Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage 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.

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.

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.

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.

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.

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.

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.

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.

Risk Factors for Carbapenem Resistant Gram Negative Bacteria (CR-GNB) Carriage Upon Admission to the Gastroenterology Department in a Tertiary First Class Hospital of China: Development and Assessment of a New Predictive Nomogram

Background

With the increasing number of critically ill patients in the gastroenterology department (GED), infections associated with Carbapenem resistant gram-negative bacteria (CR-GNB) are of great concern in GED. As the turn-around time (TAT) for a positive screening culture result is slow, contact precaution and pre-emptive isolation, cohorting methods should be undertaken immediately on admission for high-risk patients. Accurate prediction tools for CR-GNB colonization in GED can help determine target populations upon admission. And thus, clinicians and nurses can implement preventive measures more timely and effectively.

Objective

The purpose of the current study was to develop and internally validate a CR-GNB carrier risk predictive nomogram for a Chinese population in GED.

Methods

Based on a training dataset of 400 GED patients collected between January 2020 and December 2021, we developed a model to predict CR-GNB carrier risk. A rectal swab was used to evaluate the patients' CR-GNB colonization status microbiologically. We optimized features selection using the least absolute shrinkage and selection operator regression model (LASSO). In order to develop a predicting model, multivariable logistic regression analysis was then undertaken. Various aspects of the predicting model were evaluated, including discrimination, calibration, and clinical utility. We assessed internal validation using bootstrapping.

Results

The prediction nomogram includes the following predictors: Transfer from another hospital (Odds ratio [OR] 3.48), High Eastern Cooperative Oncology Group (ECOG) performance status (OR 2.61), Longterm in healthcare facility (OR 10.94), ICU admission history (OR 9.03), Blood stream infection history (OR 3.31), Liver cirrhosis (OR 4.05) and Carbapenem usage history within 3 month (OR 2.71). The model demonstrated good discrimination and good calibration.

Conclusion

With an estimate of individual risk using the nomogram developed in this study, clinicians and nurses can take more timely infection preventive measures on isolation, cohorting and medical interventions.

Construction and validation of a clinical predictive nomogram for intraductal carcinoma of the prostate based on Chinese multicenter clinical data

Introduction

Intraductal carcinoma of the prostate (IDC-P) is a special pathological type of prostate cancer that is highly aggressive with poor prognostic outcomes.

Objective

To establish an effective predictive model for predicting IDC-P.

Methods

Data for 3185 patients diagnosed with prostate cancer at three medical centers in China from October 2012 to April 2022 were retrospectively analyzed. One cohort (G cohort) consisting of 2384 patients from Zhejiang Provincial People's Hospital was selected for construction (Ga cohort) and internal validate (Gb cohort)of the model. Another cohort (I cohort) with 344 patients from Quzhou People's Hospital and 430 patients from Jiaxing Second People's Hospital was used for external validation. Univariate and multivariate binary logistic regression analyses were performed to identify the independent predictors. Then, the selected predictors were then used to establish the predictive nomogram. The apparent performance of the model was evaluated via externally validated. Decision curve analysis was also performed to assess the clinical utility of the developed model.

Results

Univariate and multivariate logistic regression analyses showed that alkaline phosphatase (ALP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), prostate specific antigen (PSA) and lactate dehydrogenase were independent predictors of IDC-P. Therefore, a predictive nomogram of IDC-P was constructed. The nomogram had a good discriminatory power (AUC = 0.794). Internal validation (AUC = 0.819)and external validation (AUC = 0.903) also revealed a good predictive ability. Calibration curves showed good agreement between the predicted and observed incidences of IDC-P.

Conclusion

We developed a clinical predictive model composed of alkaline phosphatase (ALP), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), prostate specific antigen (PSA) and lactate dehydrogenase (LDH) with a high precision and universality. This model provides a novel calculator for predicting the diagnosis of IDC-P and different treatment options for patients at an early stage.

