Acute Kidney Injury in the Surgical Patient

Perioperative fluid management for adult cardiac surgery: network meta-analysis pooling on twenty randomised controlled trials

BACKGROUND

The aim of this study was to evaluate colloids and crystalloids used in perioperative fluid therapy for cardiac surgery patients to further investigate the optimal management strategies of different solutions.

METHOD

RCTs about adult surgical patients allocated to receive perioperative fluid therapy for electronic databases, including Ovid MEDLINE, EMBase, and Cochrane Central Register of Controlled Trials, were searched up to February 15, 2023.

RESULTS

None of the results based on network comparisons, including mortality, transfuse PLA, postoperative chest tube output over the first 24 h following surgery, and length of hospital stay, were statistically significant. Due to the small number of included studies, the results, including acute kidney injury, serum creatinine, serum microglobulin, and blood urea nitrogen, are from the direct comparison. For transfusion of RBCs, significant differences were observed in the comparisons of 3% gelatine vs. 6% HES 200/0.5, 4% albumin vs. 5% albumin, 4% gelatine vs. 5% albumin, 5% albumin vs. 6% HES 200/0.5, and 6% HES 130/0.4 vs. 6% HES 200/0.5. In transfusion of FFP, significant differences were observed in comparisons of 3% gelatine vs. 4% gelatine, 3% gelatine vs. 6% HES 200/0.5, 5% albumin vs. 6% HES 200/0.5, 4% gelatine vs. 5% albumin, 4% gelatine vs. 6% HES 200/0.4, and 6% HES 130/0.4 vs. 6% HES 200/0.5. For urinary output at 24 h after surgery, the results are deposited in the main text.

CONCLUSION

This study showed that 3% gelatin and 5% albumin can reduce the transfuse RBC and FFP. In addition, the use of hypertonic saline solution can increase urine output, and 5% albumin and 6% HES can shorten the length of ICU stay. However, none of the perioperative fluids showed an objective advantage in various outcomes, including mortality, transfuse PLA, postoperative chest tube output over the first 24 h following surgery, and length of hospital stay. The reliable and sufficient evidences on the injury of the kidney, including acute kidney injury, serum creatinine, serum microglobulin, and blood urea nitrogen, was still lacking. In general, perioperative fluids had advantages and disadvantages, and there were no evidences to support the recommendation of the optimal perioperative fluid for cardiac surgery.

First do no harm: Predicting futility of intervention in geriatric emergency general surgery

BACKGROUND

Emergent surgical conditions are common in geriatric patients, often necessitating major operative procedures on frail patients. Understanding risk profiles is crucial for decision-making and establishing goals of care.

METHODS

We queried NSQIP 2015-2019 for patients ≥65 years undergoing open abdominal surgery for emergency general surgery conditions. Logistic regression was used to identify 30-day mortality predictors.

RESULTS

Of 41,029 patients, 5589 (13.6 ​%) died within 30 days of admission. The highest predictors of mortality were ASA status 5 (aOR 9.7, 95 ​% CI,3.5-26.8, p ​< ​0.001), septic shock (aOR 4.9, 95 ​% CI,4.5-5.4, p ​< ​0.001), and dialysis (aOR 2.1, 95 ​% CI,1.8-2.4, p ​< ​0.001). Without risk factors, mortality rates were 11.9 ​% after colectomy and 10.2 ​% after small bowel resection. Patients with all three risk factors had a mortality rate of 79.4 ​% and 100 ​% following colectomy and small bowel resection, respectively.

CONCLUSIONS

In older adults undergoing emergent open abdominal surgery, septic shock, ASA status, and dialysis were strongly associated with futility of surgical intervention. These findings can inform goals of care and informed decision-making.

Megalin-targeting and ROS-responsive elamipretide-conjugated polymeric prodrug for treatment of acute kidney injury

Acute kidney injury (AKI) is associated with both kidney function loss and increased mortality. In the pathological progression of ischemia-reperfusion-induced AKI, the surge of reactive oxygen species (ROS) plays a crucial role. To combat this, mitochondrial-targeted antioxidant therapy shows great promise as mitochondria are the primary source of ROS in AKI. However, most strategies aiming to target mitochondria directly result in nanodrugs that are too large to pass through the glomerular system and reach the renal tubules, which are the main site of damage in AKI. This study focused on synthesizing a Megalin receptor-targeted polymeric prodrug, low molecular weight chitosan-thioketal-elamipretide (LMWC/TK/Ela), to mitigate excessive ROS in renal tubular epithelial cells for AKI. This soluble polymeric prodrug has the ability to successfully reach the tubular site by crossing the glomerular barrier. Once there, it can responsively release elamipretide, which possesses excellent antioxidative properties. Therefore, this research offers a novel approach to actively target renal tubular epithelial cells and intracellular mitochondria for the relief of AKI.

Intraoperative hypotension and postoperative acute kidney injury: A systematic review

BACKGROUND

There is no consensus regarding safe intraoperative blood pressure thresholds that protect against postoperative acute kidney injury (AKI). This review aims to examine the existing literature to delineate safe intraoperative hypotension (IOH) parameters to prevent postoperative AKI.

METHODS

PubMed, Cochrane Central, and Web of Science were systematically searched for articles published between 2015 and 2022 relating the effects of IOH on postoperative AKI.

