Preoperative Risk Factors for Nonhome Discharge of Home-Dwelling Geriatric Patients Following Elective Surgery

Abstract

To identify patient risk factors for nonhome discharge (NHD) for home-dwelling older patients undergoing surgery, we performed a retrospective cohort study of patients aged ≥65 years undergoing elective surgery between 2014 and 2016 using the geriatric research file from the National Surgical Quality Improvement Program (NSQIP). Multivariable logistic regression examined the association between preoperative demographics, comorbidities, and functional status and NHD to determine which factors are most strongly predictive of NHD. Risk of NHD was higher among those of age >85 years, age 75 to 85 years, Black race, with body mass index (BMI) >30, dyspnea with exertion or at rest, partially or totally dependent in activities of daily living (ADLs), preoperative steroid use, preoperative wound infection, use of a mobility aid, fall within 3 months, or living alone at home without support. NHDs were statistically more likely among orthopedic, neurosurgery, or cardiac surgery interventions. Understanding individual patient’s risks and setting expectations for likely postoperative course is integral to appropriate preoperative counseling and preoperative optimization.

Keywords

surgery discharge complications home rehabilitation facility outcomes

Background

In 2016, the U.S. Census Bureau estimated that people aged 65 years or older represented approximately 16% of the total U.S. population or 49.2 million (Jablonski & Urman, 2019). This number continues to increase, with people older than 65 years making up the fastest growing component of the U.S. population (Yang et al., 2011). Understanding this growing population is critical, as approximately one third of inpatient surgeries in the United States are performed on older adults (Marwell et al., 2018).

The decision to offer and proceed with an elective surgery is fundamentally based on a calculation of the benefits and risks to the patient. Understanding this balance becomes more challenging in the older population with more complex baseline comorbidities and often unclear life expectancy. The risks of a surgical intervention are limited not only to mortality and postoperative complications but also to the postoperative course. For a patient presenting from home for elective surgery, it is important to understand if they will be able to return home after their stay or if they will require a more extended recovery in a nonhome facility (e.g., rehab or nursing facility). In addition, discharge to a nonhome facility is associated with increased complications, higher rates of readmissions, and increased mortality (Arya et al., 2016).

Risk factors for nonhome discharge (NHD) have been reported for select surgeries in the surgical literature, such as total joint arthroplasty, anterior cervical discectomy and fusion, posterior cervical fusion, colectomy, pancreatectomy, and open abdominal aortic aneurysm repair (Boitano et al., 2019; Di Capua et al., 2017; Keswani et al., 2016; Sacks et al., 2015; Ye et al., 2018). However, they have not been examined broadly across all surgical subspecialties, especially using population-based data. With an aging population and increasing numbers of older adults proceeding to surgical intervention, it is important to understand the factors that can affect the likely surgical and postoperative course of these patients, including placement in long-term care (Newcomer et al., 2018). Our study aims to understand which risk factors within a broad data set of older patients are most predictive of NHDs for home-dwelling older patients to facilitate both risk reduction and appropriate patient counseling in advance of elective surgery. We hypothesized that there are distinct preoperative risk factors that are significantly associated with NHD after elective, nonemergency surgery in older adults.

Method

Data Sources and Sample

This article adheres to the appropriate STROBE guidelines. This study was approved by the institutional review board at Brigham and Women’s Hospital and was exempted from the consent requirement due to the deidentified nature of the data.

The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) prospectively collects perioperative data for patients undergoing surgical procedures in approximately 600 institutions in the United States and the Middle East. Data collection and validation procedures have been described previously (Hall et al., 2009). Beginning in 2014, the NSQIP began collecting data on geriatric outcomes as part of a geriatric surgery pilot at 23 participating hospitals. The Geriatric Surgery Pilot Project is a feasibility study collecting data on 14 additional perioperative variables relevant to the geriatric population (Harris et al., 2019).

The initial data set comprised 32,627 surgical cases. Patients undergoing elective, nonemergent surgery admitted from home were included in the analysis. The NSQIP defines elective surgery as when “the patient is brought to the hospital or facility for a scheduled surgery from their home or normal living situation on the day that the procedure is performed” (ACS, 2018). Excluded from the analysis were emergent cases, patients where the preoperative living location was unknown, and patients with unrecorded discharge destinations. In addition, patients with missing demographic or comorbidity data and deceased patients were excluded from the analysis. Figure 1 shows the study flow diagram.

