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Escobar et al. Non-elective rehospitalizations MS

NON-ELECTIVE REHOSPITALIZATIONS AND POST-DISCHARGE MORTALITY: PREDICTIVE MODELS SUITABLE FOR USE IN REAL TIME

Gabriel J. Escobar, MD; Arona Ragins, MA; Peter Scheirer, MA; Vincent Liu, MD, MS; Jay Robles, BA; Patricia Kipnis, PhD

WEB APPENDIX FOR INTERESTED READERS

All SAS code used for this project’s data processing or analysis is available to interested readers. SAS and SQL code for the LAPS2 and LAPS2dc severity scores and the COPS2 longitudinal comorbidity score are also available to interested readers.

Number Description Pages

1 Predictors included in descriptive analyses and modeling 2 - 5

2 Additional information on cohort 6

3 Predictive modeling methodology employed 7

4 Cohort description (patient as unit of analysis) 8 - 9

5 Bivariate comparisons (patient as unit of analysis) 10 - 11

6 Comparison of 7 day EMR models to LACE 12

7 Calibration curves in validation dataset 13 - 19

8 Relative contribution of predictors 20

9 Supplemental analyses – 7 and 30 day model performance characteristics 21 - 23

10 Supplemental analyses – Impact of incorporating diagnosis in EMR models 24

11 Supplemental analyses – Model performance across subgroups 25 - 30

12 Beta Coefficients for models from derivation data 31 - 36

13 Kaplan-Meier Curves for Models 37 - 42

14 Platform presentation describing current KPNC early warning system pilot 43 – 71 (American Thoracic Society meeting, San Diego, California, 5/19/14)

15 Relationship between model predictors and disaggregated outcomes (odds ratios using univariate logistic regression) 72

– 74

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APPENDIX 1: PREDICTORS INCLUDED IN DESCRIPTIVE ANALYSES AND MODELING

The figure on page 5 shows the predictors available to us. The figure does not include some predictors that are commonly available (e.g., age and sex). As noted in the text, because we cannot capture diagnosis reliably in real time, we did not include diagnoses as predictors. Going from top to bottom and left to right, predictors were as follows:

LAPS2 (Laboratory Acute Physiology Score, version 2)

The LAPS2, described in citation 19, is based on 15 laboratory tests, vital signs (temperature, heart rate, respiratory rate, blood pressure), pulse oximetry, neurological status as documented in nursing flow sheets, and interaction terms (e.g., the shock index). The “look back” period for LAPS2 is 72 hours from the time of rooming in at the patients’ first hospital unit. Additional information on this predictor can be obtained from the principal investigator.

The figure below shows the distribution of scores in the original paper describing LAPS2, which was based on 248,383 patients.

0-24 25-49 50-74 75-99 100-124 125-149 150+ -

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COPS2 (COmorbidity Point Score, version 2)

Every month, the KPNC Decision Support Department scans data from outpatient and inpatient encounters from the entire KPNC membership. Using the ICD codes from these encounters, the MIA department assigns these codes to 70 possible Hierarchical Condition Categories (HCCs) using their inpatient and outpatient utilization during the preceding 12 month period. A given patient may have multiple HCC assignments. The ICD code categories used for these HCCs are those used by the Centers for Medicare and Medicaid Services. The COPS2 employs 45 of these HCCs to assign a point score.

The figure below shows the distribution of scores in the original paper describing COPS2, which was based on 248,383 patients (citation 19).

0-14 15-49 50-99 100-149 150-199 200+ -

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HOSPITALIZATION AND/OR ED VISIT

We scanned each patient’s records in KPNC hospitalization and emergency department databases (these are now part of the Epic inpatient record). Out of plan use was also captured for these predictors.

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LENGTH OF STAY

For hospitalizations included in the predictive model, the T0 was time of rooming in, and the TEND was the date/time stamp from the last linked hospital stay (as noted in the text, we concatenated stays of patients who experienced inter-hospital transport). Patients who experienced a non-KPNC hospital stay as part of a hospitalization episode are included (length of stay was bridged across stays).

OR (operating room stay)

KPNC bed history databases capture when patients entered or left the operating room. For the purposes of our analyses, we categorized patients as having had 0, 1, or 2+ operating room stays.

ICU (admission to Intensive Care Unit)

KPNC bed history databases capture when patients entered or left the ICU; it is also possible to determine whether a patient experienced assisted ventilation (V) or received continuous infusions of pressor agents (P). All KPNC patients admitted to the ICU are also assigned retrospective eSAPS3 scores (see citation 16). The eSAPS3 has a 12 hour “look back” time frame and a +1 hour “look forward” time frame for data capture, with the T0 being time of physical entry into the ICU.

LAPS2dc (LAPS2 at discharge)

The LAPS2dc is calculated in the same way as the LAPS2 except that (a) the T0 was set to 0800 hours on the day of discharge, (b) the “look back” time frame was set to 24 hours instead of 72, and (c) all patients were assumed to be “low risk” for the purposes of imputation of missing data (see citation 19 for additional details on the LAPS2 imputation algorithm).

CARE DIRECTIVE

We examined both admission and discharge care directives. As we have reported in citation 19, admission care directives are mandatory (“hard stop”) in the EMR, and a physician’s other orders cannot be signed without a care directive being specified. For the discharge care directive, we employed the last care directive ordered by the treating physicians. We grouped these as “full code” vs. “not full code” (which included “do not resuscitate,” “partial code,” and “comfort care only”).

