Abstract
Objectives
Our aim was to identify sociodemographic/clinical, surgical, and psychosocial predictors of postdischarge surgical recovery after laparoscopic sacrocolpopexy.
Methods
Study participants (N=171) with ≥ stage 2 pelvic organ prolapse completed a preoperative survey measuring hypothesized sociodemographic/clinical, surgical, and psychosocial recovery predictors followed by a postoperative survey at four time points (day 7, 14, 42, and 90) that included the Postdischarge Surgical Recovery (PSR)13 scale. One multivariate linear regression model was constructed for each time point to regress PSR13 scores on an a priori set of hypothesized predictors. All variables that had p values less than 0.1 were considered significant predictors of recovery because of the exploratory nature of this study and focus on model building rather than model testing.
Results
Predictors of recovery at one or more time points included the following: Sociodemographic/clinical predictors: older age, higher body mass index, fewer comorbidities, and greater preoperative pain predicted greater recovery. Surgical predictors: fewer perioperative complications and greater change in the leading edge of prolapse after surgery predicted greater recovery. Psychosocial predictors: less endorsement of doctors locus of control, greater endorsement of others locus of control, and less sick role investment predicted greater recovery.
Conclusions
Identified sociodemographic/clinical, surgical, and psychosocial predictors should provide physicians with evidence based guidance on recovery times for patients and family members. This knowledge is critical for informing future research to determine if these predictors are modifiable by changes to our narrative during the preoperative consultation visit. These efforts may reduce the postdischarge surgical recovery for patients with pelvic organ prolapse after laparoscopic sacrocolpopexy, accepting the unique demands on each individual’s time.
Keywords: Postdischarge surgical recovery, laparoscopic sacrocolpopexy, health locus of control, sick role
Introduction
Postoperative recovery is defined as an energy-requiring process of returning to normality and wholeness, as evidenced by a return to preoperative levels of independence/dependence in activities of daily living and psychological well-being (1). Patients preparing for surgical correction of pelvic organ prolapse invariably ask “what is my recovery time?” to establish their mindset following our preoperative consultation meeting. Mindsets are “mental guardrails” that guide attention and simplify complex associations (2). Recovery mindsets simplify scheduling surgery around other commitments, short-term disability and/or family medical leave form preparation, arrangement of personal postoperative or child/spousal care support at home, and post-operative activity level expectations. The meaning behind the “what is my recovery time?” question can confuse the reconstructive surgeon interested in providing recovery expectancies based on poorly characterized predictors. Unresponsive statements such as “it depends”, or “I am not sure” are largely based on observations that recovery time varies widely among patients undergoing similar surgeries (3), (4), and (5). Sociodemographic/clinical and surgical predictors including age (6), (7), (8), body mass index (7), (9), medical co-morbidities (6), (7), and surgical complications (6), (8), (9), (10) have been found to be important predictors of recovery from a variety of surgeries.
The purpose of this study was to identify modifiable psychosocial predictors of postdischarge surgical recovery after laparoscopic sacrocolopexy. Early return to the social responsibilities of daily life after surgery is a desirable outcome if 1) identified predictors are modifiable, and 2) modification of said predictors effect recovery.
Material and Methods
A convenience sample of 200 women with Stage ≥ 2 pelvic organ prolapse scheduled for laparoscopic sacrocolpopexy were recruited from a university affiliated urogynecology practice between December 2013 and October 2016 for participation in this prospective cohort study. We followed STROBE guidelines (11) for reporting observational studies in the preparation of this manuscript. Inclusion criteria were: 1) women who felt competent responding to web-based surveys delivered to their private email address, 2) had a reliable internet connection at home, and 3) comprehension of the English language at an 8th grade reading level assessed informally by the first author during the preoperative consultation visit. The Simple Measure of Gobbledygook (SMOG) formula was previously used to insure that the PSR was at a 6th grade reading level (3). The exclusion criterion was an unwillingness to commit to web-based survey completion within 3 days of their scheduled email delivery.
