13 research outputs found
Proceedings of the 4th Annual United States Army Institute of Surgical Research Summer Undergraduate Research Internship Program 2016
Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease
BACKGROUND:
Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes.
METHODS:
We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization.
RESULTS:
During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events.
CONCLUSIONS:
Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)
Prevalence of Anemia and Its Association with Frailty, Physical Function and Cognition in Community-Dwelling Older Adults: Findings from the HOPE Study
Experimental and numerical investigation on the dynamic response of pile group in liquefying ground
Pretreatment prediction of response to ursodeoxycholic acid in primary biliary cholangitis: development and validation of the UDCA Response Score
Background Treatment guidelines recommend a stepwise approach to primary biliary cholangitis: all patients begin
treatment with ursodeoxycholic acid (UDCA) monotherapy and those with an inadequate biochemical response after
12 months are subsequently considered for second-line therapies. However, as a result, patients at the highest risk
can wait the longest for effective treatment. We determined whether UDCA response can be accurately predicted
using pretreatment clinical parameters.
Methods We did logistic regression analysis of pretreatment variables in a discovery cohort of patients in the UK with
primary biliary cholangitis to derive the best-fitting model of UDCA response, defined as alkaline phosphatase less
than 1·67 times the upper limit of normal (ULN), measured after 12 months of treatment with UDCA. We validated
the model in an external cohort of patients with primary biliary cholangitis and treated with UDCA in Italy.
Additionally, we assessed correlations between model predictions and key histological features, such as biliary injury
and fibrosis, on liver biopsy samples.
Findings 2703 participants diagnosed with primary biliary cholangitis between Jan 1, 1998, and May 31, 2015, were
included in the UK-PBC cohort for derivation of the model. The following pretreatment parameters were associated with
lower probability of UDCA response: higher alkaline phosphatase concentration (p<0·0001), higher total bilirubin
concentration (p=0·0003), lower aminotransferase concentration (p=0·0012), younger age (p<0·0001), longer interval
from diagnosis to the start of UDCA treatment (treatment time lag, p<0·0001), and worsening of alkaline phosphatase
concentration from diagnosis (p<0·0001). Based on these variables, we derived a predictive score of UDCA response.
In the external validation cohort, 460 patients diagnosed with primary biliary cholangitis were treated with UDCA, with
follow-up data until May 31, 2016. In this validation cohort, the area under the receiver operating characteristic curve for
the score was 0·83 (95% CI 0·79–0·87). In 20 liver biopsy samples from patients with primary biliary cholangitis, the
UDCA response score was associated with ductular reaction (r=–0·556, p=0·0130) and intermediate hepatocytes
(probability of response was 0·90 if intermediate hepatocytes were absent vs 0·51 if present).
Interpretation We have derived and externally validated a model based on pretreatment variables that accurately
predicts UDCA response. Association with histological features provides face validity. This model provides a basis to
explore alternative approaches to treatment stratification in patients with primary biliary cholangitis
Pretreatment prediction of response to ursodeoxycholic acid in primary biliary cholangitis: development and validation of the UDCA Response Score
Background Treatment guidelines recommend a stepwise approach to primary biliary cholangitis: all patients begin
treatment with ursodeoxycholic acid (UDCA) monotherapy and those with an inadequate biochemical response after
12 months are subsequently considered for second-line therapies. However, as a result, patients at the highest risk
can wait the longest for effective treatment. We determined whether UDCA response can be accurately predicted
using pretreatment clinical parameters.
Methods We did logistic regression analysis of pretreatment variables in a discovery cohort of patients in the UK with
primary biliary cholangitis to derive the best-fitting model of UDCA response, defined as alkaline phosphatase less
than 1·67 times the upper limit of normal (ULN), measured after 12 months of treatment with UDCA. We validated
the model in an external cohort of patients with primary biliary cholangitis and treated with UDCA in Italy.
Additionally, we assessed correlations between model predictions and key histological features, such as biliary injury
and fibrosis, on liver biopsy samples.
Findings 2703 participants diagnosed with primary biliary cholangitis between Jan 1, 1998, and May 31, 2015, were
included in the UK-PBC cohort for derivation of the model. The following pretreatment parameters were associated with
lower probability of UDCA response: higher alkaline phosphatase concentration (p<0·0001), higher total bilirubin
concentration (p=0·0003), lower aminotransferase concentration (p=0·0012), younger age (p<0·0001), longer interval
from diagnosis to the start of UDCA treatment (treatment time lag, p<0·0001), and worsening of alkaline phosphatase
concentration from diagnosis (p<0·0001). Based on these variables, we derived a predictive score of UDCA response.
