Development and validation of a prognostic nomogram for myocardial infarction patients in intensive care units: a retrospective cohort study

Development of a nomogram to predict 30-day mortality of sepsis patients with gastrointestinal bleeding: An analysis of the MIMIC-IV database

Background

We aimed to establish and validate a prognostic nomogram model for improving the prediction of 30-day mortality of gastrointestinal bleeding (GIB) in critically ill patients with severe sepsis.

Methods

In this retrospective study, the current retrospective cohort study extracted data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, then partitioned the cohort randomly into training and validation subsets. The cohort was partitioned into training and validation subsets randomly. Our primary endpoint was 30-day all-cause mortality. To reduce data dimensionality and identify predictive variables, the least absolute shrinkage and selection operator (LASSO) regression was employed. A prediction model was constructed by multivariate logistic regression. Model performance was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

The analysis included 1435 total patients, comprising 1005 in the training cohort and 430 in the validation cohort. We found that age, smoking status, glucose, (BUN), lactate, Sequential Organ Failure Assessment (SOFA) score, mechanical ventilation≥48h (MV), parenteral nutrition (PN), and chronic obstructive pulmonary disease (COPD) independently influenced mortality in sepsis patients with concomitant GIB. The C-indices were 0.746 (0.700-0.792) and 0.716 (0.663-0.769) in the training and validation sets, respectively. Based on the area under the curve (AUC) and DCA, the nomogram exhibited good discrimination for 30-day all-cause mortality in sepsis with GIB.

Conclusions

For sepsis patients complicated with GIB, we created a unique nomogram model to predict the 30-day all-cause mortality. This model could be a significant therapeutic tool for clinicians in terms of personalized treatment and prognosis prediction.

Preventing secondary screw perforation following proximal humerus fracture after locking plate fixation: a new clinical prognostic risk stratification model

INTRODUCTION

After locking plate (LP) fixation, secondary screw perforation (SSP) is the most common complication in proximal humerus fracture (PHF). SSP is the main cause of glenoid destruction and always leads to reoperation. This study aimed to identify independent risk parameters for SSP and establish an individualized risk prognostic model to facilitate its clinical management.

METHODS

We retrospectively reviewed the medical information of patients with PHF who underwent open reduction and internal LP fixation at one medical center (n = 289) between June 2013 and June 2021. Uni- and multivariate regression analyses identified the independent risk factors. A novel nomogram was formulated based on the final independent risk factors for predicting the risk of SSP. We performed internal validation through concordance indices (C-index) and calibration curves. To implement the clinical use of the model, we performed decision curve analyses (DCA) and risk stratification according to the optimal cutoff value.

RESULTS

A total of 232 patients who met the inclusion criteria were enrolled. The incidence of SSP was 21.98% at the last follow-up. We found that fracture type (odds ratio [OR], 3.111; 95% confidence interval [CI], 1.223-7.914; P = 0.017), postoperative neck-shaft angle (OR, 4.270; 95% CI 1.622-11.239; P = 0.003), the absence of calcar screws (OR, 3.962; 95% CI 1.753-8.955; P = 0.003), and non-medial metaphyseal support (OR,7.066; 95% CI 2.747-18.174; P = 0.000) were independent predictors of SSP. Based on these variables, we developed a nomogram that showed good discrimination (C-index = 0.815). The predicted values of the new model were in good agreement with the actual values demonstrated by the calibration curve. Furthermore, the model's DCA and risk stratification (cutoff = 140 points) showed significantly higher clinical benefits.

CONCLUSIONS

We developed and validated a visual and personalized nomogram that could predict the individual risk of SSP and provide a decision basis for surgeons to create the most optional management plan. However, future prospective and externally validated design studies are warranted to verify our model's efficacy.

Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury

BACKGROUND

The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis.

METHODS

This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer-Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.

RESULTS

The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853-0.961) and 0.856 (95% CI = 0.746-0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147-0.940) and 0.784 (95% CI = 0.282-1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004-0.123; P = 0.037) and 0.103 (95% CI = 0.002-0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system.

CONCLUSION

We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient's status and implement interventions to improve prognosis accordingly.

