Worldwide, an estimated 382 million people live with type 2 diabetes (T2D), causing at least US 548 billion dollars in health care expenditures [
There is scarce information regarding mortality rates and causes of death in patients with T2D in LMIC. For many years, infections have been considered the main cause of mortality in LMIC, despite the fact that vascular diseases, especially stroke and myocardial infarction, are the underlying causes in high-income countries [
Information regarding inhospital mortality rates as well as clinical conditions increasing the risk of dying among T2D patients is very limited in resource-constrained settings. As a result, there is a lack of preventive policies in high-risk T2D cases that might reduce and prevent inhospital mortality. For example, a previous study, conducted in Peru in 1996, reported that infections were the main cause of morbidity and mortality in a public referral hospital [
As a result, the aim of this study was to identify potential clinical factors that might increase the risk of inhospital mortality among T2D patients. In addition, mortality rate and main causes of hospital admission and death were also determined. Results of this study can be helpful to implement public health strategies and update hospital management guidelines in order to prevent and reduce inhospital mortality among these kind of patients.
A prospective cohort study was conducted in the Internal Medicine Hospitalization Unit at the Hospital Nacional Arzobispo Loayza (HNAL) located in Lima, Peru. HNAL is a third-level public hospital located in the center of Lima, with more than 2500 hospital admissions monthly in the different available services and more than 30,000 outpatients attending every month [
Patients with T2D diagnosis, confirmed by an internal medicine physician or endocrinologist in the emergency or ambulatory room, aged 18 or more and admitted to the Internal Medicine Service of the HNAL during the year 2012 were enrolled in the study. In case of multiple hospitalizations during the year, only the first hospital admission was included in the analyses. Pregnant women and patients admitted for diagnostic or therapeutic procedures such as kidney biopsy, peritoneal dialysis catheter placement, or corticosteroid pulse were excluded.
The outcome of interest was time until event defined as the time, in days, elapsed from admission until the patient was discharged by medical indication or voluntary decision (censored) or died during hospitalization.
The exposure of interest was the cause of hospitalization defined as the diagnosis at the moment of hospital admission. Only one physician, one of the authors of this paper, was responsible for verifying and confirming the cause of hospitalization during follow-up. Causes of hospitalization were subdivided into the most common reasons previously reported including infections (urinary, respiratory, subcutaneous tissue, or diabetic foot), metabolic disorders (hypoglycemia, diabetic ketoacidosis, and hyperosmolar state), stroke, and acute and chronic renal failure (exacerbation). Definitions of the variables used are detailed in Table
Definition of causes of hospitalization assessed in the study.
Infections | |
Respiratory | Respiratory symptoms (cough or tachypnea) plus a chest X-ray with changes suggestive of viral or bacterial respiratory infection. |
Urinary | Urine sample with ≥10 leukocytes/ |
Gastrointestinal | Diarrhea < 7 days, vomiting, and dehydration. |
Subcutaneous tissue (SCT) | Cellulitis or necrotizing fasciitis in any part of the body except feet. |
Diabetic foot | Ulceration, infection, and/or gangrene of foot associated with diabetic neuropathy and different grades of peripheral artery disease [ |
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Metabolic disorders | |
Hypoglycemia | Glucose ≤70 mg/dL (3.9 mmol/L) [ |
Diabetic ketoacidosis | Glucose >250 mg/dL, pH <7.3, and bicarbonate <18 mEq/d [ |
Hyperosmolar state | Glucose >600 mg/dL, pH arterial: >7.30, bicarbonate: >18 mEq/L, anion GAP: variable, mental status: drowsy/coma, few kenotic bodies in the urine and blood, and plasmatic osmolality > 320 mOsm/kg [ |
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Vascular | |
Stroke | Fast development of clinic signs of changes in the cerebral function or global, with symptoms that persist within 24 hours or more, with no other evidence of vascular origin [ |
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Renal | |
Acute renal failure | Sudden increase (within 48 hours) of creatinine (Cr) |
Chronic renal failure | Presence of renal damage (urinary albumin excretion ≥30 mg/day) or decrease of the renal function (GFR <60 mL/min/1.73 m2) by three or more months, independent of the cause [ |
Clinical variables, especially related to T2D, were also considered including time of disease, in years, split in four categories (<5, 5–9, 10–14, and 15+), type of hospital admission (outpatient service or emergency), and medicines received prior to hospital admission (yes/no). Besides, glycated hemoglobin (HbA1c) level was categorized as noncontrolled (≥7%) and controlled (<7%), according to international guidelines [
Previous informed consent, third-year residents of Endocrinology evaluated the patients that fulfill the inclusion criteria for the study. Residents were trained before starting fieldwork activities to appropriately applied questionnaires and complete data collection templates. Sociodemographic, clinical, and laboratory data was recorded at the moment of hospital admission. Patients were then followed up for diagnosis confirmation as well as recording the outcome of interest (discharge or death).
