Abstract
Background
Holmium enucleation of the prostate (HoLEP) is becoming the gold standard for the treatment of benign prostatic hyperplasia (BPH). Our objective was to identify predictors of 30-day readmission and the impact of same-day discharge after HoLEP.
Methods
Using NSQIP data from 2011 to 2019, we identified men who underwent HoLEP for the treatment of BPH. We compared patients based on time of discharge and readmission status. We used multivariable logistic regression analysis (MLRA) to identify independent factors associated with 30-day readmission.
Results
A total of 3,489 patients met inclusion criteria with 833 (23.88%) being discharged within 24 hours and 2,656 (76.12%) discharged after 24 hours. There were 158 (4.53%) 30-day readmissions, mostly due to hematuria and urinary tract infection. Patients being readmitted were older (72 vs. 70 years old, P = 0.001), were more likely to have preoperative anemia (36.7% vs. 23.1%; P < 0.001), chronic kidney disease (29.7% vs. 19.7%; P < 0.001), bleeding disorder (10.8% vs. 2.8%; P < 0.001), higher American Society of Anesthesiologists (ASA) scores (≥3: 70.3% vs. 46.7%; P < 0.001) and a higher frailty burden (5-item modified frailty index [5i-mFI] ≥ 2: 36.1% vs. 19.1%; P < 0.001) compared to their counterparts. Factors independently associated with 30-day readmission were bleeding disorder (OR 2.89; 95% CI 1.63–5.11; P < 0.001), 5i-mFI ≥ 2 (OR 1.67; 95% CI 1.03–2.71; P = 0.038) and an ASA score ≥3 (OR 1.80; 95% CI 1.21–2.70; P = 0.004); however, same-day discharge was not found to be a significant predictor of 30-day readmissions.
Conclusion
The overall readmission rate after HoLEP is low. Patients discharged within 24 hours have similar rates of readmission compared to patients discharged after 24 hours. We found bleeding disorder, frailty burden, and ASA score to be independent predictors of 30-day readmission.
Keywords: Benign prostatic hyperplasia, Holmium laser enucleation of the prostate, Outcomes, Readmissions
1. Introduction
Benign prostatic hyperplasia (BPH) is one of the most common conditions affecting men, with up to 15 million American men affected.1 BPH, defined as the nonmalignant growth of the prostate, is found in increasing prevalence with age and on its own does not constitute a problem for the patient until lower urinary tract symptoms (LUTS) develop.2 As LUTS become more bothersome, patients may elect for medical or surgical therapy. First-line therapy for symptomatic BPH involves lifestyle modifications and medical therapy. Alpha-blockers and 5-alpha reductase inhibitors are the standard of care and may be used as monotherapy or in combination.3 Surgical management of BPH is typically offered to patients with persistent or severe BPH refractory to medical therapy. Surgical therapies include transurethral resection of the prostate (TURP), simple prostatectomy, prostatic urethral lift, prostate artery embolization, convective water vapor energy ablation therapy, and Holmium laser enucleation of the prostate (HoLEP).3
Over time, treatment paradigms for the surgical management of BPH have shifted, with open simple prostatectomy being replaced by TURP as the standard of care throughout most of the 1970s.4 As the historical gold standard, TURP previously operated as the surgical modality to which all other non-medical therapies were compared.5 A growing body of evidence has shown HoLEP to be a safe and efficient procedure with few complications, shorter catheterization times, and shorter hospital stays compared to TURP, and is currently the only procedure that is endorsed by the AUA guideline as the surgical treatment for all prostate sizes.6 HoLEP offers patients a safer, more efficient, and efficacious treatment for BPH-related LUTS and has become the procedure of choice and gold standard surgical treatment for BPH in the 21st century.5,6
As the new gold standard, HoLEP offers several advantages over TURP and other surgical modalities. Advantages include effectiveness for large prostates >200 cc,7 significantly shortened catheterization times, decreased hospital length of stay,5 and reduced blood loss.8,9 Several studies have additionally proposed HoLEP as a feasible same-day surgery.10,11 Despite the adoption of HoLEP as the gold standard for BPH, there is a paucity of studies assessing predictors of readmission after HoLEP. For this reason, we conducted a retrospective study using data from a large national database to identify the predictors of 30-day readmission and the impact of same-day discharge after HoLEP for BPH.
