Abstract
Background
Open-heart surgery is associated with substantial postoperative morbidity. Guided by the Nursing Role Effectiveness Model, this study focused on the severity of 30-day postoperative complications rather than only the presence or absence of complications.
Purpose
The study examined structural factors (health literacy, nutritional status, severity of comorbidities, anemia, and surgical mortality risk) and process factors (perception of patient-centered care, type of surgery, and cardiopulmonary bypass time) associated with complication severity after open-heart surgery.
Patients and Methods
A prospective, single-center observational prognostic factor study with medical record review was conducted from June 2022 to July 2023 among 136 adult patients undergoing open-heart surgery at a tertiary hospital in Thailand. Data were collected from patient questionnaires and medical records. The primary outcome was 30-day complication severity measured using the Comprehensive Complication Index. Bivariate analyses and multiple linear regression were performed, with prespecified covariates selected based on clinical relevance.
Results
Participants had a mean age of 61.32 years (SD = 13.09), and 61.8% were male. The mean Comprehensive Complication Index score was 37.25 (SD = 14.70). In the adjusted model, surgical mortality risk (standardized beta = 0.345, p < 0.001), anemia (standardized beta = 0.224, p = 0.018), nutritional status (standardized beta = −0.218, p = 0.011), and cardiopulmonary bypass time (standardized beta = 0.198, p = 0.034) were independently associated with complication severity. Health literacy, comorbidity severity, and perceived patient-centered care were not statistically significant.
Conclusion
Surgical mortality risk, anemia, nutritional status, and cardiopulmonary bypass time were key factors associated with 30-day complication severity. These findings support preoperative optimization of hematological and nutritional status and careful intraoperative management to reduce postoperative morbidity; however, interpretation should consider the single-center design and available covariates.
Keywords: open-heart surgery, postoperative complications, nutritional status, health literacy, patient-centered care
Introduction
Open-heart surgery remains a primary treatment for complex cardiovascular diseases, including coronary artery disease and valvular heart conditions. Despite advancements in surgical techniques and perioperative care, postoperative complications remain a significant challenge, occurring in approximately 15% to 20% of cases.1,2 These complications not only increase healthcare costs and length of hospital stay but also significantly impact the patient’s quality of life and long-term survival.3–5 Previous studies have largely focused on the binary occurrence (presence or absence) of complications; however, there is limited evidence regarding the severity of these complications in a holistic, multidisciplinary context.
To address this gap, this study utilizes the Nursing Role Effectiveness Model (NREM) as a conceptual framework variable selection and interpretation. Within this model, structural factors refer to patient or organizational characteristics that shape baseline vulnerability, whereas process factors refer to care-related or clinical activities that occur during treatment. The NREM, originally developed by Irvine, Sidani, and Hall,6 conceptualizes healthcare delivery through the interrelationships between structure, process, and outcomes, and has been widely used to evaluate nursing contributions to patient outcomes. In the present study, health literacy, nutritional status, comorbidity severity, anemia, and surgical mortality risk were conceptualized as structural factors, while perceived patient-centered care and cardiopulmonary bypass (CPB) time were conceptualized as process factors that may influence postoperative outcomes.7
Structural factors are clinically relevant because they represent the patient’s preoperative reserve and capacity to tolerate the physiological stress of surgery. Health literacy may influence patients’ understanding of perioperative instructions, engagement in care, and symptom reporting, and has been associated with poorer surgical outcomes, including longer hospital stays and reduced adherence when literacy is low. Nutritional status (assessed by the Prognostic Nutritional Index) and anemia reflect physiological reserves required for immune response, wound healing, and adequate oxygen delivery; malnutrition and low PNI have been consistently linked to higher rates of postoperative complications, infections, and prolonged recovery. Comorbidity severity and surgical mortality risk provide additional indicators of baseline vulnerability and clinical complexity, which are essential determinants of postoperative outcomes.8,9
Process factors were included because intraoperative and care-related processes may shape the early postoperative course. CPB time is a known intraoperative stressor that can trigger systemic inflammatory responses and contribute to acute kidney injury or pulmonary dysfunction.10,11 Perceived patient-centered care reflects communication, partnership, and responsiveness to patient needs, which may support early recovery, symptom reporting, and adherence to postoperative recommendations.12,13
Although postoperative complications after open-heart surgery have been widely studied, much of the existing evidence focuses on whether complications occur rather than how severe the overall complication burden becomes. Mixed evidence also remains regarding the influence of non-clinical factors, such as health literacy and patient-centered care, on short-term postoperative outcomes. A recent related cross-sectional study among elective open-heart surgery patients examined factors associated with postoperative complication severity during the first 24 hours after surgery, including frailty, kidney function, inflammation, transfusion, vasoactive-inotropic support, and operative risk.14 However, evidence remains limited regarding the 30-day severity of postoperative complications and the combined contribution of structural and process factors. Therefore, this study aims to investigate the factors predicting the severity of postoperative complications within 30 days after open-heart surgery, providing a foundation for improved multidisciplinary risk assessment and perioperative care.