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APPENDIX 2: ADDITIONAL INFORMATION ON COHORT

TABLE A: SITE OF DEATH

Mortality Inpatient Other location Total

7 day 803 7,198 8,001

30 day 4,646 18,047 22,693

TABLE B: INCLUSION CRITERIA AMONG THE REHOSPITALIZATIONS1

ACS2 Via ED3 LAPS2 ≥ 604 Number of rehospitalizations Deaths (%)

NO NO NO 17,818 241 (1.35%)

NO NO YES 2,247 189 (8.41%)

NO YES NO 15,675 409 (2.61%)

NO YES YES 41,432 3,605 (8.70%)

YES NO NO 1,113 17 (1.53%)

YES NO YES 326 22 (6.75%)

YES YES NO 1,612 21 (1.30%)

YES YES YES 9,452 431 (4.56%)

1. Top row shows that some deaths occurred in elective rehospitalizations.

2. Ambulatory Care Sensitive diagnosis; see main main text for details.

3. Emergency Department

4. Laboratory Acute Physiology Score, version 2; see main text for details.

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1. APPENDIX 3: PREDICTIVE MODELING METHODOLOGY EMPLOYED

The body of this paper discussed four models. For one of them (30-day combined outcome at discharge) we were fortunate in that the superior model ended up using the simple-to-implement logistic regression method. Other methods considered (using SAS version 9.3, JMP version 7, or R version 3 as needed) were ANCOVA, saturated ANOVA with smoothing by logistic regression when needed, random forest, conditional inference recursive partition, neural network, recursive-partition-then-logistic regression, and a nearest-neighbor Mahananobis-distance-weighted approach that does not limit itself to a fixed number of neighbors.

Some of these methods built models on a random 1/6 of patients and then tested on another random 1/6. Other methods built models on a random 1/4 of patients and then tested on another random 1/4. For all methods the selected model was based on its performance in their respective test data, and a separate random half of all patients (who had not been used previously by any method) was set aside for validation of that selected model. The best model was selected based on a high c-statistic with a subjective penalty for the number of covariates used and the complexity of the model itself.

Covariates considered were an indicator if admission was in the ED or not, the number of prior hospitalizations up to 7 days and up to 30 days prior to admission, the length of stay, age, gender, COPS2 at admission, the care order level at discharge, LAPS2 at admission and at 8am of day discharge, and the discharge day-of-week.

For when employing the logistic regression method, considered also were all possible two-way interactions and (for the continuously-valued covariates) four-knot restricted cubic splines (via Harrell’s %DASPLINE macro in SAS). Also the two parts of the combined outcome (death only or undesired rehospitalization only) were modelled separately and then blended to form an estimated combined outcome. Derived variables were considered as well, such as truncating length of stay and combining age, gender, COPS2, and LAPS2 via a quadratic response surface (which was in turn calibrated separately to the combined outcome, death only, and undesired rehospitalization only).

The other three models presented in this paper were developed by shorter routes. With the model for the 30-day combined outcome at discharge selected, we applied the same model structure onto the 7-day combined outcome at discharge and found the performance adequate. The two models to be run at admission benefitted from our past experience that a greedy recursive partition algorithm (JMP version 7) to identify six terminal nodes of a particular spread (including one node with a small sample size and a very high outcome rate and another node with a large sample size and a very low outcome rate) followed by a separate logistic regression (SAS version 9.3) of a same model structure for each terminal node generally yields satisfactory results. For this paper the covariates allowable for the recursive partition algorithm and the model structure of each of the six logistic regressions were age, gender, COPS2, and LAPS2. These four covariates were selected as they represent the classic two demographics, a comorbidity metric, and an acuity metric that all can be ascertained at admission in an emergency department. These four covariates entered the logistic regressions linearly and without interactions. Built in this fashion, the resulting two admission models were found to have sufficient performance, as demonstrated in the body of the paper.

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APPENDIX 4: COHORT DESCRIPTION (PATIENT AS UNIT OF ANALYSIS)

COHORT CHARACTERISTICS1

Derivation dataset Validation dataset Entire cohort

N (patients) 179,978 180,058 360,036N (index hospitalizations) 179,978 180,058 360,036Age (mean ± SD2) 61.7 ± 18.1 61.7 ± 18.1 61.7 ± 18.1Sex (% male) 45.8% 45.8% 45.8%Race/ethnicity

White 47.9% 47.8% 47.9%African American 6.4% 6.5% 6.5%

Asian 17.7% 17.7% 17.7%Hispanic 23.8% 23.8% 23.8%

Other or missing 4.1% 4.1% 4.1%COPS23 (mean ± SD) 21.8 ± 23.4 21.8 ± 23.5 21.8 ± 23.4Charlson score4 (median, IQR)

1 , 2 1 , 2 1 , 2

LAPS25 (mean ± SD) 49.7 ± 37.5 49.5 ± 37.4 49.6 ± 37.4LAPS2dc (mean ± SD) 39.7 ± 23.3 39.6 ± 23.2 39.7 ± 23.3% with these primary conditions6

Community-acquired pneumonia

1.6% 1.6% 1.6%

Sepsis 7.2% 7.3% 7.3%Gastrointestinal bleeding 1.4% 1.4% 1.4%

Hip fracture 1.5% 1.6% 1.6%Any malignancy 7.6% 7.5% 7.5%

Rehospitalization7

7 day (any) 4.1% 4.2% 4.2%7 day (undesirable) 3.5% 3.6% 3.6%

30 day (any) 9.6% 9.8% 9.7%30 day (undesirable) 7.9% 8.0% 7.9%

Mortality7

7 day 0.8% 0.8% 0.8%30 day 2.2% 2.2% 2.2%

Composite outcome7

7 day 4.2% 4.4% 4.3%30 day 9.4% 9.5% 9.5%

Footnotes to Appendix 4

1. This table describes study cohort using individual patients as the unit of analysis. It is a sister to Table 1 in the main manuscript (which reports data using an index hospitalization as the unit of analysis). If a patient had multiple index hospitalizations then only the first one is retained for this table.

2. SD: standard deviation; IQR: interquartile range

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3. See text and citation 19 for description of the COmorbidity Point Score, version 2. The univariate relationship of COPS2 with 30 day mortality is as follows: 0 – 39, 1.7%; 40 – 64, 5.2%, 65+, 9.0%.

4. See citations 19 and 30 for description of methodology used to assign this score.

5. See text, citation 19, and appendix for description of the Laboratory Acute Physiology Score, version 2 as well as the discharge score (LAPS2dc). The univariate relationship of an admission LAPS2 with 30 day mortality is as follows: 0 – 59, 1.0%; 60 – 109, 5.0%, 110+, 13.7%; for LAPS2dc, the relationship with 30 day mortality is as follows: 0 – 59, 2.2%; 60 – 109, 8.1%, 110+, 20.5%.

6. See citation 24 and the appendix for description of how Agency for Healthcare Research and Quality software was employed to group diagnoses into primary conditions.

