Searching for a serial killer on a hospital ward.

Abstract

The recent case of Lucy Letby in the UK shows that exposing a serial killer in healthcare is challenging. Statistical evidence has been used in the courts, but the overturning of the life sentence of a Dutch nurse in 2008 due to inadequate data analysis suggests the need for objective statistical approaches. In our case, a nurse was convicted after injecting an infant with insulin following a dispute at a family party and killing a patient with insulin in an institute for disabled persons. The nurse had previously worked in a geriatric unit for seven months, during which 29 deaths occurred, some with suspicious features. We applied statistical methods developed for the evaluation of diagnostic tests to analyze the nurse's possible involvement in these deaths. We searched for discrepancies in the death certificates and performed medico-legal autopsies on exhumed deceased patients. During the 7 months, 69 nurses and practical nurses at the geriatric ward completed 27,324 shifts. Of the deaths, 16/29 (55 %) occurred during night shifts. The convicted nurse had worked a clearly higher number of night shifts, in particular, than others. She was among the highest 10 % whose work shifts coincided with the deaths in terms of sensitivity, specificity, and likelihood ratios. Of the deaths, 11/29 (38 %) occurred during or immediately after her shift. Unexplained suspicious blood glucose fluctuations or hypoglycemia-mimicking comas unrelated to known illnesses were reported in 6 (55 %) of the 11 deaths, and in one of these cases, the incident was supported by autopsy findings. Statistical methods for diagnostic accuracy may allow the targeting of the investigation when searching for a possible serial killer in a hospital. However, a statistical approach does not allow an unambiguous determination of a perpetrator nor does it replace a thorough examination of the circumstances and cause of death when investigating a small number of suspicious deaths among patients with serious illnesses and high mortality.