Risk prediction model for major adverse cardiovascular events (MACE) during hospitalization in patients with coronary heart disease based on myocardial energy metabolic substrate

Background

The early attack of coronary heart disease (CHD) is very hidden, and clinical symptoms generally do not appear until cardiovascular events occur. Therefore, an innovative method is needed to judge the risk of cardiovascular events and guide clinical decision conveniently and sensitively. The purpose of this study is to find out the risk factors related to MACE during hospitalization. In order to develop and verify the prediction model of energy metabolism substrates, and establish a nomogram to predict the incidence of MACE during hospitalization and evaluate their performance.

Methods

The data were collected from the medical record data of Guang'anmen Hospital. This review study was collected the comprehensive clinical data of 5,935 adult patients hospitalized in the cardiovascular department from 2016 to 2021. The outcome index was the MACE during hospitalization. According to the occurrence of MACE during hospitalization, these data were divided into MACE group (n = 2,603) and non-MACE group (n = 425). Logistic regression was used to screen risk factors, and establish the nomogram to predict the risk of MACE during hospitalization. Calibration curve, C index and decision curve were used to evaluate the prediction model, and drawn ROC curve to find the best boundary value of risk factors.

Results

The logistic regression model was used to establish a risk model. Univariate logistic regression model was mainly used to screen the factors significantly related to MACE during hospitalization in the training set (each variable is put into the model in turn). According to the factors with statistical significance in univariate logistic regression, five cardiac energy metabolism risk factors, including age, albumin(ALB), free fatty acid(FFA), glucose(GLU) and apolipoprotein A1(ApoA1), were finally input into the multivariate logistic regression model as the risk model, and their nomogram were drawn. The sample size of the training set was 2,120, the sample size of the validation set was 908. The C index of the training set is 0.655 [0.621,0.689], and the C index of the validation set was 0.674 [0.623,0.724]. The calibration curve and clinical decision curve show that the model performs well. The ROC curve was used to establish the best boundary value of the five risk factors, which could quantitatively present the changes of cardiac energy metabolism substrate, and finally achieved prediction of MACE during hospitalization conveniently and sensitively.

Conclusion

Age, albumin, free fatty acid, glucose and apolipoprotein A1 are independent factors of CHD in MACE during hospitalization. The nomogram based on the above factors of myocardial energy metabolism substrate provides prognosis prediction accurately.

A Nomogram for Predicting Unplanned Intraoperative Hypothermia in Patients With Colorectal Cancer Undergoing Laparoscopic Colorectal Procedures

Unplanned intraoperative hypothermia is a complication that can lead to a variety of negative outcomes, such as cardiovascular events. We aimed to develop and validate an intraoperative hypothermia risk prediction nomogram for patients with colorectal cancer undergoing laparoscopic colorectal procedures. We conducted a prospective cohort study with 1,091 patients (ie, 765 in the training cohort, 326 in the validation cohort) from October 2020 to November 2021. We included six predictors in the nomogram model: body mass index, diabetes diagnosis, ambient temperature, ambient humidity, duration of surgery, and use of a forced-air warmer. The model performed well, and the area under the curve was 0.855. These results, together with an external validation value, mean that health care professionals can use the nomogram to calculate the intraoperative hypothermia risk for patients undergoing laparoscopic colorectal procedures and make clinical decisions based on the results.

A Predictive Model for Contrast-Induced Acute Kidney Injury After Percutaneous Coronary Intervention in Elderly Patients with ST-Segment Elevation Myocardial Infarction

Purpose

Development and validation of a nomogram model to predict the risk of Contrast-Induced Acute Kidney Injury (CI-AKI) after emergency percutaneous coronary intervention (PCI) in elderly patients with acute ST-segment elevation myocardial infarction (STEMI).

Patients and Methods

Retrospective analysis of 542 elderly (≥65 years) STEMI patients undergoing emergency PCI in our hospital from January 2019 to June 2022, with all patients randomized to the training cohort (70%; n=380) and the validation cohort (30%; n=162). Univariate analysis, LASSO regression, and multivariate logistic regression analysis were used to determine independent risk factors for developing CI-AKI in elderly STEMI patients. R software is used to generate a nomogram model. The predictive power of the nomogram model was compared with the Mehran score 2. The area under the ROC curve (AUC), calibration curves, and decision curve analysis (DCA) was used to evaluate the prediction model's discrimination, calibration, and clinical validity, respectively.

Results

The nomogram model consisted of five variables: diabetes mellitus (DM), left ventricular ejection fraction (LVEF), Systemic immune-inflammatory index (SII), N-terminal pro-brain natriuretic peptide (NT-proBNP), and highly sensitive C-reactive protein(hsCRP). In the training cohort, the AUC is 0.84 (95% CI: 0.790-0.890), and in the validation cohort, it is 0.844 (95% CI: 0.762-0.926). The nomogram model has better predictive ability than Mehran score 2. Based on the calibration curves, the predicted and observed values of the nomogram model were in good agreement between the training and validation cohort. Decision curve analysis (DCA) and clinical impact curve showed that the nomogram prediction model has good clinical utility.