Using Power and Sample Size software (PASS 2008, NCSS, UTAH, US), with information from 499 participants and assuming a level of significance of 5% and a mortality rate of 0.57 per 100 persons-day of follow-up, we have a power of 80% to detect a Hazard Ratio of 3 or more.
STATA 13 for Windows (STATA Corporation, College Station, Texas, US) was used for statistical analysis. Initially, the study population was described using mean and standard deviation for numerical variables and proportions for categorical variables. Association between inhospital mortality rate and sociodemographic, clinical variables and causes of hospitalization were assessed using Log-rank test. After that, Cox regression crude and adjusted models were fitted to evaluate associations between causes of hospitalization (individual and grouped) and our outcome of interest, reporting Hazard Ratios (HR) and their respective 95% confidence intervals (95% CI).
Because a large proportion of patients (40.2%) did not have HbA1c results and 11.8% did not have time of disease information, with both considered important confounders in the models, the associations of interest were also evaluated using multiple imputation techniques, based on data collected. As recommended in previous studies [
The study was reviewed and approved by the Institutional Ethical Committee of Universidad Peruana Cayetano Heredia, Lima, Peru. Oral informed consent was requested from participants before fieldwork activities. Data collection templates contained alphanumeric codes to avoid personal identifiers.
A total of 499 patients were enrolled during the study, 63.6% female, mean age 61.6 (SD: 13.8) years. Of note, 33.6% of patients reported 15 or more years of disease, only 343 (68.6%) were receiving treatment before hospitalization (20.5% with insulin and 62.4% with metformin), and only 71/299 (23.8%) met recommended glycemic goals (HbA1c < 7%). Among infections, the main causes of hospital admission were urinary infections (23.0%), followed by diabetic foot (22.4%). Among noncommunicable diseases, the main cause of hospitalization was exacerbation of chronic renalfailure (18.8%) and stroke (5.6%). Of note, only 4 cases (0.8%) were admitted with diagnosis of acute myocardial infarction.
During the study, 42 (8.4%) patients died with a median survival time of 7 days (interquartile range: 2–17). Detailed characteristics of the study population according to the outcome of interest are shown in Table
Sociodemographic factors associated with death during hospitalization.