In the external validation cohort, 460 patients diagnosed with primary biliary cholangitis were treated with UDCA, with
follow-up data until May 31, 2016. In this validation cohort, the area under the receiver operating characteristic curve for
the score was 0·83 (95% CI 0·79–0·87). In 20 liver biopsy samples from patients with primary biliary cholangitis, the
UDCA response score was associated with ductular reaction (r=–0·556, p=0·0130) and intermediate hepatocytes
(probability of response was 0·90 if intermediate hepatocytes were absent vs 0·51 if present).
Interpretation We have derived and externally validated a model based on pretreatment variables that accurately
predicts UDCA response. Association with histological features provides face validity. This model provides a basis to
explore alternative approaches to treatment stratification in patients with primary biliary cholangitis
Pretreatment prediction of response to ursodeoxycholic acid in primary biliary cholangitis: development and validation of the UDCA Response Score
Background Treatment guidelines recommend a stepwise approach to primary biliary cholangitis: all patients begin
treatment with ursodeoxycholic acid (UDCA) monotherapy and those with an inadequate biochemical response after
12 months are subsequently considered for second-line therapies. However, as a result, patients at the highest risk
can wait the longest for effective treatment. We determined whether UDCA response can be accurately predicted
using pretreatment clinical parameters.
Methods We did logistic regression analysis of pretreatment variables in a discovery cohort of patients in the UK with
primary biliary cholangitis to derive the best-fitting model of UDCA response, defined as alkaline phosphatase less
than 1·67 times the upper limit of normal (ULN), measured after 12 months of treatment with UDCA. We validated
the model in an external cohort of patients with primary biliary cholangitis and treated with UDCA in Italy.
Additionally, we assessed correlations between model predictions and key histological features, such as biliary injury
and fibrosis, on liver biopsy samples.
Findings 2703 participants diagnosed with primary biliary cholangitis between Jan 1, 1998, and May 31, 2015, were
included in the UK-PBC cohort for derivation of the model. The following pretreatment parameters were associated with
lower probability of UDCA response: higher alkaline phosphatase concentration (p<0·0001), higher total bilirubin
concentration (p=0·0003), lower aminotransferase concentration (p=0·0012), younger age (p<0·0001), longer interval
from diagnosis to the start of UDCA treatment (treatment time lag, p<0·0001), and worsening of alkaline phosphatase
concentration from diagnosis (p<0·0001). Based on these variables, we derived a predictive score of UDCA response.
In the external validation cohort, 460 patients diagnosed with primary biliary cholangitis were treated with UDCA, with
follow-up data until May 31, 2016. In this validation cohort, the area under the receiver operating characteristic curve for
the score was 0·83 (95% CI 0·79–0·87). In 20 liver biopsy samples from patients with primary biliary cholangitis, the
UDCA response score was associated with ductular reaction (r=–0·556, p=0·0130) and intermediate hepatocytes
(probability of response was 0·90 if intermediate hepatocytes were absent vs 0·51 if present).
