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Effect of electronic alerts on the care and outcomes in patients with acute kidney injury: a meta-analysis and trial sequential analysis

BMC Medicine volume 22, Article number: 408 (2024) Cite this article

Abstract

Background

Although electronic alerts are being increasingly implemented in patients with acute kidney injury (AKI), their effect remains unclear. Therefore, we conducted this meta-analysis aiming at investigating their impact on the care and outcomes of AKI patients.

Methods

PubMed, Embase, Cochrane Library, and Clinical Trial Registries databases were systematically searched for relevant studies from inception to March 2024. Randomized controlled trials comparing electronic alerts with usual care in patients with AKI were selected.

Results

Six studies including 40,146 patients met the inclusion criteria. The pooled results showed that electronic alerts did not improve mortality rates (relative risk (RR) = 1.02, 95% confidence interval (CI) = 0.97–1.08, P = 0.44) or reduce creatinine levels (mean difference (MD) =  − 0.21, 95% CI =  − 1.60–1.18, P = 0.77) and AKI progression (RR = 0.97, 95% CI = 0.90–1.04, P = 0.40). Instead, electronic alerts increased the odds of dialysis and AKI documentation (RR = 1.14, 95% CI = 1.05–1.25, P = 0.002; RR = 1.21, 95% CI = 1.01–1.44, P = 0.04, respectively), but the trial sequential analysis (TSA) could not confirm these results. No differences were observed in other care-centered outcomes including renal consults and investigations between the alert and usual care groups.

Conclusions

Electronic alerts increased the incidence of AKI and dialysis in AKI patients, which likely reflected improved recognition and early intervention. However, these changes did not improve the survival or kidney function of AKI patients. The findings warrant further research to comprehensively evaluate the impact of electronic alerts.

Peer Review reports

Background

Acute kidney injury (AKI), defined as a rapid increase in serum creatinine, decrease in urine output, or both, is a common complication affecting 10–15% of all hospitalized patients and up to 50% of critically ill patients [1, 2]. AKI is not only strongly associated with acute morbidity and mortality, but also subsequent chronic kidney disease (CKD) and end-stage kidney disease (ESKD), imposing a great burden on the healthcare systems [3, 4]. As AKI does not always have obvious alarm symptoms, it is easy for it to escape diagnosis [5]. Hence, early identification of patients at risk for or with AKI and timely intervention are essential for augmenting the clinical outcomes of AKI patients.

The electronic alert is a computerized system that allows real-time surveillance [6]. It is usually considered to be under the umbrella term of computerized clinical decision support systems (CDSSs) [7]. An electronic alert can automatically detect AKI on the basis of laboratory test results such as serum creatinine or others stored in electronic medical records (EMRs), facilitating early recognition and prompt intervention of AKI, which may thereby enhance the prognosis of AKI patients [8]. Recent research, including several meta-analyses, has described the implementation and effectiveness of electronic alerts but the results are inconsistent [9,10,11]. Thus, we performed this meta-analysis and trial sequential analysis (TSA) to further evaluate the effect of electronic alerts on AKI patients.

Methods

This meta-analysis was performed in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA statement) guidelines [12]. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO: CRD42024522814).

Search strategy and information sources

Electronic databases including PubMed, Embase, the Cochrane Library, and Clinical Trial Registries databases were searched from inception to March 2024, using items related to “electronic alert,” “clinical decision support,” and “AKI.” Literature search details were given in Additional file 1: Table S1. The search was limited to studies involving human subjects and published in English. The reference lists of the included articles were also scanned to identify additional relevant studies.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) study design: randomized controlled trials (RCTs); (2) population: inpatients with AKI (≥ 18 years old); (3) intervention: electronic alerts compared with usual care; and (4) outcome: assessed at least one of the following outcomes: patient-centered outcomes (mortality, dialysis, change in creatinine and AKI progression) and care-centered outcomes (documentation of AKI, inpatient renal consult, assessment or administration of fluid, urinalysis, renal ultrasound, creatinine measurement, medication review for nephrotoxins, hospital length of stay and cost of hospitalization). The exclusion criteria were as follows: (1) studies that involved pediatric inpatients or conducted only in high-risk wards; (2) studies that included inappropriate comparisons or did not include a control group; (3) studies such as secondary analysis, conference abstracts, comments, systemic reviews, and case reports.

