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Kabatoro D, Kabongo A, Kabongo K, Kabongo L, Kabongo M, Kady N, Kafu S, Kaggya M, Kaholongo B, Kairuki P, Kakololo S, Kakudji K, Kalisa A, Kalisa R, Kalufwelu M, Kalume S, Kamanda R, Kangili M, Kanoun H, Kapesa, Kapp P, Karanja J, Karar M, Kariuki K, Kaseke K, Kashuupulwa P, Kasongo K, Kassa S, Kateregga G, Kathrada M, Katompwa P, Katsukunya L, Kavuma K, Khalfallah, Khamajeet A, Khetrish S, Kibandwa, Kibochi W, Kilembe A, Kintu A, Kipng’etich B, Kiprop B, Kissoon V, Kisten TK, Kiwanuka J, Kluyts HL, Knox M, Koledale A, Koller V, Kolotsi M, Kongolo M, Konwuoh N, Koperski W, Koraz M, Kornilov A, Koto MZ, Kransingh S, Krick D, Kruger S, Kruse C, Kuhn W, Kuhn W, Kukembila A, Kule K, Kumar M, Kusel BS, Kusweje V, Kuteesa K, Kutor Y, Labib M, Laksari M, Lanos F, Lawal T, Le Manach Y, Lee C, Lekoloane R, Lelo S, Lerutla B, Lerutla M, Levin A, Likongo T, Limbajee M, Linyama D, Lionnet C, Liwani M, Loots E, Lopez AG, Lubamba C, Lumbala K, Lumbamba A, Lumona J, Lushima R, Luthuli L, Luweesi H, 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T, Mbuyi W, Mbuyisa M, Mbwele B, Mehyaoui R, Menkiti I, Mesarieki L, Metali A, Mewanou S, Mgonja L, Mgoqo N, Mhatu S, Mhlari T, Miima S, Milod I, Minani P, Mitema F, Mlotshwa A, Mmasi J, Mniki T, Mofikoya B, Mogale J, Mohamed A, Mohamed A, Mohamed A, Mohamed S, Mohamed S, Mohamed T, Mohamed A, Mohamed A, Mohamed A, Mohamed P, Mohammed I, Mohammed F, Mohammed M, Mohammed N, Mohlala M, Mokretar R, Molokoane F, Mongwe K, Montenegro L, Montwedi O, Moodie Q, Moopanar M, Morapedi M, Morulana T, Moses V, Mossy P, Mostafa H, Motilall S, Motloutsi S, Moussa K, Moutari M, Moyo O, Mphephu P, Mrara B, Msadabwe C, Mtongwe V, Mubeya F, Muchiri K, Mugambi J, Muguti G, Muhammad A, Mukama I, Mukenga M, Mukinda F, Mukuna P, Mungherera A, Munlemvo DM, Munyaradzi T, Munyika A, Muriithi J, Muroonga M, Murray R, Mushangwe V, Mushaninga M, Musiba V, Musowoya J, Mutahi S, Mutasiigwa M, Mutizira G, Muturi A, Muzenda T, Mvwala K, Mvwama N, Mwale A, Mwaluka C, Mwamba J, Mwanga H, Mwangi C, Mwansa S, Mwenda V, Mwepu I, Mwiti T, Mzezewa S, Nabela L, Nabukenya M, Nabulindo S, Naicker K, Naidoo D, Naidoo L, Naidoo L, Naidoo N, Naidoo R, Naidoo R, Naidoo S, Naidoo T, Naidu T, Najat N, Najm Y, Nakandungile F, Nakangombe P, Namata C, Namegabe E, Nansook A, Nansubuga N, Nantulu C, Nascimento R, Naude G, Nchimunya H, Ndaie M, Ndarukwa P, Ndasi H, Ndayisaba G, Ndegwa D, Ndikumana R, Ndonga AK, Ndung’u C, Neil M, Nel M, Neluheni E, Nesengani D, Nesengani N, Netshimboni L, Ngalala A, Ngari B, Ngari N, Ngatia E, Ngcobo G, Ngcobo T, Ngorora D, Ngouane D, Ngugi K, Ngumi ZW, Nibe Z, Ninise E, Niyondiko J, Njenga P, Njenga M, Njoroge M, Njoroge S, Njuguna W, Njuki P, Nkesha T, Nkuebe T, Nkuliyingoma N, Nkunjana M, Nkwabi E, Nkwine R, Nnaji C, Notoane I, Nsalamba S, Ntlhe L, Ntoto C, Ntueba B, Nyassi M, Nyatela-Akinrinmade Z, Nyawanda H, Nyokabi N, Nziene V, Obadiah S, Ochieng O, Odia P, Oduor O, Ogboli-Nwasor E, Ogendo S, Ogunbode O, Ogundiran T, Ogutu O, Ojewola R, Ojujo M, Ojuka D, Okelo O, Okiya S, Okonu N, Olang P, Omigbodun AO, Omoding S, Omoshoro-Jones J, Onyango R, Onyegbule A, Orjiako O, Osazuwa M, Oscar K, Osinaike B, Osinowo A, Othin O, Otman F, Otokwala J, Ouanes