Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Australian Health Review Australian Health Review Society
Journal of the Australian Healthcare & Hospitals Association
RESEARCH ARTICLE

External validation and comparative analysis of the HOSPITAL score and LACE index for predicting readmissions among patients hospitalised with community-acquired pneumonia in Australia

Yogesh Sharma A B * , Arduino A. Mangoni B , Chris Horwood A and Campbell Thompson C
+ Author Affiliations
- Author Affiliations

A Department of Acute and General Medicine, Flinders Medical Centre, Adelaide, SA, Australia.

B College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.

C Discipline of Medicine, The University of Adelaide, Adelaide, SA, Australia.

* Correspondence to: Yogesh.Sharma@sa.gov.au

Australian Health Review https://doi.org/10.1071/AH24204
Submitted: 28 July 2024  Accepted: 11 August 2024  Published: 2 September 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of AHHA.

Abstract

Objective

Community-acquired pneumonia (CAP) is a leading cause of emergency hospitalisations globally and is associated with high readmission rates. Specific score systems developed for all medical conditions such as the HOSPITAL score and the LACE index can also usefully predict CAP readmissions. However, there is limited evidence regarding their performance in the Australian healthcare settings.

Methods

This multicentre retrospective study analysed adult CAP discharges from two metropolitan hospitals in South Australia between 1 January 2018 and 31 December 2023. Data for determining the HOSPITAL score and the LACE index were derived from electronic medical records. Demographic characteristics of patients readmitted within 30 days were compared with those who were not readmitted. The scores were evaluated for overall performance, discriminatory power and calibration, with discriminatory power assessed using the concordance statistic (C-statistic).

Results

Over 6 years, 7245 CAP discharges were recorded, with 1329 (18.3%) readmissions within 30 days. The mean (s.d.) age of the cohort was 74.4 (17.8) years. Readmitted patients were more likely to have multiple morbidities and frailty than those not readmitted (P < 0.05). They also had a higher mean number of emergency department presentations and hospital admissions in the previous year and a longer initial hospital stay (P < 0.05). Overall, the mean (s.d.) HOSPITAL score and LACE index were 3.4 (2.1) and 9.3 (3.6), respectively. Among readmissions, 28.4% occurred in patients with a HOSPITAL score >4 (intermediate and high-risk group), while 25.8% occurred in patients in the high-risk LACE category (LACE index > 10). The C-statistic for the HOSPITAL score and LACE index was 0.62 (95% CI 0.61–0.64) and 0.63 (95% CI 0.61–0.65), respectively, with no significant difference in the area under the receiver operating characteristic curves (P > 0.05).

Conclusions

The predictive abilities of the HOSPITAL score and the LACE index for CAP readmissions are modest and comparable in an Australian setting.

Keywords: Australian health care, community-acquired pneumonia, HOSPITAL score, LACE index, readmission.

References

Campling J, Jones D, Chalmers JD, Jiang Q, Vyse A, Madhava H, et al. The impact of certain underlying comorbidities on the risk of developing hospitalised pneumonia in England. Pneumonia 2019; 11: 4.
| Crossref | Google Scholar | PubMed |

Tsirgiotis E, Ruffin R. Community acquired pneumonia. A perspective for general practice. Aust Fam Physician 2000; 29: 639-45.
| Google Scholar | PubMed |

Sharma Y, Mangoni AA, Shahi R, Horwood C, Thompson C. Recent temporal trends, characteristics and outcomes of patients with non-COVID-19 community-acquired pneumonia at two tertiary hospitals in Australia: an observational study. Intern Med J 2024;
| Crossref | Google Scholar | PubMed |

Earle K, Williams S. Burden of pneumococcal disease in adults aged 65 years and older: an Australian perspective. Pneumonia 2016; 8: 9.
| Crossref | Google Scholar | PubMed |

Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 2009; 360: 1418-28.
| Crossref | Google Scholar | PubMed |

