Risk Stratification for Postoperative Acute Kidney Injury in Major Noncardiac Surgery Using Preoperative and Intraoperative Data

Prevention of Transition from Acute Kidney Injury to Chronic Kidney Disease Using Clinical-Grade Perinatal Stem Cells in Non-Clinical Study

Acute kidney injury (AKI) is widely recognized as a precursor to the onset or rapid progression of chronic kidney disease (CKD). However, there is currently no effective treatment available for AKI, underscoring the urgent need for the development of new strategies to improve kidney function. Human placental mesenchymal stromal cells (hpMSCs) were isolated from donor placentas, cultured, and characterized with regard to yield, viability, flow cytometry, and potency. To mimic AKI and its progression to CKD in a rat model, a dedicated sensitive non-clinical bilateral kidney ischemia-reperfusion injury (IRI) model was utilized. The experimental group received 3 × 105 hpMSCs into each kidney, while the control group received IRI and saline and the untreated group received IRI only. Urine, serum, and kidney tissue samples were collected over a period of 28 days. The hpMSCs exhibited consistent yields, viability, and expression of mesenchymal lineage markers, and were also shown to suppress T cell proliferation in a dose-dependent manner. To ensure optimal donor selection, manufacturing optimization, and rigorous quality control, the rigorous Good Manufacturing Practice (GMP) conditions were utilized. The results indicated that hpMSCs increased rat survival rates and improved kidney function by decreasing serum creatinine, urea, potassium, and fractionated potassium levels. Furthermore, the study demonstrated that hpMSCs can prevent the initial stages of kidney structural fibrosis and improve kidney function in the early stages by mitigating late interstitial fibrosis and tubular atrophy. Additionally, a robust manufacturing process with consistent technical parameters was established.

An explainable machine learning model to predict early and late acute kidney injury after major hepatectomy

BACKGROUND

Risk assessment models for acute kidney injury (AKI) after major hepatectomy that differentiate between early and late AKI are lacking. This retrospective study aimed to create a model predicting AKI through machine learning and identify features that contribute to the development of early and late AKI.

METHODS

Patients that underwent major hepatectomy were categorized into the No-AKI, Early-AKI (within 48 h) or Late-AKI group (between 48 h and 7 days). Modeling was done with 20 perioperative features and the performance of prediction models were measured by the area under the receiver operating characteristic curve (AUROCC). Shapley Additive Explanation (SHAP) values were utilized to explain the outcome of the prediction model.

RESULTS

Of the 1383 patients included in this study, 1229, 110 and 44 patients were categorized into the No-AKI, Early-AKI and Late-AKI group, respectively. The CatBoost classifier exhibited the greatest AUROCC of 0.758 (95% CI: 0.671-0.847) and was found to differentiate well between Early and Late-AKI. We identified different perioperative features for predicting each outcome and found 1-year mortality to be greater for Early-AKI.

CONCLUSIONS

Our results suggest that risk factors are different for Early and Late-AKI after major hepatectomy, and 1-year mortality is greater for Early-AKI.

Impact of Operative Approach on Acute Kidney Injury Risk Prediction Models for Colectomy

INTRODUCTION

Acute kidney injury (AKI) is a serious postoperative complication associated with increased morbidity and mortality. Identifying patients at risk for AKI is important for risk stratification and management. This study aimed to develop an AKI risk prediction model for colectomy and determine if the operative approach (laparoscopic versus open) alters the influence of predictive factors through an interaction term analysis.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program database was analyzed from 2005 to 2019. Patients undergoing laparoscopic and open colectomy were identified and propensity score matched. Multivariable logistic regression identified significant preoperative demographic, comorbidity, and laboratory value predictors of AKI. The predictive ability of a baseline model consisting of these variables was compared to a proposed model incorporating interaction terms between operative approach and predictor variables using the likelihood ratio test, c-statistic, and Brier score. Shapley Additive Explanations values assessed relative importance of significant predictors.

RESULTS

252,372 patients were included in the analysis. Significant AKI predictors were hypertension, age, sex, race, body mass index, smoking, diabetes, preoperative sepsis, Congestive heart failure, preoperative creatinine, preoperative albumin, and operative approach (P < 0.001). The proposed model with interaction terms had improved predictive ability per the likelihood ratio test (P < 0.05) but had no statistically significant interaction terms. C-statistic and Brier scores did not improve. Shapley Additive Explanations analysis showed hypertension had the highest importance. The importance of age and diabetes showed some variation between operative approaches.

CONCLUSIONS

While the inclusion of interaction terms collectively improved AKI prediction, no individual operative approach interaction terms were significant. Including operative approach interactions may enhance predictive ability of AKI risk models for colectomy.

A simple machine learning model for the prediction of acute kidney injury following noncardiac surgery in geriatric patients: a prospective cohort study

BACKGROUND

Surgery in geriatric patients often poses risk of major postoperative complications. Acute kidney injury (AKI) is a common complication following noncardiac surgery and is associated with increased mortality. Early identification of geriatric patients at high risk of AKI could facilitate preventive measures and improve patient prognosis. This study used machine learning methods to identify important features and predict AKI following noncardiac surgery in geriatric patients.

METHODS

The data for this study were obtained from a prospective cohort. Patients aged ≥ 65 years who received noncardiac surgery from June 2019 to December 2021 were enrolled. Data were split into training set (from June 2019 to March 2021) and internal validation set (from April 2021 to December 2021) by time. The least absolute shrinkage and selection operator (LASSO) regularization algorithm and the random forest recursive feature elimination algorithm (RF-RFE) were used to screen important predictors. Models were trained through extreme gradient boosting (XGBoost), random forest, and LASSO. The SHapley Additive exPlanations (SHAP) package was used to interpret the machine learning model.

RESULTS

The training set included 6753 geriatric patients. Of these, 250 (3.70%) patients developed AKI. The XGBoost model with RF-RFE selected features outperformed other models with an area under the precision-recall curve (AUPRC) of 0.505 (95% confidence interval [CI]: 0.369-0.626) and an area under the receiver operating characteristic curve (AUROC) of 0.806 (95%CI: 0.733-0.875). The model incorporated ten predictors, including operation site and hypertension. The internal validation set included 3808 geriatric patients, and 96 (2.52%) patients developed AKI. The model maintained good predictive performance with an AUPRC of 0.431 (95%CI: 0.331-0.524) and an AUROC of 0.845 (95%CI: 0.796-0.888) in the internal validation.

CONCLUSIONS

This study developed a simple machine learning model and a web calculator for predicting AKI following noncardiac surgery in geriatric patients. This model may be a valuable tool for guiding preventive measures and improving patient prognosis.

TRIAL REGISTRATION

The protocol of this study was approved by the Committee of Ethics from West China Hospital of Sichuan University (2019-473) with a waiver of informed consent and registered at www.chictr.org.cn (ChiCTR1900025160, 15/08/2019).

The exploration of perioperative hypotension subtypes: a prospective, single cohort, observational pilot study

Background

Hypotension is a risk factor for postoperative complications, but evidence from randomized trials does not support that a higher blood pressure target always leads to optimized outcomes. The heterogeneity of underlying hemodynamics during hypotension may contribute to these contradictory results. Exploring the subtypes of hypotension can enable optimal management of intraoperative hypotension.

Methods

This is a prospective, observational pilot study. Patients who were ≥ 45 years old and scheduled to undergo moderate-to-high-risk noncardiac surgery were enrolled in this study. The primary objective of this pilot study was to investigate the frequency and distribution of perioperative hypotension and its subtypes (hypotension with or without cardiac output reduction). The exposure of hypotension and its subtypes in patients with and without myocardial or acute kidney injury were also explored.