RESULTS

Our search yielded 19 articles. IOH risk thresholds ranged from <50 to <75 ​mmHg for mean arterial pressure (MAP) and from <70 to <100 ​mmHg for systolic blood pressure (SBP). MAP below 65 ​mmHg for over 5 ​min was the most cited threshold (N ​= ​13) consistently associated with increased postoperative AKI. Greater magnitude and duration of MAP and SBP below the thresholds were generally associated with a dose-dependent increase in postoperative AKI incidence.

CONCLUSIONS

While a consistent definition for IOH remains elusive, the evidence suggests that MAP below 65 ​mmHg for over 5 ​min is strongly associated with postoperative AKI, with the risk increasing with the magnitude and duration of IOH.

Meaning and Management of Perioperative Oliguria

Perioperative oliguria is an alarm signal. The initial assessment includes closer patient monitoring, evaluation of volemic status, risk-benefit of fluid challenge or furosemide stress test, and investigation of possible perioperative complications.

High Intraoperative Serum Lactate Level is Associated with Acute Kidney Injury after Brain Tumor Resection

BACKGROUND

Postoperative acute kidney injury (AKI) is associated with poor clinical outcomes. Identification of risk factors for postoperative AKI is clinically important. Serum lactate can increase in situations of inadequate oxygen delivery and is widely used to assess a patient's clinical course. We investigated the association between intraoperative serum lactate levels and AKI after brain tumor resection.

METHODS

Demographics, medical and surgical history, tumor characteristics, surgery, anesthesia, preoperative and intraoperative blood test results, and postoperative clinical outcomes were retrospectively collected from 4131 patients who had undergone brain tumor resection. Patients were divided into high (n=1078) and low (n=3053) lactate groups based on an intraoperative maximum serum lactate level of 3.35 mmol/L. After propensity score matching, 1005 patients were included per group. AKI was diagnosed using the Kidney Disease Improving Global Outcomes criteria, based on serum creatinine levels within 7 days after surgery.

RESULTS

Postoperative AKI was observed in 53 (1.3%) patients and was more frequent in those with high lactate both before (3.2% [n=35] vs. 0.6% [n=18]; P < 0.001) and after (3.3% [n=33] vs. 0.6% [n=6]; P < 0.001) propensity score matching. Intraoperative predictors of postoperative AKI were maximum serum lactate levels > 3.35 mmol/L (odds ratio [95% confidence interval], 3.57 [1.45-8.74], P = 0.005), minimum blood pH (odds ratio per 1 unit, 0.01 [0.00-0.24], P = 0.004), minimum hematocrit (odds ratio per 1%, 0.91 [0.84-1.00], P = 0.037), and mean serum glucose levels > 200 mg/dL (odds ratio, 6.22 [1.75-22.16], P = 0.005).

CONCLUSION

High intraoperative serum lactate levels were associated with AKI after brain tumor resection.

Clinical Considerations for Patients Experiencing Acute Kidney Injury Following Percutaneous Nephrolithotomy

Acute kidney injury (AKI) is a common postoperative outcome in urology patients undergoing surgery for nephrolithiasis. The objective of this study was to determine the prevalence of postoperative AKI and its degrees of severity, identify risk factors, and understand the resultant outcomes of AKI in patients with nephrolithiasis undergoing percutaneous nephrolithotomy (PCNL). A cohort of patients admitted between 2012 and 2019 to a single tertiary-care institution who had undergone PCNL was retrospectively analyzed. Among 417 (n = 326 patients) encounters, 24.9% (n = 104) had AKI. Approximately one-quarter of AKI patients (n = 18) progressed to Stage 2 or higher AKI. Hypertension, peripheral vascular disease, chronic kidney disease, and chronic anemia were significant risk factors of post-PCNL AKI. Corticosteroids and antifungals were associated with increased odds of AKI. Cardiovascular, neurologic complications, sepsis, and prolonged intensive care unit (ICU) stay percentages were higher in AKI patients. Hospital and ICU length of stay was greater in the AKI group. Provided the limited literature regarding postoperative AKI following PCNL, and the detriment that AKI can have on clinical outcomes, it is important to continue studying this topic to better understand how to optimize patient care to address patient- and procedure-specific risk factors.

Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury

Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.

Strategies for post–cardiac surgery acute kidney injury prevention: A network meta-analysis of randomized controlled trials

Objects

Cardiac surgery is associated with acute kidney injury (AKI). However, the effects of various pharmacological and non-pharmacological strategies for AKI prevention have not been thoroughly investigated, and their effectiveness in preventing AKI-related adverse outcomes has not been systematically evaluated.

Methods

Studies from PubMed, Embase, and Medline and registered trials from published through December 2021 that evaluated strategies for preventing post–cardiac surgery AKI were identified. The effectiveness of these strategies was assessed through a network meta-analysis (NMA). The secondary outcomes were prevention of dialysis-requiring AKI, mortality, intensive care unit (ICU) length of stay (LOS), and hospital LOS. The interventions were ranked using the P-score method. Confidence in the results of the NMA was assessed using the Confidence in NMA (CINeMA) framework.