Materials and Methods
Study Design and Sampling
This study used a single-center observational prognostic factor design to examine factors associated with the severity of postoperative complications among patients undergoing open-heart surgery between June 2022 and July 2023. The term prognostic factor is used here to clarify that the study examined associations between candidate factors and a continuous outcome, rather than developing or validating an individualized clinical prediction model. A convenience sampling method was employed to recruit 136 participants from a tertiary hospital in Thailand. Inclusion criteria were male or female patients aged 18 years or older who underwent open-heart surgery and were eligible for 30-day postoperative follow-up. Patients were excluded if they developed life-threatening postoperative complications resulting in severe cognitive impairment or persistent unconsciousness, had incomplete medical records for key surgical or laboratory variables, or withdrew consent during data collection. This exclusion criterion was necessary because patient-reported questionnaires could not be completed reliably by patients with severe cognitive impairment; however, it may have reduced the representation of the most severely affected patients.
The sample size was determined using G*Power for linear multiple regression with a significance level of 0.05, statistical power of 0.90, and a medium effect size of 0.15. The initial calculation was based on eight independent variables because the primary focus was the candidate structural and process factors. The adjusted model also included clinically relevant covariates. Given the modest sample size and the exploratory nature of the analysis, the regression results were interpreted as prognostic factor associations rather than as a fully validated prediction model. To reduce overinterpretation, model assumptions, multicollinearity diagnostics, and adjusted model fit were examined and reported. Based on these criteria, the required sample size was calculated to be 136 participants.
Data Collection
The study was conducted following approval from the Institutional Review Board of the Faculty of Nursing, Mahidol University (MU-MOU CoA No. IRB-NS2022/675.2703) and in accordance with the Declaration of Helsinki. Eligible participants received verbal and written information about the study and provided written informed consent before participation. Health literacy and perception of patient-centered care questionnaires were administered during the postoperative period when participants were clinically stable, oriented, and able to respond. Because perceived patient-centered care was assessed postoperatively, this variable was interpreted cautiously as a care-experience factor associated with the postoperative course rather than as a purely preoperative antecedent. Clinical data, including surgical mortality risk (STS score), preoperative laboratory results (albumin and lymphocyte counts for PNI calculation), hemoglobin level for anemia assessment, and intraoperative variables such as CPB time, were extracted from electronic medical records using a predefined data extraction form. The final analytic sample included participants with complete data for variables used in the primary analysis; therefore, no imputation was performed.