7. See text for study outcomes definitions. Mortality includes deaths that occurred in and outside the hospital.

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APPENDIX 5: BIVARIATE COMPARISONS (PATIENT AS UNIT OF ANALYSIS)SELECTED CHARACTERISTICS OF INDEX HOSPITALIZATIONS WITH AND WITHOUT THE 30-DAY COMPOSITE OUTCOME1

BIVARIATE COMPARISONS1

Index hospitalizations not followed by the composite outcome

Index hospitalizations followed by the composite outcome

N 325920 34116Age (mean ± SD2) 60.8 ± 18.0 70.2 ± 16.7Sex (% male) 45.5% 48.4%COPS23 (mean ± SD) 20.5 ± 21.9 34.8 ± 32.2Charlson score4 (median, IQR) 1 , 2 2 , 2LAPS25 (mean ± SD) 47.2 ± 36.1 72.5 ± 42.3LAPS2dc (mean ± SD) 38.4 ± 22.2 51.5 ± 29.0% with these primary conditions6

Community-acquired pneumonia 1.6% 2.5%Sepsis 6.8% 11.7%

Gastrointestinal bleeding 1.4% 1.5%Hip fracture 1.5% 2.1%

Any malignancy 7.8% 5.5%“Full code” at admission (%)7 93.2% 79.5%“Full code” at time of hospital discharge (%) 92.2% 73.5%Admitted through emergency department (%) 57.4% 75.9%Length of stay (mean ± SD) 4.5 ± 5.9 6.5 ± 10.2Ever admitted to ICU8 (%) 13.1% 19.6%Experienced unplanned transfer to ICU (%) 2.1% 4.7%Experienced 1+ surgical procedure after already experiencing one such procedure9 (%)

4.7% 9.9%

Footnotes to

1. This table provides bivariate comparisons using individual patients as the unit of analysis. It is a sister to Table 2 in the main manuscript (which reports data using an index hospitalization as the unit of analysis). If a patient had multiple index hospitalizations then only the first one is retained for this table.

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1. All variables are significantly different from the mean or median with a p value of <0.0001 except for gastrointestinal bleeding. The p value for gastrointestinal bleeding is 0.0742.

2. SD: standard deviation; IQR: interquartile range

3. See text, citation 19, and Table 1 for details of the COmorbidity Point Score, version 2.

4. See citations 19 and 30 for description of methodology used to assign this score.

5. See text, citation 19, and Table 1 for details of the the Laboratory Acute Physiology Score, version 2 as well as its discharge version.

6. See citation 24 for description of how Agency for Healthcare Research and Quality software was employed to group diagnoses into primary conditions.

7. See citation 19 for details on how patient care directives are captured in the electronic record.

8. ICU: intensive care unit. See citations 13 and 19 and the Appendix for a description of how we employed bed history data to distinguish between ever admits to the ICU and unplanned transfer to the ICU.

9. This is a proxy for a major surgical complication (i.e., one requiring return to the operating room). See citations 13 and 19 for details on how we employed bed history data. For this item, the denominator consists of 158,299 hospitalizations in which the patient had surgery; of these, 9,055 experienced the combined outcome.

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APPENDIX 6: COMPARISON OF 7 DAY MODELS TO LACE

MODEL2

[95% Confidence Interval]Difference vs LACE3

Point Estimate [95% Confidence Interval]METRIC1 LACE ED 7 Discharge

Day 7EMR – admit EMR – discharge

c statistic 0.690(0.686, 0.694)

0.698(0.694, 0.701)

0.715(0.711, 0.719)

0.008(0.004, 0.011)

0.025(0.022, 0.028)

R2 0.0709(0.0679, 0.0740)

0.0778(0.0744, 0.0809)

0.0900(0.0862, 0.0934)

0.0069(0.0040, 0.0097)

0.0191(0.0163, 0.0216)

Calibration break

40% 30% 20%

NRI (vs. LACE)

0.00004(-0.00003, 0.00016)

0.0080(0.0066, 0.0095)

IDI (vs. LACE)

0.0034(0.0027, 0.0042)

0.0135(0.0126, 0.0144)

Footnotes

1. Metrics (top: value of metric; bottom: 95% confidence interval) are as follows: c statistic is the area under the receiver operator characteristic curve|cit|; R2 is the Nagelkerke pseudo-R2|cit|; calibration break refers to the percentile range at which the predictive model deteriorates (see text and appendix 7 for details and graphical displays); NRI (net reclassification improvement) and IDI (integrated discrimination improvement) are calculated according to the methodology of Pencina et al. (see citation 28); for NRI, we used a threshold of ≥ 40% risk for the composite study outcome.

2. ED = electronic medical record models using data available at the time of admission Discharge Day=electronic medical record models using data available at the time of discharge; LACE = length of stay, acuity, Charlson, and emergency visits score of van Walraven et al. (see citation 8).

3. The point estimate is the result from a simple subtraction of one column from another. The confidence intervals are the 2.5% and 97.5% quantiles after 1000 bootstrap replications.

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APPENDIX 7: CALIBRATION CURVES IN VALIDATION DATASET

The figures in the pages that follow provide a graphical illustration of the calibration of the various models we tested for a combined outcome (death or undesirable rehospitalization within 7 or 30 days).

These figures present information as follows.

TOP LEFT: The X axis shows 10 predicted ranges (< 10%, 10 to < 20%, etc.) for the combined outcome, while the Y axis shows the actual observed rate (with its associated 95% confidence interval) in the validation dataset for all observations with that predicted risk. The dotted line shows what would be found were calibration to be perfect.

TOP RIGHT: This figure shows the distribution of observations with a given probability of the outcome where the patient did not have the combined outcome (0, top) and those where the patient did (1, bottom). As can be seen by examining sequential figures of this type, as a model performs better, the “spread” between the two subsets will increase.

BOTTOM LEFT: This figure splits all observations in the validation dataset into 10 deciles on predicted probability of the outcome shows the number of index hospitalizations where the patient was expected to have the outcome (black bars) as well as the number of index hospitalizations where the patient actually had the outcome (grey bars).

BOTTOM RIGHT: The X axis shows 10 predicted outcome ranges (< 10%, 10 to < 20%, etc.), while the Y axis shows the total number of index hospitalizations that fell within each of these ranges.

