@misc{10818/61859,
year = {2024},
url = {http://hdl.handle.net/10818/61859},
abstract = {By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024.},
publisher = {Scientific Reports},
title = {At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods},
doi = {10.1038/s41598-024-63212-7},
author = {Mesinovic M and Wong X.C and Rajahram G.S and Citarella B.W and Peariasamy K.M and van Someren Greve F and Olliaro P and Merson L and Clifton L and Kartsonaki C and Abdukahil S.A and Abdulkadir N.N and Abe R and Abel L and Abrous A and Absil L and Acker A and Adachi S and Adam E and Adriano E and Adrião D and Ageel S.A and Ahmed S and Aiello M and Ainscough K and Airlangga E and Aisa T and Hssain A.A and Tamlihat Y.A and Akimoto T and Akmal E and Qasim E.A and Alalqam R and Alberti A and Al-dabbous T and Alegesan S and Alegre C and Alessi M and Alex B and Alexandre K and Al-Fares A and Alfoudri H and Ali A and Ali I and Alidjnou K.E and Aliudin J and Alkhafajee Q and Allavena C and Allou N and Alves J and Alves J.M and Alves R and Cabrita J.A and Amaral M and Amira N and Ammerlaan H and Ampaw P and Andini R and Andréjak C and Angheben A and Angoulvant F and Ansart S and Anthonidass S and Antonelli M and de Brito C.A.A and Anwar K.R and Apriyana A and Arabi Y and Aragao I and Arancibia F and Araujo C and Arcadipane A and Archambault P and Arenz L and Arlet J.-B and Arnold-Day C and Arora L and Arora R and Artaud-Macari E and Aryal D and Asaki M and Asensio A and Ashley E.A and Ashraf M and Assie J.B and Asyraf A and Atique A and Attanyake A.M.U.L and Auchabie J and Aumaitre H and Auvet A and Azemar L and Azoulay C and Bach B and Bachelet D and Badr C and Baig N and Baillie J.K and Baird J.K and Bak E and Bakakos A and Bakar N.A and Bal A and Balakrishnan M and Balan V and Bani-Sadr F and Barbalho R and Barbosa N.Y and Barclay W.S and Barnett S.U and Barnikel M and Barrasa H and Barrelet A and Barrigoto C and Bartoli M and Baruch J and Basmaci R and Basri M.F.H and Battaglini D and Bauer J and Rincon D.F.B and Dow D.B and Beane A and Bedossa A and Bee K.H and Begum H and Behilill S and Beishuizen A and Beljantsev A and Bellemare D and Beltrame A and Beltrão B.A and Beluze M and Benech N and Benjiman L.E and Benkerrou D and Bennett S and Bento L and Berdal 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B.P.S.R.S and Sriskandan S and Stabler S and Staudinger T and Stecher S.-S and Steinsvik T and Stienstra Y and Stiksrud B and Stolz E and Stone A and Streinu-Cercel A and Streinu-Cercel A and Stuart A and Stuart D and Su R and Subekti D and Suen G and Suen J.Y and Sultana A and Summers C and Supic D and Suppiah D and Surovcová M and Suwarti A and Svistunov A and Syahrin S and Syrigos K and Sztajnbok J and Szuldrzynski K and Tabrizi S and Taccone F.S and Tagherset L and Taib S.M and Talarek E and Taleb S and Talsma J and Tamisier R and Tampubolon M.L and Tan K.K and Tan L.V and Tan Y.C and Tanaka C and Tanaka H and Tanaka T and Taniguchi H and Tardivon C and Tattevin P and Taufik M.A and Tawfik H and Tedder R.S and Tee T.Y and Teixeira J and Tejada S and Tellier M.-C and Teoh S.K and Teotonio V and Téoulé F and Terpstra P and Terrier O and Terzi N and Tessier-Grenier H and Tey A and Thabit A.A.M and Tham Z.D and Thangavelu S and Thibault V and Thiberville S.-D and Thill B and Thirumanickam J and Thompson S and Thomson D and Thomson E.C and Thurai S.R.T and Thuy D.B and Thwaites R.S and Ticinesi A and Tierney P and Tieroshyn V and Timashev P.S and Timsit J.-F and Tissot N and Toh J.Z.Y and Toki M and Tonby K and Tonnii S.L and Torre M and Torres A and Torres M and Santos-Olmo R.M.T and Torres-Zevallos H and Towers M and Trapani T and Trieu H.T and Trioux T and Tromeur C and Trontzas I and Trouillon T and Truong J and Tual C and Tubiana S and Tuite H and Turgeon A.F and Turmel J.-M and Turtle L.C.W and Tveita A and Twardowski P and Uchiyama M and Udayanga P.G.I and Udy A and Ullrich R and Uribe A and Usman A and Uyeki T.M and Vajdovics C and Valentini P and Val-Flores L and Valran A and Valzano I and de Velde S.V and Berge M.V.D and Feltz M.V.D and Palen J.V.D and Valk P.V.D and Vekens N.V.D and Voort P.V.D and Werf S.V.D and Gulik L.V and Hattem J.V and Netten C.V and Greve F.V.S and Twillert G.V and Veen I.V and Willigen H.V and Vanel N and Vanoverschelde H and Varghese P and Varrone M and Vasudayan S.R and Vauchy C and Veeran S and Veislinger A and Vencken S and Ventura S and Verbon A and Vickers J and Vidal J.E and Vieira C and Villanueva J.A and Villar J and Villeneuve P.-M and Villoldo A and Chau N.V.V and Vishwanathan G and Visseaux B and Visser H and Vitiello C and Vongsouvath M and Vonkeman H and Vuotto F and Wahab N.H and Wahab S.A and Wahid N.A and Wainstein M and Webb S and Wei J and Weil K and Wen T.P and Wesselius S and West T.E and Wham M and Whelan B and Wicky P.H and Wiedemann A and Wijaya S.O and Wille K and Willems S and Williams P.J and Williams V and Wils E.-J and Yiu N.W and Wong C and Wong T.F and Wong X.C and Wong Y.S and Wright N and Xian G.E and Xian L.S and Xuan K.P and Xynogalas I and Yacoub S and Yakop S.R.B.M and Yamazaki M and Yarad E and Yazdanpanah Y and Hing N.Y.L and Yelnik C and Yeoh C.H and Yerkovich S and Yiaye T and Yokoyama T and Yonis H and Yousif O and Yuliarto S and Zaaqoq A and Zabbe M and Zabert 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}