Postoperative complications occurring within 30 days after surgery were identified from clinical records and classified according to the Clavien-Dindo classification before calculating the Comprehensive Complication Index (CCI). Events were counted as complications only when they met prespecified clinical criteria and required treatment, monitoring, pharmacologic management, or procedural intervention beyond the expected uncomplicated postoperative trajectory. For example, postoperative pain was included only when it required additional clinical management beyond routine analgesia, and bleeding was included when it required additional monitoring, intervention, transfusion, or drainage management according to clinical documentation. The primary researcher performed the initial extraction and classification using a predefined form; ambiguous cases were discussed with the clinical research team. Formal blinding to predictor variables was not feasible because several predictors were embedded in the medical record, and this potential observer bias was considered in the limitations.
Data Analysis
The collected data were analyzed using IBM SPSS Statistics version 22 (SPSS Inc). Descriptive statistics were used to summarize sample characteristics and complication prevalence. The type of surgery was treated as a nominal categorical variable and was therefore not interpreted as an ordinal Pearson correlation; in the multivariable regression model, categorical variables were entered using dummy coding. Multiple linear regression was used to examine factors associated with complication severity. Covariates in the adjusted model (age, gender, education level, BMI, diagnosis, and type of surgery) were selected based on clinical relevance and their potential association with postoperative outcomes. Regression assumptions were assessed before interpretation, including linearity, normality of residuals, homoscedasticity, independence of errors, and high-influence observations. Multicollinearity was evaluated using tolerance and variance inflation factor (VIF) values. The assumption checks indicated no serious violation that precluded interpretation of the model; tolerance values were above 0.40 and VIF values were below 2.50. All statistical tests were two-tailed, with a significance threshold of p < 0.05.
Measure
Health Literacy
Health literacy was assessed using the Health Literacy Questionnaire developed by Wanitkun et al15 This 29-item scale encompasses seven domains: perception of basic health rights (4 items), ability to access healthcare services (5 items), communication skills with healthcare providers (4 items), family health (4 items), ability to access health information (4 items), evaluation of information reliability (4 items), and individual health responsibility (4 items). Responses are recorded on an 11-point rating scale, where higher scores reflect superior health literacy. In this study, the instrument demonstrated high internal consistency with a Cronbach’s alpha coefficient of 0.95.
Nutritional Status
Preoperative nutritional status was evaluated using the Prognostic Nutritional Index (PNI), originally developed by Buzby et al16 The index is calculated based on laboratory values obtained within 30 days prior to surgery using the PNI formula. A lower PNI score signifies a higher degree of nutritional impairment.
Severity of Comorbidities
The severity of pre-existing chronic conditions was measured using the updated Charlson Comorbidity Index (CCI).17 This version evaluates 23 comorbid conditions identified through medical record reviews. Each condition is assigned a weighted score ranging from 1 to 6. The cumulative score ranges from 0 to 42, with higher scores indicating a greater burden of comorbidity and higher clinical complexity.
Anemia
Preoperative anemia was assessed using the hemoglobin value closest to the date of surgery. In the analysis, anemia was coded so that higher values represented the presence of anemia. The direction of the regression coefficient was checked against the coding scheme and bivariate findings to ensure consistent interpretation across analyses.
Surgical Mortality Risk
The Society of Thoracic Surgeons (STS) Risk Score (Adult Cardiac Surgery Version 4.20) was utilized to predict operative mortality risk.18 This comprehensive tool assesses 10 clinical dimensions, including demographic data, hospital status, preoperative risk factors, cardiac status, and surgical characteristics. However, because the STS score incorporates several clinical characteristics that may overlap with other patient-level variables, the finding should be interpreted as an adjusted prognostic association rather than evidence that the composite score has an independent causal effect. This underscores the multidisciplinary need for rigorous preoperative risk assessment and vigilant monitoring of patients categorized as high risk by standardized surgical tools.
Perception of Patient-Centered Care
The Consultation Care Measure (CCM), developed by Little et al and translated into Thai by Choocherd et al,19 was used to assess the patient’s perspective on care. The 21-item scale covers five domains: communication and partnership (11 items), interpersonal relationship (3 items), health promotion (2 items), clear problem perception (3 items), and interest in the impact on the patient’s life (2 items). The Thai version has a reported content validity index of 0.90 and demonstrated excellent reliability in the current study (α = 0.97).
