1. Literature search (Figure 1)
8033 records from the Medline, Embase, Cochrane Library, PubMed and Web of Science databases were initially identified. After filtering by title and abstract, duplicate articles, review studies, and those on unrelated topics were excluded, and 626 studies were reviewed in full text. After excluding the comment papers, studies with inconsistent control settings, articles with unspecified AKI or sepsis diagnostic criteria, studies performed in special population, and those with limited data, 47 articles met the inclusion criteria and were included in the systematic review and meta-analysis.
2. Characteristics of the Included Studies (Table 1)
The characteristics of the included articles were shown in Table 1. Studies were published between 2008 and 2019, and were from eighteen countries (Spain, Greece, United Kingdom, France, Netherlands, Sweden, Canada, United States, Brazil, China, Japan, Saudi Arabia, Turkey, Finland, Portugal, South Korea and Australia) on four continents (Europe, America, Asia and Oceania). Overall 12 retrospective cohort studies, 25 prospective cohort studies and 12 case-control studies were included, with a total of 55,911 sepsis patients. Document quality assessment showed that the methodological quality of all studies was high, achieving a quality score of 8 (≥ 6).
3. Summary data from the included studies (Table 2)
This study summarized the characteristics of sepsis patients who developed AKI. ICU mortality, hospital mortality, 28-day mortality and 90-day mortality of S-AKI were respectively reported at 45.99% (1989/4325) in 15 studies, 49.84% (2732/5481) in 10 studies, 36.67% (161/439) in 4 studies, 64.66% (2406/3721) in 5 studies. In S-AKI patients, all mortality rates of AKI caused by septic shock were the highest, while that caused by severe sepsis was the lowest.
The most prevalent comorbidity was ARDS (47.02%, 489/1040, from 3 studies), followed by hypertension (38.39%, 3263/8500, from 32 studies), diabetes (27.57%,2248/8155, from 32 studies) and stroke (22.79%, 67/294, from 4 studies), while cirrhosis and liver disease accounted for only 4.71% (99/2104, from 6 studies) and 3.74% (554/14081, from 7 studies) respectively. Hepatic failure was more common in sepsis patients compared with those with septic shock and severe sepsis. Hypertension in septic shock was less common than sepsis and severe sepsis (26.16% VS 42.28% and 58.07%), while chronic kidney disease was more prevalent (45.13% VS 15.52% and 11.02%). Hypertension and diabetes were more prevalent in severe sepsis than in sepsis and septic shock (58.7% VS 42.28% and 26.16%, 30.20% VS 20.53% and 26.75%).
On admission, patient source mainly included emergency admission (50.88%, 9235/18149, from 8 studies) and medical admission (47.02%, 8701/18506, from 7 studies), followed by operative admission and surgical ward. Vasoactive drugs were the most commonly used drugs, accounting for 64.61% (1293/2001, from 5 studies), among which vasopressors was the most frequently used, accounting for 63.22% (911/1441, from 7 studies), followed by steroids, diuretics, ACEI or ARB, stains and NSAIDS. Vasoactive drugs and vasopressors were more prevalent in septic shock and severe sepsis than in sepsis.
Six sources of infection were reported in this study, including pulmonary infection (46.05%, 1480/3214, from 19 studies), respiratory infection (32.08%, 85/273, from 7 studies), abdominal infection (30.87%, 2152/6971, from 25 studies), urinary tract infection (11.14%, 630/5653, from 19 studies), skin or soft tissue infection (6.03%, 335/5554, from 13 studies), and unknow infection (6.02%, 100/1662, from 4 studies).
Community acquired infection was reported in 3 studies with a prevalence of 57.36% (2041/3558), which was higher than nosocomial acquired infection reported in 2 studies (39.81%, 2474/6215). Twenty-four studies reported mechanical ventilation in 68.00% of the patients (7167/10539, from 24 studies), and mechanical ventilation was more frequently used in septic shock and severe sepsis cases compared with sepsis cases. Other prevalent factors included positive blood culture (41.38%, 3259/7876, from 8 studies) and smoking history (43.09%, 642/1490, from 5 studies).
4. Risk factors for AKI (Figure 2)
Comorbidities
The pooled data on hypertension from 32 studies indicated that it was a significant predictor (OR 1.43, 95%CI 1.20-1.70), with a moderate heterogeneity (I2 = 74.00%). Source of heterogeneity was not identified through subgroup analysis. The results of the sensitivity analysis were consistent. After excluding 3 studies with rather high heterogeneity, the heterogeneity decreased and the result remained stable (see Additional files 2).
The pooled data on diabetes mellitus from 32 studies indicated that it was a significant predictor (OR 1.59, 95%CI 1.47-1.71), with a moderate heterogeneity (I2 = 37.1%). The results remained stable even with random effect model (see Additional files 3).