Results

Sixty patients were included in the analysis. 83% (50/60) of the patients experienced perioperative hypotension. The median duration of hypotension for each patient was 8.0 [interquartile range, 3.1-23.3] minutes. Reduced cardiac output was present during 77% of the hypotension duration. Patients suffering from postoperative myocardial or acute kidney injury displayed longer duration and more extensive exposure in all hypotension subtypes. However, the percentage of different hypotension subtypes did not differ in patients with or without postoperative myocardial or acute kidney injury.

Conclusion

Perioperative hypotension was frequently accompanied by cardiac output reduction in moderate-to-high-risk noncardiac surgical patients. However, due to the pilot nature of this study, the relationship between hypotension subtypes and postoperative myocardial or acute kidney injury still needs further exploration.

Clinical trial registration

https://www.chictr.org.cn/showprojEN.html?proj=134260, CTR2200055929.

Internal and External Validation of Machine Learning Models for Predicting Acute Kidney Injury Following Non-Cardiac Surgery Using Open Datasets

This study developed and validated a machine learning model to accurately predict acute kidney injury (AKI) after non-cardiac surgery, aiming to improve patient outcomes by assessing its clinical feasibility and generalizability. We conducted a retrospective cohort study using data from 76,032 adults who underwent non-cardiac surgery at a single tertiary medical center between March 2019 and February 2021, and used data from 5512 patients from the VitalDB open dataset for external model validation. The predictive variables for model training consisted of demographic, preoperative laboratory, and intraoperative data, including calculated statistical values such as the minimum, maximum, and mean intraoperative blood pressure. When predicting postoperative AKI, our gradient boosting machine model incorporating all the variables achieved the best results, with AUROC values of 0.868 and 0.757 for the internal and external validations using the VitalDB dataset, respectively. The model using intraoperative data performed best in internal validation, while the model with preoperative data excelled in external validation. In this study, we developed a predictive model for postoperative AKI in adult patients undergoing non-cardiac surgery using preoperative and intraoperative data, and external validation demonstrated the efficacy of open datasets for generalization in medical artificial modeling research.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study

BACKGROUND

Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors.

MATERIALS AND METHODS

Adult patients undergoing noncardiac surgery were retrospectively included in the study (76 457 patients in the discovery cohort and 11 910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation.

RESULTS

The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance.

CONCLUSIONS

The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

Incidence and predictors of acute kidney injury after elective surgery for lumbar degenerative disease: A 13-year analysis of the US Nationwide Inpatient Sample

BACKGROUND

Acute kidney injury (AKI) is a severe postoperative complication associated with poor clinical outcomes, including the development of chronic kidney disease (CKD) and death. This study aimed to investigate the incidence and determinants of AKI following elective surgeries for degenerative lumbar spine disease.

METHODS

All patient data were extracted from the US Nationwide Inpatient Sample database. After surgery, AKI's incidence and risk factors were identified for lumbar degenerative disease. ICD-9 and ICD-10 codes defined lumbar spine degenerative disease, fusion, decompression, and AKI. The study cohort was categorized by type of surgery, that is, decompression alone or spinal fusion. Regression analysis was used to identify associations between AKI and risk factors organized by surgery type.

RESULTS

The incidence of AKI after decompression or fusion was 1.1% and 1.8%, respectively. However, the incidence of AKI in the United States is rising. The strongest predictor of AKI was underlying CKD, which was associated with an 9.0- to 12.9-fold more significant risk of AKI than in subjects without comorbid CKD. In this setting, older age, congestive heart failure, anemia, obesity, coagulopathy and hospital-acquired infections were also strong predictors of AKI. In contrast, long-term aspirin/anticoagulant usage was associated with lowered AKI risk.

CONCLUSION

Findings of this study inform risk stratification for AKI and may help to optimize treatment decisions and care planning after elective surgery for lumbar degenerative disease.

Development of a predictive model for risk stratification of acute kidney injury in patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy

Acute kidney injury (AKI) following hyperthermic intraperitoneal chemotherapy (HIPEC) is common. Identifying patients at risk could have implications for surgical and anesthetic management. We aimed to develop a predictive model that could predict AKI based on patients' preoperative characteristics and intraperitoneal chemotherapy regimen. We retrospectively gathered data of adult patients undergoing HIPEC at our health system between November 2013 and April 2022. Next, we developed a model predicting postoperative AKI using multivariable logistic regression and calculated the performance of the model (area under the receiver operating characteristics curve [AUC]) via tenfold cross-validation. A total of 412 patients were included, of which 36 (8.7%) developed postoperative AKI. Based on our multivariable logistic regression model, multiple preoperative and intraoperative characteristics were associated with AKI. We included the total intraoperative cisplatin dose, body mass index, male sex, and preoperative hemoglobin level in the final model. The mean area under the receiver operating characteristics curve value was 0.82 (95% confidence interval 0.71-0.93). Our risk model predicted AKI with high accuracy in patients undergoing HIPEC in our institution. The external validity of our model should now be tested in independent and prospective patient cohorts.

Cystatin C or creatinine for pre-operative assessment of kidney function and risk of post-operative acute kidney injury: a secondary analysis of the METS cohort study

Background

Post-operative acute kidney injury (PO-AKI) is a common surgical complication consistently associated with subsequent morbidity and mortality. Prior kidney dysfunction is a major risk factor for PO-AKI, however it is unclear whether serum creatinine, the conventional kidney function marker, is optimal in this population. Serum cystatin C is a kidney function marker less affected by body composition and might provide better prognostic information in surgical patients.

Methods

This was a pre-defined, secondary analysis of a multi-centre prospective cohort study of pre-operative functional capacity. Participants were aged ≥40 years, undergoing non-cardiac surgery. We assessed the association of pre-operative estimated glomerular filtration rate (eGFR) calculated using both serum creatinine and serum cystatin C with PO-AKI within 3 days after surgery, defined by KDIGO creatinine changes. The adjusted analysis accounted for established AKI risk factors.

Results

A total of 1347 participants were included (median age 65 years, interquartile range 56-71), of whom 775 (58%) were male. A total of 82/1347 (6%) patients developed PO-AKI. These patients were older, had higher prevalence of cardiovascular disease and related medication, were more likely to have intra-abdominal procedures, had more intraoperative transfusion, and were more likely to be dead at 1 year after surgery 6/82 (7.3%) vs 33/1265 (2.7%) (P = .038). Pre-operative eGFR was lower in AKI than non-AKI patients using both creatinine and cystatin C. When both measurements were considered in a single age- and sex-adjusted model, eGFR-Cysc was strongly associated with PO-AKI, with increasing risk of AKI as eGFR-Cysc decreased below 90, while eGFR-Cr was no longer significantly associated.

Conclusions

Data from over 1000 prospectively recruited surgical patients confirms pre-operative kidney function as major risk factor for PO-AKI. Of the kidney function markers available, compared with creatinine, cystatin C had greater strength of association with PO-AKI and merits further assessment in pre-operative assessment of surgical risk.

Development and external validation of multimodal postoperative acute kidney injury risk machine learning models

Objectives

To develop and externally validate machine learning models using structured and unstructured electronic health record data to predict postoperative acute kidney injury (AKI) across inpatient settings.

