Population-Based Trends in Complexity of Hospital Inpatients

Abstract

ImportanceClinical experience suggests that hospital inpatients have become more complex over time, but few studies have evaluated this impression.ObjectiveTo assess whether there has been an increase in measures of hospital inpatient complexity over a 15-year period.Design, Setting and ParticipantsThis cohort study used population-based administrative health data from nonelective hospitalizations from April 1, 2002, to January 31, 2017, to describe trends in the complexity of inpatients in British Columbia, Canada. Hospitalizations were included for individuals 18 years and older and for which the most responsible diagnosis did not correspond to pregnancy, childbirth, the puerperal period, or the perinatal period. Data analysis was performed from July to November 2023.ExposureThe passage of time (15-year study interval).Main Outcomes and MeasuresMeasures of complexity included patient characteristics at the time of admission (eg, advanced age, multimorbidity, polypharmacy, recent hospitalization), features of the index hospitalization (eg, admission via the emergency department, multiple acute medical problems, use of intensive care, prolonged length of stay, in-hospital adverse events, in-hospital death), and 30-day outcomes after hospital discharge (eg, unplanned readmission, all-cause mortality). Logistic regression was used to estimate the relative change in each measure of complexity over the entire 15-year study interval.ResultsThe final study cohort included 3 367 463 nonelective acute care hospital admissions occurring among 1 272 444 unique individuals (median [IQR] age, 66 [48-79] years; 49.1% female and 50.8% male individuals). Relative to the beginning of the study interval, inpatients at the end of the study interval were more likely to have been admitted via the emergency department (odds ratio [OR], 2.74; 95% CI, 2.71-2.77), to have multimorbidity (OR, 1.50; 95% CI, 1.47-1.53) and polypharmacy (OR, 1.82; 95% CI, 1.78-1.85) at presentation, to receive treatment for 5 or more acute medical issues (OR, 2.06; 95% CI, 2.02-2.09), and to experience an in-hospital adverse event (OR, 1.20; 95% CI, 1.19-1.22). The likelihood of an intensive care unit stay and of in-hospital death declined over the study interval (OR, 0.96; 95% CI, 0.95-0.97, and OR, 0.81; 95% CI, 0.80-0.83, respectively), but the risks of unplanned readmission and death in the 30 days after discharge increased (OR, 1.14; 95% CI, 1.12-1.16, and OR, 1.28; 95% CI, 1.25-1.31, respectively).Conclusions and RelevanceBy most measures, hospital inpatients have become more complex over time. Health system planning should account for these trends.

Increasing perioperative age and comorbidity: a 16-year cohort study at two University hospital sites in Sweden

“…The higher comorbidity over time found in the present study is in agreement with results from a large recent Canadian cohort study of 3.4 million hospital admissions reporting an increase in hospital inpatient complexity between 2002 and 2017 10 . One explanation could be active changes in healthcare policy, where smaller non-academic centres manage younger and healthier patients, whereas university hospitals focus more on complex cases with higher comorbidity.…”

“…Elderly patients have higher rates of major perioperative complications and mortality after non-cardiac surgery, as well as longer hospital stays 7 , 8 . Investigating changes in demography and mortality over time in large, unselected patient populations is unusual 9 , 10 , but in a study of emergency surgery in Scotland over a 20-year period (1996–2015), no significant changes in median age were found 11 . In contrast, a study of the surgical population 2000–2014 in England showed that patients were getting older 12 .…”

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“…There is strong evidence of the increasing complexity of hospitalized and surgical patients [7]. It can be hypothesized that this complexity is also associated with anemia and coagulopathy as increasingly encountered comorbid conditions, especially in surgical patients.…”

Prognosticating the outcome of intensive care in older patients—a narrative review

“…The generalisability of prediction models refers to their performance in patient groups or healthcare settings other than the one used for training. Variations of patient biographies according to geography and era as well as differences in cultural norms and practice patterns affect care processes and outcomes [4,[42][43][44], especially in very old patients with complex conditions. Thus, the generalisability of many prediction models is limited [45,46] and the performance of these models was shown to degrade when deployed in different healthcare settings or during different periods in time [47,48].…”