KR20200057628A - Predicting system and method of postoperative acute kidney injury risk after non-cardiac surgery - Google Patents

Abstract

The present invention relates to a system for predicting the risk of acute renal failure after non-cardiac surgery and a method thereof. Specifically, the system includes a variable selector, a classification reference point setting unit, and a prediction unit of risk of acute renal failure after non-cardiac surgery. The method includes a first step of selecting a variable, a second step of setting a classification reference point, and a third step of predicting the risk of acute renal failure after non-cardiac surgery. Since the present invention is simple and accurate, it is an invention with very high clinical applicability.

Description

Prediction system and method for risk of acute renal failure after non-cardiac surgery {PREDICTING SYSTEM AND METHOD OF POSTOPERATIVE ACUTE KIDNEY INJURY RISK AFTER NON-CARDIAC SURGERY}

The present invention relates to a system and method for predicting the risk of acute renal failure after non-cardiac surgery.

Acute renal injury after surgery is an important event related to the death / dialysis / extended hospital stay of the patient after surgery, which occurs at a rate of 1-30% in the patient who underwent surgery. It is known from previous reports that when acute kidney damage occurs, the risk of short- or long-term death or the risk of dialysis due to renal failure increases, and medical expenses during and during hospital stays also increase significantly. There have been many reports of renal damage after heart surgery in previous studies, but studies of renal damage after non-cardiac surgery have been relatively rare. In particular, a predictive model for evaluating patients at high risk of acute renal injury after surgery and predicting the risk of occurrence has been rarely reported. Among them, a prediction model using "clinical easy-to-use score system" that has confirmed its feasibility in an independent patient group. Had no reports.

Korean Registered Patent No. 1940014

An object of the present invention is to provide a system and method for predicting the risk of acute renal failure after non-cardiac surgery using non-cardiac clinical data from non-cardiac patients.

1. A variable selection unit for selecting factors related to the occurrence of acute renal failure after non-cardiac surgery consisting of clinical data before non-cardiac surgery of patients subject to non-cardiac surgery as variables, and setting an index set for each variable;

A classification reference point setting unit that calculates sensitivity and specificity for the sum of the combination of exponents for each index set, and sets cut-off values according to the sensitivity and specificity; And

The non-cardiac surgery that requires the prediction is calculated based on the classification reference points set in the classification reference point setting unit by calculating the sum of the indices determined according to the set of indices set for each variable selected by the variable selection unit for the non-cardiac surgery target patient Includes a prediction unit for the risk of acute renal failure after non-cardiac surgery to predict the risk of acute renal failure after non-cardiac surgery of the target patient,

The factors related to the occurrence of acute renal failure after non-cardiac surgery are age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, and expected duration of surgery (Expected) surgical duration, emergency operation, diabetes mellitus, lenin-aldosterone-angiotensin-system blockade use, RAAS blockade use, hypoalbuminemia , Anemia and hyponatremia (Hyponatremia) at least one selected from the group consisting of, a system for predicting the risk of acute renal failure after non-cardiac surgery.

2. In the above 1, the index set is set according to the selection category for each variable except for the expected surgical duration, and 0, 3, 4, 6 to 9, 13, 15, and 22 for each selection category Any one of the selected exponent is set, the total sum of the exponents by selection category of the variable is 0 to 81, the system.

3. In the above 1, the index set in the expected surgical duration (Expected surgical duration) of the variable is that the index is set to 5 times the expected surgical duration (time).

4. In the above 1, among the variables, the age is divided into selection categories under 40, under 40, under 60, over 60, under 80, and over 80,

The estimated glomerular filtration rate (eGFR) is 60 mL / min / 1.73 m ² or more, 45 mL / min / 1.73 m ² or more and 60 mL / min / 1.73 m ^{2 or} less, 30 mL / min / 1.73. m ² over 45 mL / min / 1.73 m ² below, 15 mL / min / 1.73 m 2 at least 30 mL / min / 1.73 m, the system is divided into the selection criteria of less than ^2.

5. In the above 1, factors related to the occurrence of acute renal failure after non-cardiac surgery are age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex , Expected surgical duration, Emergency operation, Diabetes mellitus, Renin-aldosterone-angiotensin-system blockade use, RAAS blockade use , Hypoalbuminemia, Anemia and Hypoonatremia.

6. In the above 1, the predicted risk of acute renal failure after non-cardiac surgery is A according to the sum of indices determined according to the set of indexes set for each variable selected in the variable selection unit for the non-cardiac surgery target patient in need of prediction. B, C, D A system classified into four grades.

7. In the above 6, wherein the grade A is the total of the index is less than 20, the grade B is the sum of the index is 20 or more and less than 40, the grade C is the sum of the index is 40 or more and less than 60, The D grade is classified when the sum of the indices is 60 or more.

8. In the above 7, the A grade is less than 2% probability of acute renal failure and severe acute renal failure after non-cardiac surgery in non-cardiac patients in need of the prediction,

In the grade B, the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 2% or more, and the probability of serious acute renal failure is less than 2%,

In the grade C, the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 10% or more, and the probability of serious acute renal failure is 2% or more,

The D-class is a system in which the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 20% or more, and a probability of serious acute renal failure is 10% or more.

9. A first step of selecting factors related to the occurrence of acute renal failure after non-cardiac surgery consisting of clinical data before non-cardiac surgery of patients subject to non-cardiac surgery as variables, and setting an index set for each of the variables;

A second step of calculating sensitivity and specificity for the sum of combinations of exponents for each set of indices, and setting cut-off values according to the sensitivity and specificity; And

A non-cardiac surgical target that requires the prediction is calculated based on a set of indexes determined in accordance with the index set set for each variable selected in the first step for a non-cardiac surgical target patient that needs prediction And a third step of predicting the risk of acute renal failure after non-cardiac surgery in a patient.

The factors related to the occurrence of acute renal failure after non-cardiac surgery are age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, and expected duration of surgery (Expected) surgical duration, emergency operation, diabetes mellitus, lenin-aldosterone-angiotensin-system blockade use, RAAS blockade use, hypoalbuminemia , Anemia and at least one selected from the group consisting of hyponatremia, a method for predicting the risk of acute renal failure after non-cardiac surgery.

10. In the above 9, the index set is set according to the selection category for each variable except for the expected surgical duration, and 0, 3, 4, 6 to 9, 13, 15, and 22 for each selection category Any one of the selected exponent is set, the total sum of the exponents by selection category of the variable is 0 to 81.

11. In the above 9, the index set in the expected surgical duration (Expected surgical duration) of the variable is that the index is set to 5 times the expected surgical duration (time).

12. In the above 9, the age (age) among the above variables are divided into selection categories under 40, under 40, under 60, over 60, under 80, and over 80,

The estimated glomerular filtration rate (eGFR) is 60 mL / min / 1.73 m ² or more, 45 mL / min / 1.73 m ² or more and 60 mL / min / 1.73 m ^{2 or} less, 30 mL / min / 1.73. m ² over 45 mL / min / 1.73 m ² below, 15 mL / min / 1.73 m 2, methods that are separated by more than 30 mL / min / 1.73 m ² under the selected category.

13. In the above 9, the factors related to the occurrence of acute renal failure after non-cardiac surgery are age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex , Expected surgical duration, Emergency operation, Diabetes mellitus, Renin-aldosterone-angiotensin-system blockade use, RAAS blockade use , Hypoalbuminemia, Anemia and Hyponatremia.

14. In the above 9, the predicted risk of acute renal failure after non-cardiac surgery is A according to the sum of indices determined according to a set of indices set for each variable selected in the variable selector for non-cardiac surgery target patients who need prediction. Methods classified into four grades B, C, and D.

15. In the above 14, wherein the grade A is the total of the index is less than 20, the grade B is the sum of the index is 20 or more and less than 40, the grade C is the sum of the index is 40 or more and less than 60, The D grade is classified when the sum of the index is 60 or more.

16. In the above 15, the A grade is less than 2% of the probability of acute renal failure and severe acute renal failure after non-cardiac surgery in non-cardiac patients in need of the prediction,

In the grade B, the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 2% or more, and the probability of serious acute renal failure is less than 2%,

In the grade C, the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 10% or more, and the probability of serious acute renal failure is 2% or more,

The D-grade is a method for predicting acute renal failure after non-cardiac surgery in patients with non-cardiac surgery in need of the prediction is 20% or more, and a probability of serious acute renal failure is 10% or more.

The present invention can predict acute renal failure or related prognosis of a patient before actual surgery is performed with measurable / predictable indicators before surgery. Furthermore, the present invention not only reduces the risk of short-term / long-term death, the risk of dialysis, etc., but also uses an accurate, simple and easy scoring system.

