Simple Postoperative AKI Risk (SPARK) Classification before Noncardiac Surgery: A Prediction Index Development Study with External Validation

Artificial intelligence and predictive models for early detection of acute kidney injury: transforming clinical practice

Acute kidney injury (AKI) presents a significant clinical challenge due to its rapid progression to kidney failure, resulting in serious complications such as electrolyte imbalances, fluid overload, and the potential need for renal replacement therapy. Early detection and prediction of AKI can improve patient outcomes through timely interventions. This review was conducted as a narrative literature review, aiming to explore state-of-the-art models for early detection and prediction of AKI. We conducted a comprehensive review of findings from various studies, highlighting their strengths, limitations, and practical considerations for implementation in healthcare settings. We highlight the potential benefits and challenges of their integration into routine clinical care and emphasize the importance of establishing robust early-detection systems before the introduction of artificial intelligence (AI)-assisted prediction models. Advances in AI for AKI detection and prediction are examined, addressing their clinical applicability, challenges, and opportunities for routine implementation.

Association of velocity-pressure loop-derived values recorded during neurosurgical procedures with postoperative organ failure biomarkers: a retrospective single-center study

BACKGROUND

Perioperative renal and myocardial protection primarily depends on preoperative prediction tools, along with intraoperative optimization of cardiac output (CO) and mean arterial pressure (MAP). We hypothesise that monitoring the intraoperative global afterload angle (GALA), a proxy of ventricular afterload derived from the velocity pressure (VP) loop, could better predict changes in postoperative biomarkers than the recommended traditional MAP and CO.

METHOD

This retrospective monocentric study included patients programmed for neurosurgery with continuous VP loop monitoring. Patients with hemodynamic instability were excluded. Those presenting a 1-day post-surgery increase in creatinine, B-type natriuretic peptide, or troponin Ic us were labelled Bio+, Bio- otherwise. Demographics, intra-operative data, and comorbidities were considered as covariates. The study aimed to determine if intraoperative GALA monitoring could predict early postoperative biomarker disruption.

RESULT

From November 2018 to November 2020, 86 patients were analysed (Bio+/Bio- = 47/39). Bio+ patients were significantly older (62 [54-69] vs. 42 [34-57] years, p <  0.0001), More often hypertensive (25% vs. 9%, p = 0.009), and more frequently treated with antihypertensive drugs (31.9% vs. 7.7%, p = 0.013). GALA was significantly larger in Bio+ patients (40 [31-56] vs. 23 [19-29] °, p < 0.0001), while CO, MAP, and cumulative time spent <65mmHg were similar between groups. GALA exhibited strong predictive performances for postoperative biological deterioration (AUC = 0.88 [0.80-0.95]), significantly outperforming MAP (MAP AUC = 0.55 [0.43-0.68], p < 0.0001).

CONCLUSION

GALA under general anaesthesia prove more effective in detecting patients at risk of early cardiac or renal biological deterioration, compared to classical hemodynamic parameters.

Complex Pathophysiology of Acute Kidney Injury (AKI) in Aging: Epigenetic Regulation, Matrix Remodeling, and the Healing Effects of H2S

The kidney is an essential excretory organ that works as a filter of toxins and metabolic by-products of the human body and maintains osmotic pressure throughout life. The kidney undergoes several physiological, morphological, and structural changes with age. As life expectancy in humans increases, cell senescence in renal aging is a growing challenge. Identifying age-related kidney disorders and their cause is one of the contemporary public health challenges. While the structural abnormalities to the extracellular matrix (ECM) occur, in part, due to changes in MMPs, EMMPRIN, and Meprin-A, a variety of epigenetic modifiers, such as DNA methylation, histone alterations, changes in small non-coding RNA, and microRNA (miRNA) expressions are proven to play pivotal roles in renal pathology. An aged kidney is vulnerable to acute injury due to ischemia-reperfusion, toxic medications, altered matrix proteins, systemic hemodynamics, etc., non-coding RNA and miRNAs play an important role in renal homeostasis, and alterations of their expressions can be considered as a good marker for AKI. Other epigenetic changes, such as histone modifications and DNA methylation, are also evident in AKI pathophysiology. The endogenous production of gaseous molecule hydrogen sulfide (H2S) was documented in the early 1980s, but its ameliorative effects, especially on kidney injury, still need further research to understand its molecular mode of action in detail. H2S donors heal fibrotic kidney tissues, attenuate oxidative stress, apoptosis, inflammation, and GFR, and also modulate the renin-angiotensin-aldosterone system (RAAS). In this review, we discuss the complex pathophysiological interplay in AKI and its available treatments along with future perspectives. The basic role of H2S in the kidney has been summarized, and recent references and knowledge gaps are also addressed. Finally, the healing effects of H2S in AKI are described with special emphasis on epigenetic regulation and matrix remodeling.

Preoperative risk prediction models for acute kidney injury after noncardiac surgery: an independent external validation cohort study

BACKGROUND

Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them.

METHODS

We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases.

RESULTS

Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis.

CONCLUSIONS

We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery.

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).

Incidence, risk factors and outcome of postoperative acute kidney injury in China

BACKGROUND

Postoperative acute kidney injury (AKI) is a common condition after surgery, however, the available data about nationwide epidemiology of postoperative AKI in China from large and high-quality studies are limited. This study aimed to determine the incidence, risk factors and outcomes of postoperative AKI among patients undergoing surgery in China.

METHODS

This was a large, multicentre, retrospective study performed in 16 tertiary medical centres in China. Adult patients (≥18 years of age) who underwent surgical procedures from 1 January 2013 to 31 December 2019 were included. Postoperative AKI was defined by the Kidney Disease: Improving Global Outcomes creatinine criteria. The associations of AKI and in-hospital outcomes were investigated using logistic regression models adjusted for potential confounders.

RESULTS

Among 520 707 patients included in our study, 25 830 (5.0%) patients developed postoperative AKI. The incidence of postoperative AKI varied by surgery type, which was highest in cardiac (34.6%), urologic (8.7%) and general (4.2%) surgeries. A total of 89.2% of postoperative AKI cases were detected in the first 2 postoperative days. However, only 584 (2.3%) patients with postoperative AKI were diagnosed with AKI on discharge. Risk factors for postoperative AKI included older age, male sex, lower baseline kidney function, pre-surgery hospital stay ≤3 days or >7 days, hypertension, diabetes mellitus and use of proton pump inhibitors or diuretics. The risk of in-hospital death increased with the stage of AKI. In addition, patients with postoperative AKI had longer lengths of hospital stay (12 versus 19 days) and were more likely to require intensive care unit care (13.1% versus 45.0%) and renal replacement therapy (0.4% versus 7.7%).

CONCLUSIONS

Postoperative AKI was common across surgery type in China, particularly for patients undergoing cardiac surgery. Implementation and evaluation of an alarm system is important for the battle against postoperative AKI.

Development and validation of a medication-based risk prediction model for acute kidney injury in older outpatients

BACKGROUND

Older adults are at an increased risk of acute kidney injury (AKI), particularly in community settings, often due to medications. Effective prevention hinges on identifying high-risk patients, yet existing models for predicting AKI risk in older outpatients are scarce, particularly those incorporating medication variables. We aimed to develop an AKI risk prediction model that included medication-related variables for older outpatients.

METHODS

We constructed a cohort of 2,272,257 outpatients aged ≥65 years using a national claims database. This cohort was split into a development (70%) and validation (30%) groups. Our primary goal was to identify newly diagnosed AKI within one month of cohort entry in an outpatient context. We screened 170 variables and developed a risk prediction model using logistic regression.

