The Predictive Value of the Inflammatory Prognostic Index for Detecting No-Reflow in ST-Elevation Myocardial Infarction Patients. / O Valor Preditivo do Índice Prognóstico Inflamatório para Detecção de No-Reflow em Pacientes com Infarto do Miocárdio com Supradesnivelamento do Segmento ST.

BACKGROUND: No-reflow (NR) is characterized by an acute reduction in coronary flow that is not accompanied by coronary spasm, thrombosis, or dissection. Inflammatory prognostic index (IPI) is a novel marker that was reported to have a prognostic role in cancer patients and is calculated by neutrophil/lymphocyte ratio (NLR) multiplied by C-reactive protein/albumin ratio. OBJECTIVE: We aimed to investigate the relationship between IPI and NR in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (pPCI). METHODS: A total of 1541 patients were enrolled in this study (178 with NR and 1363 with reflow). Lasso panelized shrinkage was used for variable selection. A nomogram was created based on IPI for detecting the risk of NR development. Internal validation with Bootstrap resampling was used for model reproducibility. A two-sided p-value <0.05 was accepted as a significance level for statistical analyses. RESULTS: IPI was higher in patients with NR than in patients with reflow. IPI was non-linearly associated with NR. IPI had a higher discriminative ability than the systemic immune-inflammation index, NLR, and CRP/albumin ratio. Adding IPI to the baseline multivariable logistic regression model improved the discrimination and net-clinical benefit effect of the model for detecting NR patients, and IPI was the most prominent variable in the full model. A nomogram was created based on IPI to predict the risk of NR. Bootstrap internal validation of nomogram showed a good calibration and discrimination ability. CONCLUSION: This is the first study that shows the association of IPI with NR in STEMI patients who undergo pPCI.

FUNDAMENTO: O no-reflow (NR) é caracterizado por uma redução aguda no fluxo coronário que não é acompanhada por espasmo coronário, trombose ou dissecção. O índice prognóstico inflamatório (IPI) é um novo marcador que foi relatado como tendo um papel prognóstico em pacientes com câncer e é calculado pela razão neutrófilos/linfócitos (NLR) multiplicada pela razão proteína C reativa/albumina. OBJETIVO: Nosso objetivo foi investigar a relação entre IPI e NR em pacientes com infarto do miocárdio com supradesnivelamento do segmento ST (IAMCSST) submetidos a intervenção coronária percutânea primária (ICPp). MÉTODOS: Um total de 1.541 pacientes foram incluídos neste estudo (178 com NR e 1.363 com refluxo). A regressão penalizada LASSO (Least Absolute Shrinkage and Select Operator) foi usada para seleção de variáveis. Foi criado um nomograma baseado no IPI para detecção do risco de desenvolvimento de NR. A validação interna com reamostragem Bootstrap foi utilizada para reprodutibilidade do modelo. Um valor de p bilateral <0,05 foi aceito como nível de significância para análises estatísticas. RESULTADOS: O IPI foi maior em pacientes com NR do que em pacientes com refluxo. O IPI esteve associado de forma não linear com a NR. O IPI apresentou maior capacidade discriminativa do que o índice de imunoinflamação sistêmica, NLR e relação PCR/albumina. A adição do IPI ao modelo de regressão logística multivariável de base melhorou a discriminação e o efeito do benefício clínico líquido do modelo para detecção de pacientes com NR, e o IPI foi a variável mais proeminente no modelo completo. Foi criado um nomograma baseado no IPI para prever o risco de NR. A validação interna do nomograma Bootstrap mostrou uma boa capacidade de calibração e discriminação. CONCLUSÃO: Este é o primeiro estudo que mostra a associação de IPI com NR em pacientes com IAMCSST submetidos a ICPp.

Is having a moderate or low history, electrocardiogram, age, risk factors, troponin risk score a handicap for long-term mortality?

OBJECTIVE: History, electrocardiogram, age, risk factors, troponin risk score and troponin level follow-up are used to safely discharge low-risk patients with suspected non-ST elevation acute coronary syndrome from the emergency department for a 1-month period. We aimed to comprehensively investigate the 6-month mortality of patients with the history, electrocardiogram, age, risk factors, troponin risk score. METHODS: A total of 949 non-ST elevation acute coronary syndrome patients admitted to the emergency department from 01.01.2019 to 01.10.2019 were included in this retrospective study. History, electrocardiogram, age, risk factors, troponin scores of all patients were calculated by two emergency clinicians and a cardiologist. We compared the 6-month mortality of the groups. RESULTS: The mean age of the patients was 67.9 (56.4-79) years; 57.3% were male and 42.7% were female. Six-month mortality was significantly lower in the high-risk history, electrocardiogram, age, risk factors, troponin score group than in the low- and moderate-risk groups: 11/80 (12.1%), 58/206 (22%), and 150/444 (25.3%), respectively (p=0.019). CONCLUSION: Patients with high history, electrocardiogram, age, risk factors, troponin risk scores are generally treated with coronary angioplasty as soon as possible. We found that the mortality rate of this group of patients was lower in the long term compared with others. Efforts are also needed to reduce the mortality of moderate and low-risk patients. Further studies are needed on the factors affecting the 6-month mortality of moderate and low-risk acute coronary syndrome patients.

