A Nomogram Based on Clinical Factors to Predict the Serum Myoglobin Levels Following Bariatric Surgery

Nomogram to predict 6-month mortality in acute ischemic stroke patients treated with endovascular treatment

Background

Acute Ischemic Stroke (AIS) presents significant challenges in evaluating the effectiveness of Endovascular Treatment (EVT). This study develops a novel prognostic model to predict 6-month mortality post-EVT, aiding in identifying patients likely to benefit less from this intervention, thus enhancing therapeutic decision-making.

Methods

We employed a cohort of AIS patients from Shenyang First People's Hospital, serving as the Validation set, to develop our model. LASSO regression was used for feature selection, followed by logistic regression to create a prognostic nomogram for predicting 6-month mortality post-EVT. The model's performance was validated using a dataset from PLA Northern Theater Command General Hospital, assessing discriminative ability (C-index), calibration (calibration plot), and clinical utility (decision curve analysis). Statistical significance was set at p < 0.05.

Results

The development cohort consisted of 219 patients. Six key predictors of 6-month mortality were identified: "Lack of Exercise" (OR, 4.792; 95% CI, 1.731-13.269), "Initial TICI Score 1" (OR, 1.334; 95% CI, 0.628-2.836), "MRS Score 5" (OR, 1.688; 95% CI, 0.754-3.78), "Neutrophil Percentage" (OR, 1.08; 95% CI, 1.042-1.121), "Onset Blood Sugar" (OR, 1.119; 95% CI, 1.007-1.245), and "Onset NIHSS Score" (OR, 1.074; 95% CI, 1.029-1.121). The nomogram demonstrated a high predictive capability with a C-index of 0.872 (95% CI, 0.830-0.911) in the development set and 0.830 (95% CI, 0.726-0.920) in the validation set.

Conclusion

Our nomogram, incorporating factors such as Lack of Exercise, Initial TICI Score 1, MRS Score 5, Neutrophil Percentage, Onset Blood Sugar, and Onset NIHSS Score, provides a valuable tool for predicting 6-month mortality in AIS patients post-EVT. It offers potential to refine early clinical decision-making and optimize patient outcomes, reflecting a shift toward more individualized patient care.

Two web-based dynamically interactive nomograms and risk stratification systems for predicting survival outcomes and guiding treatment in non-metastatic nasopharyngeal carcinoma

BACKGROUND

A nomogram is a valuable and easily accessible tool for individualizing cancer prognosis. This study aims to establish and validate two prognostic nomograms for long-term overall survival (OS) and cancer-specific survival (CSS) in non-metastatic nasopharyngeal carcinoma (NPC) patients and to investigate the treatment options for the nomogram-based risk stratification subgroups.

METHODS

A total of 3959 patients with non-metastatic NPC between 2004 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated to the training and validation cohorts in a 7:3 ratio. Prognostic nomograms were constructed to estimate OS and CSS by integrating significant variables from multivariate Cox regression employing a backward stepwise method. We examined the correlation indices (C-index) and areas under the curves (AUC) of time-dependent receiver operating characteristic curves to assess the discriminative ability of our survival models. The comprehensive enhancements of predictive performance were evaluated with net reclassification operating improvement (NRI) and integrated discrimination improvement (IDI). Reliability was validated using calibration plots. Decision curve analysis (DCA) was used to estimate clinical efficacy and capability. Finally, the nomogram-based risk stratification system used Kaplan-Meier survival analysis and log-rank tests to examine differences between subgroups.

RESULTS

The following independent parameters were significant predictors for OS: sex, age, race, marital status, histological type, median household income, AJCC stage tumor size, and lymph node size. Except for the race variables mentioned above, the rest were independent prognostic factors for CSS. The C-index, AUC, NRI, and IDI indicated satisfactory discriminating properties. The calibration curves exhibited high concordance with the exact outcomes. Moreover, the DCA demonstrated performed well for net benefits. The prognosis significantly differed between low- and high-risk patients (p < 0.001). In a treatment-based stratified survival analysis in risk-stratified subgroups, chemotherapy benefited patients in the high-risk group compared to radiotherapy alone. Radiotherapy only was recommended in the low-risk group.

CONCLUSIONS

Our nomograms have satisfactory performance and have been validated. It can assist clinicians in prognosis assessment and individualized treatment of non-metastatic NPC patients.

