Analysis of risk factors and establishment of a risk prediction model for cardiothoracic surgical intensive care unit readmission after heart valve surgery in China: A single-center study

A novel model for predicting a composite outcome of major complications after valve surgery

Background

On-pump valve surgeries are associated with high morbidity and mortality. The present study aimed to reliably predict a composite outcome of postoperative complications using a minimum of easily accessible clinical parameters.

Methods

A total of 7,441 patients who underwent valve surgery were retrospectively analyzed. Data for 6,220 patients at West China Hospital of Sichuan University were used to develop a predictive model, which was validated using data from 1,221 patients at the Second Affiliated Hospital of Zhejiang University School of Medicine. The primary outcome was a composite of major complications: all-cause death in hospital, stroke, myocardial infarction, and severe acute kidney injury. The predictive model was constructed using the least absolute shrinkage and selection operator as well as multivariable logistic regression. The model was assessed in terms of the areas under receiver operating characteristic curves, calibration, and decision curve analysis.

Results

The primary outcome occurred in 129 patients (2.1%) in the development cohort and 71 (5.8%) in the validation cohort. Six variables were retained in the predictive model: New York Heart Association class, diabetes, glucose, blood urea nitrogen, operation time, and red blood cell transfusion during surgery. The C-statistics were 0.735 (95% CI, 0.686-0.784) in the development cohort and 0.761 (95% CI, 0.694-0.828) in the validation cohort. For both cohorts, calibration plots showed good agreement between predicted and actual observations, and ecision curve analysis showed clinical usefulness. In contrast, the well-established SinoSCORE did not accurately predict the primary outcome in either cohort.

Conclusions

This predictive nomogram based on six easily accessible variables may serve as an "early warning" system to identify patients at high risk of major complications after valve surgery.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [NCT04476134].

The risk assessment tool for intensive care unit readmission: A systematic review and meta-analysis

OBJECTIVE

To review and evaluate existing risk assessment tools for intensive care unitreadmission.

METHODS

Nine electronic databases (Medline, CINAHL, Web of Science, Cochrane Library, Embase, Sino Med, CNKI, VIP, and Wan fang) were systematically searched from their inception to September 2022. Two authors independently extracted data from the literature included. Meta-analysis was performed under the bivariate modeling and summary receiver operating characteristic curve method.

RESULTS

A total of 29 studies were included in this review, among which 11 were quantitatively Meta-analyzed. The results showed Stability and Workload Index for Transfer: Sensitivity = 0.55, Specificity = 0.65, Area under curve = 0.63. And Early warning score: Sensitivity = 0.78, Specificity = 0.83, Area under curve = 0.88. The remaining tools included scores, nomograms, machine learning models, and deep learning models. These studies, with varying reports on thresholds, case selection, data preprocessing, and model performance, have a high risk of bias.

CONCLUSION

We cannot identify a tool that can be used directly in intensive care unit readmission risk assessment. Scores based on early warning score are moderately accurate in predicting readmission, but there is heterogeneity and publication bias that requires model adjustment for local factors such as resources, demographics, and case mix. Machine learning models present a promising modeling technique but have a high methodological bias and require further validation.

IMPLICATIONS FOR CLINICAL PRACTICE

Using reliable risk assessment tools is essential for the early identification of unplanned intensive care unit readmission risk in critically ill patients.  A reliable risk assessment tool must be developed, which is the focus of further research.

Predictive Modeling for Readmission to Intensive Care: A Systematic Review

To evaluate the methodologic rigor and predictive performance of models predicting ICU readmission; to understand the characteristics of ideal prediction models; and to elucidate relationships between appropriate triage decisions and patient outcomes.

DATA SOURCES

PubMed, Web of Science, Cochrane, and Embase.

STUDY SELECTION

Primary literature that reported the development or validation of ICU readmission prediction models within from 2010 to 2021.

DATA EXTRACTION

Relevant study information was extracted independently by two authors using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool. Data sources, modeling methodology, definition of outcomes, performance, and risk of bias were critically evaluated to elucidate relevant relationships.

DATA SYNTHESIS

Thirty-three articles describing models were included. Six studies had a high overall risk of bias due to improper inclusion criteria or omission of critical analysis details. Four other studies had an unclear overall risk of bias due to lack of detail describing the analysis. Overall, the most common (50% of studies) source of bias was the filtering of candidate predictors via univariate analysis. The poorest performing models used existing clinical risk or acuity scores such as Acute Physiologic Assessment and Chronic Health Evaluation II, Sequential Organ Failure Assessment, or Stability and Workload Index for Transfer as the sole predictor. The higher-performing ICU readmission prediction models used homogenous patient populations, specifically defined outcomes, and routinely collected predictors that were analyzed over time.

CONCLUSIONS

Models predicting ICU readmission can achieve performance advantages by using longitudinal time series modeling, homogenous patient populations, and predictor variables tailored to those populations.

