A nomogram for predicting survival based on hemoglobin A1c and circulating tumor cells in advanced gastric cancer patients receiving immunotherapy.

BACKGROUND: Our study aimed to investigate the correlation between hemoglobin A1c (HbA1c), circulating tumor cells (CTCs) and prognosis in advanced gastric cancer (GC) patients who received immunotherapy and explore the potential prognostic predictors to develop a nomogram. METHODS: We retrospectively enrolled 259 patients with advanced GC treated at Beijing Friendship Hospital between September 2014 and March 2024. Patients were divided into the immunochemotherapy cohort (ICT) and the chemotherapy (CT) cohort. Survival rate was calculated by Kaplan-Meier survival curve, and the differences were evaluated by log-rank test. The univariate and multivariate Cox proportional hazards regression model was used to identify factors independently associated with survival. A nomogram was developed to estimate 6-, 12-, and 18-month progression-free survival (PFS) probability based on the ICT cohort. RESULTS: Patients achieved higher PFS in the ICT cohort than the CT cohort. We focused on the ICT cohort and constructed a nomogram based on the multivariate analysis, including five variables: age, PD-L1 expression, HbA1c, CTCs and CEA*. The concordance index value was 0.82 in the training cohort and 0.75 in the validation cohort. Furthermore, we proved the nomogram was clinically useful and performed better than PD-L1 expression staging system. Notably, we found high HbA1c level but not diabetes mellitus significantly affected the efficacy of ICT. CONCLUSION: ICT showed better PFS than CT. In addition, HbA1c and CTCs were novel biomarkers to predict PFS in patients treated with ICT. The nomogram could predict PFS of advanced GC patients receiving ICT with increased accuracy and favorable clinical utility.

Identifying risk and prognostic factors for synchronous liver metastasis in small bowel adenocarcinoma: a predictive analysis using the SEER database.

Background: Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy with an increasing incidence and a high propensity for liver metastasis (LM). This study aimed to investigate the risk factors for synchronous LM and prognostic factors in patients with LM. Methods: Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, this study analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020. Logistic regression was used to determine risk factors for synchronous LM. A nomogram was developed to predict the risk of LM in SBA patients, and its predictive performance was assessed through receiver operating characteristic (ROC) curves and calibration curves. Kaplan-Meier and Cox regression analyses were conducted to evaluate survival outcomes for SBA patients with LM. Results: Synchronous LM was present in 13.4% of SBA patients (n = 276). Six independent predictive factors for LM were identified, including tumor location, T stage, N stage, surgical intervention, retrieval of regional lymph nodes (RORLN), and chemotherapy. The nomogram demonstrated good discriminative ability, with an area under the curve (AUC) of 83.8%. Patients with LM had significantly lower survival rates than those without LM (P < 0.001). Survival analysis revealed that advanced age, tumor location in the duodenum, surgery, RORLN and chemotherapy were associated with cancer-specific survival (CSS) in patients with LM originating from SBA. Conclusions: This study highlights the significant impact of LM on the survival of SBA patients and identifies key risk factors for its occurrence. The developed nomogram aids in targeted screening and personalized treatment planning.

A nomogram for individualized prediction of mild cognitive impairment in patients with subjective cognitive decline during physical examinations: a cross-sectional study.

Background and objectives: To develop a nomogram for mild cognitive impairment (MCI) in patients with subjective cognitive decline (SCD) undergoing physical examinations in China. Methods: We enrolled 370 patients undergoing physical examinations at the Medical Center of the First Hospital of Jilin University, Jilin Province, China, from October 2022 to March 2023. Of the participants, 256 were placed in the SCD group, and 74 were placed in the MCI group. The population was randomly divided into a training set and a validation set at a 7:3 ratio. A least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. The performance and clinical utility of the nomogram were determined using Harrell's concordance index, calibration curves, and decision curve analysis (DCA). Results: Cognitive reserve (CR), age, and a family history of hypertension were associated with the occurrence of MCI. The predictive nomogram showed satisfactory performance, with a concordance index of 0.755 (95% CI: 0.681-0.830) in internal verification. The Hosmer-Lemeshow test results suggested that the model exhibited good fit (p = 0.824). In addition, DCA demonstrated that the predictive nomogram had a good clinical net benefit. Discussion: We developed a simple nomogram that could help secondary preventive health care workers to identify elderly individuals with SCD at high risk of MCI during physical examinations to enable early intervention.