2. Materials and Methods
2.1. Data source
After receiving exempt status from our Institutional Review Board, we queried the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. The NSQIP Participant Use Data File (PUF) contains deidentified data on over 150 variables, including patient demographics, preoperative laboratory values, intraoperative data, and 30-day postoperative mortality and morbidity outcomes for patients undergoing major surgical procedures across the US. Trained and certified staff at participating sites collect all the data, and quality-assessment audits are performed regularly.12
2.2. Study population
Using NSQIP data from 2011 to 2019, we selected adult (≥18 years old) males who underwent HoLEP based on Current Procedural Terminology (CPT) code 52649.13 Patients were excluded if they were categorized as being “90+” years old; were ventilator-dependent at the time of surgery; had American Society of Anesthesiologists (ASA) score of “5”, history of ascites, acute renal failure, or sepsis prior to surgery. We excluded patients with unknown age, ASA score, height, weight, preoperative functional health status, preoperative hematocrit and serum creatinine, operative time, length of hospital stay, or discharge destination. Patients were dichotomized based on same-day discharge and readmission status. Same-day discharge was defined as total hospital stay <24 hours. Readmission was defined as any readmission within 30 days after HoLEP.
2.3. Variables
The preoperative variables included were age, race, Hispanic ethnicity, comorbidities (arterial hypertension [HT], diabetes mellitus [DM], chronic obstructive pulmonary disease [COPD], congestive heart failure [CHF], dyspnea, bleeding disorder, smoking status, chronic steroid use, anemia, chronic kidney disease [CKD]), functional status prior to surgery, ASA score, body mass index (BMI). The “diabetes” and “dyspnea” variables were recategorized into either “yes” or “no”. Body mass index was calculated using the formula: weight in kg/(height in m2). ASA scores were categorized into either <3 or ≥3. BMI was categorized into 5 groups according to the World Health Organization classification of BMI: underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), obese (30–39.9 kg/m2), and morbidly obese (≥40 kg/m2).14 The category of “other” for the “race” variable included patients identifying as Asian, American Indian or Alaska Native, Native Hawaiian, or Pacific Islander. According to the NSQIP data dictionary, “bleeding disorder” not only encompasses patients with increased risk of bleeding due to an underlying hematologic disorder but also secondary to chronic anticoagulation.12 Intraoperative data included wound classification (I—clean; II—clean/contaminated; III—contaminated; IV—dirty/infected) and operative time (in minutes). We calculated the frailty burden using the 5-item modified frailty index (5i-mFI), which uses the presence of CHF, HT, DM, COPD or pneumonia, and dependent functional status to determine a score between 0 and 5. Due to the small number of patients in the last three groups, we used the following categories to classify the frailty burden: 0, 1, and ≥2.15 Since the cohort was composed of males only, we defined anemia as hematocrit <39%.16,17 CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2.17 We used the Cockroft-Gault formula to calculate eGFR ([140 – age] × weight in kg)/(serum creatinine × 72).18
2.4. Outcomes
Postoperative complications were grouped as follows: neurological (stroke), cardiovascular (myocardial infarction or cardiac arrest requiring cardiopulmonary resuscitation), pulmonary (unplanned intubation, on ventilator greater than 48 hours, or pneumonia), thromboembolic (pulmonary embolism or deep venous thrombosis requiring therapy), septic (sepsis or septic shock), renal (acute renal failure, progressive renal insufficiency, or urinary tract infection), and wound-related complications (superficial incisional, deep incisional, organ/space surgical site infections, or wound dehiscence). We also examined the overall complication rate, which was defined as a composite of the previously mentioned complications plus intraoperative/postoperative blood transfusion or death. Additional outcomes evaluated independently were intraoperative/postoperative blood transfusion, reoperation, and non-home discharge. Non-home discharge was defined as a discharge to a location other than “home” or “facility which was home.”
