Frailty Driven Therapy Decisions in the Age of the Patient Driven Payment Model: A Retrospective Cohort Study

doi:10.21203/rs.3.rs-1226178/v1

PDF

Research Article

Frailty Driven Therapy Decisions in the Age of the Patient Driven Payment Model: A Retrospective Cohort Study

https://doi.org/10.21203/rs.3.rs-1226178/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

In the absence of guidance from clinicians identifying patients who have the greatest potential to improve function as a benefit of skilled therapy, and writing orders to assure adequate therapy minutes, rehabilitation for the most vulnerable might not have been possible without objective assessment tools. The Frailty Index (FI) is one objective metric for identifying rehabilitation potential. The aim of this study was to evaluate the use of routinely collected data and predictive analytics of FI related to function to estimate rehabilitative potential.

Methods

Retrospective analysis of patients admitted into a large urban skilled nursing facility (SNF) in Western New York for post-acute rehabilitation over a nine-month period (N = 341). Using data collected in the Minimum Data Set (MDS), the change in the GG function scores from admission to discharge (GG AvD/C) was computed for each patient. The study utilized a multiple regression modeling approach to evaluate the variation of the GG AvD/C score across the different categories of frailty.

Results

The results of this analysis suggest that by observing a patient's FI near time of admission, the clinician can make a recommendation, based on an objective metric, when ordering patient treatment frequencies and time, based on the patient’s potential for functional gains.

Conclusions

Using a FI to categorize patients into Frailty Risk Groups provides an opportunity to predict the amount of functional improvement from start of care to discharge as measured using MDS GG functional scores.

Frailty

Functional Outcomes

Rehabilitation

PDPM

MDSs

This study utilized a retrospective cohort design with data extracted from electronic health records. Patients in the sample were admitted to a SNF located in Western New York over a nine-month period and were excluded from the sample for the following reasons: 1) discharged against medical advice (AMA); 2) hospitalized; 3) deceased; 4) not placed on program for PT/OT; 5) under 21 years old; 6) not placed on restorative programs with functional goals; 7) incomplete data.

Functional GG scores were used to measure the level of patients’ functional status. The functional GG scores were obtained from plans of care developed by PT and OT under the MDS 3.0 section GG reporting mobility and self-care measures [31]. Scoring was completed under instruction of the Long-Term Care Facility Resident Assessment Instrument 3.0 User’s Manual v 1.14 [31]. Therapists completing the functional scores were unaware of the FI at time of completion. Scoring for GG items was based on the MDS value of each functional status, from dependent to independent. The codes of “7”, for patient refusal, “88” for not attempted due to medical condition, or “9” for not applicable, were converted to a zero. These scores do not follow the same ordinal pattern related to functional level and thus would result in inaccuracy with observing functional status. To limit type 1 error for functional change, wheelchair mobility GG measures were excluded [32]. The GG Functional scores were totaled with a score of 60 being the highest score, indicating a more independent functional status. Table 1 lists the measures included. Table 2 correlates the billing code to the converted scores for the purpose of this study. The data collected allowed for the observation of GG admission score (GG A), GG discharge score (GG D/C) and the difference in GG score from admission to discharge (GG AvD/C). Under the PDPM, the required GG codes were anticipated to become a standard of measurement in function and this expectation was a considering factor for the design of this study to improve its feasibility of the findings.

This study depended on an electronic FI calculated by Patient Pattern software produced in Buffalo, New York [33]. The FI was generated from MDS data with a focus on function, cognitive/psychological status, nutrition, motivation, and mood. The proprietary algorithm used to calculate the Patient Pattern FI was modeled after the Rockwood Deficit Accumulation model of frailty and was completed for each patient within 2 weeks of admission date [28, 34]. If a patient’s change in a condition required additional frailty assessment within the date range, the FI closest to the evaluation date was recorded. Participants were classified into the frailty risk categories based on their FI as follows: under 29.9% considered Mildly Frail/Low Risk group (MildF/LRG), between 30-39.9% as Moderately Frail/Moderate Risk group (ModF/MRG), and over 40% deemed Severely Frail/High Risk group (SevF/HRG). The descriptive text “moderate, mild, and severe risk” for each frailty category was adapted to the subacute care setting from that of the Canadian Study of Health and Aging Clinical Frailty scale [35]. Due to the inconsistency of FI cut points for frailty categories within the literature, this study utilized previously studied FI risk stratification for mortality, hospitalization, institutionalization in addition to Patient Pattern internal risk validation data to establish the FI category cut points [36, 37]. The recommended level of therapy of services for patients during this retrospective analysis were not influenced by GG score or FI. Provider recommendations were allocated based on professional judgment alone.