Time to event |
Dead/total |
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Gender | |||
Female | 9.9 | 30/314 (9.5%) | 0.15 |
Male | 17.8 | 12/180 (6.7%) | |
Age (years) | |||
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11.3 | 3/91 (3.3%) | 0.12 |
50–59 years | 10.6 | 9/132 (6.8%) | |
60–69 years | 11.7 | 14/125 (11.2%) | |
70+ years | 13.5 | 16/149 (10.7%) | |
Place of origin | |||
Coast | 11.8 | 36/418 (8.6%) | 0.90 |
Highlands | 13.0 | 4/60 (6.7%) | |
Jungle | 16.0 | 2/19 (10.5%) | |
Education level | |||
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8.6 | 21/236 (8.9%) | 0.52 |
7–11 years | 21.1 | 13/204 (6.4%) | |
12+ years | 5.0 | 5/51 (9.8%) | |
Time of disease (years) | |||
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9.8 | 13/131 (9.9%) | 0.97 |
5–9 years | 17.7 | 6/77 (7.8%) | |
10–14 years | 5.0 | 6/85 (7.1%) | |
15+ years | 13.7 | 14/148 (9.5%) | |
Hospital admission | |||
Outpatient | 12.3 | 39/465 (8.4%) | 0.73 |
Emergency | 10.0 | 3/33 (9.1%) | |
Receiving treatment before admission | |||
No | 10.5 | 15/156 (9.6%) | 0.35 |
Yes | 13.0 | 27/343 (7.9%) | |
Glycemic control (HbA1c) | |||
Controlled ( |
11.1 | 8/71 (11.3%) | 0.14 |
Uncontrolled ( |
10.7 | 13/228 (5.7%) |
Factors associated with mortality during hospital admission: crude and adjusted models using Cox regression.
Dead/total | Mortality | Crude model | Adjusted model |
Imputed model | ||
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( |
Rate (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | ||
Urinary infection | No | 35/385 (9.1%) | 0.59 (0.42–0.83) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 7/114 (6.1%) | 0.48 (0.23–1.01) | 0.77 (0.34–1.75) | 1.04 (0.27–3.98) | 0.70 (0.28–1.72) | |
Respiratory infection | No | 27/438 (6.2%) | 0.41 (0.28–0.61) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 15/61 (24.6%) | 1.68 (1.01–2.78) |
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Gastrointestinal infection | No | 38/461 (8.2%) | 0.55 (0.40–0.76) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 4/38 (10.5%) | 0.82 (0.31–2.19) | 1.49 (0.53–4.19) | 1.04 (0.12–8.79) | 1.91 (0.65–5.64) | |
Subcutaneous infection | No | 36/459 (7.8%) | 0.53 (0.38–0.74) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 6/39 (15.4%) | 1.14 (0.51–2.53) | 2.18 (0.91–5.19) | 1.71 (0.33–8.79) | 1.36 (0.45–4.09) | |
Diabetic foot | No | 36/387 (9.3%) | 0.73 (0.52–1.02) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 5/111 (4.5%) | 0.21 (0.09–0.51) |
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Hypoglycemia | No | 39/449 (8.7%) | 0.57 (0.41–0.78) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 3/50 (6.0%) | 0.68 (0.22–2.10) | 1.07 (0.33–3.50) | 0.80 (0.09–6.88) | 0.73 (0.17–3.21) | |
Diabetic ketoacidosis | No | 40/462 (8.7%) | 0.58 (0.43–0.80) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 2/36 (5.6%) | 0.44 (0.11–1.74) | 0.72 (0.17–2.97) | 4.77 (0.49–46.71) | 2.07 (0.46–9.37) | |
Hyperosmolar state | No | 40/482 (8.3%) | 0.56 (0.41–0.76) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 2/17 (11.8%) | 1.07 (0.27–4.28) | 1.79 (0.43–7.45) |
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1.66 (0.37–7.44) | |
Stroke | No | 35/471 (7.4%) | 0.50 (0.36–0.70) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 7/28 (25.0%) | 1.70 (0.81–3.56) |
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Acute renal disease | No | 39/493 (7.9%) | 0.53 (0.39–0.73) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 3/5 (60.0%) | 10.7 (3.46–33.2) |
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Chronic renal disease | No | 26/405 (6.4%) | 0.43 (0.29–0.63) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Yes | 16/94 (17.0%) | 1.24 (0.76–2.02) |
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2.69 (0.77–9.45) |
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Mortality rates were calculated per 100 persons-day of follow-up.
Respiratory infections (
Results were consistent when models were fitted using multiple imputation techniques; however, hyperosmolar state was no longer significant (
Finally, when the sum of causes of admission was assessed, an increase in the number of conditions (comorbidities) was associated with greater risk of mortality. Although results of imputed models were consistent, values of HR were markedly attenuated (Table
Cumulative effect of comorbidities and its association with death: crude and adjusted models using Cox regression.