2.5. Statistical analysis
Continuous variables are reported as median with interquartile range (IQR) and compared between groups using the Mann-Whitney test. Categorical variables are reported in frequencies as percentages and compared between groups using the chi-square test or Fisher's exact test where appropriate. We compared the preoperative, operative, and postoperative factors between patients discharged within 24 hours and patients discharged after 24 hours. Similarly, the same set of factors were compared between patients readmitted (within 30 days after surgery) and those that were not. Trend figures were graphed to evaluate changes in same-day discharge and readmission rates across time; the Cochrane-Armitage test for trends in proportions was used to assess statistical significance. To assess whether preoperative and operative factors were independently associated with 30-day readmission, we used multivariable logistic regression analysis (MLRA). A variance inflation factor (VIF) ≤2.5 was considered as acceptable when assessing multicollinearity among the covariates used in the MLRA models. The MLRA was adjusted for age, anemia, 5i-mFI, BMI groups, chronic kidney disease, ASA score, bleeding disorder, dyspnea, smoking status, chronic steroid use, wound class, operative time, and same-day discharge. Statistical significance was set at a two-tailed P < 0.05 and analysis was performed using the EZR plugin for R.19 The trend figures were prepared using GraphPad Prism version 9, and the line of the best fit with 95% confidence intervals was graphed using simple linear regression.
3. Results
After the application of inclusion and exclusion criteria, the total cohort consisted of 3,489 patients (Fig. 1). Table 1 shows the preoperative characteristics of all patients based on readmission status and day of discharge. Of the total cohort of 3,489 patients, 2,656 (76.1%) patients were discharged within 24 hours and the remaining 833 (23.9%) were discharged after 24 hours. 158 (4.5%) patients were readmitted within 30 days. Overall, patients had a median age of 70 years (IQR 64–76), were mostly Caucasian (81.6%), and overweight (42.9%) according to BMI. 63% of patients had a 5i-mFI score ≥1, mostly due to HT or DM. Patients being readmitted within 30 days were older (72 vs. 70, P = 0.001), more likely to have preoperative anemia (36.7% vs. 23.1%; P < 0.001), CKD (29.7% vs. 19.7%; P < 0.001), bleeding disorder (10.8% vs. 2.8%; P < 0.001), higher American Society of Anesthesiologists (ASA scores) (≥3: 70.3% vs. 46.7%; P < 0.001), and a higher frailty burden (5i-mFI ≥ 2: 36.1% vs. 19.1%; P < 0.001) compared to their counterparts.
Fig. 1.
Flowchart of patient inclusion and exclusion using NSQIP, 2011 to 2019
Table 1.