Comorbidity was assessed using the CCI and derived from documentation of the Physician and Nurse Practitioner at time of admission [30]. Other data, including age, sex, and length of time on skilled PT and OT, was gathered manually extracting from therapy documentation notes.

Statistical Analyses

The GG AvD/C score was computed for each patient. The study utilized a multiple regression modeling approach to evaluate the variation of the GG AvD/C score across the different levels of frailty. Patients’ sex, age, CCI, and length of time on PT program were included in the regression modeling as covariates to reduce potential confounding effects. We also noticed that GG AvD/C scores increased as age went up to a certain point but then declined afterwards. The regression analysis also included an age-squared variable to address the non-linear relationship between the age and the GG AvD/C score. The value of the coefficient of determination (R²) was computed to estimate the amount of variance accounted for by the frailty level and covariates. Coefficient estimates and their 95% confidence intervals (CI) were calculated and a two-sided alpha of less than 0.05 was defined a priori for statistical significance with p-value < 0.05. All analyses were performed using SPSS Version 24 (SPSS Inc, Chicago, IL, USA).

Over the nine-month period 498 residents were admitted to the SNF facility and 157 were excluded based on exclusion criteria The average FI of those excluded due to death (n = 7) was 39.2% (ModF/MRG). The average FI of those excluded due to hospitalizations (n = 19) was 37.8% (ModF/MRG). The sample size, after exclusion, was 341 residents. For the GG functional analysis portion of this study the average FI was 31.82%. Most patients, defined as being within 2 standard deviations of the mean, were between 61 and 87 years of age. The sample population included 118 males and 223 females. Males improved their GG AvD/C scores by an average of 12.0 points and females by 13.9 points. The mean score was a 2.0 on the CCI. Mean length of time on PT and OT were 24.95 and 24.89 days respectively.

Overall, 43.1% of residents were categorized as MildF/LRG, 39.6% as ModF/MRG, and 17.3% as SevF/HRG. On average, patients’ GG functional scores were 31.33 (SD = 11.04) at time of admission and 44.6 (SD = 15.86) at time of discharge. Mean GG A score for the MildF/LRG was 36.74, ModF/MRG was 29.16, and SevF/HRG was 22.85. Mean GG D/C score for MildF/LRG was 54.57, ModF/MRG was 40.83, and SevF/HRG was 28.05.

The average amount of change in functional score from admission to discharge was 13.27 (SD of 10.61). Figure 1 displays mean functional GG AvD/C score according to patient frailty risk category. Patients in the MildF/LRG improved their GG functional score by a mean 17.84 points, those in the ModF/MRG improved by a mean 11.81 points, and those in the SF/HRG improved by a mean 5.20 points. Figure 2 displays mean functional GG AvD/C score by age. One individual in the sample was less than 40 years old and improved their GG AvD/C score by 23 points. The remainder were as follows: 40–49 years old = 14.7 points, 50–59 years old = 16.8 points, 60–69 years old = 13.5 points, 70–79 years old = 14.0 points, 80–89 years old = 12.3 points, and > 90 years old = 8 points.

Table 3 displays the results of the multiple regression model observing GG AvD/C as the dependent variable. The model was found to be significant (p<0.001) with an r2 value = 0.7038. Frailty Index, sex, age, age², comorbidity, and length of time on PT were all found to be significant. Length of time on OT was not significant and thus excluded within this model. The length of time on OT had less of an impact potentially due to the nature that only 3 of the 10 GG Mobility-Self Care tasks observed within this study were treated and documented by occupational therapists. Due to the nonlinear relationship observed between age and the dependent variable, age²was included within our model. The parameter estimates for the MF/MRG and SF/HRG’s were -6.772 and -12.820 respectively.