Development of a predictive nomogram for intermediate-risk differentiated thyroid cancer patients after fixed 3.7GBq (100mCi) radioiodine remnant ablation.

Objectives: The objective of this study was to develop a predictive nomogram for intermediate-risk differentiated thyroid cancer (DTC) patients after fixed 3.7GBq (100mCi) radioiodine remnant ablation (RRA). Methods: Data from 265 patients who underwent total thyroidectomy with central lymph node dissection (CND) and received RRA treatment at a single institution between January 2018 and March 2023 were analyzed. Patients with certain exclusion criteria were excluded. Univariate and multivariate logistic regression analyses were performed to identify risk factors for a non-excellent response (non-ER) to RRA. A nomogram was developed based on the risk factors, and its performance was validated using the Bootstrap method with 1,000 resamplings. A web-based dynamic calculator was developed for convenient application of the nomogram. Results: The study included 265 patients with intermediate-risk DTC. Significant differences were found between the ER group and the non-ER group in terms of CLNM>5, Hashimoto's thyroiditis, sTg level, TgAb level (P < 0.05). CLNM>5 and sTg level were identified as independent risk factors for non-ER in multivariate analysis. The nomogram showed high accuracy, with an area under the curve (AUC) of 0.833 (95% CI = 0.770-0.895). The nomogram's predicted probabilities aligned closely with actual clinical outcomes. Conclusions: This study developed a predictive nomogram for intermediate-risk DTC patients after fixed 3.7GBq (100mCi) RRA. The nomogram incorporates CLNM>5 and sTg levels as risk factors for a non-ER response to RRA. The nomogram and web-based calculator can assist in treatment decision-making and improve the precision of prognosis information. Further research and validation are needed.

Prostate cancer nomograms and their application in Asian men: a review.

Nomograms help to predict outcomes in individual patients rather than whole populations and are an important part of evaluation and treatment decision making. Various nomograms have been developed in malignancies to predict and prognosticate clinical outcomes such as severity of disease, overall survival, and recurrence-free survival. In prostate cancer, nomograms were developed for determining need for biopsy, disease course, need for adjuvant therapy, and outcomes. Most of these predictive nomograms were based on Caucasian populations. Prostate cancer is significantly affected by race, and Asian men have a significantly different racial and genetic susceptibility compared to Caucasians, raising the concern in generalizability of these nomograms. We reviewed the existing literature for nomograms in prostate cancer and their application in Asian men. There are very few studies that have evaluated the applicability and validity of the existing nomograms in these men. Most have found significant differences in the performance in this population. Thus, more studies evaluating the existing nomograms in Asian men or suggesting modifications for this population are required.

Advancing Tailored Treatments: A Predictive Nomogram, Based on Ultrasound and Laboratory Data, for Assessing Nodal Involvement in Endometrial Cancer Patients.

Assessing lymph node metastasis is crucial in determining the optimal therapeutic approach for endometrial cancer (EC). Considering the impact of lymphadenectomy, there is an urgent need for a cost-effective and easily applicable method to evaluate the risk of lymph node metastasis in cases of sentinel lymph node (SLN) biopsy failure. This retrospective monocentric study enrolled EC patients, who underwent surgical staging with nodal assessment. Data concerning demographic, clinicopathological, ultrasound, and surgical characteristics were collected from medical records. Ultrasound examinations were conducted in accordance with the IETA statement. We identified 425 patients, and, after applying exclusion criteria, the analysis included 313 women. Parameters incorporated into the nomogram were selected via univariate and multivariable analyses, including platelet count, myometrial infiltration, minimal tumor-free margin, and CA 125. The nomogram exhibited good accuracy in predicting lymph node involvement, with an AUC of 0.88. Using a cutoff of 10% likelihood of nodal involvement, the nomogram displayed a low false-negative rate of 0.04 (95% CI 0.00-0.19) in the training set. The adaptability of this straightforward model renders it suitable for implementation across diverse clinical settings, aiding gynecological oncologists in preoperative patient evaluations and facilitating the design of personalized treatments. However, external validation is mandatory for confirming diagnostic accuracy.

Preoperative predictors of successful tumour localization by intraoperative molecular imaging with pafolacianine in lung cancer to create predictive nomogram.

