Latent class analysis stratifies mortality risk in patients developing acute kidney injury after high-risk intraabdominal general surgery: a historical cohort study

Kim, Minjae; Wall, Melanie M.; Kiran, Ravi P.; Li, Guohua

doi:10.1007/s12630-018-1221-0

Cited by 4 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performed almost identically in predicting 30‐day mortality in the development and validation cohorts (AUROC 0.92, 95% CI 0.913–0.917 in the development cohort) and was well‐calibrated. Subsequently this method has been applied to the prediction of major postoperative complications 57,58 …”

Section: Resultsmentioning

confidence: 99%

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non‐cardiac surgery

Reilly

Gabbe

Brown

et al. 2020

ANZ Journal of Surgery

View full text Add to dashboard Cite

Background Risk prediction tools can be used in the perioperative setting to identify high‐risk patients who may benefit from increased surveillance and monitoring in the postoperative period, to aid shared decision‐making, and to benchmark risk‐adjusted hospital performance. We evaluated perioperative risk prediction tools relevant to an Australian context. Methods A systematic review of perioperative mortality risk prediction tools used for adults undergoing inpatient noncardiac surgery, published between 2011 and 2019 (following an earlier systematic review). We searched Medline via OVID using medical subject headings consistent with the three main areas of risk, surgery and mortality/morbidity. A similar search was conducted in Embase. Tools predicting morbidity but not mortality were excluded, as were those predicting a composite outcome that did not report predictive performance for mortality separately. Tools were also excluded if they were specifically designed for use in cardiac or other highly specialized surgery, emergency surgery, paediatrics or elderly patients. Results Literature search identified 2568 studies for screening, of which 19 studies identified 21 risk prediction tools for inclusion. Conclusion Four tools are candidates for adapting in the Australian context, including the Surgical Mortality Probability Model (SMPM), Preoperative Score to Predict Postoperative Mortality (POSPOM), Surgical Outcome Risk Tool (SORT) and NZRISK. SORT has similar predictive performance to POSPOM, using only six variables instead of 17, contains all variables of the SMPM, and the original model developed in the UK has already been successfully adapted in New Zealand as NZRISK. Collecting the SORT and NZRISK variables in a national surgical outcomes study in Australia would present an opportunity to simultaneously investigate three of our four shortlisted models and to develop a locally valid perioperative mortality risk prediction model with high predictive performance.

show abstract

Section: Resultsmentioning

confidence: 99%

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non‐cardiac surgery

Reilly

Gabbe

Brown

et al. 2020

ANZ Journal of Surgery

View full text Add to dashboard Cite

show abstract

“…Notably, LCA was used to explore AKI subphenotypes in critically ill patients with cardiovascular disease. LCA tends to be a popular method applied to clinical datasets to identify subphenotypes of diseases [20]. Both BIC criteria and entropy demonstrated that it was reasonable to divide AKI patients into 4 classes.…”

Section: Discussionmentioning

confidence: 99%

Association of latent class analysis-derived subphenotypes of acute kidney injury with mortality in critically ill patients with cardiovascular disease: a retrospective cohort study

Huang

Xiao

Xie

et al. 2022

BMC Cardiovasc Disord

View full text Add to dashboard Cite

Background To explore the potential heterogeneity of acute kidney injury (AKI) and evaluate the prognostic differences among AKI subphenotypes in critically ill patients with cardiovascular diseases. Methods Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database. Latent class analysis (LCA) was used to explore the potential subphenotypes of AKI in critically ill patients with cardiovascular diseases. The number of classes was identified by the Bayesian information criterion and entropy. The differences in prognostic ability among the AKI subphenotypes were evaluated by logistic regression analysis. Result A total of 7738 AKI patients were enrolled in this study. Using LCA, AKI patients were divided into 4 heterogeneous subphenotypes, which were obviously different from the Kidney Disease: Improving Global Outcomes (KDIGO) stages. Interestingly, class 3 classified by LCA was dominated by stage 2, while the mortality rate in class 3 was significantly different from that in class 1 (15.2% vs. 1.6%, p < 0.05). After further adjustment, the mortality rate in class 3 remained higher than that in class 1, with an odds ratio of 12.31 (95% confidence interval, 8.96–16.89). Conclusions LCA was feasible for AKI classification in critically ill patients with cardiovascular disease, and 4 distinct subphenotypes of AKI patients with different prognoses were identified. Our results highlighted the potential heterogeneity of AKI patients, which is worthy of further investigation.

show abstract

“…16 Using LCA, it is possible to categorize dementia based on the numbers of patient-and disease-specific factors rather than a single individual risk factor in predicting mortality. The LCA approach has been used in several previous studies investigating the homeless veteran population, 17 acute kidney injury, 18 and type 2 diabetes. 19 To the best of our knowledge, however, no study has used LCA to predict mortality risk in patients with dementia.…”

Section: Introductionmentioning

confidence: 99%

Multimorbidity Pattern and Risk for Mortality Among Patients With Dementia: A Nationwide Cohort Study Using Latent Class Analysis

Chu,

Cheng,

Bai

et al. 2023

Psychiatry Investig

View full text Add to dashboard Cite

Objective Individuals with dementia are at a substantially elevated risk for mortality; however, few studies have examined multimorbidity patterns and determined the inter-relationship between these comorbidities in predicting mortality risk.Methods This is a prospective cohort study. Data from 6,556 patients who were diagnosed with dementia between 1997 and 2012 using the Taiwan National Health Insurance Research Database were analyzed. Latent class analysis was performed using 16 common chronic conditions to identify mortality risk among potentially different latent classes. Logistic regression was performed to determine the adjusted association of the determined latent classes with the 5-year mortality rate.Results With adjustment for age, a three-class model was identified, with 42.7% of participants classified as “low comorbidity class (cluster 1)”, 44.2% as “cardiometabolic multimorbidity class (cluster 2)”, and 13.1% as “FRINGED class (cluster 3, characterized by FRacture, Infection, NasoGastric feeding, and bleEDing over upper gastrointestinal tract).” The incidence of 5-year mortality was 17.6% in cluster 1, 26.7% in cluster 2, and 59.6% in cluster 3. Compared with cluster 1, the odds ratio for mortality was 9.828 (95% confidence interval [CI]=6.708–14.401; p<0.001) in cluster 2 and 1.582 (95% CI=1.281–1.953; p<0.001) in cluster 3.Conclusion Among patients with dementia, the risk for 5-year mortality was highest in the subpopulation characterized by fracture, urinary and pulmonary infection, upper gastrointestinal bleeding, and nasogastric intubation, rather than cancer or cardiometabolic comorbidities. These findings may improve decision-making and advance care planning for patients with dementia.

show abstract

Latent class analysis stratifies mortality risk in patients developing acute kidney injury after high-risk intraabdominal general surgery: a historical cohort study

Abstract: Among patients with AKI after high-risk general surgery, the preoperative comorbid state is associated with the time course of and survival after AKI. This knowledge can stratify mortality risk in patients who develop postoperative AKI.

Cited by 4 publications

References 33 publications

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non‐cardiac surgery

Systematic review of perioperative mortality risk prediction models for adults undergoing inpatient non‐cardiac surgery

Association of latent class analysis-derived subphenotypes of acute kidney injury with mortality in critically ill patients with cardiovascular disease: a retrospective cohort study

Multimorbidity Pattern and Risk for Mortality Among Patients With Dementia: A Nationwide Cohort Study Using Latent Class Analysis

Contact Info

Product

Resources

About