Allaudeen N, Schnipper JL, Orav EJ, Wachter RM, Vidyarthi AR. Inability of providers to predict unplanned readmissions. J Gen Intern Med 2011; 26: 771-6.
| Crossref | Google Scholar | PubMed |

Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011; 306: 1688-98.
| Crossref | Google Scholar | PubMed |

Weinreich M, Nguyen OK, Wang D, Mayo H, Mortensen EM, Halm EA, et al. Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance. Ann Am Thorac Soc 2016; 13: 1607-14.
| Crossref | Google Scholar | PubMed |

Hatipoğlu U, Wells BJ, Chagin K, Joshi D, Milinovich A, Rothberg MB. Predicting 30-Day All-Cause Readmission Risk for Subjects Admitted With Pneumonia at the Point of Care. Respir Care 2018; 63: 43-9.
| Crossref | Google Scholar | PubMed |

10  Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med 2013; 173: 632-8.
| Crossref | Google Scholar | PubMed |

11  Donzé JD, Williams MV, Robinson EJ, Zimlichman E, Aujesky D, Vasilevskis EE, et al. International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions. JAMA Intern Med 2016; 176: 496-502.
| Crossref | Google Scholar | PubMed |

12  Yazdan-Ashoori P, Lee SF, Ibrahim Q, Van Spall HG. Utility of the LACE index at the bedside in predicting 30-day readmission or death in patients hospitalized with heart failure. Am Heart J 2016; 179: 51-8.
| Crossref | Google Scholar | PubMed |

13  van Walraven C, Wong J, Forster AJ. LACE+index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med 2012; 6: e80-90.
| Google Scholar | PubMed |

14  Rajaguru V, Han W, Kim TH, Shin J, Lee SG. LACE Index to Predict the High Risk of 30-Day Readmission: A Systematic Review and Meta-Analysis. J Pers Med 2022; 12: 545.
| Crossref | Google Scholar | PubMed |

15  Burke RE, Schnipper JL, Williams MV, Robinson EJ, Vasilevskis EE, Kripalani S, et al. The HOSPITAL Score Predicts Potentially Preventable 30-Day Readmissions in Conditions Targeted by the Hospital Readmissions Reduction Program. Med Care 2017; 55: 285-90.
| Crossref | Google Scholar | PubMed |

16  Jones SL, Cheon O, Manzano JM, Park AK, Lin HY, Halm JK, et al. Comparison of LACE and HOSPITAL Readmission Risk Scores for CMS Target and Nontarget Conditions. Am J Med Qual 2022; 37: 299-306.
| Crossref | Google Scholar | PubMed |

17  Dreyer R, Gome J. Causes for 30-day readmissions and accuracy of the LACE index in regional Victoria, Australia. Intern Med J 2024; 54: 951-60.
| Crossref | Google Scholar | PubMed |

18  Dobler CC, Hakim M, Singh S, Jennings M, Waterer G, Garden FL. Ability of the LACE index to predict 30-day hospital readmissions in patients with community-acquired pneumonia. ERJ Open Res 2020; 6: 00301-2019.
| Crossref | Google Scholar | PubMed |

19  Sharma Y, Horwood C, Hakendorf P, Au J, Thompson C. Characteristics and clinical outcomes of index versus non-index hospital readmissions in Australian hospitals: a cohort study. Aust Health Rev 2020; 44: 153-9.
| Crossref | Google Scholar | PubMed |

20  Shkirkova K, Connor M, Lamorie-Foote K, Patel A, Liu Q, Ding L, et al. Frequency, predictors, and outcomes of readmission to index versus non-index hospitals after mechanical thrombectomy in patients with ischemic stroke. J Neurointerv Surg 2020; 12: 136-41.
| Crossref | Google Scholar | PubMed |

21  Independent Health and Aged Care Pricing Authority (IHACPA). Chronicle of The International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) First Edition to Eleventh Edition. IHACPA; 2019.