OBJECTIVES: Intraoperative molecular imaging (IMI) uses cancer-targeted fluorescent probe to locate nodules. Pafolacianine is a Food and Drug Administration-approved fluorescent probe for lung cancer. However, it has a 8-12% false negative rate for localization. Our goal is to define preoperative predictors of tumour localization by IMI. METHODS: We performed a retrospective review of patients who underwent IMI using pafolacianine for lung lesions from June 2015 to August 2019. Candidate predictors including sex, age, body mass index, smoking history, tumour size, distance of tumour from surface, use of neoadjuvant therapy and positron emission tomography avidity were included. The outcome was fluorescence in vivo and comprehensively included those who were true or false positives negatives. Multiple imputation was used to handle the missing data. The final model was evaluated using the area under the receiver operating characteristic curve. RESULTS: Three hundred nine patients were included in our study. The mean age was 64 (standard deviation 13) and 68% had a smoking history. The mean distance of the tumours from the pleural surface was 0.4 cm (standard deviation 0.6). Smoking in pack-years and distance from pleura had an odds ratio of 0.99 [95% confidence interval: 0.98-0.99; P = 0.03] and 0.46 [95% confidence interval: 0.27-0.78; P = 0.004], respectively. The final model had an area under the receiver operating characteristic curve of 0.68 and was used to create a nomogram that gives a probability of fluorescence in vivo. CONCLUSIONS: Primary tumours that are deeper from the pleural surface, especially in patients with a higher pack-years, are associated with a decreased likelihood of intraoperative localization. We identified a nomogram to predict the likelihood of tumour localization with IMI with pafolacianine.

Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early­stage invasive breast cancer.

Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022. Pathological ALN status was used as the reference standard. The clinicopathological factors and MMUS features were analyzed with uni- and multivariate logistic regression to construct a clinicopathological and conventional US model and a MMUS-based nomogram. The MMUS nomogram was validated with respect to discrimination, calibration, reclassification and clinical usefulness. US features of tumor size, echogenicity, stiff rim sign, perfusion defect, radial vessel and US Breast Imaging Reporting and Data System category 5 were independent risk predictors for ALNM. MMUS nomogram based on these factors demonstrated an improved calibration and favorable performance [area under the receiver operator characteristic curve (AUC), 0.927 and 0.922 in the training and validation cohorts, respectively] compared with the clinicopathological model (AUC, 0.681 and 0.670, respectively), US-depicted ALN status (AUC, 0.710 and 0.716, respectively) and the conventional US model (AUC, 0.867 and 0.894, respectively). MMUS nomogram improved the reclassification ability of the conventional US model for ALNM prediction (net reclassification improvement, 0.296 and 0.288 in the training and validation cohorts, respectively; both P<0.001). Taken together, the findings of the present study suggested that the MMUS nomogram may be a promising, non-invasive and reliable approach for predicting ALNM.

Predicting long-term prognosis after percutaneous coronary intervention in patients with acute coronary syndromes: a prospective nested case-control analysis for county-level health services.

Purpose: We aimed to establish and authenticate a clinical prognostic nomogram for predicting long-term Major Adverse Cardiovascular Events (MACEs) among high-risk patients who have undergone Percutaneous Coronary Intervention (PCI) in county-level health service. Patients and methods: This prospective study included Acute Coronary Syndrome (ACS) patients treated with PCI at six county-level hospitals between September 2018 and August 2019, selected from both the original training set and external validation set. Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques and logistic regression were used to assess potential risk factors and construct a risk predictive nomogram. Additionally, the potential non-linear relationships between continuous variables were tested using Restricted Cubic Splines (RCS). The performance of the nomogram was evaluated based on the Receiver Operating Characteristic (ROC) curve analysis, Calibration Curve, Decision Curve Analysis (DCA), and Clinical Impact Curve (CIC). Results: The original training set and external validation set comprised 520 and 1,061 patients, respectively. The final nomogram was developed using nine clinical variables: Age, Killip functional classification III-IV, Hypertension, Hyperhomocysteinemia, Heart failure, Number of stents, Multivessel disease, Low-density Lipoprotein Cholesterol, and Left Ventricular Ejection Fraction. The AUC of the nomogram was 0.79 and 0.75 in the training set and external validation set, respectively. The DCA and CIC validated the clinical value of the constructed prognostic nomogram. Conclusion: We developed and validated a prognostic nomogram for predicting the probability of 3-year MACEs in ACS patients who underwent PCI at county-level hospitals. The nomogram could provide a precise risk assessment for secondary prevention in ACS patients receiving PCI.