RESULTS

The final model integrated 12 variables: 2 demographic, 4 comorbid, and 6 medication-related. It showed good performance with acceptable calibration. In the validation cohort, the area under the receiver operating characteristic curve value was 0.720 (95% confidence interval, 0.692-0.748). Sensitivity and specificity were 69.9% and 61.9%, respectively. Notably, the model identified high-risk patients as having a 27-fold increased AKI risk compared with low-risk individuals.

CONCLUSION

We have developed a new AKI risk prediction model for older outpatients, incorporating critical medication-related variables with good discrimination. This tool may be useful in identifying and targeting patients who may require interventions to prevent AKI in an outpatient setting.

Evaluation of risk stratification for acute kidney injury: a comparative analysis of EKFC, 2009 and 2021 CKD-EPI glomerular filtration estimating equations

BACKGROUND

The adoption of the 2021 CKD-EPIcr equation for glomerular filtration rate (GFR) estimation provided a race-free eGFR calculation. However, the discriminative performance for AKI risk has been rarely validated. We aimed to evaluate the differences in acute kidney injury (AKI) prediction or reclassification power according to the three eGFR equations.

METHODS

We performed a retrospective observational study within a tertiary hospital from 2011 to 2021. Acute kidney injury was defined according to KDIGO serum creatinine criteria. Glomerular filtration rate estimates were calculated by three GFR estimating equations: 2009 and 2021 CKD-EPIcr, and EKFC. In three equations, AKI prediction performance was evaluated with area under receiver operator curves (AUROC) and reclassification power was evaluated with net reclassification improvement analysis.

RESULTS

A total of 187,139 individuals, including 27,447 (14.7%) AKI and 159,692 (85.3%) controls, were enrolled. In the multivariable regression prediction model, the 2009 CKD-EPIcr model (continuous eGFR model 2, 0.7583 [0.755-0.7617]) showed superior performance in AKI prediction to the 2021 CKD-EPIcr (0.7564 [0.7531-0.7597], < 0.001) or EKFC model in AUROC (0.7577 [0.7543-0.761], < 0.001). Moreover, in reclassification of AKI, the 2021 CKD-EPIcr and EKFC models showed a worse classification performance than the 2009 CKD-EPIcr model. (- 7.24 [- 8.21-- 6.21], - 2.38 [- 2.72-- 1.97]).

CONCLUSION

Regarding AKI risk stratification, the 2009 CKD-EPIcr equation showed better discriminative performance compared to the 2021 CKD-EPIcr equation in the study population.

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.

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.

Association between cumulative duration of deep anesthesia and postoperative acute kidney injury after noncardiac surgeries: a retrospective observational study

BACKGROUND

Bispectral index (BIS) is a processed electroencephalography monitoring tool and is widely used in anesthetic depth monitoring. Deep anesthesia exposure may be associated with multiple adverse outcomes. However, the relationship between anesthetic depth and postoperative acute kidney injury (AKI) remains unclear. We sought to determine the effect of BIS-based deep anesthesia duration on postoperative AKI following noncardiac surgery.

METHODS

This retrospective study used data from the Vital Signs DataBase, including patients undergoing noncardiac surgeries with BIS monitoring. The BIS values were collected every second during anesthesia. Restricted cubic splines and logistic regression were used to assess the association between the cumulative duration of deep anesthesia and postoperative AKI.

RESULTS

4774 patients were eligible, and 129 (2.7%) experienced postoperative AKI. Restricted cubic splines showed that a cumulative duration of BIS < 45 was nonlinearly associated with postoperative AKI (P-overall = 0.033 and P-non-linear = 0.023). Using the group with the duration of BIS < 45 less than 15 min as the reference, ORs of postoperative AKI were 2.59 (95% confidence interval [CI]:0.60 to 11.09, p = 0.200) in the 15-100 min group, and 4.04 (95%CI:0.92 to 17.76, p = 0.064) in the ≥ 100 min group after adjusting for preoperative and intraoperative covariates in multivariable logistic regression.

CONCLUSIONS

The cumulative duration of BIS < 45 was independently and nonlinearly associated with the risk of postoperative AKI in patients undergoing noncardiac surgery.

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.

Cardiometabolic disease and obesity patterns differentially predict acute kidney injury after total joint replacement: a retrospective analysis

BACKGROUND

Acute kidney injury (AKI) is a frequent yet understudied postoperative total joint arthroplasty complication. This study aimed to describe cardiometabolic disease co-occurrence using latent class analysis, and associated postoperative AKI risk.

METHODS

This retrospective analysis examined patients ≥18 years old undergoing primary total knee or hip arthroplasties within the US Multicenter Perioperative Outcomes Group of hospitals from 2008 to 2019. AKI was defined using modified Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Latent classes were constructed from eight cardiometabolic diseases including hypertension, diabetes, and coronary artery disease, excluding obesity. A mixed-effects logistic regression model was constructed for the outcome of any AKI and the exposure of interaction between latent class and obesity status adjusting for preoperative and intraoperative covariates.

RESULTS

Of 81 639 cases, 4007 (4.9%) developed AKI. Patients with AKI were more commonly older and non-Hispanic Black, with more significant comorbidity. A latent class model selected three groups of cardiometabolic patterning, labelled 'hypertension only' (n=37 223), 'metabolic syndrome (MetS)' (n=36 503), and 'MetS+cardiovascular disease (CVD)' (n=7913). After adjustment, latent class/obesity interaction groups had differential risk of AKI compared with those in 'hypertension only'/non-obese. Those 'hypertension only'/obese had 1.7-fold increased odds of AKI (95% confidence interval [CI]: 1.5-2.0). Compared with 'hypertension only'/non-obese, those 'MetS+CVD'/obese had the highest odds of AKI (odds ratio 3.1, 95% CI: 2.6-3.7), whereas 'MetS+CVD'/non-obese had 2.2 times the odds of AKI (95% CI: 1.8-2.7; model area under the curve 0.76).

CONCLUSIONS

The risk of postoperative AKI varies widely between patients. The current study suggests that the co-occurrence of metabolic conditions (diabetes mellitus, hypertension), with or without obesity, is a more important risk factor for acute kidney injury than individual comorbid diseases.

Predictors and Adverse Outcomes of Acute Kidney Injury in Hospitalized Renal Transplant Recipients

Data about in-hospital AKI in RTRs is lacking. We conducted a retrospective study of 292 RTRs, with 807 hospital admissions, to reveal predictors and outcomes of AKI during admission. In-hospital AKI developed in 149 patients (51%). AKI in a previous admission was associated with a more than twofold increased risk of AKI in subsequent admissions (OR 2.13, p &lt; 0.001). Other major significant predictors for in-hospital AKI included an infection as the major admission diagnosis (OR 2.93, p = 0.015), a medical history of hypertension (OR 1.91, p = 0.027), minimum systolic blood pressure (OR 0.98, p = 0.002), maximum tacrolimus trough level (OR 1.08, p = 0.005), hemoglobin level (OR 0.9, p = 0.016) and albumin level (OR 0.51, p = 0.025) during admission. Compared to admissions with no AKI, admissions with AKI were associated with longer length of stay (median time of 3.83 vs. 7.01 days, p &lt; 0.001). In-hospital AKI was associated with higher rates of mortality during admission, almost doubled odds for rehospitalization within 90 days from discharge and increased the risk of overall mortality in multivariable mixed effect models. In-hospital AKI is common and is associated with poor short- and long-term outcomes. Strategies to prevent AKI during admission in RTRs should be implemented to reduce re-admission rates and improve patient survival.