Data extraction and quality assessment

Two reviewers (ZF and CL) independently performed the study selection and data extraction using a standardized form. Each trial was assessed using the Cochrane risk of bias tool in Review Manager (version 5.4, The Cochrane Collaboration, Oxford, UK) from the following aspects: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective reporting and other bias. Any disagreements were solved by a third reviewer. The extracted data included first author, year of publication, patient characteristics, sample size, type of intervention, patient-centered outcomes including death, receipt of dialysis, change in creatinine and AKI progression, and care-centered outcomes such as AKI documentation, renal consult, and fluid assessment.

Data synthesis and statistical analysis

All statistical analyses were conducted using Review Manager 5.4. The effect size was measured by relative risks (RRs) with 95% confidence intervals (CIs) for dichotomous outcomes and mean differences (MDs) with 95% CIs for continuous outcomes. Heterogeneity across the trials was assessed using the I2 statistic, and I2 > 50% indicated significant heterogeneity [13]. Random-effects model was applied in pooled analyses regardless of the heterogeneity. Subgroup analysis was performed to investigate the possible sources of heterogeneity. All P-values were two-sided, and a P-value less than 0.05 was considered to indicate a statistically significant difference. If the mean or standard deviation of the outcomes could not be directly extracted from the studies, we estimated them from the sample size, median, and interquartile range [14, 15].

Trial sequential analysis

Trial sequential analysis (TSA) was used in the meta-analyses to lower the risk of obtaining a false-positive or false-negative conclusion [16] and was conducted using TSA Version 0.9.5.10 Beta (www.ctu.dk/tsa). A sufficient level of evidence for the anticipated intervention effect was reached and no further trials were needed if the cumulative Z-curve crossed the trial sequential monitoring boundary or entered the futility area, whereas if the Z-curve did not cross any of the boundaries or the required information size (RIS) has not been reached, the evidence of the conclusion was considered to be insufficient and more trials were needed to confirm the results [17]. For the TSA used in this study, the RIS was estimated based on a RR reduction of 10%. The type I error (α) = 0.05 (two-sided) and power (1-β) = 0.90. The control event proportion was calculated from the comparator group [18].

Results

Characteristics of eligible studies and quality assessment

The study selection process is presented as a flow chart in Fig. 1. After the removal of duplicates and studies that failed to meet the inclusion criteria, 29 citations were retrieved for detailed assessment. In total, 6 RCTs [19,20,21,22,23,24] spanning from the year 2012 to 2024 and involving 40,146 participants were included in this meta-analysis. The characteristics of the included trials and patient demographic data are summarized in Table 1. Most studies had good quality reporting and the details of the quality of the studies were shown in Additional file 1: Fig. S1.

Fig. 1
figure 1

Flow diagram of the study selection process for the meta-analysis

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Table 1 Characteristics of included studies
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Patient-centered outcomes

Mortality

Five studies [19,20,21,22,23] reported the mortality in AKI patients. The application of electronic alerts showed no reduction in neither overall mortality (RR = 1.02, 95% CI = 0.97–1.08, P = 0.44, I2 = 0%) nor the mortality observed at different time points (Fig. 2A). However, the TSA suggested that the evidence to reach this conclusion was insufficient, for the cumulative Z-curve did not cross the futility boundary and enter the futility area (Fig. 2B–E).