Construction and Validation of a Nomogram Predicting Depression Risk in Patients with Acute Coronary Syndrome Undergoing Coronary Stenting: A Prospective Cohort Study.

PURPOSE: To construct and validate a nomogram for predicting depression after acute coronary stent implantation for risk assessment. METHODS: This study included 150 patients with acute coronary syndrome (ACS) who underwent stent implantation. Univariate analysis was performed to identify the predictors of postoperative depression among the 24 factors. Subsequently, multivariate logistic regression was performed to incorporate the significant predictors into the prediction model. The model was developed using the "rms" software package in R software, and internal validation was performed using the bootstrap method. RESULTS: Of the 150 patients, 82 developed depressive symptoms after coronary stent implantation, resulting in an incidence of depression of 54.7%. Univariate analysis showed that sleep duration ≥7 h, baseline GAD-7 score, baseline PHQ-9 score, and postoperative GAD-7 score were associated with the occurrence of depression after stenting in ACS patients (all p < 0.05). Multivariate logistic regression analysis revealed that major life events in the past year (OR = 2.783,95%CI: 1.121-6.907, p = 0.027), GAD-7 score after operation (OR = 1.165, 95% CI: 1.275-2.097, p = 0.000), and baseline PHQ-9 score (OR = 3.221, 95%CI: 2.065-5.023, p = 0.000) were significant independent risk factors for ACS patients after stent implantation. Based on these results, a predictive nomogram was constructed. The model demonstrated good prediction ability, with an AUC of 0.857 (95% CI = 0.799-0.916). The correction curve showed a good correlation between the predicted results and the actual results (Brier score = 0.15). The decision curve analysis and prediction model curve had clinical practical value in the threshold probability range of 7 to 94%. CONCLUSIONS: This nomogram can help to predict the incidence of depression and has good clinical application value. This trial is registered with ChiCTR2300071408.

Low serum calcium is a novel predictor of unfavorable prognosis after traumatic brain injury.

Background: Accurate and convenient serological markers for prognosis after traumatic brain injury (TBI) are still lacking. We aimed to explore the predictive value of serum calcium for prognosing outcomes within 6 months after TBI. Methods: In this multicenter retrospective study, 1255 and 719 patients were included in development and validation cohorts, respectively, and their 6-month prognoses were recorded. Serum calcium was measured through routine blood tests within 24 h of hospital admission. Two multivariate predictive models with or without serum calcium for prognosis were developed. Receiver operating characteristics and calibration curves were applied to estimate their performance. Results: The patients with lower serum calcium levels had a higher frequency of unfavorable 6-month prognosis than those without. Lower serum calcium level at admission was associated with an unfavorable 6-month prognosis in a wide spectrum of patients with TBI. Lower serum calcium level and our prognostic model including calcium performed well in predicting the 6-month unfavorable outcome. The calcium nomogram maintained excellent performance in discrimination and calibration in the external validation cohort. Conclusions: Lower serum calcium level upon admission is an independent risk factor for an unfavorable 6-month prognosis after TBI. Integrating serum calcium into a multivariate predictive model improves the performance for predicting 6-month unfavorable outcomes.

A nomogram for individually predicting overall survival for elderly patients with early breast cancer: a consecutive cohort study.

Background: Elderly patients with breast cancer are highly heterogeneous, and tumor load and comorbidities affect patient prognosis. Prediction models can help clinicians to implement tailored treatment plans for elderly patients with breast cancer. This study aimed to establish a prediction model for breast cancer, including comorbidities and tumor characteristics, in elderly patients with breast cancer. Methods: All patients were ≥65 years old and admitted to the Peking Union Medical College Hospital. The clinical and pathological characteristics, recurrence, and death were observed. Overall survival (OS) was analyzed using the Kaplan-Meier curve and a prediction model was constructed using Cox proportional hazards model regression. The discriminative ability and calibration of the nomograms for predicting OS were tested using concordance (C)-statistics and calibration plots. Clinical utility was demonstrated using decision curve analysis (DCA). Results: Based on 2,231 patients, the 5- and 10-year OS was 91.3% and 78.4%, respectively. We constructed an OS prediction nomogram for elderly patients with early breast cancer (PEEBC). The C-index for OS in PEEBC in the training and validation cohorts was 0.798 and 0.793, respectively. Calibration of the nomogram revealed a good predictive capability, as indicated by the calibration plot. DCA demonstrated that our model is clinically useful. Conclusion: The nomogram accurately predicted the 3-year, 5-year, and 10-year OS in elderly patients with early breast cancer.