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Model: LACE Outcome time frame: 30 days

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Model: ED Outcome time frame: 30 days

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Model: Discharge Day Outcome time frame: 30 days

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Model: LACE Outcome time frame: 7 days

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Model: ED Outcome time frame: 7 days

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Model: Discharge Day Outcome time frame: 7 days

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APPENDIX 8: RELATIVE CONTRIBUTION OF PREDICTORS

Relative contribution of a specific covariate was calculated in the following manner. First we determined the log likelihood from the full model and also separately from dropping each covariate in turn. The relative contribution of a specific covariate was a ratio where the denominator was the sum of the changes in the log likelihood and the numerator was the change from dropping that specific covariate.

The prior hospitalization category is a four-level factor that captures any hospitalizations before admission: none up to 30 days previous; 1+ up to 7 days previous and none 8-30 days previous; none up to 7 days previous and 1+ 8-30 days previous; and 1+ up to 7 days previous and another 1+ 8-30 days previous.

Model Outcome time frame Relative contribution of predictorsAGE SEX COPS2 LAPS2

ED 7 days 5.40% 0.60% 47.40% 46.50%

ED 30 days 5.70% 0.10% 61.90% 32.30%

Model Outcome time frame Relative contribution of predictors

COPS2 LAPS2 Length of

StayDischarge care

directive Prior hospitalization

categoryDischarge

Day 7 days 22.10%

31.80% 13.30% 17.40% 15.50%

Discharge Day 30 days 36.90

%25.50

% 8.90% 13.00% 15.70%

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APPENDIX 9: SUPPLEMENTAL ANALYSES – 7 AND 30 MODEL PERFORMANCE CHARACTERISTICS

TABLE 9.1: OPERATIONAL PERFORMANCE CHARACTERISTICS OF 30 DAY MODEL IN VALIDATION DATASET

Risk threshold1

Work-up to detection ratio2 ≥ 10% ≥ 20% ≥ 30% ≥ 50%LACE 4.48 3.37 2.62 2.16ED 30 4.48 3.29 2.67 1.90Discharge Day 30 4.05 2.93 2.45 1.97

Each cell is formatted as “30 day admit model / 30 day discharge model”

Risk threshold1

≥ 10% ≥ 20% ≥ 30% ≥ 50%Work-up to detection ratio2 4.52 / 4.10 3.30 / 2.93 2.67 / 2.45 1.90 / 1.98% capture3

Death or undesirable hospitalization

84.2 / 80.3 55.6 / 51.1 30.2 / 31.1 2.5 / 10.2

Death 96.3 / 95.6 74.6 / 76.1 44.1 / 51.7 4.1 / 19.3Undesirable hospitalization 81.8 / 77.0 51.7 / 45.5 27.4 / 26.5 2.2 / 8.2Undesirable hospitalization days 83.7 / 80.9 54.7 / 50.6 31.1 / 31.2 3.2 / 10.9Any hospitalization 76.9 / 72.4 46.7 / 41.3 24.2 / 23.6 1.9 / 7.2Any hospitalization days 80.3 / 77.9 50.7 / 47.4 28.1 / 28.7 2.8 / 9.8

Footnotes to table

Results for the admit model are in plain font; for the discharge model, in bold italics font; 7 day models’ results are in the appendix.

1. Refers to the predicted risk assigned by either the admit or discharge model

2. Refers to the ratio of number of patients flagged as having at least a risk of 10, 20, 30 or 50% to the number who actually had the composite outcome within the 30 day time frame. Thus, at a ≥ 10% risk level, the admit model flags 172,667 patients in the validation dataset, of whom 38,168 had the study outcome, giving a W:D of 4.52; in contrast, this ratio falls to 2.67 at a predicted risk threshold of ≥ 30%.

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3. Refers to the proportion of indicated outcomes that are identified at the specified threshold. For example, in the entire validation dataset there are 45,304 patients with the study outcome. At a ≥ 10% risk level, the admit model flags 172,667 patients in the validation dataset, of whom 38,168 had the study outcome. Thus the percent of all outcomes caputured by this threshold is 84.2%.

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APPENDIX TABLE 4: OPERATIONAL PERFORMANCE CHARACTERISTICS OF 7 DAY MODEL IN VALIDATION DATASET

Risk threshold1

Work-up to detection ratio2 ≥ 10% ≥ 20% ≥ 30% ≥ 50%LACE 7.12 5.20 3.75 NAED 7 7.14 4.42 4.27 NADischarge Day 7 6.35 4.49 3.61 2.77

Each cell is formatted as “7 day admit model / 7 day discharge model”

Risk threshold1

≥ 10% ≥ 20% ≥ 30% ≥ 50%Work-up to detection ratio2 7.11 / 6.30 4.42 / 4.49 4.27 / 3.58 NA / 2.77% capture3

Death or undesirable hospitalization

37.6 / 37.4 4.2 / 10.2 0.2 / 3.1 NA / 0.2

Death 60.4 / 69.5 9.1 / 23.4 0.4 / 7.0 NA / 0.5Undesirable hospitalization 32.5 / 29.9 3.0 / 7.0 0.2 / 2.2 NA / 0.2

Undesirable hospitalization days 35.0 / 34.4 4.2 / 9.5 0.3 / 3.5 NA / 0.3Any hospitalization 29.5 / 27.4 2.7 / 6.4 0.2 / 2.0 NA / 0.1

Any hospitalization days 32.9 / 32.7 3.8 / 9.0 0.3 / 3.2 NA / 0.3

Footnotes to table

Results for the admit model are in plain font; for the discharge model, in bold italics font.

1. Refers to the predicted risk assigned by either the admit or discharge model

2. Refers to the ratio of number of patients flagged as having at least a risk of 10, 20, 30 or 50% to the number who actually had the composite outcome within the 7 day time frame.

