BACKGROUND: Manual analysis of data acquired from manometric studies of colonic motility is laborious, subject to laboratory bias and not specific enough to differentiate all patients from control subjects. Utilizing a cross-correlation technique, we have developed an automated analysis technique that can reliably differentiate the motor patterns of patients with slow transit constipation (STC) from those recorded in healthy controls. METHODS: Pancolonic manometric data were recorded from 17 patients with STC and 14 healthy controls. The automated analysis involved calculation of an indicator value derived from cross-correlations calculated between adjacent recording sites in a manometric trace. The automated technique was conducted on blinded real data sets (observed) and then to determine the likelihood of positive indicator values occurring by chance, the channel number within each individual data set were randomized (expected) and reanalyzed. KEY RESULTS: In controls, the observed indicator value (3.2 ± 1.4) was significantly greater than that predicted by chance (0.8 ± 1.5; P < 0.0001). In patients, the observed indicator value (-2.7 ± 1.8) did not differ from that predicted by chance (-3.5 ± 1.6; P = 0.1). The indicator value for controls differed significantly from that of patients (P < 0.0001), with all individual patients falling outside of the range of indicator values for controls. CONCLUSIONS & INFERENCES: Automated analysis of colonic manometry data using cross-correlation separated all patients from controls. This automated technique indicates that the contractile motor patterns in STC patients differ from those recorded in healthy controls. The analytical technique may represent a means for defining subtypes of constipation.
BACKGROUND: Manual analysis of data acquired from manometric studies of colonic motility is laborious, subject to laboratory bias and not specific enough to differentiate all patients from control subjects. Utilizing a cross-correlation technique, we have developed an automated analysis technique that can reliably differentiate the motor patterns of patients with slow transit constipation (STC) from those recorded in healthy controls. METHODS: Pancolonic manometric data were recorded from 17 patients with STC and 14 healthy controls. The automated analysis involved calculation of an indicator value derived from cross-correlations calculated between adjacent recording sites in a manometric trace. The automated technique was conducted on blinded real data sets (observed) and then to determine the likelihood of positive indicator values occurring by chance, the channel number within each individual data set were randomized (expected) and reanalyzed. KEY RESULTS: In controls, the observed indicator value (3.2 ± 1.4) was significantly greater than that predicted by chance (0.8 ± 1.5; P < 0.0001). In patients, the observed indicator value (-2.7 ± 1.8) did not differ from that predicted by chance (-3.5 ± 1.6; P = 0.1). The indicator value for controls differed significantly from that of patients (P < 0.0001), with all individual patients falling outside of the range of indicator values for controls. CONCLUSIONS & INFERENCES: Automated analysis of colonic manometry data using cross-correlation separated all patients from controls. This automated technique indicates that the contractile motor patterns in STC patients differ from those recorded in healthy controls. The analytical technique may represent a means for defining subtypes of constipation.
Authors: P G Dinning; L Wiklendt; L Maslen; I Gibbins; V Patton; J W Arkwright; D Z Lubowski; G O'Grady; P A Bampton; S J Brookes; M Costa Journal: Neurogastroenterol Motil Date: 2014-08-11 Impact factor: 3.598
Authors: Ji-Hong Chen; Sean P Parsons; Mitra Shokrollahi; Andrew Wan; Alexander D Vincent; Yuhong Yuan; Maham Pervez; Wu Lan Chen; Mai Xue; Kailai K Zhang; Arshia Eshtiaghi; David Armstrong; Premsyl Bercik; Paul Moayyedi; Eric Greenwald; Elyanne M Ratcliffe; Jan D Huizinga Journal: Front Physiol Date: 2018-09-20 Impact factor: 4.566