Prioritising deteriorating patients using time-to-event analysis: prediction model development and internal–external validation

Blythe R, Parsons R, White NM, Cook D, McPhail SM. A scoping review of real-time automated clinical deterioration alerts and evidence of impacts on hospitalised patient outcomes. BMJ Qual Saf. 2022;31(10):725–34.

Article  PubMed  Google Scholar 

Gerry S, Bonnici T, Birks J, Kirtley S, Virdee PS, Watkinson PJ, Collins GS. Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology. BMJ. 2020;369: m1501.

Article  PubMed  PubMed Central  Google Scholar 

Smith ME, Chiovaro JC, O’Neil M, Kansagara D, Quinones AR, Freeman M, et al. Early warning system scores for clinical deterioration in hospitalized patients: a systematic review. Ann Am Thorac Soc. 2014;11(9):1454–65.

Article  PubMed  Google Scholar 

Martinez VA, Betts RK, Scruth EA, Buckley JD, Cadiz VR, Bertrand LD, et al. The kaiser permanente northern california advance alert monitor program: an automated early warning system for adults at risk for in-hospital clinical deterioration. Jt Comm J Qual Patient Saf. 2022;48(8):370–5.

PubMed  Google Scholar 

van der Vegt AH, Campbell V, Mitchell I, Malycha J, Simpson J, Flenady T, et al. Systematic review and longitudinal analysis of implementing artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain. J Am Med Inform Assoc. 2024;31(2):509–24.

Article  PubMed  Google Scholar 

Bedoya AD, Clement ME, Phelan M, Steorts RC, O’Brien C, Goldstein BA. Minimal impact of implemented early warning score and best practice alert for patient deterioration. Crit Care Med. 2019;47(1):49–55.

Article  PubMed  PubMed Central  Google Scholar 

Romero-Brufau S, Huddleston JM, Escobar GJ, Liebow M. Why the C-statistic is not informative to evaluate early warning scores and what metrics to use. Crit Care. 2015;19:285.

Article  PubMed  PubMed Central  Google Scholar 

Parsons R, Blythe R, Cramb SM, McPhail SM. Integrating economic considerations into cutpoint selection may help align clinical decision support toward value-based healthcare. J Am Med Inform Assoc. 2023;30(6):1103–13.

Article  PubMed  PubMed Central  Google Scholar 

Wynants L, van Smeden M, McLernon DJ, Timmerman D, Steyerberg EW, Van Calster B, et al. Three myths about risk thresholds for prediction models. BMC Med. 2019;17(1):192.

Article  PubMed  PubMed Central  Google Scholar 

Harrell FE, Jr. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. 2 ed: Springer International Publishing; 2015.

Iserson KV, Moskop JC. Triage in medicine, part I: Concept, history, and types. Ann Emerg Med. 2007;49(3):275–81.

Article  PubMed  Google Scholar 

Ramos JG, Perondi B, Dias RD, Miranda LC, Cohen C, Carvalho CR, et al. Development of an algorithm to aid triage decisions for intensive care unit admission: a clinical vignette and retrospective cohort study. Crit Care. 2016;20:81.

Article  PubMed  PubMed Central  Google Scholar 

Bull LM, Lunt M, Martin GP, Hyrich K, Sergeant JC. Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods. Diagnostic Prognostic Res. 2020;4:9.

Article  Google Scholar 

Blythe R, Parsons R, Barnett AG, McPhail SM, White NM. Vital signs-based deterioration prediction model assumptions can lead to losses in prediction performance. J Clin Epidemiol. 2023;159:106–15.

Article  PubMed  Google Scholar 

Wolkewitz M, Lambert J, von Cube M, Bugiera L, Grodd M, Hazard D, et al. Statistical analysis of clinical COVID-19 data: a concise overview of lessons learned, common errors and how to avoid them. Clin Epidemiol. 2020;12:925–8.

Article  PubMed  PubMed Central  Google Scholar 

Blythe R, Naicker S, White NM, Donovan R, Scott IA, Mckelliget A, McPhail SM. Clinician preferences for clinical prediction model design in acute care settings: A case study of early warning score implementation. OSF Preprints2023.