The first author discussed study participation with eligible candidates after their preoperative surgical consultation visit. Eligible and willing participants signed an Indiana University institutional review board (IRB) approved informed consent, authorization to use de-identified protected health information, and were scheduled for surgical repair. Patients were encouraged to “listen to their own bodies, and return to normal activities, including work responsibilities, when they felt up to it” by the first author. Our surgery scheduler provided each study participant with written materials supportive of these standardized verbal postoperative activity instructions. No additional activity guidance or lifting restrictions were provided to study participants. The senior authors postoperative activity instructions have been in continued use for over 5 years given the weakness of “existing evidence […] to adequately inform a clinical practice guideline on postoperative activity restrictions” (12) and the awareness that “[ability] to comply with restrictions depends on number of dependents, at home lifestyle, support system and financial circumstances” (13). Our surgery scheduler completed Family Medical Leave Act (FMLA) or return to work forms based on the unique financial needs of each patient, independent of recovery.
Surgeons did not set recovery expectancies for study participants or family members when asked “what is the recovery time?” during their preoperative surgical consultation visit. Patient and family “what is the recovery time” questions were responded to with a specific request for study participation by the first author. Non-participants were told that recovery varies greatly between patients based on a set of poorly understood factors without mention of a specific time period.
Study participants underwent conventional laparoscopic sacrocolpopexy with concomitant procedures, including vaginal (not laparoscopic supracervical) hysterectomy, retropubic midurethral sling, and posterior colporrhaphy, when indicated. Conventional laparoscopic sacrocolpopexy (LSC) included a traditional posterior colporrhaphy without levator plication or no posterior colporrhaphy based on the surgeon's examination of the patient’s anatomy after apical suspension with anterior, posterior, apical vaginal, and sacral attachment of mesh. Conventional laparoscopic sacrocolpoperineopexy (LSCP) included an abdominovaginal posterior colporrhaphy without levator plication performed prior to anterior, posterior, apical vaginal and sacral attachment of mesh. The first stage included a vaginal dissection where a traditional posterior colporrhaphy without levator plication was augmented by the overlaid distal LSCP posterior mesh leaflet attached to the iliococcygeal fascia laterally and the perineal body distally. The second stage included attaching the mesh to the posterior vaginal wall laparoscopically in the standard technique. In both cases, Amid classification type I ultra-lightweight polypropylene mesh (Restorelle® L or M Flat Mesh, Coloplast, Minneapolis, MN) was sutured to the vagina and anterior longitudinal ligament overlying the sacrum with 2-0 polydioxanone suture (PDS® II, Ethicon, Inc., Somerville, NJ).
Neither the postoperative activity instructions, recovery expectancies, nor the surgery were considered interventions for this prospective longitudinal study. Data from this exploratory study will be used for sample size calculations during the model testing phase of our interventional research to determine if 1) identified predictors are modifiable, and 2) modification of said predictors effect recovery.
All study data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at Indiana University School of Medicine (14). REDCap is a secure, web-based application designed to support data capture for research studies, providing: 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources.
Preoperative Measures
Study participants completed a 40-minute preoperative survey measuring an a priori set of hypothesized sociodemographic/clinical, surgical, and psychosocial postdischarge surgical recovery predictors (Table 1). Race/ethnicity and marital status were not included in our regression analysis because we studied a primarily Non-Hispanic White population, and believed that the social support variable would capture the salient differences in marital status for predicting postdischarge surgical recovery. Preoperative POP-Q stage of prolapse, and leading edge of prolapse (in centimeters) were not included in our regression analysis because we believed that preoperative PFDI-20, PFIQ-7, and delta leading edge of prolapse after surgery would capture the salient differences in these predictor variables. Similarly, 7 of 8 subscales of the SF-36 were not included in our regression analysis because we believed that the disease specific PFDI-20, and PFIQ-7 would capture the salient differences in these predictor variables. This left the SF-36 preoperative pain score as the single measure of preoperative chronic pain as a potential clinical predictor of postdischarge surgical recovery.