Figure 1.

Study flow diagram.

Patients were divided into two groups based on discharge destination: home or nonhome. For the purposes of the study, NHD included discharge to nonhome facilities including acute inpatient rehabilitation facilities, skilled and nonskilled care facilities, assisted living facilities, hospice, long-term acute care facilities, or nursing homes. Only patients who survived to hospital discharge were included in our analysis.

Baseline patient demographics included age (65–75 years, 75–85 years, 85 years or older), sex, race (self-identified), body mass index (BMI; less than 18.5, 18.5–25, 25–30, above 30), functional status based on activities of daily living (ADLs; independent in ADLs, partially or totally dependent in ADLs), dyspnea (no dyspnea, dyspnea at rest or with exertion), American Society of Anesthesiologists (ASA) physical status (PS), presence of common comorbidities (smoking, hypertension, diabetes, chronic obstructive pulmonary disease [COPD], congestive heart failure [CHF], chronic kidney disease (CKD), steroid use, weight loss, preoperative wound infection [defined as a documented open wound at the time of surgery requiring drainage or wound dressings], bleeding disorder, preoperative sepsis defined as a documented infection plus at least two positive systemic inflammatory response syndrome [SIRS] criteria, use of a mobility aid), fall in the past year (no, yes within 3 months, yes 3–6 months ago, yes 6–12 months ago), home status (lives with others or has support, lives alone), laboratory evidence of renal impairment (measured by serum creatinine), laboratory evidence of anemia (measured by hematocrit), laboratory evidence of thrombocytopenia, surgical subspecialty (general surgery, orthopedics, vascular, urology, neurosurgery, gynecology, thoracic, plastics, otolaryngology, cardiac), surgical complexity (defined by work relative value units [RVUs]), and anesthetic management (general, monitored anesthesia care [MAC], spinal, epidural, regional, other). The Current Procedural Terminology (CPT) code used was that associated with the principal operative procedure. The RVUs used in the analysis were based on the principal CPT code.

Statistical Analysis

Continuous demographic variables were reported as M ± SD. Continuous variables were compared with Student’s t-test, while categorical variables were compared with Pearson’s chi-square test. To develop our multivariable model, this initial data set was analyzed for variables significantly associated with an NHD, defined as a p value of <.05 on Student’s t-test or chi-square testing as above or a 95% confidence interval (CI) not containing 1.0 on univariable logistic regression. Significantly associated covariates were subsequently included in an initial multivariable model. Bidirectional stepwise selection was performed to minimize the Akaike information criterion. These covariates were incorporated into the final multivariable model for assessment of risk factors for NHD. To account for multiple comparisons, a Bonferroni adjustment was performed to maintain a family-wise error rate of 0.05. An adjusted p value for significance of .0017 was considered significant in the analysis. All analyses were conducted using R Studio Version 1.1.456 (Boston, MA) and R Project for Statistical Computing, v3.6.2 (Vienna, Austria).

Results

Study Population

Of the 23,871 home-dwelling patients who survived to hospital discharge, the inclusion criteria for our study were met by 18,470 patients. Among this cohort, 3,798 patients (20.6%) were discharged to a nonhome location and 14,672 patients (79.4%) were discharged to home, as shown in the study flow diagram (Figure 1). As demonstrated in Table 1, the M age for NHD was 75.6 years and the average age for home discharge was 72.5 years.

Table 1.

Baseline Patient Demographics, Risk Factors, and Discharge Destinations.

Variables	NHD		Home discharge		OR [95% CI]
Variables	M	SD	M	SD	OR [95% CI]
Age	76	6.7	72	5.9	1.08 [1.07, 1.09]
BMI	30	6.5	29	5.9	1.02 [1.02, 1.03]
Labs
Creatinine	1.01	0.65	1.00	0.60	1.04 [0.98, 1.09]
Hematocrit	39.2	1.5	40.5	1.5	0.56 [0.52, 0.6]
Platelets	246	78.0	241	74.0	1.08 [1.03, 1.13]
Surgical complexity
Work RVU	23.0	8.0	21.9	9.3	1.01 [1.01, 1.02]

Note. NHD = nonhome discharge; OR = odds ratio; CI = confidence interval; BMI = body mass index; RVU = relative value unit.