Conclusion

The established nomogram model can intuitively and specifically screen high-risk groups with a high degree of discrimination and accuracy and has a specific predictive value for CI-AKI occurrence in elderly STEMI patients after PCI.

Development of a Risk Prediction Model of Subsequent Bloodstream Infection After Carbapenem-Resistant Enterobacteriaceae Isolated from Perianal Swabs in Hematological Patients

Purpose

Patients with hematological diseases are at high risk of carbapenem-resistant Enterobacteriaceae (CRE) infection, and CRE-related bloodstream infection (BSI) is associated with high mortality risk. Therefore, developing a predictive risk model for subsequent BSI in hematological patients with CRE isolated from perianal swabs could be used to guide preventive strategies.

Methods

This was a single-center retrospective cohort study at a tertiary blood diseases hospital, including all hematological patients hospitalized from 10 October 2017 to 31 July 2021. We developed a predictive model using multivariable logistic regression and internally validated it using enhanced bootstrap resampling.

Results

Of 421 included patients with CRE isolated from perianal swabs, BSI due to CRE occurred in 59. According to the multivariate logistic analysis, age (OR[odds ratio]=1.04, 95% CI[confidence interval]: 1.01-1.06, P=0.004), both meropenem and imipenem minimal inhibitory concentration (MIC) of the isolate from perianal swabs>8ug/mL (OR=5.34, 95% CI: 2.63-11.5, P<0.001), gastrointestinal symptoms (OR=3.67, 95% CI: 1.82-7.58, P<0.001), valley absolute neutrophil count (10⁹/L)>0.025 (OR=0.07, 95% CI: (0.02-0.19, P<0.001) and shaking chills at peak temperature (OR=6.94, 95% CI: (2.60-19.2, P<0.001) were independently associated with CRE BSI within 30 days and included in the prediction model. At a cut-off of prediction probability ≥ 21.5% the model exhibited a sensitivity, specificity, positive predictive value and negative predictive value of 79.7%, 85.6%, 96.27% and 47.47%. The discrimination and calibration of the prediction model were good on the derivation data (C-statistics=0.8898; Brier score=0.079) and enhanced bootstrapped validation dataset (adjusted C-statistics=0.881; adjusted Brier score=0.083). The risk prediction model is freely available as a mobile application at https://liujia1992.shinyapps.io/dynnomapp/.

Conclusion

A prediction model based on age, meropenem and imipenem MIC of isolate, gastrointestinal symptoms, valley absolute neutrophil count and shaking chills may be used to better inform interventions in hematological patients with CRE isolated from perianal swabs.

Influencing factors and risk prediction model for cervical cancer recurrence

OBJECTIVES

Cervical cancer is the most common malignant tumor in the female reproductive system worldwide. The recurrence rate for the treated cervical cancer patients is high, which seriously threatens women's lives and health. At present, the risk prediction study of cervical cancer has not been reported. Based on the influencing factors of cervical cancer recurrence, we aim to establish a risk prediction model of cervical cancer recurrence to provide a scientific basis for the prevention and treatment of cervical cancer recurrence.

METHODS

A total of 4 358 cervical cancer patients admitted to the Hunan Cancer Hospital from January 1992 to December 2005 were selected as research subjects, and the recurrence of cervical cancer patients after treatment was followed up. Univariate analysis was used to analyze the possible influencing factors. Variables that were significant in univariate analysis or those that were not significant in univariate analysis but may be considered significant were included in multivariate Cox regression analysis to establish a cervical cancer recurrence risk prediction model. Line graphs was used to show the model and it was evaluated by using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis.

RESULTS

Univariate analysis showed that the recurrence rates of cervical cancer patients with different age, age of menarche, parity, miscarriage, clinical stage, and treatment method were significantly different (all P<0.05). Multivariate Cox regression analysis showed that RR=-0.489×(age≥55 years old)+0.481×(age at menarche >15 years old)+0.459×(number of miscarriages≥3)+0.416×(clinical stage II)+0.613×(clinical stage III/IV)+0.366×(the treatment method was surgery + chemotherapy) + 0.015×(the treatment method was chemotherapy alone). The area under the ROC curve (AUC) of the Cox risk prediction model for cervical cancer recurrence constructed was 0.736 (95% CI 0.684 to 0.789), the best prediction threshold was 0.857, the sensitivity was 0.576, and the specificity was 0.810. The accuracy of the Cox risk model constructed by this model was good. From the clinical decision curve, the net benefit value was high and the validity was good.

CONCLUSIONS

Patient age, age at menarche, miscarriages, clinical stages, and treatment methods are independent factors affecting cervical cancer recurrence. The Cox proportional hazards prediction model for cervical cancer recurrence constructed in this study can be better used for predicting the risk of cervical cancer recurrence.

Predicting COVID-19 progression in hospitalized patients in Belgium from a multi-state model

Objectives

To adopt a multi-state risk prediction model for critical disease/mortality outcomes among hospitalised COVID-19 patients using nationwide COVID-19 hospital surveillance data in Belgium.