Materials and Methods

Data for adult postoperative admissions to the Loyola University Medical Center (2009-2017) were used for model development and admissions to the University of Wisconsin-Madison (2009-2020) were used for validation. Structured features included demographics, vital signs, laboratory results, and nurse-documented scores. Unstructured text from clinical notes were converted into concept unique identifiers (CUIs) using the clinical Text Analysis and Knowledge Extraction System. The primary outcome was the development of Kidney Disease Improvement Global Outcomes stage 2 AKI within 7 days after leaving the operating room. We derived unimodal extreme gradient boosting machines (XGBoost) and elastic net logistic regression (GLMNET) models using structured-only data and multimodal models combining structured data with CUI features. Model comparison was performed using the receiver operating characteristic curve (AUROC), with Delong's test for statistical differences.

Results

The study cohort included 138 389 adult patient admissions (mean [SD] age 58 [16] years; 11 506 [8%] African-American; and 70 826 [51%] female) across the 2 sites. Of those, 2959 (2.1%) developed stage 2 AKI or higher. Across all data types, XGBoost outperformed GLMNET (mean AUROC 0.81 [95% confidence interval (CI), 0.80-0.82] vs 0.78 [95% CI, 0.77-0.79]). The multimodal XGBoost model incorporating CUIs parameterized as term frequency-inverse document frequency (TF-IDF) showed the highest discrimination performance (AUROC 0.82 [95% CI, 0.81-0.83]) over unimodal models (AUROC 0.79 [95% CI, 0.78-0.80]).

Discussion

A multimodality approach with structured data and TF-IDF weighting of CUIs increased model performance over structured data-only models.

Conclusion

These findings highlight the predictive power of CUIs when merged with structured data for clinical prediction models, which may improve the detection of postoperative AKI.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery

OBJECTIVE

Postoperative acute kidney injury (PO-AKI) is a common complication after cardiac surgery. We aimed to evaluate whether machine learning algorithms could significantly improve the risk prediction of PO-AKI.

METHODS

The retrospective cohort study included 2310 adult patients undergoing cardiac surgery in a tertiary teaching hospital, China. Postoperative AKI and severe AKI were identified by the modified KDIGO definition. The sample was randomly divided into a derivation set and a validation set based on a ratio of 4:1. Exploiting conventional logistic regression (LR) and five ML algorithms including decision tree, random forest, gradient boosting classifier (GBC), Gaussian Naive Bayes and multilayer perceptron, we developed and validated the prediction models of PO-AKI. We implemented the interpretation of models using SHapley Additive exPlanation (SHAP) analysis.

RESULTS

Postoperative AKI and severe AKI occurred in 1020 (44.2%) and 286 (12.4%) patients, respectively. Compared with the five ML models, LR model for PO-AKI exhibited the largest AUC (0.812, 95%CI: 0.756, 0.860, all P < 0.05), sensitivity (0.774, 95%CI: 0.719, 0.813), accuracy (0.753, 95%CI: 0.719, 0.781) and Youden index (0.513, 95%CI: 0.451, 0.573). Regarding severe AKI, GBC algorithm showed a significantly higher AUC than the other four ML models (all P < 0.05). Although no significant difference (P = 0.173) was observed in AUCs between GBC (0.86, 95%CI: 0.808, 0.902) and conventional logistic regression (0.803, 95%CI: 0.746, 0.852), GBC achieved greater sensitivity, accuracy and Youden index than conventional LR. Notably, SHAP analyses showed that preoperative serum creatinine, hyperlipidemia, lipid-lowering agents and assisted ventilation time were consistently among the top five important predictors for both postoperative AKI and severe AKI.

CONCLUSION

Logistic regression and GBC algorithm demonstrated moderate to good discrimination and superior performance in predicting PO-AKI and severe AKI, respectively. Interpretation of the models identified the key contributors to the predictions, which could potentially inform clinical interventions.

Machine learning-based intradialytic hypotension prediction of patients undergoing hemodialysis: A multicenter retrospective study

BACKGROUND AND OBJECTIVE

Intradialytic hypotension (IDH) is closely associated with adverse clinical outcomes in HD-patients. An IDH predictor model is important for IDH risk screening and clinical decision-making. In this study, we used Machine learning (ML) to develop IDH model for risk prediction in HD patients.

METHODS

62,227 dialysis sessions were randomly partitioned into training data (70%), test data (20%), and validation data (10%). IDH-A model based on twenty-seven variables was constructed for risk prediction for the next HD treatment. IDH-B model based on ten variables from 64,870 dialysis sessions was developed for risk assessment before each HD treatment. Light Gradient Boosting Machine (LightGBM), Linear Discriminant Analysis, support vector machines, XGBoost, TabNet, and multilayer perceptron were used to develop the predictor model.

RESULTS

In IDH-A model, we identified the LightGBM method as the best-performing and interpretable model with C- statistics of 0.82 in Fall30Nadir90 definitions, which was higher than those obtained using the other models (P<0.01). In other IDH standards of Nadir90, Nadir100, Fall20, Fall30, and Fall20Nadir90, the LightGBM method had a performance with C- statistics ranged 0.77 to 0.89. As a complementary application, the LightGBM model in IDH-B model achieved C- statistics of 0.68 in Fall30Nadir90 definitions and 0.69 to 0.78 in the other five IDH standards, which were also higher than the other methods, respectively.

CONCLUSION

Use ML, we identified the LightGBM method as the good-performing and interpretable model. We identified the top variables as the high-risk factors for IDH incident in HD-patient. IDH-A and IDH-B model can usefully complement each other for risk prediction and further facilitate timely intervention through applied into different clinical setting.

A deep learning-based dynamic model for predicting acute kidney injury risk severity in postoperative patients

BACKGROUND

Acute kidney injury is a common postoperative complication affecting between 10% and 30% of surgical patients. Acute kidney injury is associated with increased resource usage and chronic kidney disease development, with more severe acute kidney injury suggesting more aggressive deterioration in clinical outcomes and mortality.

METHODS

We considered 42,906 surgical patients admitted to University of Florida Health (n = 51,806) between 2014 and 2021. Acute kidney injury stages were determined using the Kidney Disease Improving Global Outcomes serum creatinine criteria. We developed a recurrent neural network-based model to continuously predict acute kidney injury risk and state in the following 24 hours and compared it with logistic regression, random forest, and multi-layer perceptron models. We used medications, laboratory and vital measurements, and derived features from past one-year records as inputs. We analyzed the proposed model with integrated gradients for enhanced explainability.

RESULTS

Postoperative acute kidney injury at any stage developed in 20% (10,664) of the cohort. The recurrent neural network model was more accurate in predicting nearly all categories of next-day acute kidney injury stages (including the no acute kidney injury group). The area under the receiver operating curve and 95% confidence intervals for recurrent neural network and logistic regression models were for no acute kidney injury (0.98 [0.98-0.98] vs 0.93 [0.93-0.93]), stage 1 (0.95 [0.95-0.95] vs. 0.81 [0.80-0.82]), stage 2/3 (0.99 [0.99-0.99] vs 0.96 [0.96-0.97]), and stage 3 with renal replacement therapy (1.0 [1.0-1.0] vs 1.0 [1.0-1.0].

CONCLUSION

The proposed model demonstrates that temporal processing of patient information can lead to more granular and dynamic modeling of acute kidney injury status and result in more continuous and accurate acute kidney injury prediction. We showcase the integrated gradients framework's utility as a mechanism for enhancing model explainability, potentially facilitating clinical trust for future implementation.

Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors

Acute kidney injury (AKI) is a major postoperative complication that lacks established intraoperative predictors. Our objective was to develop a prediction model using preoperative and high-frequency intraoperative data for postoperative AKI. In this retrospective cohort study, we evaluated 77,428 operative cases at a single academic center between 2016 and 2022. A total of 11,212 cases with serum creatinine (sCr) data were included in the analysis. Then, 8519 cases were randomly assigned to the training set and the remainder to the validation set. Fourteen preoperative and twenty intraoperative variables were evaluated using elastic net followed by hierarchical group least absolute shrinkage and selection operator (LASSO) regression. The training set was 56% male and had a median [IQR] age of 62 (51-72) and a 6% AKI rate. Retained model variables were preoperative sCr values, the number of minutes meeting cutoffs for urine output, heart rate, perfusion index intraoperatively, and the total estimated blood loss. The area under the receiver operator characteristic curve was 0.81 (95% CI, 0.77-0.85). At a score threshold of 0.767, specificity was 77% and sensitivity was 74%. A web application that calculates the model score is available online. Our findings demonstrate the utility of intraoperative time series data for prediction problems, including a new potential use of the perfusion index. Further research is needed to evaluate the model in clinical settings.

An interpretable machine learning model for the prevention of contrast-induced nephropathy in patients undergoing lower extremity endovascular interventions for peripheral arterial disease

BACKGROUND

Contrast-induced nephropathy (CIN) is a postprocedural complication associated with increased morbidity and mortality. An important risk factor for development of CIN is renal impairment. Identification of patients at risk for acute renal failure will allow physicians to make appropriate decisions to minimize the incidence of CIN. We developed a machine learning model to stratify risk of acute renal failure that may assist in mitigating risk for CIN in patients with peripheral artery disease (PAD) undergoing endovascular interventions.

METHODS

We utilized the American College of Surgeons National Surgical Quality Improvement Program database to extract clinical and laboratory information associated with 14,444 patients who underwent lower extremity endovascular procedures between 2011 and 2018. Using 11,604 cases from 2011 to 2017 for training and 2840 cases from 2018 for testing, we developed a random forest model to predict risk of 30-day acute renal failure following infra-inguinal endovascular procedures.

RESULTS

Eight variables were identified as contributing optimally to model predictions, the most important being diabetes, preoperative BUN, and claudication. Using these variables, the model achieved an area under the receiver-operating characteristic (AU-ROC) curve of 0.81, accuracy of 0.83, sensitivity of 0.67, and specificity of 0.74. The model performed equally well on white and nonwhite patients (Delong p-value = 0.955) and patients age < 65 and patients age ≥ 65 (Delong p-value = 0.659).

CONCLUSIONS

We develop a model that fairly and accurately stratifies 30-day acute renal failure risk in patients undergoing lower extremity endovascular procedures for PAD. This model may assist in identifying patients who may benefit from strategies to prevent CIN.

Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis

Importance

Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.

Objective

To systematically review published AKI prediction models across all clinical subsettings.

Data Sources

MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.

Study Selection

All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.

Data Extraction and Synthesis

Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.

Main Outcomes and Measures

C statistic was used to measure the discrimination of prediction models.

Results

Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.

Conclusions and Relevance

In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

Pretreatment with a novel Toll-like receptor 4 agonist attenuates renal ischemia-reperfusion injury

Acute kidney injury (AKI) is common in surgical and critically ill patients. This study examined whether pretreatment with a novel Toll-like receptor 4 agonist attenuated ischemia-reperfusion injury (IRI)-induced AKI (IRI-AKI). We performed a blinded, randomized-controlled study in mice pretreated with 3-deacyl 6-acyl phosphorylated hexaacyl disaccharide (PHAD), a synthetic Toll-like receptor 4 agonist. Two cohorts of male BALB/c mice received intravenous vehicle or PHAD (2, 20, or 200 µg) at 48 and 24 h before unilateral renal pedicle clamping and simultaneous contralateral nephrectomy. A separate cohort of mice received intravenous vehicle or 200 µg PHAD followed by bilateral IRI-AKI. Mice were monitored for evidence of kidney injury for 3 days postreperfusion. Kidney function was assessed by serum blood urea nitrogen and creatinine measurements. Kidney tubular injury was assessed by semiquantitative analysis of tubular morphology on periodic acid-Schiff (PAS)-stained kidney sections and by kidney mRNA quantification of injury [neutrophil gelatinase-associated lipocalin (Ngal), kidney injury molecule-1 (Kim-1), and heme oxygenase-1 (Ho-1)] and inflammation [interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (Tnf-α)] using quantitative RT-PCR. Immunohistochemistry was used to quantify proximal tubular cell injury and renal macrophages by quantifying the areas stained with Kim-1 and F4/80 antibodies, respectively, and TUNEL staining to detect the apoptotic nuclei. PHAD pretreatment yielded dose-dependent kidney function preservation after unilateral IRI-AKI. Histological injury, apoptosis, Kim-1 staining, and Ngal mRNA were lower in PHAD-treated mice and IL-1β mRNA was higher in PHAD-treated mice. Similar pretreatment protection was noted with 200 mg PHAD after bilateral IRI-AKI, with significantly reduced Kim-1 immunostaining in the outer medulla of mice treated with PHAD after bilateral IRI-AKI. In conclusion, PHAD pretreatment leads to dose-dependent protection from renal injury after unilateral and bilateral IRI-AKI in mice.NEW & NOTEWORTHY Pretreatment with 3-deacyl 6-acyl phosphorylated hexaacyl disaccharide; a novel synthetic Toll-like receptor 4 agonist, preserves kidney function during ischemia-reperfusion injury-induced acute kidney injury.

Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery

Acute kidney injury (AKI) is a common postoperative complication among patients in the neurological intensive care unit (NICU), often resulting in poor prognosis and high mortality. In this retrospective cohort study, we established a model for predicting AKI following brain surgery based on an ensemble machine learning algorithm using data from 582 postoperative patients admitted to the NICU at the Dongyang People's Hospital from March 1, 2017, to January 31, 2020. Demographic, clinical, and intraoperative data were collected. Four machine learning algorithms (C5.0, support vector machine, Bayes, and XGBoost) were used to develop the ensemble algorithm. The AKI incidence in critically ill patients after brain surgery was 20.8%. Intraoperative blood pressure; postoperative oxygenation index; oxygen saturation; and creatinine, albumin, urea, and calcium levels were associated with the postoperative AKI occurrence. The area under the curve value for the ensembled model was 0.85. The accuracy, precision, specificity, recall, and balanced accuracy values were 0.81, 0.86, 0.44, 0.91, and 0.68, respectively, indicating good predictive ability. Ultimately, the models using perioperative variables exhibited good discriminatory ability for early prediction of postoperative AKI risk in patients admitted to the NICU. Thus, the ensemble machine learning algorithm may be a valuable tool for forecasting AKI.

Mitochondrial and NAD+ metabolism predict recovery from acute kidney injury in a diverse mouse population

Acute kidney failure and chronic kidney disease are global health issues steadily rising in incidence and prevalence. Animal models on a single genetic background have so far failed to recapitulate the clinical presentation of human nephropathies. Here, we used a simple model of folic acid-induced kidney injury in 7 highly diverse mouse strains. We measured plasma and urine parameters, as well as renal histopathology and mRNA expression data, at 1, 2, and 6 weeks after injury, covering the early recovery and long-term remission. We observed an extensive strain-specific response ranging from complete resistance of the CAST/EiJ to high sensitivity of the C57BL/6J, DBA/2J, and PWK/PhJ strains. In susceptible strains, the severe early kidney injury was accompanied by the induction of mitochondrial stress response (MSR) genes and the attenuation of NAD+ synthesis pathways. This is associated with delayed healing and a prolonged inflammatory and adaptive immune response 6 weeks after insult, heralding a transition to chronic kidney disease. Through a thorough comparison of the transcriptomic response in mouse and human disease, we show that critical metabolic gene alterations were shared across species, and we highlight the PWK/PhJ strain as an emergent model of transition from acute kidney injury to chronic disease.

Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy

BACKGROUND

Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods. We conducted this systematic review and meta-analysis to estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period.

METHODS

Pubmed, Embase, and Cochrane Library were searched to 2nd October 2021. Studies presenting diagnostic performance of artificial intelligence in the early detection of perioperative acute kidney injury were included. True positives, false positives, true negatives and false negatives were pooled to collate specificity and sensitivity with 95% CIs and results were portrayed in forest plots. The risk of bias of eligible studies was assessed using the PROBAST tool.

RESULTS

Nineteen studies involving 304,076 patients were included. Quantitative random-effects meta-analysis using the Rutter and Gatsonis hierarchical summary receiver operating characteristics (HSROC) model revealed pooled sensitivity, specificity, and diagnostic odds ratio of 0.77 (95% CI: 0.73 to 0.81),0.75 (95% CI: 0.71 to 0.80), and 10.7 (95% CI 8.5 to 13.5), respectively. Threshold effect was found to be the only source of heterogeneity, and there was no evidence of publication bias.

CONCLUSIONS

Our review demonstrates the promising performance of artificial intelligence for early prediction of perioperative AKI. The limitations of lacking external validation performance and being conducted only at a single center should be overcome.

TRIAL REGISTRATION

This study was not registered with PROSPERO.

Pharmacological inhibition of protein tyrosine kinases axl and fyn reduces TNF-α-induced endothelial inflammatory activation in vitro

Major surgery induces systemic inflammation leading to pro-inflammatory activation of endothelial cells. Endothelial inflammation is one of the drivers of postoperative organ damage, including acute kidney injury Tumour Necrosis Factor alpha (TNF-α) is an important component of surgery-induced pro-inflammatory activation of endothelial cells. Kinases, the backbone of signalling cascades, can be targeted by pharmacological inhibition. This is a promising treatment option to interfere with excessive endothelial inflammation. In this study, we identified activated kinases as potential therapeutic targets. These targets were pharmacologically inhibited to reduce TNF-α-induced pro-inflammatory signalling in endothelial cells. Kinome profiling using PamChip arrays identified 64 protein tyrosine kinases and 88 serine-threonine kinases, the activity of which was determined at various timepoints (5-240 min) following stimulation with 10 ng/ml TNF-α in Human umbilical vein endothelial cells in vitro. The PTKs Axl and Fyn were selected based on high kinase activity profiles. Co-localisation experiments with the endothelial-specific protein CD31 showed Axl expression in endothelial cells of glomeruli and Fyn in arterioles and glomeruli of both control and TNF-α-exposed mice. Pharmacological inhibition with Axl inhibitor BMS-777607 and Fyn inhibitor PP2 significantly reduced TNF-α-induced pro-inflammatory activation of E-selectin, VCAM-1, ICAM-1, IL-6 and IL-8 at mRNA and VCAM-1, ICAM-1, and IL-6 at protein level in HUVEC in vitro. Upon pharmacological inhibition with each inhibitor, leukocyte adhesion to HUVEC was also significantly reduced, however to a minor extent. In conclusion, pre-treatment of endothelial cells with kinase inhibitors BMS-777607 and PP2 reduces TNF-α-induced endothelial inflammation in vitro.

Machine learning for risk stratification in kidney disease

PURPOSE OF REVIEW

Risk stratification for chronic kidney is becoming increasingly important as a clinical tool for both treatment and prevention measures. The goal of this review is to identify how machine learning tools contribute and facilitate risk stratification in the clinical setting.

RECENT FINDINGS

The two key machine learning paradigms to predictively stratify kidney disease risk are genomics-based and electronic health record based approaches. These methods can provide both quantitative information such as relative risk and qualitative information such as characterizing risk by subphenotype.

SUMMARY

The four key methods to stratify chronic kidney disease risk are genomics, multiomics, supervised and unsupervised machine learning methods. Polygenic risk scores utilize whole genome sequencing data to generate an individual's relative risk compared with the population. Multiomic methods integrate information from multiple biomarkers to generate trajectories and prognostic different outcomes. Supervised machine learning methods can directly utilize the growing compendia of electronic health records such as laboratory results and notes to generate direct risk predictions, while unsupervised machine learning methods can cluster individuals with chronic kidney disease into subphenotypes with differing approaches to care.

Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery

IMPORTANCE

A variety of perioperative risk factors are associated with postoperative mortality risk. However, the relative contribution of routinely collected intraoperative clinical parameters to short-term and long-term mortality remains understudied.

OBJECTIVE

To examine the performance of multiple machine learning models with data from different perioperative periods to predict 30-day, 1-year, and 5-year mortality and investigate factors that contribute to these predictions.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study using prospectively collected data, risk prediction models were developed for short-term and long-term mortality after cardiac surgery. Included participants were adult patients undergoing a first-time valve operation, coronary artery bypass grafting, or a combination of both between 1997 and 2017 in a single center, the University Medical Centre Groningen in the Netherlands. Mortality data were obtained in November 2017. Data analysis took place between February 2020 and August 2021.

EXPOSURE

Cardiac surgery.

MAIN OUTCOMES AND MEASURES

Postoperative mortality rates at 30 days, 1 year, and 5 years were the primary outcomes. The area under the receiver operating characteristic curve (AUROC) was used to assess discrimination. The contribution of all preoperative, intraoperative hemodynamic and temperature, and postoperative factors to mortality was investigated using Shapley additive explanations (SHAP) values.

RESULTS

Data from 9415 patients who underwent cardiac surgery (median [IQR] age, 68 [60-74] years; 2554 [27.1%] women) were included. Overall mortality rates at 30 days, 1 year, and 5 years were 268 patients (2.8%), 420 patients (4.5%), and 612 patients (6.5%), respectively. Models including preoperative, intraoperative, and postoperative data achieved AUROC values of 0.82 (95% CI, 0.78-0.86), 0.81 (95% CI, 0.77-0.85), and 0.80 (95% CI, 0.75-0.84) for 30-day, 1-year, and 5-year mortality, respectively. Models including only postoperative data performed similarly (30 days: 0.78 [95% CI, 0.73-0.82]; 1 year: 0.79 [95% CI, 0.74-0.83]; 5 years: 0.77 [95% CI, 0.73-0.82]). However, models based on all perioperative data provided less clinically usable predictions, with lower detection rates; for example, postoperative models identified a high-risk group with a 2.8-fold increase in risk for 5-year mortality (4.1 [95% CI, 3.3-5.1]) vs an increase of 11.3 (95% CI, 6.8-18.7) for the high-risk group identified by the full perioperative model. Postoperative markers associated with metabolic dysfunction and decreased kidney function were the main factors contributing to mortality risk.

CONCLUSIONS AND RELEVANCE

This study found that the addition of continuous intraoperative hemodynamic and temperature data to postoperative data was not associated with improved machine learning-based identification of patients at increased risk of short-term and long-term mortality after cardiac operations.

Artificial Intelligence in Acute Kidney Injury Prediction

The use of artificial intelligence (AI) in nephrology and its associated clinical research is growing. Recent years have seen increased interest in utilizing AI to predict the development of hospital-based acute kidney injury (AKI). Several AI techniques have been employed to improve the ability to detect AKI across a variety of hospitalized settings. This review discusses the evolutions of AKI risk prediction discussing the static risk assessment models of yesteryear as well as the more recent trend toward AI and advanced learning techniques. We discuss the relative improvement in AKI detection as well as the relative dearth of data around the clinical implementation and patient outcomes using these models. The use of AI for AKI detection and clinical care is in its infancy, and this review describes how we arrived at our current position and hints at the promise of the future.

