Acute kidney injury is independently associated with mortality and resource use after emergency general surgery operations

National clinical and financial outcomes associated with acute kidney injury following esophagectomy for cancer

BACKGROUND

Esophagectomy is a complex oncologic operation associated with high rates of postoperative complications. While respiratory and septic complications have been well-defined, the implications of acute kidney injury (AKI) remain unclear. Using a nationally representative database, we aimed to characterize the association of AKI with mortality, resource use, and 30-day readmission.

METHODS

All adults undergoing elective esophagectomy with a diagnosis of esophageal or gastric cancer were identified in the 2010-2019 Nationwide Readmissions Database. Study cohorts were stratified based on presence of AKI. Multivariable regressions and Royston-Parmar survival analysis were used to evaluate the independent association between AKI and outcomes of interest.

RESULTS

Of an estimated 40,438 patients, 3,210 (7.9%) developed AKI. Over the 10-year study period, the incidence of AKI increased from 6.4% to 9.7%. Prior radiation/chemotherapy and minimally invasive operations were associated with reduced odds of AKI, whereas public insurance coverage and concurrent infectious and respiratory complications had greater risk of AKI. After risk adjustment, AKI remained independently associated with greater odds of in-hospital mortality (AOR: 4.59, 95% CI: 3.62-5.83) and had significantly increased attributable costs ($112,000 vs $54,000) and length of stay (25.7 vs 13.3 days) compared to patients without AKI. Furthermore, AKI demonstrated significantly increased hazard of 30-day readmission (hazard ratio: 1.16, 95% CI: 1.01-1.32).

CONCLUSIONS

AKI after esophagectomy is associated with greater risk of mortality, hospitalization costs, and 30-day readmission. Given the significant adverse consequences of AKI, careful perioperative management to mitigate this complication may improve quality of esophageal surgical care at the national level.

Machine learning-based modeling of acute respiratory failure following emergency general surgery operations

Background

Emergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored.

Objective

The present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort.

Methods

Non-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016–18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R²). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models.

Results

Of 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899–0.901 vs 0.894, 95% CI 0.862–0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R²: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8–11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0–3.2) and $11,900 (95% CI 11,600–12,300) in costs.

Conclusions

Logistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.

Outcomes of Patients with Necrotizing Soft Tissue Infections: A Propensity-Matched Analysis Using the National Inpatient Sample

Background: Necrotizing soft tissue infections (NSTIs) are severe, rapidly spreading infections with high morbidity and mortality. Attempts to identify risk factors for mortality and morbidity have produced variable results. We hope to determine which factors across the NSTI population impact mortality, morbidities, and discharge disposition. Patients and Methods: Retrospective data from the National Inpatient Sample from 2012-2018 of patients with primary diagnosis of NSTI (gas gangrene, necrotizing faciitis, cutaneous gangrene, or Fournier gangrene) were identified for analysis. A 1:4 greedy match was performed and risk factors for in-hospital mortality and discharge disposition were examined. Continuous variables were assessed using t-tests and Wilcoxon rank sum tests. Categorical variables were assessed using χ² and Fisher exact tests. Statistical significance was defined as p < 0.05. Results: A total of 6,608 patients were identified. Weighted, this represents 33,040 patients; 32,390 are in the no-mortality cohort and 650 in the mortality cohort. Advanced age group was a risk factor for both in-hospital mortality and morbidity, but not for discharge to a skilled nursing or rehabilitation facility. Having two or more comorbidities was a risk factor for mortality, morbidity, and discharge to skilled nursing or rehabilitation facility. Cancer, liver disease, and kidney disease were predictors of in-hospital mortality. Diabetes mellitus and kidney disease were predictors of experiencing an in-hospital complication. Diabetes mellitus, heart disease, and kidney disease were predictors for discharge to skilled nursing or rehabilitation facility. Conclusions: Necrotizing soft tissue infections are associated with substantial morbidity and mortality. Identifying patients at higher risk for mortality, morbidity, and higher level of care at discharge can help providers properly allocate resources to improve patient outcomes and reduce the financial burden on patients and healthcare facilities. Special attention should be paid to those with existing or acute kidney dysfunction because this was the only comorbidity associated with increased risk mortality, morbidity, and discharge to higher level of care.

The incidence, risk factors and outcomes of acute kidney injury in critically ill patients undergoing emergency surgery: a prospective observational study

BACKGROUND

Without sufficient evidence in postoperative acute kidney injury (AKI) in critically ill patients undergoing emergency surgery, it is meaningful to explore the incidence, risk factors, and prognosis of postoperative AKI.

METHODS

A prospective observational study was conducted in the general intensive care units (ICUs) from January 2014 to March 2018. Variables about preoperation, intraoperation and postoperation were collected. AKI was diagnosed using the Kidney Disease: Improving Global Outcomes criteria.

