Materiały źródłowe

• #1 Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review – PubMed

  https://pubmed.ncbi.nlm.nih.gov/39758155/

  Accurate estimation of in-hospital outcomes for patients with acute kidney injury (AKI) is crucial for aiding physicians in making optimal clinical decisions. […] We aimed to review prediction models constructed by machine learning methods for predicting AKI prognosis using administrative databases. […] In-hospital outcomes for patients with AKI included acute kidney disease, chronic kidney disease, renal function recovery or kidney failure, and mortality. […] Compared with models predicting the mortality of patients with AKI during hospitalization, the prediction performance of models on kidney function recovery was less accurate. […] Machine learning models outperformed traditional approaches in predicting mortality for patients with AKI, although they are less accurate in predicting kidney function recovery. […] Overall, these models demonstrate significant potential to help physicians improve clinical decision making and patient outcomes.

• #2 Acute kidney injury

  https://www.nhs.uk/conditions/acute-kidney-injury/

  AKI is different from chronic kidney disease, where the kidneys gradually lose function over a long period of time. […] Most people with AKI make a full recovery, but some people go on to develop chronic kidney disease or long-term kidney failure as a result.

• #3 Validated risk prediction models for outcomes of acute kidney injury: a systematic review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10170731/

  Acute Kidney Injury (AKI) is frequently seen in hospitalized and critically ill patients. Studies have shown that AKI is a risk factor for the development of acute kidney disease (AKD), chronic kidney disease (CKD), and mortality. […] Few validated risk prediction models targeting the development of renal insufficiency after experiencing AKI have been developed, most of which are based on simple statistical or machine learning models. While some of these models have been externally validated, none of these models are available in a way that can be used or evaluated in a clinical setting. […] AKI contributes to adverse short-term and long-term outcomes. Different studies have linked AKI to the development of acute kidney disease (AKD), chronic kidney disease (CKD), end-stage kidney disease, longer hospitalization time, cardiovascular disease (CVD), and other complications, suggesting that even a short episode of acute kidney injury might lead to long term morbidity and mortality.

• #4 Validated risk prediction models for outcomes of acute kidney injury: a systematic review | BMC Nephrology | Full Text

  https://bmcnephrol.biomedcentral.com/articles/10.1186/s12882-023-03150-0

  Acute Kidney Injury (AKI) is frequently seen in hospitalized and critically ill patients. Studies have shown that AKI is a risk factor for the development of acute kidney disease (AKD), chronic kidney disease (CKD), and mortality. […] Few validated risk prediction models targeting the development of renal insufficiency after experiencing AKI have been developed, most of which are based on simple statistical or machine learning models. While some of these models have been externally validated, none of these models are available in a way that can be used or evaluated in a clinical setting. […] AKI contributes to adverse short-term and long-term outcomes. Different studies have linked AKI to the development of acute kidney disease (AKD), chronic kidney disease (CKD), end-stage kidney disease, longer hospitalization time, cardiovascular disease (CVD), and other complications, suggesting that even a short episode of acute kidney injury might lead to long term morbidity and mortality.

• #5 Validated risk prediction models for outcomes of acute kidney injury: a systematic review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10170731/

  In recent years, it has become clear that AKI is not a completely reversible syndrome. It is possible that the injury that occurs may result in permanent kidney damage (e.g., CKD) and even damage to other organs. […] While AKI and CKD have been associated, confounding factors and bias can explain this, thus questioning their causal significance. […] Currently, follow-up of AKI survivors is often lacking and not regulated: follow-up of kidney function by a nephrologist in patients surviving an episode of AKI treated with renal replacement therapy (RRT) is stated in nearly one-third of the patients. […] Close follow-up and interventions aimed at preserving kidney function may positively impact long-term outcomes as major adverse kidney events have been reported that are common in AKI survivors.

• #6 Acute kidney injury

  https://www.nhs.uk/conditions/acute-kidney-injury/

  AKI is different from chronic kidney disease, where the kidneys gradually lose function over a long period of time. […] Most people with AKI make a full recovery, but some people go on to develop chronic kidney disease or long-term kidney failure as a result.