1 is a diagram summarizing an embodiment of the present invention.
2 is a diagram illustrating a variable selection process performed in the discovery cohort.
3 is a diagram showing a correction chart and a receiver operating characteristic curve.
4 is a diagram showing sensitivity and specificity to the SPARK index.
5 is a diagram showing SPARK risk classification and incidence of low-stage AKI and severe AKI.
6 is a view showing a pre-operative PO-AKI risk assessment strategy based on the proposed SPARK classification.
7 is a diagram showing a correction chart and a receiver manipulation characteristic curve of each surgical department.

Hereinafter, the present invention will be described in detail.

The present invention relates to a system for predicting the risk of acute renal failure after non-cardiac surgery.

The system of the present invention includes a variable selection unit for selecting factors related to the occurrence of acute renal failure after non-cardiac surgery consisting of clinical data before non-cardiac surgery of patients subject to non-cardiac surgery as variables, and setting an index set for each of the variables. .

The non-cardiac surgery means any surgery other than heart surgery.

The non-cardiac surgery preclinical data of the non-cardiac surgery patients may include at least one selected from an electronic medical record (EMR) of a hospital, an interview with a patient or guardian, an inpatient nursing information record, a nursing activity report, and an inpatient record. It may be by route, but is not limited thereto.

The factors related to the occurrence of acute renal failure after non-cardiac surgery may be collected from a discovery cohort and a validation cohort, but are not limited thereto.

The variable selection unit may consider statistical assumptions and multivariable analysis for a proportional odds regression technique and select factors with a large model coefficient as variables, but is limited to this. It is not.

In addition, the variable selector builds a multivariable model for ordinal variables composed of negative prognosis related to acute renal failure using the proportional odds regression technique with the selected variables. At the same time, it is possible to set the index set for each variable so that the sum of the exponents set in each variable reflects the risk by converting the model coefficients to an integer at the same time, but is not limited thereto.

The factors related to the occurrence of acute renal failure after non-cardiac surgery are age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, and expected duration of surgery (Expected) surgical duration, emergency operation, diabetes mellitus, lenin-aldosterone-angiotensin-system blockade use, RAAS blockade use, hypoalbuminemia , Anemia and hyponatremia may include at least one selected from the group consisting of, and preferably may include all, but is not limited thereto.

In an embodiment of the present invention, an index set may be set for each index for each variable except for the expected surgical duration. The index set is set to (0, X), and X is any index selected from 3, 4, 6 to 9, 13, 15 and 22. For example, the patient will have one index in the index set according to the definition of the variable. For example, when the index set of the gender variable is (0, 8), the index may be 10 if the patient is a man and 0 when the patient is a male.

In one embodiment of the present invention, each variable may vary in size of the index set by the number of selection categories. For example, if a predetermined variable has three selection categories, the set of indices set for the predetermined variable may be (0, Y, Z). At this time, Y and Z are values corresponding to any one of the above X, and Y does not have the same value as Z.

Specifically, the age (age) can be divided into a selection category of less than 40, less than 40 and less than 60, more than 60 and less than 80, and more than 80 years,

The estimated glomerular filtration rate (eGFR) is 60 mL / min / 1.73 m ² or more, 45 mL / min / 1.73 m ² or more and 60 mL / min / 1.73 m ^{2 or} less, 30 mL / min / 1.73. m ² or more and less than 45 mL / min / 1.73 m ² , 15 mL / min / 1.73 m ² or more and 30 mL / min / 1.73 m ^{2 or} less.

More specifically, the age can be set to 0 if it is less than 40 years old, and if it is 40 or more and less than 60 years old, 6, 60 or more, and if it is less than 80 years old, 9, 80 or more, 13 exponential sets may be set; The estimated glomerular filtration rate (estimated glomerular filtration rate, eGFR) was 60 mL / min / 1.73 when m ² or more 0, 45 mL / min / 1.73 m 2 is more than 60 mL / min / 1.73 m is less than ² 8, 30 mL / If min / 1.73 m ² or more and less than 45 mL / min / 1.73 m ² , 15, 15 mL / min / 1.73 m ² or more and less than 30 mL / min / 1.73 m ^2, an index set of 22 may be set; A set of exponents of 6 and 0 if Dipstick albuminuria is present can be set; A set of exponents of 8 if sex is male and 0 if female is set; If it is an emergency operation, a set of exponents of 7, or 0 can be set; A set of exponents of 4 and 0 if diabetes mellitus is present can be established; If a renin-aldosterone-angiotensin blocker use (renin-aldosterone-angiotensin-system blockade use, RAAS blockade use) is present, an exponent set of 6 can be set; An exponential set of 8 if there is hypoalbuminemia and 0 if there is none can be established; A set of exponents of 4 if there is anemia and 0 if there is no can be set; A set of exponents of 3 or 0 if hyponatremia can be established. Accordingly, the total sum of indexes for each variable excluding the expected surgical period may be calculated from a minimum of 0 to a maximum of 81.

In addition, the unit of the expected surgical duration (Expected surgical duration) of the variable may be a time, the expected surgical duration may be set to an index of 5 times that period. For example, if the expected duration of surgery is 3 hours, the index of that variable is 15.

In addition, the system for predicting the risk of acute renal failure after non-cardiac surgery of the present invention calculates sensitivity and specificity for the sum of combinations of indexes for each index set, and classifies them according to the sensitivity and specificity And a classification reference point setting unit for setting cut-off values.

The classification reference point setting unit may set the classification reference point by calculating the sensitivity and specificity for the sum of the combinations of exponents for each set of exponents using a receiver operating characteristic curve (ROC curve).

The index set means a set of indices set according to the size of a selection category of each variable, and the combination of indices for each of the index sets means each corresponding variable except for an expected surgical period obtained from clinical data of a non-cardiac patient. It means a combination of indices according to a selection category of, and the sum of the combinations of indices for each index set excluding the estimated surgical period may be a minimum of 0 to a maximum of 81.

The sensitivity and specificity of each index combination total are analyzed by the receiver manipulation characteristic curve, and the sensitivity and specificity of the variable can be calculated according to the area under the curve (AUC) value of the discovery cohort and validation cohort. Can be.

On the other hand, the classification reference point can be set as a classification reference point by performing a receiver operating characteristic (ROC) curve analysis in order to obtain a predicted probability using an estimated regression coefficient value, and by obtaining a sum of sensitivity and specificity.

More specifically, a cut-off value may be set using a youden index of a receiver operating characteristic curve (ROC curve).

In addition, the system for predicting the risk of acute renal failure after non-cardiac surgery of the present invention calculates the sum of indices determined according to a set of indices set for each variable selected in the variable selection unit for a patient in need of prediction. It includes; a prediction unit for the risk of acute renal failure after non-cardiac surgery predicting the risk of acute renal failure after non-cardiac surgery in a non-cardiac surgery target patient based on the classification reference points set in the reference point setting unit.

According to an embodiment of the present invention, the risk of acute renal failure after non-cardiac surgery is predicted according to the sum of indices determined according to a set of indices set for each variable selected in the variable selector for non-cardiac surgery target patients in need of prediction. A, B, C, D can be classified into a total of four grades.

The grade A is the sum of the indices is less than 20, the grade B is the sum of the indices is 20 or more and less than 40, the grade C is the sum of the indices is 40 or more and less than 60, the grade D is the index It may be classified when the total is 60 or more.

In addition, according to an embodiment of the present invention, the A grade may be less than 2% of the probability of acute renal failure and severe acute renal failure after non-cardiac surgery in a non-cardiac surgery patient in need of the prediction, and the B grade is the prediction The probability of occurrence of acute renal failure after non-cardiac surgery in patients in need of non-cardiac surgery may be 2% or more, and the probability of occurrence of severe acute renal failure may be less than 2%. The probability of occurrence of acute renal failure after 10% or more, and the probability of occurrence of serious acute renal failure may be 2% or more. The probability of acute renal failure may be more than 10%.

In addition, the present invention relates to a method for predicting the risk of acute renal failure after non-cardiac surgery (see FIG. 1).

The method comprises: a first step of selecting factors related to the occurrence of acute renal failure after non-cardiac surgery consisting of clinical data before non-cardiac surgery of patients subject to non-cardiac surgery as variables, and setting an index set for each variable;

A second step of calculating sensitivity and specificity for the sum of combinations of exponents for each set of indices, and setting cut-off values according to the sensitivity and specificity; And

A non-cardiac surgical target that requires the prediction is calculated based on a set of indexes determined in accordance with the index set set for each variable selected in the first step for a non-cardiac surgical target patient that needs prediction And a third step of predicting a patient's risk of developing acute renal failure after non-cardiac surgery.

The factors related to the occurrence of acute renal failure after non-cardiac surgery are age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, and expected duration of surgery (Expected) surgical duration, emergency operation, diabetes mellitus, lenin-aldosterone-angiotensin-system blockade use, RAAS blockade use, hypoalbuminemia , Anemia and hyponatremia may include at least one selected from the group consisting of, and preferably may include all, but is not limited thereto.