Fig. 2
figure 2

The incidence of mortality in the alert and usual care groups. A Forest plot of mortality. B Random effects model of the TSA of in-hospital mortality. A diversity-adjusted information size of 24,933 participants was calculated based on the mortality rate of 13.9% in the usual care group and a RR reduction of 10%, with α = 5% (two-sided), β = 10%, and I2 = 0%. The cumulative Z-curve (solid blue line) did not cross the futility boundary and enter the futility area (solid red line). C Random effects model of the TSA of 28-day/30-day mortality. A diversity-adjusted information size of 12,438 participants was calculated based on the mortality rate of 24.6% in the usual care group and a RR reduction of 10%, with α = 5% (two-sided), β = 10%, and I2 = 0%. The cumulative Z-curve (solid blue line) did not cross the futility boundary and enter the futility area (solid red line). D Random effects model of the TSA of 14-day mortality. A diversity-adjusted information size of 38,684 participants was calculated based on the mortality rate of 9.4% in the usual care group and a RR reduction of 10%, with α = 5% (two-sided), β = 10%, and I2 = 0%. The cumulative Z-curve (solid blue line) did not cross the futility boundary and enter the futility area (solid red line). E Random effects model of the TSA of 7-day mortality. A diversity-adjusted information size of 48,744 participants was calculated based on the mortality rate of 7.6% in the usual care group and a RR reduction of 10%, with α = 5% (two-sided), β = 10%, and I.2 = 0%. The cumulative Z-curve (solid blue line) did not cross the futility boundary and enter the futility area (solid red line)

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Receipt of dialysis

Regarding the rate of dialysis, four studies [19,20,21, 23] investigated this outcome in the alert and usual care groups. In the pooled analysis, electronic alerts increased the overall proportion of patients receiving dialysis (RR = 1.14, 95% CI = 1.05–1.25, P = 0.002, I2 = 0%). In the subgroup analysis according to the number of dialysis patients examined at various time points, the use of electronic alert was associated with a higher incidence of dialysis within 7 days after randomization and during the hospital admission (RR = 1.22, 95% CI = 1.02–1.46, P = 0.03, I2 = 0%; RR = 1.15, 95% CI = 1.02–1.31, P = 0.03, I2 = 0%) (Fig. 3A). However, the TSA could not confirm these results, as the cumulative Z-curve just crossed the conventional boundary but did not cross the trial sequential monitoring boundary (Fig. 3B–C). No significant trend was noted at the time point of 14 days (RR = 1.08, 95% CI = 0.93–1.26, P = 0.32, I2 = 0%) (Fig. 3A), but similarly, the TSA also could not confirm this result, because the cumulative Z-curve did not cross the futility boundary and enter the futility area (Fig. 3D).

Fig. 3
figure 3

The incidence of dialysis in the alert and usual care groups. A Forest plot of dialysis. B Random effects model of the TSA of in-hospital dialysis. A diversity-adjusted information size of 44,314 participants was calculated based on the dialysis rate of 8.3% in the usual care group and a RR reduction of 10%, with α = 5% (two-sided), β = 10%, and I2 = 0%. The cumulative Z-curve (solid blue line) crossed the conventional boundary (dashed green line) but did not cross the trial sequential monitoring boundary (solid red line). C Random effects model of the TSA of dialysis within 7 days. A diversity-adjusted information size of 44,314 participants was calculated based on the dialysis rate of 8.3% in the usual care group and a RR reduction of 10%, with α = 5% (two-sided), β = 10%, and I2 = 0%. The cumulative Z-curve (solid blue line) crossed the conventional boundary (dashed green line) but did not cross the trial sequential monitoring boundary (solid red line). D Random effects model of the TSA of dialysis within 14 days. A diversity-adjusted information size of 84,937 participants was calculated based on the dialysis rate of 4.5% in the usual care group and a RR reduction of 10%, with α = 5% (two-sided), β = 10%, and I.2 = 0%. The cumulative Z-curve (solid blue line) did not cross the futility boundary and enter the futility area (solid red line)

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Change in serum creatinine and AKI progression

Two trials [19, 23] made comparisons of the changes in serum creatinine levels. In AKI patients, electronic alert showed an effect similar to that of usual care on decreasing the creatinine level (MD =  − 0.21, 95% CI =  − 1.60–1.18, P = 0.77, I2 = 20%) (Additional file 1: Fig. S2A).

Three studies [19,20,21] showed that there was no statistically significant difference in AKI progression between the alert group and the usual care group (RR = 0.97, 95% CI = 0.90–1.04, P = 0.40, I2 = 0%) (Additional file 1: Fig. S2B).