• #7 Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup | Nature Reviews Nephrology

  https://www.nature.com/articles/s41581-023-00683-3

  Sepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients and is strongly associated with adverse outcomes, including an increased risk of chronic kidney disease, cardiovascular events and death. […] Improving outcomes in SA-AKI is challenging, as patients can present with either clinical or subclinical AKI. Early identification of patients at risk of AKI, or at risk of progressing to severe and/or persistent AKI, is crucial to the timely initiation of adequate supportive measures, including limiting further insults to the kidney. […] Although specific therapeutic options are limited, several clinical trials have evaluated the use of care bundles and extracorporeal techniques as potential therapeutic approaches. […] Sepsis-associated AKI (SA-AKI) portends a worse prognosis than either syndrome in isolation and is associated with longer intensive care unit (ICU) and hospital stays, higher mortality, increased rate of long-term disability and reduced quality of life in adult and paediatric populations.

• #8 Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup | Nature Reviews Nephrology

  https://www.nature.com/articles/s41581-023-00683-3

  Many aspects of SA-AKI remain poorly described, especially in the paediatric population, including its clinical definition, epidemiology, pathophysiology, impact of resuscitative and fluid strategies, role of biomarkers in risk stratification, diagnosis, and treatment guidance, and the effect of extracorporeal and novel therapies on patient outcomes. […] The development of AKI late in the course of sepsis is associated with worse clinical outcomes and increased mortality compared with early AKI development. […] The reported incidence of SA-AKI ranged from 14-87% and the association with mortality (including ICU mortality, hospital mortality, 28-day and 90-day mortality) was also highly variable, ranging from 11 to 77%. […] The relative contribution of one or more specific mechanisms that lead to injury defines distinct sepsis-induced AKI endotypes.

• #9 Validated risk prediction models for outcomes of acute kidney injury: a systematic review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10170731/

  Acute Kidney Injury (AKI) is frequently seen in hospitalized and critically ill patients. Studies have shown that AKI is a risk factor for the development of acute kidney disease (AKD), chronic kidney disease (CKD), and mortality. […] Few validated risk prediction models targeting the development of renal insufficiency after experiencing AKI have been developed, most of which are based on simple statistical or machine learning models. While some of these models have been externally validated, none of these models are available in a way that can be used or evaluated in a clinical setting. […] AKI contributes to adverse short-term and long-term outcomes. Different studies have linked AKI to the development of acute kidney disease (AKD), chronic kidney disease (CKD), end-stage kidney disease, longer hospitalization time, cardiovascular disease (CVD), and other complications, suggesting that even a short episode of acute kidney injury might lead to long term morbidity and mortality.

• #10 Validated risk prediction models for outcomes of acute kidney injury: a systematic review | BMC Nephrology | Full Text

  https://bmcnephrol.biomedcentral.com/articles/10.1186/s12882-023-03150-0

  Acute Kidney Injury (AKI) is frequently seen in hospitalized and critically ill patients. Studies have shown that AKI is a risk factor for the development of acute kidney disease (AKD), chronic kidney disease (CKD), and mortality. […] Few validated risk prediction models targeting the development of renal insufficiency after experiencing AKI have been developed, most of which are based on simple statistical or machine learning models. While some of these models have been externally validated, none of these models are available in a way that can be used or evaluated in a clinical setting. […] AKI contributes to adverse short-term and long-term outcomes. Different studies have linked AKI to the development of acute kidney disease (AKD), chronic kidney disease (CKD), end-stage kidney disease, longer hospitalization time, cardiovascular disease (CVD), and other complications, suggesting that even a short episode of acute kidney injury might lead to long term morbidity and mortality.

• #11 Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations | BMJ Open

  https://bmjopen.bmj.com/content/7/9/e016591

  Acute kidney injury (AKI) is defined as an acute increase in serum creatinine (SCr) or reduction in urine volume. The incidence of AKI is increasing, affecting up to one in five hospitalised adults worldwide. Associated mortality remains high, in part reflecting the severity of the underlying disease, but may also be due to the limitations of conventional markers to detect early injury. […] One suggested strategy to achieve this aim is through the implementation of clinical prediction models. […] AKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. […] The small number of externally validated models and absence of impact analysis limit the recommendation and implementation of an individual model.