Severity of Postoperative Complications
The primary outcome, severity of postoperative complications within 30 days, was quantified using the Comprehensive Complication Index (CCI) developed by Slankamenac et al (2013). Unlike traditional binary measures that classify patients only as having or not having complications, the CCI integrates all complications into a single weighted score based on the Clavien-Dindo classification. The resulting score ranges from 0 (no complications) to 100 (mortality), providing a sensitive and continuous scale for measuring the overall morbidity burden of the surgical procedure. To avoid overcounting routine postoperative symptoms, complications were classified only when they exceeded the expected uncomplicated postoperative recovery course and met the prespecified Clavien-Dindo criteria. This approach allowed this study to evaluate cumulative complication severity rather than isolate events.
Results
A total of 142 patients were initially assessed for eligibility during the study period. Of these, 6 patients were excluded due to undergoing emergency cardiac surgery. Consequently, 136 adult patients undergoing open-heart surgery met all inclusion criteria and were enrolled in the study (N = 136). All enrolled participants completed the 30-day post-discharge follow-up period, and complete data were available for the final analysis (n = 136). The detailed participant flow, following the STROBE guidelines, is depicted in Figure 1.
Figure 1.
STROBE participant flow diagram of study screening, enrollment, and follow-up.
The study included 136 participants with a mean age of 61.32 ± 13.09 years, of whom the majority were male (61.8%). Regarding educational attainment, nearly half of the sample had completed primary education (33.1%), followed by Bachelor’s degree or higher (28.7%). Clinically, a significant majority of the patients underwent their first cardiac surgery (94.9%), with coronary artery disease involving more than two vessels being the most frequent diagnosis (41.2%). The average Body Mass Index (BMI) was 24.21± 4.69 kg/m2. Preoperative laboratory assessments revealed that 19.1% of patients had abnormal albumin levels, and 25.0% presented with abnormal lymphocyte counts. Left ventricular ejection fraction was impaired (<40%) in 8.1% of the sample (Table 1). Intraoperatively, the average duration of surgery was 4.56 ± 2.01 hours, and the mean duration of mechanical ventilation postoperatively was 26.81 ± 33.42 hours.
Table 1.
Baseline Demographic and Clinical Characteristics of Participants (N = 136)
| Demographic and Clinical Characteristics | N | % |
|---|---|---|
| Age (61.32 ± 13.09 years) | ||
| 18–40 years | 9 | 6.6 |
| 41–59 years | 44 | 32.4 |
| ≥ 60 years | 83 | 61 |
| Gender | ||
| Male | 84 | 61.8 |
| Female | 52 | 38.2 |
| Education | ||
| Primary education | 45 | 33.1 |
| Secondary education/Vocational Certificate | 35 | 25.7 |
| Higher Vocational Certificate/Diploma | 17 | 12.5 |
| Bachelor’s degree or higher | 39 | 28.7 |
| History of cardiac surgery | ||
| First-time cardiac surgery | 129 | 94.9 |
| Previous cardiac surgery | 7 | 5.1 |
| Body Mass Index (24.21± 4.69 kg/m2) | ||
| < 18.5 kg/m2 | 17 | 12.7 |
| 18.5–22.9 kg/m2 | 40 | 29.9 |
| 23–24.90 kg/m2 | 23 | 17.2 |
| ≥ 25 kg/m2 | 54 | 40.3 |
| Type of surgery | ||
| CABG | 62 | 45.6 |
| Valvular surgery | 30 | 22.1 |
| Combinate | 44 | 32.3 |
| CPB Time | ||
| < 110 minutes | 51 | 37.5 |
| > 110 minutes | 85 | 62.5 |
Abbreviations: CABG, Coronary Artery Bypass Grafting; CPB, Cardiopulmonary Bypass.