Comparison between propofol and total inhalational anaesthesia on cardiovascular outcomes following on-pump cardiac surgery in higher-risk patients: a randomised controlled pilot and feasibility study.

OBJECTIVES: Myocardial revascularisation and cardiopulmonary bypass (CPB) can cause ischaemia-reperfusion injury, leading to myocardial and other end-organ damage. Volatile anaesthetics protect the myocardium in experimental studies. However, there is uncertainty about whether this translates into clinical benefits because of the coadministration of propofol and its detrimental effects, restricting myocardial protective processes. METHODS: In this single-blinded, parallel-group randomised controlled feasibility trial, higher-risk patients undergoing elective coronary artery bypass graft (CABG) surgery with an additive European System for Cardiac Operative Risk Evaluation ≥5 were randomised to receive either propofol or total inhalational anaesthesia as single agents for maintenance of anaesthesia. The primary outcome was the feasibility of recruiting and randomising 50 patients across two cardiac surgical centres, and secondary outcomes included the feasibility of collecting the planned perioperative data, clinically relevant outcomes and assessments of effective patient identification, screening and recruitment. RESULTS: All 50 patients were recruited within 11 months in two centres, allowing for a 13-month hiatus in recruitment due to the COVID-19 pandemic. Overall, 50/108 (46%) of eligible patients were recruited. One patient withdrew before surgery and one patient did not undergo surgery. All but one completed in-hospital and 30-day follow-up. CONCLUSIONS: It is feasible to recruit and randomise higher-risk patients undergoing CABG surgery to a study comparing total inhalational and propofol anaesthesia in a timely manner and with high acceptance and completion rates. TRIAL REGISTRATION NUMBER: NCT04039854.

Population-level impact of the BMJ Rapid Recommendation for colorectal cancer screening: a microsimulation analysis.

OBJECTIVE: In 2019, a BMJ Rapid Recommendation advised against colorectal cancer (CRC) screening for adults with a predicted 15-year CRC risk below 3%. Using Switzerland as a case study, we estimated the population-level impact of this recommendation. DESIGN: We predicted the CRC risk of all respondents to the population-based Swiss Health Survey. We derived the distribution of risk-based screening start age, assuming predicted risk was calculated every 5 years between ages 25 and 70 and screening started when this risk exceeded 3%. Next, the MISCAN-Colon microsimulation model evaluated biennial faecal immunochemical test (FIT) screening with this risk-based start age. As a comparison, we simulated screening initiation based on age and sex. RESULTS: Starting screening only when predicted risk exceeded 3% meant 82% of women and 90% of men would not start screening before age 65 and 60, respectively. This would require 43%-57% fewer tests, result in 8%-16% fewer CRC deaths prevented and yield 19%-33% fewer lifeyears gained compared with screening from age 50. Screening women from age 65 and men from age 60 had a similar impact as screening only when predicted risk exceeded 3%. CONCLUSION: With the recommended risk prediction tool, the population impact of the BMJ Rapid Recommendation would be similar to screening initiation based on age and sex only. It would delay screening initiation by 10-15 years. Although halving the screening burdens, screening benefits would be reduced substantially compared with screening initiation at age 50. This suggests that the 3% risk threshold to start CRC screening might be too high.

Development and validation of a predictive model for the risk of sarcopenia in the older adults in China.

BACKGROUND: Sarcopenia is a progressive age-related disease that can cause a range of adverse health outcomes in older adults, and older adults with severe sarcopenia are also at increased short-term mortality risk. The aim of this study was to construct and validate a risk prediction model for sarcopenia in Chinese older adults. METHODS: This study used data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), a high-quality micro-level data representative of households and individuals aged 45 years and older adults in China. The study analyzed 65 indicators, including sociodemographic indicators, health-related indicators, and biochemical indicators. RESULTS: 3454 older adults enrolled in the CHARLS database in 2015 were included in the final analysis. A total of 997 (28.8%) had phenotypes of sarcopenia. Multivariate logistic regression analysis showed that sex, Body Mass Index (BMI), Mean Systolic Blood Pressure (MSBP), Mean Diastolic Blood Pressure (MDBP) and pain were predictive factors for sarcopenia in older adults. These factors were used to construct a nomogram model, which showed good consistency and accuracy. The AUC value of the prediction model in the training set was 0.77 (95% CI = 0.75-0.79); the AUC value in the validation set was 0.76 (95% CI = 0.73-0.79). Hosmer-Lemeshow test values were P = 0.5041 and P = 0.2668 (both P > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA showed that the nomograms had good predictive properties. CONCLUSIONS: The constructed sarcopenia risk prediction model, incorporating factors such as sex, BMI, MSBP, MDBP, and pain, demonstrates promising predictive capabilities. This model offers valuable insights for clinical practitioners, aiding in early screening and targeted interventions for sarcopenia in Chinese older adults.