Development and validation of a nomogram for arterial stiffness

Even though as a gold standard for noninvasive measurement of arterial stiffness, carotid-femoral pulse wave velocity (cfPWV) is not widely used in primary healthcare institutions due to time-consuming and unavailable equipment. The aim of this study was to develop a convenient and low-cost nomogram model for arterial stiffness screening. A cross-sectional study was undertaken in the department of general practice, the First Affiliated Hospital of Fujian Medical University. Arterial stiffness was defined as cfPWV ≥ 10 m/s. A total of 2717 participants were recruited to construct the nomogram using the least absolute shrinkage and selection operator and logistic regressions. Receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis, clinical impact curve were used to evaluate the performance of the model. The model was validated internally and externally (399 participants) by bootstrap method. Arterial stiffness was identified in 913 participants (33.60%). Age, sex, waist to hip ratio, systolic blood pressure, duration of diabetes, heart rate were selected to construct the nomogram model. Good discrimination and accuracy were exhibited with area under curve of 0.820 (95% CI 0.803-0.837) in ROC curve and mean absolute error = 0.005 in calibration curve. A positive net benefit was shown in decision curve analysis and clinical impact curve. A satisfactory agreement was displayed in internal validation and external validation. The low cost and user-friendly nomogram is suitable for arterial stiffness screening in primary healthcare institutions.

Short-Term Prognostic Value of the Culprit-SYNTAX Score in Patients with Acute Myocardial Infarction

BACKGROUND

The SYNergy between Percutaneous Coronary Intervention with TAXus and Cardiac Surgery (SYNTAX) score is a scoring system that helps to decide on surgery or percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (MI), and studies are showing the prognostic value of this scoring system in both AMI and coronary artery disease patients undergoing PCI. In acute coronary syndrome (ACS) patients, the infarct-related artery and the complexity of the lesions are also important in terms of mortality and morbidity. Our study aimed to determine the prognostic value of the culprit vessel's SYNTAX score (cul-SS) in patients presenting with MI.

METHODS

In our study, 1284 patients presenting with MI were analyzed retrospectively. The SYNTAX scores and cul-SS of the patients were calculated. In-hospital and 30-day deaths and major complications were accepted as primary outcomes. The SYNTAX scores and cul-SS were compared in terms of predicting primary outcomes.

CONCLUSIONS

Major complications were observed in 36 (2.8%) patients, death in 42 (3.3%) patients, and stent thrombosis in 24 (1.9%) patients. The area under the curves for SYNTAX and cul-SS for predicting primary outcomes is 0.64 and 0.68 (p = 0.026), respectively. Cul-SS was as successful as the SYNTAX score in predicting stent thrombosis and was superior in predicting short-term death and major complications.

Development and validation of dynamic models to predict postdischarge mortality risk in patients with acute myocardial infarction: results from China Acute Myocardial Infarction Registry

OBJECTIVES

The risk of adverse events and prognostic factors are changing in different time phases after acute myocardial infarction (AMI). The incidence of adverse events is considerable in the early period after AMI hospitalisation. Therefore, dynamic risk prediction is needed to guide postdischarge management of AMI. This study aimed to develop a dynamic risk prediction instrument for patients following AMI.

DESIGN

A retrospective analysis of a prospective cohort.

SETTING

108 hospitals in China.

PARTICIPANTS

A total of 23 887 patients after AMI in the China Acute Myocardial Infarction Registry were included in this analysis.

PRIMARY OUTCOME MEASURES

All-cause mortality.

RESULTS

In multivariable analyses, age, prior stroke, heart rate, Killip class, left ventricular ejection fraction (LVEF), in-hospital percutaneous coronary intervention (PCI), recurrent myocardial ischaemia, recurrent myocardial infarction, heart failure (HF) during hospitalisation, antiplatelet therapy and statins at discharge were independently associated with 30-day mortality. Variables related to mortality between 30 days and 2 years included age, prior renal dysfunction, history of HF, AMI classification, heart rate, Killip class, haemoglobin, LVEF, in-hospital PCI, HF during hospitalisation, HF worsening within 30 days after discharge, antiplatelet therapy, β blocker and statin use within 30 days after discharge. The inclusion of adverse events and medications significantly improved the predictive performance of models without these indexes (likelihood ratio test p<0.0001). These two sets of predictors were used to establish dynamic prognostic nomograms for predicting mortality in patients with AMI. The C indexes of 30-day and 2-year prognostic nomograms were 0.85 (95% CI 0.83-0.88) and 0.83 (95% CI 0.81-0.84) in derivation cohort, and 0.79 (95% CI 0.71-0.86) and 0.81 (95% CI 0.79-0.84) in validation cohort, with satisfactory calibration.

