Table 1.

Strategies for predicting readmission risk within 30 days of discharge

Model name	Sample size	Population	Validation	Validation C-statistic	# of variables	Variables in model
Logistic regression models
Rico, 2016 [39]	4,879 patients 6,158 discharges	Adults with a discharge diagnosis of T2D	None	0.73	6	Age, marital status, Charlson comorbidity index, LOS, # of admissions, discharge disposition
	Strengths: outcome was unplanned readmission, few variables Weaknesses: small sample size, limited to T2D, not validated
DERRI, 2016 [9••]	17,284 patients 44,203 discharges 42,800 patients	Adults on diabetes medication preadmission or discharge diagnosis of diabetes	Internal	0.69	10	Employment status, living within 5 miles of the hospital, preadmission insulin use, burden of macrovascular diabetes complications, admission serum hematocrit, creatinine, and sodium, having a hospital discharge within 90 days before admission, most recent discharge status up to 1 year before admission, and a diagnosis of anemia
2018 [10]	105,960 discharges		External	0.63
	Strengths: decent sample size, only 10 variables, uses only pre-discharge variables, available as a web app, externally validated, racially and ethnically diverse sample Weaknesses: single center
DERRI vs HOSPITAL, 2019 [17]	200 patients 200 discharges	Adults on diabetes medication preadmission or discharge diagnosis of diabetes	External	DERRI 0.80 HOSPITAL 0.73	DERRI 10 HOSPITAL 8	HOSPITAL: hemoglobin level at discharge, discharge from oncology service, sodium level at discharge, procedure during hospital stay, index admission type, # of admissions during the last 12 months, LOS
	HOSPITAL strengths: decent sample size, only 8 variables, available as a web app, outcome was potentially avoidable readmissions, developed in patients discharged from any medical service (not limited to diabetes), externally validated [40, 41] Weaknesses: single center, not usable until day of discharge [40]
Collins, 2017 [42]	63,237 patients	Adult Medicare Advantage patients with a discharge diagnosis of T2D	Internal	0.82	14	Age, sex, # ED visits, LOS, diseases of urinary system, fluid and electrolyte disorders, diseases of WBCs, other nervous system disorders, diseases of the heart, other lower respiratory diseases, gastrointestinal hemorrhage, liver diseases, hemodialysis
	Strengths: large sample size, good performance, nearly 200 variables examined Weaknesses: did not analyze multiple hospitalizations per patient, limited target population
DERRI CVD, 2017 [43]	8,189 discharges	Adults with primary discharge diagnosis of CVD and diabetes medication preadmission treatment with diabetes medication or diagnosis of diabetes	Internal	0.68	10	Living within 5 miles of the hospital; employment status; having a hospital discharge within 90 days before admission; lower educational attainment; burden of macrovascular diabetes complications; preadmission sulfonylurea therapy, preadmission metformin; higher serum creatinine; lower serum albumin; schizophrenia or mood disorders
	Strengths: only 10 variables, uses only pre-discharge variables, racially and ethnically diverse sample Weaknesses: single center, modest sample size, not available as a web app, not externally validated
Karunakaran (DERRI-Plus), 2018 [20]	17,284 patients	Adults on diabetes medication preadmission or discharge diagnosis of diabetes	None	0.82	27	DERRI variables plus: no follow-up visit within 30 days post-index discharge, Charleston comorbidity index, LOS, insurance status, sex, race/ethnicity, preadmission glucocorticoid, preadmission thiazolidinedione, gastroparesis, WBC count, blood glucose, serum albumin, urgency of admission, cardiac dysrhythmias, schizophrenia or mood disorder, fluid or electrolyte disorder, and blood transfusion
	Strengths: decent sample size, racially and ethnically diverse sample Weaknesses: single center, not available as a web app, not validated, not feasible for manual POC use
Machine learning models
Alloghani, 2018 [44]	78,363 discharges	Adults with diabetes- related hospitalization and LOS 1–14 days, treated with diabetes medications	Internal	0.64	5	# of inpatient stays, # of emergency visits, admission source id, discharge disposition, # of diagnoses
	Strengths: large sample size Weaknesses: few variables, administrative source of data with missing values (e.g., weight in 97% of patients), narrow inclusion criteria (only considering LOS between 1 and 14 days), not available as a web app, not usable until day of discharge
Alturki, 2019 [24]	71,518 patients 101,766 discharges	Adults with diabetes- related hospitalization and LOS 1–14 days	None	0.97	15	LOS, # of procedures, # of diagnoses, # of lab procedures, # of medications, use of specific diabetes medications
	Strengths: highly accurate Weaknesses: not validated, administrative source of data with missing values (e.g., weight in 97% of patients), narrow inclusion criteria (only considering LOS between 1 and 14 days), not available as a web app, not feasible for manual POC use, not usable until day of discharge
Sarthak, 2020 [45]	70,000 patients 100,000 discharges	Adults with diabetes- related hospitalization and LOS 1–14 days	Internal	0.97	35	Count of medications (# of adjustments), diabetes medication, change in medication, comorbid diagnoses, insulin, # of lab procedures, medical specialty, discharge disposition, # of medications, payer, age, admission source, race, LOS, AIc, gender, admission type, # of diagnoses, # of procedures, service utilization (sum of inpatient, outpatient, and emergency visits), # inpatient, # outpatient, max glucose serum, # emergency, use of specific diabetes medication
	Strengths: highly accurate Weaknesses: administrative source of data with missing values (e.g., weight in 97% of patients), narrow inclusion criteria (only considering LOS between 1 and 14 days), not available as a web app, not feasible for manual POC use, not usable until day of discharge
Ossai, 2020 [46]	78,363 discharges	Adults with diabetes- related hospitalization and LOS 1–14 days, treated with diabetes medications	Internal	0.84	9	Age, LOS, insulin, use of specific diabetes medications
	Strengths: good accuracy, removed variables from consideration that were missing greater than 90% of values Weaknesses: administrative source of data with narrow inclusion criteria (only considering LOS between 1 and 14 days), not available as a web app, not usable until day of discharge

DERRI Diabetes Early Readmission Risk Indicator, ED emergency department, LOS length-of-stay, POC point-of-care, T2D type 2 diabetes, WBC white blood cells