Unplanned readmission rates following surgery gained considerable interest over the last decade because these can be a useful marker of healthcare quality, hospital performance, and cost-effectiveness. Studies have shown that as many as one in four patients returns to the hospital within 30 days of discharge, and the estimated costs in Medicare spending due to readmissions were reported to be as high as $17.6 billion [1-3]. With the introduction of the Hospital Readmissions Reduction Program, the centers for Medicare and Medicaid services can even penalize hospitals for higher readmission rates; therefore, every effort has been directed toward establishing a system to reduce the risk of readmission, which would help in curtailing the costs for the healthcare system [4].

The 30-day readmission rates in orthopedics have been reported to be between 2%–14%. In spine surgery, readmission rates range from 2.5% to as high as 9.42% given the variance of procedures studied [5-8]. However, most of these spine surgery studies were retrospective, limited to readmissions within a 30-day interval postoperatively, and the vast majority were based on elective spine surgery cases only using a specific procedure or location [7,9-14]. The existing literature reporting on 90-day readmission rates was predominantly based on a national administrative database, which is frequently error-prone due to under-reporting and lack of clinical details [15]. The available reports on economic impact analysis are most often focused on Medicare-based spending and relate specifically to total joint arthroplasty procedures, without giving a perspective on out-of-pocket expenditures [9]. In developing countries, where healthcare insurance coverage is negligible, this out-of-pocket expenditure can prove to be a substantial financial burden to the patient [16,17].

To the best of our knowledge, no previous studies analyzed hospital readmissions or their economic impact in developing countries. Moreover, a 30-day interval is considered an overly short period to properly determine the consequences of a major surgical procedure [18,19]. With this as background, we performed a prospective analysis of unplanned readmissions within 90 days following spine surgery, regardless of the type or location of the procedure, in an orthopedic specialty unit. We sought to evaluate (1) the incidence and causes for unplanned readmissions at a 90-day interval and its difference at 30-, 31–60-, and 61–90-day intervals; (2) total bed-days lost (TBL) and economic impact of readmissions; and (3) probable risk factors.

Recently, a number of studies investigated various aspects of unplanned readmissions after surgery to prevent “higher than expected” readmission rates. We conducted a prospective analysis of the readmission rates following spine surgery and its causes within the 90-day postoperative interval at a single-center. Additionally, our study attempted to identify the difference in readmission rates and its causes at 30-, 31–60-, and 61–90-day intervals following discharge along with its financial impact and probable predisposing factors.

At our institution, we had a 90-day readmission rate of 3.32% in 2,860 patients, which included patients of all ages receiving either elective and emergency spine procedures. The lowest readmission rates in spine surgery were reported by Lovecchio et al. [7] at 2.6%, anterior cervical discectomy, and fusion in 2,320 patients. In contrast, the highest reported rate was 9.42% following elective spine surgery in 1,400 patients, as reported by Adogwa et al. [8]. Both of the above-mentioned studies were retrospective studies focusing on patients readmitted at the 30-day intervals postoperatively, whereas ours was a prospective study on a higher sample size at the 3-month interval following discharge.

In a multi-centric retrospective analysis of readmissions in lumbar spine surgery, Pugely et al. [12] found that the main reasons for readmissions were wound-related (38.6%) and pain-related (22.4%) causes followed by thromboembolism (9.4%) and systemic infections (8.0%). In 2012, McCormack et al. [20] conducted a single-center study on 3,673 cases and reported that infections (32%) were the leading cause of readmissions while medical problems (excluding DVT) accounted for 22% of readmissions. Another single-center retrospective study by Adogwa et al. [8] that followed up 1,400 patients noted that SSIs (34.8%), pain (19.7%), and fever (16.7%) were the three main causes for readmission following elective spine surgery. Analogous to the above-mentioned studies, our current study also showed that SSI (n=42, 44.21%) was the most common cause for readmission followed by persistent pain (n=30, 31.58%) and medical causes (n=13, 13.68%). The low rate of medical readmissions could be attributed to the fact that our center is a specialized orthopedic institute, unlike the other multispecialty centers in which the study population might include a higher number of patients with comorbid illnesses as well.

