Discharge Prediction for Patients Undergoing Inpatient Surgery: Development and validation of the DEPENDENSE score.
patient discharge
risk assessment
skilled nursing facilities
surgical procedures
Journal
Acta anaesthesiologica Scandinavica
ISSN: 1399-6576
Titre abrégé: Acta Anaesthesiol Scand
Pays: England
ID NLM: 0370270
Informations de publication
Date de publication:
05 2021
05 2021
Historique:
revised:
09
12
2020
received:
13
06
2020
accepted:
27
12
2020
pubmed:
7
1
2021
medline:
16
10
2021
entrez:
6
1
2021
Statut:
ppublish
Résumé
A substantial proportion of patients undergoing inpatient surgery each year is at risk for postoperative institutionalization and loss of independence. Reliable individualized preoperative prediction of adverse discharge can facilitate advanced care planning and shared decision making. Using hospital registry data from previously home-dwelling adults undergoing inpatient surgery, we retrospectively developed and externally validated a score predicting adverse discharge. Multivariable logistic regression analysis and bootstrapping were used to develop the score. Adverse discharge was defined as in-hospital mortality or discharge to a skilled nursing facility. The model was subsequently externally validated in a cohort of patients from an independent hospital. In total, 106 164 patients in the development cohort and 92 962 patients in the validation cohort were included, of which 16 624 (15.7%) and 7717 (8.3%) patients experienced adverse discharge, respectively. The model was predictive of adverse discharge with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI 0.87-0.88) in the development cohort and an AUC of 0.86 (95% CI 0.86-0.87) in the validation cohort. Using preoperatively available data, we developed and validated a prediction instrument for adverse discharge following inpatient surgery. Reliable prediction of this patient centered outcome can facilitate individualized operative planning to maximize value of care.
Sections du résumé
BACKGROUND
A substantial proportion of patients undergoing inpatient surgery each year is at risk for postoperative institutionalization and loss of independence. Reliable individualized preoperative prediction of adverse discharge can facilitate advanced care planning and shared decision making.
METHODS
Using hospital registry data from previously home-dwelling adults undergoing inpatient surgery, we retrospectively developed and externally validated a score predicting adverse discharge. Multivariable logistic regression analysis and bootstrapping were used to develop the score. Adverse discharge was defined as in-hospital mortality or discharge to a skilled nursing facility. The model was subsequently externally validated in a cohort of patients from an independent hospital.
RESULTS
In total, 106 164 patients in the development cohort and 92 962 patients in the validation cohort were included, of which 16 624 (15.7%) and 7717 (8.3%) patients experienced adverse discharge, respectively. The model was predictive of adverse discharge with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI 0.87-0.88) in the development cohort and an AUC of 0.86 (95% CI 0.86-0.87) in the validation cohort.
CONCLUSION
Using preoperatively available data, we developed and validated a prediction instrument for adverse discharge following inpatient surgery. Reliable prediction of this patient centered outcome can facilitate individualized operative planning to maximize value of care.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
607-617Informations de copyright
© 2021 The Acta Anaesthesiologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.
Références
Grocott MPW, Edwards M, Mythen MG, Aronson S. Peri-operative care pathways: re-engineering care to achieve the 'triple aim'. Anaesthesia. 2019;74(Suppl 1):90-99.
Schwarze ML, Brasel KJ, Mosenthal AC. Beyond 30-day mortality: aligning surgical quality with outcomes that patients value. JAMA Surg. 2014;149:631-632.
Carlisle JB. Risk prediction models for major surgery: composing a new tune. Anaesthesia. 2019;74(Suppl 1):7-12.
Joseph B, Pandit V, Zangbar B, et al. Superiority of frailty over age in predicting outcomes among geriatric trauma patients: a prospective analysis. JAMA Surg. 2014;149:766-772.
Collaborative CO. Elective surgery cancellations due to the COVID-19 pandemic: global predictive modelling to inform surgical recovery plans. Br J Surg. 2020;107:1440-1449.
Pritchard D, Petrilla A, Hallinan S, Taylor DH Jr, Schabert VF, Dubois RW. What contributes most to high health care costs? Health care spending in high resource patients. J Manag Care Spec Pharm. 2016;22:102-109.
Toxvaerd CG, Benthien KS, Andreasen AH, Nielsen A, Osler M, Johansen NB. Chronic diseases in high-cost users of hospital, primary care, and prescription medication in the capital region of Denmark. J Gen Intern Med. 2019;34:2421-2426.
Herbold JA, Bonistall K, Walsh MB. Rehabilitation following total knee replacement, total hip replacement, and hip fracture: a case-controlled comparison. J Geriatr Phys Ther. 2011;34:155-160.
Mallinson T, Deutsch A, Bateman J, et al. Comparison of discharge functional status after rehabilitation in skilled nursing, home health, and medical rehabilitation settings for patients after hip fracture repair. Arch Phys Med Rehabil. 2014;95:209-217.
Belagaje SR, Zander K, Thackeray L, Gupta R. Disposition to home or acute rehabilitation is associated with a favorable clinical outcome in the SENTIS trial. J Neurointerv Surg. 2015;7:322-325.
Edgerton JR, Herbert MA, Mahoney C, et al. Long-term fate of patients discharged to extended care facilities after cardiovascular surgery. Ann Thorac Surg. 2013;96:871-877.
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594.