Acute Kidney Injury After General Thoracic Surgery: A Systematic Review and Meta-Analysis

INTRODUCTION

The clinical importance of postoperative acute kidney injury (AKI) in patients undergoing general thoracic surgery is unclear. We aimed to systematically review the incidence, risk factors, and prognostic implications of AKI as a complication after general thoracic surgery.

METHODS

We searched PubMed, EMBASE, and the Cochrane Library from January 2004 to September 2021. Observational or interventional studies that enrolled ≥50 patients undergoing general thoracic surgery and reported postoperative AKI defined using contemporary consensus criteria were included for meta-analysis.

RESULTS

Thirty-seven articles reporting 35 unique cohorts were eligible. In 29 studies that enrolled 58,140 consecutive patients, the pooled incidence of postoperative AKI was 8.0% (95% confidence interval [CI]: 6.2-10.0). The incidence was 3.8 (2.0-6.2) % after sublobar resection, 6.7 (4.1-9.9) % after lobectomy, 12.1 (8.1-16.6) % after bilobectomy/pneumonectomy, and 10.5 (5.6-16.7) % after esophagectomy. Considerable heterogeneity in reported incidences of AKI was observed across studies. Short-term mortality was higher (unadjusted risk ratio: 5.07, 95% CI: 2.99-8.60) and length of hospital stay was longer (weighted mean difference: 3.53, 95% CI: 2.56-4.49, d) in patients with postoperative AKI (11 studies, 28,480 patients). Several risk factors for AKI after thoracic surgery were identified.

CONCLUSIONS

AKI occurs frequently after general thoracic surgery and is associated with increased short-term mortality and length of hospital stay. For patients undergoing general thoracic surgery, AKI may be an important postoperative complication that needs early risk evaluation and mitigation.

Intraoperative mean arterial pressure and acute kidney injury after robot-assisted laparoscopic prostatectomy: a retrospective study

Intraoperative hemodynamics can affect postoperative kidney function. We aimed to investigate the effect of intraoperative mean arterial pressure (MAP) as well as other risk factors on the occurrence of acute kidney injury (AKI) after robot-assisted laparoscopic prostatectomy (RALP). We retrospectively evaluated the medical records of 750 patients who underwent RALP. The average real variability (ARV)-MAP, standard deviation (SD)-MAP, time-weighted average (TWA)-MAP, area under threshold (AUT)-65 mmHg, and area above threshold (AAT)-120 mmHg were calculated using MAPs collected within a 10-s interval. Eighteen (2.4%) patients developed postoperative AKI. There were some univariable associations between TWA-MAP, AUT-65 mmHg, and AKI occurrence; however, multivariable analysis found no association. Alternatively, American Society of Anesthesiologists physical status ≥ III and the low intraoperative urine output were independently associated with AKI occurrence. Moreover, none of the five MAP parameters could predict postoperative AKI, with the area under the receiver operating characteristic curve values for ARV-MAP, SD-MAP, TWA-MAP, AUT-65 mmHg, and AAT-120 mmHg being 0.561 (95% confidence interval [CI], 0.424-0.697), 0.561 (95% CI, 0.417-0.704), 0.584 (95% CI, 0.458-0.709), 0.590 (95% CI, 0.462-0.718), and 0.626 (95% CI, 0.499-0.753), respectively. Therefore, intraoperative MAP changes may not be a determining factor for AKI after RALP.

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.

Predicting mortality in acute kidney injury patients undergoing continuous renal replacement therapy using a visualization model: A retrospective study

Background: Patients with severe acute kidney injury (AKI) require continuous renal replacement therapy (CRRT) when hemodynamically unstable. We aimed to identify prognostic factors and develop a nomogram that could predict mortality in patients with AKI undergoing CRRT. Methods: Data were extracted from the Dryad Digital Repository. We enrolled 1,002 participants and grouped them randomly into training (n = 670) and verification (n = 332) datasets based on a 2:1 proportion. Based on Cox proportional modeling of the training set, we created a web-based dynamic nomogram to estimate all-cause mortality. Results: The model incorporated phosphate, Charlson comorbidity index, body mass index, mean arterial pressure, levels of creatinine and albumin, and sequential organ failure assessment scores as independent predictive indicators. Model calibration and discrimination were satisfactory. In the training dataset, the area under the curves (AUCs) for estimating the 28-, 56-, and 84-day all-cause mortality were 0.779, 0.780, and 0.787, respectively. The model exhibited excellent calibration and discrimination in the validation dataset, with AUC values of 0.791, 0.778, and 0.806 for estimating 28-, 56-, and 84-day all-cause mortality, respectively. The calibration curves exhibited the consistency of the model between the two cohorts. To visualize the results, we created a web-based calculator. Conclusion: We created a web-based calculator for assessing fatality risk in patients with AKI receiving CRRT, which may help rationalize clinical decision-making and personalized therapy.

Urine output and acute kidney injury following laparoscopic pancreas operations

BACKGROUND

This study aims to assess if intraoperative urine output is associated with acute kidney injury (AKI) during laparoscopic pancreas surgery.

METHODS

Medical records of adult patients who underwent laparoscopic pancreas surgery from 2010 to 2020 were reviewed to identify patients who experienced AKI (creatinine increase of 0.3 mg/dL within 72 h). Surgeries were classified as with 'vascular reconstruction' (e.g. Whipple, total pancreatectomy) versus 'without reconstruction' (e.g., distal pancreatectomy).

RESULTS

Included were 365 patients (221 with and 114 without reconstruction), and 42 (11.4%) developed AKI (32 [14.5%] reconstruction and 10 [6.9%] without reconstruction (P = 0.164)). The median urine output for AKI group was 0.79 [0.43, 1.15] mL/kg/h and 0.88 [0.55, 1.53] mL/kg/h for non-AKI group, P = 0.121. Urine output between AKI and non-AKI did not vary among reconstruction cases (P = 0.383), but was lower in AKI patients without reconstruction (P = 0.047). Older age, preexisting kidney disease, higher disease burden, and intraoperative hypotension were associated with AKI. Postoperative course was more complicated for AKI patients including rates of pancreatic fistulas and mortality.

CONCLUSION

Incidence of AKI increases with more extensive surgery, but is not associated with low urine output. However, low urine output was associated with AKI in patients undergoing operation without reconstruction.

Preoperative dipstick albuminuria is associated with acute kidney injury in high-risk patients following non-cardiac surgery: a single-center prospective cohort study

PURPOSE

This study aimed to investigate the association between preoperative dipstick albuminuria (DA) and acute kidney injury (AKI) in high-risk patients following non-cardiac surgery.

METHODS

This was a single-center prospective cohort study. Adult patients with high risk of AKI undergoing non-cardiac surgery were enrolled. The primary outcome was AKI, defined according to KDIGO criteria within 7 days following non-cardiac surgery. DA status was determined by urinalysis performed within 24 h of hospital admission. Multivariate logistic regression model was used to analyze the association between preoperative DA and postoperative AKI.

