Incidence and Predictors of Acute Kidney Injury among Patients Admitted to Adult Intensive Care Unit at West Amhara Comprehensive Specialized Hospitals, Northwest Ethiopia: A multicenter retrospective follow-up study

doi:10.21203/rs.3.rs-3734297/v1

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Incidence and Predictors of Acute Kidney Injury among Patients Admitted to Adult Intensive Care Unit at West Amhara Comprehensive Specialized Hospitals, Northwest Ethiopia: A multicenter retrospective follow-up study

https://doi.org/10.21203/rs.3.rs-3734297/v1

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Introduction: Acute kidney injury (AKI) is a clinical syndrome characterized by a sudden decrease in or loss of kidney function. In Ethiopia, the morbidity and mortality associated with acute kidney injury is an important challenge for the health community and patients. However, there is limited evidence on the incidence and predictors of acute kidney injury among intensive care unit patients in Ethiopia.

Objective: To assess incidence and predictors of acute kidney injury among intensive care unit patients in West Amhara comprehensive specialized hospitals, Northwest Ethiopia, 2023.

Methods: A multicenter institution-based retrospective follow-up study was conducted from January 1, 2020-December 31, 2022 among intensive care unit patients admitted to West Amhara comprehensive specialized Hospitals, Northwest Ethiopia. A total of 628 patient charts were chosen using systematic random sampling. Data were collected using a checklist, entered using Epi-data 4.6, and exported to STATA version 14 software for data analysis. After the bivariable and multivariable Cox regression analysis, an Adjusted Hazard Ratio (AHR) with 95% confidence intervals (CI) at p-value of <0.05 was reported to declare the strength of association and statistical significance, respectively.

Result: The overall incidence rate of acute kidney injury was 19.67 per 1000 (95% CI: 15.76-24.56) person-day of observation with a median survival time of 17 days (IQR=11–35). Sepsis (AHR= 2.02: 95% CI: 1.06, 3.85), diabetes mellitus (AHR=2.46: 95% CI: 1.44, 4.22), congestive heart failure (AHR= 3.11: 95% CI: 1.57, 6.16), Anemia (AHR=3.28: 95% CI: 1.77, 6.09), Vasopressors (AHR=2.57: 95% CI: 1.35, 4.90), and thrombocytopenia (AHR= 2.18: 95% CI: 1.20, 3.96) were found to be significant predictors of acute kidney injury among patients admitted to ICU.

Conclusion and recommendation: The overall incidence rate of acute kidney injury among patients admitted to the intensive care unit was lower as compared to studies conducted in developed countries. Sepsis, diabetes mellitus, congestive heart failure, anemia, vasopressors and thrombocytopenia were predictors of acute kidney injury. Therefore, health care providers shall give special emphasis and close follow-up for those patients to reduce the risk of AKI.

Acute kidney injury

predictors

incidence

intensive care unit

Northwest Ethiopia

Acute kidney injury is a clinical syndrome characterized by a sudden decrease in or loss of kidney function. It is an abrupt deterioration in kidney function, manifested by an increase in serum creatinine levels with or without reduced urine output, resulting in the accumulation of toxic wastes and the loss of internal homeostasis. It is defined as any of the following: an increase in SCr by 0.3 mg/dl (26.5 lmol/l) within 48 hours; an increase in SCr to 1.5 times baseline, which is known to have happened within the last seven days or is thought to have happened recently; or a drop in urine output of 0.5 ml/kg/h for six hours [1, 2]. Based on kidney disease improving global outcome (KDIGO), AKI is classified into stages I, II, and III for severity criteria [1]. The causes of acute kidney injury can be Prerenal, intrarenal, and postrenal.

Globally, acute kidney injury affects 13.3 million people annually, of whom about 85% live in developing countries [3]. Epidemiological data on AKI are only available from a small number of nations and also vary in sub-Saharan Africa (SSA). The other issue in Africa is the interpretation of data on the incidence of AKI due to the non-uniformity of cohorts. Because some studies include only adults , while others include both adults and children [4].