Clinical Features and Predictive Nomogram of Acute Kidney Injury in Aging Population Infected with SARS-CoV-2 Omicron Variant.

Background: Since little is known about the acute kidney injury (AKI) in aging population infected with SARS-CoV-2 Omicron variant, we investigated the incidence, clinical features, risk factors and mid-term outcomes of AKI in hospitalized geriatric patients with and without COVID-19 and established a prediction model for mortality. Methods: A real-time data from the Shanghai Ninth People's Hospital information system of inpatients with COVID-19 from 1 April 2022 to 30 June 2022 were extracted. Clinical spectrum, laboratory results, and clinical prognosis were included for the risk analyses. Moreover, Cox and Lasso regression analyses were applied to predict the 90-day death and a nomogram was established. Results: A total of 1607 SARS-CoV-2 infected patients were enrolled; hypertension was the most common comorbidity, followed by chronic cardiovascular disease, diabetes mellitus, and lung disease. Most of the participants were non-vaccinated and the mean age of patients was 82.6 years old (range, 60-103 years). The AKI incidence was higher in relatively older patients (16.29% vs 3.63% in patients older than 80 years and 60 to 80 years, respectively). Linear regression models identified some variables associated with the incidence of AKI, such as older age, clinical spectrum, D-dimer level, number of comorbidities, baseline eGFR, and antibiotic or corticosteroid treatment. In this cohort, 11 patients died in-hospital and 21 patients died at 90-day follow-up. The predictive nomogram of 90-day death achieved a good C-index of 0.823 by using 5 predictor variables: ICU admission, D-dimer, peak of serum creatinine, rate of serum creatinine decline and white blood cell count (WBC). Conclusion: Older age, clinical spectrum, D-dimer level, number of comorbidities, baseline eGFR, and antibiotic or corticosteroid treatment are clinical risk factors for the incidence of AKI in geriatric COVID-19 patients. The prediction nomogram achieved an excellent performance at the prediction of 90-day mortality.

Establishment and validation of a nomogram based on coagulation parameters to predict the prognosis of pancreatic cancer.

BACKGROUND: In recent years, multiple coagulation and fibrinolysis (CF) indexes have been reported to be significantly related to the progression and prognosis of some cancers. OBJECTIVE: The purpose of this study was to comprehensively analyze the value of CF parameters in prognosis prediction of pancreatic cancer (PC). METHODS: The preoperative coagulation related data, clinicopathological information, and survival data of patients with pancreatic tumor were collected retrospectively. Mann Whitney U test, Kaplan-Meier analysis, and Cox proportional hazards regression model were applied to analyze the differences of coagulation indexes between benign and malignant tumors, as well as the roles of these indexes in PC prognosis prediction. RESULTS: Compared with benign tumors, the preoperative levels of some traditional coagulation and fibrinolysis (TCF) indexes (such as TT, Fibrinogen, APTT, and D-dimer) were abnormally increased or decreased in patients with pancreatic cancer, as well as Thromboelastography (TEG) parameters (such as R, K, α Angle, MA, and CI). Kaplan Meier survival analysis based on resectable PC patients showed that the overall survival (OS) of patients with elevated α angle, MA, CI, PT, D-dimer, or decreased PDW was markedly shorter than other patients; moreover, patients with lower CI or PT have longer disease-free survival. Further univariate and multivariate analysis revealed that PT, D-dimer, PDW, vascular invasion (VI), and tumor size (TS) were independent risk factors for poor prognosis of PC. According to the results of modeling group and validation group, the nomogram model based on independent risk factors could effectively predict the postoperative survival of PC patients. CONCLUSION: Many abnormal CF parameters were remarkably correlated with PC prognosis, including α Angle, MA, CI, PT, D-dimer, and PDW. Furthermore, only PT, D-dimer, and PDW were independent prognostic indicators for poor prognosis of PC, and the prognosis prediction model based on these indicators was an effective tool to predict the postoperative survival of PC.

Development and validation of a predictive nomogram for the specific mortality risk of luminal B breast cancer patients: a competing risk model based on real populations.