Materials and methods

Information on 44,659 COVID-19 patients hospitalised between March 2020 and June 2021 with complete data on disease outcomes and candidate predictors was used to adopt a multi-state, multivariate Cox model to predict patients' probability of recovery, critical [transfer to intensive care units (ICU)] or fatal outcomes during hospital stay.

Results

Median length of hospital stay was 9 days (interquartile range: 5-14). After admission, approximately 82% of the COVID-19 patients were discharged alive, 15% of patients were admitted to ICU, and 15% died in the hospital. The main predictors of an increased probability for recovery were younger age, and to a lesser extent, a lower number of prevalent comorbidities. A patient's transition to ICU or in-hospital death had in common the following predictors: high levels of c-reactive protein (CRP) and lactate dehydrogenase (LDH), reporting lower respiratory complaints and male sex. Additionally predictors for a transfer to ICU included middle-age, obesity and reporting loss of appetite and staying at a university hospital, while advanced age and a higher number of prevalent comorbidities for in-hospital death. After ICU, younger age and low levels of CRP and LDH were the main predictors for recovery, while in-hospital death was predicted by advanced age and concurrent comorbidities.

Conclusion

As one of the very few, a multi-state model was adopted to identify key factors predicting COVID-19 progression to critical disease, and recovery or death.

A Machine Learning-Based Risk Prediction Model for Post-Traumatic Stress Disorder during the COVID-19 Pandemic

Background and Objectives: The COVID-19 pandemic has caused global public panic, leading to severe mental illnesses, such as post-traumatic stress disorder (PTSD). This study aimed to establish a risk prediction model of PTSD based on a machine learning algorithm to provide a basis for the extensive assessment and prediction of the PTSD risk status in adults during a pandemic. Materials and Methods: Model indexes were screened based on the cognitive-phenomenological-transactional (CPT) theoretical model. During the study period (1 March to 15 March 2020), 2067 Chinese residents were recruited using Research Electronic Data Capture (REDCap). Socio-demographic characteristics, PTSD, depression, anxiety, social support, general self-efficacy, coping style, and other indicators were collected in order to establish a neural network model to predict and evaluate the risk of PTSD. Results: The research findings showed that 368 of the 2067 participants (17.8%) developed PTSD. The model correctly predicted 90.0% (262) of the outcomes. Receiver operating characteristic (ROC) curves and their associated area under the ROC curve (AUC) values suggested that the prediction model possessed an accurate discrimination ability. In addition, depression, anxiety, age, coping style, whether the participants had seen a doctor during the COVID-19 quarantine period, and self-efficacy were important indexes. Conclusions: The high prediction accuracy of the model, constructed based on a machine learning algorithm, indicates its applicability in screening the public mental health status during the COVID-19 pandemic quickly and effectively. This model could also predict and identify high-risk groups early to prevent the worsening of PTSD symptoms.

External Validation of a Risk Model for Severe Complications following Pancreatoduodenectomy Based on Three Preoperative Variables

BACKGROUND

Pancreatoduodenectomy (PD) is the only cure for periampullary and pancreatic cancer. It has morbidity rates of 40-60%, with severe complications in 30%. Prediction models to predict complications are crucial. A risk model for severe complications was developed by Schroder et al. based on BMI, ASA classification and Hounsfield Units of the pancreatic body on the preoperative CT scan. These variables were independent predictors for severe complications upon internal validation. Our aim was to externally validate this model using an independent cohort of patients.

METHODS

A retrospective analysis was performed on 318 patients who underwent PD at our institution from 2013 to 2021. The outcome of interest was severe complications Clavien-Dindo ≥ IIIa. Model calibration, discrimination and performance were assessed.

RESULTS

A total of 308 patients were included. Patients with incomplete data were excluded. A total of 89 (28.9%) patients had severe complications. The externally validated model achieved: C-index = 0.67 (95% CI: 0.60-0.73), regression coefficient = 0.37, intercept = 0.13, Brier score = 0.25.

CONCLUSIONS

The performance ability, discriminative power, and calibration of this model were acceptable. Our risk calculator can help surgeons identify high-risk patients for post-operative complications to improve shared decision-making and tailor perioperative management.

Risk Prediction Models of Natural Menopause Onset: A Systematic Review

CONTEXT

Predicting the onset of menopause is important for family planning and to ensure prompt intervention in women at risk of developing menopause-related diseases.

OBJECTIVE

We aimed to summarize risk prediction models of natural menopause onset and their performance.

METHODS

Five bibliographic databases were searched up to March 2022. We included prospective studies on perimenopausal women or women in menopausal transition that reported either a univariable or multivariable model for risk prediction of natural menopause onset. Two authors independently extracted data according to the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist. Risk of bias was assessed using a prediction model risk of bias assessment tool (PROBAST).

RESULTS

Of 8132 references identified, we included 14 articles based on 8 unique studies comprising 9588 women (mainly Caucasian) and 3289 natural menopause events. All included studies used onset of natural menopause (ONM) as outcome, while 4 studies also predicted early ONM. Overall, there were 180 risk prediction models investigated, with age, anti-Müllerian hormone, and follicle-stimulating hormone being the most investigated predictors. Estimated C-statistic for the prediction models ranged from 0.62 to 0.95. Although all studies were rated at high risk of bias mainly due to the methodological concerns related to the statistical analysis, their applicability was satisfactory.

