N C Pocovi, P Kent, C-W C Lin, S D French, T F de Campos, T da Silva, M J Hancock
Recurrence of low back pain: A difficult outcome to predict. Development and validation of a multivariable prediction model for recurrence in patients recently recovered from an episode of non-specific low back pain
Musculoskeletal science & practice
acute leukaemias lifethreatening haematological cancers characterised infiltration transformed immature haematopoietic cells blood bone marrow prompt accurate diagnosis three main acute leukaemia subtypes ie acute lymphocytic leukaemia acute myeloid leukaemia aml acute promyelocytic leukaemia apl utmost importance guide initial treatment prevent early mortality requires cytological expertise always available aimed benchmark different machinelearning strategies using custom variable selection algorithm propose extreme gradient boosting model predict leukaemia subtypes basis routine laboratory parameters multicentre model development validation study conducted data six independent french university hospital databases patients aged 18 years older diagnosed aml apl one six hospital databases march 1 2012 dec 31 2021 recruited 22 routine parameters collected time initial disease evaluation variables 25 missing values two datasets used model training leading final inclusion 19 parameters performances final model evaluated internal testing external validation sets area receiver operating characteristic curves aucs clinically relevant cutoffs chosen guide clinical decision making final tool artificial intelligence prediction acute leukemia aipal developed model 1410 patients diagnosed aml apl included data quality control showed missing values cohort exception uric acid lactate dehydrogenase cohort hpital cochin 679 patients hpital lyon sud centre hospitalier universitaire de clermontferrand split training n477 internal testing n202 sets 731 patients four cohorts used external validation overall aucs across validation cohorts 097 95 ci 095099 apl 090 083097 089 082095 aml cutoffs established overall cohort 1410 patients guide clinical decisions confident cutoffs showed two 014 wrong predictions four 028 wrong predictions apl three 021 wrong predictions aml use overall cutoff greatly reduced number missing predictions diagnosis proposed 1375 975 1410 patients category slight increase wrong predictions final model evaluation across internal testing external validation sets showed accuracy 995 diagnosis 988 aml diagnosis 997 apl diagnosis confident model accuracy 879 diagnosis 863 aml diagnosis 961 apl diagnosis overall model aipal allowed accurate diagnosis three main acute leukaemia subtypes based ten simple laboratory parameters broad availability could help guide initial therapies context cytological expertise lacking lowincome countries none