Table 1.
A Priori Set of Hypothesized Sociodemographic/Clinical, Surgical, and Psychosocial Postdischarge Surgical Recovery Predictors
| Hypothesized Predictors | Scoring Range | Scoring Interpretation |
|---|---|---|
|
| ||
| Preoperative Survey | ||
|
| ||
| Sociodemographic/Clinical Predictors | ||
| • Age | ||
| • Body mass index | ||
| • SES (Hollingshead 4-factor Index of Social Position) (30) | 8–66 | ↑ SES = ↑ resources |
| • Smoking status (0 = never smoked, 1 = past/current) | ||
| • Pack year history | ↑ pack years = ↑ smoking exposure | |
| • Total number of past surgeries | ||
| • Medical Co-morbidities (Charlson Co-morbidity index) | ↑ Charlson index score = ↑ co-morbidities | |
| • Disease specific symptom bother (PFDI-20) | 0–300 | ↑PFDI-20 scores = ↑ symptom distress |
| • Disease specific impact on ADL’s (PFIQ-7) | 0–300 | ↑PFIQ scores = ↑ symptom impact on ADLs |
| • Preoperative pain scores (SF-36) | 0–100 | ↑ pain scores = ↑ pain |
| • Physical activity (Godin Leisure Time Questionnaire) | Active ≥ 24 | ↑ total activity scores = ↑ activity |
| Mod = 14–23 | ||
| Inactive < 14 | ||
|
| ||
| Psychosocial Predictors | ||
| • Health locus of control (Internal, Chance, Doctors, Others) | IHLC, CHLC 6–36 | ↑ score = ↑ belief in subscales control over ones health |
| DHLC, OHLC 3–18 | ||
| • Sick role investment (Illness Cognition Scale) | 17–85 | ↑ ICS scores = ↑ sick role investment |
| • Optimism (Life Orientation Test – Revised) | 0–24 | ↑LOT-R scores = ↑ optimism |
| • Self Efficacy (General Self Efficacy) | 10–40 | ↑ score = ↑ self efficacy |
| • Body image (Body image scale) (31) | 0–24 | ↑ scores = ↑ body image disturbance |
| • Social support (MOS social support survey) | 1–5 | ↑ scores = ↑ support |
|
| ||
| Perioperative Survey | ||
|
| ||
| Surgical predictors | ||
| • Surgical time (close time – cut time) | ||
| • Delta hemogloblin (preoperative Hgb – postoperative Hgb) | ↑delta Hgb = ↑blood loss | |
| • Delta leading edge of prolapse (preoperative leading edge – postoperative leading edge) | ↑ delta LE = ↑ change in prolapse severity | |
| • Peri-operative complications (Modified Clavien Dindo; 0 = no complications, 1 = any complications) | ↑ grade = worse complication | |
|
| ||
| Postoperative Survey | ||
|
| ||
| Postdischarge surgical recovery (PSR13) | 0–100 | ↑ PSR13 score = ↑ recovery |
SES = socioeconomic status
PFDI-20 = Pelvic Floor Disorders Inventory-20
PFIQ-7 = Pelvic Floor Impact Questionnaire-7
ADL’s = activities of daily living
Mod = moderately active
IHLC, CHLC, DHLC, OHLC = Internal, Chance, Doctors, and Others Health Locus of Control
ICS = Illness Cognition Scale
MOS = Medical Outcomes Study
Hgb = Hemoglobin
PSR13 = Postdischarge Surgical Recovery Survey
We only included psychosocial factors that our research team hypothesized prediction of postdischarge surgical recovery after laparoscopic sacrocolpopexy for model building. According to Parsons, “sick” persons have two rights: 1) exemption from normal social roles, 2) lack of responsibility for their condition; and two obligations: 1) desire to get well, 2) seek technically competent help and coordinate with the medical professional, based on social norms surrounding illness (15). Sick role investors are patients who reject their obligation to get well and become sick role winners when they are legitimized by medical professionals, through diagnosis, prescription (16), or prolonged recovery expectations. Sick role losers are investors who do not receive such legitimation. Health Locus of Control (HLC) is the belief that one’s health is dependent on internal versus external factors (17). Internal HLC involves the belief that one’s personal lifestyle choice determines health status, which has been associated with physical/mental well-being and proactive health behaviors. External factors including 1) Chance HLC involves the belief that fate or luck determines health status which has been associated with the converse of Internal HLC, and 2) Powerful Others HLC involves the belief that doctors or relations and friends determine health status which has been associated with strong adherence to recommendations but a higher likelihood of disability (18).
