title: Evaluation of an Algorithm to Predict Menstrual-Cycle Phase at the Time of Injury.
abstract: BACKGROUND
  Women are 2 to 8 times more likely to sustain an anterior cruciate ligament (ACL) injury than men, and previous studies indicated an increased risk for injury during the preovulatory phase of the menstrual cycle (MC). However, investigations of risk rely on retrospective classification of MC phase, and no tools for this have been validated.
  
  
  OBJECTIVE
  To evaluate the accuracy of an algorithm for retrospectively classifying MC phase at the time of a mock injury based on MC history and salivary progesterone (P4) concentration.
  
  
  METHODS
  Descriptive laboratory study.
  
  
  METHODS
  Research laboratory.
  
  
  METHODS
  Thirty-one healthy female collegiate athletes (age range, 18-24 years) provided serum or saliva (or both) samples at 8 visits over 1 complete MC.
  
  
  METHODS
  Self-reported MC information was obtained on a randomized date (1-45 days) after mock injury, which is the typical timeframe in which researchers have access to ACL-injured study participants. The MC phase was classified using the algorithm as applied in a stand-alone computational fashion and also by 4 clinical experts using the algorithm and additional subjective hormonal history information to help inform their decision. To assess algorithm accuracy, phase classifications were compared with the actual MC phase at the time of mock injury (ascertained using urinary luteinizing hormone tests and serial serum P4 samples). Clinical expert and computed classifications were compared using κ statistics.
  
  
  RESULTS
  Fourteen participants (45%) experienced anovulatory cycles. The algorithm correctly classified MC phase for 23 participants (74%): 22 (76%) of 29 who were preovulatory/anovulatory and 1 (50%) of 2 who were postovulatory. Agreement between expert and algorithm classifications ranged from 80.6% (κ = 0.50) to 93% (κ = 0.83). Classifications based on same-day saliva sample and optimal P4 threshold were the same as those based on MC history alone (87.1% correct). Algorithm accuracy varied during the MC but at no time were both sensitivity and specificity levels acceptable.
  
  
  CONCLUSIONS
  These findings raise concerns about the accuracy of previous retrospective MC-phase classification systems, particularly in a population with a high occurrence of anovulatory cycles.
authors.affiliation: Department of Rehabilitation and Movement Science, University of Vermont, Burlington;
authors.forename: Timothy W
authors.initials: TW
authors.lastname: Tourville
authors.affiliation: Department of Kinesiology, University of North Carolina at Greensboro;
authors.forename: Sandra J
authors.initials: SJ
authors.lastname: Shultz
authors.affiliation: Medical Biostatistics Unit, University of Vermont, Burlington;
authors.forename: Pamela M
authors.initials: PM
authors.lastname: Vacek
authors.affiliation: Dana Farber Cancer Institute, Boston, MA;
authors.forename: Emily J
authors.initials: EJ
authors.lastname: Knudsen
authors.affiliation: Department of Obstetrics, Gynecology and Reproductive Sciences, University of Vermont, Burlington;
authors.forename: Ira M
authors.initials: IM
authors.lastname: Bernstein
authors.affiliation: Department of Rehabilitation and Movement Science, University of Vermont, Burlington;
authors.forename: Kelly J
authors.initials: KJ
authors.lastname: Tourville
authors.affiliation: Department of Cell Biology and Biochemistry, Texas Tech University, Lubbock;
authors.forename: Daniel M
authors.initials: DM
authors.lastname: Hardy
authors.affiliation: Department of Obstetrics, Gynecology and Reproductive Sciences, University of Vermont, Burlington;
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Johnson
authors.affiliation: Department of Obstetrics, Gynecology and Reproductive Sciences, University of Vermont, Burlington;
authors.forename: James R
authors.initials: JR
authors.lastname: Slauterbeck
authors.affiliation: Department of Orthopaedics and Rehabilitation, University of Vermont, Burlington.
authors.forename: Bruce D
authors.initials: BD
authors.lastname: Beynnon
mesh_terms: D000293:Adolescent; D000465:Algorithms; D000070598:Anterior Cruciate Ligament Injuries; D056352:Athletes; D001265:Athletic Injuries / Q000209:etiology*; D005260:Female; D005498:Follicular Phase / Q000502:physiology; D006801:Humans; D007118:Immunoassay; D007718:Knee Injuries / Q000209:etiology; D007986:Luteinizing Hormone / Q000032:analysis; D008597:Menstrual Cycle / Q000502:physiology*; D011374:Progesterone / Q000032:analysis; D011897:Random Allocation; D012189:Retrospective Studies; D012463:Saliva / Q000737:chemistry; D055815:Young Adult
chemical_list: D011374:Progesterone; D007986:Luteinizing Hormone
keywords: anterior cruciate ligament injury; risk factors; validation