Development and validation of AKI prediction model in postoperative critically ill patients: a multicenter cohort study

BACKGROUND

Acute kidney injury (AKI) is a common complication, especially among postoperative critically ill patients. Early identification of AKI is essential for reducing mortality.

METHODS

Multicenter data were used to develop an AKI prediction model for critically ill postoperative patients. A total of 1731 patients admitted to intensive care units (ICUs) were divided into a development set (n=1196) and a validation set (n=535) according to the principle of 7:3 randomization. Multivariate logistic regression analysis was performed on the predictors identified by univariate analysis, and a nomogram was created based on the predictors. The area under the receiver operating characteristic curve (AUROC) was used to assess the discrimination of the model. Calibration curves were generated, and the Hosmer-Lemeshow (HL) goodness of fit test was carried out. Decision curve analysis (DCA) was performed to assess the net clinical benefit.

RESULTS

The final model included 7 predictors: age, emergency surgery, abnormal basal creatinine level (BCr), chronic kidney disease (CKD), use of nephrotoxic drugs, diuretic use, and the Sequential Organ Failure Assessment (SOFA) score. A nomogram was drawn based on the predictors. The AUROC of the model in the development set was 0.725 (95% confidence interval (CI): 0.696-0.754). In the validation set, the AUROC was 0.706 (95% CI: 0.656-0.744). The model showed good discrimination (>70%) in both sets, and the HL test indicated that the model fit was good (P>0.05). DCA showed that our model is clinically useful.

CONCLUSION

The novel prediction model can be used to identify high-risk postoperative patients and provide a scientific and effective basis for clinicians to identify AKI early with a nomogram.

Integration of feature vectors from raw laboratory, medication and procedure names improves the precision and recall of models to predict postoperative mortality and acute kidney injury

Manuscripts that have successfully used machine learning (ML) to predict a variety of perioperative outcomes often use only a limited number of features selected by a clinician. We hypothesized that techniques leveraging a broad set of features for patient laboratory results, medications, and the surgical procedure name would improve performance as compared to a more limited set of features chosen by clinicians. Feature vectors for laboratory results included 702 features total derived from 39 laboratory tests, medications consisted of a binary flag for 126 commonly used medications, procedure name used the Word2Vec package for create a vector of length 100. Nine models were trained: baseline features, one for each of the three types of data Baseline + Each data type, (all features, and then all features with feature reduction algorithm. Across both outcomes the models that contained all features (model 8) (Mortality ROC-AUC 94.32 ± 1.01, PR-AUC 36.80 ± 5.10 AKI ROC-AUC 92.45 ± 0.64, PR-AUC 76.22 ± 1.95) was superior to models with only subsets of features. Featurization techniques leveraging a broad away of clinical data can improve performance of perioperative prediction models.

The Predictive Value of Pre-operative N-Terminal Pro-B-Type Natriuretic Peptide in the Risk of Acute Kidney Injury After Non-cardiac Surgery

Objective

To evaluate the association between N-terminal pro-B-type natriuretic peptide (NT-proBNP) and risk of post-operative acute kidney injury (PO-AKI).

Methods

The electronic medical records and laboratory results were obtained from 3,949 adult patients (≥18 years) undergoing non-cardiac surgery performed between 1 October 2012 to 1 October 2019 at the Third Xiangya Hospital, Central South University, China. Collected data were analyzed retrospectively.

Results

In all, 5.3% (209 of 3,949) of patients developed PO-AKI. Pre-operative NT-proBNP was an independent predictor of PO-AKI. After adjustment for significant variables, OR for AKI of highest and lowest NT-proBNP quintiles was 1.96 (95% CI, 1.04–3.68, P = 0.008), OR per 1-unit increment in natural log transformed NT-proBNP was 1.20 (95% CI, 1.09–1.32, P < 0.001). Compared with clinical variables alone, the addition of NT-proBNP modestly improved the discrimination [change in area under the curve(AUC) from 0.82 to 0.83, ΔAUC=0.01, P = 0.024] and the reclassification (continuous net reclassification improvement 0.15, 95% CI, 0.01–0.29, P = 0.034, improved integrated discrimination 0.01, 95% CI, 0.002–0.02, P = 0.017) of AKI and non-AKI cases.

Conclusions

Results from our retrospective cohort study showed that the addition of pre-operative NT-proBNP concentrations could better predict post-operative AKI in a cohort of non-cardiac surgery patients and achieve higher net benefit in decision curve analysis.

Ropivacaine with intraspinal administration alleviates preeclampsia-induced kidney injury via glycocalyx /alpha 7 nicotinic acetylcholine receptor pathway

Preeclampsia is characterized by hypertension and proteinuria, which is associated with kidney injury. Glycocalyx (GCX) degradation mediated endothelial injury can result in proteinuria and kidney damage. alpha 7 nicotinic acetylcholine receptor (α7nAChR) connects nervous and immune systems to respond to stress or injury. We aimed to explore the protective effect and mechanism of intraspinal analgesia on maternal kidney injury in preeclampsia. Endotoxin-induced preeclampsia rats treated with ropivacaine via intraspinal administration. Renal histopathological examination was performed, cell apoptosis in the kidney, the levels of Glycocalyx markers of Syndecan-1 and heparin sulfate (HS) in maternal serum, Syndecan-1 along with α7nAChR in the kidney were measured. Our results showed that kidney injury was obviously in preeclampsia rats with proteinuria, endothelial damage, higher apoptosis rate, increasing levels of Syndecan-1 and HS in serum, upregulated Syndecan-1 expression but downregulated α7nAChR expression in kidney. Preeclampsia rats treated with intraspinal injected ropivacaine attenuated preeclampsia-induced kidney injury as Syndecan-1 and HS were decreased in serum, Syndecan-1 expression was suppressed as well as α7nAChR was activated in the kidney. Our results suggested that Ropivacaine administered through the spinal canal may protect preeclampsia-induced renal injury by decreasing GCX and α7nAChR activation.

Risk Classification and Subphenotyping of Acute Kidney Injury: Concepts and Methodologies

Acute kidney injury (AKI) is a complex syndrome with a paucity of therapeutic development. One aspect that could explain the lack of implementation science in the AKI field is the vast heterogeneity of the AKI syndrome, which hinders precise therapeutic applications for specific AKI subpopulations. In this context, there is a consensual focus of the scientific community toward the development and validation of tools to better subphenotype AKI and therefore facilitate precision medicine approaches. The subphenotyping of AKI requires the use of specific methodologies suitable for interrogation of multimodal data inputs from different sources such as electronic health records, organ support devices, and/or biospecimens and tissues. Over the past years, the surge of artificial intelligence applied to health care has yielded novel machine learning methodologies for data acquisition, harmonization, and interrogation that can assist with subphenotyping of AKI. However, one should recognize that although risk classification and subphenotyping of AKI is critically important, testing their potential applications is even more important to promote implementation science. For example, risk-classification should support actionable interventions that could ameliorate or prevent the occurrence of the outcome being predicted. Furthermore, subphenotyping could be applied to predict therapeutic responses to support enrichment and adaptive platforms for pragmatic clinical trials.