Preoperative prediction model for risk of readmission after total joint replacement surgery: a random forest approach leveraging NLP and unfairness mitigation for improved patient care and cost-effectiveness.

BACKGROUND: The Center for Medicare and Medicaid Services (CMS) imposes payment penalties for readmissions following total joint replacement surgeries. This study focuses on total hip, knee, and shoulder arthroplasty procedures as they account for most joint replacement surgeries. Apart from being a burden to healthcare systems, readmissions are also troublesome for patients. There are several studies which only utilized structured data from Electronic Health Records (EHR) without considering any gender and payor bias adjustments. METHODS: For this study, dataset of 38,581 total knee, hip, and shoulder replacement surgeries performed from 2015 to 2021 at Novant Health was gathered. This data was used to train a random forest machine learning model to predict the combined endpoint of emergency department (ED) visit or unplanned readmissions within 30 days of discharge or discharge to Skilled Nursing Facility (SNF) following the surgery. 98 features of laboratory results, diagnoses, vitals, medications, and utilization history were extracted. A natural language processing (NLP) model finetuned from Clinical BERT was used to generate an NLP risk score feature for each patient based on their clinical notes. To address societal biases, a feature bias analysis was performed in conjunction with propensity score matching. A threshold optimization algorithm from the Fairlearn toolkit was used to mitigate gender and payor biases to promote fairness in predictions. RESULTS: The model achieved an Area Under the Receiver Operating characteristic Curve (AUROC) of 0.738 (95% confidence interval, 0.724 to 0.754) and an Area Under the Precision-Recall Curve (AUPRC) of 0.406 (95% confidence interval, 0.384 to 0.433). Considering an outcome prevalence of 16%, these metrics indicate the model's ability to accurately discriminate between readmission and non-readmission cases within the context of total arthroplasty surgeries while adjusting patient scores in the model to mitigate bias based on patient gender and payor. CONCLUSION: This work culminated in a model that identifies the most predictive and protective features associated with the combined endpoint. This model serves as a tool to empower healthcare providers to proactively intervene based on these influential factors without introducing bias towards protected patient classes, effectively mitigating the risk of negative outcomes and ultimately improving quality of care regardless of socioeconomic factors.

Risk and prognosis of secondary lung cancer after radiation therapy for thoracic malignancies.

OBJECTIVE: Radiation therapy (RT) may increase the risk of second cancer. This study aimed to determine the association between exposure to radiotherapy for the treatment of thoracic cancer (TC) and subsequent secondary lung cancer (SLC). MATERIALS AND METHODS: The Surveillance, Epidemiology, and End Results (SEER) database (from 1975 to 2015) was queried for TC. Univariate Cox regression analyses and multiple primary standardized incidence ratios (SIRs) were used to assess the risk of SLC. Subgroup analyses of patients stratified by latency time since TC diagnosis, age at TC diagnosis, and calendar year of TC diagnosis stage were also performed. Overall survival and SLC-related death were compared among the RT and no radiation therapy (NRT) groups by using Kaplan-Meier analysis and competitive risk analysis. RESULTS: In a total of 329 129 observations, 147 847 of whom had been treated with RT. And 6799 patients developed SLC. Receiving radiotherapy was related to a higher risk of developing SLC for TC patients (adjusted HR, 1.25; 95% CI, 1.19-1.32; P < 0.001). The cumulative incidence of developing SLC in TC patients with RT (3.8%) was higher than the cumulative incidence (2.9%) in TC patients with NRT(P). The incidence risk of SLC in TC patients who received radiotherapy was significantly higher than the US general population (SIR, 1.19; 95% CI, 1.14-1.23; P < 0.050). CONCLUSIONS: Radiotherapy for TC was associated with higher risks of developing SLC compared with patients unexposed to radiotherapy.

Predicting short- to medium-term care home admission risk in older adults: a systematic review of externally validated models.

INTRODUCTION: Predicting risk of care home admission could identify older adults for early intervention to support independent living but require external validation in a different dataset before clinical use. We systematically reviewed external validations of care home admission risk prediction models in older adults. METHODS: We searched Medline, Embase and Cochrane Library until 14 August 2023 for external validations of prediction models for care home admission risk in adults aged ≥65 years with up to 3 years of follow-up. We extracted and narratively synthesised data on study design, model characteristics, and model discrimination and calibration (accuracy of predictions). We assessed risk of bias and applicability using Prediction model Risk Of Bias Assessment Tool. RESULTS: Five studies reporting validations of nine unique models were included. Model applicability was fair but risk of bias was mostly high due to not reporting model calibration. Morbidities were used as predictors in four models, most commonly neurological or psychiatric diseases. Physical function was also included in four models. For 1-year prediction, three of the six models had acceptable discrimination (area under the receiver operating characteristic curve (AUC)/c statistic 0.70-0.79) and the remaining three had poor discrimination (AUC < 0.70). No model accounted for competing mortality risk. The only study examining model calibration (but ignoring competing mortality) concluded that it was excellent. CONCLUSIONS: The reporting of models was incomplete. Model discrimination was at best acceptable, and calibration was rarely examined (and ignored competing mortality risk when examined). There is a need to derive better models that account for competing mortality risk and report calibration as well as discrimination.

Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review.

BACKGROUND: Respiratory tract infections are major contributors to the global disease burden. Quantitative microbial risk assessment (QMRA) holds potential as a rapidly deployable framework to understand respiratory pathogen transmission and inform policy on infection control. OBJECTIVES: The goal of this paper was to evaluate, motivate, and inform further development of the use of QMRA as a rapid tool to understand the transmission of respiratory pathogens and improve the evidence base for infection control policies. METHODS: We conducted a literature review to identify peer-reviewed studies of complete QMRA frameworks on aerosol inhalation or contact transmission of respiratory pathogens. From each of the identified studies, we extracted and summarized information on the applied exposure model approaches, dose-response models, and parameter values, including risk characterization. Finally, we reviewed linkages between model outcomes and policy. RESULTS: We identified 93 studies conducted in 16 different countries with complete QMRA frameworks for diverse respiratory pathogens, including SARS-CoV-2, Legionella spp., Staphylococcus aureus, influenza, and Bacillus anthracis. Six distinct exposure models were identified across diverse and complex transmission pathways. In 57 studies, exposure model frameworks were informed by their ability to model the efficacy of potential interventions. Among interventions, masking, ventilation, social distancing, and other environmental source controls were commonly assessed. Pathogen concentration, aerosol concentration, and partitioning coefficient were influential exposure parameters as identified by sensitivity analysis. Most (84%, n=78) studies presented policy-relevant content including a) determining disease burden to call for policy intervention, b) determining risk-based threshold values for regulations, c) informing intervention and control strategies, and d) making recommendations and suggestions for QMRA application in policy. CONCLUSIONS: We identified needs to further the development of QMRA frameworks for respiratory pathogens that prioritize appropriate aerosol exposure modeling approaches, consider trade-offs between model validity and complexity, and incorporate research that strengthens confidence in QMRA results. https://doi.org/10.1289/EHP12695.

Development and assessment of an RNA editing-based risk model for the prognosis of cervical cancer patients.

RNA editing, as an epigenetic mechanism, exhibits a strong correlation with the occurrence and development of cancers. Nevertheless, few studies have been conducted to investigate the impact of RNA editing on cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). In order to study the connection between RNA editing and CESC patients' prognoses, we obtained CESC-related information from The Cancer Genome Atlas (TCGA) database and randomly allocated the patients into the training group or testing group. An RNA editing-based risk model for CESC patients was established by Cox regression analysis and least absolute shrinkage and selection operator (LASSO). According to the median score generated by this RNA editing-based risk model, patients were categorized into subgroups with high and low risks. We further constructed the nomogram by risk scores and clinical characteristics and analyzed the impact of RNA editing levels on host gene expression levels and adenosine deaminase acting on RNA. Finally, we also compared the biological functions and pathways of differentially expressed genes (DEGs) between different subgroups by enrichment analysis. In this risk model, we screened out 6 RNA editing sites with significant prognostic value. The constructed nomogram performed well in forecasting patients' prognoses. Furthermore, the level of RNA editing at the prognostic site exhibited a strong correlation with host gene expression. In the high-risk subgroup, we observed multiple biological functions and pathways associated with immune response, cell proliferation, and tumor progression. This study establishes an RNA editing-based risk model that helps forecast patients' prognoses and offers a new understanding of the underlying mechanism of RNA editing in CESC.

Addition of High-Sensitivity Troponin to Perioperative Risk Assessment Improves the Predictive Ability of Death in Non-Cardiac Surgery Patients. / Adição da Troponina Ultrassensível à Avaliação de Risco Perioperatório Melhora a Capacidade Preditiva de Morte em Pacientes Submetidos à Cirurgia Não Cardíaca.

BACKGROUND: Risk stratification is an important step in perioperative evaluation. However, the main risk scores do not incorporate biomarkers in their set of variables. OBJECTIVE: Evaluate the incremental power of troponin to the usual risk stratification. METHODS: A total of 2,230 patients admitted to the intensive care unit after non-cardiac surgery were classified according to three types of risk: cardiovascular risk (CVR), Revised Cardiac Risk Index (RCRI); and inherent risk of surgery (IRS). The main outcome was all-cause mortality. Cox regression was used as well as c-statistics before and after addition of high-sensitivity troponin (at least one measurement up to three days after surgery). Finally, net reclassification index and integrated discrimination improvement were used to assess the incremental power of troponin for risk stratification. Significance level was set at 0.05. RESULTS: Mean age of patients was 63.8 years and 55.6% were women. The prevalence of myocardial injury after non-cardiac surgery (MINS) was 9.4%. High CVR-patients had a higher occurrence of MINS (40.1 x 24.8%, p<0.001), as well as high IRS-patients (21.3 x 13.9%, p=0.004) and those with a RCRI≥3 (3.0 x 0.7%, p=0.009). Patients without MINS, regardless of the assessed risk, had similar mortality rate. The addition of troponin to the risk assessment improved the predictive ability of death at 30 days and at 1 year in all risk assessments. CONCLUSION: The prevalence of MINS is higher in the high-risk population. However, its prevalence in lower-risk population is not negligible and causes a higher risk of death. The addition of high-sensitivity troponin increased the predictive ability of risk assessment in all groups.