According to a review of the literature, studies from more than 300 ICUs in various parts of the globe were studied, and the incidence of AKI varied generally, ranging from 2.5% to 92.2%. The average AKI incidence was 33.4% in developed nations and 37.7% in developing countries [5]. Studies conducted in Africa showed that the incidence of AKI ranges from 11% to 58.5% [6-12] and 14.5% to 17.76% in Ethiopia among Diabetic patients [13-15].

In low and middle income countries, AKI mortality is lower than in high-income nations. Acute kidney injury is also thought to be the cause of up to 1.7 million fatalities worldwide annually. In Sub-Saharan Africa (SSA), AKI mortality is high, and the likelihood of getting chronic kidney disease is rising [3].

According to the different studies conducted around the world age greater than sixty, male gender, hypertension, diabetes mellitus, chronic cardiovascular disease, stroke, sepsis, shock, anemia, hypotension, acute respiratory distress syndrome(ARDS), coronary artery disease, and oncologic disease, Human immunodeficiency virus (HIV), trauma, major abdominal surgery, high serum creatinine concentration, low platelet level, urea, treatments of ; diuretics, angiotensive converting enzyme inhibitors(ACEIs), non-steroidal anti-inflammatory, gentamicin, and vasopressor drugs, oliguria, low blood pressure, low Glasgow Coma Scale(GCS), high respiration rate, hypoxia, hyperthermia, serum potassium abnormality, and serum sodium abnormality were independent predictors of AKI among patients admitted to intensive care unit [6, 7, 16-23].

In order to reduce the flow of avoidable fatalities caused by untreated AKI in developing nations, the international society of nephrology and the larger health care community have teamed up [24]. The "0 by 25" A new project has been started with the goal of eliminating patient deaths due to untreated AKI by 2025. But still, the prevalence of AKI and its mortality rates are increasing.

Even though studies have been conducted in Ethiopia about the prevalence and outcomes of AKI, little has been done on the incidence rate. Therefore, this study will be important to guide evidence based information for better treatment and prevention of acute kidney injury in resource-limited settings such as, Northwest Ethiopia by assessing the incidence and predictors that have role in incidence of AKI in ICU patients.

Study design and period

A multicenter institution-based retrospective follow up study was conducted among ICU patients in West Amhara referral hospitals, North West Ethiopia, from January1^st 2020 to December 31^th 2022.

Study Setting

The study was conducted in comprehensive, specialized hospitals in West Amhara, Northwest Ethiopia, including; the University of Gondar, Felege Hiwot, Tibebe Ghion, Debre Tabor, and Debremarkos comprehensive specialized hospitals. Felege Hiwot and Tibebe Ghion hospitals are located in Bahir Dar town, which is located 565 kilometers from Ethiopia's capital city Addis Ababa. Debre Markos comprehensive specialized hospital is located in Debre Markos town, the capital city of East Gojjam zone. It is located 299 kilometers from Addis Ababa, the capital city of Ethiopia, and 254 kilometers from Bahir Dar, the capital city of the Amhara National Regional State [25]. Debre Tabor comprehensive specialized hospital is located in Debre Tabor Town, which is the capital city of South Gondar Zone. Its 765 kilometers from Addis Ababa and 102 kilometers from Bahir Dar [26]. University of Gondar Comprehensive Specialized Hospital is located in Gondar Town, which is the capital city central Gondar zone. It is 738 kilometers from Addis Ababa and 173 kilometers from Bahir Dar [14]. These hospitals currently have 51 adult ICU beds that are working and providing services to critically ill patients.

Source and study population

Source population

All adult ICU patients who were admitted to the ICU of comprehensive specialized hospitals in West Amhara.

Study population

All adult ICU admitted patients, who had been hospitalized at comprehensive specialized hospitals in West Amhara from January 1^st, 2020 to December 31^th, 2022.

Inclusion and exclusion criteria

Inclusion criteria

All adult ICU admitted patients who had been hospitalized at comprehensive specialized hospitals in West Amhara from January1^st, 2020 to December 31^th, 2022, were included in the study.

Exclusion criteria

Those patients with ICU follow-up time less than 24 hours, written diagnosis of AKI, and chronic kidney disease or end-stage kidney disease (ESKD) during admission of the ICU.

Sample size determination & sampling procedures

Sample size determination

The sample size was determined by using single population proportion formula with the following assumptions; 39% proportion of AKI among ICU admitted patients[9], 95% confidence level and 4% a tolerable margin of error n= the minimum sample size required.