Care-centered outcomes

Care-centered outcomes including AKI documentation, inpatient renal consult, fluid assessment, fluid administration, urinalysis, renal ultrasound, creatinine measurement, medication exposures (contrast, aminoglycoside, nonsteroidal anti-inflammatory drug, angiotensin-converting enzyme inhibitor, and angiotensin receptor blocker), hospital length of stay and cost of hospitalization were also examined in this meta-analysis. The results demonstrated that the alert group had more documentation of AKI compared with the usual care group (RR = 1.21, 95% CI = 1.01–1.44, P = 0.04, I2 = 96) (Table 2). In terms of other care-centered outcomes, no notable effects of the alerts were found (Table 2). It is also worth mentioning that McCoy et al. [24] conducted a trial mainly focused on the impact of the alert on reducing adverse drug events, however, the results did not differ between the alert and control groups (Table 2).

Table 2 Care-centered outcomes
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Discussion

This study provided systematically updated evidence on the effect of electronic alerts on the process of care and clinical outcomes in patients with AKI. The results showed that electronic alerts increased the proportion of patients receiving dialysis and the diagnostic rate of AKI. However, the implementation of electronic alerts did not appear to improve short-term mortality, kidney function, or other patient-centered outcomes.

Previous meta-analyses on the efficacy of electronic alerts have yielded inconsistent results. Zhao et al. concluded a positive effect of CDSSs for AKI patients, as the use of CDSSs significantly reduced the mortality and increased the recognition of AKI [11]. However, Lachance et al. found that the introduction of electronic alerts did not decrease the rates of mortality and dialysis [10]. Consistent with this meta-analysis, the present study did not support the idea that the alerts enhanced renal outcomes, but it demonstrated an improvement in AKI recognition. These discrepancies may be in part due to the involvement of non-RCTs (e.g., observational or before/after studies) in prior meta-analyses, in which the data were prone to bias. In contrast with those meta-analyses, we focused only on RCTs and included the most recent trials [19,20,21]. Additionally, we used a TSA to provide a more sufficient and conclusive evidence of the outcomes.

As AKI is a silent disease, the diagnosis of AKI is often delayed, highlighting the importance of early detection [25]. The results of our meta-analysis showed that the alert group had more patients documented with AKI, suggesting an improved recognition with the use of electronic alerts. Indeed, several studies have indicated the promise of electronic alerts in early identification of AKI [7, 26, 27]. A before and after quality improvement study found that the AKI electronic alert considerably decreased the odds of overlooked AKI incident cases [27]. The RCTs conducted by Li et al. and Wilson et al. showed that the patients in the alert group were more likely to have documentation of AKI [19, 21]. A similar effect has also been reported in the trial by Selby et al. [22] Taken together, current evidence does support that electronic alerts have the capacity to detect AKI in a timely fashion or even predict AKI when designed and executed appropriately.

In addition to a better documentation of AKI, it was also observed in our study that electronic alerts increased the use of dialysis in AKI patients. One possible interpretation of this result is that early diagnosis leads to timely initiation of dialytic treatment in severe AKI patients. Furthermore, patients admitted to the large tertiary care centers are usually more severe than those admitted to the community hospitals, which adds to the likelihood of triggering the alerts, and thereby contributing to the increase need of dialysis treatment. However, it should also be attention that although dialysis is regarded as the standard therapeutic option for severe AKI, the ideal timing of initiation of dialysis remains controversial [28, 29]. It is still not clear whether earlier initiation of dialysis would have a beneficial effect on those critically ill patients [30,31,32]. In view of this, further RCTs are needed and future electronic alerts should be designed to contain not only patient-specific information but also a continuously updated knowledge-based system, which, when combined, could provide clinicians with a more informed recommendation, preventing excessive interventions and improving patient process of care and outcomes.

No meaningful change in the care behaviors including renal consult, renal investigations, and avoidance of nephrotoxic medications was detected between the alert and usual care groups in our analysis, which was aligned with the finding of McCoy et al. [24]. This might be related to how the alerts were delivered. The means of alerting (message, telephone call, or within EMR system), hierarchy of interruption (ranging from no alarm to a hard stop without rights to override), final presented content (alert only or accompanied with care intervention recommendations such as care bundle usage) and provider acknowledgement requirements (ranging from no need for response to punitive measures if no response is provided) would all have an impact on the performance of electronic alerts [33, 34]. The effectiveness of the alerts on care process might be attenuated if the delivery method was not compatible to where the alerts were applied. It was worth mentioning that in studies that coupled the alert to a care bundle, some process measures such as intravenous fluids and urinalysis did occur more often in the alert arm [19, 21, 22], suggesting that the alerts had the potential to change the care behaviors of the providers if appropriately delivered. Since better AKI care would lead to enhanced patient outcomes, it is crucial for researchers to investigate the optimal output methods for electronic alerts in different situations.