The severity of postoperative complications within 30 days was categorized using the Clavien-Dindo classification. Grade I complications were observed in 97.1% of participants and Grade II complications in 89.0%, indicating that many patients experienced minor or pharmacologically managed postoperative events. More severe complications requiring intensive intervention (Grade IV) were found in 13.2% of the sample. The high prevalence of lower-grade complications should be interpreted in light of the use of the CCI, which captures all complications rather than only major events. The cumulative severity of these events, as measured by the CCI, yielded a mean score of 37.25 ± 14.70 (Table 2).
Table 2.
Prevalence and Distribution of Postoperative Complications by Severity
| Severity of Postoperative Complications | N | % |
|---|---|---|
| The Clavien-Dindo Classification | ||
| Grade I | 132 | 97.1 |
| Grade II | 121 | 89.0 |
| Grade III | 8 | 5.8 |
| Grade IIIa | 4 | 2.9 |
| Grade IIIb | 4 | 2.9 |
| Grade IV | 18 | 13.2 |
| Grade IVa | 16 | 11.8 |
| Grade IVb | 4 | 2.9 |
| Grade IVa and Grade IVb | 2 | 1.5 |
| Comprehensive Complication Index (37.25 ± 14.7) | ||
Bivariate correlation analysis (Pearson’s r) was used to examine the relationships between the hypothesized predictors and the CCI score. The surgical mortality risk (STS score) showed the strongest significant positive correlation with the severity of complications (r = 0.351, p < 0.01). Significant positive correlations were also observed for anemia (r = 0.215, p < 0.05), type of surgery (r = 0.190, p < 0.05), and cardiopulmonary bypass (CPB) time (r = 0.168, p < 0.05). Conversely, the Prognostic Nutritional Index (PNI) demonstrated a significant inverse correlation with complication severity (r = −0.189, p < 0.05), indicating that better nutritional status was associated with lower complication severity (Table 3). Type of surgery was retained as a categorical covariate in the adjusted regression model using dummy coding rather than interpreted as an ordinal correlation.
Table 3.
Correlation Matrix Between Independent Variables and Complication Severity
| Variable | Correlations (r) | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
| 1. Postoperative Complications | 1 | ||||||||
| 2. CPB Time | 0.168 | 1 | |||||||
| 3. Health literacy | 0.045 | 0.031 | 1 | ||||||
| 4. Nutrition | −0.189* | 0.061 | 0.155 | 1 | |||||
| 5. Anemia | 0.215* | 0.008 | 0.112 | −0.269** | 1 | ||||
| 6. Comorbidity | 0.153 | −0.001 | −0.034 | −0.065 | 0.262** | 1 | |||
| 7. Surgical mortality risk | 0.351** | −0.036 | 0.049 | −0.320** | 0.444** | 0.411** | 1 | ||
| 8. Type of surgery | 0.190* | 0.065 | −0.018 | 0.074 | 0.045 | 0.279** | 0.266** | 1 | |
| 9. Perception of care | −0.095 | −0.008 | 0.414** | −0.014 | 0.100 | −0.013 | 0.019 | −0.047 | 1 |
Notes: *p < 0.05; **p < 0.01.
Abbreviations: CPB, Cardiopulmonary Bypass; CCI, Comprehensive Complication Index.
Surgical mortality risk, health literacy, preoperative anemia, comorbidity nutritional status, perception of care, and cardiopulmonary bypass time were able to explain the variance of the severity of postoperative complications at 18.3% with statistical significance (F = 4.101, P <0.01, R2 = 0.183, adjusted R2 = 0.139). Multiple regression analysis was performed to identify factors independently associated with the severity of postoperative complications. The final model confirmed four significant factors: surgical mortality risk (standardized beta = 0.345, p < 0.001), anemia (standardized beta = 0.224, p = 0.018), nutritional status (standardized beta = −0.218, p = 0.011), and CPB time (standardized beta = 0.198, p = 0.034). The positive coefficient for anemia indicates that the presence of anemia was associated with greater complication severity after adjustment. Other factors, including health literacy, comorbidity severity, and perceived patient-centered care, did not reach statistical significance in this study (Table 4).