FUNDAMENTO: A estratificação ode risco é uma importante etapa na avaliação perioperatória. No entanto, os principais escores de risco não incorporam biomarcadores em seus conjuntos de variáveis. OBJETIVO: Avaliar o poder incremental da troponina à estratificação de risco tradicional. MÉTODOS: Um total de 2230 pacientes admitidos na unidade de terapia intensiva após cirurgia não cardíaca foram classificados de acordo com três tipos de risco: Risco Cardiovascular (RCV), Índice de Risco Cardíaco Revisado (IRCR), e Risco Inerente da Cirurgia (RIC). O principal desfecho foi mortalidade por todas as causas. A regressão de Cox foi usada, assim como a estatística C antes e após a adição de troponina ultrassensível (pelo menos uma medida até três dias após a cirurgia). Finalmente, o índice de reclassificação líquida e a melhoria de discriminação integrada foram usadas para avaliar o poder incremental da troponina para a estratificação de risco. O nível de significância usado foi de 0,05. RESULTADOS: A idade média dos pacientes foi 63,8 anos e 55,6% eram do sexo feminino. A prevalência de lesão miocárdica após cirurgia não cardíaca (MINS) foi 9,4%. Pacientes com um RCV elevado apresentaram uma maior ocorrência de MINS (40,1% x 24,8%, p<0,001), bem como pacientes com alto RIC (21,3 x 13,9%, p=0,004) e aqueles com IRCR≥3 (3,0 x 0,7%, p=0,009). Pacientes sem MINS, independentemente do risco avaliado, apresentaram taxa de mortalidade similar. A adição de troponina à avaliação de risco melhorou a capacidade preditiva de mortalidade em 30 dias e de mortalidade em um ano em todas as avaliações de risco. CONCLUSÃO: A prevalência de MINS é mais alta na população de alto risco. No entanto, sua prevalência na população de risco mais baixo não é desprezível e causa um maior risco de morte. A adição da troponina ultrassensível melhorou a capacidade preditiva da avaliação de risco em todos os grupos.

Risk Assessment Models for Venous Thromboembolism in Medical Inpatients.

Importance: Thromboprophylaxis is recommended for medical inpatients at risk of venous thromboembolism (VTE). Risk assessment models (RAMs) have been developed to stratify VTE risk, but a prospective head-to-head comparison of validated RAMs is lacking. Objectives: To prospectively validate an easy-to-use RAM, the simplified Geneva score, and compare its prognostic performance with previously validated RAMs. Design, Setting, and Participants: This prospective cohort study was conducted from June 18, 2020, to January 4, 2022, with a 90-day follow-up. A total of 4205 consecutive adults admitted to the general internal medicine departments of 3 Swiss university hospitals for hospitalization for more than 24 hours due to acute illness were screened for eligibility; 1352 without therapeutic anticoagulation were included. Exposures: At admission, items of 4 RAMs (ie, the simplified and original Geneva score, the Padua score, and the IMPROVE [International Medical Prevention Registry on Venous Thromboembolism] score) were collected. Patients were stratified into high and low VTE risk groups according to each RAM. Main Outcomes and Measures: Symptomatic VTE within 90 days. Results: Of 1352 medical inpatients (median age, 67 years [IQR, 54-77 years]; 762 men [55.4%]), 28 (2.1%) experienced VTE. Based on the simplified Geneva score, 854 patients (63.2%) were classified as high risk, with a 90-day VTE risk of 2.6% (n = 22; 95% CI, 1.7%-3.9%), and 498 patients (36.8%) were classified as low risk, with a 90-day VTE risk of 1.2% (n = 6; 95% CI, 0.6%-2.6%). Sensitivity of the simplified Geneva score was 78.6% (95% CI, 60.5%-89.8%) and specificity was 37.2% (95% CI, 34.6%-39.8%); the positive likelihood ratio of the simplified Geneva score was 1.25 (95% CI, 1.03-1.52) and the negative likelihood ratio was 0.58 (95% CI, 0.28-1.18). In head-to-head comparisons, sensitivity was highest for the original Geneva score (82.1%; 95% CI, 64.4%-92.1%), while specificity was highest for the IMPROVE score (70.4%; 95% CI, 67.9%-72.8%). After adjusting the VTE risk for thromboprophylaxis use and site, there was no significant difference between the high-risk and low-risk groups based on the simplified Geneva score (subhazard ratio, 2.04 [95% CI, 0.83-5.05]; P = .12) and other RAMs. Discriminative performance was poor for all RAMs, with an area under the receiver operating characteristic curve ranging from 53.8% (95% CI, 51.1%-56.5%) for the original Geneva score to 58.1% (95% CI, 55.4%-60.7%) for the simplified Geneva score. Conclusions and Relevance: This head-to-head comparison of validated RAMs found suboptimal accuracy and prognostic performance of the simplified Geneva score and other RAMs to predict hospital-acquired VTE in medical inpatients. Clinical usefulness of existing RAMs is questionable, highlighting the need for more accurate VTE prediction strategies.