• #12 Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review – PubMed

  https://pubmed.ncbi.nlm.nih.gov/39758155/

  Accurate estimation of in-hospital outcomes for patients with acute kidney injury (AKI) is crucial for aiding physicians in making optimal clinical decisions. […] We aimed to review prediction models constructed by machine learning methods for predicting AKI prognosis using administrative databases. […] In-hospital outcomes for patients with AKI included acute kidney disease, chronic kidney disease, renal function recovery or kidney failure, and mortality. […] Compared with models predicting the mortality of patients with AKI during hospitalization, the prediction performance of models on kidney function recovery was less accurate. […] Machine learning models outperformed traditional approaches in predicting mortality for patients with AKI, although they are less accurate in predicting kidney function recovery. […] Overall, these models demonstrate significant potential to help physicians improve clinical decision making and patient outcomes.

• #13 Validated risk prediction models for outcomes of acute kidney injury: a systematic review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10170731/

  In this systematic review, we aimed to find prediction models for the development of renal insufficiency (or recoveries) in patients who experienced AKI. We identified eight studies in which multiple prediction models were built and validated in heterogeneous cohorts of patients. The quality of the studies and the models developed are rather average in general. […] In recent years, few validated clinical models have been developed that can predict the outcomes of acute kidney injury in critically ill or hospitalized patients. The existence and use of such models, in addition to highlighting increased renal insufficiency, morbidity, and mortality following AKI, have significant implications for the future care needs of survivors. Future studies using machine learning prediction algorithms may improve the model design that can be better used in the clinical setting.

• #14 Validated risk prediction models for outcomes of acute kidney injury: a systematic review | BMC Nephrology | Full Text

  https://bmcnephrol.biomedcentral.com/articles/10.1186/s12882-023-03150-0

  In recent years, few validated clinical models have been developed that can predict the outcomes of acute kidney injury in critically ill or hospitalized patients. The existence and use of such models, in addition to highlighting increased renal insufficiency, morbidity, and mortality following AKI, have significant implications for the future care needs of survivors. Future studies using machine learning prediction algorithms may improve the model design that can be better used in the clinical setting.

• #15 Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance | npj Digital Medicine

  https://www.nature.com/articles/s41746-020-00346-8

  Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. […] In conclusion, the RNN was superior to physicians in the prediction of AKI after cardiothoracic surgery. It could potentially be integrated into hospitals electronic health records for real-time patient monitoring and may help to detect early AKI and hence modify the treatment in perioperative care. […] Early detection of patients at high risk of developing AKI allows for early therapeutic intervention prior to the onset of anuria and its complications such as acidosis, hyperkalemia, or volume overload as well as long-term complications such as lung injury, sepsis and chronic kidney disease. […] Physicians showed an overall low sensitivity of 0.594 at AKI prediction. They predicted lower risk probabilities in general.

• #16 Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance | npj Digital Medicine

  https://www.nature.com/articles/s41746-020-00346-8

  In contrast to the physicians, our RNN yielded an overall high sensitivity of 0.851 with a maximum sensitivity of 0.971 in the 26h interval before the event and a minimum sensitivity of even 0.750 in the 48168h interval before the event. In summary, our RNN was superior to experienced physicians in the prediction of AKI after cardiothoracic surgery. […] Our model achieved highly accurate results with an overall AUC of 0.893 in our internal validation. It outperformed existing classical prediction models that are based on logistic regression from static pre- and intraoperative variables, as well as a dynamic model that predicted AKI at three points in time. […] To conclude, based on a relatively small sample size, we developed a highly accurate model for the prediction of AKI after cardiac surgery that significantly outperformed experienced physicians, could potentially be integrated into EHR systems and might prevent severe complications following AKI through real-time patient surveillance.

