Reply to Letter to Editor Septic patients with cancer: Do prehospital antibiotics improve survival? Do not forget the underlying status influence!

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Dear editor,

We’ve read with interest the letter to the editor by Jouffroy et al. We would like to thank the authors for their valuable input and hereby our reaction to their comments.
Firstly, the authors incorrectly state that we did not include data regarding the resuscitation status for the treatment allocation groups (usual or intervention groups). This data was reported, as can be found in supplementary table 3 of our publication.¹ In this table, we showed that 19 (21.6%) of the patients with cancer in the usual care group and 36 (27.9%) of the patients in the intervention group had a ‘do not resuscitate’ policy.
Jouffroy et al. mention that the consequences of differences in resuscitation status might explain the fact that there was no difference in survival rate in both the usual care group and intervention group. However, a resuscitation status is always dynamic and dependent on the condition of the patient, and as we have clearly described in supplementary table 3, this was the resuscitation status at admission of the patient. We do not have data on how this changed during the course of the stay of the patients. Therefore, it is impossible to objectively state whether this influenced the mortality rates.
Secondly, the authors state that we did not exactly describe how confounders in our multivariate logistic regression analysis were detected. Although we did indeed not describe this, we used the widely accepted technique, where a variable is considered a confounder when it changes the odds ratio of the outcome by 10% or more.^2,3 All variables that had this effect on the outcome were included in our multivariate logistic regression.
We completely agree with the authors that early identification of patients with sepsis in cancer patients remains a challenge. We believe that a data-driven approach to sepsis, using predictive modelling, could play an important role in this regard in the near future.⁴ The emerging use of wearables to monitor key parameters at home will facilitate these algorithms and hopefully address these difficulties in triage, as described by the authors, to a great extent.⁵


DISCLOSURES

All authors declare no conflicts of interest. No funding or financial support was received.


REFERENCES

1. Nannan Panday RS, Wang S, Schermer EH, Cooksley T, Alam N, Nanayakkara PWB. Septic patients with cancer: Do prehospital antibiotics improve survival? A sub-analysis of the PHANTASi trial. Neth J Med. 2020;78(1):3-9.
2. Hernan MA, Hernandez-Diaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002;155(2):176-84.
3. Lee PH. Is a cutoff of 10% appropriate for the change-in-estimate criterion of confounder identification? J Epidemiol. 2014;24(2):161-7.
4. Schinkel M, Paranjape K, Nannan Panday RS, Skyttberg N, Nanayakkara PWB. Clinical applications of artificial intelligence in sepsis: A narrative review. Comput Biol Med. 2019;115:103488.
5. Nannan Panday RS, Subbe CP, van Galen LS, et al. Changes in vital signs post discharge as a potential target for intervention to avoid readmission. Acute Med. 2018;17(2):77-82.