Peri-operative Measures
The primary author abstracted study participant’s surgical data (Table 1) immediately after hospital discharge and continued to collect complication data until the three month postoperative visit. Study participants were classified by the most severe Clavien-Dindo complication category met during this time period consistent with guidelines (19). Complications were categorized as none or any during post processing because of the frequency distribution of the Modified Clavien-Dindo classifications in our study population. Surgery type and concomitant surgeries were not included in our regression analysis because we felt that surgical time, delta hemoglobin, and peri-operative complications would capture the salient differences in these predictor variables.
Postoperative Measures
Study participants completed a 15-minute postoperative survey to measure recovery on postoperative days 7, 14, 42, and 90 (± 3 days). According to Kleinbock, the PSR was designed to serve, as a dependent variable capable of measuring variations in perceived at home recovery in patients dismissed within 24 hours of their surgical procedure (3). The 15-item instrument measures this single construct without evidence for subscales or separate domains. Our research group previously reported on the psychometric properties of a validated version of the PSR [Postdischarge Surgical Recovery (PSR13) survey; Cronbach’s α’s = .911–934, MID = 5, Clinical responsiveness, Cohen’s d = 0.53 from 7 to 14 days, Cohen’s d = 0.89 from 14 to 42 days, Cohen’s d = 0.08 from 42 to 90 days] in patients with pelvic organ prolapse undergoing laparoscopic sacrocolpopexy for use in the present study (20).
We measured the recovery trajectory by computing descriptive statistics for the PSR13 at all four time points. Scores across all thirteen items were averaged to yield a single score ranging from 0–100, with higher scores indicating greater recovery. PSR13 scores were anchored to a single item recovery tool (if 100% recovery is back to your usual health, what percentage of recovery are you now?) (3) for the purpose of establishing a clinically relevant PSR13 score at which patients considered themselves fully recovered (single item score = 100).
Study sample means with standard deviations were calculated after checking normality assumptions by visual inspection of the frequency distribution for each continuous predictor variable. Study population percentages were calculated for each categorical predictor variable. We minimized missing data using repeated telephone reminders placed by the primary author preoperatively and up to 3 days after each measured postoperative time point. Surveys completed beyond the 3 days after each postoperative time point were excluded from the analysis. Missingness at any time point was not correlated with PSR13 scores or any predictor variables at the p < 0.01 level. Key sociodemographic/clinical variables were compared between included and excluded study participants to address potential sources of nonresponse bias. One multivariate linear regression model was constructed for each time point to regress PSR13 scores on an a priori set of hypothesized sociodemographic, surgical, and psychosocial predictors. All hypothesized predictor variables were entered simultaneously rather than via backward regression to reduce the likelihood that the results would be spurious due to our unique sample. Only variables that had p values less than 0.1 were considered significant predictors and reported as results because of the exploratory nature of this study and focus on model building rather than model testing.
IBM SPSS Statistics for Windows, version 24 (IBM Corp., Armonk, N.Y., USA) was used for all computational analyses.