A systematic review of robotic surgery: From supervised paradigms to fully autonomous robotic approaches

BACKGROUND

From traditional open surgery to laparoscopic surgery and robot-assisted surgery, advances in robotics, machine learning, and imaging are pushing the surgical approach to-wards better clinical outcomes. Pre-clinical and clinical evidence suggests that automation may standardise techniques, increase efficiency, and reduce clinical complications.

METHODS

A PRISMA-guided search was conducted across PubMed and OVID.

RESULTS

Of the 89 screened articles, 51 met the inclusion criteria, with 10 included in the final review. Automatic data segmentation, trajectory planning, intra-operative registration, trajectory drilling, and soft tissue robotic surgery were discussed.

CONCLUSION

Although automated surgical systems remain conceptual, several research groups have developed supervised autonomous robotic surgical systems with increasing consideration for ethico-legal issues for automation. Automation paves the way for precision surgery and improved safety and opens new possibilities for deploying more robust artificial intelligence models, better imaging modalities and robotics to improve clinical outcomes.

Artificial intelligence in perioperative medicine - a narrative review

Recent advancements in artificial intelligence (AI) techniques have enabled the development of accurate prediction models using clinical big data. AI models for perioperative risk stratification, intraoperative event prediction, biosignal analyses, and intensive care medicine have been developed in the field of perioperative medicine. Some of these models have been validated using external datasets and randomized controlled trials. Once these models are implemented in electronic health record systems or software medical devices, they could help anesthesiologists improve clinical outcomes by accurately predicting complications and suggesting optimal treatment strategies in real-time. This review provides an overview of the AI techniques used in perioperative medicine and a summary of the studies that have been published using these techniques. Understanding these techniques will aid in their appropriate application in clinical practice.

Application of Machine Learning Algorithms to Predict Acute Kidney Injury in Elderly Orthopedic Postoperative Patients

Objective

There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients’ early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models.

Methods

We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n=799) and test (20%;n=201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively.

Results

In predicting AKI, nine ML algorithms posted AUC of 0.656–1.000 in the training cohort, with the randomforest standing out and AUC of 0.674–0.821 in the test cohort, with the logistic regression model standing out. Thus, we applied the logistic regression model to establish nomogram. The nomogram was comprised of ten variables: age, body mass index, American Society of Anesthesiologists, hypoproteinemia, hypertension, diabetes, anemia, duration of low mean arterial pressure, mean arterial pressure, transfusion.The calibration curves showed good agreement between prediction and observation in both the training and test sets.

Conclusion

By including intraoperative and preoperative risk factors, ML algorithm can predict AKI and logistic regression model performing the best. Our prediction model and nomogram that are based on this ML algorithm can help lead decision-making for strategies to inhibit AKI over the perioperative duration.

The BTK Safety Score: A Novel Scoring System for Risk Stratifying Patients Undergoing Simultaneous Bilateral Total Knee Arthroplasty

Selection of appropriate candidates for simultaneous bilateral total knee arthroplasty (si-BTKA) is crucial for minimizing postoperative complications. The aim of this study was to develop a scoring system for identifying patients who may be appropriate for si-BTKA. Patients who underwent si-BTKA were identified in the National Surgical Quality Improvement Program database. Patients who experienced a major 30-day complication were identified as high-risk patients for si-BTKA who potentially would have benefitted from staged bilateral total knee arthroplasty. Major complications included deep wound infection, pneumonia, renal insufficiency or failure, cerebrovascular accident, cardiac arrest, myocardial infarction, pulmonary embolism, sepsis, or death. The predictive model was trained using randomly split 70% of the dataset and validated on the remaining 30%. The scoring system was compared against the American Society of Anesthesiologists (ASA) score, the Charlson Comorbidity Index (CCI), and legacy risk-stratification measures, using area under the curve (AUC) statistic. Total 4,630 patients undergoing si-BTKA were included in our cohort. In our model, patients are assigned points based on the following risk factors: +1 for age ≥ 75, +2 for age ≥ 82, +1 for body mass index (BMI) ≥ 34, +2 for BMI ≥ 42, +1 for hypertension requiring medication, +1 for pulmonary disease (chronic obstructive pulmonary disease or dyspnea), and +3 for end-stage renal disease. The scoring system exhibited an AUC of 0.816, which was significantly higher than the AUC of ASA (0.545; p < 0.001) and CCI (0.599; p < 0.001). The BTK Safety Score developed and validated in our study can be used by surgeons and perioperative teams to risk stratify patients undergoing si-BTKA. Future work is needed to assess this scoring system's ability to predict long-term functional outcomes.

Using Routinely Collected Electronic Health Record Data to Predict Readmission and Target Care Coordination

ABSTRACT

Patients with chronic renal failure (CRF) are at high risk of being readmitted to hospitals within 30 days. Routinely collected electronic health record (EHR) data may enable hospitals to predict CRF readmission and target interventions to increase quality and reduce readmissions. We compared the ability of manually extracted variables to predict readmission compared with EHR-based prediction using multivariate logistic regression on 1 year of admission data from an academic medical center. Categorizing three routinely collected variables (creatinine, B-type natriuretic peptide, and length of stay) increased readmission prediction by 30% compared with paper-based methods as measured by C-statistic (AUC). Marginal effects analysis using the final multivariate model provided patient-specific risk scores from 0% to 44.3%. These findings support the use of routinely collected EHR data for effectively stratifying readmission risk for patients with CRF. Generic readmission risk tools may be evidence-based but are designed for general populations and may not account for unique traits of specific patient populations-such as those with CRF. Routinely collected EHR data are a rapid, more efficient strategy for risk stratifying and strategically targeting care. Earlier risk stratification and reallocation of clinician effort may reduce readmissions. Testing this risk model in additional populations and settings is warranted.

Risk of Postoperative Renal Failure in Radical Nephrectomy and Nephroureterectomy: A Validated Risk Prediction Model

INTRODUCTION

The study aimed to construct and validate a risk prediction model for incidence of postoperative renal failure (PORF) following radical nephrectomy and nephroureterectomy.

METHODS

The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database years 2005-2014 were used for the derivation cohort. A stepwise multivariate logistic regression analysis was conducted, and the final model was validated with an independent cohort from the ACS-NSQIP database years 2015-2017.

RESULTS

In cohort of 14,519 patients, 296 (2.0%) developed PORF. The final 9-factor model included age, gender, diabetes, hypertension, BMI, preoperative creatinine, hematocrit, platelet count, and surgical approach. Model receiver-operator curve analysis provided a C-statistic of 0.79 (0.77, 0.82; p < 0.001), and overall calibration testing R2 was 0.99. Model performance in the validation cohort provided a C-statistic of 0.79 (0.76, 0.81; p < 0.001).

CONCLUSION

PORF is a known risk factor for chronic kidney disease and cardiovascular morbidity, and is a common occurrence after unilateral kidney removal. The authors propose a robust and validated risk prediction model to aid in identification of high-risk patients and optimization of perioperative care.

An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation

Background

Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making.

Methods

Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms.

Results

430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model.

Conclusions

Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02990-4.

Intraoperative Data Enhance the Detection of High-Risk Acute Kidney Injury Patients When Added to a Baseline Prediction Model

BACKGROUND

Aspects of intraoperative management (eg, hypotension) are associated with acute kidney injury (AKI) in noncardiac surgery patients. However, it is unclear if and how the addition of intraoperative data affects a baseline risk prediction model for postoperative AKI.

