had the highest relative risk (RR-15.85) of the seven potentially dubious variables evaluated, followed by systolic BP 100mmHg (RR-10.48), diabetes mellitus (RR-2.79), and age &gt;65 years (RR- 2.59). Conclusions: In patients with STEMI, the TIMI risk scoring system appears to be a straightforward, valid, and practical bedside tool for quantitative risk classification and short-term prognosis prediction.

Nomogram for the personalisation of radiotherapy treatments in breast cancer patients

IntroductionNumerous prospective studies have shown that the incorporation of genomic assays into clinical practice significantly impacts the choice of adjuvant treatments for patients with early-stage breast cancer. However, the same evidence does not exist for the treatment of locoregional recurrences. Hypothesis and objectivesThe main objective of this work was to identify the clinicopathological, molecular, and genetic parameters that allow patients to be more precisely categorised into risk groups, in order to create a locoregional recurrence riskclassification tool, the PersonalRT27. Material and methodsTo create PersonalRT27, we retrospective assessed the variables of patients with early breast cancer (stages I or II) who had undergone the OncotypeDx ® and MammaPrint ® genetic tests. These variables and factors included in the tests were categorised and weighted to obtain scores between 1 and 5 pointsto represent a lower or higher risk of relapse, respectively, based on these factors and as determined by the researchers. ResultsThe mean follow-up time was 60.5 months (range 25–96 months); locoregional progression-free survival at the time of the analysis was 98.4%, and overall survival was 97.5%, of which 0.6% of the deaths had been cancer specific. The area under the curve for the PersonalRT27 was 0.76 (95% CI [0.70, 0.81]), sensitivity was 78%, and the specificity was 58.9%. We used these factors to create an inhospital web-based nomogram. ConclusionsThe PersonalRT27 is a novel tool that integrates clinical-pathological, molecular, and genetic parameters. External and independent validation will be required to implement its clinical use.

Current Trends in Prostate Cancer Etiology, Pathogenesis and Diagnostics

Abstract Prostate cancer (PCa) remains the second most commonly diagnosed cancer in men, with an estimated 1.1 million diagnoses worldwide in 2012, accounting for 15% of all cancers diagnosed. In many cases, successful treatment of prostate cancer is difficult due to late detection and rate of metastasis. Importantly, the tumours of many patients with prostate cancer become refractory to androgen therapy and progress to metastatic castration-resistant disease. An effective treatment course of prostate cancer patients requires predictive biomarkers in metastatic castration-resistant prostate cancer that support individual treatment. Different risk classification tools have been developed to distinguish patients with early PCa according to the prognosis, including the D’Amico classification system, the Cancer of the Prostate Risk Assessment score, and the National Comprehensive Cancer Network risk groups classification. Unfortunately, there is still no single, universal, cheap, non-invasive, early prostate cancer diagnostic tool, as serum prostate specific antigen (PSA) has not approved expectations and is not implemented in standardised prostate cancer screening. The aim of this review was to provide current state of art knowledge of prostate cancer screening, diagnosis, staging and future perspectives and directions.