Two-way Interaction Effects of Perioperative Complications on 30-Day Mortality in General Surgery

Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation

BACKGROUND

Risk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify covariates as main effects, but they often ignore interactions or use stratification to account for effect modification, despite limitations due to rare events and sparse data. Three Agency for Healthcare Research and Quality (AHRQ) hospital-level Quality Indicators currently use stratified models, but their variable performance and limited interpretability motivated the design of better models.

METHODS

We analysed patient discharge de-identified data from 14 State Inpatient Databases, AHRQ Healthcare Cost and Utilization Project, California Department of Health Care Access and Information, and New York State Department of Health. We used hierarchical group lasso regularisation (HGLR) to identify first-order interactions in several AHRQ inpatient quality indicators (IQI) - IQI 09 (Pancreatic Resection Mortality Rate), IQI 11 (Abdominal Aortic Aneurysm Repair Mortality Rate), and Patient Safety Indicator 14 (Postoperative Wound Dehiscence Rate). These models were compared with stratum-specific and composite main effects models with covariates selected by least absolute shrinkage and selection operator (LASSO).

RESULTS

HGLR identified clinically meaningful interactions for all models. Synergistic IQI 11 interactions, such as between hypertension and respiratory failure, suggest patients who merit special attention in perioperative care. Antagonistic IQI 11 interactions, such as between shock and chronic comorbidities, illustrate that naïve main effects models overestimate risk in key subpopulations. Interactions for PSI 14 suggest key subpopulations for whom the risk of wound dehiscence is similar between open and laparoscopic approaches, whereas laparoscopic approach is safer for other groups. Model performance was similar or superior for composite models with HGLR-selected features, compared to those with LASSO-selected features.

CONCLUSIONS

In this application to high-profile, high-stakes risk-adjustment models, HGLR selected interactions that maintained or improved model performance in populations with heterogeneous risk, while identifying clinically important interactions. The HGLR package is scalable to handle a large number of covariates and their interactions and is customisable to use multiple CPU cores to reduce analysis time. The HGLR method will allow scholars to avoid creating stratified models on sparse data, improve model calibration, and reduce bias. Future work involves testing using other combinations of risk factors, such as vital signs and laboratory values. Our study focuses on a real-world problem of considerable importance to hospitals and policy-makers who must use RA models for statutorily mandated public reporting and payment programmes.

Prediction of complications associated with general surgery using a Bayesian network

BACKGROUND

Numerous attempts have been made to identify risk factors for surgery complications, but few studies have identified accurate methods of predicting complex outcomes involving multiple complications.

METHODS

We performed a prospective cohort study of general surgical inpatients who attended 4 regionally representative hospitals in China from January to June 2015 and January to June 2016. The risk factors were identified using logistic regression. A Bayesian network model, consisting of directed arcs and nodes, was used to analyze the relationships between risk factors and complications. Probability ratios for complications for a given node state relative to the baseline probability were calculated to quantify the potential effects of risk factors on complications or of complications on other complications.

RESULTS

We recruited 19,223 participants and identified 21 nodes, representing 9 risk factors and 12 complications, and 55 direct relationships between these. Respiratory failure was at the center of the network, directly affected by 5 risk factors, and directly affected 7 complications. Cardiopulmonary resuscitation and sepsis or septic shock also directly affected death. The area under the receiver operating characteristic curve for the ability of the network to predict complications was >0.7. Notably, the probability of other severe complications or death significantly increased when a severe complication occurred. Most importantly, there was a 141-fold higher risk of death when cardiopulmonary resuscitation was required.

CONCLUSION

We have created a Bayesian network that displays how risk factors affect complications and their interrelationships and permits the accurate prediction of complications and the creation of appropriate preventive guidelines.

Construction and evaluation of networks among multiple postoperative complications

BACKGROUND AND OBJECTIVE

Postoperative complications confer an increased risk of reoperation, prolonged length of hospital stay, and increased mortality. Many studies have attempted to identify the complex associations among complications to preemptively interrupt their progression, but few studies have looked at complications as a whole to reveal and quantify their possible trajectories of progression. The main objective of this study was to construct and quantify the association network among multiple postoperative complications from a comprehensive perspective to elucidate the possible evolution trajectories.

METHODS

In this study, a Bayesian network model was proposed to analyze the associations among 15 complications. Prior evidence and score-based hill-climbing algorithms were used to build the structure. The severity of complications was graded according to their connection to death, with the association between them quantified using conditional probabilities. The data of surgical inpatients used in this study were collected from four regionally representative academic/teaching hospitals in a prospective cohort study in China.

RESULTS

In the network obtained, 15 nodes represented complications or death, and 35 arcs with arrows represented the directly dependent relationship between them. With three grades classified on that basis, the correlation coefficients of complications within grades increased with increased grade, ranging from -0.11 to -0.06, 0.16, and 0.21 to 0.4 in grade 1 to grade 3, respectively. Moreover, the probability of each complication in the network increased with the occurrence of any other complication, even mild complications. Most seriously, once cardiac arrest requiring cardiopulmonary resuscitation occurs, the probability of death will be up to 88.1%.