A novel score for early prediction of urinary tract infection risk in patients with acute ischemic stroke: a nomogram-based retrospective cohort study.

This study aimed to construct and externally validate a user-friendly nomogram-based scoring model for predicting the risk of urinary tract infections (UTIs) in patients with acute ischemic stroke (AIS). A retrospective real-world cohort study was conducted on 1748 consecutive hospitalized patients with AIS. Out of these patients, a total of 1132 participants were ultimately included in the final analysis, with 817 used for model construction and 315 utilized for external validation. Multivariate regression analysis was applied to develop the model. The discriminative capacity, calibration ability, and clinical effectiveness of the model were evaluated. The overall incidence of UTIs was 8.13% (92/1132), with Escherichia coli being the most prevalent causative pathogen in patients with AIS. After multivariable analysis, advanced age, female gender, National Institute of Health Stroke Scale (NIHSS) score ≥ 5, and use of urinary catheters were identified as independent risk factors for UTIs. A nomogram-based SUNA model was constructed using these four factors (Area under the receiver operating characteristic curve (AUC) = 0.810), which showed good discrimination (AUC = 0.788), calibration, and clinical utility in the external validation cohort. Based on four simple and readily available factors, we derived and externally validated a novel and user-friendly nomogram-based scoring model (SUNA score) to predict the risk of UTIs in patients with AIS. The model has a good predictive value and provides valuable information for timely intervention in patients with AIS to reduce the occurrence of UTIs.

Age-adjusted Charlson comorbidity index is associated with the risk of osteoporosis in older fall-prone men: a retrospective cohort study.

BACKGROUND: There is growing evidence linking the age-adjusted Charlson comorbidity index (aCCI), an assessment tool for multimorbidity, to fragility fracture and fracture-related postoperative complications. However, the role of multimorbidity in osteoporosis has not yet been thoroughly evaluated. We aimed to investigate the association between aCCI and the risk of osteoporosis in older adults at moderate to high risk of falling. METHODS: A total of 947 men were included from January 2015 to August 2022 in a hospital in Beijing, China. The aCCI was calculated by counting age and each comorbidity according to their weighted scores, and the participants were stratified into two groups by aCCI: low (aCCI < 5), and high (aCCI ≥5). The Kaplan Meier method was used to assess the cumulative incidence of osteoporosis by different levels of aCCI. The Cox proportional hazards regression model was used to estimate the association of aCCI with the risk of osteoporosis. Receiver operating characteristic (ROC) curve was adapted to assess the performance for aCCI in osteoporosis screening. RESULTS: At baseline, the mean age of all patients was 75.7 years, the mean BMI was 24.8 kg/m2, and 531 (56.1%) patients had high aCCI while 416 (43.9%) were having low aCCI. During a median follow-up of 6.6 years, 296 participants developed osteoporosis. Kaplan-Meier survival curves showed that participants with high aCCI had significantly higher cumulative incidence of osteoporosis compared with those had low aCCI (log-rank test: P < 0.001). When aCCI was examined as a continuous variable, the multivariable-adjusted model showed that the osteoporosis risk increased by 12.1% (HR = 1.121, 95% CI 1.041-1.206, P = 0.002) as aCCI increased by one unit. When aCCI was changed to a categorical variable, the multivariable-adjusted hazard ratios associated with different levels of aCCI [low (reference group) and high] were 1.00 and 1.557 (95% CI 1.223-1.983) for osteoporosis (P <  0.001), respectively. The aCCI (cutoff ≥5) revealed an area under ROC curve (AUC) of 0.566 (95%CI 0.527-0.605, P = 0.001) in identifying osteoporosis in older fall-prone men, with sensitivity of 64.9% and specificity of 47.9%. CONCLUSIONS: The current study indicated an association of higher aCCI with an increased risk of osteoporosis among older fall-prone men, supporting the possibility of aCCI as a marker of long-term skeletal-related adverse clinical outcomes.

Risk equations for prosthetic joint infections (PJIs) in UK: a retrospective study using the Clinical Practice Research Datalink (CPRD) AURUM and GOLD databases.