Developing and Validating a New Model to Predict the Risk of Poor Neurological Status of Acute Ischemic Stroke After Intravenous Thrombolysis

OBJECTIVES

The objective of this study was to develop and validate a predictive model for the risk of poor neurological status in in-hospital patients with acute ischemic stroke (AIS) after intravenous thrombolysis.

METHODS

This 2-center retrospective study included patients with AIS treated at the Advanced Stroke Center of the Second Hospital of Hebei Medical University and Baoding No.1 Central Hospital between January 2018 and January 2020). The neurological function status at day 7 of AIS onset was used as the endpoint of the study, which was evaluated using the National Institute of Health Stroke Scale (NIHSS) score.

RESULTS

A total of 878 patients were included in the study and divided into training (n=652) and validation (n=226) sets. Seven variables were selected as predictors to establish the risk model: age, NIHSS before thrombolysis (NIHSS1), NIHSS 24 hours after thrombolysis (NIHSS3), high-density lipoprotein, antiplatelet, cerebral computed tomography after thrombolysis (CT2), and lower extremity venous color Doppler ultrasound. The risk prediction model achieved good discrimination (the areas under the Receiver Operating Characteristic curve in the training and validation sets were 0.9626 and 0.9413, respectively) and calibration (in the training set Emax=0.072, Eavg=0.01, P =0.528, and in the validation set Emax=0.123, Eavg=0.019, P =0.594, respectively). The decision curve analysis showed that the model could achieve a good net benefit.

CONCLUSIONS

The prediction model obtained in this study showed good discrimination, calibration, and clinical efficacy. This new nomogram can provide a reference for predicting the risk of poor neurological status in patients with acute ischemic stroke after intravenous thrombolysis.

Prevalence of Rhabdomyolysis Following Bariatric Surgery and its Associated Risk Factors: a Meta-Analysis

PURPOSE

This study aimed to evaluate the prevalence of rhabdomyolysis (RML) following bariatric surgery and potential associated factors.

MATERIALS AND METHODS

We systematically searched PubMed, Embase, and CENTRAL for relevant trials from database inception through August 2022. Articles were eligible for inclusion if they reported the prevalence of RML after bariatric surgery and provided at least one of the following outcome indicators: preoperative mean BMI/mean operative time for the included population.

RESULTS

Sixteen studies with a total of 1540 patients were analyzed. The mean preoperative age distribution of the included patients was centered between 32.9 and 47.0 years, and the mean preoperative BMI ranged from 42.3 to 60.0 kg/m². The operative time varied between 126.7 and 403.3 min. The overall pooled crude prevalence of post-bariatric surgery RML was 19.4%. Subgroup analyses showed the pooled prevalence of RML was 8.1% for operative duration > 120 and ≤ 180 min, 32.8% for > 180 and ≤ 240 min, and 47.4% for > 240 min. Meta-regression revealed that operation time was an independent risk factor for developing RML. Besides, BMI > 50 kg/m² and open Roux-en-Y gastric bypass (RYGB) indicated a higher risk of RML.

CONCLUSION

Post-bariatric surgery RML prevalence occurred more frequently with the extension of the operation time. For bariatric subjects with surgery time > 180 min, open RYGB, or BMI > 50 kg/m², CKP could be routinely measured early to verify the presence of RML and to actively prevent its fatal complications.

A novel nomogram to predict mortality in patients with stroke: a survival analysis based on the MIMIC-III clinical database

Background

Stroke is a disease characterized by sudden cerebral ischemia and is the second leading cause of death worldwide. We aimed to develop and validate a nomogram model to predict mortality in intensive care unit patients with stroke.

Methods

All data involved in this study were extracted from the Medical Information Mart for Intensive Care III database (MIMIC-III). The data were analyzed using multivariate Cox regression, and the performance of the novel nomogram, which assessed the patient’s overall survival at 30, 180, and 360 days after stroke, was evaluated using Harrell’s concordance index (C-index) and the area under the receiver operating characteristic curve. A calibration curve and decision curve were introduced to test the clinical value and effectiveness of our prediction model.