According to one embodiment of the present invention, the index set is set according to a selection category for each variable except for an expected surgical duration, and 0, 3, 4, 6 to 9, 13, 15 for each selection category And any one index selected from 22 is set, the total sum of the index for each selection category of the variable may be 0 to 81.

In addition, according to an embodiment of the present invention, the index set in the expected surgical duration (Expected surgical duration) among the above parameters may be set to an index of 5 times the expected surgical duration (time).

In addition, the age (age) of the variable is divided into a selection category of less than 40, less than 40 and less than 60, more than 60 and less than 80, and the estimated glomerular filtration rate (eGFR) is 60 mL / min / 1.73 m ² or more, 45 mL / min / 1.73 m ² or more, 60 mL / min / 1.73 m ^{2 or} less, 30 mL / min / 1.73 m ² or more, 45 mL / min / 1.73 m ^{2 or} less, 15 mL /min/1.73 m ² or more and 30 mL / min / 1.73 m ^{2 or} less.

In addition, according to an embodiment of the present invention, the prediction of the risk of acute renal failure after non-cardiac surgery is the sum of indices determined according to a set of indices set for each variable selected in the variable selector for non-cardiac surgery target patients in need of prediction. According to A, B, C, D can be classified into a total of four grades.

The grade A is the sum of the indices is less than 20, the grade B is the sum of the indices is 20 or more and less than 40, the grade C is the sum of the indices is 40 or more and less than 60, the grade D is the index It may be classified when the total is 60 or more.

In addition, according to an embodiment of the present invention, the A grade may be less than 2% of the probability of acute renal failure and severe acute renal failure after non-cardiac surgery in a non-cardiac surgery patient in need of the prediction, and the B grade is the prediction The probability of occurrence of acute renal failure after non-cardiac surgery in patients in need of non-cardiac surgery may be 2% or more, and the probability of occurrence of severe acute renal failure may be less than 2%. The probability of occurrence of acute renal failure after 10% or more, and the probability of occurrence of serious acute renal failure may be 2% or more. The probability of acute renal failure may be more than 10%.

The details of the first, second, and third steps of the method are respectively performed by the variable selection unit of the system of the present invention, the classification reference point setting unit, and the risk predicting unit for acute renal failure after non-cardiac surgery. Since it is the same, it will be omitted below.

Hereinafter, examples will be described in detail to specifically describe the present invention.

Example

1. Experimental method

(1) Hospital and invention design where the experiment of the present invention was carried out

The present invention relates to a retrospective observation cohort experiment conducted at a government-designated tertiary care hospital in Korea. This discovery cohort includes adult patients over the age of 18 who underwent surgery at Seoul National University Hospital between 2004 and 2013, and the validation cohort includes adults who underwent surgery at Seoul National University Bundang Hospital between 2006 and 2015. Both hospitals have more than 1000 beds and top clinicians belonging to Seoul National University School of Medicine. However, the two hospitals are located in different administrative districts in Korea and do not share patient pools or major medical staff.

We included the first surgical cases during the experimental period in the following five surgical departments: General Surgery, Orthopedic Surgery, Gynecology, Neurosurgery and Urology. Exclusion criteria were as follows: 1) cardiac surgery, 2) surgery of a deceased patient (e.g. transplantation of a deceased donor), 3) patients with nephrectomy or renal transplantation, 4) defined as the duration of surgery less than 1 hour Small surgical procedure, 5) History of renal replacement therapy, preoperative serum serum creatinine (sCr) level of 4 mg / dL or higher, estimated glomerular filtration rate (eGFR) of 15 mL / min / 1.73 Patients with pre-operative renal dysfunction, defined as an increase in baseline of sCr from less than m ² or more than 0.3 mg / dL or 1.5 times or more from a minimum value 2 weeks prior to surgery and 6) Postoperative acute kidney injury, PO-AKI) Patients without baseline or subsequent sCr values to identify events.

(2) Data collection and variables for model building

Information that could be collected or planned before surgery was included because preoperative risk classification is the object of the present invention. Most continuous variables were categorized by the ranges commonly used for practical application of the present invention. Detailed information about the collected variables is as follows.

The following demographic data were collected from the discovery and validation cohort: age, gender and baseline body mass index (BMI) at the time of acquisition. Ages were categorized as <40, ≥40 and <60, ≥60 and <80 or ≥80 years, and intervals were determined to limit the number of classifications for brevity. BMI was categorized into low weight (<18.5 kg / m ² ), normal range (≥18.5 and <25 kg / m ² ), and obesity (≥25 kg / m ² ). Co-morbidities of heart disease were collected, including a history of heart failure or coronary artery disease (eg angina or myocardial infarction), hypertension and diabetes. The history of comorbidities was confirmed primarily by reviewing the records of anesthesiologists, taking medications and diagnostic codes. Data were collected on the actual surgical duration (hours) and expected surgical duration (hours) entered and entered by the attending physician prior to performing the surgery to schedule anesthesia and to reserve the operating room. Only the expected duration of surgery was included in the model because it is a variable that can be collected before surgery. Anesthesia type (normal or non-normal) and whether surgery was performed as scheduled or as an emergency surgery were collected. Preoperative systolic and diastolic blood pressures were recorded. Among the well-known PO-AKI related drugs, preoperative use of renin-aldosterone-angiotensin-system blockers was included in the parameters of the present invention. No diuretics, nonsteroidal anti-inflammatory drugs, or nephrotoxic antibiotics were collected because they are used frequently after surgery to control dose overload, pain or infection. The laboratory values collected were the results of the final examination within 3 months prior to surgery. Baseline eGFR was calculated based on sCr levels using the CKD-EPI equation and stratified into 4 classifications (reference range = 60 mL / min / 1.73 m ² or higher; CKD 3A = 45 mL / min / 1.73 m ² or higher And less than 60 mL / min / 1.73 m ² ; CKD 3B = 30 mL / min / 1.73 m ² or greater and 45 mL / min / 1.73 m ² or greater; or CKD 4 = 15 mL / min / 1.73 m ² or greater and 30 mL /min/1.73 m ^{2 or} less). The presence of another kidney function variable, baseline proteinuria, was confirmed by a simple dipstick test. Leukocyte count abnormalities were classified into leukopenia (<4,000 / μL) and leukopenia (≥ 10,000 / μL). Anemia was defined as a hemoglobin level of <12 g / dL for women and <13 g / dL for men. Serum albumin levels below 3.5 g / dL are definitions for hypoalbuminemia. Hyponatremia (sodium in the blood <135 mEq / L), hypernatremia (sodium in the blood> 145 mEq / L), hypokalemia (potassium in the blood <3.5 mEq / L), hyperkalemia (potassium in the blood> 5.5 mEq / L) A reference electrolyte imbalance was recorded that includes.

(3) Experiment results

PO-AKI was defined according to the sCr-criterion of Kidney Disease: Improving Global Outcomes guidelines using peak values of sCr within 2 weeks after surgery. The term "PO-AKI" includes all AKIs regardless of AKI severity. To address the severity and patient-oriented outcomes of PO-AKI, we have defined results that represent the turn to build a predictive model that includes the following three outcome classifications: "No AKI", "Low AKI" and "Severe AKI" ". Among PO-AKI patients, severe AKI was defined by the appearance of two or more AKI stages and consequently AKI causing death after AKI and dialysis within 90 days. When several patients started or died of kidney transplant treatment outside of the experimental hospital, we reviewed the national death database from the National Statistical Office and the national dialysis record maintained by the Korean Kidney Society and confirmed the results. Other patients who developed stage 1 PO-AKI but did not have severe AKI were included in the "low stage AKI" classification of the sequential results.

(4) Variable selection

First, the variable selection process was performed in a discovery cohort (Figure 2). A single-variable cumulative logistic regression analysis with binary results defined by different reference values in sequential results was performed to identify variables that violated the parallel regression estimation. Parallel regression estimation was checked by examining the direction and magnitude of model coefficients rather than a semi-conservative statistical test, typically in large data sets. Next, we fit the sequential results to a multivariate proportional odds model, and only variables that appear to have statistically significant relationships with independent, sequential results remain. Finally, we reduced the number of variables included in the model according to the absolute size of the model coefficients, and excluded variables with a relatively small effect size. After the variable selection, additional procedures were performed with patients without missing values in the selected parameters.

(5) Index and classification of Simple Postoperative AKI risK (SPARK)

Additional simplification was done to establish a simple postoperative Aki RisK (SPARK) index after additional simple surgery. After confirming the calibration of the simple model, we multiplied the coefficients to set the maximum sum of model coefficients in the discovery cohort to 100, and rounded each integer to an integer to generate the SPARK index. Finally, cutoff values were defined that define the four classifications in the discovery cohort to create a comprehensive classification that can be easily interpreted in practice. The cutoff value for A / B rating was defined to present a threshold for PO-AKI screening with a high sensitivity (90%), while the threshold for B / C rating was PO-AKI Values with high specificity for (90%) were presented. Finally, in patients with a SPARK index higher than the cutoff value for the B / C grade, we further determined the cutoff value for the C / D grade and high specificity for severe AKI A threshold with (90%) was chosen to define the D grade. Considering practical problems, we rounded the threshold to the nearest 10.