The amount of functional improvement with therapy varies. The uniqueness of each patients’ outcomes results also from multiple contributing variables such as the role of a caring family/social support, age, comorbidity, intensity of therapy provided, and cognitive performance [32, 38–40]. Providing higher intensity therapy, in measure of total therapy minutes, was associated with desirable discharge outcomes, shortened length of stay, and increased likelihood of returning to the community [39]. Cognitive status has also been shown to influence a patient’s responsiveness to therapy. One study found that those who scored lower on the Brief Interview of Mental Status (BIMS) and Cognitive Performance Scale (CPS) have substantially lower functional improvement scores likely related to the ability of patients to actively engage and participate in therapy [32, 41]. The potential for functional improvement is dependent on the effect of multiple characteristics however, using the regression model within this sample 70.38% of the variance in outcomes were explained.

Figure 1 reveals a clear stepwise decrease in the average amount of functional improvement as frailty increases. Those within the Moderately Frail/Moderate Risk Group improved their function 6.77 points less than those within Mildly Frail/Low Risk Group and those within the Severely Frail/High Risk Group improved 12.82 points less than those in the Mild Frail/Low Risk Group. The same indirect trend between increasing age and functional outcomes was not observed with relation to age as demonstrated in Fig. 2.

Variables within this specific sample and regression model appear to demonstrate that older women with higher comorbidities perform poorly compared to younger men with fewer comorbidities. Table 3 details when considering the parameter estimates for age (0.505), gender female (2.584), and comorbidities (-0.718), although significant, these variables had a smaller influence of change in outcomes compared to the frailty group parameters.

Recognizing that increased frailty is predictive of poor functional recovery is consistent with findings demonstrating increased frailty is a marker of decreased resilience and poor recovery from disability among community dwelling older adults [42]. This inverse relationship between frailty and physical performance was also validated by Kim and colleagues for patients who required post-acute rehabilitation [12]. In contrast, a study by Haley and colleagues, who measured functional outcomes under the elderly mobility scale, did not find frailty as a useful predictor of improvement in mobility within the subacute population [43]. Potentially a differing standardized outcome tool to measure function resulted in the opposing results.

Currently CMS recommends the amount of therapy services be “based on characteristics of the resident”, leaving large opportunity for subjective interpretation that could potentially result in less therapy services provided for the patient [1]. The results of this analysis suggest that observing a patient's FI near time of patient admission provides an objective metric for the clinician to make a recommendation when ordering patient treatment frequencies and time based on the patient’s potential for functional gains. Using electronically documented data to produce the FI eliminates the need for completion of additional time-consuming assessments.

Further investigation examining the relationship between frailty and amount of functional improvement with skilled therapy services within larger sample sizes is recommended as the time for data gathering for this study was limited by MDS changes in regulation for GG codes and EMR available information. Examining past outcomes from therapy compared to current practices will also be beneficial prior to using the FI as a standard for service recommendations. The inconsistencies within the literature for cut off points for frailty categories should be considered by readers when interpreting this data [35–37]. Cognitive status and social support were not included within this regression model due to poor access and/or unavailable information. This sample was limited to a potentially biased population as all information was gathered from a single SNF.

Using a FI to categorize patients into Frailty Risk Groups provides an opportunity to predict the potential amount of functional improvement from start of care to discharge as measured using MDS GG functional scores. Of those patients admitted, those in the Mildly Frail/Low Risk and Moderately Frail/Moderate Risk groups demonstrated the most positive gains in function over the course of a restorative episode of PT and OT care in a subacute setting. Patients in the Severely frail/High Risk group were less likely to gain functional skills. Although clinicians must consider each patient on an individual basis, this research suggests that categorizing patients by their FI is a valuable means in predicting which patients will have the highest potential for therapeutic benefit. Prescribing therapy frequencies on the basis of an objective, validated metric and tailoring their restorative program to physiological status is in keeping with the CMS mandate to establish therapy on the basis of the patient’s characteristics. Frailty is the best predictor of outcomes and frailty-based restorative treatment plans will allow all patients to reach their full rehab potential while improving a rehabilitation facility's responsibility to maximize reimbursement [44]. A recent study examining the association between co-calibrated functional scores across post-acute care settings and the subsequent risk of hospital readmission had similar findings, using similar MDS data [45]. In that study the most dependent patients at the initial post-acute setting had a higher risk to readmit to the hospitals after discharging from the post-acute setting for 30 and 90 days, compared with patients who were more functionally independent. With the PDPM now in place, providers may find it advantageous to use such data driven models for allocating therapy resources.

