LASSO-Based Identification of Risk Factors and Development of a Prediction Model for Sepsis Patients

References

• Dellinger RP, Levy MM, Rhodes A, et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2016. Crit Care Med. 2017;45(3):486–552. doi:10.1097/CCM.0000000000002255
   PubMed Web of Science ®Google Scholar
• Shankar-Hari M, Phillips GS, Levy ML, et al. Sepsis definitions task force. developing a new definition and assessing new clinical criteria for septic shock: for the third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016;315(8):775–787. doi:10.1001/jama.2016.0289
   PubMed Web of Science ®Google Scholar
• Singer M, S DC, W SC, et al. The third international consensus definitions for sepsis and septic shock. JAMA. 2016;315(8):801–810. doi:10.1001/jama.2016.0287
   PubMed Web of Science ®Google Scholar
• Alanazi HO, Abdullah AH, Qureshi KN. A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J Med Syst. 2017;41(4):69. doi:10.1007/s10916-017-0715-6
   PubMed Web of Science ®Google Scholar
• Brdička R. Artificial intelligence and modern information and communication technologies entering medicine. Cas Lek Cesk. 2019;158(2):87–91.
   PubMedGoogle Scholar
• Chiu YM, Courteau J, Dufour I, Vanasse A, Hudon C. Machine learning to improve frequent emergency department use prediction: a retrospective cohort study. Sci Rep. 2023;13(1):1981. doi:10.1038/s41598-023-27568-6
   PubMed Web of Science ®Google Scholar
• Long J, Wang M, Li W, et al. The risk assessment tool for intensive care unit readmission: a systematic review and meta-analysis. Intensive Crit Care Nurs. 2023;76:103378. doi:10.1016/j.iccn.2022.103378
   PubMed Web of Science ®Google Scholar
• Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Statistical Soc. 1996;58(1):267–288.
   Google Scholar
• Zou H, Hastie T. Regularization and variable selection via the elastic net. J Royal Statistical Soc. 2005;67(2):301–320. doi:10.1111/j.1467-9868.2005.00503.x
   Google Scholar
• Gupta S, Hayek SS. Factors associated with death in critically ill patients with coronavirus disease 2019 in the US. JAMA Intern Med. 2021;181(4):509–515. doi:10.1001/jamainternmed.2020.8587
   PubMedGoogle Scholar
• Rudd KE, Kissoon N, Limmathurotsakul D, et al. The global burden of sepsis: barriers and potential solutions. Crit Care. 2021;25(1):348. doi:10.1186/s13054-021-03767-3
   PubMedGoogle Scholar
• Kadri SS, Rhee C, Strich JR, et al. Estimating ten-year trends in septic shock incidence and mortality in United States academic medical centers using clinical data. Chest. 2017;151(2):278–285. doi:10.1016/j.chest.2016.07.010
   PubMed Web of Science ®Google Scholar
• Rech MA, Bennett S, Chaney W, Sterk E. Risk factors for mortality in septic patients who received etomidate. Am J Emerg Med. 2015;33(10):1340–1343. doi:10.1016/j.ajem.2015.07.062
   PubMed Web of Science ®Google Scholar
• Pansiritanachot W, Ruangsomboon O, Limsuwat C, Chakorn T. Independent risk factors of mortality in patients with sepsis receiving single-dose etomidate as an induction agent during rapid sequence intubation in a large tertiary emergency department in Thailand. BMC Emerg Med. 2022;22(1):94. doi:10.1186/s12873-022-00658-w
   PubMed Web of Science ®Google Scholar
• Wang YX, Li XL, Zhang LH, et al. Machine learning algorithms assist early evaluation of enteral nutrition in ICU patients. Front Nutr. 2023;10:1060398. doi:10.3389/fnut.2023.1060398
   PubMed Web of Science ®Google Scholar
• Lu B, Pan X, Wang B, et al. Development of a nomogram for predicting mortality risk in sepsis patients during hospitalization: a retrospective study. Infect Drug Resist. 2023;16:2311–2320. doi:10.2147/IDR.S407202
   PubMed Web of Science ®Google Scholar
• Seymour CW, Gesten F, Prescott HC, et al. Time to treatment and mortality during mandated emergency care for sepsis. N Engl J Med. 2017;376(23):2235–2244. doi:10.1056/NEJMoa1703058
   PubMed Web of Science ®Google Scholar
• Liu Y, Zhang Y, Zhang X, et al. Nomogram and machine learning models predict 1-year mortality risk in patients with sepsis-induced cardiorenal syndrome. Front Med Lausanne. 2022;9:792238. doi:10.3389/fmed.2022.792238
   PubMed Web of Science ®Google Scholar
• Liu H, Zhang L, Xu F, et al. Establishment of a prognostic model for patients with sepsis based on SOFA: a retrospective cohort study. J Int Med Res. 2021;49(9):3000605211044892. doi:10.1177/03000605211044892
   Web of Science ®Google Scholar
• Ren Y, Zhang L, Xu F, et al. Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection. BMC Pulm Med. 2022;22(1):17. doi:10.1186/s12890-021-01809-8
   PubMed Web of Science ®Google Scholar
• Wang B, Chen J. Establishment and validation of a predictive model for mortality within 30 days in patients with sepsis-induced blood pressure drop: a retrospective analysis. PLoS One. 2021;16(5):1.