(6) Sensitivity analysis and other statistical analysis

Other statistical analysis methods, including sensitivity analysis, were as follows.

Classification variables are expressed as frequency (percentage), and continuous variables are expressed as median (interquartile range). The chi-squared test and Mann-Whitney U test were implemented to compare the basic characteristics of the discovery and validation cohort. The model calibration was first visually checked with a calibration plot of probability for both the estimated low-stage AKI and severe AKI. Since the invention involved a large number of patients (more than 25000), it was not recommended to apply the Hosmer-Lemeshow test as a basis. Therefore, we calculated the P value of the Hosmer-Lemeshow test on thousands of random subsamples with a fixed subsample size (n = 1000). The discriminative power of the model was confirmed by c-stat. The final test of the predictive power of the SPARK index was performed on PO-AKI and severe AKI results with analysis of the receiver operating characteristics curve (ROC curve), and the area under the curve (AUC) of 0.7 or higher was accepted. It was considered possible. Sensitivity analyzes were performed to test for the presence of significant bias due to our exclusion criteria. In the analyzes, the discriminative power of the final SPARK index for sequential results was 1) the replaced data set using additional nonlinear transformations and substitutions, 2) the input of the actual surgical period instead of the expected one, 3) after merging of this experiment and verification cohort , Data sets divided according to three ages (2004-2007, 2008-2011, 2012-2015) and 4) strict control of possible inclusions of patients with undefined AKI before surgery Regardless of the intervals between risk surgeries, patients with sCr greater than or equal to 0.3 mg / dL or 1.5 times higher than the lowest value within 3 months postoperatively after surgery were calculated with the data set after additional exclusion. In addition, the performance of the SPARK index and classification was examined in each surgical sector after combining discovery and validation cohorts. All analyzes were performed in complete cases with no missing values, except for sensitivity analysis with an attribution dataset. Statistical analyzes were performed with R (version 3.4.3, the R foundation), and P values on both sides were considered statistically significant.

2. Experimental results

(1) Characteristics of the cohort tested

A total of 162,095 patients were included in the screening in this experiment, which was the sum of 93,370 and 68,725 surgical cases tested in SNUH (Seoul National University Hospital) and SNUBH (Seoul National University Bundang Hospital), respectively (Figure 2). After the exclusion criteria were applied, 51,041 and 39,764 patients were screened for model building in a discovery and validation cohort, respectively. The number of patients with low-stage AKI and severe AKI was 2,132 (4.2%) and 605 (1.2%) in the discovery cohort. Of the cohort patients with severe AKI, 511 (1.0%), 167 (0.3%) and 88 (0.2%) of the PO-AKI patients had at least two phases of AKI, AKI death, and dialysis within 90 days, respectively. The incidence of adverse outcomes gradually increased in the discovery cohort with 1,774 (4.5%) and 727 (1.8%) patients with low-stage AKI and severe AKI. AKI in stage 2 or higher, death after AKI, and dialysis within 90 days were 644 (1.6%), 176 (0.4%) and 64 (0.2%) in the discovery cohort, respectively. Other characteristics of the two cohorts were significantly different, as the validation cohort consisted of patients with older, male proportions (Table 1). Obstetrics and gynecology surgery was relatively common in the discovery cohort, but orthopedic surgery appeared to be the largest part of the discovery cohort. Significant differences were also identified with respect to baseline experimental values and drug use.

Variables Discovery cohort (N = 51,041) Validation cohort (N = 39,764) P Demographics Age (years) 56 [44; 66] 60 [48; 70] <0.001 <40 9,206 (18.0%) 5,559 (14.0%) ≥40 and (and) <60 20,877 (40.9%) 13,492 (33.9%) ≥60 and <80 19,684 (38.6%) 18,698 (47.0%) ≥80 1,274 (2.5%) 2,015 (5.1%) Gender <0.001 Female 28,306 (55.5%) 18,706 (47.0%) Male 22,735 (44.5%) 21,058 (53.0%) Body mass index (kg / m ² ) 23.8 [21.7; 26.0] 24.1 [21.9; 26.4] <0.001 <18.5 1,919 (3.9%) 854 (3.8%) ≥18.5 and <30 45,098 (91.5%) 20,624 (90.9%) ≥30 2,294 (4.7%) 1,218 (5.4%) Existing comorbidities Heart disease 1,629 (3.2%) 1,163 (2.9%) 0.022 Hypertension 9,824 (19.2%) 9,161 (23.0%) <0.001 Diabetes mellitus 3,956 (7.8%) 3,581 (9.0%) <0.001 Surgery characteristics Departments <0.001 General surgery 22,447 (44.0%) 14,733 (37.1%) Neurosurgery 5,063 (9.9%) 3,842 (9.7%) Obstetrics and gynecology 7,894 (15.5%) 908 (2.3%) Orthopedics 11,372 (22.3%) 16,823 (42.3%) Urology surgery 4,265 (8.4%) 3,458 (8.7%) Surgery duration (hours) 2.2 [1.5; 3.3] 2.5 [1.7; 3.6] <0.001 Expected surgery duration (hours) 2.5 [2.0; 3.0] 3.0 [2.0; 4.0] <0.001 Anesthesia type <0.001 General 43,921 (86.6%) 30,570 (76.9%) Non-general 6,789 (13.4%) 9,194 (23.1%) Emergency operation 732 (1.4%) 1,965 (4.9%) <0.001 BP before operation (mmHg) Systolic blood pressure (SBP) 124 [113; 135] 128 [116; 142] <0.001 Diastolic blood pressure (DBP) 77 [70; 85] 73 [65; 81] <0.001 Normal blood pressure (Normotensive) 36,976 (75.0%) 22,934 (57.7%) Hypertensive (SBP ≥140 or DBP ≥90) 10,206 (20.7%) 11,472 (28.9%) Hypotensive (SBP <90 or DBP <60) 2,134 (4.3%) 5,358 (13.5%) Medication usage RAAS blockade 2,881 (5.6%) 2,915 (7.3%) <0.001 Laboratory findings eGFR (mL / min / 1.73 m ² ) 82.1 [71.4; 95.1] 87.7 [73.1; 99.7] <0.001 No CKD or CKD stage 1 or 2 (≥60) 46,971 (92.0%) 35,881 (90.2%) CKD stage 3A (≥45 and <60) 3,226 (6.3%) 2,918 (7.3%) CKD stage 3B (≥30 and <45) 641 (1.3%) 703 (1.8%) CKD stage 4 (≥15 and <30) 203 (0.4%) 262 (0.7%) Dipstick albuminuria (≥ 1+) 4,682 (9.3%) 2,169 (7.0%) <0.001 White blood cell count (/ mm ² ) 6,100 [5,000; 7,500] 6,500 [5,400; 8,000] <0.001 Reference range (4000 ~ 10000) 43,262 (84.8%) 33,902 (85.3%) Leukopenia (<4000) 3,934 (7.7%) 1,806 (4.5%) Leukocytosis (≥10000) 3,823 (7.5%) 4,033 (10.1%) Hemoglobin (g / dL) 13.2 [12.1; 14.4] 13.6 [12.4; 14.8] <0.001 Anemia (<12 for female, <13 for male) 14,177 (27.8%) 9,212 (23.2%) <0.001 Platelets (x10 ³ / μL) 200 [152; 256] 239 [199; 284] <0.001 Thrombocytopenia (<10) 4,160 (8.2%) 493 (1.2%) <0.001 Albumin (g / dL) 4.2 [3.9; 4.5] 4.3 [4.0; 4.5] <0.001 Hypoalbuminemia (<3.5) 5,148 (10.1%) 2,679 (6.8%) <0.001 Sodium (mEq / L) 140 [139; 142] 141 [139; 142] <0.001 Normal sodium blood concentration (Normonatremia) (135 ~ 145) 48,288 (94.8%) 31,156 (92.8%) Hypnatremia (<135) 1,291 (2.5%) 986 (2.9%) Hypernatremia (> 145) 1,361 (2.7%) 1,418 (4.2%) Potassium (mEq / L) 4.2 [4.0; 4.4] 4.2 [4.0; 4.5] <0.001 Normal potassium blood concentration (Normokaleima) (3.5 ~ 5.5) 49,711 (97.6%) 32,649 (97.3%) Hypokalemia (<3.5) 1,016 (2.0%) 711 (2.1%) Hyperkalemia (> 5.5) 213 (0.4%) 200 (0.6%)

CKD = chronic kidney disease

(2) Variable selection

Table 2 shows patient characteristics according to the sequential results tested in the discovery cohort. In the cumulative logistic regression analysis, the surgical department, body mass index and blood pressure (BP) classification, anesthesia type, and hypernatremia were excluded from building additional models because they were inappropriately met in parallel regression estimation (Table 3). Additionally, the serum potassium levels category and leukocytosis did not show a significant relationship with the sequential results in our multivariate proportional odds model (Table 4). Finally, heart disease, hypertension and leukopenia were excluded from the model because the model coefficients are relatively small compared to others (Table 5). Other variables were included for final index construction and verification.