Tables 1 –3 further detail all patient baseline demographics and risk factors. Of note, most patients in the cohort were aged 65 to 75 years (n = 1,791 for NHD and 10,020 for home discharge, total = 11,811; 63.9%). Fewer patients were aged 75 to 85 years (n = 5,587; 30.2%) or aged >85 years (n = 1,072; 5.8%). Females represented a larger percentage of the patients discharged to a nonhome location (n = 2,406; 63.3%) compared with males (n = 1,392; 36.7%). Most patients identified as White race (n = 16,331; 88.4%). Most patients had a BMI ≥ 18.5 (n = 18,261; 98.9%) with a relatively even distribution among BMI ≥ 30, 25 to 30, and 18.5 to 25 in both study groups. The majority of patients presenting for elective surgery had good functional status defined by independence in ADLs (n = 18,059; 97.8%). Most patients were classified as ASA PS 3 (n = 10,322; 55.9%). Hypertension was the most common comorbidity (n = 12,761; 69.1%), followed by use of a mobility aid (n = 5,014; 27.1%) and diabetes (n = 3,592; 19.4%). Most patients denied a fall history in the last year (n = 16,620; 90.0%). The majority of patients reported having some level of support in the home (n = 10,546; 57.1%) compared with those living alone (n = 4,498; 24.4%) or living with family or friends (n = 3,426; 18.5%). The most common surgical subspecialties were orthopedics (n = 7,139; 38.7%), general surgery (n = 5,197; 28.1%), vascular (n = 1,819; 9.8%), and neurosurgery (n = 1,374; 7.4%). Cardiac surgery represented 13 cases, accounting for 0.7% of all cases.

Table 2.

Baseline Patient Demographics and Risk Factors.

Variables	NHD		Home discharge		OR [95% CI]
Variables	No.	%	No.	%	OR [95% CI]
Age group (years)
65–75	1,791	47.2	10,020	68.3	Reference
75–85	1,543	40.6	4,044	27.6	2.13 [1.98, 2.31]
85 and above	464	12.2	608	4.1	4.27 [3.75, 4.87]
Sex
Male	1,392	36.7	6,997	47.7	Reference
Female	2,406	63.4	7,675	52.3	1.58 [1.46, 1.7]
Demographics
White	3,259	85.8	13,072	89.1	Reference
Black	394	10.4	1,029	7.0	1.54 [1.36, 1.74]
Asian	30	0.8	158	1.1	0.76 [0.51, 1.13]
Other	9	0.2	44	0.3	0.82 [0.4, 1.68]
Not reported	106	2.8	369	2.5	1.15 [0.93, 1.43]
Hispanic	115	3.0	573	3.9	0.77 [0.63, 0.94]
BMI group
<18.5	54	1.4	155	1.1	1.47 [1.07, 2.02]
18.5–25	838	22.1	3,541	24.1	Reference
25–30	1,209	31.8	5,495	37.5	0.93 [0.84, 1.03]
>30	1,697	44.7	5,481	37.4	1.31 [1.19, 1.44]
Dyspnea
No dyspnea	3,512	92.5	13,916	94.9	Reference
Dyspnea with exertion or at rest	286	7.5	756	5.2	1.5 [1.3, 1.73]
Functional status
Independent in ADLs	3,591	94.6	14,468	98.6	Reference
Partially/Totally dependent in ADLs	207	5.5	204	1.4	4.09 [3.36, 4.98]
Origin status
Lives at home with others	503	13.2	2,923	19.9	Reference
Lives with support in the home	1,807	47.6	8,739	59.6	1.2 [1.08, 1.34]
Lives alone at home	1,488	39.2	3,010	20.5	2.87 [2.57, 3.22]
ASA class
1/2	1,263	33.3	5,894	40.2	0.75 [0.69, 0.81]
3	2,301	60.6	8,021	54.7	Reference
4/5	234	6.2	757	5.2	1.08 [0.92, 1.26]

Note. NHD = nonhome discharge; OR = odds ratio; CI = confidence interval; BMI = body mass index; ADLs = activities of daily living; ASA PS = American Society of Anesthesiologists physical status.

Table 3.

Baseline Patient Comorbidities and Fall History.

