Medical practice

Mobile phone app 'helps doctors detect acute kidney injury'

BBC News reports: "A mobile phone app has speeded up the detection of a potentially fatal kidney condition in hospital patients."

Acute kidney injury (previously called acute kidney failure) is when your kidneys suddenly stop working properly, usually over hours or days. Prompt diagnosis and management is essential to give the best outlook and reduce risk of death. Experts believe that up to 30% of cases could be prevented if a doctor intervenes early enough.

While it is relatively unknown, acute kidney injury puts a considerable strain on NHS resources (estimated at £1 billion in England) and is responsible for around 100,000 deaths per year in the UK.

The app, called Streams, is a secure mobile device that brings together important medical information, like patients' blood test results, in one place.

It brings together data and test results from a range of IT systems used by the hospital and alerts medical teams if acute kidney injury has been confirmed.

Researchers compared clinical outcomes at 1 London hospital, from 8 months before the introduction of the Steams app to 4 months after. They also compared outcomes with a similar hospital that did not use the Streams app. Overall the Streams app did not improve the main outcome of rates of recovery from acute kidney injury. There were some signs of improvement, such as reduction in the number of undetected cases.

There are plans to introduce the app to another London hospital so it will be interesting to see what the outcomes will be.

Where did the story come from?

This study was conducted by researchers from University College London and the University of London. Individual researchers received funding from the National Institute of Health Research. Several authors also declare that they are paid clinical advisors to DeepMind, or have been employed there. However, it's stated that DeepMind had no involvement in the collection and analysis of data.

The study was published in the peer-reviewed Nature Digital Medicine as well as the Journal of Medical Internet Research (JMIR) and is freely available to access online.

Some headlines may lead people to think they can now download an app onto their phone that will monitor their health and alert them to when they need to consult a doctor. This is not the case. This is purely a hospital app integrated into medical systems for health professionals to use.

What kind of research was this?

This was a before-after study where researchers compared patient outcomes before and after introduction of the Streams app for detection and management of acute kidney injury (AKI).

Such studies are useful to explore the effects of an intervention, taking away many of the restrictions of performing a randomised controlled trial.

It does mean you cannot control all the other variables that could be having an influence on the outcomes, such as patient characteristics or other process change in the hospital.

However, this research benefited from comparing the same 2 before-after time periods with another hospital that did not receive the app to give a better indication of whether any change could be a direct effect of the app.

What did the research involve?

The introduction of the Streams app took place at the Royal Free Hospital in central London. The comparison hospital not receiving the app was Barnet Hospital, also part of the Royal Free London NHS Foundation Trust.

Both hospitals had similar processes before the introduction of the app, where laboratory teams would immediately alert medical teams if blood test results indicated AKI.

The Streams mobile app integrates with information previously gathered by the DeepMind system about AKI. It is then designed to process the patient's current clinical test results along with their past medical history and previous test results.

This information is then used to assess the likely level of kidney injury/failure. The specialist medical teams, including kidney specialists and resuscitation teams, would receive alerts through the app and then follow best-practice management protocols.

Exclusion criteria in this research included patients under the age of 18 or for those in critical care or with existing kidney disease.

Researchers compared outcomes at both hospitals before (May 2016 to January 2017) and after (May to September 2017) introduction of the app. At both hospitals there were around 1,700 incidents of AKI in the before phase, and around 800 after.

The main outcome of interest was recovery of kidney function, as measured by return of blood creatinine levels to normal. Creatinine is a waste product that is normally filtered out through the kidneys, so when the kidneys stop working, blood creatinine levels rise.

What were the basic results?

Introducing the app made no difference to kidney recovery rates for patients with AKI when they went to the hospital Accident and Emergency department (A&E) at Royal Free Hospital (odds ratio [OR] 1.03, 95% confidence interval [CI] 0.56 to 1.87). Neither was there any difference in kidney recovery between Royal Free and the comparison hospital Barnet.

The researchers did model there may have been a trend of improving recovery rates at Royal Free, but this effect was on the borderline of statistical significance (OR 1.04, 95% CI 1.00 to 1.08) so could be a chance finding.

Similarly there were signs the app may have reduced intensive care admissions at the Royal Free, but again this was on the threshold of statistical significance (OR 0.95, 95% CI 0.90 to 1.00).

After the introduction of the care pathway, the number of unrecognised AKI cases among patients in A&E reduced significantly from 12.4% to 3.3%. The time from A&E registration to AKI recognition in this group also reduced significantly. The median kidney recovery time for emergency patients at Royal Free was 2 days before the intervention and 3 days afterwards (no statistical difference), while at Barnet it was 2 days in both periods.

Other results included:

  • recognition of AKI improved from 87.6% to 96.7% for emergency cases
  • the average time from blood test results being available suggesting AKI to an in-application case review by a specialist was 11.5 minutes for emergency patients with AKI and 14 minutes for admitted patients. Previously it was not possible for specialists to review AKI cases arising across the hospital in real time and it could have taken several hours to identify

How did the researchers interpret the results?

The researchers conclude: "We successfully implemented a digitally enabled AKI care pathway and evaluated its impacts using interrupted time-series analysis."

They go on to say: "We demonstrate the need to consider the organisational as well as the technical aspects of digital interventions by coupling the alerting system to specific management pathways. However, we were unable to establish definitively whether early specialist input via the digitally enabled pathway improves outcome."


This is a valuable study that has explored the integration of digital technology with hospital information systems to try to enable faster recognition and management of acute kidney injury.

It found no clear evidence the app improved things. The researchers consider reasons why this may be, including the possibility that kidney injury may have typically occurred a considerable time before emergency admission, limiting the difference that detection on admission could have.

It's also important to be aware that both of these London hospitals already had lower mortality rates from AKI (15%) compared with the national average (18%). They also both have various improvement programmes in place, such as initiatives to improve management of sepsis and recognise patient deterioration.

The app could be expected to have minimal effect in hospitals where the detection and management of emergency conditions is already optimised. If the same app were introduced in other hospitals nationwide it could show more noticeable improvements.

There are some study limitations to note. As an observational study it cannot take account of all the factors that may be associated with any differences, such as patient characteristics. Also, as the researchers say, this was quite a short assessment period, and longer time periods may be needed to look at the effect.

There are plans to introduce the Streams app into another London hospital (Barnet Hospital), and the designers of the app have recently announced that they are exploring the possibility of using the technology to aid with the diagnosis of sepsis. So it will be interesting to see how the app performs in the future.

NHS Attribution