RESULTS

During the study period, 552 patients were enrolled and 8.5% of them developed postoperative AKI. The overall rate of preoperative positive DA was 26.4% with 30 and ≥ 100 mg/dL DA accounting for 19.2% and 7.2%, respectively. Patients with more severe preoperative DA had much higher rate of postoperative AKI (5.2% in patients with negative or trace DA, 13.2% in patients with 30 mg/dL DA and 30.0% in patients with ≥ 100 mg/dL DA, P < 0.001). After adjusting for several perioperative variables, preoperative 30 mg/dL DA (OR 2.575; 95% CI 1.049-6.322; P = 0.039) and ≥ 100 mg/dL DA (OR 3.868; 95% CI 1.246-12.010; P = 0.019) showed an independent association with postoperative AKI. In addition, patients with higher DA status demonstrated significantly increased level of postoperative urine biomarkers and their ratio to urine creatinine.

CONCLUSIONS

Preoperative DA was independently associated with AKI in high-risk patients following non-cardiac surgery. Preoperative routine urinalysis for determination of DA status was suggested in early risk stratification.

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.

Tools and scores for perioperative pulmonary, renal, hepatobiliary, hematological, and surgical site infection risk assessment: an update

INTRODUCTION

perioperative risk assessment is essential to mitigate surgical complications, which suggests individual and collective interest since the number of surgical procedures in Brazil has been expanding steadily. The aim of this study was to summarize and detail the main calculators, indexes and scores regarding perioperative pulmonary, renal, hepatobiliary, hematological and surgical site infection risks for general non-cardiac surgeries, which are dispersed in the literature.

METHOD

a narrative review was performed based on manuscripts in English and Portuguese found in the electronic databases Pubmed/MEDLINE and EMBASE.

RESULTS

the review included 11 tools related to the systems covered, for which the application method and its limitations are detailed.

CONCLUSION

the non-cardiovascular perioperative risk estimation tools are beneficial when disturbances are identified in the preoperative clinical examination that justify a possible increased risk to the affected system, so the use of these tools provides palpable values to aid in the judgment of surgical risk and benefit as well as it identifies factors amenable to intervention to improve outcomes.

Predictive Value of Glycosylated Hemoglobin for Post-operative Acute Kidney Injury in Non-cardiac Surgery Patients

Objective

Recent studies have indicated that patients (both with and without diabetes) with elevated hemoglobin A1c (HbA1c) have a higher rate of acute kidney injury (AKI) following cardiac surgery. However, whether HbA1c could help to predict post-operative AKI in patients after non-cardiac surgery is less clear. This study aims to explore the predictive value of pre-operative HbA1c for post-operative AKI in non-cardiac surgery.

Methods

We reviewed the medical records of patients (≥ 18 years old) who underwent non-cardiac surgery between 2011 and 2020. Patient-related variables, including demographic and laboratory and procedure-related information, were collected, and univariable and multivariable logistic regression analyses were performed to determine the association of HbA1c with AKI. The area under the receiver operating curve (AUC), net reclassification improvement index (NRI), and integrated discriminant improvement index (IDI) were used to evaluate the predictive ability of the model, and decision curve analysis was used to evaluate the clinical utility of the HbA1c-added predictive model.

Results

A total of 3.3% of patients (94 of 2,785) developed AKI within 1 week after surgery. Pre-operative HbA1c was an independent predictor of AKI after adjustment for some clinical variables (OR comparing top to bottom quintiles 5.02, 95% CI, 1.90 to 13.24, P < 0.001 for trend; OR per percentage point increment in HbA1c 1.20, 95% CI, 1.07 to 1.33). Compared to the model with only clinical variables, the incorporation of HbA1c increased the model fit, modestly improved the discrimination (change in area under the curve from 0.7387 to 0.7543) and reclassification (continuous net reclassification improvement 0.2767, 95% CI, 0.0715 to 0.4818, improved integrated discrimination 0.0048, 95% CI, -5e-04 to 0.0101) of AKI and non-AKI cases, NRI for non-AKI improvement 0.3222, 95% CI, 0.2864 to 0.3580 and achieved a higher net benefit in decision curve analysis.

Conclusion

Elevated pre-operative HbA1c was independently associated with post-operative AKI risk and provided predictive value in patients after non-cardiac surgery. HbA1c improved the predictive power of a logistic regression model based on traditional clinical risk factors for AKI. Further prospective studies are needed to demonstrate the results and clinical application.

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.

Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department

Acute kidney injury (AKI) in hospitalised patients is a common syndrome associated with poorer patient outcomes. Clinical risk scores can be used for the early identification of patients at risk of AKI. We conducted a retrospective study using electronic health records of Singapore General Hospital emergency department patients who were admitted from 2008 to 2016. The primary outcome was inpatient AKI of any stage within 7 days of admission based on the Kidney Disease Improving Global Outcome (KDIGO) 2012 guidelines. A machine learning-based framework AutoScore was used to generate clinical scores from the study sample which was randomly divided into training, validation and testing cohorts. Model performance was evaluated using area under the curve (AUC). Among the 119,468 admissions, 10,693 (9.0%) developed AKI. 8491 were stage 1 (79.4%), 906 stage 2 (8.5%) and 1296 stage 3 (12.1%). The AKI Risk Score (AKI-RiSc) was a summation of the integer scores of 6 variables: serum creatinine, serum bicarbonate, pulse, systolic blood pressure, diastolic blood pressure, and age. AUC of AKI-RiSc was 0.730 (95% CI 0.714–0.747), outperforming an existing AKI Prediction Score model which achieved AUC of 0.665 (95% CI 0.646–0.679) on the testing cohort. At a cut-off of 4 points, AKI-RiSc had a sensitivity of 82.6% and specificity of 46.7%. AKI-RiSc is a simple clinical score that can be easily implemented on the ground for early identification of AKI and potentially be applied in international settings.

Automatic assessment of adverse drug reaction reports with interactive visual exploration

A large number of adverse drug reaction (ADR) reports are collected yearly through the spontaneous report system (SRS). However, experienced experts from ADR monitoring centers (ADR experts, hereafter) reviewed only a few reports based on current policies. Moreover, the causality assessment of ADR reports was conducted according to the official approach based on the WHO-UMC system, a knowledge- and labor-intensive task that highly relies on an individual’s expertise. Our objective is to devise a method to automatically assess ADR reports and support the efficient exploration of ADRs interactively. Our method could improve the capability to assess and explore a large volume of ADR reports and aid reporters in self-improvement. We proposed a workflow for assisting the assessment of ADR reports by combining an automatic assessment prediction model and a human-centered interactive visualization method. Our automatic causality assessment model (ACA model)—an ordinal logistic regression model—automatically assesses ADR reports under the current causality category. Based on the results of the ACA model, we designed a warning signal to indicate the degree of the anomaly of ADR reports. An interactive visualization technique was used for exploring and examining reports extended by automatic assessment of the ACA model and the warning signal. We applied our method to the SRS report dataset of the year 2019, collected in Guangdong province, China. Our method is evaluated by comparing automatic assessments by the ACA model to ADR reports labeled by ADR experts, i.e., the ground truth results from the multinomial logistic regression and the decision tree. The ACA model achieves an accuracy of 85.99%, a multiclass macro-averaged area under the curve (AUC) of 0.9572, while the multinomial logistics regression and decision tree yield 80.82%, 0.8603, and 85.39%, 0.9440, respectively, on the testing set. The new warning signal is able to assist ADR experts to quickly focus on reports of interest with our interactive visualzation tool. Reports of interest that are selected with high scores of the warning signal are analyzed in details by an ADR expert. The usefulness of the overall method is further evaluated through the interactive analysis of the data by ADR expert. Our ACA model achieves good performance and is superior to the multinomial logistics and the decision tree. The warning signal we designed allows efficient filtering of the full ADR reports down to much fewer reports showing anomalies. The usefulness of our interactive visualization is demonstrated by examples of unusual reports that are quickly identified. Our overall method could potentially improve the capability of analyzing ADR reports and reduce human labor and the chance of missing critical reports.