Variables	NHD		Home discharge		OR [95% CI]
Variables	No.	%	No.	%	OR [95% CI]
Comorbidities
Smoking	270	7.1	1,408	9.6	0.72 [0.63, 0.83]
Hypertension	2,843	74.9	9,918	67.6	1.43 [1.32, 1.55]
Diabetes	847	22.3	2,745	18.7	1.25 [1.14, 1.36]
COPD	286	7.5	967	6.6	1.15 [1.01, 1.32]
CHF	33	0.9	67	0.5	1.91 [1.26, 2.9]
CKD	39	1.0	96	0.7	1.58 [1.08, 2.29]
Steroid use	226	6.0	561	3.8	1.59 [1.36, 1.87]
Weight loss	66	1.7	198	1.4	1.29 [0.98, 1.71]
Wound infection	91	2.4	200	1.4	1.78 [1.38, 2.28]
Bleeding disorder	194	5.1	659	4.5	1.14 [0.97, 1.35]
Sepsis	18	0.5	28	0.2	2.49 [1.38, 4.51]
Uses mobility aid	1,962	51.7	3,052	20.8	4.07 [3.77, 4.39]
Fall history
None	3,130	82.4	13,490	91.9	Reference
Yes, within 3 months	430	11.3	671	4.6	2.76 [2.43, 3.14]
Yes, within 3 to 6 months	125	3.3	275	1.9	1.96 [1.58, 2.43]
Yes, within 6 months to 1 year	113	3.0	236	1.6	2.06 [1.64, 2.59]
Surgical specialty
General surgery	512	13.5	4,685	31.9	Reference
Cardiac surgery	8	0.2	5	0.0	14.64 [4.77, 44.92]
Gynecology	66	1.7	940	6.4	0.64 [0.49, 0.84]
Neurosurgery	408	10.7	966	6.6	3.86 [3.34, 4.48]
Orthopedics	2,413	63.5	4,726	32.2	4.67 [4.21, 5.18]
Otolaryngology (ENT)	8	0.2	51	0.4	1.44 [0.68, 3.04]
Plastics	6	0.2	116	0.8	0.47 [0.21, 1.08]
Thoracic	73	1.9	468	3.2	1.43 [1.1, 1.86]
Urology	80	2.1	1,120	7.6	0.65 [0.51, 0.83]
Vascular	224	5.9	1,595	10.9	1.29 [1.09, 1.52]
Anesthesia type
General	2,341	61.6	10,944	74.6	Reference
MAC/IV sedation	820	21.6	2,434	16.6	1.57 [1.44, 1.72]
Spinal	594	15.6	1,204	8.2	2.31 [2.07, 2.57]
Regional	18	0.5	65	0.4	1.29 [0.77, 2.19]
Epidural	15	0.4	16	0.1	4.38 [2.16, 8.88]
Other	9	0.2	44	0.3	0.82 [0.4, 1.68]

Note. NHD = nonhome discharge; OR = odds ratio; CI = confidence interval; COPD = chronic obstructive pulmonary disease; CHF = congestive heart failure; CKD = chronic kidney disease; MAC = monitored anesthesia care; IV = intravenous.

Univariable Analysis

Tables 1 –3 further show odds ratios (ORs) for baseline demographics and risk factors. The factors most strongly linked to an NHD included advanced age above 85 years (OR = 4.27; 95% CI = [3.75, 4.87]), age 75 to 85 years (OR = 2.13; 95% CI = [1.98, 2.31]), reduced functional status with partial/total dependence in ADLs (OR = 4.09; 95% CI = [3.36, 4.98]), and use of a mobility aid at baseline (OR = 4.07; 95% CI = [3.77, 4.39]). Also, fall history within the last 3 months (OR = 2.76; 95% CI = [2.43, 3.14]), living alone at home (OR = 2.87; 95% CI = [2.57, 3.22]), neurosurgery interventions (OR = 3.86; 95% CI = [3.34, 4.48]), and orthopedic surgery interventions (OR = 4.67; 95% CI = [4.21, 5.18]) were significant risk factors for NHD.

Supplemental Table 1 shows the most common types of elective surgeries in the data set defined by the CPT code. The most common surgeries included total knee arthroplasty, total hip arthroplasty, laminectomy, facetectomy and foraminotomy, thromboendarterectomy, and laparoscopic colectomy. Supplemental Table 2 shows the most common elective surgeries within specialties predictive of NHD. The three surgical subspecialties identified to be independently predictive of NHD are orthopedic surgery, neurosurgery, and cardiac surgery. Finally, Supplemental Table 3 shows discharge destinations for NHDs. The vast majority of patients were sent to either a skilled care (55%) or an acute rehabilitation facility (44%).