AMA - Against Medical Advice (AMA)

BIMS - Brief Interview of Mental Status (BIMS)

CMS - Centers for Medicare and Medicaid Services (CMS)

CCI - Charlson Comorbidity Index (CCI)

CPS - Cognitive Performance Scale (CPS)

FI - Frailty Index (FI)

MildF/LRG: Mildly Frail/ Low Risk Group/High Rehabilitation Potential

ModF/MRG: Moderately Frail/Moderate Risk Group/ Moderate Rehabilitation Potential

SsvF/HRG: Severely Frail/High Risk Group/ Low Rehabilitation Potential

GG AvD/C: Change in functional GG score from admission to discharge

ICD - International Classification of Diseases (ICD)

MDS - Minimum Date Set (MDS)

GG Item: Funational and Self-care Assessment section within the MDS

OT - Occupational therapist (OT)

PDPM - Patient-Driven Payment Model (PDPM)

PT - Physical Therapy (PT)

PPS - Prospective Payment System (PPS)

RAI - Resident Assessment Instrument (RAI)

SNF - Skilled Nursing Facilities (SNF)

Ethics approval: Ethical approval was waived from the Pennsylvania State Institutional Review Board under Study ID number STUDY00019870 on the basis that this study was completed as a quality improvement initiative for the SNF being analyzed. All methods of this study were carried out in accordance with guidelines and regulations as outlined by the Declaration of Helsinki.

Consent to participate: Informed consent to participate was waived by the Pennsylvania State Institutional Review Board due to the depersonalization and absence of protected health information of analyzed data accessed.

Consent for publication: Not applicable.

Availability of data and materials: The datasets generated and analyzed during this study are not publicly available but are available from the corresponding author on reasonable request.

Competing interests: Margaret Sayers is a co-founder and the Vice President of Product and research for Patient Pattern. Steven Buslovich is also a co-founder and is the Chief Executive Officer of Patient Pattern. Jessica Griffiths is the Clinical Product Manager of Patient Pattern. Each of these authors contributed to guidance of experimental design, provided access to the software, and assisted in the critical revision of the manuscript but were not involved in acquisition, analysis, interpretation of data, or in the original drafting of the manuscript. Wen Jan Tuan and Robin Homan are not associated with any competing interests. At no time throughout the course of this project was there opportunity for financial or employment gain of the contributing authors who handled the acquisition or analysis of data.

Funding: Patient Pattern proprietary software was used to calculate an independent variable within the study. This research did not receive any funding from agencies in the public, commercial, or non-for-profit sectors.

Authors’ contributions: All authors were involved in revisions and approval of the final manuscript. Robin Homan, Wen-Jan Tuan, Steven Buslovich, and Margaret Sayers contributed to the planning and design of the study, interpretation, and writing of the manuscript. Wen-Jan Tuan contributed data analysis. Jessica Griffiths contributed to interpretation and writing the manuscript. Robin Homan contributed to the literature review, interpretation, writing the manuscript, and served as guarantor responsible for overall content.

