Animal models of chemotherapy-induced peripheral neuropathy: A machine-assisted systematic review and meta-analysis.
Gillian L Currie | Helena N Angel-Scott | Lesley Colvin | Fala Cramond | Kaitlyn Hair | Laila Khandoker | Jing Liao | Malcolm Macleod | Sarah K McCann | Rosie Morland | Nicki Sherratt | Robert Stewart | Ezgi Tanriver-Ayder | James Thomas | Qianying Wang | Rachel Wodarski | Ran Xiong | Andrew S C Rice | Emily S Sena
We report a systematic review and meta-analysis of research using animal models of chemotherapy-induced peripheral neuropathy (CIPN). We systematically searched 5 online databases in September 2012 and updated the search in November 2015 using machine learning and text mining to reduce the screening for inclusion workload and improve accuracy. For each comparison, we calculated a standardised mean difference (SMD) effect size, and then combined effects in a random-effects meta-analysis. We assessed the impact of study design factors and reporting of measures to reduce risks of bias. We present power analyses for the most frequently reported behavioural tests; 337 publications were included. Most studies (84%) used male animals only. The most frequently reported outcome measure was evoked limb withdrawal in response to mechanical monofilaments. There was modest reporting of measures to reduce risks of bias. The number of animals required to obtain 80% power with a significance level of 0.05 varied substantially across behavioural tests. In this comprehensive summary of the use of animal models of CIPN, we have identified areas in which the value of preclinical CIPN studies might be increased. Using both sexes of animals in the modelling of CIPN, ensuring that outcome measures align with those most relevant in the clinic, and the animal's pain contextualised ethology will likely improve external validity. Measures to reduce risk of bias should be employed to increase the internal validity of studies. Different outcome measures have different statistical power, and this can refine our approaches in the modelling of CIPN.Show more [+] Less [-]