Alive | Dead | Crude model | Adjusted model |
Imputed model | |
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( |
( |
HR (95% CI) | HR (95% CI) | HR (95% CI) | |
Number of morbidities | |||||
One | 331 (94.8%) | 18 (5.2%) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
Two | 105 (87.5%) | 15 (12.5%) |
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Three or more | 18 (69.2%) | 8 (30.8%) |
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The number of morbidities was calculated by adding proposed causes of hospitalization.
In the present study, respiratory infections, stroke, and acute renal failure were positively associated with dying during hospitalization after controlling for several potential cofounders. Conversely, a hospital admission with the diagnosis of diabetic foot was negatively associated with mortality. In addition, an increasing number of morbidities were associated with greater risk of death.
There are few prospective studies evaluating diagnosis at the moment of hospital admission as potential risk factors for inhospital mortality among T2D patients. One of the most recent studies, conducted in UK [
Two previous studies performed several years ago in Peru deserve attention. One of them reported that more than half (52%) of the hospital admissions in T2D patients were due to infections, mainly focused on soft tissues and urinary and respiratory tract, followed by chronic renal failure (24%) and heart failure (7%) [
Although infections still increase inhospital mortality among T2D patients, other causes, especially stroke, and acute renal failure, are also potential risk factors for inhospital mortality. As countries are at different stages of nutrition and epidemiological transition, it is possible that causes of death can vary according to the setting evaluated. Then, there is a need to understand characteristics associated with the risk of death in each country and even within it. For example, about 70% of patients must pay to receive health care in public hospitals, even when most of the T2D patients are covered by the national health insurance (SIS in Spanish). This might, then, imply a new challenge in the health care system as appropriate strategies are needed in order to prevent and reduce inhospital mortality.
The negative association between diabetic foot infection and mortality merits some comments. As diabetic foot infection was one of the main causes of hospital admission in Peru [
Only a quarter of hospital-admitted patients met recommended HbA1c levels, carrying out particular challenges especially in resource-constrained settings including rate of complications, number of hospitalizations, and out-of-pocket payments to cover health care [
Strength of this study included its prospective nature, the standardized assessment conducted by one health care staff to verify and confirm the diagnosis and the reason of hospitalization, and the use of one of the main referral hospitals in Lima to perform the study. This study, however, has some limitations. First, the high proportion of missing values in the case of HbA1c and time of disease variables could have affected our results. Although other studies have had the same problem [
Respiratory infections, stroke, and acute renal disease, as diagnosis of hospital admission, increased the risk of in-hospital mortality among T2D patients, whereas the presence of diabetic foot was a protective factor. In addition, an increasing number of conditions were also associated with greater risk of mortality. Results of this study confirm that infections are not the only potential cause of inhospital mortality in a developing country and certain causes of hospital admission require a more aggressive and standardized management in order to decrease mortality among T2D patients.
There are not enough prospective studies assessing risk factors of inhospital mortality in T2D patients, especially in low- and middle-income countries (LMIC), such as Peru. Different regression models were generated to evaluate the association between reason of hospitalization and inhospital mortality. We found that infections are not the only cause of inhospital mortality, as the literature used to report, but also stroke and acute renal disease increased the risk of inhospital mortality in a transitioning setting.
No one of the authors of this paper has reported a conflict of interests.
The authors would like to thank Giovanna Rodríguez, M.D., for her advisory during the study; third-year Endocrinology residents, Nancy Gonzales, M.D., and Anabella Pilar Alegria, M.D., for their active participation in collecting data; and Giancarlo Sal y Rosas, Ph.D., for his help and suggestions in statistical analysis. Antonio Bernabe-Ortiz is funded by a Wellcome Trust Research Training Fellowship in Public Health and Tropical Medicine (103994/Z/14/Z).