Acknowledgements: Not applicable

Authors’ information: Jessica Griffiths https://orcid.org/0000-0002-7622-4591

Wen-Jan Tuan https://orcid.org/0000-0003-3939-8979

Margaret Sayers https://orcid.org/0000-0001-6786-4880

Acumen. Skilled nursing facilities patient-driven payment model technical report. Centers for Medicare and Medicaid Services. April 2018. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/Downloads/PDPM_Technical_Report_508.pdf. Accessed September 19, 2018.
Patient Driven Payment Model. Centers for Medicare and Medicaid Services. December 2021. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM/. Accessed December 28, 2021.
Patient Driven Payment Model fact sheet: PDPM functional and cognitive score. Centers for Medicare and Medicaid Services. February 14, 2019. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/PDPM#fact. Accessed December 4, 2019.
McGarry B, White E, Resnik L, Rahman M, Grabowski D. Medicare’s new patient driven payment model resulted in reductions in therapy staffing in skilled nursing facilities. Health Affairs. 2021Mar;40(3):392–399.
Clegg A, Young H, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013Mar;381(9868):752–62.
Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, et al. Age-related frailty and its association with biological markers of ageing. BMC Medicine. 2015; 13(1):1–9.
Theou O, Brothers TD, Peña FG, Mitnitski A, Rockwood K. Identifying common characteristics of frailty across seven scales. J Am Geriatr Soc. 2014May; 62(5):901–906.
Bellal J, Pandit V, Zangbar B, Kulvatunyou N, Hashmi A, Green DJ, et al. Superiority of frailty over age in predicting outcomes among geriatric trauma patients: a prospective analysis. JAMA Surg. 2014Aug; 149(8):766–772.
Stineman M, Escarce J, Tassoni C, et al. Diagnostic coding and medical rehabilitation length of stay: Their relationship. Arch Phys Med Rehabil. 1998; 79:241–248.
Searle S, Mitnitski A, Gahbauer E, Gill T, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008Sep; 8(24). doi:10.1186/1471-2318-8-24
Cesari M, Gambassi G, van Kan GA, Vellas B. The frailty phenotype and the frailty index: different instruments for different purposes. Age Ageing. 2014Jan; 43(1):10–12.
Kahlon S, Pederson J, Majumdar SR, Belga S, Lau D, Fragette M, et al. Association between frailty and 30-day outcomes after discharge from hospital. CMAJ. 2015Aug; 187(11):799–804.
Kim D, Glynn R, Avorn J, Lipsitz LA, Rockwood K, Pawar A, et al. Validation of a claim-based frailty index against physical performance and adverse health outcomes in the Health and Retirement study. J Gerontol A Bio Sci Med Sci. 2019Jul; 74(8):1271–12766.
Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Bio Sci Med Sci. 2001Mar; 56(3):M146–M156.
Song X, Mitnitski A, Rockwood K. Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. J Am Geriatric Soc. 2010Apr; 58:681–687.
McMillan GJ, Hubbard RE. Frailty in older inpatients: what physicians need to know. QJM. 2012Nov; 105(11):1059–65.
Krishnan M, Beck S, Havelock W, Eeles E, Hubbard RE, Johansen A. Predicting outcome after hip fracture: using a frailty index to integrate comprehensive geriatric assessment results. Age Ageing. 2014Jan; 43:122–126.
Hubbard RE, Story DA. Patient frailty: the elephant in the operating room. Anaesthesia. 2014Jan; 69(1): 26–34.
Evans S, Sayers M, Mitnitski A, Rockwood K. The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment. Age Ageing. 2014Jan; 43(1):127–132.
Seamon BA, Simpson KN. The effect of frailty on discharge location for Medicare beneficiaries after acute stroke. Arch Phys Med Rehabil. 2019Jul; 100(7):1317–1323.
Kinosian B, Wieland D, Gu X, Stallard E, Phibbs CS, Intrator O. Validation of the JEN frailty index in the National Long-Term Care Survey community population: identifying functionally impaired older adults from claims data. BMC Health Serv Res. 2018Nov; 18(1):908.
Clegg A, Bates C, Young J, Ryan R, Nichols L, Teale EA, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016May; 45(3):353–360.
Drubbel I, Niek J. de Wit, Bleijenberg N, et al. Prediction of adverse health outcomes in older people using a frailty index based on routine primary care data. J Gerontol A. 2013Mar; 68(3):301–308.
Daha N, Immerzeel, J, Spelt I, de Waal M, van Eijck M, Numans M, et al. Risk stratification and characterization of subpopulations of frail elderly in the primary care practice using routine healthcare data. Int J Integr Care. 2018Oct; 18(2):395.
Luo H, Lum TYS, Wong GHY, Kwan JSK, Tang JYM, Chi I. Predicting adverse health outcomes in nursing homes: a 9-year longitudinal study and development of the FRAIL-Minimum Data Set (MDS) quick screening tool. J Am Med Dir Assoc. 2015Dec; 16(12):1042–1047.
Kim DH, Schneeweiss S, Glynn RJ, Lipsitz LA, Rockwood K, Avorn J. Measuring frailty in Medicare data: development and validation of a claims-based frailty index. J Gerontol A Biol Sci Med Sci. 2018Jun; 73(7):980–987.
Rockwood K, Mitnitski A. Frailty defined by deficit accumulation and geriatric medicine defined by frailty. Clin Geriatr Med. 2011Feb; 27(1):17–26.
Theou O, Walston J, Rockwood K. Operationalizing frailty using the frailty phenotype and deficit accumulation approaches. Interdiscip Top Gerontol Geriatr. 2015Jul; 41:66–73.
Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992Jun;45(6):613–619.
Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994; 47(11):1245–51.
MDS 3.0 RIA manual v.1.14. Centers for Medicare and Medicaid Services. October 2016. https://downloads.cms.gov/files/MDS-30-RAI-Manual-V114-October-2016.pdf. Accessed on September 19, 2019.
Loomer L, Downer B, Thomas K. Relationship between functional improvement and cognition in short-stay nursing home residents. J Am Geriatr Soc. 2019Mar; 67(3):553–557.
Patient Pattern, computer software. Version 1.2. Buffalo, NY: Patient Pattern, Inc; 2013
Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008Sep; 8. doi:10.1186/1471-2318-8-24.
Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005Aug; 173(5):489–495.
Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, Kirkwood TBL Age-related frailty and its association with biological markers of ageing. BMC Med. 2015Jul; 13(161). doi:10.1186/s12916-015-0400-x.
Hoover M, Rotermann M, Sanmartin C, Bernier J. Validation of an index to estimate the prevalence of frailty among community-dwelling seniors. Health Rep. 2013Sep; 24(9):10–17.
Szczepańska-Gieracha J, Kowalska J, Salamon-Krakowska K, Ochnik M, Jaworsk-Burzynsk L. The role of family in the process of rehabilitation of older adults hospitalized in a nursing home. Top Geriatr Rehabil. 2017Apr; 33(2):127–132.
O’Brien SR, Zhang N. Association between therapy intensity and discharge outcomes in aged Medicare skilled nursing facilities admissions. Arch Phys Med Rehabil. 2018Jan; 99(1):107–115.
Jung H-Y, Trivedi AN, Grabowski DC, Mor V. Does more therapy in skilled nursing facilities lead to better outcomes in patients with hip fracture? Physical Therapy. 2016Jan; 96(1):81–9.
Morghen S, Morandi A, Guccione AA, Bozzini M, Guerini F, Gatti R, et al. The association between patient participation and functional gain following inpatient rehabilitation. Aging Clin Exp Res. 2017Aug; 29(4):729–736.
Wu C, Kim D, Xue Q, Lee D, Varadhan R, Odden M. Association of frailty with recovery from disability among community-dwelling older adults: results from two large U.S. cohorts. J Gerontol A Biol Sci Med Sci. 2019; 74(4):575–581. doi:10.1093/gerona/gly080
Haley MN, Wells YD, Holland AE. Relationship between frailty and discharge outcomes in subacute care. Aust Health Rev. 2014Feb;38(1):25–9.
Wilson JR, Badhiwala JH, Moghaddamjou A, Yee A, Wilson JR, Fehlings MG. Frailty is a better predictor than age of mortality and perioperative complications after surgery for degenerative cervical myelopathy: an analysis of 41,369 patients from the NSQIP database 2010–2018. J Clin Med. 2020Oct;9(11):3491.
Chih-Ying L, Haas A, Pritchard, KT, Karmarkar A, Kuo Y-F, Hreha K, et al. Functional status across post-acute settings is associated with 30-day and 90-day hospital readmissions. JAMDA. 2021Dec;22(12):2447–2453.