Multivariable Analysis

Table 4 shows the results of the multivariable logistic regression analysis for independent risk factors predictive of an NHD. In particular, age greater than 85 years (OR = 4.04; 95% CI = [3.43, 4.77]), living alone at home (OR = 2.91; 95% CI = [2.56, 3.32]), preoperative wound infection defined as an open would at the time of surgery (OR = 2.65; 95% CI = [1.94, 3.60]), functional status defined as partial/total dependence in ADLs (OR = 2.50; 95% CI = [1.96, 3.18]), and use of a mobility aid (OR = 2.04; 95% CI = [1.87, 2.23]) are significant independent risk factors for NHD. These predictors of NHD are also shown in the forest plot, listing individual factors identified in multivariable logistic regression analysis and corresponding ORs (Figure 2).

Table 4.

Multivariable Predictors of Nonhome Discharge.

Risk factor	OR [95% CI]	p value
85 years or older	4.04 [3.43, 4.77]	<.001
Lives alone at home	2.91 [2.56, 3.32]	<.001
Preoperative wound infection	2.65 [1.94, 3.6]	<.001
Partially/Totally dependent in ADLs	2.50 [1.96, 3.18]	<.001
Uses mobility aid	2.04 [1.87, 2.23]	<.001
75–85 years old	2.01 [1.83, 2.20]	<.001
Fall within 3 months	1.65 [1.42, 1.92]	<.001
Black race	1.63 [1.4, 1.90]	<.001
Dyspnea with exertion or at rest	1.5 [1.27, 1.78]	<.001
Preoperative steroid use	1.4 [1.16, 1.69]	<.001
BMI > 30	1.31 [1.17, 1.47]	<.001
Surgical procedure types
Cardiac surgery	29.03 [8.13, 103.67]	<.001
Orthopedics	11.49 [9.88, 13.37]	<.001
Neurosurgery	7.35 [6.16, 8.78]	<.001

Note. OR = odds ratio; CI = confidence interval; ADLs = activities of daily living; BMI = body mass index.

Figure 2.

Forrest plot of the predictors of nonhome discharge identified in multivariable logistic regression analysis and corresponding odds ratios.

Discussion

We performed a retrospective cohort study of the ACS NSQIP geriatric research data set and used multivariable logistic regression to identify risk factors associated with NHDs among older adults presenting from home for elective surgery. We utilized unique data from the new Geriatric Surgery Pilot Project which is collecting data on 14 additional perioperative variables relevant to the geriatric population.

In our study, advanced age was strongly associated with NHD with ORs of 4.04 (95% CI = [3.43, 4.77]) for patients aged older than 85 years and 2.01 (95% CI = [1.83, 2.20]) for patients aged 75 to 85 years compared with adults aged 65 to 75 years. Age is a well-known risk factor from the surgical literature for NHD (Di Capua et al., 2017; Keswani et al., 2016; Sacks et al., 2015). While this is an unsurprising finding, because age is not a modifiable risk factor, it presents challenges for surgical decision-making. The likelihood of an NHD based on a patient’s risk factors can aid discussions of informed consent and goals of care. Although many different surgical subspecialties were represented in the NHD sample, those identified to be independently predictive of NHD were orthopedic surgery, neurosurgery, and cardiac surgery, which are known to be high-risk procedures with a high likelihood of a discharge to a nonhome destination (Brovman et al., 2019; Pattakos et al., 2012; Quinn et al., 2017; Stopa et al., 2019). Because our study focuses on older patients who underwent major surgery requiring an inpatient admission, it is not surprising that a large number of cases in the study sample were orthopedic and spine surgery.

It is important to understand the likelihood of an NHD for a patient who had been home-dwelling prior to elective surgical intervention because older patients continue to represent a larger share of patients presenting for elective surgery. Using risk factors identified from a broad population of older adults may help inform health care providers such as surgeons and anesthesiologists, primary care providers, and specialists of an individual’s expected postoperative course should they decide to proceed with elective surgery and design preoperative evaluation pathways (Ogink et al., 2019; Cooper et al., 2020). Our study also offers information to facilitate preoperative shared decision-making, taking into account risks of the procedure and the patient’s goals of care (Brovman et al., 2018; Quinn et al., 2018).

In 2019, the ACS released recommendations for quality improvement within geriatric surgery, suggesting 32 new surgical standards to improve surgical care and outcomes for the aging adult (ACS, 2019). These recommendations include discussions of patient goals of care, as well as interdisciplinary decision-making, preoperative screening, and postoperative management using a systems-based approach. These components are interconnected in the surgical care of the older adult because an understanding of preoperative risk factors that can lead to a higher likelihood of an NHD can help facilitate patient counseling, guide clinical care, and set plans for inhospital and posthospital management.