Comparison of preoperative characteristics based on same-day discharge and readmission status
| Factor | Overall | Same-day discharge |
P | Readmission within 30 days |
P | ||
|---|---|---|---|---|---|---|---|
| No | Yes | No | Yes | ||||
| N | 3,489 | 2,656 | 833 | 3,331 | 158 | ||
| Age (median [IQR]) | 70.00 [64.00, 76.00] | 70.00 [64.00, 76.00] | 70.00 [65.00, 76.00] | 0.19 | 70.00 [64.00, 76.00] | 72.50 [66.00, 78.00] | 0.001 |
| Anemia (%) | 826 (23.7) | 635 (23.9) | 191 (22.9) | 0.6 | 768 (23.1) | 58 (36.7) | <0.001 |
| Bleeding disorder (%) | 111 (3.2) | 81 (3.0) | 30 (3.6) | 0.4 | 94 (2.8) | 17 (10.8) | <0.001 |
| BMI (%) | 0.7 | 0.5 | |||||
| Underweight | 22 (0.5) | 14 (0.4) | 8 (0.6) | 21 (0.5) | 1 (0.6) | ||
| Normal | 954 (21.7) | 694 (22.0) | 260 (21.1) | 923 (21.9) | 31 (17.6) | ||
| Overweight | 1,887 (42.9) | 1,366 (43.2) | 521 (42.2) | 1,813 (43.0) | 74 (42.0) | ||
| Obese | 1,388 (31.6) | 987 (31.2) | 401 (32.5) | 1,323 (31.4) | 65 (36.9) | ||
| Morbidly obese | 143 (3.3) | 99 (3.1) | 44 (3.6) | 138 (3.3) | 5 (2.8) | ||
| Chronic kidney disease (%) | 704 (20.2) | 539 (20.3) | 165 (19.8) | 0.8 | 657 (19.7) | 47 (29.7) | 0.003 |
| Hispanic ethnicity (%) | 0.002 | 0.3 | |||||
| Yes | 144 (4.1) | 101 (3.8) | 43 (5.2) | 134 (4.0) | 10 (6.3) | ||
| No | 3,190 (91.4) | 2,421 (91.2) | 769 (92.3) | 3,048 (91.5) | 142 (89.9) | ||
| Unknown | 155 (4.4) | 134 (5.0) | 21 (2.5) | 149 (4.5) | 6 (3.8) | ||
| 5i-mFI score (%) | 0.4 | <0.001 | |||||
| 0 | 1,290 (37.0) | 997 (37.5) | 293 (35.2) | 1,252 (37.6) | 38 (24.1) | ||
| 1 | 1,505 (43.1) | 1,132 (42.6) | 373 (44.8) | 1,442 (43.3) | 63 (39.9) | ||
| ≥2 | 694 (19.9) | 527 (19.8) | 167 (20.0) | 637 (19.1) | 57 (36.1) | ||
| Arterial hypertension (%) | 2,040 (58.5) | 1,537 (57.9) | 503 (60.4) | 0.2 | 1,925 (57.8) | 115 (72.8) | <0.001 |
| Congestive heart failure (%) | 20 (0.6) | 13 (0.5) | 7 (0.8) | 0.3 | 18 (0.5) | 2 (1.3) | 0.2 |
| Chronic obstructive pulmonary disease (%) | 146 (4.2) | 108 (4.1) | 38 (4.6) | 0.6 | 130 (3.9) | 16 (10.1) | 0.001 |
| Diabetes (%) | 701 (20.1) | 539 (20.3) | 162 (19.4) | 0.6 | 651 (19.5) | 50 (31.6) | <0.001 |
| Any dyspnea (%) | 158 (4.5) | 119 (4.5) | 39 (4.7) | 0.8 | 142 (4.3) | 16 (10.1) | 0.002 |
| ASA (%) | 0.080 | <0.001 | |||||
| <3 | 1,823 (52.2) | 1,410 (53.1) | 413 (49.6) | 1,776 (53.3) | 47 (29.7) | ||
| ≥3 | 1,666 (47.8) | 1,246 (46.9) | 420 (50.4) | 1,555 (46.7) | 111 (70.3) | ||
| Functional status (%) | 0.9 | 0.011 | |||||
| Dependent | 55 (1.6) | 43 (1.6) | 12 (1.4) | 48 (1.4) | 7 (4.4) | ||
| Independent | 3,434 (98.4) | 2,613 (98.4) | 821 (98.6) | 3,283 (98.6) | 151 (95.6) | ||
| Race (%) | <0.001 | 0.5 | |||||
| Caucasian | 2,848 (81.6) | 2,112 (79.5) | 736 (88.4) | 2,724 (81.8) | 124 (78.5) | ||
| African American | 220 (6.3) | 172 (6.5) | 48 (5.8) | 211 (6.3) | 9 (5.7) | ||
| Other | 199 (5.7) | 183 (6.9) | 16 (1.9) | 187 (5.6) | 12 (7.6) | ||
| Unknown | 222 (6.4) | 189 (7.1) | 33 (4.0) | 209 (6.3) | 13 (8.2) | ||
| Smoker (%) | 315 (9.0) | 243 (9.1) | 72 (8.6) | 0.7 | 301 (9.0) | 14 (8.9) | 1 |
| Chronic steroid use (%) | 96 (2.8) | 77 (2.9) | 19 (2.3) | 0.4 | 88 (2.6) | 8 (5.1) | 0.078 |
| Preoperative transfusion (%) | 5 (0.1) | 5 (0.2) | 0 (0.0) | 0.6 | 5 (0.2) | 0 (0.0) | 1 |
| >10% weight loss (%) | 15 (0.4) | 10 (0.4) | 5 (0.6) | 0.4 | 14 (0.4) | 1 (0.6) | 0.5 |
ASA, American Society Anesthesiologists; BMI, body mass index; IQR, interquartile range; 5i-mFI, 5-item modified frailty index.