Table 1-3 are available in the Supplemental Files section.

Competing interest reported. Margaret Sayers is a co-founder and the Vice President of Product and research for Patient Pattern. Steven Buslovich is also a co-founder and is the Chief Executive Officer of Patient Pattern. Jessica Griffiths is the Clinical Product Manager of Patient Pattern. Each of these authors contributed to guidance of experimental design, provided access to the software, and assisted in the critical revision of the manuscript but were not involved in acquisition, analysis, interpretation of data, or in the original drafting of the manuscript. Wen Jan Tuan and Robin Homan are not associated with any competing interests. At no time throughout the course of this project was there opportunity for financial or employment gain of the contributing authors who handled the acquisition or analysis of data.

Table3.jpg
Table 3. Results of Regression Analysis on GG AvD/C

PDF

Reviews received at journal
11 May, 2022
Reviewers agreed at journal
05 May, 2022
Reviewers agreed at journal
25 Apr, 2022
Reviewers invited by journal
28 Mar, 2022
Editor assigned by journal
21 Mar, 2022
Editor invited by journal
17 Mar, 2022
Submission checks completed at journal
17 Mar, 2022
First submitted to journal
03 Jan, 2022

You are reading this latest preprint version

Frailty Driven Therapy Decisions in the Age of the Patient Driven Payment Model: A Retrospective Cohort Study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Full Text

Methods

Statistical Analyses

Results

Discussion

Limitations

Conclusion

List Of Abbreviations

Declarations

References

Tables

Competing Interests

Supplementary Files

Status:

Version 1