HOMR model accurately predicts 1-year mortality in older - - PowerPoint PPT Presentation

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HOMR model accurately predicts 1-year mortality in older - - PowerPoint PPT Presentation

Jun 15, 2023 149 likes •540 views

HOMR model accurately predicts 1-year mortality in older hospitalized patients C U R T I N D , O D O N N E L L D , D O Y L E D , G A L L A G H E R P , O M A H O N Y D . U N I V E R S I T Y C O L L E G E C O R K , I R E L A N

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C U R T I N D , O ’ D O N N E L L D , D O Y L E D , G A L L A G H E R P , O ’ M A H O N Y D .

U N I V E R S I T Y C O L L E G E C O R K , I R E L A N D

E U G M S , S E P T E M B E R 2 1 , 2 0 1 7

HOMR model accurately predicts 1-year mortality in older hospitalized patients

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Background

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George

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Estimating prognosis

 doctors are inaccurate and

  • verly optimistic

1. Parkes CM. Accuracy of predictions of survival in later stages of cancer. BMJ 1972;ii: 29-31. [PMC free article][PubMed] 2. Christakis N, Lamont E. Extent and determinants of error in doctors' prognoses. BMJ 2000;320: 469-73. [PMC free article][PubMed] 3. Vigano A, Dorgan M, Buckingham J, Bruera E, Suarez-Alzamor ME. Survival prediction in terminal cancer patients: a systematic review of the medical literature. Palliat Med 2000;14: 363-74. [PubMed] 4. Christakis NA, Lamont ER. Extent and determinants of error in physicians' prognoses in terminally ill patients. West J Med. 2000 May; 172(5): 310–313.

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Prediction Models

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Prediction Models

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Assessing Performance of Prediction models

 Discrimination (C Statistic)  Calibration  Transportability

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Assessing Performance of Prediction models

 Discrimination (C Statistic)  Calibration  Transportability

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Discrimination (C Statistic)

C statistic ≥0.9 Excellent 0.8 -0.89 Very good 0.7-0.79 Good 0.6-0.69 Fair 0.5-0.59 Poor

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Discrimination (C Statistic)

C statistic ≥0.9 Excellent 0.8 -0.89 Very good 0.7-0.79 Good 0.6-0.69 Fair 0.5-0.59 Poor

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Discrimination (C Statistic)

C statistic ≥0.9 Excellent 0.8 -0.89 Very good 0.7-0.79 Good 0.6-0.69 Fair 0.5-0.59 Poor >

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Assessing Performance of Prognostic models

 Discrimination (C Statistic)  Calibration  Transportability

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Calibration

 Agreement between observed

and predicted outcomes

 <10% difference indicates good

calibration

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Assessing Performance of Prognostic models

 Discrimination (C Statistic)  Calibration  Transportability

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Transportability

 Different population  Different location  Different investigators

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Systematic Review of Prognostic Models

 “testing of transportability was

limited”

 “insufficient evidence at this

time to recommend the widespread use..”

JAMA, 2012

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The Hospital-patient One-year Mortality Risk (HOMR) Model (2014)

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HOMR model

 Predicts 1 year mortality after

hospitalization

 Cohort >3 million; Adults of all

ages

 C statistic 0.9  <1% difference between

  • bserved and expected

mortality

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HOMR model

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HOMR model

HOMR score Predicted risk 47 70% 46 63% 45 58% 44 53% 43 50% 42 46% 41 43% 40 37% 39 32%

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Assessment of the performance of the HOMR model in an

  • lder Irish cohort
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Methods

 Adult inpatients ≥65 under

care of geriatric medicine service

 January 2013 –March 2015  Primary outcome: death within

1 year after discharge from hospital

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Results

Characteristic Male 43% Mean age 82 Emergency admission 94% Independent 67% Home care 21.3% Nursing home 7.7% 1409 patients 1150 alive 259 Dead (18.4%)

Baseline

...........................................................................