Where n=sample size, Z=critical value of 95% CI=1.96, P=proportion of AKI among ICU patients admitted to the ICU=39%, d=margin of error=0.04

After taking a 10 % for incomplete charts, the final sample size was 628.

Sampling procedure

The sampling process included all of West Amhara's comprehensive and specialized hospitals. Following the proportionate allocation of the research participants to each institution. The medical registration number of the patients was used as a sampling frame and was used to choose patient charts. Study participants were selected using a systematic random sampling method until the required sample size was obtained during the actual data collection period and was chosen as follows: first, the sampling interval (K) was determined by dividing the total admitted ICU patients in the study period by the total sample size. Finally, one number was selected from number one to the sampling interval (K), which was selected by the simple random sample method to start sample selection, and the number was included in the sample. Then, the next selection was continued every n^th unit, until a total of 628 patient records were chosen.

Data collection procedure

Data was collected from patients’ chart using a data extraction tool. The English version data extraction checklist was adapted from the different literatures [9, 18, 27-29]. The tool consists of Sociodemographic characteristics, clinical characteristics, laboratory related characteristics, date of admission, date of discharge, and AKI status of patients. The patient records and the registration book was used to extract the data. All data were collected retrospectively from the patient chart from April 17^th to May 16^th, 2023 G.C. Four qualified BSc nurses working in the ICU served as the data collectors, while one MSc nurse was selected to oversee the entire data collection procedure. The candidates were trained on data collection, record keeping, and other related issues two days before the actual data collection.

Variables of the study

Dependent variable

Incidence of AKI

Independent variables

Sociodemographic variables: Age, sex, residence

Clinical and laboratory related variables: Serum creatinine level, urea level, platelet level, blood pressure, temperature, pulse rate, oxygen saturation, GCS, respiration rate, serum potassium, white blood cells, serum sodium level, sepsis, major abdominal surgery, presence of comorbidity, hypotension, oliguria, diabetes mellitus, hypertension, HIV/AIDS, congestive heart failure, stroke, chronic liver disease, anemia, poisoning, Coronary heart disease, ICU admission(main diagnosis) diagnosis (Cardiovascular disease, trauma, Obstetric/gynecological disorders, ARDS, shock, oncologic disease), and length of stay in ICU.

Intervention related variables: Non-steroidal anti-inflammatory drugs, angiotensive converting enzyme inhibitors, diuretics, vasopressors, gentamicin, and mechanical ventilation.

Operational definition

Acute kidney injury: A written diagnosis of acute kidney injury based on the clinical decision of the physician on the patient card after admission to the ICU with in the follow-up time.

Incidence of AKI: numbers of new onsets of acute kidney injury after admission to the ICU within the follow-up period.

Follow up time: After 24 hours of ICU admission to the occurrence of an event (AKI) or ICU discharge.

Survival status: outcome of the patient; either event or censored

Oliguria: is defined as urine output less than 400 ml daily in adults.

Censor: refers to ICU patients who do not experience the desired outcome (AKI) at the end of the follow-up period. This could be as a result of failure to follow up, transferring to another institution due to death, or going against medical advice.

Event: is the occurrence of AKI among ICU patients after admission to the end of the study.

Time to event: A time between after 24 hours of admission and occurrence of AKI.

Incomplete chart: considered when 5% of the independent variable and/or the dependent variable's indicator are not recorded.

Data quality assurance and control

To confirm that the variables were present in the patients' charts, a pretest was conducted on 32 (5%) of the sample size at the comprehensive specialized hospital of the university of Gondar prior to the real data collection. This was a warranty on the validity, simplicity, and organization of the checklist. Both experienced researchers and ICU specialists reviewed the checklist's validity. As a result, the necessary modifications were made based on the inputs prior to the actual data collection. After any necessary corrections, the checklist was available for data collection. The principal investigator (PI) was reviewing the checklist each day of data collection to make sure it was comprehensive. The team supervisor and PI checked each day's checklist for accuracy and consistency at the end of the day. The data collected from the study population was carefully entered by the PI using Epi Data version 4.6.