Table 2 shows the intraoperative factors and complications of all patients based on same-day discharge and readmission status. Overall, the median operation time was 95 minutes (IQR 61–132). Patients with same-day discharge had a significantly shorter operative time (70 vs. 102, P < 0.001). There was no significant difference in operative time for patients who were readmitted versus their counterparts (89.50 vs. 95.00, P = 0.11). The overall complication rate was 4.2%, and patients who were readmitted experienced higher rates of complications than their counterparts (27.2% vs 3.1%, P < 0.001). Patients with same-day discharge did not have higher rates of complications compared to patients discharged after 24 hours (4.7% vs 4.0%, P = 0.4). The overall rate of renal complications, including UTIs, was 3.4% which was the most common type. There was no significant difference in readmission rates for patients with same-day discharge compared to their counterparts (3.7% vs 4.8%, P = 0.2). Post-hoc analysis of the reasons for readmission based on International Classification of Disease codes revealed that the top three documented reasons were: hematuria (43/158, 27.22%), UTI (10/158, 6.33%), and urinary retention (8/158, 5.06%). Fig. 2 shows an upward trend in the rate of same-day discharges (P < 0.001) from 2011 to 2019 with a concurrent slight downward trend in the rates of readmission (P = 0.03) during the same time frame. Table 3 shows a univariable and MLRA of select factors associated with 30-day readmission after HoLEP. On MLRA, three factors were associated with readmission: the presence of a bleeding disorder (OR 2.89, 95% CI 1.63–5.11, P < 0.001), 5i-mFI ≥ 2 (OR 1.67, 95% CI 1.03–2.71, P = 0.038), and an ASA score ≥3 (OR 1.80, 95% CI 1.21–2.70, P = 0.004).
Table 2.
Comparison of intraoperative factors and 30-day postoperative complications
| Factor | Overall | Same-day discharge |
P | Readmission within 30 days |
P | ||
|---|---|---|---|---|---|---|---|
| No | Yes | No | Yes | ||||
| N | 3,489 | 2,656 | 833 | 3,331 | 158 | ||
| Intraoperative factors | |||||||
| Operation time (median [IQR]) | 95.00 [61.00, 132.00] | 102.00 [69.00, 141.00] | 70.00 [47.00, 99.00] | <0.001 | 95.00 [62.00, 133.00] | 89.50 [55.00, 126.25] | 0.11 |
| Wound classification (%) | <0.001 | 0.6 | |||||
| 1-Clean | 65 (1.9) | 52 (2.0) | 13 (1.6) | 64 (1.9) | 1 (0.6) | ||
| 2-Clean/Contaminated | 3,316 (95.0) | 2,501 (94.2) | 815 (97.8) | 3,163 (95.0) | 153 (96.8) | ||
| 3-Contaminated | 76 (2.2) | 72 (2.7) | 4 (0.5) | 74 (2.2) | 2 (1.3) | ||
| 4-Dirty/Infected | 32 (0.9) | 31 (1.2) | 1 (0.1) | 30 (0.9) | 2 (1.3) | ||
| Complications | |||||||
| Overall complication (%) | 145 (4.2) | 106 (4.0) | 39 (4.7) | 0.4 | 102 (3.1) | 43 (27.2) | <0.001 |
| Mortality (%) | 3 (0.1) | 2 (0.1) | 1 (0.1) | 0.6 | 3 (0.1) | 0 (0.0) | 1 |
| Cardiovascular complication (%) | 5 (0.1) | 5 (0.2) | 0 (0.0) | 0.6 | 3 (0.1) | 2 (1.3) | 0.019 |
| Neurologic complication (%) | 4 (0.1) | 3 (0.1) | 1 (0.1) | 1 | 1 (0.0) | 3 (1.9) | <0.001 |
| Pulmonary complication (%) | 8 (0.2) | 7 (0.3) | 1 (0.1) | 0.7 | 2 (0.1) | 6 (3.8) | <0.001 |
| Renal complications (%) | 117 (3.4) | 82 (3.1) | 35 (4.2) | 0.12 | 91 (2.7) | 26 (16.5) | <0.001 |
| Septic complications (%) | 16 (0.5) | 12 (0.5) | 4 (0.5) | 1 | 3 (0.1) | 13 (8.2) | <0.001 |
| Thromboembolic complications (%) | 5 (0.1) | 3 (0.1) | 2 (0.2) | 0.3 | 0 (0.0) | 5 (3.2) | <0.001 |