A total of 49,803 and 29,715 cases, respectively, in a discovery and verification cohort with complete information of the finally selected variables were used for further analysis for simplified model construction and verification (Figure 2). The number of low-stage AKI patients included was 2,062 (4.1%) and 1,109 (3.7%) in the discovery and validation cohort, respectively, with no missing values. Serious AKI events appeared in complete cases as follows: 563 (1.1%) persons in the discovery cohort and 445 (1.5%) persons in the validation cohort. Finally, the model coefficients of the variables selected from the proportional Oz model are shown in Table 6 below.

Variables No AKI (N = 48,304) Low stage AKI (N = 2,132) Critical AKI (N = 605) P Demographics Age 55 [44; 66] 63 [53; 71] 65 [55; 73] <0.001 <40 9,016 (18.7%) 152 (7.1%) 38 (6.3%) ≥40 and <60 19,990 (41.4%) 698 (32.7%) 189 (31.2%) ≥60 and <80 18,194 (37.7%) 1,158 (54.3%) 332 (54.9%) ≥80 1,104 (2.3%) 124 (5.8%) 46 (7.6%) Sex <0.001 female 27,402 (56.7%) 706 (33.1%) 198 (32.7%) male 20,902 (43.3%) 1,426 (66.9%) 407 (67.3%) Body mass index (kg / m ² ) 23.8 [21.6; 26.0] 24.0 [21.9; 26.3] 23.7 [21.3; 26.1] 0.005 <18.5 (underweight) 1,802 (3.9%) 77 (3.8%) 40 (7.1%) ≥18.5 and <30 (normal range) 42,743 (91.5%) 1,854 (90.4%) 501 (88.5%) > 30 (obesity) 2,150 (4.6%) 119 (5.8%) 25 (4.4%) Preexisting comorbidities Heart disease 1,445 (3.0%) 132 (6.2%) 52 (8.6%) <0.001 Hypertension 8,989 (18.6%) 653 (30.6%) 182 (30.1%) <0.001 Diabetes mellitus 3,494 (7.2%) 357 (16.7%) 105 (17.4%) <0.001 Surgery characteristics Departments <0.001 General surgery 20,962 (43.4%) 1,167 (54.7%) 318 (52.6%) Neurosurgery 4,920 (10.2%) 106 (5.0%) 37 (6.1%) Obstetrics and gynecology 7,802 (16.2%) 61 (2.9%) 31 (5.1%) Orthopedics 10,834 (22.4%) 476 (22.3%) 62 (10.2%) Urologic surgery 3,786 (7.8%) 322 (15.1%) 157 (26.0%) Surgery duration (hours) 2.2 [1.4; 3.2] 3.8 [2.2; 5.8] 3.9 [2.3; 6.2] <0.001 Expected surgery duration (hours) 2.5 [2.0; 3.0] 3.0 [2.5; 5.0] 3.0 [2.5; 5.0] <0.001 Anesthesia type <0.001 General 41,480 (86.4%) 1,878 (88.6%) 563 (93.5%) Non-general 6,508 (13.6%) 242 (11.4%) 39 (6.5%) Emergency operation 613 (1.3%) 74 (3.5%) 45 (7.8%) <0.001 BP before operation SBP 123 [113; 135] 126 [114; 138] 125 [113; 137] <0.001 DBP 77 [70; 85] 76 [69; 84] 75 [68; 83] <0.001 Normotensive 35,199 (75.2%) 1,389 (69.4%) 388 (72.4%) Hypertensive (SBP ≥ 140 or DBP ≥ 90) 9,601 (20.5%) 493 (24.6%) 112 (20.9%) Hypotensive (SBP <90 or DBP <60) 1,978 (4.2%) 120 (6.0%) 36 (6.7%) Preoperative RAAS blockade 2,482 (5.1%) 294 (13.8%) 105 (17.4%) <0.001 Laboratory findings eGFR (mL / min / 1.73 m ² ) 82.3 [71.8; 95.1] 76.9 [61.9; 93.6] 74.0 [57.6; 92.9] <0.001 No CKD or CKD stage 1 or 2 (≥60) 44,900 (93.0%) 1,633 (76.6%) 438 (72.4%) CKD stage 3A (≥45 and <60) 2,836 (5.9%) 311 (14.6%) 79 (13.1%) CKD stage 3B (≥30 and <45) 459 (1.0%) 132 (6.2%) 50 (8.3%) CKD stage 4 (≥15 and <30) 109 (0.2%) 56 (2.6%) 38 (6.3%) Presence of albuminuria (dipstick ≥1 +) 4,060 (8.6%) 427 (20.5%) 195 (33.2%) <0.001 White blood cell count (1000 / mm ² ) 6.1 [5.0; 7.5] 6.3 [4.9; 7.9] 6.6 [4.9; 8.9] <0.001 Reference range (4000 ~ 10000) 41,211 (85.3%) 1,627 (76.3%) 424 (70.3%) Leukopenia (<4000) 3,581 (7.4%) 278 (13.0%) 75 (12.4%) Leukocytosis (≥10000) 3,493 (7.2%) 226 (10.6%) 104 (17.2%) Hemoglobin (g / dL) 13.3 [12.1; 14.4] 12.6 [10.9; 14.0] 12.1 [10.4; 13.8] <0.001 Anemia (<12 for female, <13 for male) 12,819 (26.6%) 1,010 (47.4%) 348 (57.6%) <0.001 Platelet (10 ³ / μL) 200 [152; 255] 206 [140; 294] 207 [135; 324] <0.001 Thrombocytopenia (<10) 3,807 (7.9%) 263 (12.6%) 90 (15.2%) <0.001 Albumin (g / dL) 4.2 [3.9; 4.5] 3.9 [3.3; 4.3] 3.7 [3.0; 4.2] <0.001 Hypoalbuminemia (<3.5) 4,322 (9.0%) 591 (27.7%) 235 (38.8%) <0.001 Sodium (mEq / L) 140 [139; 142] 140 [138; 142] 140 [137; 142] <0.001 Normonatremia (135-145) 45,900 (95.2%) 1,881 (88.2%) 507 (83.8%) Hyponatremia (<135) 1,028 (2.1%) 190 (8.9%) 73 (12.1%) Hypernatremia (> 145) 1,275 (2.6%) 61 (2.9%) 25 (4.1%) Potassium (mEq / L) 4.2 [4.0; 4.4] 4.2 [4.0; 4.5] 4.2 [3.9; 4.5] <0.001 Normokaleima (3.5 ~ 5.5) 47,108 (97.7%) 2,041 (95.7%) 562 (92.9%) Hypokalemia (<3.5) 920 (1.9%) 64 (3.0%) 32 (5.3%) Hyperkalemia (> 5.5) 175 (0.4%) 27 (1.3%) 11 (1.8%)