Results

A total of 161 trials (involving 46,619 participants) and 53 strategies were identified. Eight pharmacological strategies {natriuretic peptides [odds ratio (OR): 0.30, 95% confidence interval (CI): 0.19–0.47], nitroprusside [OR: 0.29, 95% CI: 0.12–0.68], fenoldopam [OR: 0.36, 95% CI: 0.17–0.76], tolvaptan [OR: 0.35, 95% CI: 0.14–0.90], N-acetyl cysteine with carvedilol [OR: 0.37, 95% CI: 0.16–0.85], dexmedetomidine [OR: 0.49, 95% CI: 0.32–0.76;], levosimendan [OR: 0.56, 95% CI: 0.37–0.84], and erythropoietin [OR: 0.62, 95% CI: 0.41–0.94]} and one non-pharmacological intervention (remote ischemic preconditioning, OR: 0.76, 95% CI: 0.63–0.92) were associated with a lower incidence of post–cardiac surgery AKI with moderate to low confidence. Among these nine strategies, five (fenoldopam, erythropoietin, natriuretic peptides, levosimendan, and remote ischemic preconditioning) were associated with a shorter ICU LOS, and two (natriuretic peptides [OR: 0.30, 95% CI: 0.15–0.60] and levosimendan [OR: 0.68, 95% CI: 0.49–0.95]) were associated with a lower incidence of dialysis-requiring AKI. Natriuretic peptides were also associated with a lower risk of mortality (OR: 0.50, 95% CI: 0.29–0.86). The results of a sensitivity analysis support the robustness and effectiveness of natriuretic peptides and dexmedetomidine.

Conclusion

Nine potentially effective strategies were identified. Natriuretic peptide therapy was the most effective pharmacological strategy, and remote ischemic preconditioning was the only effective non-pharmacological strategy. Preventive strategies might also help prevent AKI-related adverse outcomes. Additional studies are required to explore the optimal dosages and protocols for potentially effective AKI prevention strategies.

Abdominal compartment syndrome: an often overlooked cause of acute kidney injury

Abdominal compartment syndrome (ACS) is defined as any organ dysfunction caused by intra-abdominal hypertension (IAH), referred as intra-abdominal pressure (IAP) ≥ 12 mm Hg according to the World Society of Abdominal Compartment Syndrome. Abdominal compartment syndrome develops in most cases when IAP rises above 20 mmHg. Abdominal compartment syndrome, while being a treatable and even preventable condition if detected early in the stage of intra-abdominal hypertension, is associated with high rates of morbidity and mortality if diagnosis is delayed: therefore, early detection is essential. Acute kidney injury (AKI) is a common comorbidity, affecting approximately one in every five hospitalized patients, with a higher incidence in surgical patients. AKI in response to intra-abdominal hypertension develops as a result of a decline in cardiac output and compression of the renal vasculature and renal parenchyma. In spite of the high incidence of intra-abdominal hypertension, especially in surgical patients, its potential role in the pathophysiology of AKI has been investigated in very few clinical studies and is commonly overlooked in clinical practice despite being potentially treatable and reversible. Aim of the present review is to illustrate the current evidence on the pathophysiology, diagnosis and therapy of intra-abdominal hypertension and abdominal compartment syndrome in the context of AKI.

Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction

Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction.

Renal Recovery after the Implementation of an Electronic Alert and Biomarker-Guided Kidney-Protection Strategy following Major Surgery

Background: The facilitation of early recovery of acute kidney injury (AKI) is an important step to improve outcome, particularly because of the limited therapeutic interventions currently available for AKI. The combination of an electronic alert and biomarker-guided kidney-protection strategy implemented in the routine care may have an impact on the incidence of early complete reversal of AKI after major non-cardiac surgery. Methods: We studied 294 patients in two cohorts before (n = 151) and after protocol implementation (n = 143). Data collection required 6 months for each cohort. The kidney-protection protocol included an electronic alert to detect patients who were eligible for urinary biomarker [TIMP2 × IGFBP7]-guided kidney-protection intervention. Intervention was stratified according to three levels of immediate AKI risk: low, moderate, and high. After intervention, postoperative changes in the glomerular filtration rate (eGFR) were identified with a tracking software that included an alert for nephrology consultation if the eGFR had declined by >25% from the preoperative reference value. Primary outcome was early AKI recovery, i.e., the complete reversal of any AKI stage to absence of AKI within the first 7 postoperative days. Results: Protocol implementation significantly increased the recovery of AKI (36/46, 78% compared to control 27/48, 56%, (p = 0.025)) and reduced the length of the ICU stay (p < 0.001). There was no significant difference in the overall incidence of all AKI and moderate and severe AKI in the first 7 postoperative days: 46/143 (32%) and 12/151 (8%) in the protocol implementation group compared to 48/151 (32%) and 18/151 (12%) in the historical control group. Patients with AKI reversal within the first 7 postoperative days had lower in-hospital mortality than patients without AKI reversal. Conclusions: Implementing a combined electronic alert and biomarker-guided kidney-protection strategy in routine care improved early recovery of AKI after major surgery.

Outcomes of Patients With Parkinson Disease Undergoing Cervical Spine Surgery for Radiculopathy and Myelopathy With Minimum 2-Year Follow-up

STUDY DESIGN

This was a retrospective cohort analysis.

OBJECTIVE

To identify the impact of Parkinson disease (PD) on 2-year postoperative outcomes following cervical spine surgery (CSS).

SUMMARY OF BACKGROUND DATA

(PD) patients are prone to spine malalignment and surgical interventions, yet little is known regarding outcomes of CSS among PD patients.

MATERIALS AND METHODS

All patients from the Statewide Planning and Research Cooperative System with cervical radiculopathy or myelopathy who underwent CSS were included; among these, those with PD were identified. PD and non-PD patients (n=64 each) were 1:1 propensity score-matched by age, sex, race, surgical approach, and Deyo-Charlson Comorbidity Index (DCCI). Demographics, hospital-related parameters, and adverse postoperative outcomes were compared between cohorts. Logistic regression identified predictive factors for outcomes.