CONCLUSION

Predictive performance and generalizability of current prediction models on ONM is limited given that these models were generated from studies at high risk of bias and from specific populations/ethnicities. Although in certain settings such models may be useful, efforts to improve their performance are needed as use becomes more widespread.

Prediction of new organ onset in recurrent immunoglobulin G4-related disease during 10 years of follow-up

OBJECTIVE

Frequent relapse is a prominent challenge in managing immunoglobulin G4-related disease (IgG4-RD). According to the types of organs involved in relapse, relapse patterns were divided into recurrent organ involvement (ROI) and new organ involvement (NOI). We aimed to investigate the discrepancy in clinical relapse patterns and establish an effective prognostic nomogram for NOI.

METHODS

We retrospectively enrolled 125 IgG4-RD patients who experienced relapse during the follow-up period. Patients were classified into two groups: those with NOI (including NOI and NOI + ROI) and without NOI (ROI). Logistic regression analyses were used to assess the risk factors for NOI. The results were externally validated by a separate prospective cohort of 39 patients with relapse.

RESULTS

There were 81 (64.8%) and 44 (35.2%) patients without NOI and with NOI, respectively. Patients without NOI showed higher baseline disease activity. The most common ROIs were the lacrimal gland and submandibular gland, while the lung and urinary system were the most involved in NOI. Re-elevation of serum IgG4 level to 74.31% of baseline was associated with NOI. Multiple relapses, organ involvement type at baseline, glucocorticoids combined with immunosuppressive drugs (IM) or IM alone during the maintenance period, and relapse IgG4/baseline IgG4 ratio were included in the nomogram. Both internal and external validations showed good agreement and discrimination.

CONCLUSIONS

About one third of IgG4-RD patients with relapse suffer from NOI. We developed a risk stratification model that can effectively predict the future risk of NOI. Glucocorticoid and IM combined therapy during maintenance is also recommended.

Predicting the Risk of Dental Implant Loss Using Deep Learning

AIM

To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography.

MATERIALS AND METHODS

Six hundred and three patients who underwent implant surgery (279 high-risk patients who did and 324 low-risk patients who did not experience implant loss within 5 years) from January 2012 to January 2020 were enrolled. Three models, a logistic regression clinical model (CM) based on clinical features, a DL model based on radiography features, and an integrated model (IM) developed by combining CM with DL, were developed to predict the 5-year implant loss risk. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. Time to implant loss was considered for both groups, and Kaplan-Meier curves were created and compared by the log-rank test.

RESULTS

The IM exhibited the best performance in predicting implant loss risk [AUC = 0.90, 95% confidence interval (CI) 0.84-0.95], followed by the DL model (AUC = 0.87, 95% CI 0.80-0.92) and the CM (AUC = 0.72, 95% CI 0.63-0.79).

CONCLUSION

Our study offers preliminary evidence that both the DL model and IM performed well in predicting implant fate within 5 years and thus may greatly facilitate implant practitioners in assessing preoperative risks.

Construction and validation of an early prediction model of delirium in children after congenital heart surgery

BACKGROUND

Delirium often occurs in children with congenital heart disease in the early postoperative period, which is not conducive to the rehabilitation and prognosis. There is little evidence to prove the effectiveness and safety of drug treatment of delirium in children, and the prevention has become an important topic. The purpose of this study is to analyze the early risk factors of delirium in children after congenital heart surgery, establish a nomogram prediction model, and explore the application efficiency of the model, so as to provide reference for early prevention of delirium.

METHODS

A total of 362 children treated in the cardiac intensive care unit (CICU) of Shanghai Children's Medical Center after congenital heart surgery from February 15 to April 15, 2021 were enrolled for the construction of the model. Bedside nurses who received unified training used the Cornell Assessment of Pediatric Delirium (CAPD) to evaluate delirium and recorded sixteen preoperative- and intraoperative-related influencing factors. A nomogram prediction model was created using multivariate logistic regression. The prediction effect of the model was evaluated by C-index and Brier value, and 96 children from April 16 to May 15, 2021 were included for effect verification. The model's effectiveness was validated by comparing the occurrence of delirium in children predicted by the model with the actual occurrence.

RESULTS

Multivariate logistic regression analysis showed that male gender [odds ratio (OR) =1.786, 95% confidence interval (CI): 1.018-3.134, P=0.043], age <6.5 months (OR =0.224, 95% CI: 0.126-0.399, P=0.000), disease severity ≥4 points (OR =6.955, 95% CI: 3.564-13.576, P=0.003), and operation time ≥148 min (OR =2.401, 95%CI: 1.336-4.315, P=0.000) were independent risk factors for delirium in children after cardiac surgery. The C-index of the nomogram prediction model was 0.808, sensitivity was 76.1%, specificity was 70%, and the Brier value was 0.142. The validation of the model showed that the model predicted 20 cases and the actual occurrence was 20 cases, of which 8 cases were false negative and 8 cases were false positive, and the sensitivity, specificity, and accuracy of the model were 60%, 89.5%, and 83.3%, respectively.