RESULTS

Among 383 critically ill patients undergoing emergency surgery, 151 (39.4%) patients developed postoperative AKI. Postoperative reoperation, postoperative Acute Physiology and Chronic Health Evaluation (APACHE II) score, and postoperative serum lactic acid (LAC) were independent risk factors for postoperative AKI, with the adjusted odds ratio (ORadj) of 1.854 (95% confidence interval [CI], 1.091-3.152), 1.059 (95%CI, 1.018-1.102), and 1.239 (95%CI, 1.047-1.467), respectively. Compared with the non-AKI group, duration of mechanical ventilation, renal replacement therapy, ICU and hospital mortality, ICU and hospital length of stay, total ICU and hospital costs were higher in the AKI group.

CONCLUSIONS

Postoperative reoperation, postoperative APACHE II score, and postoperative LAC were independent risk factors of postoperative AKI in critically ill patients undergoing emergency surgery.

Simplified risk-prediction for benchmarking and quality improvement in emergency general surgery. Prospective, multicenter, observational cohort study

BACKGROUND AND AIMS

Emergency General Surgery (EGS) conditions account for millions of deaths worldwide, yet it is practiced without benchmarking-based quality improvement programs. The aim of this observational, prospective, multicenter, nationwide study was to determine the best benchmark cutoff points in EGS, as a reference to guide improvement measures.

METHODS

Over a 6-month period, 38 centers (5% of all public hospitals) attending EGS patients on a 24-h, 7-days a week basis, enrolled consecutive patients requiring an emergent/urgent surgical procedure. Patients were stratified into cohorts of low (i.e., expected morbidity risk <33%), middle and high risk using the novel m-LUCENTUM calculator.

RESULTS

A total of 7258 patients were included; age (mean ± SD) was 51.1 ± 21.5 years, 43.2% were female. Benchmark cutoffs in the low-risk cohort (5639 patients, 77.7% of total) were: use of laparoscopy ≥40.9%, length of hospital stays ≤3 days, any complication within 30 days ≤ 17.7%, and 30-day mortality ≤1.1%. The variables with the greatest impact were septicemia on length of hospital stay (21 days; adjusted beta coefficient 16.8; 95% CI: 15.3 to 18.3; P < .001), and respiratory failure on mortality (risk-adjusted population attributable fraction 44.6%, 95% CI 29.6 to 59.6, P < .001). Use of laparoscopy (odds ratio 0.764, 95% CI 0.678 to 0.861; P < .001), and intraoperative blood loss (101-500 mL: odds ratio 2.699, 95% CI 2.152 to 3.380; P < .001; and 500-1000 mL: odds ratio 2.875, 95% CI 1.403 to 5.858; P = .013) were associated with increased morbidity.

CONCLUSIONS

This study offers, for the first time, clinically-based benchmark values in EGS and identifies measures for improvement.

[Impact of oliguria during lung surgery on postoperative acute kidney injury]

OBJECTIVE

To explore the influence of intraoperative urine volume on postoperative acute kidney injury (AKI) and the independent risk factors of AKI.

METHODS

This was a retrospective cohort study recruiting patients who received selective pulmonary resection under general anesthesia in Peking University First Hospital from July, 2017 to June, 2019. The patients were divided into the AKI group and the control group according to whether they developed postoperative AKI or not. Firstly, univariate analysis was used to analyze the relationship between perioperative variables and postoperative AKI. Secondly, receiver operating characteristic (ROC) curve was used to explore the predictive value of intraoperative urine output for postoperative AKI. The nearest four cutoff values [with the interval of 0.1 mL/(kg·h)] at maximum Youden index were used as cutoff values of oliguria. Then univariate analysis was used to explore the relationship between oliguria defined by these four cutoff values and the risk of AKI. And the cutoff value with maximum OR was chosen as the threshold of oliguria in this study. Lastly, the variables with P < 0.10 in the univariate analysis were selected for inclusion in a multivariate Logistic model to analyze the independent predictors of postoperative AKI.

RESULTS

A total of 1 393 patients were enrolled in the study. The incidence of postoperative AKI was 2.2%. ROC curve analysis showed that the area under curve (AUC) of intraoperative urine volume used for predicting postoperative AKI was 0.636 (P=0.009), and the cutoff value of oliguria was 0.785 mL/(kg·h) when Youden index was maximum (Youden index =0.234, sensitivity =48.4%, specificity =75.0%). Furthermore, 0.7, 0.8, 0.9, 1.0 mL/(kg·h) and the traditional cutoff value of 0.5 mL/(kg·h) were used to analyze the influence of oliguria on postoperative AKI. Univariate analysis showed that, when 0.8 mL/(kg·h) was selected as the threshold of oliguria, the patients with oliguria had the most significantly increased risk of AKI (AKI group 48.4% vs. control group 25.3%, OR=2.774, 95%CI 1.357－5.671, P=0.004). Multivariate regression analysis showed that intraoperative urine output < 0.8 mL/(kg·h) was one of the independent risk factors of postoperative AKI (OR=2.698, 95%CI 1.260－5.778, P=0.011). The other two were preoperative hemoglobin ≤120.0 g/L (OR=3.605, 95%CI 1.545－8.412, P=0.003) and preoperative estimated glomerular filtration rate < 30 mL/(min·1.73 m²) (OR=11.009, 95%CI 1.813－66.843, P=0.009).

CONCLUSION

Oliguria is an independent risk fact or of postoperative AKI after pulmonary resection, and urine volume < 0.8 mL/(kg·h) is a possible screening criterium.