Data processing and analysis

After completion of data collection, the data was coded and entered into the Epi Data Manager Statistical software (version 4.6), and it was exported to Stata 14 for data cleaning and analysis. The descriptive statistics were described using frequencies, percentages, graphs, and means with standard deviations or medians with interquartile ranges based on the nature of the data. Each participant's outcome was divided into two categories: censored and event. After confirming that the missing data was entirely random, incomplete data that made up less than five percent of the record was handled under the assumption of multiple imputation. The incidence rate of AKI was calculated for the entire follow-up period. A Kaplan-Meier curve was used to compare survival differences and estimate the median survival time. Bivariable analysis was conducted to ascertain the relationships between each independent variable and the outcome variable (incidence) using the Cox proportional hazard model. The multivariable analysis was conducted on variables having a p-value of <0.2, and an adjusted hazard ratio (AHR) with 95% confidence interval (CI) was calculated to assess the strength of association. The proportional hazard model assumption was checked using a global test (Schoenfeld residuals=0.8287) and multicollinearity was checked by variance inflation factors (VIF=1.35). Besides, model fitness was checked using Cox-Snell residuals. Finally, statistical significance was declared at p-value of <0.05, and the strength of associations was summarized using an adjusted hazard ratio (AHR) with 95% confidence intervals.

Socio-demographic Characteristics

After reviewing 628 ICU patient records, 582(92.68%) records were included in the final analysis, as 38 records were excluded due to incompleteness and 8 records due to lost. The mean age of the participants’ at the time of follow-up was 43±16 years. Above one-fourth of participant age was under 40-50 age category. In this study, more than half of (52.23%) participants were male. Regarding to residency more than half of (55.15%) participants were from rural (Table 1).

Base line Clinical and Laboratory related Characteristics

Out of the total 582 patients, more than one fifth of participants were admitted to the ICU with the main diagnosis of cardiovascular disease (20.45%), and about 111 (19.07%) participants were admitted with the diagnosis of acute respiratory distress syndrome (ARDS). During the study follow-up period, more than three-fourths of patients were admitted to the ICU with a platelet level greater than 150,000/mm3 (84.36%). About sixty-nine (11.86%) ICU patients admitted to the ICU had sepsis at follow-up time. Regarding serum creatinine level, the majority of study participants (93.47%) had a serum creatinine level of 1.2 mg/dl during a follow-up period. On the other hand, participants were admitted with the diseases such as diabetes mellitus (12.89%), congestive heart failure (5.67%), chronic liver disease (1.37%), stroke (2.58%), and hypertension (18.07%).

During the study follow-up period, the majority of 566 (97.25%) of the study participants had urine output greater than 400 ml/day. In the study, participants on the Glasgow coma scale 135 (23.20%), 220 (23.20%), and 227 (39.00%) were classified as severe, moderate, and mild, respectively (Table 2).

Intervention related characteristics

Among the total 582 ICU patients, more than half of patients were on mechanical ventilators 339(58.25%), and more than one fourth of the participants 159(27.37 %) used non-steroidal anti-inflammatory drugs (NSAIDs). About seventy-seven (13.25%) participants were on vasopressors, whereas, 90(15.49%) were ever on diuretics (Table 3).

Incidence of AKI among ICU patients

The incidence rate of AKI in the cohort during the 3965 person days of observation (PDO) was 19.67 per 1000 (95% CI: 15.76–24.56) person-days of follow-up, with a median survival time of 17 days (IQR = 11–35). In this study, the cumulative incidence rate of AKI among patients admitted to the ICU during the follow-up period was 13.4% (95% CI: 10.86, 16.43), as the minimum and, maximum follow-up times of 3 and 48 days, respectively. Among 78 (13.4%) AKI patients, 61 (78.21%) were stage I AKI, 10 (12.82%) stage II, and 7 (8.97%) stage III AKI patients. Five hundred four (86.6%) patients were censored until the end of the study period.