CONCLUSIONS

The prediction model constructed in this study could provide early prediction of the occurrence of delirium in children after congenital heart surgery to a certain extent.

Derivation and external validation of a 30-day mortality risk prediction model for older patients having emergency general surgery

BACKGROUND

Older people (≥65 yr) are at increased risk of morbidity and mortality after emergency general surgery. Risk prediction models are needed to guide decision making in this high-risk population. Existing models have substantial limitations and lack external validation, potentially limiting their applicability in clinical use. We aimed to derive and validate, both internally and externally, a multivariable model to predict 30-day mortality risk in older patients undergoing emergency general surgery.

METHODS

After protocol publication, we used the National Surgical Quality Improvement Program (NSQIP) database (2012-6; estimated to contain 90% data from the USA and 10% from Canada) to derive and internally validate a model to predict 30-day mortality for older people having emergency general surgery using logistic regression with elastic net regularisation. Internal validation was done with 10-fold cross-validation. External validation was done using a temporally separate health administrative database exclusively from Ontario, Canada.

RESULTS

Overall, 6012 (12.0%) of the 50 221 patients died within 30 days. The model demonstrated strong discrimination (area under the curve [AUC]=0.871) and calibration across the spectrum of observed and predicted risks. Ten-fold internal cross-validation demonstrated minimal optimism (AUC=0.851, optimism 0.019 [standard deviation=0.06]) with excellent calibration. External validation demonstrated lower discrimination (AUC=0.700) and degraded calibration.

CONCLUSION

A multivariable mortality risk prediction model was strongly discriminative and well calibrated internally. However, poor external validation suggests the model may not be generalisable to non-NSQIP data and hospitals. The findings highlight the importance of external validation before clinical application of risk models.

Development and internal validation of a multivariable risk prediction model for severe rebound pain after foot and ankle surgery involving single-shot popliteal sciatic nerve block

BACKGROUND

Rebound pain occurs after up to 50% of ambulatory surgeries involving regional anaesthesia. To assist with risk stratification, we developed a model to predict severe rebound pain after foot and ankle surgery involving single-shot popliteal sciatic nerve block.

METHODS

After ethics approval, we performed a single-centre retrospective cohort study. Patients undergoing lower limb surgery with popliteal sciatic nerve block from January 2016 to November 2019 were included. Exclusion criteria were uncontrolled pain in the PACU, use of a perineural catheter, or loss to follow-up. We developed and internally validated a multivariable logistic regression model for severe rebound pain, defined as transition from well-controlled pain in the PACU (numerical rating scale [NRS] 3 or less) to severe pain (NRS ≥7) within 48 h. A priori predictors were age, sex, surgery type, planned admission, local anaesthetic type, dexamethasone use, and intraoperative anaesthesia type. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC), Nagelkerke's R², scaled Brier score, and calibration slope.

RESULTS

The cohort included 1365 patients (mean [standard deviation] age: 50 [16] yr). The primary outcome was abstracted in 1311 (96%) patients, with severe rebound pain in 652 (50%). Internal validation revealed poor model performance, with AUROC 0.632 (95% confidence interval [CI]: 0.602-0.661; bootstrap optimisation 0.021), Nagelkerke's R² 0.063, and scaled Brier score 0.047. Calibration slope was 0.832 (95% CI: 0.623-1.041).

CONCLUSIONS

We show that a multivariable risk prediction model developed using routinely collected clinical data had poor predictive performance for severe rebound pain after foot and ankle surgery. Prospective studies involving other patient-related predictors are needed.

CLINICAL TRIAL REGISTRATION

NCT05018104.

Development of Risk Prediction Model for Grade 2 or Higher Hypocalcemia in Bone Metastasis Patients Treated with Denosumab plus Cholecalciferol (Vitamin D3)/Ca Supplement

Denosumab-induced hypocalcemia is sometimes severe, and although a natural vitamin D/Ca combination is used to prevent hypocalcemia, some patients rapidly develop severe hypocalcemia even under supplementation. It is clinically important to predict this risk. This study aimed to develop a risk prediction model for grade 2 or higher hypocalcemia within 28 days after the first denosumab dose under natural vitamin D/Ca supplementation. Using a large database containing multicenter practice data, 2,399 bone metastasis patients who were treated with denosumab between June 2013 and May 2020 were retrospectively analyzed. Background factors in patients who developed grade 2 or higher hypocalcemia within 28 days after the first denosumab dose and those who did not were compared by univariate analysis. Multivariate analysis was conducted to develop a risk prediction model. The model was evaluated for discriminant performance (ROC-AUC: receiver operating characteristic - area under the curve, sensitivity, specificity) and predictive performance (calibration slope). A total of 124 patients in the hypocalcemia group and 1,191 patients in the non-hypocalcemia group were extracted. A risk prediction model consisting of sex, Ca, albumin, alkaline phosphatase, osteoporosis, breast cancer, gastric cancer, proton pump inhibitor combination, and pretreatment with zoledronic acid was developed. The ROC-AUC was 0.87. Sensitivity and specificity were 83% and 81%, respectively, and the calibration slope indicated acceptable agreement between observed and predicted risk. This model appears to be useful to predict the risk of denosumab-induced hypocalcemia and thus should be helpful for risk management of denosumab treatment in patients with bone metastases. This article is protected by copyright. All rights reserved.