CONCLUSIONS

We established dynamic risk prediction models incorporating adverse event and medications. The nomograms may be useful instruments to help prospective risk assessment and management of AMI.

TRIAL REGISTRATION NUMBER

NCT01874691.

The Application of a Multidimensional Prediction Model in the Recurrence of Female Pelvic Organ Prolapse after Surgery

Objective

The relationship between multiple indicators of women and postoperative recurrence of pelvic organ prolapse was analyzed to establish a model for predicting postoperative recurrence of female pelvic organ prolapse.

Methods

Three hundred patients with pelvic organ prolapse who underwent pelvic organ prolapse surgery at our hospital were monitored for 1-2 years to determine their prognosis. Whether there was a postoperative recurrence, they were divided into two groups. We collected the relevant data from the two groups of patients before and after surgery. Through single factor and logistic multivariate analysis, we selected the risk factors that may affect the recurrence of patients to construct a prediction model. We verified the identification ability, proofreading ability, and clinical applicability of the model.

Results

Eighty-four patients with pelvic organ prolapse who had postoperative recurrence were assigned to the recurrence group, and 216 patients were included in the nonrecurrence group. Based on the logistic multivariate analysis results, we constructed a nomogram model containing 5 dimensions of age, BMI, degree of prolapse, pubic fissure, and serum calcium to predict postoperative recurrence. The tests revealed that the model had an excellent identification ability (AUC = 0.910), and the expected recurrence rate was significantly in agreement with the actual recurrence rate (U = -0.007, Brief = 0.087). The Hosmer-Lemeshow goodness-of-fit test demonstrated that the model had good calibration (c2 = 29.352, P = 0.522), and the decision curve showed that the threshold probability was in the range of ~12% to 100%, having a high net benefit value.

Conclusion

Based on the present study findings, we concluded that the constructed nomogram model has suitable identification, calibration, and clinical applicability.

Factors contributing to 1-year dissatisfaction after total knee arthroplasty: a nomogram prediction model

Background

Identifying risk factors and early intervention are critical for improving the satisfaction rate of total knee arthroplasty (TKA). Our study aimed to identify patient-specific variables and establish a nomogram model to predict dissatisfaction at 1 year after TKA.

Methods

This prospective cohort study involved 208 consecutive primary TKA patients with end-stage arthritis who completed self-reported measures preoperatively and at 1 year postoperatively. All participants were randomized into a training cohort (n = 154) and validation cohort (n = 54). Multiple regression models with preoperative and postoperative factors were used to establish the nomogram model for dissatisfaction at 1 year postoperatively. The least absolute shrinkage and selection operator method was used to screen the suitable and effective risk factors (demographic variables, preoperative variables, surgical variable, and postoperative variables) collected. These variables were compared between the satisfied and dissatisfied groups in the training cohort. The receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis were used to validate the discrimination, calibration, and clinical usefulness of the model. Results were evaluated by internal validation of the validation cohort.

Results

The overall satisfaction rate 1 year after TKA was 77.8%. The nomogram prediction model included the following risk factors: gender; primary diagnosis; postoperative residual pain; poor postoperative range of motion; wound healing; and the rate of change in the degree of coronal lower limb alignment (hip–knee–ankle angle, HKA).The ROC curves of the training and validation cohorts were 0.9206 (95% confidence interval [CI], 0.8785–0.9627) and 0.9662 (0.9231, 1.0000) (95% CI, 0.9231, 1.0000), respectively. The Hosmer–Lemeshow test showed good calibration of the nomogram (training cohort, p = 0.218; validation cohort, p = 0.103).