Interpretation We have derived and externally validated a model based on pretreatment variables that accurately
predicts UDCA response. Association with histological features provides face validity. This model provides a basis to
explore alternative approaches to treatment stratification in patients with primary biliary cholangitis
Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study (Intensive Care Medicine, (2021), 47, 2, (160-169), 10.1007/s00134-020-06234-9)
1832The original version of this article unfortunately contained a mistake. The members of the ESICM Trials Group Collaborators were not shown in the article but only in the ESM. The full list of collaborators is shown below. The original article has been corrected.openopenLabeau S.O.; Afonso E.; Benbenishty J.; Blackwood B.; Boulanger C.; Brett S.J.; Calvino-Gunther S.; Chaboyer W.; Coyer F.; Deschepper M.; Francois G.; Honore P.M.; Jankovic R.; Khanna A.K.; Llaurado-Serra M.; Lin F.; Rose L.; Rubulotta F.; Saager L.; Williams G.; Blot S.I.; Muzha D.; Ribas A.M.; Lipovesty F.; Loudet C.; Eller P.; Mostafa N.; Honore P.M.; Telleria V.M.; Smajic J.; Nogueira P.C.; Nafees K.M.K.; Hentchoya R.; Soledad J.; Cardenas Y.; Reyes A.G.; Sustic A.; Mpouzika M.; Vymazal T.; Jensen H.I.; Aguirre-Bermeo H.; Maddison L.; Valta M.; Bloos F.; Adipa F.E.; Koulouras V.; Enamorado J.; Agoston Z.; Birgisdottir H.; Gupta A.; Gurjar M.; Kilapong B.; Hashemian S.M.; Martin-Loeches I.; Cortegiani A.; Fletcher K.; Hayashi Y.; Waweru-Siika W.; Abidi K.; Lee S.-M.; Hadri B.; Dolgusevs M.; Abillama F.F.; Jovaisa T.; Thix C.; Elhadi M.; Nor B.M.; Ratnam S.; Mazlan M.Z.; Maiyalagan S.; Sanchez-Hurtado L.; Belii A.; Naranpurev M.; Gautam P.; 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Zakalik G.; Pagella G.; Baini M.; Campos P.A.; Sabbag I.; Schmukler A.; Fonseca I.P.; Alvarez G.M.; Ramirez M.; Tapia F.; Bascary C.A.; del Valle Gimenez G.; Bertoletti F.P.; Milioto E.; Bonsignore P.J.M.; Fernandez M.A.; Smith J.; Chimunda T.; Thompson L.; Maguire T.; Watts S.; Mitchell M.; Powell M.; Lye I.; Parsons L.; Baker N.; Reynolds C.; Thompson A.; Masters K.; Sosnowski K.; Morrison L.; Leslie G.D.; Lakshmanan R.; Tabah A.; Brown W.; McDowell-Skaines S.; McLucas A.; Smith C.; Tallot M.; Jones S.; Barakat-Johnson M.; Leong T.; Butcher R.; Martin K.; Douschan P.; von Lewinski D.; Eller P.; Schmutz R.; Kolussi U.; Salman F.; Ateya Z.; Mostafa N.; De Decker K.; Van Regenmortel N.; Jans A.; Wijnands P.; Coremans S.; De Bels D.; Depuydt T.; Paillet C.; Jacquet L.-M.; Swinnen W.; Hannes F.; Mergeay M.; Van de Velde S.; Allaert S.; Hoste P.; Borin C.; Balon S.; Fraipont V.; Biston P.; De Schryver N.; Dugernier T.; Van Cotthem I.; Telleria V.M.; Smajic J.; de Almeida A.O.; Jorge S.A.; Becker D.; Schmidt R.C.; Oliveira E.; Ramalho A.; Mazocoli E.; Fioretti A.; Barros E.; Serpa L.; Bianchini S.; Campanili T.; Pantaleao T.; Garcia P.C.; Ronchini A.L.V.; Santos R.; Nafees K.M.K.; Manap N.B.A.; Hentchoya R.; Bagshaw S.; Carney D.; Bagshaw S.; Davidow J.; Bagshaw S.; Rokosh E.; Bagshaw S.; Laizner A.M.; Smith S.; McQuirter M.; Kampayana B.S.; Favre R.; Sills M.; Laizner A.M.; Dallaire J.; Laizner A.M.; Becker C.; Microys