Perioperative acute kidney injury: impact and recent update

PURPOSE OF REVIEW

Acute kidney injury (AKI) is common in hospitalized patients and is a major risk factor for increased length of stay, morbidity, and mortality in postoperative patients. There are multiple barriers to reducing perioperative AKI - the etiology is multi-factorial and the diagnosis is fraught with issues. We review the recent literature on perioperative AKI and some considerations for anesthesiologists that examine the far-reaching effects of AKI on multiple organ systems.

RECENT FINDINGS

This review will discuss recent literature that addresses the epidemiology, use of novel biomarkers in risk stratification, and therapeutic modalities for AKI in burn, pediatrics, sepsis, trauma, cardiac, and liver disease, contrast-induced AKI, as well as the evidence assessing goal-directed fluid therapy.

SUMMARY

Recent studies address the use of risk stratification models and biomarkers, more sensitive than creatinine, in the preoperative identification of patients at risk for AKI. Although exciting, these scores and models need validation. There is a need for research assessing whether early AKI detection improves outcomes. Enhanced recovery after surgery utilizing goal-directed fluid therapy has not been shown to make an appreciable difference in the incidence of AKI. Reducing perioperative AKI requires a multi-pronged and possibly disease-specific approach.

[Prevention of acute kidney injury]

Acute kidney injury contributes significantly to morbidity and mortality in hospitalized patients and is a common complication in the intensive care unit. Identification of patients at risk, elimination of modifiable risk factors and initiation of recommended preventive measures are the main cornerstones to prevent the onset and progression of acute kidney injury. Clinical and biomarker-based risk scores can help assess AKI-risk in specific patient populations. To date, there is no approved clinically effective drug to prevent AKI. Current guidelines suggest preventive care bundles that include optimizing volume status and renal perfusion by improving mean arterial pressure and using vasopressors, mainly norepinephrine. In addition, avoidance of volume overload and the targeted use of diuretics to achieve euvolemia are recommended. Nephrotoxic drugs require a critical risk-benefit assessment and therapeutic drug monitoring when appropriate. Contrast imaging should not be withheld from patients at risk of AKI when indicated but contrast medium should be limited to the smallest possible volume. Finally, recommendations include maintenance of normoglycemia and other measures to optimize organ function in specific patient populations.

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.

Acute Kidney Injury: Biomarker-Guided Diagnosis and Management

Acute kidney injury (AKI) is a common clinical syndrome that is characterized by abnormal renal function and structure. The Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference in 2019 reviewed the stages of AKI and the definitions of AKI-related terminologies, and discussed the advances in the last decade. Along with serum creatinine level and urine output, more accurate novel biomarkers for predicting AKI are being applied for the early detection of renal dysfunction. A literature search was conducted in PubMed, Scopus, Medline, and ClinicalTrials.gov using the terms AKI and biomarker, combined with diagnosis, management, or prognosis. Because of the large volume of data (160 articles) published between 2005 and 2022, representative literature was chosen. A number of studies have demonstrated that new biomarkers are more sensitive in detecting AKI in certain populations than serum creatinine and urine output according to the recommendations from the Acute Disease Quality Initiative Consensus Conference. To be specific, there is a persistently unresolved need for earlier detection of patients with AKI before AKI progresses to a need for renal replacement therapy. Biomarker-guided management may help to identify a high-risk group of patients in progression to severe AKI, and decide the initiation time to renal replacement therapy and optimal follow-up period. However, limitations such as biased data to certain studied populations and absence of cutoff values need to be solved for worldwide clinical use of biomarkers in the future. Here, we provide a comprehensive review of biomarker-based AKI diagnosis and management and highlight recent developments.

A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty

PURPOSE

Acute kidney injury (AKI) is a deleterious complication after total knee arthroplasty (TKA). The purposes of this study were to identify preoperative risk factors and develop a web-based prediction model for postoperative AKI, and assess how AKI affected the progression to ESRD.

METHOD

The study included 5757 patients treated in three tertiary teaching hospitals. The model was developed using data on 5302 patients from two hospitals and externally validated in 455 patients from the third hospital. Eighteen preoperative variables were collected and feature selection was performed. A gradient boosting machine (GBM) was used to predict AKI. A tenfold-stratified area under the curve (AUC) served as the metric for internal validation. Calibration was performed via isotonic regression and evaluated using a calibration plot. End-stage renal disease (ESRD) was followed up for an average of 41.7 months.

RESULTS

AKI develops in up to 10% of patients undergoing TKA, increasing the risk of progression to ESRD. The ESRD odds ratio of AKI patients (compared to non-AKI patients) was 9.8 (95% confidence interval 4.3-22.4). Six key predictors of postoperative AKI were selected: higher preoperative levels of creatinine in serum, the use of general anesthesia, male sex, a higher ASA class (> 3), use of a renin-angiotensin-aldosterone system inhibitor, and no use of tranexamic acid (all p < 0.001). The predictive performance of our model was good (area under the curve 0.78 [95% CI 0.74-0.81] in the developmental cohort and improved in the external validation cohort (0.89). Our model can be accessed at https://safetka.net .

CONCLUSIONS

A web-based predictive model for AKI after TKA was developed using a machine-learning algorithm featuring six preoperative variables. The model is simple and has been validated to improve both short- and long-term prognoses of TKA patients. Postoperative AKI may lead to ESRD, which surgeons should strive to avoid.

LEVEL OF EVIDENCE

Diagnostic level II.

Ferramentas e escores para avaliação de risco perioperatório pulmonar, renal, hepatobiliar, hematológico e de infecção do sítio cirúrgico: uma atualização

RESUMO Introdução: a avaliação de risco perioperatório é essencial para mitigação das complicações cirúrgicas, o que aventa interesse individual e coletivo uma vez que o número de procedimentos cirúrgicos no Brasil vem se expandindo de maneira crescente. O objetivo deste estudo foi resumir e detalhar as principais calculadoras, índices e escores dos riscos perioperatórios pulmonar, renal, hepatobiliar, hematológico e de infecção de sítio cirúrgico para cirurgias gerais não cardíacas, os quais encontram-se dispersos na literatura. Método: foi realizada revisão narrativa a partir de manuscritos em inglês e português encontrados nas bases eletrônicas Pubmed/MEDLINE e EMBASE. Resultados: a revisão incluiu 11 ferramentas relativas aos sistemas abordados, para as quais detalha-se o método de aplicação e suas limitações. Conclusão: as ferramentas de estimativa de risco perioperatório não cardiovascular encontram benefício quando se identifica no exame clínico pré-operatório alterações que justifiquem possível risco aumentado ao sistema afetado, assim a utilização destas ferramentas fornece valores palpáveis para auxílio no julgamento de risco e benefício cirúrgico bem como identifica fatores passíveis de intervenção para melhoria dos desfechos.

Acute Kidney Injury in the Outpatient Setting: Developing and Validating a Risk Prediction Model

Rationale & Objective

Risk factors for acute kidney injury (AKI) in the hospital have been well studied. Yet, risk factors for identifying high-risk patients for AKI occurring and managed in the outpatient setting are unknown and may differ.

Study Design

Predictive model development and external validation using observational electronic health record data.

Setting & Participants

Patients aged 18-90 years with recurrent primary care encounters, known baseline serum creatinine, and creatinine measured during an 18-month outcome period without established advanced kidney disease.