Results
One hundred and seventy one of 200 study participants prospectively completed the preoperative questionnaire followed by the postoperative questionnaire at ≥ 3 of the required time points. Twenty nine of 200 (14.5%) study participants were lost to attrition as follows: 14 enrolled study participants did not complete their preoperative surveys; 6 did not undergo conventional laparoscopic sacrocolpopexy (2 abdominal conversions, 4 no surgery); 4 completed the postoperative questionnaire at < 3 of the required time points; and 5 did not receive postoperative questionnaires due to research error. Enrolled but excluded study participants did not differ from included study participants with respect to age, Hollingshead SES score, Charlson Comorbidity Index, preoperative POP-Q staging, or leading edge of prolapse. Excluded participants had significantly higher BMI than included participants (29.97 vs. 28.09 kg/m2, p = 0.029).
Table 2 lists the descriptive statistics for the a priori hypothesized sociodemographic/clinical, surgical, and psychosocial predictors of postdischarge surgical recovery in our study sample. The race/ethnicity and marital status of our study population was 94.7% Non-Hispanic White and 71.3% married. The preoperative POP-Q staging distribution of our study population was 36.8% Stage 2, 58.5% Stage 3, and 4.7% Stage 4 with a mean leading edge of prolapse 2.4 ± 0.2 (minimum −1, maximum 10) centimeters beyond the introitus.
Table 2.
Descriptive Statistics for the A Priori Set of Hypothesized Sociodemographic/Clinical, Surgical, and Psychosocial Predictors of Postdischarge Surgical Recovery in our study population.
| Hypothesized Predictor | Value |
|---|---|
|
| |
| Preoperative Survey | |
|
| |
| Sociodemographic/clinical predictors | |
|
| |
| Age (yrs) | 63.25 ± 9.15 |
|
| |
| Body mass index (kg/m2) | 28.05 ± 4.18 |
|
| |
| SES | 41.92 ± 11.68 |
|
| |
| Smoking Status | |
| • Current/past smoker | 63 (36.8%) |
| • Never smoked | 105 (61.4%) |
| • Missing | 3 (1.8%) |
|
| |
| Pack year history | 6.86 ± 15.14 |
|
| |
| Total number of past surgeries | 4.27 ± 2.81 |
|
| |
| Medical Co-Morbidities | 2.47 ± 1.85 |
|
| |
| Disease specific symptom bother | |
| • UDI | 44.57 ± 28.27 |
| • POPDI | 43.84 ± 23.30 |
| • CRADI | 24.89 ± 21.66 |
| • Total (PFDI-20 Summary Score) | 113.29 (60.46) |
|
| |
| Disease specific impact on ADL’s | |
| • UIQ | 34.31 ± 27.92 |
| • POPIQ | 31.01 ± 27.21 |
| • CRAIQ | 19.05 ± 26.45 |
| • Total (PFIQ-7 Summary Score) | 84.67 ± 70.97 |
|
| |
| Preoperative pain scores | 64.64 ± 25.10 |
|
| |
| Physical activity | |
| • Total leisure activity score | 34.00 ± 40.86 |
| • Active (%) | 87 (57.9%) |
| • Moderately active (%) | 29 (17%) |
| • Insufficiently active (%) | 43 (25.1%) |
| • Missing | 12 (7%) |
|
| |
| Psychosocial predictors | |
|
| |
| Health locus of control | |
| • Internal | 19.41 ± 6.77 |
| • Chance | 11.56 ± 4.63 |
| • Doctors | 14.17 ± 3.24 |
| • Others | 9.30 ± 4.35 |
|
| |
| Sick role investment | 28.53 ± 8.99 |
|
| |
| Optimism | 23.57 ± 4.07 |
|
| |
| Self Efficacy | 31.43 ± 4.45 |
|
| |
| Body image score | |
| • Total score | 7.26 ± 6.39 |
| • Normal (% with 0 scores) | 11.7% |
| • Abnormal (% with scores > 0) | 84.2% |
| • Missing | 4.1% |
|
| |
| Social Support | |
| • Overall functional support | 4.24 ± 0.75 |
| • Tangible support | 4.24 ± 0.85 |
| • Affectionate support | 4.41 ± 0.86 |