Background: For clinical workers, disease-specific death is a better indicator of tumor severity. Breast cancer is the most prevalent malignancy in women. Luminol type B breast cancer is one of the biggest threats to women's health, and few studies have paid attention to its specific death. Early recognition of luminol type B breast cancer allows clinicians to assess the prognosis and develop more optimal treatment plans. Methods: In this study, the basic information of luminal B population, clinical and pathological characteristics, treatment regimen and survival data were collected from the SEER database. The patients were randomly divided into a training group and a validation group. The single-factor and multi-factor competitive risk models were used to analyze the independent influencing factors of tumor-specific death, and the predictive nomogram based on the competitive risk model was constructed. The consistency index (C-index) and calibration curves over time were used to evaluate the accuracy of the predicted nomograms. Results: This study included a total of 30,419 luminal B patient. The median follow-up period was 60 (IQR: 44-81) months. Among the 4,705 deaths during the follow-up period, 2,863 patients died specifically, accounting for 60.85% of the deaths. The independent predictive factors of cancer-specific mortality were: married, primary site, grade, stage, the primary site of operation, radiotherapy, chemotherapy, metastasis (lymph node, bone, brain, liver, lung), and Estrogen Receptor and Progesterone Receptor status. In the training cohort, the C-index of the predictive nomogram was 0.858, and the area under the receiver operating characteristic curve (AUC) for the first, third, and fifth years was 0.891, 0.864, and 0.845. The C-index of the validation cohort was 0.862, and the AUC for the first, third, and fifth years was 0.888, 0.872, and 0.849. The calibration curves of the training and validation cohorts showed that the predicted probability of the model was very consistent with the actual probability. And the 5-year survival rate according to the traditional survival analysis was 9.49%, while the 5-year specific mortality rate was only 8.88%. Conclusions: The luminal B competing risk model we established has ideal accuracy and calibration.

Association of Mitral Regurgitation with Postoperative Atrial Fibrillation in Critically Ill Noncardiac Surgery Patients: A Prospective Cohort Study.

Purpose: Atrial fibrillation (AF) is common in critically ill patients and can have serious consequences. Postoperative AF (POAF) in critically ill patients following noncardiac surgery has been understudied, contrary to cardiac procedures. Mitral regurgitation (MR) is associated with left ventricular dysfunction, which might contribute to the occurrence of AF in postoperative critically ill patients. This study aimed to investigate the association between MR and POAF in critically ill noncardiac surgery patients and establish a new nomogram for the prediction of POAF in critically ill noncardiac surgery patients. Patients and Methods: A prospective cohort of 2474 patients who underwent thoracic and general surgery was enrolled in this study. Data on preoperative transthoracic echocardiography (TTE), electrocardiogram (ECG), and several commonly utilized scoring systems (CHA2DS2-VASc, HATCH, COM-AF, HART, and C2HEST) and baseline clinical data were collected. Independent predictors were selected by univariate and multivariable logistic regression analysis, and a nomogram was constructed for POAF within 7 days after postoperative intensive care unit (ICU) admission. The ability of the MR-nomogram and other scoring systems to predict POAF was compared by receiver operator characteristic (ROC) curve analysis and decision curve analysis (DCA). Additional contributions were evaluated by integrated discrimination improvement (IDI) and net reclassification improvement (NRI) analysis. Results: A total of 213 (8.6%) patients developed POAF within 7 days after ICU admission. Compared to CHA2DS2-VASc, HATCH, COM-AF, HART, and C2HEST scoring systems, MR-nomogram showed better predictive ability for POAF with an area under the ROC curve of 0.824 (95% confidence interval: 0.805-0.842, p < 0.001). The improvement of the MR-nomogram in predictive value was supported by NRI and IDI analysis. The net benefit of the MR nomogram was maximal in DCA. Conclusion: MR is an independent risk factor of POAF in critically ill noncardiac surgery patients. The nomogram predicted POAF better than other scoring systems.

1H NMR-based metabolomics of paired tissue, serum and urine samples reveals an optimized panel of biofluids metabolic biomarkers for esophageal cancer.