New Predictors & Established Predictors

Established predictors for inpatient AKI were considered. Potential new predictors were hospitalization history, smoking, serum potassium levels, and prior outpatient AKI.

Outcomes

A ≥50% increase in the creatinine level above a moving baseline of the recent measurement(s) without a hospital admission within 7 days defined outpatient AKI.

Analytical Approach

Logistic regression with bootstrap sampling for backward stepwise covariate elimination was used. The model was then transformed into 2 binary tests: one identifying high-risk patients for research and another identifying patients for additional clinical monitoring or intervention.

Results

Outpatient AKI was observed in 4,611 (3.0%) and 115,744 (2.4%) patients in the development and validation cohorts, respectively. The model, with 18 variables and 3 interaction terms, produced C statistics of 0.717 (95% CI, 0.710-0.725) and 0.722 (95% CI, 0.720-0.723) in the development and validation cohorts, respectively. The research test, identifying the 5.2% most at-risk patients in the validation cohort, had a sensitivity of 0.210 (95% CI, 0.208-0.213) and specificity of 0.952 (95% CI, 0.951-0.952). The clinical test, identifying the 20% most at-risk patients, had a sensitivity of 0.494 (95% CI, 0.491-0.497) and specificity of 0.806 (95% CI, 0.806-0.807).

Limitations

Only surviving patients with measured creatinine levels during a baseline period and outcome period were included.

Conclusions

The outpatient AKI risk prediction model performed well in both the development and validation cohorts in both continuous and binary forms.

Additive harmful effects of acute kidney injury and acute heart failure on mortality in hospitalized patients

Background

Organ crosstalk between the kidney and the heart has been suggested. Acute kidney injury (AKI) and acute heart failure (AHF) are well-known independent risk factors for mortality in hospitalized patients. This study aimed to investigate if these conditions have an additive effect on mortality in hospitalized patients, as this has not been explored in previous studies.

Methods

We retrospectively reviewed the records of 101,804 hospitalized patients who visited two tertiary hospitals in the Republic of Korea over a period of 5 years. AKI was diagnosed using serum creatinine-based criteria, and AHF was classified using International Classification of Diseases codes within 2 weeks after admission. Patients were divided into four groups according to the two conditions. The primary outcome was all-cause mortality.

Results

AKI occurred in 6.8% of all patients (n = 6,920) and AHF in 1.2% (n = 1,244). Three hundred thirty-one patients (0.3%) developed both conditions while AKI alone was present in 6,589 patients (6.5%) and AHF alone in 913 patients (0.9%). Among the 5,181 patients (5.1%) who died, 20.8% died within 1 month. The hazard ratio for 1-month mortality was 29.23 in patients with both conditions, 15.00 for AKI only, and 3.39 for AHF only. The relative excess risk of interaction was 11.85 (95% confidence interval, 2.43‒21.27), and was more prominent in patients aged <75 years and those without chronic heart failure.

Conclusion

AKI and AHF have a detrimental additive effect on short-term mortality in hospitalized patients.

External Validation of a Prediction Model for Acute Kidney Injury Following Noncardiac Surgery

IMPORTANCE

The Simple Postoperative AKI Risk (SPARK) index is a prediction model for postoperative acute kidney injury (PO-AKI) in patients undergoing noncardiac surgery. External validation has not been performed.

OBJECTIVE

To externally validate the SPARK index.

DESIGN, SETTING, AND PARTICIPANTS

This single-center retrospective cohort study included adults who underwent noncardiac surgery under general anesthesia from 2007 to 2011. Those with obstetric or urological surgery, estimated glomerular filtration rate (eGFR) of less than 15 mL/min/1.73 m2, preoperative dialysis, or an expected surgical duration of less than 1 hour were excluded. The study was conducted at Nara Medical University Hospital. Data analysis was conducted from January to July 2021.

EXPOSURES

Risk factors for AKI included in SPARK index.

MAIN OUTCOMES AND MEASURES

PO-AKI, defined as an increase in serum creatinine of at least 0.3 mg/dL within 48 hours or 150% compared with preoperative baseline value or urine output of less than 0.5 mL/kg/h for at least 6 hours within 1 week after surgery, and critical AKI, defined as either AKI stage 2 or greater and/or any AKI connected to postoperative death or requiring kidney replacement therapy before discharge. The discrimination and calibration of the SPARK index were examined with area under the receiver operating characteristic curves (AUC) and calibration plots, respectively.

RESULTS

Among 5135 participants (2410 [46.9%] men), 303 (5.9%) developed PO-AKI, and 137 (2.7%) developed critical AKI. Compared with the SPARK cohort, participants in our cohort were older (median [IQR] age, 56 [44-66] years vs 63 [50-73] years), had lower baseline eGFR (median [IQR], 82.1 [71.4-95.1] mL/min/1.73 m2 vs 78.2 [65.6-92.2] mL/min/1.73 m2), and had a higher prevalence of comorbidities (eg, diabetes: 3956 of 51 041 [7.8%] vs 802 [15.6%]). The incidence of PO-AKI and critical AKI increased as the scores on the SPARK index increased. For example, 10 of 593 participants (1.7%) in SPARK class A, indicating lowest risk, experienced PO-AKI, while 53 of 332 (16.0%) in SPARK class D, indicating highest risk, experienced PO-AKI. However, AUCs for PO-AKI and critical AKI were 0.67 (95% CI, 0.63-0.70) and 0.62 (95% CI, 0.57-0.67), respectively, and the calibration was poor (PO-AKI: y = 0.24x + 3.28; R2 = 0.86; critical AKI: y = 0.20x + 2.08; R2 = 0.51). Older age, diabetes, expected surgical duration, emergency surgery, renin-angiotensin-aldosterone system blockade use, and hyponatremia were not associated with PO-AKI in our cohort, resulting in overestimation of the predicted probability of AKI in our cohort.

CONCLUSIONS AND RELEVANCE

In this study, the incidence of PO-AKI increased as the scores on the SPARK index increased. However, the predicted probability might not be accurate in cohorts with older patients with more comorbidities.

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.

Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma

The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783-0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

Derivation and External Validation of a Risk Index for Predicting Acute Kidney Injury Requiring Kidney Replacement Therapy After Noncardiac Surgery

IMPORTANCE

Severe acute kidney injury (AKI) is a serious postoperative complication. A tool for predicting the risk of AKI requiring kidney replacement therapy (KRT) after major noncardiac surgery might assist with patient counseling and targeted use of measures to reduce this risk.

OBJECTIVE

To derive and validate a predictive model for AKI requiring KRT after major noncardiac surgery.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, 5 risk prediction models were derived and internally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Alberta, Canada, between January 1, 2004, and December 31, 2013. The best performing model and corresponding risk index were externally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Ontario, Canada, between January 1, 2007, and December 31, 2017. Data analysis was conducted from September 1, 2019, to May 31, 2021.

EXPOSURES

Demographic characteristics, surgery type, laboratory measures, and comorbidities before surgery.

MAIN OUTCOMES AND MEASURES

Acute kidney injury requiring KRT within 14 days after surgery. Discrimination was assessed using the C statistic; calibration was assessed using calibration intercept and slope. Logistic recalibration was used to optimize model calibration in the external validation cohort.