RESULTS

Overall, patient demographics were comparable between cohorts, except that DCCI was higher in PD patients (1.28 vs. 0.67, P=0.028). PD patients had lengthier mean hospital stays than non-PD patients (6.4 vs. 4.1 d, P=0.046). PD patients also incurred comparable total hospital expenses ($69,565 vs. $57,388, P=0.248). Individual medical complication rates were comparable between cohorts; though PD patients had higher rates of postoperative altered mental status (4.7% vs. 0%, P=0.08) and acute renal failure (10.9% vs. 3.1%, P=0.084), these differences were not significant. Yet, PD patients experienced higher rates of overall medical complications (35.9% vs. 18.8%, P=0.029). PD patients had comparable rates of individual and overall surgical complications. The PD cohort underwent higher reoperation rates (15.6% vs. 7.8%, P=0.169) compared with non-PD patients, though this difference was not significant. Of note, PD was not a significant predictor of overall 2-year complications (odds ratio=1.57, P=0.268) or reoperations (odds ratio=2.03, P=0.251).

CONCLUSION

Overall medical complication rates were higher in patients with PD, while individual medical complications as well as surgical complication and reoperation rates after elective CSS were similar in patients with and without PD, though PD patients required longer hospital stays. Importantly, a baseline diagnosis of PD was not significantly associated with adverse two-year medical and surgical complications. This data may improve counseling and risk-stratification for PD patients before CSS.

LEVEL OF EVIDENCE

Level III.

Renal outcomes of laparoscopic versus open surgery in patients with rectal cancer: a propensity score analysis

Background

A laparoscopic approach is widely used in abdominal surgery. Although several studies have compared surgical and oncological outcomes between laparoscopic surgery (LS) and open surgery (OS) in rectal cancer patients, there have been few studies on postoperative renal outcomes.

Methods

We conducted a retrospective cohort study involving 1,633 patients who underwent rectal cancer surgery between 2003 and 2017. Postoperative acute kidney injury (AKI) was diagnosed according to the serum creatinine criteria of the Kidney Disease: Improving Global Outcomes classification.

Results

Among the 1,633 patients, 1,072 (65.6%) underwent LS. After matching propensity scores, 395 patients were included in each group. The incidence of postoperative AKI in the LS group was significantly lower than in the OS group (9.9% vs. 15.9%; p = 0.01). Operation time, estimated blood loss, and incidence of transfusion in the LS group were significantly lower than those in the OS group. Cox proportional hazard models revealed that LS was associated with decreased risk of postoperative AKI (hazard ratio [HR], 0.599; 95% confidence interval [CI], 0.402–0.893; p = 0.01) and postoperative transfusion was associated with increased risk of AKI (HR, 2.495; 95% CI, 1.529–4.072; p < 0.001). In the subgroup analysis, the incidence of postoperative AKI in patients with middle or high rectal cancer who underwent LS was much lower than in those who underwent OS (HR, 0.373; 95% CI, 0.197–0.705; p = 0.002).

Conclusion

This study showed that LS may have a favorable effect on the development of postoperative AKI in patients with rectal cancer.

Assessment of Cardiopulmonary Bypass Duration Improves Novel Biomarker Detection for Predicting Postoperative Acute Kidney Injury after Cardiovascular Surgery

Urinary liver-type fatty acid binding protein (L-FABP) is a novel biomarker with promising performance in detecting kidney injury. Previous studies reported that L-FABP showed moderate discrimination in patients that underwent cardiac surgery, and other studies revealed that longer duration of cardiopulmonary bypass (CPB) was associated with a higher risk of postoperative acute kidney injury (AKI). This study aims to examine assessing CPB duration first, then examining L-FABP can improve the discriminatory ability of L-FABP in postoperative AKI. A total of 144 patients who received cardiovascular surgery were enrolled. Urinary L-FABP levels were examined at 4 to 6 and 16 to 18 h postoperatively. In the whole study population, the AUROC of urinary L-FABP in predicting postoperative AKI within 7 days was 0.720 at 16 to 18 h postoperatively. By assessing patients according to CPB duration, the urinary L-FABP at 16 to 18 h showed more favorable discriminating ability with AUROC of 0.742. Urinary L-FABP exhibited good performance in discriminating the onset of AKI within 7 days after cardiovascular surgery. Assessing postoperative risk of AKI through CPB duration first and then using urinary L-FABP examination can provide more accurate and satisfactory performance in predicting postoperative AKI.

