U.S. Sudden Kidney Failure Involved Deaths Over a 26.5-Year Span

AKI Signal 1 and Signal 2 explained quickly

Jan 03, 2026

Happy New Year 2026.

I am going to try to put content out on Substack more frequently.

Here is yet another Acute Kidney Injury (AKI) article, though very short.

There is no question that there is a major issue with AKI and that 255,000 excess Americans lost their lives involving AKI.

I devised a new system for some research I’m working on and this will allow me to test across more variables over a longer period of time. This data is derived from CDC Wonder.

Figure 1 depicts January 1999 through June 2025 for all United States deaths involving ICD-10 code prefix, “N17,” meaning “Acute renal failure,” better known in hospital settings as “Acute Kidney Injury,” or AKI.

The top graph is raw monthly data plotted. Monthly data is itself a way to curve smooth daily death data. Detail is lost as more data is aggregated. Daily would be the best we could hope for. However, it would be interesting to get hourly or to-the-minute data. I bet we could find patterns of what time of day people in different age groups die.

Notice that the 3-month and 7-month rolling average graphs, second and third from the top, smoothed the sharp edges of raw data and are beginning to look like sine waves. From 1999 through 2019, the curves are very deterministic. There are 20 years of deterministic and stable signal until 2020, the first year of Covid.

P (period) for the waves is exactly 12 months. When the curve smoothing range is greater than P, the seasonality is lost. See the fourth graph from the top, or last graph. Notice the sharp increase in 2020 and 2021.

I did not label years because I want the reader to concentrate on disambiguating signals in these graphs. Signal 2 is the sharp rise in the fourth graph, 13-month rolling average, continuing to the end of the graph.

Signal 1 is almost gone in the 13-month rolling average, or I should say that it is not noticeable as to what it really is. The area under the graph still is the same in each plot, but the shape is muted. The area under the graphs is the total number of people who died.

In the raw data plot at the top, you see two very sharp peaks. That is Signal 1.

A graphical representation should clear up any confusion.

Figure 2 explains it all.

There are at least two signals. Signal 1 is coincident with waves of Covid, but not the first wave in many locations. Signal 2 is coincident with the uptake and vaccination status of the population over time. The two combine for an excess greater than 255,000 dead Americans.

Regardless of my assessment or attribution, the timing of these signals do not align with Covid as the cause. A disease cannot select which wave it will then induce harm into kidneys. Covid did not decide to skip over age groups and locations for the first season only to kill them en masse in the second or third season. There are other causes of AKI excess besides Covid per se.

Conclusion

While research papers assert that Covid is the cause, the data in this graph set alone is not in line with that excuse. I have many more graphs of age groups and geographically disparate states. Although I think I know what caused this epidemic of AKI, the conclusion here is not to state what caused it. The conclusions are that 1) there are indeed 255,000 excess deaths involving AKI and that 2) the CDC, FDA, CMS, NIH, and HHS have not done anything to investigate this after 3.5 years of pointing this out to them. They have shown a criminal deliberate indifference to the plight of the American people. More than a quarter of a million people are dead and probably millions injured involving this single cause of death, proven by the CDC’s own CDC Wonder data. Yet, there is no action from these agencies whose missions directly align with investigating this mass casualty event. Where is Robert F. Kennedy, Jr. on AKI? Has any big podcaster or voice in the health freedom movement asked him to look into it? Or are they all too busy talking about myocarditis in young, male athletes?

God bless you all.