The Iron-Inflammation Axis in Early-Stage Triple-Negative Breast Cancer

The iron-related homeostasis and inflammatory biomarker have been identified as prognostic factors for cancers. We aimed to explore the prognostic value of a novel comprehensive biomarker, the iron-monocyte-to-lymphocyte ratio (IronMLR) score, in patients with early-stage triple-negative breast cancer (TNBC) in this study. We retrospectively analysed a total of 257 early-stage TNBC patients treated at Sun Yat-sen University Cancer Center (SYSUCC) between March 2006 and October 2016. Their clinicopathological information and haematological data tested within 1 week of the diagnosis were collected. According to the IronMLR score cutoff value of 6.07 μmol/L determined by maximally selected rank statistics, patients were stratified into the low- and high-IronMLR groups, after a median follow-up of 92.3 months (95% confidence interval [CI] 76.0–119.3 months), significant differences in 5-years disease-free survival (DFS) rate (81.2%, 95% CI 76.2%–86.5% vs. 65.5%, 95% CI 50.3%–85.3%, p = 0.012) and 5-years overall survival (OS) rate (86.0%, 95% CI 81.6%–90.7% vs. 65.5%, 95% CI 50.3%–85.3%, p = 0.011) were seen between two groups. Further multivariate Cox regression analysis revealed the IronMLR score as an independent predictor for DFS and OS, respectively, we then established a prognostic nomogram integrating the IronMLR score, T stage and N stage for individualized survival predictions. The prognostic model showed good predictive performance with a C-index of DFS 0.725 (95% CI 0.662–0.788) and OS 0.758 (95% CI 0.689–0.826), respectively. Besides, calibration curves for 1-, 3-, 5-DFS, and OS represented satisfactory consistency between actual and nomogram predicted survival. In conclusion, the Iron-inflammation axis might be a potential prognostic biomarker of survival outcomes for patients with early-stage TNBC, prognostic nomograms based on it with good predictive performance might improve individualized survival predictions.

Prostate Magnetic Resonance Imaging Analyses, Clinical Parameters, and Preoperative Nomograms in the Prediction of Extraprostatic Extension

Introduction: Proper planning of laparoscopic radical prostatectomy (RP) in patients with prostate cancer (PCa) is crucial to achieving good oncological results with the possibility of preserving potency and continence. Aim: The aim of this study was to identify the radiological and clinical parameters that can predict the risk of extraprostatic extension (EPE) for a specific site of the prostate. Predictive models and multiparametric magnetic resonance imaging (mpMRI) data from patients qualified for RP were compared. Material and methods: The study included 61 patients who underwent laparoscopic RP. mpMRI preceded transrectal systematic and cognitive fusion biopsy. Martini, Memorial Sloan-Kettering Cancer Center (MSKCC), and Partin Tables nomograms were used to assess the risk of EPE. The area under the curve (AUC) was calculated for the models and compared. Univariate and multivariate logistic regression analyses were used to determine the combination of variables that best predicted EPE risk based on final histopathology. Results: The combination of mpMRI indicating or suspecting EPE (odds ratio (OR) = 7.49 (2.31–24.27), p < 0.001) and PSA ≥ 20 ng/mL (OR = 12.06 (1.1–132.15), p = 0.04) best predicted the risk of EPE for a specific side of the prostate. For the prediction of ipsilateral EPE risk, the AUC for Martini’s nomogram vs. mpMRI was 0.73 (p < 0.001) vs. 0.63 (p = 0.005), respectively (p = 0.131). The assessment of a non-specific site of EPE by MSKCC vs. Partin Tables showed AUC values of 0.71 (p = 0.007) vs. 0.63 (p = 0.074), respectively (p = 0.211). Conclusions: The combined use of mpMRI, the results of the systematic and targeted biopsy, and prostate-specific antigen baseline can effectively predict ipsilateral EPE (pT3 stage).

Risk factors for and predictive nomogram of postoperative hypoxaemia in elderly patients with femoral neck fractures

Objective

To investigate the related risk factors and predictive nomogram of postoperative hypoxaemia in elderly patients with femoral neck fractures.

Methods

This study included patients aged ≥65 years who underwent surgical treatment of acute femoral neck fractures. Univariate and multivariate logistic analyses were performed to determine the incidence of and risk factors for postoperative hypoxaemia. A predictive nomogram was constructed based on the multivariable model. Using the bootstrap method, discrimination was determined by the C-index and calibration plot.

Results

The logistic regression analysis showed that the anaesthesia type, surgical procedure, American Society of Anesthesiologists (ASA) classification, preoperative hypoxaemia occurrence, and age were independent predictors of development of postoperative hypoxaemia. The predictive formula for hypoxaemia was established as follows: hypoxaemia=−0.8668×spinal anaesthesia (whether)+0.1162×nerve anaesthesia (whether)+1.9555×plate/screw fixation (whether)+1.4950×hip replacement (whether)+0.4883×ASA classification+1.7153×preoperative oxygenation index+0.1608×age. With the bootstrap method, the prediction curve fit well with the ideal curve, suggesting that the prediction curve constructed in this study has good predictive ability.

Conclusions

Anaesthesia type, surgical procedure, ASA classification, preoperative hypoxaemia occurrence, and age were risk factors for postoperative hypoxaemia in elderly patients with femoral neck fractures. The predictive nomogram was designed for preoperative assessment of the risk of postoperative hypoxaemia by calculating the risk score.