| • Positive social interaction | 4.29 ± 0.79 |
| • Emotional/informational support | 4.26 ± 0.77 |
|
| |
| Perioperative Survey | |
|
| |
| Surgical Predictors | |
|
| |
| Surgical time (close time – cut time) | 4:09 ± 0:43 |
|
| |
| Delta Hemoglobin (Hgb) | −1.98 ± 1.17 |
|
| |
| Delta leading edge (cm) | 5.19 ± 1.93 |
|
| |
| Peri-operative complications | |
| • Grade 0 | 127 (74.3%) |
| • Grade I | 22 (12.9%) |
| • Grade II | 9 (5.3%) |
| • Grade IIIa | 1 (0.6%) |
| • Grade IIIb | 12 (7.0%) |
| • Grade IVa | 0 (0) |
| • Grade IVb | 0 (0) |
| • Grade V | 0 (0) |
|
| |
| Postoperative Survey | |
|
| |
| Postdischarge surgical recovery | |
| • 7 days | |
| • 14 days | See figure 1 |
| • 42 days | |
| • 90 days | |
Figure 1 illustrates the trajectory of postdischarge surgical recovery for our study population. The weighted mean PSR13 score at which patients rated themselves fully recovered was 92.02 ([23*92.38 + 14*92.08 + 2*87.5]/39). Full recovery after laparoscopic sacrocolpopexy can be expected in 0 (0%), 9 (5.3%), 34 (19.9%), and 42 (24.6%) at postdischarge day 7, 14, 42, and 90, respectively. There was clinically meaningful change, defined as an increase in PSR13 score beyond 5, in postdischarge recovery for all quartile groups from 7 to 14 days and 14 to 42 days. A clinically meaningful change from 42 to 90 days was only detected in the 50% quartile (median) group.
Figure 1.
Post Discharge Surgical Recovery Trajectory after Laparoscopic Sacrocolpopexy. A PSR13 score of 90 approximates the threshold above which study participants considered themselves fully recovered
Table 3 lists the results of the multivariate linear regression model constructed for each time point to regress PSR13 scores on the a priori set of hypothesized sociodemographic/clinical, surgical, and psychosocial predictors. 1) Sociodemographic/clinical predictors - Age predicted postdischarge surgical recovery at 7 and 14 days, with older women having higher PSR13 scores along the trajectory. Body mass index predicted postdischarge surgical recovery at 7, 42, and 90 days, with women with higher BMI having higher PSR13 scores along the trajectory. Medical co-morbidity predicted postdischarge surgical recovery at 7, 14, and 42 days, with women with greater medical co-morbidities having lower PSR13 scores along the trajectory. Preoperative pain scores predicted postdischarge surgical recovery at 7, 42, and 90 days, with women reporting greater preoperative pain having higher PSR13 scores along the trajectory. 2) Surgical predictors - Perioperative complications predicted postdischarge surgical recovery at 7 days and 14 days, with women having complications having lower PSR13 scores along the trajectory. Change in leading edge of prolapse following laparoscopic sacrocolpopexy predicted postdischarge surgical recovery at 42 and 90 days, with greater changes in leading edge having higher PSR13 scores along the trajectory. 3) Psychosocial predictors - Doctor Locus of Control predicted postdischarge surgical recovery at 7, 42, and 90 days, with women who endorsed following doctor’s orders, seeing their doctor regularly, or consulting a medically-trained professional having lower PSR13 scores along the trajectory. Conversely, Others Locus of Control predicted postdischarge surgical recovery at 6 weeks, with women who endorse other people’s role in the outcome of their condition having higher PSR13 scores along the trajectory.
Table 3.