Table 4.
Multiple Regression Analysis of Factors Predicting the Severity of Postoperative Complications (CCI)
| Variables | Crude | Adjusted | ||||
|---|---|---|---|---|---|---|
| β | 95% CI | p | β | 95% CI | p | |
| Health literacy | 0.045 | (−1.417, 2.445) | 0.599 | 0.048 | (−1.465, 2.554) | 0.593 |
| Nutrition | −0.189 | (−0.982, −0.059) | 0.027 | −0.218 | (−1.056, −0.140) | 0.011 |
| Comorbidity | 0.153 | (−0.106, 2.154) | 0.075 | 0.084 | (−0.688,1.815) | 0.375 |
| Anemia (Yes vs No) | −0.196 | (−2.865, −0.226) | 0.022 | −0.224 | (−3.220, −0.310) | 0.018 |
| Surgical mortality risk | 0.351 | (0.177, 0.473) | < 0.001 | 0.345 | (0.159, 0.480) | < 0.001 |
| CPB Time (min) | 0.168 | (0.000, 0.086) | 0.050 | 0.198 | (0.004, 0.097) | 0.034 |
| Perception of care | −0.095 | (−4.013, 1.132) | 0.270 | −0.123 | (−4.419, 0.687) | 0.150 |
Notes: The adjusted model controlled for age, gender, education level, body mass index, diagnosis, and type of surgery.
Abbreviations: CPB, Cardiopulmonary Bypass; CI, Confidence Interval.
Discussion
This study identified key structural and process factors associated with 30-day postoperative complication severity (CCI) after open-heart surgery. Utilizing the Nursing Role Effectiveness Model (NREM) as a conceptual framework, our findings demonstrate that surgical mortality risk (STS score), preoperative anemia, nutritional status (PNI), and cardiopulmonary bypass (CPB) duration serve as independent prognostic factors for cumulative complication severity.
Surgical Risk and Baseline Vulnerability
The Society of Thoracic Surgeons (STS) risk score emerged as the strongest predictor of complication severity. Because the STS score aggregates complex baseline clinical parameters, this finding highlights that overall clinical complexity remains the primary determinant of postoperative morbidity. The STS score was utilized as it represents the institutional standard at the study site, whereas EuroSCORE II was not routinely documented. Given that the STS score incorporates overlapping clinical characteristics, this result reflects an adjusted prognostic association rather than a direct causal relationship. Nevertheless, it emphasizes the importance of standardized preoperative risk screening to identify high-risk surgical candidates requiring vigilant post-procedure surveillance.
Preoperative Optimization: Anemia and Nutrition
Preoperative anemia and lower PNI scores were significantly associated with greater complication severity. Anemia compromises tissue oxygenation during surgical stress, while poor nutritional status impairs wound healing and immune function.20,21 Unlike fixed baseline risk scores, anemia and malnutrition represent modifiable factors. These results indicate that preoperative targeted interventions, such as iron therapy or nutritional support, may offer valuable opportunities to mitigate acute postoperative morbidity burden.22–24
Cardiopulmonary Bypass Duration
Regarding process factors, prolonged CPB time independently predicted higher complication severity. CPB exposure is known to induce systemic inflammatory responses, hemolysis, and coagulopathy.10,11,25 In the context of intraoperative care processes, this finding reinforces the importance of surgical efficiency and perfusion management to minimize bypass duration whenever clinically feasible.