Comparison of the CIPHER prognostic model with the existing scores in predicting severe maternal outcomes during intensive care unit admission

OBJECTIVE

To compare the performance of the Collaborative Integrated Pregnancy High-Dependency Estimate of Risk (CIPHER) model in predicting maternal death and near-miss morbidity (Severe Maternal Outcome [SMO]) with the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the Simplified Acute Physiology Score (SAPS) III scores.

METHODS

A retrospective and a prospective study was conducted at two centers in Brazil. For each score, area under curve (AUC) was used and score calibration was assessed using the Hosmer-Lemeshow statistic (H-L) test and the standardized mortality ratio (SMR).

RESULTS

A cohort of 590 women was analyzed. A SMO was observed in 216 (36.6%) women. Of these, 13 (2.2%) were maternal deaths and 203 (34.4%) met one or more maternal near-miss criteria. The CIPHER model did not show significant diagnostic ability (AUC 0.52) and consequently its calibration was poor (H-L P<0.05). The SAPS III had the best performance (AUC 0.77, H-L P>0.05 and SMR 0.85).

CONCLUSION

The performance of the CIPHER model was lower compared to the other scores. Since the CIPHER model is not ready for clinical use, the SAPS III score should be considered for the prediction of SMO.

A predictive model for postoperative cognitive dysfunction in elderly patients with gastric cancer: a retrospective study

OBJECTIVE

To explore the risk factors of postoperative cognitive dysfunction (POCD) in elderly patients with gastric cancer after radical resection and to establish a risk prediction model.

METHODS

A retrospective analysis of the clinicopathological data of 687 elderly patients who underwent radical gastric cancer surgery from January 2014 to January 2020 in the Third Department of Surgery, Fourth Hospital of Hebei Medical University was conducted. The degree of cognitive impairment was divided into POCD positive group (n=141, 20.52%) and POCD negative group (n=546, 79.48%). The general data of the two groups were compared. Multivariate logistic regression was used to analyze the risk factors for POCD in elderly gastric cancer patients after radical surgery. A risk prediction model was established. The receiver operating characteristic (ROC) curve was used to evaluate the effectiveness of the model.

RESULTS

Multivariate logistic regression analysis showed that preoperative ASA classification (OR=4.674, 95% CI: 1.610~12.651, P=0.020), age (OR=3.130, 95% CI: 1.307~8.669, P=0.001), operation time (OR=2.724, 95% CI: 1.232~7.234, P=0.031), preoperative PG-SGA score (OR=4.023, 95% CI: 1.011-10.883, P=0.048), and preoperative hemoglobin (OR=4.158, 95% CI: 2.255~8.227, P=0.001) were independent risk factors for POCD. Intraoperative application of dexmedetomidine (OR=0.172, 95% CI: 0.078~0.314, P=0.002) and maintaining a deeper anesthesia state (OR=0.151, 95% CI: 0.122~0.283, P=0.018) were protective factors. The area under the ROC curve of the POCD risk prediction model for elderly gastric cancer patients after surgery was 0.820 (95% CI: 0.742-0.899) (P<0.01).

CONCLUSION

The occurrence of postoperative POCD in elderly patients with gastric cancer is closely related to a variety of risk factors. By establishing a risk prediction model for the occurrence of POCD, high-risk patients can be effectively identified during the perioperative period, to intervene earlier.

Identification and Validation of a Potent Multi-lncRNA Molecular Model for Predicting Gastric Cancer Prognosis

Gastric cancer (GC) remains the third deadliest malignancy in China. Despite the current understanding that the long noncoding RNAs (lncRNAs) play a pivotal function in the growth and progression of cancer, their prognostic value in GC remains unclear. Therefore, we aimed to construct a polymolecular prediction model by employing a competing endogenous RNA (ceRNA) network signature obtained by integrated bioinformatics analysis to evaluate patient prognosis in GC. Overall, 1,464 mRNAs, 14,376 lncRNAs, and 73 microRNAs (miRNAs) were found to be differentially expressed in GC. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that these differentially expressed RNAs were mostly enriched in neuroactive ligand–receptor interaction, chemical carcinogenesis, epidermis development, and digestion, which were correlated with GC. A ceRNA network consisting of four lncRNAs, 21 miRNAs, and 12 mRNAs were constructed. We identified four lncRNAs (lnc00473, H19, AC079160.1, and AC093866.1) as prognostic biomarkers, and their levels were quantified by qRT-PCR in cancer and adjacent noncancerous tissue specimens. Univariable and multivariable Cox regression analyses suggested statistically significant differences in age, stage, radiotherapy, and risk score groups, which were independent predictors of prognosis. A risk prediction model was created to test whether lncRNAs could be used as an independent risk predictor of GC or not. These novel lncRNAs’ signature independently predicted overall survival in GC (p < 0.001). Taken together, this study identified a ceRNA and protein–protein interaction networks that significantly affect GC, which could be valuable for GC diagnosis and therapy.