RESULTS

The derivation cohort included 92 114 patients (52.2% female; mean [SD] age, 62.3 [18.0] years), and the external validation cohort included 709 086 patients (50.8% female; mean [SD] age, 61.0 [16.0] years). A total of 529 patients (0.6%) developed postoperative AKI requiring KRT in the derivation cohort, and 2956 (0.4%) developed postoperative AKI requiring KRT in the external validation cohort. The following factors were consistently associated with the risk of AKI requiring KRT: younger age (40-69 years: odds ratio [OR], 2.07 [95% CI, 1.69-2.53]; <40 years: OR, 3.73 [95% CI, 2.61-5.33]), male sex (OR, 1.55; 95% CI, 1.28-1.87), surgery type (colorectal: OR, 4.86 [95% CI, 3.28-7.18]; liver or pancreatic: OR, 6.46 [95% CI, 3.85-10.83]; other abdominal: OR, 2.19 [95% CI, 1.66-2.89]; abdominal aortic aneurysm repair: OR, 19.34 [95% CI, 14.31-26.14]; other vascular: OR, 7.30 [95% CI, 5.48-9.73]; thoracic: OR, 3.41 [95% CI, 2.07-5.59]), lower estimated glomerular filtration rate (OR, 0.97; 95% CI, 0.97-0.97 per 1 mL/min/1.73 m2 increase), lower hemoglobin concentration (OR, 0.99; 95% CI, 0.98-0.99 per 0.1 g/dL increase), albuminuria (mild: OR, 1.88 [95% CI, 1.52-2.33]; heavy: OR, 3.74 [95% CI, 2.98-4.69]), history of myocardial infarction (OR, 1.63; 95% CI, 1.32-2.03), and liver disease (mild: OR, 2.32 [95% CI, 1.66-3.24]; moderate or severe: OR, 4.96 [95% CI, 3.58-6.85]). In external validation, a final model including these variables showed excellent discrimination (C statistic, 0.95; 95% CI, 0.95-0.96), with sensitivity of 21.2%, specificity of 99.9%, positive predictive value of 38.1%, and negative predictive value of 99.7% at a predicted risk threshold of 10% or greater.

CONCLUSIONS AND RELEVANCE

The findings suggest that this risk model can predict AKI requiring KRT after noncardiac surgery using routine preoperative data. The model may be feasible for implementation in clinical perioperative risk stratification for severe AKI.

Postoperative complications of endovascular blunt thoracic aortic injury repair

Background

Thoracic endovascular aortic repair (TEVAR) has become the standard of care for thoracic aortic aneurysms and increasingly for blunt thoracic aortic injury (BTAI). Postoperative complications, including spinal cord ischemia and paraplegia, have been shown to be less common with elective TEVAR than with open thoracic or thoracoabdominal repair. Although small cohort studies exist, the postoperative complications of endovascular repair of traumatic aortic injury have not been described through large data set analysis.

Methods

A retrospective cohort analysis was performed of the American College of Surgeons Trauma Quality Improvement Program registry spanning from 2007 to 2017. All patients with BTAI who underwent TEVAR, as indicated by the Abbreviated Injury Scale or the International Classification of Diseases (ICD-9 or ICD-10), were included. Categorical data were presented as proportions and continuous data as mean and SD. OR was calculated for each postoperative complication.

Results

2990 patients were identified as having undergone TEVAR for BTAI. The postoperative incidence of stroke was 2.8% (83), and 4.7% (140) of patients suffered acute kidney injury or renal failure. The incidence of spinal cord ischemia was 1.9% (58), whereas 0.2% (7) of patients suffered complete paraplegia. Renal events and stroke were found to occur significantly more frequently in those undergoing TEVAR (OR 1.758, 1.449–2.134 and OR 2.489, 1.917–3.232, respectively). Notably, there was no difference between TEVAR and non-operative BTAI incidences of spinal cord ischemia or paraplegia (OR 1.061, 0.799–1.409 and OR 1.698, 0.728–3.961, respectively).

Discussion

Postoperative intensive care unit care of patients after BTAI has historically focused on awareness of spinal cord ischemia. Our analysis suggests that after endovascular repair of blunt aortic trauma, care should involve vigilance primarily against postoperative cerebrovascular and renal events. Further study is warranted to develop guidelines for the intensivist managing patients after TEVAR for BTAI.

Level of evidence

Level III.

Pre-operative proteinuria, postoperative acute kidney injury and mortality: A systematic review and meta-analysis

OBJECTIVE

To investigate the association of pre-operative proteinuria with postoperative acute kidney injury (AKI) development as well as the requirement for a renal replacement therapy (RRT) and mortality at short-term and long-term follow-up.

BACKGROUND

Postoperative AKI is associated with surgical morbidity and mortality. Pre-operative proteinuria is potentially a risk factor for postoperative AKI and mortality. However, the results in literature are conflicting.

METHODS

We searched PubMed, Embase, Scopus, Web of Science and Cochrane Library from the inception through to 3 June 2020. Observational cohort studies investigating the association of pre-operative proteinuria with postoperative AKI development, requirement for RRT, and all-cause mortality at short-term and long-term follow-up were considered eligible. Using inverse variance method with a random-effects model, the pooled effect estimates and 95% confidence interval (CI) were calculated.

RESULTS

Twenty-eight studies were included. Pre-operative proteinuria was associated with postoperative AKI development [odds ratio (OR) 1.74, 95% CI, 1.45 to 2.09], in-hospital RRT (OR 1.70, 95% CI, 1.25 to 2.32), requirement for RRT at long-term follow-up [hazard ratio (HR) 3.72, 95% CI, 2.03 to 6.82], and long-term all-cause mortality (hazard ratio 1.50, 95% CI, 1.30 to 1.73). In the subgroup analysis, pre-operative proteinuria was associated with increased odds of postoperative AKI in both cardiovascular (OR 1.77, 95% CI, 1.47 to 2.14) and noncardiovascular surgery (OR 1.63, 95% CI, 1.01 to 2.63). Moreover, there is a stepwise increase in OR of postoperative AKI development when the quantity of proteinuria increases from trace to 3+.

CONCLUSION

Pre-operative proteinuria is significantly associated with postoperative AKI and long-term mortality. Pre-operative anaesthetic assessment should take into account the presence of proteinuria to identify high-risk patients.

PROSPERO REGISTRATION

CRD42020190065.

Pre-operative N-terminal pro-B-type natriuretic peptide for prediction of acute kidney injury after noncardiac surgery: A retrospective cohort study

BACKGROUND

Acute kidney injury (AKI) is associated with poor outcomes after noncardiac surgery. Whether pre-operative N-terminal pro-B-type natriuretic peptide (NT-proBNP) predicts AKI after noncardiac surgery is unclear.

OBJECTIVE

To investigate the predictive role of pre-operative NT-proBNP on postoperative AKI.

DESIGN

Retrospective cohort study.

SETTING

Nanfang Hospital, Southern Medical University, China.

PATIENTS

Adult patients who had a serum creatinine and NT-proBNP measurement within 30 pre-operative days and at least one serum creatinine measurement within 7 days after noncardiac surgery between February 2008 and May 2018 were identified.

MAIN OUTCOME MEASURES

The primary outcome was postoperative AKI, defined by the kidney disease: improving global outcomes creatinine criteria.