A Novel Preoperative Nomogram for Predicting Lymph Node Invasion in Renal Cell Carcinoma Patients Without Metastasis

Objective

To provide a preoperative predictive model to support clinical decision-making regarding the selection of in renal cell carcinoma (RCC) patients who will benefit the most from lymph node dissection.

Methods

This retrospective analysis enrolled 374 RCC patients without distant metastasis who underwent surgical treatment from January 2006 to December 2017. The relationships between lymph node invasion (LNI) and age at surgery; gender; body mass index(BMI); the presence of clinical symptoms such as flank pain, hematuria or a palpable mass; clinical T stage (cT stage); clinical N stage (cN stage); and the results of routine hematological and serum biochemical analyses were investigated. All the variables were included in univariate and multivariate logistic regression analyses, and the significant variables were then included in a novel nomogram to predict the probability of LNI. Then, we calibrated the nomogram with an internal validation set.

Results

Six of eighteen variables were significant in the univariate logistic regression analysis. After multivariate logistic regression analysis, age at surgery (OR=0.643, 95% CI: 0.421–0.975), cT stage (OR=3.034, 95% CI: 1.541–5.926), cN stage (OR=6.353, 95% CI: 3.273–12.456), lymphocyte percentage (OR=0.481, 95% CI: 0.256–0.894), and the presence of clinical symptoms (OR=2.045, 95% CI: 1.065–3.924) were independent predictors of LNI and were included in the nomogram. The C-index of this nomogram was 0.824.

Conclusion

Preoperative basic laboratory findings combined with the results of a physical examination and radiological examination can indicate the probability of LNI in RCC patients.

AXIN2 and SNAIL expression predict the risk of recurrence in cutaneous squamous cell carcinoma after Mohs micrographic surgery

Recurrence is a common complication observed during cutaneous squamous cell carcinoma (cSCC) treatment; however, biomarkers for predicting recurrence in cSCC remain unknown. The present study aimed to investigate the predictive value of axis inhibition protein 2 (AXIN2) and SNAIL expression in cSCC recurrence. AXIN2 and SNAIL expression was evaluated using immunohistochemistry in 111 cSCC tissue samples obtained from 18 patients who presented recurrence (recurrence interval, 1–91 months) and 93 patients who did not experience recurrence following Mohs micrographic surgery (MMS) during the follow-up period (156 months). Nomogram construction was performed using patients' clinicopathological characteristics and AXIN2 and SNAIL protein expression. The results demonstrated that high AXIN2 (histoscore >100) and SNAIL (histoscore >100) expression was detected in 35 and 44 cSCC tissues, respectively. Furthermore, the expression levels of AXIN2 and SNAIL were significantly associated in patients with cSCC (P=0.001). AXIN2 and SNAIL expression levels were significantly associated with tumor size (P=0.021 and P=0.044, respectively) and recurrence of cSCC (P=0.017 and P=0.042, respectively). In addition, the results of the Kaplan-Meier curve analysis revealed that recurrence-free survival was significantly associated with tumor size (P=0.025), differentiation status (P<0.001), AXIN2 expression (P=0.001) and SNAIL expression (P=0.001). Furthermore, the results of the multivariate analysis demonstrated that age (P=0.043), AXIN2 expression (P=0.001) and SNAIL expression (P=0.045) were independent risk factors for cSCC recurrence in the present cohort. A nomogram for predicting the 1-, 2-, 3-, and 5-year recurrence-free survival was developed for patients with cSCC by including independent risk factors with a concordance index of 0.75. The results suggested that high AXIN2 and SNAIL expression may be considered as potential risk factors for cSCC recurrence. This nomogram may therefore be useful to assess the probability of recurrence in patients with cSCC following MMS.

Predicting Inpatient Status After Primary Total Knee Arthroplasty in Medicare-Aged Patients

BACKGROUND

The Centers for Medicare and Medicaid Services (CMS) removed total knee arthroplasty (TKA) from its inpatient only (IPO) list as of January 1, 2018. The purpose of this study was to establish a risk-stratifying nomogram to aid in determining the need for inpatient admission among Medicare-aged patients undergoing primary TKA.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program database was queried to identify all patients aged ≥65 years who underwent primary TKA between 2006 and 2015. The primary outcome measure was inpatient admission, as defined by hospital length of stay longer than 2 days. Multiple demographic, comorbid, and perioperative variables were incorporated in a multivariate logistic regression model to yield a risk stratification nomogram.