Eini-Porat B, Amir O, Eytan D, Shalit U. Tell me something interesting: clinical utility of machine learning prediction models in the ICU. J Biomed Inform. 2022;132: 104107.

Article  PubMed  Google Scholar 

Therneau T, Crowson C, Atkinson E. Using time dependent covariates and time dependent coefficients in the cox model. Surv Vignettes. 2017;2(3):1–25.

Google Scholar 

Zhang Z, Reinikainen J, Adeleke KA, Pieterse ME, Groothuis-Oudshoorn CGM. Time-varying covariates and coefficients in Cox regression models. Ann Transl Med. 2018;6(7):121.

Article  PubMed  PubMed Central  Google Scholar 

Mayer M. missRanger: Fast Imputation of Missing Values. 2023.

Sisk R, Sperrin M, Peek N, van Smeden M, Martin GP. Imputation and missing indicators for handling missing data in the development and deployment of clinical prediction models: a simulation study. Stat Methods Med Res. 2023;32(8):1461–77.

Article  PubMed  PubMed Central  Google Scholar 

Borzecki AM, Christiansen CL, Chew P, Loveland S, Rosen AK. Comparison of in-hospital versus 30-day mortality assessments for selected medical conditions. Med Care. 2010;48(12):1117–21.

Article  PubMed  Google Scholar 

Eskildsen MA. Long-term acute care: a review of the literature. J Am Geriatr Soc. 2007;55(5):775–9.

Article  PubMed  Google Scholar 

Ma J, Dhiman P, Qi C, Bullock G, van Smeden M, Riley RD, Collins GS. Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review. J Clin Epidemiol. 2023;2(161):140–51.

Article  Google Scholar 

Harrell FE, Jr. rms: Regression Modelling Strategies. 2023.

Ensor J, Martin EC, Riley RD. pmsampsize: Calculates the Minimum Sample Size Required for Developing a Multivariable Prediction Model. 2022.

Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019;38(7):1276–96.

Article  PubMed  Google Scholar 

Riley RD, Van Calster B, Collins GS. A note on estimating the Cox-Snell R(2) from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome. Stat Med. 2021;40(4):859–64.

Article  PubMed  Google Scholar 

Collins GS, Dhiman P, Ma J, Schlussel MM, Archer L, Van Calster B, et al. Evaluation of clinical prediction models (part 1): from development to external validation. BMJ. 2024;384: e074819.

Article  PubMed  PubMed Central  Google Scholar 

Steyerberg EW, Harrell FE Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol. 2016;69:245–7.

Article  PubMed  Google Scholar 

Uno H, Cai T, Tian L, Wei L. Evaluating prediction rules for t-year survivors with censored regression models. J Am Stat Assoc. 2007;102(478):527–37.

Article  CAS  Google Scholar 

Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32(30):5381–97.

Article  PubMed  Google Scholar 

Austin PC, Steyerberg EW. Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med. 2014;33(3):517–35.

Article  PubMed  Google Scholar 

McLernon DJ, Giardiello D, Van Calster B, Wynants L, van Geloven N, van Smeden M, et al. Assessing performance and clinical usefulness in prediction models with survival outcomes: practical guidance for cox proportional hazards models. Ann Intern Med. 2023;176(1):105–14.

Article  PubMed  Google Scholar 

R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2022.

Suresh K, Severn C, Ghosh D. Survival prediction models: an introduction to discrete-time modeling. BMC Med Res Methodol. 2022;22(1):207.

Article  PubMed  PubMed Central  Google Scholar 

Mok W, Wang W, Cooper S, Ang EN, Liaw SY. Attitudes towards vital signs monitoring in the detection of clinical deterioration: scale development and survey of ward nurses. Int J Qual Health Care. 2015;27(3):207–13.

Article  PubMed  Google Scholar 

Blackwell JN, Keim-Malpass J, Clark MT, Kowalski RL, Najjar SN, Bourque JM, et al. Early detection of in-patient deterioration: one prediction model does not fit all. Critical Care Exp. 2020;2(5): e0116.

Google Scholar 

Smith GB, Prytherch DR, Schmidt PE, Featherstone PI. Review and performance evaluation of aggregate weighted “track and trigger” systems. Resuscitation. 2008;77(2):170–9.

Article  PubMed 

留言 (0)

沒有登入
gif