S.; Bowes B.; Lajeunesse J.; Ghosh R.; Baptiste-Savoie J.; Raizman R.; Bagshaw S.; Suen G.; Taghavi N.; Smith O.; Fielding C.; Canales J.; Molina P.; Chaparro J.; Sepulveda M.I.; Zamorano M.J.F.; Rocha P.; Villanueva X.; Araya P.; Dayan M.; Avalos F.; Li X.; Liu Y.; Li X.; Chen X.; Jiang Z.; Yang J.; Chen J.; Yang L.; Wang K.; Gao J.; Fang X.; Zhao R.; Xia X.; Liu H.; Li J.; Wang H.; Meng G.; Di Y.; Wang D.; Zhao R.H.; Hu L.P.; Fang X.; Peipei X.; Jiao Q.F.; Wang H.Y.; Xia C.J.; Liu Y.; Ye M.; Wan Y.; Wang W.; Ding Y.; Ren A.; Gao Y.; Li Q.; Du G.; Yang J.; Shen Y.; Ding Y.; Li N.; Yuan C.; Li J.; Tan L.; Lin Q.; Guo H.; Yan H.; Xu X.; Zhang W.; Liang J.; Zhang L.; Tian E.; Zhao Q.; InSu L.; Dong J.; Gu Y.; Liu Y.; Zhao L.; Wang W.; Qiao H.; Tuo L.; Lv M.; Zhu J.Y.; Zhu J.F.; Wei Y.; Liu M.; He Y.; Cheng J.; Liu J.; Jia N.; Wei D.; Li Q.; Wu X.; Duan H.; Lin D.; Liang Q.; Luo X.; Xiong Y.; Huang R.F.; Fu J.; Zan T.; Ye M.; Shi Z.; Long Y.; Lei Y.; Liu X.; Yumei C.; Wang L.L.; Zhang Y.; Xu Y.; Cheng; Zhijuan W.; Sun C.; Song J.H.; Wang Y.; Liu X.M.; Liu Y.; Yuan Y.; Huang Q.; Yang F.; Wu Y.; Luo X.; Bai X.; Zheng H.; Song M.; Sun Y.; Li Z.S.; Luo F.; Liu M.; Li L.C.; Li X.; Zhang G.; Xiao L.; Yu T.; Gao G.; Wei W.; Wang F.; Han T.; Li T.; Zeng Q.; Zeng J.M.; Long Y.; Pan F.; Wang J.; He G.; Chen H.; Zhang F.; Chao Y.; Chunhua G.; Yao X.; Bai D.; Liu L.; Xu X.; Wang Y.; Liang X.; Zhang N.; Li X.; Zhang A.; Chen X.; Hu X.C.; Zhang H.; Wang R.X.; Tak P.S.; Ho S.W.; Jiang Q.X.; Ding X.; Hong L.; Miao L.; Feng Z.; Huang L.P.; Wu J.; Wang Y.; Guo J.; Zhang B.; Ma C.; Han Y.; Liu C.; Ding M.; Luan L.; Zheng J.; Lv S.; Jiang S.; Cao W.; Xue X.; Li J.; Liu G.; Wang J.; Wei X.; Zhang W.; Jiang Y.; Yao Z.; Gao L.; Li J.; Zhao W.; Jiang M.; Hao J.; Zhang J.; Song C.; Chen F.; Wang S.; Hu L.; Cao D.; Liu Y.; Wan J.; Wang X.; Shao H.; Zhang Z.; Cui X.; Liu J.; Zhao L.; Li X.; Fan L.; Zhang L.; Yu M.; Li B.; Li C.; Liu L.; Liu X.L.; Chen W.; Li Y.; Zhigang Z.; Yuchen W.; Mu C.; Zhu G.; Yang F.; Bo Q.; Li L.; Chen M.; Jiang J.H.; Yin H.; Pang X.; Gong Y.Y.; Yang S.; Yan X.; Zheng X.; Lei D.H.; Lei L.; Guo Y.; Liu L.; Yu J.; Sun W.; Bi A.P.; Li W.; Wu Y.; Li J.; Ni D.; Li X.; Liu Y.; Wu Z.; Song B.; Chen J.; Fei Q.; Xiaoyan Y.; Ran Q.; Xixi L.; Jiao X.; Ji H.; Zhiping S.; Hong M.; Jianhong M.; Hao Y.; Yin L.; Wang Y.; Hui C.; Ju W.; Xia X.; Huo Y.; Wang Y.; Chen L.; Yan Y.; Zhao Q.; Chen H.; Bao G.; Cao Y.; Hong L.; Zhang H.; Zhang Y.; Xu L.; Guixiang J.; Li Y.; Zhao H.M.; Huang X.; Dai Z.; Jian Y.; Zhang H.; Tian Z.; Cao Z.Q.; Li M.; Liu Y.; Ouyang F.; Ma F.; Jin W.; Ge L.; Wu S.F.; Li J.; Yuan W.; Chen T.; Shi G.; Chen Z.; Liu K.; Lin X.; Yuemen L.; Lijuan S.; Tian X.F.; Wang S.; Feng Z.X.; Liu X.Z.; Dong Y.; Zhang J.; Bocui N.; Jiang Z.X.; Yang J.; Wang G.X.; Zhao Y.; Wu X.; Yang Q.; Hu R.L.; Li X.Q.; Yu Z.J.; Yao Y.; Deng X.; Xiao Y.; Xie Y.; Yang Y.; Yang H.; Zhou Y.; Li Z.; Xiao M.; Yang Y.X.; Tian Y.; Gama L.M.S.; Hernandez J.S.; Cardenas Y.; Caicedo N.; Marin J.; Ochoa M.