[Development of a Risk Prediction Model and a Simple Assessment Sheet for Cold Disorder (Hiesho) in Middle-aged and Older Adults]

OBJECTIVES

In this study, we aimed to develop a risk prediction model and a simple assessment sheet for cold disorder (hiesho) in middle-aged and older adults.

METHODS

The 889 participants in this study were from a community-dwelling general population (mean age, 62.4±8.8 years). The skin surface temperatures of the face and hands of the participants were measured by thermography. The cold disorder was objectively defined as having a temperature difference of ≥8°C between the forehead and fingertips. Data on the body regions with cold perception and the various concomitant signs were collected by a self-administered questionnaire and structured interviews.

RESULTS

The objectively assessed cold disorder was observed in 22.7% of participants and strongly associated with coldness of the back of the hand, palms, fingers, dorsal torso, toes, and soles of the feet. Its prevalence was found to increase with the number of signs of coldness. Older age, being female, low body mass index, hypertension, anemia, and physical inactivity were identified as potential risk factors. A logistic model for predicting the cold disorder was designed on the basis of the perceived cold, accompanying signs, and risk factors. The model showed good discrimination (area under the curve=0.737) and calibration capabilities (Hosmer-Lemeshow test, P=0.426). On the basis of this prediction model, a simple assessment sheet was developed to estimate the individual risk of experiencing the cold disorder, in middle-aged and older adults.

CONCLUSIONS

With the proposed risk prediction model showing good discrimination capability, the assessment sheet may serve as a prescreening tool to evaluate the potential of middle-aged and older population to develop the cold disorder.

Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method

OBJECTIVES

The purpose of this study was to establish a risk prediction model for differential diagnosis of pheochromocytomas (PCCs) from lipid-poor adenomas (LPAs) using a grouping method based on tri-phasic CT image features.

METHODS

In this retrospective study, we enrolled patients that were assigned to a training set (136 PCCs and 183 LPAs) from two medical centers, along with an external independent validation set (30 PCCs and 54 LPAs) from another center. According to the attenuation values in unenhanced CT (CTu), the lesions were divided into three groups: group 1, 10 HU < CTu ≤ 25 HU; group 2, 25 HU < CTu ≤ 40 HU; and group 3, CTu > 40 HU. Quantitative and qualitative CT imaging features were calculated and evaluated. Univariate, ROC, and binary logistic regression analyses were applied to compare these features.

RESULTS

Cystic degeneration, CTu, and the peak value of enhancement in the arterial and venous phase (DEpeak) were independent risk factors for differential diagnosis of adrenal PCCs from LPAs. In all subjects (groups 1, 2, and 3), the model formula for the differentiation of PCCs was as follows: Y = -7.709 + 3.617*(cystic degeneration) + 0.175*(CTu ≥ 35.55 HU) + 0.068*(DEpeak ≥ 51.35 HU). ROC curves were drawn with an AUC of 0.95 (95% CI: 0.927-0.973) in the training set and 0.91 (95% CI: 0.860-0.929) in the external validation set.

CONCLUSION

A reliable and practical prediction model for differential diagnosis of adrenal PCCs and LPAs was established using a grouping method.

Establishment and validation of a clinical model for predicting diabetic ketosis in patients with type 2 diabetes mellitus

BACKGROUND

Diabetic ketosis (DK) is one of the leading causes of hospitalization among patients with diabetes. Failure to recognize DK symptoms may lead to complications, such as diabetic ketoacidosis, severe neurological morbidity, and death.

PURPOSE

This study aimed to develop and validate a model to predict DK in patients with type 2 diabetes mellitus (T2DM) based on both clinical and biochemical characteristics.

METHODS

A cross-sectional study was conducted by evaluating the records of 3,126 patients with T2DM, with or without DK, at The Affiliated Hospital of Qingdao University from January 2015 to May 2022. The patients were divided randomly into the model development (70%) or validation (30%) cohorts. A risk prediction model was constructed using a stepwise logistic regression analysis to assess the risk of DK in the model development cohort. This model was then validated using a second cohort of patients.

RESULTS

The stepwise logistic regression analysis showed that the independent risk factors for DK in patients with T2DM were the 2-h postprandial C-peptide (2hCP) level, age, free fatty acids (FFA), and HbA1c. Based on these factors, we constructed a risk prediction model. The final risk prediction model was L= (0.472a - 0.202b - 0.078c + 0.005d - 4.299), where a = HbA1c level, b = 2hCP, c = age, and d = FFA. The area under the curve (AUC) was 0.917 (95% confidence interval [CI], 0.899-0.934; p<0.001). The discriminatory ability of the model was equivalent in the validation cohort (AUC, 0.922; 95% CI, 0.898-0.946; p<0.001).

CONCLUSION

This study identified independent risk factors for DK in patients with T2DM and constructed a prediction model based on these factors. The present findings provide an easy-to-use, easily interpretable, and accessible clinical tool for predicting DK in patients with T2DM.