There are a paucity of data regarding the differences in incident rates and causes for readmissions at varying intervals following surgery. Within 30 days of discharge, the total percentage of readmission was 66.32%. This implies that a significant 33.68% occurred beyond this interval (23.16%, n=22 in 31–60-day interval; 10.53%, n=10 in the 61–90-day interval), which further emphasizes the importance of a longer follow-up model. Most of the Medicare bundled payment plans define the immediate postoperative period as 3 months. Moreover, in a tertiary care center like ours, follow-up appointments are given 4 to 6 weeks in advance from the date of discharge; therefore, a 90-day model would be more ideal to assess hospital performance. While SSIs remained the most common cause for readmission at the 30-day interval, aseptic pain was the leading cause in 31–60- and 61–90-day intervals (40.9% and 60%, respectively). We consider that this finding is clinically relevant as it could be useful for performing targeted interventions at the appropriate timing, thereby decreasing the readmission rates to a minimum.

With 688 bed-days being lost to readmissions and a mean of 7.33±1.5 days per readmission, a considerable number of beds essential for other elective and emergency admissions were occupied during the hospital stay of our patients. In 2004, the estimated expense due to unplanned readmissions was approximately $17.6 billion in Medicare spending [1,3]. The financial impact of readmissions was INR 41,93,660 during the study period with a mean of INR 44,143.79 (range, INR 2,625 to 2,23,955) being spent per patient. Most inpatients were from low socio-economic strata as evidenced by a mean per capita income of INR 13,400 in our study group. Besides, the Central Statistics Office on the Advance Estimate of National Income, 2012–2013 showed that India’s per capita monthly income was only INR 5,729, re-iterating the substantial economic burden posed by readmissions of patients [16,17]. We also noted that only 14.74% of the readmitted patients in our study population had health insurance coverage and none of them covered the expenses met during the readmission. In a population where a vast majority of individuals pay for healthcare from their own pockets, unplanned readmissions prove to be a costly financial burden, especially when the majority of patients belong to a lower income group [16,17]. The only authors who analyzed the economic impact of readmissions were Clement et al. [9]. They found that if Medicare stops reimbursing readmissions, the institution under review might sustain an average loss of $11,494 [9]. However, their study group comprised only of patients with total hip arthroplasties.

In a meta-analysis, Bernatz et al. [21] found that age, length of stay, discharge to a skilled nursing facility, increased body mass index, the American Society of Anesthesiologists (ASA) score ≥3, and Medicare and Medicaid insurance demonstrated a statistically positive correlation with increased 30-day readmissions in ≥75% of studies. Pugely et al. [12] observed that the predictors of readmission after lumbar spine surgery included advanced patient age ≥80 years, African American race, recent weight loss, chronic obstructive pulmonary disorder, history of cancer, creatinine levels ≥1.2, high ASA class, operative time >4 hours, and prolonged hospital stay for more than 4 days. In 2018, Elsamadicy et al. [22] reported that insurance status, having chronic obstructive pulmonary disorder, depression, hypertension, diabetes, obesity, or deficiency anemia were independently associated with 90-day readmissions following elective spine surgery. Readmissions in our series showed a statistically significant association with prolonged hospital stay (≥10 days) (p<0.001), health insurance (p=0.007), comorbid illnesses (diabetes [p=0.035], hypertension [p=0.001], and liver disease [p<0.001]) in accordance with the above-mentioned literature (Table 5).

This study has some limitations such as the fact that multivariate logistic regression analysis was not performed in our study to determine independent risk factors for readmissions at specific time intervals. The emergency department visits of patients were not tracked, and underreporting could be possible as patients with nonsurgical complications might seek medical attention at some other institution.

We believe that a 3-month interval, rather than a 30-day interval, is ideally suited for readmission analysis, especially for cases of spine surgery, in which the advantages of covering the immediate postoperative period of Medicare bundled plans as well as major adverse events that could reflect the quality of healthcare and hospital performance. In this prospective study, we provide a unique perspective on unplanned readmissions occurring within 90 days postoperatively in patients of all ages receiving spine surgery by analyzing the clinical setting of a tertiary care spine unit in a developing nation.

Our study showed that the leading causes of readmissions following spine surgery were surgical causes, including SSIs and aseptic causes of postoperative pain. Our 90-day model identified an additional 32 (33.68 %) readmissions, which included 21.1% for deep SSIs. We confirmed the importance of a 90-day study as compared to the routine 30-day analysis. The analysis of varying causes at different time intervals can allow targeted interventions at the appropriate timing which could lead to an improved quality of the healthcare delivery and reduce the financial burden on the patient.