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Table 1 Study characteristics

From: The use of predictive fall models for older adults receiving aged care, using routinely collected electronic health record data: a systematic review

Authors; Year; Country

Setting

Population aged group

Data Source

Study Design

Statistical Model

Derivation Cohort

(% of total cohort)

Internal Validation Cohort (% of total cohort)

External Validation Cohort

Falls Outcome Prediction

Falls Rate (%)

Risk Score/ Category

Number of models

Discrimination (AUC), (95% CI)

Implemented in practice

Volrathongchai, et al.; 2005; US [31]

Residential Care

65–100 years

MDS

Retrospective

LBP

9,980 (100%)

NR

Fall within 3-months

NR

No

1

NR

No

Marier, et.al.; 2016; US [30]

Residential Care

NR

EMR and MDS

Retrospective

Repeated events survival model

2,527 (49.3%)

2,602 (50.7%)

NR

2.3–32.3% across the deciles in validation cohort depending on the model used

Yes

4

1:MDS Assessments

2: MDS Assessments & EMR only

3: MDS assessments & EMR duplicates

4: MDS assessments & EMR Only & EMR Duplicates

AIC

1: 6733

2: 6749

3: 6614

4:6626

No

Kuspinar, et al.; 2019; Canada [29]

Home care

77 ± 14 years with no previous fall in the last 90 days

RAI-HC

Prospective

Decision tree

88,690 (70%)

Internal:

38,013 (30%)

External:

2,738

1,226

9,566

NR

5–35% across risk categories in derivative cohort

Yes

1

NR

No

Lo, et al.; 2019; US [32]

Home care

65 + years

OASIS and EHR

Retrospective

Random Forest Algorithm

29,514 (50%)

29,514 (50%)

NR

5.14% (for emergency care or hospitalisation)

No

3 – Validated against the MAHC-10

1: Combined

2: OASIS

3: MACH model

1: 0.67

2: 0.67 (0.66, 0.68)

3: 0.6 (0.59,0.62)

No

  1. MDS Minimum Data Set, LBP Likelihood Basis Pursuit, EMR Electronic Medical record, RAI-HC Resident Assessment Instrument-Home Care, OASIS Outcome and Assessment information Set, AUC Area under the curve, AIC Akaike Information Criteria, MACH-10 Missouri Alliance for Home Care fall risk assessment, NR Not reported