Results

A total of 767 patients with stroke were randomly divided into derivation (n = 536) and validation (n = 231) cohorts at a 7:3 ratio. Multivariate Cox regression showed that 12 independent predictors, including age, weight, ventilation, cardiac arrhythmia, metastatic cancer, explicit sepsis, Oxford Acute Severity of Illness Score or OASIS score, diastolic blood pressure, bicarbonate, chloride, red blood cell and white blood cell counts, played a significant role in the survival of individuals with stroke. The nomogram model was validated based on the C-indices, calibration plots, and decision curve analysis results.

Conclusions

The plotted nomogram accurately predicted stroke outcomes and, thus may contribute to clinical decision-making and treatment as well as consultation services for patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01836-3.

A NAC nomogram to predict the probability of three-month unfavorable outcome in Chinese acute ischemic stroke patients treated with mechanical thrombectomy

BACKGROUND AND PURPOSE

Mechanical thrombectomy (MT) is a standard care for most acute ischemic stroke (AIS) patients. For AIS patients underwent MT, predicting the patients at high risk of unfavorable outcome and adjusting therapeutic strategies accordingly can greatly improve patient outcomes. We aimed to develop and validate a nomogram for individualized prediction of Chinese AIS patients underwent MT.

METHODS

We conducted a multicenter prospective study including 238 AIS patients who underwent MT from January 2014 to December 2018. The main outcome measure was three-month unfavorable outcome (modified Rankin Scale 3-6). A nomogram was generated based on multivariate logistic model. We assessed the discriminative performance by using the area under the receiver-operating characteristic curve and calibration of risk prediction model by using the Hosmer-Lemeshow test.

RESULTS

In NAC nomogram, NIHSS (National Institutes of Health Stroke Scale) score on admission (OR: 1.193, p < 0.0001), Age (OR: 1.025, p = 0.037) and Creatinine (OR: 1.028, p < 0.0001) remained independent predictors of 3-month unfavorable outcome in Chinese AIS patients treated with MT. The NAC nomogram exhibited an area under the curve of 0.816 for predicting functional impairment. Calibration was good (p = 0.560 for the Hosmer-Lemeshow test).

CONCLUSIONS

The NAC nomogram is the first nomogram developed and validated in Chinese AIS patients treated with MT and it may be used to predict 3 months unfavorable outcome for these patients.

A NADE nomogram to predict the probability of 6-month unfavorable outcome in Chinese patients with ischemic stroke

BACKGROUND

Early prediction of unfavorable outcome after ischemic stroke is of great significance to the clinical and therapeutic management. A nomogram is a better visual tool than earlier models and prognostic scores to predict clinical outcomes, which incorporates different factors to develop a graphic continuous scoring system and calculates accurately the risk probability of poor outcome entirely based on individual characteristics. However, to date, no nomogram models have been found to predict the probability of 6-month poor outcome after ischemic stroke. We aimed to develop and validate a nomogram for individualized prediction of the probability of 6-month unfavorable outcome in Chinese patients with ischemic stroke.

METHODS

Based on the retrospective stroke registry, a single-center study which included 499 patients from May, 2013 to May, 2018 was conducted in Nanjing First Hospital (China) for ischemic stroke within 12 h of symptoms onset. The main outcome measure was 6-month unfavorable outcome (mRS > 2). To generate the nomogram, NIHSS score on admission, Age, previous Diabetes mellitus and crEatinine (NADE) were integrated into the model. We assessed the discriminative performance by using the area under the curve (AUC) of receiver-operating characteristic (ROC) and calibration of risk prediction model by using the Hosmer-Lemeshow test.

RESULTS

A visual NADE nomogram was constructed that NIHSS score on admission (OR: 1.190, 95%CI: 1.125-1.258), age (OR: 1.068, 95%CI: 1.045-1.090), previous diabetes mellitus (OR: 1.995, 95%CI: 1.236-3.221) and creatinine (OR: 1.010, 95%CI: 1.002-1.018) were found to be significant predictors of 6-month unfavorable outcome after acute ischemic stroke in Chinese patients. The AUC-ROC of nomogram was 0.791. Calibration was good (p = 0.4982 for the Hosmer-Lemeshow test).

CONCLUSION

The NADE is the first nomogram developed and validated in Chinese ischemic stroke patients to provide an individual, visual and precise prediction of the risk probability of 6-month unfavorable outcome.