Challenges in the primary prevention of sudden cardiac death in hypertrophic cardiomyopathy in the young

A case of hypertrophic cardiomyopathy in the transition from childhood to adulthood, which was low risk by the conventional risk assessment model, medium risk by the adult risk prediction model, and high risk by the paediatric risk prediction model, was inserted an implantable cardioverter-defibrillator. Three years post-implantation, the patient was resuscitated with an appropriate discharge of cardioverter-defibrillator.

Risk Prediction Model for Esophageal Cancer Among General Population: A Systematic Review

Objective: The risk prediction model is an effective tool for risk stratification and is expected to play an important role in the early detection and prevention of esophageal cancer. This study sought to summarize the available evidence of esophageal cancer risk predictions models and provide references for their development, validation, and application.

Methods: We searched PubMed, EMBASE, and Cochrane Library databases for original articles published in English up to October 22, 2021. Studies that developed or validated a risk prediction model of esophageal cancer and its precancerous lesions were included. Two reviewers independently extracted study characteristics including predictors, model performance and methodology, and assessed risk of bias and applicability with PROBAST (Prediction model Risk Of Bias Assessment Tool).

Results: A total of 20 studies including 30 original models were identified. The median area under the receiver operating characteristic curve of risk prediction models was 0.78, ranging from 0.68 to 0.94. Age, smoking, body mass index, sex, upper gastrointestinal symptoms, and family history were the most commonly included predictors. None of the models were assessed as low risk of bias based on PROBST. The major methodological deficiencies were inappropriate date sources, inconsistent definition of predictors and outcomes, and the insufficient number of participants with the outcome.

Conclusions: This study systematically reviewed available evidence on risk prediction models for esophageal cancer in general populations. The findings indicate a high risk of bias due to several methodological pitfalls in model development and validation, which limit their application in practice.

A Hypoxia-Associated Prognostic Gene Signature Risk Model and Prognosis Predictors in Gliomas

Most solid tumours are hypoxic. Tumour cell proliferation and metabolism accelerate oxygen consumption. The low oxygen supply due to vascular abnormalisation and the high oxygen demand of tumour cells give rise to an imbalance, resulting in tumour hypoxia. Hypoxia alters cellular behaviour and is associated with extracellular matrix remodelling, enhanced tumour migration, and metastatic behaviour. In light of the foregoing, more research on the progressive and prognostic impacts of hypoxia on gliomas are crucial. In this study, we analysed the expression levels of 75 hypoxia-related genes in gliomas and found that a total of 26 genes were differentially expressed in The Cancer Genome Atlas (TCGA) database samples. We also constructed protein–protein interaction networks using the STRING database and Cytoscape. We obtained a total of 10 Hub genes using the MCC algorithm screening in the cytoHubba plugin. A prognostic risk model with seven gene signatures (PSMB6, PSMD9, UBB, PSMD12, PSMB10, PSMA5, and PSMD14) was constructed based on the 10 Hub genes using LASSO–Cox regression analysis. The model was verified to be highly accurate using subject work characteristic curves. The seven-gene signatures were then analysed by univariate and multivariate Cox. Notably, PSMB10, PSMD12, UBB, PSMA5, and PSMB6 were found to be independent prognostic predictive markers for glioma. In addition, PSMB6, PSMA5, UBB, and PSMD12 were lowly expressed, while PSMB10 was highly expressed, in the TCGA and GTEx integrated glioma samples and normal samples, which were verified through protein expression levels in the Human Protein Atlas database. This study found the prognostic predictive values of the hypoxia-related genes PSMB10, PSMD12, UBB, PSMA5, and PSMB6 for glioma and provided ideas and entry points for the progress of hypoxia-related glioma.

Prediction of Graft Survival Post-liver Transplantation by L-GrAFT Risk Score Model, EASE Score, MEAF Scoring, and EAD

Background: Early allograft dysfunction (EAD) is correlated with poor patient or graft survival in liver transplantation. However, the power of distinct definitions of EAD in prediction of graft survival is unclear. Methods: This retrospective, single-center study reviewed data of 677 recipients undergoing orthotopic liver transplant between July 2015 and June 2020. The following EAD definitions were compared: liver graft assessment following transplantation (L-GrAFT) risk score model, early allograft failure simplified estimation score (EASE), model for early allograft function (MEAF) scoring, and Olthoff criteria. Risk factors for L-GrAFT₇ high risk group were evaluated with univariate and multivariable logistic regression analysis. Results: L-GrAFT₇ had a satisfied C-statistic of 0.87 in predicting a 3-month graft survival which significantly outperformed MEAF (C-statistic = 0.78, P = 0.01) and EAD (C-statistic = 0.75, P < 0.001), respectively. L-GrAFT₁₀, EASE was similar to L-GrAFT₇, and they had no statistical significance in predicting survival. Laboratory model for end-stage liver disease score and cold ischemia time are risk factors of L-GrAFT₇ high-risk group. Conclusion: L-GrAFT₇ risk score is capable for better predicting the 3-month graft survival than the MEAF and EAD in a Chinese cohort, which might standardize assessment of early graft function and serve as a surrogate endpoint in clinical trial.