RESULTS

In all, 6.1% (444 of 7248) of patients developed AKI within 1 week after surgery. Pre-operative NT-proBNP was an independent predictor of AKI after adjustment for clinical variables (OR comparing top to bottom quintiles 2.29, 95% CI, 1.47 to 3.65, P < 0.001 for trend; OR per 1-unit increment in natural log transformed NT-proBNP 1.27, 95% CI, 1.16 to 1.39). Compared with clinical variables alone, the addition of NT-proBNP improved model fit, modestly improved the discrimination (change in area under the curve from 0.764 to 0.773, P = 0.005) and reclassification (continuous net reclassification improvement 0.210, 95% CI, 0.111 to 0.308, improved integrated discrimination 0.0044, 95% CI, 0.0016 to 0.0072) of AKI and non-AKI cases, and achieved higher net benefit in decision curve analysis.

CONCLUSIONS

Pre-operative NT-proBNP concentrations provided predictive information for AKI in a cohort of patients undergoing noncardiac surgery, independent of and incremental to conventional risk factors. Prospective studies are required to confirm this finding and examine its clinical impact.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR1900024056. www.chictr.org.cn/showproj.aspx?proj=40385.

Perioperative acute kidney injury following major abdominal surgery

Major abdominal surgery is associated with significant morbidity, not least the development of acute kidney injury. As a common perioperative complication, acute kidney injury is associated with increased length of stay, increased risk of perioperative infection and the potential development of chronic kidney disease. Moreover, the development of acute kidney injury is independently associated with an increased risk of death. Perioperative acute kidney injury is not a single entity, but describes a clinical syndrome with multiple causes including physical causes related to the surgical procedure, ischaemia-reperfusion injury and the use of potential nephrotoxins. Currently, acute kidney injury is defined by changes in serum creatinine level and urine output criteria, which although robust in heterogenous populations, may not perform as accurately in the perioperative period. This article discusses these issues including the potential role of novel biomarkers for early detection of perioperative acute kidney injury, as well as the use of predictive modelling. Treatment is mainly supportive but evidence suggests that more targeted therapy may lead to improved outcomes.

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.

External Validation of the Acute Kidney Injury Risk Prediction Score for Critically Ill Surgical Patients Who Underwent Major Non-Cardiothoracic Surgery

Background: Acute kidney injury (AKI) is a common complication encountered in an intensive care unit (ICU). In 2020, the AKI prediction score was developed specifically for critically ill surgical patients who underwent major non-cardiothoracic surgeries. This study aimed to externally validate the AKI prediction score in terms of performance and clinical utility. Methods: External validation was carried out in a prospective cohort of patients admitted to the ICU of the Faculty of Medicine Vajira Hospital between September 2014 and September 2015. The endpoint was AKI within seven days following ICU admission. Discriminative ability was based on the area under the receiver operating characteristic curves (AuROC). Calibration and clinical usefulness were evaluated. Results: A total of 201 patients were included in the analysis. AKI occurred in 37 (18.4%) patients. The discriminative ability dropped from good in the derivation cohort, to acceptable in the validation cohort (0.839 (95%CI 0.825–0.852) vs. 0.745 (95%CI 0.652–0.838)). No evidence of lack-of-fit was identified (p = 0.754). The score had potential clinical usefulness across the range of threshold probability from 10 to 50%. Conclusions: The AKI prediction score showed an acceptable discriminative performance and calibration with potential clinical usefulness for predicting AKI risk in surgical patients who underwent major non-cardiothoracic surgery.

Anaemia and acute kidney injury: the tip of the iceberg?

Acute kidney injury (AKI) is a common disorder that complicates the hospital course of many patients. AKI is linked with an independent risk of death, hospital length of stay and chronic kidney disease (CKD). Several preoperative predictors are found to be associated with AKI after surgery independent of its origin (cardiac versus non-cardiac). Among these, anaemia has been widely recognized and studied. Anaemia is more common within the surgical population for various reasons (iron deficiency, blood loss, anaemia of chronic disease such as inflammatory state, malignancy or CKD). Both pre- and postoperative anaemia have a deleterious impact on different clinical outcomes including AKI. In this issue, Nishimoto et al. investigated whether AKI could be a risk factor for anaemia (and not the opposite) and whether anaemia could be an independent mediator of mortality after AKI.

Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury

OBJECTIVE

We aimed to analyze the risk factors affecting all-cause mortality in diabetic patients with acute kidney injury (AKI) and to develop and validate a nomogram for predicting the 90-day survival rate of patients.

METHODS

Clinical data of diabetic patients with AKI who were diagnosed at The First Affiliated Hospital of Guangxi Medical University from April 30, 2011, to April 30, 2021, were collected. A total of 1,042 patients were randomly divided into a development cohort and a validation cohort at a ratio of 7:3. The primary study endpoint was all-cause death within 90 days of AKI diagnosis. Clinical parameters and demographic characteristics were analyzed using Cox regression to develop a prediction model for survival in diabetic patients with AKI, and a nomogram was then constructed. The concordance index (C-index), receiver operating characteristic curve, and calibration plot were used to evaluate the prediction model.

RESULTS

The development cohort enrolled 730 patients with a median follow-up time of 87 (40-98) days, and 86 patients (11.8%) died during follow-up. The 90-day survival rate was 88.2% (644/730), and the recovery rate for renal function in survivors was 32.9% (212/644). Multivariate analysis showed that advanced age (HR = 1.064, 95% CI = 1.043-1.085), lower pulse pressure (HR = 0.964, 95% CI = 0.951-0.977), stage 3 AKI (HR = 4.803, 95% CI = 1.678-13.750), lower 25-hydroxyvitamin D3 (HR = 0.944, 95% CI = 0.930-0.960), and multiple organ dysfunction syndrome (HR = 2.056, 95% CI = 1.287-3.286) were independent risk factors affecting the all-cause death of diabetic patients with AKI (all p < 0.01). The C-indices of the prediction cohort and the validation cohort were 0.880 (95% CI = 0.839-0.921) and 0.798 (95% CI = 0.720-0.876), respectively. The calibration plot of the model showed excellent consistency between the prediction probability and the actual probability.

CONCLUSION

We developed a new prediction model that has been internally verified to have good discrimination, calibration, and clinical value for predicting the 90-day survival rate of diabetic patients with AKI.

AKI!Now Initiative: Recommendations for Awareness, Recognition, and Management of AKI

The American Society of Nephrology has established a new initiative, AKI!Now, with the goal of promoting excellence in the prevention and treatment of AKI by building a foundational program that transforms education and delivery of AKI care, aiming to reduce morbidity and associated mortality and to improve long-term outcomes. In this article, we describe our current efforts to improve early recognition and management involving inclusive interdisciplinary collaboration between providers, patients, and their families; discuss the ongoing need to change some of our current AKI paradigms and diagnostic methods; and provide specific recommendations to improve AKI recognition and care. In the hospital and the community, AKI is a common and increasingly frequent condition that generates risks of adverse events and high costs. Unfortunately, patients with AKI may frequently have received less than optimal quality of care. New classifications have facilitated understanding of AKI incidence and its impact on outcomes, but they are not always well aligned with AKI pathophysiology. Despite ongoing research efforts, treatments to promote or hasten kidney recovery remain ineffective. To avoid progression, the current approach to AKI emphasizes the promotion of early recognition and timely response. However, a lack of awareness of the importance of early recognition and treatment among health care team members and the heterogeneity of approaches within the health care teams assessing the patient remains a major challenge. Early identification is further complicated by differences in settings where AKI occurs (the community or the hospital), and by differences in patient populations and cultures between the intensive care unit and ward environments. To address these obstacles, we discuss the need to improve education at all levels of care and to generate specific guidance on AKI evaluation and management, including the development of a widely applicable education and an AKI management toolkit, engaging hospital administrators to incorporate AKI as a quality initiative, and raising awareness of AKI as a complication of other disease processes.