| Wound complications (%) | 6 (0.2) | 6 (0.2) | 0 (0.0) | 0.3 | 3 (0.1) | 3 (1.9) | 0.002 |
| Intraop/postop transfusion (%) | 50 (1.4) | 49 (1.8) | 1 (0.1) | <0.001 | 43 (1.3) | 7 (4.4) | 0.007 |
| Reoperation (%) | 68 (1.9) | 54 (2.0) | 14 (1.7) | 0.6 | 36 (1.1) | 32 (20.3) | <0.001 |
| Readmission (%) | 158 (4.5) | 127 (4.8) | 31 (3.7) | 0.2 | – | – | – |
| Non-home discharge (%) | 26 (0.7) | 23 (0.9) | 3 (0.4) | 0.17 | 23 (0.7) | 3 (1.9) | 0.11 |
IQR, interquartile range.
Fig. 2.
Percent of HoLEP cases with same-day discharge and 30-day readmission between 2011–2019
Table 3.
Univariate and multivariable logistic regression analysis of select factors associated with 30-day readmission after HoLEP
| Factor | Univariable analysis |
Multivariable analysis |
||
|---|---|---|---|---|
| OR (95% CI) | P | OR (95% CI) | P | |
| Age (per year increase) | 1.03 (1.01–1.05) | 0.002 | 1.00 (0.98–1.02) | 0.8 |
| Anemia | 1.94 (1.39–2.70) | 0.0001 | 1.30 (0.90–1.89) | 0.16 |
| Bleeding disorder | 4.15 (2.41–7.15) | <0.001 | 2.89 (1.63–5.11) | <0.001 |
| BMI underweight (vs. normal) | 1.51 (0.19–11.70) | 0.7 | 1.09 (0.14–8.78) | 0.9 |
| BMI overweight (vs. normal) | 1.38 (0.87–2.18) | 0.17 | 1.46 (0.91–2.34) | 0.12 |
| BMI obese (vs. normal) | 1.62 (1.01–2.60) | 0.045 | 1.56 (0.94–2.61) | 0.089 |
| BMI morbidly obese (vs. normal) | 1.02 (0.35–2.98) | 1.0 | 0.80 (0.26–2.45) | 0.7 |
| Chronic kidney disease | 1.72 (1.21–2.45) | 0.002 | 1.45 (0.96–2.20) | 0.080 |
| 5i-mFI = 1 (vs. 0) | 1.44 (0.96–2.17) | 0.081 | 1.05 (0.68–1.62) | 0.8 |
| 5i-mFI≥2 (vs. 0) | 2.95 (1.93–4.49) | <0.001 | 1.67 (1.03–2.71) | 0.038 |
| ASA ≥3 (vs. <3) | 2.70 (1.90–3.82) | <0.001 | 1.80 (1.21–2.70) | 0.004 |
| Any dyspnea | 2.53 (1.47–4.36) | <0.001 | 1.64 (0.93–2.92) | 0.090 |
| Smoker | 0.98 (0.56–1.72) | 0.9 | 0.98 (0.55–1.75) | 0.9 |
| Chronic steroid use | 1.97 (0.94–4.13) | 0.074 | 1.41 (0.66–3.02) | 0.4 |
| >10% body weight loss | 1.51 (0.20–11.50) | 0.7 | 0.88 (0.10–7.48) | 0.9 |
| Operative time (per minute increase) | 1.00 (1.00–1.00) | 0.11 | 1.00 (1.00–1.00) | 0.3 |
| Wound class 2 (vs. 1) | 3.10 (0.43–22.50) | 0.3 | 3.05 (0.41–22.90) | 0.3 |
| Wound class 3 (vs. 1) | 1.73 (0.15–19.50) | 0.7 | 1.98 (0.17–23.10) | 0.6 |
| Wound class 4 (vs. 1) | 4.27 (0.37–48.90) | 0.2 | 4.31 (0.35–53.10) | 0.3 |
| Early discharge | 0.77 (0.52–1.15) | 0.2 | 0.69 (0.46–1.05) | 0.086 |
ASA, American Society of Anesthesiologists; BMI, body mass index; CI, confidence interval; HoLEP, holmium enucleation of the prostate; OR, odds ratio; 5i-mFI - 5-item modified frailty index.
4. Discussion
The increasing use and availability of HoLEP represent an advancement in the surgical management of BPH. Within our cohort, the most common reason for readmission was hematuria. The commonality of this complication as a driver for readmissions after HoLEP has also been reported in other studies.20,21 Despite the increased precision granted by HoLEP compared to TURP, residual hematuria following this invasive procedure is to some degree inevitable.22 The second most common reason for readmission among our cohort was UTI. In addition to the risk of infection by virtue of this invasive procedure, previous studies have also identified de novo urinary stasis following HoLEP procedures which can further increase the likelihood of UTIs to occur.23