Overall Kaplan- Meier survival function

The overall Kaplan-Meier estimate showed that the probability of survival of patients was relatively high in the first weeks of admission, which comparatively decreased as follow-up time increased. As a result, the overall survival probability after admission to the ICU was 24.30% (95% CI: 6.63, 47.83) at 3965 days of follow-up. The estimated survival probability was 99.14% (95% CI: 97.95, 99.64) in the first three days of follow-up time, 97.94% (96.30, 98.85) in the fourth days of follow-up time, 95.50% (95% CI: 93.15, 97.06) in the fifth days of follow-up time, 77.11% (95% CI: 70.77, 82.26) in the next 10 days, and 57.31% (95% CI: 45.25, 67.65) in the next 15 days, and so on. The overall median survival time of patients admitted to the ICU in the study was 17 days (IQR: 11-35). At the end of 48 days of hospital stay, the overall survival probability of patients was 24.30% (95% CI: 6.63, 47.83) with a standard error of 0.11. As shown in the figure below, the Kaplan-Meier survival curve decreases stepwise during the ICU stay, and there is no occurrence of AKI after 35 days of follow-up (Fig.1).

Kaplan Meier-Curve and log-rank test

Based on Kaplan Meier curve with long-rank test the Kaplan-Maier survival function for congestive heart failure had a median survival time of 10 days and 17 days without congestive heart failure (CHF). As a result, the incidence rate for CHF patients was 48 per 1000 person days of observation and 17 per 1000 person days of observation for counterparts. The difference of the group was significant at P-value of <0.001(Fig.2).

Likewise, patients diagnosed with sepsis had a lower median survival time (9 days) compared to those who had not been diagnosed with sepsis, who had a median survival time of 35 days. According to our study, the incidence rate for patients diagnosed with sepsis was 63 per 1000 days of observation, and the incidence rate for patients without a sepsis diagnosis was 14 per 1000 days of observation. This survival time difference was statistically significant with a p-value of <0.001 (Fig.3).

Similarly, Patients following treatment with vasopressors had a low survival probability, with a median survival time of 9 days, and the comparative group had a median survival time of 21 days. In addition, the incidence rate for patients with treatment with vasopressors was 64 per 1000 days of observation and 13.1 per 1000 days of observation for patients without treatment with vasopressors. The difference was statistically significant at a p-value of<0.001 (Fig.4).

Study subjects who were followed with diabetes mellitus had a low survival probability with a median survival time of 11 days compared to patients who had no diabetes mellitus and a median survival times of 21 days. Also, the incidence rate for diabetic patients was 49 per 1000 days of observation and 15 per 1000 days of observation for the counterparts. The difference was statistically significant at a p-value of<0.001 (Fig.5).

The median survival time of patients who had thrombocytopenia (PLT <150,000/mm3) had a low survival probability, with a median survival time of 11 days, compared to those patients with PLT greater than 150,000/mm³. The incidence rate was 43 per 1000 days of observation and 16 per 1000 days of observation for the counterpart. The difference was significant at a p-value of<0.00 (Fig.6).

According to our study, patients diagnosed with Anemia had a median survival time of 12 days, and without anemia, the median survival time was 18 days. Our study reveals that the incidence rate for anemic patients was 54.2 per 1000 days of observation and 17 per 1000 days of observation for counterparts. The difference was statistically significant at a p-value of<0.001 (Fig.7).

Predictors of AKI among ICU patients

Initially, all variables had been entered into the bivariate Cox proportional hazard regression model. Based on this model, the Glasgow coma scale, hypertension, hypotension, congestive heart failure, pulse rate, residence, urine output, serum creatinine, low level of platelets (thrombocytopenia), length of stay, sepsis, diabetes mellitus, anemia, major abdominal surgery, Non-steroidal anti-inflammatory drugs, vasopressors, and gentamicin were candidates for multivariable analysis with a p-value of<0.2. However, in multivariable Cox regression analysis, sepsis, congestive heart failure, diabetes mellitus, thrombocytopenia, anemia, and vasopressors were statistically significant predictors of acute kidney injury among critically ill patients with p-values< 0.05.