Summary statistics, standardized beta coefficients, and p values of linear regression models for predicting postdischarge surgical recovery, as measured by the PSR13
| Standardized Beta Coefficients | |||||
|---|---|---|---|---|---|
| 7 days | 14 days | 42 days | 90 days | ||
| Sociodemographic/clinical predictors | |||||
| Age at surgery | 0.48** | 0.30* | |||
| Body mass index | 0.21* | 0.28** | 0.27* | ||
| Charlson co-morbidity index | −0.22* | −0.25* | −0.25** | ||
| SF-36 preoperative pain scores | 0.28** | 0.31** | 0.27* | ||
| Surgical predictors | |||||
| Perioperative complications | −0.26** | −0.19* | |||
| Change in leading edge of prolapse after surgery | 0.28** | 0.33** | |||
| Psychosocial predictors | |||||
| Doctors locus of control | −0.20* | −0.30** | −0.40** | ||
| Others locus of control | 0.21* | ||||
| Sick role investment | −0.22* | ||||
| Model Summary | |||||
| R2 | .445 | .337 | .481 | 405 | |
| F | 2.09 | 1.37 | 2.54 | 1.45 | |
| p | 0.012 | 0.162 | 0.002 | 0.138 | |
P < 0.1 level
P < 0.05 level
PSR13 – Postdischarge Surgical Recovery Scale 13
Finally, sick role investment predicted postdischarge surgical recovery at 6 weeks, with women who embraced their pelvic organ prolapse as illness exempting them from normal social roles and responsibility for their condition having lower PSR13 scores along the trajectory.
A priori hypothesized psychosocial factors including optimism, self efficacy, body image, and tangible social support were not associated with postdischarge surgical recovery.
Discussion
One strength of this study is its use of the validated PSR13 for confirming established predictors of postdischarge surgical recovery in both an expected and unexpected direction. Medical co-morbidities (6–7) and perioperative complications (6), (8–10), were negatively associated with postdischarge surgical recovery, beginning early in its trajectory in the expected direction. The ability to reduce the severity of prolapse with reconstructive surgery translated into better postdischarge surgical recovery as expected, at 42 to 90 days likely mediated by a “return to normalcy” once the negative impact of medical co-morbidities and perioperative complications ran their course.
The association of advanced age at surgery with better 7- and 14-day recovery in an unexpected direction may be explained by an increased confidence in the healthcare delivery system through past nonsurgical and surgical experiences (21). Advanced age could confer a neuropathic advantage over younger patients limiting their exposure to noxious stimuli experienced early in the recovery trajectory (22). Preoperative chronic pain and BMI were positively associated with postdischarge surgical recovery in an unexpected direction, along a similar time course. Patients with preoperative chronic pain may acclimate to not feeling good, which can set different expectations on postoperative recovery. This may explain better perceived recovery among women with chronic pain as they habituate to preoperative levels of discomfort (23).
A hypothesis that obese patients recover quicker because their postoperative activity levels deviate less from their sedentary lifestyles is supported by the negative association of body mass index with total leisure time score (r = −0.22, p = 0.005), despite controlling for preoperative activity levels in our regression analyses. Alternatively, their quicker postdischarge recovery may have been affected by a “healthy volunteer effect” bias as excluded participants had higher BMIs than included participants (24).
Two of six hypothesized psychosocial predictors of postdischarge recovery were confirmed, controlling for sociodemographic/clinical, and surgical predictors. We found a negative association between sick role investment and postdischarge surgical recovery 42 days after laparoscopic sacrocolpopexy independent of medical co-morbidities. Traditionally, older patients assume more sick role rights and expect more sick role legitimation than younger counterparts (25). In our study, older participants recovered faster than their younger counterparts suggesting that sick role investment may be modifiable by surgeons who create sick role losers when they set no recovery expectancies during the preoperative consultation visit (16). It was outside the scope of this study to determine what measured factors (severity of prolapse, age, co-morbidities, symptom bother, impact on activities of daily living, etc.) predict preoperative sick role investment in women with pelvic organ prolapse. This is a goal of an ongoing secondary analysis of our data.