RESULTS

Sixty-one thousand two hundred eighty-four inpatient and 26,066 outpatient admissions were analyzed. Age >80 years (odds ratio [OR] = 2.27, P < .0001, 95% confidence interval [CI] = 2.13-2.42), simultaneous bilateral TKA (OR = 2.02, P < .0001, 95% CI = 1.77-2.30), dependent functional status (OR = 1.95, P < .0001, 95% CI = 1.62-2.35), metastatic cancer (OR = 1.91, P = .055, 95% CI = 0.99-3.73), and female gender (OR = 1.76, P < .0001, 95% CI = 1.70-1.82) were the greatest determinants of inpatient stay. The resulting predictive model demonstrated acceptable discrimination and excellent calibration.

CONCLUSION

Our model enabled a reliable and straightforward identification of the most suitable candidates for inpatient admission in Medicare aged-patients undergoing primary TKA. Larger multicenter studies are necessary to externally validate the proposed predictive nomogram.

A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program

Background: Patients with early stage breast cancer with lymph nodes metastasis were proven to have more aggressive biologically phenotypes. This study aimed to build a nomogram to predict lymph node metastasis in patients with T1 breast cancer.

Methods: We identified female patients with T1 breast cancer diagnosed between 2010 and 2014 in the Surveillance, Epidemiology and End Results database. The patients were randomized into training and validation sets. Univariate and multivariate logistic regressions were carried out to assess the relationships between lymph node metastasis and clinicopathological characteristics. A nomogram was developed and validated by a calibration curve and receptor operating characteristic curve analysis.

Result: Age, race, tumour size, tumour primary site, pathological grade, oestrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2) status were independent predictive factors of positive lymph node metastasis in T1 breast cancer. Increasing age, tumour size and pathological grade were positively correlated with the risk of lymph node metastasis. We developed a nomogram to predict lymph node metastasis and further validated it in a validation set, with areas under the receiver operating characteristic curves of 0.733 and 0.741 in the training and validation sets, respectively.

Conclusions: A better understanding of the clinicopathological characteristics of T1 breast cancer patients might important for assessing their lymph node status. The nomogram developed here, if further validated in other large cohorts, might provide additional information regarding lymph node metastasis. Together with sentinel lymph node biopsy, this nomogram can help comprehensively predict lymph node metastasis.

Comparative analysis of colorectal mixed adenoneuroendocrine carcinoma and adenocarcinoma with neuroendocrine differentiation: a population-based study

BACKGROUND

Colorectal mixed adenoneuroendocrine carcinoma (MANEC) and adenocarcinoma with neuroendocrine differentiation (ANED) are recognized as different tumors pathologically and clinically. In a population-based study, the clinicopathologic characteristics and treatment strategies of the two tumors were comparatively analyzed.

METHODS

Patients with colorectal adenocarcinoma (ADEC), neuroendocrine carcinoma (NEC), MANEC and ANED were identified diagnosis from 2010 to 2014 using the Surveillance, Epidemiology, and End Results (SEER) database. The clinicopathologic data were analyzed by Chi-square test, univariable and multivariable Cox regression. Nomogram was performed to provide a prognostic evaluation for colorectal MANEC and ANED.

RESULTS

Totally 82121 patients were recruited in this cohort. There was no difference between MANEC and ANED in clinicopathologic characteristics and prognosis (P>0.05). The survival data showed that 1-year and 3-year survival rates were 84.70% and 67.83% for ADEC, 66.83% and 51.98% for NEC, and 54.27% and 37.68% for MANEC and ANED, respectively. Stage and surgery were independent prognostic factors of colorectal MANEC/ANED. We also found that the prognosis was significantly different without vs with chemotherapy (P=0.000) in stage III colorectal MANEC/ANED; without vs with surgery (P=0.007), and without vs with chemotherapy (P=0.000) in stage IV colorectal MANEC/ANED. Radiation did nothing for improving the prognosis of colorectal MANEC/ANED in stage III and stage IV (P=0.557, 0.677).

CONCLUSIONS

MANEC and ANED should be merged into the same category pathologically and clinically, and had the poorest prognosis. Stage and surgery were independent prognostic risk factors for colorectal MANEC/ANED. The prognosis of MANEC/ANED could not benefit from radiation.

Predicting Inpatient Status After Total Hip Arthroplasty in Medicare-Aged Patients

BACKGROUND

The Centers for Medicare and Medicaid Services has solicited comments regarding the removal of total hip arthroplasty (THA) from its inpatient-only list. The goal of this study is to develop and internally validate a risk stratification nomogram to aid in the identification of optimal inpatient candidates in this patient population.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program database was utilized to identify all patients >65 years of age who underwent primary THA between 2006 and 2015. Inpatient stay was the primary outcome measure, as defined by stay >2 days in length. The impact of numerous demographic, comorbid, and perioperative variables was assessed through a multivariable logistic regression analysis to construct a predictive nomogram.