1 year

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Results

C statistic 0.79 (95% CI 0.75 -0.82)

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Results

Calibration:

 Deaths:

259 (18.4%)

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Results

Calibration:

 Deaths:

259 (18.4%)

 Predicted deaths

403 (28.8%)

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Results

Calibration:

 Deaths:

259 (18.4%)

 Predicted deaths

403 (28.8%)

 Odds ratio for death:

Irish population = North American population

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Calibration

10 20 30 40 50 60 70 80 90 100 25 27 29 31 33 35 37 39 41 43 45 47 49 51 Predicted Observed

% dead at

  • ne

year HOMR score

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Calibration

10 20 30 40 50 60 70 80 90 100 25 27 29 31 33 35 37 39 41 43 45 47 49 51 Predicted Observed Medium risk

% dead at

  • ne

year HOMR score

Low risk High risk

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Re-calibration

10 20 30 40 50 60 70 80 90 100 25 27 29 31 33 35 37 39 41 43 45 47 49 51 Predicted Observed

% dead at

  • ne

year HOMR score

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Discussion

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Discussion

Model Description

C-Statistic:

Derivation Validation Independent validation

HELP, 2000 Patients ≥80 years, emergency admissions C= 0.73 (N=1266) C=0.74 (N=150)

  • Walter et al, 2001

Patients ≥70 years, discharged from general medicine service C=0.75 (N=1495) C=0.79 (N=1427) C=0.72 (N=122; patients ≥75; 5 year mortality prediction ) BISEP, 2003 Patients ≥70 years, admitted under general medicine service C=0.83 (N=525) C=0.77 (N=1246) C=0.73 (N=122; patients ≥75; 5 year mortality prediction ) Levine et al, 2007 Patients ≥65 years discharged from general medicine service C=0.67 (N=2739) C=0.65 (N=3643)

  • MPI,

2008 Patients ≥65 years admitted to geriatric unit C=0.75 C=0.75

  • Silver Code, 2010

Patients ≥75 admitted through the emergency department C=0.66 (N=5457) C=0.64 (N=5456)

  • HOMR, 2014

Adult patients admitted under non-psychiatric hospital services C=0.92 (N=319 531) C=0.89 -0.92 (N= 2 862 996) C=0.79 (N=1409; patients ≥65 years discharged from geriatric service)

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Discussion

Model Description

C-Statistic:

Derivation Validation Independent validation

HELP, 2000 Patients ≥80 years, emergency admissions C= 0.73 (N=1266) C=0.74 (N=150)

  • Walter et al, 2001

Patients ≥70 years, discharged from general medicine service C=0.75 (N=1495) C=0.79 (N=1427) C=0.72 (N=122; patients ≥75; 5 year mortality prediction ) BISEP, 2003 Patients ≥70 years, admitted under general medicine service C=0.83 (N=525) C=0.77 (N=1246) C=0.73 (N=122; patients ≥75; 5 year mortality prediction ) Levine et al, 2007 Patients ≥65 years discharged from general medicine service C=0.67 (N=2739) C=0.65 (N=3643)

  • MPI,

2008 Patients ≥65 years admitted to geriatric unit C=0.75 C=0.75

  • Silver Code, 2010

Patients ≥75 admitted through the emergency department C=0.66 (N=5457) C=0.64 (N=5456)

  • HOMR, 2014

Adult patients admitted under non-psychiatric hospital services C=0.92 (N=319 531) C=0.89 -0.92 (N= 2 862 996) C=0.79 (N=1409; patients ≥65 years discharged from geriatric service)

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Discussion

Model C statistic HOMR 0.79 CHA2DS2-VASc 0.68 HAS-BLED 0.69

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Discussion

Can the model be used to predict 1-year mortality in older hospitalized

patients?

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Conclusion

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Conclusion

 Prognostic models are important  HOMR model is robust  Compares favourably to other prognostic models  Re-calibrated model needs to be tested

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Thank you