Conclusion

This study developed a prediction nomogram model based on partially modifiable risk factors for predicting dissatisfaction 1 year after TKA. This model demonstrated good discriminative capacity for identifying those at greatest risk for dissatisfaction and may help surgeons and patients identify and evaluate the risk factors for dissatisfaction and optimize TKA outcomes.

Development, validation, and visualization of a web-based nomogram to predict 5-year mortality risk in older adults with hypertension

Background

Hypertension-related mortality has been increasing in older adults, resulting in serious burden to society and individual. However, how to identify older adults with hypertension at high-risk mortality remains a great challenge. The purpose of this study is to develop and validate the prediction nomogram for 5-year all-cause mortality in older adults with hypertension.

Methods

Data were extracted from National Health and Nutrition Examination Survey (NHANES). We recruited 2691 participants aged 65 years and over with hypertension in the NHANES 1999-2006 cycles (training cohort) and 1737 participants in the NHANES 2007-2010 cycles (validation cohort). The cohorts were selected to provide at least 5 years follow-up for evaluating all-cause mortality by linking National Death Index through December 31, 2015. We developed a web-based dynamic nomogram for predicting 5-year risk of all-cause mortality based on a logistic regression model in training cohort. We conducted internal validation by 1000 bootstrapping resamples and external validation in validation cohort. The discrimination and calibration of nomogram were evaluated using concordance index (C-index) and calibration curves.

Results

The final model included eleven independent predictors: age, sex, diabetes, cardiovascular disease, body mass index, smoking, lipid-lowering drugs, systolic blood pressure, hemoglobin, albumin, and blood urea nitrogen. The C-index of model in training and validation cohort were 0.759 (bootstrap-corrected C-index 0.750) and 0.740, respectively. The calibration curves also indicated that the model had satisfactory consistence in two cohorts. A web-based nomogram was established (https://hrzhang1993.shinyapps.io/dynnomapp).

Conclusions

The novel developed nomogram is a useful tool to accurately predict 5-year all-cause mortality in older adults with hypertension, and can provide valuable information to make individualized intervention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03087-3.

Development and validation of a prediction model for in-hospital mortality of patients with severe thrombocytopenia

Risk stratification and prognosis evaluation of severe thrombocytopenia are essential for clinical treatment and management. Currently, there is currently no reliable predictive model to identify patients at high risk of severe thrombocytopenia. This study aimed to develop and validate a prognostic nomogram model to predict in-hospital mortality in patients with severe thrombocytopenia in the intensive care unit. Patients diagnosed with severe thrombocytopenia (N = 1561) in the Medical Information Mart for Intensive Care IV database were randomly divided into training (70%) and validation (30%) cohorts. In the training cohort, univariate and multivariate logistic regression analyses with positive stepwise selection were performed to screen the candidate variables, and variables with p < 0.05 were included in the nomogram model. The nomogram model was compared with traditional severity assessment tools and included the following 13 variables: age, cerebrovascular disease, malignant cancer, oxygen saturation, heart rate, mean arterial pressure, respiration rate, mechanical ventilation, vasopressor, continuous renal replacement therapy, prothrombin time, partial thromboplastin time, and blood urea nitrogen. The nomogram was well-calibrated. According to the area under the receiver operating characteristics, reclassification improvement, and integrated discrimination improvement, the nomogram model performed better than the traditional sequential organ failure assessment (SOFA) score and simplified acute physiology score II (SAPS II). Additionally, according to decision curve analysis, a threshold probability between 0.1 and 0.75 indicated that our constructed nomogram model showed more net benefits than the SOFA score and SAPS II. The nomogram model we established showed superior predictive performance and can assist in the quantitative assessment of the prognostic risk in patients with severe thrombocytopenia.

Sequential Data-Based Patient Similarity Framework for Patient Outcome Prediction: Algorithm Development

BACKGROUND

Sequential information in electronic medical records is valuable and helpful for patient outcome prediction but is rarely used for patient similarity measurement because of its unevenness, irregularity, and heterogeneity.

OBJECTIVE

We aimed to develop a patient similarity framework for patient outcome prediction that makes use of sequential and cross-sectional information in electronic medical record systems.