Several studies have affirmed the safety and feasibility of HoLEP as a same-day procedure, with an increasing trend toward same-day discharge in patients undergoing HoLEP compared to other surgical options for BPH.10,11,21,24,25 As two of the commonly cited advantages of HoLEP over other surgical options are reduced hospital stay and catheterization times, HoLEP lends itself to earlier patient discharge, greater cost savings, improved patient satisfaction, and increased hospital efficiency.26, 27, 28, 29 The rate of early readmission (defined as readmission within 48 hours after surgical treatment) following HoLEP has continued to increase in recent years, and is hypothesized to be related to institutions increasingly advocating for same-day discharge, along with surgical staff inexperience and patient anxiety.26,27 Despite this, readmission rates remain low, even after same-day discharge.10,11,25 In the current study, same-day discharge was associated with a statistically significant increase in readmission rates among our univariate analysis of the overall cohort. However, we did not identify statistical significance when adjusting for other covariates using MLRA. This is in contrast with a recent study by Garden et al, which demonstrated same-day discharge is associated with the increased risk of readmission, after propensity score matching.25
Our study identified independent predictors of readmission including the presence of a bleeding disorder, frailty burden, and ASA score. Bleeding disorders in this patient population are most likely due to chronic anticoagulation, although further subgroup analysis to stratify acquired and congenital bleeding disorders is limited by a lack of characterization in NSQIP data. In previous studies, HoLEP has been demonstrated to be a safe and effective procedure with superior perioperative hemostatic control when compared to TURP.30 Consequently, HoLEP is specifically indicated for patients with a high risk of bleeding including patients on chronic anticoagulant and/or antiplatelet medications. This has been due to the association with minimal changes in hemoglobin, shorter catheterization time, and shorter length of hospital stay compared to robotic-assisted simple prostatectomy for the treatment of BPH.29
Frailty burden (according to the 5i-mFI) was identified to be an independent predictor of readmission within 30 days, corresponding with previous literature using a standardized frailty to examine postoperative complications and mortality in a cohort of various urologic procedures.31,32 To our current knowledge, there has not been a previous study using the 5i-mFI scoring system specifically for patients receiving HoLEP to assess outcomes of readmission.15 ASA score was additionally identified as an independent predictor of readmission within 30 days, further validating previous literature discussing its utility in predicting complications in general urologic procedures.32 For these reasons, the predictive value of frailty and ASA scoring demonstrates useful insight for determining potential candidates eligible for same-day discharge. Additionally, these scores may have clinical utility in predicting which patients may require a longer hospital stay and can aid healthcare providers in the postoperative management of this patient population. As the presence of a bleeding disorder is an indication for HoLEP over TURP, this subgroup of patients can likewise be more closely monitored for postoperative complications and can be stratified to determine the risk of readmission.