   Web of Science ®Google Scholar
• Frank CE, Buchman TG, Simpson SQ, et al. Sepsis among medicare beneficiaries: 4. precoronavirus disease 2019. Crit Care Med. 2021;49(12):2058–2069. doi:10.1097/CCM.0000000000005332
   PubMed Web of Science ®Google Scholar
• Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet. 2019;394(10212):1949–1964. doi:10.1016/S0140-6736(19)32563-2
   PubMed Web of Science ®Google Scholar
• Jolly F, Jacquier M, Pecqueur D, et al;READIAL Study group. Management of renal replacement therapy among adults in French intensive care units: a bedside practice evaluation. J Intensive Med. 2023;3(2):147–154. doi:10.1016/j.jointm.2022.10.005
   PubMedGoogle Scholar
• Wang AY, Bellomo R. Renal replacement therapy in the ICU: intermittent hemodialysis, sustained low-efficiency dialysis or continuous renal replacement therapy? Curr Opin Crit Care. 2018;24(6):437–442. doi:10.1097/MCC.0000000000000541
   PubMed Web of Science ®Google Scholar
• Ma H, Liu H, Liu Y, Wang Y, He J, Yang Q. Efficacy of continuous renal replacement therapy and intermittent hemodialysis in patients with renal failure in intensive care unit: a systemic review and meta-analysis. Evid Based Complement Alternat Med. 2023;2023:8688974. doi:10.1155/2023/8688974
   PubMedGoogle Scholar
• Klingele M, Baerens L. Impact of renal replacement therapy on mortality in critically ill patients-the nephrologist’s view within an interdisciplinary intensive care team. J Clin Med. 2021;10(15):3379. doi:10.3390/jcm10153379
   PubMed Web of Science ®Google Scholar
• Bateman RM, Sharpe MD, Jagger JE, et al. 36th international symposium on intensive care and emergency medicine: Brussels, Belgium. Crit Care. 2016;20(Suppl 2):94. doi:10.1186/s13054-016-1208-6
   PubMed Web of Science ®Google Scholar
• Evans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Crit Care Med. 2021;49(11):e1063–e1143. doi:10.1097/CCM.0000000000005337
   PubMed Web of Science ®Google Scholar
• Bitton E, Zimmerman S, Azevedo LCP, et al. An international survey of adherence to surviving sepsis Campaign guidelines 2016 regarding fluid resuscitation and vasopressors in the initial management of septic shock. J Crit Care. 2022;68:144–154. doi:10.1016/j.jcrc.2021.11.016
   PubMed Web of Science ®Google Scholar
• Tuttle E, Wang X, Modrykamien A. Sepsis mortality and ICU length of stay after the implementation of an intensive care team in the emergency department. Intern Emerg Med. 2023;2023:1–8.
   Google Scholar
• Petros S. Volumen- und vasoaktive therapie bei sepsis [fluid and vasopressor therapy in sepsis]. Med Klin Intensivmed Notfmed. 2023;118(2):163–171. doi:10.1007/s00063-022-00976-8
   PubMedGoogle Scholar
• Qaseem A, Humphrey LL, Fitterman N, Starkey M, Shekelle P. Clinical guidelines committee of the American college of physicians. Treatment of anemia in patients with heart disease: a clinical practice guideline from the American college of physicians. Ann Intern Med. 2013;159(11):770–779. doi:10.7326/0003-4819-159-11-201312030-00009
   PubMed Web of Science ®Google Scholar
• Luo M, Chen Y, Cheng Y, Li N, Qing H. Association between hematocrit and the 30-day mortality of patients with sepsis: a retrospective analysis based on the large-scale clinical database MIMIC-IV. PLoS One. 2022;17(3):1.
   Web of Science ®Google Scholar
• Wang Z, Zhang L, Li S, et al. The relationship between hematocrit and serum albumin levels difference and mortality in elderly sepsis patients in intensive care units-A retrospective study based on two large database. BMC Infect Dis. 2022;22(1):629. doi:10.1186/s12879-022-07609-7
   PubMed Web of Science ®Google Scholar
• Zhang FX, Li ZL, Zhang ZD, Ma XC. Prognostic value of red blood cell distribution width for severe acute pancreatitis. World J Gastroenterol. 2019;25(32):4739–4748. doi:10.3748/wjg.v25.i32.4739
   PubMed Web of Science ®Google Scholar
• Wu H, Liao B, Cao T, Ji T, Huang J, Ma K. Diagnostic value of RDW for the prediction of mortality in adult sepsis patients: a systematic review and meta-analysis. Front Immunol. 2022;13:997853. doi:10.3389/fimmu.2022.997853
   PubMed Web of Science ®Google Scholar
• Yu SH, Ma YT, Li X. The correlation between coagulation function and prognosis in patients with acute respiratory distress syndrome caused by extrapulmonary sepsis or pulmonary infection. Zhonghua Nei Ke Za Zhi. 2021;60(7):650–655. doi:10.3760/cma.j.cn112138-20201217-01017
   PubMedGoogle Scholar
• Chen R, Zhou X, Rui Q, Wang X. Combined predictive value of the risk factors influencing the short-term prognosis of sepsis. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020;32(3):307–312. doi:10.3760/cma.j.cn121430-20200306-00218
   PubMedGoogle Scholar
• He M, Huang J, Li X, Liang S, Wang Q, Zhang H. Risk factors for mortality in sepsis patients without lactate levels increasing early. Emerg Med Int. 2023;2023:6620157. doi:10.1155/2023/6620157
   PubMed Web of Science ®Google Scholar
• Schupp T, Weidner K, Rusnak J, et al. C-reactive protein and procalcitonin during course of sepsis and septic shock. Ir J Med Sci. 2023; 1–12. doi:10.1007/s11845-023-03385-8
   Web of Science ®Google Scholar