-E.; Gomez M.; Rojas-Suarez J.; Gonzalez J.; Reyes A.J.G.; Chapeta E.; Orozco E.; Filipovic-Grcic I.; Vukovic A.; Pecenkovic S.; Suput A.; Sustic A.; Zivanovic-Posilovic G.; Bozena A.; Udiljak N.; Milic M.; Radivojevic R.C.; Mihaljevic S.; Matas M.; Tonkovic D.; Culjak H.; Herceg I.; Pavlisa G.; Dobric M.; Beker T.; Adam V.N.; Goranovic T.; Markoulias C.; Mathaios M.; Mylordou M.; Achilleos E.; Kleanthous P.; Kotanidi V.; Foka M.; Charalabous I.; Alexandrou A.; Georgiou M.; Patsalos A.; Zepoy S.; Constantinou C.; Piza P.; Vymazal T.; Wiborg E.; Bruhn L.; Kaasby K.; Pedersen K.R.; Mikkelsen S.; Collet M.; Langvad A.; Andresen H.; Fischer S.; Kjaergard I.E.; Jepsen B.; Husted B.; Bestle M.; Kodal A.M.; Hansen T.C.B.; Pedersen A.S.B.; Thomsen T.D.; Hoegenhaven A.; From M.; Frandsen T.M.; Henning G.; Hansen A.; Jensen H.I.; Bliksted I.A.; Tamayo L.M.; Mogrovejo P.; Aguirre-Bermeo H.; Palaez C.; Tutillo D.R.M.; Hurtado C.V.; Garcia M.F.; Alvarez D.; Guerrero F.; Vasquez A.; Kutimets M.; Tamme K.; Maddison L.; Anvelt E.; Dlamini-Sserumaga L.; Lofqvist C.; Lusenius V.; Kauppi O.; Sakki J.-K.; Tervo-Heikkinen T.; Kesti U.; Merilainen M.; Karjula E.; Peltomaa M.; Palmu A.; Ahtiala M.; Valta M.A.; Mentec H.; Plantefeve G.; Besch G.; Pili-Floury S.; Ledochowski S.; Deserts M.D.; Giacardi C.; Daubin C.; Massard A.; Le Guen Y.; Blanc A.; Mandaroux S.; Gunther S.C.; Avogadro P.; Radavidson A.; Turc J.; Jochmans S.; Quintard H.; Boyer L.; Bruel C.; Philippart F.; Montravers P.; Atchade E.; Flessel N.; Chinardet B.; Soulisse L.; Pillard C.; Ngo D.; Bongiorno B.; Heitzler N.; Souppart V.; Gautheret N.; Timsit J.-F.; Essardy F.; Fartoukh M.; Mehay D.; Etourneau F.; Farkas J.-C.; Beuret P.; Preda G.; De Montmollin E.; Castelain V.; Jaschinski U.; Rothenfusser M.; Kindgen-Milles D.; Dimski T.; Fiedler C.; Heinicke T.; Meybohm P.; Schulze T.; Bota M.; Pelz S.; Odenthal T.; Christ M.; Bloos F.; Bosl K.; Chovas A.; Stehr S.; Simon P.; Grotheer S.; Schuppel S.; Schaller S.; Albrecht L.; Stubner A.; Graeser S.; Kolbe N.; Lausch M.; Diers A.; Guenther U.; Riessen R.; Roller M.; Osei I.P.; Kusi-Appiah A.-C.; Yakubu Y.H.; Guadi-Gosh B.; Dragoumanis C.; Christofis C.; Kazakos N.; Bastani S.; Martinos C.; Bekos V.; Papanikolaou M.; Papavasilopoulou T.; Efthymiou A.; Chantziara V.; Kyriakoudi A.; Kakaras N.; Diakaki C.; Flevari A.; Nikolaou C.; Katerina K.; Avramopoulou L.; Tsikritsaki K.; Gkiokas G.; Pantiora E.; Katsenos C.; Patsiou E.-C.; Alexandropoulou P.; Koutsodimitropoulos I.; Farmakis E.; Nestora K.; Chatzis M.; Kondili E.; Soundoulounaki S.; Mousafiri O.; Lepida D.; Liarmakopoulou A.; Koulouras V.; Papathanakos G.; Oikonomou M.; Ioannides P.; Papadopoulos D.; Staikos I.; Stafylaraki M.; Raitsiou B.; Mandis K.; Ravani I.; Kourelea S.; Efthimiou A.; Thoma G.; Bakas A.; Psarulis K.; Anisoglou S.; Papageorgiou E.; Michailidou E.; Tholioti T.; Lavrentieva A.; Sourla E.; Spyropoulou A.; Pantelas N.; Stalika K.M.M.; Georgakas I.; Karathanou A.; Tsikriki S.; Dimoula A.; Kanakaki S.; Vakalos A.; Pagioulas K.; Enamorado J.E.; Nardai G.; Hawchar F.; Blondal A.; Rygvadottir B.; Jonasdottir R.J.; Birgisdottir H.; Shah B.; Kaushik S.; Tripathy S.; Singh M.; Agarwal S.; Gupta M.; Ahmad M.; Mangal K.; Bhargava V.; Kushare V.; Jha S.; Bhakhtiani L.; Gupta A.; Kamal M.; Gurjar M.; Baronia A.; Kilapong B.; Susanti A.; Lestari M.I.; Zulkifli Z.; Baskoro W.; Zand F.; Zarei F.; Mahmoodpoor A.; Heidari F.; Jafaraghaee F.; O'Shea A.; O'Shea F.; O'Donnell C.; Craig G.; Fitzpatrick G.; Dunne L.; Hastings J.; Marsh B.; Cody C.; Campbell E.; Doyle D.; Pacturanan M.; 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Pretreatment prediction of response to ursodeoxycholic acid in primary biliary cholangitis: development and validation of the UDCA Response Score
