Psychiatric disease in surgically treated colorectal cancer patients

Psychiatric Diagnoses Are Associated With Postoperative Disparities in Patients Undergoing Major Colorectal Operations

BACKGROUND

Over 50% of hospitalized patients have comorbid psychiatric diagnoses, resulting in increased risk of morbidity such as longer lengths of stay, worse health-related quality of life, and increased mortality. However, data regarding colorectal surgery postoperative outcomes in patients with psychiatric diagnoses (PD) are limited.

METHODS

We queried a single institution's National Surgical Quality Improvement Program from 2013-2019 for major colorectal procedures. Postsurgical outcomes for patients with and without PD were compared. Primary outcomes were prolonged length of stay (pLOS) and 30-day readmission.

RESULTS

From a total of 1447 patients, 402 (27.8%) had PD. PD had more smokers (20.9% vs 15%) and higher mean body mass index (29.1 kg/m2 vs 28.2 kg/m2). Bivariate outcomes showed more surgical site infections (SSI) (10.2% vs 6.12%), reoperation (9.45% vs 6.35%), and pLOS (34.8% vs 29.0%) (all P values <.05) in the PD group. On multivariate analysis, PD had higher likelihood of reoperation (OR 1.53, 95% CI: [1.02-2.80]) and SSI (OR 1.82, 95% CI: [1.25-2.66]).

DISCUSSION

Psychiatric diagnoses are a risk factor for adverse outcomes after colorectal procedures. Further studies are needed to evaluate the benefit of perioperative mental health support services for these patients.

Associations Between Psychiatric Diagnoses on Length of Stay and Mortality After Rib Fracture: A Retrospective Analysis

INTRODUCTION

Concurrent psychiatric diagnoses adversely impact outcomes in surgical patients, but their relationship to patients with rib fracture after trauma is less understood. We hypothesized that psychiatric comorbidity would be associated with increases in hospital length of stay (LOS) and mortality risk after rib fracture.

MATERIALS AND METHODS

The 2017 National Inpatient Sample was queried for adult patients who were admitted with rib fracture after trauma. Mental health disorders were categorized into 34 psychiatric diagnosis groups (PDGs) using clinical classifications software refined for International Classification of Diseases-10. Outcomes of interest were LOS and mortality. Bivariable analysis determined associations between PDGs, patient demographics, hospital characteristics, and outcomes. Logistic regression was performed to identify adjusted effects on mortality, and linear regression was performed to identify effects on LOS.

RESULTS

Of 32,801 patients, median age was 61 y (IQR 46-76), and median LOS was 5 d (IQR 3-9). No PDGs were associated with increased odds of mortality. Concurrent diagnosis of schizophrenia spectrum (Coeff. 3.5, 95% CI 2.7-4.4, P < 0.001) or trauma- or stressor-related (Coeff. 1.6, 95% CI 0.9-2.5, P < 0.001) disorders demonstrated the greatest association with prolonged LOS. Increased odds of death and prolonged hospital stay were also associated with male sex, non-White patient race, and surgery occurring at urban and public hospitals.

CONCLUSIONS

Psychiatric comorbidities are associated with death after rib fracture but are associated with increased LOS. These findings may help promote multidisciplinary patient management in trauma.

Colorectal cancer treatment in people with severe mental illness: a systematic review and meta-analysis

AIMS

People with severe mental illness (SMI) have a greater risk of dying from colorectal cancer (CRC), even though the incidence is lower or similar to that of the general population This pattern is unlikely to be solely explained by lifestyle factors, while the role of differences in cancer healthcare access or treatment is uncertain.

METHODS

We undertook a systematic review and meta-analysis on access to guideline-appropriate care following CRC diagnosis in people with SMI including the receipt of surgery, chemo- or radiotherapy. We searched for full-text articles indexed by PubMed, EMBASE, PsychInfo and CINAHL that compared CRC treatment in those with and without pre-existing SMI (schizophrenia, schizoaffective, bipolar and major affective disorders). Designs included cohort or population-based case-control designs.

RESULTS

There were ten studies (sample size = 3501-591 561). People with SMI had a reduced likelihood of surgery (RR = 0.90, 95% CI 0.92-0.97; p = 0.005; k = 4). Meta-analyses were not possible for the other outcomes but in results from individual studies, people with SMI were less likely to receive radiotherapy, chemotherapy or sphincter-sparing procedures. The disparity in care was greatest for those who had been psychiatric inpatients.

CONCLUSIONS

People with SMI, including both psychotic and affective disorders, receive less CRC care than the general population. This might contribute to higher case-fatality rates for an illness where the incidence is no higher than that of the general population. The reasons for this require further investigation, as does the extent to which differences in treatment access or quality contribute to excess CRC mortality in people with SMI.

The clinical and financial cost of mental disorders among elderly patients with gastrointestinal malignancies

The clinical and financial effects of mental disorders are largely unknown among gastrointestinal (GI) cancer patients. Using the Surveillance, Epidemiology, and End Results (SEER)‐Medicare linked database, we identified patients whose first cancer was a primary colorectal, pancreatic, gastric, hepatic/biliary, esophageal, or anal cancer as well as those with coexisting depression, anxiety, psychotic, or bipolar disorder. Survival, chemotherapy use, total healthcare expenditures, and patient out‐of‐pocket expenditures were estimated and compared based on the presence of a mental disorder. We identified 112,283 patients, 23,726 (21%) of whom had a coexisting mental disorder. Median survival for patients without a mental disorder was 52 months (95% CI 50–53 months) and for patients with a mental disorder was 43 months (95% CI 42–44 months) (p < 0.001). Subgroup analysis identified patients with colorectal, gastric, or anal cancer to have a significant association between survival and presence of a mental disorder. Chemotherapy use was lower among patients with a mental disorder within regional colorectal cancer (43% vs. 41%, p = 0.01) or distant colorectal cancer subgroups (71% vs. 63%, p < 0.0001). The mean total healthcare expenditures were higher for patients with a mental disorder in first year following the cancer diagnosis (increase of $16,823, 95% CI $15,777‐$18,173), and mean patient out‐of‐pocket expenses were also higher (increase of $1,926, 95% CI $1753–$2091). There are a substantial number of GI cancer patients who have a coexisting mental disorder, which is associated with inferior survival, higher healthcare expenditures, and greater personal financial burden.

Applying Supervised Machine Learning to Identify Which Patient Characteristics Identify the Highest Rates of Mortality Post-Interhospital Transfer

Objective:

To demonstrate the usefulness of applying supervised machine-learning analyses to identify specific groups of patients that experience high levels of mortality post-interhospital transfer.

Methods:

This was a cross-sectional analysis of data from the Health Care Utilization Project 2013 National Inpatient Sample, that applied supervised machine-learning approaches that included (1) classification and regression tree to identify mutually exclusive groups of patients and their associated characteristics of those experiencing the highest levels of mortality and (2) random forest to identify the relative importance of each characteristic’s contribution to post-transfer mortality.

Results:

A total of 21 independent groups of patients were identified, with 13 of those groups exhibiting at least double the national average rate of mortality post-transfer. Patient characteristics identified as influencing post-transfer mortality the most included: diagnosis of a circulatory disorder, comorbidity of coagulopathy, diagnosis of cancer, and age.

Conclusions:

Employing supervised machine-learning analyses enabled the computational feasibility to assess all potential combinations of available patient characteristics to identify groups of patients experiencing the highest rates of mortality post-interhospital transfer, providing potentially useful data to support developing clinical decision support systems in future work.