METHODS

Sequence similarity was calculated from timestamped event sequences using edit distance, and trend similarity was calculated from time series using dynamic time warping and Haar decomposition. We also extracted cross-sectional information, namely, demographic, laboratory test, and radiological report data, for additional similarity calculations. We validated the effectiveness of the framework by constructing k-nearest neighbors classifiers to predict mortality and readmission for acute myocardial infarction patients, using data from (1) a public data set and (2) a private data set, at 3 time points-at admission, on Day 7, and at discharge-to provide early warning patient outcomes. We also constructed state-of-the-art Euclidean-distance k-nearest neighbor, logistic regression, random forest, long short-term memory network, and recurrent neural network models, which were used for comparison.

RESULTS

With all available information during a hospitalization episode, predictive models using the similarity model outperformed baseline models based on both public and private data sets. For mortality predictions, all models except for the logistic regression model showed improved performances over time. There were no such increasing trends in predictive performances for readmission predictions. The random forest and logistic regression models performed best for mortality and readmission predictions, respectively, when using information from the first week after admission.

CONCLUSIONS

For patient outcome predictions, the patient similarity framework facilitated sequential similarity calculations for uneven electronic medical record data and helped improve predictive performance.

Nomogram for Predicting Overall Survival in Acral Lentiginous Melanoma: A Population-based Study

Background

The objective of this study was to establish a nomogram for predicting the overall survival (OS) of patients with acral lentiginous melanoma (ALM).

Materials and Methods

The study sample was selected from 1785 patients diagnosed with ALM from 2004 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database, and R software was used to divide patients into the training cohort and validation cohort at a ratio of 7: 3. Stepwise selection method in the Cox regression model was used in the training cohort to select predictive variables to construct the nomogram, and model validation parameters were used in the validation cohort to evaluate the performance of the nomogram.

Results

The nomogram showed that age at diagnosis had the greatest impact on OS in patients with ALM, followed by AJCC stage, surgical treatment, SEER stage, sex, race, and marital status. The index of concordance, area under the receiver operating characteristic curve, calibration plots, net reclassification improvement, integrated discrimination improvement, and decision curve analysis demonstrate the good performance of this nomogram.

Conclusion

The prognostic value of the nomogram is superior to that of the AJCC staging system alone, and it helps clinicians to better predict 3-, 5-, and 8-year OS in patients with ALM.

A novel nomogram for predicting 3-year mortality in critically ill patients after coronary artery bypass grafting

Background

The long-term outcomes for patients after coronary artery bypass grafting (CABG) have been received more and more concern. The existing prediction models are mostly focused on in-hospital operative mortality after CABG, but there is still little research on long-term mortality prediction model for patients after CABG.

Objective

To develop and validate a novel nomogram for predicting 3-year mortality in critically ill patients after CABG.

Methods

Data for developing novel predictive model were extracted from Medical Information Mart for Intensive cart III (MIMIC-III), of which 2929 critically ill patients who underwent CABG at the first admission were enrolled.

Results

A novel prognostic nomogram for 3-year mortality was constructed with the seven independent prognostic factors, including age, congestive heart failure, white blood cell, creatinine, SpO₂, anion gap, and continuous renal replacement treatment derived from the multivariable logistic regression. The nomogram indicated accurate discrimination in primary (AUC: 0.81) and validation cohort (AUC: 0.802), which were better than traditional severity scores. And good consistency between the predictive and observed outcome was showed by the calibration curve for 3-year mortality. The decision curve analysis also showed higher clinical net benefit than traditional severity scores.

Conclusion

The novel nomogram had well performance to predict 3-year mortality in critically ill patients after CABG. The prediction model provided valuable information for treatment strategy and postdischarge management, which may be helpful in improving the long-term prognosis in critically ill patients after CABG.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12893-021-01408-8.

Clinical features and risk factors analysis of bronchitis obliterans due to refractory Mycoplasma pneumoniae pneumonia in children: a nomogram prediction model

Background

Early prediction of bronchitis obliterans (BO) is of great significance to the improvement of the long-term prognosis of children caused by refractory Mycoplasma pneumoniae pneumonia (RMPP). This study aimed to establish a nomogram model to predict the risk of BO in children due to RMPP.