ICU Readmission in Cardiac Surgical Subset: A Problem Worth Pondering

Over the past decades, there have been noteworthy advancements in the cardiac surgical practice that have assisted fast-tracking and enhanced recovery after cardiac surgery (ERACS). With that said, intensive care unit (ICU) readmission in this high-risk patient cohort entails a significant morbidity–mortality burden. As an extension of the same, there has been a heightened emphasis on a comprehensive evaluation of the predisposition to readmission following a primary ICU discharge. However, the variability of the institutional perioperative practices and the research complexities compound our understanding of this heterogeneous outcome of readmission, which is intricately linked to both patient and organizational factors. Moreover, a discussion on ICU readmission in the recent times can only be rendered comprehensive when staged in close conjunction to the fast-tracking practices in cardiac surgery. From a more positive probing of the matter, a preventative outlook can likely mitigate a part of the larger problem of ICU readmission. Herein, focused cardiac prehabilitation programs can play a potential role given the emerging literature on the positive impact of the former on the most relevant readmission causes. Therefore, the index review article aims to address the subject of cardiac surgical ICU readmission, highlighting the magnitude and burden, the causes and risk-factors, and the research complexities alongside deliberating the topic in the present-day context of ERACS and cardiac prehabilitation.

A combined postoperative nomogram for survival prediction in clear cell renal carcinoma

PURPOSE

To investigate and validate the prognostic value of nomogram models for predicting disease-free survival (DFS) and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

In this retrospective study, 223 patients (age 54.38 ± 10.93 years) with pathologically confirmed ccRCC who underwent resection and lymph node dissection between March 2010 and September 2018 were investigated. All patients were randomly divided into training (n = 155) and validation (n = 68) cohorts. Radiomics features were extracted from computed tomography (CT) images in the unenhanced, corticomedullary, and nephrographic phases. Radiomic score was calculated and combined with clinicopathological factors for model construction and nomogram development. Clinicopathological factors and imaging features were collected at initial diagnosis. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the relationship between the radiomics signature and prognosis outcomes.

RESULTS

There were four prognostic factors for predicting DFS and five factors for predicting OS in our nomogram model (P < 0.05). The radiomics signature correlated independently with DFS (hazard ratio = 27; P < 0.001) and OS (hazard ratio = 25; P < 0.001). The nomogram showed excellent performance (C-index = 0.825) for predicting DFS. The combined nomogram also showed the highest C-index for OS (C-index = 0.943), which was verified in the validation dataset.

CONCLUSION

The combined nomogram model based on radiomics, clinicopathological factors, and preoperative CT features can accurately perform prognosis and survival analysis and can potentially be used for preoperative non-invasive survival prediction in ccRCC patients.

Predicting Readmission to Intensive Care After Cardiac Surgery Within Index Hospitalization: A Systematic Review

Readmission to the cardiac intensive care unit after cardiac surgery has significant implications for both patients and healthcare providers. Identifying patients at risk of readmission potentially could improve outcomes. The objective of this systematic review was to identify risk factors and clinical prediction models for readmission within a single hospitalization to intensive care after cardiac surgery. PubMed, MEDLINE, and EMBASE databases were searched to identify candidate articles. Only studies that used multivariate analyses to identify independent predictors were included. There were 25 studies and five risk prediction models identified. The overall rate of readmission pooled across the included studies was 4.9%. In all 25 studies, in-hospital mortality and duration of hospital stay were higher in patients who experienced readmission. Recurring predictors for readmission were preoperative renal failure, age >70, diabetes, chronic obstructive pulmonary disease, preoperative left ventricular ejection fraction <30%, type and urgency of surgery, prolonged cardiopulmonary bypass time, prolonged postoperative ventilation, postoperative anemia, and neurologic dysfunction. The majority of readmissions occurred due to respiratory and cardiac complications. Four models were identified for predicting readmission, with one external validation study. As all models developed to date had limitations, further work on larger datasets is required to develop clinically useful models to identify patients at risk of readmission to the cardiac intensive care unit after cardiac surgery.

Heart valve operations associated with reduced risk of death from mitral valve disease but other operations associated with increased risk of death: a national population-based case-control study

Background

Mitral valve disease is the most common heart valve disease worldwide. Heart valve operation is the predominant treatment strategy for heart valve disease. This study analyzed the death risk from heart valve disease with respect to the frequency of heart valve operation and other operations in patients with mitral valve disease.

Materials and methods

We conducted a retrospective nationwide population-based case–control study using a claims dataset from Taiwan’s National Health Insurance Research Database. The case and control groups enrolled mitral valve disease patients from 2002 to 2013 who had either underwent an heart valve operation procedure or not, respectively. Conditional logistic regression was estimated the odds ratios (ORs) associated with various risk factors for heart valve operation-related death, including other operations and comorbidities.

Results

A total of 25,964 patients with mitral valve disease were recruited for the study and divided into heart valve operation (600 patients) and non-heart valve operation (25,364 patients) groups. After matching, a total of 1800 non-heart valve operation patients were selected for final analysis. Heart valve operation was associated with decreased risk of death (adjusted OR [aOR] 0.439), but operations related to other cardiovascular disease (CVD, aOR 3.691), respiratory conditions (aOR 3.210), and the urinary system (aOR 1.925) were associated with increased risk of death for patients with mitral valve disease. Patients with mitral valve disease and diabetes (aOR 1.505), chronic kidney disease (CKD, aOR 3.760), or emphysema (aOR 2.623) also had a higher risk of death. Patients who underwent more heart valve operations had a lower risk of death from mitral valve disease, but patients who underwent more other operations had a higher risk of death from mitral valve disease.

Conclusions

The death risk for patients with mitral valve disease patients could be lowered by more frequently performing heart valve operations. However, the risk of death is increased for patients with mitral valve disease who more frequently undergo other operations, chiefly those for other CVD system, respiratory conditions, and urinary system, or have comorbidities such as diabetes, chronic kidney disease, and emphysema.