Rostin P, Teja BJ, Friedrich S, et al. The association of early postoperative desaturation in the operating theatre with hospital discharge to a skilled nursing or long-term care facility. Anaesthesia. 2019;74:457-467.
Quality AfHRa. HCUP CCS-services and procedures. Healthcare cost and utilization project. [www document]. 2018. www.hcup-us.ahrq.gov/toolssoftware/ccs_svcsproc/ccssvcproc.jsp. Accessed August 14, 2019
Mueller N, Murthy S, Tainter CR, et al. Can sarcopenia quantified by ultrasound of the rectus femoris muscle predict adverse outcome of surgical intensive care unit patients as well as frailty? A prospective. Observational cohort study. Ann Surg. 2016;264:1116-1124.
Labarere J, Renaud B, Fine MJ. How to derive and validate clinical prediction models for use in intensive care medicine. Intensive Care Med. 2014;40:513-527.
Shin CH, Grabitz SD, Timm FP, et al. Development and validation of a Score for Preoperative Prediction of Obstructive Sleep Apnea (SPOSA) and its perioperative outcomes. BMC Anesthesiol. 2017;17:71.
Schaefer MS, Hammer M, Platzbecker K, et al. What factors predict adverse discharge disposition in patients older than 60 years undergoing lower-extremity surgery? The adverse discharge in older patients after lower-extremity surgery (ADELES) risk score. Clin Orthop Relat Res. 2020. https://doi.org/10.1097/CORR.0000000000001532
Sivasundaram L, Tanenbaum JE, Mengers SR, et al. Identifying a clinical decision tool to predict discharge disposition following operative treatment of hip fractures in the United States. Injury. 2020;51:1015-1020.
Lukannek C, Shaefi S, Platzbecker K, et al. The development and validation of the Score for the Prediction of Postoperative Respiratory Complications (SPORC-2) to predict the requirement for early postoperative tracheal re-intubation: a hospital registry study. Anaesthesia. 2019;74:1165-1174.
Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: executive summary. Am J Clin Nutr. 1998;68:899-917.
Childers CP, Dworsky JQ, Russell MM, Maggard-Gibbons M. Association of work measures and specialty with assigned work relative value units among surgeons. JAMA Surg. 2019;154:915-921.
Hamidi M, Ho C, Zeeshan M, et al. Can sarcopenia quantified by computed tomography scan predict adverse outcomes in emergency general surgery? J Surg Res. 2019;235:141-147.
Makary MA, Segev DL, Pronovost PJ, et al. Frailty as a predictor of surgical outcomes in older patients. J Am Coll Surg. 2010;210:901-908.
van der Steenhoven TJ, Staffhorst B, Van de Velde SK, Nelissen RG, Verhofstad MH. Complications and institutionalization are almost doubled after second hip fracture surgery in the elderly patient. J Orthop Trauma. 2015;29:e103-e108.
Vochteloo AJ, van Vliet-Koppert ST, Maier AB, et al. Risk factors for failure to return to the pre-fracture place of residence after hip fracture: a prospective longitudinal study of 444 patients. Arch Orthop Trauma Surg. 2012;132:823-830.
Fingar KR, Stocks C, Weiss AJ, Steiner CA. Most Frequent Operating Room Procedures Performed in U.S. Hospitals, 2003-2012: Statistical Brief #186. Rockville, MD: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs; 2006.
Medicare Payment Advisory Commission. Report to the Congress: Medicare Payment Policy. Washington, DC: Medicare Payment Advisory Commission; 2019.
Vetter TR, Barman J, Hunter JM Jr, Jones KA, Pittet JF. The effect of implementation of preoperative and postoperative care elements of a perioperative surgical home model on outcomes in patients undergoing hip arthroplasty or knee arthroplasty. Anesth Analg. 2017;124:1450-1458.
Lipshutz AK, Gropper MA. Acquired neuromuscular weakness and early mobilization in the intensive care unit. Anesthesiology. 2013;118:202-215.
Xu HF, White RS, Sastow DL, Andreae MH, Gaber-Baylis LK, Turnbull ZA. Medicaid insurance as primary payer predicts increased mortality after total hip replacement in the state inpatient databases of California, Florida and New York. J Clin Anesth. 2017;43:24-32.
Gosling AF, Hammer M, Grabitz S, et al. Development of an instrument for preoperative prediction of adverse discharge in patients scheduled for cardiac surgery. J Cardiothorac Vasc Anesth. 2020. https://doi.org/10.1053/j.jvca.2020.08.028
Mosca I, van der Wees PJ, Mot ES, Wammes JJG, Jeurissen PPT. Sustainability of long-term care: puzzling tasks ahead for policy-makers. Int J Health Policy Manag. 2017;6:195-205.
Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J. 2005;173:489-495.
Walston J, Bandeen-Roche K, Buta B, et al. Moving frailty toward clinical practice: NIA intramural frailty science symposium summary. J Am Geriatr Soc. 2019;67:1559-1564.
McIsaac DI, Wong CA, Huang A, Moloo H, van Walraven C. Derivation and validation of a generalizable preoperative frailty index using population-based health administrative data. Ann Surg. 2019;270:102-108.
Liu Y, Cohen ME, Hall BL, Ko CY, Bilimoria KY. Evaluation and enhancement of calibration in the American College of Surgeons NSQIP surgical risk calculator. J Am Coll Surg. 2016;223:231-239.