Introduction: The goal of this study was to establish an optimized metabolic panel by combining serum and urine biomarkers that could reflect the malignancy of cancer tissues to improve the non-invasive diagnosis of esophageal squamous cell cancer (ESCC). Methods: Urine and serum specimens representing the healthy and ESCC individuals, together with the paralleled ESCC cancer tissues and corresponding distant non-cancerous tissues were investigated in this study using the high-resolution 600 MHz 1H-NMR technique. Results: We identified distinct 1H NMR-based serum and urine metabolic signatures respectively, which were linked to the metabolic profiles of esophageal-cancerous tissues. Creatine and glycine in both serum and urine were selected as the optimal biofluids biomarker panel for ESCC detection, as they were the overlapping discriminative metabolites across serum, urine and cancer tissues in ESCC patients. Also, the were the major metabolites involved in the perturbation of "glycine, serine, and threonine metabolism", the significant pathway alteration associated with ESCC progression. Then a visual predictive nomogram was constructed by combining creatine and glycine in both serum and urine, which exhibited superior diagnostic efficiency (with an AUC of 0.930) than any diagnostic model constructed by a single urine or serum metabolic biomarkers. Discussion: Overall, this study highlighted that NMR-based biofluids metabolomics fingerprinting, as a non-invasive predictor, has the potential utility for ESCC detection. Further studies based on a lager number size and in combination with other omics or molecular biological approaches are needed to validate the metabolic pathway disturbances in ESCC patients.

Development and Assessment of a Novel Predictive Nomogram to Predict the Risk of Secondary CR-GNB Bloodstream Infections among CR-GNB Carriers in the Gastroenterology Department: A Retrospective Case-Control Study.

BACKGROUND: With the number of critically ill patients increasing in gastroenterology departments (GEDs), infections associated with Carbapenem-resistant Gram-negative bacteria (CR-GNB) are of great concern in GED. However, no CR-GNB bloodstream infection (BSI) risk prediction model has been established for GED patients. Almost universally, CR-GNB colonization precedes or occurs concurrently with CR-GNB BSI. The objective of this study was to develop a nomogram that could predict the risk of acquiring secondary CR-GNB BSI in GED patients who are carriers of CR-GNB. METHODS: We conducted a single-center retrospective case-control study from January 2020 to March 2022. Univariate and multivariable logistic regression analysis was used to identify independent risk factors of secondary CR-GNB bloodstream infections among CR-GNB carriers in the gastroenterology department. A nomogram was constructed according to a multivariable regression model. Various aspects of the established predicting nomogram were evaluated, including discrimination, calibration, and clinical utility. We assessed internal validation using bootstrapping. RESULTS: The prediction nomogram includes the following predictors: high ECOG PS, severe acute pancreatitis, diabetes mellitus, neutropenia, a long stay in hospital, and parenteral nutrition. The model demonstrated good discrimination and good calibration. CONCLUSIONS: With an estimate of individual risk using the nomogram developed in this study, clinicians and nurses can identify patients with a high risk of secondary CR-GNB BSI early.

Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer.

The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01-1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11-3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61-6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23-2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53-2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS-based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.

Predicting lymph node metastasis and recurrence in patients with early stage colorectal cancer.

Tumor budding (TB), a powerful, independent predictor of colorectal cancer (CRC), is important for making appropriate treatment decisions. Currently, TB is assessed only using the tumor bud count (TBC). In this study, we aimed to develop a novel prediction model, which includes different TB features, for lymph node metastasis (LNM) and local recurrence in patients with pT1 CRC. Enrolled patients (n = 354) were stratified into training and validation cohorts. Independent predictors of LNM and recurrence were identified to generate predictive nomograms that were assessed using the area under the receiver operating characteristic (AUROC) and decision curve analysis (DCA). Seven LNM predictors [gross type, histological grade, lymphovascular invasion (LVI), stroma type, TBC, TB mitosis, and TB CDX2 expression] were identified in the training cohort. LNM, histology grade, LVI, TBC, stroma type, and TB mitosis were independent predictors of recurrence. We constructed an LNM predictive nomogram with a high clinical application value using the DCA. Additionally, a nomogram predicting recurrence-free survival (RFS) was constructed. It presented an AUROC value of 0.944 for the training cohort. These models may assist surgeons in making treatment decisions. In the high-risk group, radical surgery with a postoperative adjuvant chemotherapy was associated with RFS. Postoperative chemotherapy can be better for high-risk patients with pT1 CRC. We showed that TB features besides TBC play important roles in CRC pathogenesis, and our study provides prognostic information to guide the clinical management of patients with early stage CRC.