Propofol ameliorates renal ischemia/reperfusion injury by enhancing macrophage M2 polarization through PPARγ/STAT3 signaling

Propofol (Pro) confers protection against renal ischemia/reperfusion (rI/R) injury through incompletely characterized mechanisms. Since Pro has shown net anti-inflammatory properties as part of its beneficial effects, we examined the potential role of Pro in the modulation of macrophage polarization status during both rI/R injury in vivo and exposure of cultured peritoneal macrophages (PMs) to hypoxia/reoxygenation (H/R). Rats were subjected to 45-min r/IR surgery or a sham procedure and administered PBS (vehicle) or Pro during the ischemia stage. Pro administration attenuated rI/R-induced kidney damage and renal TNF-α, IL-6, and CXCL-10 expression. Enhanced macrophage M2 polarization, evidenced by reduced iNOS and increased Arg1 and Mrc1 mRNA levels, was further detected after Pro treatment both in the kidney, after rI/R in vivo, and in H/R-treated PMs. Pro administration also repressed phosphorylated signal transducer and activator of transcription 1 (p-STAT1) and increased p-STAT3, p-STAT6, and peroxisome proliferator-activated receptor-γ (PPARγ) mRNA levels in H/R-exposed PMs. Importantly, siRNA-mediated PPARγ silencing repressed Pro-mediated STAT3 activation in PMs and restored proinflammatory cytokine levels and prevented macrophage M2 marker expression in both rI/R-treated rats and cultured PMs. These findings suggest that Pro confers renoprotection against rI/R by stimulating PPARγ/STAT3-dependent macrophage conversion to the M2 phenotype.

Optimizing predictive strategies for acute kidney injury after major vascular surgery

BACKGROUND

Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery.

METHODS

A single-center retrospective cohort of 1,531 adult patients who underwent nonemergency major vascular surgery, including open aortic, endovascular aortic, and lower extremity bypass procedures, was evaluated. The validated, automated MySurgeryRisk analytics platform used electronic health record data to forecast patient-level probabilistic risk scores for postoperative acute kidney injury using random forest models with preoperative data alone and perioperative data (preoperative plus intraoperative). The MySurgeryRisk predictions were compared with each other as well as with the American Society of Anesthesiologists physical status classification.

RESULTS

Machine learning models using perioperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification (accuracy: 0.70 vs 0.64 vs 0.62, area under the receiver operating characteristics curve: 0.77 vs 0.68 vs 0.61, area under the precision-recall curve: 0.70 vs 0.58 vs 0.48).

CONCLUSION

In predicting acute kidney injury after major vascular surgery, machine learning approaches that incorporate dynamic intraoperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification. Machine learning methods have the potential for real-time identification of high-risk patients who may benefit from personalized risk-reduction strategies.

MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After Surgery

OBJECTIVE

To accurately calculate the risk for postoperative complications and death after surgery in the preoperative period using machine-learning modeling of clinical data.

BACKGROUND

Postoperative complications cause a 2-fold increase in the 30-day mortality and cost, and are associated with long-term consequences. The ability to precisely forecast the risk for major complications before surgery is limited.

METHODS

In a single-center cohort of 51,457 surgical patients undergoing major inpatient surgery, we have developed and validated an automated analytics framework for a preoperative risk algorithm (MySurgeryRisk) that uses existing clinical data in electronic health records to forecast patient-level probabilistic risk scores for 8 major postoperative complications (acute kidney injury, sepsis, venous thromboembolism, intensive care unit admission >48 hours, mechanical ventilation >48 hours, wound, neurologic, and cardiovascular complications) and death up to 24 months after surgery. We used the area under the receiver characteristic curve (AUC) and predictiveness curves to evaluate model performance.

RESULTS

MySurgeryRisk calculates probabilistic risk scores for 8 postoperative complications with AUC values ranging between 0.82 and 0.94 [99% confidence intervals (CIs) 0.81-0.94]. The model predicts the risk for death at 1, 3, 6, 12, and 24 months with AUC values ranging between 0.77 and 0.83 (99% CI 0.76-0.85).

CONCLUSIONS

We constructed an automated predictive analytics framework for machine-learning algorithm with high discriminatory ability for assessing the risk of surgical complications and death using readily available preoperative electronic health records data. The feasibility of this novel algorithm implemented in real time clinical workflow requires further testing.

Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics

BACKGROUND

Acute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data. Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI.

METHODS

A retrospective single-center cohort of 2,911 adults who underwent surgery at the University of Florida Health between 2000 and 2010 was utilized for this study. Machine learning and statistical analysis techniques were used to develop perioperative models to predict the risk of developing AKI during the first three days after surgery, first seven days after surgery, and overall (after surgery during the index hospitalization). The improvement in risk prediction was examined by incorporating intraoperative physiological time-series variables. Our proposed model enriched a preoperative model that produced a probabilistic AKI risk score by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and Net Reclassification Improvement (NRI).

RESULTS

The predictive performance of the proposed model is better than the preoperative data only model. The proposed model had an AUC of 0.86 (accuracy of 0.78) for the seven-day AKI outcome, while the preoperative model had an AUC of 0.84 (accuracy of 0.76). Furthermore, by integrating intraoperative features, the algorithm was able to reclassify 40% of the false negative patients from the preoperative model. The NRI for each outcome was AKI at three days (8%), seven days (7%), and overall (4%).

CONCLUSIONS

Postoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.

Trending Towards Safer Breast Cancer Surgeries? Examining Acute Complication Rates from A 13-Year NSQIP Analysis

As breast cancer surgery continues to evolve, this study highlights the acute complication rates and predisposing risks following partial mastectomy (PM), mastectomy(M), mastectomy with muscular flap reconstruction (M + MF), mastectomy with implant reconstruction (M + I), and oncoplastic surgery (OPS). Data was collected from the American College of Surgeons NSQIP database (2005⁻2017). Complication rate and trend analyses were performed along with an assessment of odds ratios for predisposing risk factors using adjusted linear regression. 226,899 patients met the inclusion criteria. Complication rates have steadily increased in all mastectomy groups (p < 0.05). Cumulative complication rates between surgical categories were significantly different in each complication cluster (all p < 0.0001). Overall complication rates were: PM: 2.25%, OPS: 3.2%, M: 6.56%, M + MF: 13.04% and M + I: 5.68%. The most common predictive risk factors were mastectomy, increasing operative time, ASA class, BMI, smoking, recent weight loss, history of CHF, COPD and bleeding disorders (all p < 0.001). Patients who were non-diabetic, younger (age < 60) and treated as an outpatient all had protective OR for an acute complication (p < 0.0001). This study provides data comparing nationwide acute complication rates following different breast cancer surgeries. These can be used to inform patients during surgical decision making.

Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study

BACKGROUND

Major postoperative complications are associated with increased cost and mortality. The complexity of electronic health records overwhelms physicians' abilities to use the information for optimal and timely preoperative risk assessment. We hypothesized that data-driven, predictive-risk algorithms implemented in an intelligent decision-support platform simplify and augment physicians' risk assessments.

METHODS

This prospective, nonrandomized pilot study of 20 physicians at a quaternary academic medical center compared the usability and accuracy of preoperative risk assessment between physicians and MySurgeryRisk, a validated, machine-learning algorithm, using a simulated workflow for the real-time, intelligent decision-support platform. We used area under the receiver operating characteristic curve to compare the accuracy of physicians' risk assessment for six postoperative complications before and after interaction with the algorithm for 150 clinical cases.

RESULTS

The area under the receiver operating characteristic curve of the MySurgeryRisk algorithm ranged between 0.73 and 0.85 and was significantly better than physicians' initial risk assessments (area under the receiver operating characteristic curve between 0.47 and 0.69) for all postoperative complications except cardiovascular. After interaction with the algorithm, the physicians significantly improved their risk assessment for acute kidney injury and for an intensive care unit admission greater than 48 hours, resulting in a net improvement of reclassification of 12% and 16%, respectively. Physicians rated the algorithm as easy to use and useful.

CONCLUSION

Implementation of a validated, MySurgeryRisk computational algorithm for real-time predictive analytics with data derived from the electronic health records to augment physicians' decision-making is feasible and accepted by physicians. Early involvement of physicians as key stakeholders in both design and implementation of this technology will be crucial for its future success.

Usefulness of the preoperative prognostic nutritional index score as a predictor of the outcomes of lung transplantation: A single-institution experience

BACKGROUND & AIMS

There is increasing evidence that preoperative nutritional status is a predictor of disease severity and mortality after lung transplantation (LTX). This study aimed to evaluate preoperative nutritional assessment as a predictor of LTX outcomes.

METHODS

We included 132 patients who underwent single or double LTX at Severance Hospital, Yonsei University, between October 2010 and April 2016. The Prognostic Nutritional Index (PNI) scores were calculated as follows: 10 × serum albumin value (g/dL) + 0.005 × peripheral lymphocyte count (/mm³). The optimal cut-off PNI score for the prediction of postoperative overall survival was set at 41.15 using receiver operating characteristics analysis. The efficacies of PNI and other clinical factors in predicting LTX outcomes were determined using univariate and multivariate Cox proportional hazard analyses.

RESULTS

Patients with PNI <41.15 (PNI-low group) were older, had higher preoperative C-reactive protein levels, and had lower nutritional status scores than did those in the PNI-high group (PNI ≥ 41.15). Based on Kaplan-Meier analysis, the overall survival rate was significantly better in the PNI-high group (78.3%) than in the PNI-low group (28.6%) (P < 0.001). Age, sex, body mass index, use of preoperative mechanical ventilation, C-reactive protein level, neutrophil-to-lymphocyte ratio, and PNI score were independent prognostic factors. Survival was significantly higher in the PNI-high group (hazard ratio: 0.220; P < 0.001) than in the PNI-low group, and incidence of complications ≥ grade IV was higher in the PNI-low group than in the PNI-high group (P < 0.001). Multivariate regression analysis showed that preoperative PNI score was significantly associated with postoperative survival, even after adjusting for other confounding factors.

CONCLUSIONS

Our findings suggest that PNI is a useful prognostic marker for the identification of high-risk lung transplant recipients. Preoperative nutritional assessment using PNI may provide useful information for reducing postoperative morbidity and mortality.

Preoperative Albuminuria and Intraoperative Chloride Load: Predictors of Acute Kidney Injury Following Major Abdominal Surgery

Background: Postoperative Acute Kidney Injury (AKI) is a common and serious complication associated with significant morbidity and mortality. While several pre- and intra-operative risk factors for AKI have been recognized in cardiac surgery patients, relatively few data are available regarding the incidence and risk factors for perioperative AKI in other surgical operations. The aim of the present study was to determine the risk factors for perioperative AKI in patients undergoing major abdominal surgery. Methods: This was a prospective, observational study of patients undergoing major abdominal surgery in a tertiary care center. Postoperative AKI was diagnosed according to the Acute Kidney Injury Network criteria within 48 h after surgery. Patients with chronic kidney disease stage IV or V were excluded. Logistic regression analysis was used to evaluate the association between perioperative factors and the risk of developing postoperative AKI. Results: Eleven out of 61 patients developed postoperative AKI. Four intra-operative variables were identified as predictors of AKI: intra-operative blood loss (p = 0.002), transfusion of fresh frozen plasma (p = 0.004) and red blood cells (p = 0.038), as well as high chloride load (p = 0.033, cut-off value > 500 mEq). Multivariate analysis demonstrated an independent association between AKI development and preoperative albuminuria, defined as a urinary Albumin to Creatinine ratio ≥ 30 mg·g⁻¹ (OR = 6.88, 95% CI: 1.43–33.04, p = 0.016) as well as perioperative chloride load > 500 mEq (OR = 6.87, 95% CI: 1.46–32.4, p = 0.015). Conclusion: Preoperative albuminuria, as well as a high intraoperative chloride load, were identified as predictors of postoperative AKI in patients undergoing major abdominal surgery.