RESULTS

In total, 30,587 inpatient THAs and 17,024 outpatient THAs were analyzed. Heart failure (odds ratio [OR] 2.11, P = .001), simultaneous bilateral THA (OR 2.47, P < .0001), age >80 years (OR 2.91, P < .0001), female gender (OR 1.90, P < .0001), and dependent functional status (OR 1.89, P < .0001) were the most influential determinants of inpatient status. The final prediction algorithm showed good accuracy, excellent calibration, and internal validation (bias-corrected concordance index of 0.69).

CONCLUSION

Our model enabled accurate and simple identification of the best candidates for inpatient admission after THA in Medicare-aged patients. Given the increasing feasibility of outpatient THA coupled with the likelihood of THA being removed from the Centers for Medicare and Medicaid Services inpatient-only list, this model provides a framework to guide discussion and decision-making for stakeholders.

Refined nomogram incorporating standing cough test improves prediction of male transobturator sling success

AIMS

To develop a decision aid in predicting sling success, incorporating the Male Stress Incontinence Grading Scale (MSIGS) into existing treatment algorithms.

METHODS

We reviewed men undergoing first-time transobturator sling for stress urinary incontinence (SUI) from 2007 to 2016 at our institution. Patient demographics, reported pads per day (PPD), and Standing Cough Test (SCT) results graded 0-4, according to MSIGS, were assessed. Treatment failure was defined as subsequent need for >1 PPD or further procedures. Parameters associated with failure were included in multivariable logistic models, compared by area under the receiver-operating characteristic curves. A nomogram was generated from the model with greatest AUC and internally validated.

RESULTS

Overall 203 men (median age 67 years, IQR 63-72) were evaluated with median follow-up of 45 months (IQR 11-75 months). A total of 185 men (91%) were status-post radical prostatectomy and 29 (14%) had pelvic radiation history. Median PPD and SCT grade were both two. Eighty men (39%) failed treatment (use of ≥1 PPD or subsequent anti-incontinence procedures) at a median of 9 months. History of radiation (P = 0.03), increasing MSIGS (P < 0.0001) and increasing preoperative PPD (P < 0.0001) were associated with failure on univariate analysis. In a multivariable model with AUC 0.81, MSIGS, and PPD remained associated (P = 0.002 and <0.0001 respectively, and radiation history P = 0.06), and was superior to models incorporating PPD and radiation alone (AUC 0.77, P = 0.02), PPD alone (AUC 0.76, P = 0.02), and a cutpoint of >2 PPD alone (AUC 0.71, P = 0.0001).

CONCLUSIONS

MSIGS adds prognostic value to PPD in assessing success of transobturator sling for treatment of SUI.

Prognostic value of stromal cell-derived factor 1 expression in patients with gastric cancer after surgical resection

Aberrant chemokine stromal cell-derived factor 1 (SDF-1) expression has been shown to be involved in the development and progression of various malignancies. Our present study aims to investigate the clinical and prognostic value of SDF-1 expression and improve risk stratification in patients with gastric cancer. Peritumoral and intratumoral SDF-1 levels were assessed in 220 retrospectively enrolled gastric cancer patients, and their relations with clinicopathological features and clinical outcomes were evaluated. A predictive nomogram was created to refine risk stratification for overall survival of gastric cancer patients. Compared with peritumor tissues, tumor tissues showed decreased SDF-1 expression levels according to TNM stage progression in gastric cancer specimens. Peritumoral SDF-1 expression correlated positively with tumor invasion depth and lymph node metastasis, whereas intratumoral SDF-1 expression associated negatively with tumor size, tumor differentiation, tumor invasion depth, lymph node metastasis, and clinical TNM stage. Moreover, both low peritumoral SDF-1 expression and high intratumoral SDF-1 expression indicated favorable overall survival, and SDF-1 risk derived from the peritumoral/intratumoral SDF-1 expression signature could stratify prognosis of patients with gastric cancer. After backward elimination, SDF-1 risk was identified as an independent prognostic factor for survival. Finally, a predictive nomogram was generated with identified independent prognosticators to assess patient survival at 3 and 5 years following surgery. Conclusively, SDF-1 risk, an identified independent prognostic factor, could be developed into a nomogram with tumor invasion depth, lymph node involvement, and distant metastasis to refine predictive accuracy for survival in patients with gastric cancer after surgical resection.