Methods

A retrospective observation was conducted to study the clinical data of children with RMPP (1–14 years old) during acute infection. According to whether there is BO observed in the bronchoscope, children were divided into BO and the non-BO groups. The multivariate logistic regression model was used to construct the nomogram model.

Results

One hundred and forty-one children with RMPP were finally included, of which 65 (46.0%) children with RMPP were complicated by BO. According to the multivariate logistic regression analysis, WBC count, ALB level, consolidation range exceeding 2/3 of lung lobes, timing of macrolides, glucocorticoids or fiber bronchoscopy and plastic bronchitis were independent influencing factors for the occurrence of BO and were incorporated into the nomogram. The area under the receiver operating characteristic curve (AUC-ROC) value of nomogram was 0.899 (95% confidence interval [CI] 0.848–0.950). The Hosmer–Lemeshow test showed good calibration of the nomogram (p = 0.692).

Conclusion

A nomogram model found by seven risk factor was successfully constructed and can use to early prediction of children with BO due to RMPP.

Identifying the risk features for occupational stress in medical workers: a cross-sectional study

Objective

Occupational stress is considered a worldwide epidemic experienced by a large proportion of the working population. The identification of characteristics that place people at high risk for occupational stress is the basis of managing and intervening in this condition. In this study, we aimed to identify and validate the risk features for occupational stress among medical workers using a risk model and nomogram.

Methods

This cross-sectional study included 1988 eligible participants from Henan Province in China. Occupational stress and worker-occupation fit were measured with the Depression, Anxiety and Stress Scales (DASS-21) and Worker-Occupation Fit Inventory (WOFI). The identification of risk features was achieved through constructing multiple logistic regression model, and the risk features were used to develop the risk model and nomogram. Receiver operating characteristic (ROC) curves and calibration plots were generated to assess the effectiveness and calibration of the risk model.

Results

Among 1988 participants in our study, there were 42.5% (845/1988) medical workers experienced occupational stress. The risk features for occupational stress included poor work-occupation fit (WOF score < 25, expected risk: 77.3%), nurse population (expected risk: 63.1%), male sex (expected risk: 67.2%), work experience duration of 11–19 years (expected risk: 54.5%), experience of a traumatic event (expected risk: 65.3%) and the lack of a regular exercise habit (expected risk: 60.2%). For medical workers who have these risk features, the expected risk probability of occupational stress would be 90.2%.

Conclusion

The current data can be used to identify medical workers at risk of developing occupational stress. Identifying risk features for occupational stress and the work-occupation fit can support hierarchical stress management in hospitals.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00420-021-01762-3.

Development and Validation of Nomogram to Predict Long-Term Prognosis of Critically Ill Patients with Acute Myocardial Infarction

Purpose

Acute myocardial infarction (AMI) is a common cardiovascular disease with a poor prognosis. The aim of this study was to construct a nomogram for predicting the long-term survival of critically ill patients with AMI. This nomogram will help in assessing disease severity, guiding treatment, and improving prognosis.

Patients and Methods

The clinical data of patients with AMI were extracted from the MIMIC-III v1.4 database. Cox proportional hazards models were adopted to identify independent prognostic factors. A nomogram for predicting the long-term survival of these patients was developed on the basis of the results of multifactor analysis. The discriminative ability and accuracy of the multifactor analysis were evaluated according to concordance index (C-index) and calibration curves.

Results

A total of 1202 patients were included in the analysis. The patients were randomly divided into a training set (n = 841) and a validation set (n = 361). Multivariate analysis revealed that age, blood urea nitrogen, respiratory rate, hemoglobin, pneumonia, cardiogenic shock, dialysis, and mechanical ventilation, all of which were incorporated into the nomogram, were independent predictive factors of AMI. Moreover, the nomogram exhibited favorable performance in predicting the 4-year survival of patients with AMI. The training set and the validation set had a C-index of 0.789 (95% confidence interval [CI]: 0.765–0.813) and 0.762 (95% CI: 0.725–0.799), respectively.

Conclusion

The nomogram constructed herein can accurately predict the long-term survival of critically ill patients with AMI.