Acute kidney injury increases the rate of major morbidities in cytoreductive surgery and HIPEC

Introduction

Acute kidney injury (AKI) following cardiovascular surgery has been shown to increase costs and overall morbidity and mortality. The incidence, risk factors, and outcomes of AKI following other types of major surgeries have not been as well characterized. We sought to study the incidence of AKI following cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) per the Kidney Disease: Improving Global Outcomes (KDIGO) criteria.

Materials and methods

Patients undergoing CRS and HIPEC between 2013 and 2015 were included. Demographic and perioperative data were compared between patients who experienced AKI versus controls using appropriate statistical analysis between categorical and continuous variables. AKI was recorded by a Certified Professional in Healthcare Quality (CPHQ) and defined as a rise in serum creatinine by ≥ 0.3 mg/dL within 48 h (KDIGO criteria).

Results

Fifty-eight consecutive patients undergoing CRS and HIPEC were included. Twelve (20.7%) patients were recorded to develop AKI. This was the most common complication recorded by the CPHQ member. There was one 30-day mortality secondary to cerebral infarction. AKI patients had a longer hospitalization period (14.2 ± 6.9 vs. 9.5 ± 3.3 days, p = 0.002), and a higher rate of major complications (50.00% vs. 15.21%; p = 0.018). Readmission rate was similar (p = 0.626). Multivariate regression identified excessive blood loss during surgery as a major predictor of AKI occurrence, and pre-existing comorbidities and postoperative AKI as predictors of major morbidities following CRS and HIPEC.

Conclusion

AKI following CRS and HIPEC appears to be a common complication which is associated with further major morbidities. Current quality improvement programs may be under-reporting this incidence.

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We aim to study the incidence of acute kidney injury and renal recovery following cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC) per the Kidney Disease Improving Global Outcomes (KDIGO) criteria.

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Fifty-eight patients who underwent CRS and HIPEC at our institution were included over 2 years.

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AKI was the most common complication leading to a longer hospitalization and a higher rate of other major complications.

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The use of Mitomycin C as the HIPEC agent, as well as longer surgeries with increased blood loss were the only predictors of AKI occurrence. Our intra- and postoperative fluid management was not different between the AKI and non-AKI group.

Acute kidney injury predicts mortality in emergency general surgery patients

BACKGROUND

Patients undergoing Emergency General Surgery (EGS) have increased risk of complications and death. The risk of AKI in patients undergoing EGS, along with associated outcomes, is unknown.

METHODS

This two-institution observational study included adults admitted to intensive care units between 1997 and 2012. EGS was defined by 7 procedures occurring within 48 hours of ICU admission. The main outcome studied was AKI within 5 days, along with 90-day mortality.

RESULTS

In our cohort of 59,604 patients, 1758 (2.9%) underwent EGS. Risk of AKI in EGD patients was significantly increased relative to non-EGS patients, with adjusted odds of 1.7 (95%CI 1.40-1.94; P < 0.001). Risk of renal replacement for EGS patients was also increased, with odds of 1.8 (95%CI 1.37-2.46; P < 0.001). EGS patients were at significantly higher risk of 90-day mortality, with adjusted odds of 3.1 (95%CI 2.16-4.33,p < 0.001) for AKI and 4.5 (95%CI 2.58-7.96,p < 0.001) for AKI requiring renal replacement, relative to the absence of AKI.

CONCLUSIONS

EGS is a robust risk factor for AKI in critically ill patients, the development of which is strongly predictive of increased 90-day mortality.

Effects of differential-phase remote ischemic preconditioning intervention in laparoscopic partial nephrectomy: A single blinded, randomized controlled trial in a parallel group design

STUDY OBJECTIVE

There are two windows of protection for remote ischemic preconditioning (RIPC), an early (ERIPC) and a late-phase (LRIPC). While ERIPC has been well studied, works on LRIPC are relatively scarce, especially for the kidneys. We aimed to compare the effects of early-phase versus late-phase RIPC in patients with laparoscopic partial nephrectomy (LPN).

DESIGN

A randomized controlled study SETTING: The Second Affiliated Hospital of Anhui Medical University, 1 May 2012 to 30 October 2013 PATIENTS: Sixty-five ASA 1 to 2 patients scheduled for LPN were located randomly to ERIPC group, LRIPC group and CON group (control).

INTERVENTIONS

Three five-minute cycles of right upper limb ischaemia and reperfusion were performed after induction of anesthesia in ERIPC group. Patients in LRIPC group received similar treatment 24h before surgery, while control patients were not subjected to preconditioning.

MEASUREMENTS

Serum neutrophil gelatinase-associated lipocalin (NGAL) and serum cystatin C (CysC) were evaluated before the induction of anesthesia (0h), 2h (2h) and 6h (6h) after surgery. Unilateral glomerular filtration rates (GFR) were assessed before and after surgery to evaluate overall renal function.