According to this study, the hazards of participants with sepsis were 2.02 times more likely to develop AKI as compared to those who had no sepsis (AHR=2.02; 95% CI: 1.06, 3.85). Furthermore, the hazard of developing AKI among the participants who had diabetes mellitus was 2.46 times higher than their comparatives (AHR=2.46; 95% CI: 1.44, 4.22). The hazard of AKI among the participants who had Anemia was 3.28 times higher than that among those with no anemia (AHR=3.28; 95% CI: 1.77, 6.09). The hazard of developing AKI among the participants who had congestive heart failure was 3.11 times higher than among those who had no congestive heart failure (AHR=3.11; 95% CI: 1.57, 6.16). The hazard of developing AKI was 2.18 times higher among the participants who had thrombocytopenia as compared to those who had no thrombocytopenia (AHR=2.18; 95% CI: 1.20, 3.96). Lastly, those participants who were treated with vasopressors during the follow-up period were found to be at 2.57 times higher hazard of developing AKI compared to those who were not treated with vasopressors (AHR=2.57; 95% CI: 1.35, 4.90)(Table 4).

Model Fitness Test

The data in the Cox proportional hazard regression model's overall model fitness is shown in the image below. Residuals have a standard censored exponential distribution with hazard ratio. The hazard function follows 45˚, which is near the baseline hazard when we compare the line with the reference (cox Snell residual), showing that the model was well fitted. As a result, the Cox-Snell residuals test reveals that the model was well fitted. It was feasible to draw the conclusion that the final model fit the data well based on the residual test (Fig.8).

According to the current study, the incidence rate of AKI among patients admitted to the ICU in the study was 19.67 per 1000 person-days of observation (95% CI: 15.76, 24.56) and the median survival time was 17 days (IQR = 11–35). The cumulative incidence rate of AKI among patients admitted to the ICU during the follow-up period was 13.4% (95% CI: 10.86, 16.43). The finding is in line with a study conducted in India (16.1%) [30], and Alexandria university hospital, Egypt (11%). Moreover, this study supports the previous studies analyzed from different regions of the world, which was continuously well-collected database (2.5–92.2%) [5]. However, this finding is higher than the studies conducted in Korea (8.3%) [31], and in the city of Rio Branco, Acre, Brazil, which showed the incidence of AKI in the ICU was 37.3 per 1,000 individuals [21]. This high occurrence might be due to the difference in study population, ICU setup, and the level of service provided. The other reason might be due to our economic status differences, which affect our level of overall care as compared to those countries.

In contrast, this finding is lower than the studies conducted in Norway (53.5%) [22], Jordan (31.6%) [23], Iran (37%) [32], Indonesia (43%) [20], Brazil (40.5%) [33], Uruguay(50.1%) [17], Zambia(52.9%) [7] and Sudan (39%) [9]. The reason for this discrepancy might be due to the difference in the characteristics of study population, sampling method, level of study setting, duration of follow-up time, and sample size. And the result is lower than a cohort study done in China(51%) [19], Cameroon (22.3%) [8], Congo (52.7%) [10], Thailand (52.9%) [16], and South Africa which shows the incidence found to be 58.5% [6]. The discrepancy might be due to the difference in study design, since our study design was retrospective follow-up and their study design was a prospective cohort study. And also, the difference in Sociodemographic characteristics of study participants, mainly related to the age difference, might affect the occurrence of the case. In addition, the sample size issue might cause a discrepancy since most studies were conducted on whole ICU patients for a specified period of time. This difference might also be due to socio-economic status, level of study setting, and the availability of specialized health care professionals with a special diagnostic tool, which may increase the incidence of AKI in the ICU.

According to the Cox proportional hazard regression model analysis sepsis, Diabetes mellitus, congestive heart failure, Anemia, low levels of platelets (thrombocytopenia), and Vasopressors were independent predictors of AKI among patients admitted to the ICU. This study identified that sepsis has a significant positive predictors of AKI. Individuals who had sepsis were 2.02 times more likely to develop AKI as compared to those who had no sepsis. The finding was supported by the studies conducted in Sudan [9], Cameroon [8], South Africa [6], the Democratic Republic of the Congo [10], Indonesia [20], China [29], Malaysia [34], Egypt [18], India [35] and USA [36]. However, a study from Brazil showed that sepsis was not significantly associated with the developments of AKI among the ICU patients [27]. This could be due to recent evidence revealing that sepsis is related to cellular mechanisms, both adaptive and maladaptive, which potentiate one another and ultimately result in clinical AKI. The microcirculation may be the physiological compartment where these systems interact and have a more severe, detrimental impact. Some of these include adaptive cell responses to injury, inflammatory conditions, coagulation problems, and endothelial dysfunction. To reduce the need for energy and guarantee cell survival, these changes cause a down regulation of cell function. As a result, kidney function may decline [37].