Background: Treatment guidelines recommend a stepwise approach to primary biliary cholangitis: all patients begin treatment with ursodeoxycholic acid (UDCA) monotherapy and those with an inadequate biochemical response after 12 months are subsequently considered for second-line therapies. However, as a result, patients at the highest risk can wait the longest for effective treatment. We determined whether UDCA response can be accurately predicted using pretreatment clinical parameters. Methods: We did logistic regression analysis of pretreatment variables in a discovery cohort of patients in the UK with primary biliary cholangitis to derive the best-fitting model of UDCA response, defined as alkaline phosphatase less than 1·67 times the upper limit of normal (ULN), measured after 12 months of treatment with UDCA. We validated the model in an external cohort of patients with primary biliary cholangitis and treated with UDCA in Italy. Additionally, we assessed correlations between model predictions and key histological features, such as biliary injury and fibrosis, on liver biopsy samples. Findings: 2703 participants diagnosed with primary biliary cholangitis between Jan 1, 1998, and May 31, 2015, were included in the UK-PBC cohort for derivation of the model. The following pretreatment parameters were associated with lower probability of UDCA response: higher alkaline phosphatase concentration (p<0·0001), higher total bilirubin concentration (p=0·0003), lower aminotransferase concentration (p=0·0012), younger age (p<0·0001), longer interval from diagnosis to the start of UDCA treatment (treatment time lag, p<0·0001), and worsening of alkaline phosphatase concentration from diagnosis (p<0·0001). Based on these variables, we derived a predictive score of UDCA response. In the external validation cohort, 460 patients diagnosed with primary biliary cholangitis were treated with UDCA, with follow-up data until May 31, 2016. In this validation cohort, the area under the receiver operating characteristic curve for the score was 0·83 (95% CI 0·79–0·87). In 20 liver biopsy samples from patients with primary biliary cholangitis, the UDCA response score was associated with ductular reaction (r=–0·556, p=0·0130) and intermediate hepatocytes (probability of response was 0·90 if intermediate hepatocytes were absent vs 0·51 if present). Interpretation: We have derived and externally validated a model based on pretreatment variables that accurately predicts UDCA response. Association with histological features provides face validity. This model provides a basis to explore alternative approaches to treatment stratification in patients with primary biliary cholangitis. Funding: UK Medical Research Council and University of Milan-Bicocca