3. Refers to the proportion of indicated outcomes that are identified at the specified threshold.

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APPENDIX 10: Supplemental analyses: Impact of incorporating diagnosis in EMR models

We assessed the value of incorporating diagnosis information following the general guidance of Pepe et al. (citation 29). Each of the four models was rebuilt and we then recalculated the various fit assessment metrics (such as the c-statistic). In each of the four, this new model used an intercept, the estimated probability from the original model, the diagnosis (as a factor), and an interaction between the latter two. Using the derivation data the optimum weight for each covariate was allowed to deviate from the value of zero (for the intercept) or one (for the other covariates). Thus diagnosis adjusts the intercept and weight for the predicted value separately for each diagnosis. The results below are from the validation dataset. Details on how individual diagnoses were grouped are provided in citation 19 and also in Appendix 11.

C-statistic Nagelkerke pseudo-R2

-2*log(likelihood) NRI @ 40%

IDI

Without Dx

With Dx

Without Dx

With Dx

Without Dx

With Dx Without vs With

Without vs With

ED 7 0.698 0.702 0.0781 0.0791 134447 134319 0.0056 0.0031Discharge Day 7

0.715 0.705 0.0892 0.0797 133134 134253 0.0087 -0.0027

ED 30 0.739 0.743 0.1577 0.1596 227450 227082 0.0242 0.0051Discharge Day 30

0.755 0.749 0.1722 0.1646 224675 226133 -0.0053 -0.0038

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APPENDIX 11: Supplemental analyses – Model performance across subgroups in validation dataset

GROUPING OF ICD CODES INTO PRIMARY CONDITIONS

As described in citation 19, we combined Health Care Utilization Project (www.ahrq.gov/data/hcup) single-level diagnosis clinical classification software (CCS) categories to cluster all possible ICD admission diagnosis codes into 30 groups, which we refer to as Primary Conditions. The HCUP single-level diagnosis CCS categories were grouped based on biologic plausibility (i.e., relative similarity from a disease standpoint) and on the observed mortality rate because, for modeling purposes, it was desirable to have ~30 patient groupings with at least 30-40 deaths in the derivation dataset. The table below shows our 30 groupings with their corresponding HCUP category numbers.

Primary Condition Name HCUP single-level diagnosis clinical classification software (CCS) category number(s)

Sepsis 2Fluid and electrolyte disorders 55Coma; stupor; and brain damage 85AMI 100Cardiac arrest 107CHF 108Acute CVD 109CAP 122GI bleed 153UTI 159Hip fracture 226Residual codes 259Renal failure (all) 156, 157, 158Less severe cancer 11-16, 18, 20-26, 28-32, 34, 36, 37, 44-47, 207Endocrine & related conditions 48-51, 53, 54, 56, 57, 200, 202, 210, 211Miscellaneous GI conditions 137-140, 155, 214Primary Condition Name HCUP single-level diagnosis clinical classification

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software (CCS) category number(s)Other cardiac conditions 96-99, 103-105, 114, 116, 117, 213, 217HCUP Hyper Group 1 101, 102, 106HCUP Hyper Group 2 0, 10, 141, 144-146, 147, 154, 160-166, 168-196, 201, 215,

218-224, 241-243, 255, 256, 258, 650-652, 654-663, 670, 999, 2601-2621

HCUP Hyper Group 3 115, 129, 131, 249HCUP Hyper Group 4 127, 128, 130, 132, 133Hematologic conditions 59-64Ill-defined signs and symptoms 250-253Liver and pancreatic disorders 151, 152

Highly malignant cancer 17, 19, 27, 33, 35, 38-43Miscellaneous neurological conditions

79-84, 93-95, 110-113, 216, 245, 653

Problems with nutrition 52, 58Other infectious conditions 1, 3-9, 76-78, 90, 92, 123-126, 134, 135, 148, 197-199,

201, 246-248Miscellaneous surgical conditions 86-89, 91, 118-121, 136, 142, 143, 167, 203, 204, 206,

208, 209, 212, 237, 238, 254, 257Trauma 205, 225, 227-236, 239, 240, 244

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c statistic (area under receiver operator characteristic curve)RTCO7A RTCO7D RTCO30A RTCO30D

Gender Male 0.6910 0.7110 0.7350 0.7510Female 0.7030 0.7180 0.7430 0.7590

Age Age < 65 0.6970 0.7200 0.7430 0.7620Age ≥ 65 0.6650 0.6850 0.7010 0.7220

Bed history-based

Patients ever in ICU 0.6500 0.6810 0.6940 0.7180Patients who experienced unplanned transfer to the ICU (EDIP outcome) 0.6370 0.6760 0.6710 0.6990Patients who ever had an operating room stay 0.6860 0.7070 0.7250 0.7460

Principal HCUP SG

Sepsis 0.6680 0.6950 0.6950 0.7220Fluid and Electrolyte Disorders 0.6470 0.6510 0.6610 0.6690Coma, Stupor, and Brain Damage 0.6940 0.7230 0.6860 0.7460AMI 0.6840 0.6790 0.7200 0.7220Atherosclerosis 0.6620 0.6690 0.6950 0.6980Chest Pain 0.6750 0.6840 0.7270 0.7370Dysrhythmia 0.6390 0.6630 0.6870 0.7010Cardiac Arrest 0.6100 0.6190 0.7130 0.6890CHF 0.6150 0.6390 0.6270 0.6530Acute CVD 0.6500 0.6890 0.6860 0.7100CAP 0.6300 0.6490 0.6670 0.6860ASP Pneumonia 0.5530 0.6020 0.5850 0.6210GI Bleed 0.6520 0.6780 0.7030 0.7220UTI 0.6290 0.6620 0.6740 0.6890Hip Fracture 0.6570 0.6960 0.6940 0.7080Residual Codes 0.7240 0.7330 0.7320 0.7570Renal Failure (All) 0.6250 0.6450 0.6580 0.6880Less Severe Cancer 0.7080 0.7370 0.7320 0.7650Catastrophic Conditions 0.6200 0.6570 0.6800 0.7050COPD Asthma and Misc Lung Problems