(Low stage AKI + Critical AKI) vs. (No AKI) (Critical AKI) vs. (Low stage AKI + No AKI) Coefficient 95% CI P Coefficient 95% CI P Age <40 Reference Reference ≥40 and <60 0.745 0.588, 0.906 <0.001 0.790 0.453, 1.154 <0.001 ≥60 and <80 1.357 1.207, 1.513 <0.001 1.420 1.098, 1.773 <0.001 ≥80 1.989 1.772, 2.205 <0.001 2.201 1.769, 2.640 <0.001 Male sex (vs. female) 0.978 0.896, 1.060 <0.001 0.951 0.782, 1.123 <0.001 Departments General surgery Reference Reference Neurosurgery -0.891 -1.069, -0.720 <0.001 -0.669 -1.027, -0.341 <0.001 Obstetrics and gynecology -1.793 -2.012, -1.587 <0.001 -1.293 -1.683, -0.941 <0.001 Orthopedics -0.355 -0.457, -0.255 <0.001 -0.964 -1.246, -0.698 <0.001 Urologic surgery 0.580 0.470, 0.688 <0.001 0.978 0.782, 1.170 <0.001 BMI category Underweight (vs. reference range) 0.164 -0.032, 0.351 0.093 0.639 0.299, 0.951 <0.001 Obesity (vs. reference range) 0.195 0.017, 0.365 0.028 -0.019 -0.449, 0.362 0.925 Heart disease (vs. none) 0.849 0.688, 1.005 <0.001 1.069 0.769, 1.348 <0.001 Hypertension (vs. none) 0.652 0.567, 0.737 <0.001 0.599 0.422, 0.772 <0.001 Diabetes mellitus (vs. none) 0.957 0.850, 1.062 <0.001 0.932 0.714, 1.141 <0.001 Expected surgery duration (hours) 0.539 0.515, 0.563 <0.001 0.458 0.417, 0.500 <0.001 Non-general anesthesia (vs. general) -0.310 -0.438, -0.185 <0.001 -0.810 -1.151, -0.499 <0.001 Emergency operation (vs. non-emergency) 1.277 1.073, 1.474 <0.001 1.803 1.477, 2.105 <0.001 BP category Hypotensive before surgery (vs. normotensive) 0.222 0.126, 0.316 <0.001 0.045 -0.170, 0.253 0.675 Hypertensive before surgery (vs. normotensive) 0.446 0.273, 0.613 <0.001 0.481 0.120, 0.811 0.006 Preoperative RAAS blockade use (vs. no use) 1.148 1.033, 1.260 <0.001 1.282 1.064, 1.492 <0.001 eGFR ≥60 Reference Reference ≥45 and <60 1.092 0.977, 1.206 <0.001 0.981 0.732, 1.217 <0.001 ≥30 and <45 2.151 1.972, 2.327 <0.001 2.196 1.881, 2.490 <0.001 ≥15 and <30 2.928 2.648, 3.207 <0.001 3.197 2.819, 3.551 <0.001 Leukopenia (vs. reference range) 0.683 0.564, 0.800 <0.001 0.675 0.420, 0.916 <0.001 Leukocytosis (vs. reference range) 0.641 0.518, 0.761 <0.001 1.039 0.817, 1.251 <0.001 Anemia (vs. none) 1.004 0.926, 1.081 <0.001 1.280 1.118, 1.443 <0.001 Hypoalbuminemia (vs. none) 1.481 1.393, 1.568 <0.001 1.772 1.605, 1.937 <0.001 Hyponatremia (vs. reference range) 1.593 1.450, 1.733 <0.001 1.731 1.472, 1.976 <0.001 Hypernatremia (vs. reference range) 0.260 0.030, 0.475 0.022 0.567 0.136, 0.950 0.006 Hypokalemia (vs. reference range) 0.636 0.416, 0.844 <0.001 1.045 0.664, 1.390 <0.001 Hyperkalemia (vs. reference range) 1.369 1.002, 1.710 <0.001 1.561 0.888, 2.125 <0.001 Urine albuminuria (vs. none) 1.175 1.079, 1.270 <0.001 1.605 1.429, 1.778 <0.001 Thrombocytopenia (vs. none) 0.564 0.446, 0.680 <0.001 0.707 0.475, 0.928 <0.001

AKI = acute kidney injury, CI = confidence interval, BMI = body mass index, BP = blood pressure, RAAS = renin-angiotensin-aldosterone system, eGFR = estimated glomerular filtration rate General surgery, Neurosurgery, Obstetrics and gynecology, Orthopedics, Urologic surgery, Bodyweight Index (vs. reference range), Obesity (vs. reference range), Non-general anesthesia (vs. general), Blood pressure (Hypotensive before surgery (vs. normotensive), Hypertensive before surgery (vs. normotensive)), and hypernatremia (vs. reference range) were not included in the construction of additional models due to significant differences in coefficients at each critical point. Parallel estimation can hardly be estimated with variables.

Model coefficient 95% CI P Age <40 Reference ≥40 and <60 0.473 0.302, 0.644 <0.001 ≥60 and <80 0.787 0.617, 0.956 <0.001 80≥ 1.120 0.875, 1.366 <0.001 Male sex (vs. female) 0.714 0.624, 0.804 <0.001 Heart disease (vs. none) 0.190 0.006, 0.374 0.042 Hypertension (vs. none) 0.176 0.069, 0.282 0.001 Diabetes mellitus (vs. none) 0.263 0.135, 0.391 <0.001 Expected surgery duration (hours) 0.451 0.424, 0.478 <0.001 Emergency operation (vs. non-emergency) 0.690 0.448, 0.931 <0.001 Preoperative RAAS blockade use (vs. none) 0.473 0.338, 0.608 <0.001 eGFR ≥60 ≥45 and <60 0.663 0.532, 0.794 <0.001 ≥30 and <45 1.321 1.113, 1.528 <0.001 ≥15 and <30 1.921 1.607, 2.235 <0.001 Leukopenia (vs. reference range) 0.395 0.254, 0.536 <0.001 Leukocytosis (vs. reference range) 0.027 -0.117, 0.172 0.712 Anemia (vs. none) 0.292 0.192, 0.392 <0.001 Hypoalbuminemia (vs. none) 0.683 0.563, 0.802 <0.001 Hyponatremia (vs. reference range) 0.307 0.162, 0.452 <0.001 Hypokalemia (vs. reference range) 0.169 -0.080, 0.418 0.184 Hyperkalemia (vs. reference range) 0.270 -0.155, 0.696 0.212 Urine albuminuria (vs. none) 0.533 0.421, 0.646 <0.001 Thrombocytopenia (vs. none) 0.041 -0.090, 0.172 0.543

CI = confidence interval, RAAS = renin angiotensin aldosterone system, eGFR = estimated glomerular filtration rate, Leukocytosis of the variables in Table 4 above vs. reference range), hypokalemia (vs. reference range), hyperkalemia (vs. reference range), thrombocytopenia (vs. none) Due to differences, they were not included in the construction of additional models.

Model coefficient 95% CI P Age (vs. <40) ≥40 and <60 0.502 0.332, 0.672 <0.001 ≥60 and <80 0.807 0.638, 0.976 <0.001 ≥80 1.136 0.892, 1.379 <0.001 Male sex (vs. female) 0.705 0.616, 0.794 <0.001 Heart disease (vs. none) 0.187 0.004, 0.370 0.040 Hypertension (vs. none) 0.171 0.065, 0.276 <0.001 Diabetes mellitus (vs. none) 0.275 0.148, 0.401 <0.001 Expected surgical duration (continuous, hours) 0.458 0.432, 0.484 <0.001 Emergency operation 0.669 0.432, 0.906 <0.001 Preoperative RAAS blockade use (vs. none) 0.453 0.319, 0.588 <0.001 eGFR (vs. ≥60 mL / min / 1.73 m ² ) ≥45 and <60 0.680 0.551, 0.809 <0.001 ≥30 and <45 1.331 1.127, 1.536 <0.001 ≥15 and <30 2.021 1.714, 2.328 <0.001 Leukopenia (vs. none) 0.204 0.097, 0.310 <0.001 Anemia (vs. none) 0.305 0.206, 0.404 <0.001 Hypoalbuminemia (vs. none) 0.667 0.550, 0.784 <0.001 Hyponatremia (vs. none) 0.296 0.153, 0.438 <0.001 Albuminuria (vs. none) 0.505 0.394, 0.617 <0.001

CI = confidence interval, RAAS = renin angiotensin aldosterone system, eGFR = estimated glomerular filtration rate vs. none), hypertension (vs. none), and leukopenia (vs. none) were not included in the construction of additional models because of the relatively small model coefficients.

Model coefficients 95% CI P Age (vs. <40) ≥40 and <60 0.522 0.353, 0.691 <0.001 ≥60 and <80 0.852 0.686, 1.019 <0.001 ≥80 1.203 0.962, 1.443 <0.001 Male sex (vs. female) 0.705 0.616, 0.794 <0.001 Diabetes mellitus (vs. none) 0.347 0.227, 0.467 <0.001 Expected surgical duration (continuous, hours) 0.459 0.433, 0.484 <0.001 Emergency operation 0.678 0.441, 0.915 <0.001 RAAS blockade use (vs. none) 0.506 0.375, 0.638 <0.001 eGFR (vs. ≥60 mL / min / 1.73 m ² ) ≥45 and <60 0.690 0.561, 0.818 <0.001 ≥30 and <45 1.345 1.141, 1.549 <0.001 ≥15 and <30 2.012 1.705, 2.319 <0.001 Anemia (vs. none) 0.319 0.221, 0.418 <0.001 Hypoalbuminemia (vs. none) 0.705 0.590, 0.820 <0.001 Hyponatremia (vs. none) 0.298 0.156, 0.441 <0.001 Albuminuria (vs. none) 0.510 0.399, 0.621 <0.001

CI = confidence interval, RAAS = renin angiotensin aldosterone system, eGFR = estimated glomerular filtration rate model coefficients are multiplied by 11.0306, rounded up to an integer to form the SPARK index.