CONCLUSIONS

The present evolving network can facilitate the identification of strong associations among specific complications and provides a basis for the development of targeted measures to prevent further deterioration in high-risk patients.

Associations between preoperative risks of postoperative complications: Results of an analysis of 4.8 Million ACS-NSQIP patients

BACKGROUND

Surgical Risk Preoperative Assessment System (SURPAS) estimates patient's preoperative risk of 12 postoperative complications, yet little is known about associations between these probabilities- We sought to examine relationships between predicted probabilities.

METHODS

Risk of 12 postoperative complications was calculated using SURPAS and the 2012-2018 ACS-NSQIP database. Pearson correlation coefficients (r) were computed to examine relationships between predicted outcomes. "High-risk" was predicted risk in the 10th decile.

RESULTS

4,777,267 patients were included. 71.1% were not high risk, 10.7% were high risk for 1, and 18.2% were high risk for ≥2 complications. High mortality risk was associated with high risk for pulmonary (r = 0.94), cardiac (r = 0.98), renal (r = 0.93), and stroke (0.96) complications. Patients high-risk for ≥2 complications had the most comorbidities and actual adverse outcomes.

CONCLUSIONS

High preoperative risk for certain postoperative complications had strong correlations. 18.2% of patients were high-risk for ≥2 complications and could be targeted for risk reduction interventions.

Synergistic Effects of Perioperative Complications on 30-Day Mortality Following Hepatopancreatic Surgery

BACKGROUND

Data on the interaction effect of multiple concurrent postoperative complications relative to the risk of short-term mortality following hepatopancreatic surgery have not been reported. The objective of the current study was to define the interaction effect of postoperative complications among patients undergoing HP surgery on 30-day mortality.

METHODS

Using the ACS-NSQIP Procedure Targeted Participant Use Data File, patients who underwent HP surgery between 2014 and 2016 were identified. Hazard ratios (HRs) for 30-day mortality were estimated using Cox proportional hazard models. Two-way interaction effects assessing combinations of complications relative to 30-day mortality were calculated using the relative excess risk due to interaction (RERI) in separate adjusted Cox models.

RESULTS

Among 26,824 patients, 10,886 (40.5%) experienced at least one complication. Mortality was higher among patients who experienced at least one complication versus patients who did not experience a complication (3.0 vs 0.1%, p < 0.001). The most common complications were blood transfusion (16.9%, n = 4519), organ space infection (12.2%, n = 3273), and sepsis/septic shock (8.2%, n = 2205). Combinations associated with additive effect on mortality included transfusion + renal dysfunction (RERI 12.3, 95% CI 5.2-19.4), pulmonary dysfunction + renal dysfunction (RERI 60.9, 95% CI 38.6-83.3), pulmonary dysfunction + cardiovascular complication (RERI 144.1, 95% CI 89.3-199.0), and sepsis/septic shock + renal dysfunction (RERI 11.5, 95% CI 4.4-18.7).

CONCLUSION

Both the number and specific type of complication impacted the incidence of postoperative mortality among patients undergoing HP surgery. Certain complications interacted in a synergistic manner, leading to a greater than expected increase in the risk of short-term mortality.

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

PURPOSE

Risk stratification for postoperative acute kidney injury (AKI) evaluates a patient's risk for developing this complication using preoperative characteristics. Nevertheless, it is unclear if these characteristics are also associated with mortality in patients who actually develop this complication, so we aimed to determine these associations.

METHODS

The 2011-15 American College of Surgeons National Surgical Quality Improvement Program was used to obtain a historical, observational cohort of high-risk intraabdominal general surgery patients with AKI, which was defined as an increase in serum creatinine > 177 µmol·L^-1 (2 mg·dL^-1) above the preoperative value and/or the need for dialysis. Latent class analysis, a model-based clustering technique, classified patients based on preoperative comorbidities and risk factors. The associations between the latent classes and the time course of AKI development and mortality after AKI were assessed with the Kruskall-Wallis test and Cox models.

RESULTS

A seven-class model was fit on 3,939 observations (derivation cohort). Two patterns for the time course of AKI diagnosis emerged: an "early" group (median [interquartile range] day of diagnosis 3 [1-10]) and a "late" group (day 9 [3-16]). Three patterns of survival after AKI diagnosis were identified (groups A-C). Compared with the group with the lowest mortality risk (group A), the hazard ratios (95% confidence intervals) for 30-day mortality were 1.79 [1.55 to 2.08] for group B and 3.55 [3.06 to 4.13] for group C. These differences in relative hazard were similar after adjusting for the postoperative day of AKI diagnosis and surgical procedure category.

CONCLUSIONS

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.