0.6250 0.6550 0.6740 0.6950

Page 27 of 73


Endocrine and Related Conditions 0.6860 0.7140 0.7110 0.7410Miscellaneous GI Conditions 0.6870 0.7000 0.7010 0.7190Other Cardiac Conditions 0.6580 0.6880 0.7010 0.7190Hematologic Conditions 0.6260 0.6600 0.6670 0.6910Ill Defined Signs and Symptoms 0.6320 0.6360 0.6920 0.7000Liver and Pancreatic Disorders 0.6790 0.6960 0.7110 0.7380Miscellaneous Conditions 0.6870 0.6660 0.7230 0.7260Highly Malignant Cancer 0.7040 0.7390 0.7310 0.7650Miscellaneous Neurological Conditions 0.6670 0.6940 0.7160 0.7400Problems with Nutrition 0.7740 0.7800 0.8110 0.8220Obstruction and Diverticula 0.6380 0.6540 0.6640 0.6920Other GI 0.6130 0.6290 0.6870 0.7010Other Genitourinary Conditions 0.6390 0.6950 0.6920 0.7310Other Infectious Conditions 0.6880 0.6880 0.7320 0.7410Other Respiratory Conditions 0.6730 0.6850 0.7100 0.7140Psychiatric Conditions 0.6870 0.7190 0.6790 0.6950Miscellaneous Surgical Conditions 0.7280 0.7410 0.7680 0.7780Trauma 0.7400 0.7380 0.7750 0.7710

Page 28 of 73


Nagelkerke pseudo-R2

RTCO7A RTCO7D RTCO30A RTCO30DGender Male 0.0743 0.0883 0.1549 0.1711

Female 0.0803 0.0887 0.1590 0.1720Age Age < 65 0.0724 0.0858 0.1503 0.1630

Age ≥ 65 0.0593 0.0695 0.1237 0.1404Events Patients ever in ICU 0.0408 0.0695 0.1081 0.1374

Patients who experienced unplanned transfer to the ICU (EDIP outcome) 0.0090 0.0650 0.0565 0.1079Patients who ever had an operating room stay 0.0601 0.0670 0.1158 0.1243

Principal HCUP SG

Sepsis 0.0638 0.0850 0.1200 0.1476Fluid and Electrolyte Disorders 0.0496 0.0575 0.0720 0.0771Coma, Stupor, and Brain Damage 0.0695 0.1002 0.0950 0.1743AMI 0.0566 0.0403 0.1349 0.1303Atherosclerosis 0.0496 0.0583 0.0897 0.0970Chest Pain 0.0172 0.0389 0.0644 0.1061Dysrhythmia 0.0425 0.0576 0.0943 0.1109Cardiac Arrest 0.0143 -0.0151 0.1122 0.0803CHF 0.0296 0.0430 0.0510 0.0649Acute CVD 0.0235 0.0526 0.0875 0.1095CAP 0.0356 0.0470 0.0794 0.0963ASP Pneumonia -0.0758 -0.0274 -0.0435 -0.0167GI Bleed 0.0462 0.0685 0.1114 0.1446UTI 0.0419 0.0533 0.0980 0.1031Hip Fracture 0.0423 0.0743 0.1012 0.1155Residual Codes 0.0938 0.1028 0.1467 0.1651Renal Failure (All) 0.0295 0.0392 0.0785 0.1079Less Severe Cancer 0.0710 0.0810 0.1258 0.1534Catastrophic Conditions 0.0181 0.0398 0.0974 0.1244COPD Asthma and Misc Lung Problems

0.0196 0.0446 0.0878 0.1179

Page 29 of 73


Endocrine and Related Conditions 0.0651 0.0820 0.1236 0.1497Miscellaneous GI Conditions 0.0642 0.0778 0.1096 0.1262Other Cardiac Conditions 0.0522 0.0713 0.1172 0.1379Hematologic Conditions 0.0284 0.0532 0.0725 0.1017Ill Defined Signs and Symptoms 0.0215 0.0254 0.0963 0.0995Liver and Pancreatic Disorders 0.0590 0.0723 0.1195 0.1594Miscellaneous Conditions 0.0573 0.0415 0.1318 0.1265Highly Malignant Cancer 0.0318 0.0670 0.0490 0.1027Miscellaneous Neurological Conditions 0.0533 0.0706 0.1181 0.1432Problems with Nutrition 0.1486 0.1426 0.2713 0.2652Obstruction and Diverticula 0.0298 0.0421 0.0632 0.1008Other GI 0.0094 0.0284 0.0905 0.1105Other Genitourinary Conditions 0.0336 0.0377 0.0839 0.0900Other Infectious Conditions 0.0697 0.0679 0.1501 0.1506Other Respiratory Conditions 0.0621 0.0603 0.1307 0.1200Psychiatric Conditions 0.0567 0.0906 0.0806 0.1020Miscellaneous Surgical Conditions 0.0865 0.0899 0.1641 0.1609Trauma 0.0910 0.0818 0.1555 0.1333

Page 30 of 73


APPENDIX 12: BETA COEFFICIENTS FOR MODELS FROM DERIVATION DATA

This paper developed four models: one for ED admission for a 7-day horizon (real time combined outcome, or “RTCO7A”), one for the same with a 30-day horizon (“RTCO30A”), and two more for discharge day (“RTCO7D” and “RTCO30D”). Below are the specifications of those four models. To briefly summarize, the two at admission each are a six-node tree followed by a logistic regression for each node, and the two models at discharge are simply a single logistic regression.

Below are the tree portion of RTCO7A and then of RTCO30A. In each call the left-most terminal node as “cohort 1”, continue with the node to the right of that as “cohort 2”, etc until the right-most terminal node as “cohort 6”. These trees were grown with six nodes in this specific structure as such trees have been useful for our group in the past: cohort 1 is to have a relatively small sample size but the highest outcome rate, cohort 6 is to be with a relatively large sample size and the lowest outcome rate, and all the other cohorts are to be with different sample sizes that may end up with similar outcome rates with each other.