F, Oumar O, Ousseini A, Padayachee S, Pahlana S, Pansegrouw J, Paruk F, Patel M, Patel U, Patience A, Pearse RM, Pembe J, Pengemale G, Perez N, Aguilera Perez M, Peter AM, Phaff M, Pheeha R, Pienaar B, Pillay V, Pilusa K, Pochana M, Polishchuk O, Porrill OS, Post E, Prosper A, Pupyshev M, Rabemazava A, Rabiou M, Rademan L, Rademeyer M, Raherison R, Rajah F, Rajcoomar M, Rakhda Z, Rakotoarijaona A, Rakotoarisoa A, Rakotoarison SR, Rakotoarison R, Ramadan L, Ramananasoa M, Rambau M, Ramchurn T, Ramilson H, Ramjee RJ, Ramnarain H, Ramos R, Rampai T, Ramphal S, Ramsamy T, Ramuntshi R, Randolph R, Randriambololona D, Ras W, Rasolondraibe R, Rasolonjatovo J, Rautenbach R, Ray S, Rayne SR, Razanakoto F, Reddy S, Reed AR, Rian J, Rija F, Rink B, Robelie A, Roberts C, Rocher A, Rocher S, Rodseth RN, Rois I, Rois W, Rokhsi S, Roos J, Rorke NF, Roura H, Rousseau F, Rousseau N, Royas L, Roytowski D, Rungan D, Rwehumbiza S, Ryabchiy B, Ryndine V, Saaiman C, Sabwa H, Sadat S, Saed S, Salaheddin E, Salaou H, Saleh M, Salisu-Kabara H, Doles Sama H, Samateh AL, Sam-Awortwi W, Samuel N, Sanduku D, Sani CM, Sanyang L, Sarah H, Sarkin-Pawa A, Sathiram R, Saurombe T, Schutte H, Sebei M, Sedekounou M, Segooa M, Semenya E, Semo B, Sendagire C, Senoga S, Senusi F, Serdyn T, Seshibe M, Shah G, Shamamba R, Shambare C, Shangase T, Shanin S, Shefren I, Sheshe A, Shittu O, Shkirban A, Sholadoye T, Shubba A, Sigcu N, Sihope S, Sikazwe D, Sikombe B, Simaga Abdoul K, Simo W, Singata K, Singh A, Singh S, Singh U, Sinoamadi V, Sipuka N, Sithole N, Sitima S, Skinner DL, Skinner G, Smith O, Smits C, Sofia M, Sogoba G, Sohoub A, Sookun S, Sosinska O, Souhe R, Souley G, Souleymane T, Spicer J, Spijkerman S, Steinhaus H, Steyn A, Steyn G, Steyn H, Stoltenkamp HL, Stroyer S. The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications. Br J Anaesth 2018; 121:1357-1363. [PMID: 30442264 DOI: 10.1016/j.bja.2018.08.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 07/19/2018] [Accepted: 08/06/2018] [Indexed: 10/28/2022] Open
Abstract
BACKGROUND The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. METHODS ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. RESULTS The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. CONCLUSIONS This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. CLINICAL TRIAL REGISTRATION NCT03044899.
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P, Dinning L, Rahman S, Ray S, Dimicri C, Guppy S, Nielsen H, Vogel C, Ariza C, Morales L, Chang Y, Gabbay R, Ambrocio L, Manley L, Nemery R, Charlton W, Smith P, Kerr L, Steindel-Kopp B, Alamaguer M, Tabisola-Nuesca E, Pendersen A, Larson N, Cooper-Olviver H, Chan D, Fitz-Patrick D, Carreira T, Park Y, Ruhaak R, Liljenquist D. A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk. Diabetes Care 2018; 41:1887-1894. [PMID: 30002199 PMCID: PMC6105323 DOI: 10.2337/dc18-0087] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 06/06/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals. RESEARCH DESIGN AND METHODS We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. RESULTS Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002). CONCLUSIONS The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
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