There are limitations associated with this study that must be acknowledged. The population studied in this analysis is directly formed from submissions to hospitals exclusively associated with the ACS-NSQIP. This affects the generalizability of the results, as hospitals not participating in this program are excluded from the database. Additionally, ACS-NSQIP is a program that identifies complications associated with a variety of procedures and was not designed specifically for specific urologic procedures such as HoLEP; for this reason, relevant variables such as prostate size, medication history, previous procedures, surgeon experience, and hospital-level factors are not available to assess and therefore not accounted. As prostate size may affect postoperative bleeding, and previous studies have shown that HoLEP may be associated with a larger prostate size pre-operatively, not having this granular data is a barrier to establishing the cause of postoperative hematuria affecting readmission.33 NSQIP also does not provide information on postoperative management, including if catheters were used postoperatively, and on how soon they were removed (i.e., if patients left the hospital with a catheter in place). As HoLEP has been shown to reduce catheterization times and reduce postoperative stay, further studies are needed to analyze if these benefits of HoLEP have any effect on readmission rates.34 As NSQIP is not designed specifically for urologic procedures, this information is lacking in our analysis and represents another limitation of our study. Further studies may be needed to establish if both prostate size and catheter use affect readmission following HoLEP.
Also not present in NSQIP are changes in pertinent functional outcomes such as urodynamic changes or the impact on quality of life. Our analysis attempts to account for these possible confounding variables through MLRA, but nonetheless, the absence of the aforementioned factors represents a limitation to our study. An additional limitation to this study is the lack of granular data regarding bleeding disorders. The NSQIP data dictionary assigns “bleeding disorder” to patients with an increased risk of bleeding due to multiple collective reasons, such as an underlying hematologic disorder or chronic anticoagulation. However, the database does not provide information regarding the specifics of anticoagulation medication and if or how long anticoagulation was stopped prior to the procedure. Current literature affirms that HoLEP is a relatively safe and effective procedure for patients on chronic anticoagulation medication and that these patients should be managed using an interdisciplinary approach.35 The lack of granularity in NSQIP regarding bleeding disorders ultimately limits our study's ability to contribute to this discussion.
In our analysis, 1,031 patients were excluded (22.8%) mostly due to missing laboratory values. This substantial percentage of patients excluded from the original cohort represents a limitation to this study that affects the generalizability of these findings. This limitation also raises an important question regarding why such values are missing in the first place; it is not clear if patients undergoing HoLEP are not having these values ordered/recorded, or if these values are not being appropriately relayed/received to NSQIP. Future studies using datasets designed specifically to include these pertinent variables are needed to validate and further generalize these results.
5. Conclusion
The overall readmission rate after HoLEP is low. Patients with same-day discharge have similar rates of readmission compared to patients discharged after 24 hours. We found bleeding disorder, frailty burden, and ASA score to be independent predictors of 30-day readmission.
Disclosure
The American College of Surgeons National Surgical Quality Improvement Program and the hospitals participating in the ACS NSQIP are the source of the data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors.
Conflicts of interest
The authors have no material or financial conflicts of interest to disclose.
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