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RTCO7A

All Rows

305151Count

142033.12G 2

01

Level0.93790.0621

Prob

cops2>=35

117313Count

76148.479G 2

01

Level0.90020.0998

Prob

LAPS2>=85

46871Count

37047.19G 2

01

Level0.86530.1347

Prob

LAPS2>=125

12821Count

12079.037G 2

01

Level0.82020.1798

Prob

LAPS2<125

34050Count

24675.071G 2

01

Level0.88230.1177

Prob

LAPS2<85

70442Count

38063.867G 2

01

Level0.92350.0765

Prob

cops2<35

187838Count

61401.456G 2

01

Level0.96140.0386

Prob

LAPS2>=60

59631Count

28832.606G 2

01

Level0.93450.0655

Prob

LAPS2<60

128207Count

30994.898G 2

01

Level0.97390.0261

Prob

LAPS2>=35

43586Count

13055.588G 2

01

Level0.96560.0344

Prob

LAPS2<35

84621Count

17766.322G 2

01

Level0.97820.0218

Prob

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RTCO30A

All Rows

305151Count

257936.31G 2

01

Level0.85000.1500

Prob

cops2>=35

117313Count

133306.34G 2

01

Level0.74460.2554

Prob

LAPS2>=65

69076Count

85497.065G 2

01

Level0.69030.3097

Prob

cops2>=95

24802Count

33251.051G 2

01

Level0.60640.3936

Prob

cops2<95

44274Count

50991.894G 2

01

Level0.73730.2627

Prob

LAPS2<65

48237Count

45121.503G 2

01

Level0.82240.1776

Prob

cops2<35

187838Count

108464.85G 2

01

Level0.91590.0841

Prob

LAPS2>=60

59631Count

49531.712G 2

01

Level0.85420.1458

Prob

LAPS2<60

128207Count

54933.147G 2

01

Level0.94460.0554

Prob

age>=75

19756Count

12310.909G 2

01

Level0.90610.0939

Prob

age<75

108451Count

42048.302G 2

01

Level0.95160.0484

Prob

Page 33 of 73


Below are the results of the logistic regression fits for all four models. In terms of the discrete covariates, MALE is an indicator if the patient was male or not, and its reference level was “male”. DCO_4 is an indicator for the care level order in effect at discharge, and its reference level was “full code”. PCAT categorizes the pattern of any prior hospitalizations with these four levels, with the reference level being “4”.:

Level Meaning1 None up to 30 days ago2 1+ up to 7 days ago, and none 8-30 days ago3 None up to 7 days ago, and 1+ 8-30 days ago4 1+ up to 7 days ago, and another 1+ 8-30 days ago

Model and Cohort

Covariate Compared Level

Point Estimate Standard Error 95% Confidence IntervalLower Upper

RTCO7A

1

INTERCEPT -3.832 0.228 -4.279 -3.385AGE 0.007 0.002 0.003 0.011MALE Female -0.035 0.023 -0.080 0.011COPS2 0.003 0.000 0.003 0.004LAPS2 0.010 0.001 0.008 0.012

2

INTERCEPT -3.188 0.178 -3.537 -2.839AGE -0.001 0.001 -0.004 0.001MALE Female -0.007 0.017 -0.041 0.026COPS2 0.005 0.000 0.004 0.006LAPS2 0.008 0.002 0.005 0.011

3

INTERCEPT -3.341 0.077 -3.491 -3.190AGE 0.000 0.001 -0.002 0.002MALE Female -0.011 0.014 -0.039 0.017COPS2 0.006 0.000 0.006 0.007LAPS2 0.007 0.001 0.006 0.008

4

INTERCEPT -4.474 0.086 -4.644 -4.305AGE 0.013 0.001 0.011 0.015MALE Female -0.059 0.017 -0.092 -0.026COPS2 0.008 0.002 0.004 0.012LAPS2 0.009 0.001 0.008 0.010

Page 34 of 73


5


6


RTCO30A

1

INTERCEPT -1.526 0.098 -1.718 -1.333AGE -0.006 0.001 -0.008 -0.004MALE Female 0.014 0.013 -0.012 0.039COPS2 0.006 0.000 0.005 0.006LAPS2 0.007 0.000 0.006 0.008

2


3

INTERCEPT -2.445 0.064 -2.570 -2.319AGE -0.002 0.001 -0.004 -0.001MALE Female 0.006 0.012 -0.018 0.030COPS2 0.009 0.000 0.009 0.010LAPS2 0.010 0.001 0.008 0.011

4


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5


6


RTCO7DINTERCEPT -3.203 0.025 -3.253 -3.154COPS2 0.005 0.000 0.005 0.006LAPS2 0.008 0.000 0.008 0.008LOS_30 0.038 0.001 0.036 0.041DCO_4 Not Full Code 0.276 0.009 0.259 0.294PCAT 1 -0.469 0.016 -0.501 -0.437PCAT 2 0.062 0.024 0.015 0.110PCAT 3 -0.061 0.020 -0.101 -0.021

RTCO30DINTERCEPT -2.347 0.019 -2.384 -2.311COPS2 0.009 0.000 0.009 0.009LAPS2 0.009 0.000 0.009 0.009LOS_30 0.041 0.001 0.039 0.043DCO_4 Not Full Code 0.305 0.007 0.292 0.318PCAT 1 -0.581 0.013 -0.606 -0.557PCAT 2 0.005 0.019 -0.032 0.042PCAT 3 0.058 0.016 0.027 0.088

Page 36 of 73


APPENDIX 13: KAPLAN-MEIER CURVES FOR MODELS

Kaplan-Meier curves for patients in the validation dataset, which was divided into terciles based on predicted risk for the combined outcome (undesirable rehospitalization and/or death within 30 days). The model employed was the 30 day discharge model. The dotted line (•••) shows the lowest risk tercile (predicted risk up to 6.2%); the dashed line (- - -) hospitalizations with predicted risk of 6.2 to 9.6%; and the solid line (—) hospitalizations with predicted risk of 9.6% or more.

Page 37 of 73


Sur

viva

l

81%

82%

83%

84%

85%

86%

87%

88%

89%

90%

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%

Days After Discharge

0 10 20 30

30 Days After DischargeDischarge Model

Lowest Tercile [PE]Lowest Tercile [LCL]Lowest Tercile [UCL]

Middle Tercile [PE]Middle Tercile [LCL]Middle Tercile [UCL]Highes t Tercile [PE]

Highes t Tercile [LCL]Highes t Tercile [UCL]

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Sur

viva

l

81%

82%

83%

84%

85%

86%

87%

88%

89%

90%

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%


0 10 20 30

30 Days After DischargeAdmit Model




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Sur

viva

l

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%


0 1 2 3 4 5 6 7

7 Days After DischargeDischarge Model




Page 40 of 72


Sur

viva

l

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%


0 1 2 3 4 5 6 7

7 Days After DischargeAdmit Model

Lowes t Tercile [PE]Lowes t Tercile [LCL]Lowes t Tercile [UCL]