We found both a negative (days 7, 42, and 90) and positive (day 42) association of doctor’s and other’s health locus of control with postdischarge surgical recovery respectively. The established negative relationship between doctor’s health locus of control and postdischarge recovery after laparoscopic sacrocolpopexy is consistent with previously published work by Clayton. Return to work recommendations exist in the United Kingdom, yet only 40% of health care practitioners are aware of their existence (4). Disability guidelines following surgical procedures in the United Kingdom suggest an absence from work of 3 weeks after laparoscopic hysterectomy and 7 weeks after abdominal hysterectomy (26). Advice given by healthcare professionals regarding sickness absence following hysterectomy was much longer than evidence-based guidelines. Patients adhere to this advice, consequently delaying their return, despite feeling able to work. The average duration of absence was 14.3 weeks following hysterectomy for the 69% of subjects who received recommendations beyond guidelines compared to 7.9 weeks for the 11% of subjects who received recommendations within guidelines. Reasons for advice variation from guidelines include a lack of knowledge of their existence, unwillingness to set expectations preventing disappointment for those who experience delay, or a fundamental lack of knowledge of the factors that impact return to work following surgery (5).
The positive association between Others HLC and postdischarge surgical recovery suggests that some study participants were willing to use their social environment, including friends and family, to reinforce our surgeon’s postoperative activity instructions to optimize recovery after laparoscopic sacrocolpopexy.
The “what is my recovery time?” question suggests a strong surgical patient desire for recovery expectancies from their surgeons, resulting in sick role legitimation and postdischarge recovery delay, when prolonged. Not setting recovery expectancies in our study did not change this reality for patients who embrace doctor’s health locus of control, suggesting that surgical patients may become confused about conflicting expectancies (5) or purposefully reject advice when it contradicts previous surgical experiences (27). Surgeons must be cognizant of conflicting expectancies delivered by health care professional contact within and outside their own organization if they hope to optimize postdischarge recovery through positive expectancy management. Fifteen of sixteen studies included in a systematic review revealed that positive recovery expectancies were associated with better health outcomes controlling for the effects of biologic, physiologic, and psychosocial variables (28). Further research is needed to determine why patients might reject positive recovery expectancy advice delivered by their surgeons if it conflicts with other recommendations or contradicts previous surgical experience.
The following limitations of our study must be considered before its conclusions can be considered valid for future study. A “healthy volunteer effect” bias may have been introduced by convenience sampling rather than a complete enumeration of willing laparoscopic sacrocolpopexy patients resulting in an optimistic recovery trajectory. Our established predictors are only generalizable to a primarily non-Hispanic White population of women who understand English, have a computer, and a reliable internet connection to respond to email surveys. We hope to expand this work to an ethnic/racially diverse population of women through multi-center study participation because 90% of American adults now use the internet (29). Our a priori decision to use surgical time, delta hemoglobin, and peri-operative complications as a surrogate for surgery type and concomitant surgeries may have obscured the effect of unique tissue damage on postdischarge recovery associated with each procedure.
Identified sociodemographic/clinical, surgical, and psychosocial predictors should provide physicians with evidence-based guidance on recovery times for patients and family members. This knowledge is critical for informing future research to determine if these predictors are modifiable by changes to our narrative during the preoperative consultation visit. Less disability, and early return to the social responsibilities of daily life is a desirable public health outcome for interventions capable of predictor modification and changes in postdischarge surgical recovery for women with pelvic organ prolapse.
Acknowledgments
Financial support: Dr. Chen was supported by Grant Number 5T32 NR007066 from the National Institute of Nursing Research of the National Institutes of Health during preparation of this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Contributor Information
Michael Heit, Department of Obstetrics and Gynecology, School of Medicine, Indiana University, Indianapolis, IN.
Janet S. Carpenter, Science of Nursing Care Department, School of Nursing, Indiana University, Indianapolis, IN.
Chen X. Chen, Department of Community and Health Systems, School of Nursing, Indiana University, Indianapolis, IN.
Ryan Stewart, Department of Obstetrics and Gynecology, School of Medicine, Indiana University, Indianapolis.
Jennifer Hamner, Department of Obstetrics and Gynecology, School of Medicine, Indiana University, Indianapolis.
Kevin L. Rand, Department of Psychology, School of Science, Indiana University-Purdue University Indianapolis.
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