• #17 Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance | npj Digital Medicine

  https://www.nature.com/articles/s41746-020-00346-8

  Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early prediction of AKI could prompt preventive measures, but is challenging in the clinical routine. […] In conclusion, the RNN was superior to physicians in the prediction of AKI after cardiothoracic surgery. It could potentially be integrated into hospitals electronic health records for real-time patient monitoring and may help to detect early AKI and hence modify the treatment in perioperative care. […] Early detection of patients at high risk of developing AKI allows for early therapeutic intervention prior to the onset of anuria and its complications such as acidosis, hyperkalemia, or volume overload as well as long-term complications such as lung injury, sepsis and chronic kidney disease. […] Physicians showed an overall low sensitivity of 0.594 at AKI prediction. They predicted lower risk probabilities in general.

• #18 Urine biomarkers give early prediction of acute kidney injury and outcome after out-of-hospital cardiac arrest | Critical Care | Full Text

  https://ccforum.biomedcentral.com/articles/10.1186/s13054-016-1503-2

  Post-resuscitation care after out-of-hospital cardiac arrest (OHCA) is challenging due to the threat of organ failure and difficult prognostication. Our aim was to examine whether urine biomarkers could give an early prediction of acute kidney injury (AKI) and outcome. […] In comatose OHCA patients, urine levels of cystatin C and NGAL at admission and day 3 were independent risk factors for AKI, 6-month mortality and PNO. […] A huge challenge for clinicians is the lack of reliable predictors of AKI, mortality, and neurological outcome after OHCA. An early diagnostic and/or prognostic biomarker could potentially optimize targeted post-resuscitation care and reduce the burden of futile treatment to patients, relatives and the healthcare system. […] The primary aim of this study was to examine the ability of urine biomarkers to predict AKI, mortality and PNO after OHCA.

• #19 Urine biomarkers give early prediction of acute kidney injury and outcome after out-of-hospital cardiac arrest | Critical Care | Full Text

  https://ccforum.biomedcentral.com/articles/10.1186/s13054-016-1503-2

  Our main finding in this prospective study on resuscitated comatose OHCA patients was that the urine concentrations of cystatin C and NGAL sampled at admission and on day 3 were independent risk factors for AKI, mortality, and PNO. […] The biomarkers cystatin C, NGAL, and TIMP-2IGFBP7 have been prognostic predictors of both renal recovery and mortality in general ICU patients. […] We found that urine cystatin C and NGAL, but not urine TIMP-2IGFBP7, were statistically associated with mortality and PNO. […] In this observational study of resuscitated comatose OHCA patients, urine cystatin C and NGAL levels at admission and day 3 were independent risk factors for AKI, mortality, and PNO.

• #20 Outcome Prediction of Acute Kidney Injury Biomarkers at Initiation of Dialysis in Critical Units

  https://www.mdpi.com/2077-0383/7/8/202

  Our study showed that cFGF-23, measured at initiation of RRT in critical patients with AKI, may be a novel and distinct marker for predicting 90-day mortality after discharge and less weaning from RRT in survivors. Its predictive discrimination was superior to other established biomarkers of kidney injury, in particular creatinine, NGAL and Kim-1. Adding cFGF-23 to the traditional AKI risk predicting score may allow better risk stratification and enhance prognostic power. cFGF-23 could further be used as a surrogate marker to decide the best timing to initiate RRT.

• #21 Outcome Prediction of Acute Kidney Injury Biomarkers at Initiation of Dialysis in Critical Units

  https://www.mdpi.com/2077-0383/7/8/202

  At initializing dialysis, the discriminative power of AKI biomarkers for 90-day mortality is fair. At dialysis initiation, the discrimination of cFGF-23 is better than NGAL, KIM-1, iFGF-23 and creatinine predicting patients’ outcome. With mortality as competing risk, higher cFGF-23 levels also predicted lesser kidney recovery in survivors. More importantly, cFGF-23 had better predictive power than creatinine-adjusted urine NGAL and its integration into the AKI risk predicting score significantly enhanced the accuracy of risk stratification. At a cut-off level above 2050 RU/mL, cFGF-23 could predict of AKI mortality after adjusting for different clinical and disease severity parameters. Thus, cFGF-23 could be used as an early determinant of prognosis in ICU patients subjected at initializing RRT and also as an early determinant of the timing of dialysis initiation.