Poor preoperative functional status has long been recognized as a significant risk factor of worse postoperative outcomes, including a higher incidence of 30-day mortality and major morbidity (Scarborough et al., 2015). Our study suggests that patients who are partially or totally dependent in their ADLs have an OR of 2.50 (95% CI = [1.96, 3.18]) of an NHD, a significant finding with implications for patient counseling and surgical planning as well as development of multifaceted perioperative interventions to optimize these patients. Common surgical risk prediction models include functional status as a key component, such as the ACS NSQIP Calculator (Marwell et al., 2018). Other tools include the Hopkins Frailty Index, which assesses for several domains such as unintentional weight loss, exhaustion, low energy expenditure, low grip strength, and slowed walking speed and has been shown to predict the likelihood of a postoperative complication (Revenig et al., 2013). Another prediction tool, Death, Institutionalization in assisted living or residential care facility, Readmission to a hospital, or visit to an Emergency Department (“DIRE”), has been used to predict an adverse event (AE) within 30 days following discharge from a hospital to the community for frail patients older than 65 years (Flanagan & Kelly, 2019). Similar to functional status, a history of falls has been used as a proxy measure of baseline strength and physiologic reserve. Our study shows that patients who have a history of falls within 3 months of surgery or use a mobility aid have ORs of 1.65 (95% CI = [1.42, 1.92]) and 2.04 (95% CI = [1.87, 2.23]) of an NHD, respectively. A history of falls has previously been shown to increase the likelihood of discharge to a nonhome facility following neurosurgical operations (Bekelis et al., 2017).

Previous studies have shown that the use of mobility aids prior to hip and knee surgery is associated with increases in hospital stay, and a recent study of postoperative morbidity and discharge destinations in patients older than 85 years who underwent fast-track hip and knee arthroplasty showed that a preoperative use of a walking aid was associated with a significantly higher likelihood of an NHD (9.2% vs. 2.4%; Pitter et al., 2016). Screening tools are available to objectively assess a patient’s functional mobility, most notably, the Timed Up and Go (TUG) test, which may be used to predict risk of falls in older adults (Marwell et al., 2018). Frailty can be assessed as a risk index that takes into account a number of deficits, such as disability, diseases, physical and cognitive impairments, psychosocial risk factors, and geriatric syndromes (Xue, 2011; Arias et al., 2020). For patients identified with frailty preoperatively, a recent Statement from the Society for Perioperative Assessment and Quality Improvement (SPAQI) on frailty assessment recommends utilizing shared decision-making, prehabilitation, and interdisciplinary geriatric comanagement to potentially improve the prognosis of these patients (Alvarez-Nebreda et al., 2018). However, it is unclear if these management strategies would actually decrease NHD.

Another key finding from our study is the importance of support at home to reduce the risk of an NHD. Patients living alone at home had an OR of 2.91 (95% CI = [2.56, 3.32]) of an NHD. It is possible that patients who discharge to a nonhome location due to a lack of social support may not experience the same rate of postoperative complications seen in patients who experience NHD due to medical complexity or frailty. In fact, living alone has been associated with a decreased rate of 90-day readmissions (Pitter et al., 2016). It is possible that living alone increases the likelihood of an NHD due to reliance on family/friends to support baseline needs rather than it being a poor prognostic factor. While further research is necessary to clarify this finding, it is clear that patients who live alone should be counseled about the increased likelihood of requiring an NHD.

Despite the abundance of nonmodifiable risk factors, there may be a role for preoperative optimization of patients who have preexisting wound infections, dyspnea on exertion, are taking steroids, or have an elevated BMI (Murphy et al., 2017). Our analysis shows that the presence of a preoperative wound infection, shortness of breath, steroid use, and BMI > 30 are significant predictors of an NHD. It is important to note that a low BMI also carries a poor prognosis in geriatric patients (Saleh et al., 2017), but with a small number of patients in the low-BMI group in our study, it is not possible to show being underweight as another risk factor for NHD.