22  Sharma Y, Horwood C, Hakendorf P, Shahi R, Thompson C. External Validation of the Hospital Frailty-Risk Score in Predicting Clinical Outcomes in Older Heart-Failure Patients in Australia. J Clin Med 2022; 11: 2193.
| Crossref | Google Scholar | PubMed |

23  Sharma Y, Thompson C, Kaambwa B, Shahi R, Miller M. Validity of the Malnutrition Universal Screening Tool (MUST) in Australian hospitalized acutely unwell elderly patients. Asia Pac J Clin Nutr 2017; 26: 994-1000.
| Crossref | Google Scholar | PubMed |

24  van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ 2010; 182: 551-7.
| Crossref | Google Scholar | PubMed |

25  Robinson R, Hudali T. The HOSPITAL score and LACE index as predictors of 30 day readmission in a retrospective study at a university-affiliated community hospital. PeerJ 2017; 5: e3137.
| Crossref | Google Scholar | PubMed |

26  Rufibach K. Use of Brier score to assess binary predictions. J Clin Epidemiol 2010; 63: 938-9.
| Crossref | Google Scholar | PubMed |

27  Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 2010; 21: 128-38.
| Crossref | Google Scholar | PubMed |

28  de Hond AAH, Steyerberg EW, van Calster B. Interpreting area under the receiver operating characteristic curve. Lancet Digit Health 2022; 4: e853-5.
| Crossref | Google Scholar | PubMed |

29  Nattino G, Pennell ML, Lemeshow S. Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test. Biometrics 2020; 76: 549-60.
| Crossref | Google Scholar | PubMed |

30  DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44: 837-45.
| Crossref | Google Scholar | PubMed |

31  Kim LD, Kou L, Messinger-Rapport BJ, Rothberg MB. Validation of the HOSPITAL Score for 30-Day All-Cause Readmissions of Patients Discharged to Skilled Nursing Facilities. J Am Med Dir Assoc 2016; 17: 863.e15-8.
| Crossref | Google Scholar | PubMed |

32  Garrison GM, Robelia PM, Pecina JL, Dawson NL. Comparing performance of 30-day readmission risk classifiers among hospitalized primary care patients. J Eval Clin Pract 2017; 23: 524-9.
| Crossref | Google Scholar | PubMed |

33  Cooksley T, Nanayakkara PW, Nickel CH, Subbe CP, Kellett J, Kidney R, et al. Readmissions of medical patients: an external validation of two existing prediction scores. QJM 2016; 109: 245-8.
| Crossref | Google Scholar | PubMed |

34  Cotter PE, Bhalla VK, Wallis SJ, Biram RW. Predicting readmissions: poor performance of the LACE index in an older UK population. Age Ageing 2012; 41: 784-9.
| Crossref | Google Scholar | PubMed |

35  Patel SA, Krasnow M, Long K, Shirey T, Dickert N, Morris AA. Excess 30-Day Heart Failure Readmissions and Mortality in Black Patients Increases With Neighborhood Deprivation. Circ Heart Fail 2020; 13: e007947.
| Crossref | Google Scholar | PubMed |

36  Joynt Maddox KE, Reidhead M, Hu J, Kind AJH, Zaslavsky AM, Nagasako EM, et al. Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program. Health Serv Res 2019; 54: 327-36.
| Crossref | Google Scholar | PubMed |

37  Conroy S, Dowsing T, Reid J, Hsu R. Understanding readmissions: An in-depth review of 50 patients readmitted back to an acute hospital within 30 days. Eur Geriatr Med 2013; 4: 25-7.
| Crossref | Google Scholar |

38  Sager MA, Franke T, Inouye SK, Landefeld CS, Morgan TM, Rudberg MA, et al. Functional outcomes of acute medical illness and hospitalization in older persons. Arch Intern Med 1996; 156: 645-52.
| Crossref | Google Scholar | PubMed |