(3) SPARK index and classification

Selected variables were fitted to sequential results with a proportional odds model. Although some underestimation was found in the high probability range of the discovery cohort, the calibration plot showed an acceptable distribution of estimated and expected probability (FIG. 3). In 1000 random subsamples of fixed size (n = 1,000), the Hosmer-Lemeshow test was 0.372 (interquartile range (IQR) 0.171 to 0.592, P, for low-level AKI and severe AKI results in the discovery cohort, respectively). Median P values of 90.9% of samples with ≥0.05] and 0.485 (IQR 0.171 to 0.739, 88.3% of samples with P ≥0.05). The median P values in the cohort found from the same test also showed that the model fit was significantly acceptable (P> 0.05). However, the calibration results were relatively good within the validation cohort as a small percentage of the sub-samples showed good model fit. The median P value for low-stage AKI was 0.119 (IQR 0.032 to 0.294, 67.8% of samples with P ≥0.05), and the median P value for severe AKI was 0.130 (IQR 0.016 to 0.437, samples with P ≥0.05) (65.0%). The c-stat was 0.798 and 0.715 in the discovery and validation cohort, respectively, which was an acceptable range. After converting the model coefficients to an integer score, the final SPARK index is PO-AKI [area under the discovery cohort curve (AUC) 0.800 (95% CI 0.791-0.809), validation cohort AUC = 0.717 (95% CI 0.705-0.730)] And severe AKI [discovery cohort AUC = 0.826 (95% CI 0.810-0.843), validation cohort AUC = 0.765 (95% CI 0.743-0.786)] results showed acceptable discriminative power.

Subsequently, we tested the sensitivity / specificity values (FIG. 4) and selected cutoff values of 20 and 40 as thresholds between SPARK grades A / B and B / C, respectively (Table 7). . Additionally, a threshold of 60 was identified as the threshold between SPARK C and D grades. After classification was performed at a designated cutoff value, both the incidence of AKI and severe AKI showed a grade-dependent increase in the discovery and verification cohort (FIG. 5). As a result of the above mentioned, we built the SPARK index and classification, and proposed a preoperative AKI monitoring strategy (Fig. 6).

Sensitivity Specificity Negative predictive value Positive predictive value PO-AKI Discovery cohort Classification cutoff point (Cutoff) = 20 96.0% 27.2% 99.2% 6.8% Cutoff = 40 51.0% 88.6% 97.0% 20.0% Validation cohort Cutoff = 20 95.9% 16.0% 98.6% 5.9% Cutoff = 40 38.5% 85.8% 96.2% 13.0% Critical AKI (among SPARK index ≥40) Discovery cohort Cutoff = 60 25.8% 91.7% 96.0% 13.9% Validation cohort Cutoff = 60 18.2% 94.9% 96.0% 14.8%

PO-AKI = postoperative acute kidney injury, AKI = acute kidney injury, SPARK = simple postoperative acute kidney injury risk

(4) Sensitivity analysis

Sensitivity analysis was performed to investigate whether there was a significant bias due to our exclusion criteria. The discriminative power of the SPARK index in the attribution dataset, including cases where missing values were present, was acceptable in the discovery cohort [c-stat = 0.802 (N = 51,041)]. However, within a validation cohort with a high percentage of missing values, discriminative power, which was largely missing a large number of dipstick proteinuria variables, was relatively reduced [c-stat = 0.698 (N = 39,764)]. When the actual surgical period was included instead of the expected period, there was no significant decrease in discriminative power [c-stat = 0.810 in the discovery cohort (N = 49,803) and c-stat = 0.723 in the validation cohort (N = 29,715)]. Then, after combining the discovery and verification cohorts, we tested whether there were obvious differences over time, and no significant difference or decrease in c-stat was observed in the three periods of this experiment [2004 to 2004]. C-stat = 0.754 from 2007 (N = 18,560), c-stat = 0.779 from 2008 to 2011 (N = 34,016) and c-stat = 0.768 from 2012 to 2015 (N = 26,942)]. Finally, we have demonstrated a pre-operative creatinine greater than or equal to 0.3 mg / dL or more than 1.5 times the minimum value within 3 months prior to surgery regardless of the duration to control potential bias from a set of subacute or chronic progressive kidney damage prior to surgery. This elevated patient was excluded. Even in the analysis, the discriminative power of the SPARK index remained in an acceptable range (c-stat = 0.792 in the discovery cohort (N = 48,124) and c-stat = 0.711 in the validation cohort (N = 29,315)].

(5) Performance of SPARK index and classification in each surgical department

When we merged cases without missing values from the discovery and validation cohort, certain differences related to clinical characteristics existed between departments (Table 8). The results of performing the simplified non-regressive odds model and SPARK index of each department are shown in FIG. 7. The correction results indicated that all corrections were acceptable in general surgery and orthopedics, and were good in gynecology. However, while our model underestimates the risk of adverse outcomes in urology surgery, a significant overestimation has been identified in the neurosurgery department. This showed a similar tendency in discriminative power, and the AUC value in neurosurgery and urology departments was relatively low, less than 0.7. Nevertheless, when the SPARK classification was applied, an increase in incidence with a significant grade of adverse consequences was again observed (Table 9).

General surgery
(N = 31,810) Orthopedic surgery (N = 24,873) Obstetrics and gynecology (N = 8,351) Neurosurgery (N = 7,280) Urologic surgery (N = 7,204) P Age (years) 58 [49; 68] 59 [44; 69] 45 [36; 52] 56 [45; 65] 66 [57; 72] <0.001 <40 3,023 (9.5%) 5,171 (20.8%) 2,827 (33.9%) 1,186 (16.3%) 692 (9.6%) ≥40 and <60 13,949 (43.9%) 7,559 (30.4%) 4,332 (51.9%) 3,185 (43.8%) 1,473 (20.4%) ≥60 and <80 13,869 (43.6%) 11,149 (44.8%) 1,133 (13.6%) 2,813 (38.6%) 4,780 (66.4%) ≥80 969 (3.0%) 994 (4.0%) 59 (0.7%) 96 (1.3%) 259 (3.6%) eGFR (mL / min / 1.73 m ² ) 82.7 [71.7; 95.2] 85.7 [72.6; 98.0] 86.5 [75.4; 100.8] 85.5 [72.8; 99.4] 76.6 [64.7; 88.2] <0.001 ≥60 29,432 (92.5%) 22,516 (90.5%) 8,042 (96.3%) 6,740 (92.6%) 5,936 (82.4%) ≥45 and <60 1,895 (6.0%) 1,830 (7.4%) 250 (3.0%) 440 (6.0%) 963 (13.4%) ≥30 and <45 366 (1.2%) 401 (1.6%) 43 (0.5%) 74 (1.0%) 226 (3.1%) ≥15 and <30 117 (0.4%) 126 (0.5%) 16 (0.2%) 26 (0.4%) 79 (1.1%) Dipstick albuminuria 2,806 (8.8%) 1,459 (5.9%) 699 (8.4%) 402 (5.5%) 1,316 (18.3%) <0.001 Male sex 16,900 (53.1%) 11,025 (44.3%) 0 (0.0%) 3,366 (46.2%) 6,455 (89.6%) <0.001 Expected surgery duration (hours) 3.0 [2.0; 3.5] 2.5 [2.0; 3.0] 2.0 [2.0; 3.0] 4.0 [3.0; 5.0] 3.0 [2.0; 4.0] <0.001 Emergency department 444 (1.4%) 296 (1.2%) 164 (2.0%) 233 (3.2%) 58 (0.8%) <0.001 Diabetes mellitus 2,705 (8.5%) 2,522 (10.1%) 255 (3.1%) 595 (8.2%) 597 (8.3%) <0.001 RAAS blockade use 1,647 (5.2%) 1,756 (7.1%) 235 (2.8%) 712 (9.8%) 472 (6.6%) <0.001 Albumin (g / dL) 4.2 [3.9; 4.5] 4.4 [4.1; 4.6] 4.3 [4.1; 4.5] 4.2 [3.9; 4.5] 4.4 [4.2; 4.6] <0.001 Hypoalbuminemia (<3.5) 3,126 (9.8%) 1,478 (5.9%) 554 (6.6%) 683 (9.4%) 197 (2.7%) Hemoglobin (g / dL) 13.3 [12.0; 14.4] 13.6 [12.5; 14.9] 12.7 [11.6; 13.5] 13.4 [12.4; 14.5] 14.3 [13.2; 15.2] <0.001 Anemia (<12 for female, <13 for male) 9,425 (29.6%) 4,444 (17.9%) 2,555 (30.6%) 1,631 (22.4%) 1,280 (17.8%) Sodium (mEq / L) 141 [139; 142] 141 [139; 142] 140 [139; 141] 141 [139; 142] 141 [139; 142] <0.001 Hyponatremia (<135) 933 (2.9%) 545 (2.2%) 102 (1.2%) 169 (2.3%) 111 (1.5%)

eGFR = estimated glomerular filtration rate, RAAS = renin-angiotensin-aldosterone system