MAIN RESULTS

Serum NGAL and CysC were significantly lower in ERIPC and LRIPC groups at 2h post-operation (P<0.001), 6h post-operation (P<0.001). Additionally, The GFR were significantly lower in ERIPC and LRIPC groups than in CON group at the 3rd month after surgery (P=0.019; P<0.001). Moreover, compared to the ERIPC group, concentration of NGAL and CysC in LRIPC group decreased to a greater extent, while GFR and the percentage of decrement was significantly less in the LRIPC group (P=0.016; P<0.001).

CONCLUSIONS

Regardless of early-phase or late-phase intervention, limb remote ischemic preconditioning confers protection on renal ischemia-reperfusion injury in patients with laparoscopic partial nephrectomy, and the late-phase protection is more prominent.

Mesenchymal stem cells correct haemodynamic dysfunction associated with liver injury after extended resection in a pig model

In patients, acute kidney injury (AKI) is often due to haemodynamic impairment associated with hepatic decompensation following extended liver surgery. Mesenchymal stem cells (MSCs) supported tissue protection in a variety of acute and chronic diseases, and might hence ameliorate AKI induced by extended liver resection. Here, 70% liver resection was performed in male pigs. MSCs were infused through a central venous catheter and haemodynamic parameters as well as markers of acute kidney damage were monitored under intensive care conditions for 24 h post-surgery. Cytokine profiles were established to anticipate the MSCs’ potential mode of action. After extended liver resection, hyperdynamic circulation, associated with hyponatraemia, hyperkalaemia, an increase in serum aldosterone and low urine production developed. These signs of hepatorenal dysfunction and haemodynamic impairment were corrected by MSC treatment. MSCs elevated PDGF levels in the serum, possibly contributing to circulatory homeostasis. Another 14 cytokines were increased in the kidney, most of which are known to support tissue regeneration. In conclusion, MSCs supported kidney and liver function after extended liver resection. They probably acted through paracrine mechanisms improving haemodynamics and tissue homeostasis. They might thus provide a promising strategy to prevent acute kidney injury in the context of post-surgery acute liver failure.

Optimal Role of the Nephrologist in the Intensive Care Unit

As advances in Critical Care Medicine continue, critically ill patients are surviving despite the severity of their illness. The incidence of acute kidney injury (AKI) has increased, and its impact on clinical outcomes as well as medical expenditures has been established. The role, indications and technological advancements of renal replacement therapy (RRT) have evolved, allowing more effective therapies with less complications. With these changes, Critical Care Nephrology has become an established specialty, and ongoing collaborations between critical care physicians and nephrologist have improved education of multi-disciplinary team members and patient care in the ICU. Multidisciplinary programs to support these changes have been stablished in some hospitals to maximize the delivery of care, while other programs have continue to struggle in their ability to acquire the necessary resources to maximize outcomes, educate their staff, and develop quality initiatives to evaluate and drive improvements. Clearly, the role of the nephrologist in the ICU has evolved, and varies widely among institutions. This special article will provide insights that will hopefully optimize the role of the nephrologist as the leader of the acute care nephrology program, as clinician for critically ill patients, and as teacher for all members of the health care team.

Early detection of acute kidney injury after pediatric cardiac surgery

Acute kidney injury (AKI) is increasingly recognized as a common problem in children undergoing cardiac surgery, with well documented increases in morbidity and mortality in both the short and the long term. Traditional approaches to the identification of AKI such as changes in serum creatinine have revealed a large incidence in this population with significant negative impact on clinical outcomes. However, the traditional diagnostic approaches to AKI diagnosis have inherent limitations that may lead to under-diagnosis of this pathologic process. There is a dearth of randomized controlled trials for the prevention and treatment of AKI associated with cardiac surgery, at least in part due to the paucity of early predictive biomarkers. Novel non-invasive biomarkers have ushered in a new era that allows for earlier detection of AKI. With these new diagnostic tools, a more consistent approach can be employed across centers that may facilitate a more accurate representation of the actual prevalence of AKI and more importantly, clinical investigation that may minimize the occurrence of AKI following pediatric cardiac surgery. A thoughtful management approach is necessary to mitigate the effects of AKI after cardiac surgery, which is best accomplished in close collaboration with pediatric nephrologists. Long-term surveillance for improvement in kidney function and potential development of chronic kidney disease should also be a part of the comprehensive management strategy.

Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications

Objective

To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury.

Design

Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010.

Patients

50,318 adult patients undergoing major surgery.

Measurements

We evaluated the performance of logistic regression, generalized additive models, naïve Bayes and support vector machines for forecasting postoperative sepsis and acute kidney injury. We assessed the impact of feature reduction techniques on predictive performance. Model performance was determined using the area under the receiver operating characteristic curve, accuracy, and positive predicted value. The results were reported based on a 70/30 cross validation procedure where the data were randomly split into 70% used for training the model and the 30% for validation.

Main Results

The areas under the receiver operating characteristic curve for different models ranged between 0.797 and 0.858 for acute kidney injury and between 0.757 and 0.909 for severe sepsis. Logistic regression, generalized additive model, and support vector machines had better performance compared to Naïve Bayes model. Generalized additive models additionally accounted for non-linearity of continuous clinical variables as depicted in their risk patterns plots. Reducing the input feature space with LASSO had minimal effect on prediction performance, while feature extraction using principal component analysis improved performance of the models.

Conclusions

Generalized additive models and support vector machines had good performance as risk prediction model for postoperative sepsis and AKI. Feature extraction using principal component analysis improved the predictive performance of all models.