Results of this study did not find the benign influence of electronic alerts on patient clinical outcomes, but this does not mean that the alerts are not worth for further research. The contribution of electronic alerts to early recognition of AKI is undeniable [35,36,37]. In addition, it should be noticed that the nature of AKI is heterogeneous and the prognosis of AKI is correlated with multiple factors including human factors besides the peculiar impacts of the disease itself [7, 22]. Therefore, no standard AKI management could be easily applied in a one-size-fits-all model in response to the alerts, weakening the effects of electronic alerts [38]. Regarding this, it may be advisable for future trials to refine alert methods (e.g., be customized based on the EMR system and linking alerts to patient-specific care recommendations provided in the CDSSs) to suit various kinds of patients to ensure they benefit most from electronic alerts.

In this meta-analysis, a TSA was also used to provide more conservative estimates and to establish sufficient and conclusive evidence of the results with significant differences. However, the TSA could confirm none of those findings, indicating that additional research is required.

Limitations

This study has several limitations. First, the number of studies included in this analysis was small, and the data for some of the results were scarce, thus, we could only provide preliminary results on the effects of electronic alerts. Second, long-term follow-up results are not yet available, so the long-term effect of the alerts cannot be investigated. Third, there was significant heterogeneity in some of the outcomes in this meta-analysis, which may be due to the setting or alert formats in the included studies. Therefore, further adequate-powered RCTs are expected.

Conclusions

In conclusion, this meta-analysis showed that electronic alerts increased the AKI documentation and dialysis rates of AKI patients, which likely reflected improved recognition and early intervention of AKI. However, these changes did not translate into improved survival or kidney function. These findings suggest that further research is needed to comprehensively evaluate the value of electronic alerts in the AKI domain.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

AKI:

Acute kidney injury

CDSSs:

Clinical decision support systems

CKD:

Chronic kidney disease

EMRs:

Electronic medical records

ESKD:

End-stage kidney disease

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

RCTs:

Randomized controlled trials

TSA:

Trial sequential analysis

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Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82200780, 82070741, and 82270758), China Postdoctoral Science Foundation (2022M723899), National Key Research and Development Program of China (2018YFE0126600) and Grant for GYC (22KJLJ001).

Author information

Author notes

  1. Zhangning Fu, Xiuzhen Hao and Yangfan Lv contributed equally to this work and share first authorship.

  2. Zhe Feng and Chao Liu contributed equally to this work and share senior authorship.

Authors and Affiliations

  1. Department of Nephrology, First Medical Center of Chinese PLA General Hospital, State Key Laboratory of Kidney Diseases, National Clinical Research Center for Kidney Diseases, Beijing Key Laboratory of Kidney Diseases Research, Beijing, 100853, China

    Zhangning Fu, Quan Hong & Zhe Feng

  2. First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China

    Xiuzhen Hao

  3. Department of Pathology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China

    Yangfan Lv

  4. Department of Critical Care Medicine, First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China

    Chao Liu

Authors
  1. Zhangning Fu
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  2. Xiuzhen Hao
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  3. Yangfan Lv
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  4. Quan Hong
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  5. Zhe Feng
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  6. Chao Liu
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Contributions

ZNF conceived the study, participated in the design, collected the data, performed statistical analyses and drafted the manuscript. XZH and YFL helped provide the study materials and draft the manuscript. QH and ZF critically revised the manuscript for important intellectual content. CL conceived the study, participated in the design and revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Zhe Feng or Chao Liu.

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Fu, Z., Hao, X., Lv, Y. et al. Effect of electronic alerts on the care and outcomes in patients with acute kidney injury: a meta-analysis and trial sequential analysis. BMC Med 22, 408 (2024). https://doi.org/10.1186/s12916-024-03639-x

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  • DOI: https://doi.org/10.1186/s12916-024-03639-x

Keywords

BMC Medicine

ISSN: 1741-7015

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