2025
Mind the Gap: Wearable Lactate and Glucose Monitors for Hospitalized Patients
Guzzi J, Falter F, Kumar A, Perrino A. Mind the Gap: Wearable Lactate and Glucose Monitors for Hospitalized Patients. Cureus 2025, 17: e78536. PMID: 40062120, PMCID: PMC11886925, DOI: 10.7759/cureus.78536.Peer-Reviewed Original Research
2024
Early Warning Scores With and Without Artificial Intelligence
Edelson D, Churpek M, Carey K, Lin Z, Huang C, Siner J, Johnson J, Krumholz H, Rhodes D. Early Warning Scores With and Without Artificial Intelligence. JAMA Network Open 2024, 7: e2438986. PMID: 39405061, PMCID: PMC11544488, DOI: 10.1001/jamanetworkopen.2024.38986.Peer-Reviewed Original ResearchConceptsEarly Warning ScoreWarning ScoreCohort studyYale New Haven Health SystemClinical deterioration eventsHigh-risk thresholdHealth systemRetrospective cohort studyPatient encountersDeteriorating patientsOverall PPVMain OutcomesInpatient encountersEDI scoresHospital encountersDeterioration eventsClinical deteriorationIntensive care unitEarly warning toolCare unitDecision support toolArtificial intelligenceScoresReceiver operating characteristic curveNEWS2
2021
Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019
Churpek MM, Gupta S, Spicer AB, Hayek SS, Srivastava A, Chan L, Melamed ML, Brenner SK, Radbel J, Madhani-Lovely F, Bhatraju PK, Bansal A, Green A, Goyal N, Shaefi S, Parikh CR, Semler MW, Leaf DE, Walther C, Anumudu S, Arunthamakun J, Kopecky K, Milligan G, McCullough P, Nguyen T, Shaefi S, Krajewski M, Shankar S, Pannu A, Valencia J, Waikar S, Kibbelaar Z, Athavale A, Hart P, Ajiboye O, Itteera M, Green A, Rachoin J, Schorr C, Shea L, Edmonston D, Mosher C, Shehata A, Cohen Z, Allusson V, Bambrick-Santoyo G, Bhatti N, Metha B, Williams A, Brenner S, Walters P, Go R, Rose K, Hernán M, Zhou A, Kim E, Lisk R, Chan L, Mathews K, Coca S, Altman D, Saha A, Soh H, Wen H, Bose S, Leven E, Wang J, Mosoyan G, Nadkarni G, Friedman A, Guirguis J, Kapoor R, Meshberger C, Parikh C, Garibaldi B, Corona-Villalobos C, Wen Y, Menez S, Malik R, Cervantes C, Gautam S, Chang C, Nguyen H, Ahoubim A, Thomas L, Guru P, Bergl P, Zhou Y, Rodriguez J, Shah J, Gupta M, Kumar P, Lazarous D, Kassaye S, Melamed M, Johns T, Mocerino R, Prudhvi K, Zhu D, Levy R, Azzi Y, Fisher M, Yunes M, Sedaliu K, Golestaneh L, Brogan M, Thakkar J, Kumar N, Ross M, Chang M, Raichoudhury R, Schenck E, Cho S, Plataki M, Alvarez-Mulett S, Gomez-Escobar L, Pan D, Lee S, Krishnan J, Whalen W, Charytan D, Macina A, Ross D, Srivastava A, Leidner A, Martinez C, Kruser J, Wunderink R, Hodakowski A, Velez J, Price-Haywood E, Matute-Trochez L, Hasty A, Mohamed M, Avasare R, Zonies D, Leaf D, Gupta S, Baron R, Sise M, Newman E, Abu Omar S, Pokharel K, Sharma S, Singh H, Gaviria S, Shaukat T, Kamal O, Wang W, Yang H, Boateng J, Lee M, Strohbehn I, Li J, Muhsin S, Mandel E, Mueller A, Cairl N, Madhani-Lovely F, Rowan C, Madhai-Lovely F, Peev V, Reiser J, Byun J, Vissing A, Kapania E, Post Z, Patel N, Hermes J, Sutherland A, Patrawalla A, Finkel D, Danek B, Arikapudi S, Paer J, Radbel J, Puri S, Sunderram J, Scharf M, Ahmed A, Berim I, Vatson J, Anand S, Levitt J, Garcia P, Boyle S, Song R, Zhang J, Sharshir M, Rusnak V, Bansal A, Podoll A, Chonchol M, Sharma S, Burnham E, Rashidi A, Hejal R, Judd E, Latta L, Tolwani A, Albertson T, Adams J, Chang S, Beutler R, Schulze C, Macedo E, Rhee H, Liu K, Jotwani V, Koyner J, Shah C, Jaikaransingh V, Toth-Manikowski S, Joo M, Lash J, Neyra J, Chaaban N, Iardino A, Au E, Sharma J, Sosa M, Taldone S, Contreras G, De La Zerda D, Gershengorn H, Hayek S, Blakely P, Berlin H, Azam T, Shadid H, Pan M, Hayer P, Meloche C, Feroze R, Padalia K, Leya J, Donnelly J, Admon A, Flythe J, Tugman M, Brown B, Leonberg-Yoo A, Spiardi R, Miano T, Roche M, Vasquez C, Bansal A, Ernecoff N, Kovesdy C, Molnar M, Hedayati S, Nadamuni M, Khan S, Willett D, Short S, Renaghan A, Bhatraju P, Malik A, Semler M, Vijayan A, Joy C, Li T, Goldberg S, Kao P, Schumaker G, Goyal N, Faugno A, Schumaker G, Hsu C, Tariq A, Meyer L, Christov M, Wilson F, Arora T, Ugwuowo U. Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019. Critical Care Explorations 2021, 3: e0515. PMID: 34476402, PMCID: PMC8378790, DOI: 10.1097/cce.0000000000000515.Peer-Reviewed Original ResearchSequential Organ Failure Assessment scoreOrgan Failure Assessment scoreNational Early Warning ScoreCoronavirus disease 2019Early Warning ScoreDisease 2019CURB-65Warning ScoreIll patientsAssessment scoresModified Sequential Organ Failure Assessment scoreCharacteristic curveExternal validationCritically ill patientsClinical trial enrichmentPrognostic enrichmentICU admissionInhospital mortalityAdult patientsHighest areaICU patientsPrimary outcomeArterial pHTrial enrichmentU.S. ICUs
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