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I have undertaken my analysis with data for 79,949 patients across three NHS trust hospitals. These results represent the largest analysis to date that explores the association of Ur:Cr with in-hospital outcome for a large series of unselected emergency admissions to secondary care. In addition, I examined the value of assessing the dynamic change in Ur:Cr and the importance of its association with patient outcome.

I found that an admission Ur:Cr ratio of >80 was present in nearly half of all patients (consistent with previous smaller studies58_{), and Ur:Cr rose to >80 during hospital stay in over a quarter of those with}

a normal Ur:Cr on admission. A high Ur:Cr ratio status was strongly associated with increased LoS. Though I cannot attribute rises in Ur:Cr solely to dehydration, such data are consistent with the doubling of LoS in acute coronary syndrome patients suffering dehydration (7.8 vs 3 days), and the six- fold and tenfold rise in risk of AKI and cardiogenic shock respectively in such patients.38

AKI affected 10.7% of inpatients, matching recently published data130_{, and this figure was similar to}

that suggested by a recent meta-analysis.78 _{Likewise, my data confirmed the high prevalence of an}

elevated Ur:Cr amongst those who develop AKI, as reported by others53_{, and its association with risk}

of dying with AKI. In contrast to previous work53,130_{, I have explored the impact of initial ratio status}

and its change with mortality in AKI patients, demonstrating a wide range in risk of death.

Higher Ur:Cr on admission was non-linearly associated with in-hospital mortality, and change in Ur:Cr strongly correlated with both LoS and mortality via a complex relationship, further affected by age. The contributions of the individual components of the ratio also had a dramatic impact on the association of the ratio with mortality.

Such complex interactions suggest the need for non-linear models for the prediction of poor outcomes. The ML-Dehydration model, when used in a real-world scenario (sequential blood tests, not knowing whether it is to be the last), identifies 50% (sensitivity) of patients who will subsequently die, from only their second blood test onwards. The positive predictive value of 20% means that if the model classifies the patient as being likely to die, there is a 1:5 chance that this will subsequently occur.

Reductions in Ur:Cr, when the ratio at admission was >80, probably reflect effective intervention, though I am unable to describe the granularity of such interventions. It is likely that treatment of dehydration and sepsis (alone or in combination) might play a dominant role in generating this

reduction. Fluid therapy is an essential part of most sepsis bundles, improving relative or actual intravascular fluid depletion, and thus outcome.131,132,133, 134,135

Similarly, new or persistent rises in the Ur:Cr ratio increase relative risk of death (from 6.4 to 47.9), and may represent opportunities to improve patient management. Use of the Ur:Cr ratio and its trajectory in order to highlight patients at risk may provide guidance for interventions. Such interventions, proposed in the UK’s National Institute for Health and Care Excellence guidelines on AKI78 _{and on prescribing intravenous fluids}6_{, include automated alerts to clinicians, fluid}

administration, and appropriate training in the assessment of patients’ fluid and electrolyte needs.

This specific investigation has particular strengths. It is the largest study, in terms of both number of patients and number of blood tests analysed, to have examined the following in hospitalised patients: 1) Ur:Cr, both its absolute value and its change; and 2) AKI, using quantitative biochemical definitions. It is also the first study that examines the relationship between Ur:Cr on admission and its change during hospitalisation, with outcome. The use of minimal exclusion criteria increases the generalisability of my findings. The use of data from three hospital groups, over approximately the same time period, thus reducing biases associated with single-site studies. Finally, the study is topical and relevant, as the impact of dehydration on outcome, and the search for measures to mitigate this, has become a concern for England’s Care Quality Commission (CQC)69_{, the Patient Association}70_{, and}

The British Parliamentary Ombudsman71_{, with such concerns being echoed in an independent inquiry}72

and in the media.73,74,75,76,77

Nonetheless, this study does have limitations. The data was collected from hospitals which when compared to the national average had higher quality ratings (CQC and standardised mortality ratio) than the England average; this could introduce bias into the results. However, this bias, if it exists, would result in an underreporting of the problem of dehydration lending further support to the unreported scale of the problem, and thus the potential for improvement of patient care. It has not explored any excessive deaths amongst those who had a high Ur:Cr on admission, and who failed to survive to receive a second blood test. Nor has it examined the impact of those discharged prior to a second test. In identifying cases of AKI, baseline creatinine was defined as the earliest blood test performed within 24 hours of hospital admission; however, since these analyses were carried out, a National NHS England algorithm (NHSE-algorithm) definition of baseline creatinine has been mandated, which utilises creatinine blood results obtained up to one year prior to admission. This new NHSE-algorithm definition of AKI was not used. Therefore, a proportion of patients who presented to hospital with new AKI (based on community creatinine values) were not classified as such, nor were those who had a low out-of-hospital creatinine value. However, these limitations led to a conservative

estimate of AKI prevalence in my analyses. Data on urine output as part of the KDIGO classification were also unavailable. Further, I did not investigate interactions with co-morbid diagnoses, as the data linkage across all co-morbidities was beyond the scope of this initial investigation. Most of these limitations can be addressed in a subsequent study.

I have demonstrated that the use of two widely available blood tests (urea and creatinine), combined with two demographic variables (age and sex), when analysed in a binary or continuous manner, can be operationalised as a powerful predictor of outcomes. These variables interact with each other in a complex fashion in determining such outcomes. Reductions in the Ur:Cr ratio, when elevated, are associated with improved outcome, and rises may indicate patient management that can be improved with targeted interventions.

Dehydration, is a condition that is easy to communicate and in the majority of cases relatively simple to treat. Building care pathways that continuously track hydration status in patients, and have interventions to maintain hydration, would be simple and require relatively few resources. Such interventions could include encouragement to eat and drink more, along with nursing support to enable patients to do this, as well as robust implementation of existing guidelines on intravenous fluid administration.

Finally, from a practical viewpoint, my research demonstrated that manipulating and analysing large datasets with multiple non-numerical categorical values was extremely memory-intensive and cumbersome in MATLAB alone, despite Mathworks (the maker of MATLAB) having recently added database feature- ‘Data Tables’. Therefore, for all further data management and analysis, I made the decision to completely switch from MATLAB to a dedicated database (Microsoft SQLServer), and to an alternative analytic suite, consisting of R and h2o.