Class A Class B Class C Class D P General surgery (N = 6,228) (N = 19,988) (N = 4,988) (N = 606) Any AKI 47 (0.8%) 785 (3.9%) 827 (16.6%) 347 (57.3%) <0.001 Low-stage AKI 41 (0.7%) 606 (3.0%) 610 (12.2%) 259 (42.7%) <0.001 Critical AKI 6 (0.1%) 179 (0.9%) 217 (4.4%) 88 (14.5%) <0.001 Orthopedic surgery (N = 6,119) (N = 16,776) (N = 1,908) (N = 70) Any AKI 78 (1.3%) 604 (3.6%) 219 (11.5%) 14 (20.0%) <0.001 Low-stage AKI 71 (1.2%) 539 (3.2%) 169 (8.9%) 8 (11.4%) <0.001 Critical AKI 7 (0.1%) 65 (0.4%) 50 (2.6%) 6 (8.6%) <0.001 Obstetrics and gynecology (N = 4,300) (N = 3,900) (N = 145) (N = 6) Any AKI 26 (0.6%) 68 (1.7%) 20 (13.8%) 1 (16.7%) <0.001 Low-stage AKI 17 (0.4%) 44 (1.1%) 13 (9.0%) 0 (0.0%) <0.001 Critical AKI 9 (0.2%) 24 (0.6%) 7 (4.8%) 1 (16.7%) <0.001 Neurosurgery (N = 287) (N = 5,133) (N = 1,781) (N = 79) Any AKI 7 (2.4%) 91 (1.8%) 76 (4.3%) 15 (19.0%) <0.001 Low-stage AKI 6 (2.1%) 71 (1.4%) 52 (2.9%) 6 (7.6%) <0.001 Critical AKI 1 (0.3%) 20 (0.4%) 24 (1.3%) 9 (11.4%) <0.001 Urologic surgery (N = 552) (N = 4,935) (N = 1,603) (N = 114) Any AKI 10 (1.8%) 526 (10.7%) 361 (22.5%) 57 (50.0%) <0.001 Low-stage AKI 6 (1.1%) 376 (7.6%) 240 (15.0%) 37 (32.5%) <0.001 Critical AKI 4 (0.7%) 150 (3.0%) 121 (7.5%) 20 (17.5%) <0.001

AKI = acute kidney injury

Claims (16)

A variable selection unit for selecting factors related to the occurrence of acute renal failure after non-cardiac surgery consisting of clinical data before non-cardiac surgery of patients subject to non-cardiac surgery as variables, and setting an index set for each variable;
A classification reference point setting unit that calculates sensitivity and specificity for the sum of the combination of exponents for each index set, and sets cut-off values according to the sensitivity and specificity; And
The non-cardiac surgery that requires the prediction is calculated based on the classification reference points set in the classification reference point setting unit by calculating the sum of the indices determined according to the set of indices set for each variable selected by the variable selection unit for the non-cardiac surgery target patient Includes a prediction unit for the risk of acute renal failure after non-cardiac surgery to predict the risk of acute renal failure after non-cardiac surgery of the target patient,
The factors related to the occurrence of acute renal failure after non-cardiac surgery include age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, and expected duration of surgery (Expected) surgical duration, emergency operation, diabetes mellitus, lenin-aldosterone-angiotensin-system blockade use, RAAS blockade use, hypoalbuminemia , Anemia and hyponatremia (Hyponatremia) at least one selected from the group consisting of, a system for predicting the risk of acute renal failure after non-cardiac surgery.
The method according to claim 1, The index set is set according to the selection category for each variable except the expected surgical duration (Expected surgical duration), selected from 0, 3, 4, 6 to 9, 13, 15 and 22 for each selection category Any one index is set, the total sum of the indices by the selection category of the variable is 0 to 81, the system.
The method according to claim 1, The index set in the expected surgical duration (Expected surgical duration) of the variable is that the index is set to five times the expected surgical duration (time).
The method according to claim 1, age (age) of the variable is divided into a selection category of less than 40, less than 40 and less than 60, more than 60 and less than 80, and more than 80,
The estimated glomerular filtration rate (eGFR) is 60 mL / min / 1.73 m ² or more, 45 mL / min / 1.73 m ² or more and 60 mL / min / 1.73 m ^{2 or} less, 30 mL / min / 1.73. m ² over 45 mL / min / 1.73 m ² below, 15 mL / min / 1.73 m 2 at least 30 mL / min / 1.73 m, the system is divided into the selection criteria of less than ^2.
The method according to claim 1, The factors related to the occurrence of acute renal failure after non-cardiac surgery are age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, and expectation. Expected surgical duration, Emergency operation, Diabetes mellitus, Renin-aldosterone-angiotensin-system blockade use, RAAS blockade use, Low A system comprising albuminemia (Hypoalbuminemia), anemia and hyponatremia.
The method according to claim 1, The predicted risk of acute renal failure after non-cardiac surgery is A, B, according to the sum of indices determined according to a set of indices set for each variable selected in the variable selection unit for a patient in need of prediction. A system classified into a total of four classes, C and D.
The method according to claim 6, wherein the grade A is the sum of the index is less than 20, the grade B is the sum of the index is 20 or more and less than 40, the grade C is the sum of the index is 40 or more and less than 60, the D The system is classified when the sum of the indices is 60 or more.
The method according to claim 7, wherein the A grade is less than 2% probability of acute renal failure and severe acute renal failure after non-cardiac surgery in non-cardiac surgery patients in need of the prediction,
In the grade B, the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 2% or more, and the probability of serious acute renal failure is less than 2%,
In the grade C, the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 10% or more, and the probability of serious acute renal failure is 2% or more,
The D-class is a system in which the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 20% or more, and a probability of serious acute renal failure is 10% or more.
A first step of selecting factors related to the occurrence of acute renal failure after non-cardiac surgery consisting of clinical data before non-cardiac surgery of patients subject to non-cardiac surgery as variables, and setting an index set for each of the variables;
A second step of calculating sensitivity and specificity for the sum of combinations of exponents for each set of indices, and setting cut-off values according to the sensitivity and specificity; And
A non-cardiac surgical target that requires the prediction is calculated based on a set of indexes determined in accordance with the index set set for each variable selected in the first step for a non-cardiac surgical target patient that needs prediction And a third step of predicting the risk of acute renal failure after non-cardiac surgery in a patient.
The factors related to the occurrence of acute renal failure after non-cardiac surgery include age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, and expected duration of surgery (Expected) surgical duration, emergency operation, diabetes mellitus, lenin-aldosterone-angiotensin-system blockade use, RAAS blockade use, hypoalbuminemia , Anemia and at least one selected from the group consisting of hyponatremia, a method for predicting the risk of acute renal failure after non-cardiac surgery.
The method according to claim 9, The index set is set according to the selection category for each variable except the expected surgical duration (Expected surgical duration), selected from 0, 3, 4, 6 to 9, 13, 15 and 22 for each selection category Any one index is set, the total sum of the indices by the selection category of the variable is 0 to 81.
The method according to claim 9, wherein the index set in the expected surgical duration (Expected surgical duration) of the variable is that the index is set to 5 times the expected surgical duration (time).
The method according to claim 9, the age (age) of the variable is divided into a selection category of less than 40, less than 40 and less than 60, more than 60 and less than 80, and more than 80,
The estimated glomerular filtration rate (eGFR) is 60 mL / min / 1.73 m ² or more, 45 mL / min / 1.73 m ² or more and 60 mL / min / 1.73 m ^{2 or} less, 30 mL / min / 1.73. m ² over 45 mL / min / 1.73 m ² below, 15 mL / min / 1.73 m 2, methods that are separated by more than 30 mL / min / 1.73 m ² under the selected category.
The method according to claim 9, The factors related to the occurrence of acute renal failure after non-cardiac surgery are age, estimated glomerular filtration rate (eGFR), dipstick albuminuria, sex, and expected Expected surgical duration, Emergency operation, Diabetes mellitus, Renin-aldosterone-angiotensin-system blockade use, RAAS blockade use, Low A method comprising albuminemia (Hypoalbuminemia), anemia and hyponatremia.
The method according to claim 9, The predicted risk of acute renal failure after non-cardiac surgery is A, B, according to the sum of indices determined according to a set of indices set for each variable selected in the variable selection unit for a patient in need of prediction. C, D A method classified into a total of four grades.
The method according to claim 14, wherein the grade A is the sum of the index is less than 20, the grade B is the sum of the index is 20 or more and less than 40, the grade C is the sum of the index is 40 or more and less than 60, the D The grade is classified when the sum of the indices is 60 or more.
16. The method of claim 15, wherein the A grade has a probability of acute renal failure and severe acute renal failure of less than 2% after non-cardiac surgery in non-cardiac surgery patients in need of the prediction,
In the grade B, the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 2% or more, and the probability of serious acute renal failure is less than 2%,
In the grade C, the probability of acute renal failure after non-cardiac surgery in patients with non-cardiac surgery requiring the prediction is 10% or more, and the probability of serious acute renal failure is 2% or more,
The D-grade is a method for predicting acute renal failure after non-cardiac surgery in patients with non-cardiac surgery in need of the prediction is 20% or more, and a probability of serious acute renal failure is 10% or more.

Images