On the other hand, the hazard of developing AKI among the participants who had congestive heart failure was 3.11 times higher than participants with no congestive heart failure. The finding was similar to the study conducted in the USA and Singapore [36, 38]. This could be related to patients with congestive heart failure who have altered renal hemodynamics that result in hypoperfusion to the kidneys because of low cardiac output and impaired venous return because of blood stasis. Ischemic injury, also known as acute renal tubular necrosis, is caused by this. Additionally, reduced creatinine due to glomerular ultrafiltration pressure is caused by a number of factors, including decreased renal blood flow, casts consisting of lost epithelial cells and necrotic debris, and back leaks of filtrate through the injured tubular epithelium [39].

The hazard of developing AKI among study participants who had diabetes mellitus was found to be 2.46 times higher than that of those who did not have diabetes mellitus. This finding was in line with a study conducted in Norway, South Africa, and Sudan [6, 9, 22]. The possible reason could be those patients with diabetes mellitus, characterized by high blood glucose levels. Over time, the millions of little filtering units found inside each kidney are harmed by the blood's excessive sugar levels. In addition, diabetes mellitus significantly increases overall morbidity and mortality, particularly by elevating cardiovascular risk [40].

The hazard of developing AKI among the participants who were on treatment with vasopressor drugs during the follow-up was found to be 2.57 times higher than that of those who had not been treated with vasopressors. This finding is consistent with the studies conducted in China [29], South Africa [6] and Thailand [16]. The scientific finding showed that, because of the decreased renal blood flow (BBF) caused by vasoconstriction, it is commonly believed that using vasopressors will impair renal function. It has been shown in healthy humans that renal blood flow decreases when norepinephrine (NE) is infused [41].

The hazard of AKI among patients with a thrombocytopenia 2.18 (< 150,000/mm³) times higher as compared to those who had a platelet level of greater than 150,000/mm³. This finding was supported by studies conducted in Iran [42] and China [43]. According to scientific research, a key characteristic of AKI is the deregulation of renal hemodynamics. Changes in renal microcirculation may result in hypoxemic areas that continue to sustain renal inflammation and aggravate AKI. Platelets influence the endothelium vascular wall, such as via modifying vascular permeability, and hence play a crucial role in the regulation of hemodynamic processes. Therefore, platelet activation or malfunction may play a role in the etiology of AKI [44]. As result, health care providers should monitor platelet levels of patients regularly.

Participants who had Anemia were 3.28 times at higher hazard to develop AKI than those who had no anemia. This is supported by studies conducted in Thailand [16], USA [36], and India [35]. The possible scientific reason could be due to lower hemoglobin levels may be the cause, which predisposes patients to renal hypoxia and oxidative stress. In addition, many anemic patients also have subclinical renal illnesses, which make them more vulnerable to renal insults [45].

Limitation of the study

Due to the retrospective follow-up study, variables related to physiologic and laboratory parameters necessary to predict AKI such as acute physiologic and chronic health evaluation (APACHE) and Sequential Organ Failure Assessment (SOFA) were not analyzed.

This study has assessed incidence and predictors of AKI among patients admitted to adult intensive care unit at West Amhara comprehensive specialized hospitals, Northwest Ethiopia. Accordingly, the incidence was lower as compared to studies conducted in developed countries. Sepsis, diabetes mellitus, congestive heart failure, anemia, treatment of vasopressors, and thrombocytopenia were independent predictors of AKI among patients admitted to the ICU.

Based on the findings, the following recommendations are forwarded:

For Amhara regional health bureau:

To start clinical early screening programs for AKI among patients with diabetes mellitus, congestive heart failure, anemia, and sepsis throughout the ICU follow-up.

To strengthen close monitoring and supportive supervisions for ICU sites in such areas.

For West Amhara Comprehensive specialized hospitals:

To strengthen ICUs and monitor the quality of service in the ICU to decrease the incidence of AKI in ICU patients.
Special consideration should be given to preventing ICU patients from developing AKI by integrating different case teams.