The pooled data on chronic kidney disease from 14 studies indicated that it was a significant predictor (OR 3.49, 95%CI 2.36-5.15), with a moderate heterogeneity (I2 = 71.70%). Source of heterogeneity was not identified through subgroup analysis. The results of the sensitivity analysis were consistent. After excluding the study with high heterogeneity, the I2 was reduced to 25.6% (low heterogeneity) and the result remained stable (see Additional files 4).
Cardiovascular disease (from 14 studies, OR 1.31, 95%CI 1.24-1.40) and liver disease (from 17 studies, OR 1.68, 95%CI 1.47-1.90) were identified as risk factors with low heterogeneity, and the results remained stable even with random effect model (see Additional files 5 and 6).
The pooled data on coronary artery disease from 8 studies indicated that it was a significant predictor (OR 1.27, 95%CI 1.08-1.49), with a moderate heterogeneity (I2 = 37.1%). The results remained stable with the random effect model (see Additional files 7).
Source of infection
The pooled data on pulmonary infection from 8 studies indicated that it was a significant predictor (OR 0.77, 95%CI 0.60-0.99), with a moderate heterogeneity (I2 = 77.60%). Source of heterogeneity was not identified through subgroup analysis. The results of the sensitivity analysis were consistent (see Additional files 8).
The pooled data on abdominal infection from 25 studies indicated that it was a significant predictor (OR 1.44, 95%CI 1.32-1.58), with a moderate heterogeneity (I2 = 40.20%). The results of the sensitivity analysis were consistent. After excluding a study with high heterogeneity, the result remained stable, and the results were also stable with the fixed effect model (see Additional files 9).
The pooled data on unknown infection from 25 studies indicated that it was a significant predictor (OR 2.01, 95%CI 1.35-2.98), with a low heterogeneity (I2 = 0%). The results were still stable with the random effect model (see Additional files 10).
Medications
Vasopressors (from 7 studies, OR 3.15, 95%CI 2.00-4.96) and ACEI or ARB (from 8 studies, OR 1.61, 95%CI 1.10-2.36) were all identified as risk factors with high heterogeneity (I2 ≥ 75%). Source of heterogeneity was not identified through subgroup analysis and the sensitivity analysis results were stable (see Additional files 11).
The pooled data on diuretics from 5 studies indicated that it was a significant predictor (OR 1.40, 95%CI 1.13-1.72), with a low heterogeneity ( I2 = 0%). The results remained stable with the random effect model (see Additional files 12).
Figure 3. Forest plot for meta-analysis of the association between male sex and AKI
Other factors
The pooled data on male sex from 43 studies indicated that it was a significant predictor (OR 1.22, 95%CI 1.06-1.40), with a moderate heterogeneity (I2 = 69.80%). Source of heterogeneity was not identified through subgroup analysis. The sensitivity analysis results were consistent (see Additional files 13).
The pooled data on positive blood culture from 9 studies indicated that it was a significant predictor (OR 1.60, 95%CI 1.35-1.89), with a moderate heterogeneity (I2 = 50.20%). Source of heterogeneity was not identified through subgroup analysis. The sensitivity analysis results were consistent (see Additional files 14).
The pooled data on smoking history from 5 studies indicated that it was a significant predictor (OR 1.60, 95%CI 1.09-2.36), with a high heterogeneity (I2 = 78.30%). The sensitivity analysis results were consistent. After excluding a study with high heterogeneity, the result remained stable (see Additional files 15).
The pooled data on septic shock from 7 studies indicated that it was a significant predictor (OR 1.40, 95%CI 1.13-1.72), with a low heterogeneity (I2 = 8.2%). The results were still stable with the random effect model (see Additional files 16).
Gram-negative bacteria (from 3 studies, OR 2.19, 95%CI 1.52-3.15) and organ transplant (from 3 studies, OR 1.96, 95%CI 1.48-2.61) were all identified as risk factors with low heterogeneity (I2 = 0%), and the results remained stable with the random effect model (see Additional files 17 and 18).
The pooled data on mechanical ventilation from 24 studies indicated that it was a significant predictor (OR 1.64, 95%CI 1.24-2.16), with a high heterogeneity (I2 = 88.70%). Source of heterogeneity was not identified through subgroup analysis. The sensitivity analysis results were consistent (see Additional files 19).
5. Tests for Publication Bias (Figure 2)
The Egger’s rank correlation test and the Egger linear regression test indicated no publication bias of all risk factors (≥ 7 studies) except for cardiovascular disease (P = 0.015). Due to the limited study number (< 7 studies), publication bias was not evaluated with the predictors of smoking history, cirrhosis, multiorgan dysfunction (≥ 3),unknow infection, vasoactive drug administration, use of diuretics and organ transplant.