Much of the literature has focused on the impact of a postoperative wound complications leading to an NHD (Caldararo et al., 2016; Keswani et al., 2016; Ye et al., 2018). However, this may suggest a role for more intensive preoperative wound care. Dyspnea is a predictor of postoperative pulmonary complications, and it has been linked to higher rates of inhospital mortality and increased likelihood of requiring discharge to a skilled nursing facility (Marwell et al., 2018). Preoperative steroid use has been linked to NHD after multiple surgeries, including total joint arthroplasty (Keswani et al., 2016), anterior cervical discectomy and fusion (Di Capua et al., 2017), and posterior cervical fusion (Ye et al., 2018). Patients with a BMI ≥ 40 undergoing total joint arthroplasty and anterior cervical discectomy and fusion demonstrate higher rates of NHD in some studies (Di Capua et al., 2017). Our study suggests that this risk is also increased for patients with BMI ≥ 30. The ACS NSQIP Calculator considers dyspnea, steroid use, and BMI among other factors to measure surgical risk (Marwell et al., 2018). As shown in the retrospective study of risk factors for NHD and prolonged length of stay in cytoreductive surgery by Burguete et al., additional factors such as advanced age, low serum albumin, and multivisceral resection showed an increased risk, suggesting that adequate identification of these factors may facilitate preoperative discussion with patients and improve discharge planning and resource utilization (Burguete et al., 2019). Although our study identified several independent risk factors for NHD, one might surmise that a coexistence of several of these risk factors in a particular patient could increase the risk even more (Newcomer et al., 2018). Although the ACS NSQIP Risk Calculator has “Discharge to Nursing or Rehab Facility” as one of the outcome risks based on procedure, there is no risk calculator that specifically addresses NHD as the primary outcome. For patients discharged home, a combination of interventions such as patient needs assessment, medication reconciliation, patient education, arranging timely outpatient appointments, and providing telephone follow-up have successfully reduced readmission rates (Kripalani et al., 2014).

Limitations

The limitations of this study include the retrospective nature of the database and potentially subjective nature of some of the variables (e.g., functional status or degree of home support). In addition, the data do not show duration of time spent in the hospital, rates of readmission, or time spent in an NHD facility. There may also be oversampling or undersampling of particular case types. Our data exclude patients who were first admitted to the hospital and then operated on as inpatients. Although our study suggests some modifiable risk factors that increase the likelihood of NHD, it does not provide data on the impact of risk reduction strategies.

Conclusion

In summary, understanding an individual patient’s risks and setting expectations for likely postoperative course may enhance patient satisfaction through an operative course and help with discharge planning. Making a plan for a likely NHD may assist with care coordination prior to surgery. Understanding these risk factors is integral to appropriate preoperative counseling of older patients and may provide opportunities for preoperative optimization and clinical pathway development, especially if cost-effectiveness can be demonstrated.

Supplemental Material

NHD_SuppTable1_052020 – Supplemental material for Preoperative Risk Factors for Nonhome Discharge of Home-Dwelling Geriatric Patients Following Elective Surgery

Supplemental material, NHD_SuppTable1_052020 for Preoperative Risk Factors for Nonhome Discharge of Home-Dwelling Geriatric Patients Following Elective Surgery by John C. Warwick, Ethan Y. Brovman, Sascha S. Beutler and Richard D. Urman in Journal of Applied Gerontology

Supplemental Material

NHD_SuppTable2_052020 – Supplemental material for Preoperative Risk Factors for Nonhome Discharge of Home-Dwelling Geriatric Patients Following Elective Surgery

Supplemental material, NHD_SuppTable2_052020 for Preoperative Risk Factors for Nonhome Discharge of Home-Dwelling Geriatric Patients Following Elective Surgery by John C. Warwick, Ethan Y. Brovman, Sascha S. Beutler and Richard D. Urman in Journal of Applied Gerontology

Supplemental Material

NHD_SuppTable3_052020_RU_June – Supplemental material for Preoperative Risk Factors for Nonhome Discharge of Home-Dwelling Geriatric Patients Following Elective Surgery

Supplemental material, NHD_SuppTable3_052020_RU_June for Preoperative Risk Factors for Nonhome Discharge of Home-Dwelling Geriatric Patients Following Elective Surgery by John C. Warwick, Ethan Y. Brovman, Sascha S. Beutler and Richard D. Urman in Journal of Applied Gerontology

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Research Ethics

The study design was approved by the Institutional Review Board, Partners Health care. Protocol #2015P000551.

ORCID iD

Richard D. Urman

Supplemental Material

Supplemental material for this article is available online.

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Supplementary Material

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