• #22 Outcome Prediction of Acute Kidney Injury Biomarkers at Initiation of Dialysis in Critical Units

  https://www.mdpi.com/2077-0383/7/8/202

  Our study showed that cFGF-23, measured at initiation of RRT in critical patients with AKI, may be a novel and distinct marker for predicting 90-day mortality after discharge and less weaning from RRT in survivors. Its predictive discrimination was superior to other established biomarkers of kidney injury, in particular creatinine, NGAL and Kim-1. Adding cFGF-23 to the traditional AKI risk predicting score may allow better risk stratification and enhance prognostic power. cFGF-23 could further be used as a surrogate marker to decide the best timing to initiate RRT.

• #23 Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup | Nature Reviews Nephrology

  https://www.nature.com/articles/s41581-023-00683-3

  Many aspects of SA-AKI remain poorly described, especially in the paediatric population, including its clinical definition, epidemiology, pathophysiology, impact of resuscitative and fluid strategies, role of biomarkers in risk stratification, diagnosis, and treatment guidance, and the effect of extracorporeal and novel therapies on patient outcomes. […] The development of AKI late in the course of sepsis is associated with worse clinical outcomes and increased mortality compared with early AKI development. […] The reported incidence of SA-AKI ranged from 14-87% and the association with mortality (including ICU mortality, hospital mortality, 28-day and 90-day mortality) was also highly variable, ranging from 11 to 77%. […] The relative contribution of one or more specific mechanisms that lead to injury defines distinct sepsis-induced AKI endotypes.

• #24 Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup | Nature Reviews Nephrology

  https://www.nature.com/articles/s41581-023-00683-3

  Identifying distinct endotypes of SA-AKI might provide crucial prognostic information, help to define treatment responsiveness and enrich clinical trial populations. […] The presence of AKI in patients with sepsis is common and SA-AKI is best defined by both consensus sepsis criteria and AKI criteria, with early SA-AKI occurring within 48h of diagnosis of sepsis and late SA-AKI occurring between 48h and 7 days of diagnosis of sepsis.

• #25 Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations | BMJ Open

  https://bmjopen.bmj.com/content/7/9/e016591

  In this first systematic review of HA-AKI prediction in general hospital settings, the most common predictors were age, diabetes, CKD, drugs, heart failure, SCr and bicarbonate. Modest discrimination performance of all the models is unsurprising when attempting at a single time point to predict a future event reflecting diverse aetiologies, affecting heterogeneous patient groups. […] This systematic review suggests there are few externally validated prediction models to help identify those at risk of AKI across general hospital populations. Future research should concentrate on validation, utility of additional markers, exploration of electronic implementation to enable clinical uptake and impact analysis.

• #26 Risk prediction models for acute kidney injury in adults: An overview of systematic reviews | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248899

  The incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. […] An important step in prevention lies in AKI risk prediction. […] Our aim was to assess the quality of SRs of RPMs in AKI. […] The methodological quality of SRs of RPMs of AKI is inconsistent. […] Most SRs lack a formal risk of bias assessment. […] SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient. […] Key to preventing AKI is the ability to predict AKI before it actually occurs. […] Models that can reliably predict who is at risk for AKI, could help identify those in need of specialized care, guide decision making and avoid additional renal insults. […] A SR is considered the highest level of evidence. […] However, across different settings of AKI, SRs of RPMs show inconsistent quality. […] Individual RPM studies and SRs of RPMs that adhere to good methodological standards have the best opportunity to positively impact patient outcome, and benefit guideline development and health policy.