Middle Tercile [PE]Middle Tercile [LCL]Middle Tercile [UCL]Highest Tercile [PE]

Highest Tercile [LCL]Highest Tercile [UCL]

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0-24 25-49 50-74 75-99 100-124 125-149 150+ -

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

110,000

120,000

130,000

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

LAPS2

Num

ber o

f Hos

pita

lizati

ons

% In

patie

nt M

orta

lity

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0-14 15-49 50-99 100-149 150-199 200+ -

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

COPS2

Num

ber o

f Hos

pita

lizati

ons

% In

patie

nt M

orta

lity

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Page 60 of 72The Pop-Up will display until “Accepted”. The Link will Display the Report


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The Report Contains the Scores and Last Time it was Calculated, along with Additional Information


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The Report Contains the Scores and Last Time it was Calculated, along with Additional Information


Page 63 of 72

New columns in the Patient List activity show the latest Advance Alert (EDIP) scores & the admission LAPS2 & COPS2


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Appendix 15: Relationship between model predictors and disaggregated outcomes (odds ratios using univariate logistic regression)

The tables below show the following general trends in the relationship between model predictors and our composite outcome (death or non-elective rehospitalization within 30 days) as compared to each outcome alone (non-elective rehospitalization within 30 days only, death within 30 days only):

a. The directionality of predictor-outcomes relationships is similar across outcomes

b. Care directives are very strong predictors for mortality, but, nonetheless, they are still significant predictors for non-elective rehospitalization (even when the patient survived)

c. As is the case with care directives, physiologic derangement (LAPS2) and longitudinal comorbidity burden (COPS2) are very strong predictors for mortality (particularly when very elevated – for example, a LAPS2 of ≥ 110 has an odds ratio of 18.6 for mortality). Nonetheless, they are still strong predictors for rehospitalization even when the patient survived.

Page 70 of 72


Predictor Composite outcome (death or non-elective rehospitalization within 30 days)

Non-elective rehospitalization within 30 days Death within 30 days

With survival With in-hospital death

Any Out of hospital Any

Age in years<40 (reference)

40-64 1.42 (1.38,1.47) 1.26 (1.22,1.3) 3.57 (2.94,4.33) 1.32 (1.27,1.36) 5.62 (4.71,6.71) 4.54 (3.96,5.19)

65-79 2.25 (2.19,2.33) 1.74 (1.69,1.8) 7.83 (6.47,9.47) 1.91 (1.85,1.97) 14.62 (12.28,17.41)

11.08 (9.69,12.65)

80+ 3.39 (3.29,3.50) 1.95 (1.89,2.02) 14.38 (11.90,17.38) 2.31 (2.23,2.38) 39.37

(33.09,46.85)27.36

(23.96,31.24)Sex

Male (reference) . . . . . .Female 0.89 (0.88,0.91) 0.91 (0.90,0.93) 0.82 (0.79,0.86) 0.9 (0.88,0.91) 0.88 (0.85,0.91) 0.87 (0.85,0.89)

Full CodeYes (reference) . . . . . .

No 3.42 (3.36,3.47) 1.39 (1.36,1.42) 5.13 (4.90,5.37) 1.70 (1.67,1.73) 21.44 (20.70,22.20)

15.46 (15.01,15.91)

Hospital length of stay (calendar days)

< 2 . . . . . .2 0.96 (0.92,1.01) 0.90 (0.86,0.95) 1.67 (1.31,2.12) 0.93 (0.89,0.98) 1.26 (1.10,1.45) 1.36 (1.20,1.55)3 1.39 (1.33,1.46) 1.22 (1.16,1.28) 2.92 (2.31,3.69) 1.29 (1.23,1.35) 2.25 (1.97,2.57) 2.44 (2.15,2.76)

4-5 1.91 (1.82,1.20) 1.56 (1.49,1.64) 4.41 (3.50,5.56) 1.69 (1.61,1.77) 3.55 (3.11,4.06) 3.83 (3.39,4.33)6-30 3.08 (2.94,3.22) 2.27 (2.16,2.38) 7.66 (6.08,9.65) 2.55 (2.43,2.67) 6.30 (5.52,7.18) 6.93 (6.13,7.83)

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Predictor Composite outcome

Non-elective rehospitalization Death

With survival With in-hospital death

Any Out of hospital Any

LAPS2 (points)<50 (reference) . . . . . .

50-109 2.77 (2.73,2.82) 2.29 (2.25,2.33) 5.25 (4.87,5.66) 2.46 (2.42,2.51) 5.43 (5.17,5.71) 5.36 (5.13,5.60)

110+ 5.76 (5.64,5.88) 3.13 (3.06,3.21) 14.21 (13.14,15.36) 3.82 (3.73,3.91) 19.21

(18.25,20.22)18.63

(17.80,19.49)COPS2 (points)<40 (reference) . . . . . .

40-64 2.48 (2.43,2.53) 2.14 (2.09,2.19) 3.26 (3.04,3.50) 2.25 (2.20,2.30) 3.23 (3.09,3.37) 3.25 (3.12,3.38)65+ 4.51 (4.44,4.59) 3.54 (3.48,3.60) 6.47 (6.13,6.83) 3.92 (3.86,3.99) 5.50 (5.31,5.69) 5.82 (5.64,6.00)

Hospitalizations prior to current admission

None within previous 30 days (reference) . . . . . .

≥ 1 up to 7 days ago, and none 8-30 days ago 2.63 (2.55,2.70) 2.36 (2.29,2.44) 2.88 (2.65,3.12) 2.49 (2.42,2.57) 2.46 (2.33,2.60) 2.59 (2.47,2.72)

None up to 7 days ago, and ≥ 1 8-30 days ago 3.19 (3.12,3.25) 2.86 (2.79,2.93) 3.36 (3.16,3.56) 3.04 (2.97,3.10) 2.71 (2.60,2.82) 2.89 (2.79,3.00)

≥ 1 up to 7 days ago, and ≥ 1 8-30 days ago 5.49 (5.23,5.76) 4.68 (4.45,4.93) 5.07 (4.51,5.71) 5.15 (4.90,5.41) 3.52 (3.22,3.85) 3.99 (3.70,4.32)

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