BACKGROUND: Prosthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk scoring is currently available. OBJECTIVE: To determine a risk quantification model for the development of PJI using electronic health records. DESIGN: Records in Clinical Practice Research Datalink (CPRD) GOLD and AURUM of patients undergoing hip or knee arthroplasty between January 2007 and December 2014, with linkage to Hospital Episode Statistics and Office of National Statistics, were obtained. Cohorts' characteristics and risk equations through parametric models were developed and compared between the two databases. Pooled cohort risk equations were determined for the UK population and simplified through stepwise selection. RESULTS: After applying the inclusion/exclusion criteria, 174 905 joints (1021 developed PJI) were identified in CPRD AURUM and 48 419 joints (228 developed PJI) in CPRD GOLD. Patients undergoing hip or knee arthroplasty in both databases exhibited different sociodemographic characteristics and medical/drug history. However, the quantification of the impact of such covariates (coefficients of parametric models fitted to the survival curves) on the risk of PJI between the two cohorts was not statistically significant. The log-normal model fitted to the pooled cohorts after stepwise selection had a C-statistic >0.7. CONCLUSIONS: The risk prediction tool developed here could help prevent PJI through identifying modifiable risk factors pre-surgery and identifying the patients most likely to benefit from close monitoring/preventive actions. As derived from the UK population, such tool will help the National Health Service reduce the impact of PJI on its resources and patient lives.

Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus.

Objective: Diabetic peripheral neuropathy frequently occurs and presents severely in individuals suffering from type 2 diabetes mellitus, representing a significant complication. The objective of this research was to develop a risk nomogram for DPN, ensuring its internal validity and evaluating its capacity to predict the condition. Methods: In this retrospective analysis, Suqian First Hospital's cohort from January 2021 to June 2022 encompassed 397 individuals diagnosed with T2DM. A random number table method was utilized to allocate these patients into two groups for training and validation, following a 7:3 ratio. By applying univariate and multivariable logistic regression, predictive factors were refined to construct the nomogram. The model's prediction accuracy was assessed through metrics like the ROC area, HL test, and an analysis of the calibration curve. DCA further appraised the clinical applicability of the model. Emphasis was also placed on internal validation to confirm the model's dependability and consistency. Results: Out of 36 evaluated clinicopathological characteristics, a set of four, duration, TBIL, TG, and DPVD, were identified as key variables for constructing the predictive nomogram. The model exhibited robust discriminatory power, evidenced by an AUC of 0.771 (95% CI: 0.714-0.828) in the training cohort and an AUC of 0.754 (95% CI: 0.663-0.845) in the validation group. The congruence of the model's predictions with actual findings was corroborated by the calibration curve. Furthermore, DCA affirmed the clinical value of the model in predicting DPN. Conclusion: This research introduces an innovative risk nomogram designed for the prediction of diabetic peripheral neuropathy in individuals suffering from type 2 diabetes mellitus. It offers a valuable resource for healthcare professionals to pinpoint those at elevated risk of developing this complication. As a functional instrument, it stands as a viable option for the prognostication of DPN in clinical settings.

Could self-reported physical performance help predict individuals at the highest risk of mortality and hospital admission events in clinical practice? Findings from the Hertfordshire Cohort Study.

AIM: To consider how self-reported physical function measures relate to adverse clinical outcomes measured over 20 years of follow-up in a community-dwelling cohort (aged 59-73 at baseline) as compared with hand grip strength, a well-validated predictor of adverse events. BACKGROUND: Recent evidence has emphasized the significant association of physical activity, physical performance, and muscle strength with hospital admissions in older people. However, physical performance tests require staff availability, training, specialized equipment, and space to perform them, often not feasible or realistic in the context of a busy clinical setting. METHODS: In total, 2997 men and women were analyzed. Baseline predictors were measured grip strength (Jamar dynamometer) and the following self-reported measures: physical activity (Dallosso questionnaire); physical function score (SF-36 Health Survey); and walking speed. Participants were followed up from baseline (1998-2004) until December 2018 using UK Hospital Episode Statistics and mortality data, which report clinical outcomes using ICD-10 coding. Predictors in relation to the risk of mortality and hospital admission events were examined using Cox regression with and without adjustment for sociodemographic and lifestyle characteristics. FINDINGS: The mean age at baseline was 65.7 and 66.6 years among men and women, respectively. Over follow-up, 36% of men and 26% of women died, while 93% of men and 92% of women were admitted to hospital at least once. Physical activity, grip strength, SF-36 physical function, and walking speed were all strongly associated with adverse health outcomes in both sex- and fully adjusted analyses; poorer values for each of the predictors were related to greater risk of mortality (all-cause, cardiovascular-related) and any, neurological, cardiovascular, respiratory, any fracture, and falls admissions. SF-36 physical function and grip strength were similarly associated with the adverse health outcomes considered.

Risk factors and early prediction of cardiorenal syndrome type 3 among acute kidney injury patients: a cohort study.