• #27 The Kidney Failure Risk Equation

  https://kidneyfailurerisk.com/

  Using the patient’s Urine, Sex, Age and GFR, the kidney failure risk equation provides the 2 and 5 year probability of treated kidney failure for a potential patient with CKD stage 3 to 5. […] The four and eight variable equations accurately predict the 2 and 5 year probability of treated kidney failure (dialysis or transplantation) for a potential patient with CKD Stage 3 to 5. […] Determining the probability of kidney failure may be useful for patient and provider communication, triage and management of nephrology referrals and timing of dialysis access placement and living related kidney transplant. […] For patients with localized kidney cancer facing either a partial or radical nephrectomy, the Kidney Cancer Risk Equation (KCRE) can be used to predict the risk of kidney failure 5-years after kidney cancer surgery. Knowing your risk can help inform treatment decisions, such as surgery (partial versus radical nephrectomy), or watchful waiting. […] Patient risk of progression to kidney failure requiring dialysis or transplant after kidney cancer surgery:

• #28 The Kidney Failure Risk Equation

  https://kidneyfailurerisk.com/

  Using the patient’s Urine, Sex, Age and GFR, the kidney failure risk equation provides the 2 and 5 year probability of treated kidney failure for a potential patient with CKD stage 3 to 5. […] The four and eight variable equations accurately predict the 2 and 5 year probability of treated kidney failure (dialysis or transplantation) for a potential patient with CKD Stage 3 to 5. […] Determining the probability of kidney failure may be useful for patient and provider communication, triage and management of nephrology referrals and timing of dialysis access placement and living related kidney transplant. […] For patients with localized kidney cancer facing either a partial or radical nephrectomy, the Kidney Cancer Risk Equation (KCRE) can be used to predict the risk of kidney failure 5-years after kidney cancer surgery. Knowing your risk can help inform treatment decisions, such as surgery (partial versus radical nephrectomy), or watchful waiting. […] Patient risk of progression to kidney failure requiring dialysis or transplant after kidney cancer surgery:

• #29 Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review – PubMed

  https://pubmed.ncbi.nlm.nih.gov/39758155/

  Accurate estimation of in-hospital outcomes for patients with acute kidney injury (AKI) is crucial for aiding physicians in making optimal clinical decisions. […] We aimed to review prediction models constructed by machine learning methods for predicting AKI prognosis using administrative databases. […] In-hospital outcomes for patients with AKI included acute kidney disease, chronic kidney disease, renal function recovery or kidney failure, and mortality. […] Compared with models predicting the mortality of patients with AKI during hospitalization, the prediction performance of models on kidney function recovery was less accurate. […] Machine learning models outperformed traditional approaches in predicting mortality for patients with AKI, although they are less accurate in predicting kidney function recovery. […] Overall, these models demonstrate significant potential to help physicians improve clinical decision making and patient outcomes.

• #30 Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations | BMJ Open

  https://bmjopen.bmj.com/content/7/9/e016591

  In this first systematic review of HA-AKI prediction in general hospital settings, the most common predictors were age, diabetes, CKD, drugs, heart failure, SCr and bicarbonate. Modest discrimination performance of all the models is unsurprising when attempting at a single time point to predict a future event reflecting diverse aetiologies, affecting heterogeneous patient groups. […] This systematic review suggests there are few externally validated prediction models to help identify those at risk of AKI across general hospital populations. Future research should concentrate on validation, utility of additional markers, exploration of electronic implementation to enable clinical uptake and impact analysis.

• #31 Risk prediction models for acute kidney injury in adults: An overview of systematic reviews | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248899

  The incidence of Acute Kidney Injury (AKI) and its human and economic cost is increasing steadily. […] An important step in prevention lies in AKI risk prediction. […] Our aim was to assess the quality of SRs of RPMs in AKI. […] The methodological quality of SRs of RPMs of AKI is inconsistent. […] Most SRs lack a formal risk of bias assessment. […] SRs ought to adhere to certain standard quality criteria so that clinicians can rely on them to select a RPM for use in an individual patient. […] Key to preventing AKI is the ability to predict AKI before it actually occurs. […] Models that can reliably predict who is at risk for AKI, could help identify those in need of specialized care, guide decision making and avoid additional renal insults. […] A SR is considered the highest level of evidence. […] However, across different settings of AKI, SRs of RPMs show inconsistent quality. […] Individual RPM studies and SRs of RPMs that adhere to good methodological standards have the best opportunity to positively impact patient outcome, and benefit guideline development and health policy.

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