For Research community:

Upcoming researchers shall conduct prospective cohort study to overcome the limitation of this study.
To conduct follow-up study with an advanced model like longitudinal analysis model.

For Health care providers:

Health care providers shall give special emphasis and close follow-up for intensive care unit patients admitted with sepsis, diabetes mellitus, anemia, and congestive heart failure to reduce the risk of AKI.
Additionally, clinicians shall give more emphasis to treatment strategies, specifically those for vasopressors.

ACEIs: Angiotensive Converting Enzyme Inhibitors; AHR: Adjusted Hazard Ratio; AKI: Acute Kidney Injury; ARDS: Acute Respiratory Distress Syndrome; BPM: Beat per Minute/Breath per Minute; CHF: Congestive Heart Failure; CHR: Crude hazard ratio; CI: Confidence Interval; CKD: Chronic Kidney Disease; CVD: Cardiovascular Disease; IRB: Institutional Review Board; GCS: Glasgow coma scale; GFR: Glomerular Filtration Rate; HIV: Human Immunodeficiency Virus; ICU: Intensive Care Unit; KDIGO: Kidney Disease Improving Global Outcome; LOS: Length of stay; NSAIDs: Non-Steroidal Anti Inflammatory Drugs; SCr: Serum Creatinine; SSA: Sub-Saharan Africa

Ethics approval and consent to participant

The study was conducted in accordance with the 1964 declaration of Helsinki and following amendments. Before conducting the study, the proposal was presented and defended. Ethical clearance was obtained from school of nursing research review committee on the behalf of University of Gondar research and Institutional Review Board (IRB). The ethical review committee of the school of Nursing was send an ethically approved formal letter with an ethical clearance number of Ref.No.S/N/161/2015 on 02/08/2015 to Felege Hiwot, the University of Gondar, Tibebe Ghion, Debre Tabor, and Debremarkos comprehensive specialized hospitals. Permissions was obtained from the hospital's quality assurance office, medical directors, and ICU coordinators. After obtaining permissions, data was collected from the patient chart using checklist, and a code was used for the purpose of maintaining confidentiality. As the data gathered was secondary (based on medical card), informed consent from parents or legal guardians was not required. Besides, it was waived by the IRB of University of Gondar.

Consent to publication

Although photographs or videos linked to an individual's recorded data are not included in the study, there is no dispute about consent to publish other data obtained.

Availability of data and materials

The data set used and analyzed during the current study is available from the corresponding author on reasonable request.

Competing interests

The authors declare that, they have no competing interest.

Funding

Funding is not applicable

Author’s Contributions

M.M.B, A.W.A, and K.M.A involves on the Conceptualization, methodology, execution, data analysis, and interpretation. G.G.A, Z.B.A and B.A.G: took part in drafting, reviewing the article; wrote the manuscript, and preparing the components of manuscript. All authors critically reviewed, read, and approved the final manuscript.

Acknowledgments

First of all, we would like to give our warmest gratitude to University of Gondar, College of Medicine and Health Sciences, School of Nursing, department of emergency and critical care nursing. Secondly, we would like to thank Gondar, Felege Hiwot, Tibebe Ghion, Debre Markos, and Debre Tabor referral hospital staff working at ICU and Amhara Public Health Institute for their assistance during data collection. Finally, our special thanks and appreciation goes to the supervisor and the data collectors of the study.

Author’s Information

M.M.B: Lecturer-Department of Nursing, Bahir Dar Health Science College, Bahir Dar, Ethiopia A.W.A: Assistant Professor-Department of Medical Nursing, School of Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

K.M.A: Lecturer-Department of Emergency and Critical care Nursing, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

G.G.A: Lecturer-Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia

Z.B.A: Department of Nursing, College of Medicine and Health Sciences, Injibara University Injibara, Ethiopia

B.A.G: Lecturer-Department of Nursing, Bahir Dar Health Science College, Bahir Dar, Ethiopia

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Tables 1-4 are available in the Supplementary Files section.

No competing interests reported.

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Incidence and Predictors of Acute Kidney Injury among Patients Admitted to Adult Intensive Care Unit at West Amhara Comprehensive Specialized Hospitals, Northwest Ethiopia: A multicenter retrospective follow-up study

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