A Longitudinal Renal Health Outcome for Clinical Trials in Acute Kidney Injury?

Excellent. We did a fair amount of unpublished work studying creatinine trajectories in MIMIC in the past using what we called time series reciprocations. In our work, a reciprocation is defined as an annotated temporal pattern composed of four linked elements: 1. potential causal force, 2. perturbation, 3. potential recovery force, 4. recovery. This framework generated a structured set of relational intervals and slopes: * Pf → P (perturbation-force to perturbation interval), * Rf → R (recovery-force to recovery interval), * perturbation slopes (P), * and recovery slopes (R). We stopped the work because in those days AKI was a synthetic syndrome with a standardized consensus definition and there was little interest in TS modeling. AKI itself is a synthetic data generating process (SDGP). This is a synthetic threshold set which generates data but like sepsis and ARDS is not a biological entity but rather the data generation is derived from a heuristic grouping of different diseases (causal pathways) generated by the consensus threshold set. As such, a cause agnostic RCT of AKI will generate the third estimand as I have described previously. Happy to show you how we approached the study in a Zoom review of the TS patterns but we were in the discovery mode when we stopped. As far as an ordinal of multiple signal thresholds (a renal SOFA), I understand the desire for this but this is problematic. SOFA like SIRS is a SDGP and to my knowledge, neither has ever been useful in RCT since they were created in 1996 and 1987 respectively. The key is to identify and link the perturbation forces to the extent possible with the perturbations associated with renal dysfunction and the recovery forces and the recoveries of the perturbed signals. There is a need to study the relational time series patterns (RTP) of these reciprocations for each signal and the signal together. One problem with TS patterns is the application of statistical analysis. Our view was that given the complexity the TS patterns should be objectified and we accomplished this by 6 scales. The most important first step is to avoid creating and then studying a new synthetic data generating process (which is what AKI as defined by consensus thresholds, was).