BACKGROUND: Type 3 cardiorenal syndrome (CRS type 3) triggers acute cardiac injury from acute kidney injury (AKI), raising mortality in AKI patients. We aimed to identify risk factors for CRS type 3 and develop a predictive nomogram. METHODS: In this retrospective study, 805 AKI patients admitted at the Department of Nephrology, Second Hospital of Shanxi Medical University from 1 January 2017, to 31 December 2021, were categorized into a study cohort (406 patients from 2017.1.1-2021.6.30, with 63 CRS type 3 cases) and a validation cohort (126 patients from 1 July 2021 to 31 Dec 2021, with 22 CRS type 3 cases). Risk factors for CRS type 3, identified by logistic regression, informed the construction of a predictive nomogram. Its performance and accuracy were evaluated by the area under the curve (AUC), calibration curve and decision curve analysis, with further validation through a validation cohort. RESULTS: The nomogram included 6 risk factors: age (OR = 1.03; 95%CI = 1.009-1.052; p = 0.006), cardiovascular disease (CVD) history (OR = 2.802; 95%CI = 1.193-6.582; p = 0.018), mean artery pressure (MAP) (OR = 1.033; 95%CI = 1.012-1.054; p = 0.002), hemoglobin (OR = 0.973; 95%CI = 0.96--0.987; p < 0.001), homocysteine (OR = 1.05; 95%CI = 1.03-1.069; p < 0.001), AKI stage [(stage 1: reference), (stage 2: OR = 5.427; 95%CI = 1.781-16.534; p = 0.003), (stage 3: OR = 5.554; 95%CI = 2.234-13.805; p < 0.001)]. The nomogram exhibited excellent predictive performance with an AUC of 0.907 in the study cohort and 0.892 in the validation cohort. Calibration and decision curve analyses upheld its accuracy and clinical utility. CONCLUSIONS: We developed a nomogram predicting CRS type 3 in AKI patients, incorporating 6 risk factors: age, CVD history, MAP, hemoglobin, homocysteine, and AKI stage, enhancing early risk identification and patient management.

Identifying Risk Indicators of Cardiovascular Disease in Fasa Cohort Study (FACS): An Application of Generalized Linear Mixed-Model Tree.

BACKGROUND: Today, cardiovascular disease (CVD) is the most important cause of death around the world. In this study, our main aim was to predict CVD using some of the most important indicators of this disease and present a tree-based statistical framework for detecting CVD patients according to these indicators. METHODS: We used data from the baseline phase of the Fasa Cohort Study (FACS). The outcome variable was the presence of CVD. The ordinary Tree and generalized linear mixed models (GLMM) were fitted to the data and their predictive power for detecting CVD was compared with the obtained results from the GLMM tree. Statistical analysis was performed using the RStudio software. RESULTS: Data of 9499 participants aged 35‒70 years were analyzed. The results of the multivariable mixed-effects logistic regression model revealed that participants' age, total cholesterol, marital status, smoking status, glucose, history of cardiac disease or myocardial infarction (MI) in first- and second-degree relatives, and presence of other diseases (like hypertension, depression, chronic headaches, and thyroid disease) were significantly related to the presence of CVD (P<0.05). Fitting the ordinary tree, GLMM, and GLMM tree resulted in area under the curve (AUC) values of 0.58 (0.56, 0.61), 0.81 (0.77, 0.84), and 0.80 (0.76, 0.83), respectively, among the study population. In addition, the tree model had the best specificity at 81% but the lowest sensitivity at 65% compared to the other models. CONCLUSION: Given the superior performance of the GLMM tree compared with the standard tree and the lack of significant difference with the GLMM, using this model is suggested due to its simpler interpretation and fewer assumptions. Using updated statistical models for more accurate CVD prediction can result in more precise frameworks to aid in proactive patient detection planning.

A nomogram to predict the risk of venous thromboembolism in patients with colon cancer in China.

OBJECTIVE: To create a nomogram for predicting the likelihood of venous thromboembolism (VTE) in colon cancer patients from China. METHODS: The data of colon cancer patients from Chongqing University Cancer Hospital between 2019 and 2022 were analyzed. Patients were divided into training set and internal validation set by random split-sample method in a split ratio of 7:3. The univariable and multivariable logistic analysis gradually identified the independent risk factors for VTE. A nomogram was created using all the variables that had a significance level of p < 0.05 in the multivariable logistic analysis and those with clinical significance. Calibration curves and clinical decision curve analysis (DCA) were used to assess model's fitting performance and clinical value. Harrell's C-index (concordance statistic) and the area under the receiver operating characteristic curves (AUC) were used to evaluate the predictive effectiveness of models. RESULTS: A total of 1996 patients were ultimately included. There were 1398 patients in the training set and 598 patients in the internal validation set. The nomogram included age, chemotherapy, targeted therapy, hypertension, activated partial thromboplastin time, prothrombin time, platelet, absolute lymphocyte count, and D-dimer. The C-index of nomogram and Khorana score were 0.754 (95% CI 0.711-0.798), 0.520 (95% CI 0.477-0.563) in the training cohort and 0.713 (95% CI 0.643-0.784), 0.542 (95% CI 0.473-0.612) in the internal validation cohort. CONCLUSIONS: We have established and validated a nomogram to predict the VTE risk of colon cancer patients in China, which encompasses a diverse age range, a significant population size, and various clinical factors. It facilitates the identification of high-risk groups and may enable the implementation of targeted preventive measures.