METHODS

With institutional review board (IRB) approval, an institutional cohort (2005-2015) of inpatient intra-abdominal surgery patients without preoperative AKI was identified. Data from the American College of Surgeons National Surgical Quality Improvement Program (preoperative and procedure data), Anesthesia Information Management System (intraoperative data), and electronic health record (postoperative laboratory data) were linked. The sample was split into derivation/validation (70%/30%) cohorts. AKI was defined as an increase in serum creatinine ≥0.3 mg/dL within 48 hours or >50% within 7 days of surgery. Forward logistic regression fit a baseline model incorporating preoperative variables and surgical procedure. Forward logistic regression fit a second model incorporating the previously selected baseline variables, as well as additional intraoperative variables. Intraoperative variables reflected the following aspects of intraoperative management: anesthetics, beta-blockers, blood pressure, diuretics, fluids, operative time, opioids, and vasopressors. The baseline and intraoperative models were evaluated based on statistical significance and discriminative ability (c-statistic). The risk threshold equalizing sensitivity and specificity in the intraoperative model was identified.

RESULTS

Of 2691 patients in the derivation cohort, 234 (8.7%) developed AKI. The baseline model had c-statistic 0.77 (95% confidence interval [CI], 0.74-0.80). The additional variables added to the intraoperative model were significantly associated with AKI (P < .0001) and the intraoperative model had c-statistic 0.81 (95% CI, 0.78-0.83). Sensitivity and specificity were equalized at a risk threshold of 9.0% in the intraoperative model. At this threshold, the baseline model had sensitivity and specificity of 71% (95% CI, 65-76) and 69% (95% CI, 67-70), respectively, and the intraoperative model had sensitivity and specificity of 74% (95% CI, 69-80) and 74% (95% CI, 73-76), respectively. The high-risk group had an AKI risk of 18% (95% CI, 15-20) in the baseline model and 22% (95% CI, 19-25) in the intraoperative model.

CONCLUSIONS

Intraoperative data, when added to a baseline risk prediction model for postoperative AKI in intra-abdominal surgery patients, improves the performance of the model.

Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review

PURPOSE OF REVIEW

Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associated with high morbidity and mortality. The risk of developing AKI depends on the presence of preexisting comorbidities and the cause of the current disease. Besides, many other parameters affect the kidney function, such as the state of other vital organs, the host response, and the initiated treatment. Advancements in the field of informatics have led to the opportunity to store and utilize the patient-related data to train and validate models to detect specific patterns and, as such, predict disease states or outcomes.

RECENT FINDINGS

Machine-learning techniques have also been applied to predict AKI, as well as the patients' outcomes related to their AKI, such as mortality or the need for kidney replacement therapy. Several models have recently been developed, but only a few of them have been validated in external cohorts.

SUMMARY

In this article, we provide an overview of the machine-learning prediction models for AKI and its outcomes in critically ill patients and individuals undergoing major surgery. We also discuss the pitfalls and the opportunities related to the implementation of these models in clinical practices.

Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models

Artificial intelligence (AI) is the development of computer systems that normally require human intelligence. In the field of acute kidney injury (AKI) AI has led to an evolution of risk prediction models. In the past, static prediction models were developed using baseline (eg, preoperative) data to evaluate AKI risk. Newer models which incorporated baseline as well as evolving data collected during a hospital admission have shown improved predicative abilities. In this review, we will summarize the advances made in AKI risk prediction over the last several years, including a shift toward more dynamic, real-time, electronic medical record-based models. In addition, we will be discussing the role of electronic AKI alerts and decision support tools. Recent studies have demonstrated improved patient outcomes through the use of these tools which monitor for nephrotoxin medication exposures as well as provide kidney focused care bundles for patients at high risk for severe AKI. Finally, we will briefly discuss the pitfalls and implications of implementing these scores, alerts, and support tools.

The role of artificial intelligence in surgical patient perioperative management

Perioperative medicine is a patient-centered, multidisciplinary and integrated clinical practice that starts from the moment of contemplation of surgery until full recovery. Every perioperative phase (preoperative, intraoperative and postoperative) must be studied and planned in order to optimize the entire patient management. Perioperative optimization does not only concern a short-term outcome improvement, but it has also a strong impact on long term survival. Clinical cases variability leads to the collection and analysis of a huge amount of different data, coming from multiple sources, making perioperative management standardization very difficult. Artificial Intelligence (AI) can play a primary role in this challenge, helping human mind in perioperative practice planning and decision-making process. AI refers to the ability of a computer system to perform functions and reasoning typical of the human mind; Machine Learning (ML) could play a fundamental role in presurgical planning, during intraoperative phase and postoperative management. Perioperative medicine is the cornerstone of surgical patient management and the tools deriving from the application of AI seem very promising as a support in optimizing the management of each individual patient. Despite the increasing help that will derive from the use of AI tools, the uniqueness of the patient and the particularity of each individual clinical case will always keep the role of the human mind central in clinical and perioperative management. The role of the physician, who must analyze the outputs provided by AI by following his own experience and knowledge, remains and will always be essential.

Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury

IMPORTANCE

Acute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patients at high risk of AKI are limited, and few prediction models have been externally validated.

OBJECTIVE

To internally and externally validate a machine learning risk score to detect AKI in hospitalized patients.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study included 495 971 adult hospital admissions at the University of Chicago (UC) from 2008 to 2016 (n = 48 463), at Loyola University Medical Center (LUMC) from 2007 to 2017 (n = 200 613), and at NorthShore University Health System (NUS) from 2006 to 2016 (n = 246 895) with serum creatinine (SCr) measurements. Patients with an SCr concentration at admission greater than 3.0 mg/dL, with a prior diagnostic code for chronic kidney disease stage 4 or higher, or who received kidney replacement therapy within 48 hours of admission were excluded. A simplified version of a previously published gradient boosted machine AKI prediction algorithm was used; it was validated internally among patients at UC and externally among patients at NUS and LUMC.

MAIN OUTCOMES AND MEASURES

Prediction of Kidney Disease Improving Global Outcomes SCr-defined stage 2 AKI within a 48-hour interval was the primary outcome. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC).

RESULTS

The study included 495 971 adult admissions (mean [SD] age, 63 [18] years; 87 689 [17.7%] African American; and 266 866 [53.8%] women) across 3 health systems. The development of stage 2 or higher AKI occurred in 15 664 of 48 463 patients (3.4%) in the UC cohort, 5711 of 200 613 (2.8%) in the LUMC cohort, and 3499 of 246 895 (1.4%) in the NUS cohort. In the UC cohort, 332 patients (0.7%) required kidney replacement therapy compared with 672 patients (0.3%) in the LUMC cohort and 440 patients (0.2%) in the NUS cohort. The AUCs for predicting at least stage 2 AKI in the next 48 hours were 0.86 (95% CI, 0.86-0.86) in the UC cohort, 0.85 (95% CI, 0.84-0.85) in the LUMC cohort, and 0.86 (95% CI, 0.86-0.86) in the NUS cohort. The AUCs for receipt of kidney replacement therapy within 48 hours were 0.96 (95% CI, 0.96-0.96) in the UC cohort, 0.95 (95% CI, 0.94-0.95) in the LUMC cohort, and 0.95 (95% CI, 0.94-0.95) in the NUS cohort. In time-to-event analysis, a probability cutoff of at least 0.057 predicted the onset of stage 2 AKI a median (IQR) of 27 (6.5-93) hours before the eventual doubling in SCr concentrations in the UC cohort, 34.5 (19-85) hours in the NUS cohort, and 39 (19-108) hours in the LUMC cohort.

CONCLUSIONS AND RELEVANCE

In this study, the machine learning algorithm demonstrated excellent discrimination in both internal and external validation, supporting its generalizability and potential as a clinical decision support tool to improve AKI detection and outcomes.