Non-Significant Predictors
Health literacy, comorbidity burden, and perceived patient-centered care were not statistically significant predictors in this model. While these variables are central to patient engagement and communication within the NREM framework, acute physiological stressors and intraoperative events appear to exert a more dominant influence on biological complication severity during the early 30-day window. These non-significant findings do not diminish the value of patient-reported attributes; rather, their impact may be more pronounced in long-term recovery, functional rehabilitation, and quality-of-life outcomes post-discharge. Additional unmeasured clinical variables—such as ejection fraction, reoperations, or acute renal failure—were either partially captured within the composite STS score or restricted by sample size constraints. Future prospective, multi-center studies with larger samples should directly evaluate these granular perioperative parameters alongside alternative risk models like EuroSCORE II.
Limitations
This study has several limitations that should be considered when interpreting the findings. First, the use of convenience sampling at a single tertiary care center may limit the generalizability of the results to other healthcare settings or different geographical populations. The sample may also reflect the clinical characteristics and surgical case mix of a tertiary cardiac center, and therefore the findings should be compared cautiously with broader cardiac surgery populations. Second, patients with severe postoperative cognitive impairment or persistent unconsciousness were excluded because they could not complete patient-reported questionnaires; this may have reduced the representation of patients with the most severe postoperative courses and may partly influence the observed severity distribution. Third, perceived patient-centered care was assessed postoperatively and may have been influenced by the patient’s postoperative experience and complications; therefore, this variable should be interpreted as an associated care-experience factor rather than a clear antecedent predictor. Fourth, the high prevalence of lower-grade complications may reflect the use of the CCI, which captures cumulative morbidity and minor events in addition to major complications, rather than a simple binary definition. Fifth, although complications were classified using prespecified criteria, formal blinding of outcome adjudication to all predictor variables was not feasible, which may introduce observer bias. Sixth, the adjusted regression model was exploratory and was not internally validated using bootstrap or split-sample validation; therefore, the findings should not be interpreted as a validated clinical prediction model for individual risk estimation. Seventh, the 30-day follow-up period, while standard for surgical outcomes, may not capture late-onset complications or long-term recovery trajectories. Eighth, EuroSCORE II was not included because it was not routinely documented for all participants, limiting comparison with another widely used cardiac surgical risk score. Ninth, several clinically important factors, such as emergency operation status, renal failure, perioperative heart failure, and coronary endarterectomy, were not analyzed as separate predictors and should be considered in future studies. Finally, the study relied partly on retrospective medical record reviews for clinical data, which is subject to the accuracy and completeness of hospital documentation. As an observational prognostic factor study, this research can identify associations but cannot definitively establish causal relationships.
Implications for Practice
The findings have implications for multidisciplinary healthcare teams involved in perioperative cardiac care. Because surgical mortality risk (STS score), anemia, and nutritional status (PNI) were identified as key structural predictors, these assessments should be integrated into preoperative risk screening and optimization protocols. For elective cases, nurses and clinicians may prioritize correction of modifiable factors, including evaluation and management of anemia and nutritional support for patients with low PNI scores, before surgery when clinically appropriate.
Furthermore, the significant impact of CPB time suggests that intraoperative teams should continue to refine surgical and perfusion processes to minimize bypass duration while maintaining patient safety. From a nursing and multidisciplinary perspective, the CCI offers a more nuanced tool for monitoring patient recovery than simple occurrence tracking, allowing clinicians to identify patients with a greater cumulative morbidity burden and allocate postoperative surveillance and resources more precisely.
Conclusion
The severity of complications after open-heart surgery is multifactorial, driven by both structural and process factors. Surgical mortality risk, anemia, nutritional status, and CPB time were significantly associated with 30-day complication severity. These findings support the Nursing Role Effectiveness Model and suggest that preoperative optimization of hematological and nutritional status, together with careful intraoperative management, may help reduce postoperative morbidity. However, interpretation should consider the single-center design, convenience sampling, exclusion of patients with severe postoperative cognitive impairment, absence of EuroSCORE II assessment, and limited availability of some operative risk variables.
Disclosure
The authors report no conflicts of interest in this work.
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